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Physiological strategies of resilience to environmental change in larval stages of marine invertebrates
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Physiological strategies of resilience to environmental change in larval stages of marine invertebrates
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PHYSIOLOGICAL STRATEGIES OF RESILIENCE TO ENVIRONMENTAL CHANGE
IN LARVAL STAGES OF MARINE INVERTEBRATES
by
Melissa Beth DellaTorre
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
BIOLOGICAL SCIENCES
(BIOLOGY)
May 2022
Copyright 2022 Melissa Beth DellaTorre
ii
ACKNOWLEDGMENTS
There are numerous people who supported me throughout my time at USC and this
dissertation could not have been completed without their help. Firstly, I would like to thank my
advisor, Professor Donal Manahan, for the countless hours of discussions, research planning,
always answering urgent phone calls during experiments, and the rigorous editing process of
this dissertation. I also thank my dissertation and qualifying committee members: Professor
David Bottjer, Professor Dennis Hedgecock, Professor Carly Kenkel, Professor Rob Maxson, and
Professor Sergio Sanudo-Wilhelmy. Their comments and support during my time at USC have
been sincerely appreciated.
The past and present Manahan Lab members including Dr. Scott Applebaum, Dr.
Christina Frieder, Dr. Andrew Griffith, Dr. Ning Li, Dr. Francis Pan and former graduate student
Jason Wang all helped me to develop new research skills and laboratory techniques that
enabled me to do the work presented in this dissertation. The collaborative efforts with
culturing and group discussions helped provide the basis for these experiments and were
tremendously constructive and beneficial to the research presented here. I would also like to
thank Dave Anderson, who provided valuable assistance with maintaining algal cultures on
Catalina Island which were necessary for larval feeding experiments.
The entire faculty and staff of the Marine and Environmental Biology Section,
Department of Biological Sciences, have been instrumental in my time at USC as course
instructors and mentors. The staff and facilities team at USC is strongly appreciated for their
efforts of keeping the lab running, as well as the administrative help and support I received
iii
from Don Bingham, Doug Burleson, and Adolfo Dela Rosa, among others. Additionally, I valued
the guidance from the lab managers of my teaching assistantships in the Biological Sciences,
including Gorjana Bezminalovic, Eric Noakes, and Michael Moore. I am also very grateful for my
fellow graduate students who have been great friends and moral support over the years.
The staff at the Wrigley Marine Science Center on Catalina Island, including Juan Aguilar,
Sean Conner, Linda Duguay, Karen Erickson, John Heidelberg, Holly Nielson, Lauren Czarnecki
Oudin, Trevor Oudin, Kellie Spafford, and Victoria Sperow, provided assistance in preparations
and coordination of research and accommodations on Catalina Island, and offered a great
amount of aid with technical problems, maintenance, and overall support. Additionally, a
special thank you to Randy Phelps, Steve McElroy and Mark Van Liew for mechanical and
plumbing repairs, sometimes at very inconvenient hours. All of you made my time at the
Wrigley Marine Science Center a very positive experience that I cherish.
I would like to thank the National Science Foundation, the USC Wrigley Institute for
Environmental Studies, and the USC Dornsife College of Letters, Arts, and Sciences, which
provided funding for the research in this dissertation.
Lastly, I need to thank my family, specifically my mom, dad, and sister Caitlyn, and my
friends who have all offered great encouragement and motivation for my efforts while
completing this work.
iv
TABLE OF CONTENTS
Acknowledgments.......................................................................................................................... ii
List of Tables .................................................................................................................................. v
List of Figures ................................................................................................................................ vi
Abstract .......................................................................................................................................... x
Introduction ................................................................................................................................... 1
Introduction References .................................................................................................. 20
Chapter 1: Influence of food quality and quantity on biochemical and physiological
processes in larvae of the white sea urchin, Lytechinus pictus ...................................... 31
Chapter 1 References ....................................................................................................... 65
Chapter 2: Differential temperature sensitivity among physiological processes in
larvae of the white sea urchin, Lytechinus pictus ............................................................ 92
Chapter 2 References ..................................................................................................... 125
Chapter 3: Experimental studies of temperature acclimation on protein synthesis
rates in developing sea urchins ..................................................................................... 147
Chapter 3 References ..................................................................................................... 160
Chapter 4: Thermal sensitivities of respiration and protein synthesis differ among
larval families of the Pacific Oyster, Crassostrea gigas ................................................. 173
Chapter 4 References .................................................................................................... 195
References ................................................................................................................................. 207
Appendices ................................................................................................................................. 232
Appendix A: The intercalibration of micro-respiration methods using
polarographic oxygen sensors and optode technology for larvae of the purple
sea urchin, Stronglyocentrotus purpuratus ................................................................... 232
Appendix B: Comparisons between field-reared and laboratory-reared larvae ........... 240
Appendix C: Impact of varied exposure to food and temperature on
morphological growth, survival, feeding rate, respiration rate, and protein
synthesis rates in larvae of the purple sea urchin Stronglyocentrotus
purpuratus and the Pacific oyster, Crassostrea gigas ................................................... 258
Appendix D: Measurements of protein synthesis ......................................................... 263
v
LIST OF TABLES
Table 1.1: Survivorship of larvae of Lytechinus pictus fed two diets of differing
biochemical quality .......................................................................................................... 90
Table 1.2: Protein depositional efficiency for larvae of Lytechinus pictus .................................. 91
Table 2.1: Survivorship for three cohorts of larvae of Lytechinus pictus reared at 15°C
and 20°C ......................................................................................................................... 141
Table 2.2: Thermal sensitivities of four physiological processes (Q 10 values ± S.E. of the
slope, see Figs. 1-4) measured for three different larval cohorts of Lytechinus
pictus .............................................................................................................................. 142
Table 2.3: Summary of thermal sensitivities (Q 10 values from Table 2) of four
physiological processes for larvae of Lytechinus pictus reared at either 15 or 20°C .... 144
Table 2.4: The energy cost of protein synthesis for early developmental stages of
Lytechinus pictus reared at 15°C .................................................................................... 145
Table 2.5: The percent allocation of available energy (measured by respiration rate) to
protein synthesis in larvae of Lytechinus pictus ............................................................ 146
Table B.1: Survival percentages of four independent cohorts of larvae of Lytechinus
pictus, during the series of experiments of laboratory- and ocean-fed treatments ..... 257
vi
LIST OF FIGURES
Figure 1.1: Morphological growth (size measured as midline body length) for two
cohorts of larvae of Lytechinus pictus reared under three different algal-fed
treatments. ...................................................................................................................... 72
Figure 1.2: Changes in larval morphology of Lytechinus pictus fed at 30 algal cells µl
-1
with either Rhodomonas lens (closed symbols) or Dunaliella tertiolecta (open
symbols) from Cohort 1. .................................................................................................. 73
Figure 1.3: Protein accretion for two cohorts of larvae of Lytechinus pictus reared under
three different algal-fed treatments. .............................................................................. 74
Figure 1.4: Size frequency distributions of individual cells for the algal species
Rhodomonas lens (closed symbols) and Dunaliella tertiolecta (open symbols). ............ 75
Figure 1.5: Biochemical constituents of the algae Rhodomonas lens and Dunaliella
tertiolecta measured as total protein, the lipid classes triacylglycerol and
phospholipid, and total carbohydrate. ............................................................................ 76
Figure 1.6: Respiration rate of larvae of Lytechinus pictus, reared under three different
algal-fed treatments, in relation to age, size, and protein content for Cohort 1
(circle symbols) and Cohort 2 (triangle symbols). ........................................................... 78
Figure 1.7: Cumulative energy usage for larvae of Lytechinus pictus during growth when
fed a diet of either the alga R. lens or D. tertiolecta ....................................................... 79
Figure 1.8: Protein synthesis rate of larvae of Lytechinus pictus, reared under three
different algal-fed treatments, in relation to age and protein content .......................... 80
Figure 1.9: Modeling the relationship between rates of protein synthesis, rates of
protein accretion, and protein depositional efficiencies (ratio of accretion to
synthesis) for larvae of Lytechinus pictus reared with two different diets (algal-
fed treatments) ................................................................................................................ 81
Figure 1.10: Experimental maintenance over time of the concentration of algal cells in
20-l culture vessels ........................................................................................................... 83
Figure 1.11: Feeding rates for larvae of Lytechinus pictus fed at either 50,000 cells ml
-1
(circles) or 5,000 cells ml
-1
(triangles) for Cohorts 3 and 4 .............................................. 84
Figure 1.12: Morphological growth (size measured as midline body length) for two
cohorts of larvae of Lytechinus pictus reared under four different algal-fed
treatments ....................................................................................................................... 85
vii
Figure 1.13: Ammonia excretion rate for larvae of Lytechinus pictus from Cohorts 3 and
4 fed Rhodomonas lens (closed symbols) and Dunaliella tertiolecta (open
symbols) algal species at 5,000 cells ml
-1
(triangles) and 50,000 cells ml
-1
(circles) ............................................................................................................................ 86
Figure 1.14: Larvae of Lytechinus pictus from Cohort 5, fed constant rations of
Rhodomonas lens at 10,000 cells ml
-1
, 30,000 cells ml
-1
, and 50,000 cells ml
-1
,
with two replicate culture vessels per ration .................................................................. 87
Figure 1.15: Flow diagram illustrating biochemical basis for Rhodomonas lens (coded R.l)
as a superior algal diet to Dunaliella tertiolecta (coded D.t), resulting in a 2-fold
differential protein accretion rate calculated for 7-day-old larvae of Lytechinus
pictus when fed equal ration (DIET) of both algal species .............................................. 89
Figure 2.1: Respiration rate measurements and calculation of Q 10 value using six-day-old,
314-µm sized, larvae of Lytechinus pictus from Cohort 1 reared at 15°C ..................... 132
Figure 2.2: Alanine transport rate measurements transformed into Q 10 value for eight-
day-old, 340-µm larvae of Lytechinus pictus from Cohort 1A reared at 15°C ............... 133
Figure 2.3: Protein synthesis rate measurements transformed into Q 10 value of four-day-
old, 303-µm sized, larvae of Lytechinus pictus from Cohort 1A reared at 15°C ............ 134
Figure 2.4: Ammonia excretion rate measurements transformed into Q 10 value for three-
day-old, 300-µm sized, larvae of Lytechinus pictus from Cohort 1A reared at 20°C ..... 135
Figure 2.5: Growth rates for larvae of Lytechinus pictus ........................................................... 136
Figure 2.6: Relationship of protein content to midline body length for larvae of
Lytechinus pictus reared at either 15 or 20°C ................................................................ 138
Figure 2.7: Protein metabolic dynamics of larvae of Lytechinus pictus reared at 15°C
(closed) and 20°C (open) ................................................................................................ 139
Figure 2.8: A model of the differential sensitivity of protein synthesis and respiration
with rising temperature ................................................................................................. 140
Figure 3.1: Experimental design used for testing the length of acclimation on
temperature sensitivity for larvae of Lytechinus pictus and Stronglyocentrotus
purpuratus...................................................................................................................... 165
Figure 3.2: Size of midline body length for larvae of Lytechinus pictus reared chronically
from fertilization at 15°C (closed symbols) and 20°C (open symbols) .......................... 166
viii
Figure 3.3: Larvae of Lytechinus pictus have no significant differences in protein
synthesis rates normalized to total protein content when chronically reared at
15°C or 20°C ................................................................................................................... 167
Figure 3.4: Protein content (A, C, E) and protein synthesis rates (B, D, F) for three
cohorts of larvae of Lytechinus pictus at increasing lengths of acclimation time
from 15°C to 20°C .......................................................................................................... 168
Figure 3.5: Protein content (A, C, E) and rates of trichloroacetic acid (TCA)-insoluble
protein incorporation (B, D, F) for three cohorts of larvae of Stronglyocentrotus
purpuratus at increasing lengths of acclimation time from 15°C to 20°C ..................... 170
Figure 3.6: Analysis of the relationship of the amount of
14
C-alanine incorporated into
trichloroacetic acid (TCA)-precipitable protein as a predictor of absolute rate of
protein synthesis in larvae of Stronglyocentrotus purpuratus ...................................... 172
Figure 4.1: Pedigree of twelve families of Crassostrea gigas used for the experiments in
this study, based on a breeding program initiated in 2016 ........................................... 199
Figure 4.2: Respiration rate measurements and calculation of Q 10 value using larvae of
Crassostrea gigas from Family 56B (A-C), and Family 60 (D-F) ..................................... 200
Figure 4.3: Protein synthesis rate measurements and calculation of Q 10 value using
larvae of Crassostrea gigas from Family 59 (A-C), and Family 60 (D-F) ........................ 201
Figure 4.4: Respiration Q 10 values by size for larvae of Crassostrea gigas in twelve larval
families reared at 25°C, exposed acutely to temperatures of 14-28°C ......................... 202
Figure 4.5: Respiration Q 10 values for larvae of Crassostrea gigas were evaluated as
shown per each family (represented by bar symbol) .................................................... 203
Figure 4.6: Larvae of Crassostrea gigas from two families (Family 59, closed symbols;
Family 60, open symbols) temperature sensitivities to (A) protein synthesis and
(B) respiration ................................................................................................................ 204
Figure 4.7: Glycine transport rate measurements used for calculation of Q 10 value for
147 µm-sized larvae of Crassostrea gigas from Families 59 and 60, reared at 25°C .... 205
Figure 4.8: Larvae of Crassostrea gigas at 147 µm shell length from two families (Family
59 and Family 60) show a significant effect of family on temperature sensitivities
in terms of energy allocation to protein synthesis ........................................................ 206
ix
Figure A.1: Inter-calibration of two methods used for micro-respirometry, measured by
time-course assays using optode technology (closed symbols) and end-point
assays by polarographic oxygen sensor (POS) (open symbols) ..................................... 236
Figure B.1: Photographs of the in-situ larval culturing chamber deployed in the ocean, in
Big Fisherman’s Cove, Santa Catalina Island, CA ........................................................... 250
Figure B.2: Temperature profiles for both the laboratory- and ocean-reared larval
cultures. For the data points indicated as ‘Laboratory’ each was measured with a
digital data logger (HOBO) ............................................................................................. 251
Figure B.3: Larvae of Lytechinus pictus fed the alga Rhodomonas lens in laboratory-
reared 20-liter culture vessels at constant amounts of 5,000 cells ml
-1
(closed
circles), 10,000 cells ml
-1
(open circles), 30,000 cells ml
-1
(closed triangles) and
50,000 cells ml
-1
(open triangles).................................................................................... 252
Figure B.4: Protein content for three cohorts of 5-day-old larvae of Lytechinus pictus,
reared under four different algal rations of Rhodomonas lens, and one unfed
treatment (filtered seawater) ........................................................................................ 253
Figure B.5: Respiration rate for four cohorts of 5-day-old larvae of Lytechinus pictus,
reared under four different algal rations of Rhodomonas lens, and one unfed
treatment (filtered seawater) ........................................................................................ 254
Figure B.6: Incorporation rates of
14
C-alanine into trichloroacetic acid precipitable
protein in 5-day-old larvae of Lytechinus pictus, reared under four different algal
rations of Rhodomonas lens, and one unfed treatment (filtered seawater) ................ 255
Figure B.7: Continuous monitoring (15-minute time increments) of the amount of
chlorophyll a in Big Fisherman’s Cove, Santa Catalina Island ........................................ 256
Figure D.1: The relationship between the rate of incorporation of
14
C-alanine into the
trichloroacetic acid (TCA)-precipitable protein fraction (x-axis), and the absolute
rate of protein synthesis (y-axis) for different ages and sizes of larvae of
Lytechinus pictus ............................................................................................................ 266
Figure D.2: The partitioning of the individual data points from Figure 1 into a series of
analyses that are temperature specific for different ages and sizes of larvae of
Lytechinus pictus reared at four temperatures ............................................................. 267
x
ABSTRACT
Understanding how the environment drives physiological change in organisms is a
critical component to understand the rules of life and predictions of phenotype. For studies of
marine invertebrates, early life-history stages offer distinct advantages for evaluating response
to environmental change, due to high fecundity and experimental tractability for rearing
millions of individuals under controlled conditions. This dissertation evaluates the physiological
and biochemical mechanisms in marine larval forms that underlie responses to variable food
and temperature – two major environmental variables with important implications for
predictions of organismal resilience in a changing global ocean.
Biological response to environmental variability is investigated under three major
themes: (I) study of varied quantity and quality of food; (II) response to short- and long-term
temperature change; and (III) cellular energy allocation strategy. To address these central
themes, a suite of integrated measurements is applied to determine how larvae respond to
experimental treatments designed to simulate environmental variability. Those specific
analytical measurements are: (a) at the level of the whole organism, analyses of morphological
growth and survival; (b) physiological analyses of feeding and metabolic rates; (c) biochemical
measurements of protein, lipid, and carbohydrate content; (d) protein metabolic dynamics (i.e.,
protein synthesis, turnover, and accretion); and (e) nitrogen excretion rates to determine
substrate preference utilization to support metabolism.
xi
Different food quality impacts the cost of growth and dynamics of protein metabolism
Analyses are undertaken to determine the effect of a lower-quality algal diet on larval
growth and physiology of the white sea urchin, Lytechinus pictus. When fed the alga
Rhodomonas lens, larvae grow faster and have a higher growth efficiency than when fed the
alga Dunaliella tertiolecta. Notably, no amount of increased food ration of the lower quality diet
compensates for lower growth. Significantly, in terms of the cost of living, the energy cost of
growth to a given size is 1.4-fold higher for larvae fed on the lower quality diet. The mechanistic
basis for the more costly growth is due to increased amounts of protein synthesis required to
deposit a unit-mass of protein accretion (Chapter 1). In addition, there is a 3.5-fold lower
protein depositional efficiency (the ratio of protein accretion to protein synthesis) for larvae fed
the lower quality diet. The lower quality diet is attributed to algal cells of D. tertiolecta that
have half the cellular protein content relative to R. lens. This difference in protein content is
key, since algal size, lipid, and carbohydrate contents are similar between the two algal species.
Larval feeding rates on the two species of algae are similar; hence, total protein intake by larvae
is double for the higher quality alga. The ratio of algal protein ingested to protein accreted in
the larva (gross protein accretion efficiency) is the same for larvae fed on either algal diet.
These measurements provide a biochemical explanation for a long-standing question about the
mechanistic basis of why one algal species is better than another for supporting the growth of
larvae.
xii
High growth rate can be maintained with low food ration
A ten-fold increase in an experimentally-provided ration of the alga R. lens results in a
six-fold increase in the rate of cell ingestion by larvae of L. pictus. Counterintuitively, however,
this increase in algal ingestion rate does not result in an increase in either growth, size-specific
metabolic rate, or excretion rate. An explanation for this finding is that larvae compensate for
the lower food ration by increasing gross protein accretion efficiency and also by increasing
rates of protein synthesis, relative to larvae that are fed a higher ration (Chapter 1). These
findings provide new insights into the mechanisms of resilience, regarding how larvae can
compensate for food limitation in highly variable and nutritionally-dilute oceanic environments.
Specific physiological processes have different responses to temperature increase
The major physiological components of growth are measured to determine the
sensitivity of specific processes to rising temperature. Specifically, the temperature sensitivity
(quantified as a Q 10 value) is measured for respiration, amino acid transport, protein synthesis,
and ammonia excretion (Chapter 2). Of the processes measured, protein synthesis has the
highest sensitivity to temperature, with a Q
10
value of 3.7 ± 0.2 (SE). This contrasts with a lower
Q 10 value for respiration of 2.4 ± 0.2 (SE). For the other processes measured, amino acid
transport has a Q 10 of 1.6 ± 0.1 (SE) and ammonia excretion 2.7 ± 0.2 (SE). A major conclusion
from this analysis is that the energy supplied through respiration responds to temperature at a
slower rate than does the energy demand to support protein synthesis. Given this differential
xiii
physiology, in a warming ocean larval forms will allocate a greater proportion of their available
energy to support protein synthesis for growth. A quantitative model is provided of energy
allocation and predictive limits of this strategy under scenarios of rising temperature.
The experimental protocols developed in this study to test for the effect of temperature
on protein synthesis are fundamental to the conclusions presented. Further evaluations of
these protocols, based on acute temperature exposures, are provided for a range of
experimental temperature acclimations for two species of sea urchin larvae (L. pictus and
Stronglyocentrotus purpuratus). Acclimation durations up to 32-hours prior to the initiation of
protein synthesis assays do not change the conclusions of temperature sensitivity (i.e., high Q 10
values) for larvae of L. pictus. Larvae of S. purpuratus are more sensitive to the duration of
acclimation (Chapter 3).
Specific larval families can maintain energy homeostasis under temperature increase
Eggs and sperm obtained from gravid, pedigreed adult Pacific oysters (Crassostrea
gigas) are fertilized in a series of controlled crosses to yield nine different families of larvae.
These larvae are then used to test for family-specific differences in physiological responses to
rising temperature (Chapter 4). A physiological mechanism of resilience to rising temperature is
identified, based on the differential sensitivity of respiration and protein synthesis across
different families. Specifically, a family is identified that has a low sensitivity of protein
synthesis to temperature increase, coupled with a high sensitivity of respiration to temperature
increase. Notably, these animals can sustain physiological homeostasis of energy supply-and-
xiv
demand under rising temperatures. This phenotype is a good candidate for breeding programs
in commercial aquaculture to meet the challenge of “Blue Food” production in a warming
ocean.
Significance and Impact
The diversity of animal life on Earth is comprised of 35-40 phyla. Over half of these phyla
are exclusively aquatic and most of those are only found in the marine environment. Complex
life-history strategies are dominant in marine organisms. For marine animals, the vast majority
of species have a larval stage of development. Many ideas and postulates have been proposed
regarding how these planktonic stages of early development live in the world’s largest
environment (by volume, the pelagic zone of the oceans represent over 90% of the living
biosphere). There is a large literature in the field of larval biology, most of which is focused on
ecology and recruitment. Conversely, there is also an extensive literature in the field of cell and
animal developmental biology. Information is scant, however, in areas of biology that could link
ecology and cell biology – specifically, an understanding of whole organisms studied at the
biochemical and physiological levels of analyses. It is the latter approach that is the focus of this
dissertation.
Selected species of sea urchins and oysters are the experimental organisms of choice for
the studies presented in this dissertation. These are well-studied species, providing an
extensive background upon which to build novel approaches and analyses to understand the
biology of larval forms. A new discovery presented in this dissertation is that larval growth is
xv
more dependent on food quality than food quantity. A low food ration yields the same growth
rate as a high food ration. This finding is in apparent contradiction to published reports in the
literature, but can easily be defended by the experimental protocols utilized in the current
study – namely, using digital cell-counting technology to ensure that all food rations tested are
kept constant. An analysis of energy metabolism shows that larvae growing on a lower quality
diet have a higher cumulative cost of metabolism to reach a given size. The integrative
measures of morphology, physiology, and biochemistry provide mechanistic explanations of the
observations at the level of the whole organism, between food quantity, quality, and growth
rate. Specifically, rates of protein synthesis are up-regulated at low ration, whereas in response
to food rations of variable quality, the ratio of protein synthesis to accretion (protein
depositional efficiency) is up-regulated in response to a higher quality diet.
Rising temperature impacts physiology, often by constraining the allocation of cellular
energy, leading to stress and death of organisms. The biological variance of this process has
long been recognized, but has been challenging to study. Here, such Darwinian performance
has been analyzed by partitioning variance using crosses of pedigreed animals. A discovery of
high significance is that some families of larvae have the ability to perform more optimally than
others under increased temperatures, based on analyses of cellular-level energy supply and
demand. These analyses of the dynamics of energy budgets provide new insight into the
metabolic strategies of coping with rapid environmental change. In addition to contributing to
fundamental science, this finding has valuable implications for the aquaculture industry, with
the possibility of selecting resilient genotypes for breeding programs to meet the increased
global demand for food from the sea.
1
INTRODUCTION
The oceans comprise 97% of Earth’s bio-volume and the largest phyletic diversity of
animals, of which most are invertebrates with complex early life histories consisting of larval
stages. The focus of the present research is an integrative analysis of how development of
marine larvae is impacted by environment, especially by diet and temperature. These
environmental indices have been largely studied in the context of their impacts on growth and
survival. Investigations into the biochemical and physiological mechanisms underlying the
responses of marine invertebrate larvae to varied food and temperature is critical to
understanding energetic limitations and resilience under changing ocean conditions. By
identifying the mechanistic functions underlying growth and development and how these
responses are impacted by food availability and temperature, predictions of resilient
phenotypes to environmental change can be established. These phenotypes could be good
candidates for breeding programs to meet the growing demand for industry production of the
future of food from the sea.
1. Does food limit larval survival and growth?
Marine invertebrates with a larval stage have a type III survivorship in the ocean,
experiencing large early mortalities during development with losses averaging 99%, followed by
a more stable existence from metamorphosis to reproductive adults (Pearl & Miner, 1935;
Deevey, 1947). Reasons for this high mortality could be due to a combination of factors
2
including food limitation, predation, dispersal, and genetics; some of which have been modeled
by Vance (1973). Starvation has been a long-standing area of study as a major factor driving
poor survival of larval forms in the ocean (Hjort, 1914; Thorson, 1950; Anger & Dawirs, 1981;
Nguyen et al., 2021).
The uncertainty of starvation and its impacts on survival for marine larval forms has
been deliberated for over 100 years. This began with Hjort’s (1914) “critical period” hypothesis
of larval mortality predicted to be because of a failure to find sufficient food, followed with the
finding by Zeuthen (1947) that early-life stages of invertebrate larvae require more frequent
access to food than the adult stage, and Thorson’s (1950) elucidation that low food availability
slows larval development, prolonging the duration prior to metamorphosis and thus increasing
risks of predation or dispersal into uninhabitable areas. Yet the extent to which larvae are
food-limited in the ocean remains unclear. Observations in the wild are highly variable and
difficult for studies of microscopic larvae.
(a) Feeding behavior of larvae: Larval feeding studies have been conducted since the
early 1900s (Gemmill, 1914). Strathmann (1971) observed currents and particle movements
with feeding larvae. In planktotrophic marine larvae, feeding is a result of ciliary action
transporting particles to the mouth (Strathmann, 1971). Ciliated bands produce a current away
from the circumoral field and at right angles to the band, and cilia transport particles toward
the mouth. When larvae ingest particles, muscles surrounding the esophagus contract in a
wave, opening the cardiac sphincter, pushing particles into the stomach. Some particles in the
stomach are digested and transported by cilia, while some particles pass through to the
intestine via the pyloric sphincter (Strathmann, 1971). When the pyloric sphincter opens,
3
particles are carried to the end of the intestine by cilia to be excreted. A long history of research
suggests that larvae grow fastest on a high food ration and with a mixed-algal diet
(Hinegardner, 1969; Fenaux et al., 1994; Pechenik et al., 1996). Yet, more food does not always
increase growth, and the complex processes and physiological functioning responsible for
growth rate under varied diets is not yet understood.
(b) Starvation resistance: While the importance of larval nutrition is evident, marine
planktotrophic larvae can survive several days without food. For instance, larvae of C. gigas can
survive up to 33 days with no food (Moran & Manahan, 2004). This survival far exceeds
thermodynamic calculations of a point of no return, where maternal egg investment would be
depleted based on metabolic demands. Notably, upon feeding after prolonged starvation,
larvae can recover growth and physiological rates (Moran & Manahan, 2004; McFarland et al.,
2020). This capability of survival and recovery after prolonged periods of food limitation can be
partially explained by larval ability to uptake dissolved organic matter from seawater (Jaeckle &
Manahan, 1989; Manahan, 1990; Hoegh-Guldberg, 1994). This uptake of dissolved nutrients
may be vital to survival under conditions of minimal algal food availability, serving as an
alternate source of energy used to maintain metabolism (Jaeckle & Manahan, 1992; Moran &
Manahan, 2004). Additionally, uptake of dissolved substrates has been shown to account for
contributions to echinoderm growth and development during pre-feeding stages of sea urchin,
for L. pictus and S. purpuratus (Shilling & Manahan, 1990). Development of a high performance
liquid chromatography (HPLC) protocol to test small changes of amino acids at the picomole
level has enabled calculations of larval uptake of amino acids from the seawater (Manahan &
Stephens, 1983).
4
(c) Excretory examination of larval resources: Excretion functions to maintain
homeostatic concentrations of solutes and water content, and removal of metabolic end
products (Schmidt-Nielsen, 1997). When amino acids are metabolized, the amino group NH 2 is
removed by deamination and forms ammonia (NH 3) which is excreted by marine invertebrates.
Ammonia has high solubility and small molecular size, resulting in rapid diffusion. In marine
organisms, rates of excretion of ammonia are continuous but can increase during nutritive or
temperature stress (Bayne, 1973; Clark et al., 2013). For example, adults of the blue mussel,
Mytilus edulis, increase nitrogen excretion when food is limited, indicating protein catabolism
from utilization of protein energy reserves when carbohydrate reserves are low (Bayne, 1973).
Increased temperature has similarly been implicated with an increased use of protein as a fuel
source in Pacific oysters, C. gigas (Clark et al., 2013).
The amount of energy attributable to protein metabolism can be calculated by the ratio
of oxygen consumption to nitrogen (ammonia) excretion, i.e. O:N ratios. The O:N ratio is a
valuable tool to describe metabolism; protein catabolism is characterized by a low O:N, below
20, and equal amounts of protein and lipid catabolism are characterized by O:N values between
50 and 60 (Mayzaud & Conover, 1988). While the rate of respiration follows a relatively
predictable pattern based upon size and temperature across species, changes in ammonia
excretion can vary widely across species, presumably based upon biochemical constituent
differences (Mayzaud & Conover, 1988). Under periods of stress, O:N ratios decrease to reflect
the increase in nitrogen excretion relative to oxygen consumption (Bayne, 1973, Clark et al.,
2013).
5
(d) Ocean phenology and laboratory-based food studies: The patchiness of algal
availability in the natural ocean, and the vast range of particles and plankton of various sizes,
nutritional quality, and digestibility as a food source, renders mechanistic studies of larval
feeding in the natural environment impractical. In the ocean, phytoplankton abundance is
typically measured by chlorophyll a concentration. In the oligotrophic ocean this value typically
ranges from 0.01-0.1 g L
-1
, while during a large phytoplankton bloom these values can reach as
high as 15-20 g L
-1,
differing over two orders of magnitude (Ryther, 1969; Yoder et al., 1993).
The amount of phytoplankton in the ocean is rarely present at concentrations necessary to
sustain maximum larval growth, and the risk of harmful algae and viruses is of growing concern
for many marine species (Tang et al., 2006; Talmage & Gobler, 2012). Although studies of larval
phenology and trophic mismatches of food availability and larval duration of the planktonic
stage is of importance, the focus of the current studies is the understanding of the properties of
a diet and the physiological-based mechanisms required to reach successful development of a
larva (Somero, 2012).
The invention of Guillard’s F/2 nutrient media for rapid growth of algal cultures has had
a major impact on the ability to perform laboratory experiments with marine larvae to
determine effects of algal diet. Larval growth in laboratory conditions by enhancing natural
seawater with supplemented algae has been extensively reported to yield faster growth than in
natural seawater without algal supplements (Fenaux et al., 1994; Paulay et al., 1985). For
example, three weeks of growth in a laboratory under optimal food conditions can match
annual growth in situ of an arctic bivalve, Hiatella arctica (Sejr et al., 2004). The quality of
laboratory diet is not meant to be representative of food in the ocean, but rather used as a
6
controlled variable that can help to advance knowledge of developmental biology by
determination of the components and mechanisms implicated in feeding behavior.
2. What are the functional impacts of diet on growth and development?
It is well established that food impacts growth and survival of all heterotrophic
organisms. Food availability has also been shown to impact physiological state, but quantitative
analyses on the mechanistic responses in marine invertebrates to food quantity and quality are
not yet fully understood, i.e. what are the particular bases underlying fast or slow growth?
Physiological experiments of varied food rations are needed to understand the mechanistic
bases underlying larval changes in growth and development due to diet.
(a) Food quantity and quality: Effects of the quantities of food on development have
been studied extensively in terms of growth and morphology; several studies conclude that
increasing food concentrations can lead to faster growth (Tang et al., 2006; Pace & Manahan
2007; Thomsen et al., 2013). In response to food availability, larvae have demonstrated
tradeoffs in morphological growth. For example, in food scarcities pluteus larvae can extend
arm length to increase the capacity to capture food particles, but in food abundance arms
remain short and the stomach lengthens (Boidron-Metairon, 1988; Hart & Strathmann, 1994;
Carrier et al., 2015).
In terms of food quality, mixed algal diets have been shown to be beneficial for growth
(Helm & Bourne, 2004). Growth rates and survival of marine invertebrates, both larval and
adult, are largely impacted by algal diet species (clam larvae, Meretrix meretrix: Tang et al.,
7
2006; sea urchin, Paracentrotus lividus: Castilla-Gavilan et al., 2018, Gomes et al., 2021). In
larvae of P. lividus, a single algal diet of Rhodomonas sp. outperformed algal diets of Dunaliella
tertiolecta, T-isochrysis, and mixed-species diets in terms of survival to metamorphosis,
morphological size, and faster development (Castilla-Gavilan et al., 2018).
(b) Biochemical constituents: Although the above studies demonstrate measurable
effects of food supply on growth and weight, the mechanisms of these impacts are required to
understand why food availability matters. Biochemical composition of organisms (lipid,
carbohydrate, and protein) can be instrumental in understanding the use of energy substrates
and stored energy reserves during growth and development (Mayzaud & Conover, 1988;
Wikfors et al., 1992). How these constituents are altered under environmental influences can
be used to interpret indices of metabolism (Holland & Gabbott, 1971; Bayne, 1973; Gnaiger,
1983). Specifically, lipids are divided into classes that serve different purposes within an
organism. The importance of neutral lipids has been identified as important for successful
metamorphosis since the 1970’s (Holland & Spencer, 1973). Neutral lipids, such as
triacylglycerols, have been shown to quickly deplete in unfed larvae of oysters, sea urchins, and
abalone (Holland & Spencer, 1973, Moran & Manahan, 2003; Moran & Manahan, 2004; Sewell,
2005). In contrast, phospholipids, which are primarily structural, can remain constant during
larval development and beyond metamorphosis, even in the absence of food (Holland &
Spencer, 1973; Moran & Manahan, 2003; Moran & Manahan, 2004; Sewell, 2005). Knowing
changes in lipid classes is essential for understanding structural and bioenergetic bases of early
development.
8
Carbohydrates also have impacts on metabolic substrates and corresponding energy
availability in marine organisms (Bayne, 1973; Shilling & Manahan, 1990) but have been shown
to be minimally available for larvae of C. gigas (Moran & Manahan, 2004) and L. pictus (this
dissertation). Biochemical studies conducted on larvae of the sea star, Asterina miniata, in the
presence and absence of food revealed that glycine and phospholipid contents may be of value
for determining the nutritional state of larvae (Meyer et al., 2007).
Proteins have a pivotal function in growth and maintenance of physiological
homeostasis (Fry et al., 2018). A major discovery by Rudolf Schoenheimer (1964) found that
proteins do not become permanently incorporated into a structure, and instead are continually
synthesized, degraded, and deposited as mass, even when an organism is not growing. This
concept of protein turnover has become a major field of study in biological sciences and
resulted in the 2016 Nobel Prize in Physiology or Medicine (Schoenheimer, 1964; Tsukada &
Ohsumi, 1993; Marsh et al., 2001; Mathieson et al., 2018).
(c) Physiological impacts due to food availability: Understanding how food impacts
physiology is necessary to determine the significance of food availability. Measurements of
metabolism are an important tool for evaluating the potential of energy limitations. During
starvation, the loss of biochemical reserves during periods without food results in reduced
metabolic rates (Crisp, 1976; Dawirs, 1983; Moran & Manahan, 2004; McFarland et al., 2020).
Reduced metabolic rate under periods of starvation indicates reduced energy availability. Yet,
studies of metabolism have found that marine invertebrate larvae can maintain low respiration
rates in the absence of food for several days (C. gigas, Moran & Manahan, 2004; A. miniata,
Pace & Manahan, 2007). Analysis of varied rations of food found variable dynamics of protein
9
synthesis and accretion dependent on ration, whereby larvae of the sea star, Asterina miniata,
can both increase growth and utilize less energy when fed higher rations (Pace & Manahan,
2007). Similarly, faster growth rate was also associated with a lower energy cost in adult
oysters, Saccostrea commercialis (Bayne, 2000).
Physiological dynamics of protein synthesis, turnover, and accretion can drive
differences in growth and development. The energetic importance of protein turnover and
depositional efficiency has been a strong predictor of growth rate (Bayne & Hawkins, 1997).
High rates of protein turnover can be achieved with a low cost, for instance in the Antarctic sea
urchin, S. neumayeri, the cost of protein synthesis is just 0.45 Joules mg
-1
protein, the lowest
recorded for any animal (Marsh et al., 2001). A metabolic efficiency model proposed by Bayne
(2000) for adult mollusks predicts that lower metabolic cost of growth, high ratio of growth to
energy intake, and faster feeding rates correspond with faster growth rates. An energy
perspective is a critical component to understand drivers of growth and strategies of resilience
to stress.
3. What sets thermal tolerance limits?
(a) Temperature effects on metabolism: Temperature is a major determinant in a
species energy budget, often used to define total available energy, specifically for marine
invertebrates, since water temperature controls metabolic rate (Helm & Bourne, 2004). The
concept of oxygen- and capacity-limited thermal tolerance (OCLTT) characterizes sub-lethal
limits on species performance and the corresponding consequences for ecosystems (Pörtner &
10
Gutt, 2016). The limit of thermal tolerance is based on the capacity and function of oxygen
supply mechanisms to cover oxygen demand. More complex animals will have lower thermal
limits. Physiology of thermal limitation and adaptation shapes the responses of species to
climate change. For example, factors of heat death include denaturation of proteins or thermal
inactivation of enzymes, inadequate oxygen, or differing temperature sensitivities on
interdependent metabolic reactions (Schmidt-Nielsen, 1997).
If one physiological process is
accelerated by increased temperature more than another, an organism may need to change its
metabolic allocation to maintain homeostasis. If a process of energy demand outpaces the
increase with temperature compared to a process of energy supply, the organism may no
longer have sufficient energy to fulfill that metabolic process.
A common descriptor of the effects on metabolic rates to temperature is the Q 10
temperature coefficient, which defines the factor of exponential increasing rates from an
increase of temperature by 10 C as Q 10 =(Rate 2/Rate 1)
10/(Temp2-Temp1)
. The Q 10 can be determined
in response to both acute and chronic temperature changes. In general, a chronic response Q 10
value is lower than acute, because of acclimation and compensation of metabolic rates.
Physiological parameters, including feeding, respiration, and biosynthesis, will increase
with temperature until a thermal capacity is reached, at which metabolic depression and
physiological collapse occurs (Sokolova & Pörtner, 2003; Delorme & Sewell, 2016). Climate
change models suggest that surface temperatures of the ocean may be increased by 1.5 C at
the end of this century due to increasing atmospheric CO 2, making it critical to isolate and
understand these effects (Collins et al., 2013; Hoegh-Guldberg et al., 2018). Temperature is one
of the most widely studied environmental variables, yet further investigations are urgent, as
11
the ocean heat content is rapidly accelerating, and the top 20 ocean heat content records have
all occurred within the last 20 years (Cheng et al. 2021). As the ocean temperatures continue
rise, biology of marine organisms will continue to be impacted.
(b) Thermal tolerance and acclimation impacts: Small temperature changes of 1-4 C can
have large effects on survival when organisms are approaching thermal tolerance limits (Helm
& Bourne, 2004; Collin & Chan, 2016). Since some species are currently approaching thermal
limits in the wild, it is of particular importance to determine which biological processes are
most affected by temperature. The thermal tolerances of marine species can vary in different
geographic regions, and are currently mainly informed by mortalities. For example, the upper
thermal boundary of C. gigas is varied. Rico-Villa et al. (2009) found very low mortality (<10%)
at 32 C, while Helm & Millican (1977) reported 65% mortality at 32 C. These discrepancies
could be attributable to environment, phenotype, or acclimation. The reared temperature of an
ectotherm can regulate the ability to cope with rising temperature, and may be a major
mechanism of resilience to increasing temperatures (Tewksbury et al., 2008; Seebacher et al.,
2015).
The role of acclimation is also known to help organisms tolerate thermal stress.
Acclimation is defined as a response mounted due to an isolated specific stressor;
acclimatization is inclusive of other correlated factors to a particular stressor that would
correspondingly change with the stressor in a natural environment (Collier et al., 2019).
Acclimatization in regions of warmer temperatures has been suggested to confer increased
thermal tolerances in marine larvae (barnacles, Pollicipes elegans, Crickenberger et al., 2015;
ascidians, Ciona intestinalis, Clutton et al., 2021). It is challenging to make broad conclusions
12
from acclimation studies, because of large variations in degrees of change, speed of change,
and lengths of acclimation time.
(c) Energetic costs of increasing temperature: Understanding interactions between
organismal processes is important for predicting responses. Rising temperatures can induce
mismatches between energy supply and demand within an organism. Measuring the Q 10
temperature sensitivities of individual physiological processes is of value to determine if and
where mismatches exist. Of particular importance, there was a differential in temperature
sensitivity recorded in the Pacific oyster, C. gigas, between the energy supply (respiration) and
energy utilization (protein synthesis) processes (Pan et al., 2021). The cost of repairing thermal
damage and acclimatization may also contribute to energy budgets. These costs can arise from
activation and operation of the heat-shock response, replacing denatured proteins,
restructuring cell membranes, or shifts in gene expression (Somero, 2002). These costs from
sub-lethal perturbations may establish an energetics-based limitation on a species distribution.
Organisms in extreme environments have capabilities of compensating for temperature. For
instance, despite temperatures of -1.5 C, Antarctic sea urchin embryos matched protein
synthesis rates and RNA synthesis rates to that of temperate sea urchins (Marsh et al., 2001).
This was due to an extremely low energy cost of protein synthesis, making it possible for a high
protein metabolism even with a low metabolic rate (Marsh et al., 2001).
(d). Interactive effects – additive, antagonistic, synergistic: Increases in temperature
may have interactive effects (additive, antagonistic, synergistic) with other environmental
factors such as food and ocean acidification. For instance, increased food may be able to
compensate for the negative impacts of increased temperature or CO 2 (Bayne, 1998; Hettinger
13
et al., 2013; Thomsen et al., 2013). Each species may have its own complex responses affecting
its growth and survival under stressful conditions but are likely linked by a common mechanism
(Lannig et al., 2010; Todgham & Stillman, 2013). Understanding how these interactions affect
the allocation of energy within an organism is important. Integrating physiology with climate
models simulating future environments, and combining genetic variability and biological
mechanisms, are necessary to develop a full understanding of the future (Applebaum et al.,
2014).
4. Will marine organisms be able to respond to increasing temperatures?
It has become increasingly clear that anthropogenic modifications to ecosystems are
creating challenges for several species. A shift in climate forces an individual to compensate. If
the change is tolerable, a species can either acclimatize (adjust physiology within individuals) or
adapt (increase proportion of tolerant genotypes). If the change is not tolerable, a species will
migrate, change its phenology, or die. Shifting climate conditions will bring about species that
are ‘winners’ who thrive, and those that are ‘losers’ who suffer under the changing
environment (Somero, 2012; Applebaum et al., 2014). Climate variability has influenced animal
evolution throughout earth’s history; a further understanding of this evolution from an energy
perspective will enhance knowledge of the impacts of future climate change (Pörtner & Gutt,
2016).
Heat stress causes many physiological changes including modification of proteins,
variation in protein stability and function, cardiac function, and stress induced gene expression
14
changes (Somero, 2012). Organisms can withstand stress through physiological changes to their
regulatory mechanisms to maintain homeostasis (Applebaum et al., 2014). Rapid evolution has
been known to occur in a common benthic worm, Limnodrilus hoffmeisteri, when exposed to
extremely high concentrations of cadmium and nickel metal pollution in Foundry Cove, New
York. The worms adapted genetic resistance to the toxins in just one to four generations, and
that resistance remained after two generations (Klerks & Levinton, 1989). A large genetic
variation to generate multiple phenotypes will aid species in thermal tolerance (Somero, 2010).
Building an energy budget, with respiration rate providing a total estimate of ATP, and
known costs per unit activity for major ATP-consuming processes, has helped to understand the
mechanistic basis of metabolic responses (Applebaum et al., 2014). Notably, the costs of some
physiological processes have been calculated and used to create energy budgets, i.e. protein
synthesis, calcification, protein degradation, ion transport (Lee et al., 2016; Frieder et al., 2017;
Pan et al., 2018). The rates of these processes, and corresponding energy demands of an
organism, can largely vary based on environment, phenotype, and genotype (Lee et al., 2016;
Frieder et al., 2017; Pan et al., 2018). This variation in physiological performance has
implications for predicting growth, metabolism, and survival of larvae of different genotypes
and under different environmental conditions.
Other traits can either hinder development, for example a poor maternal egg quality, or
facilitate optimal development, for example by providing a phenotype that thrives under its
given environmental conditions (Monaghan, 2007). Understanding the mechanistic basis of
metabolic responses requires building an energy budget defining availability and utilization of
energy and biochemical bases for changes in uses (Applebaum et al., 2014). Pan et al. (2016)
15
also depict the need to identify biomarkers predictive of growth potential. Measurements of
physiology (ion transport), biochemistry (enzyme activity) and molecular biology (gene
expression) in larvae demonstrate that physiological variation cannot be predicted by
biochemical or molecular markers (Pan et al., 2016). Variation in physiological performance has
implications for predicting growth, metabolism, and survival of larvae of different genotypes
and under different environmental conditions. Although environmental conditions like food and
temperature are commonly manipulated, using wild-type species prevents the ability to
separate genotype and the environment (nature and nurture) due to standing genetic variation
within the population. Systematizing this information, and partitioning variance, is necessary to
predict resilience and adaptation potential, as well as establish biomarkers (Pan et al., 2016).
5. Study organisms and rationale: sea urchins and bivalves
Sea urchins and bivalves are valuable model organisms for physiological research owing
to several factors. Both are tractable for large scale culturing and have high fecundity with the
ability to rear millions of larvae under controlled conditions and manipulations. These species
allow for complete life cycle propagation to enable quantitative genetic analyses, from adults
that are grown-out in the natural ocean habitat. These advantageous aspects as research
organisms have led to wide-ranging uses in biological and ecological research enabling
comparisons among literature values. This breadth of knowledge facilitates evaluations of
complicated interactions between biological and environmental variables (exogenous and
endogenous factors).
16
(a) Sea urchins: Echinoderm larvae have been studied for over a hundred years (Grave,
1903; Gemmill, 1914). Sea urchin larvae are valuable for studies of early developmental stages
because of synchronicity of growth within cohorts in the same environment, which, in turn,
provides low variability in morphology and facilitating mass measurements for parallel
biochemical and physiological analyses. Having relatively large and semi-transparent cells
allows for convenient observation of fertilization and early development. Several important
scientific benchmarks, from elucidating mechanisms of fertilization and gastrulation to those
with implications for medical research have been determined using the sea urchin. Nobel prize
winner Tim Hunt in 2001 used the sea urchin to discover the cell cycle protein cyclin, having
major implications for cancer research. Before Hunt, Oskar Hertwig used the sea urchin to
discern the mechanism for sexual reproduction in 1875 with the discovery that fertilization
occurs from the fusion between sperm and egg (Hertwig, 1875).
In more recent years, sea urchins have been categorized as both robust and vulnerable.
Purple sea urchins, Stronglyocentrotus purpuratus, in both the larval and adult stages, are
known to survive long periods of starvation by reducing metabolic activity and can recover once
resources become available (Meyer et al., 2007; Dolinar & Edwards, 2021). At the same time,
mass mortalities of purple sea urchins and sea stars have been caused by harmful algal blooms,
which are expected to increase in frequency with climate change (Jurgens et al., 2015). These
findings highlight the need to determine the mechanisms underlying sensitivities, to enable
predictions of future impacts on coastal ecosystems.
(b) Bivalves: There are several reasons to focus on bivalves for studying the effects of
environmental stressors; one major reason is their potential to provide future food from the
17
sea (Costello et al., 2020). Specifically, oysters are an important source of food and comprise
over 80% of the industry’s total annual shellfish production, which supports over $270 million
per year in economic activity and over 3,000 jobs throughout the Pacific Northwest (Barton et
al., 2015). Within the last decade oyster production facilities have suffered from large
mortalities of over 75% of stocks (Barton et al., 2012; Barton et al., 2015; Gray et al., 2022).
These mortalities have been linked to harmful algal blooms, pathogens, and decreasing
aragonite saturation state of seawater from increasing CO 2 (Barton et al., 2012, 2015; De Rijcke
et al., 2015). Hatcheries can now monitor aragonite saturation state in real time, enabling
informed decisions of water treatment systems based on carbonate chemistry (Barton et al.,
2015). This has succeeded in partial recovery of oysters, but shellfish crashes continue to be a
persistent problem, sometimes with no determinable cause of failure (Gray et al., 2022).
Studies of physiological, genetic, and biochemical analyses under varied environmental
conditions are crucial to identify and solve issues of survivorship in shellfish hatcheries.
Aquaculture production has been accelerating since the 1990s and has now overpassed
production of wild capture fisheries (Pernet & Browman, 2021). Bivalves, and specifically
oysters, are low on the trophic scale, rendering them efficient to grow because there is no
high-energy requirement to produce additional food. Once oysters outgrow the hatchery stage
of development, they are moved to cages in the ocean where they filter-feed the surrounding
water and no longer need to be provided supplementary food. When compared to common
agriculture species and marine species higher up on the trophic scale, oysters have much lower
energy requirements and production costs (Palma & Viegas, 2020). Filter-feeders provide an
additional benefit to the surrounding ecosystem by improving water quality and clarity and
18
preventing eutrophication (Irkin, 2021). Additionally, oysters have high nutrition value for
human consumption including high protein content and low fat (Zhu et al., 2018). Pacific
oysters are a good source of valuable minerals including calcium, magnesium, zinc, iron, and
qualities that many humans are deficient in, including the amino acid taurine and
polyunsaturated fatty acids (Zhu et al., 2018).
6. Significance and Impact
Fluctuations in fish populations in the late 1800s initiated marine biology and fisheries
research. Fish stocks in the ocean were not the unlimited supply they were assumed to be. This
realization from Hjort (1914), led to theories of migration, distribution, and spawning
seasonality. Annual variations in population numbers, distributions, and rates of growth
allowed for hypotheses on why fluctuations in fish populations are observed. This question of
why such large fluctuations occur persists as a challenge for scientists to this day. Hjort (1914)
established that early developmental stages are the most important factor in determining if a
year-class of fish will have high or low abundance and that hydrographical and biological
conditions cause fluctuations of fish and yield of stock.
The responses of marine organisms to global environmental changes will have
significant implications for the aquaculture industry. Shellfish farming is an important economic
and cultural component of coastal communities. With space for agriculture nearly at maximum
capacity, there is a major need for aquaculture to meet the demands for a growing human
19
population. Aquaculture is the fastest growing sector of the food industry (Nature editorial,
2021).
Integrative analyses are needed to determine impacts of environmental change in an
organism. Physiological studies can provide a link between ecology and cellular biology. Studies
of protein metabolic dynamics and allocation of energy enables this link and will help in
understanding the performance and limitations of growth under environmental change.
Determining the environmental conditions for optimal growth and development, and research
on resilience to varied environments, can rapidly accelerate the ability of industry to enhance
food production.
20
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31
CHAPTER ONE
Influence of food quality and quantity on biochemical and physiological processes in larvae of
the white sea urchin, Lytechinus pictus
ABSTRACT
Understanding how food impacts physiological processes of organisms is important for
biology. While much is known about how nutrient availability can alter growth rates of marine
organisms, much less is understood about the biochemical bases underlying changes in growth
rates. The hypothesis being tested in this chapter is that protein synthetic dynamics
compensates for variable food environments. To address this hypothesis, a suite of biochemical
and physiological measurements was undertaken using larvae of the white sea urchin,
Lytechinus pictus. Specifically, measurements were made of growth rate (morphological size
and protein content), in parallel with measurements of feeding rate, respiration rate, protein
synthesis rate, and ammonia excretion rate (an index of protein catabolism). Larvae were fed a
range of dietary rations using the algae Rhodomonas lens and Dunaliella tertiolecta. For a given
ration, larvae had similar feeding rates (cells larva
-1
h
-1
) for both R. lens and D. tertiolecta. When
ration was increased, larval feeding increased 6-fold at the higher food ration (50,000
compared to 5,000 cells ml
-1
). Growth rate, however, did not increase. The mechanistic basis of
this observation was protein synthetic dynamics, where rates of protein synthesis increased to
compensate growth at lower food ration. For the different rations of R. lens and D. tertiolecta,
32
larvae fed R. lens had significantly faster growth rates. On a per cell basis, R. lens had twice the
amount of protein than D. tertiolecta. Yet, even when the ration of D. tertiolecta was increased,
larvae did not compensate growth. The basis of this observation was again, protein synthetic
dynamics but in this case related to the increased ratio of protein accretion to ingestion at
lower rations. Respiration rates were size-dependent, not changed by food quantity or quality.
Due to slower growth rates of larvae fed D. tertiolecta, the cumulative energy cost measured by
respiration to reach a given size larva (340 µm) required 1.4-fold more energy for the slower
growing larva. These studies have revealed biochemical analyses focused on the dynamics of
protein acquisition, synthesis, and turnover that provide new insights into the mechanisms of
growth for a marine larval form.
INTRODUCTION
Food availability limits the growth and survival of heterotrophic organisms. From an
energy perspective, ingestion is thought to be the most important interaction between an
organism and its environment (Spomer, 1973). It is important to analyze the multifaceted
effects of food quality and quantity underlying the ability of an organism to produce and utilize
energy. Feeding behaviors in several species, larval and adult, have been observed to vary with
food availability in natural environments, based on complex interactions between behavior,
morphology, and physiology (Bayne, 1998). Preferential feeding has been observed in marine
larvae based on particle size (Baldwin, 1995; Okaji et al., 1997). Baldwin (1995) further
observed that larvae preferred feeding on algae harvested in exponential growth phase
33
compared to stationary phase. Morphological differences, such as extending larval arm length
and increasing time in the planktonic larval stage, have been shown to be adaptive responses to
food limitation (Boidron-Metairon, 1988; Hart & Strathmann, 1994). One issue not addressed in
prior studies is the cost of growth, and does that cost vary with nutrient limitation?
Food quality in combination with quantity has been investigated in adult bivalves by
mixing silt particles with different microalgae, demonstrating adjustments in feeding rate,
rejection rate, and digestive behavior (Navarro et al., 1994; Ibarrola et al., 2000). Other studies
have shown certain larval species grow better with certain algal diets, for example larvae of the
clam, Meretrix meretrix, grew faster with a diet of Isochrysis galbana than with Phaeodactylum
tricomutum, Platymonas subcordiformis, Dunaliella sp., or Pavlova viridus (Tang et al., 2006).
Size, biochemical content, and ingestibility are all factors that contribute to the quality
of an algal diet for larvae (Helm & Bourne, 2004). Although studies have shown variable species
of larvae preferentially select particles based on size, not all suitably sized algal species are
nutritious. For instance, Dunaliella tertiolecta lacks certain fatty acids that are required for
bivalve larval development, giving it presumably a lower nutritional value than other species of
algae of similar size and digestibility (Helm & Bourne, 2004). Hinegardner (1969) reported that
rearing larvae of L. pictus with either D. tertiolecta or R. lens as the sole algal diet resulted in
successful metamorphoses.
Rhodomonas lens has been shown to be a good diet for several species of marine
invertebrates. In adult mussels, Mytilus galloprovincialis, R. lens was shown to confer several
advantages compared to a diet of Tetraselmis sp. or Tahitian-Isochrysis galbana (T-ISO) algae,
including higher absorption efficiency, higher protein content, higher clearance and ingestion
34
rates, and higher total energy absorbed, as measured by the absorption efficiency and organic
ingestion rate of protein, lipid, and carbohydrates (Fernandez-Reirez et al., 2015). Fernandez-
Reirez et al. (2015) determined several factors that contribute to nutritional quality, e.g. cell
size, digestibility, and biochemical composition. Protein has the highest absorptive efficiency, as
calculated from the percentages of absorption efficiency by the ratio of a biochemical
component in the feces to the diet (Fernandez-Reirez et al., 2015).
Given the importance of protein as a major biochemical component of early
developmental stages of marine invertebrates, information still remains scant on the effects of
algal quality in terms of physiological dynamics of protein metabolism and bio-energetics. The
energetic importance of protein turnover and depositional efficiency has been shown as a
strong predictor of growth rate (Bayne & Hawkins, 1997). A metabolic efficiency model
proposed by Bayne (2000) for adult mollusks predicts that a low metabolic cost of growth, high
ratio of growth to energy intake, and fast feeding rates correspond with fast growth rates.
Physiological and biochemical analyses have been well established in fed versus unfed
sea urchin larvae (Pace & Manahan, 2006; Meyer et al., 2007). On a per-cell basis (calculation
based on known DNA content), protein content, enzyme activities, and respiration rates of fed
and unfed larvae were not statistically different, determining that different nutritional states
are best evaluated in differences at the whole-organism level normalized to size or total protein
content in regard to these processes (Meyer et al., 2007). Protein synthesis rates of several
larval species, including the white sea urchin, Lytechinus pictus, the Pacific oyster, Crassostrea
gigas, and the asteroid, Asterina miniata, all have a fixed cost of synthesis, despite large
differences in synthesis rates between fed and unfed larvae (Pace & Manahan, 2006; Pace &
35
Manahan, 2007; Lee et al., 2016). Larvae of the asteroid, A. miniata, when fed at high food
concentration had faster growth, higher proportion of protein, and higher protein depositional
efficiencies than larvae fed at intermediate or low amounts (Pace & Manahan, 2007).
Proportional differences in energy acquisition and available algae were shown for larvae of the
Pacific sand dollar, Dendraster excentricus, with increased respiration rates correlated with
increased size (Rendleman et al., 2018). From comparisons of assimilation, gross growth, and
protein growth Rendleman et al. (2018) concluded that both morphological and physiological
variation is driven by higher ingestion and metabolic rates in higher fed treatments.
Adult mussels that live intertidally with limited access to food, have enhanced digestion
and absorption of food compared to mussels in the subtidal zone, which have continuous food
access (Charles & Newell, 1997). Physiological responses can compensate for reduced or
intermittent times for feeding from larval to adult life stages. The present study seeks to
determine physiological effects on variable food quantities maintained at constant amounts,
without periods of food depletion. Even a short period of starvation could elicit a long-term
effect on growth and development (Pechenik et al., 2002; Tang et al., 2006).
A major goal of the current study was to determine growth and physiological impacts of
varying qualities and rations of algal food in larvae of L. pictus. These algal species, R. lens and
D. tertiolecta, have been previously shown to support larval growth and development (Pace &
Manahan, 2006). By incorporating larval respiration and protein synthesis rates, simultaneously
with protein content and size, the depositional efficiency of protein required to deposit a unit-
mass of protein on a given diet can be calculated. This efficiency can be used to establish a
physiological mechanism for the impact of food quality. Another major goal of the current
36
study was to evaluate if varied food ration could compensate for quality, and if physiological
functioning is impacted by food ration when held constant. This research helps to establish a
mechanistic understanding of the impact of food quality and quantity on physiological
processes during larval development and growth.
METHODS
Approach and Rationale
A total of five independent cohorts (referred to as Cohorts 1-5) of larvae of the white
sea urchin, Lytechinus pictus, were used for experimental analyses. Each cohort was spawned
from a mix of adult males and females collected from the Southern California Bight. Across all
five cohorts, there was no significant difference in the relationship of protein growth and
respiration rate. This relationship was based on an analysis of 54 samples across five different
cohorts of larvae, and is described by the equation: Metabolic rate (pmol O 2 larva
-1
h
-1
) = 0.45x
+ 1.4 where x is protein content (ng larva
-1
), R
2
= 0.78. Hence different cohorts used for
different experiments were physiologically comparable. To address the biochemical bases
underlying differences in growth, studies were undertaken to evaluate the effects of diet
quality of algal species known to yield differences in growth rate: Rhodomonas lens, Dunaliella
tertiolecta, or a 50:50 mixed diet, each stocked at 30,000 cells ml
-1
. To evaluate the ability for
larvae to compensate for an inferior diet, additional studies of larvae reared with the algae R.
lens and D. tertiolecta evaluated the effects of dietary quantity and quality of food ration. To
37
determine the biochemical and physiological bases underlying differential growth under
different food treatments, an analysis was conducted of protein depositional efficiencies.
Experimental Design and Sampling Protocol
During this study, a total of five cohorts of larvae were reared from gametes obtained
from different males and females of L. pictus. More than 276,000 larvae were used for assays of
feeding rates; 3,800 larvae were measured for morphometric size; 291,000 larvae were used for
determinations of protein content; 175,500 larvae for determination of lipid content; 18,000
larvae were used for determination of carbohydrate content; 203,500 larvae for respiration and
corresponding ammonia excretion; and 1,320,000 larvae for determinations of protein
synthesis. Additionally, over 50 million algal cells each for R. lens and D. tertiolecta were used
for determinations of total protein, lipid, and carbohydrate contents.
Larvae of L. pictus were reared from fertilization in 20-liter culture vessels initially
stocked at 20 fertilized eggs ml
-1
. Upon reaching the pluteus feeding stage at four-days-old,
larvae were divided into separate feeding treatments as follows.
For a series of studies that investigated larval physiology as a function of two different
algal species fed at constant ration, larvae were allocated into twelve 20-liter culturing vessels:
four culture vessels held larvae that were fed R. lens; four culture vessels held larvae fed D.
tertiolecta; and four culture vessels held larvae fed a 50:50 mix of R. lens and D. tertiolecta.
For an additional set of physiological studies that focused on variable rations of food
quality, larvae were allocated into a series of eight 20-liter culturing vessels. Four feeding
38
treatments were tested, each with two replicates: 1) R. lens maintained at constant ration of
5,000 cells ml
-1
; 2) R. lens maintained at constant ration of 50,000 cells ml
-1
, 3) D. tertiolecta
maintained at constant ration of 5,000 cells ml
-1
, 4) D. tertiolecta maintained at constant ration
of 50,000 cells ml
-1
.
For studies of protein synthesis and depositional efficiency as a function of different
food rations, larvae were allocated into six 20-liter culturing vessels. Three feeding treatments
were tested, each with two replicates: 1) R. lens maintained at constant ration of 10,000 cells
ml
-1
, 2) R. lens maintained at constant ration of 30,000 cells ml
-1
, 3) R. lens maintained at
constant ration of 50,000 cells ml
-1
.
For all cohorts, initial larval culture vessels had between 133,000 and 255,000 total
larvae, dependent on available larvae and at no point did cultures exceed 15 larva ml
-1
in any
given 20-liter culture vessel. Importantly, larval numbers in this range did not impact growth,
survival, or metabolic rate. For instance, survival from days four to six for Cohort 1 was
increased at the higher larval concentration (Table 1).
Across all different cohorts and food treatments, larvae were assayed multiple times
throughout the experimental period to test for changes in morphological size, with
measurements of midline body length and post-oral arm length – two metrics of growth known
to be impacted by food ration. Additionally, larvae were measured for biochemical protein
content, rate of respiration, and rate of protein synthesis. Some cultures, in addition to protein
content, also measured both lipid and carbohydrate contents to examine primary sources of
energy reserves. To evaluate ingestion and metabolic efficiencies, larval rates of feeding on
algae and rates of ammonia excretion were measured.
39
For each dimension of morphological size, 50 different individual larvae were measured.
For biochemical assays, five replicates of 1,000 individuals of a given size were sampled and
frozen at -80
o
C for subsequent analysis of protein, lipid, and carbohydrate content. For
respiration and corresponding rates of ammonia excretion, eight replicates of between 250 to
500 larvae (dependent on larval size) were assayed in Biological Oxygen Demand micro-
respiration vials (µBOD vials). For the respiration assay in an individual µBOD vial, a time-course
with up to five time-points was used to measure the rate of depletion of oxygen. For assays of
the rate of algal feeding, three replicates of between 1,500 to 3,000 larvae (dependent on larval
size) were used in each assay, with a series of time-course samples taken from each feeding
assay vial. For measuring rates of protein synthesis, two replicates of 10,000 larvae (each in 10
ml of seawater in a 20 ml vial) were used. During an approximately 30-min time-course assay,
five time-point samples were taken from each vial.
Larval Culturing
Spawning of adult white sea urchins was induced by intracoelemic injection of 0.5 M
potassium chloride. A small aliquot of each possible combination of eggs and sperm was tested
and confirmed by microscopy to have greater than 90% fertilization success prior to formation
of the five cohorts. All culture vessels were kept in a dark temperature-controlled room, with
temperature verified by recording water temperature every 30-minutes for the duration of the
culturing experiments (digital data loggers, HOBO U12, Onset Computer Corp., MA, USA; 16.2 C
± 0.005 S.E.M.). Filtered (0.2- m pore-size) seawater from a continuous flow-through,
40
temperature-controlled filtering system of seawater off the coast in a marine protected area at
the Wrigley Marine Science Center, Catalina Island, CA, was replaced in each culturing vessel
every other day. These culturing systems yielded high survivorship; the average survival from
fertilization to the plutei-stage across the five cohorts was 82 ± 6%. Feeding began at day four
when L. pictus reached the plutei stage of development and were allocated into feeding
treatments as described above (see ‘Experimental Design’). For each sampling day (every other
day), larval cultures were condensed by filtering onto a 45-µm mesh-size sieve (Nitex) and
resuspended in a known volume for enumeration. Enumerations were used for calculations of
survival, and for allocation of larvae into subsequent assays of feeding rates, total protein, lipid,
and carbohydrate contents, respiration and ammonia excretion, and protein synthesis.
Algal Culturing
Algae of R. lens and D. tertiolecta were grown in aerated 20-liter polycarbonate carboys
in a temperature-controlled room (18 C) under controlled light conditions, with algal culture
media F/2 A and B added (Guillard & Rhyther, 1963). F/2 A contains the following minerals:
iron, manganese, cobalt, zinc, copper, and molybdate. F/2 B contains the following nutrients:
nitrogen (9.3%), phosphate (2%), Vitamin B1 (thiamine), Vitamin B12 (cobalamin), Vitamin B7
(biotin) (Guillard & Rhyther, 1963). Nutrients were added at 20-ml each per 20-liter algal
culturing vessel immediately prior to addition of algal stock used for inoculation.
Algae grew to at least 900,000 and up to 3,500,000 cells ml
-1
prior to being harvested for
feeding larvae in culture vessels. The doubling time of algal cell populations in a given algal
41
culture was approximately every two days over the course of the larval culturing experiments.
The average number of R. lens was 2,261,000 ± 170,000 (S.E.M.) cells ml
-1
, and the average
number of D. tertiolecta was 2,136,000 ± 220,000 (S.E.M.) cells ml
-1
. Algae was counted visually
using a hemocytometer, and cell counts were cross-calibrated with automatic counting
technology using a microparticle counter (Beckman Coulter Counter, Z series, Beckman Coulter
Inc., Brea, CA, USA). Protein, lipid, and carbohydrate contents of the algal species R. lens and D.
tertiolecta were measured every other day throughout the duration of the larval culture
experiments.
Survivorship and Growth
Survivorship and growth were measured for all five cohorts during the larval rearing
period tested. Survival was based on enumeration of small aliquots of 30 to 50- l of individuals
that were condensed and gently hand mixed in a 30 to 50-ml volume, allowing each count to
have greater than 100 individuals for statistical precision. Volumetric counts were calculated at
least three times, or until the coefficient of variation became less than ten percent. Survival was
corrected for the number of individuals removed for sampling purposes. Statistical evaluation
of survivorship under varied feeding treatments were compared by ANCOVA with age.
Measurements of morphology, via midline body length and post-oral arm length, were
used to evaluate growth. Over 50 larvae from each culture vessel were photographed at 40x
magnification, and morphological measurements were made using ImageJ software (National
Institutes of Health, Bethesda, MD, USA). Midline body length was measured from the dorsal
42
tip to the oral hood, and post-oral arm length was measured by the skeletal element from the
tip of the post-oral arm to the body rod (see diagram, Chapter 2, Fig. 6B). Growth was
evaluated by linear regression of length over age from the initiation of feeding.
Biochemical Composition
Larvae and algae were measured for biochemical compositions including total protein
content, analyses of lipid classes, and total carbohydrate content. Five replicate samples of
1,000 larvae or 1 million algal cells, were allocated from each culture vessel for each
biochemical component (protein, lipid, carbohydrate) and kept frozen at -80°C until analyzed.
Protein content was determined using a Bradford assay (Bradford, 1976), modified for larval
forms as described by Jaeckle and Manahan (1989), with quantified absorbance at 595-nm.
Lipid content was determined using a modified method of Bligh and Dyer (1959) as described
by Moran and Manahan (2003). Carbohydrate content was determined using an alkaline
ferricyanide reduction, a modified method of Holland and Gabbott (1971), as described by
Moran and Manahan (2004). For the present study, carbohydrate samples were incubated for
one hour with 5% cold trichloroacetic acid for extraction, followed by the addition of 6 M
hydrochloric acid incubated for two hours at 95°C for hydrolysation.
43
Metabolic Rate
Metabolic rate was evaluated by oxygen consumption over time. A known number of
larvae, ranging from 250 to 500 individuals (size-dependent) were allocated into micro-
Biological Oxygen Demand ( BOD) vials with 8-10 replicate vials per culture vessel. Rates were
quantified with optode technology, such that measurements of larval respiration can be made
non-invasively by taking multiple measurements on the same BOD respiration vial during a
series of individual time-course assays. This optode technology was previously calibrated to
match polarographic oxygen sensor systems for marine invertebrate larvae (Pan et al., 2021,
Pacific oyster larvae; this dissertation, see Appendix A for larvae of the sea urchin
Stronglyocentrotus purpuratus). A fiber optic cable transmits signals to a Witrox receiver (Loligo
Systems, Denmark) and used to calculate oxygen concentration ( M O 2) when held against an
optode sensor spot (Presens, Regensburg, Germany) in each BOD vial. All respiration vials
were incubated at 15 C in a constant temperature water bath and inverted prior to each
measurement to mix larvae in the respiration vial. Five samples were taken during each
individual time-course assay for each respiration vial over a 3-to-5-hour period. A linear
regression of oxygen depletion over time was used to calculate the oxygen consumption per
larva per hour. Calculated respiration rates were corrected for any background respiration
using control vials containing only 0.2- m (pore-size) filtered seawater with no larvae present.
The average percentage of oxygen decrease in control vials was only ~2% of the starting oxygen
amount (see Chapter 2, Fig. 1) and not biologically significant. Respiration rates were compared
44
between feeding treatments using ANCOVA analyses of rate increases with age, midline body
length, and protein content.
Protein Synthesis Rate
Protein synthesis assays were performed by in vivo time-courses based on larval
transport from seawater of a
14
C-alanine tracer used to measure rates of protein synthesis and
incorporation into protein. Each assay was performed in duplicate with 10,000 larvae in 10-ml
of seawater, 2- Ci
14
C-alanine supplemented with non-radioactive alanine to yield a final
concentration of 10- M alanine (Sigma Aldrich, St Louis, MO, USA). All assays were conducted
in a 15 C water bath. Immediately upon addition of alanine solution, an aliquot was removed
for calculation of specific activity and placed on ice. At exact 6 or 8-minute increments, a 1-ml
aliquot from the assay vial (containing ~1,000 larvae) was transferred onto an 8- m pore-sized
membrane filter (Nuclepore, GE Healthcare, Pittsburgh, PA, USA) and under low vacuum, excess
seawater was removed. Filters were rinsed with seawater to remove excess radioactivity and
placed immediately into a microtube on ice. During an approximately 30-minute time-course
assay, five samples of larvae were collected; samples were stored at -80 C until processing.
Processing of Samples for Protein Synthesis
Samples were processed by homogenizing larvae with Nanopure water (Barnstead
TM
Nanopure Bioresearch Deionization System, Dubuque, IA, USA) via sonication with a Vibra-cell
45
ultrasonic processor and probe (Sonics & Materials, Inc., Newtown, CT, USA). Protein synthesis
rates were determined by calculations of the total amount of radioactivity incorporated into
trichloroacetic acid (TCA)-insoluble protein, and the intracellular-specific activity of the
14
C-alanine in the free amino acid pool, as described by Pace and Manahan (2006).
To process the fraction for TCA precipitated protein, TCA was added to form a 5% TCA in
homogenate solution that was incubated on ice for at least 30-minutes, then filtered onto a
GF/C glass microfiber filter (Whatman grade, Tisch Scientific, North Bend, OH, USA) on a
vacuum and rinsed with 5% TCA followed by methanol. The filter containing larvae was
transferred into a 7-ml scintillation vial with 4-ml Ultima Gold scintillation fluid (PerkinElmer
Inc., USA), and counted with appropriate quench correction.
To process the fraction of alanine in the free amino acid pool of larvae, ethanol was
added to sample homogenate for 70% total ethanol concentration, incubated at 4 C for at least
24-hours. Samples were centrifuged at 12,000-rpm for five minutes prior to analyses by high
performance liquid chromatography (HPLC), as described by Pace and Manahan (2006). Amino
acids were separated by HPLC, and the alanine chromatographic peak was collected via fraction
collector (Model FC 203B, Gilson Inc., Middleton, WI, USA) into a scintillation vial, for
determination of
14
C-alanine in the free amino acid pool.
The change in specific activity of
14
C-alanine in combination with the rate of
incorporation of
14
C-alanine into protein over time were used to calculate an absolute rate of
protein synthesis (ng protein larva
-1
h
-1
), given by the following equation:
Equation 1: Protein Synthesis = d/dt(Sp/Sfaa) x 129.4/7.8
46
In this equation, 7.8 is the percent of alanine in the amino acid composition of whole-
body protein, and 129.4 (g mol
-1
) is the mole-percent corrected molecular mass of all amino
acids that constituted whole-body protein, t is time (hours), S p is the amount of radioactivity
(disintegrations per minute, dpm) in the protein fraction of larval tissue, and S faa is the amount
of radioactivity (dpm) in the free amino acid pool of larval tissue. Protein synthesis rates were
compared between feeding treatments using ANCOVA analyses of rate increases with age,
midline body length, or protein content.
Protein depositional efficiency of larvae was calculated by the ratio of protein accretion
(ng protein day
-1
) to protein synthesis (ng protein day
-1
). Depositional efficiencies were
compared between feeding treatments using ANCOVA analyses of rate increases with age,
midline body length, or protein content.
Feeding Rate
Feeding rates were determined through time-course assays by utilization of a
microparticle counter (Beckman Coulter Counter, Z series, Beckman Coulter Inc., Brea, CA, USA)
as described by Pace et al. (2006) for the Pacific oyster, Crassostrea gigas, modified as follows
for larvae of L. pictus. A standard curve was created using known algal cell numbers determined
by hemocytometer counts. A sample of 1,500 larvae was allocated into 150-ml of seawater and
kept at 15°C. After a 30-minute equilibration period, algae were added and the initial algal cell
number was confirmed by particle counter. Initial algal ration added was varied experimentally
for each of the algal feeding treatments. For example, initial algal amount was 50,000 cells ml
-1
47
for larval feeding treatments reared at 50,000 cells ml
-1
, and initial algal amount was 5,000 cells
ml
-1
for larval feeding treatments reared at 5,000 cells ml
-1
. A series of larval controls (larvae
present, with no algae added) and algal controls (algae present, with no larvae added) were
conducted in parallel to the experimental treatments to correct for particle contamination or
instrument drift. A five-point time-course assay of larval feeding was completed, with the
duration of the assay being set by when the algal amount decreased by at least 20%. Particle
counter (algal cell) measurements quantified the rate of decrease in algal cell number in
seawater over time. The slope of this decrease, corrected for the experimental volume and
number of larvae in that volume, was used to determine the rate at which larvae fed on algae,
using the following equation:
Equation 2: Clearance Rate ( l larva
-1
h
-1
) =
𝑙𝑛 𝐶 𝑖𝑛𝑖𝑡𝑖𝑎𝑙 −𝑙𝑛 𝐶 𝑓𝑖𝑛𝑎𝑙 𝑁 × 𝑇 × 𝑉
In this equation, C initial and C final are the algal concentrations at the start and end of the
experiment, respectively, N is the number of larvae used in each assay, T is time, and V is
volume. In this analysis the rate of interest is the feeding rate by larvae on algae, calculated as
clearance rate, and corrected for the volume of seawater filtered over time (i.e., the number of
algal cells depleted over time).
48
Ammonia Excretion Rate
Ammonia excretion rates were determined using the seawater remaining in each µBOD
vial at the termination of respiration rate (see ‘Metabolic Rate’ above). Initial seawater
samples, from both control assay vials of filtered seawater (no larvae present) and larval
culture-vessel seawater (with no larvae present) were taken at the start of the respiration assay
as the initial control and background time-points. All samples were immediately frozen at -80°C
until analyses. The amount of ammonia was measured by the Betholot color reaction
(Weatherburn, 1967) modified such that reagents were prepared with Nanopure water at room
temperature. Phenol, nitroprusside, and an oxidizing reagent (85.4% by volume alkaline reagent
[0.2 g ml
-1
sodium citrate; 0.01 g ml
-1
sodium hydroxide dissolved in Nanopure water]; 14.6%
commercially available bleach, [Clorox® Disinfecting Bleach, The Clorox Co., CA, USA) were
added to each sample, vortexing to mix following each addition. Standard curves of ammonia
(12 concentrations spanning 0.5 to 100 μM NH 4) were analyzed on a microplate reader in
parallel to assays of all experimental samples (Spectramax M2e Microplate Reader, Softmax Pro
Software, Molecular Devices, LLC., San Jose, CA, USA). Plates were incubated in the dark at
room temperature for 90-minutes, prior to reading absorbance at 640-nm, in modification from
Weatherburn (1967). Amount of ammonia was determined by the following equation:
Equation 3: pmol NH4
+
larva
-1
h
-1
= [Δ[NH4
+
]larvae/Δt – Δ[NH4
+
]control/Δt] × V/N
49
In this equation, the change in ammonia is equal to the difference from the start and
end points measured for both larval and control (seawater) samples. V refers to volume, t to
total time, and N to number of larvae per vial. Control vials were used to correct for changes in
background levels of ammonia in seawater over time that were not attributable to larval
excretion. Ammonia excretion rates were compared between feeding treatments using
ANCOVA analyses of rate increases with age.
Calculation of Oxygen-to-Nitrogen Ratios
Rates of respiration (oxygen consumption) and ammonia (nitrogen excretion) were used
to calculate the ratio of atomic oxygen to nitrogen (O:N). These rates were calculated for each
individual BOD vial, where each O and N value came from the same vial that was used
sequentially for respiration rate followed by ammonia excretion rate. Respiration rates,
calculated as pmol O 2 larva
-1
h
-1
, were converted from moles of oxygen to atoms, prior to
analyses of the atomic ratio, using the ammonia excretion rate, as calculated by pmol N larva
-1
h
-1
(Mayzaud & Conover, 1988). O:N ratios were compared between feeding treatments using
ANCOVA analyses of rate with age.
50
RESULTS
Survivorship
Survivorship of larvae of Lytechinus pictus did not significantly differ (Table 1: ANCOVA
slope F 1,42 = 0.24, p = 0.8) when larvae were reared on different species of algae. This
conclusion applies to larvae fed either on a diet of only Rhodomonas lens, only Dunaliella
tertiolecta, or a 50:50 mixed diet of both species (Table 1). Survival was calculated beginning
from four-days-old, when larvae reached the plutei stage and became capable of feeding. While
survivorship did not vary with diet, there was a difference between the cohorts tested. For all
dietary treatments for larvae, the average survival was 30% for Cohort 1 and 57% for Cohort 2
(Table 1: ANCOVA slope F 1,44 = 12.68, p = 0.0009). Such variation between cohorts is commonly
observed for larval cultures initiated from separate pools of gametes from different adult males
and females.
Growth
Larval growth varied when fed different algal species. In the dataset shown (Fig. 1), a
total of 2,157 midline body length measurements were made on individual larvae, for three
different algal diets (R. lens, D. tertiolecta, and 50:50 mixed), each with culture vessel
replication and cohort replication (Cohort 1 and 2). Across all twelve culture vessels, larvae
grew faster in midline body length when fed R. lens or a mixed diet, than when fed only D.
51
tertiolecta (Fig. 1A: Cohort 1 ANCOVA slope: F 2,1096 = 69.1, p < 0.0001) (Fig. 1B: Cohort 2
ANCOVA slope: F 2,1055 = 88.4, p < 0.0001). There was consistency of growth rates between the
two cohorts of larvae tested (Fig. 1A, B). Larvae fed R. lens had growth rates of 13.0 2.0
(S.E.M.) m day
-1
and 13.3 2.3 m day
-1
, for Cohorts 1 and 2, respectively (ANCOVA slope
F 1,714 = 0, p = 1.0). Larvae fed the 50:50 mixed diet of R. lens and D. tertiolecta also had
consistent growth rates for Cohorts 1 and 2 of 12.1 0.2 (S.E.M.) m day
-1
and 13.3 0.8 m
day
-1
, respectively (ANCOVA slope F 1,719 = 1.7, p = 0.2). Larvae had the same growth rate
whether fed R. lens or the mixed diet (Fig. 1A: Cohort 1 ANCOVA slope: F 1,732 = 0.59, p = 0.4)
(Fig. 1B: Cohort 2 ANCOVA slope: F 1,701 = 0.24, p = 0.6). When fed only D. tertiolecta, larvae had
on average a 2.6-fold lower growth rate of 4.2 0.1 m day
-1
and 5.9 0.3 m day
-1
for Cohort
1 and Cohort 2, respectively.
The relationship between midline body length and post-oral arm length did not
significantly differ between algal-fed treatments (Fig. 2A). However, due to the slow growth of
larvae fed D. tertiolecta, there was a significantly different relationship between post-oral arm
length and age between larvae fed R. lens and D. tertiolecta. Notably, post-oral arm length
increased with age for larvae fed R. lens but did not increase with age for the larval rearing
duration tested for larvae fed D. tertiolecta (Fig. 2B: 9-day-old).
Protein, the major biochemical component of larvae of L. pictus (see Chapter 2: Results,
Biochemical Composition) also differed in accretion rate for larvae fed differing algal diets. For
Cohorts 1 and 2, larvae fed either R. lens or a mixed diet had faster protein accretion rates than
larvae fed D. tertiolecta (Fig. 3A and B: ANCOVA F
2,136
= 35.98, p < 0.0001). For 10-day-old larvae
(Cohort 1), this difference was 2-fold (averaging 240 ng protein larva
-1
for larvae fed R. lens and
52
117 ng protein larva
-1
for larvae fed D. tertiolecta) (Fig. 3A). Midline body length predicted the
protein content of larvae, independent of algal diet and cohort (Fig. 3C).
Algal Size and Biochemical Composition
An analysis of the size frequency distributions for the two algal species used to rear
larvae of L. pictus revealed that these algal cells had an average cell diameter of 7.0 µm ± 0.01
(S.E.M.) for R. lens and 6.5 µm ± 0.01 (S.E.M.) for D. tertiolecta (Fig. 4). The protein, lipid, and
carbohydrate content of these algal species was measured during the time-course of larval
growth experiments, and showed a consistent biochemical composition (Fig. 5A, B). Protein
content, for instance stayed consistent for R. lens as a dietary source (linear ANOVA regression
F 1,6 = 2.37, p = 0.2). A similar result was observed for D. tertiolecta (linear ANOVA regression F 1,6
= 0.16, p = 0.7). Carbohydrate was the least abundant biochemical component in both algal
species, comprising 11-12% by biomass composition of each cell (Fig. 5C). Measurements of
total lipid classes detected phospholipids and triacylglycerols in both algal species. For R. lens,
each cell contained 9.5 0.9 (S.E.M.) pg phospholipid (13%) and 13.5 1.0 (S.E.M.) pg
triacylglycerol (19%). For D. tertiolecta, each cell contained 9.1 1.2 (S.E.M.) pg phospholipid
(18%) and 15.3 0.4 (S.E.M.) pg triacylglycerol (31%). Protein content was the major
biochemical component observed to differ between the two algal species. The protein content
of R. lens was 40.6 0.9 (S.E.M) pg cell
-1
, which was double that of D. tertiolecta at 19.1 0.2
(S.E.M.) pg cell
-1
(ANOVA F
1,13
= 35.69, p < 0.0001) (Fig. 5C). Protein content comprised 57% of
53
the biomass composition measured for R. lens, and 39% of the biomass composition for D.
tertiolecta.
Metabolic Rates
Respiration rates differed by diet with age for larvae of L. pictus (ANCOVA F 2,29 = 5.1, p =
0.01) for both Cohorts 1 and 2 (Fig. 6A). Specifically, respiration rates were significantly higher
for larvae fed R. lens compared to larvae fed D. tertiolecta with age, because larvae fed R. lens
were larger (ANCOVA F 1,19 = 12.6, p = 0.002). There was no difference between respiration rate
with age between larvae fed R. lens compared to larvae fed the mixed (50:50) diet (ANCOVA
F 1,20 = 0.03, p = 0.9). Notably 10-day-old larvae fed either R. lens or a mixed diet had a
respiration rate of 2.6 times that of larvae fed D. tertiolecta, with a rate of 113 5 (S.E.M.) pmol
O 2 larva
-1
h
-1
for larvae fed R. lens compared to 44 3 (S.E.M.) pmol O 2 larva
-1
h
-1
for larvae fed
D. tertiolecta. From these data, a quantitative scaling relationship is derived that describes the
change in the rate of respiration from the change in size, as measured by midline body length
and protein content (Figs. 6B, C). The cumulative energy usage for larvae can be calculated by
converting oxygen to energy using an oxyenthalpic equivalent value of 484 kJ mol
-1
O 2 (Gnaiger,
1983) based on lipid and protein being the predominant biochemical constituents of
metabolism in larvae of L. pictus (see Chapter 2: Results, Biochemical Composition). Cumulative
energy calculated per day resulted in a 1.8-fold increase in amount of energy cost for larvae fed
R. lens compared to D. tertiolecta by 10-days-old, due to faster growth and consequently bigger
size (Fig. 7A). The cumulative energy cost to reach a given size was greater for slower-growing
54
larvae fed D. tertiolecta compared to faster-growing larvae fed R. lens (Fig. 7B). For example, for
a 340-µm larva (the largest size reached for larvae fed D. tertiolecta in the 10-day-experiment),
the total energy cost of growth for larvae fed D. tertiolecta was 1413 µJ, compared to the cost
of 1030 µJ for larvae fed R. lens (a 1.4-fold difference). Following this trajectory up to 390-µm,
the difference increases to a 1656 µJ higher energy cost for larvae fed D. tertiolecta.
Protein Synthesis and Depositional Efficiency under Different Algal Diets
Algal diet had a significant effect on protein synthesis rates with age due to increased
size for larvae fed R. lens compared to D. tertiolecta for both Cohort 1 (ANCOVA slope F 1,12 =
6.38, p = 0.03) (Fig. 8A) and Cohort 2 (ANCOVA slope F 1,6 = 9.38, p = 0.02) (Fig. 8B). Notably, by
day ten protein synthesis rates of larvae fed R. lens were over three times that of larvae fed D.
tertiolecta for either cohort. For Cohort 1 protein synthesis rates averaged 23.2 1.2 (S.E.M.)
ng protein larva
-1
h
-1
for larvae fed R. lens, compared to 7.3 1.2 (S.E.M.) ng protein larva
-1
h
-1
for larvae fed D. tertiolecta. For Cohort 2 by day eleven, protein synthesis rates averaged 10.0
0.06 (S.E.M.) ng protein larva
-1
h
-1
for larvae fed R. lens, compared to 2.9 0.3 (S.E.M.) ng
protein larva
-1
h
-1
for larvae fed D. tertiolecta. Total protein content of a larva was a predictor of
protein synthesis rate across both cohorts and all diets (Fig. 8C).
From the ratio of protein accretion to protein synthesis, the depositional efficiency of
protein synthesis can be calculated as a function of age (Fig. 9A and B) and for different sizes
(Fig. 9C). The rate of protein synthesis increases significantly faster than that of protein
accretion for both larvae fed R. lens and larvae fed D. tertiolecta, indicating lower protein
55
depositional efficiency with age as the difference in ratio of synthesis to accretion increases.
Protein depositional efficiency for larvae at a given size differed dramatically between diet. For
example, a 340-µm larva fed R. lens had a protein depositional efficiency of 39%, while a
340-µm larva fed D. tertiolecta had a depositional efficiency of just 11%. The implication of this
difference is that for a given mass-unit of protein synthesized for a larva of a given size, larvae
fed R. lens have a higher protein depositional efficiency than larvae fed D. tertiolecta.
Algal Quality versus Quantity
To evaluate the ability of algal quantity to compensate for algal quality, additional
cohorts of larvae of L. pictus were fed varied quantities of algae maintained in culture vessels at
constant amounts (Fig. 10). For Cohort 3 (Fig. 10A) and Cohort 4 (Fig. 10B), larval culture vessels
were held at constant amounts of 5,000 and 50,000 cells ml
-1
of R. lens and D. tertiolecta.
Feeding rates of larvae were higher when fed at a higher amount, averaging a feeding rate of
224 14 (S.E.M.) algal cells larva
-1
h
-1
at an algal ration of 50,000 cells ml
-1
, compared to a
feeding rate of 35 3 (S.E.M.) algal cells larva
-1
h
-1
when fed an algal ration of 5,000 cells ml
-1
(Fig. 11). Notably, there was no significant difference in feeding rate between algal diet at a
specific algal quantity; larvae fed on R. lens at the same rate as larvae fed on D. tertiolecta (Fig.
11).
Larval growth rate by midline body length did not change with food quantity. Larvae fed
at a constant ration of 5,000 cells ml
-1
had the same growth rate as larvae fed at a constant
ration of 50,000 cells ml
-1
(Fig. 12). Differences in growth rate dependent on algal quality
56
persisted regardless of quantity; regardless of food quantity, larvae fed R. lens grew faster than
larvae fed D. tertiolecta.
Ammonia excretion rate did not significantly vary in larvae for algal quantity or quality
(Fig. 13). Ammonia excretion rates increased from 0.5 0.1 pmol NH 3 larva
-1
h
-1
for 4-day-old
larvae, to 2.0 0.3 pmol NH 3 larva
-1
h
-1
for 7-day-old larvae. This subsequently resulted in a
decrease of O:N ratios, averaging 35 5 (S.E.M.) at day four, lowering to 20 2 (S.E.M.) at day
seven. This indicates larvae were metabolizing proteins as the dominant macromolecule. As
growth proceeded, ammonia excretion increased significantly by day 9 to average 16 2
(S.E.M.) (Fig. 13). Since O:N ratios also decreased, this further indicated that metabolism was
protein driven. Since respiration rates of larvae fed R. lens increased at a faster rate than larvae
fed D. tertiolecta (see inset, Fig. 13), but ammonia excretion rates were similar regardless of
diet, the calculated O:N ratios decreased more with age for larvae fed D. tertiolecta than larvae
fed R. lens, again indicating a protein-based metabolism.
Increased depositional efficiency at higher food ration
The effect of food rations on protein depositional efficiency were evaluated to address
the mechanisms that enable larvae to ingest more food (i.e. a higher feeding rate, Fig. 11), yet
maintained the same growth as measured by midline body length, the same protein accretion
rate, and the same respiration and excretion rates as larvae that ingest less algal food. Figure 14
provides an insight into this apparent paradox. The ratio of protein accretion to protein
synthesis (protein depositional efficiency) increased for larvae fed at higher rations. This results
57
in a similar protein accretion rate that is independent of algal ration, when larvae were fed the
alga R. lens maintained at constant rations of 10,000, 30,000 and 50,000 cells ml
-1
(Fig. 10C).
Regardless of the ration of R. lens provided, larval protein accretion rates did not significantly
differ with algal ration (Fig. 14A). Protein synthesis rate decreased with increasing algal ration
(Fig. 14B). Larvae that were fed R. lens maintained at 10,000 cells ml
-1
had higher protein
synthesis rates than larvae fed R. lens at a higher ration of 50,000 cells ml
-1
(Fig. 14B).
In summary, larvae fed a lower ration have a higher protein synthesis rate, but maintain
the same accretion rate compared to larvae fed a higher ration. This results in a lower protein
depositional efficiency at lower rations (Fig. 14C, Table 2). While larvae that are fed a higher
ration ingest more food, they are more efficient at depositing food into protein accreted. For
example, a 6-day-old larva fed a ration of 10,000 cells ml
-1
has a depositional efficiency of 26%,
in contrast to the depositional efficiency of 46% for this same aged larva fed a ration of 50,000
cells ml
-1
(Table 2). In conclusion, a lower rate of protein synthesis can maintain a similar rate of
protein accretion across 5-fold differences in food ration.
DISCUSSION
The present study highlights several major findings, one of which is that slower growing
larvae had a 3.5-fold lower protein depositional efficiency (Fig. 9C). Another is that a lower rate
of protein synthesis can maintain a similar rate of protein accretion across 5-fold differences in
food ration (Fig. 14). These novel findings provide new insights into the biochemical bases of
differential growth as a function of food quality and quantity.
58
Growth Rate
Differences in diet are well-known to affect growth rates and morphology of larval sea
urchins (Hart & Scheibling, 1988; Fenaux et al., 1994; Bertram & Strathmann, 1998; Carboni et
al., 2012). Specifically, increased growth rate for larvae fed R. lens compared to D. tertiolecta
has been shown in L. variegatus (McEdward & Herrera, 1999). Shorter arm length for larvae of
the same age fed D. tertiolecta were also consistent in L. variegatus, as well as shorter ciliated
band length (McEdward & Herrera, 1999). In early developmental stages, longer arm length
under low food ration is commonly observed in larvae to extend ability to capture food
particles (Hinegardner, 1969; Boidron-Metairon, 1988; Strathmann et al., 1992; Adams et al.,
2011; McAlister & Miner, 2018). Studies have indicated that larval stomach size for sea urchins
is a marker of algal nutritional value (Qi et al., 2018; Gomes et al., 2021). In the current study,
increased algal ration did not compensate for differences in growth due to algal quality (Fig.
12). Protein content in algae of D. tertiolecta, being half the amount of R. lens, was a key basis
for reduced growth on equal food ration (Fig. 5).
Algal Diet
Several studies have established R. lens as a high-quality algal species for echinoderm
larval diets, having different biochemical composition advantageous for larval development
(Fernandez-Reiriz et al., 1989; Gomes et al., 2021). Other studies of diet using varied algal
species found D. tertiolecta to be a lesser quality algal species than Chaetoceros muelleri for
59
larvae of Stronglyocentrotus intermedius and S. nudus (Qi et al., 2018). Qi et al. (2018)
determined the alga D. tertiolecta had less protein content than the alga C. muelleri, and
corresponding larval protein content was less, in agreement with the present study that
increased algal protein content can correspond to increased larval protein content. In Qi et al.
(2018) the alga Isochrysis galbana also had a higher protein content than D. tertiolecta, but
larval protein content remained significantly lower for larvae fed I. galbana than for larvae fed
D. tertiolecta, suggesting other factors impact larval protein accretion (such as efficiency,
discussed in ‘Feeding and Protein Accretion Efficiency’ below). Castilla-Gavilan et al. (2018) also
reported higher amounts of protein in R. lens than in D. tertiolecta.
In general, mixed algal diets have been recognized to be more nutritious than a diet of a
single algal species (Helm & Bourne, 2004). Notably, in the current experiments, a mixed algal
diet of R. lens and D. tertiolecta showed no evidence of being more nutritious, based on larval
growth rates, than the single algal diet of R. lens. Of particular interest, no amount of increased
algal quantity could compensate for decreased growth of larvae fed D. tertiolecta.
Feeding and Protein Accretion Efficiency
In the present study, there were no differences in feeding rate of larvae over the age
range tested when fed either R. lens or D. tertiolecta at 5,000 cells ml
-1
(Fig. 11). Similarly, there
were no differences in feeding rate of larvae when fed R. lens or D. tertiolecta at 50,000 cells
ml
-1
. For either low or high feeding ration, larvae fed on R. lens and D. tertiolecta at the same
rate for a given amount (5,000 or 50,000 cells ml
-1
). Feeding rates at 50,000 cells ml
-1
for both R.
60
lens and D. tertiolecta were 6.4-fold higher than feeding rates at 5,000 cells ml
-1
(Fig. 11).
Surprisingly, feeding rates at either 5,000 or 50,000 cells ml
-1
do not account for the different
growth rates when fed either R. lens or D. tertiolecta (Fig. 15, ‘DIET Column’). At a feeding
ration of 50,000 algal cells ml
-1
larvae ingest 224 cells hour
-1
. The alga R. lens has double the
protein content than the alga D. tertiolecta (40.6 and 19.1 pg algal cell
-1
, respectively), hence a
larva feeding on R. lens ingests 218 ng of protein per day, while a larva feeding on D. tertiolecta
ingests less than half, at 103 ng of protein per day (Fig. 15, ‘FEEDING Column’). Applying the
increased feeding rates with increased food, larvae fed D. tertiolecta would need to have a
feeding rate of 475 algal cells h
-1
to ingest the same amount of protein per hour as larvae fed R.
lens. If larvae continued to increase feeding with increased algal ration at the rate observed in
this experiment, this would require a ration of over 110,000 cells ml
-1
to ingest the same
amount of protein per day as larvae fed R. lens at 50,000 cells ml
-1
. Specifically, 40.6 pg R. lens
cell
-1
× 224 cells h
-1
(feeding rate at 50,000 cells ml
-1
) = 9.1 ng protein larva
-1
h
-1
(Fig. 15,
‘FEEDING Column’); 19.1 pg D. tertiolecta cell
-1
× 475 cells h
-1
= 9.1 ng protein larva
-1
h
-1
(at
110,000 cells ml
-1
ration [estimated from relationship of increased feeding rate with ration]). It
is unlikely, however, that increase of food ration to over 110,000 cells ml
-1
would in fact result
in increased feeding rate by larvae on cells of D. tertiolecta, since a food ration saturation point
would be reached prior to that large food ration (Strathmann, 1987).
The protein accretion rates of a 7-day-old larva, when fed R. lens, resulted in a protein
accretion rate of 23 ng protein per day, and when fed D. tertiolecta resulted in a much lower
protein accretion rate of 10 ng protein per day. The ratio of protein accreted to protein
ingested (gross protein accretion efficiency) was the same for larvae fed either diet at ~10%
61
(Fig. 15, ‘DIGESTION & ASSIMILATION Column’). Specifically, for the R. lens diet, 218 ng ingested
to 23 ng accreted, was equivalent to the D. tertiolecta diet, 103 ng ingested to 10 ng accreted.
The observed differences in protein accretion can be attributed directly to the difference in
algal protein content, and explicitly not due to ingestion rate or protein accretion efficiency. For
larva of a given size (and not age), protein depositional efficiency was significantly lower for
larvae fed D. tertiolecta, and also contributed to the slower growth rate of larvae fed D.
tertiolecta (see ‘Protein Dynamics with food quality’ below). The differences in protein growth
under the varied food qualities (R. lens and D. tertiolecta) can be explained by the lowered
protein ingestion and same protein accretion efficiency for a given age, and lowered protein
depositional efficiency at a given size promoting slower growth (Fig. 9).
For larvae fed varied rations of the same algal species, a primary reason that a 6.4-fold
higher feeding rate did not result in a higher growth rate was due to increased gross protein
accretion efficiency at lower food rations. Larvae fed rations of 5,000 and 50,000 cells ml
-1
were
feeding 6.4-fold more at the higher ration, yet maintained the same growth rates by size and
protein accretion. Larvae fed high and low rations also had the same respiration rates and same
excretion rates. So how could the larvae fed more not be bigger? Larvae fed the low ration
ingested just 34 ng protein larva
-1
day
-1
, compared to 218 ng for larvae fed the high ration,
(6.4-fold increased, due to feeding rate). While larvae fed the high ration had 10.5% gross
protein accretion efficiency (Fig. 15 'DIGESTION & ASSIMILIATION Column’), larvae fed the low
ration had 67.6% gross protein accretion efficiency (23 ng larva
-1
day
-1
protein growth / 34 ng
larva
-1
day
-1
protein ingestion) and maintained the same growth rate (Fig. 12). This 6.4-fold
increase in gross protein accretion efficiency is likely linked to increased protein synthesis rates
62
(Table 2). Protein synthesis rates of larvae fed 50,000 cells ml
-1
were 44 ng larva
-1
day
-1
, with
increasing synthesis rates at 30,000 and 10,000 cells ml
-1
. Using these measurements to
estimate synthesis at 5,000 cells ml
-1
equals 85 ng protein synthesized larva
-1
day
-1
, which is
1.9-fold higher than protein synthesis rates of larvae fed at 50,000 cells ml
-1
. These findings are
discussed in detail below, in ‘Protein Dynamics with food quantity’.
Protein Dynamics with Food Quality
For a given size, respiration, protein content and protein synthesis rate were not
significantly different, regardless of diet. Differences in these physiological processes shown by
age were due to slower growth, and when normalized to size, protein content (Fig. 3C),
respiration (Fig. 6B, C), and protein synthesis rates (Fig. 8C) yielded predictive equations. The
ratio of protein accretion to synthesis (protein depositional efficiency), was significantly
different between feeding ration treatments for larvae of a given size (Fig. 9C). Larvae fed R.
lens had a 3.5-fold higher protein depositional efficiency than larvae fed D. tertiolecta (Fig. 9C).
This also corresponds to a higher percentage of energy allocation to protein synthesis for larvae
of the same size. For example, for a 340-µm sized larva, using the cost of 2.3 µJ per nanogram
protein synthesized (see Chapter 2), a larva fed R. lens synthesized 78 ng protein day
-1
at a cost
of 179 µJ, with a corresponding respiration rate of 960 pmol O 2 per day, which converts to 465
µJ (Gnaiger, 1983, as applied in Results ‘Metabolic Rates’). This results in a 38% energy
allocation (ratio of 179 µJ to 465 µJ) towards protein synthesis. For the same 340-µm sized larva
fed D. tertiolecta, synthesis is 127 ng protein per day (at 10-days-old, Fig. 9B), at a cost of 292 µJ
63
(127 ng protein synthesized x 2.3 µJ), with a corresponding respiration rate of 1,062 pmol O 2
per day (Fig. 6A, correcting a rate per hour to per day), which converts to 514 µJ. Relative to a
larva fed on R. lens, this results in a higher allocation of 57% energy to protein synthesis (ratio
of 292 µJ to 514 µJ) for larvae fed D. tertiolecta. A 340-µm sized larva was 10 days old when fed
D. tertiolecta, but 7 days-old when fed R. lens. Comparison of larvae fed different diets at the
same age yields similar depositional efficiency. Notably, it takes 1.4-fold more energy to grow a
larva to 340-µm when fed on a diet of D. tertiolecta than it takes to reach the same size when
fed on a diet of R. lens (Fig. 7B). This fold difference is remarkably close to fully explaining the
protein synthetic bases for this energy difference (1.5-fold difference, ratio of 57% to 38%
energy allocation).
Protein Dynamics with Food Quantity
Higher protein depositional efficiency was observed for larvae fed at higher algal
quantities of R. lens (Table 2). This finding was also reported by Pace and Manahan (2007) for
larvae of the asteroid, Asterina miniata, where larvae fed at a high food ration had a high
protein depositional efficiency, which decreased at lower rations. In the present study, new
insights are presented for analyses of protein synthesis rates, where it is shown that at low food
rations synthesis rates were higher (Table 2).
The study of protein turnover is a major field of cell biology (Tsukada & Ohsumi, 1993;
Hinkson & Elias, 2011; Mathieson et al., 2018). Yet, the need to understand the dynamics of
protein metabolism is often underappreciated in the field of marine biology. The experiments
64
presented in the current study emphasize this level of analysis, highlighting the importance of
understanding not just static end-point biochemical constituents of dietary quality of algal
foods and resultant changes in larvae, but the dynamics of the process in a developing marine
organism. Additionally, the work presented increases understanding of how dietary food is
converted into animal biomass. Such insights are relevant to the production of seafood from
aquaculture industries. There is considerable interest in understanding how to increase the
production of “Blue Food” (Nature editorial, 2021 and linked articles) to help meet the rising
demand for protein for human consumption in the coming decades.
ACKNOWLEDGMENTS
The data presented in this chapter on rates of feeding on algae by larvae were collected
by Jason Wang, a PhD student in the laboratory group of Professor Donal T. Manahan at the
time these experiments were conducted. My fellow graduate student Jason also assisted in the
extensive set of larval culturing efforts that were part of the studies presented in this
dissertation.
65
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FIGURES
Figure 1. Morphological growth (size measured as midline body length) for two cohorts of
larvae of Lytechinus pictus reared under three different algal-fed treatments. (A) Cohort 1
(circles); (B) Cohort 2 (triangles). Growth in size was measured for two cohorts of larvae; each
cohort was reared in two replicate culture vessels for each of the three different algal-fed
treatments. Larvae were fed 30 algal cells µl
-1
with either (i) the alga Rhodomonas lens (closed
symbols), (ii) the alga Dunaliella tertiolecta (open symbols), or (iii) a 50:50 mixture of R. lens and
D. tertiolecta (gray symbols). Algal food was added to culture vessels when the feeding larval
stage (pluteus) was reached at 4-days old. For each data point: N ≥ 50 larval measurements of
midline body length; error bars ± S.E.M. Growth rates of larvae were significantly reduced when
fed a diet of D. tertiolecta, compared to R. lens (Cohort 1: ANCOVA F 1,12 = 13.1, p = 0.004;
Cohort 2: ANCOVA F 1,12 = 12.5, p = 0.004). The growth rate of larvae fed a 50:50 mixture of R.
lens and D. tertiolecta (gray symbols) was similar to larvae fed only R. lens. For Cohort 1:
ANCOVA F 1,12 = 0.03, p = 0.9 single regression line shown for combined data. For Cohort 2:
ANCOVA F 1,12 = 0.04, p = 0.9, single regression line shown for combined data.
73
Figure 2. Changes in larval morphology of Lytechinus pictus fed at 30 algal cells µl
-1
with either
Rhodomonas lens (closed symbols) or Dunaliella tertiolecta (open symbols) from Cohort 1.
(A) The relationship between midline body length and post-oral arm length was not statistically
different between algal-fed treatments (ANCOVA F 1,6 = 1.91, p = 0.2; single regression line
shown for combined data). For each data point: N ≥ 50 larval measurements of both midline
body length and post-oral arm length; error bars are ± S.E.M.
(B) The relationship between age and post-oral arm length for larval fed with either R. lens
(closed symbols) or D. tertiolecta (open symbols). Four-day-old pre-feeding larvae were
measured (gray symbol) prior to initiation of algal-fed treatments. For each data point: N ≥ 50
larval measurements of post-oral arm length; error bars are ± S.E.M. Post-oral arm length
increased with age for larvae fed R. lens, but not for larvae fed D. tertiolecta (Fed R. lens: Linear
ANOVA regression F 1,3 = 78.6, p = 0.003; Fed D. tertiolecta: Linear ANOVA regression F 1,3 = 0.02,
p = 0.9).
74
Figure 3. Protein accretion for two cohorts of larvae of Lytechinus pictus reared under three
different algal-fed treatments.
(A) and (B). Larvae from both Cohort 1 and Cohort 2 were fed 30 algal cells µl
-1
with either (i)
the alga Rhodomonas lens (closed symbols), (ii) the alga Dunaliella tertiolecta (open symbols),
or (iii) a 50:50 mixture of R. lens and D. tertiolecta (gray symbols). For each data point: N = 5
measurements of protein content; error bars ± S.E.M.
(C) The relationship between midline-body length and total protein content for larvae of L.
pictus did not differ between the three algal-fed treatments. Fed R. lens (closed symbols); fed
D. tertiolecta (open symbols); 50:50 mixture of R. lens and D. tertiolecta (gray symbols). For
Cohorts 1 and 2, a single regression line is shown for all combined data since ANCOVA revealed
no difference between cohorts (F 2,42 = 0.2, p = 0.8). For each data point: N ≥ 50 larval
measurements of midline body length; N = 5 measurements of protein content; error bars ±
S.E.M.
75
Figure 4. Size frequency distributions of individual cells for the algal species Rhodomonas lens
(closed symbols) and Dunaliella tertiolecta (open symbols). Each data point represents the
number of cells counted with a Beckman Coulter Z2 Particle Counter (Number of cells, y-axis)
for a specific size (Diameter, x-axis). N > 10,000 individual algal cells measured for each algal
species. The average cell diameter for R. lens was 7.0 µm ± 0.01 (S.E.M.) and 6.5 µm ± 0.01
(S.E.M.) for D. tertiolecta.
76
Figure 5. Biochemical constituents of the algae Rhodomonas lens and Dunaliella tertiolecta
measured as total protein, the lipid classes triacylglycerol and phospholipid, and total
carbohydrate.
(A) Protein, triacylglycerol (TG), phospholipids (PL), and carbohydrate (Carb) content of algal
cells of R. lens used as food ration for larvae under the different feeding rations tested. The x-
axis term “Larval Feeding Duration” refers to the duration of the feeding ration treatment.
Symbols: Cohort 1, circles; Cohort 2, triangles; Cohorts, 3 and 4 (performed synchronously with
same algal cultures), squares. The biochemical constituents measured in R. lens did not change
77
significantly across the days of the feeding experiments analyzed (e.g. Protein content: ANOVA
regression F 1,6 = 2.37, p = 0.2).
(B) Similar labeling and symbols to (A), but for cells of D. tertiolecta (Protein content: ANOVA
regression F 1,6 = 0.16, p = 0.7).
(C) Average biochemical constituents of the algae R. lens and D. tertiolecta measured across all
larval feeding treatments. Protein was the largest measured biochemical constituent and
significantly differed between the two algal species (ANOVA F 1,13 = 35.69, p < 0.0001). Other
lipid classes (cholesterol, hydrocarbons, wax esters) were below the limit of detection in each
species of alga.
78
Figure 6. Respiration rate of larvae of Lytechinus pictus, reared under three different algal-fed
treatments, in relation to age, size, and protein content for Cohort 1 (circle symbols) and
Cohort 2 (triangle symbols). N = 8-10 replicate measurements of respiration rate per data point;
error bars ± S.E.M.)
(A) Respiration rate of larvae fed 30 algal cells µl
-1
with either (i) the alga Rhodomonas lens
(closed symbols), (ii) the alga Dunaliella tertiolecta (open symbols), or (iii) a 50:50 mixture of R.
lens and D. tertiolecta (gray symbols). Four-day-old pre-feeding larvae were measured prior to
initiation of algal-fed treatments. For graphical visualization, some data points have been
moved laterally (x-axis).
(B) Size-specific respiration rate of larvae fed 30 algal cells µl
-1
with either R. lens (closed
symbols), D. tertiolecta (open symbols), or a 50:50 mixture of R. lens and D. tertiolecta (gray
symbols) as a function of midline body length (size). By ANCOVA there is no difference in size-
specific respiration rate (F 2,29 = 1.6, p = 0.2; single regression line shown for combined data).
(C) Protein-specific respiration rate of larvae fed 30 algal cells µl
-1
with either R. lens (closed
symbols), D. tertiolecta (open symbols), or a 50:50 mixture of R. lens and D. tertiolecta (gray
symbols) as a function of protein content of larvae. By ANCOVA there is no difference in
protein-specific respiration rate (F 2,29 = 0.62, p = 0.5; single regression line shown for combined
data).
79
Figure 7. Cumulative energy usage for larvae of Lytechinus pictus during growth when fed a diet
of either the alga R. lens or D. tertiolecta. The total (cumulative) energy use was calculated by
applying an oxyenthalpic equivalent of 484 kJ per mole O 2 to the respiration rates calculated
from Cohort 1.
(A) Cumulative energy with time for larvae fed either R. lens (R.l) or D. tertiolecta (D.t). By 10
days of growth, larvae fed R. lens had a cumulative energy cost 1.8-fold higher than that of
larvae fed D. tertiolecta (the ratio of 3801 to 2092).
(B) Cumulative energy with size. Solid line represents data for sizes reached during
experimental analyses; dashed line represents the trajectory for energy use for larvae fed the
alga D. tertiolecta (D.t) for continued growth past the experimental duration tested. The
cumulative amount of energy required for a larva to grow to 340-µm in size on a diet of D.
tertiolecta is 1,413 µJ, compared to a cost of 1,030 µJ on a diet of R. lens (a difference of 383
µJ). This difference becomes larger with increasing size (e.g., at 390-µm, the energetic cost is
6,946 µJ for larvae fed D. tertiolecta and for 5,290 µJ for larvae fed R. lens, a difference of 1,656
µJ).
80
Figure 8. Protein synthesis rate of larvae of Lytechinus pictus, reared under three different
algal-fed treatments, in relation to age and protein content.
(A) and (B) Larvae from both Cohort 1 (A, circle symbols) and Cohort 2 (B, triangle symbols)
were fed 30 algal cells µl
-1
with either (i) the alga Rhodomonas lens (closed symbols), (ii) the
alga Dunaliella tertiolecta (open symbols), or (iii) a 50:50 mixture of R. lens and D. tertiolecta
(gray symbols). Four-day-old pre-feeding larvae were measured prior to initiation of algal-fed
treatments. For each data point for protein synthesis rate, two replicate time-course protein
synthesis assays were conducted for larvae of a given age (N = 6 time points per assay),
replicated for larvae from Cohort 1 and 2.
(C) Protein synthesis rates, normalized to total protein content, for larvae from Cohort 1 and 2.
Different symbols represent different algal-fed treatments (as in A and B). By ANCOVA there is
no difference between the three algal-fed treatments for protein-specific rates of protein
synthesis by larvae (F 1,17 = 0.56, p = 0.5; single regression line shown for combined data). For
each protein content data point, N = 5 replicate measurements; error bars ± S.E.M.
81
Figure 9. Modeling the relationship between rates of protein synthesis, rates of protein
accretion, and protein depositional efficiencies (ratio of accretion to synthesis) for larvae of
Lytechinus pictus reared with two different diets (algal-fed treatments).
(A) Rates of protein synthesis and rates of protein accretion for larvae reared on a diet of 30
algal cells µl
-1
of the alga Rhodomonas lens. Calculations for lines shown are based on equations
for protein synthesis and accretion rates per day measured for larvae from Cohort 1 (see Figs.
3A and 8A, converted to rates per day above).
82
(B) Rates of protein synthesis and rates of protein accretion for larvae reared on a diet of 30
algal cells µl
-1
of the alga Dunaliella tertiolecta. Calculations for lines shown are based on
equations for protein synthesis and accretion rates measured for larvae from Cohort 1 (see Figs.
3A and 8A, converted to rates per day above).
(C) Changes in protein depositional efficiency as a function of size (midline body length) for
larvae fed either the alga R. lens, or the alga D. tertiolecta. The slopes of the two solid lines
shown are not significantly different (ANCOVA F 1,6 = 0.05, p = 0.8); the intercepts are
significantly different (ANCOVA F 1,5 = 16.49, p = 0.03).
Conclusion: Per mass-unit of protein synthesized, same-sized larvae fed R. lens have a higher
protein depositional efficiency than larvae fed D. tertiolecta. For the example shown (dashed
line), a 340-µm sized-larva fed R. lens had a protein depositional efficiency of 39% (-0.0056*340
+ 2.3). The corresponding value for larvae fed D. tertiolecta is 3.5-fold lower at 11%
(-0.0064*340 + 2.3).
83
Figure 10. Algal concentrations.
(A, Cohort 3) Experimental maintenance over time of the concentration of algal cells in 20-l
culture vessels used to rear Cohort 3 larvae of Lytechinus pictus at constant food rations.
Rhodomonas lens (closed symbols) and Dunaliella tertiolecta (open symbols) held at food
rations of either 50,000 cells ml
-1
(circles) or 5,000 cells ml
-1
(triangles).
(B, Cohort 4) Similar treatments and symbols as in (A), repeated for another set of 20-l culture
vessels used to rear Cohort 4 larvae of L. pictus at constant food rations.
(C, Cohort 5) Larvae of Lytechinus pictus fed the alga Rhodomonas lens at constant amounts of
50,000 cells ml
-1
(square symbols), 30,000 cells ml
-1
(triangle symbols), and 10,000 cells ml
-1
(circle symbols). Two culture vessel replicates of Cohort 5 at each algal ration were maintained
(open and closed symbols).
84
Age (Days)
4 5 6 7 8 9 10
Feeding Rate (algal cells larva
-1
h
-1
)
0
100
200
300
224 ± 14 (S.E.M.)
35 ± 3 (S.E.M.)
Figure 11. Feeding rates for larvae of Lytechinus pictus fed at either 50,000 cells ml
-1
(circles) or
5,000 cells ml
-1
(triangles) for Cohorts 3 and 4. Larvae fed Rhodomonas lens (closed symbols)
and larvae fed Dunaliella tertiolecta (open symbols) had no significant differences in feeding
rate at either feeding ration: ANOVA 50,000 cells ml
-1
F 1,21 = 3.34, p = 0.1; ANOVA 5,000 cells ml
-
1
F 1,18 = 0.57, p = 0.5. There was a significant difference in feeding rate based on algal ration;
larvae fed at a higher amount have a faster feeding rate (ANOVA F 1,41 = 153.6, p < 0.0001).
85
Figure 12. Morphological growth (size measured as midline body length) for two cohorts of
larvae of Lytechinus pictus reared under four different algal-fed treatments. Larvae from
Cohorts 3 and 4 fed the algae Rhodomonas lens (closed symbols) or Dunaliella tertiolecta (open
symbols) at either 5,000 cells ml
-1
(triangles) or 50,000 (circles) cells ml
-1
. Gray squares
represent initial size of the 4-day-old pre-feeding larval stage for Cohorts 3 and 4, with each of
these initial points being included in each regression shown.
The relationship between growth (size-at-age) and quantity of available food (5,000 or 50,000
cells ml
-1
) was not significantly different for larvae fed R. lens (ANCOVA F 1,12 = 2.05, p = 0.2 for
Cohorts 3 and 4 combined; single regression line shown for combined data). Similarly, growth
of larvae fed either 5,000 or 50,000 cells ml
-1
of D. tertiolecta was not significantly different
(ANCOVA F 1,12 = 0.16, p = 0.7 for Cohorts 3 and 4 combined; single regression line shown for
combined data). Regardless of food quantity, larvae fed R. lens (closed symbols) grew faster
than larvae fed D. tertiolecta (open symbols) (ANCOVA F 1,20 = 18.63, p = 0.0003).
86
Figure 13. Ammonia excretion rate for larvae of Lytechinus pictus from Cohorts 3 and 4 fed
Rhodomonas lens (closed symbols) and Dunaliella tertiolecta (open symbols) algal species at
5,000 cells ml
-1
(triangles) and 50,000 cells ml
-1
(circles). Pre-feeding larvae from each Cohort (3
and 4) represented by gray squares. Inset: Respiration rate regressions for larvae fed R. lens
(R.l) and larvae fed D. tertiolecta (D.t) (see Fig. 6A).
87
Figure 14. Larvae of Lytechinus pictus from Cohort 5, fed constant rations of Rhodomonas lens
at 10,000 cells ml
-1
, 30,000 cells ml
-1
, and 50,000 cells ml
-1
, with two replicate culture vessels
per ration.
(A) No difference in the rate of total protein accretion on different food rations of 10,000 cells
ml
-1
(circles), 30,000 cells ml
-1
(triangles) and 50,000 cells ml
-1
(squares).
88
(B) Significant increase in protein synthesis for larvae fed at lower food rations of 10,000 cells
ml
-1
(closed symbols) compared to 50,000 cells ml
-1
(gray symbols). Newly formed 4-day-old
pre-feeding larval stage shown as open symbol.
(C) Modeled depiction of increasing protein synthesis with lower amounts of food, with same
rate of protein accretion regardless of food ration. When larvae are fed lower food ration, they
have lower protein depositional efficiencies (ratio of protein accretion to synthesis).
89
Figure 15. Flow diagram illustrating biochemical basis for Rhodomonas lens (coded R.l) as a
superior algal diet to Dunaliella tertiolecta (coded D.t), resulting in a 2-fold differential protein
accretion rate calculated for 7-day-old larvae of Lytechinus pictus when fed equal ration (DIET)
of both algal species (from Fig. 3A).
FEEDING: Larvae ingested the equivalent number of algal cells of R. lens and D. tertiolecta (Fig.
11: modeled using a feeding rate of 224 cells larva
-1
h
-1
at a food ration of 50,000 cells ml
-1
). Due
to the different protein content of each algal species (Fig. 5), rate of protein ingestion by larvae
is 2.1-fold higher when feeding on R. lens (218 ng algal protein larva
-1
day
-1
), compared to D.
tertiolecta (103 ng algal protein larva
-1
day
-1
).
DIGESTION & ASSIMILATION: Larvae fed on R. lens had a protein accretion rate of 23 ng algal
protein larva
-1
day
-1
(Fig. 3A), which represents a gross protein accretion efficiency of 10.5%
(ratio of protein accretion to protein ingested: 23/218). Equivalent calculation for D. tertiolecta
yields a near-identical gross protein accretion efficiency of 10.3% (ratio of protein ingested to
protein accretion: 10/103).
CONCLUSION: Larvae ingest the same number of similar-sized algal cells (224 cells larva
-1
h
-1
)
and have the same gross protein accretion efficiency (~10%). The observation that larvae fed R.
lens grow at double the rate of larvae fed D. tertiolecta, can be explained by the measurement
that R. lens contains double the protein content per cell than D. tertiolecta (40.6 and 19.1 pg
cell
-1
, respectively).
90
TABLES
Table 1. Survivorship of larvae of Lytechinus pictus fed two diets of differing biochemical quality
(Fig. 5). Larvae from two cohorts, each with two replicate 20-l culture vessels per treatment (A
and B), were fed at 30,000 cells ml
-1
of either Rhodomonas lens, Dunaliella tertiolecta, or a
50:50 mix of R. lens and D. tertiolecta (mixed diet). Survival of larvae was calculated from the
initiation of feeding (4-day-old), when the plutei stage was reached. Each data point represents
the percentage of survival from one culture vessel calculated from enumerated aliquots of
larvae relative to the starting number of larvae at day-four, and corrected for the number of
larvae removed for experimental analyses. No significant differences were found in the
survivorship of any feeding treatment (ANCOVA slope F 1,42 = 0.24, p = 0.8; intercept F 1,44 = 0.81,
p = 0.5). However, there were differences in survival due to cohort (ANCOVA F 1,44 = 12.68, p =
0.0009).
Diet Cohort
Feeding larvae,
Day 4 Stocked
Population
Starting
% Day 4
% Survival
Day 6
% Survival
Day 8
%
Survival
Day 10
Rhodomonas
1A 255,000 ± 12,000 100% 100% 85% 25%
1B 255,000 ± 12,000 100% 100% 82% 25%
Rhodomonas-fed Mean
100% 84% 25%
Dunaliella
1A 255,000 ± 12,000 100% 100% 78% 37%
1B 255,000 ± 12,000 100% 94% 84% 27%
Dunaliella-fed Mean 97% 81% 32%
Mixed
1A 255,000 ± 12,000 100% 100% 80% 18%
1B 255,000 ± 12,000 100% 100% 88% 47%
Mixed-fed Mean 100% 84% 33%
Overall Mean 30%
Diet Cohort
Feeding larvae,
Day 4 Stocked
Population
Starting
% Day 4
% Survival
Day 6
% Survival
Day 8
%
Survival
Day 11
Rhodomonas
2A 133,000 ± 8,000 100% 75% 71% 55%
2B 133,000 ± 8,000 100% 85% 71% 50%
Rhodomonas-fed Mean 80% 71% 53%
Dunaliella
2A 133,000 ± 8,000 100% 80% 80% 66%
2B 133,000 ± 8,000 100% 65% 58% 44%
Dunaliella-fed Mean 73% 69% 55%
Mixed
2A 133,000 ± 8,000 100% 72% 77% 68%
2B 133,000 ± 8,000 100% 100% 100% 56%
Mixed-fed Mean 86% 89% 62%
Overall Mean 57%
91
Table 2. Protein depositional efficiency for larvae of Lytechinus pictus (Cohort 5; see Fig. 14,
6-day and 8-days-old), fed a diet of Rhodomonas lens at three different constant rations (Fig.
10C). Depositional efficiency is calculated by the ratio of protein accretion (Fig. 14A) to protein
synthesis (Fig. 14B).
Age
(Days)
Ration
(cells ml
-1
)
Protein Accretion
(day
-1
± S.E. slope)
Protein Synthesis
(day
-1
± S.E.M)
Depositional Efficiency
(%)
6
10,000 20.3 ± 4.5 78.4 ± 8.3 26%
6 30,000 20.3 ± 4.5 67.8 ± 8.8 30%
6
50,000 20.3 ± 4.5 43.8 ± 6.4 46%
8
8
8
10,000
30,000
50,000
42.1 ± 4.5
42.1 ± 4.5
42.1 ± 4.5
165.6 ± 9.9
111.4 ± 3.1
93.8 ± 0.4
25%
38%
45%
92
CHAPTER TWO
Differential temperature sensitivity among physiological processes
in larvae of the white sea urchin, Lytechinus pictus
ABSTRACT
Approximately 90% of excess planetary heat has been absorbed by the ocean in the past
1-2 decades. Thermal stress elicits energetically costly responses from organisms. The
hypothesis tested in this chapter is that, in developing sea urchins, differential sensitivity of
specific biochemical and physiological processes results in energy demand exceeding energy
supply (i.e., a predictive “tipping point” of thermal tolerance). Measuring how different
physiological processes within an organism respond to short- and long-term temperature
changes (acute and chronic) helps to define physiological limits with rising ocean temperatures.
Larvae of the white sea urchin, Lytechinus pictus, were reared over a temperature range
experienced by these species in their ambient habitat. Thermal sensitivities (Q 10) were
determined for physiological rate processes of respiration, amino acid transport, protein
synthesis, and ammonia excretion at four temperatures ranging from 10 to 25
o
C. The chronic
temperature used to rear larvae had no effect on the acute Q 10 sensitivities for respiration,
amino acid transport, protein synthesis, and ammonia excretion rates. However, among
processes there were significant differences in Q
10
sensitivities. Protein synthesis (Q
10
= 3.7 ±
0.2 (SE)) was shown to be a more sensitive process to thermal change than respiration rate (Q 10
93
= 2.4 ± 0.2 (SE)). These different Q 10 values have implications for physiological response to a
changing ocean.
INTRODUCTION
Records of ocean heat content have consistently increased over the past 60 years, and
the year 2020 has continued the record-breaking trend of the hottest level yet, accelerating
each decade since the 1990s (Cheng et al., 2021, Cheng et al., 2019). Over 90% of the heat from
carbon emissions is absorbed by the ocean, and models project continued increases in global
ocean temperature, which will continue to impact the biology of marine organisms (Collins et
al., 2013; Frolicher et al., 2018; Cheng et al., 2021).
Increasing metabolic rates is one established strategy for organisms to manage
increased temperature. Q 10 values are a common measure of sensitivity in response to
temperature changes, where a Q 10 value of 2 indicates a doubling of rate with a corresponding
10
o
C change in temperature. Q 10 values of metabolic rates calculated for several adult marine
species have been found to be between 2-3 (blue mussel, Mytilus edulis, Q 10 of 2.2, Zittier et al.,
2015; Pacific oyster, Crassostrea gigas, Q 10 of 2.5, Lannig et al., 2010; Antarctic sea urchin,
Sterechinus neumayeri, Q
10
of 2.5, Brockington & Clarke, 2001; black sea urchin, Arbacia
stellate, Q 10 of 2.1, Diaz et al., 2017). Evaluation of Q 10 values for metabolic rates have also
been determined for various species of marine invertebrate larvae (sea stars, Odontaster
validus, Q
10
of 2.2 and Asterina miniata, Q
10
of 1.9, Hoegh-Guldberg & Pearse, 1995; Pacific
oyster, C. gigas, Q 10 of 2.0, Pan et al., 2021).
94
Temperature condition heavily impacts sea urchin development from the tropics to the
Antarctic (Marsh et al., 2001; Collin & Chan, 2016). Lethal temperature has been shown to be
lower for sea urchins in early life stages than adults for both acute and chronic temperature
stress (Collin & Chan, 2016). Upper and lower thermal boundaries for organisms can vary
considerably based on other compounding environmental factors (Helm & Millican, 1977; Rico-
Villa et al., 2009; Garcia et al., 2018). While these studies have found predominantly species-
specific results when measuring survival and growth effects due to temperature change, other
studies have hypothesized that oxygen supply versus demand may be a critical aspect to
thermal tolerance (Pörtner & Gutt, 2016; Leung et al., 2020).
Physiological functioning is a major driving factor of thermal tolerance. It is well-
established that temperature affects physiological rates, but if a mismatch exists between the
temperature sensitivities of varied physiological processes, specifically between those
responsible for energy acquisition versus expenditure, physiological collapse will occur. Studies
of temperature sensitivity comparisons of specific physiological processes are much less
understood, although an increase in protein synthesis compared to respiration rates has been
found in Pacific oyster larvae (Pan et al., 2021).
Protein synthesis is well-known to be a costly process in organisms, and this has been
established in larvae of L. pictus (Pace & Manahan, 2006). Protein synthesis was shown to
consume up to 75% of the larval energy budget of L. pictus, and had a fixed cost regardless of
size and variable rates of synthesis (Pace & Manahan, 2006). Regulation of protein synthesis
rates is a critical factor in determining a mechanistic response to thermal stress.
95
In the present study, the determination of the temperature sensitivities of various
physiological responses throughout larval development was measured. Protein synthesis was
shown to be more sensitive to temperature increases than respiration rates. Subsequently, the
increase in energy demands (protein synthesis) after acute thermal increases is a factor greater
than that of energy acquisition (respiration). Hence, increasing ocean temperature
disproportionately affects interdependent physiological processes, altering the protein
metabolism of developing larvae. This provides new insight for understanding the mechanisms
of thermal tolerances in marine organisms.
MATERIALS AND METHODS
Approach and Rationale
A total of five independent cohorts (herein referred to as Cohorts 1-5) of developmental
stages of the white sea urchin, Lytechinus pictus, were analyzed during these experiments. Each
cohort was started by fertilizing eggs and sperm obtained from different, reproductively ripe
adults collected from the Southern California Bight region. Cohorts 1-3 were used for studies of
temperature sensitivity and Cohorts 4-5 were used for studies of the cost of protein synthesis.
For a given cohort, fertilized eggs were added to a series of 20-liter culture vessels each
containing filtered seawater (0.2-µm pore-size), yielding an initial stocking of 20 eggs per
ml. Within Cohorts 1-3, larvae of varied ages and sizes were measured for seven sets of
measurements during the time course of each experiment. Specifically, (1) morphological size,
96
(2) lipid and (3) protein content, (4) respiration, (5) amino acid transport, (6) protein synthesis,
and (7) ammonia excretion rates were measured. Within a set of measurements for a specific
cohort, (e.g., for Cohort 1, from each of the two culture replicates) per sampling day, 50
individual larvae were measured for morphological size, 1,000 were sampled for protein and
lipid content (with five replicates, each stored in 1.5 ml test tubes). For respiration, at each
sampling day eight replicate (micro-respiration vial) time-course assays of oxygen depletion
were conducted at each temperature tested and, simultaneously, two replicate time-course
assays for amino acid transport and protein synthesis were conducted for each temperature
treatment. In addition, ammonia excretion rates were measured in each vial used to measure
respiration. The same experimental design, with modifications described below, was used for
the additional larval cohorts tested. In total, 1,227 individual larvae were measured for
determinations of morphological size, 170 independent samples were analyzed for total protein
content, 135 for lipid class analysis, 743 determinations of metabolic rate were measured from
3,715 time points, 960,000 larvae were used for 192 in vivo alanine transport and protein
synthesis time-course assays (960 time point measurements), and 831 independent samples
were analyzed for ammonia excretion rate. For analysis of the cost of protein synthesis, an
additional four sets of protein synthesis rates and respiration rates were measured using
Cohorts 4 and 5.
97
Experimental Design
Larvae of L. pictus were reared chronically from fertilization at constant temperatures of
15°C or 20°C. Upon development to the feeding pluteus stage, larvae were sampled every other
day, and exposed to an acute 30-minute acclimation to 10, 15, 20 or 25°C prior to beginning
assays of respiration rate, alanine transport rate, protein synthesis rate, and ammonia excretion
rate. Thirty-minute acclimation was found to have the same response as longer acclimation
lengths up to 32 hours in larvae of L. pictus (details in Chapter 3). Measurements of
morphological size using midline body length and post-oral arm length were used to determine
growth rates, in addition to biochemical assays of whole-body protein content.
Larval Culturing
Sea urchins were spawned by intracoelomic injection of 0.5 M potassium chloride
(Hinegardner, 1975). Five separate cohorts of larvae of L. pictus were reared from eggs and
sperm obtained from different males and females (four to nine females and three to four males
were used per cohort). Aliquots of male and female gametes were confirmed by microscopy 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 maintained in
a temperature-controlled room in water baths at constant temperatures of 15 and 20°C (15.9 ±
0.32 (S.D.); 19.6 ± 0.06 (S.D.)) verified with data loggers at 30-minute increments (HOBO U12,
Onset Computer Corp., MA, USA). Filtered (0.2-μm pore-size) seawater was completely
98
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 alga Rhodomonas lens at 30 cells µl
-1
and sampled every other day. Depending upon the size of larvae, the entire larval culture was
condensed by filtering onto either a 30 µm or a 50 µm mesh-size sieve (Nitex). After gently
mixing larvae on mesh sieve with filtered seawater, the entire larval culture was resuspended in
a known volume of filtered seawater (range 30-50 ml) from which aliquots were removed to
enumerate larvae (maximum coefficient of variation was less than 10%) on 1 ml Sedgewick-
Rafter counting cells for subsequent assays of total protein content, respiration, amino acid
transport, protein synthesis, and ammonia excretion rates. The use of replicate 20-l culture
vessels, (Cohort 1A, B) verified that the culturing protocols used in the present study yielded
consistent measurements of morphological size, protein accretion, and the other physiological
rates tested (statistical details given in results) with no significant differences, analyzed by
factorial ANCOVAs of size and feeding day, and protein content and feeding day (p > 0.05).
Survivorship and Growth
Growth and survival of larvae from each of the different cohorts were evaluated at both
15 and 20°C chronic rearing temperatures. All culturing was done with ocean waters off-shore
at the Wrigley Marine Science Center on Catalina Island, CA, filtered through 0.2-µm pore-size.
Survival was calculated based on enumerations of larval aliquots and corrected for the number
of larvae removed for experimental measurements from each sampling day. Survivorship of
99
larvae at 15°C and 20°C was analyzed by two-way ANOVA with factors of day and temperature,
with logistic transformation logit(p) = log(p/1-p) of proportions of surviving larvae. Under the
culturing conditions used in this study, survivorship always exceeded 80% (see Results section).
At least 50 different larvae from each culture vessel were photographed at 40x
magnification and analyzed for size using ImageJ software (National Institutes of Health,
Bethesda, MD, USA) at each sampling interval. Size was measured with two morphological
measurements on the same individual larvae: midline body length, from the dorsal tip to the
oral hood, and post-oral arm length, measured along the skeletal element from the tip of the
arm to the body rod (diagram insets in results). Larvae in each individual culture vessel were
analyzed via the slope of a linear regression modeled with increases in midline body length and
post-oral arm length over age from the day of feeding initiation.
Biochemical Composition
Whole-body protein content was determined using a Bradford assay (Bradford, 1976)
modified for larvae as described by Jaeckle and Manahan (1989), using five replicates of 500 or
1,000 individuals based on developmental stage. Larval protein accretion rates were
determined from the slope of a linear regression of increasing total protein with age. Lipid
content was determined using a methanol chloroform extraction process and thin-layer
chromatography to separate and quantify lipid classes, using a modified method of Bligh and
Dyer (1959) as described previously (Moran & Manahan, 2003, 2004; Moore & Manahan,
100
2007). Carbohydrate content was determined using a modified method of Holland and Gabbott
(1971) (details in Moran & Manahan, 2003).
Metabolic Rate
Metabolic rates of larvae, measured as oxygen consumption (pmol O 2 larva
-1
h
-1
), were
quantified with optode technology (Witrox-1 Oxygen Meter, Loligo Systems). The Witrox
system includes a digital temperature probe placed in the sampling temperature water bath.
Pressure and salinity were manually input and calibrated, and oxygen readings were converted
to moles of oxygen. A known number of larvae, from 250-500 individuals (dependent on size),
were allocated into a series of micro-respiration (micro-Biological Oxygen Demand, µBOD) vials.
Each vial was custom made and the series of vials used ranged in volume from 400-700 μl, with
each vial individually calibrated for its specific volume. Each vial has a 2-mm diameter sensor
spot (Presens, Regensburg, Germany). For each cohort on each sampling day, eight replicate
µBOD vials were used at each acute temperature (10, 15, 20 and 25°C), with additional control
µBOD vials per temperature that contained only filtered sea water, to correct for any
background changes in oxygen. A time course of five different optode measurements were
taken for each individual µBOD vial during the 3 to 5-hour experimental period. A linear
regression analysis of these data was used to determine the oxygen consumption per larva per
hour (Fig. 1A). The average percentage of oxygen decrease in control µBOD vials that contained
no larvae was, at the end of the experimental period, only 2.2% of the starting oxygen amount;
this 2.2% change was not statistically significant (e.g., for the regression shown from Fig. 1A, the
101
amount of oxygen in each of the two µBOD control respiration vials had no significant change in
slope (ANOVA: F 1,8 = 2.8, p = 0.13). These statistical analyses were undertaken for all of the
control µBOD vials tested during the course of the experimental series presented here (N = 60
control µBOD vials, with an average of 2% change that was not statistically significant). The rate
of oxygen consumption per individual larva was calculated by the following equation:
Equation 1: Respiration (pmol O2 larva
-1
h
-1
) =
𝑠𝑙𝑜𝑝𝑒 (
𝑜𝑥𝑦𝑔𝑒𝑛 µ𝑚𝑜𝑙 𝑡𝑖𝑚𝑒 ) 𝑥 µ𝐵𝑂𝐷 µ𝑙
24 ℎ 𝑥 𝑛
In this equation each μBOD vial is calibrated to its known volume, and the value (n)
represents the total number of larvae allocated into the vial. This process was repeated in each
of the four experimental temperatures (Fig. 1B). Assays from each of the four temperatures
were used to determine the Q 10 of respiration (Fig. 1C).
Alanine Transport Rate
Transport rates were measured from in vivo time course assays based on larval
transport of
14
C radio-labeled alanine from seawater, as previously described (Pace & Manahan,
2006). Specifically, 10,000 larvae were allocated into a 20-ml capped glass vial containing 10-ml
of 0.2-µm (pore-size) filtered seawater with 74-kBq of
14
C-alanine (Perkin Elmer, Wellesley, MA,
USA). The final alanine concentration was adjusted to 10 µM with the addition of non-
radioactive alanine (Sigma Aldrich, St Louis, MO, USA). Larvae from each culture vessel were
quantitatively allocated into duplicate 20-ml capped glass vials containing 10-ml of filtered
102
seawater, with the larvae in each vial being incubated at each of four different temperatures
(10, 15, 20, 25°C) (i.e. eight total vials, two per temperature). The specific activity of
14
C-alanine
in each experimental vial was determined at the start of each transport assay; this value was
used to correct for the calculated amount of total alanine transport (moles) from seawater into
larvae. For each vial, a time-course assay was conducted during which a series of samples were
collected to determine the rate of alanine transport by larvae (Fig. 2A). Since by ANCOVA the
linear regressions for each of the two assay vials were not statistically different (ANCOVA F 1,7 =
0.84, p = 0.39), these regressions were pooled into a single rate (shown in Fig. 2A). Prior to each
sample being taken, the experimental vial was gently hand-mixed with pipettor, and 1-ml
samples of seawater containing 1,000 larvae were transferred onto an 8-µm pore-sized
membrane filter (Nuclepore, GE Healthcare, Pittsburgh, PA, USA). Seawater was removed with
gentle vacuum, and the larvae were rinsed with filtered seawater to remove excess isotope.
Larvae on each filter were transferred to a 1.5-ml test tube and immediately frozen at -80°C
until further processing.
To process samples, the frozen larvae were first freeze-dried to remove any excess
seawater, and then 400 µl of deionized Nanopure water (Barnstead
TM
Nanopure Bioresearch
Deionization System, Dubuque, IA, USA) was added to each 1.5-ml test tube. Samples held on
ice were homogenized with a Vibra-cell ultrasonic processor and probe (Sonics & Materials,
Inc., Newtown, CT, USA) for two 15-second intervals to disrupt larval tissues. A 15 µl aliquot of
the vortexed-mixed sonicate was transferred to a 7-ml polyethylene plastic scintillation vial
(Kimble, DWK Life Sciences, LLC, Millville, NJ, USA) to which was added 200 µl of 1M sodium
hydroxide (to digest tissue) and 5 ml of scintillation fluid (Ultima Gold
TM
, Perkin Elmer). The
103
amount of radioactivity was measured by liquid scintillation counting with appropriate quench
correction (Beckman Coulter Liquid Scintillation Counter, Model 6500). The amount of
14
C-alanine transported was then corrected for the specific activity of
14
C-alanine in the
experimental vial, and the total moles of alanine transported was calculated by the equation:
Equation 2: Transport (pmol Ala larva
-1
h
-1
) =
𝑠𝑙𝑜𝑝𝑒 (
𝐷𝑃𝑀 14𝐶 𝑚𝑖𝑛 )
𝑆𝐴 𝑥 𝑛 x 10
6
(µmol to pmol) x 60 (min to h)
DPM signifies the designations per minute from liquid scintillation counting after
appropriate quench correction, SA represents the specific activity of the isotope in seawater
(DPM/µmol), and the value (n) is the number of homogenized larvae in the sample. Duplicate
independent time-course assays were used to measure the rate of transport as the slope ± SE
of increasing transport over time (Fig. 2A). The transport rate was linear throughout the time
course of all assays (Fig. 2A). These assays were replicated at each of four temperatures to
determine the Q 10 value (Fig. 2B-C).
Protein Synthesis Rate
Protein synthesis rates were measured from in vivo time course assays based on larval
transport of
14
C-labeled alanine from seawater and incorporation into protein, as described by
Pace & Manahan (2006). Assays were measured in vials as described above (see ‘Alanine
transport rate’) in duplicate to determine the absolute rate of protein synthesis as the slope ±
S.E. of increasing protein synthesized over time (Fig. 3A). Specifically, to measure the rate that
104
alanine became incorporated into larval protein, 300 μl from the sonicated larval sample (see
‘Alanine transport rate’ above) was transferred to a 1.5-ml microcentrifuge tube with 300 μl of
10% trichloroacetic acid (TCA) diluted with deionized Nanopure water, to yield 5% TCA solution.
After a 30-minute incubation on ice, samples were vortexed and pipetted onto a GF/C glass
microfiber filter (Whatman, Tisch Scientific, North Bend, OH, USA) on vacuum, rinsed with 5%
TCA, and washed with methanol. The filter was transferred into a 7-ml scintillation vial with 4
ml of scintillation fluid and counted with appropriate quench correction to a statistical precision
of 1% (based on the absolute number of radioactive counts).
To determine the change of intracellular specific activity of
14
C-Alanine in the free amino
acid pool of larvae, an aliquot from the sonicated larval sample (see ‘Alanine transport rate’
above) was transferred into a 1.5-ml tube to which was added an aliquot of ethanol (high-
performance liquid chromatography-grade), to yield a 70% ethanol extraction of free amino
acid. Samples were refrigerated at 4°C until analyzed by high-performance liquid
chromatography (HPLC). Details of the HPLC protocol are given in Lee et al. (2016), modified to
use
14
C-alanine as the tracer as described in Pace & Manahan (2006).
The amount (moles) of alanine in each sample was separated by HPLC, and that
chromatographic peak was collected by fraction collector (Model FC 203B, Gilson Inc.,
Middleton, WI, USA) into a 20-ml scintillation vial (for determination of the amount of
14
C-alanine by liquid scintillation counting). To calculate the rate of protein synthesis from each
transport rate of
14
C-alanine by larvae (Fig. 3B), the analysis and calculations detailed in Lee et
al. (2016) were used. For each assay composed of two duplicate vials, an ANCOVA of the two
linear regressions for the two assay vials revealed no significant difference in slopes (ANCOVA
105
F 1,7 = 0.04, p = 0.85): hence, these two regressions were pooled into a single rate (shown in Fig.
3A). In brief, the percent composition of all amino acids in proteins of larvae of L. pictus was
determined by Pace & Manahan (2006): alanine is 7.8% of the total amino acid pool, and the
mole-percent corrected molecular mass of all amino acids is 129.4 g mol
-1
. These values were
used to calculate the rate of protein synthesis in the present study, using the following
equation:
Equation 3: Protein Synthesis = d/dt(Sp/Sfaa) x 129.4/7.8
In this equation from Pace & Manahan (2006), t is time (h), S p is the amount of
radioactivity in the protein fraction of larval tissue, and S faa is the amount of radioactivity in the
free amino acid pool of larval tissue. These assays were replicated at each of the four
temperatures to determine the Q 10 value (Fig. 3B-C).
Ammonia Excretion Rate
Ammonia was measured using a 250 μl aliquot of seawater removed from each µBOD
vial (see ‘Metabolic Rate’ above) immediately after completion of respirometry. For a given
sampling interval, the seawater in each replicate μBOD vial was used to measure ammonia
(ammonium in seawater). The amount of ammonia was determined by the Berthelot color
reaction (Weatherburn, 1967) with the exception that reagents were prepared with Nanopure
water at room temperature. In brief, 10 μl of phenol, 10 μl nitroprusside, and 25 μl oxidizing
106
reagent (85.4% by volume alkaline reagent [0.2 g ml
-1
sodium citrate; 0.01 g ml
-1
sodium
hydroxide dissolved in Nanopure water]; 14.6% commercially available bleach, [Clorox®
Disinfecting Bleach, The Clorox Co., CA, USA], made fresh daily) were added to each sample,
vortexing between each addition of chemical. A standard curve of 12 concentrations from 0.5
μM to 100 μM NH 4
+
was analyzed with each individual 96-well flat-bottom microplate (Sigma-
Aldrich). Modified from Weatherburn (1967), plates were incubated in the dark at room
temperature for 90-minutes prior to analyses at 640 nm absorbance (Spectramax M2e
Microplate Reader, Softmax Pro Software, Molecular Devices, LLC., San Jose, CA, USA).
Ammonia was calculated by the following equation:
Equation 4A: ΔNH4
+
(μM) =
(𝐴𝑏𝑠𝑜𝑟𝑏𝑎𝑛𝑐𝑒 −𝐼𝑛𝑡𝑒𝑟𝑐𝑒𝑝𝑡 )
𝑆𝑙𝑜𝑝𝑒
Equation 4B: pmol NH4
+
larva
-1
h
-1
= {Δ[NH4
+
] larvae/Δt – Δ[NH4
+
] control/Δt} * V
In Equation 4A, the slope and intercept are input from the standard curve linear
equation specific to the 12 prepared standard dilutions in each individual 96 well-plate. In
Equation 4B, rates were converted to pmol NH 4
+
larva
-1
h
-1
by correcting for the change in
ammonia in control μBOD vials, containing only filtered seawater and no larvae, from the start
and end time points of the assay [volume (v) of each vial, number (n) of larvae per vial], and
total measurement time (Δt, 3-5 hours) (Fig. 4A). These assays were replicated at each of four
temperatures to determine the Q 10 value (Fig. 4B-C). Control vials included in each set of
measurements at each temperature were used to correct for background changes in ammonia
107
that were not attributable to larval excretion (e.g., for the bars shown from Fig. 4A, the average
amount of ammonia in the µBOD control vials at time zero, and after 5-hours (t-test: t = -4.8, df
= 11, p < 0.01). The average ammonia background increase in control vials that contained no
larvae was, at the end of the five-hour experimental period, 0.5 µM ammonia per hour. These
statistical analyses were undertaken for all of the control vials tested during the course of the
experimental series presented here and corrected accordingly (N = 108 control vials for initial
time point analyses, and N = 60 control vials for end point analyses).
Calculation of Oxygen-to-Nitrogen Ratios
Specific ratios of atomic oxygen to atomic nitrogen (O:N ratios) were calculated for
larvae in each independent μBOD vial used to measure respiration. Due to the non-invasive
sampling across time points for the optode respiratory measurements, the exact vial of larvae
used for micro-respirometry rate was also used for measurements of ammonia (NH 3) excretory
rate. Consequently a specific O:N value for each independent vial of larvae was determined.
Ratios below 30 indicate that larvae are catabolizing mostly protein, and over 40 indicate larvae
are catabolizing lipid or carbohydrates (Clark et al., 2013). Atomic O:N ratios were calculated
from eight replicate μBOD vials from each cohort at each of the four acute temperatures used
to calculate Q10 values. The respiration rates (pmol O 2 larva
-1
h
-1
) were corrected to convert
moles of oxygen to atoms, prior to comparing to the ammonia excretion rate (pmol N larva
-1
h
-1
) (Mayzaud & Conover, 1988). O:N ratios were analyzed by linear regression at 10, 15, 20 and
108
25°C. Differences in chronic temperature (15°C-reared larvae measured at 15°C, and 20°C-
reared larvae measured at 20°C) were analyzed by ANOVA.
Thermal Sensitivity (Q 10 Values)
Q 10 values were calculated for all physiological assays (Fig. 1-4) using the following
equations (Equation 5A-C) where R and T represent the corresponding rate and temperature,
respectively (e.g., R1 is the physiological rate measured at T1).
Equation 5A: 𝑄 10
= (
𝑅 2
𝑅 1
)
10
𝑇 2−𝑇 1
Equation 5B: 𝑙𝑜𝑔 𝑅 2
= 𝑙𝑜𝑔 𝑅
1
+
𝑇 2−𝑇 1
10
× 𝑙𝑜𝑔 𝑄 10
Equation 5C: Q 10 = 10
slope
A linear transformation of Equation 5A is necessary to apply data from more than two
temperatures, shown in Equation 5B. In this experiment several replicates (number varied
based on physiological assay) were tested at four temperatures; using Equation 5B allowed the
incorporation of all data points in every replicate to be included in the Q 10 calculation. Solving
for the Q 10 variable in Equation 5B results in Equation 5C, where the slope is the linear
regression of log R 2 against
𝑇 2−𝑇 1
10
. Each Q 10 value is calculated from the slope ± S.E. of the semi-
109
logarithmic regression relationship between physiological rate and temperature (Schmidt-
Nielsen, 1997, page 220). ANOVA was used to test for (1) possible changes in Q 10 values as
development proceeded within each of the three cohorts tested for each of the physiological
processes measured, and (2) possible changes in Q 10 values among the different cohorts (Table
2: respiration, transport, protein synthesis, ammonia excretion).
Protein Metabolic Dynamics
Protein metabolic dynamics were studied at 15°C and 20°C by measuring rates of
protein accretion and rates of protein synthesis in different development stages. Changes in
these processes were used to calculate (1) protein depositional efficiency (Equation 6A) (2)
fractional rates of protein synthesis (Equation 6B) and (3) protein turnover (Equation 6C).
Equation 6A: Protein depositional efficiency =
𝑃𝑟𝑜𝑡𝑒𝑖𝑛 𝑎𝑐𝑐𝑟𝑒𝑡𝑖𝑜𝑛 𝑃𝑟𝑜𝑡𝑒𝑖𝑛 𝑠𝑦𝑛𝑡 ℎ𝑒𝑠𝑖𝑠 × 100
Equation 6B: Fractional rate =
𝑃𝑟𝑜𝑡𝑒𝑖𝑛 𝑠𝑦𝑛𝑡 ℎ𝑒𝑠𝑖𝑠 𝑃𝑟𝑜𝑡𝑒𝑖𝑛 𝑐𝑜𝑛𝑡𝑒𝑛𝑡 × 100
Equation 6C: Protein turnover = Protein synthesis – Protein accretion
110
Cost of Protein Synthesis and Allocation of Metabolic Energy towards Protein Synthesis
The cost of protein synthesis was measured for L. pictus in early developmental stages
of gastrula (two-day-old) and pluteus (four-day-old) for two cohorts of individuals reared at
15°C. The energetic cost of a unit of protein synthesized was calculated from simultaneous
measurements of protein synthesis and respiration, with and without a protein synthesis
inhibitor, emetine. Emetine was added at 100 µM concentration to a subset of 5,000 or 10,000
individuals in 10-ml of fresh filtered seawater (0.2-µm pore-size), for respiration and protein
synthesis, respectively. Emetine is a specific inhibitor of protein synthesis, previously
determined to have no non-specific effects on physiology of developing sea urchins at this
concentration (Pace & Manahan, 2006; Pan et al., 2015). Additionally, in the current study, the
inhibitor had no effect on alanine transport rate, further indicating that there were no non-
specific effects. By ANOVA the rates of alanine transport with and without emetine were not
significantly different (F 1,6 = 3.56, p = 0.1). The rate of alanine transport with no emetine
present (control) was 3.0 ± 0.2 (S.E.M.) pmol alanine larva
-1
h
-1
; rate of alanine transport with
emetine present (experimental treatment) was 2.4 ± 0.2 (S.E.M.) pmol alanine larva
-1
h
-1
. For
both respiration and protein synthesis assays, embryos or larvae were pre-incubated in emetine
solution (100 µM) for one hour before allocation into the experimental vials and the initiation
of measurements. The measurements of protein synthesis rate were completed in parallel with
the corresponding respiration rates on the same larval cohort, size and age.
The energetic cost of protein synthesis was calculated from the difference in respiration
rate and protein synthesis rates in the presence and absence of emetine (E) (Equation 7). The
111
rates of oxygen consumption were converted into energy equivalents based on equal
oxyenthalpic values of lipid and protein, 484 kJ mol
-1
O 2 (Gnaiger, 1983).
Equation 7: Cost [µJ ng
-1
] = (Resp. E present – Resp. E absent [µJ]) ÷ (P.S. E present – P.S. E absent[ng])
Using the cost of protein synthesis, the proportion of metabolic energy allocated
towards protein synthesis was calculated. For a given sample of individuals that were measured
simultaneously for determination of respiration and protein synthesis rates, both processes can
be converted into energetic equivalents, with respiration being the measure of total available
energy for allocation to specific biochemical processes (i.e., protein synthesis).
RESULTS
Survivorship and Growth
Throughout this study, survivorship of larvae of L. pictus averaged 80% or greater from
the initiation of the first feeding stage through the duration of the experiment (Table 1).
Survivorship varied between the three cohorts tested, with the highest survival observed at
94% (Cohort 3 at 15°C, day 8), and the lowest survival observed at 73% (Cohort 1A at 20°C, day
7).
Growth varied between cohorts and temperature. A total of 1,227 size measurements
were made across eight culture vessels of larvae. Replicate culture vessels of the same cohort
112
(1A and 1B) showed consistent growth rates in size (Fig. 5A: ANCOVA slope F 1,306 = 28.7, p >
0.05), confirming repeatability based on the optimized culturing procedures used for the
current study. Across the three different cohorts tested, midline body length growth rates
ranged from 7.5 – 12.4 µm day
-1
at 15°C and 16.4 – 19.4 µm day
-1
at 20°C (Fig. 5C: slope ±
S.E.S.). Such intra-cohort variation is expected for larval cultures initiated from separate pools
of males and females. On average, the midline body lengths of larvae increased at a rate of 9.8
± 0.1 (S.E.S.) µm day
-1
at 15°C compared to 17.6 ± 0.1 µm day
-1
at 20°C (ANCOVA of slopes of
midline body length by day for three cohorts compared for two temperatures, F 1,1223 = 90.08, p
< 0.0001).
Biochemical Composition
Biochemical composition of larvae was measured for protein, lipid, and carbohydrate.
Protein was the dominant component of the measured organic biomass, followed by lipid and
carbohydrate. Measurements of carbohydrate content for four-day-old larvae (Cohort 1)
yielded 12.6 ± 1.6 (S.E.M.) ng per larva. The lipid and protein contents in these same larvae
were 36.0 ± 2.9 ng lipid and 47.7 ± 1.2 ng protein. As a percent of biomass composition
measured, carbohydrates represented only 13%. Measurements of total lipid content
performed for a total of 15 different ages and sizes (spanning eggs to eight-day-old larvae)
revealed no significant change in total lipid content, averaging 41.0 ± 4.5 (S.E.M.) ng total lipid
per individual. Notably, across all samples analyzed, phospholipid content averaged 87% ± 3%
(S.E.M.) of all lipid classes measured, with trace amounts of triacylglycerol, hydrocarbons, and
113
cholesterol detected. Also, no difference in total lipid content or composition was observed for
larvae reared under the two different temperature culturing treatments of 15°C and 20°C.
Protein was the dynamic biochemical component that was observed to change and,
hence, is the primary focus of this study. Larvae from culture replicates of the same Cohort 1A
and 1B, reared at the same temperature, did not differ in protein accretion rates (Fig. 5B:
ANCOVA slope F 1,25 = 2.4, p>0.05). Corresponding with changes in morphological size, as
measured by midline body length (Fig. 5C), larvae reared at 15°C accreted protein at a slower
rate of 9.2 ± 0.1 ng larva
-1
day
-1
, compared to larvae reared at 20°C (11.9 ± 0.2 (S.E.S.) ng larva
-1
day
-1
) (Fig. 5D: ANCOVA of slopes of protein accretion by day for three cohorts compared for
two temperatures, F 1,113, p = 0.049). Across the three different cohorts tested, protein accretion
ranged from 7.1 – 12.1 ng day
-1
at 15°C and from 8.3 – 16.7 ng day
-1
at 20°C (Fig. 5D: slope ±
S.E.S.). Overall, higher temperature resulted in faster growth as measured by size and protein
content. Notably, midline body length predicted protein content independent of rearing
temperature (Fig. 6A). However, rearing temperature did affect morphology, with larvae reared
at the higher temperature of 20°C having a higher ratio of post-oral arm length to midline body
length compared to larvae of the same cohort reared at 15°C (Fig. 6B).
Temperature Sensitivities of Physiological Processes (Q
10
)
Larvae of L. pictus increased all measured physiological rates from the lowest to highest
temperatures examined (10°C – 25°C) (Fig. 1-4). All Q
10
values were calculated from the slope (±
S.E.S.) of the semi-logarithmic regression of physiological rate and acute temperature exposure
114
at 10, 15, 20 and 25°C. The Q 10 values of respiration, alanine transport, protein synthesis, and
ammonia excretion from each cohort chronically reared at 15 or 20°C are presented in Table 2.
For larvae reared at 15°C, the highest value measured for Q 10 was for protein synthesis rates, at
3.6 ± 0.2 (S.E.M.). This value for protein synthesis was significantly higher (ANOVA, p < 0.05)
than the Q 10 for alanine transport (1.6), respiration (2.4), and ammonia excretion (2.6). Notably,
these averaged Q 10 values for each physiological process were consistent across the three
different cohorts tested. A similar hierarchy of Q 10 values was observed for larvae that were
chronically reared at the higher temperature of 20°C (Table 3, ANOVA, p > 0.05).
During the course of these experiments, a wide range of ages and sizes of larvae were
tested for physiological responses to rising temperature. The possibility was tested that Q 10
values might change with age and size. (Table 2: Linear Regressions of Q 10 x Midline Body
Length). These ANOVAs revealed that the values of Q 10 for protein synthesis, respiration, and
ammonia excretion did not change with morphological size. The Q 10 values for alanine transport
had the lowest averaged values of 1.6 and 1.5 at 15 and 20°C, respectively, and showed a small
but significantly lower temperature sensitivity with increasing size.
Importantly, the Q 10 values for protein synthesis were always higher than those of
respiration (Table 3). This differential temperature sensitivity has important consequences for
energy allocation with rising temperature (discussed below).
115
Oxygen-to-Nitrogen Ratios (O:N)
Atomic O:N ratios are used to indicate whether catabolism is protein-driven (values
<30), or lipid or carbohydrate driven (values > 30) (Mayzaud & Conover, 1988; Clark et al.,
2013). For the studies reported here with larvae of L. pictus, the maximum O:N ratios measured
averaged 25, indicating that larvae were catabolizing protein as the major biochemical
substrate. O:N ratios ranged from an average of 20.9 ± 1.0 (S.E.M.) at 15°C, to 25.2 ± 1.9 at
20°C. These O:N values were not significantly different across three cohorts (ANOVA F 2,21 =
0.15, p = 0.9), nor for larvae of different ages or sizes (ANOVA F 1,160 = 4.03, p = 0.05).
Protein Synthesis, Turnover, and Depositional Efficiency under Different Rearing Temperature
Chronic rearing temperature had no effect on acute temperature sensitivities for any of
the physiological processes measured (respiration, alanine transport, protein synthesis, or
excretion rates; Table 3). Also, protein depositional efficiency (the ratio of protein accreted to
amount synthesized) remained consistent whether larvae were reared at 15°C or 20°C (Fig. 7A);
however, the percentage of depositional efficiency decreased with size (Fig. 7A). Specifically,
depositional efficiency was 58% for larvae of 270-µm midline body length and decreased to 6%
for larger larvae (380-µm) (Fig. 7A).
Similarly, fractional protein synthesis rates, the rates of protein synthesis relative to
whole-body protein contents, were not affected by rearing temperature (Fig. 7B). In contrast to
the decrease in protein depositional efficiency as larvae grew, the fractional rates of protein
116
synthesis increased with size, from 2% h
-1
for 270-µm larvae to 8% h
-1
for 380-µm larvae (Fig.
7B). Again, fractional synthesis rates did not differ between rearing temperatures. Since
depositional efficiency decreased with size and fractional synthesis rates increased, protein
turnover rates increased correspondingly with age, ranging from 5 to 264 ng larva
-1
day
-1
for
270-µm and 380-µm sized larvae, respectively (Fig. 7C). Protein turnover rates also did not
differ between larvae chronically reared at 15 or 20°C. In summary, larvae decreased
depositional efficiency with size, but increased fractional synthesis, indicating that larvae
synthesize more protein per unit growth with age. Notably, none of these protein metabolic
dynamic processes were affected at the temperatures tested (15 or 20°C).
Cost of Protein Synthesis
The energetic cost of protein synthesis was determined to be 2.3 ± 0.3 (S.E.M.) µJ ng
-1
protein synthesized. This calculation was based on four independent sets of concurrent
respiration and protein synthesis rates measured for two cohorts and two stages of
development (gastrula and pluteus) (Table 4). As illustrated in Table 4, the rates of protein
synthesis and respiration were reduced in the presence of the protein synthesis inhibitor,
emetine. Emetine was an effective inhibitor of protein synthesis, and decreased the rate of
synthesis by 90-97%, relative to controls (no emetine present). The presence of emetine also
resulted in a decrease in respiration rate, ranging from 13-32% relative to controls. For
instance, for two-day-old gastrulae (Cohort 4; Table 4), the presence of emetine reduced the
rate of protein synthesis from 0.76 ± 0.023 ng embryo
-1
hour
-1
, to 0.02 ± 0.007 ng embryo
-1
117
hour
-1
(i.e. a difference of 0.7 ng embryo
-1
hour
-1
). The corresponding decrease in respiration
was 4.5 pmol O 2 embryo
-1
hour
-1
(the difference between 13.9 ± 1.4 and 9.4 ± 1.1 pmol O 2
embryo
-1
hour
-1
). A respiration rate difference of 4.5 pmol O 2 embryo
-1
hour
-1
was 2.2 µJ per
embryo
-1
hour
-1
(based on an oxyenthaplic equivalent of 484 µJ per picomole of oxygen;
Gnaiger, 1983). Hence, this decrease of 2.2 µJ embryo
-1
hour
-1
corresponding to a decrease in
protein synthesis of 0.7 ng protein embryo
-1
hour
-1
, yields the cost of protein synthesis for two-
day-old gastrula from Cohort 4 is 3.0 µJ ng
-1
protein synthesized (0.7/2.2). A similar set of
analyses conducted for different cohorts and developmental stages (Table 4), revealed an
average cost of protein synthesis for developmental stages of L. pictus of 2.3 ± 0.3 (S.E.M.) µJ
ng
-1
. The values for gastrula and pluteus were not statistically different (ANOVA: F = 6.42 p =
0.13).
Allocation of Energy to Protein Synthesis with Rising Temperature
Applying this cost of 2.3 µJ ng
-1
protein synthesized results in a disproportionate amount
of energy being allocated to protein synthesis as temperature increases. This is illustrated for
the metabolism of four-day-old larvae from Cohort 1 as environmental temperature increases
(Table 5). From 10°C to 20°C, there was an approximate two-fold increase in the allocation of
energy to protein synthesis (Table 5). An allocation value of 16% at 10°C, for instance, is
calculated as follows. Larvae from Cohort 1, reared in two replicate 20-l culture vessels (Table 5:
Cohort 1A, 1B) had respiration rates of 12.4 and 11.6 pmol O
2
larva
-1
h
-1
(equivalent to 6.0 and
5.6 µJ larva
-1
h
-1
, respectively). The corresponding rates of protein synthesis were 0.4 ng protein
118
larva
-1
h
-1
for both Cohorts 1A and 1B (equivalent to 0.9 µJ larva
-1
h
-1
). The percent allocation of
energy to protein synthesis is 15% (Cohort 1A) and 16% (Cohort 1B): i.e., the ratio of 0.9 to 6.0
and 0.9 to 5.6, respectively. With increasing temperature to 25°C, the allocation to support the
energy requirements of protein synthesis increases to 37% (Cohort 1B) and 42% (Cohort 1A) of
larval metabolism.
DISCUSSION
While it is well established that metabolic rates increase with rising temperature,
(Somero, 2010; Somero, 2012; Pörtner & Gutt, 2016) the underlying physiological mechanisms
are less well known in developmental stages. The present study contributed four main findings.
Firstly, the Q 10 values of protein synthesis were greater than that of respiration (Q 10: 3.7 vs 2.4
respectively; Table 3). This finding is of significance because it reveals a major disproportionate
increase in the costly process of biosynthesis compared to the energetic production from
respiration. Secondly, when larvae were chronically exposed (reared) to a higher temperature
(20°C) there was no difference in acute temperature sensitivity (Q 10 values) for a range of
physiological processes, compared to larvae of the same cohort chronically exposed to a lower
temperature (15°C). Thirdly, depositional efficiency (ratio of protein accretion to protein
synthesis) significantly decreased with size; correspondingly, fractional protein synthesis and
turnover rates increased with size. Notably, there was no effect of chronic temperature on
rates of protein depositional efficiency, despite larvae reared at higher temperatures having
faster protein synthesis and accretion rates, and also faster metabolic rates. Fourthly, because
119
of the differential sensitivity of protein synthesis and respiration with rising temperature, larvae
allocate a higher percentage of total energy towards protein synthesis as temperatures
increase.
Growth Rate and Temperature
As expected with rising temperature larval growth rates – measured by morphological
increase in midline body-length size and biochemical increase in protein content – were faster
in larvae reared at 20°C than at 15°C (Fig. 5). Also, the relationship between midline body-
length and protein content was linear (Fig. 6A), providing evidence that temperature did not
change the condition index of growing larvae (i.e., similar ratio of size to biomass). However,
there was a surprising effect showing a contrasting relationship between post-oral arm length
and midline body-length for larvae reared at either 15 or 20°C. Larvae reared at 20°C had
increased post-oral arm lengths compared to larvae of the same midline-body length reared at
15°C (Fig. 6B). Previously, it has been demonstrated that arm length in echinoderm larvae
becomes longer under treatments of limited food supply and shorter when abundant food is
present (Boidron-Metairon, 1988; Sewell et al., 2004; Adams et al., 2011). In larvae of the
purple sea urchin, S. purpuratus, Adams et al. (2011) determined that a neuro-signaling
pathway of dopamine is responsible for regulating larval arm length. Specifically, the
mechanism acts by inhibiting the growth of post-oral arms under conditions of high food
amounts, as opposed to promoting the growth of post-oral arms under limited food (Adams et
al., 2011). A novel result in the present study is that temperature also alters larval morphology.
120
The mechanism of regulating arm length in response to rising temperature in the presence of
varied food treatments remains to be determined. It might be that under higher temperatures,
and resulting higher metabolic rates, larvae increase arm length to increase food acquisition to
compensate for additional energy needs.
Respiration Rate and Temperature
Q 10 values for respiration have been calculated for adult stages of several marine
invertebrate species and are generally reported between 2 and 3 (Lawrence, 1987; Sibly et al.,
2012). The blue mussel, Mytilus edulis, had a Q 10 of 2.2 from exposure to a range of 10-28 C,
with a critical temperature of metabolic decline being reached between 28 and 31 C (Zittier et
al., 2015). The sea star, Pisaster ochraceus, also had a Q 10 of 2.2 exposed from 10-20 C (Fly et
al., 2012). Respiration Q10 of Pacific oyster larvae, Crassostrea gigas, measured from 15-25 C
was determined to be 2.5 (Lannig et al., 2010). Adult urchins, Heliocidaris erythrogamma had a
Q 10 of 1.4 from 18 to 23˚C (Carey et al., 2016), and the adult brittle star, Ophionereis schayeri,
had a Q 10 2.6, when acclimated to 19 and 25˚C (Christensen et al. 2011).
With continually increasing temperatures, metabolic rates often decrease (thermal
optimum: Pörtner & Farrell, 2008). For instance, rocky-shore snails, Echinolittorina malaccana,
which experience temperatures from 25-50 C, frequently showed higher metabolic rates at
30 C than at 40 C (Marshall & McQuaid, 2011). For adult sea urchins, Heliocidaris
erythrogramma, Harianto et al., (2018) reported decreasing survival and metabolic Q 10 values
from 1.67 to 0.88 with increasing temperatures. In the current study, larvae of L. pictus
121
continue to increase rates at higher temperature, suggesting that acute exposure as high as
25 C is tolerable (Figs. 1-4).
Cost of Protein Synthesis
The cost of protein synthesis in larvae of L. pictus has been determined to be fixed in
terms of larval stages of development (blastula, gastrula, plutei) and in physiological state
(unfed larvae and larvae fed ad libitum) (Pace & Manahan, 2006). Additionally, larval protein
synthesis had a fixed cost even when protein synthesis rates increased under ocean
acidification treatments in the purple sea urchin, Stronglyocentrotus purpuratus (Pan et al.,
2015). Larval protein synthesis has been determined to have a fixed cost regardless of
environmental temperature in the Pacific oyster, C. gigas (Lee et al., 2016). Hence, the fixed
cost calculated in this study of L. pictus at 2.3 ± 0.2 (S.E.M.) µJ ng
-1
was applied to larvae of L.
pictus across a temperature range of 10-25 ̊C (Tables 4, 5). The cost of protein synthesis for
larvae of L. pictus in the current study was lower than previously reported for this species (8.4 ±
1 µJ ng
-1
, Pace & Manahan, 2006). In the current study, using a protein synthesis cost of 8.4 µJ
ng
-1
would result in over 100% of energy allocation towards protein synthesis alone. Thus, the
cost of protein synthesis was independently measured to be 2.3 ± 0.2 µJ ng
-1
for cohorts of
larvae used in the current study (Table 4). Notably, this value is consistent with other studies on
different species of marine invertebrate larvae [e.g., 2.1 µJ ng
-1
for C. gigas (Lee et al., 2016);
2.4 µJ ng
-1
for S. purpuratus (Pan et al., 2015)].
122
Protein synthesis represents one of the major cellular costs in living organisms, and can
account for over half of the energy budget in marine ectotherm larvae, even prior to
responding to environmental stressors such as ocean acidification and temperature (Pan et al.,
2015; Lee et al., 2016; Frieder et al., 2018; Pan et al., 2018; Pan et al., 2021). Any environmental
stress that results in an increasing allocation of the organism’s available energy (ATP pool) to
protein synthesis leaves less available energy to support other essential physiological processes.
Direct comparisons between Q 10 values of respiration and protein synthesis are limited
but have been studied in oyster larvae (Pan et al., 2021). The Q 10 of protein synthesis for larvae
of C. gigas were consistently higher than that of concurrently measured respiration (Pan et al.,
2021). Due to the fixed cost of protein synthesis, the disproportionate increase in energy
allocation to protein synthesis constituted a significant disruption of homeostatic energy
balance.
Protein Dynamics at Increased Temperatures
The increase in temperature sensitivity of protein synthesis compared to respiration
indicates that energy demand to support protein synthesis increases at a greater rate than the
supply of energy from respiration (Fig. 8). The energy required to meet the increased demands
of protein synthesis at higher temperatures can be accommodated within the available pool of
energy, at least partially attributable to an increase in the available pool at higher temperatures
(Fig. 8, Table 5).
123
Regarding which biochemical substrate is used by larvae to support energy metabolism,
the observation that O:N ratios did not change with rising temperature suggests a critical role
for protein catabolism irrespective of environmental temperature. O:N ratios are routinely used
as an indicator of catabolism source. A low O:N ratio (<30) indicates a predominantly protein-
based metabolism, and higher O:N ratio indicates lipid or carbohydrate-based metabolism
(Mayzaud & Conover, 1988). Larvae of L. pictus did not significantly alter O:N ratios at 15
compared to 20 C. The measured ratios ranged from 20 to 25, indicating that protein was the
dominant substrate being degraded and oxidized. While organisms have shown mixed
responses of O:N ratios with temperature change (Mayzaud & Conover, 1988; Ikeda et al.,
2001), temperature stress has been linked to decreasing O:N ratio (Robertson et al., 2001; Clark
et al., 2013). When C. gigas adults were exposed to temperatures of 19 C they had O:N ratios
of 55.8 (low pH) and 68.8 (Normal pH), these high ratios indicating oxidation of mostly
carbohydrates or lipids as energy source. In contrast at 24 C, the O:N ratio was 26.7, indicating
predominately oxidation of protein (Clark et al., 2013). The static O:N ratio seen in the current
study of L. pictus suggests that larvae are tolerant to the temperature increase tested, and that
temperatures as high as 20 C are not physiologically stressful in the context of catabolism.
Conclusion
L. pictus are a summer-spawning species taken from the Southern California Bight for
these experiments, where average temperatures range from 15 to 20°C, hence the chosen
rearing temperatures. The average annual temperature in the Southern California Bight has
124
been increasing since the 1960s, and experienced four of the top five highest annual surface
temperatures since 2015 in a 105-year dataset (Rasmussen et al., 2020). Thus, it is of
significance to know how organisms respond to temperature change. While many studies focus
on the lethal or organismal-level growth effects of increasing temperature, much less is
understood about the underlying physiological changes that occur to mitigate sub-lethal
temperature increases. Understanding these mechanisms of physiological homeostasis will help
in accurate predictions of resilience to global ocean change.
Acknowledgments
Jason Wang, a fellow PhD student in the laboratory group of Professor Donal T.
Manahan at the time when these experiments were undertaken, assisted in the large number
of larval cultures that were necessary to complete the experiments presented here. Dr. Andrew
Griffith, a postdoctoral fellow in the laboratory group of Professor Donal T. Manahan, analyzed
the carbohydrate content for 4-day-old larvae reported in the ‘Biochemical Composition’ results
section.
125
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FIGURES
Figure 1. Respiration rate measurements and calculation of Q 10 value using six-day-old, 314-µm
sized, larvae of Lytechinus pictus from Cohort 1 reared at 15°C.
(A) Oxygen depletion by larvae in micro-biological oxygen demand (𝜇 BOD) respiration vials;
larvae from replicate culture vessel Cohort 1A (closed triangles) and replicate culture vessel
Cohort 1B (closed diamonds). Two control respiration vials containing only filtered seawater
(0.2-µm pore size) (open symbols) at 15°C.
(B) Average of oxygen depletion rates calculated for larvae from Cohort 1A, B from Fig. A is
represented as two data points for the assays conducted at 15°C; the data shown in (B) are sets
of replicate assays each conducted at each temperature shown. Respiration rates of larval
Cohorts 1A, B, sampled from each replicate culture vessel, were not statistically different
(ANCOVA F 1,52 = 0.96, p = 0.3). Error bars ± 1 S.E.M.
(C) The log value of the average respiration rate at each temperature shown in Fig. B for Cohort
1A is plotted on the y-axis of Fig. C; the x-axis is the temperature incremental change [T2 –
T1)/10] (see Schmidt-Nielson, 1997, p.219-220). An ANOVA regression (line shown is described
by the equation: y = 0.38x + 1.27) gives the Q 10 value as 10
slope
(i.e., 10
0.38
) and is 2.4 ± 0.2 (S.E.
of the slope) for larval respiration rate at 314-µm.
133
Figure 2. Alanine transport rate measurements transformed into Q
10
value for eight-day-old,
340-µm larvae of Lytechinus pictus from Cohort 1A reared at 15°C.
(A) Replicate time-course assays (closed and open symbols), each conducted in a separate 10
ml assay vial, of alanine transport at 15°C; each replicate assay vial contained 10 µM alanine
and 74 kBq of
14
C-alanine tracer. Absolute rates of alanine transport from seawater were
determined by correcting for the specific activity of
14
C-alanine in each assay vial. Replicate
transport assays did not differ statistically (ANCOVA F 1,6 = 0.21, p = 0.7), and both independent
assays were pooled to yield a single rate (regression line shown) for 360 µm larvae.
(B) Average of alanine transport rates for larvae from Fig. A is represented as the data point for
the assay conducted at 15°C; the data shown in (B) are sets of assays each conducted at the
different temperatures shown. Error bars ± 1 S.E.M.
(C) The log value of the average alanine transport rate at each temperature shown in Fig. B is
plotted on the y-axis of Fig. C; the x-axis is the temperature incremental change [(T2 – T1)/10]
(see Schmidt-Nielson, 1997, p.219-220). An ANOVA regression (y = 0.103 + 0.55) gives the Q 10
value as 10
slope
(i.e., 10
0.103
) and is 1.3 ± 0.1 (S.E. of the slope) for transport of alanine by larvae
of 360-µm.
134
Figure 3. Protein synthesis rate measurements transformed into Q 10 value of four-day-old,
303-µm sized, larvae of Lytechinus pictus from Cohort 1A reared at 15°C.
(A) Replicate time-course assays (closed and open symbols), each conducted in a separate
10-ml assay vial of protein synthesis rate for larvae at 15°C. Replicate protein synthesis assays
did not differ statistically (ANCOVA F 1,7 = 0.04, P = 0.85) and both independent assays were
pooled to yield a single rate (regression line shown) for 303 µm larvae. Rate of incorporation of
14
C-alanine into the protein fraction was corrected for changes of intracellular specific activity
and calculated based on known mole-percent of amino acids in larval proteins (see Equation 3).
(B) Average of protein synthesis rate for larvae from Fig. A is represented as the data point for
the assay conducted at 15°C; the data shown in (B) are sets of assays each conducted at the
different temperatures shown. Closed triangle and diamond symbols represent assays of larvae
from two replicate culture vessels for Cohort 1A, B. Error bars ± 1 S.E.M.
(C) The log value of the average protein synthesis rate at each temperature shown in Fig. B is
plotted on the y-axis of Fig. C; the x-axis is the temperature incremental change [(T 2 – T 1)/10]
(see Schmidt-Nielson, 1997, p.219-220). An ANOVA regression (y = 0.574x – 0.41) gives the Q 10
value as 10
slope
(i.e., 10
0.574
) and is 3.8 ± 0.3 (S.E. of the slope) for protein synthesis for larvae of
303-µm.
135
Figure 4. Ammonia excretion rate measurements transformed into Q 10 value for three-day-old,
300-µm sized, larvae of Lytechinus pictus from Cohort 1A reared at 20°C.
(A) ‘Control T-0’ is filtered seawater (0.2 𝜇 m pore size) only (no larvae present), sampled from
the same µBOD vial used to assay rates of oxygen depletion; ‘Larval T-0’ represents the
concentration of ammonia in the µBOD vial at the start of the assay. ‘Control T-5h’ represents
the concentration of ammonia in the control µBOD vial that contained no larvae, at the end of
the 5-hr assay; ‘Larval T-5h’ represents the concentration of ammonia in the experimental
µBOD vial that contained larvae, at the end of the 5-hr assay. From these data, the rate of
ammonia produced larva
-1
h
-1
was calculated, corrected for the rate of ammonia increase in the
control seawater vials. Error bars ± 1 S.E.M. (N = 3 for Control T-0, Larval T-0, and Control T-5h
vials; N = 7 for Larval T-5h vials).
(B) Average of ammonia excretion rates for larvae from Fig. A is represented as the data point
for the assay conducted at 20°C; the data shown in (B) are sets of assays each conducted at the
different temperatures shown. Ammonia excretion rates ± 1 S.E.M. (N = 7 per temperature
assay).
(C) The log value of the average ammonia excretion rate at each temperature shown in Fig. B is
plotted on the y-axis of Fig. C; the x-axis is the temperature incremental change [(T 2 – T 1)/10]
(see Schmidt-Nielson, 1997, p.219-220). An ANOVA regression (y = 0.38x + 0.0074) gives the Q 10
value as 10
slope
(i.e., 10
0.38
) and is 2.4 ± 0.2 (S.E. of the slope) for ammonia excretion for larvae
of 300-µm.
136
Figure 5. Growth rates for larvae of Lytechinus pictus.
(A) Midline body-length sizes for larvae from two replicate culture vessels (represented by black
and gray symbols) of Cohort 1 reared at 15°C. Error bars ± 1 SEM; each data represents 50
individual larval size measurements. Inset depicts the measured size variable for midline body
length (red line). There are no significant differences between replicate culture vessels of the
same cohort for midline body length (ANCOVA F 1,305 = 28.7, p = 0.7); replicates were pooled to
yield a single rate (regression line shown).
100 m
137
(B) Protein content for larvae from the same two replicate culture vessels as in Fig. A, (black
and gray symbols represent five replicate protein assays from each of the two replicate culture
vessels). There are no significant differences between replicate culture vessels of the same
cohort for protein content (ANCOVA F 1,24 = 2.38, p = 0.1); replicates were pooled to yield a
single rate (regression line shown).
(C) The data from Fig. A for Cohort 1 larvae grown at 15°C are represented by the closed bar,
Cohort 1. A similar analysis was conducted for Cohort 1 larvae grown at 20°C, represented by
the open bar, Cohort 1. This analysis was repeated for an additional two cohorts of larvae
(Cohort 2 and 3), each grown at two temperatures; these midline body-length growth rates are
represented in Fig. C (15°C, closed bar; 20°C open bar). Error bars represent S.E. of the slope.
(D) Corresponding analyses for protein content for larval Cohorts 1-3 reared at 15 and 20°C.
138
Figure 6. Protein and post-oral arm growth relative to body length.
(A) Relationship of protein content to midline body length for larvae of Lytechinus pictus reared
at either 15 or 20°C. There is no significant difference in the slopes of larvae grown at either
temperature with respect to the relationship of protein content and midline body length
(ANCOVA F 1,20 = 0.53, p = 0.5), hence all data were pooled to yield a single regression line as
shown. Each data point for midline body length represents 50 individual larval size
measurements and 5 protein content assays per data point. Error bars represent 1 SEM of
midline body length (x-axis) and protein content (y-axis).
(B) Relationship of post-oral arm length to midline body length for individual larvae reared at
either 15 or 20°C. Inset depicts the measured post-oral arm length (red line). Each data point
represents 50 individual larvae for which both midline body length and post-oral arm length
was measured. The slopes of the relationship of post-oral arm length to midline body length
were not different (ANCOVA F 1,8 = 0.62, p = 0.5); the intercepts of these relationships were
significantly different (ANCOVA F 1,9 = 142.8, p < 0.0001), with larvae grown at 15°C having
smaller post-oral arm lengths at the same midline body-length.
100 m
139
Figure 7. Protein metabolic dynamics of larvae of Lytechinus pictus reared at 15°C (closed) and
20°C (open).
(A) The negative relationship of protein depositional efficiency (ratio of protein accretion to
protein synthesis) with increasing midline body-length size. There is no significant difference in
the slopes of these processes for larvae grown at either 15 or 20°C (ANCOVA F 1,20 = 0.42, p =
0.5), hence all data were pooled to yield a single regression line as shown.
(B) The positive relationship of fractional protein synthesis rates (ratio of protein synthesis rate
to total larval protein content) with increasing midline body-length size. There is no significant
difference in the slopes of this process for larvae grown at either 15 or 20°C (ANCOVA F 1,20 =
0.02, p = 0.9), hence all data were pooled to yield a single regression line as shown.
(C) The positive relationship of protein turnover rates with increasing midline body-length size
(i.e., protein degradation being the product of the rate of protein synthesis minus the rate of
protein accretion). There is no significant difference in the slopes of this process for larvae
grown at either 15 or 20°C (ANCOVA F 1,20 = 0.15, p = 0.7), hence all data were pooled to yield a
single regression line as shown.
140
Figure 8. A model of the differential sensitivity of protein synthesis and respiration with rising
temperature. The calculations of the application of a Q 10 value of protein synthesis of 3.7 and a
Q 10 value of respiration of 2.4 are given in Table 3. The model illustrates the disproportionate
demand with rising temperature for energy by protein synthesis, compared to the supply of
energy through respiration. The pie charts depict this energy differential of demand and supply,
by converting the rate of protein synthesis to energy equivalents (Table 4). From Table 5, the
energy supply from respiration is depicted as the area of each pie chart at three different
temperatures (15, 20, 25°C). The black section of each pie chart represents the energy allocated
to support the rate of protein synthesis with increasing temperature.
141
TABLES
Table 1. Survivorship for three cohorts of larvae of Lytechinus pictus reared at 15°C and 20°C.
Percent survival was determined for each culture vessel by enumeration of aliquots of larvae
through microscopy. Cohort 1A, B represent survival in replicate culture vessels. Each calculated
percentage represents larval survival relative to the day when the feeding stage developed; Day
4 for larvae reared at 15°C and Day 3 for larvae reared at 20°C. All data were corrected for the
sample removal of larvae for experimental purposes.
Cohort at
15°C
Feeding larvae, Day 4 ±
SEM
Starting %
Day 4
% Survival
Day 6
% Survival
Day 8
1A 475,833 ± 2,000 100% 88% 83%
1B 477,500 ± 11,000 100% 94% 88%
2 464,167 ± 8,000 100% 94% 89%
3 435,833 ± 16,000 100% 100% 94%
Mean
89%
Cohort at
20°C
Feeding larvae, Day 3 ±
SEM
Starting %
Day 3
% Survival
Day 5
% Survival
Day 7
1A 587,500 ± 14,000 100% 85% 73%
1B 585,750 ± 19,000 100% 82% 79%
2 413,929 ± 10,000 100% 89% 89%
3 463,642 ± 10,000 100% 78% 77%
Mean
80%
142
Table 2. Thermal sensitivities of four physiological processes (Q 10 values ± S.E. of the slope, see
Figs. 1-4) measured for three different larval cohorts of Lytechinus pictus. Cohort 1A, B
represent culture replicates from the same cohort. Measurements of each physiological process
were made on three different midline body-length sizes of larvae per cohort. Error bars
represent ± 1 S.E.M. By ANOVA (Linear Regr. Q 10 x Length), there is no significant effect of
midline body-length size on Q 10 values for respiration, protein synthesis, or ammonia excretion
for larvae reared at either 15°C or 20°C.
Reared from fertilization at 15°C
Cohort
Midline Body
Length (µm)
Respiration
Alanine
Transport
Protein
Synthesis
Ammonia
Excretion
1A
303 ± 2 1.9 ± 0.1 1.9 ± 0.1 3.8 ± 0.3 1.9 ± 0.1
314 ± 2 2.4 ± 0.2 1.8 ± 0.2 4.2 ± 0.5 4.6 ± 0.7
340 ± 3 2.4 ± 0.3 1.3 ± 0.1 2.2 ± 0.3 2.1 ± 0.2
1B
294 ± 2 2.0 ± 0.1 1.9 ± 0.2 3.3 ± 0.4 2.2 ± 0.2
320 ± 2 2.3 ± 0.2 1.5 ± 0.1 3.0 ± 0.4 4.0 ± 0.5
356 ± 3 3.0 ± 0.4 1.1 ± 0.1 3.8 ± 1.2 3.4 ± 0.6
2
289 ± 2 2.1 ± 0.1 1.7 ± 0.1 3.8 ± 0.7 2.3 ± 0.2
316 ± 2 2.1 ± 0.1 1.8 ± 0.1 4.5 ± 1.0 2.2 ± 0.3
320 ± 3 3.0 ± 0.3 1.3 ± 0.1 3.1 ± 0.8 1.5 ± 0.2
3
294 ± 2 2.4 ± 0.1 1.4 ± 0.1 2.9 ± 0.4 2.8 ± 0.3
328 ± 3 2.1 ± 0.1 1.6 ± 0.1 5.1 ± 1.4 2.7 ± 0.4
332 ± 3 3.6 ± 0.3 1.4 ± 0.1 2.9 ± 0.4 1.9 ± 0.2
Average Q 10 ± SEM: 2.4 ± 0.2 1.6 ± 0.1 3.6 ± 0.2 2.6 ± 0.3
Linear Regr. Q 10 x Length: 0.1 0.01 0.9 0.7
143
Reared from fertilization at 20°C
Cohort
Midline Body
Length (µm)
Respiration
Alanine
Transport
Protein
Synthesis
Ammonia
Excretion
1A
300 ± 2 2.3 ± 0.2 1.9 ± 0.1 5.4 ± 0.5 2.4 ± 0.2
330 ± 2 2.1 ± 0.2 1.3 ± 0.1 2.5 ± 0.2 2.4 ± 0.2
369 ± 3 2.8 ± 0.3 1.3 ± 0.1 3.8 ± 0.9 3.1 ± 0.2
1B
310 ± 1 2.1 ± 0.1 1.6 ± 0.1 5.6 ± 1.9 2.6 ± 0.2
340 ± 2 2.3 ± 0.1 1.3 ± 0.2 2.8 ± 0.5 2.7 ± 0.2
377 ± 3 2.6 ± 0.1 1.2 ± 0.1 3.9 ± 0.3 3.3 ± 0.2
2
274 ± 3 2.3 ± 0.1 2.3 ± 0.3 3.7 ± 0.5 3.4 ± 0.6
322 ± 3 2.4 ± 0.2 1.8 ± 0.3 3.6 ± 0.6 2.0 ± 0.2
340 ± 4 2.7 ± 0.2 1.3 ± 0.1 4.9 ± 1.5 2.8 ± 0.3
3
287 ± 2 2.2 ± 0.2 1.5 ± 0.1 3.4 ± 0.5 3.6 ± 0.8
327 ± 3 2.1 ± 0.2 1.6 ± 0.1 4.3 ± 0.8 1.6 ± 0.2
365 ± 4 2.2 ± 0.1 1.4 ± 0.1 3.3 ± 0.8 3.8 ± 0.8
Average Q 10 ± SEM: 2.3 ± 0.2 1.5 ± 0.1 3.9 ± 0.3 2.8 ± 0.2
Linear Regr. Q 10 x Length: 0.1 0.002 0.5 0.7
144
Table 3. Summary of thermal sensitivities (Q 10 values from Table 2) of four physiological
processes for larvae of Lytechinus pictus reared at either 15 or 20°C. A series of ANOVAs were
conducted for the Q 10 values for each physiological process to test for effects of chronic rearing
temperature on acutely measured values of Q 10. In all cases, there were no significant
differences (e.g. the Q 10 values for respiration for chronically reared larvae at either 15 or 20°C,
were not significantly different: ANOVA F 1,22 = 0.38, p = 0.5). The mean Q 10 values for
respiration, alanine transport, protein synthesis, and ammonia excretion were calculated by
combining all data at each rearing temperature for each physiological process. Error bars ±
S.E.M. (N = 24).
Process 15°C 20°C Mean
Respiration
2.4 ± 0.2 (12) 2.3 ± 0.2 (12) 2.4 ± 0.1 (24)
Alanine Transport
1.6 ± 0.1 (12) 1.5 ± 0.1 (12) 1.6 ± 0.1 (24)
Protein Synthesis
3.6 ± 0.2 (12) 3.9 ± 0.3 (12) 3.7 ± 0.2 (24)
Ammonia Excretion
2.6 ± 0.3 (12) 2.8 ± 0.2 (12) 2.7 ± 0.2 (24)
145
Table 4. The energy cost of protein synthesis for early developmental stages of Lytechinus
pictus reared at 15°C. The cost of a unit mass of protein synthesis (ng) was calculated from the
simultaneous decrease in protein synthesis rate and respiration rate in the presence of the
inhibitor emetine. The mean cost of protein synthesis for all pooled data was 2.3 ± 0.3 µJ (ng
protein synthesized)
-1
.
Rate Avg ± SEM
Respiration
µJ ind
-1
h
-1
(484
kJ/mol O 2)
Difference
(emetine
presence –
absence)
Cost
µJ (ng protein
synthesized)
-1
Cohort
4
Gastrula
(Day 2)
Protein Synthesis 0.76 ± 0.023 ng ind
-1
h
-1
0.7 ng ind
-1
h
-1
3.0
Protein Synthesis
w/ emetine
0.02 ± 0.007 ng ind
-1
h
-1
Respiration 13.9 ± 1.4 pmol O 2 ind
-1
h
-1
6.7
2.1 µJ ind
-1
h
-1
Respiration
w/ emetine
9.4 ± 1.1 pmol O
2
ind
-1
h
-1
4.6
Cohort
5
Gastrula
(Day 2)
Protein Synthesis 0.82 ± 0.004 ng ind
-1
h
-1
0.8 ng ind
-1
h
-1
2.3
Protein Synthesis
w/ emetine
0.03 ± 0.007 ng ind
-1
h
-1
Respiration 13.9 ± 1.5 pmol O 2 ind
-1
h
-1
6.7
1.8 µJ ind
-1
h
-1
Respiration
w/ emetine
10.1 ± 1.0 pmol O 2 ind
-1
h
-1
4.9
Cohort
4
Early
Pluteus
(Day 4)
Protein Synthesis 0.67 ± 0.002 ng ind
-1
h
-1
0.6 ng ind
-1
h
-1
1.7
Protein Synthesis
w/ emetine
0.05 ± 0.002 ng ind
-1
h
-1
Respiration 15.4 ± 0.8 pmol O 2 ind
-1
h
-1
7.5
1.0 µJ ind
-1
h
-1
Respiration
w/ emetine
13.4 ± 1.1 pmol O 2 ind
-1
h
-1
6.5
Cohort
5
Early
Pluteus
(Day 4)
Protein Synthesis 0.68 ± 0.002 ng ind
-1
h
-1
0.6 ng ind
-1
h
-1
2.0
Protein Synthesis
w/ emetine
0.09 ± 0.001 ng ind
-1
h
-1
Respiration 15.0 ± 1.0 pmol O 2 ind
-1
h
-1
7.3
1.2 µJ ind
-1
h
-1
Respiration
w/ emetine
12.7 ± 0.9 pmol O 2 ind
-1
h
-1
6.1
2.3 ± 0.3
146
Table 5. The percent allocation of available energy (measured by respiration rate) to protein
synthesis in larvae of Lytechinus pictus. Developmental stages from Cohort 1 were reared at
15°C in two replicate culture vessels (A, B) to the four-day-old pluteus larval stage. Rates of
respiration and protein synthesis were then measured at four different temperatures (10, 15,
20, 25°C). Respiration (Column I, pmol O 2 larva
-1
h
-1
) was converted to µJ (Column II) using an
oxyenthalpic equivalent of 484 kJ per mole of O 2. Protein synthesis rates (Column III, ng protein
larva
-1
h
-1
) were converted to µJ (Column IV) using the value of 2.3 µJ per ng protein
synthesized (see Table 4). The ratio of these two energy equivalents was calculated to
determine the percent of total available energy allocated to protein synthesis (Column V).
I II III IV V
Cohort
Temperature
(°C)
Respiration
(pmol O 2
larva
-1
h
-1
)
Total
Energy
(µJ)
Protein
Synthesis
(ng
protein
larva
-1
h
-1
)
Protein
Synthesis
Cost (µJ)
% Allocation
to Protein
Synthesis
1A 10
12.4 6.0 0.4 0.9 15%
1B 10 11.6 5.6 0.4 0.9 16%
1A 15 16.4 8.0 0.8 1.8 23%
1B 15 21.6 10.5 0.8 1.8 17%
1A 20 24.2 11.7 1.4 3.2 27%
1B 20 24.3 11.8 1.7 3.9 33%
1A 25 33.2 16.1 2.9 6.7 42%
1B 25 32.7 15.8 2.5 5.8 37%
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CHAPTER THREE
Experimental studies of temperature acclimation on protein synthesis rates
in developing sea urchins
ABSTRACT
The concept of acclimation, the process by which an organism can respond to a change,
is a critical component in understanding the ability of an organism to respond to environmental
change. It is necessary to distinguish between the differential impact on a physiological process
of short-term or long-term periods of acclimation to the environmental variable of interest. In
this study, the physiological process of interest is protein synthesis and the response of that
biosynthetic process to temperature increase. Analyses are presented that compare short-term
acute acclimation periods (minutes) to long-term (days) pre-exposure to elevated temperatures
for larval stages of two species of sea urchin (Lytechinus pictus and Stronglyocentrotus
purpuratus). Acclimation temperatures of 15°C to 20°C were chosen because this is the natural
temperature range of fluctuations experienced by these species in their natural habitats. Each
length of acclimation of up to 72 hours was compared to a simultaneous acclimation period of
only 30 minutes. The major conclusion from these time-dependent analyses of acclimation is
that short, acute exposures to temperature are an accurate reflection of longer exposures. This
finding simplifies the experimental manipulation of rapidly developing marine animals for
studies of physiological responses to temperature.
148
INTRODUCTION
Consequential to understanding the impact of environmental change on the biology of
organisms, is the requirement to understand the degree to which organisms can respond (i.e.
their tolerance). Approaches to understand this central question in biology have included a
wide range of experimental methods that encompass the complicated concepts of
acclimatization, acclimation, and adaptation. Acclimatization is the process of physiological
changes to a new environment in the natural habitat that incorporates multiple factors of
change, whereas acclimation is the process of physiological changes to one specific factor that
can be isolated, and adaptation involves a trait that enhances the fitness of an organism due to
a selective advantage (Hochachka & Somero, 2002). The potential for acclimation to increase
tolerance to temperature change is widely acknowledged from the systematic to cellular level
(Wood & McDonald, 1997; Pörtner, 2002; Logan & Somero, 2011; Collier et al., 2019).
Temperature acclimation is based on both the intensity and the duration of exposure.
Prolonged acclimation time can decrease sensitivity to temperature. The variabilities across
studies in duration of acclimation, stepwise versus abrupt acclimation, and rearing
temperatures, all create challenges for interpretations of temperature stress. It remains of
significant interest to understand the capacity of organisms to acclimate, and to what extent
the ability of acclimation can keep pace with the increases in global temperature (Seebacher et
al., 2015; Pörtner & Farrell, 2008). Physiological studies are necessary to characterize these
capacities and determine the mechanistic causes underlying these responses.
149
The significance of aerobic scope and oxygen limited thermal tolerances is implicated in
acclimation capacity (Pörtner, 2002; Guderley & Pörtner, 2010; Somero, 2011). Distinguishing
between acute (short-term) and chronic (long-term) temperature acclimation will help establish
the impact of acclimation duration on physiological response. Specifically, thermal acclimation
of physiological rate depends on the temperature sensitivity of the species under study (Schulte
et al., 2011). In general, the term acute response refers to studies from exposures of minutes to
days (Horowitz, 2002; Kingsolver et al., 2016; Schulte et al., 2011), and chronic response has
been applied to studies from exposures of weeks to years (Collier et al., 2019)
Many studies have reported instances of acclimation, where a new physiological state
was reached after a prolonged period of temperature change, but the length of time to reach
compensation can take months (Suckling et al., 2015; Nyboer & Chapman, 2017). For instance,
Nile perch, Lates niloticus, that had been acclimated to a range increased temperatures for a
long-term (3-week) period had lower metabolic rates compared to fish exposed for a short-
term (3-day) period (Nyboer & Chapman, 2017). Long term acclimation can increase resilience
and reduce sensitivity to increased temperatures (Stillman, 2003).
Different species, and different populations within species, clearly differ in capacity to
acclimate (Angilletta & Angilletta, 2009; Seebacher et al., 2012). Small changes in the 1-2°C
range can impact physiological performance when the experimental organism is close to its
upper thermal limit (Wood & McDonald, 1997; Collin & Chan, 2016). The effects of temperature
changes as single, repeated, or continuous exposures can modify tolerance (Kingsolver et al.,
2016).
150
Studies have determined that acute (short-term) responses of 30 minutes at different
temperatures yields significantly different physiological rates (Pan et al., 2021; this dissertation:
Chapter 2, Chapter 4) and can be lethal (Edney, 1964). But how are those responses impacted
by increasing lengths of acclimation? The present study determines that neither the white sea
urchin, L. pictus, nor the purple sea urchin, S. purpuratus, are impacted by acclimation ranges in
periods up to 16-hours. In the context of rising ocean temperature, it is important to distinguish
between the differences in short- and long-term exposure to understand how physiological
processes will respond. The experiments presented in this study will help to establish the
effects of acclimation across time scales of physiological response, an important component to
determining resilience to rising environmental temperature.
METHODS
Scale of Experiments
Large-scale larval culturing experiments were undertaken using six different cohorts of
larvae: three cohorts of the white sea urchin, Lytechinus pictus, and three cohorts of the purple
sea urchin, Stronglyocentrotus purpuratus. A total of over 5 million fertilized eggs of L. pictus,
and 5 million fertilized eggs of S. purpuratus were placed in a total of 28 different 20-liter
culture vessels of filtered seawater. Across all experiments, 520 individual samples containing
500 larvae each (a total of 260,000) larvae were measured for total protein content. For assays
151
of protein synthesis, a total of 1,950 individual time-point samples were measured, using a total
of 4 million larvae.
Approach and Rationale
Larvae of L. pictus and S. purpuratus reared at 15°C were transferred at specified times
to a higher temperature of 20°C. To test the possibility that protein synthesis assays conducted
during approximately 30-minute assays were not confounded by the pre-incubation time at the
higher temperature, a series of acclimation times were undertaken up to 3-days (Fig. 1). From
these analyses it can be determined whether increasing acclimation time elicits compensation
in physiological rate. In parallel with the acclimation experiments, larval cultures were reared
for eight days (from fertilization), chronically at both 15 and 20°C.
Experimental Design
For L. pictus, acclimation experiments for each of the three cohorts were initiated with
5-day-old larvae for a 32-hour acclimation duration. Larvae from each cohort were held in four,
20-liter vessels: 1) chronic (from fertilization) at 15°C, 2) chronic (from fertilization) at 20°C, 3)
reared at 15°C and transferred to 20°C for a duration up to 32-hours, i.e. used for testing of
acclimation lengths of 0.5, 1, 2, 4, 8, 16, and 32 hours, and 4) reared at 15°C, transferred to
20°C for a 30-minute acclimation prior to each longer time-point listed in 3).
152
For S. purpuratus, acclimation experiments were conducted with 4-to-8-day old larvae
across cohorts. Experiments were extended for acclimation durations up to 72-hours. Larvae
from each cohort were held in four, 20-liter vessels: 1) chronic (from fertilization) at 15°C, 2)
chronic (from fertilization) at 20°C, 3) reared at 15°C, transferred to 20°C for a duration up to
72-hours, i.e. used for testing of acclimation lengths of 0.5, 1, 2, 4, 8, 16, 32, 48, and 72 hours,
and 4) reared at 15°C, transferred to 20°C for a 30-minute acclimation prior to each longer
time-point listed in 3).
Larval Culturing and Growth
Adult L. pictus and S. purpuratus were induced to spawn by intracoelemic injection of
0.5 M potassium chloride. Three separate cohorts for each species were created from the eggs
and sperm collected from different males and females. Each male and female combination of
gametes was tested by microscopic examination of a small aliquot of sperm and eggs to confirm
greater than 90% fertilization success prior to combining gametes for the creation of cohorts.
All cultures were maintained in a temperature-controlled room using 0.2- m (pore-size) filtered
seawater from a continuous flow through temperature-controlled filtering system at the
Wrigley Marine Science Center, Catalina Island, CA, maintained at 15°C or 20°C. The seawater
temperature in larval culturing vessels was recorded every 30 minutes (digital data loggers,
HOBO U12, Onset Computer Corp., MA, USA), and the seawater in each culture vessel was
changed every other day. At each water change, larvae were drained onto a mesh-sieve and
resuspended in a known volume for volumetric counts of survival. Three aliquots of 100-200
153
larvae were counted from each culturing vessel. Enumerations were used to allocate larvae into
subsequent aliquots for protein content and assays for protein synthesis. Photographs at 40x
magnification were taken of each larval sample for later analysis of morphological size. Size was
measured by midline body length, from the dorsal tip to the oral hood of a larva, calculated
using ImageJ software (National Institutes of Health, Bethesda, MD, USA). Protein content was
determined every other day, and additionally for each acclimation time of 8-hours onwards for
all experimental larval cultures. Protein content was quantified by use of the Bradford assay
(1976), as modified for larval forms by Jaeckle and Manahan (1989). Three to five samples of
500 larvae from each culturing vessel were measured at each sampling interval.
Protein Synthesis
Protein synthesis rates were determined by in vivo time course assays using
14
C-alanine
in seawater as a tracer. Each assay consisted of 10,000 larvae in 10-ml of seawater, in which
2- Ci
14
C-alanine (Perkin Elmer, stock source of 170 µCi µmole
-1
) and non-radioactive alanine
were added to yield a final concentration of 10 M alanine (Sigma Aldrich, St Louis, MO, USA).
The assay was initiated immediately upon addition of alanine into seawater. Larvae were
sampled at exact 4-minute intervals, for five time-points during the 20-minute assay. At each
sampling interval, a 1-ml aliquot was pipetted of 1,000 larvae from the 10-ml assay vial
containing 10,000 larvae. The aliquot of 1,000 larvae was transferred onto an 8- m pore-sized
membrane filter (Nuclepore, GE Healthcare, Pittsburgh, PA, USA), under a low vacuum, and the
154
larvae were rinsed with seawater to remove excess radioactivity. Filters containing larvae were
immediately placed into a microcentrifuge tube on ice and stored at -80°C until processing.
Samples were processed by sonication of larvae with Nanopure water (Barnstead
TM
Nanopure Bioresearch Deionization System, Dubuque, IA, USA) by a Vibra-cell ultrasonic
processor and probe (Sonics & Materials, Inc., Newtown, CT, USA). The protein synthesis rate
was calculated by 1) analyzing the total amount of radioactivity incorporated into
trichloroacetic acid (TCA)-insoluble protein and 2) correcting that amount of radioactivity from
an analysis of the intracellular specific activity of
14
C-alanine in the free amino acid pool of
larvae (see Chapters 1 and 2 for analysis of protein synthesis rates in developing sea urchins).
To calculate the total amount of radioactivity incorporated into TCA-insoluble protein,
TCA was added to samples in Nanopure water to a 5% total concentration and incubated on ice
for at least 30-minutes. Samples were vacuum-filtered onto a GF/C glass microfiber filter
(Whatman grade, Tisch Scientific, North Bend, OH, USA) and rinsed with 5% TCA, then 100%
methanol under gentle vacuum. The filter containing the TCA protein precipitate of
homogenized larvae was transferred to a scintillation vial with 4 ml Ultima Gold scintillation
fluid (PerkinElmer Inc., USA) and the amount of radioactivity was counted (Model LS 6000
Liquid Scintillation Counter, Beckman Coulter Inc., Brea, CA, USA).
To calculate the intracellular specific activity of alanine in the free amino acid pool of
larvae, ethanol was added to samples in Nanopure water to a 70% total ethanol concentration
and incubated at 4°C for at least 24-hours. Samples were centrifuged at 12,000 rpm for five
minutes and analyzed by high performance liquid chromatography (HPLC), as described by Pace
and Manahan (2006). HPLC separation of amino acids enabled quantification of the isolated
155
alanine chromatographic peak, and collection of the peak by fraction collector (Model FC 203B,
Gilson Inc., Middleton, WI, USA) for counts of
14
C radioactivity by liquid scintillation counting.
The rate of incorporation of
14
C-alanine into precipitable larval protein, and correction
for intracellular specific activity in the free amino acid pools, were combined to calculate the
rate of protein synthesis by the following equation:
Equation 1: Protein Synthesis = d/dt(Sp/Sfaa) x MWp/Sm
In this equation from Pace & Manahan (2006), t is time (h), S p is the amount of
radioactivity in the protein fraction of larval tissue, and S faa is the amount of radioactivity in the
free amino acid pool of larval tissue. MW p represents the mole-percent corrected molecular
mass of all amino acids that constituted whole-body protein of a larva, which is 129.4 g mol
-1
for L. pictus (Pace & Manahan, 2006) and 127.4 g mol
-1
for S. purpuratus (Pan et al., 2015). S m
represents the percentage of alanine in the amino acid pool of a larva, which is 7.8% for larvae
of L. pictus (Pace & Manahan, 2006) and 7.9% for larvae of S. purpuratus (Pan et al., 2015).
RESULTS
Chronic Temperature Growth and Physiology
When larval cohorts of Lytechinus pictus were reared chronically from fertilization at
15°C and 20°C, growth was faster at 20°C as measured by midline-body length size (Fig. 2A:
156
ANCOVA slopes of midline body lengths day
-1
F 1,20 = 7.35, p = 0.013) and protein accretion (Fig.
2B: ANCOVA slopes of total protein content day
-1
F 1,53 = 3.89, p = 0.049).
Protein synthesis measurements were performed over a range of ages and sizes for
chronically-reared larvae at 15°C and 20°C. Protein synthesis rates, normalized to mass by the
total protein content of the larvae tested, did not significantly differ between larvae chronically
reared at 15°C or 20°C, when measured at the rearing temperature (i.e. reared at 15°C and
measured at 15°C) for larvae of L. pictus (Fig. 3, ANCOVA of fitted line F 1,7 = 0.47, p = 0.5).
Impact of Varied Temperature Acclimation Durations
Larvae of L. pictus were reared for acclimation durations up to 32-hours; each
acclimation length was compared simultaneously with a 30-minute acute acclimation from 15°C
to 20°C. During the 32-hour acclimation experiments, protein content did not significantly differ
between the temperature acclimation durations for any of the three cohorts tested (Fig. 4A, C,
E: see legend for ANCOVA regression statistics). Notably, for the three cohorts tested, all
showed a similar pattern that short acute exposures to temperature resulted in similar rates as
larvae that were acclimated for up to 32-hours (Fig. 4B, D, F: see legend for ANCOVA regression
statistics). These data for L. pictus support the conclusion that experiments on protein synthesis
can be conducted effectively with short acute exposures to the temperature of experimental
interest.
A similar experimental design was performed for larvae of Stronglyocentrotus
purpuratus, for acclimation durations up to 72-hours. During the course of the acclimation
157
experiments, protein content did not significantly differ between the temperature acclimation
durations for any of the three cohorts tested, up to 16-hours (Fig. 5A, C, E: see legend for
ANCOVA regression statistics). For larvae of S. purpuratus, each cohort did exhibit differences in
protein synthesis with increased acclimation durations, after 16-hours (Fig. 5B, D, F). For
example, Cohort 1 did not show a significant difference between acclimation durations of
30-minutes compared to longer lengths up through 16-hours (Fig. 5B: see legend for ANCOVA
regression statistics for specific varied acclimation lengths). At 32-hours and 72-hours, the
protein synthesis rates of larvae transferred to 20°C for those lengths of time, were significantly
higher than larvae from the same cohort measured simultaneously at those time points, but
transferred to 20°C just 30-minutes prior to measurements (Fig. 5B: see legend for ANCOVA
regression statistics for specific varied acclimation lengths). These data for S. purpuratus
support the conclusion that short acute exposures to temperature adequately represent rates
of protein synthesis for up to 16-hours for the temperature difference tested (15 to 20°C).
Relationship of rate of
14
C-alanine incorporation into trichloroacetic acid (TCA)-precipitable
protein as a predictor for absolute rates of protein synthesis
Estimates of protein synthesis rates shown in Fig. 5B, D, F are reported in terms of
trichloroacetic acid (TCA)-precipitable protein for larvae of S. purpuratus. This relationship
between radioactivity in the TCA-precipitate and measured rates of protein synthesis was
shown to be highly correlated with a R
2
of 0.96 (Fig. 6). These data were analyzed for larvae
from both acclimation treatments in Cohort 1, using a subset of 16 protein synthesis assays (at
158
acclimation lengths of 0.5, 4, 16, and 72-hours), totaling 80 samples of measurements. Thus,
the resulting data can be interpreted with confidence to absolute rates of protein synthesis.
DISCUSSION
Prolonged acclimation time has been often shown to decrease sensitivity to
temperature (Schmidt-Nielsen, 1997, p.237). In this study, varied lengths of acclimation had no
measurable impact on the rates of protein accretion or synthesis during a series of 32-hour
acclimation experiments for larvae of Lytechinus pictus. Increasing acclimation duration
increased sensitivity of protein synthesis, normalized to protein accretion for larvae of
Stronglyocentrotus purpuratus, but not until 32-hours of acclimation time. These experiments
provide support that short, acute experimental acclimation durations are an accurate reflection
of longer exposures for analyses of protein synthesis rates of sea urchin larvae at the
temperatures of interest (Figs. 4 and 5).
Modification of Thermal Tolerance
All species have different thermal tolerances based on geographic range, physiology,
and adaptation potential. Specifically, to respond to environmental change, animals either alter
their physiological rates, show long-term adaptation, or migrate (Peck, 2005; Peck et al., 2010;
Somero, 2012; Applebaum et al., 2014). Organisms have previously been shown to increase
thermal tolerance based on acclimation to higher temperature (Stillman, 2003; Somero, 2010;
159
Sorte et al., 2011; Kingsolver et al., 2016). The importance of oxygen limitations on thermal
tolerance in supplying energy for organism functioning has been often reported (Pörtner &
Knust, 2007; Pörtner et al., 2017). With increasing ocean temperatures, the ability of
acclimation to maintain homeostasis is obviously an important issue. Flexible changes in
thermal physiology can enhance the ability of an organism to maintain homeostasis at
increasing temperature.
The major question addressed in the current study is the degree to which experiments
need to be designed reflecting the much-debated limitations of acute exposures. The findings
presented in the current study directly addressed this issue, specifically for protein synthesis
rates in sea urchin larvae. Since acclimation durations of up to 32-hours for L. pictus, and
16-hours for S. purpuratus did not confound the interpretations of short-term acute exposures,
this study provides a valuable contribution to the large literature on the topic of the design of
temperature sensitivity experiments (e.g. measurement of Q 10 values).
160
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165
FIGURES
Figure 1. Experimental design used for testing the length of acclimation on temperature
sensitivity for larvae of Lytechinus pictus and Stronglyocentrotus purpuratus. Larvae were
reared at 15°C until the initial acclimation period at which time larvae were transferred to 20°C.
One subset of larvae (open symbols) was then held continuously at 20°C, with larvae removed
for sampling at varied time-lengths beginning at 30-minutes, and up to 72-hours (x-axis). The
other subset of larvae (closed symbols) was transferred from 15 to 20°C exactly 30-minutes
prior to each longer acclimation time-length assay. This design enabled direct assessments of
an acute 30-minute acclimation time compared to increasing acclimation lengths.
166
Figure 2. Size and protein content of developing larvae of Lytechinus pictus.
(A) Size of midline body length for larvae of Lytechinus pictus reared chronically from
fertilization at 15°C (closed symbols) and 20°C (open symbols). Cohort 1 (circles – replicate
culture vessels), Cohort 2 (triangles), and Cohort 3 (squares) did not differ in size by midline
body length over the course of these experiments when reared at the same temperature,
hence are plotted on a single regression line. Growth by size was significantly greater for larvae
reared at 20°C compared to 15°C (ANCOVA slope F 1,20 = 7.35, p = 0.013). Error bars are ± S.E.M.;
N = 50 per data point.
(B) Protein content of larvae reared chronically from fertilization at 15°C (closed symbols) and
20°C (open symbols) for Cohort 1. There was no difference in protein content between the two
replicate culture vessels of Cohort 1, hence they are plotted on a single regression line. Protein
accretion was significantly greater for larvae reared at 20°C compared to 15°C (ANCOVA slope
F 1,53 = 3.89, p = 0.049). Error bars are ± S.E.M.; N = 5 per data point.
167
Figure 3. Larvae of Lytechinus pictus have no significant differences in protein synthesis rates
normalized to total protein content when chronically reared at 15°C or 20°C (ANCOVA of fitted
line F 1,7 = 0.47, p = 0.5). Error bars ± S.E. slope (y-axis [N = 2 time-course assays]) and ± S.E.M.
(x-axis [N=5]).
168
Figure 4. Protein content (A, C, E) and protein synthesis rates (B, D, F) for three cohorts of
larvae of Lytechinus pictus at increasing lengths of acclimation time from 15°C to 20°C. Larvae
from each cohort (Cohort 1: A, B; Cohort 2: C, D; Cohort 3: E, F) were transferred from 15°C to
169
20°C for time points indicated on the x-axis (open symbols), and each compared to a 30-minute
acute acclimation time (closed symbols). Protein content did not significantly change between
the acclimation treatments for the duration of the acclimation experiment. Protein synthesis
rates did not differ significantly across any of the treatments of increased acclimation time
compared to a shorter 30-minute acute acclimation.
(A, Cohort 1) Protein content between the acclimation treatments for the duration of the
acclimation experiment was not significantly different (ANCOVA slope F 1,4 = 0.48, p = 0.5).
(B, Cohort 1) Protein synthesis between the acclimation treatments for the duration of the
acclimation experiment was not significantly different (ANCOVA slope F 1,24 = 0.01, p = 0.9).
(C, Cohort 2) Protein content between the acclimation treatments for the duration of the
acclimation experiment was not significantly different (ANCOVA slope F
1,2
= 0.52, p = 0.5).
(D, Cohort 2) Protein synthesis between the acclimation treatments for the duration of the
acclimation experiment was not significantly different (ANCOVA slope F 1,11 = 0.03, p = 0.9).
(E, Cohort 3) Protein content between the acclimation treatments for the duration of the
acclimation experiment was not significantly different (ANCOVA slope F 1,4 = 0.02, p = 0.9).
(F, Cohort 3) Protein synthesis between the acclimation treatments for the duration of the
acclimation experiment was not significantly different (ANCOVA slope F 1,14 = 0.01, p = 0.9).
170
Figure 5. Protein content (A, C, E) and rates of trichloroacetic acid (TCA)-insoluble protein
incorporation (B, D, F) for three cohorts of larvae of Stronglyocentrotus purpuratus at increasing
lengths of acclimation time from 15°C to 20°C. Larvae from each cohort (Cohort 1: A, B; Cohort
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2: C, D; Cohort 3: E, F) were transferred from 15°C to 20°C for time points indicated on the x-
axis (open symbols), and each compared to a short 30-minute acute acclimation time (closed
symbols).
(A, Cohort 1) Protein content between the acclimation treatments for the duration of the
acclimation experiment was not significantly different (ANCOVA slope F 1,8 = 5.3, p = 0.05).
(B, Cohort 1) Rates of incorporation of
14
C-alanine into the TCA-protein fraction across the
different acclimation treatments for the duration of the experiment was significantly different
(ANCOVA slope F 1,32 = 24.6, p < 0.0001).
Specific ANCOVA comparisons of rates based on acclimation time comparisons (numerals below
are depicted at time-point on graph):
(i) pre-exposure of 0.5 h F 1,16 = 0.73, p = 0.4 (NS)
(ii) pre-exposure of 4 h F
1,16
= 0.39, p = 0.5 (NS)
(iii) pre-exposure of 16 h F 1,16 = 9.49, p = 0.27 (NS)
(iv) pre-exposure of 32 h F 1,16 = 66.07, p = 0.000389
(v) pre-exposure of 72 h F 1,16 = 63.14, p = 0.000001
(C, Cohort 2) Protein content between the acclimation treatments for the duration of the
acclimation experiment was not significantly different (ANCOVA slope F 1,6 = 0.69, p = 0.4).
(D, Cohort 2) Rates of incorporation of
14
C-alanine into the TCA-protein fraction across the
different acclimation treatments for the duration of the experiment was significantly different
(ANCOVA slope F 1,28 = 17.5, p = 0.0003).
(E, Cohort 3) Protein content between the acclimation treatments for the duration of the
acclimation experiment was not significantly different (ANCOVA slope F 1,4 = 1.2, p = 0.3).
(F, Cohort 3) Rates of incorporation of
14
C-alanine into the TCA-protein fraction across the
different acclimation treatments for the duration of the experiment was significantly different
(ANCOVA slope F 1,24 = 10.1, p = 0.004).
172
Figure 6. Analysis of the relationship of the amount of
14
C-alanine incorporated into
trichloroacetic acid (TCA)-precipitable protein as a predictor of absolute rate of protein
synthesis in larvae of Stronglyocentrotus purpuratus. The relationship between
14
C-alanine
incorporated into TCA-precipitable protein (x-axis) and absolute protein synthesis rates (y-axis).
Absolute protein synthesis rates were corrected for 1) the specific activity of
14
C-alanine in the
free amino acid pool of larvae, 2) the 7.9% alanine present in the total amino acid pool, and 3)
the mole-percent corrected molecular mass of all amino acids of 127.4 g mol
-1
(see Equation 1;
specific to S. purpuratus). The predictability of this relationship resulted in an R
2
value of 0.96. N
= 16 data points, each data point represents the slope of a five-point time-series assay, x- and y-
error bars ± S.E. of the slope.
173
CHAPTER FOUR
Thermal sensitivities of respiration and protein synthesis differ among larval families of the
Pacific Oyster, Crassostrea gigas
ABSTRACT
Understanding biological and physiological responses to environmental change is a
major challenge with rising temperature. Organisms may be able to compensate for increasing
temperatures by shifting their metabolic allocation of energy. By evaluating physiological
responses to temperature increase using twelve different larval families of the Pacific oyster,
Crassostrea gigas, the present study determined differences in temperature sensitivities (Q 10
value) for physiological process of both energy supply and energy demand, integrated at the
level of the whole organism. Energy supply was measured via respiration rate, converted to
total amount of energy available. Energy demand was measured via protein synthesis rates, a
metabolically costly process that is well established to account for a large proportion of the
energy budget of marine invertebrate larvae. These differences were assessed throughout
larval development and were consistent by family, regardless of size. Of particular significance
is that one larval family was found to function at physiological homeostasis (with respect to
cellular energy allocation) at increased temperature, by having both increased energy supply,
via a high temperature sensitivity of respiration, and also a lowered energy demand, via a low
temperature sensitivity of protein synthesis, compared to the other larval families analyzed.
174
These findings revealed distinctions among families that have variability in physiological
functioning, dependent on temperature. Combined genetic and physiological studies have
important implications for determining resilience to environmental change. Specifically for
bivalves of commercial interest, understanding such variance could contribute a significant
impact on improving the aquaculture industry.
INTRODUCTION
Temperature has a large influence on animal physiology. In response to increases in
temperature, the physiological rates of organisms increase. Organisms differ in their range of
temperature tolerances across latitudes, depths, and throughout life stages (Schmidt-Nielson,
1997; Pörtner, 2002; Somero 2010). For instance, early developmental stages, such as larvae,
are often more vulnerable to temperature and other environmental perturbations, than adults
(Dupont et al., 2010; Ross et al., 2011). Research on fish populations for over 100 years,
beginning with Hjort (1914), have established that early developmental stages are the most
important factor in determining the yield successes of fish classes, and have since been
extended to include physiological and biochemical explanations of this vulnerability in early
development stages (Pankhurst & Munday, 2011; Dahlke et al., 2020). One mechanism for
varied thermal tolerance within a life span is due to the relationship between aerobic oxygen
capacity and demand (Pörtner & Knust, 2007; Dahlke et al., 2020).
Understanding how animals can adjust physiological functioning in response to
temperature will enable predictions on resilience to climate change (Seebacher et al., 2015).
175
While most studies focus on increases in metabolic rate in response to temperature, one major
question remains as to which physiological systems are responsible for setting thermal limits
(Somero, 2010). Recent research has determined that there are differences in temperature
sensitivity specific to individual physiological processes (Pan et al., 2021). Genotype-
environment interactions remain less understood for temperature responses. In the present
study, the impact of rising temperature on physiological processes was investigated using a
series of larval families generated from crosses of adults of known genotype.
Metabolic rate and protein synthesis rates are two of the most critical processes for the
energetic budget of marine invertebrate larvae. Metabolic rate, as measured by oxygen
consumption, can be converted by oxyenthaplic equivalents (Gnaiger, 1983). For small
organisms, measured under oxygenated conditions (normoxic), the total amount of energy can
be converted into universal energy units (Joules) from rates of oxygen consumption (Hand,
1999). Protein synthesis is one of the most energetically costly processes of a marine
invertebrate larva, at times utilizing over 50% of the total energetic budget (e.g. white sea
urchin, Lytechinus pictus, Pace & Manahan, 2006; purple sea urchin, Strongylocentrotus
purpuratus, Pan et al., 2015a; Pacific oyster, Crassostrea gigas, Frieder et al., 2018). The
thermodynamic cost of synthesizing a unit of protein for these larval species has been
previously determined and, importantly, this cost is fixed and independent of environment,
genotype, and phenotype for larvae of C. gigas (Lee et al., 2016). This allows for the total
amount of energy allocated towards protein synthesis to be calculated. Proteins are constantly
being degraded and recycled in organisms, and the functioning of protein synthesis is essential
for maintaining homeostasis and growth under changing environmental conditions (Hawkins,
176
1991; Tsukada & Ohsumi, 1993; Vavra & Manahan, 1999; Frieder et al., 2018). The process of
protein turnover is a very important one in biology; the work of Ohsumi referenced above was
awarded the 2016 Nobel Prize in Physiology or Medicine.
Environmental stress can cause changes in the allocation of energy for marine
invertebrate larvae. Larvae of S. purpuratus significantly increase metabolic allocation towards
supporting the cost of increased protein synthesis and turnover under conditions of ocean
acidification (Pan et al., 2015a). Notably, Pan et al. (2015a) found no effects of ocean
acidification on growth, metabolic rate, or biochemical composition, underlining the
significance of protein metabolic dynamics as a physiological response to stress. Similarly,
larvae of C. gigas also increase rates of protein synthesis and turnover under increased ocean
acidification (Frieder et al., 2018). Frieder et al. (2018) determined two strategies used by
larvae to support the increased demand for protein synthesis: reallocating a larger portion of
the available energy, or, increasing metabolic rate to increase the amount of total energy
available. This concept has been further analyzed for larvae in response to temperature
sensitivities, where temperature sensitivity (Q 10 values) for respiration was shown to have a
lower Q 10 than protein synthesis (Pan et al., 2021). This has negative implications for larvae, as
rising energy costs increase at a greater rate than the ability to increase the metabolic supply of
energy. There may, however, be sufficient biological variance within a population of a given
species that could confer resilience to stress. This is the focus of the present study.
Physiological responses can change due to either genotype or environment (nature and
nurture). Under the same environment, of either controlled or ocean acidification treatments,
differences in growth have been observed among different larval families of C. gigas (Pan et al.,
177
2015b; Frieder et al. 2016). Thus, it is hypothesized that there may be family differences in
temperature sensitivity (Q 10 values) for the physiological processes of respiration and protein
synthesis. If these family differences exist for both respiration and protein synthesis
temperature sensitivities, then it can be established whether some larval families are optimally
equipped to cope with increased temperature, while others are sub-optimal and cannot
respond physiologically to stress. The physiological mechanisms of resilience, and
determination of families that perform optimally under increased temperature is of importance
to enhancing the aquaculture industry. Selecting and breeding of families that thrive under
increased temperature will be of value to generate large yields for industries growing shellfish
in warmer oceans.
METHODS
Experimental Scale
A total of 24 million fertilized eggs were used to generate 12 larval families for the
experiments presented (2 million gametes per family, each family in a 200-liter culturing
vessel). Of the adult pedigreed lines of Crassostrea gigas, started in 2016 by crosses of wild-
caught oysters from Willapa Bay, WA (Fig. 1), individual males and females were confirmed by
genotyping the adult broodstock, using a panel of DNA markers developed by Sun et al. (2015)
and maximum likelihood assignment methods (Kalinowski et al. 2007). Gametes obtained from
these adults were crossed to produce either a new set of larval families (G 0) or first generation
178
(G 1) larval families. This analysis resulted in four G 1 families and eight other larval families (G 0)
that were produced from single crosses of an individual male and female of mixed genetic
pedigree. Throughout these experiments over 3,900 time-point respiratory measurements
were conducted that resulted in 28 Q 10 values for the temperature sensitivity of respiration in
multiple larval families, using over 200,000 larvae. An additional set of four Q 10 values for the
temperature sensitivity of protein synthesis were generated for two of the families, using a
total of 160,000 larvae.
Larval Culturing
All larval families were reared in the custom designed, large scale culturing facilities at
the Wrigley Marine Science Center on Catalina Island, CA. The seawater used to culture these
larval families was from the pristine, offshore ocean waters surrounding Catalina. This ambient
seawater was filtered down to 0.2-µm (pore-size) and heated with inert titanium heat
exchangers to the required experimental temperatures. Eggs and sperm were directly removed
using Pasteur pipettes from the gonads of gravid adult oysters. Eggs were placed into 1-liter of
seawater for at least one hour to confirm egg quality (microscopic observation of egg
rounding). Aliquots of sperm were pipetted into micro-centrifuge tubes and kept “dry” (no
seawater added) on ice at 4°C. Once egg quality was confirmed, seawater was added to activate
sperm prior to fertilization. The fertilization success of eggs from each family was confirmed by
observation of first polar body formation. Fertilized eggs were stocked at an initial
concentration of 10 ml
-1
in 200-liter culture vessels. During the larval rearing process, the
179
seawater in each culture vessel was completely replaced every other day by condensing larvae
onto mesh sieves. Over the course of the experiments, temperature was maintained stably at
25°C; temperature was measured every 30 minutes (HOBO data logger U12, Onset Computer
Corp., MA, USA) in selected 200-liter culture vessels. A total of 2,439 temperature
measurements were made yielding an average temperature of 24.9°C ± 0.01 (S.E.M). At this
temperature, the first larval feeding stage (D-hinge veliger larva) developed by two days. The
alga Isochrysis galbana (T-ISO strain) was then added to each larval culture vessel at a ration of
30,000 cells ml
-1
. Algal ration was later increased to 50,000 cells ml
-1
when larvae increased in
size (per standard culturing protocols described by Breese & Malouf, 1975; Helm & Bourne,
2004, for larvae of C. gigas).
Growth Rate and Size
Growth and survival of larvae from each of the families were evaluated throughout the
course of the experiments. Survival was based on enumerations of aliquots of larvae that were
size-selected using appropriate mesh sieve and resuspended in a volume of 200-500 ml
(dependent on numbers of larvae collected from each culture vessel). Growth rate was
measured from the shell lengths of larvae over time. Larvae were photographed at 40x
magnification on Sedgewick Rafter counting chambers, with 1-2 drops of ethanol added for
immobilization. At least 50 larvae at each specific time point selected within each culture vessel
were measured using ImageJ (National Institutes of Health, Bethesda, MD, USA) software for
shell length analyses. Enumerations were completed of larvae (to coefficient of variations less
180
than 10%) for subsequent allocation to assays of respiration, amino acid transport, and protein
synthesis rates.
Metabolic Rate and Q 10
Metabolic rate of larvae was measured as oxygen consumption over time, using optode
technology (Witrox-1 Oxygen Meter, Loligo Systems). The optode technology was previously
calibrated and determined consistent with respirometry measurements using polarographic
oxygen sensors for larvae of C. gigas (Pan et al., 2021). A known number of larvae, between 250
and 400, size-dependent, were placed in micro-Biological Oxygen Demand (µBOD) glass vials,
each individually calibrated for volume, between 400-700 µl. Each vial has a 2-mm diameter
sensor spot (Presens, Regensburg, Germany), in which a fiber optic cable is non-invasively
pressed against for each measurement of oxygen amount, permitting time course assays for
each individual µBOD respiration vial. For each larval family on a given day, larval consumption
of oxygen was measured in 7-10 replicate µBOD respiration vials over a range of selected
temperatures (14-28°C). Oxygen depletion in each vial was measured 4-5 times over a time
course of approximately 3 hours. Control µBOD vials containing just filtered 0.2 µm (pore-size)
seawater, without larvae, were included at each temperature. A linear regression of decreasing
oxygen amounts over time, corrected for µBOD volume and number of larvae per vial
quantified respiration rate as pmol O 2 larva
-1
h
-1
(Fig. 2A). This process was repeated for each of
the four experimental temperatures (Fig. 2B). Assays from each of the four temperatures were
181
used to determine the Q 10 of respiration (Fig. 2C), using the following equations to calculate
Q 10:
Equation 1A: 𝑄 10
= (
𝑅 2
𝑅 1
)
10
𝑇 2−𝑇 1
Equation 1B: 𝑙𝑜𝑔 𝑅 2
= 𝑙𝑜𝑔 𝑅
1
+
𝑇 2−𝑇 1
10
× 𝑙𝑜𝑔 𝑄 10
Equation 1C: Q 10 = 10
slope
In these Q 10 equations, the rate, R, and temperature, T, correspond to two separate
rates at two temperatures, 1 and 2. In the original equation (Equation 1A) the Q 10 value is
limited to two rates at two temperatures. In the current experiment, replicates were tested at
four temperatures, and thus need to be log transformed to include all data points (Equation
1B). After log transformation, the Q 10 value becomes equal to 10
slope
of the regression between
physiological rate measurement and temperature (Equation 1C).
Amino Acid Transport Rate and Q 10
Amino acid transport rates were determined by in vivo time course assays using
14
C-glycine, as previously used for larvae of C. gigas (Lee et al., 2016; Pan et al., 2018; Pan et al.,
2021).
14
C-glycine was chosen for the radioisotope based on the composition of the intracellular
free amino acid pool. Each protein synthesis assay was measured in duplicate vials, each
182
containing 10,000 larvae in 10 ml of filtered seawater. Addition of 74 kBq of
14
C-glycine,
supplemented with non-radioactive glycine to 10 µM total glycine concentration, initiated the
assay. A small aliquot of seawater was taken at the start of each assay to quantify the specific
activity of
14
C-glycine in each experimental vial. The specific activity was used to correct for the
amount of total glycine transport from seawater into larvae. Samples of 1 ml, containing
approximately 1,000 larvae, were pipetted every 8 minutes for a 6-time point, 48-minute assay.
Samples were pipetted onto an 8 µm pore-size (Nuclepore, GE Healthcare, Pittsburgh, PA, USA)
membrane filter on vacuum, and rinsed with seawater to remove excess radioactivity. Filters
were immediately placed in tubes on ice, and frozen at -80°C until processing.
Frozen larvae were processed by adding 400 µl Nanopure water (Barnstead
TM
Nanopure
Bioresearch Deionization System, Dubuque, IA, USA) to each sample tube and homogenized
with a Vibra-cell ultrasonic processor and probe (Sonics & Materials, Inc., Newton, CT, USA) for
two 15-second intervals. A 15 µl aliquot of the homogenate was put directly into scintillation
vials with 4 ml scintillation fluid (Ultima Gold
TM
, Perkin Elmer), and counted with appropriate
quench correction (Beckman Coulter Liquid Scintillation Counter, Model 6500). The rate of
glycine transport was calculated from the slope of
14
C designations per minute (DPM, as
quantified by scintillation count) over time, corrected by the starting specific activity of each
assay vial and the number of larvae per sample. Duplicate assays were replicated at four
temperatures to determine Q 10 value. ANCOVA was used to compare Q 10 values within and
between families.
183
Protein Synthesis Rate and Q 10
Protein synthesis rates were calculated from the same in vivo time course assay as
amino acid transport rate, above. Of the 400 µl larval homogenate samples from each time
point of the time course assay as described above, a portion was used to determine the amount
of
14
C-glycine that became incorporated into protein, and a portion was used to determine the
amount of intracellular specific activity of
14
C-glycine in the free amino acid pool of larvae.
Combining, 1) the analyses of the rate of incorporation of
14
C-glycine into the trichloroacetic
acid (TCA)-precipitable protein fraction, and 2) the intracellular specific activity of
14
C-glycine in
the free amino acid pool, was used to calculate rates of protein synthesis.
To determine the amount of
14
C-glycine that became incorporated into protein, 300 µl
of the larval homogenate was combined with trichloroacetic acid (TCA) in Nanopure water to a
final concentration of 5%. After incubation on ice for greater than 30 minutes, samples were
vortexed and pipetted onto a GF/C glass microfiber filter (Whatman, Tisch Scientific, North
Bend, OH, USA), onto a vacuum, rinsed with 5% TCA and methanol, and transferred into
scintillation vial with fluid for counting.
The intracellular specific activity of
14
C-glycine was determined by high performance
liquid chromatography (HPLC). A 42 µl aliquot from the 400 µl homogenate (same as above),
was combined with 100 µl HPLC-grade ethanol for a final concentration of 70%. Samples were
incubated at 4°C for at least one day, and centrifuged for 5 minutes at 12,000 rpm prior to
analyses by HPLC, as described by Lee et al. (2016). In brief, HPLC separated the moles of
glycine in each sample, and the chromatographic peak for glycine was collected by fraction
184
collector (Model FC 203B, Gilson Inc., Middleton, WI, USA) into a scintillation vial for
quantification of the amount of
14
C-glycine in the free amino acid pool of larvae.
Protein synthesis was calculated from the following equation:
Equation 2: Protein Synthesis = d/dt(S p/S faa) x 126.6/12
In this equation, the value 12 is the percent glycine in the whole-body protein content of
the protein amino acid composition in larvae of C. gigas; the value 126.6 is grams per mole of
the mole-percent corrected molecular mass of all amino acids comprising the whole-body
protein content of larvae (Lee et al., 2016). S p represents the amount of radioactivity in the
protein fraction of larval tissue, and S faa represents the amount of radioactivity in the free
amino acid pool of larval tissue, in time, t. The slope of increasing protein synthesized over each
time point of the assay, was measured in duplicate for a given family at a given temperature
(Fig. 3A). This process was repeated for four total temperatures, with the linear regression of
the slope at each temperature used for determination of a single rate at each temperature (Fig.
3B). The duplicate assays at four temperatures were log transformed for analyses of Q 10, as
described above (Fig. 3C). ANCOVA was used to compare Q 10 values within and between
families.
185
Allocation of Metabolic Energy Towards Protein Synthesis
The measurements of metabolic rate and protein synthesis can be converted to
energetic equivalents for calculation of percent of total available energy allocated towards
protein synthesis. The cost of protein synthesis has been previously determined for larvae of C.
gigas to be 2.1 ± 0.2 (S.E.M.) µJ per ng of protein synthesized (Lee et al., 2016). Of importance,
this cost was shown to be fixed regardless of genotype, phenotype, or temperature (Lee et al.,
2016). The amount of total energy available is determined by converting respiration rate into
Joules, using the oxyenthalpic equivalent determined by Gnaiger (1983) to be 484 kJ per mole
of oxygen.
RESULTS
Measurements of Family-specific Differences in Respiration and Protein Synthesis
The temperature sensitivity of respiration rate for larvae of different families was
different, as measured by different Q 10 values (Fig. 2). Fig. 2A shows the replication of
measured metabolic rates in a series of independent micro-Biological Oxygen Demand (µBOD)
vials for larval Family 56B. Also shown in this figure are the control values for the insignificant
change in oxygen amount in µBOD vials with no larvae present (seawater only). All of the
independent rates of oxygen depletion shown in Fig. 2A are graphed as a single point with
appropriate measurement error in Fig. 2B (data point for temperature of 24°C). Similar sets of
186
respiration analyses were also conducted on larval Family 56B, but at different temperatures,
and are graphed in Fig. 2B. These data were then used to calculate a Q 10 value for larval Family
56B, which had a Q 10 value of 2.8 ± 0.1 (S.E.) (Fig. 2C).
Figures 2D, E, F illustrate a similar analysis for an additional larval family, Family 60.
Figure 2F shows the calculation of the Q 10 value for larval Family 60, which is 1.7 ± 0.05 (S.E.).
An ANOVA of the comparison of the regressions of the log values of the change in respiration
rates with increasing temperature shown in Figure 2C and 2F for larval Family 56B and 60,
respectively, revealed that these slopes (i.e. Q 10 values) are significantly different (ANCOVA F 1,52
= 57.4, p < 0.00001). This analysis shows that there is a significant difference between larval
families with respect to response to temperature, with Family 60 having a lower sensitivity to
temperature rise than Family 56B. Specifically, these differences in Q 10 values would result in a
1.6-fold difference (the ratio of Q 10 values 2.8 to 1.7) in respiration rate for a larva experiencing
a 10°C increase.
The change in the rate of protein synthesis with increasing temperature was measured
for Family 59 and 60 (Fig. 3). Protein synthesis, like respiration, also showed significant family
differences in response to temperature. Figure 3A illustrates the rate of protein synthesis
measured in duplicate assays for Family 59 at a temperature of 24°C. A single regression line is
shown since the two replicate assays were not significantly different (see Figure 3A legend for
ANCOVA statistics). This rate of synthesis at 24°C was repeated for the range of temperature
shown in Fig. 3B, yielding a Q 10 value for protein synthesis in larval Family 59 of 1.7 ± 0.05 (S.E.
slope) (Fig. 3C). This series of analyses (Fig. 3D, E) was repeated for larval Family 60, which had
a Q 10 value of 3.1 ± 0.1 (S.E. slope) (Fig. 3F). A statistical comparison by ANCOVA regression
187
revealed that there is a difference between the Q 10 value of 1.7 for larval Family 59 and Q 10
value for 3.1 for larval Family 60 (ANCOVA F 1,12 = 42.95, p = 0.00003). For protein synthesis,
there is a 1.8-fold difference (the ratio of Q 10 values 3.1 to 1.7) in the rate of protein synthesis
for a 10°C rise in temperature between larval Family 59 and 60.
Measurements of Respiration Q 10 Values for Multiple Families
Respiration Q 10 values were determined for larvae of different sizes (shell-length range
from 80 to 280 µm) for all twelve families studied (Fig. 4). Notably, the Q 10 values within a larval
family did not change during growth, since at any given size the Q 10 values were similar (Fig. 4).
Differences in Q 10 values seen between families (i.e. Family 56 and Family 60), were repeatable
throughout larval development at different ages and sizes (ovalized circles, Fig. 4). Specifically,
ANCOVA linear regressions for Family 56 and 60, which represent the highest and lowest family
Q 10 values, show no change in Q 10 values with increasing size (Family 56 ANOVA Regression F 1,4
= 0.014, p = 0.9; Family 60 ANOVA Regression F 1,2 = 0.03, p = 0.9). The average Q 10 value across
all families was 2.2 ± 0.1 (S.E.M.) (Figs. 4, 5). Respiration measurements to determine Q 10 values
across the twelve families were confirmed by repeating temperature sensitivity assays across
different larval growth sizes (e.g., Family 56, five different larval sizes were measured).
The average Q 10 value across all larval families studied was 2.2 ± 0.1 (S.E.M.) (Fig. 5).
Within each larval family, the Q 10 values were consistent (Fig. 5). There was an important
variance to the mean Q
10
value of 2.2, and this variance was biological and not due to
experimental error. This conclusion was reached from a Tukey’s HSD pairwise slope comparison
188
following ANOVA (see legend of Fig. 5 for statistics), which revealed that larval Family 56 had a
significantly higher respiration Q 10 of 2.9 ± 0.07 (S.E.M.) compared to all other families tested.
In contrast, larval Family 60 had a significantly lower respiration Q 10 of 1.6 ± 0.05 (S.E.M.)
compared to Family 56 that had the highest Q 10 (2.9), and Family 59 that had a Q 10 value of 2.4
± 0.06 (S.E.M.).
Contrasts in Q 10 Values for Protein Synthesis and Respiration in Specific Larval Families
The analysis of differences in Q 10 values for respiration highlighted significant
differences between larval Family 59 and 60 (Fig. 5). These two larval families were chosen for
further analyses of the temperature response of protein synthesis. Figure 6 shows the
difference in Q 10 values of protein synthesis for larval Family 59 and 60. This comparison of
families was based on larvae of similar size, averaging 147 µm [Family 59, 148 µm ± 1.3 (S.E.M.);
Family 60, 146 µm ± 1.4 (S.E.M.)]. Larval Family 60 had a Q 10 value of 3.1 ± 0.1 (S.E. of the
slope), in contrast to larval Family 59, that had a Q 10 of 1.7 ± 0.3 (Fig. 6A). These Q 10 values were
significantly different (see Figure 6A legend for ANCOVA statistics).
The corresponding changes in Q 10 values for respiration in these same two families (59
and 60) are shown in Fig. 6B. Larval Family 59 had a higher temperature sensitivity (Q
10
= 2.4 ±
0.07) compared to Family 60, which had a significantly lower sensitivity (Q 10 = 1.7 ± 0.01) (Fig.
6B). An important conclusion illustrated in Figure 6 is that there was a physiological contrast
between larval Families 59 and 60 with respect to the supply of energy (respiration) and the
demand for energy (protein synthesis), which alternated in each family.
189
The above analysis highlights the physiological contrast amongst families with respect to
respiration and protein synthesis. As a “control” for these contrasts, a further physiological
variable was measured. Specifically, the rate of transport of glycine from seawater was
equivalent, with a Q 10 value of 1.8 ± 0.2 (S.E. slope) for the two larval families studied (59, 60).
In Figure 7, a single regression line is shown (see Fig. 7 legend for ANCOVA of regression
slopes).
Differing Energy Allocation Across Larval Families in Response to Temperature
The respiration and protein synthesis values determined for Families 59 and 60, across
the range of temperatures tested, has significant consequences for energy allocation and
availability (Fig. 8). In this analysis, the rates of respiration and protein synthesis were
converted into energy equivalents to compare energy supply (respiration) to energy demand
(protein synthesis). The total area illustrated by each pie-chart represents the total energy from
respiration, calculated by converting moles of oxygen to joules, using an oxyenthalpic
equivalent of 484 kJ (mole of O 2)
-1
(Gnaiger, 1983). The percent allocation to protein synthesis
(the gray area in each pie chart) is calculated by converting the measured synthesis rate to
Joules, using an energy equivalence of 2.1 µJ (ng protein synthesized)
-1
(Lee et al., 2016). For
example, for Family 59 at 18°C, the allocation to protein synthesis was 42%. This is calculated as
follows: (A) respiration rate of 680 pmol O 2 larva
-1
day
-1
(based on 28.3 pmol O 2 larva
-1
h
-1
from
Fig. 6B) converts to 329 µJ day
-1
(680*484/1000); (B) protein synthesis rate of 65 ng protein
larva
-1
day
-1
(based on 2.7 ng protein larva
-1
h
-1
from Fig. 6A) converts to 137 µJ day
-1
(65*2.1).
190
Thus, the ratio of the energy cost to support protein synthesis (137 µJ day
-1
) to the total energy
supply (329 µJ day
-1
) was 42% as modeled in Figure 8, based on a 147-µm sized larva of Family
59 at 18°C (Fig. 6A, B). For the same larvae from Family 59 exposed to a temperature of 28°C,
the high Q 10 value of 2.4 for respiration would supply sufficient energy to meet the increased
rate of protein synthesis at this higher temperature, because protein synthesis has a lower Q 10
of 1.7 relative to respiration. As illustrated in Figure 8, this scenario is considered “Optimal”
since a larva has sufficient energy to exceed the increased cost of an increased rate of protein
synthesis that would require 28% of total energy.
In contrast, a 147-µm sized larva of Family 60 would be considered in a “Stressful”
scenario under the same temperature scenario of 28°C. Stressful is herein considered an
allocation that exceeds 50% of the total available energy. In this case, the cost of supporting the
increased rate of protein synthesis would require an allocation of 67% of the total available
energy (Fig. 8, Family 60, 28°C, “Stressful” scenario). It is notable in these scenarios that larvae
of each different family (59, 60) allocated between 42% (Family 59) and 34% (Family 60) of total
energy to protein synthesis – a sustainable physiological scenario at 18°C. It is only under
conditions of rising temperature (28°C) that the unsustainable physiological allocation (i.e. 67%)
becomes evident for Family 60. This temperature range is within the global geographic range of
temperatures in the natural habitat for C. gigas.
191
DISCUSSION
The ability to utilize different larval families of the Pacific oyster, Crassostrea gigas,
offers unique experimental advantages for insights into the biological variance and resilience of
response to rising temperature. That such resilience has now been demonstrated to exist in
current populations (Families 59 and 60 are G 0, Fig. 1) illustrates the importance of
understanding standing genetic variation in current populations of the Pacific oyster (Sun &
Hedgecock, 2017). In the present study, family specific differences were found in temperature
sensitivity of the fundamental physiological processes of respiration and protein synthesis (Fig.
6). These differences in temperature sensitivity for respiration were maintained during larval
growth (Fig. 4). A major outcome of these analyses is that certain larval families can function
optimally as temperature increases (Fig. 8, Family 59) while other larval families analyzed in the
present study do not have that physiological capacity (Fig. 8 Family 60).
Energetic Allocation under Increased Temperature
The current study identified a larval family (59) that can be considered to be a sub-
population of a much larger natural population of C. gigas. Similar findings have been reported
for the identification of specific families of C. gigas that show resilience to ocean acidification
(Frieder et al., 2017) and temperature (Pan et al., 2021). The fraction of protein synthesis that is
reported in the analysis of energy allocation presented in Figure 8, is consistent with previous
studies (Lee et al., 2016; Frieder et al., 2017; Pan et al., 2018, 2021). In the pie-charts of Figure
192
8, all energy not accounted for by protein synthesis is labeled “Unaccounted”. For larval stages
of C. gigas, this “Unaccounted” fraction has been characterized biochemically, with the sodium
pump (Na
+
, K
+
-ATPase activity), calcification, and nucleic acid biosynthesis comprising the
majority of the fraction of energy allocation not accounted for by protein synthesis (Pan et al.,
2016, 2018). Obviously, as one of these specific allocation values is altered in response to
environmental change – such as protein synthesis increasing to 67% allocation under rising
temperature (Fig. 8: Family 60) – the support of other essential physiological processes will be
compromised through energy limitation. Pan et al. (2021) provides an analysis of the allocation
of the ATP pool, in which at least 50% needs to be allocated to support other physiological
processes (e.g., maintenance of ion gradients; cost of calcification).
Identification of “Winners” under Scenarios of Environmental Change
The ability for a species to cope with environmental change sets evolutionary potential
of “winners and losers” (Somero, 2010). Success of a species with high fecundity can be
attributed to the progeny of just a few individuals in a population contributing to the successful
recruitment of the next generation (Hedgecock & Pudovkin, 2011). The findings in the current
study support, in general terms, the concept of a few larval family “winners” that have
resilience to change and have the capacity to survive and recruit the next generation of
reproductive adults (Fig. 8). Although the present studies were not specifically designed to
allow quantitative analyses of genetic-based variation, the finding that even a G
0
generation of
larvae has the physiological capacity to respond to rising temperature is notable regarding the
193
identification of a resilient phenotype. Importantly, a physiological explanation for resilience is
now presented from an analysis of the biochemical strategies of energy allocation (Fig. 6).
In conclusion, the identification of family-specific environment interactions has
important implications for studies of adaptations to global change. Understanding the genetic
and physiological bases of adaptation will be of particular importance for those more limited
set of species that form the basis of human food security. “Blue Food” production is receiving
increased awareness regarding the ability to provide sufficient food in the foreseeable future
(decadal scale, Nature editorial, 2021). Species such as bivalves are a major component of the
commercial aquaculture industry. Information that leads to improving yields will be highly
prized for future food production. Understanding the mechanisms of survival and resilience will
be valuable for such selective breeding programs.
Acknowledgments
The results in this chapter were part of a larger project of spawning and rearing several
larval families of Crassostrea gigas through metamorphosis to create and breed pedigreed
lines. The data presented here were possible due to a group effort of the members in the
laboratory group of Professor Donal Manahan, including Dr. Francis Pan, who genotyped each
adult oyster spawned to produce the larvae in these analyses, and Drs. Andrew Griffith, Ning Li,
and Francis Pan, who assisted with culturing, larval counting, and respirometry measurements
for some of the data presented in this dissertation chapter.
194
Melissa DellaTorre was responsible for collecting data for respiration for larval Families
14, 56, 58, 59, 60, and for collecting all measurements of protein synthesis rates for larval
Families 59 and 60.
195
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199
FIGURES
Figure 1. Pedigree of twelve families of Crassostrea gigas used for the experiments in this
study, based on a breeding program initiated in 2016. Closed squares represent males, open
circles represent females. Adults used to create families identified as G 1 have been genotyped
to confirm parentage. Families identified as G 0 had one or both parents confirmed not to match
the family number (i.e. Families 14 and 52D the female genotype was a mismatch, Families 16
and 56(A-B) the male genotype was a mismatch). Families 58-60 were spawned either with
both male and female mismatched genotypes, or with a single remaining individual from a prior
family. Families with a letter value indicate either half siblings, or individuals spawned from
separate males and females of adult siblings (from the same line). Families 39 and 56 are half-
siblings, where Family 39 A and B share the same female parent, and Family 56 A and B share
the same, unrelated, male parent. Families 52 A and D were spawned from separate males and
females paired from sibling adult broodstock.
200
Figure 2. Respiration rate measurements and calculation of Q 10 value using larvae of
Crassostrea gigas from Family 56B (A-C), and Family 60 (D-F).
(A and D) Oxygen depletion by larvae in micro-biological oxygen demand respiration vials from
Family 56B (A), and Family 60 (D) (closed symbols) at 24°C. Control respiration vials (open
symbols) contain only filtered seawater (0.2-µm pore-size). Each micro-biological oxygen
demand vial for Family 56 contained 400 larvae; each vial for Family 60 contained 250 larvae.
(B and E) Average of oxygen depletion rates measured acutely at four temperatures for larvae
of C. gigas from Family 56B (B) and Family 60 (E). Average of oxygen depletion rates at 24°C are
calculated from the data points for the assays conducted at 24°C shown in Figs. A and D. Error
bars ± S.E.M (N = 7 vials per each data point shown).
(C and F) The log value of the average respiration rate at each temperature shown in Figs. B, E
for Families 56B and 60, respectively, is plotted on the y-axis of Figs. C, F. The x-axis is the
temperature incremental change, [T2 – T1)/10] (see Schmidt-Nielson, 1997, p.219-220). An
ANOVA regression gives the Q 10 value as 10
slope
and is 2.8 ± 0.1 (S.E. of the slope) for larval
respiration rate for Family 56B, and 1.7 ± 0.05 (S.E. of the slope) for larval respiration rate for
Family 60.
201
Figure 3. Protein synthesis rate measurements and calculation of Q 10 value using larvae of
Crassostrea gigas from Family 59 (A-C), and Family 60 (D-F).
(A and D) Replicate time-course assays (closed and open symbols), each conducted in a
separate 10 ml sample of 10,000 larvae, of protein synthesis from Family 59 (A), and Family 60
(D) at 24°C. Replicate protein synthesis assays did not differ statistically (Family 59 ANCOVA
slope F 1,6 = 0.19, p = 0.7; Family 60 ANCOVA slope F 1,6 = 0.05, p = 0.8) and both independent
assays were pooled to yield a single rate (regression line shown) for ~150 𝜇 m larvae. Rate of
incorporation of
14
C-glycine into the protein fraction was corrected for changes of intracellular
specific activity and calculated based on known mole-percent of amino acids in larval proteins.
(B and E) Average of protein synthesis rates for larvae from Figs. A, D for Family 59 and 60,
respectively, are represented as the data points for the assays conducted at 24°C. The data
shown in (B, E) are sets of assays conducted at each of the four temperatures shown. Open and
closed symbols represent replicate assays of larvae. Error bars ± 1 S.E.M.
(C and F) The log value of the average protein synthesis rate at each temperature shown in Figs.
B, E for Families 59 and 60, respectively, is plotted on the y-axis of Figs. C, F. The x-axis is the
temperature incremental change, [T2 – T1)/10] (see Schmidt-Nielson, 1997, p.219-220). An
ANOVA regression gives the Q 10 value as 10
slope
and is 1.7 ± 0.05 (S.E. of the slope) for larval
protein synthesis rate for Family 59, and 3.1 ± 0.1 (S.E. of the slope) for larval protein synthesis
rate for Family 60.
202
Figure 4. Respiration Q 10 values by size for larvae of Crassostrea gigas in twelve larval families
reared at 25°C, exposed acutely to temperatures of 14-28°C. The overall average respiration Q 10
was 2.2 ± 0.1 (S.E.M.) for all families. There was no significant relationship between Q 10 value
and shell length (ANCOVA linear regression F 1,26 = 3.3, p = 0.1). Specifically, for Family 56 (data
points circled in upper section of graph), a linear regression of size and Q 10 is not significant
(ANOVA F 1,4 = 0.014, p = 0.9), and the same was determined for Family 60 (data points circled in
lower section of graph) (ANOVA F 1,2 = 0.03, p = 0.9). Each data point represents the Q 10 value
derived from a series of 4-5 time point measurements at four temperatures, with 7-10
respirometry vials measured per temperature over time. Family 56 (closed symbols), Family 60
(open symbols), and all other families (gray symbols, family identified next to each symbol)
were measured across developmental size range (x-axis).
203
Figure 5. Respiration Q 10 values for larvae of Crassostrea gigas were evaluated as shown per
each family (represented by bar symbol). Larvae were reared at 25°C, exposed acutely to
temperatures of 14-28°C. Family 16B-1 and 16B-2 represent larvae reared in duplicate culture
vessels, having the same parents. The average Q 10 value for respiration across all families was
2.2 ± 0.1. There were significant differences across families in Q 10 (ANOVA F 9,19 = 24.3, p <
0.0001). Follow-up Tukey’s HSD test (α = 0.05) revealed Family 56 (black bars) had a
significantly higher respiration Q 10 compared to all other families tested, averaging 2.9 ± 0.07
(S.E.M.). Tukey’s HSD for Family 60 (white bars) revealed significantly lower respiration Q 10
compared to most other families (excluding Families 14, 58, and 39), averaging 1.6 ± 0.05
(S.E.M.). The remaining 7 families, shown in varied gray bars (14, 39, 58, 11, 16, 52, and 59) did
not significantly vary amongst each other (Tukey’s HSD).
204
Figure 6. Larvae of Crassostrea gigas from two families (Family 59, closed symbols; Family 60,
open symbols) temperature sensitivities to (A) protein synthesis and (B) respiration. Larvae
were size-fractionated to ~147 µm: Family 59 had a measured shell length of 148 µm ± 1.3
(S.E.M); Family 60 had a measured shell length of 146 µm ± 1.4 (S.E.M.) (ANOVA F 1,107 = 1.02, p
= 0.3). Larvae were reared at 25°C.
(A) Larvae from Family 60 had a significantly higher temperature sensitivity for protein
synthesis rates than larvae from Family 59 (ANCOVA slope F 1,12 = 42.79, p < 0.0001)
(B) Larvae from Family 60 had a significantly lower temperature sensitivity for respiration rates
than larvae from Family 59 (ANCOVA slope F 1,50 = 5.56, p = 0.02).
205
Figure 7. Glycine transport rate measurements used for calculation of Q 10 value for 147
µm-sized larvae of Crassostrea gigas from Families 59 and 60, reared at 25°C. Average of
glycine transport rates at each temperature represent the slope of a five-point time-series
assay, error bars ± S.E. of the slope. Transport rates by Families 59 and 60 were not significantly
different (ANCOVA F 1,13 = 0.35, p = 0.6).
206
Figure 8. Larvae of Crassostrea gigas at 147 µm shell length from two families (Family 59 and
Family 60) show a significant effect of family on temperature sensitivities in terms of energy
allocation to protein synthesis. Family 59 and 60 have 34-42% of energy allocated towards
protein synthesis at 18°C (respiration and protein synthesis rates shown in Fig. 6A-B). Due to
the difference in temperature sensitivities of these two families in both respiration (increased
total available for Family 59, compared to Family 60 at 28°C), and protein synthesis (decreased
total energy demand for Family 59, compared to Family 60 at 28°C) (statistics in Fig. 6A-B). This
analysis proposes a scenario whereby larval Family 59 has an “Optimal” energy allocation
strategy in contrast to larval Family 60 which has a “Stressful” allocation strategy. The size of
each pie-chart represents the total amount of available energy, calculated by conversion of
respiration rates to energetic units using an oxyenthalpic equivalent of 484 kJ per mole of O 2
(Gnaiger, 1983). Protein synthesis rates were converted to energy equivalents using the cost of
protein synthesis for C. gigas of 2.1 ± 0.2 µJ (ng protein synthesized)
−1
(Lee et al., 2016).
207
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APPENDIX A
The intercalibration of micro-respiration methods using polarographic oxygen sensors and
optode technology for larvae of the purple sea urchin, Stronglyocentrotus purpuratus
INTRODUCTION
Accurate and precise measurements of the low rates of metabolism in small marine
organisms are challenging to make, but of importance in biology and specifically for studies of
the energy requirements. Oxygen consumption rates have been evaluated for larvae for the last
70 years (Scholander et al., 1952). Throughout these years, several advances in technology have
enabled improvements of accuracy and precision for oxygen consumption measurements. Each
modification must be validated prior to use. It is important to calibrate the various methods of
oxygen consumption to determine accurate and robust measurements. These types of
intercalibrations have been previously performed with marine larvae using coulometric
respirometry, Winkler’s titration of dissolved oxygen, and polarographic oxygen sensors (POS)
(Hoegh-Guldberg & Manahan, 1995). While POS measurements of oxygen in blood were
accurate in calibrations (Bridges, 1983), the application of these methods underestimated the
metabolic rates of marine invertebrate larvae (Hoegh-Guldberg & Manahan, 1995). Hoegh-
Guldberg & Manahan (1995) compared measurements of POS with small respiration chambers,
which resulted in significantly lower respiration rates compared to Winkler’s titrations or
coulometric respirometry. Thereafter, a micro-Biological Oxygen Demand vial (µBOD) method
233
using custom-made glass vials of specific volumes between 400-700 µl was developed to
measure accurate rates of respiration in marine invertebrate larvae (Marsh & Manahan, 1999).
This method established an accurate calibration with Winkler’s titrations and coulometric
respirometry, and the added advantage of ease-of-use that enabled a large number of samples
to be measured quickly. This progress was imperative for larval biology in quantifying oxygen
consumption rates.
While optode technology of quenching luminescence by oxygen has been recognized
since 1939 (Kautsky, 1939), the development and insight into the advantages of an oxygen
optode sensor as compared to polarographic oxygen electrodes did not arise until much later
(Optiz & Lubbers, 1987; Lubbers 1995), and subsequent applications into oceanographic
measurements followed (Tengberg et al., 2006; Bittig et al., 2018).
The optode has added advantages over other respirometry methods including ability
for a time-course assay, consisting of multiple time-points using the same individual vial. This
enables a higher precision than the end-point respirometry POS method. The POS and optode
methods have been calibrated to be accurate for studies using larvae of the Pacific oyster,
Crassostrea gigas (Pan et al., 2021). Since different organisms may be affected differently by
rate measurements of oxygen consumption, (in addition to bases of size, concentration, and
duration of assay), it is important to assess calibrations for multiple species in order to validate
the use of the method with the species and life stages being investigated. In the present study,
calibrations of POS and optode respirometry were assessed using larvae of the purple sea
urchin, Stronglyocentrotus purpuratus.
234
METHODS
Metabolic rates of larvae, measured as oxygen consumption (pmol O 2 larva
-1
h
-1
), were
quantified with optode technology (Witrox-1 Oxygen Meter, Loligo Systems) and polarographic
oxygen sensors (POS) (Model 1302, Strathkelvin, North Lanarkshire, UK) for cross-calibration. In
both systems, larvae of S. purpuratus were held at 15°C (at 8-days-old) and 20°C (at 6-days-old),
with 500 larvae allocated into a series of replicate, custom-made sealed micro-respiration
chambers (micro-Biological Oxygen Demand (μBOD) vials). For the optode method, 10 such
μBOD vials were used, with subsequent use for the polarographic oxygen sensor method.
Volumes of the vials ranged from 400-700 μl, each vial individually calibrated for its specific
volume. Additional control µBOD vials that contained only filtered sea water, were measured to
correct for any background changes in oxygen.
The Witrox optode system includes a digital temperature probe placed in the sampling
temperature water bath. Pressure and salinity were manually input and calibrated, and oxygen
readings were converted to moles of oxygen. The POS system relies on end-point
determinations of O 2 consumption, while the optode system allowed for an optical sensor spot
(2 mm diameter; Presens, Regensburg, Germany) to be placed inside the micro-biological
oxygen demand (µBOD) vials, to which a fiber optic cable can be non-invasively pressed against
the sensor spot from the outside of the vial, allowing time-course measurements of O 2
consumption from each vial. Five optode measurements were taken over a time-course of 3 to
5 hours to determine the oxygen consumption per larva per hour and compared with end-point
235
POS measurements of initial and final oxygen done in parallel. The rate of oxygen consumption
per individual larva was calculated by the following equation:
Equation 1: Respiration (pmol O2 larva
-1
h
-1
) =
𝑠𝑙𝑜𝑝𝑒 (
𝑜𝑥𝑦𝑔𝑒𝑛 µ𝑚𝑜𝑙 𝑡𝑖𝑚𝑒 ) 𝑥 µ𝐵𝑂𝐷 µ𝑙
24 ℎ 𝑥 500
In this equation each vial was calibrated to its known volume (µBOD µl), and the value (500)
represents the 500 total number of larvae allocated into each vial for this experiment.
RESULTS
The two independent micro-respirometry methods of optode and polarographic oxygen
sensors demonstrated no significant difference (t-test below) in respiration rates for larvae of
the purple sea urchin, Stronglyocentrotus purpuratus (Fig. 1).
236
Figure 1. Intercalibration of two methods used for micro-respirometry, measured by time-
course assays using optode technology (closed symbols) and end-point assays by polarographic
oxygen sensor (POS) (open symbols). Measurements were recorded at 33.5 psu and 101.3 kPa
barometric pressure. (A) Oxygen depletion of micro-biological oxygen demand (µBOD) vials
containing 500 larvae of 6-day-old S. purpuratus at 20°C showed no significant differences
between the two methods (t-test, t = 1.13, p = 0.27; N = 10). (B) Oxygen depletion of µBOD vials
containing 500 larvae of 8-day-old S. purpuratus at 15°C showed no significant differences
between the two methods (t-test, t = 0.79, p = 0.44; N = 10).
DISCUSSION
The optode measurements of oxygen consumption in larvae of Stronglyocentrotus
purpuratus demonstrated a higher precision, due to multiple time-points, and the same
accuracy, due to no significant differences in respiration rate, as the polarographic oxygen
sensor (POS).
Due to this higher precision and equal accuracy of the optode compared to POS
methods, the optode system should be used for further respirometry experiments of marine
echinoid larvae. The linearity of decreasing oxygen over time reported in Figure 1 shows that
237
larvae of S. purpuratus are oxy-regulators. This level of regulation is commonly reported in the
literature for a range of species that do not adjust metabolic rate due to decreasing amount of
oxygen in the sealed respiratory vials at the amounts tested (Van Winkle & Mangum, 1975;
Ryabushko et al., 1980; Pörtner et al., 2010). This technology is of great value for use in marine
invertebrate systems to yield accurate and precise measurements of larval respirometry over
varied lengths of time and with a robust number of samples, only limited by number of µBOD
vials available. These types of measurements are crucial to further knowledge of energy
metabolism.
APPENDIX A REFERENCES
Bittig, H. C., Körtzinger, A., Neill, C., van Ooijen, E., Plant, J. N., Hahn, J., Johnson, K. S., Yang, B.,
& Emerson, S. R. (2018). Oxygen optode sensors: principle, characterization, calibration,
and application in the ocean. Frontiers in Marine Science, 4, 429.
Bridges, C. R. (1983). pO 2 and oxygen content measurement in blood samples using
polarographic oxygen sensors. Polarographic Oxygen Sensors (ed. by Gnaiger/Forstner),
219-233.
Hoegh-Guldberg O., & Manahan D. T. (1995). Coulometric measurement of oxygen
consumption during development of marine invertebrate embryos and larvae. Journal of
Experimental Biology, 198(1), 19-30.
Kautsky, H. (1939). Quenching of luminescence by oxygen. Transactions of the Faraday
Society, 35, 216-219.
238
Lübbers, D. W. (1995). Optical sensors for clinical monitoring. Acta Anaesthesiologica
Scandinavica Supplementum, 104, 37–54.
Marsh A. G., & 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.
Opitz, N., & Lübbers, D. W. (1987). Theory and development of fluorescence-based
optochemical oxygen sensors: oxygen optodes. International Anesthesiology
Clinics, 25(3), 177–197.
Pan, T.-C. F., Applebaum, S. L., & Manahan, D. T. (2021). Differing thermal sensitivities of
physiological processes alter ATP allocation. Journal of Experimental Biology, 224(2),
jeb233379.
Pörtner, H. O., Dupont, S., Melzner, F., Storch, D., & Thorndyke, M. (2010). Studies of metabolic
rate and other characters across life stages. Publications Office of the European Union.
Ryabushko, V. I., Zhuchikhina, A. A., & Lutsik, N. V. (1980). Effects of environmental oxygen
concentrations on the level of metabolism of some echinoderms from the Sea of Japan.
Computational Biochemical Physiology, 67(1), 171-174.
Scholander, P. F., Claff, C. L., Sveinsson, S. L., & Scholander, S. I. (1952). Respiratory studies of
single cells. III. Oxygen consumption during cell division. The Biological Bulletin, 102(3),
185–199.
239
Tengberg, A., Hovdenes, J., Andersson, J. H., Brocandel, O., Diaz, R., Hebert, D., Arnerich, T.,
Huber, C., Körtzinger, A., Khripounoff, A., Rey, F., Rönning, C., Schimanski, J., Sommer,
S., & Stangelmayer, A. (2006). Evaluation of a lifetime-based optode to measure oxygen
in aquatic systems. Limnology and Oceanography, Methods, 4(2), 7–17.
Van Winkle, W., & Mangum, C. (1975). Oxyconformers and oxyregulators: A quantitative index.
Journal of Experimental Marine Biology & Ecology, 17(2), 103-110.
240
APPENDIX B
Comparisons between ocean-reared and laboratory-reared larvae
of the white sea urchin, Lytechinus pictus
Note: This study was impacted by the limited access imposed through COVID-19 protocols
which prevented the required access to the Wrigley Marine Science Center on Santa Catalina
Island, CA.
INTRODUCTION
Objective: To understand the physiological state of larvae growing in-situ in the ocean.
Approach: The present study used a custom made 20-liter in-situ culturing chamber that was
continuously flushed with ambient seawater off Santa Catalina Island.
Investigatory Questions: 1) How do physiological rates of laboratory-reared larvae compare to
larvae reared in-situ under natural ocean conditions? 2) Are larval stages food-limited in the
ocean?
241
METHODS
Experimental Design: All experiments were conducted at the Wrigley Marine Science Center.
This facility offers a unique experimental setting to compare the physiology of larvae grown
under natural ocean conditions with the same larval cohort grown under controlled laboratory
conditions. A custom built 20-liter plastic (transparent) cylindrical culturing chamber with a
detachable mesh top and bottom (53-μm pore-size) was used to enclose larvae. This chamber
was continually vertically mixed using two plastic rods attached to the chamber and a motor-
driven spinning metal disk (Fig. 1A, B). Four separate cohorts (each obtained from different
males and females) of larvae of Lytechinus pictus were tested in a series of experiments by
rearing larvae in the in-situ chamber and in parallel under controlled laboratory conditions.
Embryos were reared in the laboratory at the average ocean temperature measured during the
course of these experiments of 21°C (Fig. 2). Once the feeding larval stage developed (pluteus)
after three days, known numbers of larvae were allocated into a series of different feeding
treatments to compare laboratory-fed and ocean-fed larvae of the same cohort.
General Animal Culturing: Adult L. pictus were spawned by intracoelemic injection of 0.5 M
potassium chloride, and fertilization success of eggs was confirmed to be >90% for all male-
female pairings. Four separate spawning events were conducted. Laboratory cultures were held
in a temperature-controlled room at an average temperature of 20.6°C ± 0.3 (S.D.), with
temperature recorded every 30-minutes (HOBO U12, Onset Computer Corp., MA, USA). This
corresponded with the average ocean temperature at the Wrigley Marine Science Center
242
(WMSC) during the same time-period at 21.4°C ± 0.8 (S.D.), recorded every 15-minutes (WQM
Water Quality Monitor, Sea-Bird Scientific, Bellevue, WA, USA Sea) (Fig. 2). Embryos were
reared in a series of 20-liter culturing vessels at 20 individuals ml
-1
from fertilization until the
feeding pluteus larval form developed at 3-days old.
Specific Protocol for Laboratory-fed Larvae: Vessels were filled with filtered (0.2-μm pore-size)
seawater from the pristine ocean waters off the coast of Santa Catalina, and each vessel was
continuously mixed with a motorized rotating paddle attached to the lid of each vessel.
Beginning at 3-days-old, when the plutei stage was reached, each cohort was allocated into six
experimental treatments, stocked at 2 larva ml
-1
. For the five laboratory treatments, feeding
was held at constant rations using the alga Rhodomonas lens at 5,000 cells ml
-1
, 10,000 cells
ml
-1
, 30,000 cells ml
-1
, 50,000 cells ml
-1
, and unfed (Fig. 3). At 5-days-old, larvae were
enumerated for subsequent measurements of survival, growth, metabolic rate, and protein
synthesis.
Specific Protocol for Ocean-fed Larvae: In parallel with the feeding laboratory treatments
described above, an additional number of 3-day-old larvae (2 ml
-1
), taken from each of the four
cohorts was placed into the 20-liter in-situ culturing chamber, providing larvae with a natural
ocean-fed treatment. The placement of the in-situ culturing chamber in Santa Catalina’s Big
Fisherman’s Cove was selected due to its proximity to an ocean data monitoring system that
recorded chlorophyll a every 15 minutes (WQM Water Quality Monitor, Sea-Bird Scientific,
Bellevue, WA, USA) (Fig. 1A, B). After a two-day exposure of larvae to ambient food levels,
243
larvae were removed from the in-situ culturing chamber for comparative physiological
measurements to those undertaken in the laboratory-fed treatments.
Survival and Growth: Survival was calculated from enumerated volumetric counts, as a
percentage of the 40,000 larvae input into each of the six treatments at 3-days-old. Growth was
measured via protein accretion for 3- and 5-day-old larvae through whole-body protein content
using a Bradford assay (Bradford, 1976, as modified for larvae by Jaeckle & Manahan, 1989).
Three sample replicates of 300 larvae were analyzed from each treatment. Differences between
treatments and between cohorts were analyzed by ANOVA.
Respiration Rate: Respiration rate of larvae was measured by oxygen consumption and
quantified with optode technology (Witrox-1 Oxygen Meter, Loligo Systems). For 3-day-old
larvae, aliquots of 500 larvae were allocated into 7 to 8 replicate micro-Biological Oxygen
Demand vials. For 5-day-old larvae, aliquots of 250 larvae were used. Vials ranged in size from
400 to 700 µl, each calibrated to its exact volume. A 5-point time-course assay of optode
measurements was taken over a 3-to-5-hour duration. A linear regression of decreasing oxygen
over time was used to calculate respiration rate (pmol O 2 larva
-1
h
-1
) and was corrected for any
background oxygen changes in seawater by use of control micro-respiration vials measured in
parallel with just seawater, no larvae. Differences between treatments and between cohorts
were analyzed by ANOVA.
244
Protein Synthesis: An index of protein synthesis was obtained by measuring the rate of
incorporation of the rate
14
C-alanine into the trichloroacetic acid precipitate. For each assay,
10,000 larvae were allocated into 10 ml of seawater (0.2-µm pore-size filtered), to which 74 kBq
of
14
C-alanine (Perkin Elmer, Wellesley, MA, USA) and cold-carrier alanine (Sigma Aldrich, St
Louis, MO, USA), were added to a final alanine concentration of 10 µM. Addition of alanine
initiated the time-sensitive assay. At exact 8-minute increments thereafter, samples of ~1,000
larvae were removed (by 1 ml pipetting), and transferred onto 8-µm pore-sized membrane
filter (Nuclepore, GE Healthcare, Pittsburgh, PA, USA) on a vacuum-filter. The filter containing
larvae was rinsed with seawater, transferred to a test tube and immediately frozen at -80°C
until processing.
To process samples for incorporation into protein, 400 µl of deionized Nanopure water
(Barnstead
TM
Nanopure Bioresearch Deionization System, Dubuque, IA, USA) was added to each
1.5 ml test tube, and samples were sonicated with a Vibra-cell ultrasonic processor and probe
(Sonics & Materials, Inc., Newtown, CT, USA). An aliquot of 300 µl from this homogenate was
transferred and incubated on ice with tri-chloroacetic acid (5% TCA total solution) to extract
proteins. After an approximate 30-minute incubation, samples were vortexed and vacuum-
filtered onto a GF/C glass microfiber filter (Whatman, Tisch Scientific, North Bend, OH, USA)
rinsed with 5% TCA then methanol. The filter containing the larval protein was then immersed
in scintillation fluid and counted with appropriate quench correction. Differences between
treatments and between cohorts were analyzed by ANOVA.
245
RESULTS
Survival and Growth: All laboratory-reared larvae had over 90% survival from 3-days-old to
5-days-old, the experimental period tested. Larvae reared in the in-situ chamber had lower
survival, averaging 81% and ranging from 67 – 100% across the four cohorts tested (Table 1).
Protein content of ocean-fed larvae (5-days-old) was intermediate between unfed and fed
laboratory treatments (Fig. 4). For laboratory-fed larvae, there were no significant differences
in total protein content regardless of the 10-fold difference in the amount of algal food present
across the four different laboratory-fed treatments (ANOVA F 3,35 = 1.86, p = 0.2). Protein
content for 5-day old larvae among the laboratory-fed treatments had a narrow range of
averages from 58 ± 2 (S.E.M.) ng protein larva
-1
(larvae fed 50,000 cells ml
-1
) to 66 ± 3 (S.E.M.)
ng protein larva
-1
(larvae fed 10,000 cells ml
-1
). All laboratory-fed larvae, averaging 62 ± 2
(S.E.M.) ng protein larva
-1
, had a 1.9-fold higher protein content than unfed larvae at 33 ± 1
(S.E.M.) ng protein larva
-1
(ANOVA F 4,43 = 34.95, Post-hoc Tukey HSD Test p < 0.01), and a
1.4-fold higher protein content than ocean-fed larvae at 44 ± 4 (S.E.M.) ng protein larva
-1
(ANOVA F 4,42 = 13.53, Post-hoc Tukey HSD Test p < 0.01). Notably, ocean-fed larvae had a
significantly higher protein content than unfed larvae (ANOVA F 1,15 = 11.18, p = 0.004) (Fig. 4).
Respiration: Respiration rates of ocean-fed larvae were intermediate between unfed and fed
laboratory treatments (Fig. 5). For laboratory-fed larvae, there were no significant differences
in respiration rate regardless of a 10-fold difference across the four different algal feeding
treatments. Respiration rate had a narrow range across the laboratory-fed treatments of 43 to
246
49 pmol O 2 larva
-1
h
-1
(not statistically different by ANOVA F 3,14 = 0.85, p = 0.5) (Fig. 5).
Laboratory-fed larvae averaged 44 ± 2 (S.E.M.) pmol O 2 larva
-1
h
-1
, a respiration rate that was
2.6-fold higher than unfed larvae [17 ± 1 (S.E.M.) pmol O 2 larva
-1
h
-1
] (difference is significant by
ANOVA F 4,17 = 18.3, p = <0.0001, Post-hoc Tukey HSD test p < 0.01). Laboratory-fed larvae also
had 1.9-fold higher respiration rates than ocean-fed larvae [24 ± 2 (S.E.M.) pmol O 2 larva
-1
h
-1
]
(ANOVA F 4,17 = 9.5, p = <0.0001, Post-hoc Tukey HSD test p < 0.01). Ocean-fed larvae had a
marginally similar (p = 0.08) respiration rate to unfed larvae (F 1,6 = 4.36, p = 0.08) (Fig. 5).
Protein Synthesis: Rates of protein synthesis (estimated by incorporation of
14
C-alanine into
protein) of ocean-fed larvae, unfed larvae, and fed larval treatments are presented in Figure 6.
There were differences between Cohorts 1 and 2, when each were fed the same food ration in
protein synthesis rates (ANOVA of 2 Cohorts and 4 food treatments, p < 0.0001). For Cohort 1,
rates of protein synthesis for ocean-fed larvae were significantly lower than larvae fed rations
of 10,000, 30,000, and 50,000 cells ml
-1
(ANOVA F 4,7 = 81.87, p < 0.001, Post-hoc Tukey HSD
Test, p < 01) (Fig. 6A). For Cohort 2, rates of protein synthesis for ocean-fed laboratory were
also lower than laboratory-fed larvae (ANOVA F 1,6 = 8.17, p = 0.03). Protein synthesis rates for
laboratory-fed larvae were 2.6-fold greater than unfed larvae (ANOVA F 1,6 = 71.5, p = 0.0001)
(Fig. 6B). Notably, ocean-fed larvae of Cohort 2 had a significantly higher protein synthesis rate
than unfed larvae (ANCOVA of time-course regressions F 1,16 = 11.98, p = 0.003).
Environmental Monitoring: Figure 7 gives the results of the seawater quality monitoring
(WQM, Sea-Bird Scientific, Bellevue, WA, USA) that quantified the amount of chlorophyll a in
247
Big Fisherman’s Cove, Santa Catalina Island, during the time when the experiments were
conducted on the larval cohorts analyzed in the present study. The average chlorophyll a
concentration was 1.1 µg l
-1
.
DISCUSSION
Physiological State of Ocean-fed Larvae: In terms of growth (protein accretion), ocean-fed
larvae are intermediate between unfed larvae and well-fed larvae (Fig. 4). For metabolism, the
rate of respiration of ocean-fed larvae was similar to unfed larvae (Fig. 5). For protein synthesis,
the rate in ocean-fed larvae in Cohort 2 was higher than unfed larvae (Fig. 6). In conclusion,
under the experimental conditions tested, ocean-fed larvae are near to physiological starvation
conditions.
Laboratory Versus Ocean Larval Food Availability: Chlorophyll a levels in the ocean waters
adjacent to the in-situ culturing chamber averaged 1.1 µg l
-1
and showed little variation for the
duration of the larval rearing experiments (Fig. 7). There is a large literature questioning the
value of chlorophyll a as an index of food for meroplankton (Olson, 1987; Paulay et al., 1985;
Fenaux et al., 1994). A value of total chlorophyll a is likely an overestimate of the food ration
available to larvae, because not all of the phytoplankton containing chlorophyll a would be of
the proper size and digestibility for echinoderm larvae to capture and ingest. A comparison of
possible food availability can be obtained by converting cells of Rhodomonas lens into
chlorophyll a equivalents. Coutinho et al. (2020) report that R. lens has 1.5 pg chlorophyll a
248
cell
-1
and a protein content range between 36 and 63 pg cell
-1
. This average protein content is
consistent with analyses of protein content of R. lens for the algal culturing methods used in the
present study (see Chapter 1: Fig. 5). From Coutinho et al. (2020) analyses, it can be estimated
that the laboratory-fed chlorophyll a content used in this study (Fig. 3) is 0, 7.5, 15, 37.5 and 75
µg chlorophyll a l
-1
for the algal feeding rations of 0, 5,000, 10,000, 30,000 and 50,000 cells ml
-1
,
respectively. These calculations reveal that if algal species in the ambient seawater for the
ocean-fed treatments were accessible as food to larvae, then ~700 cells ml
-1
in chlorophyll a
equivalents are present. Clearly, even at 700 cells ml
-1
larvae would be close to starvation, and
this general conclusion is supported by the evidence presented in this study. So, the paradox
remains as to how larvae grow in the food limited environment of the natural ocean.
APPENDIX B REFERENCES
Bradford, 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.
Coutinho, P., Ferreira, M., Freire, I., & Otero, A. (2020). Enriching rotifers with “Premium”
microalgae: Rhodomonas lens. Marine Biotechnology, 22, 118-129.
Fenaux, L., Strathmann, M. F., & 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.
249
Jaeckle, W. B., & 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.
Olson, R. R. (1987). In situ culturing as a test of the larval starvation hypothesis for the crown‐
of‐thoms starfish, Acanthaster planci. Limnology and Oceanography, 32(4), 895-904.
Paulay, G., Boring, L., & 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.
250
FIGURES & TABLE
Figure 1. Photographs of the in-situ larval culturing chamber deployed in the ocean, in Big
Fisherman’s Cove, Santa Catalina Island, CA.
(A) An above water view of the platform used to support the in-situ culturing chamber,
displaying the circular metal disk that rotated and provided continual vertical mixing with the
surrounding sea water.
(B) An underwater view of the in-situ culturing chamber, a 20-l volume plastic cylinder, with top
and bottom detachable mesh enclosures of 53-µm pore-size.
A. B.
251
Figure 2. Temperature profiles for both the laboratory- and ocean-reared larval cultures. For
the data points indicated as ‘Laboratory’ each was measured with a digital data logger (HOBO).
For the data points indicated as ‘Ocean’ each was measured with a WQM data logger (see
methods). The average temperature for the ‘Laboratory’ was 20.6°C ± 0.3 (S.D.) and 21.4°C ±
0.8 (S.D.) for the ‘Ocean’. Given the variability in these data, these temperature profiles were
not significantly different (ANCOVA F 1,1137 = 96.9, p = 0.9). The dates indicated on the x-axis
represent the time period during which the experiments were conducted on larvae of
Lytechinus pictus.
252
Figure 3. Larvae of Lytechinus pictus fed the alga Rhodomonas lens in laboratory-reared 20-liter
culture vessels at constant amounts of 5,000 cells ml
-1
(closed circles), 10,000 cells ml
-1
(open
circles), 30,000 cells ml
-1
(closed triangles) and 50,000 cells ml
-1
(open triangles). Each data point
represents a measurement of cell number by digital cell-counting technology (Z2 Beckman
Coulter Counter) of algal cells in suspension in the larval culture vessels.
253
Figure 4. Protein content for three cohorts of 5-day-old larvae of Lytechinus pictus, reared
under four different algal rations of Rhodomonas lens, and one unfed treatment (filtered
seawater). The bars labeled in-situ represent the protein content of larvae measured in the
ocean-fed treatment. Individual bars in a single treatment represent replicate culture vessels.
Treatments at 10,000 cells ml
-1
included additional replicate culturing vessels within two of the
three cohorts. N = 5 independent samples of protein content in different larvae; error bars ±
S.E.M.
254
Figure 5. Respiration rate for four cohorts of 5-day-old larvae of Lytechinus pictus, reared under
four different algal rations of Rhodomonas lens, and one unfed treatment (filtered seawater).
The bars labeled in-situ represent the respiration rate of larvae measured in the ocean-fed
treatment. Individual bars in a single treatment represent replicate culture vessels. Treatments
at 10,000 cells ml
-1
included additional replicate culturing vessels within three of the four
cohorts. N = 8 independent measurements of respiration rate (each a micro-respiration vial
used for a time-course assay); error bars ± S.E.M.
Inset: Respiration rate increased between 3- and 5-day-old larvae; respiration rates were similar
for similar aged larvae from different cohorts, represented by different symbols. This
relationship of increased respiration with age was based on an analysis of 156 micro-respiration
vials across four different cohorts of larvae. Line shown is the pooled respiration data for all
larval cohorts, and is described by the equation: Metabolic Rate (pmol O 2 larva
-1
h
-1
) = 12.9x -
20.9, where x is larval age (days), R² = 0.92.
255
Figure 6. Incorporation rates of
14
C-alanine into trichloroacetic acid precipitable protein in
5-day-old larvae of Lytechinus pictus, reared under four different algal rations of Rhodomonas
lens, and one unfed treatment (filtered seawater). The bars labeled in-situ represent the
estimate of relative protein synthesis in larvae measured in the ocean-fed treatment. Bars
represent the average of replicates from Cohort 1 (A) and Cohort 2 (B) in each feeding
treatment listed on the x-axis. For each bar: N = 2 assay vials; N = 4 assay vials for treatment at
10,000 cells ml
-1
; error bars ± S.E.M.
256
Figure 7. Continuous monitoring (15-minute time increments) of the amount of chlorophyll a in
Big Fisherman’s Cove, Santa Catalina Island. Each data point was measured with a water quality
monitoring system moored adjacent to the in-situ chamber for the ocean-fed treatment of
larvae of Lytechinus pictus. The red line indicated the average chlorophyll a concentration of 1.1
µg l
-1
that was measured during the experimental period of larval biological assays conducted
during August 2019. Data provided by Matt Ragan and Carl Oberg (USC Wrigley Marine Science
Center).
257
Table 1. Survival percentages of four independent cohorts of larvae of Lytechinus pictus, during
the series of experiments of laboratory- and ocean-fed treatments. Each percentage given
represents the survival of 5-day-old larvae compared to the initial allocation of 3-day-old larvae
in each feeding treatment shown. The initial stocking density of larvae was 2 ml
-1
in all
treatments. The column of feeding treatment (cells ml
-1
) represents the algal ration fed to
larvae in those treatments using different amounts of the alga Rhodomonas lens. Unfed
treatment represents larvae in filtered seawater. In-situ treatment represents ocean-fed larvae
suspended in natural seawaters of Big Fisherman’s Cove, Santa Catalina Island. The coefficient
of variation for all larval counts used to calculate percent survival were less than 10%.
Cohort
Feeding Treatment
(cells ml
-1
)
1 2 3 4 Average
IN-SITU 67% 79% 100% 78% 81%
UNFED 100% 100% 90% 100% 98%
5,000 100% 100% 100% 100% 100%
10,000 (A) 100% 100% 99% 100% 100%
10,000 (B) 99% - 100% 100% 100%
30,000 94% 100% 100% 100% 98%
50,000 100% - 100% 100% 100%
258
APPENDIX C
Impact of varied exposure to food and temperature on morphological growth, survival,
feeding rate, respiration rate, and protein synthesis rates in larvae of the purple sea urchin
Stronglyocentrotus purpuratus and the Pacific oyster, Crassostrea gigas
The images and text listed below are a summary of a series of studies, as per the title above.
These images were summarized in a seminar presentation to Marine and Environmental
Biology, Department of Biological Sciences at the University of Southern California.
These data were relevant to the development of ideas and protocols presented throughout this
dissertation (Chapters 1-4).
Above: Temperature and food are two major environmental variables affecting larval growth.
The theoretical lines on the graph represent hypotheses to be tested regarding the relationship
of growth to changing temperature and food ration. Specifically, to test these hypotheses,
larvae of S. purpuratus and C. gigas were exposed to four treatments:
• Low temperature & Low food
• Low temperature & High food
• High temperature & Low food
• High temperature & High food
259
Above:
(Left) Size growth rate: All treatments significantly differ in growth by midline body length
(ANCOVA F 6,24 = 15.59 p < 0.0001).
14°C Fed 5 cells µl
-1
vs 25 cells µl
-1
: ANCOVA F 2,12 = 29.92, p < 0.0001
19°C Fed 5 cells µl
-1
vs 25 cells µl
-1
: ANCOVA F 2,12 = 4.8, p = 0.03
14°C vs 19°C: ANCOVA F 2,4 = 24.83, p = 0.0056
(Right) Protein Accretion: Temperature differences impact protein accretion rate, food
treatment did not impact total protein content (ANCOVA F 6,75 = 34.95, p < 0.0001)
2x20-liter vessels 2x20-liter vessels
2x20-liter vessels 2x20-liter vessels
Stronglyocentrotus purpuratus
Temperature and food, significantly
affected growth by midline body length
Temperature, but not food, affected
protein accretion
High Food
(25 algal cells µ l
-1
)
Low Food
(5 algal cells µ l
-1
)
High Temp.
(19 °C)
Control Temp.
(14 °C)
20
19
16.3 16.3
13.
12
10.8 10.5
0.0
5.0
10.0
15.0
20.0
25.0
5A 5B 25A 25B 5A 5B 25A 25B
19 C 14 C
Growth Rate ( µm day
-1
)
Feeding Ration (algal cells µl
-1
) and
Temperature Condition
0
10
20
30
40
50
60
70
80
90
2 4 6 8 10 12
Protein Content (ng larva
-1
)
Age (Day)
260
Above:
Statistical basis to show no difference in the relationship of total protein to protein synthesis
rate for larvae of S. purpuratus: Equation y = 0.017x – 0.45 (ANCOVA F 3,44 = 1.1, p = 0.4)
261
Above:
Left: Regardless of feeding treatment, larvae reared at 28 C had a higher mortality compared to
larvae reared at 24 C (ANCOVA F 1,9 = 14.1, p = 0.005). This is a highly temperature-tolerant
species, routinely reared at temperatures of approximately 25°C.
Middle: High-fed larvae increased size compared to low-fed larvae at 28 C (ANCOVA F 1,8 =
37.47, p = 0.0003). Food amount had no effect on size at 24 C (ANCOVA F 1,8 = 3.24, p = 0.1)
Right: Feeding rate (measured as clearance rate: volume of seawater filtered per individual
larva per hour), does not differ between high- and low-fed larvae (not shown). Consequently
higher-fed larvae consume more food in high-food concentrations. Decreased growth rate
under low-food conditions larvae at 28 C is related to decreased feeding rate (ANCOVA F 1,9 =
4.74, p = 0.05). At 24 C, regardless of feeding rate, larvae maintain similar growth rates.
262
Above:
At higher temperature, larvae of C. gigas increased respiration rates. Larvae at 28 C had higher
respiration rates than larvae of the same size at 24 C (ANCOVA F 1,9 = 7.56, p = 0.02).
Food amount did not change respiration rate (ANCOVA F 1,8 = 0.1, p = 0.8).
263
APPENDIX D
Measurements of the rates of protein synthesis
INTRODUCTION
These data were relevant to the development of approaches to measure rates of protein
synthesis presented in Chapter 3 as rates of
14
C-alanine incorporation into protein. These data
presented for temperatures of 10, 15, 20 and 25°C allow for estimates of absolute rates of
protein synthesis analyzed by high performance liquid chromatography (measurement of
intracellular specific activity) and compared to rates based only on the incorporation of
14
C-alanine into protein.
Objective: Quantification of protein synthesis rates in larvae of the white sea urchin, Lytechinus
pictus. To understand the significance of correcting for intracellular specific activity of
radioactive precursors (
14
C-alanine use as a tracer of protein synthesis) in the free amino acid
pool, when measuring absolute rates of protein synthesis in marine invertebrate larvae.
Approach: The present study analyzed, by high-performance liquid chromatography, 160
measurements of protein synthesis rates, totaling 800 time-course samples. In each sample the
specific activity of
14
C-alanine in the free amino acid pool of larvae was measured. Larvae of L.
pictus were studied across four temperatures to quantify the relationship between the rate that
264
14
C-alanine was incorporated into protein, and the absolute rate of protein synthesis. Absolute
rates of protein synthesis were calculated based on (1) rate of incorporation of
14
C-alanine into
the trichloroacetic acid (TCA)-precipitable protein fraction of larvae; 2) corrections for changes
in specific activity of
14
C-alanine in the intracellular free amino acid pool; (3) measurement of
the percent of alanine in the amino acid composition of whole-body larval protein, and (4) the
mole-corrected amino acid composition of whole-body larval protein.
METHODS
Experimental Design: Samples were obtained from the series of larval samples and
methodology used for analysis of protein synthesis, as described in Chapter Two ‘Materials &
Methods’. In brief, in vivo assays were performed by adding
14
C-alanine to seawater, from
which measurements of the transport of that substrate and incorporation into larval protein
can be made (30-minute, five-point time-course assay, performed in duplicate). The rate of
14
C-alanine incorporated protein was analyzed by liquid scintillation counting of the
radioactivity in extractions of trichloroacetic acid (TCA)-precipitable protein. Additional
corrections for absolute protein synthesis rate were performed by incubating an aliquot of the
samples in 70% ethanol to extract the pool of free amino acids, which were then quantified by
high-performance liquid chromatography. During chromatographic separation of amino acids,
the specific alanine peak was isolated and collected via fraction collector for counting the
amount of
14
C-alanine in the free amino acid pools. Using both the rates of
14
C-alanine
incorporated into protein and the correction for the specific activity of alanine in the free amino
265
acid pool, absolute rates of protein synthesis were calculated. The equation for calculating
protein synthesis is:
Equation 1: Protein Synthesis = d/dt(Sp/Sfaa) x 129.4/7.8
The term S p is the amount of radioactivity in the TCA-precipitable protein; the term S faa is the
amount of radioactivity in the free amino acid pool; the number 7.8 is the mole percent of
alanine in the whole-body protein of larvae of L. pictus; the number 129.4 is the mole-percent
corrected amino acid composition in whole-body protein (g mole
-1
).
266
RESULTS & INTERPRETATION
Figure 1. The relationship between the rate of incorporation of
14
C-alanine into the
trichloroacetic acid (TCA)-precipitable protein fraction (x-axis), and the absolute rate of protein
synthesis (y-axis) for different ages and sizes of larvae of Lytechinus pictus. From these data, a
single regression analysis is plotted to predict the quantitative rate of protein synthesis from
the index of protein synthesis assayed by the rate of incorporation of
14
C-alanine into the
protein fraction. Notable is the scatter of individual data points around the regression (R
2
=
0.69), highlighting a limitation of using TCA assays to quantify rates of protein synthesis. The
required conversion of the amount of
14
C-alanine incorporated into protein is achieved by
quantifying the intracellular specific activity of
14
C-alanine in the free amino acid pool of larvae.
Details of those methods are given in the text of this appendix and in further detail in Chapter
2.
267
Figure 2. The partitioning of the individual data points from Figure 1 into a series of analyses
that are temperature specific for different ages and sizes of larvae of Lytechinus pictus reared at
four temperatures. The exponential relationships of protein synthesis rates and rates of
trichloroacetic acid (TCA)-incorporation into protein are specific to each temperature tested.
The equations for each temperature relationship are:
10°C: y = 0.1439e
0.4656x
15°C: y = 0.1754e
0.2762x
20°C: y = 0.4017e
0.1254x
25°C: y = 0.529e
0.0754x
These equations quantify the increases in protein synthesis rates with increasing temperature
and size-age (i.e., higher rates as larvae grow in size).
Abstract (if available)
Abstract
Understanding how the environment drives physiological change in organisms is a critical component to understand the rules of life and predictions of phenotype. For studies of marine invertebrates, early life-history stages offer distinct advantages for evaluating response to environmental change, due to high fecundity and experimental tractability for rearing millions of individuals under controlled conditions. This dissertation evaluates the physiological and biochemical mechanisms in marine larval forms that underlie responses to variable food and temperature – two major environmental variables with important implications for predictions of organismal resilience in a changing global ocean. Biological response to environmental variability is investigated under three major themes: (I) study of varied quantity and quality of food; (II) response to short- and long-term temperature change; and (III) cellular energy allocation strategy. To address these central themes, a suite of integrated measurements is applied to determine how larvae respond to experimental treatments designed to simulate environmental variability. Those specific analytical measurements are: (a) at the level of the whole organism, analyses of morphological growth and survival; (b) physiological analyses of feeding and metabolic rates; (c) biochemical measurements of protein, lipid, and carbohydrate content; (d) protein metabolic dynamics (i.e., protein synthesis, turnover, and accretion); and (e) nitrogen excretion rates to determine substrate preference utilization to support metabolism.
Different food quality impacts the cost of growth and dynamics of protein metabolism
Analyses are undertaken to determine the effect of a lower-quality algal diet on larval growth and physiology of the white sea urchin, Lytechinus pictus. When fed the alga Rhodomonas lens, larvae grow faster and have a higher growth efficiency than when fed the alga Dunaliella tertiolecta. Notably, no amount of increased food ration of the lower quality diet compensates for lower growth. Significantly, in terms of the cost of living, the energy cost of growth to a given size is 1.4-fold higher for larvae fed on the lower quality diet. The mechanistic basis for the more costly growth is due to increased amounts of protein synthesis required to deposit a unit-mass of protein accretion (Chapter 1). In addition, there is a 3.5-fold lower protein depositional efficiency (the ratio of protein accretion to protein synthesis) for larvae fed the lower quality diet. The lower quality diet is attributed to algal cells of D. tertiolecta that have half the cellular protein content relative to R. lens. This difference in protein content is key, since algal size, lipid, and carbohydrate contents are similar between the two algal species. Larval feeding rates on the two species of algae are similar; hence, total protein intake by larvae is double for the higher quality alga. The ratio of algal protein ingested to protein accreted in the larva (gross protein accretion efficiency) is the same for larvae fed on either algal diet. These measurements provide a biochemical explanation for a long-standing question about the mechanistic basis of why one algal species is better than another for supporting the growth of larvae.
High growth rate can be maintained with low food ration
A ten-fold increase in an experimentally-provided ration of the alga R. lens results in a six-fold increase in the rate of cell ingestion by larvae of L. pictus. Counterintuitively, however, this increase in algal ingestion rate does not result in an increase in either growth, size-specific metabolic rate, or excretion rate. An explanation for this finding is that larvae compensate for the lower food ration by increasing gross protein accretion efficiency and also by increasing rates of protein synthesis, relative to larvae that are fed a higher ration (Chapter 1). These findings provide new insights into the mechanisms of resilience, regarding how larvae can compensate for food limitation in highly variable and nutritionally-dilute oceanic environments.
Specific physiological processes have different responses to temperature increase
The major physiological components of growth are measured to determine the sensitivity of specific processes to rising temperature. Specifically, the temperature sensitivity (quantified as a Q10 value) is measured for respiration, amino acid transport, protein synthesis, and ammonia excretion (Chapter 2). Of the processes measured, protein synthesis has the highest sensitivity to temperature, with a Q10 value of 3.7 ± 0.2 (SE). This contrasts with a lower Q10 value for respiration of 2.4 ± 0.2 (SE). For the other processes measured, amino acid transport has a Q10 of 1.6 ± 0.1 (SE) and ammonia excretion 2.7 ± 0.2 (SE). A major conclusion from this analysis is that the energy supplied through respiration responds to temperature at a slower rate than does the energy demand to support protein synthesis. Given this differential physiology, in a warming ocean larval forms will allocate a greater proportion of their available energy to support protein synthesis for growth. A quantitative model is provided of energy allocation and predictive limits of this strategy under scenarios of rising temperature. The experimental protocols developed in this study to test for the effect of temperature on protein synthesis are fundamental to the conclusions presented. Further evaluations of these protocols, based on acute temperature exposures, are provided for a range of experimental temperature acclimations for two species of sea urchin larvae (L. pictus and Stronglyocentrotus purpuratus). Acclimation durations up to 32-hours prior to the initiation of protein synthesis assays do not change the conclusions of temperature sensitivity (i.e., high Q10 values) for larvae of L. pictus. Larvae of S. purpuratus are more sensitive to the duration of acclimation (Chapter 3).
Specific larval families can maintain energy homeostasis under temperature increase
Eggs and sperm obtained from gravid, pedigreed adult Pacific oysters (Crassostrea gigas) are fertilized in a series of controlled crosses to yield nine different families of larvae. These larvae are then used to test for family-specific differences in physiological responses to rising temperature (Chapter 4). A physiological mechanism of resilience to rising temperature is identified, based on the differential sensitivity of respiration and protein synthesis across different families. Specifically, a family is identified that has a low sensitivity of protein synthesis to temperature increase, coupled with a high sensitivity of respiration to temperature increase. Notably, these animals can sustain physiological homeostasis of energy supply-and-demand under rising temperatures. This phenotype is a good candidate for breeding programs in commercial aquaculture to meet the challenge of “Blue Food” production in a warming ocean.
Significance and Impact
The diversity of animal life on Earth is comprised of 35-40 phyla. Over half of these phyla are exclusively aquatic and most of those are only found in the marine environment. Complex life-history strategies are dominant in marine organisms. For marine animals, the vast majority of species have a larval stage of development. Many ideas and postulates have been proposed regarding how these planktonic stages of early development live in the world’s largest environment (by volume, the pelagic zone of the oceans represent over 90% of the living biosphere). There is a large literature in the field of larval biology, most of which is focused on ecology and recruitment. Conversely, there is also an extensive literature in the field of cell and animal developmental biology. Information is scant, however, in areas of biology that could link ecology and cell biology – specifically, an understanding of whole organisms studied at the biochemical and physiological levels of analyses. It is the latter approach that is the focus of this dissertation.
Selected species of sea urchins and oysters are the experimental organisms of choice for the studies presented in this dissertation. These are well-studied species, providing an extensive background upon which to build novel approaches and analyses to understand the biology of larval forms. A new discovery presented in this dissertation is that larval growth is more dependent on food quality than food quantity. A low food ration yields the same growth rate as a high food ration. This finding is in apparent contradiction to published reports in the literature, but can easily be defended by the experimental protocols utilized in the current study – namely, using digital cell-counting technology to ensure that all food rations tested are kept constant. An analysis of energy metabolism shows that larvae growing on a lower quality diet have a higher cumulative cost of metabolism to reach a given size. The integrative measures of morphology, physiology, and biochemistry provide mechanistic explanations of the observations at the level of the whole organism, between food quantity, quality, and growth rate. Specifically, rates of protein synthesis are up-regulated at low ration, whereas in response to food rations of variable quality, the ratio of protein synthesis to accretion (protein depositional efficiency) is up-regulated in response to a higher quality diet.
Rising temperature impacts physiology, often by constraining the allocation of cellular energy, leading to stress and death of organisms. The biological variance of this process has long been recognized, but has been challenging to study. Here, such Darwinian performance has been analyzed by partitioning variance using crosses of pedigreed animals. A discovery of high significance is that some families of larvae have the ability to perform more optimally than others under increased temperatures, based on analyses of cellular-level energy supply and demand. These analyses of the dynamics of energy budgets provide new insight into the metabolic strategies of coping with rapid environmental change. In addition to contributing to fundamental science, this finding has valuable implications for the aquaculture industry, with the possibility of selecting resilient genotypes for breeding programs to meet the increased global demand for food from the sea.
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Creator
DellaTorre, Melissa Beth
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Core Title
Physiological strategies of resilience to environmental change in larval stages of marine invertebrates
School
College of Letters, Arts and Sciences
Degree
Doctor of Philosophy
Degree Program
Biology
Degree Conferral Date
2022-05
Publication Date
04/13/2022
Defense Date
03/02/2022
Publisher
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Tag
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), Hedgecock, Dennis (
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), Kenkel, Carly (
committee member
), Maxson, Robert (
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), Sañudo-Wilhelmy, Sergio (
committee member
)
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Tags
energy allocation
food availability
growth
larvae
metabolism
oyster
physiology
protein
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protein turnover
respiration
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