Close
About
FAQ
Home
Collections
Login
USC Login
Register
0
Selected
Invert selection
Deselect all
Deselect all
Click here to refresh results
Click here to refresh results
USC
/
Digital Library
/
University of Southern California Dissertations and Theses
/
Dynamics of protein metabolism in larvae of marine invertebrates
(USC Thesis Other)
Dynamics of protein metabolism in larvae of marine invertebrates
PDF
Download
Share
Open document
Flip pages
Contact Us
Contact Us
Copy asset link
Request this asset
Transcript (if available)
Content
DYNAMICS OF PROTEIN METABOLISM IN LARVAE OF MARINE INVERTEBRATES
by
Jason Wang
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
BIOLOGY
(MARINE BIOLOGY AND BIOLOGICAL OCEANOGRAPHY)
August 2021
Copyright 2021 Jason Wang
ii
ACKNOWLEDGEMENTS
There are many people I would like to thank for supporting me in my research efforts at
the Marine Biology and Biological Oceanography program at USC. Thanks to my advisor
Professor Donal Manahan and my dissertation committee Professor Dennis Hedgecock,
Professor Sergio Sañudo-Wilhelmy, and Professor Robert Maxson for all their advice, training,
and feedback on my research. Professors Suzanne Edmands and Dale Kiefer also participated in
my qualifying committee, and I thank them for their feedback throughout the process. I would
like to thank all the members of the Manahan lab during my time here: Dr. Scott Applebaum, Dr.
Francis Pan, Dr. Christina Frieder, Dr. Andrew Griffith, Dr. Ning Li, Melissa DellaTorre, and
Charles Capron. It has been a great learning experience to work closely with a team with diverse
research expertise. Melissa provided the raw data for respiration and ammonia excretion in
Chapter 3. Andrew provided an estimation of larval carbohydrate content for considerations of
bioenergetic conversion factors in Chapter 3. Scott, Francis, and Ning assisted with the
identification of amino acid transporter genes in Chapter 4. Thanks also to Professor Gage
Crump and Dr. Joanna Smeeton for providing zebrafish embryos for studies in Chapter 4, and for
their perspectives on testing gene function in this system. Professor Enrique Navarro shared
invaluable advice on feeding experiments during his time here as a visiting scientist. Dr. Patrick
Sun assisted with tests of proteasomal activity in urchin larvae.
Thanks to everyone from the Wrigley Institute for Environmental Studies who supported
my research; especially to Linda Duguay, Kellie Spafford, Lauren Czarnecki Oudin for
assistance during the Wrigley Summer Fellowship durations and research operations. Thanks
also to Dave Anderson for helping with the larval culturing efforts early on at Catalina. Finally, a
iii
personal thanks to all my friends and family who have been there for me during my time as a
student. Special thanks to Benjamin Wu and David Fan for their moral support and friendship.
And to my family, you have helped to keep things in perspective and to maintain a foundation
for me – I am deeply grateful.
This work was made possible with financial support from the U.S. National Science
Foundation, the USC Wrigley Institute for Environmental Studies, and the USC Dornsife
College of Letters, Arts, and Sciences.
iv
TABLE OF CONTENTS
Acknowledgements ......................................................................................................................... ii
List of Tables ................................................................................................................................. vi
List of Figures .............................................................................................................................. viii
Abstract ........................................................................................................................................ xiii
Introduction ......................................................................................................................................1
Introduction References .....................................................................................................24
Chapter 1: Protein Synthesis Rates Exceed Ingestion Rates in Growing Larval Stages ...............42
Chapter 1 References .........................................................................................................86
Chapter 2: Differences in Protein Food Conversion Efficiency Maintain Accretion Rates
Across Food Rations ..........................................................................................................93
Chapter 2 References .......................................................................................................128
Chapter 3: Effects of Temperature on Feeding and Protein Metabolism ....................................135
Chapter 3 References .......................................................................................................189
Chapter 4: Molecular Characterization and Sequence Diversity of Solute Carrier Family 6
Amino Acid Transporters in Marine Invertebrates ..........................................................195
Chapter 4 References .......................................................................................................239
Dissertation Synthesis: Dynamics of Protein Metabolism in Larvae of Marine Invertebrates ...246
Dissertation Synthesis References ...................................................................................261
References ....................................................................................................................................267
Appendices ...................................................................................................................................298
Appendix A: Synchronicity of Larval Cultures for the Purple Sea Urchin
Strongylocentrotus purpuratus ...........................................................................298
Appendix B: Protein Content and Growth of the Larval Algal Food
Rhodomonas lens ................................................................................................307
Appendix C: Protein and Lipid Content of Larvae of the Pacific Oyster
Crassostrea gigas Reared Under Varying Food Rations ....................................314
v
Appendix D: Changes in Feeding Rates for larvae of the Pacific Oyster
Crassostrea gigas to Variable Temperature and Food Ration ............................324
Appendix E: Thermal Sensitivity of Feeding Rates for Larvae of the
Painted Sea Urchin Lytechinus pictus .................................................................339
Appendix F: Neurotransmitter Control of Feeding Rates for larvae of the
Purple Sea Urchin Strongylocentrotus purpuratus .............................................345
Appendix G: In Vitro Proteasomal Enzyme Activity in Larvae of the
Purple Sea Urchin Strongylocentrotus purpuratus .............................................351
Appendix H: The Zebrafish Embryo as a Heterologous Expression System for
Assays of Amino Acid Transport .......................................................................356
Appendix I: RNAscope Probes for In Situ Hybridization of SLC6 Transporter Genes
for Strongylocentrotus purpuratus ......................................................................364
vi
LIST OF TABLES
Table 2.1: Example calculation of protein synthesis rates based on
14
C-alanine incorporation
into the trichloroacetic acid (TCA) protein fraction ........................................................108
Table 2.2: Food rations maintained in culture vessels used for experiments on growth and
protein metabolic dynamics in larvae of Lytechinus pictus .............................................112
Table 3.1: Example calculation of protein catabolism for a 5-day old larva (241.5 ± 2.1 µm
midline body length) of Strongylocentrotus purpuratus chronically reared at 20°C ......158
Table 3.2: Example calculation of the contribution of protein catabolism to the energy
needs of a 5-day old larva (241.5 ± 2.1 µm midline body length) of
Strongylocentrotus purpuratus chronically reared at 20°C .............................................159
Table 3.3: Thermal sensitivity, Q10 [average ± s.e.m (n)], of physiological and whole-
organismal processes for larvae of Strongylocentrotus purpuratus chronically reared
at 15 or 20°C ....................................................................................................................169
Table 3.4: Thermal sensitivity of larval physiological rates (Q 10 ± s.e.) of
Strongylocentrotus purpuratus chronically reared at 15°C .............................................170
Table 3.5: Thermal sensitivity of larval physiological rates (Q 10 ± s.e.) of
Strongylocentrotus purpuratus chronically reared at 20°C .............................................171
Table 4.1: Primer and sequence information for cloning 13 full-length SoLute Carrier
family 6 (SLC6) coding sequences from Crassostrea gigas ............................................206
Table 4.2: Sequencing primers targeting sequences within the coding sequence of cloned
SoLute Carrier family 6 (SLC6) genes .............................................................................209
Table 4.3: Twenty-four SoLute Carrier family 6 (SLC6) genes identified from the genome
of Crassostrea gigas assembled by the Institute of Oceanology .....................................225
Table 4.4: Twenty-four SoLute Carrier family 6 (SLC6) genes identified from the genome
of Crassostrea gigas assembled by the Roslin Institute ..................................................226
Table 4.5: Twenty-two SoLute Carrier family 6 (SLC6) genes identified from the genome
of Strongylocentrotus purpuratus ....................................................................................227
Table 4.6: Closest matching proteins for 13 cloned SoLute Carrier family 6 (SLC6) amino
acid transporter genes from Crassostrea gigas ................................................................229
vii
Table C1: Biochemical composition of larvae of Crassostrea gigas reared under different
food rations ......................................................................................................................320
Table D1. Thermal sensitivities (Q10) of clearance rates for larvae of Crassostrea gigas ..........330
Table D2. Comparison of models of the change in clearance rates for larvae of Crassostrea
gigas to food ration ..........................................................................................................331
Table D3. Comparison of models of the change in ingestion rates for larvae of Crassostrea
gigas to food ration ..........................................................................................................332
Table E1. Thermal sensitivities of clearance rates for larvae of Lytechinus pictus reared at
15 or 20°C ........................................................................................................................343
Table I1. Ten putative SLC6 amino acid transporters were identified for Strongylocentrotus
purpuratus ........................................................................................................................367
Table I2. Percent identity matrix of Strongylocentrotus purpuratus SLC6 amino acid
transporter genes ..............................................................................................................368
Table I3. RNAscope
®
(ACDBio) ZZ probes designed for in situ hybridization of
Strongylocentrotus purpuratus transcripts .......................................................................369
viii
LIST OF FIGURES
Figure 1.1: Conceptual model of protein metabolic dynamics ......................................................48
Figure 1.2: Determination using particle counting of feeding rates by larvae of
Strongylocentrotus purpuratus ..........................................................................................55
Figure 1.3: Determination of in vivo protein synthesis rates using
14
C-alanine radiotracer ..........59
Figure 1.4: Feeding rates of larvae of Strongylocentrotus purpuratus from three
replicate culture vessels of the same cohort represented by different symbols ................63
Figure 1.5: Predicted food availability and feeding in culture vessels containing larvae of
Strongylocentrotus purpuratus fed 20,000 cells ml
−1
........................................................64
Figure 1.6: Comparison of food rations maintained with different frequencies within culture
vessels containing feeding-stage larvae of Strongylocentrotus purpuratus ......................65
Figure 1.7: Determination of protein content for larvae of Strongylocentrotus purpuratus fed
20,000 cells ml
−1
Rhodomonas lens ...................................................................................68
Figure 1.8: Growth of larvae of Strongylocentrotus purpuratus provided with 20,000
cells ml
−1
of Rhodomonas lens ...........................................................................................71
Figure 1.9: Lipid class contents of larvae of Strongylocentrotus purpuratus fed a constant
ration of 20,000 cells ml
−1
Rhodomonas lens ....................................................................72
Figure 1.10: Dynamics of protein synthesis and ingestion for larvae of Strongylocentrotus
purpuratus ..........................................................................................................................75
Figure 1.11: Protein growth efficiencies for larvae of Strongylocentrotus purpuratus reared
at a constant food ration of 20,000 cells ml
−1
Rhodomonas lens .......................................76
Figure 1.12: The relationship of protein synthesis rate to rates of accretion are strongly
correlated with protein accretion rates for larvae of S. purpuratus ...................................77
Figure 2.1: Experimental design and approach encompassing two primary objectives on the
study of larval growth for Lytechinus pictus ......................................................................99
Figure 2.2: Changes in feeding rates by larvae of Lytechinus pictus exposed to food rations
ranging from 1,000 to 100,000 cells ml
−1
........................................................................111
Figure 2.3: Monitoring of food rations within 18 culture vessels of larvae of Lytechinus
pictus fed Rhodomonas lens.............................................................................................113
ix
Figure 2.4: The effect of food ration on the morphological size and protein content of
larvae of Lytechinus pictus fed between 5,000 – 50,000 cells ml
−1
.................................116
Figure 2.5: Lipid class contents (phospholipids, triacylglycerols, free fatty acids,
hydrocarbons, and cholesterols) for larvae of Lytechinus pictus reared at different
food rations ......................................................................................................................117
Figure 2.6: Changes in protein food conversion efficiency for larvae of Lytechinus pictus
provided with different food rations ................................................................................119
Figure 2.7: Dynamics of protein synthesis, degradation, and accretion for larvae of four
cohorts (different symbol shapes) and 18 culture vessels reared at different food
rations ...............................................................................................................................121
Figure 3.1: Experimental design for determining the chronic and acute thermal sensitivities
of physiological processes ...............................................................................................141
Figure 3.2: Stability of long-term (chronic) rearing temperatures of the seawater used to
culture larvae ....................................................................................................................143
Figure 3.3: Feeding rates were determined by particle counting of Rhodomonas lens
depletion by larvae assayed at different temperatures .....................................................146
Figure 3.4: Intercalibration of methods for determining respiration rates for larvae of
Strongylocentrotus purpuratus, and the determination of the thermal sensitivity of
respiration ........................................................................................................................148
Figure 3.5: Alanine transport rates were determined for larvae of Strongylocentrotus
purpuratus following incubation in 10 µM alanine with 74 kBq of
14
C-alanine tracer ..150
Figure 3.6: In vivo protein synthesis rates were determined for larvae of Strongylocentrotus
purpuratus by measuring rates of
14
C-alanine incorporation into the protein fraction....152
Figure 3.7: Determination of ammonia excretion rates for 8-day old larvae (263.4 ± 2.1 µm
in midline body length) of Strongylocentrotus purpuratus at different temperatures .....155
Figure 3.8: Survivorship of larvae of Strongylocentrotus purpuratus reared at either 15°C
(closed symbols) or 20°C (open symbols) .......................................................................165
Figure 3.9: Growth rates of larvae of Strongylocentrotus purpuratus larvae reared either at
15°C or 20°C ....................................................................................................................166
Figure 3.10: Q10 for protein synthesis rates (white) and respiration rates (black) in larvae of
Strongylocentrotus purpuratus reared at (A) 15°C or (B) 20°C ......................................172
x
Figure 3.11: Utilization of protein as a metabolic substrate in larvae of Strongylocentrotus
purpuratus reared at 15 or 20°C ......................................................................................174
Figure 3.12: Protein metabolic dynamics of five larval cohorts (Cohorts 1 – 5, see
Tables 3.4 and 3.5) of Strongylocentrotus purpuratus reared at 15° (closed symbols)
or 20°C (open symbols) ...................................................................................................177
Figure 3.13: The disproportionate effect of temperature on physiological processes creates
energetic imbalances and impacts whole-organism physiology ......................................182
Figure 4.1: Sanger sequencing of an example cloned SoLute Carrier family 6 (SLC6) gene
insert in pGEM-T vector ..................................................................................................210
Figure 4.2: Example transmembrane structure of a cloned SoLute Carrier family 6 (SLC6)
amino acid transporter gene from Crassostrea gigas (Clone 10, EKC35081.1 of
Institute of Oceanology v1 assembly, g27427 of Institute of Oceanology v2
assembly, LOC105321334 of Roslin Institute assembly)................................................215
Figure 4.3: Protein alignment of 13 cloned SoLute Carrier family 6 (SLC6) genes from
Crassostrea gigas with the reference leucine transporter of Aquifex aeolicus
(Aa_LeuT) and reference glycine transporter of Homo sapiens (Hs_SLC6A9; GlyT1) ...216
Figure 4.4: Phylogenetic relationships of SoLute Carrier family 6 (SLC6) proteins from
Crassostrea gigas (Cg), Strongylocentrotus purpuratus (Sp), Drosophila
melanogaster (Dm), and Homo sapiens (Hs) ..................................................................228
Figure A1: Temperature stability in larval cultures of Strongylocentrotus purpuratus were
measured once every 30 minutes throughout the culturing period ..................................302
Figure A2: Survival of larvae of Strongylocentrotus purpuratus reared in three replicate
culture vessels over 13 days of culturing .........................................................................303
Figure A3: Lengths of Strongylocentrotus purpuratus in three replicate larval cultures
measured over 13 days of growth and development ........................................................304
Figure A4: Embryonic and larval stages of Strongylocentrotus purpuratus observed in the
first 13 days of growth and development .........................................................................305
Figure A5: Synchronicity of larval stages across three replicate culture vessels of
Strongylocentrotus purpuratus .......................................................................................306
Figure B1: Comparison of Rhodomonas lens growth from different algal sources and
different culturing volumes ..............................................................................................311
Figure B2: Growth of Rhodomonas lens cultured in 1-liter flasks containing 700 ml filtered
seawater (0.2 µm filtered) and 0.625 ml f/2 media ..........................................................312
xi
Figure B3: Analyses of the relationship between the change in protein content of
Rhodomonas lens with time after inoculation ..................................................................313
Figure C1: Food rations (T-Isochrysis galbana) within larval cultures of Crassostrea gigas
monitored twice daily by particle counting (Z1 Particle Counter, Beckman Coulter) ....321
Figure C2: Feeding rates of larvae of Crassostrea gigas reared at different food rations ...........322
Figure C3: Growth of larvae of Crassostrea gigas reared at 5 (circles), 30 (triangles), or 50
(squares) cells µl
−1
...........................................................................................................323
Figure D1: Replicate time-course clearance rate assays used to determine the thermal
sensitivity of clearance rates to temperature ....................................................................333
Figure D2: Temperature dependence of clearance rates of Crassostrea gigas and
determination of Q10 values for the larval cohort of Crassostrea gigas,
Wild-Type 2 Day 5 (WT2D5) ..........................................................................................334
Figure D3: Q10 values of larval clearance rates of Crassostrea gigas determined from 14
separate trials for seven larval cohort families ................................................................335
Figure D4: Functional response of 11-day old larval clearance rates of Crassostrea gigas
and ingestion rates to food ration .....................................................................................336
Figure D5: Changes in clearance rates for larvae of Crassostrea gigas and ingestion rates
to food amount .................................................................................................................337
Figure D6: Size-specific changes in larval clearance rates of Crassostrea gigas and
ingestion rates with food ration........................................................................................338
Figure E1: Determination of clearance rate thermal sensitivity (Q 10) .........................................344
Figure F1: Inhibition of larval clearance rates of Strongylocentrotus purpuratus by 1 µM
dopamine ..........................................................................................................................349
Figure F2: Dose-response of larval clearance rates of Strongylocentrotus purpuratus to
serotonin (0 – 10 µM) ......................................................................................................350
Figure G1: Standard curves for fluorescence of free 7-amino-r-methylcoumarin (AMC) ..........354
Figure G2: Caspase-like/β1 proteosomal activity in 4-day old larvae of Strongylocentrotus
purpuratus determined by in vitro cleavage of the fluoropeptide Z-LLE-AMC and
fluorescent reporting of unbound AMC ...........................................................................355
Figure H1: Free amino acid profiles of embryos of Danio rerio after 0, 5, and 26 hours
post-fertilization (hpf) ......................................................................................................361
xii
Figure H2: Transport rates of alanine from the media for embryos of Danio rerio over one
hour .................................................................................................................................362
Figure H3: Comparisons of alanine transport by live or dead zebrafish embryos .......................363
xiii
ABSTRACT
The planktonic larval stages of marine invertebrates represent vulnerable periods when
fast growth rates are crucial for survival and success. Physiological rates of feeding, nutrient
uptake, ion transport, and protein synthesis have been shown to correlate with fast growth rates
in larvae. In particular, the dynamics between processes related to protein metabolism (e.g.,
ingestion, synthesis, and degradation) are a major driver of larval growth. In the present
dissertation, a conceptual model based on protein metabolism was applied to study how larvae
grow under various environmental conditions of food ration and temperature. Under this
conceptual model, proteins and amino acids are ingested or transported into the organism for use
either as metabolic substrates or for protein synthesis. Synthesized proteins are either accreted as
growth or turned over. Catabolized proteins are then excreted as nitrogenous wastes. Following
this model, each of these processes of ingestion, protein synthesis, protein accretion, protein
degradation, metabolism and ammonia excretion was measured for larvae under various
conditions. Studying these processes revealed the importance of the dynamics of protein
metabolism in response to environmental change.
In Chapter One, the dynamics of protein metabolism were examined under a constant
food ration of 20,000 cells ml
−1
for larvae of the purple urchin Strongylocentrotus purpuratus.
Protein synthesis rates of larvae always exceed rates of protein ingestion and accretion. For every
unit of protein ingested, only 33% is accreted as growth (i.e., protein food conversion efficiency)
for a 4-day old larva of 20 ng total protein. For every unit of protein synthesized by a 4-day old
larva with 20 ng total protein, 37% is accreted as growth (i.e., protein depositional efficiency),
and this efficiency decreased to 18% in later feeding larval stages (of 120 ng total protein). These
xiv
findings show that most protein mass synthesized is degraded and highlight the importance of
integrating the dynamics of protein ingestion, synthesis, degradation, and accretion as the key
mechanisms that regulate growth.
In Chapter Two, larvae of the sea urchin Lytechinus pictus were reared under constant,
experimental rations ranging from 5,000 – 50,000 cells ml
−1
to study their protein metabolic
dynamics. Changes in larval feeding rates across food rations between 1,000 – 100,000 cells ml
−1
were also tested. Larvae were shown to upregulate feeding rates up to 10-fold when exposed to
low food rations below approximately 5,000 cells ml
−1
. Larvae reared at experimental rations
between 5,000 – 50,000 cells ml
−1
had high rates of protein synthesis exceeding rates of protein
ingestion and accretion. These findings are consistent with the dynamics of protein metabolism
seen in larvae of S. purpuratus from Chapter One. At all food rations, larval protein synthesis
and degradation rates did not differ. Larvae maintained a near-constant rate of protein accretion
(16 ng day
−1
) which, surprisingly, did not change even when larvae were reared in 10-fold higher
food environments (5,000 cf. 50,000 cells ml
−1
). At the biochemical level, the growth efficiencies
of larvae changed with different rations to maintain constant protein accretion rates. At 5,000
cells ml
−1
, larvae increased feeding rates and accreted 77% of ingested protein mass. Larvae
reared at 10-fold increases in food ration (50,000 cells ml
−1
) only accreted 17% of the ingested
protein mass. Since protein synthesis and degradation rates did not differ for larvae at each food
ration, changes in protein food conversion efficiencies allowed larvae to have similar protein
accretion rates.
Chapter Three demonstrated the decoupling of processes related to protein metabolism
under different temperatures. Larvae of S. purpuratus were reared at 15 or 20°C and assayed
periodically at four temperatures (10 – 25°C) to determine the thermal sensitivities (Q 10) of each
xv
process. Rates of protein synthesis and catabolism (measured by ammonia excretion rates), were
more sensitive to temperature changes than rates of respiration (Q 10 values of ~3 compared to ~2,
respectively). These differences in thermal sensitivity were exacerbated when larvae were reared
at 20°C. Based on these thermal sensitivities, and constitutively high rates of protein synthesis,
higher allocations of ATP to protein synthesis were predicted for larvae exposed to high
temperatures. For example, using Q10 values of 3 and 2 for protein synthesis and respiration,
respectively, larvae would increase ATP allocation from 19% to 35% between 10 and 20°C
respectively. Furthermore, the high thermal sensitivity of ammonia excretion rates (Q 10 of 3)
relative to respiration rates (Q10 of 2) results in decreases in the ratio of atomic oxygen to atomic
nitrogen (an index of a protein-based metabolism), implying that more protein is catabolized to
support larval metabolism at higher temperatures. Determinations of atomic oxygen to atomic
nitrogen ratios showed that larvae derived 47% (chronically reared at 15°C) and 77%
(chronically reared at 20°C) of their metabolic energy from protein catabolism. High rates of
both protein synthesis and protein catabolism are taxing for larvae growing in elevated
temperatures.
In Chapter Four, an analysis is presented of putative genes responsible for the transport of
amino acids from seawater. Amino acid transport capacities have been shown to predict growth
rates in larvae of the Pacific oyster Crassostrea gigas. Twenty-seven genes of the SoLute Carrier
family 6 (SLC6) amino acid transporter family were identified from three genome assemblies of
C. gigas, and 22 genes were identified for S. purpuratus. Compared to the four SLC6 amino acid
transporter genes in humans, 27 and 22 genes found in the marine invertebrate species represent
a significant gene expansion. Polymerase chain reaction primers were designed to amplify full-
length coding sequences of 13 genes which were successfully cloned from RNA pools of C.
xvi
gigas. Phylogenetic and sequence analyses of these genes show putative differences in substrate-
binding residues amongst the cloned sequences when compared to reference sequences from the
bacterial homologue LeuT and the human homologue GlyT1. For example, four of the 13 cloned
sequences showed an alanine-tryptophan-glycine motif responsible for glycine substrate binding
in GlyT1. The cloned sequences were also used to support gene identification from three
different assemblies of the genome of C. gigas. Analyses of these sequences can aid reverse-
genetic, functional studies of transcript localization or substrate binding kinetics. The high
degree of gene expansion in SLC6 genes along with the sequence diversity suggests that these
genes have different functions. Reverse genetic approaches manipulating genes in expression
systems will permit future analysis to define physiological functions of this expanded gene
family of amino acid transporters, and their evolution.
In summary, this dissertation presents a conceptual model of protein metabolism and
growth from the biological levels of molecules to physiology and whole-organismal growth. By
studying these processes under varying conditions of food availability and temperature, different
physiological strategies were revealed which highlight the importance of protein synthesis and
turnover in driving growth in variable environments.
1
INTRODUCTION
Rationale
The studies presented in this dissertation build upon a conceptual model of protein
metabolism as a driver of larval growth and bioenergetics. Planktonic larvae of marine
invertebrates have diverse life history strategies with respect to where they derive their metabolic
energy from and how quickly they grow. For planktotrophic larvae, physiological rates of the
consumption of phytoplankton and the uptake of dissolved organic matter from the environment
are important physiological predictors of growth rate (Pace et al., 2006; Pan et al., 2015a). Once
assimilated and digested, amino acids from proteins from food are used for biosynthesis or
catabolism. The constant synthesis and degradation of proteins represents a significant portion of
ATP utilization, and these dynamics also result in changes in net protein accretion. Because of
the importance of these processes in all living organisms, protein metabolism is the focus of this
dissertation. Studies rearing larvae under varying food availability and temperatures revealed
different metabolic and biosynthetic strategies for growth. The decoupling of interdependent
processes with higher temperatures also revealed limitations to thermal tolerance in larvae. These
studies highlight the importance in considering the dynamics of protein metabolism in the
growth of marine invertebrate larvae under different environmental conditions.
The marine larval stage
Planktonic larval stages of marine invertebrates are often characterized by high rates of
mortality (Rumrill, 1990). These taxa follow a “Type III” survivorship curve where the early
stages have high mortality rates which decrease sharply to later life stages with lower mortality
2
(Pearl and Miner, 1935; Deevy, 1947). For benthic marine invertebrate taxa, the planktonic
larval stage precedes a juvenile stage when the organism will metamorphose and settle in a
benthic habitat. The evolution of a planktonic larval form has several advantages and
disadvantages related to dispersal and propagation (Pechenik, 1999). Dispersal of offspring away
from parental habitats can reduce intraspecific competition for resources as well as the chance of
inbreeding (Trivers, 1974; Starrfelt and Kokko, 2010). Large scale dispersal can also favor faster
population growth; but beyond a certain range, the detriments of dispersal out of favorable
habitats or into highly variable habitats can outweigh these benefits (Palmer and Strathmann,
1981).
Dispersal distance is largely affected by the planktonic larval duration and a combination
of oceanographic and biological processes (Sponaugle et al., 2002; Shanks et al., 2003; Shanks,
2009). Behaviorally passive larvae drift in the water column, and their dispersal distances are
largely determined by water currents. Actively swimming larvae can vertically migrate to be
retained in tidal currents or to track coastal features (Olmi, 1994; Sponaugle et al., 2002).
Planktonic larval duration, or the time to metamorphosis, is also affected by larval size and
growth rates with faster growing larvae having better survival and quicker competency for
metamorphosis (Marshall et al., 2010). Larval size and age can affect predation risk from
suspension feeders with certain predators preferring to capture smaller and younger larvae
(Allen, 2008). Faster growth rates can also allow for larvae to delay metamorphosis until a
favorable habitat is encountered (Pechenik, 1980; Strathmann, 1985). By growing quickly to a
metamorphically competent state, larvae have the best chances to respond to environmental cues
and select the most favorable habitat (Hadfield et al., 2001).
3
Growth rates during the planktonic larval duration are influenced by how larvae acquire
nutrients from the environment. Marine invertebrate taxa utilize several different life history
strategies for planktonic larvae including lecithotrophy, planktotrophy, and a spectrum of
strategies in between (Herrera et al., 1996). Species with lecithotrophic larval stages generally
carry high amounts of yolk which fuel development through metamorphosis. Species with
planktotrophic larval stages produce many smaller eggs which quickly develop to a feeding stage
which feeds in the water column to grow to a metamorphic stage. Historically, the larval life
history strategy was thought to correlate with biogeographical considerations such as food
availability or temperature (Thorson, 1950). “Thorson’s rule” was broadly based on the premise
that high-latitude, cold environments are food-limited and thus favor aplanktonic or non-feeding
larval forms. While weak correlations exist between larval feeding mode and latitude,
temperature, or chlorophyll, Thorson’s rule has largely been rejected as there are a plethora of
examples contradicting this hypothesis (Pearse, 1994; Marshall et al., 2012).
The relationship between biogeographical food availability and larval form is further
complicated by ambiguity in the delineation between feeding and non-feeding strategies.
Lecithotrophic larvae have been shown to uptake enough nutrients from the environment to
contribute to metabolism and growth independently of the use of maternal reserves (Jaeckle and
Manahan, 1989a; Jaeckle and Manahan, 1989b). Similarly, planktotrophic larvae are also able to
account for metabolic demands by the uptake of dissolved organic matter providing further
evidence that the biogeography of larval forms is not necessarily correlated with food availability
(Shilling and Manahan, 1990; Shilling and Manahan, 1994; Hoegh-Guldberg, 1994). Regardless
of the primary feeding mode of larvae, growth rates are influenced both by maternal provisioning
and environmental sources whether they be planktonic foods or dissolved organic matter.
4
Planktotrophic larval feeding
Many marine invertebrate larvae exhibit morphological plasticity in the morphological
size of feeding structures such as ciliary band lengths in response to low food rations (Boidron-
Metairon, 1988; Sewell, et al., 2004; Miner, 2005). For example, pluteus arm lengths were
longer for echinoderm larvae reared under lower food rations (Sewell, et al., 2004). While arm
lengths increased with lower food rations, stomach sizes were reduced at low rations compared
to high rations (Miner, 2005). In echinoderm larvae, ciliary bands line the arms and oral hood of
the pluteus larva, and maximum clearance rates (C) were described to be a function of ciliary
band length (lb), cilium height (lc), and ciliary beating current velocity (v) according to the
equation C = lb(le – lr)v, with lr representing a correction factor for return-stroke timing
(Strathmann 1972). According to this relationship, increases in ciliary band lengths, which
include pluteus arm lengths, yield higher maximum clearance rates. These relationships were
observed in nine echinoderm species with maximum clearance rates increasing with ciliated band
lengths (Hart, 1996). Plasticity in the length of ciliated pluteus arms allowed for higher rates of
feeding, but the growth of longer arms also resulted in longer times required to reach
metamorphosis (Hart and Strathmann, 1994). These trade-offs in morphological traits and
growth rates indicate that larvae respond to feeding environments with a degree of plasticity and
regulation.
Since plasticity in ciliary band lengths present potential costs to growth and development
(Hart and Strathmann, 1994), changing ciliary beating frequency is an alternative mechanism for
increasing maximum clearance rates. Along the ciliary band, ciliary movement can be controlled
with changes of direction resulting in either swimming or feeding behaviors (Strathmann and
Grunbaum, 2006). Neurotransmitters such as dopamine, serotonin, or epinephrine act to change
5
the frequency or direction of ciliary beating patterns resulting in changes in the rate of swimming
or feeding (Lacalli and Gilmour, 1990; Beiras and Widdows, 1995; Wada et al., 1997). For sea
urchin larvae, dopamine was shown to reduce swimming speed while serotonin increased
swimming speed (Yoshihiro et al., 1992). Ciliary bands along the arms of the pluteus beat in one
direction while swimming and prior to encountering food particles. However, once the food
particles have been encountered by the cilia, larvae are able to respond within 0.02 to 0.06
seconds to reverse the direction of ciliary beating and direct algal food towards the mouth
(Strathmann, 2007). Larvae of the purple urchin Strongylocentrotus purpuratus have been shown
to be capable of behavioral regulation of feeding by reducing ciliary beat frequency in high food
conditions through the neurotransmitter dopamine (Adams et al., 2011). Dopamine was also
shown to reduce the growth of postoral arms in larvae of other urchin species Mesocentrotus
nudus and Strongylocentrotus intermedius (Kalachev, 2020). Given the patchiness of food
availability in the water column (Mackas et al., 1985), the regulation of ciliary beating frequency
allows for a faster response to food availability than changes in morphological traits.
Zooplankton grazers or suspension feeders exhibit a functional response for feeding rates
dependent on food ration. These responses are well-studied in adult-forms of zooplankton such
as krill or copepods where clearance rates are dependent on feeding history, food amount, size,
and quality (Harvey, 1937; Huntley, 1988; Thor and Wendt, 2010; Kiørboe et al., 2018; Cabrol
et al., 2020). For copepod species of the genus Calanus, maximal clearance rates occur at low
food rations and decrease non-linearly with increasing rations (Frost, 1972). These clearance
rates correspond to ingestion rates which increase linearly with food ration up to a maximal
saturated ingestion rate. For copepods, these threshold and maxima are influenced by food
particle size and other factors (Frost, 1975; Frost, 1977).
6
Unlike the feeding mechanisms of copepods, suspension feeding by bivalve and
echinoderm larvae relies on the use of cilia across ciliated bands or a specialized velum structure
(Strathmann, 1971; Strathmann and Leise, 1979). For pluteus larvae of echinoderms, the ciliated
band around the arms and oral hood produce currents which pass seawater and food particles
across the mouth (Strathmann, 1971). Veliger larvae have angled cilia along the length of the
velum which can act like sieves to push food particles faster than the surrounding seawater
(Strathmann and Leise, 1979). Regardless of the mechanism of suspension feeding, pluteus
larvae also exhibit a functional response where maximum clearance rates are achieved at low
rations, with rates decreasing with higher rations (Conover, 1968; Strathmann, 1971). For larvae
of the oyster Crassostrea virginica, the regulation of clearance rates across different food rations
resulted in ingestion rates which increased to saturation according to a Hollings type III
functional response (Baldwin and Newell, 1995). Similar functional responses in clearance rate
and ingestion rates were seen in larvae of the European flat oyster Ostrea edulis, the Pacific
oyster Crassostrea gigas, and the geoduck Panopea globosa (Crisp et al., 1985; Robert et al.,
2017; Rico-Villa et al., 2009; Rico-Villa et al., 2010; Ferreira-Arrieta et al., 2015). At low food
rations, larvae of these species have higher clearance rates, and ingestion rates increase with food
ration up to a saturated maximum rate.
Planktotrophic larvae of marine invertebrates exhibit morphological plasticity as well as
behavioral control of feeding in response to environmental food availability. The regulation of
feeding processes is often invoked as a compensatory mechanism that achieves some optimal or
target physiological state (Taghon, 1981; Calow, 1982). In optimal foraging theory, the plasticity
in feeding processes balances the costs and gains of feeding behaviors to optimize for some
fitness attribute such as growth (Schoener, 1971). Since the adoption of the optimal foraging
7
theory, more focus has been shifted towards the optimal nutritional gains resulting from feeding
behaviors in different environments (Raubenheimer and Simpson, 1993; Raubenheimer and
Simpson, 2018). This led to the consideration of compensatory feeding mainly in the contexts of
food type and quality, as defined by nutritional content and digestibility. To optimize growth,
larvae require specific amounts of proteins, lipids, carbohydrates, and other nutrients (Marshall
et al., 2010). While dietary requirements vary between species, marine organisms typically
require high amounts of dietary protein (Kaushik and Seiliez, 2010; Fry et al., 2018).
Transport of dissolved organic matter from seawater
In addition to the nutritional value of feeding and maternal reserves, the transport of
dissolved organic matter directly from the seawater accounts for a significant portion of
metabolic energy (Jaeckle and Manahan, 1989a; Jaeckle and Manahan, 1989b; Shilling and
Manahan, 1990; Shilling and Manahan, 1994). The question of whether aquatic organisms
utilized dissolved organic matter from the environment was posed in a quantitative context by
Pütter (1909) following his calculations that measured respiratory rates could not be fully
accounted for by realistic rates of feeding. He argued that the transport of dissolved organic
matter from small volumes of water rather than solely feeding on plankton through
unrealistically large volumes of water would better support observed respiratory rates. While the
uptake of dissolved organic matter could not be ruled out entirely for many soft-bodied species,
Pütter’s hypothesis was met with skepticism concerning estimations of food availability and the
mechanisms by which dissolved organic matter could cross integumental tissues (Krogh, 1931).
Interest in the ability of aquatic organisms to uptake dissolved organic matter was renewed when
Stephens and Schinske (1961) used a colorimetric method of amino acid detection to show that
8
species from 35 genera across 11 phyla, with the exception of arthropods, could remove glycine
from seawater. In marine invertebrates, intracellular free amino acid pools range from 0.1 – 0.5
M concentrations, and since the amino acid concentrations in natural seawater often range in the
nanomolar range, transport occurs across greater than 10
6
-fold gradients (Wright and Secomb,
1986; Lee and Bada, 1977; Braven et al., 1984; Stephens 1988).
The use of
14
C-labelled amino acids and high-performance liquid chromatography
increased the sensitivity of detection of amino acids and allowed for better characterization of
amino acid substrates and enzyme kinetics involved in uptake (Manahan et al., 1983).
Furthermore, experiments on axenic cultures of larvae provided evidence for larval uptake of
amino acids independently of bacteria (Manahan et al., 1983; Manahan, 1989). The uptake of
amino acids could account for a significant proportion of metabolic demands even in
lecithotrophic larvae which were thought to rely solely on embryonic reserves for growth
(Jaeckle and Manahan, 1989a). Similarly, invertebrate larvae living in food-limiting waters of
the Antarctic have amino acid transport rates with the potential to account for 32% of oxidative
requirements (Shilling and Manahan, 1994). For adult stages of bivalves, the uptake of amino
acids through epithelia can also contribute to a significant proportion of metabolic rates (Wright,
1982). The aforementioned studies highlight the metabolic importance of the uptake of dissolved
organic matter directly from seawater by larvae.
Marine invertebrates transport amino acids using homologous protein families shared by
well-studied mammals and even bacteria and plants (Wipf et al., 2002; Boudko et al., 2005;
Yamashita et al., 2005; Meyer and Manahan, 2009; Koito et al., 2010; Pan et al., 2015a;
Bouchard et al., 2019). This protein family, solute carrier family 6 (SLC6), is defined by the
transmembrane transport of neurotransmitters (including amino acids) along with Na
+
or Cl
-
9
cotransport. The SLC6 family can be further divided into four groups in mammals: amino acid
transporter (I), amino acid transporter (II), monoamine, and GABA (γ-Aminobutyric Acid)
transporters (Brӧer and Gether, 2012). These protein groups play a large role in the nervous
system by transporting neurotransmitters such as serotonin, dopamine, GABA, and other amino
acids. The amino acid transporter groups I and II co-transport neutral amino acids with sodium,
and in mammals, the two groups are expressed in different tissues. A SLC6 gene was also shown
to localize in the neurons of the jellyfish Cyanea capillata representing an evolutionarily distant
neural system compared to vertebrates (Bouchard et al., 2019). The SLC6 gene in jellyfish was
grouped as a nutrient amino acid transporter based on phylogenetic analysis. In insects, the
expanded subfamily of nutrient amino acid transporters was named for its role in amino acid
uptake related to nutrition (Boudko, 2012). Given the deep evolutionary history of the SLC6 gene
family and the high degree of gene expansion, reverse genetic approaches to testing gene
function are required to confirm putative functions based on protein sequences.
Without directly testing the functions of each SLC6 transporter gene, function can only be
inferred by comparisons to a homologous reference with a solved crystal structure. Crystal
structures for proteins in different substrate-binding states reveal which amino acid residues are
involved in molecular interactions. The crystal structure of a homologous SLC6 transporter LeuT
found in the bacteria Aquifex aeolicus has been solved, providing insights into the mechanisms
of amino acid and Na
+
ion co-transport (Yamashita et al., 2005). The SLC6 protein contains 12
transmembrane domains, two of which are involved in dimerization. The remaining 10
transmembrane domains are involved in a “LeuT fold” with two sets of five symmetrically
inverted transmembrane domains (transmembrane domains 1-5 and transmembrane domains 6-
10). Residues on extracellular loops two and four extend from between transmembrane domains
10
3 and 4 and transmembrane domains 7 and 8 respectively and are related to substrate binding.
Sodium ions bind to the 1
st
and 6
th
transmembrane domain causing a receptive extracellular
conformation which can bind substrate (Yamashita et al., 2005; Claxton et al., 2010). Once
substrate is bound, protein conformation changes to transport it across the membrane and reset
the conformation. In eukaryotes, crystal structures have also been solved for members of the
SLC6 amino acid transport and mono-amine transporter subfamilies (dopamine transporter,
Penmasta et al., 2013; serotonin transporter, Coleman et al., 2016; glycine transporter, Shahsavar
et al., 2021). In addition to the co-transport of Na
+
seen for the bacterial homologue LeuT,
eukaryotic SLC6 transporters also co-transport Cl
-
with coordination from residues in
transmembrane domains 6 and 7 (Shahsavar et al., 2021). Eukaryotic SLC6 transporters also
contain conserved glycosylation sites at the extracellular loops, with a conserved asparagine in
extracellular loop 2 responsible for the trafficking of the SLC6 protein to the membrane
(Shahsavar et al., 2021). Importantly, for the human glycine transporter gene, there is a three-
residue motif found in transmembrane domain six which varies in sequence depending on
substrate specificity. Analyses of residues in these positions for sequences of interest can provide
insights into their putative functions.
While SLC6 transporter function has been extensively studied in mammals or in the
context of neural signaling, few studies exist characterizing the function of SLC6 transporters in
marine animals. In one case, SLC6 transporters have been functionally characterized in deep-sea
mussels which exist in symbiotic relationships with sulfur-oxidizing bacteria (Inoue et al., 2008).
Taurine transporters (TAUT) found in the mussel gills were cloned and able to transport taurine
in heterologous systems; the proven function suggests that transport in mussels is possible for the
purposes of detoxifying sulfides. The functional characterization of SLC6 transporters has been
11
described in the purple urchin Strongylocentrotus purpuratus (Meyer and Manahan, 2009). In
this species, 3 transporters were identified, cloned, and expressed in heterologous systems
(oocytes of Xenopus laevis). This study allowed for confirmation of substrate binding as well as
the localization of proteins within the epithelial layers of larvae of S. purpuratus. The importance
of SLC6 genes in transporting nutrients from the environment was also see in a study of the
deep-sea bone worm Osedax japonicus which localized SLC6 transporters at the root epidermis
(Miyamoto et al., 2017).
Studies proving the functions of genes are important given the high degree of genomic
expansion seen in the SLC6 family for marine invertebrates. For polar echinoderm larvae and
adults 11 putative SLC6 transporter genes were identified, some of which were expressed as
mRNA throughout life history (Applebaum et al., 2013). Pan et al. (2015a) also identified 23
putative amino acid transporters that are in the AATI subgroup (part of the SLC6 family) from C.
gigas. This expansion of transporter genes in this species is noteworthy, especially when
considered in the context of the expansion of other gene families in C. gigas, such as heat shock
genes (Zhang et al., 2012). Investigations of the SLC6 gene family for the model sea squirt
Ciona savignyi resulted in 40 putative SLC6 genes, six of which belonged to the amino acid
transporter (AATI) subgroup (Ren et al., 2019). A recent study of a scallop (Patinopecten
yessoensis) genome revealed up to 90 SLC6 genes in total across several subgroups (Xun et al.,
2020). This represents a significant expansion in bivalves compared to other systems such as
fruit flies or vertebrates. Given the large genomic expansion of SLC6 genes in marine
invertebrates as well as their diverse functional roles in nutrient uptake and neuron activity, it is
important to characterize which genes are important for driving larval growth and the uptake of
dissolved organic matter from seawater.
12
Theories of growth
From a whole-organismal perspective, the growth of animals can be described broadly by
a bioenergetic model of the energy fluxes into and out of an organism. Energetic inputs into an
organism (consumption) are balanced by growth, respiration, feces, and excreta (Bayne and
Newell, 1983, pp. 408-409). Within the total energetic value of foods consumed, a portion is lost
as feces, while the remainder is considered “absorbed” and biologically available to the
organism. Energy from absorbed rations is then utilized in respiration, growth, and excretion.
These energetic components can be further subdivided. For example, respiration, R, is the sum of
specific dynamic action R’ and metabolism R”; and growth, P, is the sum of gamete production
Pr and somatic growth Pg. When studying marine invertebrate larvae, gamete production is non-
existent, so only somatic growth Pg is considered. Other variations on this bioenergetic model
may also partition respiration due to animal activity (Nisbet et al., 2012).
This traditional bioenergetic model has been utilized for comparative studies in attempts
to build allometric models for the growth of animals (von Bertalanffy, 1938; von Bertalanffy,
1957; Winberg, 1956). Early uses of this model apply only to organisms living in static
environment which are not influenced by variations in food ration or temperature. Comparisons
of bioenergetic parameters (e.g., metabolic rate) between species were made by establishing
parabolic, allometric relationships between a parameter and size (von Bertalanffy, 1957;
Ostrovsky, 1995). The parabolic or power function follows the formula Y = aW
b
where a variable
(e.g., metabolic rate) Y will scale with size W according to species-specific allometric constants
“a” and “b”. The coefficient “b” has often been generalized for rates of consumption based on
surface area (b = 2/3) or allometric studies of respiration (2/3 < b < 1) for ectotherms (Koojiman,
13
1986). By describing the bioenergetics of growth in an allometric manner, growth could be
modeled throughout the life of an organism.
Later improvements on bioenergetic models gave rise to dynamic energy budget models
which provide a framework which should theoretically apply to all living organisms in all life
stages (Sousa et al., 2020). The dynamic energy budget theory is based on conservations of
energy and matter such that assimilated matter is divided into structure (growth), reserves, or a
reproductive or developmental component depending on stage (Nisbet et al., 2012).
Biochemical components (e.g., proteins, lipids, carbohydrates) are considered as atomic,
stoichiometric ratios of carbon, hydrogen, oxygen, and nitrogen. Model components such as
consumption are also heavily parameterized (e.g., terms for assimilation, allometric scaling,
temperature dependence) to allow for generalizability of parameters to each system. For
example, the dynamic energy budget model was applied to Pacific oysters with a feeding term
accounting for allometry of body volume and a Holling’s type II function response of ingestion
rates to food ration or a clearance rate modelled to depend on temperature (Ren and Ross, 2001;
Pouvreau et al., 2006). By testing how predictions of these model fit with actual growth data
under different conditions, optimal parameter values can be found. However, it is often
ambiguous as to what these parameter values entail on a biological level. Direct studies of the
physiological processes which the parameters attempt to describe can elucidate the biological
significance or drivers of growth. Many of these physiological processes which predict growth
are related to bioenergetics.
14
Predictors of growth
Growth rates during planktonic larval stages have a strong influence on the survival and
recruitment of marine organisms (Searcy and Sponaugle, 2001; Bergenius et al., 2002;
McCormick and Hoey, 2004). In turn, larval growth rates are often hypothesized to depend on
genetic, environmental, and maternal effects. Biological rates often exhibit large ranges of
natural variation which can be reduced by parsing out the contributions of genetic,
environmental, and interactive effects (Applebaum et al., 2014). At the whole-organismal levels,
several physiological processes have been found to predict larval growth rates independently of
gene expression at the transcript and protein levels (Pace et al., 2006; Pan et al., 2015a; Pan et
al., 2016; Pan et al., 2018). A comparison of larvae from pedigree lines of Crassostrea gigas
showed that 51% of the variation in growth rates could be attributed to clearance rates (Pace et
al., 2006). In larvae of the same species, Pan et al. (2015a) also showed that maximum amino
acid transport capacities at 10 days post-fertilization were predictive of growth rates (R
2
= 0.70)
for larvae from pedigree lines.
Since variation in growth rates between larvae of different pedigree lines could largely be
explained by rates of feeding and nutrient acquisition from the environment, and food is used to
fuel metabolism, it is reasonable to hypothesize that variation in metabolic costs might also
predict growth rates. In cells of living organisms, most of the ATP is consumed by processes
related to biosynthesis (e.g., proteins or nucleic acids) or ion transport (Buttgereit and Brand,
1995). The sodium potassium ATPase hydrolyses ATP to exchange two potassium ions for three
sodium ions, and accounts for a significant fraction of ATP expenditure (Morth et al., 2007). For
larvae from pedigreed genetic lines of C. gigas, the sodium potassium ATPase accounted for
17% of in vivo ATP allocation with the capacity to account for up to 41% of ATP allocation
15
based on enzyme abundance (Pan et al., 2016). The size-specific rate of in vivo transport of
sodium and potassium ions (Na
+
/K
+
-ATPase activity) explained 43% of the variation in growth
rates between larvae from different pedigree lines. The corresponding level of gene expression
for Na
+
/K
+
-ATPase and the total enzyme activity in vitro assay did not predict variance in
growth rate. Pan et al. (2016) concluded from these analyses that gene expression and
biochemical content did not predict physiological rates.
Another metabolically costly process, protein synthesis, was also seen to predict variation
in larval growth. Amongst larvae of pedigree lines of C. gigas, protein synthesis rates 72% of the
variation in growth (Pan et al., 2018). While physiological processes of feeding, nutrient uptake,
ion transport, and biosynthesis can each explain a significant portion of variation in larval
growth, there is still considerable unexplained biological variation. These processes point to an
energetic perspective towards understanding the drivers of larval growth, and there is a need to
study their dynamics and relationships in larvae growing under varying environmental
conditions.
Protein metabolism
Cellular components such as proteins were first described to be in dynamic states of
turnover by Schoenheimer (1942). Enabled, at the time, by the new availability of radioisotopes,
Schoenheimer (1942, pp. 25) noticed that animals were in a “nitrogen equilibrium” where
nitrogenous excretion matched dietary intake. As a result, protein synthesis rates in a non-
growing animal must have been balanced with equal rates of protein degradation. With the
discovery of the lysosome (De Duve et al., 1955), the mechanisms of protein degradation as a
regulator of cellular function and homeostasis were beginning to be established. Since then, the
16
mechanisms of autophagy and the ubiquitin-proteasomal protein degradation systems have also
been elucidated, resulting in Nobel Prize awards in 1974, 2004, and 2016 (De Duve et al., 1955;
Hershko et al., 1979; Ohsumi, 2014). The continual synthesis and degradation of proteins is
necessary for proper maintenance of the cell cycle, homeostasis, and response to the
environment. Protein quality control systems exist in cells to either refold or degrade misfolded
proteins, many of which are newly synthesized (Wickner et al., 1999). Protein degradation has
also been of great interest to studying the mechanisms of growth, particularly in skeletal-muscle
systems where decreases in protein degradation could explain growth (Millward et al., 1975).
Depending on nutritional state or environmental stressors, faster protein growth is either
driven by increases in synthesis relative to degradation, or decreases in protein degradation
(Millward et al., 1975; Houlihan et al., 1988; Mente et al., 2001; Frieder et al., 2018). In these
cases, slower growth under low food or environmental stress is mainly driven by increases in
protein degradation rather than protein synthesis. In scenarios where faster growth occurred, both
protein synthesis and protein degradation rates increased with a net amount of proteins
synthesized for growth. Because protein synthesis is an energetically expensive process
compared to protein degradation (2.1 cf. 0.1 kJ g
−1
protein, Pan et al., 2018), there is likely an
energetic limitation to growth which favors shifts in protein degradation rather than protein
synthesis (Millward et al., 1975). Protein degradation allows for the maintenance of an adequate
amino acid supply for building proteins.
Food availability
The natural food availability for larvae in the wild is often considered to be limiting
(Conover, 1968; Reitzel et al., 2004). Patchiness of plankton in the field is mainly driven by
17
hydrodynamic processes as well as biological factors of species interaction, reproduction, and
behavior (Mackas et al., 1985). Because of the patchiness and dilute resources available in the
plankton, there are evolutionary benefits to matching the timing of plankton abundance. The
match-mismatch hypothesis proposes that the timing in spawns of plankton coincides with the
timing of spawning of their phytoplanktonic foods (Cushing, 1990). This hypothesis has been
supported in several studies showing that larval fish survival is highest during spring blooms,
and that green urchins and blue mussels time their spawning to coincide with phytoplankton
metabolite cues (Starr et al., 1990; Platt et al., 2003). These synchronous examples of phenology
in the planktonic environment demonstrate increased fitness benefits to matching the availability
of food with a vulnerable larval period requiring fast growth. Correlative studies of plankton
populations in the wild are often difficult to study, so other metrics or indices of food limitation
have been tested under laboratory conditions (Olson and Olson, 1989).
Laboratory experiments examining food limitation in planktonic larvae generally involve
comparing larvae reared under “natural” diets or enhanced diets and measuring indices of
morphology, biochemistry, developmental rates, or nutrition (Olson and Olson, 1989). For
example, the measurement of lipid reserves, particularly triacylglycerols, can be used as an index
of food limitation (Gallager et al., 1986). Other lipids and intracellular molecules such as
glycine, cholesterols, free fatty acids, and hydrocarbons were also seen to correlate negatively
with food-limited larvae (Meyer et al., 2007). From a morphological perspective, arm lengths
can also be used as a metric for food limitation in echinoderm larvae (Miner, 2005; Olson and
Olson, 1989). Since there are many different indices of food-limitation, Fenaux et al. (1994)
utilized five different indices to describe food-limited larvae in echinoderm species. By several
comparisons, food-limited larvae grew faster in enhanced laboratory diets as well as in the spring
18
bloom compared to natural diets in the autumn when natural chlorophyll levels are lower
(Fenaux et al., 1994). Larvae in the field also had longer arms and a delayed rudiment than
larvae reared under an enhanced ration. Together, these indices provided evidence that larvae in
the field are often food limited.
Food limitation in the water column also affects growth and metamorphosis during the
planktonic larval duration on a species-specific basis. In an experiment involving planktonic
larvae of invertebrate species, four out of five species tested grew faster on an enhanced
laboratory diet compared to a diet from natural sea water (Paulay et al., 1985). The enhanced diet
also resulted in earlier metamorphosis of larvae to the juvenile stage, decreasing the planktonic
larval duration. In contrast, larvae of the gastropod Crepidula fornicate reached metamorphosis
faster under food-limited conditions rather than enhanced dietary conditions (Pechenik et al.,
1996). The gastropod larvae also grew more slowly during the juvenile stage after larval
exposure to food-limitation. Further studies on the crab Carcinus maenas show that continual
food-limitation can reduce survival beyond the zoea I stage and affect the size at metamorphosis
(Giménez and Anger, 2005; Giménez, 2010).
While food-limitation is generally detrimental to larval stages, larvae can resist the
effects of starvation through the uptake of dissolved organic matter from the environment
(Moran and Manahan, 2004). Studies of the effects of starvation on larvae of the Pacific oyster
C. gigas showed that larvae were able to resume growth after 17 days of starvation at same rates
compared to larvae fed from 2-days old (Moran and Manahan, 2004). Even after 33 days of
starvation, larvae of C. gigas were able to resume feeding and swimming behaviors. Since
metabolism was maintained throughout the starvation period, metabolic demands were met by
the utilization of dissolved organic matter from the environment. Direct experiments enriching
19
the dissolved organic matter in seawater used to grow bivalve larvae under laboratory conditions
suggest that dissolved organic matter can contribute to larval growth (Courtright et al., 1971;
Langdon, 1983). While dissolved organic matter can be used to maintain metabolic rates,
eventually feeding on particulate algal food is required to fuel growth (Moran and Manahan,
2004). These results indicate that while particulate food-limitation may affect larval growth,
dissolved organic matter can partially compensate for metabolic demands until larvae encounter
favorable food environments.
In marine organisms which are starved, protein synthesis rates and degradation rates
remain high (Houlihan et al., 1988; Houlihan et al., 1989; Pace and Manahan, 2006). The
degradation and recycling of proteins during starvation or food limitation is a conserved
mechanism across living organisms. In the yeast Saccharomyces cerevisiae, cells starved of
nitrogen had higher rates of protein degradation and turnover than those that were provided
nutrients (Halvorson 1958; Lopez and Gancedo 1979). For organisms growing with different
food rations, protein growth rates can be explained by excesses in protein synthesis relative to
degradation or nitrogenous excretion. For example, the European lobster Homarus gammarus
had 2-fold higher protein degradation rates when given low rations, 5% body weight, of food
compared to higher rations of 10% body weight (Mente et al., 2001). On the other hand, lobsters
given higher rations of food synthesized the same amounts of proteins but grew faster due to
lower protein degradation rates.
Thermal stress
While there is evidence that food-limitation has an influence on larval growth in natural
conditions, a comparative study of lecithotrophic and planktotrophic larvae suggests that
20
temperature may be a more important driver of larval growth than food-limitation (Hoegh-
Guldberg and Pearse, 1995). Temperature is one of the most significant drivers of species
distribution and biodiversity. As such, its effects on organismal fitness and physiology have long
been of interest, especially in ectotherms. Earlier, seminal work on thermal performance curves
has provided a basic framework for understanding the relationship between fitness and
temperature (Huey and Kingsolver, 1993). Performance traits increase non-linearly from a
critical thermal minimum to an optimum before declining rapidly at the thermal maximum (Huey
and Stevenson, 1979; Schulte et al., 2011). Comparative approaches often have attempted to
relate thermal optimums of performance with habitat distributions or other fitness parameters.
However, interpretations of thermal performance curves can vary widely depending on the
performance trait selected and are often complicated by natural temperature fluctuations, life
history stages, and exposure time (Sinclair et al., 2016).
Performance traits can have interactive effects (antagonistic, additive, or synergistic) that
complicate a measure of overall fitness with temperature. For example, the optimal temperature
for growth rates in salmon was significantly dependent on the thermal sensitivity of feeding
physiology (Brett, 1971). This phenomenon has led others to describe a “metabolic meltdown”
scenario where increased temperatures increase metabolic demands, but sometimes limit
foraging activity or food availability (Huey and Kingsolver, 2019). In these cases, thermal
performance curves of energy consuming processes can be subtracted from energy-gaining
processes (such as feeding) to give a performance curve of net energy gained or lost. Another
example of the underlying principle for the complexities of interacting thermal performances was
described by Kellermann et al. (2019) who showed that the metrics of thermal performance
curves of several traits (standard metabolic rate, fecundity, activity, and egg-to-adult viability)
21
varied more between traits than within traits for Drosophila melanogaster reared at different
temperatures. Thus, understanding the energetic or fitness tradeoffs and interactions that occur
between traits with temperature is important.
While there have been extensive studies using organismal-level methodologies of
understanding thermal performance, others have provided a bottom-up, biochemical, and
mechanistic perspective to thermal tolerance. These studies involve understanding protein
folding, intracellular pH balance, lipid fluidity, and regulatory heat shock responses to changing
temperature (Hochachka and Somero, 2002). In particular, enzyme functions such as catalytic
rates are highly sensitive to temperature as are protein conformation and folding. Organisms of
different genotypes or environments have varying abilities to respond to heat shock, but some
mechanisms for thermal responses are often rapid and reversible such as RNA thermosensors
(Somero, 2018). Given adequate responses to preserving protein functionality and maintaining
adequate oxygen supply, Schmidt-Nielsen (1997, pp. 224-225) also suggests that disparities in
thermal sensitivities of interdependent processes may contribute to thermal death (e.g., when two
processes share a common substrate, a more thermal-sensitive process may deprive the other
process by competitive depletion of the common substrate). From a cellular energetics
perspective, ATP can be considered a “shared substrate” which may be consumed more quickly
by processes (e.g., protein synthesis) that are more sensitive to temperature than others.
One of the most cellularly expensive processes in living organisms is protein synthesis.
Several studies have shown that the cost of protein synthesis in marine invertebrates can account
for over half of the cellular ATP budget (Pace and Manahan, 2006; Pan et al., 2015b, Lee et al.,
2016). Despite the high cost of protein synthesis, it is an essential and necessary cost for driving
marine larval growth. Up to 72% of the growth variation in Crassostrea gigas larvae is explained
22
by protein synthesis rates (Pan et al., 2018). However, few studies have attempted to determine
the relationship between protein synthesis rates and temperature in marine invertebrates, much
less the energetic consequences. Instead, most of the studies for marine organism have centered
around vertebrate fish species. Haschemeyer et al. (1979) measured protein synthesis rates of
several fish species and several tissue types and concluded that the Q 10 of protein synthesis
ranged from 2.5 to 3.5. Similar thermal sensitivities were also measured in rainbow trout muscle
tissue with Q10 of 3.07 – 3.57 depending on thermal acclimation (Loughna and Goldspink, 1985).
However, other experiments have shown that thermal sensitivities can range depending on tissue
types or acclimation temperature (Das and Prosser, 1967). Thermal sensitivities of ATP
production through respiration are more commonly studied in marine invertebrates in the context
of developing an indicator of performance. A comparative review of respiratory Q 10 in adults of
14 urchin species in 28 studies yielded an average Q 10 of 2.15 ± 0.13 (Hughes et al., 2011). In
theory, processes with Q10 values higher than that of respiration would disproportionately
consume the ATP pool at higher temperatures leaving less energy available for other processes.
Pan et al. (2021) demonstrated that high Q10 values of protein synthesis (~3) compared to
respiration (~2) resulted in increases in ATP allocation to protein synthesis which left little ATP
remaining for other processes in C. gigas larvae. These results provided an example where the
uncoupling of protein metabolic processes resulted in a stressful allocation of ATP to the
determinant of growing organisms.
Integrating analyses of feeding and protein metabolic dynamics to study growth
The dynamics of the continuous synthesis and degradation of proteins is central to all of
life whether organisms are growing or not. Because of this, the studies in the present dissertation
23
examine these dynamics in growing larval stages of marine invertebrate species under different
environmental conditions (e.g., food ration and temperature). Additionally, these protein
metabolic dynamics are also examined in relation to rates of feeding to describe changes in the
efficiencies of growth and the utilization of ATP for cellular processes. Overall, protein synthesis
and degradation rates are constitutively high, resulting in low proportions of synthesized proteins
being accreted as growth. Changes in feeding rates and the utilization of ATP under differences
in the environment affect the energetics of growth. The present dissertation highlights the
importance of protein metabolic dynamics in the study of growing organisms.
24
Introduction References
Adams, D.K., Sewell, M.A., Angerer, R.C., and Angerer, L.M. (2011). Rapid adaptation to food
availability by a dopamine-mediated morphogenetic response. Nature Communications,
2, 592.
Allen, J.D. (2008). Size-specific predation on marine invertebrate larvae. The Biological
Bulletin, 214(1), 42-49.
Applebaum, S.L., Ginsburg, D.W., Capron, C.S., and Manahan, D.T. (2013). Expression of
amino acid transporter genes in developmental stages and adult tissues of Antarctic
echinoderms. Polar Biology, 36(9), 1257-1267.
Applebaum, S.L., Pan, T.C.F., Hedgecock, D., and Manahan, D.T. (2014). Separating the nature
and nurture of the allocation of energy in response to global change. Integrative and
Comparative Biology, 54(2), 284-295.
Baldwin, B.S., and Newell, R.I. (1995). Feeding rate responses of oyster larvae (Crassostrea
virginica) to seston quantity and composition. Journal of Experimental Marine Biology
and Ecology, 189(1-2), 77-91.
Bayne, B.L., and Newell, R.C. (1983). Physiological energetics of marine molluscs. In The
Mollusca (pp. 407-515). Academic Press.
Beiras, R., and Widdows, J. (1995). Effect of the neurotransmitters dopamine, serotonin and
norepinephrine on the ciliary activity of mussel (Mytilus edulis) larvae. Marine Biology,
122(4), 597-603.
Bergenius, M.A., Meekan, M.G., Robertson, R.D., and McCormick, M.I. (2002). Larval growth
predicts the recruitment success of a coral reef fish. Oecologia, 131(4), 521-525.
25
Boidron-Metairon, I.F. (1988). Morphological plasticity in laboratory-reared echinoplutei of
Dendraster excentricus (Eschscholtz) and Lytechinus variegatus (Lamarck) in response
to food conditions. Journal of Experimental Marine Biology and Ecology, 119(1), 31-41.
Bouchard, C., Boudko, D.Y., and Jiang, R.H. (2019). A SLC6 transporter cloned from the lion's
mane jellyfish (Cnidaria, Scyphozoa) is expressed in neurons. PloS one, 14(6), DOI:
e0218806.
Boudko, D. Y. (2012). Molecular basis of essential amino acid transport from studies of insect
nutrient amino acid transporters of the SLC6 family (NAT-SLC6). Journal of Insect
Physiology, 58(4), 433-449.
Boudko, D.Y., Kohn, A.B., Meleshkevitch, E.A., Dasher, M.K., Seron, T.J., Stevens, B.R., and
Harvey, W.R. (2005). Ancestry and progeny of nutrient amino acid transporters.
Proceedings of the National Academy of Sciences, 102(5), 1360-1365.
Braven, J., Evens, R., and Butler, E.I. (1984). Amino acids in sea water. Chemistry in Ecology,
2(1), 11-21.
Brett, J.R. (1971). Energetic responses of salmon to temperature. A study of some thermal
relations in the physiology and freshwater ecology of sockeye salmon (Oncorhynchus
nerkd). American Zoologist, 11(1), 99-113.
Brӧer, S., and Gether, U. (2012). The solute carrier 6 family of transporters. British Journal of
Pharmacology, 167(2), 256-278.
Buttgereit, F., and Brand, M.D. (1995). A hierarchy of ATP-consuming processes in mammalian
cells. Biochemical Journal, 312(1), 163-167.
26
Cabrol, J., Fabre, A., Nozais, C., Tremblay, R., Starr, M., Plourde, S., and Winkler, G. (2020).
Functional feeding response of Nordic and Arctic krill on natural phytoplankton and
zooplankton. Journal of Plankton Research, 42(2), 239-252.
Calow, P. (1982). Homeostasis and fitness. The American Naturalist, 120(3), 416-419.
Claxton, D.P., Quick, M., Shi, L., Delmondes de Carvalho, F., Weinstein, H., Javitch, J.A., and
Mchaourab, H.S. (2010). Ion/substrate-dependent conformational dynamics of a bacterial
homolog of neurotransmitter:sodium symporters. Nature Structural and Molecular
Biology, 17(7), 822-829.
Coleman, J.A., Green, E.M., and Gouaux, E. (2016). X-ray structures and mechanism of the
human serotonin transporter. Nature, 532(7599), 334-339.
Conover, R.J. (1968). Zooplankton—life in a nutritionally dilute environment. American
Zoologist, 8(1), 107-118.
Courtright, R.C., Breese, W.P., and Krueger, H. (1971). Formulation of a synthetic seawater for
bioassays with Mytilus edulis embryos. Water Research, 5(10), 877-888.
Crisp, D.J., Yule, A.B., and White, K.N. (1985). Feeding by oyster larvae: the functional
response, energy budget and a comparison with mussel larvae. Journal of the Marine
Biological Association of the United Kingdom, 65(3), 759-783.
Cushing, D.H. (1990). Plankton production and year-class strength in fish populations: an update
of the match/mismatch hypothesis. In: Advances in Marine Biology (Vol. 26, pp. 249-
293). Academic Press.
Das, A.B., and Prosser, C.L. (1967). Biochemical changes in tissues of goldfish acclimated to
high and low temperatures—I. Protein synthesis. Comparative Biochemistry and
Physiology, 21(3), 449-467.
27
De Duve, C., Pressman, B.C., Gianetto, R., Wattiaux, R., and Appelmans, F. (1955). Tissue
fractionation studies. 6. Intracellular distribution patterns of enzymes in rat-liver tissue.
Biochemical Journal, 60(4), 604.
Deevey Jr, E.S. (1947). Life tables for natural populations of animals. The Quarterly Review of
Biology, 22(4), 283-314.
Fenaux, L., Strathmann, M.F., and Strathmann, R.A. (1994). Five tests of food‐limited growth of
larvae in coastal waters by comparisons of rates of development and form of
echinoplutei. Limnology and Oceanography, 39(1), 84-98.
Ferreira-Arrieta, A., García-Esquivel, Z., González-Gómez, M.A., and Valenzuela-Espinoza, E.
(2015). Growth, survival, and feeding rates for the geoduck Panopea globosa during
larval development. Journal of Shellfish Research, 34(1), 55-61.
Frieder, C.A., Applebaum, S.L., Pan, T.C.F., and Manahan, D.T. (2018). Shifting balance of
protein synthesis and degradation sets a threshold for larval growth under environmental
stress. The Biological Bulletin, 234(1), 45-57.
Frost, B.W. (1972). Effects of size and concentration of food particles on the feeding behavior of
the marine planktonic copepod Calanus pacificus 1. Limnology and oceanography, 17(6),
805-815.
Frost, B.W. (1975). A threshold feeding behavior in Calanus pacificus 1. Limnology and
Oceanography, 20(2), 263-266.
Frost, B.W. (1977). Feeding behavior of Calanus pacificus in mixtures of food particles 1.
Limnology and Oceanography, 22(3), 472-491.
28
Fry, J.P., Mailloux, N.A., Love, D.C., Milli, M.C., and Cao, L. (2018). Feed conversion
efficiency in aquaculture: do we measure it correctly? Environmental Research Letters,
13(2), 024017.
Gallager, S.M., Mann, R., and Sasaki, G.C. (1986). Lipid as an index of growth and viability in
three species of bivalve larvae. Aquaculture, 56(2), 81-103.
Giménez, L. (2010). Relationships between habitat conditions, larval traits, and juvenile
performance in a marine invertebrate. Ecology, 91(5), 1401-1413.
Gimenez, L., and Anger, K. (2005). Effects of temporary food limitation on survival and
development of brachyuran crab larvae. Journal of Plankton Research, 27(5), 485-494.
Hadfield, M.G., Carpizo-Ituarte, E.J., Del Carmen, K., and Nedved, B.T. (2001). Metamorphic
competence, a major adaptive convergence in marine invertebrate larvae. American
Zoologist, 41(5), 1123-1131.
Halvorson, H. (1958). Intracellular protein and nucleic acid turnover in resting yeast cells.
Biochimica et Biophysica Acta, 27, 255-266.
Hart, M.W. (1996). Variation in suspension feeding rates among larvae of some temperate,
Eastern Pacific echinoderms. Invertebrate Biology, 115(1), 30-45.
Hart, M.W., and Strathmann, R.R. (1994). Functional consequences of phenotypic plasticity in
echinoid larvae. The Biological Bulletin, 186(3), 291-299.
Harvey, H.W. (1937). Note on selective feeding by Calanus. Journal of the Marine Biological
Association of the United Kingdom, 22(1), 97-100.
Haschemeyer, A.E., Persell, R., and Smith, M.A. (1979). Effect of temperature on protein
synthesis in fish of the Galapagos and Perlas Islands. Comparative Biochemistry and
Physiology. B, Comparative biochemistry, 64(1), 91-95.
29
Herrera, J.C., McWeeny, S.K., and McEdward, L.R., (1996). Diversity of energetic strategies
among echinoid larvae and the transition from feeding to nonfeeding development.
Oceanologica, 19(3-4), 313-321.
Hershko, A., Ciechanover, A., and Rose, I.A. (1979). Resolution of the ATP-dependent
proteolytic system from reticulocytes: a component that interacts with ATP. Proceedings
of the National Academy of Sciences, 76(7), 3107-3110.
Hochachka, P.W., and Somero, G.N. (2002). Biochemical Adaptation: Mechanism and Process
in Physiological Evolution. Oxford university press.
Hoegh-Guldberg, O. (1994). Uptake of dissolved organic matter by larval stage of the crown-of-
thorns starfish Acanthaster planci. Marine Biology, 120(1), 55-63.
Hoegh-Guldberg, O.V.E., and Pearse, J.S. (1995). Temperature, food availability, and the
development of marine invertebrate larvae. American Zoologist, 35(4), 415-425.
Houlihan, D.F., Hall, S.J., and Gray, C. (1989). Effects of ration on protein turnover in cod.
Aquaculture, 79(1-4), 103-110.
Houlihan, D.F., Hall, S.J., Gray, C., and Noble, B.S. (1988). Growth rates and protein turnover in
Atlantic cod, Gadus morhua. Canadian Journal of Fisheries and Aquatic Sciences, 45(6),
951-964.
Huey, R.B., and Kingsolver, J.G. (1993). Evolution of resistance to high temperature in
ectotherms. The American Naturalist, 142, S21-S46.
Huey, R.B., and Kingsolver, J.G. (2019). Climate warming, resource availability, and the
metabolic meltdown of ectotherms. The American Naturalist, 194(6), E140-E150.
Huey, R.B., and Stevenson, R.D. (1979). Integrating thermal physiology and ecology of
ectotherms: a discussion of approaches. American Zoologist, 19(1), 357-366.
30
Hughes, S.J.M., Ruhl, H.A., Hawkins, L.E., Hauton, C., Boorman, B., and Billett, D.S. (2011).
Deep-sea echinoderm oxygen consumption rates and an interclass comparison of
metabolic rates in Asteroidea, Crinoidea, Echinoidea, Holothuroidea and Ophiuroidea.
Journal of Experimental Biology, 214(15), 2512-2521.
Huntley, M. (1988). Feeding biology of Calanus: a new perspective. In Biology of Copepods (pp.
83-99). Springer, Dordrecht.
Inoue, K., Tsukuda, K., Koito, T., Miyazaki, Y., Hosoi, M., Kado, R., Miyazaki, N., and
Toyohara, H. (2008). Possible role of a taurine transporter in the deep-sea mussel
Bathymodiolus septemdierum in adaptation to hydrothermal vents. FEBS Letters,
582(10), 1542-1546.
Jaeckle, W.B., and Manahan, D.T. (1989a). Feeding by a “nonfeeding” larva: uptake of
dissolved amino acids from seawater by lecithotrophic larvae of the gastropod Haliotis
rufescens. Marine Biology, 103(1), 87-94.
Jaeckle, W.B., and Manahan, D.T. (1989b). Growth and energy imbalance during the
development of a lecithotrophic molluscan larva (Haliotis rufescens). The Biological
Bulletin, 177(2), 237-246.
Kalachev, A.V. (2020). Effect of dopamine on early larvae of sea urchins, Mesocentrotus nudus
and Strongylocentrotus intermedius. Journal of Experimental Zoology Part B: Molecular
and Developmental Evolution, 334(6), 373-380.
Kaushik, S.J., and Seiliez, I. (2010). Protein and amino acid nutrition and metabolism in fish:
current knowledge and future needs. Aquaculture Research, 41(3), 322-332.
31
Kellermann, V., Chown, S.L., Schou, M.F., Aitkenhead, I., Janion-Scheepers, C., Clemson, A.,
Scott, M.T., and Sgrò, C.M. (2019). Comparing thermal performance curves across traits:
how consistent are they? Journal of Experimental Biology, 222(11), DOI: jeb193433.
Kiørboe, T., Saiz, E., Tiselius, P., and Andersen, K.H. (2018). Adaptive feeding behavior and
functional responses in zooplankton. Limnology and Oceanography, 63(1), 308-321.
Koito, T., Nakamura-Kusakabe, I., Yoshida, T., Maruyama, T., Omata, T., Miyazaki, N., and
Inoue, K. (2010). Effect of long-term exposure to sulfides on taurine transporter gene
expression in the gill of the deep-sea mussel Bathymodiolus platifrons, which harbors a
methanotrophic symbiont. Fisheries Science, 76(2), 381-388.
Kooijman, S.A.L.M. (1986). Energy budgets can explain body size relations. Journal of
Theoretical Biology, 121(3), 269-282.
Krogh, A. (1931). Dissolved substances as food of aquatic organisms. Biological Reviews, 6(4),
412-442.
Lacalli, T.C., and Gilmour, T.H.J. (1990). Ciliary reversal and locomotory control in the pluteus
larva of Lytechinus pictus. Philosophical Transactions: Biological Sciences, 330, 391-
396.
Langdon, C.J. (1983). Growth studies with bacteria-free oyster (Crassostrea gigas) larvae fed on
semi-defined artificial diets. The Biological Bulletin, 164(2), 227-235.
Lee, J.W., Applebaum, S.L., and Manahan, D.T. (2016). Metabolic cost of protein synthesis in
larvae of the Pacific oyster (Crassostrea gigas) is fixed across genotype, phenotype, and
environmental temperature. The Biological Bulletin, 230(3), 175-187.
Lee, C., and Bada, J.L. (1977). Dissolved amino acids in the equatorial Pacific, the Sargasso Sea,
and Biscayne Bay 1. Limnology and Oceanography, 22(3), 502-510.
32
López, S., and Gancedo, J.M. (1979). Effect of metabolic conditions on protein turnover in yeast.
Biochemical Journal, 178(3), 769-776.
Loughna, P.T., and Goldspink, G. (1985). Muscle protein synthesis rates during temperature
acclimation in a eurythermal (Cyprinus carpio) and a stenothermal (Salmo gairdneri)
species of teleost. Journal of Experimental Biology, 118(1), 267-276.
Mackas, D.L., Denman, K.L., and Abbott, M.R. (1985). Plankton patchiness: biology in the
physical vernacular. Bulletin of Marine Science, 37(2), 652-674.
Manahan, D.T. (1989). Amino acid fluxes to and from seawater in axenic veliger larvae of a
bivalve (Crassostrea gigas). Marine Ecology Progress Series, 53(3), 247-255.
Manahan, D.T., Davis, J.P., and Stephens, G.C. (1983). Bacteria-free sea urchin larvae: selective
uptake of neutral amino acids from seawater. Science, 220(4593), 204-206.
Marshall, D.J., Krug, P.J., Kupriyanova, E.K., Byrne, M., and Emlet, R.B. (2012). The
biogeography of marine invertebrate life histories. Annual Review of Ecology, Evolution,
and Systematics, 43, 97-114.
Marshall, R., McKinley, S., and Pearce, C.M. (2010). Effects of nutrition on larval growth and
survival in bivalves. Reviews in Aquaculture, 2(1), 33-55.
McCormick, M.I., and Hoey, A.S. (2004). Larval growth history determines juvenile growth and
survival in a tropical marine fish. Oikos, 106(2), 225-242.
Mente, E., Houlihan, D.F., and Smith, K. (2001). Growth, feeding frequency, protein turnover,
and amino acid metabolism in European lobster Homarus gammarus L. Journal of
Experimental Zoology, 289(7), 419-432.
33
Meyer, E., Green, A.J., Moore, M., and Manahan, D.T. (2007). Food availability and
physiological state of sea urchin larvae (Strongylocentrotus purpuratus). Marine Biology,
152(1), 179-191.
Meyer, E., and Manahan, D.T. (2009). Nutrient uptake by marine invertebrates: cloning and
functional analysis of amino acid transporter genes in developing sea urchins
(Strongylocentrotus purpuratus). Biological Bulletin, 217(1), 6-24.
Moran, A.L., and Manahan, D.T. (2004). Physiological recovery from prolonged ‘starvation’ in
larvae of the Pacific oyster Crassostrea gigas. Journal of Experimental Marine Biology
and Ecology, 306(1), 17-36.
Morth, J.P., et al. (2007). Crystal structure of the sodium–potassium pump. Nature, 450(7172),
1043-1049.
Millward, D.J., Garlick, P.J., Stewart, R.J., Nnanyelugo, D.O., and Waterlow, J.C. (1975).
Skeletal-muscle growth and protein turnover. Biochemical Journal, 150(2), 235-243.
Miner, B.G. (2005). Evolution of feeding structure plasticity in marine invertebrate larvae: a
possible trade-off between arm length and stomach size. Journal of Experimental Marine
Biology and Ecology, 315(2), 117-125.
Miyamoto, N., Yoshida, M.A., Koga, H., and Fujiwara, Y. (2017). Genetic mechanisms of bone
digestion and nutrient absorption in the bone-eating worm Osedax japonicus inferred
from transcriptome and gene expression analyses. BMC Evolutionary Biology, 17(1), 1-
13.
Nisbet, R.M., Jusup, M., Klanjscek, T., and Pecquerie, L. (2012). Integrating dynamic energy
budget (DEB) theory with traditional bioenergetic models. Journal of Experimental
Biology, 215(6), 892-902.
34
Ohsumi, Y. (2014). Historical landmarks of autophagy research. Cell Research, 24, 9-23.
Olmi, E.J. (1994). Vertical migration of blue crab Callinectes sapidus megalopae: implications
for transport in estuaries. Marine Ecology Progress Series, 113, 39-54.
Olson, R.R., and Olson, M.H. (1989). Food limitation of planktotrophic marine invertebrate
larvae: does it control recruitment success? Annual Review of Ecology and Systematics,
20(1), 225-247.
Ostrovsky, I. (1995). The parabolic pattern of animal growth: determination of equation
parameters and their temperature dependencies. Freshwater Biology, 33(3), 357-371.
Pace, D.A., and Manahan, D.T. (2006). Fixed metabolic costs for highly variable rates of protein
synthesis in sea urchin embryos and larvae. Journal of Experimental Biology, 209(1),
158-170.
Pace, D.A., Marsh, A.G., Leong, P.K., Green, A.J., Hedgecock, D., and Manahan, D.T. (2006).
Physiological bases of genetically determined variation in growth of marine invertebrate
larvae: a study of growth heterosis in the bivalve Crassostrea gigas. Journal of
Experimental Marine Biology and Ecology, 335(2), 188-209.
Palmer, A.R., and Strathmann, R.R. (1981). Scale of dispersal in varying environments and its
implications for life histories of marine invertebrates. Oecologia, 48(3), 308-318.
Pan, T.C.F., Applebaum, S.L., Frieder, C.A., and Manahan, D.T. (2018). Biochemical bases of
growth variation during development: a study of protein turnover in pedigreed families of
bivalve larvae (Crassostrea gigas). Journal of Experimental Biology, 221(10), DOI:
jeb.171967.
35
Pan, T.C.F., Applebaum, S.L., Lentz, B.A., and Manahan, D.T. (2016). Predicting phenotypic
variation in growth and metabolism of marine invertebrate larvae. Journal of
Experimental Marine Biology and Ecology, 483, 64-73.
Pan, T.C.F., Applebaum, S.L., and Manahan, D.T. (2015a). Genetically determined variation in
developmental physiology of bivalve larvae (Crassostrea gigas). Physiological and
Biochemical Zoology, 88(2), 128-136.
Pan, T.C.F, Applebaum, S.L., and Manahan, D.T. (2015b). Experimental ocean acidification
alters the allocation of metabolic energy. Proceedings of the National Academy of
Sciences, 112(15), 4696-4701.
Pan, T.C.F., Applebaum, S.L., and Manahan, D.T. (2021). Differing thermal sensitivities of
physiological processes alter ATP allocation. Journal of Experimental Biology, 224(2),
DOI: jeb233379.
Paulay, G., Boring, L., and Strathmann, R.R. (1985). Food limited growth and development of
larvae: experiments with natural sea water. Journal of Experimental Marine Biology and
Ecology, 93(1-2), 1-10.
Pearl, R., and Miner, J.R. (1935). Experimental studies on the duration of life. XIV. The
comparative mortality of certain lower organisms. The Quarterly Review of Biology,
10(1), 60-79.
Pearse, J.S. (1994). Cold-water echinoderms break Thorson’s rule. Reproduction, Larval Biology
and Recruitment in the Deep-sea Benthos, 27-43.
Pechenik, J.A. (1980). Growth and energy balance during the larval lives of three prosobranch
gastropods. Journal of Experimental Marine Biology and Ecology, 44(1), 1-28.
36
Pechenik, J.A. (1999). On the advantages and disadvantages of larval stages in benthic marine
invertebrate life cycles. Marine Ecology Progress Series, 177, 269-297.
Pechenik, J.A., Estrella, M.S., and Hammer, K. (1996). Food limitation stimulates
metamorphosis of competent larvae and alters postmetamorphic growth rate in the marine
prosobranch gastropod Crepidula fornicata. Marine Biology, 127(2), 267-275.
Penmatsa, A., Wang, K.H., and Gouaux, E. (2013). X-ray structure of dopamine transporter
elucidates antidepressant mechanism. Nature, 503(7474), 85-90.
Platt, T., Fuentes-Yaco, C., and Frank, K.T. (2003). Spring algal bloom and larval fish survival.
Nature, 423(6938), 398-399.
Pouvreau, S., Bourles, Y., Lefebvre, S., Gangnery, A., and Alunno-Bruscia, M. (2006).
Application of a dynamic energy budget model to the Pacific oyster, Crassostrea gigas,
reared under various environmental conditions. Journal of Sea Research, 56(2), 156-167.
Pütter, A. (1909). Der ernahrung der wassertiere und der stoffhaushalt der gewasser”. Gustav
Fischer, Jena.
Raubenheimer, D., and Simpson, S.J. (1993). The geometry of compensatory feeding in the
locust. Animal Behaviour, 45(5), 953-964.
Raubenheimer, D., and Simpson, S.J. (2018). Nutritional ecology and foraging theory. Current
Opinion in Insect Science, 27, 38-45.
Reitzel, A.M., Webb, J., and Arellano, S. (2004). Growth, development and condition of
Dendraster excentricus (Eschscholtz) larvae reared on natural and laboratory diets.
Journal of Plankton Research, 26(8), 901-908.
Ren, J.S., and Ross, A.H. (2001). A dynamic energy budget model of the Pacific oyster
Crassostrea gigas. Ecological Modelling, 142(1-2), 105-120.
37
Ren, P., Wei, J., Yu, H., and Dong, B. (2019). Identification and functional characterization of
solute carrier family 6 genes in Ciona savignyi. Gene, 705, 142-148.
Rico-Villa, B., Pouvreau, S., and Robert, R. (2009). Influence of food density and temperature
on ingestion, growth and settlement of Pacific oyster larvae, Crassostrea gigas.
Aquaculture, 287(3-4), 395-401.
Rico-Villa, B., Bernard, I., Robert, R., and Pouvreau, S. (2010). A Dynamic Energy Budget
(DEB) growth model for Pacific oyster larvae, Crassostrea gigas. Aquaculture, 305(1-4),
84-94.
Robert, R., Vignier, J., and Petton, B. (2017). Influence of feeding regime and temperature on
development and settlement of oyster Ostrea edulis (Linnaeus, 1758) larvae. Aquaculture
Research, 48(9), 4756-4773.
Rumrill, S.S. (1990). Natural mortality of marine invertebrate larvae. Ophelia, 32(1-2), 163-198.
Schmidt-Nielsen, K. (1997). Animal physiology: adaptation and environment. Cambridge
University Press.
Schoener, T.W. (1971). Theory of feeding strategies. Annual Review of Ecology and Systematics,
2(1), 369-404.
Schoenheimer, R. (1942). The dynamic state of body constituents. The Dynamic State of Body
Constituents.
Schulte, P.M., Healy, T.M., and Fangue, N.A. (2011). Thermal performance curves, phenotypic
plasticity, and the time scales of temperature exposure. Integrative and Comparative
Biology, 51(5), 691-702.
Searcy, S.P., and Sponaugle, S.U. (2001). Selective mortality during the larval–juvenile
transition in two coral reef fishes. Ecology, 82(9), 2452-2470.
38
Sewell, M.A., Cameron, M.J., and McArdle, B.H. (2004). Developmental plasticity in larval
development in the echinometrid sea urchin Evechinus chloroticus with varying food
ration. Journal of Experimental Marine Biology and Ecology, 309, 219-237.
Shahsavar, A., et al. (2021). Structural insights into the inhibition of glycine reuptake. Nature,
591(7851), 677-681.
Shanks, A L. (2009). Pelagic larval duration and dispersal distance revisited. The Biological
Bulletin, 216(3), 373-385.
Shanks, A.L., Grantham, B.A., and Carr, M.H. (2003). Propagule dispersal distance and the size
and spacing of marine reserves. Ecological Applications, 13(1), 159-169.
Shilling, F.M., and Manahan, D.T. (1990). Energetics of early development for the sea urchins
Strongylocentrotus purpuratus and Lytechinus pictus and the crustacean Artemia sp.
Marine Biology, 106(1), 119-127.
Shilling, F.M., and Manahan, D.T. (1994). Energy metabolism and amino acid transport during
early development of Antarctic and temperate echinoderms. The Biological Bulletin,
187(3), 398-407.
Sinclair, B.J., Marshall, K.E., Sewell, M.A., Levesque, D.L., Willett, C.S., Slotsbo, S., Dong, Y.,
Harley, C.D.G., Marshall, D.J., Helmuth, B.S., and Huey, R.B. (2016). Can we predict
ectotherm responses to climate change using thermal performance curves and body
temperatures? Ecology Letters, 19(11), 1372-1385.
Somero, G.N. (2018). RNA thermosensors: how might animals exploit their regulatory potential?
Journal of Experimental Biology, 221(4), DOI: jeb.162842.
39
Sousa, T., Domingos, T., Poggiale, J.C., and Kooijman, S.A.L.M. (2010). Dynamic energy
budget theory restores coherence in biology. Philosophical Transactions of the Royal
Society B: Biological Sciences, 365(1557), 3413-3428.
Sponaugle, S., et al. (2002). Predicting self-recruitment in marine populations: biophysical
correlates and mechanisms. Bulletin of Marine Science, 70(1), 341-375.
Starr, M., Himmelman, J.H., and Therriault, J.C. (1990). Direct coupling of marine invertebrate
spawning with phytoplankton blooms. Science, 247(4946), 1071-1074.
Starrfelt, J., and Kokko, H. (2010). Parent-offspring conflict and the evolution of dispersal
distance. The American Naturalist, 175(1), 38-49.
Stephens, G.C. (1988). Epidermal amino acid transport in marine invertebrates. Biochemica et
Biophysica, 947(1), 113-138.
Stephens, G.C., and Schinske, R.A. (1961). Uptake of amino acids by marine invertebrates.
Limnology and Oceanography, 6(2), 175-181.
Strathmann, R.R. (1971). The feeding behavior of planktotrophic echinoderm larvae:
mechanisms, regulation, and rates of suspension feeding. Journal of Experimental
Marine Biology and Ecology, 6(2), 109-160.
Strathmann, R.R. (1985). Feeding and nonfeeding larval development and life-history evolution
in marine invertebrates. Annual Review of Ecology and Systematics, 16(1), 339-361.
Strathmann, R.R. (2007). Time and extent of ciliary response to particles in non-filtering feeding
Mechanism. The Biological Bulletin, 212, 93-103.
Strathmann, R.R., and Grunbaum, D., (2006). Good eaters, poor swimmers: compromises in
larval form. Integrative and Comparative Biology, 46(3), 312-322.
40
Strathmann, R.R., and Leise, E. (1979). On feeding mechanisms and clearance rates of
molluscan veligers. The Biological Bulletin, 157(3), 524-535.
Strathmann, R.R., Jahn, T.L., and Fonseca, J.R. (1972). Suspension feeding by marine
invertebrate larvae: clearance of particles by ciliated bands of a rotifer, pluteus, and
trochophore. The Biological Bulletin, 142(3), 505-519.
Taghon, G.L. (1981). Beyond selection: optimal ingestion rate as a function of food value. The
American Naturalist, 118(2), 202-214.
Thor, P., and Wendt, I. (2010). Functional response of carbon absorption efficiency in the
pelagic calanoid copepod Acartia tonsa. Limnology and Oceanography, 55(4), 1779-
1789.
Thorson, G. (1950). Reproductive and larval ecology of marine bottom invertebrates. Biological
Reviews, 25(1), 1-45.
Trivers, R.L. (1974). Parent-offspring conflict. Integrative and Comparative Biology, 14(1), 249-
264.
Von Bertalanffy, L. (1938). A quantitative theory of organic growth (inquiries on growth laws.
II). Human Biology, 10(2), 181-213.
Von Bertalanffy, L. (1957). Quantitative laws in metabolism and growth. The Quarterly Review
of Biology, 32(3), 217-231.
Wada, Y., Mogami, Y., and Baba, S.A. (1997). Modification of ciliary beating in sea urchin
larvae induced by neurotransmitters: Beat-plane rotation and control of frequency
fluctuation. Journal of Experimental Biology, 200, 9-18.
Wickner, S., Maurizi, M.R., and Gottesman, S. (1999). Posttranslational quality control: folding,
refolding, and degrading proteins. Science, 286(5446), 1888-1893.
41
Winberg, G.G. (1956). Rate of metabolism and food requirements of fish. Journal of the
Fisheries Research Board of Canada, Translation Series, 194, 1-253.
Wipf, D., Ludewig, U., Tegeder, M., Rentsch, D., Koch, W., and Frommer, W.B. (2002).
Conservation of amino acid transporters in fungi, plants, and animals. Trends in
Biochemical Sciences, 27(3), 139-147.
Wright, S.H. (1982). A nutritional role for amino acid transport in filter-feeding marine
invertebrates. American Zoologist, 22(3), 621-634.
Wright, S.H., and Secomb, T.W. (1986). Epithelial amino acid transport in marine mussels: role
in net exchange of taurine between gills and sea water. Journal of Experimental Biology,
121(1), 251-270.
Xun, X., et al. (2020). Solute carriers in scallop genome: Gene expansion and expression
regulation after exposure to toxic dinoflagellate. Chemosphere, 241, DOI:
j.chemosphere.2019.124968.
Yamashita, A., Singh, S.K., Kawate, T., Jin, Y., and Gouaux, E. (2005). Crystal structure of a
bacterial homologue of Na
+
/Cl
-
-dependent neurotransmitter transporters. Nature,
437(7056), 215-223.
Yoshihiro, M., Keiko, W., Chieko, O., Akemi, K., and Baba, S.A. (1992). Regulation of ciliary
movement in sea urchin embryos: dopamine and 5-HT change the swimming behaviour.
Comparative Biochemistry and Physiology Part C: Comparative Pharmacology, 101(2),
251-254.
Zhang, G., et al. (2012). The oyster genome reveals stress adaptation and complexity of shell
formation. Nature, 490(7418), 49-54.
42
CHAPTER 1
Protein Synthesis Rates Exceed Ingestion Rates in Growing Larval Stages
ABSTRACT
The efficiency with which marine invertebrate larvae use food for growth has strong
influences on their biology. The biochemical mechanisms of larval growth at the initiation of
feeding remain poorly understood. In this study, larvae of Strongylocentrotus purpuratus were
reared at a constant algal diet of 20,000 cells ml
-1
, and rates of protein ingestion, synthesis,
degradation, and accretion were measured. Proteins were a major biochemical constituent in
growing larvae with 3-fold higher protein masses compared to lipids by the end of the period of
study. Larvae grew in midline body length by 1.4-fold (244 ± 7 to 350 ± 16 µm) over the study
period. Larvae also accreted protein mass resulting in 6.7-fold increases in total body protein
(18.2 ± 1.3 to 121.0 ± 12.4 ng) over the study period. Protein synthesis rates exceeded protein
accretion rates across the range of protein contents measured (ANCOVA F1,45 = 207.34, p <
0.0001), with the proportions of protein accretion to synthesis decreasing from 37% to 18% with
mass (ANOVA regression, p = 0.004). The protein-food conversion efficiency, a ratio of protein
accretion to ingestion, increased with growth from 33% to 45% (ANOVA regression, p = 0.015),
with an average of 37.4% ± 2.2 (n=22). Notably, protein synthesis rates exceeded rates of
protein ingestion from the onset of feeding (larvae of approximately 18 ng total protein), and the
magnitude of this difference in protein rates increased as larvae grew (ANCOVA Slope: F 1,41 =
16.67, p = 0.0002). Since the amount of protein biomass synthesized exceeded the protein mass
43
acquired from ingestion, degradation of proteins was required to supply biomass (i.e., amino
acids) for protein synthesis. As larvae grew, protein degradation rates increased such that 72% of
the mass of proteins synthesized by 12-day old larvae (121 ng total protein) were degraded, and
only 18% was accreted as growth. Protein synthesis rates explained most of the variation in
protein accretion rates (R
2
= 0.898). This work highlights the importance of the dynamics of
protein degradation and food conversion efficiency in understanding the growth of larvae.
44
INTRODUCTION
Cellular components such as proteins were first described to be in dynamic states of
turnover by Schoenheimer (1942) (reviewed by Ohsumi, 2014). Enabled, at the time, by the new
availability of radioisotopes, Schoenheimer (1942, pp. 25) noticed that animals were in a
“nitrogen equilibrium” where nitrogenous excretion matched dietary intake. As a result, protein
synthesis rates in a non-growing animal must have been balanced with equal rates of protein
degradation. With the discovery of the lysosome (De Duve et al., 1955), the mechanisms of
protein degradation as a regulator of cellular function and homeostasis were beginning to be
established. Since then, the mechanisms of autophagy and the ubiquitin-proteasomal protein
degradation systems have also been elucidated, resulting in Nobel Prize awards in 1974, 2004,
and 2016 (De Duve et al., 1955; Hershko et al., 1979; Ohsumi, 2014). The continual synthesis
and degradation of proteins is necessary for proper maintenance of the cell cycle, homeostasis,
and response to the environment. Protein degradation has also been of great interest to studying
the mechanisms of growth, particularly in skeletal-muscle systems where decreases in protein
degradation could explain growth (Millward et al., 1975). The role of protein degradation in
growth has continued to be studied in taxa ranging from bacteria to plants, fungi, and vertebrates
(Houlihan et al., 1988; Lahtvee et al., 2014; Li et al., 2017; Martin-Perez and Villén, 2017).
Even in prokaryotes, protein turnover increases 7-fold with corresponding 5-fold increases in
growth rate (Lahtvee et al., 2014). While protein degradation has been studied in vertebrate and
other systems, little is known about the roles of protein degradation in marine invertebrates.
Understanding how these processes drive growth has important implications for species with
growing larval stages.
45
Growing in the water column, the larvae of benthic marine invertebrates are subject to
many vulnerabilities. Survivorship or recruitment are often thought to depend on larval growth
through a planktonic duration (Rumril, 1990). For planktotrophic larvae, feeding and nutrient
transport is essential for larval survival and growth within a limited time frame (Strathmann,
1985; Moran and Manahan, 2004). While embryos contain maternally derived biochemical
substrates, the environment plays a strong role in determining the amount of energy that is
available to larvae for growth and their duration in the planktonic environment (Jaeckle and
Manahan, 1989; Bertram and Strathmann, 1998). The abilities of marine invertebrate larvae to
feed in the plankton is subject to endogenous biological variation. For example, genetic crosses
of the Pacific oyster Crassostrea gigas (Thunberg, 1793) produced larval families in which
larval feeding rates explained over 50% of variation in growth rates between families (Pace et
al., 2006). While it is logical that higher feeding may result in faster growth, the mechanisms,
and efficiencies of food conversion to biomass are less understood in marine larvae.
Food conversion efficiency (i.e., the ratio of mass grown to mass consumed) in marine
organisms is often studied in the context of aquaculture production of commercially important
species (Sun et al., 2016). In marine systems, food conversion efficiency is often described as a
function of protein content or caloric potential of feeds (Sedgwick 1979; Winfree and Stickney
1981; Kaushik and Seiliez, 2010). Compared to livestock production, many marine aquaculture
species have higher food conversion efficiencies (e.g., 10%-17% for cattle compared to >50%
for fish and shrimp), and require high protein diets (Kaushik and Seiliez, 2010; Fry et al., 2018).
In addition to species differences, environmental factors such as temperature or salinity may also
provide a range of optimal conditions for higher food conversion efficiencies in fish species
(Imsland et al., 2001; Handeland et al., 2008). For marine larvae, the ability to grow to a larger
46
size is related to benefits in survivorship and the probability of settling in a higher quality habitat
(Marshall and Keough, 2008). Because an organism’s efficiency to grow biomass from its diet
affects its survival in the plankton, it is important to understand the mechanisms that drive
protein metabolism.
A common indicator of dietary quality is protein content (Winfree and Stickney 1981;
Singh et al., 2018; Hua et al., 2019). Within organisms, protein is constantly being synthesized
and degraded in a process of degradation which ultimately affects growth (Waterlow, 1995;
Ohsumi, 2014). Due to the balance between synthesis and degradation, protein accretion can be
viewed as an excess of protein synthesis relative to protein degradation (Houlihan et al., 1988).
From an energetic perspective, ATP allocated to the process of protein synthesis can also
account for more than half of the cellular ATP budget in marine invertebrates (Pan et al., 2015).
With these dynamics in consideration, protein growth in marine organisms is an energetically
costly process that requires on dietary input and cellular recycling of protein biomass.
The present study examined the balance between protein ingestion, synthesis,
degradation, and accretion in larvae of Strongylocentrotus purpuratus reared in a stable food
environment to elucidate the importance of protein metabolic dynamics in feeding-stage larvae.
Together, these processes were integrated into a conceptual model of larval growth that involves
the constant recycling of proteins. Results revealed that most of the whole-body protein mass
synthesized daily was not accreted as growth. Furthermore, protein synthesis rates exceeded rates
of ingestion at higher magnitudes as larvae grew. High rates of protein synthesis compared to
ingestion and accretion means that rates of protein degradation were also high. The dynamics of
protein ingestion, synthesis, degradation, and accretion affect the growth efficiencies of larvae.
47
METHODS
Experimental rationale and design
The following study was designed to examine the protein metabolic dynamics of a
growing larvae of S. purpuratus. The rationale behind which physiological or biochemical
processes to measure involved a conceptual model in which protein accretion is affected by
dynamical processes of feeding, protein synthesis, and protein degradation (Fig. 1.1).
Biochemical substrates (e.g., proteins) are either oxidized for ATP production or used in
biosynthesis (e.g., protein synthesis). Proteins are synthesized while consuming a significant
fraction of the ATP pool. As proteins are degraded or turned over, the net excess of proteins
synthesized relative to those degraded are accreted as growth. Following this conceptual model
of larval metabolism and growth, measurements were made on larval protein ingestion,
synthesis, accretion, and degradation to describe the protein metabolic dynamics during the
growth processes.
Given the small size of larvae and the biomass requirements for biochemical or
physiological assays, large-scale culturing experiments were conducted. A total of seven cohorts
of larvae (represented by 3,990,000 embryos) were reared across 11 culture vessels at the 7- or
20-liter scale. For larvae across the 11 culture vessels, 1,900 size measurements, 190
determinations of protein content (190,000 larvae), 33 determinations of lipid content (68,000
larvae), 62 protein synthesis assays (620,000 larvae), and 62 assays of feeding rate (250,250
larvae) were conducted. Feeding rates determined from larvae of four culture vessels were used
in a quantitative model to determine the optimal feeding frequency that would allow for a near-
constant food ration.
48
Figure 1.1. Conceptual model of protein metabolic dynamics. Intracellular free amino acids which are used to synthesize proteins are
supplied by ingestion and transport from the environment. Proteins are synthesized using the free pool of amino acids and degraded as
proteins in a process of turnover. A portion of proteins not degraded are accreted as growth. These processes utilize the ATP pool
produced by respiration. Processes highlighted in red text were measured in the present study.
49
Larval Culturing
Purple sea urchins, S. purpuratus, were spawned by intracoelomic injection of 0.5 M
KCl. Fertilized embryos were stocked at 20 embryos ml
−1
in culture vessels and constantly
stirred using a paddle attached to a gearhead motor (Herbach and Rademan, Raleigh, NC). All
culture vessels were kept in a temperature-controlled room set at 15 °C. A total of 1,262
temperature measurements were taken using a temperature data logger (Model Pro v2 HOBO
water temperature data logger, Onset, Cape Cod, MA) giving an average temperature of 14.7 ±
0.04°C. On days 0, 4, 6, 8, 10, and 12, larvae were sampled by gently pouring them onto a Nitex
mesh with 53 µm sieve openings. Larvae were resuspended in <50 ml volumes of filtered
seawater (0.2 µm pore size, Nucleopore) treated with ultraviolet light. Seawater was collected
and filtered from the Wrigley Marine Science Center (Santa Catalina Island, CA). Larvae were
mixed gently by inversion and subsampled for enumeration. To ensure the accuracy and
precision of counting, greater than 100 larvae were counted per aliquot, and replicate counts had
coefficients of variation below 10%. After four days of culturing, larvae had reached the 4-arm
pluteus stage with fully developed stomachs and were fed a microalgal diet of Rhodomonas lens
at 20,000 cells ml
−1
. Accounting for the number of larvae per vessel, this food ration was
selected to provide satiating rations described from well-established culturing methods (Leahy,
1986; Strathmann, 1987).
Growth rates of larvae
Growth rates were determined morphometrically and biochemically by measuring
changes in midline body length (see Fig. 1.8A for photomicrograph) and total protein through
time. Midline body lengths, defined by the line from the posterior end to the edge of the oral
50
hood, were measured from 50 randomly selected larvae using ImageJ software (v1.49,
https://imagej.nih.gov/ij/) analysis of photomicrographic images and calibrated to a standard
micrometer at 40x total magnification. Total protein contents were determined using a Bradford
(1976) assay modified for use with marine larvae (Jaeckle and Manahan, 1989). Briefly, replicate
tubes of 1,000 larvae from a culture vessel were freeze-dried using SpeedVac Plus (Model
SC110A SpeedVac Plus, Savant, part of Thermo Fisher Scientific; Flexi-Dry MP, FTS systems,
Stone Ridge, NY). Larvae were then resuspended in deionized water (NanoPure) and sonicated
(Model Vibracel VC50, Sonics & Materials, Inc., Newtown, CT). Proteins were then precipitated
in 5% trichloroacetic acid (TCA) on ice for 30 minutes. Precipitated proteins were centrifuged at
12,000 rcf for 20 minutes at 4°C. TCA was aspirated using a glass pipette under vacuum.
Proteins were then resolubilized using 500µl 1M NaOH and neutralized with 300 µl 1.67M HCl.
Bradford reagent (200 µl) was then added to samples and mixed before transferring 200 µl
volumes to three replicate wells in a 96-well plate. Absorbances at 595 nm were measured
(Model M2E SpectraMax multi-mode microplate reader, Molecular Devices, San Jose, CA) and
converted to units of mass using a dilution series of bovine serum albumin (BSA) standards
prepared in the same method. Growth in midline body length and protein accretion was
calculated based on the change in each metric with respect to time for larvae within each culture
vessel.
Lipid content
Lipid class analysis of larvae were determined by flame-ionization detection of lipids
separated by thin-layer chromatography following the protocols by Moran and Manahan (2004).
Lipid extraction were performed on larvae from replicate tubes (2,000 larvae tube
−1
) from each
51
culture vessel of each cohort. Larvae were sonicated in deionized water and extracted in glass V-
vials (1 ml #986254, Wheaton) using 1:1:0.5 (v:v:v) water, methanol, chloroform. An internal
standard of known amount (0.5 µg 1-octadecanol stearyl alcohol) was added to the extraction to
determine final extraction efficiency by quantifying the extracted stearyl alcohol relative to the
expected amount (0.5 µg). Lipids in extraction solutions were mixed and centrifuged at 8,000 rcf
for 10 minutes at 4°C. Following phase separation, both liquid phases of methanol and
chloroform were transferred to clean V-vials. Water, methanol, and chloroform were then added
to the initial extraction to a final ratio of 0.9:1:1 (v:v:v). Samples were mixed and centrifuged
again as described above. The bottom chloroform phase layer was then transferred to a
borosilicate shell vial (1.5 ml #5182-0714, Agilent, Santa Clara, CA). Chloroform (400 µl) was
added to the v-vial and allowed to separate again. The chloroform was then transferred to the
borosilicate shell vial and heated at 40°C under a gentle stream of N 2 gas. After the chloroform
was evaporated, the lipid residue was resuspended in a known amount of chloroform (50 µl) and
transferred to a thin glass insert (Agilent #5181-3377) to reduce surface area. Thin-layer
chromatography rods (Chromarod-S III; Iatron Laboratories, Inc., Tokyo, Japan) were spotted
with 1-2 µl of sample using a 1 µl glass capillary tube. Three rods were spotted for each sample,
as well as two sets of standards of known concentration (Standard 1: squalene, tripalmitin, 1-
octadecanol, L-phosphatidylcholine; Standard 2: Lauric acid palmityl ester, palmitic acid, 1-
octadecanol, cholesterol). The use of these standards allowed for the identification and
quantification of phospholipids, cholesterols, stearyl alcohol, free fatty acids, triacylglycerols,
wax esters, and hydrocarbons. Rods were developed in chromatography chambers using a polar
solvent (60 ml hexane, 17 ml ethyl ether, 10 drops of glacial acetic acid) for 22-25 minutes. Rods
were then dried on wooden rack holders for 10 minutes at 100°C. Lipids on each Chromarod
52
were detected by H2 flame ionization detection (Model MK-5 Iatroscan, Iatron Laboratories Inc.,
Tokyo, Japan) with a 30 second scan for each rod. Peak areas were converted to µg lipids based
on standards mentioned above. Lipid quantities were adjusted for extraction efficiencies based
on the amount of stearyl alcohol recovered from the 0.5 µg added to the extraction.
Feeding rates of larvae
Once larvae reached the pluteus stage (4 days post-fertilization), larvae were sampled and
fed a ration of 20,000 cells ml
−1
Rhodomonas lens. Food amounts in each culture vessel were
measured every 4 – 6 hours by particle counting (Model Z2 Particle Counter, Beckman Coulter,
Carlsbad, CA) cross-calibrated to visual hemocytometer counts (Fig. 1.2A). A dilution series of
algae with amounts ranging from 0 – 30,000 cells ml
−1
was prepared in a series of vials. Each
vial was mixed and counted three times on the particle counter to create a standard curve for the
relationship between cell amounts and particle counts. If food rations within a culture vessel fell
below 18,000 cells ml
−1
(a decline of 10%), appropriate amounts of algal food cells were added
to the culture vessel. Larvae from each culture vessel were then distributed into 20 assay vials
(Accuvette Cups, Beckman Coulter, Carlsbad, CA) in 10 ml of seawater and 20 – 40 larvae ml
−1
for determination of feeding rates (Fig. 1.2B). Ten of the 20 assay vials containing larvae
received 20 cells µl
−1
R. lens while the remaining 10 assay vials were left as larvae-only controls.
An additional 10 assay vials were filled with 10 ml seawater and 20,000 cells ml
−1
Rhodomonas
lens as algae-only controls containing no larvae. All assay vials were kept in a water bath set to
15°C using a digitally controlled thermostat. Every 60 – 90 minutes, two assay vials from each
treatment (larvae-only, algae-only, or larvae with algae) were sampled by pouring the filtrate
through a Nitex mesh (20 µm pore size). The filtrate from each vial was then counted twice on
53
the Z2 particle counter and converted to cells ml
−1
according to a standard curve of visual
hemocytometer counts (Fig. 1.2A).
Clearance rates (the volumes of water processed by suspension feeders per unit time)
were calculated using Equation 1 (Gauld, 1951; Strathmann, 1972; Pace et al., 2006) and
Equation 2 where F is the clearance rate, Ct is the measured number of algae (cells ml
−1
) at time
t, C0 is the initial amount of algae, N is the total larvae in each assay vial, and V is the volume of
the assay vial. Equation 1 is useful for assays including an initial and ending timepoint for
sampling. In that case, C0 and Cf are measured and used to calculate the clearance rate after a
sampling time. However, if sampling times are not chosen properly, large changes in cell
amounts may lead to variability in the range of food amounts experienced by larvae. Instead, a
time-course sampling method was used in this present study. Equation 1 was solved for Cf to
give Equation 2. This equation is a general exponential function which allows for the estimation
of clearance rate, F, as the slope parameter of a plot of the decline in algal amounts over time.
This allows for the use of many time-points of algal counts over time. The equations for
clearance rate yield units of volume cleared per unit time, which broadly follows an exponential
decline of algal cells (cf. linear) due to the asymptotic nature of suspension feeding where
volumes of water are cleared (Gauld, 1951).
Ingestion rates, defined as the number of cells removed from the seawater by suspension
feeders over time, are commonly calculated from clearance rates by multiplying the volumes of
seawater cleared by the number of algal cells in that volume. However, since the measurement
itself is dependent on changing cell amounts, choosing which cell amount to multiply requires
additional consideration (Marin et al., 1986). Since the decline in algal amounts within short
ranges is approximately linear, ingestion rates (cells larva
−1
h
−1
) in the present study were
54
estimated by the slope of the linear regressions. This allowed for the use of the same data to
calculate both clearance rates and derive corresponding ingestion rates. When changes in algal
concentration are corrected for any changes in particle counts in larvae-only and algae-only
controls, the decline of algal cells from suspension media can be accurately attributed to larval
feeding. Daily protein ingestion rates (ng larva d
−1
) were calculated based on determinations of
algal total protein content using the same methods for larval protein content (see “Growth rates
of larvae” above).
Equation 1. 𝐹 =
× ×
Equation 2. 𝐶 = 𝐶 × 𝑒 ×
55
Figure 1.2. Determination using particle counting of feeding rates by larvae of
Strongylocentrotus purpuratus. (A) All particle counts were cross calibrated with visual
hemocytometer counts of the algal food Rhodomonas lens. Each data point represents a replicate
particle count of a standard vial containing algae visually counted by hemocytometer. (B)
Feeding rate assay data showing declines in experimental assay vials. Gray symbols represent
assays with larvae only (control). White symbols and the dashed line represent assays with algae
only (control). Black symbols and the solid line represent assays with both larvae and algae.
Each data point represents a particle count of a subsample of assay seawater.
56
Predicting algal depletion by larvae within culture vessels
Under established culturing conditions for urchin larvae, rations are typically provided to
culture vessels once every day or two days (Hinegardner, 1969). However, a culture vessel fed
every two days would yield different food amounts as larvae consumed food over the interval
between provisions. Predicted food amounts in culture vessels containing feeding-stage larvae
were modelled in RStudio software (v1.2.5033). The model was based on the relationship
between clearance rates and larval size and the predicted depletion of algae between a feeding
interval of two days (Eq. 2). For each modelled time step (2 hours), the amount of food
remaining within culture vessels was calculated based on how much food would be ingested by
larvae of the respective age. Amounts of cells ingested were added to calculate the cumulative
numbers of cells ingested. The adjusted culture vessel ration was iteratively adjusted based on
feeding rates and age until each interval of 48 hours when rations were returned to 20,000 cells
ml
−1
. Based on these model predictions, the appropriate frequency of providing rations was
determined which would maintain food rations within 10% of a target ration. These feeding
frequencies were utilized to maintain rations in culture vessels at 20,000 cells ml
−1
.
57
In vivo protein synthesis rates
Protein synthesis rates were measured from in vivo time course assays based on methods
for larvae of S. purpuratus used by Pan et al., (2015). Radioactive
14
C-alanine was used in tracer-
amounts to quantify the incorporation of alanine into protein. Alanine was chosen because that
amino acid has a distinct peak when separated by high-performance liquid chromatography. This
allows for the collection of the alanine fraction for analyses of radioactivity and the
determination of the specific activity of 14C-alanine in the intracellular free amino acid pool.
Assays of protein synthesis rate were performed by incubating known amounts of larvae in 74
kBq
14
C-alanine with 10 µM of cold-carrier alanine. Assays were conducted in duplicate 20 ml
glass vials at 15°C, and each vial contained 10,000 larvae in 10 ml of sea water and the alanine
solution described. Assays were subsampled at known intervals (four to six minutes) between
five time points by removing 1 ml (containing 1,000 larvae) and retaining larvae on a filter (8µm
pore size; Nucleopore). Excess radioactivity was washed away using > 10 ml seawater. Larvae
were frozen at -80°C until processing.
To determine the specific activity of
14
C-alanine in the free amino acid pool, the frozen
larvae were resuspended in a known volume (400 µl) of deionized water and disrupted by
sonication. Free amino acids were extracted in 70% ethanol at 4°C overnight, and proteins were
precipitated for a separate subsample in 5% trichloroacetic acid (TCA) on ice for > 30 minutes.
The alanine fraction was analyzed by separating extracted amino acids using reverse-phase high-
performance following labelling with ortho-pthaldealdehyde (Fig. 1.3A). The
14
C-alanine
radioactivity in the protein fraction was determined by scintillation counting of the TCA-
precipitable fraction (Fig. 1.3B). Rates of in vivo protein synthesis were calculated according to
Equation 3 (Pace and Manahan, 2006). For S. purpuratus, MWp is the average molecular weight
58
of amino acid residues in protein (127.4 g mol
−1
; Pan et al., 2015), Sm is the mole-percent of
alanine in protein of the larvae (7.9%; Pan et al., 2015), Sp is radioactivity of alanine in the TCA-
precipitated fraction (protein fraction), and Sfaa is the specific-activity of
14
C-alanine in the free
amino acid pool (Fig. 1.3C). The rate of protein synthesis was calculated as the slope ± s.e. of
increase in protein synthesized through time. Slopes of duplicate assays did not differ
significantly (ANCOVA), and all data points were pooled together to give a single protein
synthesis rate.
Equation 3. 𝑃𝑟𝑜𝑡𝑒𝑖𝑛𝑠 𝑆𝑦𝑛𝑡 ℎ𝑒𝑠𝑖𝑧𝑒𝑑 =
×
59
Figure 1.3. Determination of in vivo protein synthesis rates using
14
C-alanine radiotracer. Closed
and open symbols represent values from duplicate assays. (A) The specific activity of
14
C-
alanine in the free amino acid pool in duplicate assays. (B) The
14
C radioactivity of the TCA-
precipitate protein fraction of larvae in duplicate assays. (C) Amounts of alanine incorporated
into the protein fraction. Minor differences in the intercepts were caused by differences in
washes of larvae. Since protein synthesis rates were calculated based on the slopes, these
differences did not affect the calculation of protein synthesis rates.
60
Protein synthetic dynamics
The relationships between protein ingestion, synthesis, and accretion rates were
quantified to build a conceptual model of larval growth and metabolism (Fig. 1.1). Protein
degradation was calculated by subtracting rates of protein synthesis and accretion according to
Equation 4. Protein degradation is the excess difference in protein synthesis relative to accretion.
The ratio of protein accretion to protein synthesis (Equation 5) gives the protein depositional
efficiency. The ratio of protein accretion rates to ingestion rates (Equation 6) gives the protein
food conversion efficiency. Lastly, the fractional protein synthesis rate (percent total protein
mass per hour) was calculated as the rate of protein synthesis relative to the total protein content
of the larvae (Equation 7). These metrics were quantified throughout the experimental period and
covaried with total protein content (log-transformed ANOVA regression). The metrics across a
range of larval protein masses were compared statistically by ANCOVA. Differences in
regression intercepts (ANCOVA) indicated that a given process was higher or lower than another
while differences in slope indicated that a respective metric increased faster with mass than
another.
Equation 4. 𝑃𝑟𝑜𝑡𝑒𝑖𝑛 𝐴𝑐𝑐𝑟𝑒𝑡𝑖𝑜𝑛 = 𝑃𝑟𝑜𝑡𝑒𝑖𝑛 𝑆𝑦𝑛𝑡 ℎ𝑒𝑠𝑖𝑠 − 𝑃𝑟𝑜𝑡𝑒𝑖𝑛 𝐷𝑒𝑔𝑟𝑎𝑑𝑎𝑡𝑖𝑜𝑛
Equation 5. 𝑃𝑟𝑜𝑡𝑒𝑖𝑛 𝐷𝑒𝑝𝑜𝑠𝑖𝑡𝑖𝑜𝑛𝑎𝑙 𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 =
Equation 6. 𝑃𝑟𝑜𝑡𝑒𝑖𝑛 𝐹𝑜𝑜𝑑 𝐶𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜𝑛 𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 =
61
Equation 7. 𝐹𝑟𝑎𝑐𝑡𝑖𝑜𝑛𝑎𝑙 𝑃𝑟𝑜𝑡𝑒𝑖𝑛 𝑆𝑦𝑛𝑡 ℎ𝑒𝑠𝑖𝑠 𝑅𝑎𝑡𝑒 =
× 100
RESULTS
Comparisons of food ration stability monitored at different frequencies
Standard rations of the algal food Rhodomonas lens provided every 2-days were
predicted to result in the near-depletion of food availability in culture vessels as larvae grew.
These predictions are based on measured increases in feeding rates of larvae under standard
culturing conditions (Fig. 1.4). From these feeding rate assays performed on larvae in three
replicate culture vessels of the same cohort, a relationship between clearance rate, F, and age
(days) was determined to be F = 0.363 × days – 0.689 (Fig. 1.4, p < 0.0001; R
2
= 0.8511). Using
Equation 2 and the predicted feeding rate of larva at a given time, food availability was predicted
to drop below 10% of target rations within a few hours (Fig. 1.5). On day 4, this threshold was
crossed between 8 – 10 hours while on day 12, the threshold was crossed under 4 hours. Based
on this model, different frequencies at which food is provided to larvae can be estimated to
achieve the desired stability in food rations within culture vessels containing feeding-staged
larvae.
The effects that different frequencies of food provisioning have on food ration stability
within culture vessels was seen from comparisons of two sets of cultures monitored either every
6 – 12 hours, or every 4 – 6 hours (Fig. 1.6). Both food ration monitoring frequencies allowed for
the maintenance of average rations of approximately 20,000 cells ml
−1
. Culture vessels
monitored and provided with rations every 6 – 12 hours averaged 21,184 ± 176 (s.e.m. n=4
culture vessels) cells ml
−1
(Fig. 1.6A). The average food rations within each of these culture
62
vessels did not differ significantly from each other (ANOVA, F 3,186 = 0.33; ns). Culture vessels
monitored and provided with rations more often (every 4 – 6 hours) averaged 20,453 ± 147
(s.e.m., n=4 culture vessels) cells ml
−1
(Fig. 1.6B). There were also no significant differences in
average food rations between these culture vessels (ANOVA; F3,92 = 0.12, ns). While both food
ration monitoring frequencies produced consistent average food rations between culture vessels,
the ranges of food rations were smaller in culture vessels monitored every 4 – 6 hours (cf.
monitored every 6 – 12 hours). Culture vessels monitored every 6 – 12 hours had food rations
reaching as low as 10,168 cells
ml
−1
(Fig. 1.6A) while food rations in culture vessels monitored
every 4 – 6 hours did not decrease below 15,000 cells ml
−1
(Fig. 1.6B). For studies where
quantitative assessments of feeding and growth are required, monitoring food rations several
times a day is suggested to maintain stable rations within culture vessels.
63
Figure 1.4. Feeding rates of larvae of Strongylocentrotus purpuratus from three replicate culture
vessels of the same cohort represented by different symbols. Clearance rates increased with age
(and size) according to the equation (Clearance Rate) = 0.363 × days – 0.689 (p < 0.0001; R
2
=
0.8511). The predicted clearance rates were used to model the decline in ration within culture
vessels (see Fig. 1.5).
64
Figure 1.5. Predicted food availability and feeding in culture vessels containing larvae of
Strongylocentrotus purpuratus fed 20,000 cells ml
−1
. (A) Food availability in a culture vessel
provided with 20,000 cells ml
−1
every two days. Values are modelled based on measured feeding
rates of larvae of different age and size (see Fig. 1.4). The horizontal reference line indicates a
10% decrease from 20,000 cells ml
−1
. and each data point is separated by 2 hours. (B)
Cumulative number of algal cells ingested by larvae, from Panel A.
65
Figure 1.6: Comparison of food rations maintained with different frequencies within culture
vessels containing feeding-stage larvae of Strongylocentrotus purpuratus. (A) Algal food rations
maintained every 6 – 12 hours did not differ significantly across four different culture vessels
(each symbol represents a different culture vessel) (ANOVA F3,186 = 0.33; ns). These culture
vessels averaged 21,184 ± 176 cells ml
−1
(s.e.m., n=4 culture vessels). Symbols represent
replicate culture vessels for a single cohort of larvae. (B) Algal food rations maintained at 4 – 6-
hour intervals did not differ significantly across four different culture vessels, for separate
cohorts of larvae than shown in Panel A (each symbol represents a different culture vessel)
(ANOVA; F3,92 = 0.12, ns). These culture vessels averaged 20,453 ± 147 cells ml
−1
.
66
Feeding rates of larvae
The relationships between particle counts and visually counted cells were strong, with an
average R
2
value of 0.997 ± 0.002 (s.e.m., n=9) in the tested range up to 30,000 cells ml
−1
(Fig.
1.2A). Higher counts were not tested in the present study because feeding rations were only
20,000 cells ml
−1
, which is within the range of calibration. The calibration curves had an average
slope of 1.12 ± 0.05 (s.e.m, n=9) particles counted per visually confirmed cell of R. lens.
Experimentally, production of particles by larvae or algae alone were also accounted for by using
algal-only and larvae-only controls in parallel to each feeding assay. Particle counts of larvae-
only and algae-only controls did not change over time (ANOVA regression p > 0.05) indicating
that algal depletion in experimental vials was due to larval feeding.
A total of approximately 250,000 larvae from 11 culture vessels representing seven
cohorts were utilized in assays of feeding rates including control assays. This amounted to 62
feeding rate assays, 310 experimental timepoints (five per assay), and 620 particle counts (two
per timepoint) over the period of study. Feeding rates of larvae from these seven cohorts
increased nonlinearly with age (Fig. 1.7A,B). From these data, clearance rates were predicted by
the equation y = 0.32e
0.226x
(Fig. 1.7A), and ingestion rates were predicted by the equation y =
6.14e
0.21x
(Fig. 1.7B) where x is the age (days) of larvae. At the onset of feeding (4 days post-
fertilization), larvae had predicted clearance rates of 0.79 µl larva
−1
h
−1
and predicted ingestion
rates of 14 cells larva
−1
h
−1
when assayed at approximately 20,000 cells ml
−1
(Fig. 1.7A,B). By
the end of the period of study (12 days post-fertilization), larvae had predicted clearance rates
increasing to 4.8 µl larva
−1
h
−1
and predicted ingestion rates of 77 cells larvae
−1
h
−1
.
The protein content of cells of Rhodomonas lens from two batches of growing algae
provided as food to larvae did not change over the time periods of this study (regression p > 0.05
67
for both batches; Fig. 1.7C). Algae from the two batches provided to larvae of different culture
vessels had protein contents of 39.5 ± 1.4 (Fig. 1.7C, black symbols) and 34.9 ± 1.9 pg cell
−1
(Fig. 1.7C, white symbols). There were no statistical differences between the protein contents of
different batches of algae (ANOVA F1,17 = 3.46, ns). While the standard error in measured
protein content of algae were small (4-5% error), protein ingestion rates were calculated using
daily measured protein values to account for this deviation. Based on these values for the protein
content of R. lens, the assayed ingestion rates equated to daily protein ingestion rates of 12.3 ±
2.3 ng protein larva
−1
d
−1
(4-day old larvae of n = 6 culture vessels of different cohorts) to 66.4 ±
9.8 ng protein larva
−1
d
−1
(12-day old larvae of n = 5 culture vessels representing 3 cohorts) (Fig.
1.7D). By scaling protein ingestion rates to total larval protein content, the dynamics of protein
food conversion efficiency can be quantified for larvae of S. purpuratus.
68
Figure 1.7: Determination of protein content for larvae of Strongylocentrotus purpuratus fed
20,000 cells ml
−1
Rhodomonas lens. Symbols represent seven different cohorts of larvae. (A)
Clearance rates, defined as volumes of water processed by larvae, over a period of 12 days. Each
data point represents the clearance rate ± s.e. (n = 20 algal counts). (B) Ingestion rates, defined as
the number of cells consumed, of larvae throughout the period of study. (C) Protein content of R.
lens from two batches (open and closed symbols) did not change over the period of study (linear
regression; p = 0.774 and p = 0.292 for batches 1 (black symbols) and 2 (white symbols)
respectively. Protein content did not differ between batches of algae (ANOVA F 1,17 = 3.46, ns)
and averaged 37 ± 1 pg cell
−1
(indicated by the dashed reference line). (D) Protein ingestion rates
for larvae of different total protein mass. Range of data points, shown by solid bars, represent
values for different cohorts of 4-day old and 12-day old larvae.
69
Larval growth
Growth rates with respect to morphological size (midline body length) and total protein
were examined for S. purpuratus. Larval midline body lengths increased with age (Fig. 1.8A).
For larvae from seven cohorts in 11 culture vessels, average midline body lengths increased from
244 ± 7 (4-day old larvae) to 350 ± 16 µm (12-day old larvae) over the study period (Fig. 1.8A).
This equates to an increase in 106 µm over eight days of feeding, and an overall growth rate of
13 µm day
−1
.
High rates of protein accretion were also seen for larvae. Over the period of study, total
protein content increased for larvae by over 100 ng protein (Fig. 1.8B). For larvae of seven
cohorts in 11 culture vessels, total protein increased, on average, from 18 ± 1 (4-day old larvae)
to 121 ± 12 ng protein (12-day old larvae) (Fig. 1.8B). This equates to a 6.7-fold increase in total
protein content over eight days of feeding. Protein accretion rates also increased with mass
according to the equation log10(Protein Accretion) = 1.004 × log 10(Total Protein) – 0.614 (Fig.
1.8D, p < 0.0001; R
2
= 0.947). Based on this relationship, a 4-day old larva of 18 ng total protein
is predicted to accrete 4.4 ng protein day
−1
; and a 12-day old larva of 121 ng total protein is
predicted to accrete 30 ng protein day
−1
. These predicted protein accretion rates can be used for
comparisons of the dynamics of protein metabolism.
A comparison of the measurements of the different lipid classes in larvae of S.
purpuratus with corresponding protein content, showed that protein is the major biochemical
constituent of larvae. At the onset of feeding, 4-day old larvae from four cohorts and six culture
vessels averaged 18 ng total lipid (Fig. 1.9) which is approximately the same mass as total
protein (see previous paragraph). After feeding for 8 days, 12-day old larvae had 40 ng total lipid
(Fig. 1.9), compared to 121 ng protein. This represents a 3-fold higher mass of total protein
70
compared to lipids. The high increase in total protein content relative to lipids indicates that most
of the biomass grown by larvae during the period of study was protein.
71
Figure 1.8: Growth of larvae of Strongylocentrotus purpuratus provided with 20,000 cells ml
−1
of Rhodomonas lens. Cohorts are indicated by different symbols. (A) Morphological features
used to define midline body length (yellow line) in a pluteus larva. The white scale bar is 100
µm. (B) Midline body lengths for larvae representing seven different cohorts. Each different
symbol represents a different cohort grown under similar conditions. Each data point represents
the average midline body length ± 1 s.e.m (n=50 larvae). (C) Total protein contents for larvae
throughout the period of study. Each point represents the average ± s.e.m (n = 5 protein assays).
(D) Protein accretion rates were calculated from the change in total protein with respect to time
for larvae. A positive linear relationship between accretion rates and total protein was found such
that log10(Protein Accretion) = 1.004 × log10(Total Protein) – 0.614 (p < 0.0001; R
2
= 0.947).
72
Figure 1.9. Lipid class contents of larvae of Strongylocentrotus purpuratus fed a constant ration
of 20,000 cells ml
−1
Rhodomonas lens. PL: phospholipids; CH: cholesterols; FFA & TAG: free
fatty acids & triacylglycerols; HC: hydrocarbons.
73
Dynamics of protein ingestion, synthesis, and accretion
Rates of protein synthesis for larvae of S. purpuratus increased over 15-fold over the
period of study (Fig. 1.10A). For larvae from seven cohorts, the relationship between protein
synthesis rates and total protein content can be described by the equation log 10(Protein Synthesis
[ng day
−1
]) = 1.269 × log10(Total Protein) – 0.452 (regression p < 0.0001). Based on this
predictive relationship (R
2
= 0.795), a 4-day old larva of 18 ng total protein is predicted to
synthesize 14 ng protein day
−1
, equating to a fractional synthesis rate of 3.2% of whole-body
protein mass hour
−1
(Eq. 7). A 12-day old larva of 121 ng total protein is predicted to synthesize
155 ng protein day
−1
, equating to a fractional synthesis rate of 5.3% of whole-body protein mass
hour
−1
.
The relationship between protein ingestion rates and total protein content is described by
the equation log10(Protein Ingestion) = 0.895 × log10(Total Protein) – 0.017 (regression p <
0.0001; R
2
= 0.841; Fig. 1.10B). Based on this predictive relationship (R
2
= 0.841), a newly
formed 4-day old larva of 18 ng total protein is predicted to ingest 13 ng protein day
−1
. This rate
of ingestion could supply the rate of protein synthesis at 14 ng day
−1
(Fig. 1.10A). A 12-day old
larva of 121 ng total protein is predicted to ingest 70 ng protein day
−1
, compared to 155 ng
protein synthesis day
−1
. Comparisons of the dynamics of protein ingestion (Fig. 1.10B) and
synthesis (Fig. 1.10A) predict that protein synthesis rates exceed protein ingestion rates
(ANCOVA log-transformed data: Slope: F1,41 = 16.7, p = 0.0002; Fig. 1.10A,B). Because the
slopes differed (note: log-log relationship of protein synthesis rate slope = 1.269 and log-log
protein ingestion slope = 0.895), the 1.4-fold difference in slope illustrates that the processes of
protein ingestion and protein synthesis diverge as larvae grow. For 12-day old larvae, protein
synthesis rates (155 ng day
−1
) are 2.2-fold higher than protein ingestion rates (70 ng day
−1
).
74
Comparisons of high protein synthesis rates (e.g., 155 ng day
−1
for 12-day old larvae)
with protein ingestion and accretion rates yield protein growth efficiencies that change as larvae
grow. Protein food conversion efficiencies, defined as the percentage of protein mass ingested
that is accreted as growth (Eq. 6), increased with larval total protein content (Fig. 1.11A). The
relationship between protein food conversion efficiency and total protein is described by the
equation Protein Food Conversion Efficiency = 0.12 × (Total Protein) + 30.5. A 4-day old larva
of 18 ng total protein is predicted to accrete 33% of the protein mass ingested as growth. This
efficiency increases for a 12-day old larvae of 121 ng total protein which would accrete 45% of
ingested protein mass as growth. Protein depositional efficiency, the percentage of proteins
synthesized that are accreted (Eq. 5), decreased with total protein according to the equation
Protein Depositional Efficiency = -0.19 × (Total Protein) + 40.7 (Fig. 1.11B). A 4-day old larva
of 18 ng total protein is predicted to accrete 37% of the protein mass synthesized as growth. The
protein depositional efficiency decreases for a 12-day old larva of 121 ng total protein to 18%.
Protein synthesis rates could explain 90% of the variation in protein accretion rate based on
larvae from seven different cohorts reared in 11 culture vessels (ANOVA regression, p < 0.0001,
R
2
= 0.898, Fig. 1.12). Importantly, this relationship between protein accretion and synthesis
rates had a slope of 0.15, meaning that little of the protein mass synthesized is accreted. Since
protein depositional efficiencies are so low (18% for 12-day old larvae), 72% of proteins
synthesized are degraded (Eq. 4). While protein synthesis rates are highly predictive (90%) of
protein accretion rates, these predictions depend on knowledge of protein depositional
efficiencies to avoid up to ~70% error on estimates of growth based on incorporation of isotopes
into macromolecules.
75
Figure 1.10: Dynamics of protein synthesis and ingestion for larvae of Strongylocentrotus
purpuratus. Symbols represent larvae from different cohorts. (A) Protein synthesis rates are
described by the linear regression of log data: log 10(protein synthesis) = 1.269 × log10(total
protein) – 0.452 (regression p < 0.0001; R
2
= 0.795). (B) Protein ingestion rates are described by
the linear regression of log data: log 10(protein ingestion) = 0.895 × log10(total protein) – 0.017
(regression p < 0.0001; R
2
= 0.841). A Comparison of these two slopes reveals significant
difference (ANCOVA log-transformed data: Slope: F 1,41 = 16.7, p = 0.0002).
76
Figure 1.11: Protein growth efficiencies for larvae of Strongylocentrotus purpuratus reared at a
constant food ration of 20,000 cells ml
−1
Rhodomonas lens. Data point symbols represent larvae
from different cohorts. (A) Protein food conversion efficiency, the percentage of protein mass
ingested that is accreted, increased as larvae grew (linear regression; y = 0.0012x + 0.305; p =
0.01), with an average efficiency of 37.4% ± 2.2 (n= 22). (B) Protein depositional efficiency, the
percentage of protein mass synthesized that is accreted, decreased as larvae grew (linear
regression; y = -0.0019x + 0.407; p = 0.004).
77
Figure 1.12. The relationship of protein synthesis rate to rates of accretion are strongly
correlated with protein accretion rates for larvae of S. purpuratus. Equation: (Protein Accretion)
= 4.41 + 0.15 × (Protein Synthesis) (ANOVA regression; p < 0.0001, R
2
= 0.898). Different
symbols represent 5 cohorts. Dashed reference lines for a larva with a protein synthesis rate of
100 ng day
−1
corresponds to 19 ng day
−1
protein accretion and 81% degradation.
78
DISCUSSION
Protein synthesis rates are predictive of protein accretion with low depositional efficiency
Over the period of study, larvae from the seven cohorts (11 culture vessels) accreted 4.7-
fold more protein (103 ng of accretion) as growth, compared to lipids (22 ng). At the onset of
feeding (4-day old larvae), the total protein content of 18 ng larva
−1
was the same as the total
lipid content of 18 ng larva
−1
(Fig. 1.8B, Fig. 1.9). After feeding for eight days, larvae accreted
protein to 121 ng and lipids to 40 ng. Protein mass was accreted 6.7-fold high as larvae grew
(Fig. 1.8C). Previous studies on the biochemical content of feeding-stage larvae of S. purpuratus
have also shown high growth rates in protein relative to lipid, with proteins being approximately
double the mass of lipids in fed larvae (Meyer et al., 2007). Because proteins constitute a large
portion of larval biomass and growth, protein growth dynamics and efficiencies were the focus
of the present study.
Maximum protein accretion rates were 30 ng day
−1
(Fig. 1.8D) in the present study.
Comparisons of rates of protein synthesis revealed that supporting this protein accretion rate was
a very inefficient process, due to high rates of protein degradation. Protein depositional
efficiencies, the percentage of protein mass synthesized that is accreted (Eq. 5) decreased from
37% (4-day old larva of 18 ng total protein) to 18% (12-day old larva of 121 ng total protein)
(Fig. 1.11B). Hence, between 63% (100% - 37%) and 72% (100% - 18%) of the total mass of
protein synthesized was degraded and not accreted.
Protein depositional efficiencies have been previously quantified in marine larvae of
Lytechinus pictus, ranging from 21 – 37% depending upon the stage of development (Pace and
Manahan, 2006). For larvae of S. purpuratus, a value of 34% depositional efficiency for was
79
reported by Pan et al. (2015). For larvae of the Pacific oyster, Crassostrea gigas, a low
depositional efficiency of 14% was measured throughout the larval life span (Pan et al., 2018).
In the present study, low depositional efficiencies reflect progressively higher rates of
protein synthesis (Fig. 1.10A) relative to protein accretion (Fig. 1.8D). While larvae accreted
6.7-fold of their initial protein mass within the first eight days of feeding (18 – 121 ng larva
−1
;
Fig. 1.8C), synthesis rates were high, with larvae synthesizing 5.3% of their protein mass per
hour by the end of the period of study (by day 12). This equates to 127% of the whole-body
protein mass being synthesized per day, which is only possible through the degradation of
proteins and reutilization of amino acid substrates from degraded proteins. Because of this,
protein accretion is equivalent to the difference between protein synthesis and degradation (Eq.
4). For a 12-day old larva of 121 ng total protein, 72% of the mass of proteins synthesized are
degraded, leaving 18% accreted as growth. These high rates of protein degradation highlight the
importance of examining protein metabolic dynamics in growing larvae.
Protein synthesis rates explained 90% of the variation in protein accretion rates for larvae
from seven cohorts (Fig. 1.12). However, due to the dynamics of protein degradation described
above, protein depositional efficiencies must be taken into account to predict accretion rates from
rates of synthesis. While protein synthesis rates are predictive of protein accretion, the slope of
this relationship depends on protein depositional efficiency. Similar findings were reported by
Pan et al. (2018) for larvae of C. gigas that most of the variation in protein accretion rates was
explained by rates of protein synthesis, with only 14% protein depositional efficiency. In general,
the importance of protein synthesis and degradation quantified in the present study is an
important extension for this field regarding the biology of growth regulation across the tree of
life (bacteria: Borek et al., 1958, Lahtvee et al., 2014; yeast: López and Gancedo, 1979, Martin-
80
Perez and Villén, 2017; plants: Nelson and Millar, 2015, Li et al., 2017; mammals: Waterlow et
al., 1978).
The application of electronic particle counters for studies of feeding
Since a major goal of this study is to quantify the ingestion of algal protein by developing
larvae, several considerations were taken with the use of electronic particle counting in algal
depletion assays. Methods and practices for the quantification of phytoplankton and other
particles in seawater have progressed throughout the last half century with the development of
new technologies each with their own benefits and limitations. Phytoplankton amounts have
historically been determined by visual counting (microscopy), spectrophotometric properties,
electronic particle counting, fluorescence properties, and organic content measurements of
suspensions (Butterwick et al., 1982). The introduction of electronic particle counting by
instruments such as the Beckman Coulter particle counter allowed for rapid quantification of
particles based on the impedance of electrical currents through an aperture by particles of
varying volumes (Sheldon and Parsons, 1967). However, the use of particle counters with
biological particles requires simplified assumptions about irregular particle shapes of cells,
particle paths through apertures, and electrical properties of suspension media and particles
(Harbison and McAlister, 1980; Peters, 1984). The relationship between electrical current
impedance and particle number is calculated based on idealized spherical volumes and density-
dependent coincidences when multiple particles pass through an aperture at a time. As a result,
heterogenous suspensions of particles of varying sizes and shapes can only be interpreted as
idealized spheres of the same volume.
81
Furthermore, biological processes related to feeding may also alter the size distribution of
particles in seawater. For example, copepod feeding and particle handling mechanisms may
cause large food particles to break into several smaller debris particles complicating the
interpretation of selective feeding across cell sizes (Deason, 1980). Over the time course of
feeding assays, larvae may also produce fecal pellets which contribute to particle counts in size
classes of interest. Larvae of the oyster Crassostrea virginica were observed to produce fecal
particles between 2 – 15 µm and evacuated their guts as much as four times per hour (Baldwin
and Newell, 1995). These size classes overlap with those of microalgae commonly fed to this
species. Because of these processes and limitations, studies attempting to address feeding
behavior (e.g., feeding rates or preferences for shapes and sizes) using electronic particle
counting must be interpreted by categorizing particles by volume (rather than diameter), visual,
microscopic inspection, or by introducing experimental controls. The use of flow cytometry
added the ability to distinguish different-sized particles by the fluorescence of algal foods due to
chlorophyll or phycoerythrins and phycocyanins and has been used to monitor the algal food
ration in culture vessels used to rear sand dollar larvae, Dendraster excentricus (Rendleman et
al., 2018). While this approach resolves some of the issues of biological handling, pseudo-fecal
production, and fecal production (Cucci et al., 1985; Baldwin and Newell, 1995), it does not
resolve feeding rates at the level of the individual algal cell as was undertaken in this study (Fig.
1.2A: direct, by visual hemocytometer counts compared to digital analysis by particle counter).
In the present study, feeding rate assays were conducted in replicate assay vials using
larvae removed from their culture vessel into a controlled assay environment. Previous studies
have utilized flow cytometry to estimate algal depletion directly in culture vessels through the
loss of fluorescent signals from algal cells (Cucci et al., 1985; Rendleman and Pace, 2018;
82
Rendleman et al., 2018). In the present study, since algal rations were maintained above satiation
levels (>15,000 cells ml
−1
), the result of independent assays of feeding rates to matched target
rations in comparable environments of the larval culture vessel. This additional experimental
step also avoided the complications from the increase in non-food particles or pseudo-fecal or
fecal pellets in the larval culture vessels where the seawater was changed only every two days.
Given the critical cross-calibration of particle counts to visual hemocytometer counts of cells
(algal food R. lens, Fig. 1.2A), the experimental controls employed, and the appropriate assay
times used to detect algal depletion, the use of the particle counter enabled accurate and precise
determinations of larval feeding rates. This experimental approach and rationale used in the
present study allowed for the quantitative comparison of feeding rates to independently be
measured as a key component of protein metabolism in growing larvae (i.e., ingestion, synthesis,
degradation, and accretion).
Quantification of protein content
Aside from accurate measurements of larval feeding, the daily determination of protein
contents of R. lens using the same methodology for larvae was important in quantifying protein
ingestion rates. Comparisons of these growth efficiency models are subject to variation
depending on the methods used for quantification of proteins (e.g., Bradford assay cf.
bicinchonininc acid methods) (Martínez et al., 2020). For R. lens, different protein contents have
been reported based on various methodologies and culturing conditions (Renaud et al., 2002;
Seixas et al., 2009; Rendleman et al., 2018). Since protein ingestion rates in the present study
were quantitatively compared to protein synthesis and accretion rates, the total protein content of
each batch of algal cells was directly measured and matched daily with feeding rate assays. Even
83
though the protein contents of R. lens in the present study did not differ statistically with time or
between batches (averaging 37 ± 1 pg cell
−1
), the standard error for the protein contents of algae
were approximately 4 – 5%. By matching feeding rates to daily measurements of algal protein
content, this error was minimized. In the present study, algal protein content was also
determined using the modified Bradford (1976) assay that was also used for larvae (Fig. 1.7C;
Jaeckle and Manahan, 1989). Previous comparisons of protein contents determined by this
method with those quantified by high performance liquid chromatographic analyses of acid-
hydrolyzed proteins found only approximately 7% differences (Shilling and Manahan, 1994). By
measuring daily protein contents of algae using consistent methodology, protein ingestion rates
could accurately be quantified for examinations of the dynamics of protein metabolism.
Protein accretion and food conversion efficiencies
Larvae of S. purpuratus increased their ingestion rates by nearly 5.4-fold (13 ng day
−1
a
4-days old to 70 ng day
−1
at 12-days old, from regression shown in Fig. 1.10B). During this same
period, larvae accreted 103 ng of protein (18 ng total protein increasing to 121 ng total protein,
from equation shown in Fig. 1.8C). There was a significant relationship between protein food
conversion efficiency, the proportion of proteins ingested that were accreted, and total protein
content (linear regression, p = 0.004, Fig. 1.11A), from 33% (4-day old larva of 18 ng total
protein) to 45% (12-day old larva of 121 ng total protein). Since these two values are close in
number and given the compounding statistical errors associated with these kinds of biochemical
assays, a mean value of 37.4% ± 2.2provides a reasonable estimate for larvae of S. purpuratus.
Importantly, predicted rates of protein ingestion and synthesis (based on linear
regressions of log-log transformed data) always exceeded accretion rates (Fig. 1.8C; Fig. 1.10).
84
Furthermore, rates of protein synthesis also increased faster than rates of protein ingestion as
larvae grew, as indicated by the differences in slopes (Fig. 1.10A,B; ANCOVA log-log
transformed data: Slope: F1,41 = 16.7, p = 0.0002). A 12-day old larva of 121 ng total protein had
a protein synthesis rate of 155 ng larva
−1
day
−1
– a rate that is 2.2-fold higher than ingestion at 70
ng larva
−1
day
−1
. This indicates that protein degradation is necessary to account for high rates of
protein synthesis, since insufficient amino acid mass is available from ingestion. When protein
degradation is considered in the conceptual model of growth (Fig. 1.1), it is then possible to
account for increases in protein accretion rates despite significant decreases in protein
depositional efficiencies (Fig. 1.11B) and a near-constant protein food conversion efficiency of
37.4% ± 2.2 (Fig. 1.11A). In this sense, simply feeding larvae more food cannot necessarily
drive a faster growth rate without modifications to the complexity of protein metabolic
dynamics. In addition, it is important to note that simplistic interpretations of a one-way flux of
radioisotopes into macromolecules is not a predictor of growth rates. As illustrated in this study,
it is the dynamics of protein metabolism that regulates growth (Fig. 1.1 cf. Fig. 1.12, slope =
0.15).
High rates of protein degradation are now reported for larval stages of S. purpuratus.
From Fig. 1.12, a larva synthesizing 100 ng larva
−1
hour
−1
only retains 19% of synthesized
protein as protein mass (i.e., protein degradation is 81%). This finding for larval stages of marine
invertebrates fits the conceptual model of protein metabolism and the “nitrogen equilibrium”
described by Schoenheimer (1942). Since, in many animals, rates of protein ingestion and
excretion were equivalent, high rates of protein synthesis utilized amino acids from degraded
proteins in a steady-state, non-growing organism (Schoenheimer, 1942). For organisms that are
growing, differences in protein degradation rates create net imbalances with proteins
85
synthesized, resulting in changes in protein growth (Millward et al., 1975; Houlihan, 1988;
Frieder et al., 2018). Integrated measurements of protein metabolic dynamics across marine
invertebrates are revealing underlying physiological mechanisms that drive growth (Pan et al.,
2018). The findings from the present study demonstrate this conceptual model (Fig. 1.1) of
protein metabolism and growth for larvae of an echinoderm species S. purpuratus. The high rates
of protein synthesis relative to rates of protein ingestion and accretion highlight the importance
of protein degradation as a key mechanism in regulating larval growth.
86
Chapter 1 References
Baldwin, B.S., and Newell, R.I.E. (1995). Feeding rate responses of oyster larvae (Crassostrea
virginica) to seston quantity and composition. Journal of Experimental Marine Biology
and Ecology, 189, 77-91.
Bertram, D.F., and Strathmann, R.R. (1998). Effects of maternal and larval nutrition on growth
and form of planktotrophic larvae. Ecology, 79(1), 315-327.
Borek, E., Ponticorvo, L., and Rittenberg, D. (1958). Protein turnover in micro-organisms.
Proceedings of the National Academy of Sciences of the United States of America, 44(5),
369.
Bradford, M.M. (1976). A rapid and sensitive method for the quantitation of microgram
quantities of protein utilizing the principle of protein-dye binding. Analytical
Biochemistry, 72(1-2), 248-254.
Butterwick, C., Heaney, S.I., and Talling, J.F. (1982). A comparison of eight methods for
estimating the biomass and growth of planktonic algae. British Phycological Journal,
17(1), 69-79.
Cucci, T.L., Shumway, S.E., Newell, R.C., Selvin, R., Guillard, R.R.L., and Yentsch, C.M.
(1985). Flow cytometry: a new method for characterization of differential ingestion,
digestion and egestion by suspension feeders. Marine Ecology Progress Series, 24, 201-
204.
Deason, E.E. (1980). Potential effect of phytoplankton colony breakage on the calculation of
zooplankton filtration rates. Marine Biology, 57, 279-286.
87
De Duve, C., Pressman, B.C., Gianetto, R., Wattiaux, R., and Appelmans, F. (1955). Tissue
fractionation studies. 6. Intracellular distribution patterns of enzymes in rat-liver tissue.
Biochemical Journal, 60(4), 604.
Frieder, C.A., Applebaum, S.L., Pan, T.C.F., and Manahan, D.T. (2018). Shifting balance of
protein synthesis and degradation sets a threshold for larval growth under environmental
stress. Biological Bulletin, 234(1), 45-57.
Fry, J.P., Mailloux, N.A., Love, D.C., Milli, M.C., and Cao, L. (2018). Feed conversion
efficiency in aquaculture: do we measure it correctly? Environmental Research Letters,
13(2), 024017.
Gauld, D.T. (1951). The grazing rate of planktonic copepods. Journal of the Marine Biological
Association of the United Kingdom, 29(3), 695-706.
Handeland, S.O., Imsland, A.K., and Stefansson, S.O. (2008). The effect of temperature and fish
size on growth, feed intake, food conversion efficiency and stomach evacuation rate of
Atlantic salmon post-smolts. Aquaculture 283, 36-42.
Harbison, G.R., and McAlister, V.L. (1980). Fact and artifact in copepod feeding experiments 1.
Limnology and Oceanography, 25(6), 971-981.
Hershko, A., Ciechanover, A., and Rose, I.A. (1979). Resolution of the ATP-dependent
proteolytic system from reticulocytes: a component that interacts with ATP. Proceedings
of the National Academy of Sciences, 76(7), 3107-3110.
Hinegardner, R.T. (1969). Growth and development of the laboratory cultured sea urchin. The
Biological Bulletin, 137(3), 465-475.
88
Houlihan, D.F., Hall, S.J., Gray, C., and Noble, B.S. (1988). Growth rates and protein turnover in
Atlantic cod, Gadus morhua. Canadian Journal of Fisheries and Aquatic Sciences, 45,
951-964.
Hua, K., et al. (2019). The future of aquatic protein: implications for protein sources in
aquaculture diets. One Earth, 1(3), 316-329.
Imsland, A.K., Foss, A., Gunnarsson, S., Berntssen, M.H.G., FitzGerald, R., Bonga, S.W., Ham,
E., Naevdal, G., and Stefansson, S.O. (2001). The interaction of temperature and salinity
on growth and food conversion in juvenile turbot (Scophthalmus maximus). Aquaculture,
198, 353-367.
Jaeckle, W., and Manahan, D.T. (1989). Growth and energy imbalance during the development
of a lecithotrophic molluscan larva (Haliotis rufescens). The Biological Bulletin, 177,
237–246.
Kaushik, S.J., and Seiliez, I. (2010). Protein and amino acid nutrition and metabolism in fish:
current knowledge and future needs. Aquaculture Research, 41(3), 322-332.
Lahtvee, P.J., Seiman, A., Arike, L., Adamberg, K., and Vilu, R. (2014). Protein turnover forms
one of the highest maintenance costs in Lactococcus lactis. Microbiology, 160(7), 1501-
1512.
Leahy, P.S. (1986). Laboratory culture of Strongylocentrotus purpuratus adults, embryos, and
larvae. Methods in Cell Biology, 27, 1-13.
Li, L., Nelson, C.J., Trösch, J., Castleden, I., Huang, S., and Millar, A.H. (2017). Protein
degradation rate in Arabidopsis thaliana leaf growth and development. The Plant Cell,
29(2), 207-228.
89
López, S., and Gancedo, J.M. (1979). Effect of metabolic conditions on protein turnover in yeast.
Biochemical Journal, 178(3), 769-776.
Marin, V., Huntley, M.E., and Frost, B. (1986). Measuring feeding rates of pelagic herbivores:
analysis of experimental design and methods. Marine Biology, 93(1), 49-58.
Marshall, D.J., Keough, M.J. (2008). The evolutionary ecology of offspring size in marine
invertebrates. Advances in Marine Biology, 53, 1–60.
Martínez, I., Herrera, A., Tames-Espinosa, M., Bondyale-Juez, D.R., Romero-Kutzner, V.,
Packard, T.T., and Gómez, M. (2020). Protein in marine plankton: a comparison of
spectrophotometric methods. Journal of Experimental Marine Biology and Ecology, 526,
DOI: j.jembe.2020.151357.
Martin-Perez, M., and Villén, J. (2017). Determinants and regulation of protein turnover in yeast.
Cell systems, 5(3), 283-294.
Meyer, E., Green, A.J., Moore, M., and Manahan, D.T. (2007). Food availability and
physiological state of sea urchin larvae (Strongylocentrotus purpuratus). Marine Biology,
152(1), 179-191.
Millward, D.J., Garlick, P.J., Stewart, R.J., Nnanyelugo, D.O., and Waterlow, J.C. (1975).
Skeletal-muscle growth and protein turnover. Biochemical Journal, 150(2), 235-243.
Moran, A.L., and Manahan, D.T. (2004). Physiological recovery from prolonged ‘starvation’ in
larvae of the Pacific oyster Crassostrea gigas. Journal of Experimental Marine Biology
and Ecology, 306(1), 17-36.
Nelson, C.J., and Millar, A.H. (2015). Protein turnover in plant biology. Nature plants, 1(3), 1-7.
Ohsumi, Y. (2014). Historical landmarks of autophagy research. Cell Research, 24, 9-23.
90
Pace, D.A., Marsh, A.G., Leong, P.K., Green, A.J., Hedgecock, D., and Manahan, D.T. (2006).
Physiological bases of genetically determined variation in growth of marine invertebrate
larvae: a study of growth heterosis in the bivalve Crassostrea gigas. Journal of
Experimental Marine Biology and Ecology, 335(2), 188-209.
Pace, D.A., and Manahan, D.T. (2006). Fixed metabolic costs for highly variable rates of protein
synthesis in sea urchin embryos and larvae. Journal of Experimental Biology, 209(1),
158-170.
Pan, T.C.F., Applebaum, S.L., and Manahan, D.T. (2015). Experimental ocean acidification
alters the allocation of metabolic energy. Proceedings of the National Academy of
Sciences, 112(15), 4696-4701.
Pan, T.C.F., Applebaum, S.L., Frieder, C.A., and Manahan, D.T. (2018). Biochemical bases of
growth variation during development: a study of protein turnover in pedigreed families of
bivalve larvae (Crassostrea gigas). Journal of Experimental Biology, 221, DOI:
jeb.171967.
Peters, R.H. (1984). Methods for the study of feeding, filtering and assimilation by zooplankton.
In: Downing, J.A., Rigler, F.H. (eds) A manual on methods for the assessment of
secondary productivity in fresh waters, second edition. Blackwell Scientific Publications,
Oxford London Edinburgh Boston Melbourne, pp. 336-412.
Renaud, S.M., Thinh, L.V., Lambrinidis, G., and Parry, D.L. (2002). Effect of temperature on
growth, chemical composition and fatty acid composition of tropical Australian
microalgae grown in batch cultures. Aquaculture, 211, 195-214.
91
Rendleman, A.J., Rodriguez, J.A., Ohanian, A., and Pace, D.A. (2018). More than morphology:
Differences in food ration drive physiological plasticity in echinoid larvae. Journal of
Experimental Marine Biology and Ecology, 501, 1-15.
Rendleman, A.J., and Pace, D.A. (2018). Physiology of growth in typical and transversus
echinopluteus larvae. Invertebrate Biology, 137(4), 289-307.
Rumrill, S.S. (1990). Natural mortality of marine invertebrate larvae. Ophelia, 32(1-2), 163-198.
Schoenheimer, R. (1942). The dynamic state of body constituents. The dynamic state of body
constituents.
Sedgwick, R.W. (1979). Influence of dietary protein and energy on growth, food consumption
and food conversion efficiency in Penaeus merguiensis de Man. Aquaculture 16(1), 7-30.
Seixas, P., Coutinho, P., Ferreira, M., and Otero, A. (2009). Nutritional value of the cryptophyte
Rhodomonas lens for Artemia sp. Journal of Experimental Marine Biology and Ecology,
381, 1-9.
Sheldon, R.W., and Parsons, T.R. (1967). A practical manual on the use of the Coulter counter in
marine research. Coulter Electronics Sales Company, Toronto, Ontario.
Shilling, F.M., and Manahan, D.T. (1990). Energetics of early development for the sea urchins
Strongylocentrotus purpuratus and Lytechinus pictus and the crustacean Artemia sp.
Marine Biology, 106(1), 119-127.
Shilling, F.M., and Manahan, D.T. (1994). Energy metabolism and amino acid transport during
early development of Antarctic and temperate echinoderms. The Biological Bulletin,
187(3), 398-407.
Singh, P., Paul, B.N., and Giri, S.S. (2018). Potentiality of new feed ingredients for aquaculture:
A review. Agricultural Reviews, 39(4), 282-291.
92
Strathmann, R.R., Jahn, T.L., and Fonseca, J.R. (1972). Suspension feeding by marine
invertebrate larvae: clearance of particles by ciliated bands of a rotifer, pluteus, and
trochophore. The Biological Bulletin, 142(3), 505-519.
Strathmann, R.R. (1985). Feeding and nonfeeding larval development and life-history evolution
in marine invertebrates. Annual Review of Ecology and Systematics, 16, 339-361.
Strathmann, M.F. (1987). Phylum Echinodermata, class Echinoidea. Reproduction and
development of marine invertebrates of the Northern Pacific Coast. University of
Washington Press, Seattle, 511-534.
Sun, M., Hassan, S.G., and Li, D. (2016). Models for estimating feed intake in aquaculture: A
review. Computers and Electronics in Agriculture, 127, 425-438.
Waterlow, J.C. (1995). Whole-body protein turnover in humans – past, present, and future.
Annual Review of Nutrition, 15, 57-92.
Waterlow, J.C., Garlick, P.J., and Millward, D.J. (1978). Protein turnover in mammalian tissues
and in the whole body. Amsterdam, The Netherlands. North-Holland Biomedical Press,
335.
Winfree, R.A., and Stickney, R.R. (1981). Effects of dietary protein and energy on growth, feed
conversion efficiency and body composition of Tilapia aurea. The Journal of Nutrition,
111(6), 1001-1012.
93
CHAPTER 2
Differences in Protein Food Conversion Efficiency Maintain
Accretion Rates Across Food Rations
ABSTRACT
Feeding rates of planktotrophic marine larvae drive larval growth in the water column to
metamorphosis. In natural environments, food availability is often limited or variable in space
and time. The present study examined the feeding rates and growth physiology of larval stages of
the sea urchin Lytechinus pictus, exposed to a range of food rations spanning two orders of
magnitude. Larval feeding rates exhibited a nonlinear, ration-dependent response where feeding
rates increased up to 10-fold at rations of 1,000 cell ml
−1
compared to higher rations above 5,000
cells ml
−1
. Ten-fold increases in feeding rates result in an ingestion excess of 6.5 ng protein day
−1
at a ration of 1,000 cells ml
−1
, compared to 0.7 ng day
−1
at feeding rates were typical of those
measured for higher food rations. Surprisingly, larvae given constant rations of 5,000, 10,000,
30,000, and 50,000 cells ml
−1
of the algal diet Rhodomonas lens accreted the same amount of
protein, 16 ± 2 ng protein day
−1
. Furthermore, rates of protein synthesis (81 ± 6 ng protein
larva
−1
day
−1
) and degradation (65 ± 6 ng protein day
−1
) also did not vary with food ration.
Protein synthesis rates were 5-fold higher than rates of protein accretion. This resulted in only
20% of the mass of proteins synthesized being accreted, while 80% was degraded. Since the
dynamics of protein synthesis and degradation did not differ, changes in feeding and protein food
conversion efficiency were responsible for maintaining stable accretion rates of 16 ± 2 ng protein
94
day
−1
at rations spanning from 5,000 to 50,000 algal cells ml
−1
. Larvae reared at 5,000 cells ml
−1
accreted 71% of the mass of proteins ingested while larvae reared at 10-fold higher rations of
50,000 cells ml
−1
only accreted 17% of the mass of proteins ingested. These results show that
5,000 cells ml
−1
is not a food-limiting ration for larvae of L. pictus. Instead, larvae are able to
maintain the dynamics of protein synthesis, degradation, and accretion by changing efficiencies
in feeding and protein food conversion efficiency.
95
INTRODUCTION
Planktonic marine invertebrate larvae utilize a spectrum of ways to acquire nutrients,
ranging from maternal reserves to suspension feeding and facultative feeding strategies (Herrera
et al., 1996). Each of these metabolic strategies differ in the source of substrates used to drive
growth to a metamorphic stage. Many studies have focused on maternally endowed biochemical
reserves or egg size as predictors for larval success (Sinervo and McEdward, 1988; Emlet and
Hoegh-Guldberg, 1997). While some effects of maternal investment on growth have been
reported, studies show that the energetics of larval growth is dependent on environmental
conditions such as food availability and the uptake of dissolved organic matter (Jaeckle and
Manahan, 1989; Bertram and Strathmann, 1998). Even for planktotrophic larvae, the uptake of
dissolved organic matter plays a significant role for larvae to resist starvation until conditions for
phytoplankton availability become more favorable (Moran and Manahan, 2004). Larvae that do
not feed will deplete their energetic reserves and must utilize environmental resources to
maintain metabolism (Meyer et al., 2007). As a result, understanding the feeding physiology of
marine larvae in different food environments will provide insight into the metabolic dynamics of
growth.
Many marine invertebrate larvae exhibit morphological plasticity in response to their
food environment such that the efficiencies of capture or assimilation may be enhanced in
environments of low food (Boidron-Metairon, 1988; Sewell, et al., 2004; Miner, 2005). For
example, arm lengths of pluteus-form larvae were longer for larvae reared under lower food
rations (Sewell, et al., 2004). In echinoderm larvae, ciliary bands line the arms and surrounding
oral region of the pluteus larva, and maximum feeding rates were described to be a function of
96
ciliary band length, cilium height, and ciliary beating current velocity (Strathmann 1972). Along
the ciliary band, ciliary movement can be controlled with changes of direction resulting in either
swimming or feeding behaviors depending on the stroke direction (Strathmann and Grunbaum,
2006). Neurotransmitters such as dopamine, serotonin, or epinephrine can act to change the
frequency or direction of ciliary beating patterns (Lacalli and Gilmour, 1990; Wada et al., 1997).
Ciliary bands along the arms of the pluteus beat in one direction while swimming and prior to
encountering food particles. However, once the food particles have been encountered by the
cilia, larvae are able to respond within 0.02 to 0.06 seconds to reverse the direction of ciliary
beating and direct algal food towards the mouth (Strathmann, 2007). Whether through
morphological plasticity or neuronal control, the regulation of feeding rates by echinoderm
larvae is a direct response to environmental food availability. The regulation of feeding will
heavily influence a larva’s ability to acquire nutrients in rapidly changing environments or in
patches of food-limited environments.
The ability to regulate feeding rates, whether through morphological changes or
physiological changes, suggests that there is an optimal food ration for growth (Taghon 1981,
Calow 1982; Kiørboe et al., 2018; Raubenheimer and Simpson, 2018). Protein content is often
used as a metric for food quality in marine systems due to its importance in growth, metabolism,
and homeostasis (Sedgwick 1979; Winfree and Stickney 1981; Fry et al., 2018). However,
proteins are in a dynamic state of constantly being synthesized and degraded in living organisms
(Schoenheimer, 1942; Hawkins 1991; Ohsumi, 2014). In marine organisms which are starved,
protein synthesis rates and degradation rates remain high (Houlihan et al., 1988; Houlihan et al.,
1989; Pace and Manahan, 2006). The degradation and recycling of proteins during starvation or
food limitation is a conserved mechanism across living organisms. In the yeast Saccharomyces
97
cerevisiae, cells starved of nitrogen had higher rates of protein degradation than those that were
provided nutrients (Halvorson 1958; López and Gancedo, 1979; Helbig et al., 2011). For
organisms growing with different food rations, protein accretion is dependent on changes in the
dynamics of protein synthesis and degradation (Houlihan et al., 1988; Mente et al., 2001; Ellison
et al., 2021).
Given the plasticity of marine larvae to regulate feeding, and the changing dynamics of
protein metabolism in different food environments, the present study was conducted to examine
the effects of food ration on protein metabolism and growth in larvae of Lytechinus pictus. By
concurrently measuring protein ingestion, synthesis, degradation, and accretion rates, 4-fold
increases in protein food conversion efficiencies were found for larvae reared at different rations.
These differences were seen while rates of protein synthesis and degradation remained constant
across all rations. The key protein metabolic dynamic step in controlling growth in highly
variable food environments is change in the conversion efficiency of protein ingested.
METHODS
Experimental design and approach
To test the effects of various food rations on larval growth and protein metabolism,
several experiments were performed to address two primary objectives: (1) determine changes in
larval feeding rates in response to a range of food rations, and (2) assess the dynamics of protein
metabolism for growing larvae reared at four different food rations (Fig. 2.1). Larvae from two
culture vessels of different cohorts were used for Objective 1, and larvae from a total of 18
culture vessels representing four cohorts were used for Objective 2 (Fig. 2.1).
98
To accomplish Objective 1, a total of 62,000 larvae were used in 26 feeding rate assays
with assay rations ranging from 1,000 to 100,000 cells ml
−1
of the algal food Rhodomonas lens.
Larvae from each culture vessel were divided into assays of feeding rate across 14 rations to
characterize changes in feeding rates due food ration. Since the feeding rates across this range of
food rations was tested on the same-sized larvae at the same time, changes in feeding rates could
be attributed to differences in food ration.
For Objective 2, a total of 1,244,600 larvae were sampled across 18 culture vessels
representing four cohorts and four target food ration treatments across a 10-fold range from
5,000 to 50,000 cells ml
−1
(Fig. 2.1). These larvae were utilized in 2,400 measurements of size,
240 determinations of protein content (120,000 larvae), 84 determinations of lipid content
(123,000 larvae), 80 assays of protein synthesis rate (800,000 larvae), and 81 assays of feeding
rate (199,200 larvae). Together, these assays provide an integrative view of the dynamics of
protein metabolism and growth efficiencies in larvae reared at different rations.
99
Figure 2.1. Experimental design and approach encompassing two primary objectives on the
study of larval growth for Lytechinus pictus. Objective 1 was to characterize the changes in
feeding rates at rations ranging from 1,000 to 100,000 cells ml
−1
of the algal food Rhodomonas
lens. Larvae from two cohorts were used. Objective 2 was to rear larvae in replicate culture
vessels given four different food rations, and to measure larval growth and protein metabolism.
Four cohorts were spawned (Obj. 2: blue boxes) and larvae were divided into 18 culture vessels
across four food rations.
100
Larval culturing
Gametes were collected from several adult white urchins (Lytechinus pictus obtained
from Marinus Scientific, Long Beach, CA) following intracelomic injection of 0.5 M KCl.
Fertilization success rates, determined by the rapid formation of the fertilization envelope, was
observed to be > 99%. Fertilized embryos were stocked at 20 embryos ml
−1
in 20-liter culture
vessels held in a temperature-controlled room with a temperature data logger (Model Pro v2
HOBO water temperature data logger, Onset, Cape Cod, MA). The water temperature averaged
16.3 ± 0.01°C (s.e.m, n=692 measurements). On days 4, 6, and 8 larvae were sampled by gently
pouring them onto a Nitex mesh with 53 µm sieve sizes. Larvae were resuspended in <50 ml
volumes of UV treated seawater (0.2 µm filtered) from the coastal waters of Santa Catalina
Island, CA. Larvae were mixed gently, and aliquots were subsampled for counting under 40x
total magnification. All larval counts were greater than 100 per aliquot and had coefficients of
variation below 10%. By day four, larvae had reached the 4-arm pluteus stage with fully
developed stomachs and were fed a microalgal diet of Rhodomonas lens at rations of 5,000,
10,000, 30,000, or 50,000 cells ml
−1
(Objective 2; Fig. 2.1). Cultures of Rhodomonas lens were
grown in f/2 medium (Guillard and Ryther, 1962) and visually counted using a hemocytometer
prior to being provided as algal food rations to larval cultures. Food rations within culture vessels
were monitored every 4-6 hours to maintain target rations. Water samples (10 ml) were taken
from well-mixed culture vessels every 4-8 hours and passed through a 20 µm mesh. The filtrate
was collected in a vial (25 ml Accuvette Cup, Beckman Coulter, Carlsbad, CA), and particles
were counted using a particle counter (Model Z2 particle counter, Beckman Coulter, Carlsbad,
CA). Particle counts were cross-calibrated to visual hemocytometer counts of R. lens to account
101
for non-algal particles. Cells of the algal food R. lens were provided to culture vessels to
maintain rations within 10% of the target ration.
Larval feeding rates
Feeding rates were determined by measuring rates of algal depletion from the seawater in
time-course assays. Algal amounts in assays were determined using a particle counter (Model Z2
particle counter, Beckman Coulter) cross-calibrated to a standard curve of visual hemocytometer
counts (see Chapter 1 for detailed justification). Independent assays with larvae-only or algae-
only were also sampled in parallel to control for changes in particle counts not attributable to
feeding by larvae (e.g., production of pseudo-feces and feces). Clearance rates, defined as
volumes of water processed by larvae over time, were used as the measure for feeding rate
(Strathmann et al., 1972). Clearance rates are calculated based on changes in the amount of cells
between two timepoints according to Equation 1 (Gauld, 1951; Pace et al., 2006). This equation
can be solved for Ct to yield Equation 2 where F is the clearance rate, Ct is the measured cell
ration at time t, C0 is the initial cell ration, N is the number of larvae in each assay vial, and V is
the volume of the assay vial. In an exponential plot of the amount of cells in the volume over
time, Equation 2 allows for the estimation of clearance rate as the slope term, F. The equations
for clearance rate yield units of volumes of seawater cleared per unit time, which follows an
exponential decline due to the asymptotic nature of suspension feeding where volumes of water
are cleared rather than cells being targeted and captured (Gauld, 1951). Corresponding ingestion
rates (cells larva
−1
h
−1
) were calculated based on the depletion of cells over time from seawater
and adjusted for assay volumes and numbers of larvae. At the short time scales during which the
102
assays occurred, the algal declines were approximately linear, and ingestion rates could be
estimated by the slope of the linear regressions.
Equation 1. 𝐹 =
( ) × ×
Equation 2. 𝐶 = 𝐶 × 𝑒 ×
For Objective 1 (Fig. 2.1), the dependence of feeding rates on food ration was examined.
Larvae from culture vessels of two cohorts were divided into assays spanning a range of initial
algal rations (1,000 to 100,000 cells ml
−1
). These assays were initiated at the same time, and
time-course samples (five timepoints) were taken from all treatments at regular intervals. Figure
2.2A shows 14 assays run in parallel, with the x-axis of time adjusted to show the continuous
decline in algal amounts by larvae across different rations (y-axis). This sampling regime was
chosen so that assays at each ration lasted for less than six hours total while covering a range of
algal rations between 1,000 to 100,000 cells ml
−1
. It was important to limit the assay time (and
thus the range of cell amounts encountered) since the dependence of feeding rates on food
rations was in question (Marin et al., 1986). This way, measured clearance rates are better
attributed to the respective food ration at which they were measured. For each assay, the
clearance rate was calculated according to Equation 2 using the time-course data of algal
depletion.
For Objective 2 (Fig. 2.1), which examined larval growth and physiology when fed
rations of 5,000, 10,000, 30,000, or 50,000 cells ml
−1
, larval clearance and ingestion rates were
assayed at rations matching those measured in their culture vessel. Assayed ingestion rates were
103
used to determine the total number of cells ingested over the feeding period by multiplying
ingestion rates by the duration of feeding. Protein content of Rhodomonas lens was also
determined using the same method for larvae (see section “Larval growth rates” below) for each
batch of algae used. Together with the total amount of cells ingested, the protein content of R.
lens was used to calculate the amount of protein ingested, given the known number of algal cells
ingested.
Larval growth rates
Larval morphology was measured as change in size every two days from
microphotographs from 50 larvae taken from each culture vessel. Two morphological
measurements were made on each larval size measured. Midline body length was quantified as a
measure of total body length (Fig. 2.4A). In addition, the postoral arm length was quantified as
this variable is a measure of feeding structures related to ciliary band length (known to vary with
food ration) (Fig. 2.4A). A standard micrometer was used to calibrate measurements using
ImageJ software (v1.49, https://imagej.nih.gov/ij/). Midline body lengths were defined as the
straight line between the posterior end and the edge of the oral hood (Fig. 2.4A). Postoral arm
lengths were measured along the length of the spicule spanning the longest of the two postoral
arms. The total protein content of larvae was determined by Bradford (1976) assay modified for
larvae (Jaeckle and Manahan, 1989). The Bradford assay was chosen based upon a comparison
of amino acid content in whole-body protein that provided an accuracy within 7% of protein
content in a larval form (Shilling and Manahan, 1994). In the current study, five replicate tubes
containing 500 larvae each were freeze-dried (Model SC110A SpeedVac Plus, Savant, part of
Thermo Fisher Scientific; Flexi-Dry MP, FTS systems, Stone Ridge, NY). Larvae were then
104
resuspended in deionized water (NanoPure) and sonicated (Model VC50, Vibracel, Sonics &
Materials, Inc., Newtown, CT). Proteins were then precipitated in 5% trichloroacetic acid (TCA)
on ice for 30 minutes. Precipitated proteins were centrifuged at 12,000 rcf for 20 minutes at 4°C.
TCA was aspirated using a glass pipette under vacuum. Proteins were then resolubilized using
500 µl 1M NaOH and neutralized with 300 µl 1.67M HCl. Bradford reagent (200 µl) was then
added to samples and mixed before aliquoting three technical replicate volumes to 96-well
plates. Absorbances at 595 nm were measured (Model M2E SpectraMax multi-mode microplate
reader, Molecular Devices, San Jose, CA) and quantified in units of mass using a standard curve
of bovine serum albumin standards prepared by the same method. Daily growth rates for midline
body length, postoral arm length, and total protein were determined for larvae from each culture
by calculating the change in size or protein content over time.
Lipid classes and amounts in larvae
Lipid contents were determined for larvae grown at 5,000, 10,000, 30,000, and 50,000
cells ml
−1
following the protocols by Moran and Manahan (2004). Replicate vials containing
known amounts of larvae (1,000 – 2,000, depending on size) were sonicated in 200 µl deionized
water to rupture tissues and cells. The homogenate was transferred to a glass V-vial (#986254,
Wheaton
®
DWK Life Sciences, Millville, NJ) that was washed with chloroform. Methanol and
chloroform were added to the sonicated sample in the glass V-vial to a final ratio of 1:1:0.5
(v:v:v) water, methanol, chloroform. Known amounts (0.5 – 0.9 µg) of 1-octadecanol (stearyl
alcohol) internal standard were also added for the determination extraction efficiency. The
solution was mixed and centrifuged at 8,000 rcf for 10 minutes at 4°C to allow for phase
separation of the methanol and chloroform phases. Following phase separation, both liquid
105
phases of methanol (top) and chloroform (bottom) were transferred to clean V-vials using glass
syringes (Hamilton Company, Reno, NV), avoiding any solid debris. Water, methanol, and
chloroform were then added to the liquid phase to a final ratio of 0.9:1:1 (v:v:v). Samples were
mixed and centrifuged again as described above. The bottom chloroform phase layer was then
transferred to a borosilicate shell vial (1.5 ml #5182-0714, Agilent, Santa Clara, CA). A
chloroform rinse (400 µl) was added to the V-vial and allowed to phase separate again. Lipids in
the chloroform rinse were combined in the borosilicate shell vial and heated at 40 °C under a
gentle stream of N2 gas. The dried lipid was then resuspended in 50 µl chloroform and
transferred to a thin glass insert (Agilent #5181-3377). Thin-layer chromatography rods
(Chromarod-S III, Iatron Laboratories, Inc., Tokyo, Japan) were spotted with known amounts (1-
2 µl) of sample using a 1 µl glass capillary tube. Three rods were spotted for each sample, as
well as two sets of standards of known concentration (Standard 1: squalene, tripalmitin, 1-
octadecanol, L-phosphatidylcholine; Standard 2: Lauric acid palmityl ester, palmitic acid, 1-
octadecanol, cholesterol). Rods were developed in chromatography chambers using a polar
solvent (60 ml hexane, 17 ml ethyl ether, 10 drops of glacial acetic acid) for between 22-25
minutes. Rods were then dried for 10 minutes at 100°C. Lipid classes on each Chromarod were
detected by H2 flame ionization detection (Model MK-5 Iatroscan, Iatron Laboratories Inc.,
Tokyo, Japan) with a 30 second scan for each rod. Peak areas were converted to units of mass
based on standards for hydrocarbon, wax ester, cholesterol, triacylglycerol, free fatty acid,
stearyl alcohol, and phospholipid. Lipid quantities from extracted samples were corrected for
extraction efficiencies based on the amount of internal standard (1-octadecanol) detected. The
total lipid content was calculated by adding the masses of all classes of lipids detected.
106
In vivo protein synthesis rates
Protein synthesis rates were measured from in vivo time course assays based on larval
incorporation of
14
C-alanine into protein (Pace and Manahan, 2006). Alanine was chosen
because it is readily quantified following separation by high-performance liquid
chromatography. Separation by high-performance liquid chromatography also allows for the
collection of the alanine fraction for scintillation counting. To assay
14
C-alanine incorporation
into protein, known amounts of larvae (10,000 larvae in 10 ml) were incubated in 74 kBq
14
C-
alanine with 10 µM of non-radioactive alanine. This total concentration of alanine resulted in
transport rates that allow for a quantifiable signal of radioactivity. Assays were conducted in
duplicate 20 ml glass vials at 15°C, and each contained 10,000 larvae in 10 ml of seawater
(filtered through a Nucleopore filter, 0.2 µm pore size) and the alanine solution described. Every
six minutes for five time points, 1,000 larvae (1 ml) were removed and retaining on a Nucleopore
filter (8 µm pore size) under vacuum filtration. Excess radioactivity was washed away using > 10
ml volumes of filtered seawater. Larvae were frozen at -80°C to halt further synthesis of
proteins. To measure the amount of
14
C-alanine transported and incorporated into protein, larvae
were sonicated in known amounts (400 µl) of deionized water (Nanopure) and divided into
aliquots for quantification of radioactivity in the free amino acid pool and the protein fraction.
The free amino acid pool of larvae was extracted in 70% ethanol at 4°C overnight. The
specific activity of
14
C-alanine in the free amino acid pool was analyzed using reverse-phase
high-performance liquid chromatography and labelling of amino acid primary amines with ortho-
pthaldealdehyde. The protein fraction was extracted following precipitation in 5% trichloroacetic
acid (TCA) on ice for 30 minutes. The TCA fraction was retained on a glass microfiber filter
(Whatman
®
, grade GF/F) under vacuum filtration and rinsed with successive rinses of 5% TCA
107
and methanol. The TCA-precipitable protein fraction retained on the glass microfiber filter was
then vortexed in scintillation fluid (Ultima Gold, Perkin Elmer, Santa Clara, CA) before being
counted by scintillation counting (Model LS 6500, Beckman Coulter, Carlsbad, CA). Rates of in
vivo protein synthesis were calculated according to Equation 5 where MWp is the average
molecular weight of amino acid residues in whole-body protein of L. pictus (129.4 g mol
−1
; Pace
and Manahan, 2006), Sm is the mole-percent of alanine in protein of the larvae (L. pictus alanine:
7.8%; Pace and Manahan, 2006), Sp is radioactivity of alanine in the TCA-precipitated fraction
(proteins), and Sfaa is the specific-activity of
14
C-alanine in the intracellular free amino acid pool.
For an example calculation of protein synthesis rate, see Table 2.1. As expected, given the small
amounts of
14
C-alanine transported into the intracellular “free” amino acid pool, the amount of
alanine did not change during the course of each assay (Table 2.1, column 2). The specific
activity of
14
C-alanine in the intracellular “free” amino acid pool increased due to transport of the
radio-labelled tracer (Table 2.1, column 3). Meanwhile, the
14
C-alanine radioactivity increases in
the protein fraction (Table 2.1, column 5). The rate of protein synthesis was calculated as the
slope ± s.e. of increase in protein synthesized through time. The slopes of duplicate assays did
not differ significantly by ANCOVA, so data points were pooled together from duplicate assays
to give a single protein synthesis rate.
Equation 5. 𝑃𝑟𝑜𝑡𝑒𝑖𝑛𝑠 𝑆𝑦𝑛𝑡 ℎ𝑒𝑠𝑖𝑧𝑒𝑑 =
×
108
Table 2.1. Example calculation of protein synthesis rates based on
14
C-alanine incorporation into the trichloroacetic acid (TCA)
protein fraction. (1) The alanine chromatographic peak area as determined by ortho-pthaldealdehyde-labelling of intracellular free
amino acids separated by reverse-phase high performance liquid chromatography (HPLC). (2) The amount of alanine in the HPLC
peak determined using an alanine standard with known quantities. (3) The
14
C radioactivity in the alanine peak fraction. (4) The
14
C
specific activity of alanine in the intracellular free amino acid pool (Sfaa). (5) The
14
C radioactivity in the trichloroacetic acid
precipitated TCA fraction. (6) The
14
C radioactivity in the TCA protein fraction was calculated for a single larva (from a total assay of
750 pooled larvae) (Sp). (7) The change in Sp from the previous timepoint. (8) The average Sfaa between assay timepoints. (9) Amount
of alanine incorporated into proteins between assay timepoints calculated by dividing values in column 8 by values in column 7. (10)
The cumulative amount of alanine incorporated into proteins. (11) The linear regression of the time-course data in Column 10 yielded
a slope that is the rate of alanine incorporation into protein. (12) The mass of protein synthesized per hour calculated by multiplying
the rate of alanine incorporation into protein by MWp/Sm and converted to units of ng hour
−1
.
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
Minutes
Alanine
(fluorescence
units)
Alanine
(pmol)
Alanine
(dpm)
Sfaa
(dpm/pmol)
Protein
(dpm) Sp ΔSp
(tn − tn−1)
Avg. Sfaa
(tn − tn−1)
Ala in protein
(pmol)
Total Ala in
protein
(pmol)
6 5.68E+06 74 356 4.8 1,232 1.64 1.64 2.42 0.68 0.68
12 6.82E+06 88 576 6.5 2,436 3.25 1.60 5.68 0.28 0.96
18 5.72E+06 74 731 9.9 4,022 5.36 2.11 8.20 0.26 1.22
24 6.44E+06 83 888 10.7 5,664 7.55 2.19 10.26 0.21 1.43
30 5.78E+06 75 947 12.7 6,620 8.83 1.28 11.66 0.11 1.54
(11) Slope: 0.037 pmol Ala min
−1
(12) MWp/Sm: 3.65
ng protein h
−1
Lytechinus pictus: MWp = 129.4 g mol
−1
; Sm = 7.8% (Pace and Manahan, 2006)
Alanine standard: 1.29E-05 pmol (fluorescence unit)
−1
Larvae used in TCA-fraction: 750 larvae
109
Protein metabolic dynamics
For Objective 2, total amounts of protein ingestion, protein synthesis, and protein
accretion were determined for larvae reared at food rations between 5,000 to 50,000 cells ml
−1
.
By measuring rates of protein synthesis and accretion, protein degradation could be calculated
according to Equation 6. The protein depositional efficiency was calculated as the percentage of
proteins accreted relative to proteins synthesized (Equation 7). Protein food conversion
efficiency was calculated as the ratio of total protein accretion to total protein ingestion
(Equation 8). The relationship between food ration and each process of protein ingestion,
synthesis, degradation, accretion depositional efficiency, or food conversion efficiency, was
statistically tested (ANOVA regression).
Equation 6. 𝑃𝑟𝑜𝑡𝑒𝑖𝑛𝑠 𝐴𝑐𝑐𝑟𝑒𝑡𝑖𝑜𝑛 = 𝑃𝑟𝑜𝑡𝑒𝑖𝑛 𝑆𝑦𝑛𝑡 ℎ𝑒𝑠𝑖𝑠 − 𝑃𝑟𝑜𝑡𝑒𝑖𝑛 𝐷𝑒𝑔𝑟𝑎𝑑𝑎𝑡𝑖𝑜𝑛
Equation 7. 𝑃𝑟𝑜𝑡𝑒𝑖𝑛𝑠 𝐷𝑒𝑝𝑜𝑠𝑖𝑡𝑖𝑜𝑛𝑎𝑙 𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 =
Equation 8. 𝑃𝑟𝑜𝑡𝑒𝑖𝑛𝑠 𝐹𝑜𝑜𝑑 𝐶𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜𝑛 𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 =
RESULTS
Objective 1: Dependence of feeding rates on food ration
To determine the functional response of feeding rates of L. pictus to food ration, a total of
62,000 larvae from two culture vessels were used in 26 feeding rate assays with initial rations
110
ranging from 1,000 to 100,000 cells ml
−1
. A set of 14 concurrent assays in Fig. 2.2A shows the
range of food rations assayed. These assays resulted in feeding rates increasing from 0.8 ± 0.3 µl
seawater larva
−1
h
−1
(at ~100,000 cells ml
−1
) to 8.7 ± 0.2 µl seawater larva
−1
h
−1
(at ~1,000 cells
ml
−1
) (Fig. 2.2B,C). This range in feeding rates represents >10-fold changes in feeding rates from
larvae of 271 ± 2.1 µm midline body length. Feeding rates were normalized to midline body
length and plotted for each replicate culture vessel and cohort tested (Fig. 2.2D). The relationship
between feeding rate and food ration was non-linear at low food rations.
Objective 2: Larval growth and physiology at different food rations
To assess the growth and protein metabolism of larvae reared at different food rations, it
was necessary to maintain stable rations of algae within culture vessels. A total of 18 culture
vessels were maintained for this objective. The culture vessels were fed from three batches of
Rhodomonas lens, and ration levels were monitored by particle counting every 4 – 6 hours. The
protein contents of each batch of algae were determined prior to being provided as rations to
each culture vessel. The three batches of algae had protein contents of 34.3 ± 3.5 (Fig. 2.3A),
26.8 ± 1.3 (Fig. 2.3B), and 30.6 ± 1.7 pg cell
−1
(Fig. 2.3C; Table 2.2). The protein content did not
differ significantly between batches of algae (ANOVA F 2,10 = 3.32, ns). When pooled together,
the three batches of algae yield an average protein content of 30.0 ± 1.3 pg cell
−1
. Culture vessel
rations were all maintained within <1,000 cells ml
−1
s.e.m of the target ration in all culture
vessels (Table 2.2).
111
Figure 2.2. Changes in feeding rates by larvae of Lytechinus pictus exposed to food rations
ranging from 1,000 to 100,000 cells ml
−1
. (A) Fourteen independent time course assays,
corresponding to different symbol shapes, were performed at various initial food rations. Start
times (x-axis values) were adjusted for each assay based on the regression of each time course to
model a continuous decline in food ration. (B) Changes in feeding rates with food ration based
on assays on larvae of size 299 ± 2.3 µm midline body length. Feeding rates ± s.e. were
determined from assays shown in panel A. (C) Changes in feeding rates with food ration based
on assays on larvae of size 271 ± 2.1 µm midline body length. (D) Changes in size-corrected
feeding rates with food ration. Closed symbols represent assays reported in panel B while open
symbols represent assays reported in Panel C.
112
Table 2.2. Food rations maintained in culture vessels used for experiments on growth and
protein metabolic dynamics in larvae of Lytechinus pictus. Food rations are reported as averages
± s.e.m in the rows under the respective batch of algae provided to larvae. The protein contents
of three batches of Rhodomonas are reported as averages ± s.e.m. There were no significant
differences between protein contents for algae from each batch (ANOVA F 2,10 = 3.32, ns).
Batch 1 Batch 2 Batch 3
R. lens protein
pg cell
−1
34.3 ± 3.5
26.8 ± 1.3
30.6 ± 1.7
Culture vessel
food rations
(10
3
cells ml
−1
)
10.1 ± 0.3
5.2 ± 0.2
4.7 ± 0.2
9.9 ± 0.3
5.0 ± 0.2
4.6 ± 0.2
32.3 ± 0.6
10.0 ± 0.2
30.2 ± 0.5
32.1 ± 0.6
10.2 ± 0.2
30.1 ± 0.6
54.9 ± 0.9
32.2 ± 0.3
51.2 ± 0.9
55.3 ± 0.7 32.4 ± 0.4 53.1 ± 0.8
113
Figure 2.3. Monitoring of food rations within 18 culture vessels of larvae of Lytechinus pictus
fed Rhodomonas lens. Symbols in each panel represent culture vessels with target rations of (A)
10,000, 30,000, and 50,000 cells ml
−1
; (B) 5,000, 10,000, and 30,000 cells ml
−1
; and (C) 5,000,
30,000, and 50,000 cells ml
−1
. Lines represent the average ration for larvae from a replicate
culture vessel for a ration.
114
Larval growth at different food rations
Growth rates in midline body length and postoral arm length (see Fig. 2.4A for
measurements) were determined from measurements on 2,400 larvae from 18 culture vessels.
Protein accretion was determined for larvae (240 assays; 120,000 larvae) from 18 culture vessels.
The lowest ration of 5,000 cells ml
−1
was not a growth-limiting ration as growth rates in size for
both morphological characteristics measured (midline body length and postoral arm length) did
not differ for larvae reared at rations between 5,000 and 50,000 cells ml
−1
(ANOVA regression;
midline body length p = 0.17; postoral arm length p = 0.48; Fig. 2.4B,C). The protein accretion
rates also did not differ for larvae from 18 culture vessels provided with different food rations
(ANOVA regression; p = 0.07; Fig. 2.4D). In addition, at the biochemical level, protein accretion
rates also remained constant across food rations for larvae within the same cohort (ANOVA
regressions; p = 0.33, p = 0.25, p = 0.93, p = 0.84 respectively; Fig. 2.4D). Since morphological
growth rates did not differ for larvae provided with different food rations, values were pooled
together to calculate overall growth rates for larvae. On average larvae grew 11 ± 1 µm day
−1
midline body length, 18 ± 1 µm day
−1
postoral arm length, and 16 ± 2 ng day
−1
total protein (Fig.
2.4).
An analysis of the lipid class contents of larvae reared at rations of 5,000 to 50,000 cells
ml
−1
is reported in Fig. 2.5. The amounts of phospholipids, triacylglycerols, free fatty acids,
hydrocarbons, and sterols were summed to determine the total mass of lipids in larvae (across
three different cohorts). At the onset of feeding, 4-day old larvae had 22 ng total lipid. For 8-day
old larvae (after four days of feeding), total lipid contents increased to 29, 42, 46, and 41 ng
when provided rations of 5,000, 10,000, 30,000, or 50,000 cells ml
−1
, respectively. These
amounts of total lipid accretion were less than half of that of total protein accretion. The average
115
rate of protein accretion was 16 ± 2 ng protein day
−1
(n=18 culture vessels) (Fig. 2.4D). In
contrast, the rate of lipid accretion for larvae provided with food rations of either 5,000, 10,000,
30,000, or 50,000 cells ml
−1
were 2, 5, 6, and 5 ng lipid day
−1
, respectively. These lipid
accretion values are calculated from the difference in total lipid contents (the daily change in
lipid content averaged between 4-day old and 8-day old larvae, Fig. 2.5). Firstly, these data on
the biochemical composition of larvae of L. pictus show that most of the growth in biomass was
related to proteins. Secondly, these data illustrate that maximum lipid accretion rates were
obtained once the algal ration exceeded 10,000 cells ml
−1
.
116
Figure 2.4. The effect of food ration on the morphological size and protein content of larvae of
Lytechinus pictus fed between 5,000 – 50,000 cells ml
−1
. (A) Measurements of morphological
size for an 8-day old pluteus larva of L. pictus fed 50,000 cells ml
−1
indicated by thin, black lines.
Midline body length was measured from the posterior end to the edge of the oral hood. Postoral
arm length was measured along the spicule of the postoral arm. (B) The effect of food ration on
the growth rate of midline body length; growth rate did not change significantly (ANOVA
regression, p = 0.17) with ration and averaged 11.1 ± 0.8 µm day
−1
. (C) The effect of food ration
on the growth rate of postoral arm length; growth rate did not change significantly (ANOVA
regression, p = 0.48) with ration and averaged 18.1 ± 1.1 µm day
−1
. (D) The effect of food ration
on protein accretion rates; growth rate did not differ significantly (ANOVA regression, p = 0.07)
with ration, and averaged 16 ± 2 ng day
−1
. Data points with different symbols represent larvae
from 18 culture vessels representing four different cohorts; within each panel, individual data
point symbols represent the same larval culture.
117
Figure 2.5. Lipid class contents (phospholipids, triacylglycerols, free fatty acids, hydrocarbons,
and cholesterols) for larvae of Lytechinus pictus reared at different food rations. The standard
error (ng larva
−1
) is shown for phospholipids. Errors for other lipid classes are excluded for
visual clarity, and the errors averaged (ng larva
−1
± s.e.m): hydrocarbons ± 1.5, triacylglycerols
and free fatty acids ± 0.5, cholesterols ± 0.2.
118
Larval protein food conversion efficiency
Protein food conversion efficiencies (Eq. 8) were determined for larvae provided with
rations between 5,000 to 50,000 cells ml
−1
. Protein ingestion rates were calculated using assayed
feeding rates and the protein contents measured for R. lens reported in Table 2.2. Based on the
measured protein ingestion rates, the rates of protein ingestion were compared with rates of
protein accretion in the same larval culture. Comparisons of protein accretion and ingestion were
used to calculate a protein food conversion efficiency (Eq. 8). There was a significant
relationship between protein food conversion efficiency and food rations (ANOVA regression, p
= 0.004, R
2
= 0.63; Fig. 2.6). The relationship between protein food conversion efficiency and
food ration is described by the equation: Protein Food Conversion Efficiency = 77.24 – 0.0012 ×
(Ration cells ml
−1
). Based on this regression, larvae provided with a ration of 5,000 cells ml
−1
would accrete 71% of the protein mass ingested. The protein food conversion efficiency for
larvae is predicted to decrease for larvae provided with a ration of 50,000 cells ml
−1
with only
17% of protein mass ingested accreted as growth (Fig. 2.6).
119
Figure 2.6. Changes in protein food conversion efficiency for larvae of Lytechinus pictus
provided with different food rations. Closed circles represent values for larvae from culture
vessels of the same cohort. The experiment was repeated for three different cohorts of larvae,
each represented by a different symbol. There is a statistically significant relationship between
protein food conversion efficiency and food ration which can be predicted by y = 77.24 –
0.0012x (ANOVA regression; p = 0.004).
120
Dynamics of protein synthesis and accretion
Protein synthesis rates and accretion rates were calculated based on 240 determinations of
protein content (120,000 larvae), 80 assays of protein synthesis (800,000 larvae). These assayed
rates were used to calculate protein degradation rates (Eq. 6) and protein depositional efficiencies
(Eq. 7). Larvae maintained a constant protein synthesis rate even at the lowest food ration tested
of 5,000 cells ml
−1
. Notably, rates did not increase as food rations increased up to 50,000 cells
ml
−1
(ANOVA regression p = 0.64; Fig. 2.7A). Examination of protein synthesis rates within
each of four cohorts also showed no significant relationship between protein synthesis rates and
food ration (ANOVA regressions; p = 0.054, p = 0.511, p = 0.182, p = 0.306 within each cohort,
respectively). Pooling rates together from larvae of four cohorts (18 culture vessels), larvae
synthesized 81 ± 6 ng protein larva
−1
day
−1
(Fig. 2.7A). Protein synthesis rates were 5-fold higher
than rates of protein accretion (ANOVA F1,34 = 102.21, p < 0.001). Compared to protein
accretion rates of 16 ± 2 ng day
−1
(Fig. 2.7A, black symbols), which also did not change with
food ration, the higher rate of protein synthesis (Fig. 2.7A, white symbols) relative to protein
accretion rate results in a low protein depositional efficiency of 20% (16 / 81 ng day
−1
). This
means that only 20% of the mass of proteins synthesized are accreted, and 80% of the whole-
body protein mass is degraded (Eq. 6). On average, larvae of the 18 culture vessels degraded
over 65 ± 6 ng protein larva
−1
day
−1
(Fig. 2.7B). Protein degradation rates also did not differ for
larvae across four cohorts (18 culture vessels) depending on food ration (ANOVA regression p =
0.33; Fig. 2.7B). These results suggest that stable food rations across a 10-fold range from 5,000
to 50,000 cells ml
−1
have no effect on rates of protein synthesis and degradation.
121
Figure 2.7. Dynamics of protein synthesis, degradation, and accretion for larvae of four cohorts
(different symbol shapes) and 18 culture vessels reared at different food rations. (A) Protein
synthesis rates (open symbols) averaged 81 ± 6 ng day
−1
and were 5-fold higher than protein
accretion rates (closed symbols) which averaged 16 ± 2 ng day
−1
for larvae reared at all food
rations tested (ANOVA F1,34 = 102.21, p < 0.001). (B) Protein degradation rates, calculated as
the difference between protein synthesis rates and accretion rates, did not change with food
ration (ANOVA regression, p = 0.33), and averaged 65 ± 6 ng day
−1
.
122
DISCUSSION
Constitutively high rates of protein synthesis and degradation regardless of food ration
Despite ten-fold differences in food ration (5,000 – 50,000 cells ml
−1
), growth rates in
morphological size (midline body length: 11 ± 1 µm day
−1
, Fig. 2.4B; postoral arm length: 18 ± 1
µm day
−1
, Fig. 2.4C) and protein accretion (16 ± 2 ng day
−1
; Fig. 2.4D) did not differ for larvae
of L. pictus from four cohorts and 18 culture vessels. In particular, the consistency of the postoral
arm lengths indicate that algal food was never limiting across the experimental rations tested.
Even at the lowest algal ration tested of 5,000 cells ml
−1
, food was not a limiting factor since a
key component of the experimental design in the present study maintained stable rations in the
larval culture vessels (Fig. 2.3).
Most of the growth in biomass was due to accretion of proteins, which were greater than
2-fold higher than lipid mass. High protein synthesis rates (81 ± 6 ng larva
−1
day
−1
, Fig. 2.7A)
that are 5-fold higher than rates of protein accretion (16 ± 2 ng larva
−1
day
−1
, Fig. 2.4D, ANOVA
F1,34 = 102.21, p < 0.001) were required for growth. Surprisingly, neither protein synthesis rates
nor protein degradation rates differed according to the food ration provided to larvae (Fig.
2.7A,B). Comparisons of protein accretion rates and protein synthesis rates showed that only
20% (16 / 81 ng larva
−1
day
−1
) of the protein mass synthesized by larvae was accreted. This
means that 80% (100%-20%) of the protein mass synthesized is degraded regardless of the food
ration provided to larvae (Eq. 6; Fig. 2.7B). The maintenance of protein synthesis, degradation,
and accretion rates at constant levels across 10-fold changes in food ration (5,000 to 50,000 cells
ml
−1
) suggest that differences in feeding efficiencies can support nominal protein metabolic
dynamics at these rations.
123
High protein food conversion efficiency at low food ration
There were significant increases in protein food conversion efficiencies for larvae reared
at the lowest food ration tested of 5,000 cells ml
−1
compared to 10-fold higher at 50,000 cells
ml
−1
(Fig. 2.6). Based on the relationship between food conversion efficiency and food ration
(Fig. 2.6), larvae provided with 5,000 cells ml
−1
accreted 71% of the mass of protein ingestion
while larvae reared at rations of 50,000 cells ml
−1
accreted only 17% or the mass of protein
ingestion. Increases in protein food conversion efficiency at low food rations provides an
explanation for how larvae can maintain the same protein accretion rate (16 ± 2 ng larva
−1
day
−1
,
Fig. 2.4D) when fed 5,000 cells ml
−1
compared to 10-fold higher rations of 50,000 cells ml
−1
.
Larvae of L. pictus accrete such a large portion of ingested protein mass at low food rations, as
long as rations are maintained at stable levels (Fig. 2.3). This finding suggests that modifying
standard larval culturing protocols to maintain stable rations for marine larvae could greatly
reduce the demand for large-scale algal culturing for aquacultural production, by taking
advantage of the physiological changes in protein food conversion efficiency at low rations (Fig.
2.6: 71% conversion efficiency at 5,000 cells ml
−1
cf. only 17% at 50,000 cells ml
−1
).
Compensatory feeding by larvae at low food rations
Plasticity in feeding rates is often invoked as a compensatory mechanism to achieve
optimal growth (Taghon 1981, Calow 1982). For example, changes in the morphology of feeding
structures that influence feeding rates is thought to only be necessary when they improve
performance or growth (Boidron-Metairon, 1988; Sewell, et al., 2004; Miner, 2005; Adams et
al., 2011). In the ecological optimal foraging theory, the plasticity in feeding rates balance the
costs and benefits of increased feeding rates (e.g., costly behaviors of search time or costly
124
morphological changes in feeding structures) to optimize for some fitness attribute such as
growth (Schoener, 1971). Since the adoption of the optimal foraging theory, more focus has been
shifted towards the optimal nutritional gains resulting from feeding behaviors in different
environments (Raubenheimer and Simpson, 1993; Raubenheimer and Simpson, 2018). This led
to the consideration of compensatory feeding mainly in the contexts of food type and quality, as
defined by nutritional content and digestibility. In the present study, compensatory feeding was
observed for changes in food quantity from a single food source (R. lens) with known protein
nutritional values (Table 2.2). By comparing the increased energetic gains provided by
compensatory feeding, to that of non-compensatory rates, the excess energetic benefits can be
quantified.
The present study provides evidence for the broad regulation of feeding rates in larvae
independent of size and morphology (Fig. 2.2B,C data for larvae of the same size within a
cohort). Feeding rates for marine invertebrate larvae have previously been described to be a
function of the ciliary band length, length of cilium, and velocity of ciliary beating (Strathmann
et al., 1972). Studies on the regulation of feeding rates examined changes in the growth of
pluteus arms or ciliary band lengths when reared at different food rations (Hart and Strathmann,
1994; Sewell et al., 2004). Interestingly, Adams et al. (2011) pharmaceutically manipulated the
postoral arm lengths of feeding-stage larvae of S. purpuratus and showed a decrease in feeding
rates. In that study, the authors used artificial “food” using inert beads instead of algal cells. At
treatments greater than 4,000 beads ml
−1
, changes in postoral arm length explained >25% of the
variation in feeding rate, but only 20% of the variation at lower amounts of artificial food, 1,500
beads ml
−1
. The high amount of unexplained variance (>75%) in feeding rates at rations below
4,000 beads ml
−1
might better be explained by regulated feeding rates rather than ciliary band
125
length. In the present study, larvae of L. pictus exposed to rations of 1,000 cell ml
−1
, larvae were
observed to have feeding rates 10-fold higher than rates measured at higher food rations, above
5,000 cells ml
−1
(Fig. 2.2B,C). While changes in morphological size (i.e., postoral arm lengths)
can affect feeding rates, the physiological regulation of feeding rates at low rations provides a
more immediate explanation for changes in feeding.
Based on the increased feeding rates at an algal ration of 1,000 cell ml
−1
, the increased
clearance rate at this low ration was approximately 10 µl seawater larva
−1
hour
−1
(see highest-
value data points in Fig. 2.2C). This clearance rate approximately a 10-fold increase from the
minimal clearance rate at higher food rations of approximately 1 µl seawater larva
−1
hour
−1
(see
lowest-value data points in Fig. 2.2C). This difference results in a gain of 216 algal cells ingested
larva
−1
day
−1
at 1,000 cells ml
−1
. This calculation is based on a clearance rate of 10 µl seawater
larva
−1
hour
−1
at a ration of 1,000 cells ml
−1
(240 cells day
−1
). If larvae did not upregulate their
clearance rate at low food ration but maintained clearance at the same rate as higher food rations,
the clearance rate of 1 µl seawater larva
−1
hour
−1
at a ration of 1,000 cells ml
−1
would only be 24
cells day
−1
. Hence, the upregulation of feeding rate at low ration results in a net gain of 216 algal
cells day
−1
. Knowing the average protein content per algal cell of R. lens at 30.0 ± 1.3 pg cell
−1
(Table 2.2), 216 algal cells is equivalent to 6.5 ng protein (216 cells × 30 pg cell
−1
). This
additional number of 216 algal cells obtained an upregulated feeding rate [cf. 24 algal cells
day
−1
, equivalent to 0.7 ng protein day
−1
(24 cells × 30 pg cell
−1
), if feeding was not upregulated
at low rations], results in a total dietary input of 240 algal cells day
−1
(7.2 ng protein day
−1
). This
is a 10-fold increase in protein mass ingested due to changes in feeding rates at low food ration.
126
Food availability in laboratory versus natural environments
A common metric of the nutritional state of growing larvae is the abundance of
triacylglycerols and free fatty acids which accumulate through feeding as an energy reserve for
metamorphosis (Olson and Olson, 1989; Fraser, 1989; Meyer et al., 2007; Prowse et al., 2017).
As energy from foods are consumed, excess energetic substrates in the form of triacylglycerols
are stored, and the body content of these lipids is correlated with nutritional state and size. In the
present study, free fatty acids and triacylglycerols were considered together because they share
the same specific enthalpy of combustion (Gnaiger and Bitterlich, 1984). In the present study,
free fatty acids and triacylglycerols were found in amounts greater than 4 ng larva
−1
regardless of
the food ration provided (Fig. 2.5). There was no significant difference in free fatty acid and
triacylglycerol contents in larvae reared at different food rations from 5,000 to 50,000 cells ml
−1
(Two-way ANOVA, effect of ration: F3,130 = 1.36, ns; Fig. 2.5). The presence of triacylglycerols
and free fatty acids (>4 ng larva
−1
) at the lowest ration of 5,000 cells ml
−1
further supports the
conclusion that the low ration is not limiting to growth, but only if low rations are maintained at
constant levels (Fig. 2.3).
This study shows that physiological rates were maintained under laboratory conditions
independent of the food rations tested. In the natural world, however, food availability for
zooplankton in the ocean is considered to be limiting (Conover, 1968; Reitzel et al., 2004).
Chlorophyll concentrations, a proxy for phytoplankton abundance, are typically low (0.59 µg
liter
−1
) in the summers in the Southern California Bight (Nezlin and Li, 2003), in environments
relevant to early life stages of L. pictus. Chlorophyll concentrations can vary seasonally within
the surrounding region with levels exceeding 20 µg liter
−1
in winter or spring months in the
Santa Barbara Channel (Gelpi, 2018). Gelpi (2018) also reported a sinusoidal fluctuation of
127
chlorophyll content around Santa Catalina Island, CA, averaging 1.04 µg liter
−1
± 0.36 µg liter
−1
at a seasonal low during the summer, and a phase of 60 days. While the chlorophyll content of
phytoplankton is dependent on many environmental factors, the chlorophyll content of R. lens
(the food used in this study) was recently reported at approximately 2.0 pg cell
−1
(Coutinho et
al., 2020). Using this chlorophyll concentration and the chlorophyll content of R. lens, a
maximum of 296 cells ml
−1
(0.591 µg chlorophyll liter
−1
) would be predicted in the near the
coast of San Pedro, CA; and 340 cells ml
−1
(0.68 µg chlorophyll liter
−1
) off the coast of Santa
Catalina, CA (Nezlin and Li, 2003; Coutinho et al., 2020; Gelpi, 2018). These estimates of
phytoplankton abundance (340 cells ml
−1
) are more than 10-fold lower than the lowest laboratory
feeding conditions of 5,000 cells ml
−1
(approximately 10 µg chlorophyll liter
−1
) in the present
study (2.0 pg cell
−1
; Coutinho et al., 2020). The estimated natural food availability is further
reduced when considering that only certain particle classes may be palatable to the larvae in the
natural environment (Pernet, 2018). In these natural environments where particulate food
availability is low (cf. laboratory conditions), the uptake of dissolved organic matter from the
environment becomes important (Manahan et al., 1983; Manahan, 1990). Results from the
present study also show that physiological changes in larvae growing in environments with low
food compensate to maintain growth rates through a 10-fold upregulation of ingestion rates and a
4-fold increase in the conversion of ingested protein mass to larval biomass. In natural
environments where food rations are low or patchy, compensatory feeding and high protein food
conversion efficiencies are necessary to maintain high rates of protein synthesis, degradation,
and accretion.
128
Chapter 2 References
Adams, D.K., Sewell, M.A., Angerer, R.C., and Angerer, L.M. (2011). Rapid adaptation to food
availability by a dopamine-mediated morphogenetic response. Nature communications,
2(1), 1-7.
Bertram, D.F., and Strathmann, R.R. (1998). Effects of maternal and larval nutrition on growth
and form of planktotrophic larvae. Ecology, 79(1), 315-327.
Boidron-Metairon, I.F. (1988). Morphological plasticity in laboratory-reared echinoplutei of
Dendraster excentricus (Eschscholtz) and Lytechinus variegatus (Lamarck) in response
to food conditions. Journal of Experimental Marine Biology and Ecology, 119(1), 31-41.
Bradford, M.M. (1976). A rapid and sensitive method for the quantitation of microgram
quantities of protein utilizing the principle of protein-dye binding. Analytical
Biochemistry, 72(1-2), 248-254.
Calow, P. (1982). Homeostasis and fitness. The American Naturalist, 120(3), 416-419.
Conover, R.J. (1968). Zooplankton—life in a nutritionally dilute environment. American
Zoologist, 8(1), 107-118.
Coutinho, P., Ferreira, M., Freire, I., and Otero, A. (2020). Enriching rotifers with ‘premium’
microalgae: Rhodomonas lens. Marine Biotechnology, 22, 118-129.
Ellison, A., Pouv, A., and Pace, D.A. (2021). Different protein metabolic strategies for growth
during food-induced physiological plasticity in echinoid larvae. Journal of Experimental
Biology, 224(4), DOI: jeb230748.
Emlet, R.B., and Hoegh‐Guldberg, O. (1997). Effects of egg size on postlarval performance:
experimental evidence from a sea urchin. Evolution, 51(1), 141-152.
129
Fraser, A.J. (1989). Triacylglycerol content as a condition index for fish, bivalve, and crustacean
larvae. Canadian Journal of Fisheries and Aquatic Science, 46, 1868-1873.
Fry, J.P., Mailloux, N.A., Love, D.C., Milli, M.C., and Cao, L. (2018). Feed conversion
efficiency in aquaculture: do we measure it correctly? Environmental Research Letters,
13(2), 024017.
Gauld, D.T. (1951). The grazing rate of planktonic copepods. Journal of the Marine Biological
Association of the United Kingdom, 29(3), 695-706.
Gelpi, C. (2018). Chlorophyll Dynamics Around the Southern Channel Islands. Western North
American Naturalist, 78(4), 590-604.
Gnaiger, E. (1983). Calculation of energetic and biochemical equivalents of respiratory oxygen
consumption. In E. Gnaiger and H. Forstner (Eds.), Polarographic oxygen sensors (pp.
337-345). Berlin, Germany: Springer.
Gnaiger, E., and Bitterlich, G. (1984). Proximate biochemical composition and caloric content
calculated from elemental CHN analysis: a stoichiometric concept. Oecologia, 62(3),
289-298.
Guillard, R.R., and Ryther, J.H. (1962). Studies of marine planktonic diatoms: I. Cyclotella nana
Hustedt, and Detonula confervacea (Cleve) Gran. Canadian journal of microbiology,
8(2), 229-239.
Halvorson, H. (1958). Intracellular protein and nucleic acid turnover in resting yeast cells.
Biochimica et Biophysica Acta, 27, 255-266.
Hart, M.W., and Strathmann, R.R. (1994). Functional consequences of phenotypic plasticity in
echinoid larvae. Biological Bulletin, 186, 291-299.
Hawkins, A.J.S. (1991). Protein turnover: a functional appraisal. Functional Ecology, 222-233.
130
Helbig, et al. (2011). The diversity of protein turnover and abundance under nitrogen-limited
steady-state conditions in Saccharomyces cerevisiae. Molecular Biosystems, 7(12), 3316-
3326.
Herrera, J.C., McWeeny, S.K., and McEdward, L.R., (1996). Diversity of energetic strategies
among echinoid larvae and the transition from feeding to nonfeeding development.
Oceanologica, 19(3-4), 313-321.
Houlihan, D.F., Hall, S.J., Gray, C., and Noble, B.S. (1988). Growth rates and protein turnover in
Atlantic cod, Gadus morhua. Canadian Journal of Fisheries and Aquatic Sciences, 45(6),
951-964.
Houlihan, D.F., Hall, S.J., and Gray, C. (1989). Effects of ration on protein turnover in cod.
Aquaculture, 79(1-4), 103-110.
Jaeckle, W., and Manahan, D.T. (1989). Growth and energy imbalance during the development
of a lecithotrophic molluscan larva (Haliotis rufescens). The Biological Bulletin, 177,
237–246.
Kiørboe, T., Saiz, E., Tiselius, P., and Andersen, K.H. (2018). Adaptive feeding behavior and
functional responses in zooplankton. Limnology and Oceanography, 63(1), 308-321.
Lacalli, T.C., and Gilmour, T.H.J. (1990). Ciliary reversal and locomotory control in the pluteus
larva of Lytechinus pictus. Philosophical Transactions: Biological Sciences, 330, 391-
396.
López, S., and Gancedo, J.M. (1979). Effect of metabolic conditions on protein turnover in yeast.
Biochemical Journal, 178(3), 769-776.
Manahan, D.T. (1990). Adaptations by invertebrate larvae for nutrient acquisition from seawater.
American Zoologist, 30(1), 147-160.
131
Manahan, D.T., Davis, J.P., and Stephens, G.C. (1983). Bacteria-free sea urchin larvae: selective
uptake of neutral amino acids from seawater. Science, 220(4593), 204-206.
Marin, V., Huntley, M.E., and Frost, B. (1986). Measuring feeding rates of pelagic herbivores:
analysis of experimental design and methods. Marine Biology, 93(1), 49-58.
Mente, E., Houlihan, D.F., and Smith, K. (2001). Growth, feeding frequency, protein turnover,
and amino acid metabolism in European lobster Homarus gammarus L. Journal of
Experimental Zoology, 289(7), 419-432.
Meyer, E., Green, A.J., Moore, M., and Manahan, D.T. (2007). Food availability and
physiological state of sea urchin larvae (Strongylocentrotus purpuratus). Marine Biology,
152(1), 179-191.
Miner, B.G. (2005). Evolution of feeding structure plasticity in marine invertebrate larvae: a
possible trade-off between arm length and stomach size. Journal of Experimental Marine
Biology and Ecology, 315(2), 117-125.
Moran, A.L., and Manahan, D.T. (2004). Physiological recovery from prolonged ‘starvation’ in
larvae of the Pacific oyster Crassostrea gigas. Journal of Experimental Marine Biology
and Ecology, 306(1), 17-36.
Nezlin, N.P., and Li, B.L. (2003). Time-series analysis of remote-sensed chlorophyll and
environmental factors in the Santa Monica-San Pedro Basin off Southern California.
Journal of Marine Systems, 39, 185-202.
Ohsumi, Y. (2014). Historical landmarks of autophagy research. Cell Research, 24, 9-23.
Olson, R.R., and Olson, M.H. (1989). Food limitation of planktotrophic marine invertebrate
larvae: does it control recruitment success? Annual Review of Ecology and Systematics,
20(1), 225-247.
132
Pace, D.A., Marsh, A.G., Leong, P.K., Green, A.J., Hedgecock, D., and Manahan, D.T. (2006).
Physiological bases of genetically determined variation in growth of marine invertebrate
larvae: a study of growth heterosis in the bivalve Crassostrea gigas. Journal of
Experimental Marine Biology and Ecology, 335(2), 188-209.
Pace, D.A., and Manahan, D.T. (2006). Fixed metabolic costs for highly variable rates of protein
synthesis in sea urchin embryos and larvae. Journal of Experimental Biology, 209(1),
158-170.
Pernet, B. (2018). Larval feeding: mechanisms, rates, and performance in nature (pp. 87-102).
Oxford University Press, Oxford.
Prowse, T.A., Sewell, M.A., and Byrne, M. (2017). Three-stage lipid dynamics during
development of planktotrophic echinoderm larvae. Marine Ecology Progress Series, 583,
149-161.
Raubenheimer, D., and Simpson, S.J. (1993). The geometry of compensatory feeding in the
locust. Animal Behaviour, 45(5), 953-964.
Raubenheimer, D., and Simpson, S.J. (2018). Nutritional ecology and foraging theory. Current
opinion in insect science, 27, 38-45.
Reitzel, A.M., Webb, J., and Arellano, S. (2004). Growth, development and condition of
Dendraster excentricus (Eschscholtz) larvae reared on natural and laboratory diets.
Journal of plankton research, 26(8), 901-908.
Schoener, T.W. (1971). Theory of feeding strategies. Annual review of ecology and systematics,
2(1), 369-404.
Schoenheimer, R. (1942). The dynamic state of body constituents. The dynamic state of body
constituents.
133
Sedgwick, R.W. (1979). Influence of dietary protein and energy on growth, food consumption
and food conversion efficiency in Penaeus merguiensis de Man. Aquaculture 16(1), 7-30.
Sewell, M.A., Cameron, M.J., and McArdle, B.H. (2004). Developmental plasticity in larval
development in the echinometrid sea urchin Evechinus chloroticus with varying food
ration. Journal of Experimental Marine Biology and Ecology, 309, 219-237.
Shilling, F.M., and Manahan, D.T. (1994). Energy metabolism and amino acid transport during
early development of Antarctic and temperate echinoderms. The Biological Bulletin,
187(3), 398-407.
Sinervo, B., and McEdward, L.R. (1988). Developmental consequences of an evolutionary
change in egg size: an experimental test. Evolution, 42(5), 885-899.
Strathmann, R.R., Jahn, T.L., and Fonseca, J.R. (1972). Suspension feeding by marine
invertebrate larvae: clearance of particles by ciliated bands of a rotifer, pluteus, and
trochophore. The Biological Bulletin, 142(3), 505-519.
Strathmann, R.R. (2007). Time and extent of ciliary response to particles in non-filtering feeding
Mechanism. Biological Bulletin, 212, 93-103.
Strathmann, R.R., and Grunbaum, D., (2006). Good eaters, poor swimmers: compromises in
larval form. Integrative and Comparative Biology, 46(3), 312-322.
Taghon, G.L. (1981). Beyond selection: optimal ingestion rate as a function of food value. The
American Naturalist, 118(2), 202-214.
Wada, Y., Mogami, Y., and Baba, S.A. (1997). Modification of ciliary beating in sea urchin
larvae induced by neurotransmitters: Beat-plane rotation and control of frequency
fluctuation. Journal of Experimental Biology, 200, 9-18.
134
Winfree, R.A., and Stickney, R.R. (1981). Effects of dietary protein and energy on growth, feed
conversion efficiency and body composition of Tilapia aurea. The Journal of Nutrition,
111(6), 1001-1012.
135
CHAPTER 3
Effects of Temperature on Feeding and Protein Metabolism
ABSTRACT
Thermal stress can elicit energetically costly responses from organisms. Measuring how
different physiological processes within an organism respond to acute and chronic temperature
changes can provide a valuable approach to defining physiological limits in a changing ocean. In
this study, larvae of the winter-spawning purple sea urchin Strongylocentrotus purpuratus, were
chronically reared at 15 or 20°C. Thermal sensitivities (Q 10) of physiological processes including
feeding, amino acid transport, morphological growth, protein accretion, protein synthesis, protein
degradation, respiration, and ammonia excretion were determined to model the allocation of
ATP. There were significant differences in Q 10 values of different physiological processes.
Notably, the Q10 of protein synthesis (2.9 ± 0.2) was significantly higher than respiration (2.0 ±
0.1). This discrepancy in thermal sensitivities was further exacerbated when larvae were
chronically reared at 20°C, with protein synthesis Q10 values increasing to 3.7 ± 0.3. The
differences in thermal sensitivity between protein synthesis and respiration rates cause an
uncoupling of ATP consumption and production. For larvae of S. purpuratus, a protein synthesis
Q10 of 3.7 and a respiration Q10 of 2.0 increases allocation of ATP to protein synthesis from 19%
to 35% between 10 and 20°C respectively. Determinations of atomic oxygen to atomic nitrogen
ratios showed that larvae shift to a protein-dominated metabolism at higher temperatures
increasing from 46.5% ± 2.4 to 77.0% ± 4.8 of metabolic energy derived from protein catabolism
136
at chronic rearing temperatures of 15 and 20°C. This study demonstrates that disproportionate
thermal sensitivities of interdependent physiological processes alter the protein metabolism and
growth of developing larval stages. This provides an energetic perspective for thermal tolerances
and biological tipping points of marine organisms.
137
INTRODUCTION
Temperature is one of the most significant drivers of species distribution and
biodiversity. Earlier, seminal work on thermal performance curves has provided a basic
framework for understanding the relationship between fitness and temperature (Huey and
Kingsolver, 1993). Performance traits increase non-linearly from a critical thermal minimum to
an optimum before declining rapidly at the thermal maximum (Huey and Stevenson, 1979;
Schulte et al., 2011). Comparative approaches often have attempted to relate thermal optimums
of performance with habitat distributions or other fitness parameters. However, interpretations of
thermal performance curves can vary widely depending on the performance trait selected and are
often complicated by natural temperature fluctuations, life history stages, and exposure time
(Sinclair et al., 2016).
Performance traits can have interactive effects (antagonistic, additive, or synergistic) that
complicate a measure of performance with temperature. For example, the optimal temperature
for growth rates in salmon was significantly dependent on the thermal sensitivity of feeding
physiology (Brett, 1971). This phenomenon has led others to describe a “metabolic meltdown”
scenario where increased temperatures increase metabolic demands, but sometimes limit
foraging activity or food availability (Huey and Kingsolver, 2019). In these cases, thermal
performance curves of energy consuming processes can be subtracted from energy-gaining
processes (such as feeding) to give a performance curve of net energy gained or lost. Another
example of the underlying principle for the complexities of interacting thermal performances was
described by Kellermann et al. (2019) who showed that the metrics of thermal performance
curves of several traits (standard metabolic rate, fecundity, activity, and egg-to-adult viability)
138
varied more between traits than within traits for Drosophila melanogaster reared at different
temperatures. Thus, understanding the energetic or fitness tradeoffs and interactions that occur
between traits with temperature is important.
While there have been extensive studies using organismal-level methodologies of
understanding thermal performance, others have provided a bottom-up, biochemical, and
mechanistic perspective to thermal tolerance. These studies involve understanding protein
folding, intracellular pH balance, lipid fluidity, and regulatory heat shock responses to changing
temperature (Hochachka and Somero, 2002). In particular, enzyme functions such as catalytic
rates are highly sensitive to temperature, as are protein conformation and folding. Organisms of
different genotypes or environments have varying abilities to respond to heat shock, but some
mechanisms for thermal responses are often rapid and reversible such as RNA thermosensors
(Somero, 2018). Given adequate responses to preserving protein functionality and maintaining
adequate oxygen supply, Schmidt-Nielsen (1997, pp. 224-225) also suggests that disparities in
thermal sensitivities of interdependent processes are a factor leading to thermal death. For
example, when two processes share a common substrate, a more thermal-sensitive process may
deprive the other process by competitive depletion of the common substrate. Following this
concept, it is hypothesized in the present study, that ATP acts as the “shared substrate” which
may be consumed more quickly by cellular processes that are more sensitive to temperature than
others.
One of the most cellularly expensive processes in living organisms is protein synthesis.
Several studies have shown that the cost of protein synthesis in marine invertebrates can account
for over half of the cellular ATP budget (Pace and Manahan, 2006; Pan et al., 2015, Lee et al.,
2016). Despite the high cost of protein synthesis, it is an essential and necessary cost for driving
139
marine larval growth. Up to 72% of the growth variation in larvae of Crassostrea gigas was
explained by protein synthesis rates (Pan et al., 2018). However, few studies have attempted to
determine the relationship between protein synthesis rates and temperature in marine
invertebrates, much less the energetic consequences. Instead, most of the studies for marine
organism have centered around vertebrate fish species. Haschemeyer et al. (1979) measured
protein synthesis rates of several fish species and several tissue types and concluded that the Q 10
of protein synthesis ranged from 2.5 to 3.5. Similar thermal sensitivities were also measured in
rainbow trout muscle tissue with Q10 of 3.07 – 3.57 depending on thermal acclimation (Loughna
and Goldspink, 1985). However, other experiments have yielded wide-ranging thermal
sensitivities depending on tissue or acclimation temperature (Das and Prosser, 1967). On the
other hand, the thermal sensitivities of ATP production through respiration are more commonly
studied in marine invertebrates in the context of developing an indicator of fitness. A
comparative review of respiratory Q10 in adults of 14 urchin species in 28 studies yielded an
average Q10 of 2.15 ± 0.13 (Hughes et al., 2011). In theory, processes with Q10 values higher than
that of respiration would disproportionately consume the ATP pool at higher temperatures
leaving less energy available for other processes. Pan et al. (2021) demonstrated that high Q10
values of protein synthesis (~3) compared to respiration (~2) resulted in increases in ATP
allocation to protein synthesis which left little ATP remaining for other processes in larvae of C.
gigas.
The present study builds upon the findings of Pan et al. (2021) by examining the thermal
sensitivities of physiological processes involved in protein metabolism in the larvae of
Strongylocentrotus purpuratus reared at two environmentally relevant temperatures (Strathmann,
1987) and including physiological processes of protein ingestion and ammonia excretion. By
140
taking a systematic approach to simultaneously measuring feeding, amino acid transport,
morphological growth, protein accretion, protein synthesis, protein degradation, respiration,
ammonia excretion the energetic balance of larvae could be modeled as they grow in different
temperature environments. This study shows a biological decoupling of physiological processes
with temperature in echinoderm larvae and provides insights into the mechanisms for thermal
tolerance with respect to an integrative physiological perspective.
MATERIALS AND METHODS
Experimental design
A total of seven different cohorts of larvae of S. purpuratus were reared in multiple,
independent culture vessels (n=16) during the course of this study. Each cohort was started from
gametes obtained from different males and females. Fertilized eggs were placed into different
thermal treatments: a total of seven cohorts were divided evenly between two long-term
(chronic) rearing temperatures (15 or 20°C). By doing so, the following goals were sought: (1) to
compare the short-term, acute thermal sensitivity (Q 10) of feeding, amino acid transport, protein
synthesis, protein degradation, respiration, ammonia excretion rates, (2) to determine the long-
term, chronic thermal sensitivities of morphological growth and protein accretion, and (3) to
examine whether long-term (chronic) temperature treatments affected short-term, acute Q 10
values for each process. Cohorts 1-5 were assayed for feeding rates, alanine transport rates,
protein synthesis rates, respiration rates, and ammonia excretion rates. Cohorts 6 and 7 were
assayed for additional measurements of feeding rates. Survival was tabulated for cohorts 1, 2, 3,
6, and 7.
141
Figure 3.1. Experimental design for determining the chronic and acute thermal sensitivities of
physiological processes. Larvae of Strongylocentrotus purpuratus were reared at either 15 or
20°C. On multiple days, larvae from each long-term (chronic) rearing temperature were pre-
exposed to 10, 15, 20, or 25°C for physiological assays of feeding, alanine transport, protein
synthesis, respiration, and ammonia excretion. Rates at each temperature were used to calculate a
Q10 value for each process. Growth rates (midline body length and protein accretion) of larvae
reared at 15 or 20°C were used to calculate growth Q 10 values.
142
Larval culturing
Sea urchins (S. purpuratus) were spawned by intracelomic injection of 0.5 M KCl
following standard culturing protocols by Hinegardner (1975). Gametes of S. purpuratus were
spawned from one to five females and one to three males during each spawn. Mixtures of male
and female gametes were observed to have > 90% fertilization success from the rapid
development of the fertilization envelope. Embryos were stocked initially at 25 ml
−1
in 20-liter
culture vessels. Cultures were held in water baths set to 15 and 20°C. A temperature data logger
(Model Pro v2 HOBO water temperature data logger, Onset, Cape Cod, MA) was kept in a
culture vessel to monitor temperatures every 30 seconds (Fig. 3.2). Filtered (0.2 μm pore-size;
Nucleopore) seawater was completely replaced in each culture vessel every other day.
Competency for feeding (4-arm pluteus stage) was reached at three days for larvae reared at
20°C, and at four days for larvae reared at 15°C. Beginning on the initial feeding day, larvae
were fed the algae Rhodomonas lens at 20,000 cells ml
−1
and sampled every other day. Larval
cultures were condensed by filtering onto a Nitex mesh sieve (53 µm pore size) and resuspended
in 30-50 ml of filtered sea water to be enumerated and divided into physiological assays.
143
Figure 3.2. Stability of long-term (chronic) rearing temperatures of the seawater used to culture
larvae. Culture vessels at two different (chronic) treatment temperatures were used in this study
monitored by temperature data loggers every 30 seconds for the eight-day period presented.
144
Growth and survivorship
From each culture vessel, larvae from four sub-sampled aliquots were enumerated (<
10% coefficient of variation) for survival data, and >50 larvae were photographed at 40x
magnification. Digital images were analyzed using ImageJ software (version 1.49,
http://imagej.net) for midline body length size measurements (from the dorsal end to the oral
hood). Total protein content was determined using a Bradford (1976) assay modified for larvae
as described by Jaeckle and Manahan (1989), using five replicate assays of 1,000 individuals per
culture vessel. Larval growth rates were determined from the slope ± s.e. of increasing midline
body length or total protein with age. Survivorship of larvae reared at 15 or 20°C was compared
by two-way ANOVA with factors of day and temperature. Percent survival data were logit-
transformed to normalize the variances at low and high percentages.
Feeding rates
Clearance rates, the rates at which volumes of water are processed by suspension feeders,
were determined using a particle counter (Model Z2 Particle Counter, Beckman Coulter,
Carlsbad, CA) to measure the time-course depletion of microalgal food (Rhodomonas lens) from
suspension by larvae. Particle counts were cross-calibrated with visual, microscopic
hemocytometer counts and the number of particles correlated strongly with the number of cells
in suspension (Fig. 3.3A). Replicate controlled assays of larvae-only, algae-only, and
experimental assays of larvae + algae were carried out using a food ration of 20,000 cells ml
−1
which matched culturing rations (Fig. 3.3B). Assay vials were mixed frequently and subsampled
for five time points over four to six hours. Clearance rates were calculated based on Equations 1
and 2, where F is the clearance rate (µl larva
−1
h
−1
), C is the concentration of algae in suspension,
145
V is the volume of the assay, N is the number of larvae, and t is the time. Equation 1 was
previously used (Gauld, 1951; Pace et al., 2006) for calculation of clearance rates based on
changes in initial (C0) and end-point (Cf) food amounts sampled after an assay period (t). To
avoid large decreases in assay food amounts that may confound clearance rates (see Chapter 2
for ration-dependent feeding rates), a time-course method of sampling was used for the
determination of clearance rates in the present study. To calculate clearance rates from time-
course data (replicate samples at each of five time-points), Equation 1 is solved for Cf to give the
exponential function shown in Equation 2. In Equation 2, the slope ± s.e. of exponential function
gives the clearance rate (F) of the larvae within an assay vial. Assays were replicated at each of
the four temperatures 10, 15, 20, and 25°C to determine the Q 10 of clearance rates (Fig. 3.3C).
Equation 1. 𝐹 =
× ×
Equation 2. 𝐶 = 𝐶 × 𝑒 ×
146
Figure 3.3. Feeding rates were determined by particle counting of Rhodomonas lens depletion
by larvae assayed at different temperatures. (A) Cross-calibration of particle counts with visual
hemocytometer counts. (B) Representative assays of algal depletion by known numbers of
larvae. Open symbols replicate assays of algae-only (top) and larvae-only (bottom). Closed
symbols represent particle counts for vials containing larvae + algae. (C) Clearance rates ± s.e.
for replicate assays at four acute temperatures (10, 15, 20, and 25°C) increased nonlinearly with
temperature. (D) log-transformed rates from panel (C) were plotted against
such that Q10
is calculated as 10
slope
.
147
Respiration rate
Respiration rates of larvae, were determined by measurements of oxygen consumption
(pmol larva
−1
h
−1
), using optode technology (Model Witrox-1 Oxygen Meter, Loligo Systems,
Viborg, Denmark). This optode system was cross-calibrated for larvae of S. purpuratus reared at
15°C and 20°C compared to polarographic oxygen sensors previously utilized by Marsh and
Manahan (1999) (Fig. 3.4A,B). The use of polarographic oxygen sensors relies on end-point
determinations of O2 consumption by diffusion of O2 in samples through a membrane to a Clark
electrode. The optode system uses an optical sensor spot inside of micro-biological oxygen
demand vials which can be measured by a detector without disturbing the sample. This allows
for time-course, repeated measurements of O2 consumption. Cross-calibrations were conducted
for larvae of S. purpuratus at 15°C (Fig. 3.4A) and 20°C (Fig. 3.4B) with no statistical difference
in rates determined by either method (at 15°C: ANOVA, F 1,18 = 0.59, ns; at 20°C: ANOVA, F1,16
= 1.79, ns). Subsequently, the optode system was used for the remainder of the study.
The use of the optode system for determinations of larval respiration rates involved the
air-tight-sealing of a known number of larvae (250-500 individuals depending on size), inside
custom-made micro-biological oxygen demand vials with known volumes (400-700 μl). Seven
replicate micro-biological oxygen demand vials were used for each culture vessel at each short-
term, acute temperature (10, 15, 20 and 25°C). Oxygen concentrations were measured for
replicate vials at five time points for each vial over the course of 3 to 5 hours (Fig. 3.4C). The
rate of oxygen consumption per individual larva was calculated by the slope of depletion, the
known volume of each respiration vial, and the number of larvae in each vial. Respiration rates
were plotted against temperature to examine the thermal sensitivity (Fig. 3.4D). The log-
transformed rates were plotted against (T n-Tf)/10 to calculate the Q10 (Fig. 3.4E).
148
Figure 3.4. Intercalibration of methods for determining respiration rates for larvae of
Strongylocentrotus purpuratus, and the determination of the thermal sensitivity of respiration.
(A) Respiration rates determined by time-course assays using an optode (closed symbols) did not
differ from rates determined by end-point assays using a Clark electrode (open symbols) for 8-
day old larvae reared at 15°C (ANOVA, F1,18 = 0.59, ns). Data points represent the average ±
s.e.m (n=10 assays). (B) Respiration rates determined by time-course assays using an optode
(closed symbols) did not differ from rates determined by end-point assays using a Clark
electrode (open symbols) for 6-day old larvae reared at 20°C (ANOVA, F 1,16 = 1.79, ns). (C)
Depletion of oxygen by larvae in micro-biological oxygen demand vials. Closed symbols
represent replicate assays, and open symbols represent seawater-controls. (C) Respiration rates
for larvae assayed at four acute temperatures. (D) log-transformed rates from panel (C) were
plotted against
such that Q10 is calculated as 10
slope
. This figure was redrawn from data
provided by Melissa B. DellaTorre with permission (see section “Acknowledgements and
contributions to Chapter 3”).
149
Alanine transport rate
Alanine transport rates were determined by incubating known amounts of larvae in 74
kBq
14
C-alanine with 10 µM of non-radioactive alanine (Fig. 3.5). Assays were conducted in
duplicate at each of the four temperatures (10, 15, 20, 25°C), and each contained 10,000 larvae in
10 ml of sea water and alanine. Assays were subsampled every five to eight minutes (depending
on larval size or temperature) for five time points by removing 1 ml (1,000 larvae) and retaining
larvae on a filter (8µm pore size; Nucleopore) and rinsing away excess radioactivity from the
media with > 10 ml seawater. Larvae were frozen at -80°C. To measure the amount of
14
C-
alanine transported by larvae, larvae were ruptured by sonication in known amounts of deionized
water (Nanopure), and
14
C-alanine activity was measured by scintillation counting (Model LS
6500, Beckman Coulter, Carlsbad, CA). The radioactivity of
14
C-alanine was measured in the
assay media to determine the relationship between scintillation counts (disintegrations per
minute) and moles of alanine (Fig. 3.5A). Assays were repeated at four temperatures to
determine the thermal sensitivity of amino acid transport rates (Fig. 3.5B). The log-transformed
rates were plotted against (Tn-Tf)/10 to calculate the Q10 (Fig. 3.5C).
150
Figure 3.5. Alanine transport rates were determined for larvae of Strongylocentrotus purpuratus
following incubation in 10 µM alanine with 74 kBq of
14
C-alanine tracer. (A) Time-course
assays of alanine transport by larvae sampled at 5 time points over 20 minutes. Open and closed
symbols represent measurements from duplicate assays. A single regression line was fit to all
data points since the slopes did not differ (ANCOVA, slope F 1,6 = 0.73, ns). (B) Alanine
transport (rates ± s.e.) for duplicate assays at four acute temperatures. (D) log-transformed rates
from panel (C) were plotted against
such that Q10 is calculated as 10
slope
.
151
Protein synthesis rate
Protein synthesis rates were measured from in vivo time course assays (see section
“Alanine transport rate” above) based on larval transport of
14
C-alanine from seawater and
incorporation into protein (Pan et al., 2015). The total amount of radioactivity incorporated into
trichloroacetic acid (TCA)-precipitable total protein fraction, and the intracellular-specific
activity of
14
C-alanine in the free amino acid pool, were analyzed using reverse-phase high
performance liquid chromatography. Total protein synthesis rates were calculated according to
Equation 3 where MWp is the average molecular weight of amino acid residues in protein (124.7
g mol
−1
; Pan et al., 2015), Sm is the mole-percent of alanine in protein of the larvae (7.9%; Pan et
al., 2015), Sp is radioactivity of alanine in the TCA-precipitated fraction (proteins), and Sfaa is the
intracellular specific-activity of
14
C-alanine. The rate of protein synthesis was calculated as the
slope ± s.e. of increase in protein synthesized through time (Fig. 3.6A). The majority of slopes
from duplicate assays did not differ (ANCOVA, Slope: F 1,6 = 1.01, ns), and a single rate was
determined using all data points (Fig. 3.6A). Protein synthesis rates were measured at each of
four acute temperatures to determine the Q10 (Fig. 3.6B). The log-transformed rates were plotted
against (Tn-Tf)/10 to calculate the Q10 (Fig. 3.6C).
Equation 3. 𝑃𝑟𝑜𝑡𝑒𝑖𝑛𝑠 𝑆𝑦𝑛𝑡 ℎ𝑒𝑠𝑖𝑧𝑒𝑑 =
×
152
Figure 3.6. In vivo protein synthesis rates were determined for larvae of Strongylocentrotus
purpuratus by measuring rates of
14
C-alanine incorporation into the protein fraction. (A)
Duplicate time-course assays (open and closed symbols) of protein synthesis by larvae sampled
at 5 time points over 40 minutes. A single regression line was fit to all data points since the
slopes did not differ (ANCOVA, F1,6 = 1.01, ns). (B) Protein synthesis (rates ± s.e.) for duplicate
assays at four temperatures. (C) log-transformed rates from panel (B) were plotted against
such that Q10 is calculated as 10
slope
.
153
Ammonia excretion rate
Ammonia is the primary nitrogenous waste product of marine invertebrates. Excreted
ammonia, ionized as ammonium when in seawater, was measured from micro-biological oxygen
demand vials immediately following assays of respiration rate (see “Respiration rate” section
above). Sample-matched values of ammonia excretion and respiration rates from the same vial
allows for the comparison of atomic oxygen to atomic nitrogen ratios (O:N). Ammonium was
also measured in seawater-only controls as well as an initial time-point of seawater containing
larvae to determine ammonia excretion rates as the change on ammonia over a period of
approximately five hours (Fig. 3.7A). Ammonium concentrations from 250 µl samples were
quantified using Berthelot’s reagent (phenol and hypochlorite) to produce indophenol, which is
quantified at 640 nm wavelength absorbance (Weatherburn, 1967). Briefly, the following
reagents were added and mixed in sequence: 10 µl phenol (10% w:v in ethanol), 10 µl
nitroprusside (0.5% w:v), and 25 µl oxidizing reagent [17.09% sodium citrate (w:v), 0.85%
NaOH (w:v), 14.56% hypochlorite (v:v); pH 9.8, made fresh daily]. The reaction was incubated
in the dark for 90 minutes before measuring the absorbance of light by indophenol at 640 nm.
Absorbances were compared to a standard curve of ammonium sulfate standards ranging from
0.5 – 100 µM NH4
+
yielding the concentration of ammonium. Ammonia excretion rates were
calculated according to Equation 4 where Δ[NH 4
+
]larvae is the change in ammonium concentration
between initial and final time-points for samples containing larvae, Δ[NH 4
+
]control is the change in
ammonium concentration of the seawater control, V is the total volume of the micro-biological
oxygen demand vial, and N is the number of larvae in the micro-biological oxygen demand vial.
These assays were conducted at four temperatures to determine the thermal sensitivity of
154
ammonia excretion rates (Fig. 3.7B). The log-transformed rates were plotted against (T n-Tf)/10
to calculate the Q10 (Fig. 3.7C).
Equation 4. 𝐴𝑚𝑚𝑜𝑛𝑖𝑢𝑚 𝐸𝑥𝑐𝑟𝑒𝑡𝑖𝑜𝑛 = ∆[ ]
∆ −
∆[ ]
∆ ×
155
Figure 3.7. Determination of ammonia excretion rates for 8-day old larvae (263.4 ± 2.1 µm in
midline body length) of Strongylocentrotus purpuratus at different temperatures. (A) Assays of
ammonia concentrations in filtered seawater (0.2 µm pore-size) control (white) and larval (gray)
micro-biological oxygen demand vials following incubation at zero and five (dashed bar) hours.
“N.D.” indicates that ammonia was not detected in this control. (B) Ammonia excretion rates
determined at four acute temperatures. (C) log-transformed rates from panel (B) were plotted
against
such that Q10 is calculated as 10
slope
. This figure was redrawn from data provided
by Melissa B. DellaTorre with permission (see section “Acknowledgements and contributions to
Chapter 3”).
156
Atomic O:N ratios and protein catabolism
Atomic O:N ratios represent the ratio of atomic oxygen consumed per atom of nitrogen
excreted as ammonia. These ratios give an index of the amount of respiratory energy derived by
catabolism of nitrogenous substrates such as protein (Clark et al., 2013; Bayne 2017). For
example, high amounts of protein or amino acid catabolism result in lower O:N ratios. Since
respiration rates were measured as O2 consumption, the numerator of the O:N ratio is twice the
respiration rate (two oxygen atoms per O2 molecule). Because the nitrogenous waste of S.
purpuratus is ammonia, containing a single nitrogen atom, the denominator of the O:N ratio is
equal to the ammonia excretion rate. Thus, the O:N ratio can be calculated as 2× (respiration
rate)/(ammonia excretion rate). Since respiration was measured as the consumption of molecules
of O2 (containing 2 atoms of oxygen), rates were multiplied by two for the conversion of
molecule amounts to atomic amounts of oxygen. Given that both respiration rate and ammonia
excretion rate were measured from the same µBOD vial each time, the O:N ratio could be
determined for each replicate assay.
Protein catabolism was calculated from ammonia excretion by utilizing the amino acid
composition of larvae of S. purpuratus to estimate a relationship between ammonia excretion and
the mass of proteins catabolized (Mayzaud and Conover, 1988; Pan et al., 2015; Table 3.1). This
estimation assumes that all nitrogenous waste is the product of protein catabolism and represents
a maximum mass of protein catabolism. Mole fractions of amino acids were converted to moles
of excretable nitrogen based on the number of excretable nitrogen atoms from each amino acid
(Mayzaud and Conover, 1988). The nitrogen mole fraction of each amino acid was then
multiplied by the measured ammonia excretion rate to estimate the moles of each amino acid that
were catabolized. The moles of each amino acid were then multiplied by the residue mass
157
(corrected for the release of H2O from the peptide bond formation). The sum of all of the residue
masses estimates the total mass of protein catabolized. The energetic value of the estimated mass
of proteins catabolized was then calculated as a percent of total energy (ATP pool estimated
from respiration) from either lipid or protein, but not carbohydrates (Gnaiger, 1983; Table 3.2).
Carbohydrates were not considered since Shilling and Manahan (1990) showed that
carbohydrates were found in lower amounts (7 ng for 4-day old larva) compared to lipid and
protein (>30 ng for 4-day old larva) in S. purpuratus. A recent analysis of carbohydrate contents
in larvae of S. purpuratus (A.W. Griffith, 2021; personal communication, see Chapter 3
Acknowledgements section) confirmed that carbohydrates are not a substantial fraction of larval
biochemical composition. For a 4-day old larva of S. purpuratus, Griffith (2021; personal
communication, see Chapter 3 Acknowledgements section) detected 3.74 ± 1.30 ng carbohydrate
larva
−1
compared to 16.5 ± 1.50 ng protein larva
−1
and 8.7 ± 2.80 ng lipid larva
−1
. Using specific
enthalpies of combustion for carbohydrates (17.5 kJ g
−1
), protein (24.0 kJ g
−1
), and lipid (39.5 kJ
g
−1
), carbohydrates would provide 65.5 µJ out of 805.1 µJ, or 8% of metabolic energy derived
from all three biochemical constituents (Gnaiger, 1983). Due to the low contribution of
carbohydrates to metabolic reserves, oxyenthalpic equivalents used in the present study focused
on proteins and lipids where equal utilization of each substrate equates to 484 kJ (mol O 2)
−1
(Gnaiger, 1983).
158
Table 3.1. Example calculation of protein catabolism for a 5-day old larva (241.5 ± 2.1 µm
midline body length) of Strongylocentrotus purpuratus chronically reared at 20°C. This example
is based on a measured ammonia excretion rate of 72 pmol larva
−1
day
−1
(E), that results in a
mass of 8.1 ng protein catabolized by larvae of this stage of development. This represents an
average case of protein metabolism (see Table 3.2 and Fig. 3.11B). The calculation is as follows:
(A) The mole percent of amino acids in average whole-body protein is referenced. (B) The
stoichiometric ratio of ammonia (NH3) for each amino acid is referenced. (C) Values from (A)
and (B) are multiplied to give the moles of NH3 for each amino acid. (D) The mole fraction of
NH3 for each amino acid from column (C) is calculated. (E) The ammonia excretion rate (72
pmol larva
−1
day
−1
) is multiplied by the mole percent of NH 3 for each amino acid to estimate the
amount of each amino acid catabolized and excreted. (F) The mass of water is subtracted from
the molecular weight of each amino acid due to account for the liberation of H 2O from a peptide
bond in protein. (G) The amount of each amino acid contributing to ammonia excretion (E) is
multiplied by the corrected molecular weight of the amino acid residue in protein (F) to give the
mass of each amino acid in proteins that are catabolized. Summing column (G) gives the total
mass of proteins catabolized (8.1 ng larva
−1
day
−1
) shown in the bottom row.
A B C D E F G
AA mol
%
*
NH3 : AA
†
(A × B)
NH3 mol
(Ci / ΣCi)
NH3 mol fraction
(D × 72)
‡
AA pmol
AA Da
(-H2O)
(E × F) 10
-3
AA ng
Alanine 7.9 1 7.9 0.07 4.74 71 0.3
Glycine 9.4 1 9.4 0.08 5.64 57 0.3
Threonine 6.3 1 6.3 0.05 3.78 101 0.4
Serine 6.5 1 6.5 0.05 3.90 87 0.3
Cysteine 1.0 1 1 0.01 0.60 103 0.1
Phenylalanine 4.2 1 4.2 0.04 2.52 147 0.4
Tyrosine 3.0 1 3 0.03 1.80 163 0.3
Leucine 7.8 1 7.8 0.07 4.68 113 0.5
Lysine 6.6 2 13.2 0.11 7.93 128 1.0
Tryptophan 0.8 2 1.6 0.01 0.96 186 0.2
Glutamate/glutamine 11.2 2 22.4 0.19 13.45 129 1.7
Arginine 4.7 1 4.7 0.04 2.82 156 0.4
Histidine 1.7 1 1.7 0.01 1.02 137 0.1
Proline 5.1 1 5.1 0.04 3.06 97 0.3
Methionine 1.4 2 2.8 0.02 1.68 131 0.2
Valine 6.8 1 6.8 0.06 4.08 99 0.4
Isoleucine 5.1 1 5.1 0.04 3.06 113 0.3
Aspartate/asparagine 10.4 1 10.4 0.09 6.24 115 0.7
Total Protein Catabolism (ng day
−1
): 8.1
* Pan et al., 2015
† Mayzaud and Conover, 1988
‡ Ammonia excretion rate (pmol day
−1
) for a 5-day old larva of 241.5 ± 2.1 midline body length reared chronically at 20°C
159
Table 3.2. Example calculation of the contribution of protein catabolism to the energy needs of a
5-day old larva (241.5 ± 2.1 µm midline body length) of Strongylocentrotus purpuratus
chronically reared at 20°C. This example is based on a measured respiration rate of 494 pmol O 2
larva
−1
day
−1
(4), that results in a calculation of 78% of energy derived from protein catabolism
by larvae of this stage of development. This represents an average case of protein metabolism
(see Table 3.1 and Fig. 3.11B). From this calculation, 78% of the energy requirement for a larva
at this stage of development is supplied by catabolism of protein. The calculation is as follows:
(1) The value of 8.1 ng protein catabolized is taken from Table 3.1 (measured from the same
larvae). (2) Based on the specific enthalpy of combustion for proteins, the energy from
catabolism of 8.1 ng protein is 195 µJ. (3) The oxyenthalpic value of proteins is used to calculate
how much O2 is consumed to produce the energy in row (2). (4) The respiration rate measured
for a larva at this stage of development is 494 pmol O 2 larva
−1
day
−1
. (5) The O2 used for protein
catabolism is subtracted from the total respiration rate. (6) The oxyenthalpic value of lipids is
used for the remaining O2 to calculate the energy derived from lipids. (7) The energy from lipids
and proteins are summed to give the total metabolic energy. (8) The percent of energy from
protein catabolism is calculated to be 78%.
Calculation Value
(1) Protein catabolized See Table 3.1 8.1 ng
(2) Energy from protein (1) x (24 µJ ng
−1
)
*
195 µJ
(3) O2 utilized by protein catabolism (2) / [0.527 µJ (pmol O2)
−1
]* 370 (pmol O2)
−1
(4) Respiration rate Measured
†
494 (pmol O2)
−1
(5) O2 utilized by lipid catabolism (4) - (3) 124 (pmol O2)
−1
(6) Energy from lipid oxidation (5) x [0.441 µJ (pmol O2)
−1
]
*
55 µJ
(7) Total respiratory energy (6) + (2) 250 µJ
(8) Energy % from protein catabolism (2) / (7) x 100 78 %
* Gnaiger, 1983: 24 µJ (ng protein)
−1
; 527 kJ (mol O2)
−1
using only protein substrate; 441 kJ (mol O2) using only lipid substrate
† Respiration rate (pmol day
−1
) for a 5-day old larva of 241.5 ± 2.1 midline body length reared chronically at 20°C
160
Thermal sensitivity Q10
Q10 values were calculated for all processes of feeding, amino acid transport,
morphological growth, protein accretion, protein synthesis, protein degradation, respiration, and
ammonia excretion using Equations 5 and 6 where R represents the physiological rate at
temperature T. Equations 5 is commonly used for the calculation of Q 10 at two temperatures (T1
and T2). However, assays were conducted at four acute temperatures in the present study, so
Equation 5 was log-transformed and solved for log 10R2 to produce a linear function (Schmidt-
Nielsen, 1997, pg. 220). The log-transformation seen in Equation 6 allows for the determination
of Q10 by linear regression of the log10(R2) as the variable dependent on the constant
multiplied by the slope (log10Q10). Since four temperatures were tested with 2 – 8 replicates per
temperature depending on the assay, Equation 6 allows for the incorporation of all data points in
calculating the Q10. In this case, Q10 = 10
slope
of this line. Because Q10 values were determined
over several days while larvae were growing in size, the relationships between Q 10 and midline
body length were examined by ANOVA linear regression. When no relationship was found
between size and Q10
values, the values were averaged together (average ± s.e.m) for comparison
between temperature treatments by analysis of variance. Specific comparisons between Q 10
values of physiological processes of interest (i.e., protein synthesis vs respiration and ammonia
excretion vs respiration) were compared directly by 2-way ANOVA with long-term (chronic)
rearing temperature and process as factors. Finally, the effect of long-term (chronic) rearing
temperature on growth was determined by comparing cohort-specific growth rates of larvae
reared at 15 or 20°C and reported as Q10 ± s.e. of the slope of the linear transformation described
above.
161
Equation 5. 𝑄 =
Equation 6. log 𝑅 = log 𝑅 +
× log 𝑄
Protein metabolic dynamics
Protein depositional efficiencies, i.e., the percentage of protein synthesized that was
accreted as body mass, was calculated as protein accretion (ng day
−1
) divided by protein
synthesis (ng day
−1
) x 100. Fractional rates of protein turnover, i.e., the percentage of total body
protein turned over each day, was calculated as protein synthesis (ng day
−1
) divided by total
protein content (ng larva
−1
) x 100. Protein turnover (degradation) was calculated as protein
synthesis (ng day
−1
) minus protein accretion (ng day
−1
). Measurements were calculated for larvae
(1) reared at 15°C and tested at 15°C; cf. (2) reared at 20°C and tested at 20°C and corrected for
size. The relationship between protein metabolism (depositional efficiency, fractional synthesis,
and log-transformed turnover) and size was compared between larvae reared at 15 and 20°C and
analyzed by ANCOVA.
Energetic budgets
Rates of protein synthesis, respiration, and ammonia excretion were converted into
energetic equivalents. Oxygen consumption from respiration rates were converted to energetic
equivalents using an oxyenthalpic conversion of 484 kJ (mol O 2)
−1
based upon the major
biochemical constituents of larvae of S. purpuratus which assumes equal utilization of protein
(527 kJ mol
−1
) and lipid (441 kJ mol
−1
) (Gnaiger, 1983; Pan et al., 2015). Since the oxyenthalpic
162
equivalent values for carbohydrate, lipid, and protein are similar in range (10%), the use of 484
kJ (mol O2)
−1
, at most, results in <6% error in estimations of metabolic energy (Schmidt-
Nielsen, 1997, pg. 170). These proportions were based on assumptions that only lipid and protein
were used as metabolic substrates (see section “Atomic O:N ratios and protein catabolism”
above). To calculate the proportion of energy allocated to protein synthesis, the cost of 2.4 J (mg
protein synthesized)
−1
as determined by Pan et al. (2015), was used. The energy (J) expended for
protein synthesis relative to the total energy from respiration represents the proportion of energy
allocated to protein synthesis. The protein mass-equivalent lost per mass of NH 4
+
excreted was
estimated using the average amino acid composition of S. purpuratus determined by Pan et al.
(2015) and assuming no bias in amino acid catabolism and catabolism of all nitrogenous
functional groups (See section “Atomic O:N ratios and protein catabolism” above; Mayzaud and
Conover, 1988; Table 3.1).
RESULTS
Growth and survivorship
Survivorship of larvae of S. purpuratus was measured for five cohorts of larvae reared at
15°C and 20°C (Fig. 3.8, Cohorts 1, 2, 3, 6, 7). The average survivorship for larvae of S.
purpuratus chronically reared at 15°C for eight-days was 67% ± 2. Survivorship was more
variable for larvae of different cohorts chronically reared at 20°C. Notably, larvae from Cohorts
2 and 3 experienced 100% mortality beyond four-days, while larvae of the same cohorts had
>60% survival at 15°C (Fig. 3.8B,C). For the cohorts chronically reared at 20°C that survived
past five days (Cohorts 1, 6, and 7), the survivorship was 59% ± 4 (Fig. 3.8). For the larvae from
163
Cohorts 1, 6, and 7, there was a statistical effect of temperature and age on survival (2-way
repeated measures ANOVA on logit-transformed data; Temperature F 1,13 = 8.02, p = 0.014; Day
F2,26 = 16.59, p < 0.0001; Interaction F2,26 = 0.090, ns).
A total of 2,122 midline body length measurements and 209 protein content
determinations were made through the course of this study of larvae to determine growth rates at
different chronic rearing temperatures. Growth rates for larvae from replicate culture vessels of
the same cohort (Cohort 1) were highly reproducible for midline body length (ANCOVA,
midline body length slope: F1,318 = 2.88; ns; Fig. 3.9A) and protein accretion (ANCOVA, protein
accretion slope: F1,25 = 2.0; ns). Therefore, differences in growth rates, if any, are attributed to
biological variation between larvae of different cohorts. Growth in midline body length between
larvae of five cohorts (Cohorts 1, 2, 3, 6, and 7) chronically reared at 15°C was remarkably
consistent, ranging between 11.2 – 13.2 µm day
−1
, a difference in rates of 2 µm day
−1
(Fig.
3.9C). A wider range of cohort variation in the growth of midline body length (Cohorts 1, 4, 5, 6,
and 7), was found for larvae chronically reared at the higher temperature of 20°C (13.3 – 18.8
µm day
−1
; Fig. 3.9C). This means that there is cohort-variation in tolerance and growth at 20°C.
When larvae from all seven cohorts are considered together, larvae reared at 15°C grew on
average 11.4 ± 0.4 (slope ± s.e.) µm midline body length compared to larvae reared at 20°C
which grew on average 16.6 ± 0.5 (slope ± s.e.) µm midline body length. Differences in growth
rates (midline body length) at 15°C and 20°C result in a midline body length growth Q 10 of 1.8 ±
0.2 (Table 3.3). This means that growth rates in midline body length increase by 180% for every
10°C increase in temperature.
Cohort-specific variability in protein accretion rates was seen for larvae chronically
reared at 15°C and 20°C (Fig. 3.9D). These differences were not attributed to culturing methods
164
since larvae from replicate culture vessels had highly reproducible protein accretion rates (Fig.
3.9B, by ANCOVA rates of independent culture vessels for the same cohort could be combined).
Larvae from different cohorts of S. purpuratus chronically reared at 15°C showed different rates
of accretion (Fig. 3.9D. Cohort 2: 3.3; Cohort 6: 9.5 ng day
−1
) compared to larvae chronically
reared at 20°C (Fig. 3.9D. Cohort 5: 4.3; Cohort 7: 12.3 ng day
−1
). When larvae of S. purpuratus
from all seven cohorts are considered together, larvae reared at 15°C grew on average 5.7 ± 0.3
ng protein day
−1
and 7.7 ± 0.4 ng protein day
−1
at 20°C (Fig. 3.9D). Comparisons of protein
accretion rates for larvae chronically reared at 15°C and 20°C give a protein accretion rate Q 10 of
1.5 ± 0.6 (Table 3.3). This means that protein accretion rates increase by 150% for every 10°C
increase in temperature.
165
Figure 3.8. Survivorship of larvae of Strongylocentrotus purpuratus reared at either 15°C
(closed symbols) or 20°C (open symbols). Each panel represents a different cohort of larvae
numbered according to Table 3.3: (A) Cohort 1; (B) Cohort 2; (C) Cohort 3; (D) Cohort 6; (E)
Cohort 7. Symbol types are used throughout this study for respective cohorts.
166
Figure 3.9. Growth rates of larvae of Strongylocentrotus purpuratus larvae reared either at 15°C
or 20°C. (A) Midline body lengths (average ± s.e.m, n=50; see labelled photomicrograph in
Chapter 1, Fig. 1.8A) of larvae in replicate culture vessels of Cohort 1. (B) Total protein content
(average ± s.e.m, n=5) of larvae in replicate culture vessels of Cohort 1. Data point x-axis values
are offset for clarity. (C) Midline body length growth rates (± s.e.) for seven cohorts of larvae
chronically reared at 15°C (black bars) or 20°C (white bars). (D) Protein accretion rates (± s.e.)
for seven cohorts of larvae chronically reared at 15°C (black bars) or 20°C (white bars).
167
Acute temperature sensitivities (Q10) of physiological processes
A total of 228 in vivo protein synthesis assays (1,140 time-points), 232 feeding rate
assays (1,160 time-points), 808 respiration rate assays (4,040 time-points), and 461 ammonia
excretion assays were performed across seven cohorts of larvae chronically reared at 15 and
20°C. Table 3.3 provides a summary of the Q 10 values for feeding, amino acid transport,
morphological growth, protein accretion, protein synthesis, respiration, ammonia excretion. The
detailed values for summary Table 3.3 for the effect of temperature on feeding, amino acid
transport, protein synthesis, respiration, and ammonia excretion rates are presented in Tables 3.4
and 3.5. A major conclusion from these analyses of thermal sensitivity is that the Q 10 values of
protein synthesis (Q10 = 2.9 at 15°C; 3.7 at 20°C) and ammonia excretion (Q 10 = 2.9 at 15°C; 3.4
at 20°C) are higher than corresponding Q10 values for feeding, amino acid transport, and
respiration. In particular, the higher thermal sensitivity for protein synthesis relative to
respiration suggests an uncoupling of ATP demand and supply (ANOVA for Q 10 values for
protein synthesis and respiration: F1,34 = 20.08; p < 0.0001).
The Q10 values for processes related to protein metabolic dynamics, namely protein
synthesis and ammonia excretion rates, had higher temperature sensitivity with increasing
temperature (15°C cf. 20°C) [Two-way ANOVA of the physiological processes listed in Table
3.3: F1,56 = 41.53, p < 0.0001; Temperature F1,56 = 4.5, p = 0.0383; Interaction F1,56 = 4.2, p =
0.0451; Honest Significant Difference at 0.05 for effect sizes greater than 0.73; both protein
synthesis and ammonia excretion, p < 0.05]. Notably, there is an enhanced thermal stress for
developmental stages of S. purpuratus with rising temperature, with a further uncoupling of
thermal sensitivity for protein synthesis (Q10 = 3.7) relative to respiration (Q10 = 2.0) at 20°C.
168
Figure 3.10 illustrates the biological variation of thermal sensitivity for protein synthesis and
respiration for five larval cohorts of S. purpuratus.
169
Table 3.3. Thermal sensitivity, Q10 [average ± s.e.m (n)], of physiological and whole-organismal
processes for larvae of Strongylocentrotus purpuratus chronically reared at 15 or 20°C.
Process
15 °C 20 °C
Protein Synthesis 2.9 ± 0.2 (18) 3.7 ± 0.3 (12)
Ammonia Excretion 2.9 ± 0.1 (12) 3.4 ± 0.2 (6)
Respiration 2.0 ± 0.1 (18) 2.0 ± 0.1 (12)
Feeding Rate 1.7 ± 0.1 (12) 1.8 ± 0.1 (16)
Amino Acid Transport 1.4 ± 0.1 (18) 1.6 ± 0.1 (12)
*Growth in Midline Body Length 1.8 ± 0.2
*Protein Accretion 1.5 ± 0.6
*Process for which Q10 was calculated based on chronic-time-scale differences.
170
Table 3.4. Thermal sensitivity of larval physiological rates (Q 10 ± s.e.) of Strongylocentrotus
purpuratus chronically reared at 15°C. Assays were repeated across seven cohorts of larvae with
different sizes (midline body lengths). Linear regressions between midline body length and Q 10
indicate whether Q10 values covaried with midline body length.
S. purpuratus larvae reared at 15 °C
Cohort
Midline Body
Length (µm)
Protein
Synthesis
Respiration
Rate
Alanine
Transport
Feeding
Rate
Ammonia
Excretion
1a* 251.9 ± 1.6 3.0 ± 0.4 2.0 ± 0.2 1.7 ± 0.1 1.8 ± 0.2
274.4 ± 2.4 3.6 ± 0.7 1.9 ± 0.1 1.6 ± 0.1 1.8 ± 0.4
305.1 ± 1.6 3.2 ± 0.7 1.7 ± 0.3 1.6 ± 0.1 1.4 ± 0.1
1b* 257.2 ± 2.0 2.4 ± 0.8 1.8 ± 0.1 1.8 ± 0.2 1.2 ± 0.2
286.5 ± 1.8 4.1 ± 1.1 1.4 ± 0.1 1.6 ± 0.1 1.7 ± 0.1
303.2 ± 1.2 2.4 ± 0.5 1.5 ± 0.1 1.5 ± 0.1 2.1 ± 0.7
2 221.7 ± 1.6 2.0 ± 0.2 2.2 ± 0.2 1.1 ± 0.0
3.9 ± 0.4
227.4 ± 2.0 2.9 ± 0.3 2.2 ± 0.2 1.2 ± 0.1
2.6 ± 0.4
228.2 ± 2.5 3.9 ± 1.0 2.1 ± 0.3 1.0 ± 0.1
3.2 ± 0.4
254.0 ± 1.8 3.1 ± 0.3 2.2 ± 0.3 1.2 ± 0.1
2.5 ± 0.5
263.4 ± 2.1 2.6 ± 0.3 3.6 ± 0.6 1.2 ± 0.1
2.8 ± 0.3
3 227.6 ± 1.8 1.4 ± 0.3 1.9 ± 0.1 1.4 ± 0.1
2.6 ± 0.6
228.6 ± 1.9 3.1 ± 0.6 1.8 ± 0.2 1.8 ± 0.6
2.4 ± 0.2
233.0 ± 1.7 2.3 ± 0.6 2.4 ± 0.4 1.1 ± 0.1
3.1 ± 0.2
257.2 ± 2.3 3.3 ± 0.8 1.2 ± 0.1 1.4 ± 0.5
3.4 ± 1.2
267.3 ± 2.2 3.4 ± 0.6 1.8 ± 0.2 0.9 ± 0.1
2.8 ± 0.3
4 234.1 ± 2.7 2.9 ± 0.8 1.7 ± 0.1 1.2 ± 0.1
2.3 ± 0.3
5 247.7 ± 2.2 2.0 ± 0.2 1.9 ± 0.2 1.7 ± 0.1
2.7 ± 0.5
6 244.0 ± 2.4
1.8 ± 0.2
255.6 ± 1.9
1.6 ± 0.3
296.5 ± 2.0
1.4 ± 0.2
7 242.2 ± 2.0
2.2 ± 0.3
256.0 ± 1.9
1.5 ± 0.2
287.7 ± 2.2
1.6 ± 0.3
Average ± s.e.m 2.9 ± 0.2 2.0 ± 0.1 1.4 ± 0.1 1.7 ± 0.1 2.9 ± 0.1
Linear regr. Q 10 x Length p = 0.19 p = 0.28 p = 0.18 p = 0.57 p = 0.38
*1a and 1b represent replicate culture vessels of the same cohort.
171
Table 3.5. Thermal sensitivity of larval physiological rates (Q 10 ± s.e.) of Strongylocentrotus
purpuratus chronically reared at 20°C. Assays were repeated across seven cohorts of larvae with
different sizes (midline body lengths). Linear regressions between midline body length and Q 10
indicate whether Q10 values covaried with midline body length.
S. purpuratus larvae reared at 20 °C
Cohort
Midline Body
Length (µm)
Protein
Synthesis
Respiration
Rate
Alanine
Transport
Feeding
Rate
Ammonia
Excretion
1a* 206.4 ± 2.1 5.0 ± 0.6 2.0 ± 0.2 1.7 ± 0.1 2.2 ± 0.6
236.4 ± 2.0 2.3 ± 0.4 1.6 ± 0.1 1.4 ± 0.1 2.7 ± 0.6
276.8 ± 2.1 5.8 ± 2.3 2.6 ± 0.7 1.8 ± 0.2 2.7 ± 0.4
1b* 216.2 ± 1.7 4.2 ± 0.4 1.7 ± 0.2 1.7 ± 0.2 1.6 ± 0.2
238.2 ± 2.0 2.3 ± 0.5 1.8 ± 0.1 1.5 ± 0.1 2.2 ± 0.3
266.0 ± 2.4 3.4 ± 1.0 1.9 ± 0.4 1.7 ± 0.2 2.1 ± 0.4
2 178.8 ± 2.6 3.4 ± 0.9 1.9 ± 0.2 1.8 ± 0.4
3.6 ± 0.5
3 177.8 ± 2.1 2.5 ± 0.4 1.8 ± 0.1 1.7 ± 0.2
4.0 ± 0.5
4 207.0 ± 2.3 5.5 ± 2.4 1.7 ± 0.1 1.5 ± 0.2 1.5 ± 0.3 2.3 ± 0.9
241.5 ± 2.1 3.8 ± 0.3 2.4 ± 0.3 1.4 ± 0.1 2.3 ± 0.4 3.7 ± 0.8
5 211.0 ± 2.0 3.1 ± 0.4 1.8 ± 0.1 2.1 ± 0.2 1.2 ± 0.9 3.4 ± 0.8
246.4 ± 1.8 2.8 ± 0.3 2.5 ± 0.3 1.3 ± 0.1 2.5 ± 0.8 3.6 ± 0.5
6 204.0 ± 2.4
1.2 ± 0.3
230.9 ± 2.6
1.3 ± 0.1
261.6 ± 3.9
1.5 ± 0.2
7 197.7 ± 1.9
1.0 ± 0.1
215.5 ± 2.6
1.6 ± 0.1
251.7 ± 3.1
1.7 ± 0.5
Average ± s.e.m 3.7 ± 0.3 2.0 ± 0.1 1.6 ± 0.1 1.8 ± 0.1 3.4 ± 0.2
Linear regr. Q 10 x Length p = 0.64 p = 0.07 p = 0.39 p = 0.01 p = 0.07
*1a and 1b represent replicate culture vessels of the same cohort.
172
Figure 3.10. Q10 for protein synthesis rates (white) and respiration rates (black) in larvae of Strongylocentrotus purpuratus reared at
(A) 15°C or (B) 20°C. Values represent average ± s.e.m for different larval sizes within cohorts numbered on the x-axis (see Tables
3.4 and 3.5). Different symbols, used throughout this study, represent respective cohorts: Cohort 1 (circles), Cohort 2 (squares),
Cohort 3 (triangles), Cohort 4 (crossed circles), and Cohort 5 (stars). Values given as Cohort 1A and 1B represent larvae reared in
replicate 20-liter culture vessels for the same cohort.
173
Protein catabolism
There was a significant difference between the Q 10 values of ammonia excretion and
respiration rates at both chronic rearing temperatures [Two-way ANOVA of the physiological
processes listed in Table 3.3: F1,56 = 41.53, p < 0.0001; Temperature F1,56 = 4.5, p = 0.0383;
Interaction F1,56 = 4.2, p = 0.0451; Honest Significant Difference at 0.05 for effect sizes greater
than 0.73]. Pairwise comparisons (Holm-Sidak method) revealed that chronic rearing
temperature also had an effect on ammonia excretion Q 10 values (difference of 0.575, t = 2.458, p
= 0.018). The Q10 values of ammonia excretion rates increased from 2.9 ± 0.1 at 15°C chronic
rearing temperature to 3.4 ± 0.2 at 20°C chronic rearing temperature. Respiration rate Q 10 values
remained at 2.0 ± 0.1 at both chronic rearing temperatures. The differences in Q 10 values
between ammonia excretion (2.9 or 3.4) and respiration rate (2.0) indicate a disproportionate
increase in protein catabolism relative to ATP production at higher temperatures.
Evidence for increased protein catabolism at higher chronic rearing temperatures was supported
by lower atomic O:N ratios at 20°C. Atomic O:N ratios decreased significantly from 30.8 ± 1.9
for larvae reared at 15°C to 18.6 ± 3.1 when reared at 20°C (Fig. 3.11A; ANOVA, F 1,103 = 11.46;
p = 0.001). Based on the estimated relationship between ammonia excretion and protein
catabolism (Tables 3.1 and 3.2), the decrease in O:N ratio at 20°C reflects an increase in the
proportion of metabolic energy derived from protein substrates from 46.5% ± 2.4 to 77.0% ± 4.8
of metabolic energy derived from protein catabolism (Fig. 3.11B; ANOVA F 1,103 = 39.58; p <
0.0001). These estimates assume that the remainder of the metabolic energy came from lipid
oxidation, and not carbohydrate metabolism (Shilling and Manahan, 1990; Griffith, 2021
personal communication; see section “Atomic O:N ratios and protein catabolism” for
justification).
174
Figure 3.11. Utilization of protein as a metabolic substrate in larvae of Strongylocentrotus
purpuratus reared at 15 or 20°C. (A) Atomic O:N ratios were significantly lower in larvae reared
at 20°C (18.6 ± 3.1) compared to larvae reared at 15°C (30.8 ± 1.9) (ANOVA F 1,103 = 11.46; p =
0.001). (B) The proportion of metabolic energy attributed to protein catabolism (as calculated in
Table 3.1; Table 3.2) increased from 46.5 ± 2.4 to 77.0 ± 4.8% for larvae reared at 15 or 20°C
respectively (ANOVA F1,103 = 39.58; p < 0.0001). Bars indicate averages ± s.e.m (15°C: n = 75;
20°C: n = 30).
175
Protein metabolic dynamics under different chronic rearing temperatures
Long-term, chronically exposed rearing temperatures of 15°C or 20°C had no effect on
the short-term, acute measured values of Q10 for feeding rate, alanine transport rate, and
respiration rate for larvae of S. purpuratus [Two-way ANOVA of the physiological processes
listed in Table 3.3: F1,56 = 41.53, p < 0.0001; Temperature F1,56 = 4.5, p = 0.0383; Interaction
F1,56 = 4.2, p = 0.0451; Honest Significant Difference at 0.05 for effect sizes greater than 0.73].
However, protein synthesis and ammonia excretion Q 10 increased significantly (protein
synthesis: 2.9 ± 0.2 (s.e.m.) to 3.7 ± 0.3 (s.e.m.); ammonia excretion: 2.9 ± 0.1 (s.e.m.) to 3.4 ±
0.2 (s.e.m.) when larvae were reared at 20°C. Based on cohort-specific growth rates compared
between chronic rearing temperatures, larvae of S. purpuratus had a midline body length growth
Q10 of 1.8 ± 0.2 (s.e.) and a protein accretion rate Q 10 of 1.5 ± 0.6 (s.e.) (Table 3.3).
They dynamics of protein synthesis, degradation, and accretion remained the same
regardless of whether larvae were reared at 15°C or 20°C. Protein depositional efficiencies (i.e.,
the ratio of protein accretion to protein synthesis) did not change with morphological size
(midline body length) (ANOVA regression p = 0.7986 at 15°C; ANOVA regression p = 0.8239
at 20°C; Fig. 3.12A), nor was there a difference in protein depositional efficiencies with chronic
rearing temperature (ANOVA F1,24 = 0.04, ns; Fig. 3.12A). Because protein depositional
efficiency did not differ statistically between chronic rearing temperatures or size, data were
pooled together for all cohorts (Cohorts 1 – 5) and temperatures to yield an average, size-
independent protein depositional efficiency of 27% ± 2 (Fig. 3.12A, mean ± s.e.m. of all data
points n=26). Fractional protein synthesis rates, (i.e., the rate of protein synthesis per unit mass
of total protein), also did not change with morphological size (midline body length) (ANOVA
regression p = 0.09), nor was there a difference in fraction protein synthesis rates between larvae
176
chronically reared at 15°C or 20°C (Fig. 3.12B; F 1,24 = 0.44, ns). On average, larvae of five
cohorts (Cohorts 1 – 5) of S. purpuratus synthesized 3.6% ± 0.2 of their total body protein mass
per hour when chronically reared at either 15°C or 20°C.
Protein degradation, (determined from the excess of protein synthesis relative to protein
accretion), increased with size, from 9.8 ng day
−1
for a larva of 200 µm midline body length to
36 ng day
−1
for a larva of 300 µm (ANOVA regression on log-transformed turnover rates: p <
0.0001; log10(Turnover) = -0.1873 + 0.0057× (midline body length); Fig. 3.12C). This trend was
the same for larvae chronically reared at different temperatures (15°C and 20°C) such that
protein turnover rates were not explained by chronic rearing temperature (ANCOVA intercept
F1,23 = 2.04, ns; slope F1,22 = 0.02, ns)
177
Figure 3.12. Protein metabolic dynamics of five larval cohorts (Cohorts 1 – 5, see Tables 3.4 and
3.5) of Strongylocentrotus purpuratus reared at 15° (closed symbols) or 20°C (open symbols).
(A) Protein depositional efficiencies (i.e., the ratio of protein accretion to protein synthesis) did
not differ between larvae raised at either temperature (ANOVA F1,24 = 0.03; ns). Protein
depositional efficiency averaged 27 ± 2% (s.e.m, n = 26). (B) Average fractional protein
synthesis rates did not differ between larvae reared at either temperature (ANOVA F 1,24 = 0.44;
ns). Fractional protein synthesis rates averaged 3.6 ± 0.2% h
−1
. (C) Protein degradation rate (i.e.,
protein turnover, the difference between protein synthesis and accretion rates), increased with
midline body length (ANOVA regression, log-transformed turnover rates; 15°C: p = 0.041;
20°C: p = 0.009). Protein turnover rates were indistinguishable between larvae chronically reared
at 15 or 20°C (ANCOVA intercept F1,23 = 2.04, ns; slope F1,22 = 0.02, ns). Protein degradation
rates can be predicted from midline body length according to the equation log 10(protein
degradation) = -0.182 + 0.00565× (midline body length). (D) Relationship between midline body
length and total protein content for larvae chronically reared at two different temperatures. For a
given size, larvae chronically reared at 20°C had higher protein (ANCOVA Intercept: F 1,35 =
15.68, p = 0.0004). Different symbols, used throughout this study, represent respective cohorts:
Cohort 1 (circles), Cohort 2 (squares), Cohort 3 (triangles), Cohort 4 (crossed circles), and
Cohort 5 (stars). For symbols used for Panel D, black represents 15°C and white represents
20°C.
178
DISCUSSION
The present study supports evidence that the decoupling of physiological processes with
increased temperature creates imbalances in ATP production and utilization in marine larvae
(Pan et al., 2021). A thorough investigation for larvae of S. purpuratus included 228 in vivo
protein synthesis assays (1,140 time-points), 232 feeding rate assays (1,160 time-points), 808
respiration rate assays (4,040 time-points), and 461 ammonia excretion assays performed across
multiple cohorts at two chronic rearing temperatures (Fig. 3.1) allowed for the comparison of
interdependent physiological processes in the context of protein metabolism and energetics. Of
particular interest, these data indicate the decoupling of a major energetically demanding
process, protein synthesis, with ATP producing process, respiration, observed in larvae tested
(Fig. 3.10). Furthermore, larvae of S. purpuratus shift to a protein-dominated metabolism at
higher temperatures to meet these energetic demands (Fig. 3.11). These data provide evidence
for a disproportionate allocation of ATP to protein synthesis compounded by increases in protein
catabolism that disrupts the dynamics of protein metabolism.
Comparative investigations of physiological rates revealed that protein metabolic
processes become uncoupled with increasing temperature due to differences in temperature
sensitivity (Q10) between rates. These uncoupling effects were exemplified by significant
differences between the Q10 values of interconnected processes (c.f. protein synthesis Q10 > 2.9
and respiration Q10 = 2.0). Processes are interconnected when they utilize a common substrate
(e.g., ATP), but become decoupled when differences in thermal sensitivities cause one processes
rate to increase in a manner that deprives the common substrate from another less-thermally-
sensitive process (Schmidt-Nielsen, 1997, pp. 224-225). At both chronic rearing temperatures,
179
protein synthesis rates of S. purpuratus had a higher thermal sensitivity than respiration (Q 10 > 3
for protein synthesis compared to Q10 of 2 for respiration; Table 3.3). Because protein synthesis
represents a major proportion of ATP demand (Pan et al., 2015; Pan et al., 2018, Pan et al.,
2021), the common “substrate” of ATP becomes disproportionally consumed by protein
synthesis relative to production by respiration. This is defined as a “stressful allocation” (Pan et
al., 2021), and the present study examines how other processes of feeding, amino acid transport,
growth, and protein metabolism are affected during exposure to high temperature.
Limitations of ATP production for protein synthesis
Protein synthesis represents one of the major cellular costs in living organisms, and the
cost of protein synthesis in larvae of S. purpuratus has been determined to be 2.4 ± 0.2 J (mg
protein synthesized)
−1
(Pan et al., 2015). Based on measurements in this study at 15°C, the
relationship between respiration rate and larval protein mass was modelled as log 10(respiration
rate) = -0.93 + 1.46 × log 10(total protein). A growing larva of S. purpuratus with 25 ng total
protein has a respiration rate of 310 pmol O 2 day
−1
. This equates to 150 µJ day
−1
based on an
oxyenthalpic value of 484 kJ (mol O2)
−1
(Gnaiger, 1983; Schmidt-Nielsen, 1997, pg. 170). A
relationship for protein synthesis rates with total protein was also found such that log 10(protein
synthesis) = -2.97 + 2.00 × log10(total protein). A larva of 25 ng total protein would have a
protein synthesis rate of 16 ng day
−1
. This equates to a cost of 38.6 µJ day
−1
for protein synthesis
or an allocation of 26% of ATP to protein synthesis (38.6 µJ / 150 µJ) (2.4 ± 0.2 J mg
−1
protein
synthesized, Pan et al., 2015).
The predicted values presented above for larvae chronically reared at 15°C were used to
predict energy allocation at different temperatures (10°C and 20°C) based on the thermal
180
sensitivities determined in the present study (Table 3.3). The thermal sensitivities of respiration
and protein synthesis are 2.0 and 3.7 respectively if larvae are chronically reared at 20 C (Table
3.3). Based on these values, a larva with 25 ng total protein would allocate 19% (20 µJ / 106 µJ;
Fig. 3.13) of its ATP pool to protein synthesis at 10 C and increase to a higher allocation of 35%
(74 µJ / 212 µJ) at 20°C (Fig. 3.13). The 1.8-fold increase a whole organism’s ATP pool to
protein synthesis leaves less available energy for other important processes such as ion transport
or the biosynthesis (e.g., nucleic acids). Pan et al. (2021) also determined the Q10 values of
protein synthesis and respiration to be 2.9 ± 0.18 and 2.0 ± 0.15 respectively for larvae of C.
gigas. In their study, the difference in Q10 values for C. gigas resulted in increases in ATP
allocation to protein synthesis from 35% to 65%. ATP allocations exceeding 50% may define the
limits of thermal tolerance for different species.
There are only a few studies of marine organisms which compare the thermal sensitivities
of both respiration rates and protein synthesis rates (Robertson et al., 2001; Whiteley and
Faulkner, 2005; Pan et al., 2021). The intertidal isopod, Ligia oceanica, has a respiration Q10 of
2.2, while the thermal sensitivity of protein synthesis rates for winter-acclimated animals was 2.9
(Whiteley and Faulkner, 2005). For Antarctic isopods, Glyptonotus antarcticus, the Q10 values
(approximately 2.2) of protein synthesis were found not to be statistically different from the Q 10
of oxygen consumption (Robertson et al., 2001). While these studies attempted to use
radioisotopes for determinations of protein synthesis rates, the use of bivalve larvae, which
naturally transport amino acids from seawater, allowed for more thorough investigations of
protein synthesis rates (Pan et al., 2021). Pan et al., (2021) describe a thermal tipping point for
larvae of C. gigas based on differences in Q10 values for respiration (2.0) compared to protein
synthesis rates (2.9). In the present study, protein synthesis rates were also consistently more
181
sensitive to temperature than concurrently measured respiration rates (Table 3.3). These studies
together provide further evidence that the increased allocation of ATP to protein synthesis may
be responsible for thermal limits to larval growth and survival.
Disproportionate increases in protein synthesis relative to respiration at high temperatures
may, in part, be due to the heat shock response (Weitzel et al., 1987). In the yeast model
Saccharomyces cerevisiae, protein synthesis rates were observed to increase for four hours after
heat shock (Weitzel et al., 1987). This increase in protein synthesis corresponded with
production of heat shock proteins as well as a decrease in cellular pH and ATP levels. Weitzel et
al. (1987) also found that increases in protein synthesis in response to temperature were
independent of oxidative phosphorylation by comparison of a mitochondrial mutant strain.
Strongylocentrotus purpuratus and C. gigas have 39 and 88 heat shock protein 70 genes
respectively, compared to 17 in humans (Sodergren et al., 2006; Zhang et al., 2012). The over-
representation of heat shock proteins suggests that they are rapidly regulated during stress
responses (Zhang et al., 2012). For example, five heat shock proteins were up-regulated more
than 2,000-fold in C. gigas exposed to heat stress (Zhang et al., 2012). High rates of protein
synthesis and the rapid up-regulation of heat shock response proteins may contribute to the
higher Q10 values observed for protein synthesis compared to respiration. Pan et al. (2015)
examined protein synthesis and degradation in response to the stress of experimental ocean
acidification for larvae of S. purpuratus but found no preferential synthesis of protein classes (by
molecular weight). Further experiments should examine whether this is true for larvae of S.
purpuratus exposed to thermal stress.
182
Figure 3.13. The disproportionate effect of temperature on physiological processes creates
energetic imbalances and impacts whole-organism physiology. Pie charts represent the
proportion of the cellular ATP budget (circle areas scaled to µJ from respiration listed below pie
charts) allocated to protein synthesis (gray). Rates at 10 and 20°C were calculated using the Q 10
values of protein synthesis and respiration for S. purpuratus chronically reared at 20°C (Table
3.3). Proportional ATP allocation to protein synthesis increased by 1.8-fold between the two
temperatures due to a higher sensitivity of protein synthesis to temperature (Q 10 = 3.7) relative to
respiration (Q10 = 2.0).
183
A shift towards protein-dominated metabolism at high temperatures
Thermal sensitivities of metabolic rates appear to be more constrained, (between 2 – 3),
for marine organisms (Mytilus edulis, Zittier et al., 2015; Pisaster ochraceus, Fly et al., 2012; C.
gigas, Lannig et al., 2010, Pan et al., 2021; Ophionereis schayeri, Christensen et al., 2011). The
Q10 of respiration rates appear to remain fixed at Q 10 of approximately 2.0 for larvae of S.
purpuratus independent of chronic rearing temperature (Table 3.3). This suggests a physiological
or biochemical constraint to maximal ATP production with temperature. Instead of higher
thermal sensitivities for respiration rates, larvae in the present study shift to a protein-dominated
metabolism to meet their respiratory needs at high temperatures (Fig. 3.11). Atomic ratios of
oxygen consumption to nitrogen excretion (O:N ratios) can be used as an indicator of metabolic
substrate utilization in respiration. Larvae increased the proportion of energy derived from
protein catabolism from 46.5% ± 2.4 (O:N ratio of 30.8 ± 1.9) to 77.0% ± 4.8 (O:N ratio of 18.6
± 3.1) when chronically reared at 15°C and 20°C respectively (Fig. 3.11). Proteins have a higher
oxyenthalpic value than lipids (527 kJ mol
−1
O2 cf. 441 kJ mol
−1
O2). Because of this, a shift
towards a protein dominated metabolism may yield slightly more energy for a given rate of
oxygen consumption. However, the catabolism of proteins is typically not preferred by other
organisms. When larvae of C. gigas were exposed to temperatures of 19 C, the larvae had O:N
ratios of 68.8 which decreased to 26.7 at 24 C (Clark et al., 2013). This suggests that oyster
larvae also shift towards a protein-dominated metabolism with increases in temperature.
Furthermore, the loss of protein biomass to respiration and ammonia excretion creates a greater
demand for ingestion to maintain protein mass. Nonetheless, larvae in the present study
increased protein catabolism by 1.7-fold (77% cf. 46%) when chronically reared at 20°C, so
there must be a necessity for this shift to protein-dominated metabolism.
184
Cohort variation may determine physiological compensation to high temperature
Based on the thermal sensitivities for feeding processes and the dynamics of protein
metabolism, compensation of larvae growing at stressful temperatures is hypothesized to be due
to cohort variation rather than changes in physiology. The decoupling of ATP production and
utilization was more pronounced for S. purpuratus at higher chronic rearing temperatures of
20°C compared to 15°C (see Q10 values Table 3.3). Notably, the discrepancy between Q 10 values
for protein synthesis, which increased from 2.9 to 3.7, and ammonia excretion, which also
increased in this case from 2.9 to 3.4 (Table 3.3). Since respiration Q 10 values were 2.0
regardless of chronic rearing temperature, this resulted in higher increased allocation to protein
synthesis compounded by higher proportions of metabolic energy derived from protein
catabolism. Both the demand for high rates of protein synthesis and demand for the catabolism of
proteins to fuel energetic requirements present a stressful competition for ATP and biochemical
substrates. One possible solution to compensating for an increased demand for both protein
synthesis and catabolism would be to increase protein ingestion. However, the thermal sensitivity
of feeding rates was approximately the same as that of respiration (Q 10: ~1.8 vs. 2.0 respectively;
Table 3.3). This means that the elevated rates of protein synthesis and catabolism would not be
compensated for by increases in feeding at higher temperature since ingestion would only
increase proportionally with respiration. Similarly, amino acid transport rates had an even lower
thermal sensitivity (Q10 ~1.5; Table 3.3), so the transport of dissolved organic matter at higher
temperatures would also fail to supply energy proportional to the demands of protein synthesis.
As more protein mass is catabolized and respired and excreted, more protein and amino acid
mass are required for structural growth. Based on differences in thermal sensitivity, feeding and
amino acid transport physiology cannot compensate for the stressful allocation of ATP to protein
185
synthesis at high temperatures, yet larvae from several cohorts of S. purpuratus were able to
increase protein accretion rates when chronically reared at 20°C.
From the seven larval cohorts of S. purpuratus in the present study, there was a range of
biological variation in growth, especially for protein accretion rates when chronically reared at
20°C (Fig. 3.9D). Biological variation between cohorts was also observed in the dynamics of
protein synthesis and degradation (Fig. 3.12), but surprisingly, there were no differences in these
processes between larvae reared at different temperatures. Regardless of the chronic rearing
temperature, larvae synthesized 3.6% ± 0.2 of their total protein masses per hour and accreted
27% ± 2 of the protein masses synthesized (Fig. 3.12A). While protein synthesis rates and
depositional efficiencies were indistinguishable between cohorts reared at 15°C or 20°C, larvae
are predicted to increase protein accretion rates with temperature according to a Q 10 value of 1.5
(Table 3.3). This further suggests that compensation for growth in high temperatures may be
driven by cohort variation rather than changes in physiology within a cohort. The ability to
maintain similar growth efficiencies despite a stressful allocation of ATP to protein synthesis
presents an interesting phenotype on which to perform studies of genotype-by-environment
interactions.
In the present study, there is evidence that larvae of different cohorts have different
thermal tolerances. Cohorts 2 and 3 were reared at both temperatures, but larvae of these cohorts
chronically reared at 20°C died between three to five days, while larvae of the same cohorts kept
at 15°C had >60% survival through the period of study (Fig. 3.8B,C). Of the other cohorts with
larvae surviving at 20°C for which survival was tabulated (Cohorts 1, 6, and 7), larvae had 59%
± 4 survivorship, which is comparable (within 8%) to larvae at 15°C (67% ± 2). Since many
aspects of protein metabolic dynamics were the same for larvae chronically reared at 15°C and
186
20°C (e.g., thermal sensitivities of feeding, respiration, fractional protein synthesis rates, protein
depositional efficiencies), it is possible that the larvae from the cohorts with 100% mortality
(Cohorts 2 and 3 at 20°C) did not have the potential to achieve the appropriate physiological
rates at the higher temperature of 20°C. Most physiological or performance traits of ectotherms
follow a thermal performance curve (Huey and Stevenson, 1979; Schulte et al., 2011). Thermal
performance curves increase nonlinearly from a lower thermal limit to reach a thermal maximum
(or thermal optimum) before declining sharply. The inability to continue increasing respiration
rates beyond 20°C together with the decreased survival (Fig. 3.8) in larvae reared at 20°C
suggests that respiration rates may be reaching a thermal maximum with increasing
temperatures. Thermal limits above 20°C are further supported since adults of
Strongylocentrotus purpuratus typically spawn in the winter or spring and lose their ability to
spawn in temperatures above 17°C (Cochran and Engelmann, 1975; Strathmann, 1987). In a
warming ocean, these limitations are alarming since temperatures are already within these ranges
(Rasmussen et al., 2021). Animals, or specific genotypes (Pan et al., 2021), that can maintain
growth despite stressful allocations of ATP will be successful in future scenarios of ocean
warming.
Conclusion
Physiological processes decoupled with increases in temperature, resulting in the
disproportionate increase of energy required for protein synthesis compared to energy produced
from respiration. In addition to a higher demand for ATP allocated to protein synthesis, more
protein must be catabolized to meet energetic demands as evident from the high thermal
sensitivity of ammonia excretion. The increased energetic demand from high rates of protein
187
synthesis, together with the increased catabolism of proteins, presents a mass-balance tradeoff
with larval utilization of proteins to support respiration while maintaining high rates of protein
synthesis. Therefore, protein metabolic dynamics are an important indicator of thermal tolerance,
and the “winners” of ocean warming scenarios are those that can maintain cellular energetic
homeostasis to grow more efficiently despite changing allocations of energetically demanding
processes.
A recent summary analysis of 102-years of temperature data at the Scripps Institute of
Oceanography Pier was reported by Rasmussen et al. (2021). Over the 102-years of study,
waters near the Southern Bight of California have a mean temperature of 17.45°C. Since 1982,
the 1-year running average temperature has been predominantly higher than 17.45°C, with a
recent (approximately 2015) annual average temperature high at approximately 19.5°C.
Rasmussen et al. (2021) also conclude that the average surface water temperature is increasing
by 1.24°C per century and that average bottom water temperatures are increasing by 1.67°C per
century. Adults of S. purpuratus cannot tolerate temperatures above 23.5°C, and experience
mortality within a few days of exposure >24°C (Farmanfarmaian and Giese, 1963). In the
present study, physiological rates of larvae are approaching a thermal limit at high temperatures,
since several larval cohorts perished when chronically reared at 20°C for three to five days (cf.
>60% survival for the same cohorts reared at 15°C). Since physiological compensation for
stressful ATP allocations (e.g., compensatory feeding) was not seen in the present study, the
survival, and species distribution of S. purpuratus in a warming Southern Bight ocean will likely
be impacted. Some preliminary evidence reflecting biological variability is presented in Figure
3.8, where two out of seven cohorts tested at 20°C had high mortality while larvae from other
cohorts did not. This suggests the possibility of cohort-specific tolerances to elevated
188
temperatures. Further studies are warranted to address the biological bases of such variability in
a changing ocean.
Acknowledgements and contributions to Chapter 3
Results in this chapter were part of a larger project including experiments on
Strongylocentrotus purpuratus and Lytechinus pictus. Due to the complexity of measuring
multiple in vivo physiological processes at the same time, some of the data presented here were
collected by Ph.D. student Melissa B. DellaTorre (in the laboratory group of Professor Donal T.
Manahan). Only data pertaining to S. purpuratus are presented here since the author Jason Wang
led the experiments for this species. Efforts towards larval culturing, counting, and assaying of
protein content were divided evenly between Jason Wang and Melissa B. DellaTorre.
Respiration and ammonia excretion data were provided by and used with permission from
Melissa B. DellaTorre (Modified versions of Fig. 3.4 and Fig. 3.7). Downstream analyses of
these raw data were performed by Jason Wang (i.e., calculations of Q10, O:N, energy from
protein catabolism, ATP allocation models). All assays of protein synthesis and feeding were
conducted and analyzed by Jason Wang. All statistical analyses and calculations here, including
the ATP allocation to protein synthesis, protein accretion rates, protein depositional efficiencies,
protein turnover rates, percent energy from protein catabolism, and Q 10 values were calculated
by Jason Wang. Carbohydrate analyses were provided by Dr. Andrew Griffith (University of
Southern California postdoctoral research associate in the laboratory group of Professor Donal T.
Manahan) on samples for which the lipid and protein contents were analyzed by Jason Wang.
189
Chapter 3 References
Bayne, B.L. (2017). Biology of oysters. Academic press.
Bradford, M.M. (1976). A rapid and sensitive method for the quantitation of microgram
quantities of protein utilizing the principle of protein-dye binding. Analytical
Biochemistry, 72(1-2), 248-254.
Brett, J.R. (1971). Energetic responses of salmon to temperature. A study of some thermal
relations in the physiology and freshwater ecology of sockeye salmon (Oncorhynchus
nerkd). American Zoologist, 11(1), 99-113.
Christensen, A.B., Nguyen, H.D., and Byrne, M. (2011). Thermotolerance and the effects of
hypercapnia on the metabolic rate of the ophiuroid Ophionereis schayeri: inferences for
survivorship in a changing ocean. Journal of Experimental Marine Biology and Ecology,
403(1-2), 31-38.
Cochran, R.C., and Engelmann, F. (1975). Environmental regulation of the annual reproductive
season of Strongylocentrotus purpuratus (Stimpson). Biological Bulletin, 148, 393-401.
Clark, M S., Thorne, M.A., Amaral, A., Vieira, F., Batista, F. M., Reis, J., and Power, D.M.
(2013). Identification of molecular and physiological responses to chronic environmental
challenge in an invasive species: the Pacific oyster, Crassostrea gigas. Ecology and
Evolution, 3(10), 3283-3297.
Das, A.B., and Prosser, C.L. (1967). Biochemical changes in tissues of goldfish acclimated to
high and low temperatures—I. Protein synthesis. Comparative Biochemistry and
Physiology, 21(3), 449-467.
Farmanfarmaian, A., and Giese, A.C. (1963). Thermal tolerance and acclimation in the western
purple sea urchin, Strongylocentrotus purpuratus. Physiological Zoology, 36(3), 237-243.
190
Fly, E.K., Monaco, C.J., Pincebourde, S., and Tullis, A. (2012). The influence of intertidal
location and temperature on the metabolic cost of emersion in Pisaster ochraceus.
Journal of Experimental Marine Biology and Ecology, 422, 20-28.
Gauld, D. T. (1951). The grazing rate of planktonic copepods. Journal of the Marine Biological
Association of the United Kingdom, 29(3), 695-706.
Gnaiger, E. (1983). Calculation of energetic and biochemical equivalents of respiratory oxygen
consumption. In Polarographic oxygen sensors (pp. 337-345). Springer, Berlin,
Heidelberg.
Haschemeyer, A.E., Persell, R., and Smith, M.A. (1979). Effect of temperature on protein
synthesis in fish of the Galapagos and Perlas Islands. Comparative Biochemistry and
Physiology. B, Comparative Biochemistry, 64(1), 91-95.
Hinegardner, R.T. (1975). Morphology and genetics of sea-urchin development. American
Zoologist, 15(3), 679-689.
Hochachka, P.W., and Somero, G.N. (2002). Biochemical adaptation: mechanism and process in
physiological evolution. Oxford university press.
Huey, R.B., and Kingsolver, J.G. (1993). Evolution of resistance to high temperature in
ectotherms. The American Naturalist, 142, S21-S46.
Huey, R.B., and Kingsolver, J.G. (2019). Climate warming, resource availability, and the
metabolic meltdown of ectotherms. The American Naturalist, 194(6), E140-E150.
Huey, R.B., and Stevenson, R.D. (1979). Integrating thermal physiology and ecology of
ectotherms: a discussion of approaches. American Zoologist, 19(1), 357-366.
191
Hughes, S.J.M., Ruhl, H.A., Hawkins, L.E., Hauton, C., Boorman, B., and Billett, D.S. (2011).
Deep-sea echinoderm oxygen consumption rates and an interclass comparison of
metabolic rates in Asteroidea, Crinoidea, Echinoidea, Holothuroidea and Ophiuroidea.
Journal of Experimental Biology, 214(15), 2512-2521.
Jaeckle, W.B., and Manahan, D.T. (1989). Growth and energy imbalance during the
development of a lecithotrophic molluscan larva (Haliotis rufescens). The Biological
Bulletin, 177(2), 237-246.
Kellermann, V., Chown, S.L., Schou, M.F., Aitkenhead, I., Janion-Scheepers, C., Clemson, A.,
Scott, M.T., and Sgrò, C.M. (2019). Comparing thermal performance curves across traits:
how consistent are they? Journal of Experimental Biology, 222, DOI: jeb193433.
Lannig, G., Eilers, S., Pörtner, H.O., Sokolova, I.M., and Bock, C. (2010). Impact of ocean
acidification on energy metabolism of oyster, Crassostrea gigas—changes in metabolic
pathways and thermal response. Marine drugs, 8(8), 2318-2339.
Lee, J.W., Applebaum, S.L., and Manahan, D.T. (2016). Metabolic cost of protein synthesis in
larvae of the Pacific oyster (Crassostrea gigas) is fixed across genotype, phenotype, and
environmental temperature. The Biological Bulletin, 230(3), 175-187.
Loughna, P.T., and Goldspink, G. (1985). Muscle protein synthesis rates during temperature
acclimation in a eurythermal (Cyprinus carpio) and a stenothermal (Salmo gairdneri)
species of teleost. Journal of Experimental Biology, 118(1), 267-276.
Marsh, A.G., and Manahan, D.T. (1999). A method for accurate measurements of the respiration
rates of marine invertebrate embryos and larvae. Marine Ecology Progress Series, 184, 1-
10.
192
Mayzaud, P., and Conover, R.J. (1988). O: N atomic ratio as a tool to describe zooplankton
metabolism. Marine ecology progress series. Oldendorf, 45(3), 289-302.
Pace, D.A., and Manahan, D.T. (2006). Fixed metabolic costs for highly variable rates of protein
synthesis in sea urchin embryos and larvae. Journal of Experimental Biology, 209(1),
158-170.
Pace, D.A., Marsh, A.G., Leong, P.K., Green, A.J., Hedgecock, D., and Manahan, D.T. (2006).
Physiological bases of genetically determined variation in growth of marine invertebrate
larvae: a study of growth heterosis in the bivalve Crassostrea gigas. Journal of
experimental marine biology and ecology, 335(2), 188-209.
Pan, T.C.F, Applebaum, S.L., and Manahan, D.T. (2015). Experimental ocean acidification alters
the allocation of metabolic energy. Proceedings of the National Academy of Sciences,
112(15), 4696-4701.
Pan, T.C.F., Applebaum, S.L., Frieder, C.A., and Manahan, D.T. (2018). Biochemical bases of
growth variation during development: a study of protein turnover in pedigreed families of
bivalve larvae (Crassostrea gigas). Journal of Experimental Biology, 221(10), DOI:
jeb.171967.
Pan, T.C.F., Applebaum, S.L., and Manahan, D.T. (2021). Differing thermal sensitivities of
physiological processes alter ATP allocation. Journal of Experimental Biology, 224(2),
DOI: jeb233379.
Rasmussen, L.L., et al. (2020). A century of Southern California coastal ocean temperature
measurements. Journal of Geophysical Research: Oceans, 125(5), DOI: e2019JC015673.
193
Robertson, R., El-Haj, A., Clarke, A., Peck, L., and Taylor, E. (2001). The effects of temperature
on metabolic rate and protein synthesis following a meal in the isopod Glyptonotus
antarcticus Eights (1852). Polar Biology, 24(9), 677-686.
Schmidt-Nielsen, K. (1997). Animal physiology: adaptation and environment. Cambridge
University Press.
Schulte, P.M., Healy, T.M., and Fangue, N.A. (2011). Thermal performance curves, phenotypic
plasticity, and the time scales of temperature exposure. Integrative and Comparative
Biology, 51(5), 691-702.
Shilling, F.M., and Manahan, D.T. (1990). Energetics of early development for the sea urchins
Strongylocentrotus purpuratus and Lytechinus pictus and the crustacean Artemia sp.
Marine Biology, 106(1), 119-127.
Sinclair, B.J., Marshall, K.E., Sewell, M.A., Levesque, D.L., Willett, C.S., Slotsbo, S., Dong, Y.,
Harley, C.D.G., Marshall, D.J., Helmuth, B.S., and Huey, R.B. (2016). Can we predict
ectotherm responses to climate change using thermal performance curves and body
temperatures? Ecology Letters, 19(11), 1372-1385.
Sodergren, E. et al. (2006). The genome of the sea urchin Strongylocentrotus purpuratus.
Science, 314(5801), 941-952.
Somero, G.N. (2018). RNA thermosensors: how might animals exploit their regulatory potential?
Journal of Experimental Biology, 221(4), DOI: jeb162842.
Strathmann, M.F. (1987). Phylum Echinodermata, class Echinoidea. Reproduction and
development of marine invertebrates of the Northern Pacific Coast. University of
Washington Press, Seattle, 511-534.
194
Weatherburn, M.W. (1967). Phenol-hypochlorite reaction for determination of ammonia.
Analytical chemistry, 39(8), 971-974.
Weitzel, G., Pilatus, U., and Rensing, L. (1987). The cytoplasmic pH, ATP content and total
protein synthesis rate during heat-shock protein inducing treatments in yeast.
Experimental Cell Research, 170(1), 64-79.
Whiteley, N., and Faulkner, L.S. (2005). Temperature influences whole-animal rates of
metabolism but not protein synthesis in a temperate intertidal isopod. Physiological and
Biochemical Zoology, 78(2), 227-238.
Zhang, G., et al. (2012). The oyster genome reveals stress adaptation and complexity of shell
formation. Nature, 490(7418), 49-54.
Zittier, Z.M., Bock, C., Lannig, G., and Pörtner, H.O. (2015). Impact of ocean acidification on
thermal tolerance and acid–base regulation of Mytilus edulis (L.) from the North Sea.
Journal of Experimental Marine Biology and Ecology, 473, 16-25.
195
CHAPTER 4
Molecular Characterization and Sequence Diversity of Solute Carrier Family 6 Amino
Acid Transporters in Marine Invertebrates
ABSTRACT
The transport of amino acids and other dissolved organic matter (DOM) from seawater is
important for the development and physiology of many marine invertebrate larvae. Aside from
forming proteins, amino acids are used by marine invertebrate larvae as metabolic substrates to
supplement significant portions of energetic demands, and they also act as neural transmitters,
osmolytes, and signaling molecules. Many amino acid transporters belong to the Na
+
/Cl
-
dependent SoLute Carrier family 6 (SLC6) of transporter proteins which has been well-studied in
humans and model organisms. Previous research has highlighted the importance of DOM
transport in marine organisms, so it is important to understand the genes that may be responsible
for these processes. In the present study, 27 SLC6 amino acid transporter genes were identified
from three genome assembly versions for Crassostrea gigas (Thunberg, 1793). Separately, there
was disagreement between the genome assemblies on the number and sequences of SLC6 genes
in C. gigas. By cross-referencing genomes and by cloning sequences, the total complement of
SLC6 genes could be confirmed. Thirteen full length coding sequences for SLC6 genes were
cloned and sequenced from C. gigas, including one sequence that was not found in one of the
genome assemblies. Phylogenetic comparisons of the cloned sequences showed that all but one
cloned sequence belonged to the amino acid (I) transporter subfamily while one sequence closely
196
matched the inebriated gene subfamily of Drosophila melanogaster. Alignments of cloned
sequences with the bacterial homologue leucine transporter and the human homologue glycine
transporter showed highly conserved sequences at the transmembrane domain sites with a highly
variable extracellular loop 2 with putative substrate binding function. Sequence comparisons
reveal that four cloned sequences may have substrate specificities for glycine while other cloned
sequences may differ in substrate specificity. Cloned sequences matched predicted proteins from
three different genome assembly versions with >99% amino acid identity. However, no single
genome assembly could match all cloned sequences indicating a high degree of sequence
variance (as much as 7% differences in sequence). Results from the present study show that
SLC6 amino acid transporter diversity and gene expansion is higher (27 genes compared to 23)
than previously thought. The large number of amino acid transporter genes suggests that they
play important roles in the physiology of C. gigas.
197
INTRODUCTION
The question of whether aquatic organisms utilized dissolved organic matter from the
environment was posed in a quantitative manner by Pütter (1909) following his calculations that
respiratory rates were better explained by uptake of dissolved organic matter from small volumes
of water rather than solely feeding on plankton through unrealistically large volumes of water.
While the uptake of dissolved organic matter could not be ruled out entirely for many soft-
bodied species, Pütter’s hypothesis was met with skepticism concerning estimations of food
availability and the mechanisms by which dissolved organic matter could cross integumental
tissues (Krogh, 1931). Interest in the ability of aquatic organisms to uptake dissolved organic
matter was renewed when Stephens and Schinske (1961) used a colorimetric method of amino
acid detection to show that species from 35 genera across 11 phyla (excluding arthropods) could
remove glycine from seawater. In marine invertebrates, intracellular free amino acid pools range
from 0.1 – 0.5 M concentrations, and amino acids could be transported across concentration
gradients as high as 10
6
-fold given environmental amounts of amino acids in the tens of
nanomoles liter
−1
to the micromole liter
−1
range for coastal or oceanic waters respectively
(Wright and Secomb, 1986; Lee and Bada, 1977; Braven et al., 1984; reviewed by Stephens
1988). The use of
14
C-labelled amino acids and high-performance liquid chromatography
increased the sensitivity of detection of amino acids and allowed for broader characterization of
amino acid substrates involved in uptake (Manahan et al., 1983). The uptake of amino acids
could account for a significant proportion of metabolic energy depending on age, environment,
and developmental stage (Wright, 1982; Jaeckle and Manahan, 1989; Shilling and Manahan,
1994).
198
Marine invertebrates transport amino acids using homologous protein families shared by
well-studied mammals and even bacteria and plants (Wipf et al., 2002; Boudko et al., 2005;
Yamashita et al., 2005; Meyer and Manahan, 2009; Koito et al., 2010; Bouchard et al., 2019).
This protein family, SoLute Carrier family 6 (SLC6), is defined by the transmembrane transport
of neurotransmitters (including amino acids) along with Na
+
or Cl
-
cotransport. The SLC6 family
can be further divided into four groups in mammals: amino acid transporter (I), amino acid
transporter (II), monoamine, and GABA (γ-Aminobutyric Acid) transporters (Brӧer and Gether,
2012). These protein groups play a large role in the nervous system by transporting
neurotransmitters such as serotonin, dopamine, GABA, and other amino acids. The amino acid
transporter groups I and II co-transport neutral amino acids with sodium, and in mammals, the
two groups are expressed in different tissues. A SLC6 gene was also shown to localize in the
neurons of the jellyfish Cyanea capillata representing an evolutionarily distant neural system
than vertebrates (Bouchard et al., 2019). The SLC6 gene in jellyfish was grouped as a nutrient
amino acid transporter. In insects, the expanded subfamily of nutrient amino acid transporters
was first named for its role in amino acid uptake related to nutrition (Boudko, 2012).
The crystal structure of an SLC6 transporter LeuT found in the bacteria Aquifex aeolicus
has been reported, elucidating the structural mechanisms of amino acid and Na
+
transport
(Yamashita et al., 2005). Since the structure of LeuT was reported, structures from eukaryotes
have also been described for the dopamine transporter DAT from Drosophila melanogaster
(Penmasta et al., 2013), the serotonin transporter hSERT from Homo sapiens (Coleman et al.,
2016), and the glycine transporter GlyT1 (SLC6A9) from H. sapiens (Shahsavar et al., 2021).
The serotonin and dopamine transporters belong to the monoamine subfamily of SLC6 while the
human glycine transporter GlyT1 belongs to the amino acid transporter I subfamily (Bröer and
199
Gether, 2012). Based on analyses of these crystalized structures, several conserved features
common to all SLC6 genes have been described (Yamashita et al., 2005; Penmasta et al., 2013;
Coleman et al., 2016; Shahsavar et al., 2021). The SLC6 protein family typically contains 12
transmembrane domains, two of which are involved in dimerization. The remaining 10
transmembrane domains are two sets of five symmetrically inverted repeats called a LeuT fold
(transmembrane domains 1-5 and transmembrane domains 6-10). Residues on extracellular loops
two and four extend from between transmembrane domains three and four and transmembrane
domains seven and eight respectively. These residues act as binding sites for substrates and may
have different lengths or patterns of glycosylation in different taxa. Sodium ions bind to the 1
st
and 6
th
transmembrane domain causing a receptive extracellular conformation which can bind
substrate (Yamashita et al., 2005; Claxton et al., 2010). In eukaryotes, Cl
-
binding is also
required for the symport of amino acid substrates (Shahsavar et al., 2021). For humans, glycine
is co-transported with two Na
+
ions and one Cl
-
ion. The Cl
-
ion coordinates with conserved
amino acid residues in transmembrane domains 6 and 7. Transmembrane 6 in GlyT1 also
contains an alanine-tryptophan-glycine sequence responsible for binding glycine (Shahsavar et
al., 2021). This three-residue motif varies in sequence to affect substrate selectivity. In
eukaryotes, there is also a conserved asparagine at position 237 (extracellular loop 2) which must
be glycosylated for correct trafficking of the SLC6 protein to the membrane (Shahsavar et al.,
2021). Once substrate is bound, protein conformation changes to an inward facing state to
transport the substrate across the membrane.
While SLC6 transporter function has been extensively studied in mammals or in the
context of neural signaling, the amount of sequence diversity found amongst taxa requires more
tests of function to understand the roles different genes play in physiology. Reverse genetic
200
approaches are taken to prove gene function through direct manipulations of genes and
measurements of changes in their enzymatic properties. In one case, SLC6 transporters have been
functionally characterized in deep-sea mussels which exist in symbiotic relationships with sulfur-
oxidizing bacteria (Inoue et al., 2008). Taurine transporters (TAUT) found in the mussel gills
were cloned and expressed in a heterologous system (oocytes of Xenopus laevis) and the putative
transporter genes shown to transport taurine. This proof of function supports the conclusion that
transport in mussels is related to detoxifying sulfides. The use of heterologous systems allows for
the expression of a foreign gene of interest. Because the gene of interest is foreign to the system,
changes of function due to the expression of the gene are directly attributed to the gene. In a
more generalized case, studies of polar echinoderm larvae and adults identified 11 putative SLC6
transporter genes, some of which were expressed as mRNA throughout life history (Applebaum
et al., 2013). While amino acid transport may play a role in nutritional requirement (Shilling and
Manahan 1994), the function of these 11 putative SLC6 transporters is yet to be tested.
Meanwhile, a comprehensive functional characterization of SLC6 transporters has been
described in the purple urchin Strongylocentrotus purpuratus (Meyer and Manahan, 2009). In
this species, 3 transporters were identified, cloned, and expressed in the oocytes of Xenopus
laevis (a heterologous system) for tests of function. This study allowed for confirmation of
substrate binding as well as the localization of proteins within the epithelial layers of larvae of S.
purpuratus.
More studies characterizing the diversity and function of SLC6 transporters are important
in understanding the mechanisms behind physiological processes in marine organisms. In the
larvae of the Pacific oyster Crassostrea gigas (Thunberg, 1793), physiological amino acid
transport rates have been shown to be strongly predictive of larval growth rates (Pan et al.,
201
2015). Since the publishing of the C. gigas genome (Zhang et al., 2012), 23 putative SLC6
transporters have been identified in this oyster species (Pan et al., 2015). Given the predictive
power of amino acid transport rates on larval growth, studying the substrate specificities, enzyme
kinetics, and localization of the genes underlying the physiology could provide insights into the
mechanisms of growth. In the present study, SLC6 amino acid transporter genes were identified
and analyzed in the genomes of C. gigas and S. purpuratus. Thirteen full length genes were
cloned from C. gigas, and phylogenetic analyses were performed to elucidate the evolutionary
relationships between paralogues and potential implications for the functional diversity of these
genes.
METHODS
Approach and Rationale
Given the degree of gene duplication and genome expansion for the SLC6 gene family
across taxa, genes were cloned from C. gigas in the present study to examine sequence diversity
for future tests of function. Cloned sequences were compared to three different sequencing
projects and genome assemblies for C. gigas (Zhang et al., 2012; Qi et al., 2021; Peñaloza et al.,
2021) as well as SLC6 amino acid transporter genes identified from the genome of S. purpuratus
(GenBank GCA 000002235.4; Ning Li, personal communication, see acknowledgements section
for Chapter 4). The three genome assembly versions for C. gigas are herein referred to as
“Institute of Oceanology v1” (Zhang et al., 2012), “Institute of Oceanology v2” (Qi et al., 2021),
and “Roslin Institute” (Peñaloza et al., 2021). Sequences were analyzed in silico by alignments
and comparisons of conserved amino acid residues in reference transporter genes from A.
202
aeolicus (LeuT; Yamashita et al., 2005) and Homo sapiens (GlyT1, SLC6A9; Shahsavar et al.,
2021).
RNA Extraction and cDNA Synthesis
Total RNA was extracted from larvae, juveniles, and adult ovary tissue of C. gigas using
the TRIzol method (Invitrogen, Carlsbad, CA). Aliquots of samples were centrifuged at 2,000 x
g for 3 minutes at 4°C to remove sea water. Sufficient numbers of larvae were selected based
upon preliminary estimates of the amount of total RNA, with approximately 4 µg of RNA being
used for the protocol described below. Samples were resuspended in 1 ml TRIzol reagent and
lysed in PowerBead tubes (1.4 mm ceramic, Qiagen, Redwood City, CA) using a Tissuelyser
(Qiagen, Redwood City, CA). An aliquot of 200 µl chloroform was added to the lysed tissue
(total volume 1.2 ml) and centrifuged at 12,000 x g for 15 minutes at 4°C. To precipitate the
nucleic acids, the aqueous layer was then combined with 500 µl isopropanol in a nuclease-free
tube, and the supernatant removed after centrifuging at 12,000 x g for 10 minutes. The
precipitate was then washed with 75% ethanol by vortexing and re-centrifuging. Remaining
ethanol was pipetted away and air-dried in a sterile hood. Precipitates were then resuspended in
30 µl of nuclease-free water (ThermoFisher Scientific, Canoga Park, CA). To purify the RNA,
20 µl of resuspended nucleic acids were incubated with RNase-free DNase I (Life Technologies,
Carlsbad, CA) for 1 h at 37°C according to the manufacturer’s instructions. The treated mixture
was then further purified using an RNA Clean and Concentrator spin column kit (Zymo
Research, Irvine, CA). Purified RNA was eluted in 12 µl of nuclease free water, and RNA
quality was confirmed using a Nanodrop (ThermoFisher Scientific, Canoga Park, CA) measuring
260/280 and 260/230 ratios ca. 2.0.
203
First-strand cDNA was synthesized from the purified RNA samples using SuperScript III
Reverse Transcriptase Kit (Life Technologies, Carlsbad, CA) according to the manufacturer’s
instructions. Using reagents from the kit, RNA was mixed with 0.1 M dithiothreitol (DTT),
RNase OUT, reverse transcriptase, and random hexamer primers. Mixtures were cycled at 65°C
for 5 minutes, 25°C for 5 minutes, 50°C for 60 minutes, and 70°C for 15 minutes (C1000
Thermal Cycler; BioRad, Irvine, CA). First-strand cDNA was then treated with E. coli RNase H
to remove remaining RNA template. Concentrations of cDNA were measured using Nanodrop
(ThermoFisher Scientific, Canoga Park, CA), diluted to working amounts, and stored at -20°C
until amplification by polymerase chain reaction (PCR).
Amplification of full length SLC6 genes
The sequences from 23 putative SLC6 amino acid transporter sequences identified from
the genome of C. gigas (Zhang et al., 2012) by Pan et al. (2015) were used to design primers for
polymerase chain reaction (PCR). The 5’ and 3’ untranslated region sequences were targeted for
PCR primer binding to capture the full-length coding sequence. Sequences from the untranslated
regions were also identified using sequences found from the National Center for Biotechnology
Information (NCBI) databases, sequences from OysterDB database, and raw transcriptome
sequence data from the NCBI Sequence Read Archive (SRA) for transcriptomes of C. gigas.
These databases were cross-referenced to determine the most reliable target regions within 5’
and 3’ untranslated regions for primer annealing.
Primer sets were synthesized and obtained from Eurofins Genomics (Louisville, KY) and
tested on cDNA templates from C. gigas at a gradient of annealing temperatures between 45 –
60°C (Table 4.1). Polymerase chain reactions were performed using GoTaq Polymerase and
204
Green GoTaq buffer (Promega, San Francisco, CA). The suggested extension phase length for
the polymerase used was 1 minute for every kilobase of amplicon. Since the full-length target
amplicons were approximately 2 kilobases, the PCR extension phases were two minutes in
duration. In some cases, amplicons were only successfully amplified after repeated PCR
amplification or nested PCR using several combinations of nested primers (Table 4.1).
Successful amplification products were separated by gel electrophoresis (1.5% agarose) in Tris-
acetate-ethylenediaminetetraacetic acid buffer at 70 volts. Gels were stained with ethidium
bromide and imaged under illumination by ultraviolet light (Model Gel Doc XR
+
, BioRad,
Irvine, CA).
Bacterial transformation and cloning of SLC6 plasmid inserts
Bands at the target 2 kb region of gels were excised and purified using a kit (Wizard SV
Gel and PCR Kit, Promega, San Francisco, CA). Isolated PCR products were then resuspended
in nuclease-free water and inserted into pGEM-T vectors at a 1:1 insert:vector ratio. Inserts were
ligated using T4 DNA ligase (Life Technologies, Carlsbad, CA) overnight at 4
o
C and
transformed into competent E. coli (JM109) cells according to the manufacturer’s instructions
(Mix & Go Competent Cells, Zymo Research, Irvine, CA). Cells were then plated onto Lysogeny
Broth (LB) agar plates containing Isopropyl β-D-1-thiogalactopyranoside, 5-Bromo-4-Chloro-3-
Indolyl -D-Galactopyranoside, and ampicillin for blue/white screening. Following overnight
incubation at 37°C, 10 replicate colonies with inserts (white colonies) were selected and
transferred in sterile conditions to 1 ml of LB broth containing ampicillin. These monocultures
were then incubated at 37°C overnight on an orbital shaker.
205
Plasmids were purified from clones using the ZR Plasmid Miniprep Kit (Zymo Research,
Irvine, CA), and successful clones were identified following restriction digests of sites flanking
the pGEM-T insert region (Not I, Nco I restriction enzymes). Colonies with insert fragments of
length ca. 2 kbp visualized on 1.5% agarose gels were used for further Sanger sequencing
(Laragen Sequencing, Culver City, CA). Aliquots of successfully cloned colonies were stored in
40% glycerol at stored at -80°C.
206
Table 4.1. Primer and sequence information for cloning 13 full-length SoLute Carrier family 6 (SLC6) coding sequences from
Crassostrea gigas. Clones with two sets of primers indicate that a nested set was used in amplification. Protein amino acid residue
numbers and predicted transmembrane domains were determined in silico (ExPASy Translate tool; Protter interactive protein feature
visualization, https://wlab.ethz.ch/protter/).
Clone Forward (5' - 3') Reverse (5' - 3')
Annealing
°C
Amplicon
Length
Amino
Acid
Residues
Predicted
TMD
SLC6
group
Clones
Sequenced
1 ATGTTCGTGGTGTCGGTGTT GGCTGAAGTGTGATTCCTATCTG 64.1
1,646
520 10 AAT1 3
2 CTCCAGAAATGAGAGAGATAATCG TCATGAGCTTGGCAAACGCA 60.0
1,995
662 12 AAT1 4
3 GGAACTCATTGGCATTTACT CGTTACTCTGATAACTGCGT 60.0
2,033
618 12 AAT1 4
ACTGAACTCCAAGCGAAGAA TAATGGTGACTTGGGCATAC 53.0
4 ATGCTGCTGCTGCTGGGACT TCTTTGAGGCTGAGCAAATG 64.0
1,812
562 9 AAT1 5
5 CAGTGATACAGGGTACTTGC CCTGTTGCCACACTAGAGAT 60.0
1,920
631 12 AAT1 4
ATTTGGCATCATGGGGGAGA CCAGCCACTCATTGCTAGAT 62.5
6 ATAATGAGTGTTTCAGAGAA GCCCAATCTGACACATATCAC 60.0
2,004
666 12 AAT1 4
ATAATGAGTGTTTCAGAGAA TCATAACTGCTGGCTTGCTG 50.0
7 TGCTGCATATTGCGAAAGAA TTGATCCAATCAATGCGAAG 62.5
2,041
652 12 AAT1 10
8 GAAGAGTCAGAAGATGTTAGC GTGTTGCTCAGAAAGAGTGA 62.5
1,779
580 12 INE 2
9 GATTGAATGTCAGTTCACCT TTATTGGTGACGGTTCCC 59.5
1,945
415 7 AAT1 7
10 ATGGCTGCTTACGAAACG TCACACCCGAATATTGGAAA 59.5
2,007
668 12 AAT1 7
11 GTCCTCGCCAATAAAGAAAC TATCCGACTTGTAGGCGTAG 55.9
2,177
678 12 AAT1 2
12 CGAAACGTAAACACTAATCTGTGC GTTCCGCACCGGAATATTGT 40.0
2,264
669 12 AAT1 9
TCTGTGCTGTGTTACACCAGTT ACCGGAATATTGTCCAGCTT 59.5
13 ATGACTACCGGCCAAAAGGG TCTAACTTGAACACAGAAAAACAGC 60.0
1,965
637 12 AAT1 9
207
Sequence analyses of cloned SLC6 gene inserts
Full-length genes successfully inserted into pGEM-T vectors were isolated from
transformed colonies following the method described above. Plasmids were then sequenced from
multiple isolated clones using the Sanger method (Laragen Sequencing, Culver City, CA) using
T7 and SP6 primer sites flanking the insert region (see Table 4.1 for number of clones). Because
full-length sequences often exceeded the reliable sequencing range of the Sanger method (>1,000
nucleotides; Laragen Sequencing, Culver City, CA), an additional internal primer was also used
to verify sequences towards the center of the sequence (Table 4.2). The internal sequencing
primer was not required for Clone 1 which was only 1,646 nucleotides in length. For Clone 1,
sequencing from each end was enough to achieve full coverage of the amplicon. Figure 4.1
provides an example of the sequence assembly for Clone 10, which was chosen as an example
since it corresponds to a highly expressed gene (EKC35081; Zhang et al., 2012).
All sequence data from clones were assembled and aligned using the default assembly
tools in DNA Baser software (v5.15.0.0BT). Contigs were further analyzed by translating the
coding sequence in silico (Expasy translate tool) and protein basic local alignment search tool
(BLAST) of the resulting translation. Protein sequences were aligned with the bacterial
homologue LeuT from Aquifex aeolicus (accession 2A65), and the human homologue GlyT1
(SLC6A9) for which the crystal structures are known (Yamashita et al., 2005; Shahsavar et al.,
2021). Comparisons with these reference sequences allow for the identification of conserved
residue positions with known function (e.g., Na
+
or Cl
-
binding). Alignment was performed in
MEGA X (v10.2.2) using the ClustalW algorithm (Kumar et al., 2018), and the alignment was
visualized using BoxShade online tool (v3.21; https://embnet.vital-
it.ch/software/BOX_form.html) to highlight regions of >50% sequence agreement. Annotation of
208
transmembrane domains and substrate-binding extracellular loops were made based on the
alignment positions of the human SLC6A9 sequence (Shahsavar et al., 2021). Transmembrane
domains were also independently verified using the online Protter interactive protein feature
visualization (https://wlab.ethz.ch/protter/). This tool uses the Phobius (Stockholm
Bioinformatics Centre) transmembrane topology prediction method (Käll et al., 2004; Käll et al.,
2007).
209
Table 4.2. Sequencing primers targeting sequences within the coding sequence of cloned SoLute
Carrier family 6 (SLC6) genes. Since cloned sequences exceeded the 1,000 bp reliability of
Sanger Sequencing (Laragen Sequencing, Culver City, CA), primers were designed to target the
middle of the coding sequence. Clone 1 did not require an internal sequencing primer due to its
shorter length (1,646 bp).
Clone Internal Sequencing Primer (5' - 3') Direction
1 Not applicable
2 CCTCCTTGGCTGAATCTGTG Reverse
3 ACTGAACTCCAAGCGAAGAA Forward
GCAAGGTTGTCTATGTCACGG Reverse
4 AGTCATCATCGTCTCCAGCA Reverse
5 CCTGTGGATACTCGTCTTTCTC Forward
6 AATGTAGCACAGTACCAGTC Reverse
7 GTGCAGCCCCATCTTATTGT Forward
TGAGGCTGCATACCAATCAA Reverse
8 TGACTTCAAAGAATGCCAAG Reverse
9 CTGACTGTCCACGCCAACTG Reverse
10 GTGACACAGGGCATCTGCTA Forward
ACAGGTCATGACGGTGAACA Reverse
11 GTCCTCGCCAATAAAGAAAC Forward
AGTACAATGCTACCACTGAC Forward
12 CAGCACGGAAGAGGATTCTC Reverse
13 CTTCCACTGTCGCAAACTGA Reverse
210
Figure 4.1. Sanger sequencing of an example cloned SoLute Carrier family 6 (SLC6) gene insert
in pGEM-T vector. Clone 10 was chosen as an example since it corresponds to a highly
expressed gene (EKC35081; Zhang et al., 2012). Sequencing primers, T7, SP6, and internal
primers were used to sequence the full length of the coding sequence (2,218 bp). Sequenced
segments were assembled using DNA Baser (v5.15.0.0BT) and aligned to a reference sequence.
Consensus contigs were determined based on the majority nucleotide at each position. Letters
(‘a’ – ‘g’) indicate different isolated clones and start sites, where each row represents the
sequence obtained using T7, SP6, or different internal sequencing primers.
211
Genomic and phylogenetic analyses
Cloned sequences were compared to SLC6 amino acid transporter genes identified from
three genomes of C. gigas: (1) Institute of Oceanology v1 (Zhang et al., 2012), (2) Institute of
Oceanology v2 (Qi et al., 2021), and (3) Roslin Institute (Peñaloza et al., 2021). Since the
Institute of Oceanology v1 genome was the earliest, the 23 putative SLC6 amino acid
transporters identified by Pan et al. (2015) from that genome were queried against the later
assemblies (translated BLAST; tBLASTn tool) to identify gene identities. The genomic locus
with the highest query coverage and percent identity was selected for examination of predicted
genes. For each predicted gene, the pseudo-chromosome number or scaffold, gene positions,
number of exons, predicted transcripts, predicted proteins, and lengths of coding sequences and
proteins were recorded. The number of transmembrane domains was predicted for each predicted
protein sequence using Protter interactive protein feature visualization. Two genes identified
from the Roslin Institute assembly were predicted to have only 2 – 3 transmembrane domains
and one gene was predicted to have 26 transmembrane domains (cf. 12 transmembrane domains
typical of SLC6 amino acid transporters), so they were not considered to be annotated correctly
as SLC6 amino acid transporter genes. Five genes from the Institute of Oceanology v2 assembly
were also eliminated for the same reasons. With 2 – 3 or 26 transmembrane domains, the LeuT
fold structure (inverted two-fold symmetry of transmembranes 1-5 with transmembranes 6-10)
characteristic of SLC6 genes is not possible (Yamashita et al., 2005; Shahsavar et al., 2021).
Protein sequences of the 13 cloned genes and the genes identified from the three genome
assemblies of C. gigas were compared with SLC6 genes from Strongylocentrotus purpuratus,
Drosophila melanogaster and Homo sapiens. SLC6 genes for S. purpuratus were identified with
the assistance of Dr. Ning Li (personal communication, see acknowledgements section of
212
Chapter 4). Briefly, Sp-AAT1-3 sequences cloned by Meyer and Manahan (2009) and SLC6
genes from C. gigas were queried against the genome (GenBank GCA_000002235.4) of S.
purpuratus using BLAST. Twenty-two SLC6 amino acid transporter genes were identified for S.
purpuratus. Twenty-one proteins were used from D. melanogaster representing all subfamilies
of SLC6 (Supplemental Table S1 from Thimgan et al., 2006). Nineteen SLC6 protein sequences
were used from H. sapiens representing all subfamilies of SLC6 (Supplemental Data from Ren et
al., 2019). Protein alignment was performed in MEGA X software (v10.2.2) using the ClustalW
alignment algorithm (Kumar et al., 2018).
For phylogenetic analyses, the protein alignment was trimmed at beginning and ending
positions represented by only one protein sequence. A total of 145 sequences were used to build
a phylogenetic tree using the neighbor-joining method (Saitou and Nei, 1987). Pairwise deletion
of ambiguous positions was performed resulting in a total of 1,195 positions. Protein distances
were calculated using the Jones-Taylor-Thornton matrix-based method (Jones et al., 1992). The
optimal tree was determined using the bootstrap test with 1,000 replicates (Felsenstein, 1985).
The subfamilies of SLC6 (i.e., GABA, Inebriated, Monoamine, Amino Acid Transporter I,
Amino Acid Transporter II) were annotated based on groupings with 20 human reference genes,
four of which belong to the Amino Acid Transporter II subfamily (Bröer and Gether, 2012;
Perland and Fredriksson, 2017). Reference sequences from D. melanogaster were included to
represent the inebriated subfamily of SLC6 genes found in invertebrates (Thimgan et al., 2006;
Borycz et al., 2018).
213
RESULTS
Sequence analyses of cloned SLC6 genes
Primer sets for 13 SLC6 genes of C. gigas were successful in amplifying full-length
products ~2 kb in length (Table 4.1). Following cloning and sequencing of the 13 full-length
genes, Clones 1, 4, and 9 were predicted to have 10, 9, and 7 transmembrane domains while the
remaining cloned sequences were predicted to have 12 predicted transmembrane domains (Table
4.1). An example transmembrane secondary structure of Clone 10 shows the characteristic 12
transmembrane domain structure (Fig. 4.2). Clone 10 was chosen as an example because it
represents a highly expressed gene in C. gigas (EKC35081; Zhang et al., 2012). Based on these
predicted conformations (Fig. 4.2), the N- and C- terminals are located intracellularly in the
cytoplasm with extracellular loops 2 and 4 being the longest extracellular chains. Glycosylation
sites in extracellular loop 2, responsible for membrane-based ligand binding and transporter
trafficking, were also seen in predictions consistent to the crystalized structure of GlyT1
(SLC6A9) in humans (Shahsavar et al., 2021).
Alignment of the 13 cloned SLC6 proteins with references from the bacterial homologue
LeuT of A. aeolicus (Yamashita et al., 2005) and human GlyT1 (SLC6A9; Shahsavar et al., 2021)
sequences allowed for the identification of conserved residues (Fig. 4.3). Cloned genes had
diverse protein sequences sharing between 13 and 26% identity with LeuT, and between 20 and
46% identity with human GlyT1. Most of the conserved sequences were in transmembrane
domain regions while there was little sequence agreement at the N- and C- terminal regions and
extracellular loop two (Fig. 4.3). Conserved residues for putative substrate and ion binding in
extracellular loops and transmembrane domains were annotated based on the protein alignment.
214
Extracellular loop 4 was highly conserved with >50% sequence agreement at all but 4 out of 35
positions. The other putative substrate-binding region, extracellular loop 2, was highly variable.
In this region, only 30 out of 105 positions had >50% sequence agreement. The high sequence
variability in extracellular loop 2, a putative substrate-binding region, suggests functional
differences in substrate binding. The alanine-tryptophan-glycine (AWG) sequence of GlyT1
located in transmembrane domain 6 is responsible for glycine specificity (Shahsavar et al.,
2021). Cloned sequence residues aligning with this motif showed variation in sequence with
sequences of (single letter codes for amino acid residues): CWG, CSG, VWG, VWT, AFG,
SWG, AGG, CSG, and GWG. Clones 4, 6, 7, and 9 shared the AWG motif found in the human
glycine transporter GlyT1 (Fig. 4.3).
215
Figure 4.2: Example transmembrane structure of a cloned SoLute Carrier family 6 (SLC6) amino
acid transporter gene from Crassostrea gigas (Clone 10, EKC35081.1 of Institute of Oceanology
v1 assembly, g27427 of Institute of Oceanology v2 assembly, LOC105321334 of Roslin Institute
assembly). Clone 10 was chosen as an example because it corresponds to a highly expressed
gene (Zhang et al., 2012). The representative structure consists of 12 transmembrane domains
with intracellular N- and C-terminals (cis-conformation). Extracellular loops 2 and 4 are putative
substrate-binding domains based on the crystal structure of LeuT Aa (Yamashita et al., 2005).
216
Figure 4.3 (continued below). Protein alignment of 13 cloned SoLute Carrier family 6 (SLC6)
genes from Crassostrea gigas with the reference leucine transporter of Aquifex aeolicus
(Aa_LeuT) and reference glycine transporter of Homo sapiens (Hs_SLC6A9; GlyT1). Alignments
and visualizations were created using the ClustalW algorithm (MEGA X v10.2.2) and BoxShade
online tool (v3.21). Shading represents >50% sequence agreement (dark: identical; light:
similar). Transmembrane domains (TMDs) and extracellular loops (EL) are based on the
reference crystal structure of Aa_LeuT (Yamashita et al., 2005). Symbols above sequences are
based on reference LeuT annotation (Yamashita et al., 2005), and symbols below sequences are
based on SLC6A9 reference annotations (Shahsavar et al., 2021). Filled red triangles indicate
binding site of a sodium ion (Na1), open red triangles indicate a second binding site (Na2), blue
triangles indicate Cl
-
binding (Hs_SLC6A9), filled circles indicate leucine binding (Aa_LeuT),
open circles represent glycine binding (Hs_SLC6A9), open stars represent extracellular positive
charges, and filled stars represent cytoplasmic positive charge.
217
Figure 4.3 (continued)
Aa_LeuT 1 -----------------------------------------------------------------------------------------------MEVKREHWATRLGLILAMAGNAVGL
Hs_SLC6A9 1 MSGGDTRAAIARPRMAAAHGPVAPSSPEQVTLLPVQRSFFLPPFSGATPSTSLAESVLKVWHGAYNSGLLPQLMAQHSLAMAQNGAVPSEATKRDQNLKRGNWGNQIEFVLTSVGYAVGL
Clone_1 1 ------------------------------------------------------------------------------------------------------------------------
Clone_2 1 --------------------------------MREIIDPSADQGAIEPTCRITSADGSESEYMFDEDADEVQVVAIKEIAEGDDVSQRSTESSGSVEDDRAQWGGKLEFLLTCIGYAVGL
Clone_3 1 ------------------------------------------------------------MELKEDHEKIT------LSEDTESEETE----------EREKWDKKAESILSMPGFCVGL
Clone_4 1 ------------------------------------------------------------------------------------------------------------------------
Clone_5 1 ---------------------------------------------------------------------------------------------------------MGETFAQWNLLQLSL
Clone_6 1 ------------------------------------------------MSVSEKNVFTGNQEYLKEKELALE-----ETESSSAGSSQSSLESGDENVGRGNWSGRLDFLLSCVGFAVGL
Clone_7 1 -----------------------------------------------MGGIRTDQVLCIGLKMRKTNGLAKQPPMEMQE-EGKLEESDSSSSSSEGDYHRGTWSHKLDFLLSVIGYSVGV
Clone_8 1 ------------------------------------------------------------------------------------------------MLARQNWSKNVEFLLAAIGYCVGL
Clone_9 1 -----------------------------------------------MS-VHLDRFKSIGLIMSKETNVS----MDVLENQSSLLDSESDSS---GLQERAVWGRKIEYLLSLFGYSVGL
Clone_10 1 -----------------------------------------------------------------------MAAYETDVETHKAKLADRDDESGDENTERGNWSSKLDFLLSCLGYAVGL
Clone_11 1 ------------------------MPGLQRKEKWESSMSDITGDTASVSIPPDLLTNNIDPGVYRKGPRASNGASQVTIATEETEDSRKQFFGKDENEERGNWTGRFDFLLSLLGYAVGL
Clone_12 1 ---------------------------------------------------------------------------------------------MGEEAKRAGWSNQVEFILTLIGYAVGL
Clone_13 1 -----------------------------------------------------MTTGQKGMEMYAECAVEEP-----LSDDQSSDDIESDQP----EVERGRWSAKLDYMLSMIGYCVGL
Aa_LeuT 26 GNFLRFPVQAAENGGGAFMIPYIIAFLLVGIPLMWIEWAMGRYGGAQGHGTTPAIFYLLWRNRFAKILGVFGLWIPLVVAIYYV-YIESWTLGFAIKFLVGLVPEPPPNATDPD------
Hs_SLC6A9 121 GNVWRFPYLCYRNGGGAFMFPYFIMLIFCGIPLFFMELSFGQFASQG------CLG-VWRISPMFKGVGYGMMVVSTYIGIYYN-VVICIAFYYFFSSMTHVLPWAYCNNPWNTHDCAG-
Clone_1 1 -----------------------------------------------------------------------MFVVSVFICIYYN-MIIAYTLYYLFASFAANLPWAECG-EWATEACSTE
Clone_2 89 GNVWRFPYLCYKNGGGAFLIPYTIMLALVGLPLFYMEVVLGQYASLG------PISIWR-INPLFKGVGYAMVIVSWLIGLYYN-VIIAHVLFYLFASFTSELPWKHCNNEWNTPSCREY
Clone_3 45 GNIWRFPYLCMRNGGGAFLIPFLFFLFFCGLPLYTLEVTVGQFSGKG------IVK-VWDVCPIFRGVGFGISILSTISCSYYI-IIISWTTFYLINSFKSPLPWTLCLEDWNTPFCRKK
Clone_4 1 ------------------------MLLLLGLPLMFLELALGQYAALG------PSVVFDRLCPLFHGMGYGMISVSGIVSLYYT-VIIAWCILYLFTSFTSELPWEKCHPEWASEHCY--
Clone_5 16 IAVFPAYLAYIRTVDGMALISLYVYLFVLCMPAMLIQMKLDGYNQKG------IVGLLSQHLPISKGVGVALLVDLFLTCLYVAPLVCHFGMYAILSMMEQPYVWSSCGNEWNTENCVDI
Clone_6 68 GNIWRFPYLCYQSGGGAFLIPYVIFLFLCGVPLFFLEISYGQFASLS------PIT-VWKISPLFKGVGYGMIIISGIVCVYYN-IIITWTIYFLYHSFKAVLPWSTCGNPWNTEKCYIR
Clone_7 73 SNIWRFPYLCIRNGGGAFLIPFFFFLIFCGIPLYFLELCLGQFSGVS------SIF-VWKLCPLFKGLGFLMVTVSAMMSWYYI-TVLAWVLYYLVNSFYHPLPWSDCSESWNTAHCIES
Clone_8 25 GNVWRFPYLCYSSGGGAFLVPFFLMLILCAVPLLYMELAVGQYTQNG------PVGALAKLCPLFKGAGLATVVISFLFTTYYN-VIIAWAFYYLFHCFQSVLPWTACDHDWNSPNCWD-
Clone_9 66 GNIWRFPYLCMRNGGGAFLIPFFILLIFCGVPLYFLEVSLGQFTGKS------PVI-VWSISPLFKGLGWLMMTISFVVAWYYN-TVIAWVIYYFVHAFLPKIPWSTCDNWWNTDHCIVS
Clone_10 50 GNVWRFPYLCYRNGGGAFFIPYCIALAFLGIPIFLLELAIGQYSSAG------PFTCWK-YSPIFTGIGYGMFIVSALVAIYYN-MIIAWAFFYLFASFTDELPWQSCG-DWSTNLCYDN
Clone_11 97 GNVWRFPYLCYRNGGGAFLIPFVLMMILVGVPLFFMEAALGQFCSSG------PMTCWR-FAPLFKGVGIAMVAVSALTSLYYN-MILAWSYYYFFASFTSDLPWVSCDNSWNTRDCS--
Clone_12 28 GNVWRFPYLAYKNGGGAFLIPYFVSLALIGVPLFFLELSFGQFASLG------PIKIWI-VNPAFKGLGFAMTIVSALIALYYN-VVIAWCLYYLFASMTSYLPWQDCDNEWNTCSCADA
Clone_13 59 GNIWRFPYLCMRNGGGAFLIPFLFFLLAAGLPLYFMEVSLGQFTGRG------CFH-VWEASPIFKGIGVGMFNVLFVVTLYYN-IINTWTLYYLGSSFISPLPWTGCDNDWNTPRCFHR
TMD 1
TMD 1 TMD 2 TMD 3
218
Figure 4.3 (continued)
Aa_LeuT 139 -------------------------------------------------------SILRPFKEFLYSYIGVP---KGDEPILKPSLFAYIVFLITMFINVSILIRGISKGIERFAKIAMP
Hs_SLC6A9 232 -VLDASNLTNGSR--------------------------PAALPSNLSHLLNHSLQRTSPSEEYWRLYVLKL--SDDIGNFGEVRLPLLGCLGVSWLVVFLCLIRGV-KSSGKVVYFTAT
Clone_1 48 TKVQILKCKRYNG---------TWCDN-QCVNATAVDFSYMNCTFYNGTS-----KLKSPSDDYFHQSVLDI--TDGIHTIGGIKWELVGCLLLAWVLVCICLAKGI-KTSGKAVYFTAF
Clone_2 201 NYQPPSALGNGTYN-------STAWNATYVVNTTFTHNSTYSPIIHHSLI-------TTPSEEYYNNHVLGK--SSGLDEIGGVQPYLALTLLASWVTVYLVLLKGI-QSLGKVVYFTAI
Clone_3 157 NHGPAGESVNGTQ--------YNVT------SSG-----YSNTSITLRNQSE--LFSTP-EEEFWQGEVLHL--SGGLESIGQIQWHLALCFLAAWVMVYLCLVKGV-KTVGKVVYVTAT
Clone_4 88 SFKEADLCHQVPG---------QVYYKQVCYNATVSLERNLSLLAQNVTR-------HPPAQDFFERGVLDQ--SNGIENIGVPQWKITLCLLCAWLMTFGALSRGV-KSTGKVVYFTAL
Clone_5 130 HQKVQAAGGNSEK--------------------TYTINNGVAQNLRVMPELEFYHNAYLGLSDSIGNVTGFPVWNFTTQLQNVGISLLPVTLAVLWILVFLFTAFGARVSGWILFVLGLA
Clone_6 180 GEINLTQVDNATAN---GTIGYNVNTTSLLNLSDVTVAFVGGTSNTSKSDLVNLTNKVTASEEFWQNEVLQI--TDGIEDLGTIRWELLICLAIAWIVVFLCLCKGI-KSSGRVVYVTAT
Clone_7 185 RIKDFNNVTNTIV--------QNIT------SNSSS---LYNISLASETGIK--KNATTAAKEFWQRRVLRI--SEGFDEAGSVEPHLIVGLLVAWVLVFLCLMKGV-KSVGKVVYVTAV
Clone_8 137 -GTATQNYTN-----------------------------STTNTSYLGPAMKPN-GTISPTEDFYQSYVLER--SSGIEETGRLKWELALILLMCWVIVYFCIWKGP-KSTGKVVYFTAT
Clone_9 178 HKDRLTLNKTAVN--------QNLTGLLFNETDASY---QYNSTHDNHS-----LTYNTAAHEFWQFNVLRR--SEGIEHPGSVQWHLVLVLFASWVLTFCCLIKGV-RSVGKVVYVTVI
Clone_10 161 VKILNKTWESCNGNNRWGKVWEANVTQGICYETGMLNKKEGVRAYLDADKAKDYFPRTLPSQEYFDNYVTGSGYSSGYHDLGGVRWQLALCYLLAFICVILALSKSI-KTSGKVVYFTAT
Clone_11 207 TMLPLTDCADSVG---------QKYDNGTCLNGDTFVGLWDVKKFTSATGR----KRKLASEEYWEKIALDQ--SSGIEDFGQPKWDLVLCLMLAWIVCFLCLIKGI-KSTGKVVYFTAV
Clone_12 140 ---------------------TTNFSSPDPWNGRRPDCLADLASIKNGSK-------NTPSSEYFTRKVLGV--TDSWTDPGGLKWDVTLCNLLAWIIVFLVLSKGI-KSLGKVVYFTAT
Clone_13 171 SSIMTQRIDNDTG--------YDLF------NASSTILQDQNATVSAAIKAS--SNSTTSQEEFWFHRVLTI--SRGLDEVGGLNWQLTLCFLGAWLAVFLCLIKGV-KSLGKVVYVTAT
Aa_LeuT 201 TLFILAVFLVIRVFLLETPNGTAADGLNFLWTP-DFEKLKDPGVWIAAVGQIFFTLS---LGFGAIITYASYVRKDQDIVLSGLTAATLNEKAEVILGGSISIPAAVAFFGVANAVAIAK
Hs_SLC6A9 322 FPYVVLTILFVRGVTLEG----AFDGIMYYLTP-QWDKILEAKVWGDAASQIFYSLG---CAWGGLITMASYNKFHNNCYRDSVIISITNCATSVYAG--FVIFSILGFMANHLGVDVSR
Clone_1 150 FPYVVLLILLIRGLTLPG----AMNGIVYYLTP-QWDKLGRAQVWGDAAVQIFFSLS---PCWGGLITLASYNKFNNNALMDAIIVSVLDSVTSVFAG--LVIFSIIGYMAEVLEQPIKD
Clone_2 304 FPYLMLIVLMFRGVTLPG----AVDGMIYYLKP-DFNKLLEPRVWSDACTQIFYSLS---ACSGGLIAMSSYNKFKNNCYKDAVIVCVINCGTSVFAG--FVIFSILGFMANEKNVPVSE
Clone_3 252 LPYILLLVILIRGLXLPX----SLDGVLFYITP-DFERLKDGKVWAEAAIQIFYSSG---IVWGALITMASYNKFHNKCLRDCYALVLAGEGTSIFGG--FVVFSVLGYMAHENGVSIEK
Clone_4 189 FPYVVLVILFFRGVTLPG----AADGIIYYLTP-RFDKLGEAKVWSDAAAQIFFALS---PAWGGLITLSSYNQFHNNCFKDSLIVGIGNICTSIFAG--FVIFSIIGYLAHDLQMPIDK
Clone_5 230 FVGMLLAVLGYGYNSLDSDSSNSFLLTFYNLNFRGFLQPVDTRVLINTASEGFDLLMNSLPVWTAIPVTMGKFTGQGKISRNLGWLLVIVTYALIIQVPQLAMAPYIGNLLTKVKDTKVL
Clone_6 294 FPYLVLTILLIRGVTLPG----AGAGIYFYLVP-EWEKLLTFKVWGDAAVQIFYSVG---MAWGGLITMASYNKFNNNCYRDAMIVPLINCGTSVFAG--LVIFSVLGFMSHETGIGIKN
Clone_7 283 LPYILLTTFLIRGLMLEG----SMEGIRHYLTP-DFSRLTDFQVWLEAGIQVFYSLG---PAWGGLITMSSYNKFNNNCFRDAIIGSLADGLTSFYAG--FVIFALLGHLAAMTGLKISD
Clone_8 223 FPYVVLVVLLVRGVTLPG----SLNGILFFIRP-KWELLLDPKVWVNAAAQNFNSIG---IAFGGIITMSSYNKFNNRIIKDVLVIAVVDAITCLLGG--FAIFSILGNLAENQGKDVGD
Clone_9 279 FPYILLTVILIRGLTLDG----AIDGIIFYLKP-DFTKVFNFQVWMEAGMQVFFSLG---PAWGGLITMSSYNRFNNKCMKDAWYGTLADGLTSFYAG--FVVFSILGFMAKDAGLTMEE
Clone_10 280 FPYIVLVILFFRGLMLEG----MEEGIKFYITP-DLKRLSDAQVWKDAAVQIFFSLS---ASWGGLIALASYNKFHNDLLRDTLIVTFGNCLTSVFAG--FVIFSYLGYLSTYMGLPVDE
Clone_11 311 FPYVVLLILFFNGIFLSN----AGEGIYFYIVP-DFSRLLDAGVWKDAAVQIFFSMS---IAGGGLVTLSSYNRFHNNILLDSIIVSIGDTVTCIFAG--FVIFSYLGHMAGELNVKVED
Clone_12 229 FPYVLLTVLLVRGLTLEG----SHEGVMYYLTP-NWERLSDASVWSDAAVQIFYSLS---ACSGGLIAMASYNKFNNNVLRDSLVVPLINCLTSFYAG--FVIFSVLGFMANYKHDTMEH
Clone_13 272 LPYLLLAVILIKGLTLPG----AVDGILFYIRP-DFNKLANVQVWLEAGLQVFYSLG---PGWGPLITMASYNKFRNNCYRDAVTLTFISEGTSIFGG--FAIFTIVGFMAHQAGKPVDE
TMD 4 TMD 5
TMD 5
EL2 (Substrate)
TMD 6 TMD 7
219
Figure 4.3 (continued)
Aa_LeuT 317 AGA----FNLGFITLPAIFSQTAGGTFLGFLWFFLLFFAGLTSSIAIMQPMIAFLEDELKLS-------RK---HAVLWTAAIVFFSAHLVMFLNKS---LDEMDFWAGTIGVVFFGLTE
Hs_SLC6A9 432 VAD--HGPGLAFVAYPEALTLLPISPLWSLLFFFMLILLGLGTQFCLLETLVTAIVDEVG---NEWILQKK---TYVTLGVAVAGFLLGIPLTSQAGIYWLLLMDNYAASFSLVVISCIM
Clone_1 260 VAT--EGAGLAFVAYPDVVTKLPLSQLWSVLFFAMLITLGLETQIATATTVHTTLLDQFPQ--CRKGYRKT---ILL-VVIAVVCLLIGLIFCSQGGMYMLQLFDNYAATYSLLFIGTIE
Clone_2 414 VAD--GGPGSAFIVYPEALTRMPIAPLWSILFFIMMATLGFGSEFSIVECVLSALTDVFPQ--IQPRRAN----IIFRSVFTAICFLLGLPMVCKGGIYLLNLVDFSVGGFPLLIVGLFE
Clone_3 362 VVK--SGPGLGFIAYPEALAKLPLPNLWAVLFFIMLLTVGLDSTFGTIEPVFTALSDSFR-----IWRKRR---ALLTALFSAVCFLVGLIMCTEGGMYVFQLIDWYSAAIAVPLFGFLE
Clone_4 299 VVD--QGAGLAFIVYPDVVTRLPISPLWSILFFVMMITLGMGSEFALLETMMTAVQDTFPQ--LRA--KKT---YVV-AVVCLIGFLGGLSVTCNGGMYILQLMDNYVSSWSVFLMAGLE
Clone_5 350 REGSLNGIQLVFNVMPAAFAELEIPPVYAMLFFLSLFICGFMFLCVAMLTIVDNIVDSLTTRFVRLHDRKHCCTFVTTFFLMVALICMGLLMTTTAGFYYVILLDQSVTRLRFITVFILA
Clone_6 404 VVT--QGPGLTFVAYPEAVARLPISPLWAVLFFLMLFTIGLDSQFGMFETMTSAFVDEFP----HLLKNRK---VLFTAFFCFIEFLLGIPCIMEGGIYVLQIMDWYCATFSLMLLSLTE
Clone_7 393 MAD--SGPGLALVVYPEAMLTLPFPHLWAVLFFLMLLTLGIDSQFGTFETVSSGLADAFPQ--YFSGGKRK---TLLTAALCAVLFVLGIPFSTNGGMYYFQIVDWYAASLCVTLTSFLE
Clone_8 333 VIQ--QGPGLVFVIYPEAFTTMPVPQLFAAVFFFMLINLGIDSQFASTEVIVTTINDHFHPQVKKYLKRKE---VLVAVVCALS-FLCGLPNVTQGGYYFFSLIDHYAAAVSLMYLAFFE
Clone_9 389 ISKSASGPGLVFVAYPEALTKLPMPHLH--------------------------------------------------------------------------------------------
Clone_10 390 VAK--SGPSLTFVVYPFAVTKMPISPLWAILFFVMLITLGVDSEFVLVETVITSLMDRFPK--LRK--YKL---FTV-MTCCLLFFLLGLTLTTDGGIYMLSIMDTYSGGWSILFIAIFE
Clone_11 421 VAQ--SGAGLAFVVYPTAVASLPASPFWSALFFFMLITLGLDSQFAMLETVLTGVMDQYPT--LRP--HKT---LVI-LAISIVFFIIGLPLCAPGGVYLVQILDNYVGGWTLLIIGFAE
Clone_12 339 VAK--GGPGLTFIVYPEALAQMPVAPLWAILFFFMMATLGFSSQFSITETVITGVMDEFPG--FFSSRVKR---ILFRAGVCAVAFILGLPMTCRGGEYTLNLMDTFVGGFPLLFVGLFE
Clone_13 382 VVQ--AGPGLAYLVYPEALSQLPFPNMWAVVFFVMLFTVGLDSQFATVEACMTAVSDIVP-----SMRRRK---ALLAFLTCSFFVLTGLILCTECGLYIFQLLDWYIAAFSLPVFALLE
Aa_LeuT 420 LIIFFWIFGADKAWEEINRGGIIKVP---------RIYYYVMRYITPAFLAVLLVVWAREYIPKIMEETHWTVWITRFYIIGLFLFLTFLVFLAERRRNHESAG------TLVPR-----
Hs_SLC6A9 544 CVAIMYIYGHRNYFQDIQMMLGFPPP---------LFFQICWRFVSPAIIFFILVFTVIQYQPITYNHYQYPGWAVAIGFLMALSSVLCIPLYAMFRLCRTDGD---TLLQRLKNATKPS
Clone_1 372 CIGIAWVYGNDRFLTDIELMLGS---------RPSNIWKYSWKFVAPVALLAILLFTAIDFKSTEYNKMKFPGWADGVGSLISISSISMIPIVAIIMVIRERRKFSGTFMELIKYLSKPL
Clone_2 526 LVAISWIYGYNRFSDNIFAMLGKRP----TKYWEICWKFVSLLVIGITVLMNIIMY----TEPELDG-QTYSDWAKSLGWLIVAFPIVVIPLWFLLRYCSDG------GWKLLREGVKPL
Clone_3 472 CIAFGWIYGAEMFSRDVRMMTGQGIP---------PLIRICRCFITPTILLIIMIVTLNSYVPPEYGSYKYPPWAQVFGWFVAVIPLIPIPVFAYREIKIANGR---TYMEKLKNSLRPX
Clone_4 409 SVTIGWIYGGDRFISDIEMMIGVRG------NAFHNFFKLFWKFLSPMTLAFVFVFNLIQYKPLEFGDYIYPMWSNGIGWILSLVPIIFISTIAITKILKGPKEMS--IMERVRFLLKPS
Clone_5 470 VSVIIVYVKHTFSIVER-----------------ILISIWCILAATATAGFWLYSFIMYLGFPLSYNGHEYTKVFDLISWIISAVPYIAIPAAAMHSCGLFEGTCKERMQYMFCGVQDEP
Clone_6 515 CVVIAWIYGADRFYKDIELMIGYQPG---------VWWKICWKYITPATITFVWLFSVTQLSPVTYGDYEYPDGAIVFGWMLGLASIVPVPVCAIIAILGEKG----SFVQRIKKLVHHT
Clone_7 506 CVIVGWIYGADRFSKDVKLMLGTEVN---------VVIRFCWCIVTPLAMLAAFILTLTNYQPPTYEGYTYPVHARVIGFLLSLVPLIPLPVTAVYYFRKVDG----TFLQRLHKLTQPS
Clone_8 447 VIAITWFYGARRLGRNIKEMNGSTPN---------IFFIVCWYFISPLFIFGIWLFSMISYRPFQLDGYDYPVWATVLGWFIAALSVLCVPIGMVHSIYQAKGN---NLWQKLKNS----
Clone_9 ------------------------------------------------------------------------------------------------------------------------
Clone_10 500 CISVGWVYGIFRFLDDIKLMTGNTFCCCVPFIAFKYWWMLCWCFITPLLAAIIMVFSWVDYTGMDNG-AEYDVYADLMGWGMTLVTIVCIVGTAIYVICKT---------GGIKEAMKPN
Clone_11 531 VTCITYVYGVNRFIRDIEIMLGK---------KMIIWWKICWMVISPIAILLIFIATFVEYEASTYGDYTFPAWADALGWIMAVAIILAIPITMLYQVNKEDEVTS--FWGKIKLQCIPS
Clone_12 452 IIAIIYVYGFFRFKRDIELMLGSNI----VISAFFLYFGATWLLISPVALLAVIIFKAIQTKPATDGSYVYPEGGQALGWLIVLAPLIWIPVIFFFEAFRNG------LGKLLKKINSPE
Clone_13 492 CIVFGWIYGADNLSRDIEMMLGRGVP---------VYMRIMWCIVTPVILTLLLISTIYTYRPPMVGDYTYPLYARAIGWLVAALPLIPIPVCALHAVRKSPGN---TWIQRFYSSFQPS
TMD 8 TMD 9 TMD 10
TMD 11 TMD 12
EL4 (Substrate)
220
Figure 4.3 (continued)
Aa_LeuT ------------------------------------------------------------------------------------------------------------------------
Hs_SLC6A9 652 RDWGPALLEHRTGRYAPTIAPSPED--------GFEVQPLHPDKAQIPIVGSNGSSRLQDSRIP--------------------------------------------------------
Clone_1 483 DTWGP-------------YLKEHR---------AQRQMMGDGYNQECQIPLKEQGEGQKV------------------------------------------------------------
Clone_2 631 KSWGP-------------SEANKKF--------EYRD-------------SGVTYKSDRSAFAKLM------------------------------------------------------
Clone_3 580 DQWGP-------------NHTEERK--------RYKG--ENR-EYTGSLLENIVINVTGKEIS---------------------------------------------------------
Clone_4 521 QSWGP-------------AHKVPT---------FDISEPDDGIISNPNFSGTVIPNFDSRDPKVL-------------------------------------------------------
Clone_5 573 RDYQG-----------YYPAPEPSAP----PAYSYNTNSYNRNGAMLYPMADVHKDMYDAELEPLDTRLRSSQI----------------------------------------------
Clone_6 622 DDWGP-------------AVEKHRI--------RYLQSLQNSQESLDSVVCTPLVDAKNMSASQQL------------------------------------------------------
Clone_7 613 EDWGP-------------HVSKYKK--------GYNE--IHHNTPDETPLLTIKRNLLGSPSH---------------------------------------------------------
Clone_8 551 ------------------IVSPLDD--------PNEIFHLNPYNG-----GPEKKENFDAI-----------------------------------------------------------
Clone_9 ------------------------------------------------------------------------------------------------------------------------
Clone_10 610 IEWGP-------------ALVRHRREAEYHTKRYASDFIVDPWGLETASPYDVQLQSNKKGVDNEGFSNIRV------------------------------------------------
Clone_11 640 REWGP-------------ALVRHR---------ELVDYV-DGFVQD---PWAEKGAFVNYAYKSD-------------------------------------------------------
Clone_12 562 ENWGP-------------ALSENKTG---RYSDEYKERMKQRTGNNGYTISNPEEQSTSEKYARLQATFVTSNQANIPNGKEEHTVDMENGTKQNGTAQVKDGTGETGTSGQFQNPAFVK
Clone_13 600 DKWKP-------------ISARACE--------KYKV--DNK-LFRGGFKAVVLRNLLGKNS----------------------------------------------------------
Aa_LeuT ----
Hs_SLC6A9 ----
Clone_1 ----
Clone_2 ----
Clone_3 ----
Clone_4 ----
Clone_5 ----
Clone_6 ----
Clone_7 ----
Clone_8 ----
Clone_9 ----
Clone_10 ----
Clone_11 ----
Clone_12 666 DDDK
Clone_13 ----
221
Genome expansion of SLC6 genes for Crassostrea gigas and Strongylocentrotus purpuratus
SoLute Carrier family 6 amino acid transporter genes were identified from three genome
assemblies of C. gigas: (1) Institute of Oceanology v1 (Zhang et al., 2012), (2) Institute of
Oceanology v2 (Qi et al., 2021), and (3) Roslin Institute (Peñaloza et al., 2021). Twenty-three
SLC6 amino acid transporter genes from the Institute of Oceanology v1 genome assembly were
previously identified by Pan et al. (2015) and used to query assemblies by the Institute of
Oceanology v2 and Roslin Institute (Qi et al., 2021; Peñaloza et al., 2021). From the Institute of
Oceanology v2 assembly (Qi et al., 2021), 29 results were returned from initial searches (Dr.
Ning Li, personal communication). Five of these genes had a predicted number of
transmembrane domains (1, 3, 26, or 27 transmembrane domains) that are not characteristic of
the 12 membrane-spanning regions for LeuT and GlyT1 (Yamashita et al., 2005; Shahsavar et al.,
2021). The remaining 24 genes were aligned with other SLC6 genes (see ‘Phylogenetic
comparison of SLC6 genes’ below; Fig. 4.4) to confirm their identity in the amino acid
transporter I subfamily and are reported in Table 4.3.
From the Roslin Institute assembly for the genome of C. gigas (Peñaloza et al., 2021), 27
results were returned from initial searches of SLC6 genes. Three of these results had total
predicted transmembrane domains of 2, 3, or 26, and were subsequently removed from analyses.
The remaining 24 genes were compared to other SLC6 genes (see ‘Phylogenetic comparison of
SLC6 genes’ below; Fig. 4.4) to confirm their identity in the amino acid transporter I subfamily
of SLC6. The 24 SLC6 amino acid transporter genes identified from the Roslin Institute assembly
are reported in Table 4.4.
Based on these results, the Institute of Oceanology v1 assembly contained 23 genes
(Zhang et al., 2012; Pan et al., 2015), the Institute of Oceanology v2 assembly contained 24
222
genes (Table 4.3), and the Roslin Institute assembly contained 24 genes (Table 4.4). The SLC6
amino acid transporter genes identified from all three assemblies were aligned by ClustalW to
cross-reference genes. The Institute of Oceanology v1 assembly (Zhang et al., 2012) contained
two genes that did not share >90% identity with genes found in other assemblies (EKC22732,
45.02% closest match; EKC22426, 88.99% closest match). The Institute of Oceanology v2
assembly (Qi et al., 2021) contained one gene that did not share >90% identity with genes found
in other assemblies (g04052, 43.08% closest match). The Roslin Institute assembly (Peñaloza et
al., 2021) contained two genes that did not share >90% identity with genes found other
assemblies (LOC117684712, XP_034313861.1, 74.28% closest match; and LOC105322944,
XP_034313861.1, 82.41% closest match). Based on the 24 genes identified in the Roslin institute
assembly, the 2 unique genes from the Institute of Oceanology v1 assembly, and the 1 unique
gene from the Institute of Oceanology v2 assembly, there are a total of 27 different (<90%
identity) SLC6 amino acid transporter genes identified from all three assemblies together. For
comparison, 22 SLC6 amino acid transporter genes were also identified from the genome of S.
purpuratus (Ning Li, personal communication; sequences curated and shown in Table 4.5). Upon
phylogenetic analysis (see ‘Phylogenetic comparison of SLC6 genes’ below; Fig. 4.4), two of the
genes from S. purpuratus belonged to the inebriated subfamily of SLC6 leaving 20 belonging to
the amino acid transporter I subfamily of SLC6.
Phylogenetic comparison of SLC6 genes
Cloned genes were aligned with SLC6 genes identified from genomes of C. gigas and S.
purpuratus as well as reference genes from D. melanogaster and H. sapiens. A total of 145
protein sequences were used to create a consensus neighbor-joining tree based on 1,000
223
bootstrap replicates (Fig. 4.4). Based on phylogenetic analyses, the cloned SLC6 genes belong to
the SLC6 amino acid transporter I with the exception of Clone 8 which belongs to the inebriated
subfamily (Fig. 4.4). The inebriated subfamily also contained genes EKC33430 (Institute of
Oceanology v1 assembly), g06237 (Institute of Oceanology v2 assembly), and XP_011450046.2
(Roslin Institute assembly) for C. gigas. There were two SLC6 genes identified from S.
purpuratus in the inebriated subfamily as well (NP_001139666.1 and XP_030827856).
Subfamily designation was based on the branches containing well-characterized SLC6 genes of
H. sapiens and D. melanogaster (Thimgan et al., 2006; Bröer and Gether, 2012; Perland and
Fredricksson, 2017). The amino acid (I) subfamily contained Hs_SLC6A5, A7, A9, and A14; the
amino acid (II) subfamily contained Hs_SLC6A15 – A20; the monoamine subfamily contained
Hs_SLC6A2 – A4; and the GABA (gamma-aminobutryric acid) subfamily contained
Hs_SLC6A1, A6, A8, A11, A12, and A13.
The 13 cloned SLC6 genes of C. gigas are denoted by red arrows in Fig. 4.4. Based on
protein distances calculated by the Jones-Taylor-Thornton (1992) method and a bootstrap tested
(n = 1,000) optimal tree, the closest matching proteins from three genome assemblies of C. gigas
were identified for each cloned sequence (Table 4.6). Of the 13 cloned sequences, top matches
appeared in all three genome assemblies. Six cloned sequences matched closest with sequences
from the Institute of Oceanology v1 assembly, three cloned sequences matched closest with the
Institute of Oceanology v2 assembly, three matched closest with the Roslin Institute assembly,
and one sequence matched closest with both the Institute of Oceanology v1 and Roslin Institute
assemblies (Table 4.6; gray shading). Pairwise alignments of cloned sequences and
corresponding genes from resulted in >99% sequence identity for each cloned sequence.
Interestingly, the sequence of Clone 5 was not found in the Institute of Oceanology v2 assembly
224
but was found (>98.4% identity) in the Institute of Oceanology v1 assembly and Roslin Institute
assembly (99.52% identity). On average, cloned sequences had 612 ± 21 (s.e.m, n=13) amino
acid residues. A difference of 1% identity equates to approximately six amino acid residue
differences.
225
Table 4.3. Twenty-four SoLute Carrier family 6 (SLC6) genes identified from the genome of
Crassostrea gigas assembled by the Institute of Oceanology (version 2; Qi et al., 2021).
Transmembrane domains (TMDs) were predicted using Protter interactive protein feature
visualization (https://wlab.ethz.ch/protter/).
Gene
Inst. Oceanology
v2 Scaffold # Start End Exons
Amino acid
residues
Predicted
TMDs
1
g04052 2
9,516,728 9,524,939 15 637 12
2 g04865
2
22,146,994 22,160,741 13 589 11
3
g04967 2
23,831,532 23,843,946 13 631 12
4
g04968 2
23,889,211 23,955,646 13 631 12
5
g05313 2
29,396,577 29,408,243 15 671 12
6
g06237 2
44,389,281 44,419,742 13 580 12
7
g06302 2
45,736,545 45,750,294 14 677 12
8
g11001 4
22,445,745 22,460,986 17 826 12
9 g11002
4
22,463,199 22,472,208 16 719 12
10
g14849 5
30,122,825 30,138,127 14 627 12
11
g15139 5
35,323,245 35,331,803 15 658 12
12
g15332 5
38,131,956 38,149,442 14 661 12
13
g16156 5
51,550,869 51,558,227 15 651 12
14
g16158 5
51,560,637 51,569,198 12 471 10
15
g16169 5
51,682,215 51,686,904 15 663 12
16
g16170 5
51,687,604 51,699,253 15 669 12
17
g16231 5
52,666,828 52,688,292 13 646 12
18
g16232 5
52,695,064 52,704,803 13 666 12
19
g16292 5
53,445,609 53,455,387 19 878 12
20
g20135 7
2,292,725 2,313,410 14 637 12
21
g20591 7
11,645,282 11,654,058 13 618 12
22
g20832 7
16,081,669 16,095,115 16 712 12
23
g21142 7
21,777,976 21,785,459 13 692 12
24 g27427
9 30,362,992
30,377,743 15 674 12
226
Table 4.4. Twenty-four SoLute Carrier family 6 (SLC6) genes identified from the genome of Crassostrea gigas assembled by the
Roslin Institute (GCA_902806645.1; Peñaloza et al., 2021). Transmembrane domains (TMDs) were predicted using Protter interactive
protein feature visualization (https://wlab.ethz.ch/protter/).
Gene
Roslin Inst.
Pseudo-
chromosome
Genomic
accession Start End Exons
Predicted
transcript bp
Predicted
protein
Amino
acid
residues
Predicted
TMDs
1 LOC105334710 7 NC_047565.1 23,972,826 23,982,785 15 XM_011438267.3
1,977
XP_011436569.2
658
12
2 LOC105324421 7 NC_047565.1 29,363,660 29,384,010 15 XM_034444685.1
1,884
XP_034300576.1
627
12
3 LOC105346523 6 NC_047564.1 40,011,694 40,026,974 13 XM_011455142.3
1,770
XP_011453444.2
589
11
4 LOC105333539 7 NC_047565.1 21,423,288 21,447,851 17 XM_034446168.1
2,073
XP_034302059.1
690
12
5 LOC105347527 Unplaced NW_022994991.1 45,538 63,512 17 XM_034462490.1
2,172
XP_034318381.1
723
12
6 LOC105344109 6 NC_047564.1 19,457,043 19,486,909 14 XM_011451744.3
1,743
XP_011450046.2
580
12
7 LOC105337679 1 NC_047559.1 33,530,470 33,539,857 14 NM_001305316.1
1,989
NP_001292245.1
662
12
8 LOC105347233 2 NC_047560.1 65,589,988 65,617,374 18 XM_034466182.1
1,896
XP_034322073.1
631
11
9 LOC105321334 3 NC_047561.1 35,055,179 35,069,109 17 XM_011419600.3
2,007
XP_011417902.2
668
12
10 LOC105319178 7 NC_047565.1 8,396,082 8,406,798 13 XM_011416591.3
2,001
XP_011414893.2
666
12
11 LOC105322942 6 NC_047564.1 38,466,738 38,492,197 16 XM_034480136.1
1,896
XP_034336027.1
631
12
12 LOC105339780 1 NC_047559.1 43,877,299 43,899,559 17 XM_034464681.1
1,857
XP_034320572.1
618
12
13 LOC105343702 6 NC_047564.1 33,102,584 33,110,917 15 XM_011451163.3
2,397
XP_011449465.2
671
12
14 LOC105321422 8 NC_047566.1 21,386,742 21,396,698 16 XM_011419689.3
2,160
XP_011417991.2
719
12
15 LOC105321421 8 NC_047566.1 21,373,388 21,385,493 19 XM_034449984.1
2,517
XP_034305875.1
838
12
16 LOC105348180 Unplaced NW_022994957.1 28,183 49,023 15 XM_011457505.3
2,034
XP_011455807.2
677
12
17 LOC105338902 7 NC_047565.1 9,448,648 9,462,572 15 XM_011444202.3
2,010
XP_011442504.2
669
12
18 LOC105338903 7 NC_047565.1 9,462,614 9,470,613 15 XM_011444203.3
1,842
XP_011442505.2
613
12
19 LOC105329093 7 NC_047565.1 7,509,234 7,516,751 17 XM_011430256.3
1,926
XP_011428558.1
641
12
20 LOC105342669 1 NC_047559.1 53,378,319 53,397,179 14 XM_011449677.3
1,914
XP_011447979.2
637
12
21 LOC105348927 7 NC_047565.1 8,412,257 8,434,461 13 XM_011458536.3
1,941
XP_011456838.2
646
12
22 LOC105334296 6 NC_047564.1 38,415,979 38,451,362 16 XM_034480130.1
1,938
XP_034336021.1
645
12
23 LOC105322944 6 NC_047564.1 38,370,960 38,400,469 15 XM_034480132.1
1,902
XP_034336023.1
633
12
24 LOC117684712 1 NC_047559.1 1,221,459 1,244,214 22 XM_034457970.1
3,777
XP_034313861.1
1258
12
227
Table 4.5. Twenty-two SoLute Carrier family 6 (SLC6) genes identified from the genome of Strongylocentrotus purpuratus (GenBank
GCA_000002235.4). Transmembrane domains (TMDs) were predicted using Protter interactive protein feature visualization
(https://wlab.ethz.ch/protter/).
Gene
Genomic
Scaffold Start End
Predicted
transcript bp
Predicted
protein
Amino
acid
residues
Predicted
TMDs
1 LOC755109 NW_022145545.1 1,648,145 1,666,805 XM_030987417.1 1,657 XP_030843277.1 410 7
2 LOC115919132 NW_022145571.1 325,977 370,474 XM_030971996.1 6,117 XP_030827856.1 638 12
3 LOC115918386 NW_022145578.1 375,963 405,113 XM_030976099.1 4,420 XP_030831959.1 659 12
4 LOC577626 NW_022145596.1 2,979,571 3,017,962 XM_777840.5 2,162 XP_782933.4 643 12
5 LOC100889790 NW_022145596.1 16,725,985 16,786,317 XM_011670127.2 3,925 XP_011668429.2 1,093 14
6 LOC590977 NW_022145596.1 15,697,327 15,751,243 XM_030980171.1 8,310 XP_030836031.1 800 13
7 LOC580968 NW_022145596.1 4,075,952 4,123,612 XM_030980414.1 2,660 XP_030836274.1 611 12
8 LOC105442292 NW_022145596.1 6,396,317 6,446,911 XM_030980479.1 2,782 XP_030836339.1 638 12
9 LOC584025 NW_022145596.1 9,530,485 9,577,712 XM_030980574.1 4,797 XP_030836434.1 739 12
10 LOC587709 NW_022145596.1 11,423,881 11,470,051 XM_030980644.1 5,688 XP_030836504.1 655 12
11 LOC578449 NW_022145605.1 42,242,258 42,281,383 NM_001146194.1 2,850 NP_001139666.1 606 12
12 LOC100889772 NW_022145606.1 51,125,844 51,151,309 XM_030994052.1 2,872 XP_030849912.1 675 12
13 LOC577731 NW_022145609.1 16,561,244 16,590,568 XM_777943.5 7,434 XP_003729242.1 685 12
14 LOC575259 NW_022145609.1 32,636,099 32,644,990 XM_775027.5 5,122 XP_780120.1 699 12
15 LOC594544 NW_022145610.1 20,067,240 20,095,994 XM_793980.5 4,430 XP_799073.3 659 12
16 LOC580659 NW_022145610.1 5,973,825 5,993,004 XM_780705.4 2,821 XP_003726159.2 717 12
17 LOC580597 NW_022145610.1 5,999,176 6,022,073 XM_030997699.1 3,310 XP_030853559.1 664 12
18 LOC586383 NW_022145610.1 27,943,904 27,973,532 XM_030998205.1 3,068 XP_030854065.1 613 10
19 LOC589614 NW_022145610.1 27,976,700 28,002,841 XM_030998206.1 2,746 XP_030854066.1 683 12
20 LOC592693 NW_022145610.1 28,181,711 28,209,937 XM_011665831.2 6,452 XP_011664133.2 896 12
21 LOC115918334 NW_022145613.1 6,088,216 6,110,535 XM_030973964.1 1,853 XP_030829824.1 583 10
22 LOC754651 XM_030973983.1 6,056,198 6,081,958 XM_030973983.1 4,533 XP_030829843.1 597 10
228
Figure 4.4. Phylogenetic relationships of SoLute Carrier family 6 (SLC6) proteins from
Crassostrea gigas (Cg), Strongylocentrotus purpuratus (Sp), Drosophila melanogaster (Dm),
and Homo sapiens (Hs). A total of 145 protein sequences were aligned (ClustalW; 1,195
positions), including SLC6 amino acid transporters from different genome assemblies of C. gigas
(Cg1 by the Institute of Oceanology v1, Cg2 by the Institute of Oceanology v2, Cg3 by the
Roslin Institute) and the 13 cloned sequences from the present study. Protein distances were
computed using the Jones-Taylor-Thornton (1992) method. The neighbor-joining method was
used, and the optimal tree was determined by the bootstrap test (1,000 replicates). Branch values
represent the percentages of bootstrap trees where a cluster was found. Red arrows indicate
cloned sequences from the present study. SLC6 subfamilies (AAT1, AAT2, Ine, Monoamine,
GABA) were annotated based on proteins of H. sapiens (Bröer and Gether, 2012).
229
Table 4.6. Closest matching proteins for 13 cloned SoLute Carrier family 6 (SLC6) amino acid transporter genes from Crassostrea
gigas. Gray highlighting represents the match with the highest percent identity among three genome assemblies for Crassostrea gigas.
Institute of Oceanology Assembly v1 Institute of Oceanology Assembly v2 Roslin Institute Assembly
Clone Protein Percent ID Gene Percent ID Gene Protein Percent ID
1
EKC17288 99.04%
g14849 98.46%
LOC105324421 XP_034300576.1 98.65%
2
EKC19003 93.19%
g21142 99.40%
LOC105337679 NP_001292245.1 93.19%
3
EKC24094 99.03%
g20591 97.73%
LOC105339780 XP_034320572.1 98.54%
4
EKC24822 99.82%
g20832 99.47%
LOC105347527 XP_034318381.1 99.82%
5
EKC29047 98.40%
g15332 21.94%
LOC105347233 XP_034322073.1 99.52%
6
EKC30006 97.60%
g16232 98.50%
LOC105319178 XP_011414893.2 99.10%
7
EKC31399 97.98%
g06302 99.54%
LOC105348180 XP_011455807.2 98.77%
8
EKC33430 99.83%
g06237 99.31%
LOC105344109 XP_011450046.2 99.66%
9
EKC33781 100.00%
g04967 99.25%
LOC105322942 XP_034336027.1 99.50%
10
EKC35081 99.10%
g27427 97.46%
LOC105321334 XP_011417902.2 98.35%
11
EKC36021 96.56%
g11002 99.12%
LOC105321422 XP_011417991.2 100.00%
12
EKC37562 100.00%
g16170 99.55%
LOC105338902 XP_011442504.2 99.25%
13 EKC40069 99.04% g20135 99.37% LOC105342669 XP_011447979.2 99.22%
Institute of Oceanology Assembly v1 (Zhang et al., 2012; Pan et al., 2015)
Institute of Oceanology Assembly v2 (Qi et al., 2021)
Roslin Institute Assembly (Peñaloza et al., 2021)
230
DISCUSSION
Genome expansion of SLC6 transporters in marine invertebrates
By cross-referencing three different genome assemblies for C. gigas, 27 different SLC6
amino acid transporter genes were identified in the present study. Previously, 23 SLC6 amino
acid transporter genes were identified from the original genome assembly (Zhang et al., 2012;
Pan et al., 2015). From each separate genome assembly, unique genes were identified, so the
cloning and sequencing of genes in the present studied further clarified the existence of certain
genes. For example, Clone 5 is missing from the Institute of Oceanology v2 assembly with the
closest match being only 22% identical in sequence (Table 4.6). The 27 SLC6 amino acid
transporter genes represent a significant expansion of this family of genes in animals. Well-
characterized SLC6 genes in humans have been subdivided into four groups: amino acid
transporters (I), amino acid transporters (II), monoamine, and GABA transporters (Brӧer and
Gether, 2012). There are a total of 20 human SLC6 transporters, and only four belong to the
amino acid transporter (I) subfamily: SLC6A5, SLC6A7, SLC6A9, and SLC6A14 (Brӧer and
Gether, 2012; Perland and Fredricksson, 2017). In contrast, C. gigas have 27 genes belonging to
the amino acid I subfamily alone (cf. four in humans), representing a large expansion in this
species. Furthermore, only three of the 13 cloned SLC6 sequences in the present study had 100%
protein sequence identity to sequences predicted in genome assemblies (Table 4.6). The
remaining cloned sequences shared >99% amino acid identity with corresponding predicted
proteins and differed by up to 7% amino acid identities between genome assemblies. The degree
of sequence diversity provides a basis for further functional diversity of SLC6 genes.
231
While the functions of this suite of genes remains unknown, there is evidence that the
genes are expressed at different times during development or in different tissues (Zhang et al.,
2012; Pan et al., 2015). For example, four transcripts were only detected in the veliger stage and
not the preceding embryo or trochophore stages (EKC33781, EKC25312, EKC21522,
EKC18061) and another set of four transcripts were only detected in trochophore and later stages
(EKC40069, EKC37562, EKC37563, EKC40294; Zhang et al., 2012; Pan et al., 2015). In adult
C. gigas, EKC2532, EKC35081, and EKC37562 were the most highly expressed SLC6 amino
acid transporter genes across 11 tissues tested (Table S14 from Zhang et al., 2012). Differences
in expression between tissues were also seen with hemocytes expressing high amounts of
EKC25312 transcripts and digestive glands expressing high amounts of EKC37562 transcripts
(Table S14 from Zhang et al., 2012). Spatial and temporal differences in the expression of SLC6
amino acid transporters suggest that they have different functions, but further studies are required
to elucidate these functions.
In the present study, 13 full-length sequences of putative SLC6 transporter genes were
cloned from the mRNA of the bivalve C. gigas. Aside from transcriptional patterns, cloning of
these sequences allows for tests of gene function. For example, three SLC6 transporter sequences
cloned from the purple urchin S. purpuratus (Sp-AAT1-3) have been expressed in heterologous
systems (oocytes of Xenopus laevis) revealing information on transporter substrate specificity
(Meyer and Manahan 2009). Since the original cloning of SLC6 genes in S. purpuratus (Meyer
and Manahan, 2009), additional amino acid transporters of distinct sequences have been
identified in the genome of that species (Appendix I). In the present study, the three genes of S.
purpuratus grouped closely with seven genes identified in the genome of C. gigas (Fig. 4.4).
Both genomes of S. purpuratus and C. gigas have been sequenced, revealing a high degree of
232
genome expansion specifically for C. gigas compared to S. purpuratus seen in the abundance of
heat shock proteins, cytochrome P450, multi-copper oxidases, inhibitors of apoptosis, and SLC6
genes (Sodergren et al., 2006; Zhang et al., 2012). The multiplicity of gene families seen in C.
gigas suggest that processes related to these genes are highly regulated and important in essential
physiological processes such as stress responses (Zhang et al., 2012). Physiological rates of
amino acid transport have also been shown to be correlated with larval growth rates for C. gigas,
so the regulation and role of 25 different SLC6 amino acid transporters is particularly interesting
(Pan et al., 2015).
Aside from S. purpuratus and C. gigas, SLC6 genes have also been studied in a few other
marine taxa for a variety of functional contexts. Sequences and transcriptional regulation of
SLC6 genes were reported for Antarctic echinoderm larvae with implications for improving the
understanding of lecithotrophic and planktotrophic life history strategies in food-limited waters
(Applebaum et al., 2013). The importance of SLC6 genes in transporting nutrients from the
environment was also seen in a study of the deep-sea bone worm Osedax japonicus which
localized SLC6 transporters at the root epidermis (Miyamoto et al., 2017). Taurine and GABA
transporters belonging to the SLC6 transporter family have also been studied in relation to deep-
sea mussel adaptations to sulfide toxicity (Inoue et al., 2008), but this represents a somewhat
specialized symbiotic function uncommon amongst molluscan taxa. The function of SLC6 genes
in neuronal function have also been documented in jellyfish Cyanea capillata (Bouchard et al.,
2019). The gene found in C. capillata most closely matched other nutrient amino acid transport
sequences from other organisms. Comparative studies of SLC6 genes also provide evolutionary
information apart from information on gene function. Comparisons of deep-sea mussel GABA
transporters with Antarctic krill provided evidence for the evolution of creatine and GABA
233
transporters through tandem duplications occurring before the deuterostome and protostome
divergence (Kinjo et al., 2013). Investigations of the SLC6 gene family for the model sea squirt
Ciona savignyi resulted in 29 complete and 11 putative SLC6 genes, six of which belonged to the
amino acid transporter (I) subgroup (Ren et al., 2019). As sea squirts are more closely related to
vertebrates, many of the SLC6 genes were conserved when compared to zebrafish (Danio rerio)
and humans. Given that SLC6 homologues are found in bacteria (Yamashita et al., 2005), and the
high degree of gene duplication and loss in metazoans, characterizing the species-specific
functions of these genes is important.
Sequence characteristics of SLC6 transporters
Sodium or Cl
-
ion co-transport are characteristic properties of the SLC6 family of
proteins. By referencing the crystalized structure of a bacterial SLC6 homolog LeuT (Yamashita
et al., 2005) and human glycine transport GlyT1 (Shahsavar et al., 2021), the role of SLC6
domains and residues within the protein can be inferred. Typical SLC6 genes have 12
transmembrane domains with N- and C- terminal residues residing in the cytoplasm. These
transmembrane domains exist in a 5 – 5 inverted symmetry called a LeuT fold (transmembrane
domains grouped 1-5, 6-10) while the remaining two transmembrane domains are thought to be
involved in dimerization (Yamishita et al., 2005; Claxton et al., 2010). Extracellular loops two
and four were described to be involved with substrate binding. Among the cloned sequences in
the present study, extracellular loop four was highly conserved with >50% sequence agreement
at all but 4 out of 35 positions in the loop (Fig. 4.3). The other putative substrate-binding region,
extracellular loop 2, was highly variable. In extracellular loop 2, only 30 out of 105 positions had
>50% sequence agreement (Fig. 4.3).
234
For the LeuT reference protein, the crystal structure allowed for the identification of
conserved charged sites as well as residues involved with sodium binding and substrate binding
(Yamashita et al., 2005). The charged residues, transmembrane domains, and sodium binding
residues were also conserved in the human GlyT1 gene (Shahsavar et al., 2021). The
extracellular charge (LeuT, R30) and cytoplasmic charges (LeuT, R5, D369) were also conserved
amongst all cloned sequences except for Clone 1, 4, and 5 which did not align to R5 or R30 and
Clone 9 which did not align to D369 (Fig. 4.3). For LeuT two sodium ions are coordinated with
the co-transport of leucine (Yamashita et al., 2005). In eukaryotes and the reference structure of
human GlyT1, the two sodium ions are also co-transported with Cl
-
and the substrate (Shahsavar
et al., 2021). The first sodium (Na1) interacts with the protein at residues LeuT, A22, N27, T254,
and N286 while the second sodium (Na2) interacts with the protein at residues LeuT, G20, V23,
A351, T354, and S355 (Yamashita et al., 2005; Fig. 4.3 of present study). The Cl
-
ion interacts
with residues in transmembranes 6 and 7 in GlyT1 and eukaryotic SLC6 genes in general
(Shahsavar et al., 2021; Fig. 4.3 of present study). Based on the alignment of the 13 cloned genes
from C. gigas, some sequence variation was observed at these sites. In particular, Clone 5 did not
align to these sodium binding residue positions in transmembrane 1 (Fig. 4.3). The Na1 binding
residue A22 was found in sequence from five clones but was also variable among other cloned
sequences with substitutions of serine, cysteine, and glutamine in place of alanine. The Na1
binding residue in transmembrane 7 (LeuT, N286) was also variable among cloned sequences
with substitutions of aspartic acid, glycine, valine, and serine in place of asparagine. Sodium 2
binding residues also differed from LeuT for clones in transmembrane domain 8. For eight of the
cloned sequences, a leucine was found in place of alanine at Na2 binding site A351. Site T354
was also variable among clones with replacements of aspartic acid, serine, glycine, and glutamic
235
acid. Twelve residues from LeuT were identified to be involved with leucine substrate binding
(Fig. 4.3; filled circles). Substitutions were found at these sites for cloned sequences, and
generally had two to four substitutions represented by the 13 clones. The human GlyT1 gene is
more similar to cloned sequences as a eukaryotic homologue. The alanine-tryptophan-glycine
(AWG) sequence of GlyT1 located in transmembrane domain six is responsible for glycine
specificity (Shahsavar et al., 2021). Cloned sequence residues aligning with this motif showed
variation in sequence with sequences of (single-letter amino acid codes): CWG, CSG, VWG,
VWT, AFG, SWG, AGG, CSG, and GWG. Clones 4, 6, 7, and 9 shared the AWG motif found in
the human glycine transporter GlyT1 (Fig. 4.3). Based on conserved sequences alone, Clones 4,
6, 7, and 9 could have a substrate specificity for glycine. These clones most closely matched
EKC24822, XP_011414893.2, g06302, and EKC33781 respectively (Table 4.6). Differences in
these residues for other clones provide evidence for differential substrate binding functions or
kinetics.
SLC6 function in Crassostrea gigas and Strongylocentrotus purpuratus
As membrane-bound transporter proteins, potential heterologous systems to test the
function and kinetics of the cloned SLC6 proteins include the oocyte of Xenopus laevis because it
is a large, single eukaryotic cell with an active membrane structure as well as embryos of the
zebrafish Danio rerio (Hyatt and Ekker, 1998; Wagner et al., 2000). The use of the X. laevis
system has allowed for the characterization of amino acid transport of SLC6 genes from marine
species Bathymodiolus septemdierum, S. purpuratus, C. capillata (Inoue et al., 2008; Meyer and
Manahan 2009; Bouchard et al., 2019). Aside from substrate specificity, SLC6 function is largely
a function of regulation and localization. Several residues within the open reading frame of these
236
genes are responsible for mediating protein-protein interactions which determine the fate of
where the protein is transported and localized within cells and tissues. Given the wide range of
functions for amino acids, localization and expression can result in the transport of amino acids
for very different purposes (e.g., as neurotransmitters or as osmolytes). Finally, as the function of
these putative SLC6 transporters are characterized, studies can be conducted examining their
potential as biomarkers or predictors of growth success in marine larvae. There is already
evidence that physiological rates of amino acid transport can predict early growth rates in C.
gigas larvae (Pan et al., 2015). However, it is unclear whether these physiological rates are
correlated to protein expression or localization, and which of the transporters may be better
predictors than others. With cloned sequence data on hand, 13 putative SLC6 transporter genes
from C. gigas can now be tested in these capacities.
Several attempts at localizing and testing the functions of SLC6 transporters from C.
gigas and S. purpuratus have been made (Appendices H and I; briefly described below).
Embryos of D. rerio were tested as a heterologous system for transporter expression (Appendix
H). Amino acids play a large role in embryonic development of many fish species with yolk
proteins being a major contributor to energy reserves (Finn and Fyhn 2010). The ability to
transport amino acids from the environment in addition to the yolk sac may be specific to fish
species or developmental stage (Siebers and Rosenthal, 1977; Korsgaard 1991). Preliminary
studies were conducted to first characterize the free amino acid composition of D. rerio embryos
reverse phase HPLC (Appendix H). These free amino acid profiles would determine appropriate
substrates for which a reliable signal could be detected. Next, endogenous amino acid transport
capabilities were tested using
14
C-alanine in various media. Live and cold-killed, dechorionated
embryos were incubated in media containing
14
C-alanine. There was no evidence for
237
endogenous, active transport in embryos. Because D. rerio embryos did not show signs of
endogenous transporter activity despite having SLC6 transporters identified in the genome, there
was no evidence to suggest that foreign expression constructs of SLC6 genes would be properly
expressed and localized along the membranes for heterologous tests of function.
Following the negative results for the use of D. rerio embryos as a heterologous system,
probes were designed to localize SLC6 transporter transcripts from S. purpuratus using
RNAscope for in situ hybridization (Appendix I). Meyer and Manahan (2009) had cloned three
SLC6 transporter genes and tested their functions for amino acid transport. Further analyses of
the genome of S. purpuratus resulted in the identification of 10 SLC6 transporter sequences
matching the cloned sequences (Meyer and Manahan, 2009; Appendix I). SpAAT 1-3 were the
most dissimilar with regards to sequence, and the remaining 7 transporters had > 90% similarity
to each of SpAAT 1, 2, or 3. RNAscope probes (ACD Bio.) were designed with enough
specificity to distinguish SpAAT1-3 with cross-reactivity to the other 7 genes with similar
sequences. Tests were being conducted for fixation protocols compatible with RNAscope probe
technology following methods used by Arenas-Mena et al. (2000) as well as confirmed
RNAscope protocols for zebrafish (ACD Bio.). The preliminary results from both the testing of
embryos of D. rerio as heterologous systems as well as the efforts in developing RNAscope
assays for S. purpuratus SLC6 transporters are reported in Appendices H and I of this
dissertation, respectively.
238
Acknowledgements for assistance for Chapter 4
I would like to thank the members of the laboratory group of Professor Donal T.
Manahan for their assistance. Specifically, Dr. Scott Applebaum, Dr. Francis T.C. Pan and Dr.
Ning Li for their help with identification of SLC6 genes from the genomes of C. gigas and S.
purpuratus. Thank you to Dr. Ning Li specifically for initial identification of SLC6 genes from
the genome assembly of C. gigas (Institute of Oceanology Assembly version 2) and S.
purpuratus (GenBank GCA_000002235.4). Thank you to Dr. Scott Applebaum for providing
tissues from C. gigas for RNA extraction.
My thanks also to Professor Robert Maxson and Professor Gage Crump for their advice
on reverse genetic approaches to examining the function and localization of transporter genes.
Dr. Joanna Smeeton and Professor Gage Crump provided zebrafish embryos and training for
tests of amino acid transport function in this species.
COVID-19 Impact Statement
The COVID-19 pandemic interrupted studies of the analyses of the function of the genes
identified in this chapter. Preliminary analyses are presented in Appendices H and I.
239
Chapter 4 References
Applebaum, S.L., Ginsburg, D.W., Capron, C.S., and Manahan, D.T. (2013). Expression of
amino acid transporter genes in developmental stages and adult tissues of Antarctic
echinoderms. Polar Biology, 36(9), 1257-1267.
Arenas-Mena, C., Cameron, A.R., and Davidson, E.H. (2000). Spatial expression of Hox cluster
genes in the ontogeny of a sea urchin. Development, 127(21), 4631-4643.
Borycz, J., et al. (2018). Location and functions of Inebriated in the Drosophila eye. Biology
Open, 7(7), 1-9.
Bouchard, C., Boudko, D.Y., and Jiang, R.H. (2019). A SLC6 transporter cloned from the lion's
mane jellyfish (Cnidaria, Scyphozoa) is expressed in neurons. PloS One, 14(6), DOI:
e0218806.
Boudko, D.Y. (2012). Molecular basis of essential amino acid transport from studies of insect
nutrient amino acid transporters of the SLC6 family (NAT-SLC6). Journal of Insect
Physiology, 58(4), 433-449.
Boudko, D.Y., Kohn, A.B., Meleshkevitch, E.A., Dasher, M.K., Seron, T.J., Stevens, B.R., and
Harvey, W.R. (2005). Ancestry and progeny of nutrient amino acid transporters.
Proceedings of the National Academy of Sciences, 102(5), 1360-1365.
Braven, J., Evens, R., and Butler, E.I. (1984). Amino acids in sea water. Chemistry in Ecology,
2(1), 11-21.
Brӧer, S., and Gether, U. (2012). The solute carrier 6 family of transporters. British Journal of
Pharmacology, 167(2), 256-278.
240
Claxton, D.P., Quick, M., Shi, L., Delmondes de Carvalho, F., Weinstein, H., Javitch, J.A., and
Mchaourab, H.S. (2010). Ion/substrate-dependent conformational dynamics of a bacterial
homolog of neurotransmitter:sodium symporters. Nature Structural and Molecular
Biology, 17(7), 822-829.
Coleman, J.A., Green, E.M., and Gouaux, E. (2016). X-ray structures and mechanism of the
human serotonin transporter. Nature, 532(7599), 334-339.
Felsenstein, J. (1985). Confidence limits on phylogenies: an approach using the bootstrap.
Evolution, 39(4), 783-791.
Finn, R.N., and Fyhn, H.J. (2010). Requirement for amino acids in ontogeny of fish. Aquaculture
Research, 41(5), 684-716.
Hyatt, T.M., and Ekker, S.C. (1998). Vectors and techniques for ectopic gene expression in
zebrafish. Methods in Cell Biology, 59, 117-126.
Inoue, K., Tsukuda, K., Koito, T., Miyazaki, Y., Hosoi, M., Kado, R., Miyazaki, N., and
Toyohara, H. (2008). Possible role of a taurine transporter in the deep-sea mussel
Bathymodiolus septemdierum in adaptation to hydrothermal vents. FEBS Letters,
582(10), 1542-1546.
Jaeckle, W.B., and Manahan, D.T. (1989). Feeding by a “nonfeeding” larva: uptake of dissolved
amino acids from seawater by lecithotrophic larvae of the gastropod Haliotis
rufescens. Marine Biology, 103(1), 87-94.
Jones, D.T., Taylor, W.R., and Thornton, J.M. (1992). The rapid generation of mutation data
matrices from protein sequences. Bioinformatics, 8(3), 275-282.
Käll, L., Krogh, A., and Sonnhammer, E.L. (2004). A combined transmembrane topology and
signal peptide prediction method. Journal of Molecular Biology, 338(5), 1027-1036.
241
Käll, L., Krogh, A., and Sonnhammer, E.L. (2007). Advantages of combined transmembrane
topology and signal peptide prediction—the Phobius web server. Nucleic Acids Research,
35(suppl_2), W429-W432.
Kinjo, A., Koito, T., Kawaguchi, S., and Inoue, K. (2013). Evolutionary history of the GABA
transporter (GAT) group revealed by marine invertebrate GAT-1. PLoS One, 8(12), DOI:
e82410.
Korsgaard, B. (1991). Metabolism of larval turbot Scophthalmus maximus (L.) and uptake of
amino acids from seawater studied by autoradiographic and radiochemical methods.
Journal of Experimental Marine Biology and Ecology, 148(1), 1-10.
Krogh, A. (1931). Dissolved substances as food of aquatic organisms. Biological Reviews, 6(4),
412-442.
Kumar, S., Stecher, G., Li, M., Knyaz, C., and Tamura, K. (2018). MEGA X: molecular
evolutionary genetics analysis across computing platforms. Molecular Biology and
Evolution, 35(6), 1547-1549.
Lee, C., and Bada, J.L. (1977). Dissolved amino acids in the equatorial Pacific, the Sargasso Sea,
and Biscayne Bay 1. Limnology and Oceanography, 22(3), 502-510.
Manahan, D.T. (1983). The uptake and metabolism of dissolved amino acids by bivalve larvae.
The Biological Bulletin, 164(2), 236-250.
Manahan, D.T. (1989). Amino acid fluxes to and from seawater in axenic veliger larvae of a
bivalve (Crassostrea gigas). Marine Ecology Progress Series, 53(3), 247-255.
Manahan, D.T., Davis, J.P., and Stephens, G.C. (1983). Bacteria-free sea urchin larvae: selective
uptake of neutral amino acids from seawater. Science, 220(4593), 204-206.
242
Manahan, D.T., Wright, S.H., and Stephens, G.C. (1983). Simultaneous determination of net
uptake of 16 amino acids by a marine bivalve. American Journal of Physiology -
Regulatory, Integrative and Comparative Physiology, 244(6), 832-838.
Manahan, D.T., and Crisp, D.J. (1983). Autoradiographic studies of the uptake of dissolved
amino acids from sea water by bivalve larvae. Journal of the Marine Biological
Association of the United Kingdom, 63(3), 673-682.
Meyer, E., and Manahan, D.T. (2009). Nutrient uptake by marine invertebrates: cloning and
functional analysis of amino acid transporter genes in developing sea urchins
(Strongylocentrotus purpuratus). Biological Bulletin, 217(1), 6-24.
Miyamoto, N., Yoshida, M.A., Koga, H., and Fujiwara, Y. (2017). Genetic mechanisms of bone
digestion and nutrient absorption in the bone-eating worm Osedax japonicus inferred
from transcriptome and gene expression analyses. BMC Evolutionary Biology, 17(1), 1-
13.
Pan, T.C.F., Applebaum, S.L., and Manahan, D.T. (2015). Genetically determined variation in
developmental physiology of bivalve larvae (Crassostrea gigas). Physiological and
Biochemical Zoology, 88(2), 128-136.
Peñaloza, C., et al. (2021). A chromosome-level genome assembly for the Pacific oyster
Crassostrea gigas. GigaScience, 10(3), DOI: giab020.
Penmatsa, A., Wang, K.H., and Gouaux, E. (2013). X-ray structure of dopamine transporter
elucidates antidepressant mechanism. Nature, 503(7474), 85-90.
Perland, E., and Fredriksson, R. (2017). Classification systems of secondary active transporters.
Trends in Pharmacological Sciences, 38(3), 305-315.
243
Pütter, A. (1909). Der ernahrung der wassertiere und der stoffhaushalt der gewasser”. Gustav
Fischer, Jena.
Qi, H., Li, L., and Zhang, G. (2021). Construction of a chromosome‐level genome and variation
map for the Pacific oyster Crassostrea gigas. Molecular Ecology Resources, 21, 1670-
1685.
Ren, P., Wei, J., Yu, H., and Dong, B. (2019). Identification and functional characterization of
solute carrier family 6 genes in Ciona savignyi. Gene, 705, 142-148.
Saitou, N., and Nei, M. (1987). The neighbor-joining method: a new method for reconstructing
phylogenetic trees. Molecular Biology and Evolution, 4(4), 406-425.
Shahsavar, A., et al. (2021). Structural insights into the inhibition of glycine reuptake. Nature,
591(7851), 677-681.
Shilling, F.M., and Manahan, D.T. (1994). Energy metabolism and amino acid transport during
early development of Antarctic and temperate echinoderms. Biological Bulletin, 187(3),
398-407.
Siebers, D., and Rosenthal, H. (1977). Amino-acid absorption by developing herring eggs.
Helgoländer wissenschaftliche Meeresuntersuchungen, 29(4), 464-472.
Sodergren, E., et al. (2006). The genome of the sea urchin Strongylocentrotus purpuratus.
Science, 314(5801), 941-952.
Stephens, G.C. (1988). Epidermal amino acid transport in marine invertebrates. Biochemica et
Biophysica, 947(1), 113-138.
Stephens, G.C., and Schinske, R.A. (1961). Uptake of amino acids by marine invertebrates.
Limnology and Oceanography, 6(2), 175-181.
244
Thimgan, M.S., Berg, J.S., and Stuart, A.E. (2006). Comparative sequence analysis and tissue
localization of members of the SLC6 family of transporters in adult Drosophila
melanogaster. Journal of Experimental Biology, 209(17), 3383-3404.
Wagner, C.A., Friedrich, B., Setiawan, I., Lang, F., and Bröer, S. (2000). The use of Xenopus
laevis oocytes for the functional characterization of heterologously expressed membrane
proteins. Cellular Physiology and Biochemistry, 10(1-2), 1-12.
Wipf, D., Ludewig, U., Tegeder, M., Rentsch, D., Koch, W., and Frommer, W.B. (2002).
Conservation of amino acid transporters in fungi, plants, and animals. TRENDS in
Biochemical Sciences, 27(3), 139-147.
Wright, S.H. (1982). A nutritional role for amino acid transport in filter-feeding marine
invertebrates. American Zoologist, 22(3), 621-634.
Wright, S.H. (1987). Alanine and taurine transport by the gill epithelium of a marine bivalve:
effect of sodium on influx. The Journal of Membrane Biology, 95(1), 37-45.
Wright, S.H., and Secomb, T.W. (1986). Epithelial amino acid transport in marine mussels: role
in net exchange of taurine between gills and sea water. Journal of Experimental Biology,
121(1), 251-270.
Wright, S.H., Moon, D.A., and Silva, A.L. (1989). Intracellular Na
+
and the control of amino
acid fluxes in the integumental epithelium of a marine bivalve. Journal of Experimental
Biology, 142(1), 293-310.
Yamashita, A., Singh, S.K., Kawate, T., Jin, Y., and Gouaux, E. (2005). Crystal structure of a
bacterial homologue of Na
+
/Cl
-
-dependent neurotransmitter transporters. Nature,
437(7056), 215-223.
245
Zhang, G., et al. (2012). The oyster genome reveals stress adaptation and complexity of shell
formation. Nature, 490(7418), 49-54.
246
DISSERTATION SYNTHESIS
Dynamics of Protein Metabolism in Larvae of Marine Invertebrates
Major findings
This dissertation is a study of the biology of growing organisms, and how growth is
regulated in variable environments. The study is focused on developmental stages of marine
invertebrates that have complex life history strategies, representative of many animal species.
Protein depositional efficiency is low during growth
Protein synthesis rates exceeded rates of protein ingestion and protein accretion for larvae
of the sea urchins Strongylocentrotus purpuratus and Lytechinus pictus. This highlights the
importance of the dynamics of protein synthesis and protein turnover in regulating growth. The
majority of protein mass synthesized is degraded, with approximately 20% of synthesized
protein mass kept as accreted biomass in larval stages of both species of sea urchin studied. This
very low efficiency has important implications for the biochemical bases of net protein accretion,
in addition to interpreting how the incorporation of radioisotopes into macromolecules can be
used to predict growth rates.
Increases in feeding rates and protein food conversion efficiency compensate for low rations
Larvae increased feeding rates by 10-fold at low food rations, a rate that was independent
of larval mass and morphology under conditions of low food. This results in a gain of protein
ingestion even at a low ration of 1,000 algal cells ml
-1
. Notably, when food rations were
maintained at stable levels, growth rates did not differ for larvae reared across a 10-fold
247
difference in food ration. Greater than 3-fold increases in protein food conversion efficiency into
body protein mass could maintain these growth rates at low rations. These findings provide new
evidence for processes that permit growth under low food rations. High protein food conversion
efficiencies supporting growth at low rations and support the general conclusion that larval forms
are not necessarily food limited at the low amounts of particulate food known to exist in the
ocean.
Supply and demand of ATP are altered with increased temperature
The cellular supply and demand of ATP could become limiting with rising temperature.
Protein synthesis rates have a higher sensitivity to temperature (Q 10 ~3), compared to respiration
rates (Q10 ~2). Furthermore, a higher proportion of ATP supply is derived from protein
catabolism, as observed by higher thermal sensitivities (Q 10 ~3) of ammonia excretion compared
to respiration. These findings highlight the need to understand the biochemical and energetic
bases of substrate oxidation in support of metabolism under conditions of rising temperature.
Gene expansion of amino acid transporters in marine invertebrates
Significant gene expansion of amino acid transporters from the SoLute Carrier Family 6
proteins was found in S. purpuratus (22 genes), with additional analyses in the Pacific oyster
Crassostrea gigas (27 genes) also supporting this conclusion of gene expansion for amino acid
transporters in marine larval forms. Analyses of amino acid sequences suggest that these genes
may differ in substrate specificity. The high degree of gene expansion in this subfamily of amino
acid transporters implies multiple physiological functions and mechanisms for acquiring
nutrients.
248
Implications of major findings
Methods of isotope incorporation cannot predict growth rate
Because proteins are in a constant state of turnover through synthesis and degradation,
estimates of growth based on this process should not be made without careful consideration of
the dynamics of turnover. For instance, studies of bacteria show that ~5% of protein mass was
turned over every hour in Escherichia coli (Borek et al., 1958). More recently, Lahtvee et al.
(2014) demonstrated that there was a 7-fold increase in protein turnover rates occur for every 5-
fold increase in growth rates for Lactococcus lactis. In yeast, approximately 2% of protein mass
was degraded every hour, with 5-fold increases in protein degradation following nitrogen
starvation (López and Gancedo, 1979). In marine organisms, species of fish had ~60% of the
mass of synthesized protein degraded and turned over (Houlihan et al., 1989). In marine
invertebrates, protein turnover is also a dominant component of the biology of growth (bivalves:
Bayne, 2017; Pan et al., 2018; echinoderms: Pace and Manahan, 2006; Pan et al., 2015a). Many
studies of microbial growth in the oceans have utilized radioisotope analyses (e.g.,
3
H- or
14
C-
leucine, or
13
N) to estimate growth rates based on the rate of their incorporation into protein
(Kirchman et al., 1986; Chin-Leo and Kirchman, 1988; Zehr et al., 1988; Miranda et al., 2007;
Kirchman, 2018). While the process of protein turnover is often acknowledged in these studies,
the interpretation of the effects on predicting growth rates is often minimized.
Based on studies in the present dissertation, strong evidence is presented for the
importance of analysis of protein turnover in any attempt to predict protein accretion and growth
based on the use of radioisotopes. Differences between protein synthesis and accretion were
routinely found at ~80% (Chapters 1, 2, and 3), i.e., the majority of protein mass synthesized is
degraded. Predicting accretion rates based solely on the rate of incorporation of radioisotopes
249
into protein would lead to a 5-fold over-estimation of accretion rate if protein turnover is not
accounted for. Because of the commonality of high protein turnover in all biological systems, the
interpretation based solely on rates of isotope incorporation into protein should not be used to
predict growth rates without accounting for turnover.
Regulation of feeding rates at low ration
Findings that feeding rates can be regulated to increase by 10-fold at low food ration
independent of morphology and size suggest that a rethinking of suspension feeding physiology
in marine invertebrate larval stages is warranted. Earlier work on echinoderm larval feeding has
described maximum feeding rates based on morphological metrics such as the length of the
ciliary band (Strathmann et al., 1972). This morphological explanation defines future studies that
interpreted feeding rate as a function of ciliary band length, cilium height, the water current
velocity produced by ciliary beating, and a correction factor for stroke timing. The scaling of
feeding rates with the ciliary band length provides an appealing way to estimate physiological
rates from simple morphological measurements. Furthermore, plasticity in the lengths of post-
oral arms (a major length of the ciliary band) in different food environments reinforced the idea
that feeding rates are largely explained by size and morphology (Boidron-Metairon, 1988; Miner,
2005). A study by Adams et al. (2011) was able to experimentally manipulate post-oral arm
lengths to show a decreased feeding rate. However, at low food rations of an artificial “food” (<
4,000 inert beads ml
-1
), post-oral arm lengths were only able to explain < 25% of the variance in
feeding rates. In Chapter 2 of this dissertation, size and morphology were controlled for by using
same-sized larvae exposed to 100-fold different treatments of food ration. This experimental
design produced a finding that feeding rates depended on food ration, and rates could increase by
250
10-fold for same-sized larvae. Since larvae can control such a wide range of feeding rates
morphological estimators of feeding rate are unreliable (10-fold error) without considering food
ration and regulation. This can lead to wrongful estimations of food intake in bioenergetic
models of growth. For example, 10-fold upregulation of feeding rates presented in Chapter 2
equate to increased ingestion of 6.5 ng protein at rations of 1,000 cells ml
-1
. Larvae reared at
5,000 cells ml
-1
accreted 16 ± 2 ng protein day
-1
. A gain of 6.5 ng protein through ingestion
represents a significant amount relative to the order of magnitude in accretion. Furthermore,
larvae at low rations accreted 71% of protein mass ingested, making this upregulated gain in
ingestion more critical for protein accretion. In summary, estimations of feeding rate that are
based solely on morphological size are insufficient to account for the size-independent regulation
of feeding rate under environmental conditions of low food.
Redefining food limitation to account for changes in growth efficiency
Food limitation in natural populations is of interest due to the patchiness of
phytoplankton abundance and its effects on grazers such as planktonic marine invertebrate larvae
(Mackas et al., 1985). A common observation made to imply food limitation is an increased
capacity for growth following enrichment of food-limited diets (Fenaux et al., 1994; Olson and
Olson, 1989). By this standard, if larvae do not reach maximum growth rates, then the food
environment must be limiting. While this definition utilizes a whole-organismal metric of
growth, it does not encompass an understanding of the underlying physiology that must occur to
grow in different food environments. In the present dissertation, substantial changes in growth
efficiency and physiology at different food environments were observed, resulting in growth
maintenance being achieved despite 10-fold differences in food ration. Even if larvae would not
251
be considered food-limited, the ability for larvae to change their growth physiology will
determine what is considered food-limitation under natural oceanographic conditions.
Previous studies examining the protein metabolic dynamics of marine invertebrate larvae
have adhered to standard culturing practices by feeding larvae intermittently every couple of
days, with rations adjusted based on larval sizes (Hinegardner, 1969; Breese and Malouf, 1975).
While these recommended feeding schedules ensure that larvae do not face starvation, the food
ration varies between the intervals of feeding as larvae remove algal cells from suspension. Since
the role of protein ingestion in larval growth was a focus of several studies in this dissertation
(Fig. 1), food rations were carefully monitored to ensure constant amounts (Chapters 1 and 2).
These considerations made it possible to better compare effects of different rations on feeding
and growth physiology. Better experimental designs of the effects of food limitation on larval
growth are possible using the methods employed in this study and provide new insights showing
that larvae are not as food-limited, in contrast to many conclusions in the literature.
Allocation of ATP changes with rising temperature
Recent frameworks for understanding thermal limits point numerous biochemical and
physiological strategies (Somero et al., 2017). More specifically, hypotheses related to
limitations in respiratory oxygen demand are also considered to be a major determinant of
thermal tolerance (Pörtner et al., 2017). Under this framework, stressors decrease the aerobic
scope of an organism by eliciting physiological responses that require a higher cost for the
maintenance of “normal” function. However, there is a need to better characterize what these
costs are, and what bioenergetic processes contribute the most to a stress response (Sokolova,
2021). In Chapter 3, I show that the process of protein synthesis has a clear impact on ATP
252
consumption at high temperatures, and I propose that protein metabolism should be a
bioenergetic marker for thermal tolerance. Different thermal sensitivities of protein synthesis and
ammonia excretion (an index of protein catabolism) compared to ATP supplied by respiration
result in consequential shifts in ATP allocation. Larvae of the echinoderm S. purpuratus had a
protein synthesis Q10 of 2.9 ± 0.2 (s.e.m, n=18) compared to a respiration Q 10 of 2.0 ± 0.1 (s.e.m,
n=18) (Chapter 3, Table 3). The difference in thermal sensitivity was further exacerbated when
larvae were reared at 20°C, which is near the natural thermal limit for this species (Strathmann,
1987). When reared at 20°C, the Q10 of protein synthesis increased to 3.7 ± 0.3 (s.e.m, n=12)
while the Q10 of respiration remained at 2.0 ± 0.1 (Chapter 3, Table 3). Because protein synthesis
rates were more sensitive to temperature changes than respiration, there is an increase in the
proportion of ATP consumption for protein synthesis. Based on these Q 10 values, the cost of
supporting protein synthesis rates results in a higher requirement of the available ATP pool,
increasing from 19% to 35% at 10°C and 20°C, respectively (Chapter 3, Fig. 13). Higher ATP
allocations to protein synthesis could result in less ATP being available to support other essential
processes (e.g., ion transport; Pan et al., 2021).
To meet energetic demands at higher temperatures, higher amounts of protein were
catabolized. Disproportionate increases in ammonia excretion rates relative to respiration result
in lower ratios at higher temperatures of atomic oxygen to atomic nitrogen (an index of protein-
dominated metabolism). This results in a shift towards high proportions of metabolic energy
derived from protein catabolism. For larvae of S. purpuratus, ammonia excretion rate Q10 values
were 2.9 ± 0.1 (s.e.m, n=12) when reared at 15°C, and 3.4 ± 0.2 (s.e.m, n=6) when chronically
reared at 20°C (cf. Q10 values of 2.0 for respiration at both chronic temperatures) (Chapter 3,
Table 3). Larvae of S. purpuratus reared at 20°C derived 77.0% ± 4.8 of the metabolic energy
253
from protein catabolism (O:N ratio of 18.6 ± 3.1) compared to 46.5 ± 2.4% when reared at 15°C
(O:N ratio of 30.8 ± 1.9) (Chapter 3, Fig. 11). The increased catabolism of proteins also
represents a loss of protein biomass from the organism (from respiration and ammonia excretion)
which altered the protein metabolic dynamics of growth. A recent study on bivalve larvae shows
that protein synthesis and metabolic rate have different thermal sensitivities (Pan et al., 2021).
For larvae of C. gigas, protein synthesis rates tripled (Q10 ~3) with each increase in 10°C, while
metabolic rates only double (Q10 ~2) within the same increase in temperature. This differential
thermal sensitivity was modelled for larvae of C. gigas to identify potential “tipping points”
based on stressful allocations of >50% of ATP supply to protein synthesis alone. Results from
Chapter 3 further emphasize the importance of protein metabolism as a definer of thermal
tolerance.
Broader impacts
Understanding resilience to climate change – ocean warming
Studies of organismal performance at increased temperatures have focused biochemical
strategies and oxygen capacity limitations (Pörtner, 2010; Bozinovic and Pörtner, 2015; Somero
et al., 2017). However, the interactions between metabolic processes and the major components
of ATP utilization are not well understood. The energetics perspective of protein metabolism is
critical in understanding organismal resilience to ocean warming. According to the
Intergovernmental Panel on Climate Change Special Report on the Ocean and Cryosphere in a
Changing Climate, an increase in average sea surface temperatures of up to 3.51°C is predicted
to occur by the year 2100 under high-CO 2 emission scenarios (Abram et al., 2019). This
represents an accelerated rate of temperature increase compared to a historical temperature
254
increase of 0.63°C spanning pre-industrial times (1850-1900) to more recent times (1986-2005).
At the Scripps Institute of Oceanography Pier in the Southern California Bight, most annual
average surface temperatures recorded since 1982 have been above 17.45°C, with a peak in
average sea surface temperature of 19.5°C around the year 2015 (Rasmussen et al., 2021). Given
the predicted rate of increasing average temperatures and the interannual variability in
temperature, marine invertebrate larvae will more frequently experience stressful temperatures
over the next century. Results from Chapter 3 of this dissertation show that temperatures above
20°C cause larvae to allocate disproportionately high amounts of ATP to protein synthesis alone,
leaving a lower proportion of the ATP pool for other cellular processes. To compensate for this
increased energetic demand, larvae catabolized proteins, highlighting the importance of
understanding the dynamics of protein metabolism under conditions of environmental change.
By identifying protein metabolism as a major determinant of ATP supply (protein catabolism),
and demand (protein synthesis) at high temperatures, these important phenotypes can be further
studied to understand how organisms may adapt to warming oceans. For example, Pan et al.
(2021) identified specific full-sibling families of larvae which were either vulnerable or resilient
to ocean warming due to differences in thermal sensitivities between protein synthesis and
respiration. These metrics provide powerful insights into the physiology behind thermal
tolerance and help to define biological tipping points for these limits.
Understanding resilience to climate change – food limitation
A generalized consequence of ocean warming is the increased stratification of surface
water layers that occurs with a deepening of the thermocline (Li et al., 2020). One major
implication of this stratification is that high-nutrient deep waters overlap less with the photic
255
zone where photosynthesis is optimal. Decreases in primary production can affect higher trophic
levels with mismatches in spatiotemporal abundances of phytoplankton and their zooplankton
grazers (e.g., marine invertebrate larvae) (Cushing, 1990). Previously, food limitation of
planktonic grazers has been tested by measuring changes in growth rate and morphology
following enrichment of natural food abundances (Fenaux et al., 1994). Studies following this
design conclude that food limitation occurs when growth rates in natural food environments are
lower than those in enriched environments, or when larvae exhibit morphological plasticity to
maximize feeding structure size. However, studies in this dissertation (Chapter 2) show that even
in variable food environments across a 10-fold range in ration, morphological size and protein
mass were similar. Yet, examining these biological processes just at those levels of analyses did
not give the new insight into changes in larval physiology that are presented in this dissertation
(e.g., upregulated rates of feeding and protein food conversion efficiency). Studies of resilience
to food limitation need to incorporate analyses of physiological mechanisms to understand the
biological bases of potential compensation to low food environments.
Improving aquaculture production
For the aquaculture of marine invertebrate species with planktonic larval stages, fast
growth to a metamorphically competent stage is beneficial in streamlining the production
process. Standard feeding strategies for the aquaculture production of bivalves include either
providing a target food density daily or to increase rations according to size of larvae (Helm et
al., 2004). The latter strategy is based on the notion that feeding rates increase with size, and that
optimum rations also increase as larvae grow. While feeding rates do increase with size, results
from this dissertation show that physiological changes in feeding rates, independent of
256
morphology and size, can increase 10-fold to compensate for low food rations. Furthermore,
larvae have increased food conversion efficiencies at low ration that result in indistinguishable
growth rates between a 10-fold difference in ration. Understanding the degree to which larvae of
different species are able to upregulate feeding (independently of size) and food conversion
efficiency allows for the reduction of algal culturing demand and presents a desirable trait to
select for in breeding programs aimed to improve yields and dietary efficiencies.
Future work
Genotype-environment interactions for protein metabolism
Studies of protein metabolism in the present dissertation across many larval cohorts of
sea urchin larvae revealed that there was a significant amount of biological variation in growth
rates and protein synthesis rates. These studies included multiple cohorts and replicate culture
vessels to find trends in protein metabolism under environmental changes to food availability
and temperature. Future studies partitioning genetic variance through the use of pedigreed lines
will help to partition the variance in the measured traits and identify favorable genotypes under
certain environments (Applebaum et al., 2014). For example, in Chapter 3, two cohorts of larvae
experienced significant mortality at 20°C compared to 15°C while three other cohorts of larvae
had >60% survivorship at both temperatures. Similarly, Pan et al. (2021) identified genetic
families of larvae of C. gigas which permitted physiological homeostasis under temperature
change. Understanding the genetic variance associated with traits like thermal sensitivity, food
conversion efficiencies, or protein synthesis rates will have implications for the adaptive
potential of larval stages under future climate scenarios.
257
Regulatory pathways for protein metabolism
Given the importance of the dynamics of protein synthesis and degradation for animals
growing under different environmental conditions, it is important to understand the mechanisms
of regulating these processes. Understanding how these mechanisms are induced or inhibited will
provide a better framework for predicting how these processes will change under different
scenarios. A regulatory pathway that has been identified in a range of organisms (for example,
yeast, mammalian cell lines) is the target of rapamycin (named mTOR for its discovery in
mammalian systems). The mTOR kinase is involved in a complex of proteins which respond to
amino acids (Kim, 2009; Jewell et al., 2013; Chen et al., 2014) among other molecules, and
work downstream to increase protein synthesis rates (Wang and Proud, 2006). The inhibition of
this pathway leads to increased degradation of proteins through both the autophagy-lysosome
system and the ubiquitin-proteasome system (Zhao et al., 2015). An example of this is seen
under starvation conditions where inactivation of the mTOR pathway increases protein
degradation to support steady-state protein synthesis rates. In developing sea urchins, inhibition
of mTOR has been shown to decrease global protein synthesis rates and suppress the expression
of cyclin B, a regulator of the cell cycle (Chassé et al., 2016). Further knowledge of these
signaling pathways in growing larval forms could be of benefit to understand biochemical
regulation of protein metabolic dynamics under different environmental conditions.
Improving measurements of protein synthesis
Echinoderm and bivalve larvae are ideal organisms for studying the dynamics of protein
synthesis due to their natural ability to transport amino acids from the seawater. This ability is
taken advantage of in measurements of protein synthesis with the use of
14
C-labelled amino
258
acids. However, the determination of absolute rates of protein synthesis (grams per unit time)
relies on accurate measurements of the precursor pool of amino acids used to synthesize proteins
(Davidson, 1976, pp. 87-90). This is often challenging because amino acids can be
compartmentalized within cells, and not all
14
C-labelled amino acids are available for protein
synthesis. Previous determinations of the precursor pool were used to measure directly the
specific activity of amino acids that are amino-acylated to tRNA. In sea urchins, the tRNA-
bound amino acids are indistinguishable in quantity to those found in the intracellular amino acid
pools, which allows for the estimation of absolute protein synthesis rates through measurements
of the specific activity of the intracellular amino acid pool (Regier and Kafatos, 1977). More
recent studies also support the conclusion that compartmentalization is not a dominant
component of regulating protein synthesis in sea urchin development (Pace and Manahan, 2006).
A future focus on which individual protein molecules are contributing to the overall pattern of
whole-body protein turnover would be a valuable contribution to understanding the dynamics of
protein metabolism during growth.
Proof of function for putative amino acid transporter genes
Amino acid transport capacity has been shown to predict growth rates for early larval
stages of Crassostrea gigas (Pan et al., 2015b). In addition, these authors identified by in silico
analysis of the genome 23 genes underlying this physiological process. In Chapter 4 of this
dissertation, the number of identified genes has been expanded to 27 (SoLute Carrier Family 6
amino acid I subfamily). Characterizing the function and localization of these genes may allow
for the identification of gene biomarkers for predicting growth rate. Furthermore, the regulation
and expression of certain amino acid transporter genes can be studied in the context of nutritional
259
limitation. In the present dissertation, 13 putative SoLute Carrier Family 6 amino acid
transporter genes were cloned from Crassostrea gigas. In silico comparisons of amino acid
sequences with a reference human homologue GlyT1 revealed motifs with variable putative
substrate specificity. Cloned sequences can be ligated to expression constructs for tests of
function in heterologous systems such as embryos of Danio rerio or Xenopus laevis (Meyer and
Manahan, 2009). These studies could examine transport kinetics across a range of neutral amino
acid substrates and substrate concentrations. Multiple amino acid substrates can also be used for
tests of substrate competition and specificity. Finally, the in vivo functions of these genes can be
examined through the localization of gene expression with visualization of transporter genes by
in situ hybridization. Starvation experiments can also be conducted to test the transcriptional and
protein-level regulation of amino acid transporters following different treatments of exposure to
nutrients. One hypothesis for the high degree of gene expansion is that the molecular biological
diversity of this class of transporter proteins allows for rapid physiological response to changes
in environmental conditions. By measuring the spatial and temporal expression of amino acid
transporters in larvae, the functions of these transporters and their role in larval physiology can
be fully characterized. This approach would represent a novel integration of molecular biological
and physiological analyses in marine organisms.
Conclusion
Studies that focus on only single components of the dynamics of protein metabolism
(ingestion, synthesis, accretion, and turnover) can lead to misinterpretations of estimated growth
rate. In the present dissertation, the study of protein metabolic dynamics illustrates that protein
ingestion, synthesis, degradation, accretion, and catabolism are in a dynamical balance which
260
determines the efficiencies of growth in marine invertebrate larvae. Protein synthesis alone
accounts for a significant proportion of the whole-organismal ATP budget. Furthermore, these
processes operate under a dynamic balance between rates of protein accretion which are
equivalent to the net excess of protein mass synthesis relative to degradation. Notably, studies
from this dissertation show that protein synthesis and degradation rates were constitutively high
for larvae reared under different environmental conditions of food ration and temperature. For
larvae reared under different rations, the protein metabolic dynamics were balanced by
regulation of feeding and protein food conversion efficiency that underlie adaptation to changing
environments. Under stressful conditions such as elevated temperatures, the protein metabolic
dynamics of sea urchin larvae appear to cope with change by differential reallocation of ATP. At
high temperatures, disproportional increases in protein synthesis rates relative to respiration
resulted in higher ATP allocations to support protein synthesis rates. The atomic O:N ratio also
decreased at high temperatures indicating a shift in larval metabolism to a protein-dominated
energy source. Both the disproportional increases in protein synthesis and catabolism relative to
respiration rates present a limitation to protein growth at high temperatures. In summary, studies
protein metabolic dynamics during larval growth under different environmental conditions
highlight the important physiological processes that hither to have been under-appreciated in
environmental biology.
261
Dissertation Synthesis References
Abram, N., et al. (2019). Framing and Context of the Report. In: IPCC Special Report on the
Ocean and Cryosphere in a Changing Climate [H.-O. Pörtner, D.C. Roberts, V. Masson-
Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A.
Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)].
Adams, D.K., Sewell, M.A., Angerer, R.C., and Angerer, L.M. (2011). Rapid adaptation to food
availability by a dopamine-mediated morphogenetic response. Nature communications,
2(1), 1-7.
Applebaum, S.L., Pan, T.C.F., Hedgecock, D., and Manahan, D.T. (2014). Separating the nature
and nurture of the allocation of energy in response to global change. Integrative and
Comparative Biology, 54(2), 284-295.
Bayne, B.L. (2017). Biology of oysters. Academic press.
Boidron-Metairon, I.F. (1988). Morphological plasticity in laboratory-reared echinoplutei of
Dendraster excentricus (Eschscholtz) and Lytechinus variegatus (Lamarck) in response
to food conditions. Journal of Experimental Marine Biology and Ecology, 119(1), 31-41.
Borek, E., Ponticorvo, L., and Rittenberg, D. (1958). Protein turnover in micro-organisms.
Proceedings of the National Academy of Sciences, 44(5), 369-374.
Bozinovic, F., and Pörtner, H.O. (2015). Physiological ecology meets climate change. Ecology
and Evolution, 5(5), 1025-1030.
Breese, W.P., and Malouf, R.E. (1975). Hatchery manual for the Pacific oyster. Oregon State
University Sea Grant College Program, Special Publication.
Brӧer, S., and Gether, U. (2012). The solute carrier 6 family of transporters. British Journal of
Pharmacology, 167(2), 256-278.
262
Chassé, H., Mulner-Lorillon, O., Boulben, S., Glippa, V., Morales, J., and Cormier, P. (2016).
Cyclin B translation depends on mTOR activity after fertilization in sea urchin embryos.
PloS One, 11(3), DOI: e0150318.
Chen, R., et al. (2014). The general amino acid control pathway regulates mTOR and autophagy
during serum/glutamine starvation. Journal of Cell Biology, 206(2), 173-182.
Chin-Leo, G., and Kirchman, D.L. (1988). Estimating bacterial production in marine waters from
the simultaneous incorporation of thymidine and leucine. Applied and Environmental
Microbiology, 54(8), 1934-1939.
Cushing, D. H. (1990). Plankton production and year-class strength in fish populations: an
update of the match/mismatch hypothesis. In Advances in Marine Biology (Vol. 26, pp.
249-293). Academic Press.
Fenaux, L., Strathmann, M.F., and Strathmann, R.A. (1994). Five tests of food‐limited growth of
larvae in coastal waters by comparisons of rates of development and form of
echinoplutei. Limnology and Oceanography, 39(1), 84-98.
Davidson, E.H. (1976). Gene activity in early development. Elsevier.
Helm, M.M., Bourne, N., and Lovatelli, A. (2004). Hatchery Culture of Bivalves: A Practical
Manual. Food and Agriculture Organization of the United Nations, Rome, Italy.
Hinegardner, R.T. (1969). Growth and development of the laboratory cultured sea urchin. The
Biological Bulletin, 137(3), 465-475.
Houlihan, D.F., Hall, S.J., and Gray, C. (1989). Effects of ration on protein turnover in cod.
Aquaculture, 79(1-4), 103-110.
Jewell, J.L., Russell, R.C., and Guan, K.L. (2013). Amino acid signaling upstream of mTOR.
Nature Reviews Molecular Cell Biology, 14(3), 133-139.
263
Kim, E. (2009). Mechanisms of amino acid sensing in mTOR signaling pathway. Nutrition
Research and Practice, 3(1), 64.
Kirchman, D.L. (2018). Leucine incorporation as a measure of biomass production by
heterotrophic bacteria. In Handbook of Methods in Aquatic Microbial Ecology (pp. 509-
512). CRC Press.
Kirchman, D.L., Newell, S.Y., and Hodson, R.E. (1986). Incorporation versus biosynthesis of
leucine: implications for measuring rates of protein synthesis and biomass production by
bacteria in marine systems. Marine Ecology Progress Series, 32, 47-59.
Lahtvee, P.J., Seiman, A., Arike, L., Adamberg, K., and Vilu, R. (2014). Protein turnover forms
one of the highest maintenance costs in Lactococcus lactis. Microbiology, 160(7), 1501-
1512.
Li, G., Cheng, L., Zhu, J., Trenberth, K.E., Mann, M.E., and Abraham, J.P. (2020). Increasing
ocean stratification over the past half-century. Nature Climate Change, 10(12), 1116-
1123.
López, S., and Gancedo, J.M. (1979). Effect of metabolic conditions on protein turnover in yeast.
Biochemical Journal, 178(3), 769-776.
Meyer, E., and Manahan, D.T. (2009). Nutrient uptake by marine invertebrates: cloning and
functional analysis of amino acid transporter genes in developing sea urchins
(Strongylocentrotus purpuratus). The Biological Bulletin, 217(1), 6-24.
Miner, B.G. (2005). Evolution of feeding structure plasticity in marine invertebrate larvae: a
possible trade-off between arm length and stomach size. Journal of Experimental Marine
Biology and Ecology, 315(2), 117-125.
264
Miranda, M.R., Guimarães, J.R.D., and Coelho-Souza, A.S. (2007). [
3
H] Leucine incorporation
method as a tool to measure secondary production by periphytic bacteria associated to the
roots of floating aquatic macrophyte. Journal of Microbiological Methods, 71(1), 23-31.
Olson, R.R., and Olson, M.H. (1989). Food limitation of planktotrophic marine invertebrate
larvae: does it control recruitment success? Annual Review of Ecology and Systematics,
20(1), 225-247.
Pace, D.A., and Manahan, D.T. (2006). Fixed metabolic costs for highly variable rates of protein
synthesis in sea urchin embryos and larvae. Journal of Experimental Biology, 209(1),
158-170.
Pan, T.C.F., Applebaum, S.L., Frieder, C.A., and Manahan, D.T. (2018). Biochemical bases of
growth variation during development: a study of protein turnover in pedigreed families of
bivalve larvae (Crassostrea gigas). Journal of Experimental Biology, 221(10), DOI:
jeb.171967.
Pan, T.C.F., Applebaum, S.L., and Manahan, D.T. (2015a). Experimental ocean acidification
alters the allocation of metabolic energy. Proceedings of the National Academy of
Sciences, 112(15), 4696-4701.
Pan, T.C.F., Applebaum, S.L., and Manahan, D.T. (2015b). Genetically determined variation in
developmental physiology of bivalve larvae (Crassostrea gigas). Physiological and
Biochemical Zoology, 88(2), 128-136.
Pan, T.C.F., Applebaum, S.L., and Manahan, D.T. (2021). Differing thermal sensitivities of
physiological processes alter ATP allocation. Journal of Experimental Biology, 224(2),
DOI: jeb233379.
265
Pörtner, H.O. (2010). Oxygen-and capacity-limitation of thermal tolerance: a matrix for
integrating climate-related stressor effects in marine ecosystems. Journal of Experimental
Biology, 213(6), 881-893.
Pörtner, H.O., Bock, C., and Mark, F.C. (2017). Oxygen-and capacity-limited thermal tolerance:
bridging ecology and physiology. Journal of Experimental Biology, 220(15), 2685-2696.
Rasmussen, L.L., et al. (2020). A century of Southern California coastal ocean temperature
measurements. Journal of Geophysical Research: Oceans, 125(5), e2019JC015673.
Regier, J.C., and Kafatos, F.C. (1977). Absolute rates of protein synthesis in sea urchins with
specific activity measurements of radioactive leucine and leucyl-tRNA. Developmental
Biology, 57(2), 270-283.
Sokolova, I. (2021). Bioenergetics in environmental adaptation and stress tolerance of aquatic
ectotherms: linking physiology and ecology in a multi-stressor landscape. Journal of
Experimental Biology, 224, DOI: jeb236802.
Somero, G.N., Lockwood, B.L. and Tomanek, L. (2017). Biochemical Adaptation: Response to
Environmental Challenges, from Life’s Origins to the Anthropocene. Sunderland, MA,
USA: Sinauer Associates, Inc.
Strathmann, M.F. (1987). Phylum Echinodermata, class Echinoidea. Reproduction and
development of marine invertebrates of the Northern Pacific Coast. University of
Washington Press, Seattle, 511-534.
Strathmann, R.R., Jahn, T.L., and Fonseca, J.R. (1972). Suspension feeding by marine
invertebrate larvae: clearance of particles by ciliated bands of a rotifer, pluteus, and
trochophore. The Biological Bulletin, 142(3), 505-519.
266
Wang, X., and Proud, C.G. (2006). The mTOR pathway in the control of protein synthesis.
Physiology, 21(5), 362-369.
Zehr, J.P., Falkowski, P.G., Fowler, J., and Capone, D.G. (1988). Coupling between ammonium
uptake and incorporation in a marine diatom: Experiments with the short‐lived
radioisotope 13N. Limnology and Oceanography, 33(4), 518-527.
Zhao, J., Zhai, B., Gygi, S. P., and Goldberg, A.L. (2015). mTOR inhibition activates overall
protein degradation by the ubiquitin proteasome system as well as by autophagy.
Proceedings of the National Academy of Sciences, 112(52), 15790-15797.
267
REFERENCES
Abram, N., et al. (2019). Framing and Context of the Report. In: IPCC Special Report on the
Ocean and Cryosphere in a Changing Climate [H.-O. Pörtner, D.C. Roberts, V. Masson-
Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A.
Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)].
Adams, D.K., Sewell, M.A., Angerer, R.C., and Angerer, L.M. (2011). Rapid adaptation to food
availability by a dopamine-mediated morphogenetic response. Nature communications,
2(1), 1-7.
Allen, J.D. (2008). Size-specific predation on marine invertebrate larvae. The Biological
Bulletin, 214(1), 42-49.
Applebaum, S.L., Ginsburg, D.W., Capron, C.S., and Manahan, D.T. (2013). Expression of
amino acid transporter genes in developmental stages and adult tissues of Antarctic
echinoderms. Polar Biology, 36(9), 1257-1267.
Applebaum, S.L., Pan, T.C.F., Hedgecock, D., and Manahan, D.T. (2014). Separating the nature
and nurture of the allocation of energy in response to global change. Integrative and
Comparative Biology, 54(2), 284-295.
Arenas-Mena, C., Cameron, A.R., and Davidson, E.H. (2000). Spatial expression of Hox cluster
genes in the ontogeny of a sea urchin. Development, 127(21), 4631-4643.
Baldwin, B.S., and Newell, R.I. (1995). Feeding rate responses of oyster larvae (Crassostrea
virginica) to seston quantity and composition. Journal of Experimental Marine Biology
and Ecology, 189(1-2), 77-91.
Bayne, B.L. (2017). Biology of oysters. Academic press.
268
Bayne, B.L., and Newell, R.C. (1983). Physiological energetics of marine molluscs. In The
Mollusca (pp. 407-515). Academic Press.
Beiras, R., and Widdows, J. (1995). Effect of the neurotransmitters dopamine, serotonin and
norepinephrine on the ciliary activity of mussel (Mytilus edulis) larvae. Marine Biology,
122(4), 597-603.
Bergenius, M.A., Meekan, M.G., Robertson, R.D., and McCormick, M.I. (2002). Larval growth
predicts the recruitment success of a coral reef fish. Oecologia, 131(4), 521-525.
Bertram, D.F., and Strathmann, R.R. (1998). Effects of maternal and larval nutrition on growth
and form of planktotrophic larvae. Ecology, 79(1), 315-327.
Boidron-Metairon, I.F. (1988). Morphological plasticity in laboratory-reared echinoplutei of
Dendraster excentricus (Eschscholtz) and Lytechinus variegatus (Lamarck) in response
to food conditions. Journal of Experimental Marine Biology and Ecology, 119(1), 31-41.
Borek, E., Ponticorvo, L., and Rittenberg, D. (1958). Protein turnover in micro-organisms.
Proceedings of the National Academy of Sciences, 44(5), 369.
Borycz, J., et al. (2018). Location and functions of Inebriated in the Drosophila eye. Biology
Open, 7(7), 1-9.
Bouchard, C., Boudko, D.Y., and Jiang, R.H. (2019). A SLC6 transporter cloned from the lion's
mane jellyfish (Cnidaria, Scyphozoa) is expressed in neurons. PloS one, 14(6), DOI:
e0218806.
Boudko, D. Y. (2012). Molecular basis of essential amino acid transport from studies of insect
nutrient amino acid transporters of the SLC6 family (NAT-SLC6). Journal of Insect
Physiology, 58(4), 433-449.
269
Boudko, D.Y., Kohn, A.B., Meleshkevitch, E.A., Dasher, M.K., Seron, T.J., Stevens, B.R., and
Harvey, W.R. (2005). Ancestry and progeny of nutrient amino acid transporters.
Proceedings of the National Academy of Sciences, 102(5), 1360-1365.
Bozinovic, F., and Pörtner, H.O. (2015). Physiological ecology meets climate change. Ecology
and Evolution, 5(5), 1025-1030.
Bradford, M.M. (1976). A rapid and sensitive method for the quantitation of microgram
quantities of protein utilizing the principle of protein-dye binding. Analytical
Biochemistry, 72(1-2), 248-254.
Braven, J., Evens, R., and Butler, E. I. (1984). Amino acids in sea water. Chemistry in Ecology,
2(1), 11-21.
Breese, W.P., and Malouf, R.E. (1975). Hatchery manual for the Pacific oyster. Oregon State
University Sea Grant College Program, Special Publication.
Brett, J.R. (1971). Energetic responses of salmon to temperature. A study of some thermal
relations in the physiology and freshwater ecology of sockeye salmon (Oncorhynchus
nerkd). American Zoologist, 11(1), 99-113.
Brӧer, S., and Gether, U. (2012). The solute carrier 6 family of transporters. British Journal of
Pharmacology, 167(2), 256-278.
Butterwick, C., Heaney, S.I., and Talling, J.F. (1982). A comparison of eight methods for
estimating the biomass and growth of planktonic algae. British Phycological Journal,
17(1), 69-79.
Buttgereit, F., and Brand, M.D. (1995). A hierarchy of ATP-consuming processes in mammalian
cells. Biochemical Journal, 312(1), 163-167.
270
Cabrol, J., Fabre, A., Nozais, C., Tremblay, R., Starr, M., Plourde, S., and Winkler, G. (2020).
Functional feeding response of Nordic and Arctic krill on natural phytoplankton and
zooplankton. Journal of Plankton Research, 42(2), 239-252.
Calow, P. (1982). Homeostasis and fitness. The American Naturalist, 120(3), 416-419.
Chassé, H., Mulner-Lorillon, O., Boulben, S., Glippa, V., Morales, J., and Cormier, P. (2016).
Cyclin B translation depends on mTOR activity after fertilization in sea urchin embryos.
PloS One, 11(3), DOI: e0150318.
Chen, R., et al. (2014). The general amino acid control pathway regulates mTOR and autophagy
during serum/glutamine starvation. Journal of Cell Biology, 206(2), 173-182.
Chin-Leo, G., and Kirchman, D.L. (1988). Estimating bacterial production in marine waters from
the simultaneous incorporation of thymidine and leucine. Applied and Environmental
Microbiology, 54(8), 1934-1939.
Christensen, A.B., Nguyen, H.D., and Byrne, M. (2011). Thermotolerance and the effects of
hypercapnia on the metabolic rate of the ophiuroid Ophionereis schayeri: inferences for
survivorship in a changing ocean. Journal of Experimental Marine Biology and Ecology,
403(1-2), 31-38.
Clark, M S., Thorne, M.A., Amaral, A., Vieira, F., Batista, F. M., Reis, J., and Power, D.M.
(2013). Identification of molecular and physiological responses to chronic environmental
challenge in an invasive species: the Pacific oyster, Crassostrea gigas. Ecology and
Evolution, 3(10), 3283-3297.
271
Claxton, D.P., Quick, M., Shi, L., Delmondes de Carvalho, F., Weinstein, H., Javitch, J.A., and
Mchaourab, H.S. (2010). Ion/substrate-dependent conformational dynamics of a bacterial
homolog of neurotransmitter:sodium symporters. Nature Structural and Molecular
Biology, 17(7), 822-829.
Cochran, R.C., and Engelmann, F. (1975). Environmental regulation of the annual reproductive
season of Strongylocentrotus purpuratus (Stimpson). Biological Bulletin, 148, 393-401.
Coleman, J.A., Green, E.M., and Gouaux, E. (2016). X-ray structures and mechanism of the
human serotonin transporter. Nature, 532(7599), 334-339.
Conover, R.J. (1968). Zooplankton—life in a nutritionally dilute environment. American
Zoologist, 8(1), 107-118.
Courtright, R.C., Breese, W.P., and Krueger, H. (1971). Formulation of a synthetic seawater for
bioassays with Mytilus edulis embryos. Water Research, 5(10), 877-888.
Coutinho, P., Ferreira, M., Freire, I., and Otero, A. (2020). Enriching rotifers with ‘premium’
microalgae: Rhodomonas lens. Marine Biotechnology, 22, 118-129.
Crisp, D.J., Yule, A.B., and White, K.N. (1985). Feeding by oyster larvae: the functional
response, energy budget and a comparison with mussel larvae. Journal of the Marine
Biological Association of the United Kingdom, 65(3), 759-783.
Cucci, T.L., Shumway, S.E., Newell, R.C., Selvin, R., Guillard, R.R.L., and Yentsch, C.M.
(1985). Flow cytometry: a new method for characterization of differential ingestion,
digestion and egestion by suspension feeders. Marine Ecology Progress Series, 24, 201-
204.
272
Cushing, D.H. (1990). Plankton production and year-class strength in fish populations: an update
of the match/mismatch hypothesis. In: Advances in Marine Biology (Vol. 26, pp. 249-
293). Academic Press.
Das, A.B., and Prosser, C.L. (1967). Biochemical changes in tissues of goldfish acclimated to
high and low temperatures—I. Protein synthesis. Comparative Biochemistry and
Physiology, 21(3), 449-467.
Davidson, E.H. (1976). Gene activity in early development. Elsevier.
De Duve, C., Pressman, B.C., Gianetto, R., Wattiaux, R., and Appelmans, F. (1955). Tissue
fractionation studies. 6. Intracellular distribution patterns of enzymes in rat-liver tissue.
Biochemical Journal, 60(4), 604.
Deason, E.E. (1980). Potential effect of phytoplankton colony breakage on the calculation of
zooplankton filtration rates. Marine Biology, 57, 279-286.
Deevey Jr, E.S. (1947). Life tables for natural populations of animals. The Quarterly Review of
Biology, 22(4), 283-314.
Ellison, A., Pouv, A., and Pace, D.A. (2021). Different protein metabolic strategies for growth
during food-induced physiological plasticity in echinoid larvae. Journal of Experimental
Biology, 224(4), DOI: jeb230748.
Emlet, R.B., and Hoegh‐Guldberg, O. (1997). Effects of egg size on postlarval performance:
experimental evidence from a sea urchin. Evolution, 51(1), 141-152.
Farmanfarmaian, A., and Giese, A.C. (1963). Thermal tolerance and acclimation in the western
purple sea urchin, Strongylocentrotus purpuratus. Physiological Zoology, 36(3), 237-243.
Felsenstein, J. (1985). Confidence limits on phylogenies: an approach using the bootstrap.
Evolution, 39(4), 783-791.
273
Fenaux, L., Strathmann, M.F., and Strathmann, R.A. (1994). Five tests of food‐limited growth of
larvae in coastal waters by comparisons of rates of development and form of
echinoplutei. Limnology and Oceanography, 39(1), 84-98.
Ferreira-Arrieta, A., García-Esquivel, Z., González-Gómez, M.A., and Valenzuela-Espinoza, E.
(2015). Growth, survival, and feeding rates for the geoduck Panopea globosa during
larval development. Journal of Shellfish Research, 34(1), 55-61.
Finn, R.N., and Fyhn, H.J. (2010). Requirement for amino acids in ontogeny of fish. Aquaculture
Research, 41(5), 684-716.
Fly, E.K., Monaco, C.J., Pincebourde, S., and Tullis, A. (2012). The influence of intertidal
location and temperature on the metabolic cost of emersion in Pisaster ochraceus.
Journal of Experimental Marine Biology and Ecology, 422, 20-28.
Fraser, A.J. (1989). Triacylglycerol content as a condition index for fish, bivalve, and crustacean
larvae. Canadian Journal of Fisheries and Aquatic Science, 46, 1868-1873.
Frieder, C.A., Applebaum, S.L., Pan, T.C.F., and Manahan, D.T. (2018). Shifting balance of
protein synthesis and degradation sets a threshold for larval growth under environmental
stress. The Biological Bulletin, 234(1), 45-57.
Frost, B.W. (1972). Effects of size and concentration of food particles on the feeding behavior of
the marine planktonic copepod Calanus pacificus 1. Limnology and oceanography, 17(6),
805-815.
Frost, B.W. (1975). A threshold feeding behavior in Calanus pacificus 1. Limnology and
Oceanography, 20(2), 263-266.
Frost, B.W. (1977). Feeding behavior of Calanus pacificus in mixtures of food particles 1.
Limnology and Oceanography, 22(3), 472-491.
274
Fry, J.P., Mailloux, N.A., Love, D.C., Milli, M.C., and Cao, L. (2018). Feed conversion
efficiency in aquaculture: do we measure it correctly? Environmental Research Letters,
13(2), 024017.
Gallager, S.M., Mann, R., and Sasaki, G.C. (1986). Lipid as an index of growth and viability in
three species of bivalve larvae. Aquaculture, 56(2), 81-103.
Gauld, D. T. (1951). The grazing rate of planktonic copepods. Journal of the Marine Biological
Association of the United Kingdom, 29(3), 695-706.
Gelpi, C. (2018). Chlorophyll Dynamics Around the Southern Channel Islands. Western North
American Naturalist, 78(4), 590-604.
Giménez, L. (2010). Relationships between habitat conditions, larval traits, and juvenile
performance in a marine invertebrate. Ecology, 91(5), 1401-1413.
Gimenez, L., and Anger, K. (2005). Effects of temporary food limitation on survival and
development of brachyuran crab larvae. Journal of Plankton Research, 27(5), 485-494.
Gnaiger, E. (1983). Calculation of energetic and biochemical equivalents of respiratory oxygen
consumption. In E. Gnaiger and H. Forstner (Eds.), Polarographic oxygen sensors (pp.
337-345). Berlin, Germany: Springer.
Gnaiger, E., and Bitterlich, G. (1984). Proximate biochemical composition and caloric content
calculated from elemental CHN analysis: a stoichiometric concept. Oecologia, 62(3),
289-298.
Guillard, R.R., and Ryther, J.H. (1962). Studies of marine planktonic diatoms: I. Cyclotella nana
Hustedt, and Detonula confervacea (Cleve) Gran. Canadian journal of microbiology,
8(2), 229-239.
275
Hadfield, M.G., Carpizo-Ituarte, E.J., Del Carmen, K., and Nedved, B.T. (2001). Metamorphic
competence, a major adaptive convergence in marine invertebrate larvae. American
Zoologist, 41(5), 1123-1131.
Halvorson, H. (1958). Intracellular protein and nucleic acid turnover in resting yeast cells.
Biochimica et Biophysica Acta, 27, 255-266.
Handeland, S.O., Imsland, A.K., and Stefansson, S.O. (2008). The effect of temperature and fish
size on growth, feed intake, food conversion efficiency and stomach evacuation rate of
Atlantic salmon post-smolts. Aquaculture 283, 36-42.
Harbison, G.R., and McAlister, V.L. (1980). Fact and artifact in copepod feeding experiments 1.
Limnology and Oceanography, 25(6), 971-981.
Hart, M.W. (1996). Variation in suspension feeding rates among larvae of some temperate,
Eastern Pacific echinoderms. Invertebrate Biology, 115(1), 30-45.
Hart, M.W., and Strathmann, R.R. (1994). Functional consequences of phenotypic plasticity in
echinoid larvae. The Biological Bulletin, 186(3), 291-299.
Harvey, H.W. (1937). Note on selective feeding by Calanus. Journal of the Marine Biological
Association of the United Kingdom, 22(1), 97-100.
Haschemeyer, A.E., Persell, R., and Smith, M.A. (1979). Effect of temperature on protein
synthesis in fish of the Galapagos and Perlas Islands. Comparative Biochemistry and
Physiology. B, Comparative biochemistry, 64(1), 91-95.
Hawkins, A.J.S. (1991). Protein turnover: a functional appraisal. Functional Ecology, 222-233.
Helbig, et al. (2011). The diversity of protein turnover and abundance under nitrogen-limited
steady-state conditions in Saccharomyces cerevisiae. Molecular Biosystems, 7(12), 3316-
3326.
276
Helm, M.M., Bourne, N., and Lovatelli, A. (2004). Hatchery Culture of Bivalves: A Practical
Manual. Food and Agriculture Organization of the United Nations, Rome, Italy.
Herrera, J.C., McWeeny, S.K., and McEdward, L.R., (1996). Diversity of energetic strategies
among echinoid larvae and the transition from feeding to nonfeeding development.
Oceanologica, 19(3-4), 313-321.
Hershko, A., Ciechanover, A., and Rose, I.A. (1979). Resolution of the ATP-dependent
proteolytic system from reticulocytes: a component that interacts with ATP. Proceedings
of the National Academy of Sciences, 76(7), 3107-3110.
Hinegardner, R.T. (1969). Growth and development of the laboratory cultured sea urchin. The
Biological Bulletin, 137(3), 465-475.
Hinegardner, R.T. (1975). Morphology and genetics of sea-urchin development. American
Zoologist, 15(3), 679-689.
Hochachka, P.W., and Somero, G.N. (2002). Biochemical Adaptation: Mechanism and Process
in Physiological Evolution. Oxford university press.
Hoegh-Guldberg, O. (1994). Uptake of dissolved organic matter by larval stage of the crown-of-
thorns starfish Acanthaster planci. Marine Biology, 120(1), 55-63.
Hoegh-Guldberg, O.V.E., and Pearse, J.S. (1995). Temperature, food availability, and the
development of marine invertebrate larvae. American Zoologist, 35(4), 415-425.
Houlihan, D.F., Hall, S.J., and Gray, C. (1989). Effects of ration on protein turnover in cod.
Aquaculture, 79(1-4), 103-110.
Houlihan, D.F., Hall, S.J., Gray, C., and Noble, B.S. (1988). Growth rates and protein turnover in
Atlantic cod, Gadus morhua. Canadian Journal of Fisheries and Aquatic Sciences, 45,
951-964.
277
Hua, K., et al. (2019). The future of aquatic protein: implications for protein sources in
aquaculture diets. One Earth, 1(3), 316-329.
Huey, R.B., and Kingsolver, J.G. (1993). Evolution of resistance to high temperature in
ectotherms. The American Naturalist, 142, S21-S46.
Huey, R.B., and Kingsolver, J.G. (2019). Climate warming, resource availability, and the
metabolic meltdown of ectotherms. The American Naturalist, 194(6), E140-E150.
Huey, R.B., and Stevenson, R.D. (1979). Integrating thermal physiology and ecology of
ectotherms: a discussion of approaches. American Zoologist, 19(1), 357-366.
Hughes, S.J.M., Ruhl, H.A., Hawkins, L.E., Hauton, C., Boorman, B., and Billett, D.S. (2011).
Deep-sea echinoderm oxygen consumption rates and an interclass comparison of
metabolic rates in Asteroidea, Crinoidea, Echinoidea, Holothuroidea and Ophiuroidea.
Journal of Experimental Biology, 214(15), 2512-2521.
Huntley, M. (1988). Feeding biology of Calanus: a new perspective. In Biology of Copepods (pp.
83-99). Springer, Dordrecht.
Hyatt, T.M., and Ekker, S.C. (1998). Vectors and techniques for ectopic gene expression in
zebrafish. Methods in Cell Biology, 59, 117-126.
Imsland, A.K., Foss, A., Gunnarsson, S., Berntssen, M.H.G., FitzGerald, R., Bonga, S.W., Ham,
E., Naevdal, G., and Stefansson, S.O. (2001). The interaction of temperature and salinity
on growth and food conversion in juvenile turbot (Scophthalmus maximus). Aquaculture,
198, 353-367.
278
Inoue, K., Tsukuda, K., Koito, T., Miyazaki, Y., Hosoi, M., Kado, R., Miyazaki, N., and
Toyohara, H. (2008). Possible role of a taurine transporter in the deep-sea mussel
Bathymodiolus septemdierum in adaptation to hydrothermal vents. FEBS Letters,
582(10), 1542-1546.
Jaeckle, W.B., and Manahan, D.T. (1989). Feeding by a “nonfeeding” larva: uptake of dissolved
amino acids from seawater by lecithotrophic larvae of the gastropod Haliotis
rufescens. Marine Biology, 103(1), 87-94.
Jaeckle, W.B., and Manahan, D.T. (1989). Growth and energy imbalance during the
development of a lecithotrophic molluscan larva (Haliotis rufescens). The Biological
Bulletin, 177(2), 237-246.
Jewell, J.L., Russell, R.C., and Guan, K.L. (2013). Amino acid signaling upstream of mTOR.
Nature Reviews Molecular Cell Biology, 14(3), 133-139.
Jones, D.T., Taylor, W.R., and Thornton, J.M. (1992). The rapid generation of mutation data
matrices from protein sequences. Bioinformatics, 8(3), 275-282.
Kalachev, A.V. (2020). Effect of dopamine on early larvae of sea urchins, Mesocentrotus nudus
and Strongylocentrotus intermedius. Journal of Experimental Zoology Part B: Molecular
and Developmental Evolution, 334(6), 373-380.
Käll, L., Krogh, A., and Sonnhammer, E.L. (2004). A combined transmembrane topology and
signal peptide prediction method. Journal of Molecular Biology, 338(5), 1027-1036.
Käll, L., Krogh, A., and Sonnhammer, E.L. (2007). Advantages of combined transmembrane
topology and signal peptide prediction—the Phobius web server. Nucleic Acids Research,
35(suppl_2), W429-W432.
279
Kaushik, S.J., and Seiliez, I. (2010). Protein and amino acid nutrition and metabolism in fish:
current knowledge and future needs. Aquaculture Research, 41(3), 322-332.
Kellermann, V., Chown, S.L., Schou, M.F., Aitkenhead, I., Janion-Scheepers, C., Clemson, A.,
Scott, M.T., and Sgrò, C.M. (2019). Comparing thermal performance curves across traits:
how consistent are they? Journal of Experimental Biology, 222(11), DOI: jeb193433.
Kim, E. (2009). Mechanisms of amino acid sensing in mTOR signaling pathway. Nutrition
Research and Practice, 3(1), 64.
Kinjo, A., Koito, T., Kawaguchi, S., and Inoue, K. (2013). Evolutionary history of the GABA
transporter (GAT) group revealed by marine invertebrate GAT-1. PLoS One, 8(12), DOI:
e82410.
Kiørboe, T., Saiz, E., Tiselius, P., and Andersen, K.H. (2018). Adaptive feeding behavior and
functional responses in zooplankton. Limnology and Oceanography, 63(1), 308-321.
Kirchman, D.L. (2018). Leucine incorporation as a measure of biomass production by
heterotrophic bacteria. In Handbook of Methods in Aquatic Microbial Ecology (pp. 509-
512). CRC Press.
Kirchman, D.L., Newell, S.Y., and Hodson, R.E. (1986). Incorporation versus biosynthesis of
leucine: implications for measuring rates of protein synthesis and biomass production by
bacteria in marine systems. Marine Ecology Progress Series, 32, 47-59.
Koito, T., Nakamura-Kusakabe, I., Yoshida, T., Maruyama, T., Omata, T., Miyazaki, N., and
Inoue, K. (2010). Effect of long-term exposure to sulfides on taurine transporter gene
expression in the gill of the deep-sea mussel Bathymodiolus platifrons, which harbors a
methanotrophic symbiont. Fisheries Science, 76(2), 381-388.
280
Kooijman, S.A.L.M. (1986). Energy budgets can explain body size relations. Journal of
Theoretical Biology, 121(3), 269-282.
Korsgaard, B. (1991). Metabolism of larval turbot Scophthalmus maximus (L.) and uptake of
amino acids from seawater studied by autoradiographic and radiochemical methods.
Journal of Experimental Marine Biology and Ecology, 148(1), 1-10.
Krogh, A. (1931). Dissolved substances as food of aquatic organisms. Biological Reviews, 6(4),
412-442.
Kumar, S., Stecher, G., Li, M., Knyaz, C., and Tamura, K. (2018). MEGA X: molecular
evolutionary genetics analysis across computing platforms. Molecular Biology and
Evolution, 35(6), 1547-1549.
Lacalli, T.C., and Gilmour, T.H.J. (1990). Ciliary reversal and locomotory control in the pluteus
larva of Lytechinus pictus. Philosophical Transactions: Biological Sciences, 330, 391-
396.
Lahtvee, P.J., Seiman, A., Arike, L., Adamberg, K., and Vilu, R. (2014). Protein turnover forms
one of the highest maintenance costs in Lactococcus lactis. Microbiology, 160(7), 1501-
1512.
Langdon, C.J. (1983). Growth studies with bacteria-free oyster (Crassostrea gigas) larvae fed on
semi-defined artificial diets. The Biological Bulletin, 164(2), 227-235.
Lannig, G., Eilers, S., Pörtner, H.O., Sokolova, I.M., and Bock, C. (2010). Impact of ocean
acidification on energy metabolism of oyster, Crassostrea gigas—changes in metabolic
pathways and thermal response. Marine drugs, 8(8), 2318-2339.
Leahy, P.S. (1986). Laboratory culture of Strongylocentrotus purpuratus adults, embryos, and
larvae. Methods in Cell Biology, 27, 1-13.
281
Lee, C., and Bada, J.L. (1977). Dissolved amino acids in the equatorial Pacific, the Sargasso Sea,
and Biscayne Bay 1. Limnology and Oceanography, 22(3), 502-510.
Lee, J.W., Applebaum, S.L., and Manahan, D.T. (2016). Metabolic cost of protein synthesis in
larvae of the Pacific oyster (Crassostrea gigas) is fixed across genotype, phenotype, and
environmental temperature. The Biological Bulletin, 230(3), 175-187.
Li, G., Cheng, L., Zhu, J., Trenberth, K.E., Mann, M.E., and Abraham, J.P. (2020). Increasing
ocean stratification over the past half-century. Nature Climate Change, 10(12), 1116-
1123.
Li, L., Nelson, C.J., Trösch, J., Castleden, I., Huang, S., and Millar, A.H. (2017). Protein
degradation rate in Arabidopsis thaliana leaf growth and development. The Plant Cell,
29(2), 207-228.
López, S., and Gancedo, J.M. (1979). Effect of metabolic conditions on protein turnover in yeast.
Biochemical Journal, 178(3), 769-776.
Loughna, P.T., and Goldspink, G. (1985). Muscle protein synthesis rates during temperature
acclimation in a eurythermal (Cyprinus carpio) and a stenothermal (Salmo gairdneri)
species of teleost. Journal of Experimental Biology, 118(1), 267-276.
Mackas, D.L., Denman, K.L., and Abbott, M.R. (1985). Plankton patchiness: biology in the
physical vernacular. Bulletin of Marine Science, 37(2), 652-674.
Manahan, D.T. (1983). The uptake and metabolism of dissolved amino acids by bivalve larvae.
The Biological Bulletin, 164(2), 236-250.
Manahan, D.T. (1989). Amino acid fluxes to and from seawater in axenic veliger larvae of a
bivalve (Crassostrea gigas). Marine Ecology Progress Series, 53(3), 247-255.
282
Manahan, D.T. (1990). Adaptations by invertebrate larvae for nutrient acquisition from seawater.
American Zoologist, 30(1), 147-160.
Manahan, D.T., and Crisp, D.J. (1983). Autoradiographic studies of the uptake of dissolved
amino acids from sea water by bivalve larvae. Journal of the Marine Biological
Association of the United Kingdom, 63(3), 673-682.
Manahan, D.T., Davis, J.P., and Stephens, G.C. (1983). Bacteria-free sea urchin larvae: selective
uptake of neutral amino acids from seawater. Science, 220(4593), 204-206.
Manahan, D.T., Wright, S.H., and Stephens, G.C. (1983). Simultaneous determination of net
uptake of 16 amino acids by a marine bivalve. American Journal of Physiology -
Regulatory, Integrative and Comparative Physiology, 244(6), 832-838.
Marin, V., Huntley, M.E., and Frost, B. (1986). Measuring feeding rates of pelagic herbivores:
analysis of experimental design and methods. Marine Biology, 93(1), 49-58.
Marsh, A.G., and Manahan, D.T. (1999). A method for accurate measurements of the respiration
rates of marine invertebrate embryos and larvae. Marine Ecology Progress Series, 184, 1-
10.
Marshall, D.J., Keough, M.J. (2008). The evolutionary ecology of offspring size in marine
invertebrates. Advances in Marine Biology, 53, 1–60.
Marshall, D.J., Krug, P.J., Kupriyanova, E.K., Byrne, M., and Emlet, R.B. (2012). The
biogeography of marine invertebrate life histories. Annual Review of Ecology, Evolution,
and Systematics, 43, 97-114.
Marshall, R., McKinley, S., and Pearce, C.M. (2010). Effects of nutrition on larval growth and
survival in bivalves. Reviews in Aquaculture, 2(1), 33-55.
283
Martínez, I., Herrera, A., Tames-Espinosa, M., Bondyale-Juez, D.R., Romero-Kutzner, V.,
Packard, T.T., and Gómez, M. (2020). Protein in marine plankton: a comparison of
spectrophotometric methods. Journal of Experimental Marine Biology and Ecology, 526,
DOI: j.jembe.2020.151357.
Martin-Perez, M., and Villén, J. (2017). Determinants and regulation of protein turnover in yeast.
Cell systems, 5(3), 283-294.
Mayzaud, P., and Conover, R.J. (1988). O: N atomic ratio as a tool to describe zooplankton
metabolism. Marine ecology progress series. Oldendorf, 45(3), 289-302.
McCormick, M.I., and Hoey, A.S. (2004). Larval growth history determines juvenile growth and
survival in a tropical marine fish. Oikos, 106(2), 225-242.
Mente, E., Houlihan, D.F., and Smith, K. (2001). Growth, feeding frequency, protein turnover,
and amino acid metabolism in European lobster Homarus gammarus L. Journal of
Experimental Zoology, 289(7), 419-432.
Meyer, E., and Manahan, D.T. (2009). Nutrient uptake by marine invertebrates: cloning and
functional analysis of amino acid transporter genes in developing sea urchins
(Strongylocentrotus purpuratus). Biological Bulletin, 217(1), 6-24.
Meyer, E., Green, A.J., Moore, M., and Manahan, D.T. (2007). Food availability and
physiological state of sea urchin larvae (Strongylocentrotus purpuratus). Marine Biology,
152(1), 179-191.
Millward, D.J., Garlick, P.J., Stewart, R.J., Nnanyelugo, D.O., and Waterlow, J.C. (1975).
Skeletal-muscle growth and protein turnover. Biochemical Journal, 150(2), 235-243.
284
Miner, B.G. (2005). Evolution of feeding structure plasticity in marine invertebrate larvae: a
possible trade-off between arm length and stomach size. Journal of Experimental Marine
Biology and Ecology, 315(2), 117-125.
Miranda, M.R., Guimarães, J.R.D., and Coelho-Souza, A.S. (2007). [
3
H] Leucine incorporation
method as a tool to measure secondary production by periphytic bacteria associated to the
roots of floating aquatic macrophyte. Journal of Microbiological Methods, 71(1), 23-31.
Miyamoto, N., Yoshida, M.A., Koga, H., and Fujiwara, Y. (2017). Genetic mechanisms of bone
digestion and nutrient absorption in the bone-eating worm Osedax japonicus inferred
from transcriptome and gene expression analyses. BMC Evolutionary Biology, 17(1), 1-
13.
Moran, A.L., and Manahan, D.T. (2004). Physiological recovery from prolonged ‘starvation’ in
larvae of the Pacific oyster Crassostrea gigas. Journal of Experimental Marine Biology
and Ecology, 306(1), 17-36.
Morth, J.P., et al. (2007). Crystal structure of the sodium–potassium pump. Nature, 450(7172),
1043-1049.
Nelson, C.J., and Millar, A.H. (2015). Protein turnover in plant biology. Nature plants, 1(3), 1-7.
Nezlin, N.P., and Li, B.L. (2003). Time-series analysis of remote-sensed chlorophyll and
environmental factors in the Santa Monica-San Pedro Basin off Southern California.
Journal of Marine Systems, 39, 185-202.
Nisbet, R.M., Jusup, M., Klanjscek, T., and Pecquerie, L. (2012). Integrating dynamic energy
budget (DEB) theory with traditional bioenergetic models. Journal of Experimental
Biology, 215(6), 892-902.
Ohsumi, Y. (2014). Historical landmarks of autophagy research. Cell Research, 24, 9-23.
285
Olmi, E.J. (1994). Vertical migration of blue crab Callinectes sapidus megalopae: implications
for transport in estuaries. Marine Ecology Progress Series, 113, 39-54.
Olson, R.R., and Olson, M.H. (1989). Food limitation of planktotrophic marine invertebrate
larvae: does it control recruitment success? Annual Review of Ecology and Systematics,
20(1), 225-247.
Ostrovsky, I. (1995). The parabolic pattern of animal growth: determination of equation
parameters and their temperature dependencies. Freshwater Biology, 33(3), 357-371.
Pace, D.A., and Manahan, D.T. (2006). Fixed metabolic costs for highly variable rates of protein
synthesis in sea urchin embryos and larvae. Journal of Experimental Biology, 209(1),
158-170.
Pace, D.A., Marsh, A.G., Leong, P.K., Green, A.J., Hedgecock, D., and Manahan, D.T. (2006).
Physiological bases of genetically determined variation in growth of marine invertebrate
larvae: a study of growth heterosis in the bivalve Crassostrea gigas. Journal of
Experimental Marine Biology and Ecology, 335(2), 188-209.
Palmer, A.R., and Strathmann, R.R. (1981). Scale of dispersal in varying environments and its
implications for life histories of marine invertebrates. Oecologia, 48(3), 308-318.
Pan, T.C.F., Applebaum, S.L., and Manahan, D.T. (2015). Experimental ocean acidification
alters the allocation of metabolic energy. Proceedings of the National Academy of
Sciences, 112(15), 4696-4701.
Pan, T.C.F., Applebaum, S.L., and Manahan, D.T. (2015). Genetically determined variation in
developmental physiology of bivalve larvae (Crassostrea gigas). Physiological and
Biochemical Zoology, 88(2), 128-136.
286
Pan, T.C.F., Applebaum, S.L., and Manahan, D.T. (2021). Differing thermal sensitivities of
physiological processes alter ATP allocation. Journal of Experimental Biology, 224(2),
DOI: jeb233379.
Pan, T.C.F., Applebaum, S.L., Frieder, C.A., and Manahan, D.T. (2018). Biochemical bases of
growth variation during development: a study of protein turnover in pedigreed families of
bivalve larvae (Crassostrea gigas). Journal of Experimental Biology, 221(10), DOI:
jeb.171967.
Pan, T.C.F., Applebaum, S.L., Lentz, B.A., and Manahan, D.T. (2016). Predicting phenotypic
variation in growth and metabolism of marine invertebrate larvae. Journal of
Experimental Marine Biology and Ecology, 483, 64-73.
Paulay, G., Boring, L., and Strathmann, R.R. (1985). Food limited growth and development of
larvae: experiments with natural sea water. Journal of Experimental Marine Biology and
Ecology, 93(1-2), 1-10.
Pearl, R., and Miner, J.R. (1935). Experimental studies on the duration of life. XIV. The
comparative mortality of certain lower organisms. The Quarterly Review of Biology,
10(1), 60-79.
Pearse, J.S. (1994). Cold-water echinoderms break Thorson’s rule. Reproduction, Larval Biology
and Recruitment in the Deep-sea Benthos, 27-43.
Pechenik, J.A. (1980). Growth and energy balance during the larval lives of three prosobranch
gastropods. Journal of Experimental Marine Biology and Ecology, 44(1), 1-28.
Pechenik, J.A. (1999). On the advantages and disadvantages of larval stages in benthic marine
invertebrate life cycles. Marine Ecology Progress Series, 177, 269-297.
287
Pechenik, J.A., Estrella, M.S., and Hammer, K. (1996). Food limitation stimulates
metamorphosis of competent larvae and alters postmetamorphic growth rate in the marine
prosobranch gastropod Crepidula fornicata. Marine Biology, 127(2), 267-275.
Peñaloza, C., et al. (2021). A chromosome-level genome assembly for the Pacific oyster
Crassostrea gigas. GigaScience, 10(3), DOI: giab020.
Penmatsa, A., Wang, K.H., and Gouaux, E. (2013). X-ray structure of dopamine transporter
elucidates antidepressant mechanism. Nature, 503(7474), 85-90.
Perland, E., and Fredriksson, R. (2017). Classification systems of secondary active transporters.
Trends in Pharmacological Sciences, 38(3), 305-315.
Pernet, B. (2018). Larval feeding: mechanisms, rates, and performance in nature (pp. 87-102).
Oxford University Press, Oxford.
Peters, R.H. (1984). Methods for the study of feeding, filtering and assimilation by zooplankton.
In: Downing, J.A., Rigler, F.H. (eds) A manual on methods for the assessment of
secondary productivity in fresh waters, second edition. Blackwell Scientific Publications,
Oxford London Edinburgh Boston Melbourne, pp. 336-412.
Platt, T., Fuentes-Yaco, C., and Frank, K.T. (2003). Spring algal bloom and larval fish survival.
Nature, 423(6938), 398-399.
Pörtner, H.O. (2010). Oxygen-and capacity-limitation of thermal tolerance: a matrix for
integrating climate-related stressor effects in marine ecosystems. Journal of Experimental
Biology, 213(6), 881-893.
Pörtner, H.O., Bock, C., and Mark, F.C. (2017). Oxygen-and capacity-limited thermal tolerance:
bridging ecology and physiology. Journal of Experimental Biology, 220(15), 2685-2696.
288
Pouvreau, S., Bourles, Y., Lefebvre, S., Gangnery, A., and Alunno-Bruscia, M. (2006).
Application of a dynamic energy budget model to the Pacific oyster, Crassostrea gigas,
reared under various environmental conditions. Journal of Sea Research, 56(2), 156-167.
Prowse, T.A., Sewell, M.A., and Byrne, M. (2017). Three-stage lipid dynamics during
development of planktotrophic echinoderm larvae. Marine Ecology Progress Series, 583,
149-161.
Pütter, A. (1909). Der ernahrung der wassertiere und der stoffhaushalt der gewasser. Gustav
Fischer, Jena.
Qi, H., Li, L., and Zhang, G. (2021). Construction of a chromosome‐level genome and variation
map for the Pacific oyster Crassostrea gigas. Molecular Ecology Resources. 21, 1670-
1685.
Rasmussen, L.L., et al. (2020). A century of Southern California coastal ocean temperature
measurements. Journal of Geophysical Research: Oceans, 125(5), DOI: e2019JC015673.
Raubenheimer, D., and Simpson, S.J. (1993). The geometry of compensatory feeding in the
locust. Animal Behaviour, 45(5), 953-964.
Raubenheimer, D., and Simpson, S.J. (2018). Nutritional ecology and foraging theory. Current
Opinion in Insect Science, 27, 38-45.
Regier, J.C., and Kafatos, F.C. (1977). Absolute rates of protein synthesis in sea urchins with
specific activity measurements of radioactive leucine and leucyl-tRNA. Developmental
Biology, 57(2), 270-283.
Reitzel, A.M., Webb, J., and Arellano, S. (2004). Growth, development and condition of
Dendraster excentricus (Eschscholtz) larvae reared on natural and laboratory diets.
Journal of Plankton Research, 26(8), 901-908.
289
Ren, J.S., and Ross, A.H. (2001). A dynamic energy budget model of the Pacific oyster
Crassostrea gigas. Ecological Modelling, 142(1-2), 105-120.
Ren, P., Wei, J., Yu, H., and Dong, B. (2019). Identification and functional characterization of
solute carrier family 6 genes in Ciona savignyi. Gene, 705, 142-148.
Renaud, S.M., Thinh, L.V., Lambrinidis, G., and Parry, D.L. (2002). Effect of temperature on
growth, chemical composition and fatty acid composition of tropical Australian
microalgae grown in batch cultures. Aquaculture, 211, 195-214.
Rendleman, A.J., and Pace, D.A. (2018). Physiology of growth in typical and transversus
echinopluteus larvae. Invertebrate Biology, 137(4), 289-307.
Rendleman, A.J., Rodriguez, J.A., Ohanian, A., and Pace, D.A. (2018). More than morphology:
Differences in food ration drive physiological plasticity in echinoid larvae. Journal of
Experimental Marine Biology and Ecology, 501, 1-15.
Rico-Villa, B., Bernard, I., Robert, R., and Pouvreau, S. (2010). A Dynamic Energy Budget
(DEB) growth model for Pacific oyster larvae, Crassostrea gigas. Aquaculture, 305(1-4),
84-94.
Rico-Villa, B., Pouvreau, S., and Robert, R. (2009). Influence of food density and temperature
on ingestion, growth and settlement of Pacific oyster larvae, Crassostrea gigas.
Aquaculture, 287(3-4), 395-401.
Robert, R., Vignier, J., and Petton, B. (2017). Influence of feeding regime and temperature on
development and settlement of oyster Ostrea edulis (Linnaeus, 1758) larvae. Aquaculture
Research, 48(9), 4756-4773.
290
Robertson, R., El-Haj, A., Clarke, A., Peck, L., and Taylor, E. (2001). The effects of temperature
on metabolic rate and protein synthesis following a meal in the isopod Glyptonotus
antarcticus Eights (1852). Polar Biology, 24(9), 677-686.
Rumrill, S.S. (1990). Natural mortality of marine invertebrate larvae. Ophelia, 32(1-2), 163-198.
Saitou, N., and Nei, M. (1987). The neighbor-joining method: a new method for reconstructing
phylogenetic trees. Molecular Biology and Evolution, 4(4), 406-425.
Schmidt-Nielsen, K. (1997). Animal physiology: adaptation and environment. Cambridge
University Press.
Schoener, T.W. (1971). Theory of feeding strategies. Annual Review of Ecology and Systematics,
2(1), 369-404.
Schoenheimer, R. (1942). The dynamic state of body constituents. The dynamic state of body
constituents.
Schulte, P.M., Healy, T.M., and Fangue, N.A. (2011). Thermal performance curves, phenotypic
plasticity, and the time scales of temperature exposure. Integrative and Comparative
Biology, 51(5), 691-702.
Searcy, S.P., and Sponaugle, S.U. (2001). Selective mortality during the larval–juvenile
transition in two coral reef fishes. Ecology, 82(9), 2452-2470.
Sedgwick, R.W. (1979). Influence of dietary protein and energy on growth, food consumption
and food conversion efficiency in Penaeus merguiensis de Man. Aquaculture 16(1), 7-30.
Seixas, P., Coutinho, P., Ferreira, M., and Otero, A. (2009). Nutritional value of the cryptophyte
Rhodomonas lens for Artemia sp. Journal of Experimental Marine Biology and Ecology,
381, 1-9.
291
Sewell, M.A., Cameron, M.J., and McArdle, B.H. (2004). Developmental plasticity in larval
development in the echinometrid sea urchin Evechinus chloroticus with varying food
ration. Journal of Experimental Marine Biology and Ecology, 309, 219-237.
Shahsavar, A., et al. (2021). Structural insights into the inhibition of glycine reuptake. Nature,
591(7851), 677-681.
Shanks, A L. (2009). Pelagic larval duration and dispersal distance revisited. The Biological
Bulletin, 216(3), 373-385.
Shanks, A.L., Grantham, B.A., and Carr, M.H. (2003). Propagule dispersal distance and the size
and spacing of marine reserves. Ecological Applications, 13(1), 159-169.
Sheldon, R.W., and Parsons, T.R. (1967). A practical manual on the use of the Coulter counter in
marine research. Coulter Electronics Sales Company, Toronto, Ontario.
Shilling, F.M., and Manahan, D.T. (1990). Energetics of early development for the sea urchins
Strongylocentrotus purpuratus and Lytechinus pictus and the crustacean Artemia sp.
Marine Biology, 106(1), 119-127.
Shilling, F.M., and Manahan, D.T. (1994). Energy metabolism and amino acid transport during
early development of Antarctic and temperate echinoderms. The Biological Bulletin,
187(3), 398-407.
Siebers, D., and Rosenthal, H. (1977). Amino-acid absorption by developing herring eggs.
Helgoländer wissenschaftliche Meeresuntersuchungen, 29(4), 464-472.
Sinclair, B.J., Marshall, K.E., Sewell, M.A., Levesque, D.L., Willett, C.S., Slotsbo, S., Dong, Y.,
Harley, C.D.G., Marshall, D.J., Helmuth, B.S., and Huey, R.B. (2016). Can we predict
ectotherm responses to climate change using thermal performance curves and body
temperatures? Ecology Letters, 19(11), 1372-1385.
292
Sinervo, B., and McEdward, L.R. (1988). Developmental consequences of an evolutionary
change in egg size: an experimental test. Evolution, 42(5), 885-899.
Singh, P., Paul, B.N., and Giri, S.S. (2018). Potentiality of new feed ingredients for aquaculture:
A review. Agricultural Reviews, 39(4), 282-291.
Sodergren, E. et al. (2006). The genome of the sea urchin Strongylocentrotus purpuratus.
Science, 314(5801), 941-952.
Sokolova, I. (2021). Bioenergetics in environmental adaptation and stress tolerance of aquatic
ectotherms: linking physiology and ecology in a multi-stressor landscape. Journal of
Experimental Biology, 224, DOI: jeb236802.
Somero, G.N. (2018). RNA thermosensors: how might animals exploit their regulatory potential?
Journal of Experimental Biology, 221(4), DOI: jeb.162842.
Somero, G.N., Lockwood, B.L. and Tomanek, L. (2017). Biochemical Adaptation: Response to
Environmental Challenges, from Life’s Origins to the Anthropocene. Sunderland, MA,
USA: Sinauer Associates, Inc.
Sousa, T., Domingos, T., Poggiale, J.C., and Kooijman, S.A.L.M. (2010). Dynamic energy
budget theory restores coherence in biology. Philosophical Transactions of the Royal
Society B: Biological Sciences, 365(1557), 3413-3428.
Sponaugle, S., et al. (2002). Predicting self-recruitment in marine populations: biophysical
correlates and mechanisms. Bulletin of Marine Science, 70(1), 341-375.
Starr, M., Himmelman, J.H., and Therriault, J.C. (1990). Direct coupling of marine invertebrate
spawning with phytoplankton blooms. Science, 247(4946), 1071-1074.
Starrfelt, J., and Kokko, H. (2010). Parent-offspring conflict and the evolution of dispersal
distance. The American Naturalist, 175(1), 38-49.
293
Stephens, G.C. (1988). Epidermal amino acid transport in marine invertebrates. Biochemica et
Biophysica, 947(1), 113-138.
Stephens, G.C., and Schinske, R.A. (1961). Uptake of amino acids by marine invertebrates.
Limnology and Oceanography, 6(2), 175-181.
Strathmann, M.F. (1987). Phylum Echinodermata, class Echinoidea. Reproduction and
development of marine invertebrates of the Northern Pacific Coast. University of
Washington Press, Seattle, 511-534.
Strathmann, R.R. (1971). The feeding behavior of planktotrophic echinoderm larvae:
mechanisms, regulation, and rates of suspension feeding. Journal of Experimental
Marine Biology and Ecology, 6(2), 109-160.
Strathmann, R.R. (1985). Feeding and nonfeeding larval development and life-history evolution
in marine invertebrates. Annual Review of Ecology and Systematics, 16, 339-361.
Strathmann, R.R. (2007). Time and extent of ciliary response to particles in non-filtering feeding
Mechanism. The Biological Bulletin, 212, 93-103.
Strathmann, R.R., and Grunbaum, D., (2006). Good eaters, poor swimmers: compromises in
larval form. Integrative and Comparative Biology, 46(3), 312-322.
Strathmann, R.R., and Leise, E. (1979). On feeding mechanisms and clearance rates of
molluscan veligers. The Biological Bulletin, 157(3), 524-535.
Strathmann, R.R., Jahn, T.L., and Fonseca, J.R. (1972). Suspension feeding by marine
invertebrate larvae: clearance of particles by ciliated bands of a rotifer, pluteus, and
trochophore. The Biological Bulletin, 142(3), 505-519.
Sun, M., Hassan, S.G., and Li, D. (2016). Models for estimating feed intake in aquaculture: A
review. Computers and Electronics in Agriculture, 127, 425-438.
294
Taghon, G.L. (1981). Beyond selection: optimal ingestion rate as a function of food value. The
American Naturalist, 118(2), 202-214.
Thimgan, M.S., Berg, J.S., and Stuart, A.E. (2006). Comparative sequence analysis and tissue
localization of members of the SLC6 family of transporters in adult Drosophila
melanogaster. Journal of Experimental Biology, 209(17), 3383-3404.
Thor, P., and Wendt, I. (2010). Functional response of carbon absorption efficiency in the
pelagic calanoid copepod Acartia tonsa. Limnology and Oceanography, 55(4), 1779-
1789.
Thorson, G. (1950). Reproductive and larval ecology of marine bottom invertebrates. Biological
Reviews, 25(1), 1-45.
Trivers, R.L. (1974). Parent-offspring conflict. Integrative and Comparative Biology, 14(1), 249-
264.
Von Bertalanffy, L. (1938). A quantitative theory of organic growth (inquiries on growth laws.
II). Human Biology, 10(2), 181-213.
Von Bertalanffy, L. (1957). Quantitative laws in metabolism and growth. The Quarterly Review
of Biology, 32(3), 217-231.
Wada, Y., Mogami, Y., and Baba, S.A. (1997). Modification of ciliary beating in sea urchin
larvae induced by neurotransmitters: Beat-plane rotation and control of frequency
fluctuation. Journal of Experimental Biology, 200, 9-18.
Wagner, C.A., Friedrich, B., Setiawan, I., Lang, F., and Bröer, S. (2000). The use of Xenopus
laevis oocytes for the functional characterization of heterologously expressed membrane
proteins. Cellular Physiology and Biochemistry, 10(1-2), 1-12.
295
Wang, X., and Proud, C.G. (2006). The mTOR pathway in the control of protein synthesis.
Physiology, 21(5), 362-369.
Wang, F., Flanagan, J., Su, N., Wang, L.C., Bui, S., Nielson, A., Wu, X., Vo, H.T., Ma, X.J., and
Luo, Y. (2012). RNAscope: a novel in situ RNA analysis platform for formalin-fixed,
paraffin-embedded tissues. The Journal of Molecular Diagnostics: JMD, 14(1), 22–29.
Waterlow, J.C. (1995). Whole-body protein turnover in humans – past, present, and future.
Annual Review of Nutrition, 15, 57-92.
Waterlow, J.C., Garlick, P.J., and Millward, D.J. (1978). Protein turnover in mammalian tissues
and in the whole body. Amsterdam, The Netherlands. North-Holland Biomedical Press,
335.
Weatherburn, M.W. (1967). Phenol-hypochlorite reaction for determination of ammonia.
Analytical chemistry, 39(8), 971-974.
Weitzel, G., Pilatus, U., and Rensing, L. (1987). The cytoplasmic pH, ATP content and total
protein synthesis rate during heat-shock protein inducing treatments in yeast.
Experimental Cell Research, 170(1), 64-79.
Whiteley, N., and Faulkner, L.S. (2005). Temperature influences whole-animal rates of
metabolism but not protein synthesis in a temperate intertidal isopod. Physiological and
Biochemical Zoology, 78(2), 227-238.
Wickner, S., Maurizi, M.R., and Gottesman, S. (1999). Posttranslational quality control: folding,
refolding, and degrading proteins. Science, 286(5446), 1888-1893.
Winberg, G.G. (1956). Rate of metabolism and food requirements of fish. Journal of the
Fisheries Research Board of Canada, Translation Series, 194, 1-253.
296
Winfree, R.A., and Stickney, R.R. (1981). Effects of dietary protein and energy on growth, feed
conversion efficiency and body composition of Tilapia aurea. The Journal of Nutrition,
111(6), 1001-1012.
Wipf, D., Ludewig, U., Tegeder, M., Rentsch, D., Koch, W., and Frommer, W.B. (2002).
Conservation of amino acid transporters in fungi, plants, and animals. Trends in
Biochemical Sciences, 27(3), 139-147.
Wright, S.H. (1982). A nutritional role for amino acid transport in filter-feeding marine
invertebrates. American Zoologist, 22(3), 621-634.
Wright, S.H. (1987). Alanine and taurine transport by the gill epithelium of a marine bivalve:
effect of sodium on influx. The Journal of Membrane Biology, 95(1), 37-45.
Wright, S.H., and Secomb, T.W. (1986). Epithelial amino acid transport in marine mussels: role
in net exchange of taurine between gills and sea water. Journal of Experimental Biology,
121(1), 251-270.
Wright, S.H., Moon, D.A., and Silva, A.L. (1989). Intracellular Na
+
and the control of amino
acid fluxes in the integumental epithelium of a marine bivalve. Journal of Experimental
Biology, 142(1), 293-310.
Xun, X., et al. (2020). Solute carriers in scallop genome: Gene expansion and expression
regulation after exposure to toxic dinoflagellate. Chemosphere, 241, DOI:
j.chemosphere.2019.124968.
Yamashita, A., Singh, S.K., Kawate, T., Jin, Y., and Gouaux, E. (2005). Crystal structure of a
bacterial homologue of Na
+
/Cl
-
-dependent neurotransmitter transporters. Nature,
437(7056), 215-223.
297
Yoshihiro, M., Keiko, W., Chieko, O., Akemi, K., and Baba, S.A. (1992). Regulation of ciliary
movement in sea urchin embryos: dopamine and 5-HT change the swimming behaviour.
Comparative Biochemistry and Physiology Part C: Comparative Pharmacology, 101(2),
251-254.
Zehr, J.P., Falkowski, P.G., Fowler, J., and Capone, D.G. (1988). Coupling between ammonium
uptake and incorporation in a marine diatom: Experiments with the short‐lived
radioisotope 13N. Limnology and oceanography, 33(4), 518-527.
Zhang, G., et al. (2012). The oyster genome reveals stress adaptation and complexity of shell
formation. Nature, 490(7418), 49-54.
Zhao, J., Zhai, B., Gygi, S. P., and Goldberg, A.L. (2015). mTOR inhibition activates overall
protein degradation by the ubiquitin proteasome system as well as by autophagy.
Proceedings of the National Academy of Sciences, 112(52), 15790-15797.
Zittier, Z.M., Bock, C., Lannig, G., and Pörtner, H.O. (2015). Impact of ocean acidification on
thermal tolerance and acid–base regulation of Mytilus edulis (L.) from the North Sea.
Journal of Experimental Marine Biology and Ecology, 473, 16-25.
298
APPENDIX A
Synchronicity of Larval Cultures for the Purple Sea Urchin Strongylocentrotus purpuratus
Objectives: To test the synchronicity in development and growth of larvae of Strongylocentrotus
purpuratus cultured at the 7-liter scale.
Larval Culturing
Eggs from three female adults of Strongylocentrotus purpuratus were spawned following
intracelomic injection of 0.5 M KCl. Fertilized embryos were divided into three 7-liter culture
vessels at 10 individuals ml
−1
. The culture vessels being tested were 7.6-liter round food-grade
storage containers (CAMBRO Camwear
®
Rounds) made of polycarbonate. Lids were modified
to support a 24 VDC reversible gearhead motor (Herbach and Rademan, Raleigh, NC). Gearhead
motors were wired to an adapter with adjustable currents. A diamond-shaped polycarbonate
paddle was attached to each motor rotating at the lowest setting (3 mA) to mix the seawater.
Culture vessels were kept in a temperature-controlled room, and a temperature logger was kept
in a 1-liter beaker of seawater next to the culture vessels. Throughout the culturing period, the
temperature averaged 13.42 ± 0.01 (S.E.M; n = 624 measurements) °C (Fig. A1). Larvae from
each culture vessel were subsampled at various times (Fig. A4, A5) for measurements of size,
and for scoring of developmental stage. Survival within each culture vessel was determined by
enumerating larvae (Fig. A2). On the fourth day, larvae were fed a ration of 20 cells µl
−1
Rhodomonas lens every two days. Sampling was conducted by retaining embryos or larvae on a
Nitex mesh (53 µm pore size) and resuspending them in smaller volumes of seawater (20 – 30
ml). Resuspended larvae were mixed gently and subsampled three to four times (50 – 75 µl each)
299
to be imaged and counted on a Sedgewick rafter slide (1 ml) at 40x magnification. Counts of
subsamples were extrapolated to total amounts of larvae based on the volumes of resuspension
and subsampling. For example, a resuspension volume of 20 ml, a subsample volume of 50 µl,
and a subsample count of 100 larvae would equate to 2 larvae (subsample µl)
−1
or 40,000 larvae
in the total resuspension volume.
Survival and growth
After 13 days of culturing, the three replicate culture vessels had 34,700 ± 900, 55,500 ±
1,942, and 50,900 ± 2,705 (s.e.m, n = 4 counts) total larvae (Fig. A2). There was a significant
effect of culture vessel on survival at day 13 (ANOVA F2,9 = 30.1; p = 0.0001). Post-hoc
analysis of this effect revealed that survival in the culture vessel with 34,700 ± 900 larvae was
significantly lower than the others (Tukey HSD p < 0.01 for each comparison against this vessel;
Fig. A2 closed circles). By day 13, the midline body lengths of larvae did not differ significantly
between culture vessels (ANOVA F2,147 = 1.84; ns; Fig. A3). Larval midline body lengths were
344.5 ± 4.6, 341.2 ± 3.6, and 333.0 ± 4.8 (S.E.M; n = 50 measurements) µm by day 13. A
regression analysis of size and time (hpf) gave an adjusted R
2
value of 0.8635 with residuals
increasing with fitted values below 200 µm and decreasing with fitted values above 200 µm. A
piece-wise linear model improved the adjusted R
2
to 0.9834 and gave a breakpoint of 3.397 ±
0.198 days post-fertilization. When x < 3.397 days, size = 63.015 + 49.513x; and when x > 3.397
days, size = 193.3851 + 11.135x. Since all larvae had reached the pluteus stage by 3.397 days
post-fertilization, this slope represents a midline body length growth rate of 11.1 µm day
−1
.
Before reaching the pluteus stage, embryos were growing in size by 49.5 µm day
−1
.
300
During development and growth, 13 stages were identified (Fig A4). These stages
included the 1-, 2-, 4-, 8-, and 16-cell stages, two blastula stages, three gastrula stages, a prism, a
4-arm pluteus, and a 6-arm pluteus stage. The late blastula stage was distinguished from the early
gastrula stage by the thickening of the primary mesenchymal cell layer. The gastrula stage was
distinguished by the full formation of the gastrula and the late gastrula stage by the appearance of
body rods. The prism stage was reached with the formation of a stomach and a concavity at the
ventral end. The pluteus stages were distinguished by the number of arms (4 or 6 arms). At each
time-point (3, 21, 28, 46, 52, 75, 95, 144, 192, 240, and 312 hpf), fifty individuals from each
culture vessel were scored to determine the proportion of individuals within each stage of
development (Fig. A5). After 3 hpf, embryos were mostly in the 4- or 8-cell stage. Embryos
reached the blastula stage by 21 hpf and the gastrula stages by 28 hpf. Prism stages were
identified by 52 hpf, and by 75 hpf, the majority of individuals had reached the 4-arm pluteus
stage. The 6-arm pluteus stage was not seen until 13 dpf when cultures consisted of 85.3 ± 2.4
(S.E.M, n = 3 culture vessels) % 6-arm plutei.
Conclusions
Culturing S. purpuratus larvae at the 7-liter scale resulted in consistent development and
growth between replicate cultures. Amongst the three replicate culture vessels, survival in one
culture vessel was lower than the other two (ANOVA F2,9 = 30.1; p = 0.0001; Tukey HSD p <
0.01). Despite differences in survival, there were no differences in size between culture vessels
after 13 days of culturing (ANOVA F2,147 = 1.84; ns). Embryos grew rapidly in size (49.5 µm
day
−1
) until 3.397 ± 0.198 dpf (81.528 ± 4.752 hpf) where midline body length growth rates
slowed to 11.1 µm day
−1
. Pluteus-stage larvae were observed between 75 hpf and 10 dpf, and 6-
301
arm plutei developed between 10 – 13 dpf. The synchronicity of growth and development within
replicate culture vessels at the 7-liter scale makes this a feasible system to investigate the
physiology of larval growth in this species.
302
Appendix A Figures
Figure A1. Temperature stability in larval cultures of Strongylocentrotus purpuratus were
measured once every 30 minutes throughout the culturing period. Across the culturing period,
the water temperature averaged 13.42 ± 0.01 (S.E.M; n = 624) °C.
303
Figure A2. Survival of larvae of Strongylocentrotus purpuratus reared in three replicate culture
vessels over 13 days of culturing. Embryos were initially stocked at 10 ml
−1
(70,000 individuals
per vessel).
304
Figure A3. Lengths of Strongylocentrotus purpuratus in three replicate larval cultures measured
over 13 days of growth and development. Different symbols indicate three different replicate
culture vessels, and each data point is the average ± S.E.M (n = 50 measurements) for a
respective culture vessel. Before the prism stage, lengths were defined as the maximum length,
and after the prism stage, lengths were defined as the midline body length. The solid line
represents a piece-wise linear fit (adjusted R
2
= 0.9834) where y = 63.015 + 49.513x (when x <
3.397); and y = 193.3851 + 11.135x (when x > 3.397).
305
Figure A4. Embryonic and larval stages of Strongylocentrotus purpuratus observed in the first
13 days of growth and development. At the rearing temperature used in this study, the
developmental stages above were reached at the times specified in the next figure (Fig. A4B)
306
Figure A4. Synchronicity of larval stages across three replicate culture vessels of
Strongylocentrotus purpuratus. Fifty individuals were scored on 3, 21, 28, 46, 52, 75, 95, 144,
192, 240, and 312 hours post-fertilization (hpf). Histogram bars represent the average proportion
± S.E.M (n = 3 culture vessels) of each stage.
307
APPENDIX B
Protein Content and Growth of the Larval Algal Food Rhodomonas lens
Objectives: To characterize the growth curve of Rhodomonas lens under different culturing
conditions and to determine the protein content of the microalgae throughout the exponential
phase.
Growth curves for the algal food Rhodomonas lens used to culture larvae
A new stock of Rhodomonas lens was purchased (AlgaGen LLC, Vero Beach, FL, USA).
Growth rates were compared between the new stock and the old stocks by enumerating cells over
time using a hemocytometer. Old stocks and new stocks were used to inoculate either 2-liter
flasks or 250 ml flasks in duplicate. Two-liter flasks were filled with 1.6 liters of autoclaved sea
water and 250 ml flasks were filled with 150 ml of autoclaved sea water. Two-liter flasks
received 1.25 ml f/2 media parts A and B, while 250 ml flasks received 4 drops from a Pasteur
pipette of f/2 media parts A and B. Flasks were kept in a 15°C temperature-controlled room on a
12:12 light:dark cycle. The new AlgaGen stocks were dense enough to be counted after 5 days of
growth (Fig. B1a). The older stock was enumerated beginning 12 days after inoculation (Fig.
B1b). Both stocks cultured in 250 ml flasks reached 3 × 10
6
cells ml
−1
by day 20 (Fig. B1c; B1d).
However, the new AlgaGen stock cultured in the 2-liter flask reached 2 × 10
6
cells ml
−1
(Fig.
B1a). Three out of the four cultures from the old stock became contaminated and crashed after
day 19.
308
Protein content of the algal food Rhodomonas lens
Three replicate flasks were inoculated to determine the growth and protein content of
Rhodomonas lens under a single culturing condition. Each 1-liter flask contained 700 ml of
autoclaved seawater (0.2 µm pore-size; Nucleopore) and 0.625 ml of f/2 media. One hundred ml
of the previous culture (stationary phase) was used to inoculate each replicate flask, and flasks
were gently bubbled with serological pipettes connected to an air pump by a 0.2 µm hosing filter.
Immediately after inoculation, an initial sample was taken and concentrated by centrifugation for
counting in smaller volumes using a hemocytometer. Over the next 8 days, samples were taken
daily for enumeration of cells and 5 aliquots were taken for determination of protein content
using a Bradford (1976) assay modified by Jaeckle and Manahan (1989). In each replicate flask,
cells reached 4.7 ± 0.1 (s.e.m; n = 4 counts), 4.3 ± 0.3, and 4.3 ± 0.3 × 10
6
cells ml
−1
by day 8
(Fig. B2a). Samples for total protein taken immediately after inoculation were 25.3 ± 0.8 (s.e.m;
n = 5 determinations), 46.1 ± 1.4, and 28.6 ± 1.1 pg cell
−1
. These values represented the protein
content of the inoculant cultures. After one day of culturing, the protein contents increased to
45.6 ± 5.2, 39.1 ± 0.7, and 50.0 ± 0.8 pg cell
−1
within each flask respectively. As the cell
populations in each culture flask grew, protein content decreased in cells (linear regression H 0: β1
= 0; p < 0.0001 for each replicate culture; Fig. B2b).
The slopes and intercepts of protein decline with time did not differ significantly between
culture vessels (ANCOVA Intercept: F2,116 = 2.8, ns; Slope: F2,114 = 0.47, ns), so all data points
were subsequently pooled to determine a rate of protein decline: (Protein content, pg cell
−1
) =
44.99 – 3.05 × (Day) during the 8-day culturing period (R
2
= 0.6497, SSE = 3162.5, df = 118;
Fig. B3a). However, the residual errors appeared to be heteroscedastic (Fig. B3b). A piece-wise
linear regression was performed using the R segmented package v1.3-1 (https://cran.r-
309
project.org/web/packages/segmented/index.html). A break-point at 5 ± 0.487 (S.E.) days after
inoculation was identified where the relationship of protein decline over time changed from y =
49.49 – 4.75x to y = 28.52 – 0.56x (Fig. B3c). The piece-wise regression model improved upon
the original linear model fit with an adjusted R
2
= 0.719, and sum of squares of unexplained error
of 2474.0 (df = 116). The residual errors also appeared less heteroscedastic (Fig. B3d). Beyond 5
± 0.487 (S.E.) after inoculation, protein content within algal cells only decreased by
approximately 0.5 pg day
−1
and averaged 24.7 ± 0.5 (s.e.m.; n = 45) pg cell
−1
. Meanwhile, cell
densities increased from 2.8 ± 0.2 ×10
6
(S.E.M; n = 15) to 4.3 ± 0.1 ×10
6
(s.e.m.; n = 12) cells
ml
−1
over the three days past the break-point.
Conclusion
Cultures of Rhodomonas lens can reach cell densities of 4 × 10
6
cells ml
−1
within 8 days
with constant light and gentle bubbling when grown in f/2 media at a ratio of 0.625 ml f/2 to 700
ml seawater. Based on the relationship between protein content and days after inoculation,
protein contents decreased by half (approximately 42 to 21 pg cell
−1
) during that period.
However, the relationship of protein decline with time changed after 5 ± 0.487 (s.e.) days post-
inoculation when protein contents averaged 24.7 ± 0.5 (s.e.m.; n = 45) pg cell
−1
. The biological
significance of this shift may reflect a lower bound of protein content in cells of Rhodomonas
lens. The cell population still grew over 10
6
cells ml
−1
in this period despite a relatively stable
level of total protein cell
−1
. Because of the 2-fold variation in protein content of algal cells across
the exponential phase, experiments utilizing Rhodomonas lens as a food source for zooplankton
should measure the total protein content of algae on each feeding day to accurately model protein
310
ingestion rates by larvae rather than relying on keeping cells in the exponential growth phase
alone.
Appendix B References
Bradford, M.M (1976). A rapid and sensitive method for the quantitation of microgram
quantities of protein utilizing the principle of protein-dye binding. Analytical
Biochemistry, 72(1-2), 248-254.
Jaeckle, W.B., and Manahan, D.T. (1989). Growth and energy imbalance during the
development of a lecithotrophic molluscan larva (Haliotis rufescens). The Biological
Bulletin, 177(2), 237-246.
311
Appendix B Figures
Figure B1. Comparison of Rhodomonas lens growth from different algal sources and different
culturing volumes. (A) R. lens from the new AlgaGen (Vero Beach, FL, USA) supply cultured in
a 2-liter flask with 1.25 ml f/2 media per 1.6 liters. (B) R. lens from an algal stock cultured in a
2-liter flask with 1.25 ml f/2 media per 1.6 liters. (C) R. lens from a second stock from AlgaGen
supply cultured in a 250 ml flask with 4 drops of f/2 media per 150 ml. (D) R. lens from a
previous stock cultured in a 250 ml flask with 4 drops of f/2 media per 150 ml. Different
symbols (open and closed) represent replicate flasks, and each data point represents one visual
hemocytometer count. Lines connect daily average values within each replicate flask.
312
Figure B2. Growth of Rhodomonas lens cultured in 1-liter flasks containing 700 ml filtered
seawater (0.2 µm filtered) and 0.625 ml f/2 media. All cultures were bubbled gently and kept 20
cm from an algal growth light source and held at room temperature. (A) Cell counts in replicate
culture flasks increased to approximately 4 × 10
6
cells ml
−1
by the eighth day of culturing. Each
data point represents a hemocytometer count, and different symbols represent replicate culture
flasks. Lines connect the average daily counts within a replicate flask. (B) Total protein content
decreased throughout the culture period. Each data point represents a Bradford assay, and
different symbols represent replicate culture flasks. Lines connect the average protein content
within a replicate flask.
313
Figure B3. Analyses of the relationship between the change in protein content of Rhodomonas
lens with time after inoculation. (A) A linear model fit through the data such that y = 44.99 –
3.05x (p < 0.0001, R
2
= 0.6497, SSE = 3162.5, df = 118). (B) The variance of the linear model
residuals did not appear to be homoscedastic. (C) A piece-wise linear regression was performed
using the R segmented package v1.3-1
(https://cran.r-project.org/web/packages/segmented/index.html) yielding a break-point at day 5 ±
0.487 (S.E.). Between days 1 – 5, y = 49.49 – 4.75x and between days 5 – 8, y = 28.52 – 0.56x (p
< 0.0001, adj. R
2
= 0.719, SSE = 2474.0, df = 116). (D) The variance of the piece-wise linear
model residuals appeared less heteroscedastic than the original linear model.
314
APPENDIX C
Protein and Lipid Content of Larvae of the Pacific Oyster Crassostrea gigas
Reared Under Varying Food Rations
Objectives: To examine changes biochemical composition of Crassostrea gigas larvae reared at
different food rations.
Experimental design and rationale
Larvae of Crassostrea gigas were sampled on day 4 of development and divided into six
20-liter culture vessels (three feeding treatments with two replicate culture vessels per
treatment). Over the course of seven days of feeding, a total of 411,350 larvae were sampled for
determinations of clearance rate, protein content, lipid content, and shell length. All
measurements were performed evenly on all culture vessels on all sampling days (day 4 pool,
day 7, day 9, and day 11). The three feeding treatments consisted of targets of 10, 30, and 50
cells µl
−1
T-Isochrysis galbana. Algal food was counted using a hemocytometer and monitored
two times per day to account for depletion due to feeding. During food ration monitoring,
particles were counted on a particle counter (Model Z1 Particle Counter, Beckman Coulter) and
cross-calibrated to hemocytometer counts. Culture vessels with rations below 10% of target
values were given enough algae to return to target values. A total of 78 particle counts (13 per
culture vessel) were determined to maintain food rations. Near-constant rations were maintained
in each culture vessel (Fig. C1).
On each sampling day, larvae were gently poured onto a Nitex mesh (20 µm pore size)
and resuspended in less than 50 ml of seawater for enumeration. Four 40 µl subsamples were
315
counted for each culture vessel such that subsamples contained > 100 larvae, and the coefficient
of variation between counts was < 10%. More than 50 larvae were imaged following
enumeration in order to measure shell lengths using ImageJ software calibrated to a micrometer
standard. Larvae were then divided into assays of clearance rate and samples for biochemical
composition analyses.
Feeding rates of larvae
Clearance rates were assayed by particle counting and end-point determination of algal
depletion. Larvae from each culture vessel were divided into six experimental assay vials and six
larvae-only control vials (Beckman Coulter Accuvette Cups). Each vial contained 300 larvae in
10 ml seawater. Larvae were allowed to acclimate to the vial for > 30 minutes. An additional set
of six algae-only control vials were filled with 30 cells µl
−1
T-Isochrysis galbana. Experimental
assay vials containing larvae were initiated by the addition of 30 cells µl
−1
algae. Three vials
were then sampled to determine the initial amount of cells in suspension. Sampling involved
pouring the vial filtrate through a Nitex mesh (20 µm) and counting particles on the Z1 particle
counter. After 3 to 5 hours, the remaining three vials were sampled in the same way to determine
the end point amount of cells in suspension. Particle counts were cross-calibrated to
hemocytometer counts and corrected for particles found in the larvae-only controls. Clearance
rates (F) were calculated using initial (C0) and final (Cf) cell amounts according to Equation C1,
where V is the volume, N is the number of larvae, and t is the time:
Equation C1. 𝐹 =
× ×
316
On day 4, the initial pool of larvae had clearance rates of 1.6 µl larva
−1
h
−1
(Fig. C2).
Clearance rates increased to greater than 20 µl larva
−1
h
−1
by day 11. The increases in clearance
rates over time were modelled by linear regressions for larvae from each culture vessel.
Integrations of the areas under these regressions with respect to time yielded net volumes cleared
during the culturing period (Fig. C2). Using the average culture vessel food rations (Fig. C1),
cumulative feeding rates were calculated by multiplying the net volumes cleared by the average
culture vessel food rations. These calculations were made possible since culture food rations
were maintained near target levels. Larvae from the replicate culture vessels fed 10 cells µl
−1
consumed 15,400 and 16,764 cells during the culture period (Fig. C2a-b). Larvae from the
replicate culture vessels fed 30 cells µl
−1
consumed 54,774 and 68,311 cells during the culture
period (Fig. C2c-d). Larvae from the replicate culture vessels fed 50 cells µl
−1
consumed 98,268
and 109,753 cells during the culture period (Fig. C2e-f).
Growth and biochemical composition of larvae
Total protein content of larvae of Crassostrea gigas was measured for each culture vessel
on days 4 (pooled larvae), 7, 9, and 11. A total of 51 assays were conducted, three per culture
vessel per day. Total protein was assayed by a Bradford (1976) method modified for larvae by
Jaeckle and Manahan (1989). Replicate samples of 3,000 larvae in individual test tubes were
dried using SpeedVac Plus (Savant Flexi-Dry MP system). Larvae were then resuspended in
deionized water (NanoPure) and sonicated. Proteins were then precipitated in 5% trichloroacetic
acid (TCA) on ice for 30 minutes. Precipitated proteins were centrifuged at 12,000 rcf for 20
minutes at 4°C. TCA was aspirated using a glass pipette under vacuum. Proteins were then
resolubilized using 1M NaOH and neutralized with 1.67M HCl. Bradford reagent was then
317
added to samples before transferring three technical replicates to 96-well plates. Absorbances at
595 nm were measured in a plate reader and converted to µg using a dilution series of BSA
standards prepared in the same method.
Lipid compositions of larvae were also determined for each culture following protocols
by Moran and Manahan (2004). A total of 51 assays were conducted, three per culture vessel per
day. Larvae were dried and sonicated as described above, and lipids were extracted in Wheaton
(1 ml #986254) glass V-vials using water, methanol, chloroform (1:1:0.5; v/v/v) after adding
0.857 µg 1-octadecanol (a stearyl alcohol internal standard). Solutions were mixed and
centrifuged at 8,000 rcf for 10 minutes at 4°C. Following phase separation, liquid phases were
transferred to a clean v-vial. Deionized water, methanol, and chloroform were added to the liquid
phase to a final ratio of 0.9:1:1 water:methanol:chloroform (v/v/v). Samples were mixed and
centrifuged again as described above. The bottom chloroform layer was then transferred to a
borosilicate shell vial (1.5 ml Agilent #5182-0714). Four hundred µl of chloroform was added to
the v-vial and allowed to separate phases. The chloroform was then added to the shell vial, and
the shell vial was heated at 40 °C and dried with a gentle stream of N 2 gas. Dried lipids were
then resuspended in 50 µl chloroform and transferred to a thin glass insert (Agilent #5181-3377).
Thin-layer chromatography rods (Chromarod-S III; Iatron Laboratories, Inc.) were spotted with 2
µl of sample using a 1 µl glass capillary tube. Three rods were spotted for each sample, as well
as two sets of standards of known concentration (Standard 1: squalene, tripalmitin, 1-
octadecanol, L-phosphatidylcholine; Standard 2: Lauric acid palmityl ester, palmitic acid, 1-
octadecanol, cholesterol). Rods were developed in chromatography chambers over a polar
solvent (60 ml hexane, 17 ml ethyl ether, 10 drops of glacial acetic acid) for 22 minutes. Rods
were then dried on wooden rack holders for 10 minutes at 100 °C. Lipids were detected by H 2
318
flame ionization detection (IATROSCAN MK-5). Peak areas were converted to µg lipids based
on standards for hydrocarbon, wax ester, cholesterol, triacylglycerol, free fatty acid, stearyl
alcohol, and phospholipid. Lipid quantities were corrected for extraction efficiencies based on
the amount of internal standard (1-octadecanol) detected. Hydrocarbons were only detected in
larvae on days 4 and 7 (Table C1). Triacylglycerols, fatty acids, sterols, and phospholipids were
detected in all samples. Phospholipids accounted for the majority of total lipids at 47 – 81% of
total lipid mass (w/w).
Differences in shell length, protein content, and total lipids were analyzed by nested
ANOVA of replicate measurements (3 for protein or lipid, 50 for shell length) from each culture
vessel on day 11 (two culture replicates nested in three feeding treatments). F statistics were
calculated as MStreatment/MSreplicate vessels (for replicate vessels nested under treatments). When
significant effects of feeding treatments were found, Tukey’s honest significant difference (HSD)
test was performed post hoc. Normality of residuals was examined by assessing quantile-quantile
(QQ) plots and Shapiro-Wilk normality test of the residuals (H 0 = normal distribution).
Heteroscedasticity of residuals were examined by plotting residuals against fitted values to
visually assess patterns of variance. Total protein content differed significantly between larvae
reared at different feeding treatments (Nested ANOVA, p = 0.044; Fig. C3a). All pairwise
comparisons of total protein content between larvae at each food ration (10, 30, and 50 cells µl
−1
)
were significantly different (Tukey’s HSD; 10 vs 30 p adj. = 0.0002; 10 vs 50 p adj. = 0.017; 30
vs 50 p adj. = 0.043). There was no significant effect of feeding ration on total lipid content
(Nested ANOVA, p = 0.28; Fig. C3b). Finally, there was no significant effect of food ration on
shell length (Nested ANOVA, p = 0.064; Fig. C3c). The residuals of shell length were not
normally distributed (Shapiro-Wilk test, p = 2.039×10
-8
). Examinations of size histograms
319
revealed a left-skew of sizes in the 10 and 50 cells µl
−1
treatments. Deletions of skewing outliers
and transformations of data did not improve the distributions and decreased the statistical
significance of treatment effects. With a 7-fold difference in feeding rate (Fig. C2), protein
content and size increased (Fig. C3a). Protein constituted the majority of the biochemical content
of larvae at an average of 74 ± 2% (w/w) of lipid and protein mass combined (Table C1).
Appendix C References
Bradford, M.M (1976). A rapid and sensitive method for the quantitation of microgram
quantities of protein utilizing the principle of protein-dye binding. Analytical
Biochemistry, 72(1-2), 248-254.
Jaeckle, W.B., and Manahan, D.T. (1989). Growth and energy imbalance during the
development of a lecithotrophic molluscan larva (Haliotis rufescens). The Biological
Bulletin, 177(2), 237-246.
Moran, A.L., and Manahan, D.T. (2004). Physiological recovery from prolonged ‘starvation’ in
larvae of the Pacific oyster Crassostrea gigas. Journal of Experimental Marine Biology
and Ecology, 306(1), 17-36.
320
Appendix C Tables and Figures
Table C1. Biochemical composition of larvae of Crassostrea gigas reared under different food rations. Values represent average ±
S.E.M (n = 3 assays) for each biochemical class. The percent of phospholipid represents the proportion of total lipid that is
phospholipid (w/w). The percent of protein represents the percent mass of protein out of total lipid and protein combined.
Age (d) Cells µl
−1
Hydrocarbon Triacylglycerol
Fatty
Acid Sterol Phospholipid
Total
Lipid
Phospholipid
%
Total
Protein
Protein
%
4 N/A 1.0 ± 0.4 0.9 ± 0.2 N.D. N.D. 1.6 ± 0.3 3.5 ± 0.9 47% 10.4 ± 0.9 75%
7 8.3 0.4 ± 0.2 0.4 ± 0.0 0.3 ± 0.1 0.5 ± 0.0 5.7 ± 0.8 7.3 ± 1.1 78% 29.3 ± 0.8 80%
9
N.D. 0.2 ± 0.0 0.5 ± 0.1 0.7 ± 0.1 5.7 ± 0.8 7.1 ± 1.0 80% 32.1 ± 1.8 82%
11
N.D. 1.4 ± 0.4 1.5 ± 0.2 2.0 ± 0.1 16.0 ± 1.8 20.8 ± 2.5 77% 81.1 ± 3.3 80%
7 7.8 0.1 ± 0.1 1.0 ± 0.2 0.3 ± 0.1 0.6 ± 0.1 8.3 ± 1.6 10.3 ± 2.0 81% 14.2 ± 4.3 58%
9
N.D. 0.2 ± 0.1 0.3 ± 0.2 1.4 ± 1.2 7.3 ± 2.0 9.1 ± 3.5 79% 33.5 ± 2.4 79%
11
N.D. 0.7 ± 0.3 0.7 ± 0.3 1.1 ± 0.5 15.6 ± 4.5 18.1 ± 5.6 86% 75.2 ± 11.4 81%
7 26.6 3.5 ± 2.2 3.2 ± 1.2 1.1 ± 0.3 1.1 ± 0.0 12.6 ± 1.1 21.5 ± 4.8 59% 37.3 ± 1.8 63%
9
N.D. 0.7 ± 0.1 1.2 ± 0.1 1.2 ± 0.0 9.9 ± 1.3 13.0 ± 1.5 76% 47.1 ± 3.7 78%
11
N.D. 1.5 ± 0.6 2.3 ± 0.3 0.7 ± 0.6 17.5 ± 4.3 22.0 ± 5.7 79% 123.6 ± 6.7 85%
7 31.7 0.5 ± 0.4 11.9 ± 1.2 0.6 ± 0.1 1.7 ± 0.1 10.9 ± 0.8 25.6 ± 2.6 43% 48.8 ± 1.3 66%
9
N.D. 2.5 ± 0.2 1.1 ± 0.1 0.9 ± 0.1 7.1 ± 1.2 11.5 ± 1.6 61% 29.7 ± 7.4 72%
11
N.D. 5.4 ± 0.3 3.7 ± 0.8 2.5 ± 0.1 18.8 ± 0.8 30.3 ± 2.0 62% 141.3 ± 9.2 82%
7 51.6 0.2 ± 0.1 6.7 ± 0.4 0.3 ± 0.1 1.1 ± 0.1 11.2 ± 1.0 19.6 ± 1.7 57% 49.8 ± 8.8 72%
9
N.D. 4.4 ± 0.2 1.0 ± 0.1 1.4 ± 0.1 7.8 ± 1.1 14.5 ± 1.4 54% 45.1 ± 4.3 76%
11
N.D. 5.5 ± 1.0 0.7 ± 0.2 0.5 ± 0.2 22.6 ± 2.8 29.3 ± 4.2 77% 96.4 ± 12.8 77%
7 51.1 0.6 ± 0.5 6.0 ± 0.8 1.1 ± 0.2 1.9 ± 0.3 11.2 ± 0.7 20.8 ± 2.3 54% 30.9 ± 8.6 60%
9
N.D. 3.1 ± 0.4 1.0 ± 0.2 1.5 ± 0.2 9.0 ± 1.0 14.6 ± 1.7 62% 57.1 ± 2.3 80%
11 N.D. 14.7 ± 1.0 1.4 ± 0.3 4.3 ± 0.2 37.4 ± 4.0 57.8 ± 5.5 65% 118.8 ± 7.6 67%
321
Figure C1. Food rations (T-Isochrysis galbana) within larval cultures of Crassostrea gigas
monitored twice daily by particle counting (Z1 Particle Counter, Beckman Coulter). Lines
represent average food rations across the entire period. Closed/solid symbols and open/dashed
symbols represent replicate culture vessels within a feeding treatment. Circles represent a target
ration of 10 cells µl
−1
, triangles represent a target ration of 30 cells µl
−1
, and squares represent a
target ration of 50 cells µl
−1
.
322
Figure C2. Feeding rates of larvae of Crassostrea gigas reared at different food rations. The
value in the top left of each panel indicates the average food ration measured within culture
vessels over the course of the experiment (A) 8.3 cells µl
−1
, (B) 7.9 cells µl
−1
, (C) 26.6 cells µl
−1
,
(D) 31.7 cells µl
−1
, (E) 51.6, and (F) 51.1 cells µl
−1
. A linear regression was fit for the larvae of
each culture vessel such that the area under the line (shaded in gray in panel A) represents the
total volume cleared over time. Total volumes cleared were multiplied by food rations to indicate
the total cells consumed larva
−1
during the experiment (bottom right value in each panel).
323
Figure C3. Growth of larvae of Crassostrea gigas reared at 5 (circles), 30 (triangles), or 50
(squares) cells µl
−1
. Open and closed symbols represent replicate culture vessels. (A) Total
protein content ± S.E.M (n = 3 assays) of larvae. (B). Total lipid content ± S.E.M (n = 3 assays)
of larvae. (C) Shell length ± S.E.M (n = 50 measurements) of larvae.
324
APPENDIX D
Changes in Feeding Rates for larvae of the Pacific Oyster Crassostrea gigas to
Variable Temperature and Food Ration
Objectives: To determine the changes in feeding rates of Crassostrea gigas to a range of food
rations, and to determine the thermal sensitivity (Q10) of feeding rates of C. gigas to temperature.
Determining thermal sensitivity of clearance rates
Clearance rates were determined from assays of algal depletion at several temperatures
ranging from 13.6 to 28°C. An example data set for the determination of clearance rate Q 10 is
shown in Figures F1 and F2 for a larval cohort of C. gigas (Family Wild-Type 2, 5-day old
larvae) (herein designated WT2D5). Clearance rate assays in this example were conducted in 250
ml beakers containing 200 ml filtered seawater (Nucleopore, 0.2 µm pore size). Beakers were
mixed by gentle stirring using a polycarbonate paddle attached to a gearhead motor (Herbach and
Rademan, Raleigh, NC). Each beaker contained 75 larvae ml
−1
and an initial ration of 40,000
cells ml
−1
T-Isochrysis galbana. Assays were conducted in water baths at 15, 20, 24, and 28°C
with each temperature treatment represented by three replicate beakers (A total of 12 beakers).
Each beaker was sampled twice during five time-points separated by 30 - 60-minute intervals,
for a total of 10 measurements per beaker. Sampling was achieved by drawing up 5 ml of assay
media through a Nitex mesh (20 µm pore size) to exclude larvae. Samples were counted on the
Z2 particle counter, cross-calibrated to hemocytometer counts and corrected for larvae-only-
control particle counts. Each particle count consumed 0.5 ml of the sample. The remaining 4.5
ml were returned to the assay beaker within 5 minutes to maintain a near-constant volume (< 5%
325
reduction in volume due to sampling over the course of the assay). Clearance rates were
calculated based on the depletion of algae from assay media using Equation F1 where F is the
clearance rate, Ct is the measured cell concentration at time t, C0 is the initial cell concentration,
N is the total larvae in each assay vial, and V is the volume of the assay vial.
Equation D1. 𝐶 = 𝐶 × 𝑒 ×
For the example above (using larvae from WT2D5), a total of 120 measurements were
taken across 5 time points and 4 temperatures. Figure D1 shows the depletion of algae by larvae
within assay beakers at 15 (panel A), 20 (panel B), 24 (panel C), and 28°C (panel D). At 15°C,
WT2D5 larvae had clearance rates of 0.44 ± 0.05, 0.44 ± 0.04, and 0.55 ± 0.07 µl larva
−1
h
−1
,
increasing to 2.47 ±0.11, 3.38 ± 0.15, and 3.00 ± 0.08 µl larva
−1
h
−1
at 25°C (Fig. D2a). The
equation for Q10 (Eq. D2) was linearly transformed to incorporate data across multiple
temperatures (Eq. D3). Using Eq. D3, the Q10 can be derived from a graph of log10(Clearance
rate) against (T2-T1)/10 where T1 is a reference temperature and T2 is the temperature of the
assay. In this case, the Q10 is equal to 10
slope
of a line fit through this graph. For example,
WT2D5, this calculation yielded a Q10 of 4.09 derived from 120 measurements (Fig. D2b).
Equation D2. 𝑄 =
Equation D3. log 𝑅 = log 𝑅 +
× log 𝑄
326
A total of 14 determinations of the Q10 of clearance rates were conducted (Table D1).
These measurements were made for larvae of C. gigas between 2- and 36-days old, between 81.6
and 239.6 µm shell length, and at rearing temperatures between 21 and 28°C. There was no
relationship between clearance rate Q10 and age (linear regression p = 0.381), shell length (p =
0.39) or rearing temperature (p = 0.23) (Fig. D3). As a result, Q 10 values across all experiments
were averaged, yielding an estimate of clearance rate Q 10 to be 2.92 ± 0.25 (S.E.M, n = 14; Table
D1).
Determining the functional response of clearance rates to food amounts
Clearance rate assays were conducted in 250 ml beakers containing 200 ml filtered
seawater (Nucleopore filter, 0.2 µm pore size). Beakers were mixed by gentle stirring using a
polycarbonate paddle attached to a gearhead motor (Herbach and Rademan, Raleigh, NC). Each
beaker contained 30 larvae ml
−1
and an initial target ration of 2, 4, 6, 8, 10, 15, 20, 30, 50, 70, or
100,000 cells ml
−1
T-Isochrysis galbana. A hemocytometer was used to count algae, and to
cross-calibrate a Z2 particle counter (Beckman Coulter). Each beaker was sampled twice during
five time-points separated by 15 – 30-minute intervals, for a total of 10 measurements per
beaker. Sampling was achieved by drawing up 5 ml of assay media through a Nitex mesh (20 µm
pore size) to exclude larvae. Samples were counted on the Z2 particle counter, cross-calibrated to
hemocytometer counts and corrected for larvae-only-control particle counts. Each particle count
consumed 0.5 ml of the sample. The remaining 4.5 ml were returned to the assay beaker within 5
minutes to maintain a near-constant volume (< 5% reduction in volume due to sampling over the
course of the assay). Clearance rates were calculated based on the depletion of algae from assay
327
media using Equation D1. Because initial food amounts declined throughout the assay, the
median amount of algae was used to represent each food ration and clearance rate data pair.
Changes in feeding rates with food ration
A total of 53 feeding assays were performed (5 time-points assay
−1
) across five cohorts of
larvae and eleven food rations. The larvae tested had similar shell lengths 124.5 ± 1.7 (Fig. D4a-
b), 129.1 ± 3.6 (Fig. D4c-d), 127.5 ± 2.6 (Fig. D5a-b), 129.9.1 ± 3.1 (Fig. D5c-d), except for one
batch with 170.7 ± 1.8 µm (Fig. D5e-f). In all cases, clearance rates were higher at low food
rations, and ingestion rates were highest at high food rations. The relationship between clearance
rates or ingestion rates with food ration did not appear to be linear. There were clear patterns in
the residual errors across fitted values in a linear regression. As a result, the functional responses
of clearance rates to food ration were also modelled with a 2-piece-wise linear regression using
the ‘segmented’ package in R
(v1.3-1; https://cran.r-project.org/web/packages/segmented/index.html)
as well as an exponential decay model using the exponential decay function in the ‘drc’ package
in R (v3.0-1; https://cran.r-project.org/web/packages/drc/drc.pdf). Mean-squared errors (MSE) as
well as corrected Akaike’s Information Criterion (AIC C) were calculated for each model fit.
Based on these models, the exponential decay model had the lowest AIC C scores and were the
most likely model describing the functional response of clearance rates to food ration (Table
D2). However, the piece-wise linear model yielded the lowest MSE in three of the five batches
of larvae making this the more predictive model. The interpretation of the two models
(exponential versus piece-wise linear) depends on the proximate biological cause for the
observations. For example, an exponential biological model would describe a continuous
328
regulatory physiology driving the functional response of clearance rates to food ration.
Meanwhile, a piece-wise linear biological model would describe a biological threshold around
which the regulation of clearance rates changes. After size-correcting the clearance rates of the
five batches of larvae, the overall size-specific functional response was modelled for all of the
data together (Fig. D6a). Bootstrapping methods were used to estimate parameters for both
exponential and piece-wise models. Briefly, 20 data points were randomly sampled with
replacement from the 53 size-corrected data points to generate parameters for the exponential
model (d, e; y = d × e
(-x/e)
) and the piece-wise linear model (b0, b1, break-point, b2). Five
thousand bootstraps replicates (random data sampling with replacement) were repeated to
estimate model parameters. The exponential decay model parameters were d = 0.27 [95% CI:
0.21 – 0.34] and e = 40.91 [95% CI: 21.24 – 65.51]. Bootstrapping of the piece-wise linear
model yielded an initial intercept b 0 = 0.30, b1 = -0.0088, break-point = 22.02, and b 2 = -0.0011.
The functional responses of ingestion rates to food ration were modelled as a Hollings
type II saturation curve using the 2-parameter Michaelis-Menten function in the ‘drc’ package in
R. The model fit was compared to a linear regression using MSE and AIC C. The Hollings Type
II model was a more likely and predictive model than a linear model in all cases (Table D3). This
model was based on two parameters: d, representing an asymptotic maximum ingestion rate and
e, representing the food ration at which half the maximum ingestion rate is predicted. Bootstrap
analysis was performed for parameters d and e on the size-corrected, pooled ingestions rate data
as described above for clearance rates. After 5,000 bootstraps, the maximum size-specific
ingestion rate was estimated as 21 cells (larval µm)
−1
h
−1
[95% CI: 10.0 – 71.9], and the half-
saturation food ration was estimated to be 6,300 cells ml
−1
[95% CI: 4.1 – 12.5].
329
Conclusion
Clearance rates for larvae of Crassostrea gigas changed in feeding rates in response to
temperature and food ration. Clearance rates increased with temperature between 15 – 28°C with
a Q10 of 2.92 ± 0.25 (S.E.M, n = 14; Table D1). There were no relationships between Q 10 values
and either shell length, rearing temperature, or age (i.e., the physiology which sets Q10 values is
constant as larvae grow). Clearance rates also showed a functional response to food rations
between 2,000 – 100,000 cells ml
−1
T-Isochrysis galbana. Clearance rates could be modelled as
either an exponential function or a piece-wise linear regression with a break-point around 22,000
cells ml
−1
. Ingestion rates could be modelled as a saturating Hollings type II function yielding
size-specific maximal ingestion rates of 21 cells (larval µm)
−1
h
−1
. These models describe how
larvae are able to regulate feeding in response to temperature and food rations. The model
parameters are useful in predicting feeding rates under different scenarios such as aquacultural
production or environmental resilience.
330
Appendix D Tables and Figures
Table D1. Thermal sensitivities (Q10) of clearance rates for larvae of Crassostrea gigas. Multiple
cohorts of larvae were assayed across age, size, chronic rearing temperature, and acute assay
temperatures. The Q10 for clearance rate averaged 2.92 ± 0.25 (s.e.m; n = 14).
Family Age (d)
Shell Length
(µm)
Rearing
Temperature
Assay
Temperatures Q10
56x11 (G 0 parents) 2 81.6 ± 0.3 24°C 15, 20, 24, 28°C 2.88
5 91.1 ± 0.8 24°C 15, 20, 24, 28°C 3.82
G 0 pool 14 184.4 ± 2.2 25°C 13.6, 24.4°C 2.12
Carlsbad WT 6 99.1 ± 0.4 25°C
14.2, 17.4,
23.9°C 1.55
WT1 6 90.6 ± 0.6 24°C 15, 20, 24, 28°C 2.71
12 107 ± 1.1 24°C 15, 20, 24, 28°C 2.60
WT2 5 94.3 ± 0.8 24°C 15, 20, 24, 28°C 4.09
10 113.9 ± 2.7 24°C 15, 20, 24, 28°C 1.61
Cg 2017x3 27 232.1 ± 2.4 21°C 21, 25, 28°C 4.77
27 224.8 ± 2.9 25°C 21, 25, 28°C 3.15
27 233.4 ± 3 28°C 21, 25, 28°C 2.58
36 239.4 ± 2.7 21°C 21, 25, 28°C 2.61
36 237.6 ± 2.2 25°C 21, 25, 28°C 3.78
Cg 2018x4 6 93.2 ± 0.7 24°C 15, 20, 24, 28°C 2.65
Average ± s.e.m: 2.92 ± 0.25
331
Table D2. Comparison of models of the change in clearance rates for larvae of Crassostrea gigas to food ration. Linear models,
exponential decay models, and piece-wise linear models were fit using R packages ‘segmented’ and ‘drc’. Mean-squared errors (MSE)
and corrected Akaike’s Information Criterion (AIC C) are listed for each model.
Linear Model Exponential Model Piece-wise Linear
y = b0 + x*b1 y = b0*e^(-x/b1) y = b0 + x*b1 [x < break-point]
Shell
length b0 b1 MSE AICC b0 b1 MSE AICC b0 b1 break-point b2 MSE AICC
124.5 ± 1.7 31.9 ± 3.8 -0.39 ± 0.1 65.4 51.8 44.4 ± 5.3 17.3 ± 5.8 23.1 41.4 51.3 ± 4.5 -4.1 ± 1.1 7.7 ± 1.4 3.9 ± 1.1 11.7 47.4
129.1 ± 3.6 34.1 ± 2.8 -0.4 ± 0.1 36.3 45.9 40.2 ± 3.1 30.8 ± 7.6 16.2 37.9 49.4 ± 4.2 -3.5 ± 1.1 7.0 ± 1.4 3.3 ± 1.1 9.1 44.8
127.5 ± 2.6 20.4 ± 2.1 -0.23 ± 0.1 21.9 43.4 22.8 ± 2.4 37.6 ± 13.1 16.3 40.2 22.8 ± 2.6 -0.5 ± 0.2 33.0 ± 16.5 0.5 ± 0.3 18.1 51.7
129.9 ± 3.1 36.7 ± 2.5 -0.36 ± 0.1 30.4 47.0 40.0 ± 2.6 50.2 ± 11.0 20.0 42.4 39.8 ± 2.9 -0.7 ± 0.3 40.9 ± 15.5 0.6 ± 0.3 21.5 53.6
170.7 ± 1.8 41.7 ± 2.1 -0.39 ± 0.1 22.3 43.6 44.0 ± 2.3 61.6 ± 11.6 17.4 40.8 50.9 ± 4.1 -2.3 ± 1.2 7.2 ± 2.8 2.0 ± 1.2 10.9 46.1
332
Table D3. Comparison of models of the change in ingestion rates for larvae of Crassostrea gigas to food ration. Linear models and
Hollings type II models were fit using the R package ‘drc’. Mean-squared errors (MSE) and corrected Akaike’s Information Criterion
(AICC) are listed for each model.
Linear Model Hollings Type II Model
y = b 0 + x*b 1
y = d/[1+(e/x)]
Shell length b 0 b 1 MSE AIC C d e MSE AIC C
124.5 ± 1.7
137.6 ± 26.6 5.0 ± 0.7 3198.4 90.7
579.8 ± 29.7 16.1 ± 2.2 422.5 70.5
129.1 ± 3.6
169.9 ± 42.7 6.2 ± 1.2 8241.8 100.2
707.4 ± 30.5 15.2 ± 1.8 505.9 72.3
127.5 ± 2.6
79.3 ± 26.1 4.5 ± 0.8 3531.7 99.3
459.1 ± 75.7 18.3 ± 8.1 2209.7 94.1
129.9 ± 3.1
173.6 ± 49.1 9.9 ± 1.3 12058.3 112.8
1143.8 ± 152.6 25.7 ± 8.3 6201.6 105.5
170.7 ± 1.8 195.3 ± 72.8 13.3 ± 2.1 26774.8 121.6 1512.3 ± 173.6 28.4 ± 7.3 7053.2 106.9
333
Figure D1. Replicate time-course clearance rate assays used to determine the thermal sensitivity
of clearance rates to temperature. Data from the larval cohort of Crassostrea gigas, Wild-Type 2
Day 5 (WT2D5) are shown as an example. Three replicate assays at each temperature are
represented by different symbols (n = 10 measurements each). (A) Assays at 15°C. (B) Assays at
20°C. (C) Assays at 24°C. (D) Assays at 28°C.
334
Figure D2. Temperature dependence of clearance rates of Crassostrea gigas and determination
of Q10 values for the larval cohort of Crassostrea gigas, Wild-Type 2 Day 5 (WT2D5). (A)
Clearance rates ± S.E. (n = 10 time-course measurements) at four temperatures tested. Data
points are off-set along the x-axis for clarity. (B) Semi-log plot of clearance rates against
normalized temperatures yields a Q 10 value of 4.09 based on the slope of the solid line.
335
Figure D3. Q10 values of larval clearance rates of Crassostrea gigas determined from 14
separate trials for seven larval cohort families. (A) There was no correlation between Q 10 and
larval age (linear regression p = 0.381). (B) There was no correlation between Q 10 and larval
shell length (p = 0.39). (C) There was no relationship between Q 10 and larval rearing temperature
(p = 0.23).
336
Figure D4. Functional response of 11-day old larval clearance rates of Crassostrea gigas and
ingestion rates to food ration. (A) Clearance rates of larvae at shell lengths of 124.5 ± 1.7
(S.E.M, n = 50 measurements) µm decreased at higher food amounts. (B) Ingestion rates of
larvae at shell lengths of 124.5 ± 1.7 (S.E.M, n = 50 measurements) µm decreased at higher food
amounts. (C) Clearance rates of larvae at shell lengths of 129.1 ± 3.6 (S.E.M, n = 50
measurements) µm decreased at higher food amounts. (D) Ingestion rates of larvae at shell
lengths of 129.1 ± 3.6 (S.E.M, n = 50 measurements) µm decreased at higher food amounts.
Each datum point is a clearance rate ± S.E (n = 10 measurements). Symbol fills indicate larvae of
the same average shell length.
337
Figure D5. Changes in clearance rates for larvae of Crassostrea gigas and ingestion rates to food
amount. (A) Clearance rates of larvae at shell lengths of 127.5 ± 2.6 (S.E.M, n = 50
measurements) µm decreased at higher food amounts. (B) Ingestion rates of larvae at shell
lengths of 127.5 ± 2.6 (S.E.M, n = 50 measurements) µm decreased at higher food amounts. (C)
Clearance rates of larvae at shell lengths of 129.9.1 ± 3.1 (S.E.M, n = 50 measurements) µm
decreased at higher food amounts. (D) Ingestion rates of larvae at shell lengths of 129.9.1 ± 3.1
(S.E.M, n = 50 measurements) µm decreased at higher food amounts. (E) Clearance rates of
larvae at shell lengths of 170.7 ± 1.8 (S.E.M, n = 50 measurements) µm decreased at higher food
amounts. (F) Ingestion rates of larvae at shell lengths of 170.7 ± 1.8 (S.E.M, n = 50
measurements) µm decreased at higher food amounts. Each datum point is a clearance rate ± S.E
(n = 10 measurements). Symbol fills indicate larvae of the same average shell length.
338
Figure D6. Size-specific changes in larval clearance rates of Crassostrea gigas and ingestion
rates with food ration. (A) Clearance rates ± s.e. (n = 10 time-course measurements) normalized
to shell length decreased with higher food rations. (B) Ingestion rates ± s.e. (n = 10 time-course
measurements) normalized to shell length increased with food ration.
339
APPENDIX E
Thermal Sensitivity of Feeding Rates for Larvae of the
Painted Sea Urchin Lytechinus pictus
Objective: To determine the thermal sensitivity (Q10) of feeding for larvae of Lytechinus pictus
reared at 15 and 20°C.
Larval culturing
A total of 270,000 larvae of L. pictus were used for the determination of thermal
sensitivities of clearance rates. To gather enough embryos, gametes were collected from several
females following injection of 0.5 M KCl. Fertilized embryos were pooled and divided into four
20-liter culture vessels. Two culture vessels were held at 15°C while the other two culture
vessels were held at 20°C water baths. Larvae reared at 15°C were sampled for clearance rates
on days 4, 6, and 8. Larvae reared at 20°C were sampled for clearance rates on days 3, 5, and 7.
During sampling, larvae were gently poured onto a Nitex mesh (54 µm pore size) and
resuspended in 50 – 130 ml filtered seawater (Nucleopore, 0.2 µm pore size). Larvae were gently
mixed and subsampled four times for enumeration. All counts exceeded 100 individuals, and
replicate counts had coefficients of variation below 10%. Following enumeration, larvae were
divided into replicate assay vials at four temperatures for determinations of clearance rate.
Larvae were held in assay vials for 30 minutes prior to assay initiation. On each sampling day, a
Q10 value was determined for larvae from each culture vessel (12 determinations total).
340
Determination of clearance rates
Clearance rate assays in this study were conducted in 250 ml beakers containing 150 ml
filtered seawater (Nucleopore, 0.2 µm pore size). Beakers were mixed by gentle stirring using a
polycarbonate paddle attached to a gearhead motor (Herbach and Rademan, Raleigh, NC). Each
beaker contained known amounts of larvae (10-30 larvae ml
−1
) and an initial ration of 20,000
cells ml
−1
Rhodomonas lens. Particle counts (Z2 Particle Counter; Beckman Coulter) were cross-
calibrated to hemocytometer counts (Fig. E1a). Assays were kept in water baths at 10, 15, 20,
and 25°C with each temperature treatment represented by two replicate beakers (A total of 8
experimental beakers). Additional beakers containing only larvae or only algae were also used as
controls. Each beaker was sampled twice during five time-points separated by 1-hour intervals,
for a total of 10 measurements per beaker (Fig. E1b). Sampling was achieved by drawing up 5
ml of assay media through a Nitex mesh (20 µm pore size) to exclude larvae. Each particle count
consumed 0.5 ml of the sample. The remaining 4.5 ml were returned to the assay beaker within 5
minutes to maintain a near-constant volume (< 5% reduction in volume due to sampling over the
course of the assay). Clearance rates were calculated based on the depletion of algae from assay
media using Equation E1 where F is the clearance rate, Ct is the measured cell concentration at
time t, C0 is the initial cell concentration, N is the total larvae in each assay vial, and V is the
volume of the assay vial. Clearance rates for larvae from a given culture vessel were compared
across four assay temperatures for determination of thermal sensitivity (Fig. E1c).
Equation E1. 𝐶 = 𝐶 × 𝑒 ×
341
Thermal sensitivities of clearance rates
The thermal sensitivity of clearance rates (Q10) was determined for larvae from each
culture vessel using clearance rates determined from four temperatures (10, 15, 20, and 25°C).
The Q10 values were calculated according to Eq. E2 and Eq. E3. Equation E3 is the log-
transformed equivalent of Eq. E2 and allows for the use of more than two data points. From Eq.
E3, the Q10 can be derived from a plot of log10(R) against (T2-T1)/10 where R is the clearance
rate, T1 is a reference temperature and T2 is the temperature of the assay. In this case, the Q10 is
equal to 10
slope
of a line fit through this graph (Fig. E1d).
Equation E2. 𝑄 =
Equation E3. log 𝑅 = log 𝑅 +
× log 𝑄
With four culture vessels each sampled on three days with duplicate assays at four
temperatures, a total of 96 assays (960 timepoint measurements) were used for determinations of
clearance rate thermal sensitivity. These 96 assays were used to calculate 12 Q 10 values of
clearance rate (Table E1). Clearance rates had no significant correlation with size for larvae
reared at either temperature (ANOVA regression; 15°C: p = 0.34; 20°C: p = 0.81). The Q 10
values for larvae reared at 15°C averaged 2.6 ± 0.2 (s.e.m.; n = 6), and the Q 10 values for larvae
reared at 20°C averaged 3.1 ± 0.2 (s.e.m.; n = 6). There was no significant difference in Q 10
between larvae long-term (chronically) reared at either 15 or 20°C (ANOVA F 1,10 = 2.28, ns).
The Q10 averaged across all determinations is 2.8 ± 0.2 (s.e.m.; n = 12). This means that the
342
thermal sensitivity of feeding rates is independent of the long-term rearing temperature of larvae
of L. pictus.
343
Appendix E Tables and Figures
Table E1. Thermal sensitivities of clearance rates for larvae of Lytechinus pictus reared at 15 or
20°C. Midline body lengths are reported as average ± S.E.M (n = 50 measurements), and Q 10
values are reported as Q10 ± S.E.
L. pictus larvae reared at 15 °C L. pictus larvae reared at 20 °C
Culture
Vessel
Midline Body
Length (µm)
Clearance
Rate Q 10
Culture
Vessel
Midline Body
Length (µm)
Clearance
Rate Q 10
1
289.3 ± 1.8 2.5 ± 0.5
3
273.9 ± 2.8 3.3 ± 0.3
315.8 ± 2.3 2.0 ± 0.4
322.2 ± 2.6 2.6 ± 0.5
319.5 ± 2.6 2.8 ± 0.5
339.7 ± 3.5 3.1 ± 0.5
2
294.4 ± 2.0 3.6 ± 0.4
4
287.4 ± 1.9 3.1 ± 0.3
327.7 ± 2.7 2.1 ± 0.3
327.0 ± 3.3 2.6 ± 0.4
331.8 ± 2.9 2.6 ± 0.4
365.1 ± 4.1 3.6 ± 0.7
Average ± s.e.m: 2.6 ± 0.2 Average ± s.e.m: 3.1 ± 0.2
344
Figure E1. Determination of clearance rate thermal sensitivity (Q10). (A) Cross-calibration of
particle counts to visual hemocytometer counts of the algal food Rhodomonas lens. (B)
Depletion of particles from clearance rate assays. Black symbols represent replicate
measurements from an assay vial containing larvae and algae. White symbols represent replicate
measurements from a control assay containing only algae. Gray symbols represent replicate
measurements from a control assay containing only larvae. (C) Replicate clearance rates assayed
for larvae at four temperatures. Each data point is a clearance rate ± S.E. (D) Log-transformed
clearance rate data from panel C plotted against (T n-Tf)/10 where Tf = 10°C. The Q10 value is
10
slope
of the regression line.
345
APPENDIX F
Neurotransmitter Control of Feeding Rates for larvae of the
Purple Sea Urchin Strongylocentrotus purpuratus
Objectives: To examine the effects of dopamine and serotonin on feeding rates for larvae of
Strongylocentrotus purpuratus.
Determination of clearance rates
Clearance rates were determined by particle counting (Z2 particle counter; Beckman
Coulter) of the depletion of algal food from assay media. All particle counts were cross-
calibrated with hemocytometer counts of the algal food Rhodomonas lens. Assays were
conducted using 120 ml graduated cylinders with glass stoppers containing 100 ml seawater
(filtered through 0.2 µm pore-size Nucleopore filters). All vials were sealed and held in a water
bath set to 15°C. Algae-only control vials contained 15,000 cells ml
−1
R. lens and larvae-only
control vials contained 20 larvae ml
−1
. Experimental vials contained both larvae and algae, and a
range of dopamine or serotonin concentrations. The algal particles in each assay vial were
subsampled across 5 time points spanning 5 – 6 hours. During each timepoint, the vials were
mixed gently, and two 10 ml aliquots were passed through a Nitex mesh (20 µm pore size). The
filtrates (excluding larvae) were collected in vials (Accuvette cups, Beckman Coulter) and
counted twice on the Z2 particle counter. The depletion of particles was corrected for baseline
particle counts in the larvae-only controls, and clearance rates were calculated using Equation F1
where F is the clearance rate, Ct is the measured cell concentration at time t, C0 is the initial cell
concentration, N is the total larvae in each assay vial, and V is the volume of the assay vial. In
346
assays where algal concentrations did not change with time, larvae were described as having no
feeding rate. Larvae were assayed with and without dopamine or serotonin to examine functional
responses to each neurotransmitter. Larvae treated with dopamine or serotonin were exposed 30
minutes prior to measurements of clearance rate.
Equation F1. 𝐶 = 𝐶 × 𝑒 ×
Dopamine response
Changes in feeding rates of Strongylocentrotus purpuratus in response to 1 µM dopamine
was examined at 6,000 and 15,000 cells ml
−1
. Two food rations were selected because larvae
have been observed to increase feeding rates at lower food rations (see Chapter 2). Larvae-only
and algae-only controls were also assayed at 0 and 1 µM dopamine. Particles in control assays
with 0 µM dopamine did not change over time (Fig. F1a). However, larvae-only controls in the
presence of 1 µM dopamine increased particle abundances over time (linear regression; p <
0.0001; Fig. F1b). As a result, assays with 1 µM dopamine were corrected based on time-
matched values of larvae-only control particle counts. At 15,000 cells ml
−1
, algal particles did
not change in abundance over time when 1 µM dopamine was added (linear regression p = 0.09;
Fig. F1c). Meanwhile, when no dopamine was added, larvae cleared algae at a rate of 2.4 ± 0.4
(S.E.; n = 20) µl larva
−1
h
−1
. A similar effect of 1 µM dopamine was observed at 6,000 cells ml
−1
.
Larvae exposed to dopamine did not clear particles from the media (linear regression p = 0.637;
Fig. F1d). Larvae assayed at 6,000 cells ml
−1
without dopamine cleared algae at a rate of 4.4 ±
0.4 (S.E.; n = 20) µl larva
−1
h
−1
. Clearance rates of larvae increased for larvae not exposed to
347
dopamine between assays at 15,000 and 6,000 cells ml
−1
. In both cases, the addition of 1 µM
dopamine inhibited clearance of algal particles.
Serotonin response
The dose response of clearance rates of S. purpuratus to 0, 001, 0.1, 1, 5, and 5 µM
serotonin was tested at 15,000 cells ml
−1
for a set of larvae of size 270 ± 2 (s.e.m; n = 50) µm
midline body length. An additional assay at 5,000 cells ml
−1
was conducted in the absence of
serotonin as a positive control for upregulation of clearance rates. There was no relationship
between clearance rates at 15,000 cells ml
−1
and serotonin dose between 0 – 5 µM (linear
regression p = 0.148; Fig. F2a). At 15,000 cells ml
−1
, larval clearance rates averaged 1.27 ± 0.15
(S.E.M, n = 5 doses) across all serotonin doses. Larvae assayed at 5,000 cells ml
−1
did increase
their clearance rates to 3.1 ± 0.4 (S.E. n = 20 time points) in the absence of serotonin. An
additional comparison of 0 and 10 µM serotonin doses was conducted for another set of larvae of
size 261 ± 2.4 (s.e.m; n = 50) µm midline body length (Fig. F2b). However, there was no
significant difference in clearance rates between 0 and 10 µM serotonin at either food ration, as
indicated by the overlapping 95% confidence intervals (Fig. F2b). Additionally, clearance rates
were elevated at 5,000 cells ml
−1
relative to at 15,000 cells ml
−1
regardless of serotonin exposure.
As a result, serotonin had no effect on larval clearance rates with doses between 0 – 10 µM.
Conclusion
Dopamine (1 µM) inhibited clearance rates in larvae at both food rations of 6,000 and
15,000 cells ml
−1
(Fig. F1c,d). However, serotonin had no effects on clearance rates at doses
between 0 – 10 µM. In the absence of either dopamine or serotonin, larvae had elevated
348
clearance rates at low food rations (5,000 or 6,000 cells ml
−1
) compared to a higher food ration
of 15,000 cells ml
−1
. Upon visual inspection of larvae exposed to dopamine at 200x
magnification, cilia were observed to still be actively beating. The inhibition of clearance rates
may be due to a reversal in ciliary beating direction or another rejection mechanism. The
appearance of particles in larvae-only controls exposed to dopamine suggest that larvae may be
eliminating particles from the gut. The use of dopamine as an inhibitor of feeding might be a
valuable approach to study the effects of experimentally inhibited feeding rates at a constant food
ration on subsequent protein metabolic dynamics.
349
Appendix F Figures
Figure F1. Inhibition of larval clearance rates of Strongylocentrotus purpuratus by 1 µM
dopamine. (A) Algae-only (circles) and larvae-only (triangles) controls did not vary with time
(regression; p = 0.286; p – 0.569 respectively). (B) The addition of 1 µM to algae-only (circles)
and larvae-only (triangles) controls resulted in increases in particles in larvae-only controls (p <
0.0001). Experimental assays containing 1 µM dopamine would be corrected for these time-
matched increases. (C) Algal depletion by larvae without dopamine (closed circles) compared to
those with 1 µM dopamine (open circles) at a ration of 15,000 cells ml
−1
. Algal cells were not
cleared by larvae when dopamine was added. (D) Algal depletion by larvae without dopamine
(closed circles) compared to those with 1 µM dopamine (open circles) at a ration of 6,000 cells
ml
−1
. Algal cells were not cleared by larvae when dopamine was added.
350
Figure F2. Dose-response of larval clearance rates of Strongylocentrotus purpuratus to serotonin
(0 – 10 µM). (A) Clearance rates of larvae (270 ± 2 (s.e.m; n = 50) µm midline body length)
assayed at 15 cells µl
−1
(black symbols) were not correlated with serotonin doses between 0 – 5
µM (linear regression; p = 0.148). An independent assay at 5 cells µl
−1
(yellow symbol) result in
higher clearance rates than those assayed at 15 cells µl
−1
indicating that a functional response
was possible. Each symbol represents a clearance rate ± s.e. (n = 20 time-course measurements).
(B) Clearance rates of larvae (261 ± 2.4 (s.e.m; n = 50) µm midline body length)) were assayed
at either 6 or 15 cells µl
−1
with either 0 (gray bars) or 10 (black bars) µM serotonin. Error bars
represent upper 95% confidence intervals of the clearance rate estimate [t 0.025 = 2.101; df = 18;
SEb1 = 0.46, 0.43, 0.15, and 0.26 for clearance rates at treatments 6 - 0µM, 6 - 10µM, 15 - 0µM,
and 15 - 10µM respectively]. Clearance rates were elevated at 6 cells µl
−1
, but there was no
effect of 10 µM serotonin on clearance rate at either food amount.
351
APPENDIX G
In Vitro Proteasomal Enzyme Activity in Larvae of the
Purple Sea Urchin Strongylocentrotus purpuratus
Objectives: To test an assay of in vitro proteasomal enzyme activity on larvae of
Strongylocentrotus purpuratus reared at different temperatures.
Protein extraction and quantification
Embryos of Strongylocentrotus purpuratus were fertilized following spawning by
intracelomic injection of 0.5 M KCl. Larvae were reared in 20-liter culture vessels in water baths
set at either 15 or 20°C. On day four of development, larvae were retained on a 53 µm (mesh
size) Nitex mesh and resuspended in 50 ml of sea water. Four aliquots of 50 µl were subsampled
to enumerate larvae. Counts for each aliquot exceeded 100 larvae, and the coefficient of variation
between all four counts was < 10%. 8 samples of 5,000 larvae each were taken from the 15°C
culture vessel while 4 samples of 5,000 larvae were taken from the 20°C culture vessel. Seawater
was removed following gentle centrifugation, and larvae were frozen at -20°C until use.
Each sample of larvae was resuspended in 100 µl proteolytic buffer (25 mM KCL, 10
mM NaCl, 1 mM MgCl2, 0.1 mM DTT, 50 mM Tris, pH 7.51). Larvae were disrupted using an
electronic pestle (Disposable pellet mixer, VWR) for multiple pulses of 10 seconds each. Tissues
were further lysed by three freeze-thaw cycles of 5 minutes on dry ice followed by 5 minutes on
room temperature water. Lysates were centrifuged at 10,000 rcf for 10 minutes at 4°C.
Supernatants were then transferred to new microcentrifuge tubes on ice. Protein content was
determined in duplicate for each sample on a 96-well plate. Each technical replicate used 5 µl of
352
lysate for the Bicinchoninic acid assay according to the manufacturer’s protocol (Pierce
TM
BCA
Protein Assay Kit – Reducing Agent Compatible, Thermofisher Scientific). Bovine serum
albumin (0.125 – 2.0 mg ml
−1
was used as a standard.
Proteolytic activity
In vitro proteasomal activity was determined by monitoring the liberation of unbound
fluorogenic substrates (7-amino-4-methylcoumarin) from standard peptides (Caspase-like/β1
activity, Z-LLE-AMC; no. 539141, Calbiochem). Five µg of each sample was brought to 100 µl
volumes with proteolytic buffer. Ten µl of peptide substrate bound to 7-amino-4-
methylcoumarin (AMC) was then added to each sample. A serial dilution of unbound AMC
(1.25 – 40 µM) was prepared as well as a bound standard acting as a blank (Fig. G1a). Triplicate
wells of 100 µl of samples and unbound AMC standards were loaded onto a 96-well for
fluorescence-based assays. Twenty-four fluorescence readings were taken at 10-minute intervals
using an excitation wavelength of 355 nm and monitoring emissions at 405 nm. Background
fluorescence units from the bound AMC standard were subtracted from readings, and
fluorescence units were converted to moles of unbound AMC using the standard curve (Fig.
G1b). Fluorescence of unbound AMC increased in wells containing sample lysate (Fig. G2a;
G2b).
The maximum increases across a 10-minute interval for a given biological replicate were
averaged for each larval rearing temperature (Fig. H2c). Larvae reared at 15°C had Caspase-
like/β1 specific activities of 2.3 ± 0.2 (S.E.M; n = 8) pmol AMC min
−1
(µg total protein)
−1
while
larvae reared at 20°C had specific activities of 2.1 ± 0.3 (S.E.M; n = 4) pmol AMC min
−1
(µg
353
total protein)
−1
. There was no significant difference between the maximum specific activities for
larvae reared at either temperature (ANOVA, F1,10 = 0.36, ns).
354
Appendix G Figures
Figure G1. Standard curves for fluorescence of free 7-amino-r-methylcoumarin (AMC). (A) A
series of AMC (1.25, 2.5, 5, 10, 20, and 40 µM) were monitored at 405 nm emission following
excitation at 355 nm. Twenty-four measurements were taken at 10-minute intervals. (B) Average
fluorescence (± 1 SD) accounting for the background noise of bound Z-LLE-AMC for each
standard amount were plotted against the concentration of AMC such that (Fluorescence Units) =
11.649 × [AMC] + 7.4725 (R
2
= 0.9982).
355
Figure G2. Caspase-like/β1 proteosomal activity in 4-day old larvae of Strongylocentrotus
purpuratus determined by in vitro cleavage of the fluoropeptide Z-LLE-AMC and fluorescent
reporting of unbound AMC. (A) Time-course AMC fluorescence of eight replicate samples of 5
µg total protein (determined by BCA assay) taken from larvae reared at 15°C. Each symbol
represents one the average of biological replicate ± 1 SD (n = 3 technical replicates). (B) Time-
course AMC fluorescence of eight replicate samples of 5 µg total protein (determined by BCA
assay) taken from larvae reared at 20°C. Each symbol represents one the average of biological
replicate ± 1 SD (n = 3 technical replicates). (C) Maximum rates of proteasomal specific activity
did not differ between larvae reared at either 15 or 20°C (ANOVA F1,10 = 0.36, ns). Bars
represent average maximum rates ± 1 S.E.M (n = 8, n = 4 for 15 and 20°C respectively).
356
APPENDIX H
The Zebrafish Embryo as a Heterologous Expression System for
Assays of Amino Acid Transport
Objectives: To determine whether zebrafish (Danio rerio) embryos could be used as a
heterologous expression system for measuring amino acid transporter activity.
Free amino acid profiles of zebrafish embryos
Zebrafish embryos were obtained from three wildtype breeding tanks (USC Stem Cell
Department; Crump Lab). In each tank, two males and three females were separated by a divider
overnight. The next morning, spawning was induced following a water change and the removal
of the divider in shallow, freshly-changed water. Eggs were collected using a tea strainer and
concentrated onto a petri dish. Unhealthy eggs were removed using a glass pipette, and the
remaining healthy eggs were transferred into fresh zebrafish embryo media in a sterile flask held
in a water bath at 25°C. After 0, 5, 7.5, 26.5, and 31 hours post-fertilization (hpf), tubes
containing five embryos were collected. Media in the tubes were replaced by 200 µl 70% ethanol
(high performance liquid chromatography grade). Embryos were homogenized with a pestle and
sonicated for 5 seconds. Free amino acids were extracted overnight at 4°C and centrifuged at
12,000 rcf for 5 minutes at 4°C.
Free amino acids were separated by reverse phase high performance liquid
chromatography (HPLC). 50 µl of each sample was mixed with 150 µl of HPLC-grade water
before adding 50 µl ortho-phthalaldehyde (OPA) to a final volume of 250 µl. Samples were
loaded onto a 100 µl injection loop and separated on a 100 C-18 column (5µm; 15 x 4.6 mm;
357
HAISIL) using a gradient of 20% to 80% methanol in 50 mM sodium acetate (pH 6.8). 200 µl
Standards containing 1 µM of amino acids were also mixed with 50 µl OPA and loaded on 100
µl injection loops for reference. By 26 hpf, all 15 amino acids from the standard were also
identified in the embryos (Fig. H1). The most abundant amino acids appeared to be aspartic acid,
glutamic acid, serine, arginine, and lysine. Alanine was present, though in relatively low
amounts.
Amino acid transport by zebrafish embryos
Amino acid transport was tested by incubating embryos in known concentrations of
14
C-
alanine and measuring the radioactivity of embryos after one hour. An initial trial was conducted
to test the effects of assay media, de-chorionation, and developmental stage on uptake rates.
After 5 and 26.5 hpf, zebrafish were divided into groups which were either dechorionated or not.
De-chorionation was achieved by gentle tearing of the chorion using needles under a dissection
scope. Tubes of five embryos were then assayed in 1 ml zebrafish embryo media containing 7 –
8 kBq
14
C-alanine in 100 µM cold alanine carrier at 25°C. For embryos tested after 26.5 hpf,
assays were created using either embryo media or 2 PSU sea water (0.2 µm filtered). At the
initiation of each assay, 20 µl of assay media were sampled to measure the
14
C-alanine specific
activity of the assay. The assays were stopped after 1 hour by fully replacing the assay media
with deionized water three times. Embryos were then disintegrated overnight in 500 µl Solvable
solution and
14
C radioactivity was counted following the addition of 4 ml scintillation cocktail
(Ultima Gold) using a scintillation counter (Beckman LS6000 SC). Radioactivity was converted
to pmol alanine based on the specific activity of the assay media. Regardless of treatment,
embryos with chorions intact incorporated higher amounts of alanine than dechorionated
358
embryos (Fig. H2). Embryos at 26.5 hpf also had higher amounts of incorporation than those at 5
hpf. The use of 2 PSU seawater for media also increased the incorporation rate relative to
embryos assayed in zebrafish embryo media. As a caveat, these trials were conducted with only
one assay per treatment. Subsequent trials would involve embryos > 25 hpf and assay media
made using 2 PSU seawater.
A time-course assay comparing alanine uptake rates of live or dead zebrafish embryos
was conducted. Wildtype zebrafish embryos were collected and held in a water bath at 28°C. A
total of 130 embryos were dechorionated between 21 – 23 hpf. Seventy-five dechorionated
embryos were then transferred to 9 ml of ice-cold 2 PSU seawater (0.2 µm filtered). After 30
minutes on ice, 20 cold-shocked embryos were collected to verify mortality visually. Fifty-five
dead embryos and 55 live embryos (both sets dechorionated) were then re-acclimated to 28°C in
9-ml assay vials of 2 PSU seawater for 30 minutes. At approximately 25 hpf, 1 ml of 10x
transport cocktail was added to each assay vial. Assay vials contained a total final volume of 10
ml with approximately 74 kBq
14
C-alanine and 100 µM cold carrier alanine in 2 PSU seawater.
At the initiation of each assay, 20 µl of assay media were immediately removed to determine the
specific activity of
14
C-alanine in the media. Subsamples of 5 embryos were then removed at 10
time points spanning 5 hours. Subsamples were collected by pipetting 5 embryos into a new
microcentrifuge tube and replacing the assay media three times with 1.5 ml 2 PSU seawater. All
samples were then disintegrated in 500 µl Solvable solution overnight. Three tubes of embryo
control samples were also processed using embryos that were not exposed to assay media. Three
tubes of wash control samples were also processed using 20 µl of assay media without embryos
to account for any effects of the washing steps.
359
14
C radioactivity increased above the baseline in both live and dead zebrafish embryos
over five hours (Fig. H3). Embryo and wash controls had background radioactivity levels of 0.28
± 0.02 and 0.26 ± 0.01 Bq respectively while live and dead embryos increased to 1 – 2 Bq after
five hours (Fig. H3a). Based on the initial
14
C-alanine specific activity in the assay media,
radioactivity in embryos was converted to pmol alanine incorporation (Fig. H3b). Across the ten
time points for each treatment, there were significant linear relationships between time and
incorporated alanine (linear regression; live p < 0.0001, and dead p = 0.015). Live embryos
incorporated alanine at a rate of 0.4 ± 0.1 (s.e.) pmol alanine embryo
−1
h
−1
while dead embryos
incorporated 0.8 ± 0.3 (s.e.) pmol alanine embryo
−1
h
−1
. However, there were no significant
differences between the rates of amino acid transport in live versus control (cold-killed) embryos
(ANCOVA Slope: F1,16
= 2.75; ns).
Conclusion
The successful use of a heterologous expression system for testing the function of amino
acid transporter genes relies on the ability to detect signals, the reduction of endogenous noise,
and the physiological compatibility of the system to utilize the protein properly. Based upon the
free amino acid profiles of zebrafish embryos, free amino acids increase throughout the first 31
hours of development. While glycine and alanine are detectable by 5 hpf, their relative
abundances are low. Meyer and Manahan (2009) utilized Xenopus laevis as an expression system
which resulted in rates of approximately 600 pmol cell
−1
h
−1
. A comparable over-expression
pattern in zebrafish could produce a signal strong enough to be detected by HPLC. However,
based on the results of the current study, zebrafish embryos did not actively uptake alanine. This
was evident by statistically indistinguishable incorporation rates between live and dead embryos
360
(Fig. H3b). It is possible that alanine incorporation was due to diffusion or adsorption. Because
of the lack of endogenous alanine uptake activity, it is unknown whether the expression of
heterologous SLC6 transporter genes will allow of proper localization and function. As a result,
further tests of this system were discontinued.
Appendix H References
Meyer, E., and Manahan, D.T. (2009). Nutrient uptake by marine invertebrates: cloning and
functional analysis of amino acid transporter genes in developing sea urchins
(Strongylocentrotus purpuratus). The Biological Bulletin, 217(1), 6-24.
361
Appendix H Figures
Figure H1. Free amino acid profiles of embryos of Danio rerio after 0, 5, and 26 hours post-
fertilization (hpf). Amino acid peaks were identified by fluorescent labelling (ortho-
phthalaldehyde) of amino acids separated by reverse phase high performance liquid
chromatography. A standard of 15 known amino acids (top panel) was used for comparison of
retention times.
362
Figure H2. Transport rates of alanine from the media for embryos of Danio rerio over one hour.
Embryos were assayed either 5 or 26.5 hours post-fertilization (hpf). Black bars represent
embryos which had their chorions intact while gray bars represent embryos which were de-
chorionated mechanically. All assays were conducted in zebrafish embryo media containing
14
C-
Alanine (approximately 7 – 8 kBq) in 100 µM cold carrier with the exception of the indicated
assays where zebrafish embryo media was replaced with 2 PSU filtered sea water (0.2 µm
filtered).
363
Figure H3. Comparisons of alanine transport by live or dead zebrafish embryos. De-chorionated
embryos were assayed 25 hours post-fertilization in 2 PSU sea water containing
14
C-Alanine (74
kBq) in 100 µM cold carrier. (A) Radioactivity increased significantly in both live (black
symbols) and dead (white symbols) embryos (linear regression; p < 0.0001 and p = 0.015
respectively). The radioactivity in blank embryo controls averaged 0.28 ± 0.02 (s.e.m; n = 3) Bq
(blue solid line) while the radioactivity in blank wash controls averaged 0.26 ± 0.01 (s.e.m; n =
3) Bq (red dashed line). Embryo controls were never exposed to
14
C-Alanine, and Wash controls
were 20 µl of assay media, excluding embryos, washed identically to samples. (B) Absolute rates
of alanine incorporation by live and dead zebrafish embryos over five hours. Live embryos
incorporated 0.4 ± 0.1 (s.e.) pmol alanine embryo
−1
while dead embryos incorporated 0.8 ± 0.3
pmol alanine embryo
−1
. However, alanine incorporation rates did not differ significantly between
live and dead embryos (ANCOVA Slope: F 1,16
= 2.75; ns).
364
APPENDIX I
RNAscope Probes for In Situ Hybridization of SLC6 Transporter Genes for
Strongylocentrotus purpuratus
Objectives: To simultaneously localize the SLC6 amino acid transporter transcripts in
developing larvae of Strongylocentrotus purpuratus using RNAscope
®
(ACDBio) probes for in
situ hybridization.
RNAscope Principles
The use of RNAscope
®
probes for in situ hybridization of transcripts allows for multiplex
visualization of up to 12 genes in a tissue. The “ZZ” probes are named for their structure as the
bottom of each “Z” probe binds 18-25 base pair (bp) sequences on the target and the top of each
“Z” contains a 14 bp sequence to bind one half of a pre-amplifier structure (Wang et al., 2012).
Because pre-amplifiers must bind to two probes (ZZ pair), successful binding indicates that ~50
bp of the target was hybridized. Once the pre-amplifier binds to a ZZ pair, amplifiers then bind
to the pre-amplifier which then greatly increases the binding of fluorescent probes. The use of
different fluorescent probes in the same tissue sample is possible due to the different
combinations of amplifiers and targets. Twenty ZZ probes are typically designed for each target,
but a minimum of 6 ZZ probes are required. This requires a specificity of at least 300 bp (50 bp
per ZZ pair).
365
RNAscope probes for Strongylocentrotus purpuratus
Amino acid transporters in the SoLute Carrier family 6 (SLC6) gene family for
Strongylocentrotus purpuratus were identified by querying the Sp-AT1, 2, and 3 sequences
cloned by Meyer and Manahan (2009) using the Basic Local Alignment Search Tool (BLAST).
The BLAST queries against the databases for Strongylocentrotus purpuratus resulted in a total of
14 hits (Table I1). Three hits (XR_004064482.1, XR_143681.3, and XM_775027.5) were
eliminated since their query coverages were < 22%. The remaining hits had query coverages of
68 – 100% and sequence identities of 76 – 100% compared to their respective queries. The
sequence hit NM_001146195.2 was considered a repeat and eliminated since it only differed
from Sp-AT2 by one missing nucleotide at the 3’ end of the sequence in the non-coding region.
As a result, 10 total SLC6 amino acid transporter genes were identified in Strongylocentrotus
purpuratus. The ten putative SLC6 amino acid transporter genes were aligned using Clustal
Omega multiple sequence alignment (https://www.ebi.ac.uk/Tools/msa/clustalo/). Sp-AT1, 2, and
3 shared 52 – 58% sequence identity with each other (Table I2). The remaining seven transporter
genes were highly similar (> 98% identity) to either of Sp-AT1, 2, or 3.
The ten identified S. purpuratus SLC6 putative amino acid transporter sequences were
submitted online to Advanced Cell Diagnostics, Inc. (https://acdbio.com/custom/probe/request)
for use in the RNAscope Multiplex Fluorescent Assay v2 (ACDBio). Additionally, four
candidate control genes were also submitted: 18S rRNA (L28056.1), Ubiquitin (M61772.1),
EF1-α (NM_001123497), and Actin (R62049). RNAscope
®
probes were designed and
catalogued for each submission including the number of ZZ probes per target as well as the
target sequence and cross-detection (Table I3). Targets for SpAT-1, 2, and 3 were specific
366
enough to distinguish between the three genes. However, the remaining seven putative
transporter genes were predicted to be cross-detected using these probes.
Statement regarding impact of COVID-19 on research
Due to the COVD-19 pandemic, further exploration of RNAscope probes as a viable
method for in situ hybridization of SLC6 genes in S. purpuratus was halted. The university-
mandated hiatus of laboratory research during the purple urchin spawning season, and the limited
accessibility of larval rearing facilities prevented the testing of tissue fixation methods that
would be compatible with the RNAscope protocol. These unrealized experiments would have
also involved the testing of positive and negative control probes with properly fixed larval
tissues. Furthermore, these experiments would have required the purchase of the Multiplex
Fluorescent Reagent Kit v2, as well as the use of a specialized oven (HybEZ
TM
Hybridization
System; ACDBio) and a fluorescent microscope. Given these limitations and the inability the
work between laboratories safely, the project was discontinued.
Appendix I References
Meyer, E., and Manahan, D.T. (2009). Nutrient uptake by marine invertebrates: Cloning and
functional analysis of amino acid transporter genes in developing sea urchins
(Strongylocentrotus purpuratus). Biological Bulletin, 217, 6-24.
Wang, F., Flanagan, J., Su, N., Wang, L.C., Bui, S., Nielson, A., Wu, X., Vo, H.T., Ma, X.J., and
Luo, Y. (2012). RNAscope: a novel in situ RNA analysis platform for formalin-fixed,
paraffin-embedded tissues. The Journal of Molecular Diagnostics: JMD, 14(1), 22–29.
367
Appendix I Tables
Table I1. Ten putative SLC6 amino acid transporters were identified for Strongylocentrotus purpuratus. Sp-AT1-3 sequences were
queried using Basic Local Alignment Search Tool (BLAST) against all nucleotide sequences for S. purpuratus. Results highlighted in
gray were used for RNAscope
®
(ACDBio) probe design.
Query Description
Max
Score
Total
Score
Query
Cover E value
Per.
Ident Acc. Len Accession
EF538763.1 Strongylocentrotus purpuratus sodium-dependent alanine
transporter 1 mRNA, complete cds
3033 3033 100% 0 100.00 1642 EF538763.1
(Sp-AT1) PREDICTED: Strongylocentrotus purpuratus sodium-dependent
alanine transporter 1 (LOC594544), mRNA
2617 2617 88% 0 99.24 4430 XM_793980.5
PREDICTED: Strongylocentrotus purpuratus sodium- and chloride-
dependent neutral and basic amino acid transporter B(0+)-like
(LOC115918386), mRNA
2595 2595 88% 0 99.10 4420 XM_030976099.1
PREDICTED: Strongylocentrotus purpuratus uncharacterized
LOC577041 (LOC577041), transcript variant X2, ncRNA
207 207 10% 6.00E-52 88.57 2364 XR_004064482.1
PREDICTED: Strongylocentrotus purpuratus uncharacterized
LOC577041 (LOC577041), transcript variant X1, ncRNA
207 207 10% 6.00E-52 88.57 2363 XR_143681.3
PREDICTED: Strongylocentrotus purpuratus sodium- and chloride-
dependent glycine transporter 1 (LOC575259), mRNA
176 176 21% 2.00E-42 75.98 5122 XM_775027.5
EF538764.1 Strongylocentrotus purpuratus sodium-dependent alanine
transporter 2 mRNA, complete cds
5007 5007 100.00% 0 100.00 2711 EF538764.1
(Sp-AT2) Strongylocentrotus purpuratus sodium-dependent alanine
transporter 2 (LOC590977), mRNA
5005 5005 99.00% 0 100.00 2710 NM_001146195.2
PREDICTED: Strongylocentrotus purpuratus sodium-dependent
alanine transporter 2 (LOC590977), transcript variant X1, mRNA
4307 4307 87.00% 0 99.41 8310 XM_030980171.1
PREDICTED: Strongylocentrotus purpuratus sodium- and chloride-
dependent glycine transporter 1-like (LOC115922253), misc_RNA
4233 4233 87.00% 0 98.86 8916 XR_004063382.1
EF538765.1 Strongylocentrotus purpuratus sodium-dependent alanine transporter
3 (LOC578449), mRNA
5264 5264 100.00% 0 100.00 2850 NM_001146194.1
(Sp-AT3) PREDICTED: Strongylocentrotus purpuratus sodium- and chloride-
dependent GABA transporter ine-like (LOC115919132), transcript
variant X2, mRNA
4828 4828 95.00% 0 98.64 6117 XM_030971996.1
PREDICTED: Strongylocentrotus purpuratus sodium- and chloride-
dependent GABA transporter ine-like (LOC115919132), transcript
variant X1, mRNA
4828 4828 95.00% 0 98.64 6117 XM_030971991.1
PREDICTED: Strongylocentrotus purpuratus sodium- and chloride-
dependent GABA transporter ine-like (LOC115919132), transcript
variant X3, mRNA
3446 3446 68.00% 0 98.52 2006 XM_030972005.1
368
Table I2. Percent identity matrix of Strongylocentrotus purpuratus SLC6 amino acid transporter genes. Multiple sequence alignment
was performed using Clustal Omega (https://www.ebi.ac.uk/Tools/msa/clustalo/). Groups of highly similar sequences are highlighted
by different colors.
1 2 3 4 5 6 7 8 9 10
1 XR_004063382.1 100 98.61 98.94 52.04 51.7 51.63 51.63 57.08 54.13 53.91
2 EF538764.1 (Sp-AT2) 98.61 100 99.35 52.88 52.58 52.52 52.48 57.59 54.96 54.83
3 XM_030980171.1 98.94 99.35 100 52.82 52.46 52.4 52.42 57.26 54.8 54.67
4 EF538765.1 (Sp-AT3) 52.04 52.88 52.82 100 98.63 98.63 98.63 54.35 53.54 53.74
5 XM_030971996.1 51.7 52.58 52.46 98.63 100 99.9 99.89 54.61 53.44 53.64
6 XM_030971991.1 51.63 52.52 52.4 98.63 99.9 100 100 54.61 53.55 53.76
7 XM_030972005.1 51.63 52.48 52.42 98.63 99.89 100 100 54.52 53.48 53.69
8 EF538763.1 (Sp-AT1) 57.08 57.59 57.26 54.35 54.61 54.61 54.52 100 96.99 97.44
9 9: XM_793980.5 54.13 54.96 54.8 53.54 53.44 53.55 53.48 96.99 100 99.55
10 10: XM_030976099.1 53.91 54.83 54.67 53.74 53.64 53.76 53.69 97.44 99.55 100
369
Table I3. RNAscope
®
(ACDBio) ZZ probes designed for in situ hybridization of
Strongylocentrotus purpuratus transcripts. Probe specificity is enough to distinguish Sp-AT1, 2,
and 3.
Gene Accession # ZZ Target (bp) RNAscope Probe Cross detection
Sp-AT1 EF538763.1 11 170-834 Spu-AT1 XM_793980.5
XM_030976099.1
Sp-AT2 EF538764.1 20 1438-2424 Spu-LOC590977 XR_004063382.1
XM_030980171.1
Sp-AT3 EF538765.1 20 416-1416 Spu-LOC578449 XM_030971996.1
XM_030971991.1
XM_030972005.1
EF1-α (+control) NM_001123497 20 70-935 Spu-LOC548620 none
18S rRNA (+control) L28056.1 1 1025-1084 Spu-18S none
Ubiquitin (+control) M61772.1 13 2-712 Spu-polyubiquitin XM_030976721.1
XM_030977236.1
XM_030977702.1
XM_030977701.1
XM_030999610.1
Actin (+control) R62049 7 2-322 Spu-actin XM_011665468.2
XM_030997002.1
XM_030997000.1
XM_030997550.1
XM_011665467.2
NM_214469.1
NM_214528.1
NM_001037157.2
XR_004066345.1
XM_030997507.1
Abstract (if available)
Abstract
The planktonic larval stages of marine invertebrates represent vulnerable periods when fast growth rates are crucial for survival and success. Physiological rates of feeding, nutrient uptake, ion transport, and protein synthesis have been shown to correlate with fast growth rates in larvae. In particular, the dynamics between processes related to protein metabolism (e.g., ingestion, synthesis, and degradation) are a major driver of larval growth. In the present dissertation, a conceptual model based on protein metabolism was applied to study how larvae grow under various environmental conditions of food ration and temperature. Under this conceptual model, proteins and amino acids are ingested or transported into the organism for use either as metabolic substrates or for protein synthesis. Synthesized proteins are either accreted as growth or turned over. Catabolized proteins are then excreted as nitrogenous wastes. Following this model, each of these processes of ingestion, protein synthesis, protein accretion, protein degradation, metabolism and ammonia excretion was measured for larvae under various conditions. Studying these processes revealed the importance of the dynamics of protein metabolism in response to environmental change. ❧ In Chapter One, the dynamics of protein metabolism were examined under a constant food ration of 20,000 cells ml⁻¹ for larvae of the purple urchin Strongylocentrotus purpuratus. Protein synthesis rates of larvae always exceed rates of protein ingestion and accretion. For every unit of protein ingested, only 33% is accreted as growth (i.e., protein food conversion efficiency) for a 4-day old larva of 20 ng total protein. For every unit of protein synthesized by a 4-day old larva with 20 ng total protein, 37% is accreted as growth (i.e., protein depositional efficiency), and this efficiency decreased to 18% in later feeding larval stages (of 120 ng total protein). These findings show that most protein mass synthesized is degraded and highlight the importance of integrating the dynamics of protein ingestion, synthesis, degradation, and accretion as the key mechanisms that regulate growth. ❧ In Chapter Two, larvae of the sea urchin Lytechinus pictus were reared under constant, experimental rations ranging from 5,000−50,000 cells ml⁻¹ to study their protein metabolic dynamics. Changes in larval feeding rates across food rations between 1,000−100,000 cells ml⁻¹ were also tested. Larvae were shown to upregulate feeding rates up to 10-fold when exposed to low food rations below approximately 5,000 cells ml⁻¹. Larvae reared at experimental rations between 5,000−50,000 cells ml⁻¹ had high rates of protein synthesis exceeding rates of protein ingestion and accretion. These findings are consistent with the dynamics of protein metabolism seen in larvae of S. purpuratus from Chapter One. At all food rations, larval protein synthesis and degradation rates did not differ. Larvae maintained a near-constant rate of protein accretion (16 ng day⁻¹) which, surprisingly, did not change even when larvae were reared in 10-fold higher food environments (5,000 cf. 50,000 cells ml⁻¹). At the biochemical level, the growth efficiencies of larvae changed with different rations to maintain constant protein accretion rates. At 5,000 cells ml⁻¹, larvae increased feeding rates and accreted 77% of ingested protein mass. Larvae reared at 10-fold increases in food ration (50,000 cells ml⁻¹) only accreted 17% of the ingested protein mass. Since protein synthesis and degradation rates did not differ for larvae at each food ration, changes in protein food conversion efficiencies allowed larvae to have similar protein accretion rates. ❧ Chapter Three demonstrated the decoupling of processes related to protein metabolism under different temperatures. Larvae of S. purpuratus were reared at 15 or 20℃ and assayed periodically at four temperatures (10−25℃) to determine the thermal sensitivities (Q₁₀) of each process. Rates of protein synthesis and catabolism (measured by ammonia excretion rates), were more sensitive to temperature changes than rates of respiration (Q₁₀ values of ~3 compared to ~2, respectively). These differences in thermal sensitivity were exacerbated when larvae were reared at 20℃. Based on these thermal sensitivities, and constitutively high rates of protein synthesis, higher allocations of ATP to protein synthesis were predicted for larvae exposed to high temperatures. For example, using Q₁₀ values of 3 and 2 for protein synthesis and respiration, respectively, larvae would increase ATP allocation from 19% to 35% between 10 and 20℃ respectively. Furthermore, the high thermal sensitivity of ammonia excretion rates (Q₁₀ of 3) relative to respiration rates (Q₁₀ of 2) results in decreases in the ratio of atomic oxygen to atomic nitrogen (an index of a protein-based metabolism), implying that more protein is catabolized to support larval metabolism at higher temperatures. Determinations of atomic oxygen to atomic nitrogen ratios showed that larvae derived 47% (chronically reared at 15℃) and 77% (chronically reared at 20℃) of their metabolic energy from protein catabolism. High rates of both protein synthesis and protein catabolism are taxing for larvae growing in elevated temperatures. ❧ In Chapter Four, an analysis is presented of putative genes responsible for the transport of amino acids from seawater. Amino acid transport capacities have been shown to predict growth rates in larvae of the Pacific oyster Crassostrea gigas. Twenty-seven genes of the SoLute Carrier family 6 (SLC6) amino acid transporter family were identified from three genome assemblies of C. gigas, and 22 genes were identified for S. purpuratus. Compared to the four SLC6 amino acid transporter genes in humans, 27 and 22 genes found in the marine invertebrate species represent a significant gene expansion. Polymerase chain reaction primers were designed to amplify full-length coding sequences of 13 genes which were successfully cloned from RNA pools of C. gigas. Phylogenetic and sequence analyses of these genes show putative differences in substrate-binding residues amongst the cloned sequences when compared to reference sequences from the bacterial homologue LeuT and the human homologue GlyT1. For example, four of the 13 cloned sequences showed an alanine-tryptophan-glycine motif responsible for glycine substrate binding in GlyT1. The cloned sequences were also used to support gene identification from three different assemblies of the genome of C. gigas. Analyses of these sequences can aid reverse-genetic, functional studies of transcript localization or substrate binding kinetics. The high degree of gene expansion in SLC6 genes along with the sequence diversity suggests that these genes have different functions. Reverse genetic approaches manipulating genes in expression systems will permit future analysis to define physiological functions of this expanded gene family of amino acid transporters, and their evolution. ❧ In summary, this dissertation presents a conceptual model of protein metabolism and growth from the biological levels of molecules to physiology and whole-organismal growth. By studying these processes under varying conditions of food availability and temperature, different physiological strategies were revealed which highlight the importance of protein synthesis and turnover in driving growth in variable environments.
Linked assets
University of Southern California Dissertations and Theses
Conceptually similar
PDF
Physiological strategies of resilience to environmental change in larval stages of marine invertebrates
PDF
Physiological rates during development of marine invertebrates in temperate and polar oceans
PDF
Comparative physiological studies of marine invertebrate larvae from Antarctic and temperate environments
PDF
Physiology and biochemistry of food limitation in marine invertebrate larvae
PDF
Thermal acclimation and adaptation of key phytoplankton groups and interactions with other global change variables
PDF
Unexplored microbial communities in marine sediment porewater
PDF
The molecular adaptation of Trichodesmium to long-term CO₂-selection under multiple nutrient limitation regimes
Asset Metadata
Creator
Wang, Jason
(author)
Core Title
Dynamics of protein metabolism in larvae of marine invertebrates
School
College of Letters, Arts and Sciences
Degree
Doctor of Philosophy
Degree Program
Biology (Marine Biology and Biological Oceanography)
Degree Conferral Date
2021-08
Publication Date
07/09/2021
Defense Date
06/08/2021
Tag
Growth,larvae,OAI-PMH Harvest,protein conversion efficiency,protein metabolism,protein turnover
Format
application/pdf
(imt)
Language
English
Contributor
Electronically uploaded by the author
(provenance)
Advisor
Manahan, Donal (
committee chair
), Hedgecock, Dennis (
committee member
), Maxson, Robert (
committee member
), Sañudo-Wilhelmy, Sergio (
committee member
)
Creator Email
jasonwang103@gmail.com,wang402@usc.edu
Permanent Link (DOI)
https://doi.org/10.25549/usctheses-oUC15293613
Unique identifier
UC15293613
Legacy Identifier
etd-WangJason-9718
Document Type
Dissertation
Format
application/pdf (imt)
Rights
Wang\, Jason
Type
texts
Source
University of Southern California
(contributing entity),
University of Southern California Dissertations and Theses
(collection)
Access Conditions
The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given.
Repository Name
University of Southern California Digital Library
Repository Location
USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA
Repository Email
cisadmin@lib.usc.edu
Tags
larvae
protein conversion efficiency
protein metabolism
protein turnover