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Simulated and field environmental effects on the transcriptome and metabolome of mussel Mytilus californianus
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Simulated and field environmental effects on the transcriptome and metabolome of mussel Mytilus californianus
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Content
SIMULATED and FIELD ENVIRONMENTAL EFFECTS on the
TRANSCRIPTOME and METABOLOME of MUSSEL
MYTILUS CALIFORNIANUS
By
Kwasi M. Connor
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(BIOLOGY)
December 2012
Copyright 2012 Kwasi M. Connor
ii
Acknowledgements
Firstly, I wish to express my sincere thanks to my advisor Dr. Andrew Gracey for instilling
in me skills to become an advanced molecular biologist and providing a pathway to
reach my lifelong dream of becoming a professional scientist. He is an outsanding
mentor, who supported all of my research endeavors, and with patience, allowed me to
use creativity in those efforts. I would also like to thank the members of the
dissertation committee Dr. Suzanne Edmands, Dr. Dennis Hedgecock and Dr. Dale Kiefer
who have been very supportive throughout the dissertation process.
My gratitude extends to the staff Linda Brazilian, Don Bingham and Adolfo De La Rosa
for faciliting an overmwhelming amount of requests for help; fellow labmates and
fellow students Megan Hall, Jacqueline Lin, Chase Femrite and Diane Kim who provided
needed assistance down the home stretch; and mentors Dr. Mark Todd, Dr. Linda
Duguay, Dr. Doug Capone, Dr. Sergio Sanudo-Wilhelmy, Dr. Jed Fuhrman and Dr. Dave
Caron and Dr. Carlos Robles for their unwaivering support.
This dissertation would not be possible without the support of my wife Krishna; children
Camilla and Julian; mother Dr. Rosa Kincaid; father Norman Connors; sisters Afrisha and
Keloi; mother-in-law Dr. Francis Smith Foster; father-in-law Dr. Warren Foster; and
James Buchanan.
iii
Table of Contents
Acknowledgements ii
List of Tables iv
List of Figures v
Abstract vii
Introduction: 1
Chapter 1: Circadian Cycles are the Dominant Transcriptional Rhythm in 12
the Intertidal Mussel Mytilus californianus
Chapter 1 References 31
Chapter 2: High-resolution Analysis of Metabolic Cycles in the Intertidal 66
Mussel Mytilus californianus
Chapter 2 References 95
Chapter 3: Molecular and Biochemical Observations of Mytilus californianus 100
under Constant Submergence
Chapter 3 References 129
Chapter 4: Transcriptome-wide Gene expression in Mytilus californianus 142
under Simulated Combined Stresses of Aerial Exposure and Solar
Radiation
Chapter 4 References 176
Bibliography 233
Appendix: The Intertidal Simulation System 244
iv
List of Tables
Table 1. Metabolites that oscillate during the tidal cycle. 81
List of Supplemental Information Tables
SI Table 1: List of transcripts represented in heatmap shown in Fig. 3A. 48
SI Table 2: List of transcripts represented in heatmap shown in Fig. 3B. 54
SI Table 3: List of tidally-regulated transcripts represented in heatmap 60
shown in Fig. 4B.
SI Table 4: List of tidally-regulated transcripts represented in heatmap 64
shown in Fig. 4C.
SI Table 5: List of transcripts represented in heatmap shown in Fig. 13A. 136
SI Table 6: List of transcripts represented in heatmap shown in Fig. 13B. 139
SI Table 7: List of transcripts represented in heatmap shown in Fig. 16A. 184
SI Table 8: List of transcripts represented in heatmap shown in Fig. 16B. 208
v
List of Figures
Figure 1: Gene expression profiling reveals tidal and circadian rhythms 17
in a simulated intertidal environment.
Figure 2: Gene expression patterns correspond to tidal height and dusk 18
and dawn.
Figure 3: Rhythmic expression of transcripts correlated with low and 24
high tide.
Figure 4: Cycles of gene expression show a strong circadian rhythm in 28
field intertidal and subtidal environments.
Figure 5: Cardiac activity is correlated with the tidal cycle in a 80
simulated intertidal environment.
Figure 6: Products of anaerobiosis exhibit a cyclical abundance profile 82
and accumulate during low tide.
Figure 7: Substrates for the TCA cycle accumulate as carnitine conjugates 83
during low tide.
Figure 8: Fatty acids and BCKA degradation products accumulate as 84
carnitine conjugates during low tide
Figure 9: Diverse metabolites show rhythmic abundance profiles and 85
accumulate during low tide.
Figure 10: Simplified metabolic schema showing the key pathways that 94
are altered during low tide.
Figure 11: Cardiograms showing cardiac activity in well fed mussels held 116
under constant submergence.
Figure 12: Comparisons of periods of cardiac activity and intervals of 117
bradycardia in mussels under low nutrient conditions.
vi
Figure 13: Concordance in gene expression between intertidal and subtidal 118
mussels.
Figure 14: Metabolite screen comparisons of the Active and Bradycardia 119
mussel roups by Welch’s T-test *p<0.05 & **p<0.01.
Figure 15: Gene expression profiling reveals rhythmically expressed genes in 156
a high-stress tidal simulation.
Figure 16: Rhythmic expression of AER-genes under a high stress tidal 157
Simulation.
Figure 17: Pattern of expression of top 100 thermally-responsive genes, 158
discovered following a single thermal event under the low-stress
tidal simulation.
Figure 18: Patterns of heat shock protein gene expression under repeated 159
thermal events in the high-stress tidal simulation.
Figure 19: Gene expression patterns of particular genes under a high stress 164
tidal simulation.
List of Supplemental Information Figures
SI Figure. 1: Periodicity Analysis Using the COSOPT algorithm. 45
SI Figure 2: Modest Heating Results in the Differential Expression of 24% of 46
the Transcriptome.
SI Figure 3: Implementation of High Resolution Sampling in the Field. 47
SI Figure 4: CEBPE pattern of gene expression. 181
SI Figure 5: Distribution of peak gene expression of subsets of the early and 182
late-phase gene lists over 48 phase hrs.
SI Figure 6: The gene expression pattern of Calmodulin. 183
vii
Abstract
Mussels of the genus Mytilus are distributed world-wide and are commercially cultured
as a food source for humans. They are also an important ecological species that provide
substrate for hundreds of invertebrate and vertebrate organisms as well as an energy
source for a variety of marine species. Because of their commercial and ecological
importance many studies have been conducted to understand aspects of their
physiology. The dominant species on north-western rocky shorelines of North America is
Mytilus californianus. As a sessile species M. californianus must endure fluctuations in
temperature, salinity, food and oxygen due to the ebb and flood of the tide. During
periods of low tide, mussels are exposed to the terrestrial environment where they
cannot feed or breathe oxygen and are exposed to temperature fluctuations as a result
of solar radiation, cloud cover, wave splash and wind shear. Mussels counteract these
stresses by closing their valves to avoid dessication, and switching to anaerobic ATP-
producing pathways as well as depressing their metabolism. Thus, M. californianus is
well adapted to the highly variable environment of the intertidal zone. Using
microarray-based gene expression profiling and metabolite screens, we performed a
series of experiments aimed at understanding the fundamental mechanisms driving
physiology in an intertidal marine mollusc. Experiments were performed in a custom
built aquarium that simulated the intertidal zone, including precision control of tide,
solar radiation, day:night cycles, and food levels. In our first experiment, we subjected
mussels to balanced cycles of aerial emergence and submergence at constant
viii
temperature. Our findings revealed that >40% of the transcriptome exhibited rhythmic
gene expression and that depending on the specific tidal conditions 80-90% of the
rhythmic transcripts followed a circadian pattern of expression pattern with a period of
24-26 hr, while <2% followed a tidal pattern 10-14hr. Our data indicate that the
circadian 24 hr cycle is the dominant driver of rhythmic gene expression in this intertidal
inhabitant despite the profound environmental and physiological changes associated
with aerial exposure during tidal emergence. Metabolite profiles of the same samples
revealed that 24 metabolites oscillated significantly with a 12 hr period that was linked
to the tidal cycle. These data confirmed the presence of alternating phases of
fermentation and aerobic metabolism and highlight a role for carnitine conjugated
metabolites during the anaerobic phase of this cycle. We also observed mussels that
spontaneously open and close their valves in constant submerged conditions and a
comparison of the expression and metabolite abundances revealed a close similarity in
gene expression and utilization of metabolic pathways between subtidal and intertidal
physiology as it relates to valve gape state. Lastly, we subjected mussels to an extreme
environment that consisted of cycles of long aerial emergence periods combined with a
daily heat stress. Surprisingly, the molecular phenotype was notably different from that
observed under our more benign conditions, suggesting that M. californianus has a
highly flexible physiology that allows it to make acute and complex cellular adjustments
that allow it to buffer intense fluctuations in the often unpredictable environment
within the intertidal zone. These experiments provide new insights and interpretations
ix
of intertidal physiology that can be used as a reference source for comparative studies
of rhythmic biology in other organisms.
1
Introduction
Mytilus californianus as Sentinels of the Intertidal Zone
Mytilus californianus beds comprise the highest single species biomass of the near shore
ecosystem of the northeastern Pacific rocky shoreline. M. californianus populations
range from Baja, California to the Aleutian Islands of Alaska. The matrices of the beds
harbor over 300 species of intertidal organisms throughout this geographic range and
therefore it is one of the most diverse habitats on earth (27). As prey, M. californianus
provides a source of energy for a variety of near shore invertebrates, fish, birds, and
mammals. Because they are filter feeders, they play a role in controlling concentrations
of suspended detritus, phytoplankton, and zooplankton, and in turn release nitrogenous
and phosphoric waste material into the water column (3) which can foster the growth of
suspended phytoplankton (21). Furthermore, released fecal matter sinks to the
surrounding seabed, and the added organic material may promote the growth of
established benthic bacterial communities (19). Therefore, M. californianus plays a
central role in bio-geochemical, trophic, and energy conversion processes that occur
within the boundaries of the coastal ecosystem. Because of their varied impacts on the
intertidal zone, the ecological success of this foundation species underpins the stability
of community structure of northeastern Pacific shores.
2
Life in a Fluctuating Environment
Mytilus californianus are sessile and exhibit limited mobility after larval settlement.
Because they are ectotherms held stationary to the rocky surface by fibrous byssus
threads, mussels must possess a well-adapted physiology that can tolerate the large
variability in the physical and biological factors associated with the intertidal zone.
These alterations in environment occur over hourly, daily, and seasonal time scales and
are closely associated with the ebb and flood of the tide. During high tide, mussels are
immersed, which exposes them to food, oxygen, and cool temperatures. During low
tide, mussels face climatic temperatures which can often be higher than those of the
prevailing sea surface and close their valves to avoid dessication. During this period of
low tide associated stress, homeostasis is possibly maintained by suppression of high
energy demanding processes, such as digestion and protein synthesis (14). Because M.
californianus have limited air breathing capacity when aerially exposed (4), efficient,
fermentation metabolic pathways, such as the glucose-succinate pathway, are used to
synthesize ATP (9). These mechanisms allow mussels to survive lengthy bouts of aerial
exposure, which is a common occurrence in high shore microhabitats (10, 13).
Environmental factors that have been demonstrated to mediate mussel physiology
include variation in food concentration (4), level of aerial exposure (10), temperature
(15, 12, 24, 1), salinity (5, 18), dissolved oxygen (2, 17), and wave force (26). As
ectotherms their body temperatures reflect that of the prevailing environment and as a
3
consequence, fluctuating environmental temperature typifies the challenges mussels
face as inhabitants of the intertidal zone. Its importance is underscored by the large
volume of studies focused on the effects of temperature on mussel biology. A two-year
study of a single shore in Monterey Bay, California revealed that the average July water
temperature was 10°C, while the maximum air temperature during the same month was
35°C (15). Therefore, in a single day, mussels can experience radical changes in
temperature as a result of tidal fluctuations and these thermal fluxes bring forth marked
changes in physiology. For example, mussels exposed to higher temperatures reveal
greater levels of heat shock proteins (15, 16, 23). Hence, one of the physiological traits
that mussels possess is a tightly coupled sensory–physical response system that reacts
concomitantly to changes in the prevailing environmental conditions. Their stationary
lifestyle and acute, plastic responses to the environment make mussels exemplary test
subjects in studies that can broaden our insights into how animals maintain homeostasis
in the face of a constantly changing environment. The focus of this dissertation is to
investigate the molecular mechanisms mussels use to deal with regular cycles of two
radically different environmental states, aerial emergence and submergence, by
measuring the global expression of their genes and metabolites under simulated and
field conditions. Using global screens to relate the plastic response of genes to core
metabolic pathways significantly advances our knowledge of the synergy of the
biological systems necessary to persist under the harsh conditions of the intertidal zone.
4
Interpreting Mussel Physiology using Molecular Techniques
The number of experiments that have documented the relationships between mussel
physiology and cycles of the ebb and flood of the tide is limited. The studies that do
exist are limited to observations of behavior (6), foraging (22), metabolism (25), and
biological membranes (28). These studies contribute significantly to our understanding
of the survival strategies used by intertidal organisms that are subjected to cycles of
aerial emergence and submergence. However, cDNA microarray technology provides a
broader analysis of the potential mechanisms that give rise to the processes observed in
the aforementioned studies. The use of cDNA microarrays allows for the monitoring of
mRNA abundance of thousands of genes simultaneously. The basic procedure of
microarray analysis is to deposit a small amount of sequenced cDNA probes on a glass
slide, and then hybridize fluorescent dye-labeled mRNA targets to the cDNA probes.
The amount of target that hybridizes to its corresponding probe is proportional to the
abundance of the transcript in the sample, and is detected as fluorescence intensity.
Microarray data are enormously powerful because they expose how the transcriptome
is behaving at a particular moment in time, which in turn gives some indication of
broader physiological functions that may be operating simultaneously. Indeed,
transcript abundance does not fully account for the variation in enzyme and protein
abundance. This is partially because mRNA undergoes post-modifications and the decay
rates of mRNA and the proteins they synthesize differ. However, there are published
physiologic studies, most notably those that investigate the heat shock response, that
5
reveal direct links between mRNA abundance and initiation of enzymatic responses
(reviewed in 11). Microarray-based transcriptome-wide profiling is especially powerful,
when used in time series studies because not only can these data allow inferences to be
drawn about co-expressed genes and organismal demand but they can also elucidate
how genes operate together as temporally sensitive transcriptional networks.
Furthermore, measurements of physiological process such as behavior, enzyme activity
or metabolites, in conjunction with measurements of mRNA abundance provide a sound
set of data from which associations can be made between the environment, cellular
signaling, gene expression, organismal response, ecology and adaptation. These
associations can eventually give insight into the evolution of the organism under study.
The first use of microarrays in studies involving M. californianus was achieved in 2008.
Place (20) prepared expression profiles of field mussels sampled simultaneously from
numerous populations separated by large (km) geographic scales. In the same year, our
laboratory published the first-ever observation of global gene expression over a series of
natural tides (12). Mussels were sampled every three hours from a high shore micro-
environment that experienced cyclical patterns of aerial emergence and submergence,
and also from a low shore environment with greater wave splash and shorter periods of
aerial exposure. Results revealed broad oscillations in gene expression in mussels from
both environments. Mussels sampled from the high shore showed a greater fold
change in transcript abundance than mussels lower on the shore. The oscillations
6
observed in high shore mussels revealed patterns of expression that could theoretically
be associated with tidal cycle; however, the peak timings and periods could not be
statistically linked to the environment. We postulated that the highly variable nature of
the intertidal environment, in particular the large thermal cycles associated with
exposure to summer mid-day low tides, might have masked a robust tidal pattern of
gene expression. In conclusion, our study resulted in the discovery of molecular states
in M. californianus and represents the first high-resolution, time series analysis of global
gene expression of any marine invertebrate in the natural environment.
Gene expression Under a Simulated Environment
The major goal of this study is to measure physiology of M californianus under
simulated environmental conditions in the laboratory. Eliminating the stochasticty of
the field environment allows a more controlled assessment of how environmental cues
are transduced to the genome. We designed a ≈50 gallon aquarium fitted with pumps
that create a rising and lowering water level. We arranged heat lamps above the tank in
order to simulate warming patterns that occur during midday low tides and overhead
lights were used to simulate day:night cycles. Using a multi-parameter computerized
controller, we were able to program the timing of the simulated environmental
variables with a resolution of one minute.
7
This thesis integrates four experiments, each described in separate chapters. In Chapter
1, “Circadian Cycles are the Dominant Transcriptional Rhythm in the Intertidal Mussel
Mytilus californianus”, we measured transcript abundance over evenly spaced cycles of
aerial emergence/submerges and day:night patterns (7). We also measured gene
expression in a field environment with limited stochasticity and compared the results to
the simulation data. In Chapter 2, “High-resolution Analysis of Metabolic Cycles in the
Intertidal Mussel Mytilus californianus” (8), we measured the metabolites in gill tissue
over cycles of aerial emergence and immersion. In Chapter 3, “Molecular and
Biochemical Observations of Mytilus californianus under Constant Submergence”, we
compared gene expression of mussels with closed valves versus open valves under
constantly submerged conditions. Finally, in Chapter 4 “Transcriptome-wide Gene
Expression in Mytilus californianus under Simulated Combined Stresses of Aerial
Exposure and Solar Radiation”, we examined expression under prolonged aerial
exposure periods while simultaneous subjecting mussels to thermal warming during
daytime low tide.
8
Introduction: References
1. Anestis A, Lazou A, Portner HO , Michaelidis B (2007). Behavioral, metabolic, and
molecular stress responses of marine bivalve Mytilus galloprovincialis during
long-term acclimation at increasing ambient temperature. American Journal of
Physiology - Regulatory, Integrative and Comparative Physiology. 293, R911-
R921.
2. Anestis A, Portner HO , Michaelidis B (2010). Anaerobic metabolic patterns
related to stress responses in hypoxia exposed mussels Mytilus galloprovincialis.
Journal of Experimental Marine Biology and Ecology. 394, 123-133.
3. Asmus RM , Asmus H (1991). Mussel beds: limiting or promoting phytoplankton?
Journal of Experimental Marine Biology and Ecology. 148, 215-232.
4. Bayne B, Hawkins A, Navarro E , Iglesias I (1989). Effects of seston concentration
on feeding, digestion and growth in the mussel Mytilus edulis. Marine ecology
progress series. 55, 47-54.
5. Braby CE , Somero GN (2006). Following the heart: temperature and salinity
effects on heart rate in native and invasive species of blue mussels (genus
Mytilus). Journal of Experimental Biology. 209, 2554-2566.
6. Coleman N , Trueman ER (1971). The effect of aerial exposure on the activity of
the mussels Mytilus edulis L. and Modiolus modiolus (L.). Journal of Experimental
Marine Biology and Ecology. 7, 295-304.
7. Connor KM , Gracey AY (2011). Circadian cycles are the dominant transcriptional
rhythm in the intertidal mussel Mytilus californianus. Proceedings of the National
Academy of Sciences of the United States of America. 108, 16110-16115.
8. Connor KM , Gracey AY (2011). High-resolution analysis of metabolic cycles in
the intertidal mussel Mytilus californianus. American Journal of Physiology-
Regulatory Integrative and Comparative Physiology. 302, R103-R111.
9. de Zwaan A (1977). Anaerobic energy metabolism in bivalve molluscs.
Oceanography and Marine Biology: An Annual Review. 15, 103-187.
9
10. Dehnel PA (1956). Growth Rates in latitudinally and vertically Separated
populations of Mytilus californianus. The Biological Bulletin. 110, 43-53.
11. Gracey A , Cossins AR (2003). Application of microarray technology in
environmental and comparative physiology. Annual Reviews Physiology. 65, 231-
259.
12. Gracey AY, Chaney ML, Boomhower JP, Tyburczy WR, Connor K , Somero GN
(2008). Rhythms of gene expression in a fluctuating intertidal environment.
Current Biology. 18, 1501-1507.
13. Griffiths C , Hockey P (1987). A model describing the interactive roles of
predation, competition and tidal elevation in structuring mussel populations.
South African Journal of Marine Science, 547-556.
14. Hand SC , Hardewig I (1996). Downregulation of cellular metabolism during
environmental stress: Mechanisms and implications. Annual Review of
Physiology. 58, 539-563.
15. Hofmann G , Somero G (1995). Evidence for protein damage at environmental
temperatures: seasonal changes in levels of ubiquitin conjugates and hsp70 in
the intertidal mussel Mytilus trossulus. Journal of Experimental Biology. 198,
1509-1518.
16. Hofmann GE , Somero GN (1996). Interspecific variation in thermal denaturation
of proteins in the congeneric mussels; Mytilus trossulus and M. galloprovincialis:
evidence from the heat-shock response and protein ubiquitination. Marine
Biology. 126, 65-75.
17. Hole LM, Moore MN , Bellamy D (1995). Age-related cellular and physiological
reactions to hypoxia and hyperthermia in marine mussels. Marine Ecology
Progress Series. 122, 173-178.
18. Lockwood BL , Somero GN (2011). Transcriptomic responses to salinity stress in
invasive and native blue mussels (genus Mytilus). Molecular Ecology. 20, 517-
529.
10
19. Newell RIE, Fisher TR, Holyoke RR, Cornwell JC, Dame RF , Olenin S (2005).
Influence of eastern oysters on nitrogen and phosphorus regeneration in
Chesapeake Bay, USA (The Comparative Roles of Suspension-Feeders in
Ecosystems): Springer Netherlands, pp. 93-120.
20. Place SP, O’Donnell MJ , G.E. Hofmann (2008). Gene expression in the intertidal
mussel Mytilus californianus: physiological response to environmental factors on
a biogeographic scale. Marine Ecology Progress Series. 356, 1-14.
21. Prins TC , Smaal AC (1994). The role of the blue mussel Mytilus edulis in the
cycling of nutrients in the Oosterschelde estuary (The Netherlands).
Hydrobiologia. 282-283, 413-429.
22. Reinsel KA (2004). Impact of fiddler crab foraging and tidal inundation on an
intertidal sandflat: season-dependent effects in one tidal cycle. Journal of
Experimental Marine Biology and Ecology. 313, 1-17.
23. Roberts DA, Hofmann GE , Somero GN (1997). Heat-Shock Protein Expression in
Mytilus californianus: Acclimatization (seasonal and tidal-height comparisons)
and acclimation effects. The Biological Bulletin. 192, 309-320.
24. Schneider KR, Van Thiel LE , Helmuth B Interactive effects of food availability
and aerial body temperature on the survival of two intertidal Mytilus species.
Journal of Thermal Biology. 35, 161-166.
25. Simpfendorfer RW, Vial MV , Monsalve A (1997). The adductor muscle pyruvate
kinase from the intertidal bivalve Mytilus chilensis (Hupe) : evidence of the
presence of a phosphorylated form of the enzyme during the entire tidal cycle.
Journal of Experimental Marine Biology and Ecology. 213, 169-179.
26. Steffani C , Branch G (2003). Growth rate, condition, and shell shape of Mytilus
galloprovincialis: responses to wave exposure. Marine Ecology Progress Series.
246, 197–209.
27. Suchanek TH (1981). The role of disturbance in the evolution of life history
strategies in the intertidal mussels Mytilus edulis and & Mytilus californianus.
Oecologia. 50, 143-152.
11
28. Tremblay Rj , Pellerin-Massicotte J (1997). Effect of the tidal cycle on lysosomal
membrane stability in the digestive gland of Mya arenaria and Mytilus edulis L.
Comparative Biochemistry and Physiology Part A: Physiology. 117, 99-104.
12
Chapter 1: Circadian cycles are the dominant transcriptional
rhythm in the intertidal mussel Mytilus californianus
Chapter 1: Abstract
Residents in the marine intertidal, the zone where terrestrial and marine habitats
converge, inhabit an environment that is subject to both the 24 hr day and night daily
rhythm of the terrestrial earth and also the 12.4 hr ebb and flow of the tidal cycle. Here,
we investigate the relative contribution of the daily and tidal cycle on the physiology of
intertidal mussels, Mytilus californianus, by monitoring rhythms of gene expression in
both simulated and natural tidal environments. We report that >40% of the
transcriptome exhibits rhythmic gene expression and that depending on the specific
tidal conditions between 80-90% of the rhythmic transcripts follow a circadian
expression pattern with a period of 24-26 hr. Consistent with the dominant effect of the
circadian cycle we show that the expression of clock genes oscillates with a 24 hr period.
Our data indicate that the circadian 24 hr cycle is the dominant driver of rhythmic gene
expression in this intertidal inhabitant despite the profound environmental and
physiological changes associated with aerial exposure during tidal emergence.
13
Chapter 1: Introduction
The biology of organisms is closely linked to temporal changes in their environmental
surroundings. The earth, revolving every 24 hrs, exposes animals to highly predictable
daily patterns of light and temperature. To match these cycles, most living organisms
have evolved biological rhythms that are manifest in daily patterns of behavior and
physiology. These biological rhythms are accompanied by large-scale oscillations in
gene expression that regulate the timing of many physiological processes (1). For
inhabitants of the intertidal, superimposed on this daily cycle are environmental
changes linked to the tidal cycle (2). In this paper we explore how environmental
changes in this habitat are transduced into coordinated changes in physiological
processes by examining gene expression cycles in a sessile intertidal inhabitant, the
California ribbed mussel, Mytilus californianus Conrad.
14
Chapter 1: Materials and Methods
M. californianus were acclimated to lab simulated or natural tidal cycles for >4 weeks
prior to sampling. Pooled total RNA was prepared from 4 mussels sampled at 2 hr
intervals, amplified, and hybridized to cDNA microarrays. Periodic transcripts were
detected using JTK_CYCLE (3). Over-represented classes of genes were identified using
DAVID (35) by mapping the mussel sequences to their putative human orthologs. Full
details of the methods can be found in the Supplemental Information.
15
Chapter 1: Results and Discussion
To investigate biological rhythms in intertidal inhabitants we created a simulated
intertidal environment in which mussels were acclimated to alternating high and low
tides of 6 hr duration, with a 12 hr cycle of light and dark. Gill samples from 4 mussels
per time-point were collected every 2 hr for 96 hr, covering 8 high tides and 8 low tides.
The simulation allowed the effects of aerial warming such as occurs during low tide on a
sunny day to be tested, and on day 4 (at the 80 hr time-point) a warm low tide was
simulated in which body temperature was allowed to increase by 7°C compared to the
normal low tide temperature (Fig. 1A).
Relative transcript abundance was measured by hybridization to microarrays and in the
first instance, we identified rhythmically expressed transcripts across the entire 96 hr
time-series using the JTK_CYCLE algorithm (3). At a false discovery rate of <0.05, 2,756
transcripts were found to oscillate with a period of between 10 and 28 hr. Two groups
of rhythmic transcripts were identified, one group that oscillated with a period of 12 hr,
and a larger group that oscillated at longer periods with a broad peak centered at 26 hr
(Fig. 1B). Just 236 transcripts oscillated with what we defined as a tidal rhythm of 10-14
hr, while 2,365 transcripts followed what we defined as a circadian rhythm of 22-28 hr.
Thus, there were approximately 10-fold more transcripts whose rhythm followed a
circadian pattern rather than that of the simulated tidal cycle. A similar pattern was
detected when the data were analyzed using a second algorithm, COSOPT (4) (SI Fig. 1).
16
Analysis of the phase of the transcripts that comprised the 12 and 26 hr peaks, that is to
say the time of day at which expression is highest, reveal that the expression of tidal
transcripts tends to peak at the middle of low tide (Fig. 1C), while the peak phase of
circadian transcripts occurs at dusk and coincides with the first hour of darkness (Fig.
1D). Inspection of a heatmap showing the rhythmic expression of each transcript sorted
according to phase, shows that circadian transcripts fall into two groups that share
similar phases, one expressed around dawn and the other at dusk (Fig. 2A). This pattern
is similar to the circadian rhythm observed in plants (5) and contrasts that of mammals
in which circadian transcripts show limited bias for phase and peak at all times of the
day (6).
17
0 2 4 6 8 10 12 14 16 18 20 22 24 26
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Fig 1. Gene expression profiling reveals tidal and circadian rhythms in a simulated intertidal
environment. (A) Representation of the environmental conditions used in the simulated tidal
environment. Animals were sampled every 2 hr starting at 7am. Animals were emerged during low tides
which occurred from 12 am – 6 am and from 12pm – 6pm, while sunrise and sunset occurred at 6am and
6pm. At 80 hr the mussels were warmed to 24°C during low tide. (B) Histogram showing the period
length of 2,756 statistically significant rhythmic transcripts. (C) Histogram showing the phase of 236
transcripts that had a period of 10-14 hr. A phase of zero means that the peak expression of the
transcript coincided with the 7 am start of the time-course. (D) Histogram showing the phase of 2,365
transcripts that had a period of 22-28 hr.
18
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
10-14 hr 25-28 hr 22-24 hr Number of rhythmic transcripts identified
885 1716 636
96 hrs 78 hrs
transcript #
Rhythmic transcripts over first 78 hrs only
Tidal
mRNA expression (log
2
)
Rhythmic transcripts over 96 hrs
236
981
1,384
138
260
238
>2.0
Relative expression
<0.5 1.0
A B
C
D
-4
-3
-2
-1
0
1
2
-3
-2
-1
0
1
2
RORB
CRY1
Circadian
NAMPT
HDAC1
Time (days)
Fig. 2. Gene expression patterns correspond to tidal height and dusk and dawn. (A) Heatmap showing
the rhythmic expression of tidal and circadian transcripts that were identified as rhythmic across the
entire 96 hr time-course or (B) across the first 78 hr only. Yellow or blue color indicates that the
expression of a transcript was greater or less than the median expression of the transcript,
respectively. Transcripts were grouped according to period and then ordered according to their
phase. (C) Venn diagram shows the overlap between the lists of transcripts that were rhythmic across
the 96 hr time-course versus transcripts which were rhythmic only prior to the heating episode at 80
hr. (D) Gene expression profiles of RORB and CRY1 determined by qPCR, and NAMPT versus HDAC1
quantified by microarray.
19
Effect of Temperature on Circadian and Tidal Rhythms
As an ectotherm the body temperature of mussels fluctuates with that of the
environment. The ebb and flow of the tide can create thermal cycles, with body
temperature at low tide often increasing during the day due to solar heating (7, 8), and
declining at night due to colder air temperatures. To investigate the role that a low tide
heating episode would have on gene expression rhythms, we imposed a modest heating
episode of +7°C during a low tide on the fourth day of the simulation, and identified a
subset of 636 transcripts that lost their periodicity in the time following the heating
episode (Figs. 2B and 2C). In total, we identified 374 and 2,863 transcripts with a tidal or
circadian period respectively, after merging the lists of genes that were periodic over
the entire time-course as well as those that lost their periodicity due to heating.
Proportionally, tidal transcripts were more affected by heating than circadian
transcripts, with 37% of tidal transcripts affected (138/374 transcripts), compared to
just 17% of circadian transcripts (498/2,863 transcripts) (Fig. 2B). This may indicate that
the regulatory mechanisms underlying the circadian cycle of mussels is able to
compensate better for temperature changes than the pathway that controls tidally-
regulated gene expression. The data also revealed that moderate heating has a
profound effect on gene expression and 24% of the transcriptome (2,484/10,410
transcripts) exhibited a significant change in expression in the 18 hr that followed the
onset of the low tide warming episode (SI Fig. 2). Elevated environmental temperatures
have been shown to have deleterious effects on intertidal mussels particularly in the
20
context of thermally-induced protein denaturation (9), and our data indicate that
temperature perturbations have further consequences through their effect on the
biological rhythms of intertidal inhabitants.
Clock Genes Oscillate with a Circadian Rhythm in the Intertidal
Circadian oscillations in gene expression have been observed in a wide range of
organisms, including mammals (10), plants (11), and flies (12), and are regulated by a set
of so-called ‘clock’ genes which comprise a set of core circadian oscillators that activate
the transcription of a range of target genes (1), and are themselves often rhythmically
expressed (6). To explore the expression patterns of clock genes in mussels we
identified mussel orthologs for the mammalian clock genes CLOCK, BMAL, CRY1, and
RORB by sequencing the M. californianus transcriptome. We monitored their
expression under our simulated tidal conditions using quantitative reverse-transcriptase
PCR (qPCR) which revealed that the expression of CRY1 and RORB (NR1F2) was rhythmic
(p<0.05, JTK_CYCLE) (Fig. 2D). Cryptochrome 1 is a key component of the circadian
oscillator (13) while RAR-related orphan receptor B is a nuclear receptor which
coordinates metabolism with the circadian clock (14). The detection of rhythmic clock
gene expression is strong evidence that the core circadian oscillator is active in mussels
and thus may play a role in orchestrating the circadian transcriptional cycles that are
manifest in mussel gill tissue. Further evidence that the circadian rhythm detected in
mussels shares characteristics with that of mammals comes from the discovery that
21
Histone deacetylase 1 (HDAC1) and Nicotinamide phosphoribosyltransferase (NAMPT)
display a circadian profile in mussels (Fig. 2D). Circadian control of transcription is
believed to be mediated by histone acetylation, regulated in part by HDACs, which by
altering the structure of the epigenome can alter the expression of large numbers of
genes (15, 16), while circadian regulation of cellular metabolism is mediated through a
feedback loop involving NAMPT which catalyzes the rate-limiting step in
NAD
+
production (17).
Functional Composition of Rhythmic Tidal Transcripts
Analysis revealed that the transcripts up-regulated at low tide (Fig. 3A) were enriched
for genes involved in GO Biological Process category of regulation of transcription
(p=0.005) (SI Table 1), while no over-represented themes were detected for the
transcripts up-regulated at high tide (Fig. 3B) (SI Table 2). For example, the low tide list
included 3 early genes, JUN, FOSL2 and IER5L (Fig. 3C) consistent with their role as genes
which respond immediately to an extracellular stimulus which in the context of this
study was aerial emergence. Early genes are regulators of cell proliferation and the list
of low tide genes includes anti-proliferative genes such as GADD45 and BTG1,
transcription factors such as CEBPE, DUSP7, MAP4K3, as well as 8 zinc finger proteins (SI
Table 1). Elevated expression of Ceramide kinase and Ceramide glucosyltransferase
during low tide was further evidence that low tide is a period of increased regulatory
activity because ceramide 1-phosphate is a potent signaling molecule (18). A number of
22
stress-related proteins were induced at low tide such as heat shock proteins 70 and 90
(HSP70, HSP90), 3 apoptosis-inhibiting baculoviral IAP repeat-containing proteins
(BIRC2, BIRC3, BIRC8), and 2 inhibitors of NF-kappa-B (IKBA, IKBE) (Fig. 3D). This
observation is consistent with a previous study in which we reported that HSP70 is
coexpressed with anti-apoptosis genes (19), suggesting either that aerial exposure is
perceived as an inducer of cell stress by mussels, or that this expression program is
anticipatory, induced in preparation for heat stress which can occur at low tide.
Anticipatory programs of heat shock and cold shock protein expression in mornings and
evenings respectively have been reported in mammals (20, 21), suggesting that
transcriptional programs that anticipate temperature regimes might be conserved
ancestral traits.
During low tide, M. californianus, like most bivalves, close their shells, enter hypoxia,
and switch to anaerobic metabolism (22) and our data revealed a number of transcripts
whose expression could be linked to hypoxia. For example, we observed that transcript
levels of 2 isoforms of carbonic anhydrase (CA2, CA13) were elevated during low tide
(Fig. 3E). Members of this gene family are hypoxia-inducible in mammals (23), and their
induction at low tide suggests that they may play a functional role in counteracting
tissue acidosis that has been reported during emergence (24) by catalyzing the
conversion of CO
2
and protons to bicarbonate. Transcripts for cAMP responsive element
binding protein-like 2 (CRBL2) exhibited particularly robust tidal oscillations (Fig. 3F),
23
suggesting that the cAMP-dependent pathway is activated during aerial emergence,
consistent with the role of cAMP as a signal of redox state (25) and as a key signaling
molecule in bivalves (26).
24
-4
-2
0
2
4
6
-1.0
-0.5
0.0
0.5
1.0
-1.0
-0.5
0.0
0.5
1.0
169 transcripts
peak at high
tide
205 transcripts
peak at low
tide
Fos-related antigen 2
Immediate early response
gene 5
Transcription factor AP-1
cAMP-responsive
element-binding protein
like 2
qPCR
microarray
A
E
D
C
B
F
Carbonic anhydrase 13
Carbonic anhydrase 3
-1.0
-0.5
0.0
0.5
1.0
NF-kappa-B inhibitor alpha
NF-kappa-B inhibitor epsilon
mRNA expression (log
2
)
>2.0
Relative expression
<0.5 1.0
Time (tidal intervals) Time (tidal intervals)
Fig. 3. Rhythmic expression of transcripts correlated with low and high tide. Gene expression profile of
transcripts whose expression peaks during low tide (A), or high tide (B). Transcripts are ordered with respect
to the relative change in expression they exhibit following the heating episode at 80 hr. Rhythmic expression
of (C) early genes, (D) NF-kappa B inhibitor genes, (E) carbonic anhydrase genes which exhibit peak
expression during low tide, and (F) CRBL2 whose transcript accumulates during low tide quantified by both
microarray and qPCR.
25
Concordance between Simulated and Field Intertidal Environments
Next, we sought to verify that the rhythms of gene expression observed under
simulated tidal conditions are evident under natural tidal regimes in the marine
environment. To accomplish this, mussels were acclimatized in the field to mid-
intertidal conditions which ensured that mussels spent approximately half the duration
of each tidal cycle submerged and half emerged (Fig. 4A, SI Fig. 3). Gill samples from 4
mussels per time-point were collected every 2 hr for 50 hr, and a search for periodic
genes identified 697 rhythmic transcripts, of which 72 exhibited a tidal periodicity of 10-
14 hr, and 501 exhibited a circadian rhythm of 22-28 hr. A priori, we predicted that
transcripts in the field would not exhibit a constant tidal period because the time and
height of each high and low tide shifts with each tidal cycle (Fig. 4A). Despite this, 28 of
the 72 transcripts which exhibited a statistically significant 10-14 hr period in the field
also exhibited a tidal rhythm in the simulation study (Fig. 4B, SI Table 3). Closer
inspection of the field data revealed that a number of the transcripts that were
identified as tidal in the simulated experiment displayed rhythmic transcriptional
oscillation in the field and that their profile matched the tidal cycle observed in the field.
Using the profile of these transcripts as a guide we identified an additional 37 transcripts
whose expression follows a tidal rhythm in the field, yielded a total of 109 tidal genes.
Thus, there are over 4 times as many genes that follow a circadian versus a tidal rhythm
at this particular intertidal location. Overall, we identified fewer rhythmic transcripts in
the field time-course compared to the laboratory simulation which we speculate is
26
attributed in part to the higher temperature variation which can affect the periodicity of
some transcripts, as evident in our simulated dataset.
The alternating bouts of submergence and aerial emergence represent candidate cues
responsible for the tidal pattern of rhythmic gene expression. To investigate this we
collected a corresponding set of gene expression data from field animals which were
held under subtidal conditions of almost constant temperature (16-17°C) and which
never experienced aerial emergence (Fig. 4C). These data revealed that just 41
transcripts exhibited an expression cycle with a tidal period, of which just 1 transcript
overlapped with those identified as tidal in both the simulated or field intertidal
datasets (Fig. 4D, SI Table 4). These data support the hypothesis that the dominant
driver of tidal gene expression rhythms is the episodes that intertidal inhabitants spend
in and out of the water. In contrast, the molecular signature of subtidal animals was
dominated by a pattern of circadian gene expression with over twice as many genes
exhibiting a circadian rhythm in subtidal versus intertidal field animals (1,224 versus 501
transcripts respectively). Interestingly, the heatmaps reveal that the phase of circadian
gene expression was similar in both subtidal and intertidal field mussels suggesting that
the same cues may be driving circadian expression cycles in both habitats.
Combining the lists of rhythmic transcripts identified under both simulated and natural
field conditions revealed that >40% of gill transcripts exhibited rhythmic gene
27
expression in at least one of the environmental conditions studied (3,934 and 476
transcripts with circadian or tidal rhythms respectively, of 10,410 total transcripts) (Fig.
4D). These data indicate that while some transcripts show rhythmic expression in all the
datasets, others are only rhythmic under specific conditions. The source of this
variability is unclear but results obtained in other organisms have reported similar
findings of inter-laboratory and inter-experimental variation in circadian gene
expression (27). For example, studies on Arabidopsis have revealed that 89% of the
transcriptome cycles under at least one set of environmental conditions, but that only
<51% of the transcriptome cycles in any single experiment, and that <1% of transcripts
cycle in every experiment (5). Together with similar findings in mammals (6, 20), it has
become clear that circadian cycles of gene expression are driven by endogenous internal
clocks, local or systemic cues, as well as external environmental factors (1). In the case
of the current study, differences in the lists of cycling transcripts are most likely
attributed to differences in the prevailing environmental factors in each of the habitats
studied. For example, the temporal separation between low and high tide was abrupt in
the simulated experiment with mussels being either submerged or emerged, whereas
wave action in the field ensures that the delimitation between high and low tide is more
diffuse, and mussels may experience alternating bouts of submergence and aerial
emergence with each passing wave. Similarly, tidal cycles in the field were often
superimposed by thermal cycles, resulting in significant day-to-day and tide-to-tide
variation in environmental conditions.
28
Fig. 4. Cycles of gene expression show a
strong circadian rhythm in field intertidal
and subtidal environments. (A)
Environmental conditions experienced by
mussels located in intertidal and subtidal
locations in the field. Upper panel shows
the conditions experienced by the
mussels during the 3 weeks leading up to
the sampling period, and lower panel
shows the detailed conditions during the
50 hrs over which the animals were
sampled. Estimated body temperature is
illustrated by the red line and the
predicted tidal cycle by the blue line.
Dashed lines indicate the tidal height
inhabited by each set of mussels, and
circle symbols the sample time-points. (B)
Heatmap showing the rhythmic
expression of tidal and circadian
transcripts that were identified as
rhythmic in the field intertidal location
and (C) subtidal location. Bars above
heatmap indicate the predicted periods
of emergence based on the tidal cycle
and the times of dusk and dawn. (D) Venn
diagram showing the relative number of
tidal and circadian transcripts identified
in the simulated intertidal and field
intertidal and subtidal datasets.
-1
0
1
2 15
20
25
-1
0
1
2
3
10
15
20
25
Temperature (°C)
Tide height (m)
A
Intertidal height
Subtidal height
Temperature
(°C)
12am 12pm 12am 12pm 12am 12pm 12am
Tide height (m)
Sample time-points
Light:Dark
Intertidal height
B
Time
Circadian Tidal
109
152
349
Emergence
Emerged Emerge Emerged Emerge
Light:Dark
22-24hr 25-28hr
D
64 1 37 268 68 735
Tidal genes Circadian genes
Intertidal Subtidal Intertidal Subtidal
43
1
2
328
129 385
2313
36
Simulation Simulation
Circadian Tidal
C
41
480
744
22-24hr 25-28hr
>2.0
Relative expression
<0.5 1.0
Time (days)
29
Chapter 1: Conclusions
Previously we have reported that high intertidal mussels that experience a single brief
episode of submergence per day exhibit temporal compartmentalization of their
physiological processes, alternating between states of metabolism, cell division, or
thermal stress (19). In the current study we examined mussels living under relatively
benign tidal conditions to reveal that most rhythmic gene expression adheres to a 24 hr
cycle. This finding is surprising given that each episode of aerial emergence is associated
with profound physiological changes including physiological hypoxia and a cessation of
cardiac activity (22).
We do not know the extent to which either the circadian or tidal transcriptional rhythms
observed in our data are entrained and will persist in the absence of external cues. A
number of intertidal organisms, particularly crustaceans, display entrained behavioral
rhythms that follow persistent circadian (28) and tidal patterns (29, 30) even when held
under constant conditions. Similarly, mussels are reported to show entrained tidal
patterns of cell division (31). This has led to questions regarding the existence of a tidal
clock and the nature of its regulation, with some data suggesting the presence of a
circatidal clock with a period of 12.4 hrs (28), while other data support an alternative
hypothesis that postulates that two circalunidian clocks with 24.8 hr periods running in
antiphase to one another could generate peaks in activity every 12.4 hrs (32). While the
molecular basis of the tidal clock remains to be elucidated (2), our observation that 2
30
clock genes, CRY1 and RORB, exhibit a circadian cycle suggests that the circadian clock
apparatus is not co-opted to oscillate with a tidal rhythm in mussels.
The abundance of circadian transcripts raises questions regarding the nature of the
zeitgeber or exogenous cue that is responsible for synchronizing the mussel’s
transcriptional rhythm to the 24 hr rotation of the earth. The general consensus is that
bivalves possess photoreceptor cells (33) and will respond to light (34), and so we
speculate that light is the most plausible exogenous cue for synchronizing the mussel’s
transcriptional rhythm to the 24 hr rotation of the earth. Further experiments that
include light/dark and tidal manipulations will be required to elucidate further the
nature of endogenous rhythms in intertidal organisms and their relationship to
environmental cues. Overall, our results emphasize the central role that the circadian
rhythm plays in physiology even in organisms that experience strong sub-daily external
cues arising from the tidal cycle.
31
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35
Chapter 1: Supplemental Information
SI Material and Methods
Tidal simulation and sampling
Mytilus californianus of 4-5 cm length were collected at Zuma Beach, north of Los
Angeles, CA. In order to simulate intertidal conditions in the lab, the mussels were
maintained in aquaria and grown as a single layer of animals on shelves at the mid-level
height of the aquarium. The ebb and flow of the tide was simulated using computer-
controlled water pumps that regulated the depth of water in the aquaria by pumping
seawater in and out of the aquarium. The room was maintained at a constant
temperature of 17°C to ensure that the body temperature of the mussels remained
constant whether submerged or emerged in air. Ceramic heat lamps suspended above
the shelves were used to simulate the solar radiation and warming that the mussels
would experience during a midday summer low tide. The output from the lamps was
controlled by a computer program that defined the rate of warming, the upper limit,
and duration of heating episode, and this was fine-controlled using feedback sensor
comprised of a thermocouple which has been surgically inserted into the body cavity of
one of the mussels. Food as liquid algal cultures was continuously added to the water
during each episode of high tide submergence. The seawater in the aquaria was
constantly replaced such that 10% of the volume of the system was changed every day.
36
A tidal regime of alternating periods of 6 hrs in and 6 hrs out of the water was
established and mussels were acclimated to this regime for 4 weeks prior to the
commencement of sampling. Low tides occurred from 12am to 6am and 12pm to 6pm.
A light/dark cycle was imposed with period of darkness occurring from 6pm to 6am.
Animals were sampled every 2 hrs over a period of 96 hrs with the initial sample
collected at 7 am on day 1 of the experiment. This sampling regime ensured that 3
samples were collected per episode of low or high tide, with the first sample collected 1
hr into the episode, the second sample taken at the middle 3 hr time-point and the third
sample taken 1 hr prior to the change in tidal episode. Four individual mussels were
collected at each time-point. A warming event was simulated on day 4 of the
experiment by warming the mussels with the ceramic lamps during a low time episode
that starting at 12pm. The body temperature of the animals increased from 17 to 24°C
during this low tide episode.
Field sampling
Mussels collected from Zuma Beach were acclimatized to intertidal and subtidal
conditions in cages suspended from the dock at Wrigley Marine Science Center, Catalina
Island, CA (Supplemental Fig. 3). The tidal height of the intertidal cage was 0.92 m and
the subtidal cage was -0.85 m. Mussel body temperatures at each site were
approximated by the deployment of ‘robomussels’ that comprised a temperature
datalogger embedded in silicon sealant inside a mussel shell (1). Mussels were
37
simultaneously sampled from both cages every 2 hr over a period of 50 hr. Four
individual mussels were collected at each time-point.
M. californianus microarray construction
The M. californianus cDNA microarray was constructed from PCR amplicons derived
from cDNAs picked from7 high-quality cDNA libraries prepared from adult gill, adductor
muscle, mantle tissues, and early larval RNA samples. To ensure a good representation
of environmentally regulated genes, the adult tissue mRNA was isolated at two time-
points following exposures of the animals to either heat, cold, hypoxia, hypo-osmotic
stress, aerial emergence, or oxidative stress. The libraries were normalized and serially
subtracted so that cDNAs that had been isolated from previous libraries were physically
subtracted during the construction of each new library (2) 5’ and 3’ EST sequences were
generated from the clones and the sequences annotated using EST-Ferret, a custom
annotation pipeline (3). The EST sequences were clustered with CAP3 and the resulting
non-redundant contigs were queried against the public protein databases using BLASTX.
Each cDNA was assigned a putative identity based on search results against the
SwissProt and RefSeq protein databases and sequences that yielded a hit with an E-
value =<1e
-5
were annotated using the name of the gene for which it had the greatest
identity. Gene Ontology terms were assigned to cDNAs with hits in SwissProt by parsing
the SwissProt GOA association file with the SwissProt IDs obtained by the BLASTX
38
search. A putative non-redundant set of 10,410 cDNAs was identified and printed on in-
house prepared poly-L-lysine coated microarray slides.
RNA isolation and microarray hybridization
Total RNA was isolated from gill tissue using Trizol (Invitrogen) according to the
manufacturer’s instructions. The total RNA was purified further across glass-fiber filter
columns (Qiagen) according to the manufacturer’s instructions. An equal amount of
total RNA from 4 individual animals sampled at each time-point was pooled and
amplified RNA was prepared as previously described (4). Briefly, double-stranded cDNA
was prepared by reverse-transcribing 2 µ g pooled total RNA a 20 µ l reaction containing
20 pmols of T7-dT
15
VN primer, 50 mM Tris-HCl (pH 8.3), 75 mM KCl, 3 mM MgCl
2
, 50
µMs dNTP, 20U of RNaseOut (Invitrogen) and 100U of MMLV-reverse transcriptase
(Epicentre). The RNA and primer mixture was heat-denatured at 65°C for 10 min, then
the remaining components added, and the reaction incubated at 40°C for 2 hrs, and
then stopped by heating to 65°C for 15 min. Next, 60 µ l of in vitro transcription reaction
mixture, containing 53 mM Tris-HCl (pH 7.5), 13 mM NaCl, 8 mM MgCl
2
, 5.3% PEG 8000,
2.6 mM spermidine, 3.33 mM each (ATP, GTP, CTP), 2.5 mM UTP, 0.83 mM amino-allyl
UTP (Epicentre), 0.12U organic pyrophosphatase, 20U RNaseOut, 500U T7 RNA
polymerase (Epicentre), was added and the reaction incubated at 40°C for 18hr. The
resulting aRNA was purified using a Qiagen RNeasy kit, and half the aRNA was labeled
39
with Cy5 and the other half with Cy3. Fluorescently-labeled aRNA samples were
hybridized to the M. californianus array using an interwoven loop design which yielded a
balanced design in which each RNA sample was hybridized to either 2 or 4 arrays with
fluor-reversal. This loop hybridization design yields improved statistical inference of
microarray data (5).
Gene expression analysis
TIFF images of hybridized arrays were captured with an Agilent scanner (Agilent
technologies) and spot intensities quantified with Agilent Feature Extraction software
(ver. 9.5.1). Spot median pixel intensities without background correction were
collected and spatial intensity trends removed and individual channels normalized using
joint lowess transformation. Relative expression of each gene in each hybridization loop
was estimated using MAANOVA Version 0.98-7 for R using an ANOVA model in which
dye and sample were treated as fixed effects, and array was treated as random effects
(6). Gene expression data were centered by dividing the relative expression of each
gene by the median expression of that gene across the samples in the dataset.
Genes that exhibited rhythmic expression were identified using JTK_CYCLE (7) or
COSOPT (8) and p-values were corrected for a false discovery rate of <0.05 using a
standard method (9). Genes whose expression was altered as a result of the heating
event were identified using MAANOVA Version 0.98-7 in a model in which dye was
40
treated as a fixed effect, array was treated as a random effect, testing for differences in
expression between samples collected during the first 78 hr versus samples collected
post heating at time-points 80-96 hr. The p-values were adjusted for false discovery
rate (10) and a statistical threshold of p<0.01 defined a list of genes that were
statistically significantly differentially expressed between the samples collected prior to
and after the heating event. To aid analysis of the field intertidal expression dataset and
identify additional rhythmic transcripts, we selected 5 genes which exhibited highly
significant periodic expression in the simulated tidal dataset and used these as guide
genes to identify a set of transcripts that shared a Pearson correlation coefficient >0.7
with these guide genes in the field intertidal dataset.
We used DAVID (11) to investigate whether particular GO Biological Processes
categories were enriched in the lists of transcripts with a tidal rhythm. Putative human
orthologs of the Mytilus genes were identified using BLASTX homology searches
between the arrayed mussel 5’ EST sequences and the human protein collection in the
SwissProt database. We assigned the mussel genes with a putative human gene
identifier and used the lists of arrayed genes and mortality signature genes as the
reference and query gene lists in the analysis. The lists were first dereplicated so that
only one instance of a gene identifier was input into the analysis. A right-tailed Fisher’s
exact test was used to calculate a p-value which was adjusted using the Benjamini-
41
Hochberg false discovery rate procedure to yield a corrected p-value that determined
the probability that an enriched GO- was due to chance alone.
Quantitative RT-PCR analysis
For quantitative RT-PCR, 2 µ g of each experimental RNA sample was reverse-transcribed
with T
15
VN (1 µ g each per reaction) in a 20 µ l reaction. The product was diluted to 100
µ l with water and aliquots of 2 µ l of the resulting diluted cDNA were amplified in the
presence of SYBR Green (Takara PreMix ExTaq II) and intercalation of the dye was
monitored on a Stratagene MX4000 thermal cycler. Sequences the mussel clock genes
were identified from 60,000,000 single-ended 100bp RNAseq reads generated from M.
californianus gill tissue on the Illumina platform. Reads were assembled with Velvet
using multiple kmers lengths of 31-81. Clock orthologs were identified by BLASTX
analysis between the resulting contigs and the human clock gene sequences in the
SwissProt database. Forward and reverse primers for RORB were 5’-3’
AAACCCTTGCAACCTTCACA and CCCACGTAAACCACATGAAA, for CRY1 were 5’-3’
GTGTTTCTGCCCAGTTGGAT and CCTTCAACACGGGCAAGTAT and for Alpha-tubulin were
5’-3’ TCCAAGACACGGCAAATACA and TTGAAACCAGTTGGACACCA. All primers pairs
exhibited an amplification efficiency between 95% and 105%, and relative expression
was measured using the ∆ -∆ Ct method, using Alpha-tubulin as the reference transcript.
42
Accession numbers
The accession numbers associated with the tidally-regulated genes are provided in the
online supplemental material. An ArrayExpress accession number of the microarray
data is pending.
43
Chapter 1: References for Supplemental Materials and Methods
1. Helmuth B, Harley CDG, Halpin PM, O'Donnell M, Hofmann GE , Blanchette CA
(2002). Climate change and latitudinal patterns of Intertidal Thermal Stress.
Science. 298, 1015-1017.
2. Carninci P, Shibata Y, Hayatsu N, Sugahara Y, Shibata K, Itoh M, Konno H, Okazaki
Y, Muramatsu M , Hayashizaki Y (2000). Normalization and subtraction of cap-
trapper-selected cDNAs to prepare full-length cDNA libraries for rapid discovery
of new genes. Genome Research. 10, 1617-1630.
3. Gracey AY, Fraser EJ, Li W, Fang Y, Taylor RR, Rogers J, Brass A , Cossins AR
(2004). Coping with cold: An integrative, multitissue analysis of the
transcriptome of a poikilothermic vertebrate. Proceedings of the National
Academy of Sciences of the United States of America. 101, 16970-16975.
4. Brandish PE, Su M, Holder DJ, Hodor P, Szumiloski J, Kleinhanz RR, Forbes JE,
McWhorter ME, Duenwald SJ, Parrish ML, Na S, Liu Y, Phillips RL, Renger JJ,
Sankaranarayanan S, Simon AJ , Scolnick EM (2005). Regulation of gene
expression by lithium and depletion of inositol in slices of adult rat cortex.
Neuron. 45, 861-872.
5. Kerr MK CG (2001). Statistical design and the analysis of gene expression
microarray data. Genetical Research. 77, 123-128.
6. Kerr KM, Martin, M., and G. A. Churchill (2000). Analysis of variance for gene
expression microarray data. Journal of Computational Biology. 7, 819-837.
7. Hughes ME, Hogenesch JB , Kornacker K (2010). JTK_CYCLE: An efficient
nonparametric algorithm for detecting rhythmic components in genome-scale
data sets. Journal of Biological Rhythms. 25, 372-380.
8. Straume M (2004). DNA microarray time series analysis: Automated statistical
assessment of circadian rhythms in gene expression patterning. Numerical
Computer Methods, Pt D. 383, 149-166.
9. Storey JD , Tibshirani R (2003). Statistical significance for genomewide studies.
Proceedings of the National Academy of Sciences. 100, 9440-9445.
44
10. Benjamin Y HY (2001). The control of the false discovery rate in multiple testing
under dependency. The Annals of Statistics. 29, 1665-1668.
11. Dennis G BTS, Douglas A Hosack, Jun Yang, Wei Gao HCL, Richard A Lempicki
(2003). DAVID: Database for annotation, visualization, and integrated discovery.
Genome Biology. 4, P53.
45
SI Fig. 1. Periodicity Analysis Using the COSOPT algorithm. (A) Heatmap showing the rhythmic expression
of tidal and daily transcripts that were identified as rhythmic across the entire 96 hr time-course using
COSOPT. Yellow or blue color indicates that the expression of a transcript was greater or less than the
median expression of the transcript, respectively. Transcripts were grouped according to period and then
ordered according to their phase. (B) Heatmap showing the rhythmic expression of tidal and daily
transcripts that were only identified as rhythmic across the first 78 hr of the time-course. (C) Venn
diagram shows the overlap between the lists of transcripts that were identified as having a tidal rhythm
by JTK_CYCLE versus COSOPT. (D) Venn diagram shows the overlap between the lists of transcripts that
were identified as having a daily rhythm by JTK_CYCLE versus COSOPT.
SI Figures and Tables
10-14 hr 25-28 hr 22-24 hr
Number of tidal transcripts identified
36 338 620
JTK_CYCLE COSOPT
transcript #
Rhythmic transcripts over first 78 hrs only
Tidal
Circacdian
Rhythmic transcripts over 96 hrs
543
1016
1,163
415
364
311
>2.0
Relative expression
<0.5 1.0
A B
C D
Number of circadian transcripts identified
646 2217 637
JTK_CYCLE COSOPT
46
>2.0
Relative expression
<0.5 1.0
SI Fig. 2. Modest Heating Results in the Differential Expression of 24% of the Transcriptome. Heatmap
showing the 2,484 transcripts whose expression is significantly elevated following a 7°C warming
event at the 80 hr time-point (FDR corrected, p<0.01).
47
Field Location:
Wrigley Marine
Science Center,
Catalina Island, CA
Cages used as
surrogate tidal
habitats. Cage in
upper part of picture
served as intertidal
habitat and lower
cage served as
subtidal habitat
Cages were
suspended by rope at
appropriate height
from dock. Rope
allowed cages to be
quickly hauled up
allowing easy fast
access to samples.
SI Fig. 3. Implementation of High Resolution Sampling in the Field. Photographs show the
location and design of the field sampling experiment.
48
SI Table 1. List of transcripts represented in heatmap shown in Fig. 3A.
USC ID 5' Acc. # 3' Acc. # Putative ID
Myt_09K13 ES735872 NA Heat shock 70 kDa protein
Myt_35L14 ES402053 ES405899
Myt_44A08 GE760632 GE763165 cAMP-responsive element-binding protein-like
2
Myt_31M21 ES398477 ES400974 Unclassifiable EST
Myt_50N22 GE761909 GE762395 Unclassifiable EST
Myt_62O20 GE747701 GE747736 Unclassifiable EST
Myt_34B19 ES402562 ES405580 Complement C1q-like protein 3
Myt_35K17 ES406375 ES401992 Unclassifiable EST
Myt_23B16 ES391808 ES390603 Unclassifiable EST
Myt_34K09 ES398967 ES403458 Cdc42 homolog
Myt_27J01 ES393846 ES396736 Unclassifiable EST
Myt_46F05 NA GE758361 Unclassifiable EST
Myt_59A24 GE750456 GE748635 Guanine nucleotide-binding protein subunit
beta-2-like 1
Myt_64J04 GE753627 GE751297 Unclassifiable EST
Myt_21B04 ES395775 ES390564 Iduronate 2-sulfatase
Myt_29A07 ES407426 ES400247 CCAAT/enhancer-binding protein epsilon
Myt_30G23 ES401162 ES406590 Prolyl-tRNA synthetase
Myt_12B08 ES391882 ES396917 ETS homologous factor
Myt_39M08 ES405297 ES405150 Ring canal kelch homolog
Myt_37J11 NA ES406441 Unclassifiable EST
Myt_21F24 ES391202 ES389269 T-box transcription factor TBX20
Myt_63A13 GE753108 GE752493 Dolichyl-diphosphooligosaccharide--protein
glycosyltransferase 48 kDa
Myt_29C06 ES400276 ES400573 Feline leukemia virus subgroup C receptor-
related protein 2
Myt_12K08 ES394544 ES394518 Unclassifiable EST
Myt_42F24 ES398180 ES400550 Unclassifiable EST
Myt_33M03 ES405647 ES407224 Transcription factor AP-1
Myt_28B23 ES396056 ES396935 DNA-binding protein inhibitor ID-2-A
Myt_32C04 ES403448 ES405227 Baculoviral IAP repeat-containing protein 7
Myt_42O15 ES408060 ES401400 Unclassifiable EST
Myt_15E22 ES391539 ES395307 Unclassifiable EST
Myt_30I10 ES407353 ES407627 Unclassifiable EST
Myt_04B22 ES738439 NA Krueppel-like factor 6
Myt_09P07 ES738196 NA Myosin-2 essential light chain
Myt_33E23 ES401131 ES399668 NF-kappa-B inhibitor alpha
49
Myt_30C19 ES397849 ES403259 Transmembrane protein 47
Myt_39A14 ES399875 ES406885 Eukaryotic translation initiation factor 3 subunit
G
Myt_21L09 ES395521 ES395093 Myeloid differentiation primary response
protein MyD88
Myt_22O21 ES738842 NA Transcription factor kayak
Myt_49B15 GE761756 GE755590 UPF0392 protein F13G3.3
Myt_17P22 ES389869 ES392617 Suppressor of cytokine signaling 2
Myt_30D02 ES404598 ES398694 Carbonic anhydrase 2
Myt_33L18 ES400306 ES397560 Unclassifiable EST
Myt_42D08 ES401128 ES406433 MAM and LDL-receptor class A domain-
containing protein C10orf112
Myt_34J16 ES397636 ES405597 Unclassifiable EST
Myt_50J03 GE764656 GE756923 Headcase protein
Myt_31K21 ES399589 ES401243 Unclassifiable EST
Myt_27A08 ES390371 ES389785 NF-kappa-B inhibitor cactus
Myt_47B22 GE761378 GE762508 PR domain zinc finger protein 16
Myt_62M13 GE753109 GE750465 Growth arrest and DNA damage-inducible
protein GADD45 gamma
Myt_65M21 GE753758 GE750851 Mannose-1-phosphate guanyltransferase beta-
B
Myt_35N15 ES407351 ES404083 Homeobox protein Mohawk
Myt_25B01 ES393224 ES393820 Unclassifiable EST
Myt_16N01 ES390243 ES387973 Unclassifiable EST
Myt_43D24 ES401812 ES406131 Target of rapamycin complex 2 subunit
MAPKAP1
Myt_39H21 ES402904 ES407283 Unclassifiable EST
Myt_30A17 ES406859 ES400385 3-demethylubiquinone-9 3-methyltransferase
Myt_46G07 GE756697 GE754463 Selenoprotein T2
Myt_15O12 ES389830 ES387975 DNA-binding protein D-ETS-4
Myt_27L14 ES393836 ES395689 Kinase D-interacting substrate of 220 kDa
Myt_20O09 ES738358 NA Neuropeptides B/W receptor type 2
Myt_19F06 ES736041 NA Unclassifiable EST
Myt_16F16 ES395614 ES395941 Unclassifiable EST
Myt_46G20 GE760050 GE757791 DNA damage-inducible transcript 4-like protein
Myt_30C20 ES398247 ES397985 Protein quiver
Myt_55I13 GE757708 GE763769 Unclassifiable EST
Myt_12K13 ES736089 NA Sodium- and chloride-dependent creatine
transporter 1
Myt_15M13 ES393463 ES395113 Heat shock protein HSP 90-alpha
Myt_45F02 GE759300 GE760446 Unclassifiable EST
Myt_15M03 ES394526 ES388663 E3 ubiquitin-protein ligase RFWD2
50
Myt_34A21 ES401701 ES406483 Probable isocitrate dehydrogenase [NAD]
subunit alpha,
Myt_16O23 ES396410 ES394787 Unclassifiable EST
Myt_22A21 ES738852 NA Galactoside 2-alpha-L-fucosyltransferase 2
Myt_35J11 ES407208 ES399791 Immediate early response gene 5-like protein
Myt_35C17 ES403991 ES397366 Transcription factor HES-4-A
Myt_28A01 ES388137 ES389083 Zinc finger C4H2 domain-containing protein
Myt_42M12 ES406836 ES401806 Zinc finger protein 597
Myt_14P21 ES396911 ES389082 Unclassifiable EST
Myt_36D19 ES402241 ES406573 Myosin light chain kinase, smooth muscle
Myt_34K10 ES398715 ES406147 Carbonic anhydrase
Myt_59M05 GE747641 GE748715
Myt_45O16 GE765349 GE764386 Dual specificity protein phosphatase 7
Myt_45F04 GE764513 GE764076 Uncharacterized protein C1orf222 homolog
Myt_51A11 GE760271 GE757642 Pleckstrin homology domain-containing family
G member 5
Myt_26N05 ES389042 ES390390 Unclassifiable EST
Myt_17K22 ES736679 NA Unclassifiable EST
Myt_61B20 GE747862 NA Protein BTG1
Myt_47N02 GE763659 GE758554 Photoreceptor-specific nuclear receptor
Myt_21D20 ES392532 ES388633 Protein four-jointed
Myt_66F11 GE751506 GE753546
Myt_55J12 GE759523 GE757659 Zinc finger protein 227
Myt_33L16 ES400776 ES397487 Protein toll
Myt_33O20 ES401389 ES405811 Unclassifiable EST
Myt_21O17 ES390838 ES393405 Unclassifiable EST
Myt_51D05 GE760458 GE755054 AAC-rich mRNA clone AAC4 protein
Myt_38M22 ES405689 ES405398 Unclassifiable EST
Myt_14C12 ES389735 ES392694 Unclassifiable EST
Myt_36H19 ES399937 ES403643 Unclassifiable EST
Myt_42E08 ES407286 ES406310 Unclassifiable EST
Myt_34K23 ES398535 ES402064 ATP synthase-coupling factor 6, mitochondrial
Myt_55C20 GE765319 GE758076 Unclassifiable EST
Myt_17G09 ES388644 ES395245 Cytoplasmic FMR1-interacting protein 2
Myt_39D03 ES407877 ES404822 RNA polymerase II-associated factor 1 homolog
Myt_26O05 ES391633 NA Uncharacterized protein C18H10.09
Myt_54N01 GE755377 GE755848 Ubiquitin-fold modifier 1
Myt_30H17 ES406041 ES400575 Unclassifiable EST
Myt_26N19 ES396845 ES390501 Unclassifiable EST
Myt_42O17 ES401458 ES404845 Unclassifiable EST
Myt_34A16 ES399810 ES397392 Ceramide kinase
51
Myt_38H19 ES408081 ES400839 Unclassifiable EST
Myt_33N03 ES402999 ES405320 Unclassifiable EST
Myt_33M24 ES400352 ES408102 Dystrophin, isoforms A/C/F/G/H
Myt_42N16 ES401217 ES402289 Unclassifiable EST
Myt_23I04 ES396385 ES387529 Unclassifiable EST
Myt_33F15 ES400025 ES400432 Unclassifiable EST
Myt_29A09 ES403602 ES407965 SET and MYND domain-containing protein 5
Myt_21F23 ES389670 ES397006 Unclassifiable EST
Myt_31B24 ES408004 ES401808 Unclassifiable EST
Myt_29J21 ES406320 ES402295 Actin-like protein 6B
Myt_13O01 ES392813 ES388358 Unclassifiable EST
Myt_19D22 ES736637 NA Zinc finger protein 112 homolog
Myt_12L09 ES389400 NA Unclassifiable EST
Myt_20O16 ES390833 ES392477 Arrestin domain-containing protein 3
Myt_52D06 GE757495 GE758608 Unclassifiable EST
Myt_24A22 NA NA Baculoviral IAP repeat-containing protein 7
Myt_62H22 GE748295 GE752208 Uncharacterized glycosyltransferase AGO61
Myt_31P04 ES405890 ES400890 M-phase inducer phosphatase 1
Myt_24B07 ES395810 ES393019 Unclassifiable EST
Myt_14I18 ES396059 ES391125 Solute carrier family 25 member 38-A
Myt_45D05 GE762194 GE757501 Baculoviral IAP repeat-containing protein 4
Myt_59L22 GE751370 GE748468 Zinc finger matrin-type protein 2
Myt_47O04 GE754402 GE757122 mTERF domain-containing protein 1,
mitochondrial
Myt_54H08 GE758666 GE757807 Unclassifiable EST
Myt_42G24 NA ES406834 Mdm2-binding protein
Myt_32D01 ES400118 ES397499 Alcohol dehydrogenase class-3
Myt_24B21 ES391953 ES396024 Unclassifiable EST
Myt_46A17 GE764986 GE762754 Tetratricopeptide repeat protein 26
Myt_22B20 ES737023 NA Molybdopterin synthase catalytic subunit
Myt_26M15 ES396265 ES391251 MFS-type transporter C6orf192
Myt_34M15 ES404635 ES399171 Zinc finger protein 233
Myt_15C18 ES389033 ES393080 Unclassifiable EST
Myt_24A23 ES396331 ES387840 Homeobox protein SIX1
Myt_43N20 ES400777 ES401187 Serine/threonine-protein kinase pim-3
Myt_44I21 GE765166 GE764204 Peroxisome biogenesis factor 2
Myt_17E03 ES391754 ES396190 Unclassifiable EST
Myt_32B17 ES403536 ES405882 MRG-binding protein
Myt_55C17 NA GE764100 PIF
Myt_35F20 ES400321 ES401954 Unclassifiable EST
Myt_47K03 GE763700 GE759250 cAMP-responsive element modulator
52
Myt_37I12 ES407868 ES406802 Unclassifiable EST
Myt_38A02 ES397836 ES406307 Acetylcholine receptor subunit alpha-like 2
Myt_19K22 ES738468 NA
Myt_38M10 ES400223 ES400202 tRNA-splicing endonuclease subunit Sen2
Myt_42N17 ES408003 ES405527 Gamma-secretase subunit PEN-2
Myt_52H11 GE758871 GE755992 Unclassifiable EST
Myt_30N23 ES405858 ES402103 Serine/threonine-protein kinase Nek6
Myt_24L24 ES389145 NA Chaperone protein dnaJ
Myt_21D19 ES394697 ES394305 Myosin-1
Myt_12I10 ES392217 ES389535 Unclassifiable EST
Myt_36F22 ES406518 ES405950 E3 ubiquitin-protein ligase TRIM33
Myt_38O01 ES404420 ES406530 Unclassifiable EST
Myt_26J21 ES393632 ES393680 Transmembrane protein C20orf46
Myt_34K08 ES397150 ES405249 Universal stress protein YxiE
Myt_20A12 NA ES391856
Myt_65L08 GE750101 GE750581 Unclassifiable EST
Myt_30N22 ES404356 ES406767 PCI domain-containing protein 2
Myt_09D08 ES736011 NA Zinc finger and BTB domain-containing protein
48
Myt_32M01 ES399301 ES402435 Galectin-3-binding protein A
Myt_52G18 GE756610 GE765179 Peroxidasin homolog
Myt_35J13 ES404629 ES399630 Ethanolaminephosphotransferase 1
Myt_21D08 ES387695 ES391731 Protein ariadne-1 homolog
Myt_46F14 GE763118 GE755280 Unclassifiable EST
Myt_08K08 NA NA Unclassifiable EST
Myt_19K02 ES736078 NA Clathrin light chain B
Myt_43L19 ES402609 ES401643 Sulfotransferase 1C2
Myt_26A15 ES389548 NA Ras-related protein Rab-18
Myt_31P06 ES406388 ES402270 Unclassifiable EST
Myt_68D18 GE753025 GE750507 Unclassifiable EST
Myt_26A14 ES396088 ES391266 Unclassifiable EST
Myt_39M01 ES403280 ES397437 Dynactin subunit 2
Myt_43H01 ES403797 ES398973 Transcription initiation protein SPT3 homolog
Myt_33J10 ES403344 ES400999 Monocarboxylate transporter 2
Myt_47B18 GE763713 GE757070 Zinc finger protein 227
Myt_35A10 NA ES402045
Myt_41G02 ES402990 ES404174 Uncharacterized protein C12orf26 homolog
Myt_42P21 ES399563 ES402212 Tripartite motif-containing protein 2
Myt_31C21 ES405927 ES399629 Probable phytanoyl-CoA dioxygenase
Myt_59A10 GE752061 GE750573 Unclassifiable EST
Myt_26J06 NA ES388086 Unclassifiable EST
53
Myt_25K20 ES395363 ES392611 Unclassifiable EST
Myt_29N04 ES400865 NA Ceramide glucosyltransferase
Myt_48A21 GE762749 GE759415 Guanine nucleotide exchange factor DBS
Myt_38N12 ES404462 ES398970 Unclassifiable EST
Myt_47O07 GE758982 GE757393 General transcription factor IIH subunit 4
Myt_26C13 ES390687 ES392692 Enolase-phosphatase E1
Myt_09E15 ES737737 NA 14-3-3 protein epsilon
Myt_20M04 ES387607 ES388865 Unclassifiable EST
Myt_53P15 GE757883 GE760428 Unclassifiable EST
Myt_09G03 ES737042 NA Unclassifiable EST
Myt_50L18 GE765296 GE762249 Unclassifiable EST
Myt_23C18 ES391483 ES394925 Unclassifiable EST
Myt_39B12 ES404888 ES402617 Unclassifiable EST
Myt_35N12 ES398293 ES407862 Armadillo repeat-containing protein 4
Myt_25F09 ES390504 ES390027 Unclassifiable EST
Myt_06O22 NA NA Unclassifiable EST
Myt_34D02 ES402870 ES401976 Unclassifiable EST
54
SI Table 2. List of transcripts represented in heatmap shown in Fig. 3B.
USC ID 5' Acc. # 3' Acc. # Putative ID
Myt_09K13 ES735872 NA Heat shock 70 kDa protein
Myt_35L14 ES402053 ES405899
Myt_44A08 GE760632 GE763165 cAMP-responsive element-binding protein-like
2
Myt_31M21 ES398477 ES400974 Unclassifiable EST
Myt_50N22 GE761909 GE762395 Unclassifiable EST
Myt_62O20 GE747701 GE747736 Unclassifiable EST
Myt_34B19 ES402562 ES405580 Complement C1q-like protein 3
Myt_35K17 ES406375 ES401992 Unclassifiable EST
Myt_23B16 ES391808 ES390603 Unclassifiable EST
Myt_34K09 ES398967 ES403458 Cdc42 homolog
Myt_27J01 ES393846 ES396736 Unclassifiable EST
Myt_46F05 NA GE758361 Unclassifiable EST
Myt_59A24 GE750456 GE748635 Guanine nucleotide-binding protein subunit
beta-2-like 1
Myt_64J04 GE753627 GE751297 Unclassifiable EST
Myt_21B04 ES395775 ES390564 Iduronate 2-sulfatase
Myt_29A07 ES407426 ES400247 CCAAT/enhancer-binding protein epsilon
Myt_30G23 ES401162 ES406590 Prolyl-tRNA synthetase
Myt_12B08 ES391882 ES396917 ETS homologous factor
Myt_39M08 ES405297 ES405150 Ring canal kelch homolog
Myt_37J11 NA ES406441 Unclassifiable EST
Myt_21F24 ES391202 ES389269 T-box transcription factor TBX20
Myt_63A13 GE753108 GE752493 Dolichyl-diphosphooligosaccharide--protein
glycosyltransferase 48 kDa
Myt_29C06 ES400276 ES400573 Feline leukemia virus subgroup C receptor-
related protein 2
Myt_12K08 ES394544 ES394518 Unclassifiable EST
Myt_42F24 ES398180 ES400550 Unclassifiable EST
Myt_33M03 ES405647 ES407224 Transcription factor AP-1
Myt_28B23 ES396056 ES396935 DNA-binding protein inhibitor ID-2-A
Myt_32C04 ES403448 ES405227 Baculoviral IAP repeat-containing protein 7
Myt_42O15 ES408060 ES401400 Unclassifiable EST
Myt_15E22 ES391539 ES395307 Unclassifiable EST
Myt_30I10 ES407353 ES407627 Unclassifiable EST
Myt_04B22 ES738439 NA Krueppel-like factor 6
55
Myt_09P07 ES738196 NA Myosin-2 essential light chain
Myt_33E23 ES401131 ES399668 NF-kappa-B inhibitor alpha
Myt_30C19 ES397849 ES403259 Transmembrane protein 47
Myt_39A14 ES399875 ES406885 Eukaryotic translation initiation factor 3
subunit G
Myt_21L09 ES395521 ES395093 Myeloid differentiation primary response
protein MyD88
Myt_22O21 ES738842 NA Transcription factor kayak
Myt_49B15 GE761756 GE755590 UPF0392 protein F13G3.3
Myt_17P22 ES389869 ES392617 Suppressor of cytokine signaling 2
Myt_30D02 ES404598 ES398694 Carbonic anhydrase 2
Myt_33L18 ES400306 ES397560 Unclassifiable EST
Myt_42D08 ES401128 ES406433 MAM and LDL-receptor class A domain-
containing protein C10orf112
Myt_34J16 ES397636 ES405597 Unclassifiable EST
Myt_50J03 GE764656 GE756923 Headcase protein
Myt_31K21 ES399589 ES401243 Unclassifiable EST
Myt_27A08 ES390371 ES389785 NF-kappa-B inhibitor cactus
Myt_47B22 GE761378 GE762508 PR domain zinc finger protein 16
Myt_62M13 GE753109 GE750465 Growth arrest and DNA damage-inducible
protein GADD45 gamma
Myt_65M21 GE753758 GE750851 Mannose-1-phosphate guanyltransferase
beta-B
Myt_35N15 ES407351 ES404083 Homeobox protein Mohawk
Myt_25B01 ES393224 ES393820 Unclassifiable EST
Myt_16N01 ES390243 ES387973 Unclassifiable EST
Myt_43D24 ES401812 ES406131 Target of rapamycin complex 2 subunit
MAPKAP1
Myt_39H21 ES402904 ES407283 Unclassifiable EST
Myt_30A17 ES406859 ES400385 3-demethylubiquinone-9 3-methyltransferase
Myt_46G07 GE756697 GE754463 Selenoprotein T2
Myt_15O12 ES389830 ES387975 DNA-binding protein D-ETS-4
Myt_27L14 ES393836 ES395689 Kinase D-interacting substrate of 220 kDa
Myt_20O09 ES738358 NA Neuropeptides B/W receptor type 2
Myt_19F06 ES736041 NA Unclassifiable EST
Myt_16F16 ES395614 ES395941 Unclassifiable EST
Myt_46G20 GE760050 GE757791 DNA damage-inducible transcript 4-like
protein
Myt_30C20 ES398247 ES397985 Protein quiver
Myt_55I13 GE757708 GE763769 Unclassifiable EST
Myt_12K13 ES736089 NA Sodium- and chloride-dependent creatine
56
transporter 1
Myt_15M13 ES393463 ES395113 Heat shock protein HSP 90-alpha
Myt_45F02 GE759300 GE760446 Unclassifiable EST
Myt_15M03 ES394526 ES388663 E3 ubiquitin-protein ligase RFWD2
Myt_34A21 ES401701 ES406483 Probable isocitrate dehydrogenase [NAD]
subunit alpha,
Myt_16O23 ES396410 ES394787 Unclassifiable EST
Myt_22A21 ES738852 NA Galactoside 2-alpha-L-fucosyltransferase 2
Myt_35J11 ES407208 ES399791 Immediate early response gene 5-like protein
Myt_35C17 ES403991 ES397366 Transcription factor HES-4-A
Myt_28A01 ES388137 ES389083 Zinc finger C4H2 domain-containing protein
Myt_42M12 ES406836 ES401806 Zinc finger protein 597
Myt_14P21 ES396911 ES389082 Unclassifiable EST
Myt_36D19 ES402241 ES406573 Myosin light chain kinase, smooth muscle
Myt_34K10 ES398715 ES406147 Carbonic anhydrase
Myt_59M05 GE747641 GE748715
Myt_45O16 GE765349 GE764386 Dual specificity protein phosphatase 7
Myt_45F04 GE764513 GE764076 Uncharacterized protein C1orf222 homolog
Myt_51A11 GE760271 GE757642 Pleckstrin homology domain-containing family
G member 5
Myt_26N05 ES389042 ES390390 Unclassifiable EST
Myt_17K22 ES736679 NA Unclassifiable EST
Myt_61B20 GE747862 NA Protein BTG1
Myt_47N02 GE763659 GE758554 Photoreceptor-specific nuclear receptor
Myt_21D20 ES392532 ES388633 Protein four-jointed
Myt_66F11 GE751506 GE753546
Myt_55J12 GE759523 GE757659 Zinc finger protein 227
Myt_33L16 ES400776 ES397487 Protein toll
Myt_33O20 ES401389 ES405811 Unclassifiable EST
Myt_21O17 ES390838 ES393405 Unclassifiable EST
Myt_51D05 GE760458 GE755054 AAC-rich mRNA clone AAC4 protein
Myt_38M22 ES405689 ES405398 Unclassifiable EST
Myt_14C12 ES389735 ES392694 Unclassifiable EST
Myt_36H19 ES399937 ES403643 Unclassifiable EST
Myt_42E08 ES407286 ES406310 Unclassifiable EST
Myt_34K23 ES398535 ES402064 ATP synthase-coupling factor 6, mitochondrial
Myt_55C20 GE765319 GE758076 Unclassifiable EST
Myt_17G09 ES388644 ES395245 Cytoplasmic FMR1-interacting protein 2
Myt_39D03 ES407877 ES404822 RNA polymerase II-associated factor 1
homolog
57
Myt_26O05 ES391633 NA Uncharacterized protein C18H10.09
Myt_54N01 GE755377 GE755848 Ubiquitin-fold modifier 1
Myt_30H17 ES406041 ES400575 Unclassifiable EST
Myt_26N19 ES396845 ES390501 Unclassifiable EST
Myt_42O17 ES401458 ES404845 Unclassifiable EST
Myt_34A16 ES399810 ES397392 Ceramide kinase
Myt_38H19 ES408081 ES400839 Unclassifiable EST
Myt_33N03 ES402999 ES405320 Unclassifiable EST
Myt_33M24 ES400352 ES408102 Dystrophin, isoforms A/C/F/G/H
Myt_42N16 ES401217 ES402289 Unclassifiable EST
Myt_23I04 ES396385 ES387529 Unclassifiable EST
Myt_33F15 ES400025 ES400432 Unclassifiable EST
Myt_29A09 ES403602 ES407965 SET and MYND domain-containing protein 5
Myt_21F23 ES389670 ES397006 Unclassifiable EST
Myt_31B24 ES408004 ES401808 Unclassifiable EST
Myt_29J21 ES406320 ES402295 Actin-like protein 6B
Myt_13O01 ES392813 ES388358 Unclassifiable EST
Myt_19D22 ES736637 NA Zinc finger protein 112 homolog
Myt_12L09 ES389400 NA Unclassifiable EST
Myt_20O16 ES390833 ES392477 Arrestin domain-containing protein 3
Myt_52D06 GE757495 GE758608 Unclassifiable EST
Myt_24A22 NA NA Baculoviral IAP repeat-containing protein 7
Myt_62H22 GE748295 GE752208 Uncharacterized glycosyltransferase AGO61
Myt_31P04 ES405890 ES400890 M-phase inducer phosphatase 1
Myt_24B07 ES395810 ES393019 Unclassifiable EST
Myt_14I18 ES396059 ES391125 Solute carrier family 25 member 38-A
Myt_45D05 GE762194 GE757501 Baculoviral IAP repeat-containing protein 4
Myt_59L22 GE751370 GE748468 Zinc finger matrin-type protein 2
Myt_47O04 GE754402 GE757122 mTERF domain-containing protein 1,
mitochondrial
Myt_54H08 GE758666 GE757807 Unclassifiable EST
Myt_42G24 NA ES406834 Mdm2-binding protein
Myt_32D01 ES400118 ES397499 Alcohol dehydrogenase class-3
Myt_24B21 ES391953 ES396024 Unclassifiable EST
Myt_46A17 GE764986 GE762754 Tetratricopeptide repeat protein 26
Myt_22B20 ES737023 NA Molybdopterin synthase catalytic subunit
Myt_26M15 ES396265 ES391251 MFS-type transporter C6orf192
Myt_34M15 ES404635 ES399171 Zinc finger protein 233
Myt_15C18 ES389033 ES393080 Unclassifiable EST
58
Myt_24A23 ES396331 ES387840 Homeobox protein SIX1
Myt_43N20 ES400777 ES401187 Serine/threonine-protein kinase pim-3
Myt_44I21 GE765166 GE764204 Peroxisome biogenesis factor 2
Myt_17E03 ES391754 ES396190 Unclassifiable EST
Myt_32B17 ES403536 ES405882 MRG-binding protein
Myt_55C17 NA GE764100 PIF
Myt_35F20 ES400321 ES401954 Unclassifiable EST
Myt_47K03 GE763700 GE759250 cAMP-responsive element modulator
Myt_37I12 ES407868 ES406802 Unclassifiable EST
Myt_38A02 ES397836 ES406307 Acetylcholine receptor subunit alpha-like 2
Myt_19K22 ES738468 NA
Myt_38M10 ES400223 ES400202 tRNA-splicing endonuclease subunit Sen2
Myt_42N17 ES408003 ES405527 Gamma-secretase subunit PEN-2
Myt_52H11 GE758871 GE755992 Unclassifiable EST
Myt_30N23 ES405858 ES402103 Serine/threonine-protein kinase Nek6
Myt_24L24 ES389145 NA Chaperone protein dnaJ
Myt_21D19 ES394697 ES394305 Myosin-1
Myt_12I10 ES392217 ES389535 Unclassifiable EST
Myt_36F22 ES406518 ES405950 E3 ubiquitin-protein ligase TRIM33
Myt_38O01 ES404420 ES406530 Unclassifiable EST
Myt_26J21 ES393632 ES393680 Transmembrane protein C20orf46
Myt_34K08 ES397150 ES405249 Universal stress protein YxiE
Myt_20A12 NA ES391856
Myt_65L08 GE750101 GE750581 Unclassifiable EST
Myt_30N22 ES404356 ES406767 PCI domain-containing protein 2
Myt_09D08 ES736011 NA Zinc finger and BTB domain-containing protein
48
Myt_32M01 ES399301 ES402435 Galectin-3-binding protein A
Myt_52G18 GE756610 GE765179 Peroxidasin homolog
Myt_35J13 ES404629 ES399630 Ethanolaminephosphotransferase 1
Myt_21D08 ES387695 ES391731 Protein ariadne-1 homolog
Myt_46F14 GE763118 GE755280 Unclassifiable EST
Myt_08K08 NA NA Unclassifiable EST
Myt_19K02 ES736078 NA Clathrin light chain B
Myt_43L19 ES402609 ES401643 Sulfotransferase 1C2
Myt_26A15 ES389548 NA Ras-related protein Rab-18
Myt_31P06 ES406388 ES402270 Unclassifiable EST
Myt_68D18 GE753025 GE750507 Unclassifiable EST
Myt_26A14 ES396088 ES391266 Unclassifiable EST
59
Myt_39M01 ES403280 ES397437 Dynactin subunit 2
Myt_43H01 ES403797 ES398973 Transcription initiation protein SPT3 homolog
Myt_33J10 ES403344 ES400999 Monocarboxylate transporter 2
Myt_47B18 GE763713 GE757070 Zinc finger protein 227
Myt_35A10 NA ES402045
Myt_41G02 ES402990 ES404174 Uncharacterized protein C12orf26 homolog
Myt_42P21 ES399563 ES402212 Tripartite motif-containing protein 2
Myt_31C21 ES405927 ES399629 Probable phytanoyl-CoA dioxygenase
Myt_59A10 GE752061 GE750573 Unclassifiable EST
Myt_26J06 NA ES388086 Unclassifiable EST
Myt_25K20 ES395363 ES392611 Unclassifiable EST
Myt_29N04 ES400865 NA Ceramide glucosyltransferase
Myt_48A21 GE762749 GE759415 Guanine nucleotide exchange factor DBS
Myt_38N12 ES404462 ES398970 Unclassifiable EST
Myt_47O07 GE758982 GE757393 General transcription factor IIH subunit 4
Myt_26C13 ES390687 ES392692 Enolase-phosphatase E1
Myt_09E15 ES737737 NA 14-3-3 protein epsilon
Myt_20M04 ES387607 ES388865 Unclassifiable EST
Myt_53P15 GE757883 GE760428 Unclassifiable EST
Myt_09G03 ES737042 NA Unclassifiable EST
Myt_50L18 GE765296 GE762249 Unclassifiable EST
Myt_23C18 ES391483 ES394925 Unclassifiable EST
Myt_39B12 ES404888 ES402617 Unclassifiable EST
Myt_35N12 ES398293 ES407862 Armadillo repeat-containing protein 4
Myt_25F09 ES390504 ES390027 Unclassifiable EST
Myt_06O22 NA NA Unclassifiable EST
Myt_34D02 ES402870 ES401976 Unclassifiable EST
SI Table 3. List of tidally-regulated transcripts represented in heatmap shown in Fig. 4B.
USC ID 5' Acc. # 3' Acc. # Putative ID
Elevated at
high tide
Myt_12O24 ES389000 ES387956 Acidic leucine-rich nuclear phosphoprotein 32
family member A
Myt_61F11 GE749575 GE752226 Unclassifiable EST
Myt_24E17 ES392743 NA Unclassifiable EST
60
Myt_44D22 GE760449 GE757163 Unclassifiable EST
Myt_13G06 ES389354 ES390396 Unclassifiable EST
Myt_32I18 ES402750 ES407926 Unclassifiable EST
Myt_19C22 ES736768 NA Kielin/chordin-like protein
Myt_33H07 ES401262 ES406706 Unclassifiable EST
Myt_23M19 ES390708 ES392196 Cysteine proteinase RD19a
Myt_16M02 ES390513 ES389760 Unclassifiable EST
Myt_27N15 ES388613 NA Radial spoke head 1 homolog
Myt_26O21 ES395074 ES396893 Plasminogen activator inhibitor 1 RNA-binding
protein
Myt_13K23 ES391680 ES390495 Unclassifiable EST
Myt_30G03 ES399619 ES399903 Radial spoke head protein 4 homolog A
Myt_17M21 ES388585 ES390540 Unclassifiable EST
Myt_34G21 ES402090 ES405631 Unclassifiable EST
Myt_39I01 ES407007 ES402451 Unclassifiable EST
Myt_16I11 ES396607 ES387824 Unclassifiable EST
Myt_33F18 ES401450 ES402807 Vacuolar protein sorting-associated protein 4B
Myt_07J22 ES736356 NA Coiled-coil domain-containing protein 147
Myt_09G14 ES738032 NA Unclassifiable EST
Myt_09K02 ES737576 NA Homeobox protein cut-like 1
Myt_14P05 ES392680 ES392998 Unclassifiable EST
Myt_22I02 ES738926 NA E3 ubiquitin-protein ligase TRIM33
Myt_39M10 ES405060 ES403982 Serine/threonine-protein kinase RIO1
Myt_60E02 GE753741 GE753196 RNA-binding protein 5
Elevated at
low tide
Myt_33E23 ES401131 ES399668 NF-kappa-B inhibitor alpha
Myt_16N01 ES390243 ES387973 Unclassifiable EST
Myt_59L20 GE747420 GE747595 Cholecystokinin receptor
Myt_23B08 ES394028 ES394779 Plasminogen
Myt_43N20 ES400777 ES401187 Serine/threonine-protein kinase pim-3
Myt_59L24 GE750947 GE753368 Probable G-protein coupled receptor 157
Myt_42D08 ES401128 ES406433 MAM and LDL-receptor class A domain-
containing protein C10orf112
Myt_59E21 GE749296 GE749931 Unclassifiable EST
Myt_17F09 ES396626 ES387848 Toll-like receptor 6
Myt_17P22 ES389869 ES392617 Suppressor of cytokine signaling 2
Myt_59O20 GE749868 GE748420 Unclassifiable EST
Myt_17E01 ES390942 ES392894 Unclassifiable EST
61
Myt_42O15 ES408060 ES401400 Unclassifiable EST
Myt_25K20 ES395363 ES392611 Unclassifiable EST
Myt_29A09 ES403602 ES407965 SET and MYND domain-containing protein 5
Myt_32J12 ES398866 ES402293 BTB/POZ domain-containing protein 2
Myt_17F08 ES395180 ES388612 Endoglucanase
Myt_21B04 ES395775 ES390564 Iduronate 2-sulfatase
Myt_55C20 GE765319 GE758076 Unclassifiable EST
Myt_37J16 ES400061 ES398996 Cytochrome P450 2C41
Myt_33K15 ES405299 ES406459 OTU domain-containing protein 1
Myt_42G24 NA ES406834 Mdm2-binding protein
Myt_29C06 ES400276 ES400573 Feline leukemia virus subgroup C receptor-
related protein 2
Myt_39M08 ES405297 ES405150 Ring canal kelch homolog
Myt_12B08 ES391882 ES396917 ETS homologous factor
Myt_31M21 ES398477 ES400974 Unclassifiable EST
Myt_44D06 GE758660 GE762627 Tubulin beta chain
Myt_47O04 GE754402 GE757122 mTERF domain-containing protein 1,
mitochondrial
Myt_60O22 GE751436 GE752949 Unclassifiable EST
Myt_17D23 ES396017 ES388392 Polyubiquitin-C
Myt_12K08 ES394544 ES394518 Unclassifiable EST
Myt_15C18 ES389033 ES393080 Unclassifiable EST
Myt_20G16 ES389076 ES391317 EF-hand domain-containing family member A2
Myt_15O12 ES389830 ES387975 DNA-binding protein D-ETS-4
Myt_44I13 GE757138 GE760784 Slit homolog 2 protein (Fragment)
Myt_09E14 ES738537 NA Unclassifiable EST
Myt_29A07 ES407426 ES400247 CCAAT/enhancer-binding protein epsilon
Myt_30D02 ES404598 ES398694 Carbonic anhydrase 2
Myt_42O17 ES401458 ES404845 Unclassifiable EST
Myt_35J11 ES407208 ES399791 Immediate early response gene 5-like protein
Myt_30C19 ES397849 ES403259 Transmembrane protein 47
Myt_42F24 ES398180 ES400550 Unclassifiable EST
Myt_50N22 GE761909 GE762395 Unclassifiable EST
Myt_26O05 ES391633 NA Uncharacterized protein C18H10.09
Myt_34B19 ES402562 ES405580 Complement C1q-like protein 3
Myt_31P07 ES403728 ES402414
Myt_43K22 ES403803 ES402898 Unclassifiable EST
Myt_47N02 GE763659 GE758554 Photoreceptor-specific nuclear receptor
Myt_12D18 ES394320 ES394928 Unclassifiable EST
62
Myt_22O21 ES738842 NA Transcription factor kayak
Myt_32D01 ES400118 ES397499 Alcohol dehydrogenase class-3
Myt_30C20 ES398247 ES397985 Protein quiver
Myt_15P11 ES396157 ES394494 Unclassifiable EST
Myt_63A11 GE747206 GE748729 Unclassifiable EST
Myt_68K19 GE748741 GE750700 Unclassifiable EST
Myt_60K14 GE753958 GE749057 Unclassifiable EST
Myt_22K22 ES738094 NA Unclassifiable EST
Myt_04B22 ES738439 NA Krueppel-like factor 6
Myt_34A21 ES401701 ES406483 Probable isocitrate dehydrogenase [NAD]
subunit alpha,
Myt_43L21 ES399947 ES402636 Unclassifiable EST
Myt_61J15 GE751389 GE753826 Checkpoint protein HUS1
Myt_28D13 ES391647 ES395642 Protein FEV
Myt_43K21 ES405620 ES407803 Baculoviral IAP repeat-containing protein 7
Myt_24D17 ES393049 NA Tumor necrosis factor receptor superfamily
member 19
Myt_16C08 ES396697 ES389060 Unclassifiable EST
Myt_62D16 GE747864 GE750054 Stomatin-2
Myt_49F08 GE764920 GE762527 EGF-like module-containing mucin-like hormone
receptor-like 3
Myt_31D14 ES401242 ES397510 Unclassifiable EST
Myt_27L23 ES391369 ES389049 Rho-related GTP-binding protein RhoQ
Myt_55J12 GE759523 GE757659 Zinc finger protein 227
Myt_26A02 ES390532 ES391252 Unclassifiable EST
Myt_55I13 GE757708 GE763769 Unclassifiable EST
Myt_09G03 ES737042 NA Unclassifiable EST
Myt_60F21 GE749808 GE748816 Serine/threonine-protein phosphatase 2A 65
kDa regulatory subunit A
Myt_36D19 ES402241 ES406573 Myosin light chain kinase, smooth muscle
Myt_49B15 GE761756 GE755590 UPF0392 protein F13G3.3
Myt_21M16 ES388562 ES389391 Unclassifiable EST
Myt_64F19 GE750650 GE753746 Integrator complex subunit 7
Myt_11K18 ES738280 NA Ganglioside GM2 activator
Myt_21K03 ES393341 ES391690 Ephrin type-A receptor 2
Myt_35L14 ES402053 ES405899
Myt_44A08 GE760632 GE763165 cAMP-responsive element-binding protein-like 2
Myt_46M11 GE762537 GE758210 Baculoviral IAP repeat-containing protein 3
63
SI Table 4. List of tidally-regulated transcripts represented in heatmap shown in Fig. 4C.
Myt_13C09 ES396889 ES389343 Polycomb group RING finger protein 1
Myt_12B08 ES391882 ES396917 ETS homologous factor
Myt_19L19 ES738307 NA Unclassifiable EST
Myt_21K15 ES393297 ES394066 Unclassifiable EST
Myt_12O02 ES396541 ES393621 Unclassifiable EST
Myt_14A03 ES389465 ES395174 Unclassifiable EST
Myt_52D05 GE755702 GE758465 Unclassifiable EST
Myt_16E19 ES393007 ES390342 Starch-binding domain-containingprotein
1
Myt_30N10 ES404792 ES403833
Myt_12F06 ES393400 ES393485 Unclassifiable EST
Myt_28L20 ES396324 ES392215 Unclassifiable EST
Myt_41F13 ES407352 ES397682 Unclassifiable EST
Myt_12B18 ES388574 ES395968 RUN domain-containing protein 2A
Myt_22K22 ES738094 NA Unclassifiable EST
Myt_14D06 ES388804 ES394782 Alpha N-terminal protein
methyltransferase 1A
Myt_20O09 ES738358 NA Neuropeptides B/W receptor type 2
Myt_11C21 ES737602 NA Unclassifiable EST
Myt_23N09 ES389162 ES394275 L-amino-acid oxidase
Myt_14K15 ES388589 ES391704 Cytochrome P450 3A4
Myt_35I14 ES399035 ES397415 Collagen alpha-2(I) chain
Myt_29I15 ES403999 ES403841 Unclassifiable EST
Myt_13L12 ES391641 ES390283 Unclassifiable EST
Myt_31K22 ES405415 ES404376 Phosphatidate phosphatase PPAPDC1B
Myt_27J20 ES389288 ES388969 Tripartite motif-containing protein 71
Myt_19I10 ES738242 NA Uncharacterized protein C2orf63 homolog
Myt_39F11 ES404600 ES400864 cAMP-specific 3',5'-cyclic
phosphodiesterase 4B
Myt_63P14 GE747946 GE753599 Growth hormone secretagogue receptor
type 1
Myt_36I09 ES408082 ES399253 Unclassifiable EST
Myt_27N02 ES389940 ES396891 Unclassifiable EST
Myt_36B12 ES401232 ES403615 Unclassifiable EST
Myt_58A02 GE757897 GE757758 Unclassifiable EST
64
Myt_42K19 ES400979 ES404898 Solute carrier organic anion transporter
family member 5A1
Myt_64B12 GE753170 GE751374 DNA-directed RNA polymerase III subunit
RPC7-like
65
Chapter 2: High resolution analysis of metabolic cycles in the
intertidal mussel Mytilus californianus
Chapter 2: Abstract
Inhabitants of the marine rocky intertidal live in an environment that alternates
between aquatic and terrestrial due to the rise and fall of the tide. The tide creates a
cyclical availability of oxygen with animals having access to oxygenated water during
episodes of submergence, while access to oxygen is restricted during aerial emergence.
Here we performed liquid chromatography and gas chromatography-mass spectrometry
enabled metabolomic profiling of gill samples isolated from the California ribbed
mussel, Mytilus californianus, to investigate how metabolism is orchestrated in this
variable environment. We created a simulated intertidal environment in which mussels
were acclimated to alternating high and low tides of 6 hr duration, and samples were
taken every 2hr for 72 hr, to capture reproducible changes in metabolite levels over 6
high and 6 low tides. We quantified 169 named metabolites of which 24 metabolites
cycled significantly with a 12 hr period that was linked to the tidal cycle. These data
confirmed the presence of alternating phases of fermentation and aerobic metabolism
and highlight a role for carnitine conjugated metabolites during the anaerobic phase of
this cycle. Mussels at low tide accumulated 8 carnitine-conjugated metabolites, arising
from the degradation of fatty acids, branched-chain amino acids, and mitochondrial β-
oxidation end products. The data also implicate sphingosine as a potential signaling
66
molecule during aerial emergence. These findings identify new levels of metabolic
control whose role in intertidal adaptation remains to be elucidated.
67
Chapter 2: Introduction
The biology of organisms is strongly influenced by temporal changes of environmental
factors in their habitat. The marine intertidal is a particularly variable habitat because it
represents the zone where the marine aquatic and terrestrial environments merge, and
the inhabitants are subjected to aquatic factors during high tide and terrestrial factors
during low tide. This has made intertidal inhabitants valuable study systems for
investigating the mechanisms that allow life to flourish in a highly variable environment
(18). Mussels, of the genus Mytilus, have long been a favored study organism in the
intertidal because they have a worldwide distribution, and being sessile, must endure
fluctuations in their environment. Therefore, they are likely to possess particularly
robust mechanisms to deal with changing environmental conditions. During immersion
at high tide, the seawater provides mussels access to food and dissolved oxygen, and
body temperatures are similar to the prevailing sea surface temperature. In contrast,
during aerial exposure the mussels cannot filter feed and body temperatures can rise or
fall due to differences between air and seawater temperature or because of the heating
effects of solar irradiance (21, 41). Furthermore, access to oxygen is severely restricted
because emerged mussels must close their valves to prevent desiccation (10).
Studies in Mytilus edulis have documented a number of physiological changes that are
associated with alternating periods of immersion and aerial emergence that include
compensatory changes in metabolic rate (41), oxygen consumption (9), heart rate (20),
68
valve opening/gape (33, 40). Further insights into the functional significance of these
responses arose from analysis of intermediary metabolites and how their abundance
changes with the ebb and flow of the tide, as well as other environmental variables (41).
The results of these studies, along with similar studies of other invertebrates (27), have
yielded a model that defines the core metabolic pathways associated with low tide
aerial exposure (41). According to this model, immersed mussels undergo aerobic
metabolism and synthesize ATP, using coupled citric acid cycle/electron transport
pathways. Upon emergence, bivalves close their valves, oxygen concentrations in the
mantle cavity drop quickly, and anaerobic metabolism commences at the onset of
hypoxia (2). Under these hypoxic conditions, glucose and aspartate are fermented to
produce succinate and alanine via the glucose-succinate, and aspartate-succinate
pathways, respectively (24). If the duration of hypoxia extends for days then succinate
is further reduced to propionate which yields additional ATP, as well as aiding in acid-
base balance via the production of bicarbonate (24, 30). Studies into these pathways
indicate that these end-products produce a greater amount of ATP per unit of glucose,
and produce less metabolic protons, compared to the chief anaerobic pathway in
vertebrates in which glucose is reduced to lactate (27). At the same time, mussels
reduce the activities of many processes, such as digestion, respiration, and heart
activity, and glycolysis, which allow mussels to maintain energy balance and reserve
glycogen stores in anticipation of long term periods of hypoxia (27, 28, 41). Upon re-
submergence the valves open within minutes, nourishing tissues with oxygen and food,
69
and this period is characterized by increased heart rate and heat production linked to
the re-synthesis of aspartate and glycogen, filter feeding, digestion, and excretion of
anaerobic end-products into the aquatic medium (see review in (15)).
Technological advances in metabolite analysis have the potential to reveal new insights
into metabolic adaptations to intertidal life. Whereas earlier studies were limited to
measurements of a single metabolite at a time, contemporary techniques such as
1
H
NMR, liquid chromatography (LC)-mass spectrometry, and gas chromatography (GC)-
mass spectrometry (LC/MS and GC/MS respectively) have the potential to quantify
hundreds to thousands of metabolites in a single sample. Recent studies employing
1
H
NMR have shown that succinate and alanine accumulate in M. edulis tissue during
hypoxia (22, 38), confirming previous studies, but have yet to yield a truly global insight
into the metabolic reprogramming that occurs with each period of aerial emergence and
immersion. Here we report results from an unbiased LC/MS and GC/MS metabolomic
screen of a simulated tidal cycle in the California ribbed mussel, Mytilus californianus
Conrad, the species that dominates rocky intertidal, wave-exposed sites from Baja
California to Alaska (37). M. californianus is well adapted to periods of immersion and
aerial emergence and appears to share all of the characteristic responses exhibited by
M. edulis (2). By taking repeated measurements of metabolites every 2 hrs over 3 days
and 6 tidal cycles we provide an unprecedented temporal overview of the relationship
70
between tidal cycles of immersion and aerial emergence and changes in the
metabolome.
71
Chapter 2: Materials and Methods
Animals
Mytilus californianus of 4-5 cm length were collected at Zuma Beach, north of Los
Angeles, CA. In order to simulate intertidal conditions in the lab, the mussels were
maintained in aquaria and grown as a single layer of animals on shelves at the mid-level
height of the aquarium. The ebb and flow of the tide was simulated using computer-
controlled water pumps that regulated the depth of water by pumping seawater into
and out of the aquarium. The room was maintained at a constant temperature of 17°C
to ensure that the body temperature of the mussels remained constant whether
submerged or emerged in air. Food as liquid algal cultures (Shellfish Diet 1800, Reed
Mariculture, Campbell, California) was continuously added to the water during each
episode of high tide submergence. The seawater in the aquaria was constantly
replaced such that 10% of the volume of the system was changed every day. Cardiac
activity was monitored continuously in a subset of mussels non-invasively using an
infra-red phototransducer that was attached to the exterior of the shell and allows
heart rate to be measured (13).
A tidal regime of alternating periods of 6 hrs in and 6 hrs out of the water was
established and mussels were acclimated to this regime for 4 weeks prior to the
commencement of sampling. Low tides occurred from 12am to 6am and 12pm to 6pm.
A light/dark cycle was imposed with period of darkness occurring from 6pm to 6am.
72
Animals were sampled every 2 hrs over a period of 72 hrs with the initial sample
collected at 7 am on day 1 of the experiment (Fig. 5A). This sampling regime ensured
that 3 samples were collected per episode of low or high tide, with the first sample
collected 1 hr into the episode, the second sample taken at the middle 3 hr time-point
and the third sample taken 1 hr prior to the change in tidal episode. Four individual
mussels were collected at each time-point. An equal mass of gill tissue (50 mg) was
dissected from each individual, pooled into a 2 ml cryovial, snap frozen in liquid
nitrogen, and stored at -80°C.
Metabolite analysis
Gill samples were shipped on dry ice to Metabolon (Durham, NC) for metabolite
analysis. Samples were prepared for LC and GC separation and MS analysis at
Metabolon. The tissue samples were ground (Glen Mills Genogrinder 2000) in methanol
for 2 mins which served to dissociate small molecules bound to protein and precipitate
proteins. The sample was centrifuged and the resulting supernatant was split into equal
volumes for analysis on the LC+, LC-, and GC platforms, and vacuum-dried. The LC/MS
portion of the platform incorporated a Waters Acquity UPLC system and a Thermo-
Finnigan LTQ mass spectrometer, including an electrospray ionization (ESI) source and
linear ion-trap (LIT) mass analyzer. Aliquots of the vacuum-dried sample were
reconstituted, one each in acidic or basic LC-compatible solvents containing 8 or more
injection standards at fixed concentrations (to both ensure injection and
73
chromatographic consistency). Extracts were loaded onto columns (Waters UPLC BEH
C18-2.1 x 100 mm, 1.7 μm) and gradient-eluted with water and 95% methanol
containing 0.1% formic acid (acidic extracts) or 6.5 mM ammonium bicarbonate (basic
extracts). Samples for GC/MS analysis were dried under vacuum desiccation for a
minimum of 18 hours prior to being derivatized under nitrogen using bistrimethyl-silyl-
trifluoroacetamide (BSTFA). The GC column was 5% phenyl dimethyl silicone and the
temperature ramp was from 60° to 340° C in a 17 minute period. All samples were then
analyzed on a Thermo-Finnigan Trace DSQ fast-scanning single-quadrupole mass
spectrometer using electron impact ionization. The instrument was tuned and
calibrated for mass resolution and mass accuracy daily. For monitoring of data quality
and process variation, multiple replicates of a pool of human plasma were injected
throughout the run, interspersed among the experimental samples in order to serve as
technical replicates for calculation of precision. Signatures for each metabolite were
identified by matching to a database of 1,205 authentic compound standards (16).
Quantitative comparisons of each compound in each sample were based on integrated
peak ion counts of the quantification ion peak and were adjusted for minor day-to-day
instrument gain drift by Metabolon as described (26). Null values were imputed with
the minimum value detected for that compound among all samples based on the
assumption that the values were below the level of detection.
74
Metabolites that exhibited a rhythmic pattern of abundance were identified using
JTK_CYCLE (23) and p-values were corrected for a false discovery rate of <0.05 using a
standard method (36). As an additional measure of significance a Welch’s t-test was
performed to test the null-hypothesis that metabolite levels were unchanged between
the 18 samples collected during low tide and the 18 samples collected during high tide.
75
Chapter 2: Results
Metabolomic profiling identified a total of 169 named compounds in gill samples
isolated from mussels during a simulated tidal regime. To identify metabolites whose
abundance was linked to the tidal cycle we used the JTK_CYCLE algorithm to identify 24
metabolites which exhibited statistically significant rhythmic changes in abundance
(Table 1). The JTK_CYCLE algorithm was developed to identify rhythmic patterns in large
scale time-series, and is a non-parametric test that detects orderings of values that
correlate with predefined period lengths (23). The metabolites that were assigned the
most significant p-values by JTK_CYCLE were those that exhibited cyclical levels of
abundance of large amplitude. Overall, the relative abundance of 17 metabolites
peaked in the samples collected during low tide, while abundance of 7 metabolites
peaked in high tide collected samples. Interestingly, the abundance of all 24
metabolites peaked every 12 hrs, indicating that their rhythmic pattern was associated
with changes in the tidal cycle, and we found no evidence for metabolite abundance
patterns that adhered to a circadian cycle. All but 2 of these metabolites were deemed
statistically significant in a Welch’s t-test that compared all the samples collected during
low tide to those collected during high tide (Table 1). Simultaneous monitoring of
cardiac activity confirmed previous studies (19) and showed that aerial emergence
caused an abrupt cessation of heart rate which persisted for the duration of low tide,
and that cardiac activity resumed quickly upon submergence at high tide (Fig. 5B).
76
The most significantly oscillating metabolite in this dataset was succinate, the end-
product of anaerobiosis in mussels (12), which exhibited a strong rhythmic pattern of
abundance, with levels increasing upon aerial emergence and then declining rapidly
during the first hour of immersion (Fig. 6A). Similarly, levels of malate, which is the
intermediate product formed during the reduction of oxaloacetate to succinate,
oscillated and increased during low tide (Fig. 6A). Consistent with glucose serving as the
primary substrate for anaerobiosis (12) our data revealed that glucose abundance
generally declined during low tide (Fig. 6B). The other anaerobic pathway in mussels is
the reduction of aspartate resulting in the formation of succinate as well as alanine via
glutamate. The abundance of these three metabolites was cyclical, with alanine and
aspartate exhibiting broadly anti-correlated abundance profiles (Fig. 6C). As evidence of
a resumption of aerobic metabolism at high tide, we observed a sharp rise in citrate
levels after 1 hr of immersion (Fig. 6D) suggesting that the accumulated malate and
succinate were metabolized through an active TCA cycle. Propionate which is produced
by a further reduction of succinate was not detected in any of these samples consistent
with reports that propionate production is initiated only after longer periods of hypoxia
(24).
A recurring pattern in these metabolomic data was the increase in carnitine-conjugated
metabolites during low tide, with a total of 8 carnitine-conjugated metabolites
exhibiting a rhythmic abundance profile. For example, we identified that the carnitine
77
derivatives of the end products of β-oxidation of fatty acids with even or odd numbers
of carbons, acetylcarnitine and propionylcarnitine, respectively, oscillated and peaked
during low tide (Fig. 7A). We detected corresponding changes in free carnitine whose
level trended towards decreasing at low tide (Fig. 7B). Long-chain fatty acids have to be
conjugated to carnitine for transfer into the mitochondrial matrix for β-oxidation, and
samples taken during low tide had significantly higher levels of both stearoylcarnitine
and butyrylcarnitine (Fig. 8A). Similarly, animals at low tide had significantly higher
levels of 4 carnitine-conjugated intermediates of branched-chain amino acid (BCKA)
catabolism, isobutyrylcarnitine, 2-methylbutyroylcarnitine, isovalerylcarnitine and
hydroxyisovaleroyl carnitine (Figs 8B & 8C). The primary step in their catabolism is the
formation of branched-chain keto acids which are then oxidized in the mitochrondria to
form acetyl-CoA and propionyl-CoA through a pathway that requires the formation of
CoA intermediates, but instead these intermediates accumulated as carnitine-
conjugates during low tide. Closer inspection of the time-course of the accumulation of
these carnitine-conjugates indicates that while acetylcarnitine and propionylcarnitine
tended to continue to accumulate for the duration of the low tide episode, the
abundance of the other carnitine-conjugates often peaked at the mid 3 hr time-point of
low tide. Considered together, these data indicate that intermediate metabolites that
are destined for metabolism in the mitochondria accumulate during low tide, and that
rather than accumulating as CoA derivatives, are conjugated to carnitine. Levels of the
CoA derivatives of these metabolites were always below the detection threshold of our
78
metabolomic screen suggesting that CoA conjugates are rapidly metabolized and do not
accumulate in this tissue. Consistent with this pattern, the abundance profile of
pantothenate, a precursor for CoA synthesis, exhibited a low amplitude cyclical
abundance pattern (Fig. 7C) with levels on average being elevated during low tide (Table
1), suggesting that the demand for CoA is decreased during periods of aerial emergence.
These data revealed other metabolites whose abundance was correlated with the tidal
cycle but whose function in intertidal physiology remains cryptic. For example, levels of
sphingosine, a signaling lipid and the backbone molecule of sphingolipids, cycled during
the tidal regime and its abundance declined during low tide (Fig. 9A). Our metabolomic
data revealed that two metabolites in pathways regulating sulfur metabolism exhibited
cycles of abundance, with levels of S-adenosylhomo-cysteine (SAH) declining during low
tide, whereas levels of cysteine-glutathione disulfide, an oxidative stress protectant (3),
were elevated at low tide (Fig. 9C). Also peaking during low tide was gamma-
glutamylalanine (Fig. 9C), which is formed by the transfer of the γ-glutamyl moiety
of glutathione to extracellular alanine by γ-glutamyl transpeptidase. Another
metabolite whose abundance peaked during low tide was aminoadipate which is
formed during the catabolism of lysine (Fig. 9D).
79
0
5
10
15
20
25
15
20
Temperature (°C)
Tidal cycle
High
Low
Light/Dark
Time-points
Time of day
6am 6pm 6am 6pm 6am 6pm 6am
Heart rate (bpm)
Time of day
6am 6pm 6am 6pm 6am 6pm 6am
A
B
Fig. 5. Cardiac activity is correlated with the tidal cycle in a simulated intertidal environment. A:
Representation of the environmental conditions used in the simulated tidal environment. Animals were
sampled every 2 hr starting at 7am. Animals were emerged during low tides which occurred from 12am –
6am and from 12pm – 6pm, while sunrise and sunset occurred at 6am and 6pm. B: Heart rate of two
representative mussels illustrates the rapid cessation of cardiac activity upon aerial emergence.
80
Compound HMDB # Pathway Platform Low tide/
High tide
Welch’s
t-test p-
value
JTK_CYCL
E
p-value
succinate
malate
citrate
alanine
aspartate
glutamate
acetylcarnitine
stearoylcarnitine
carnitine
butyrylcarnitine
propionylcarnitine
isobutyrylcarnitine
2-methylbutyroylcarnitine
isovalerylcarnitine
hydroxyisovaleroyl
carnitine
S-adenosylhomocysteine
gamma-glutamylalanine
gamma-glutamylleucine
cysteine-glutathione
disulfide
glucose
2-aminoadipate
pantothenate
phenylacetate
sphingosine
HMDB00254
HMDB00156
HMDB00094
HMDB00161
HMDB00191
HMDB03339
HMDB00201
HMDB00848
HMDB00062
HMDB02013
HMDB00824
HMDB00736
HMDB00378
HMDB00688
NA
HMDB00939
NA
HMDB11171
HMDB00656
HMDB00122
HMDB00510
HMDB00210
HMDB00209
HMDB00252
Krebs cycle
Krebs cycle
Krebs cycle
Alanine/aspartate
metabolism
Alanine/aspartate
metabolism
Glutamate metabolism
Carnitine metabolism
Carnitine metabolism
Carnitine metabolism
Fatty acid metabolism
Fatty acid metabolism
Amino-acid metabolism
Amino-acid metabolism
Amino-acid metabolism
Amino-acid metabolism
Cysteine metabolism
Peptide metabolism
Peptide metabolism
Glutathione metabolism
Glycolysis/gluconeogenesis,
Lysine metabolism
CoA metabolism
Phenylalanine metabolism
Sphingolipid metabolism
LC/MS neg
GC/MS
LC/MS neg
GC/MS
GC/MS
LC/MS neg
LC/MS pos
LC/MS pos
LC/MS pos
LC/MS pos
LC/MS pos
LC/MS pos
LC/MS pos
LC/MS pos
LC/MS pos
LC/MS neg
LC/MS pos
LC/MS pos
LC/MS pos
GC/MS
LC/MS pos
LC/MS pos
LC/MS neg
LC/MS pos
21.48
2.11
0.35
1.27
0.86
0.86
2.50
3.48
0.85
2.94
2.64
2.88
1.85
2.02
1.24
0.80
1.63
0.84
1.46
0.55
1.10
1.15
1.49
0.66
<0.001
<0.001
0.0477
0.0037
0.0211
<0.001
<0.001
<0.001
0.0737
<0.001
<0.001
<0.001
<0.001
<0.001
0.0532
<0.001
0.0142
0.0583
0.1047
0.0027
0.3957
0.0059
0.0050
0.0077
<0.0001
<0.0001
0.0435
<0.0001
0.0133
0.0031
<0.0001
<0.0001
0.0224
<0.0001
<0.0001
<0.0001
0.0001
<0.0001
0.0474
0.0020
0.0004
0.0269
0.0351
0.0214
0.0003
0.0400
0.0435
0.0008
Table 1. Metabolites that oscillate during the tidal cycle. HDMB refers to the Human Metabolome
Database entry for each identified metabolite. Platform indicates the platform on which the
compound was detected, and pos/neg signifies whether the extract was eluted under acidic or basic
conditions. Low tide/high tide reports the average relative abundance of the compound in samples
collected during low versus high tide.
81
0.5
1
2
0.1
0.25
0.5
1
2
5
10
20
0.25
0.5
1
2
5
10
Tidal cycle
Relative abundance Relative abundance
Citrate
Malate
Succinate
A
C
Aspartate
Alanine
Relative abundance
D
0.1
0.25
0.5
1
2
4
Relative abundance
Glucose
B
Fig. 6. Products of anaerobiosis exhibit a cyclical abundance profile and accumulate during low tide. x-
Axes, time as represented by the tidal cycle; y-axis, relative metabolite abundance plotted on a log
10
scale.
A: Succinate and malate. B: Glucose. C: Aspartate and alanine. D: Citrate.
82
0.5
1
2
4 A
Relative abundance
Propionylcarnitine
Acetylcarnitine
0.5
1
2
Carnitine
B
Relative abundance
Tidal cycle
0.5
1
2
Pentothenate
Relative abundance
C
Fig. 7 Substrates for the TCA cycle accumulate as carnitine conjugates during low tide. A:
Propionylcarnitine and acetylcarntine. B: Free carnitine. C: Pentothenate
83
0.25
0.5
1
2
4
0.25
0.5
1
2
4
0.1
0.25
0.5
1
2
4
Isobutyrylcarnitine
2-methylbutyryl-
carnitine
Stearoylcarnitine
Butyrylcarnitine
Relative abundance Relative abundance
A
B
Relative abundance
C
Isovalerylcarnitine
Hydroxyisovaleroyl -
carnitine
Tidal cycle
Fig. 8. Fatty acids and BCKA degradation products accumulate as carnitine conjugates during low tide. A:
Fatty acid degradation products stearoylcarnitine and butyrylcarnitine. B: Valine and isoleucine
degradation products ssobutyrylcarnitine and 2-methylbutyroyl-carnitine. C: Leucine degradation
products isovalerylcarnitine and hydroxyisovaleroyl-carnitine.
84
0.25
0.5
1
2
4
Sphingosine
Relative abundance
A
0.5
1
2
Relative abundance
B
S-adenosylhomo-
cysteine (SAH)
Tidal cycle
Relative abundance
D
0.25
0.5
1
2
4
Gamma-
glutamylalanine
Cysteine-glutathione
disulfide
0.25
0.5
1
2
C
Relative abundance
Aminoadipate
Tidal cycle
Fig. 9. Diverse metabolites show rhythmic abundance profiles and accumulate during low tide. A:
Sphingosine. B: S-adenosylhomo-cysteine. C: Gamma-glutamylalanine and cysteine-glutathione disulfide.
D: Aminoadipate.
85
Chapter 2: Discussion
The ebb and flow of ocean tides are one of the most predictable forces on earth and in
this study we present the most comprehensive screen to date of metabolite abundance
in bivalves with respect to this tidal cycle. Our findings confirm previous studies that
have examined metabolism in mussels during aerial emergence, and in addition
implicate additional levels of metabolic control whose role in intertidal adaptation
remains to be elucidated. Previous work in bivalves has reported tissue differences in
the levels of both substrates and accumulated products, as well as their relative
proportions during hypoxia (25), and indicate that this variation probably is linked to
differences in the prevailing composition of the energy stores in the tissues (11). This
suggests that strategies of metabolic reorganization will vary according to tissue and the
metabolic status of the animal. Despite these sources of variation, our data
recapitulated much of what is already understood regarding the pathways and
metabolites that participate in the switch to anaerobic metabolism that occurs during
low tide aerial emergence (Fig. 10A). Metabolites associated with glucose and aspartate
fermentation displayed particularly robust oscillations in this dataset. Malate and
succinate as intermediate and end-products of these pathways accumulated quickly
following emergence, increasing ~8-fold during the first hour in air, suggesting that this
anaerobic pathway was active within a short period of emergence. Upon immersion,
succinate levels fell rapidly and had returned to base-line levels within the first hour.
Studies in other mollusks have indicated that much of the accumulated succinate is
86
simply released into the external environment, rather than being metabolized (17), but
our observation of a large increase in citrate during the first hour of immersion suggests
that a proportion of the succinate is recovered and metabolized by the TCA cycle.
Unfortunately, our data provides only relative abundance levels which do not allow us
to estimate the proportion of the accumulated succinate that is metabolized to citrate.
The reduction of aspartate is believed to be the first anaerobic pathway that is activated
in response to hypoxia and our metabolomic data confirm that the aspartate-succinate
pathway was also active in emerged M. californianus, and revealed that the relative
abundance of aspartate and alanine is in anti-phase to one another.
One of the most striking aspects of these data was the rapid increase of carnitine-
conjugated metabolites during low tide aerial emergence. Carnitine is a low molecular-
weight compound obtained from the diet but which can also be synthesized from the
essential amino acids lysine and methionine. Carnitine has an obligate role in the
mitochondrial oxidation of long-chain fatty acids because long-chain fatty acid acyl
groups must be transferred from CoA derivatives to carnitine in order to enter the
mitochondria because the mitochondrial inner membrane is impermeable to polar
molecules such as CoA. Carnitine serves as a carrier for this transport in a process called
the carnitine shuttle and the conjugation reaction is catalyzed by Carnitine
acyltransferases. Consistent with this role, gill samples collected during low tide had
significantly higher levels of stearoylcarnitine and butyrylcarnitine indicating that these
87
fatty acid transfer molecules are accumulating rather than entering the mitochondria
and being catabolized. Therefore, we interpret the increase in long-chain fatty acyl
carnitine conjugates as evidence that fatty acid catabolism is halted during low tide,
leading to the accumulation of these intermediate metabolites (Fig. 10B). The other
sources of carnitine-conjugated compounds were acyl-chain intermediary metabolites
of BCKA catabolism, as well as acetylcarnitine and propionylcarnitine (Fig. 10C). In
contrast to the established role that carnitine plays in fatty acid catabolism, carnitine
does not play an obligate role in BCKA metabolism, nor in the metabolism of acetyl-CoA
or propionyl-CoA, suggesting that carnitine-conjugation may serve a specific role in
regulating these pathways during low tide. While the role that carnitines play in
branched-chain amino-acid catabolism is poorly understood studies in mammals
indicate that their carnitine-conjugates can be found in most mammalian tissues (4).
These data raise questions regarding the role that carnitine plays in the control of
metabolism during low tide. Conjugation and removal of carnitine is regulated by
Carnitine acyltransferase 1 and Carnitine acyltransferase 2, located on the outer and
inner layers of the inner mitochondrial membrane respectively. In addition to
carnitine’s role in fatty acid transport, it has been proposed that the formation of
carnitine conjugates may represent a safety mechanism to prevent acyl-CoA
accumulation in the cytoplasm and mitochondria (5). Carnitine binds acyl residues and
helps in their elimination by decreasing the number of acyl residues conjugated with
88
CoA and increasing the ratio between free and acylated CoA. In turn, changes in this
ratio alter the activity of many mitochondrial enzymes involved in the citric acid cycle,
gluconeogenesis, and fatty acid oxidation (35). Evidence of the detrimental effects of
elevated levels of acyl-CoA arises from research into genetic disorders affecting acyl-CoA
metabolism, which results in elevated levels of acyl-CoA in the mitochondria and to
serious health problems and early death (34). Investigations into these diseases report
that the mitochondria of affected individuals compensate for acyl-CoA imbalances by
converting acyl-CoA to acyl-carnitines and that the capacity of the patient to correct this
imbalance is limited by available carnitine and that dietary carnitine supplements help
to relieve these metabolic diseases (31). We hypothesize that the transfer of carnitine
to acyl-CoA molecules during low tide may represent a mechanism to offset the
debilitating effects that elevated levels of acyl-CoA may present. In the context of fatty
acid metabolism, this means that acyl-CoA molecules remain conjugated to carnitine
and are not metabolized further, and in the context of BCKAs, that intermediate acyl-
CoA products are transferred to carnitine. Similarly, we interpreted the increases in
acetylcarnitine and propionylcarnitine as a reflection of a buildup of acetyl-CoA and
propionyl-CoA during low tide which resulted in their transfer to carnitine. However,
growing evidence indicates that both acetylcarnitine and propionylcarnitine play a role
in the cellular stress response to oxidative damage (6), and that increased levels of these
metabolites have therapeutic effects and reduce ischemia-reperfusion injury (7).
Furthermore, the ratio of acetyl-CoA to CoA has important effects on overall
89
mitochondrial metabolism by modulating the activity of Pyruvate dehydrogenase with
low ratios enhancing the metabolism of carbohydrates (8). Therefore the functional
significance of the increases in these metabolites during low tide remains cryptic.
Finally, our metabolomic screen revealed that the CoA precursor, pantothenate,
increased during low tide, suggesting that de novo synthesis of CoA is depressed. The
production of CoA from pantothenate requires a large amount of energy (4 ATP) and
therefore the transfer of carnitines to intermediate metabolites may represent an
energetically favorable strategy to salvage CoA during low tide. Further investigations
will be required to test these hypotheses starting with comparisons of the relative levels
of total carnitine and carnitine acyltransferase activity in mussels growing under tidal
versus subtidal conditions. Increases in intertidal mussels would suggest that an
elevated capacity for carnitine-conjugation may serve an adaptive purpose for intertidal
mussels.
While most compounds that exhibited cyclical changes in abundance tended to
accumulate during emergence, levels of sphingosine declined ~2-fold. Sphingosine
comprises an 18 carbon unsaturated hydrocarbon chain with an amino alcohol terminus
and is component of sphingolipids, such as ceramide and sphingomyelins. Sphingosine
is also involved in cell signaling and can be phosphorylated by Sphingosine kinase (SK1)
to produce sphingosine 1-phosphate, and both sphingosine and sphingosine 1-
phosphate are important lipid signaling molecules and have implicated as important
90
regulators of many cellular processes, particularly cell survival, proliferation, and death
(29). Of particular relevance to this study is that SK1 contains a HIF-1α element binding
site that enhances the conversion of sphingosine to sphingosine-1-phosphate during
hypoxia (1), with evidence that sphingosine 1-phosphate functions to reduce hypoxia-
reoxygenation injury (39). Sphingosine levels declined during aerial emergence when
the mussel tissues were hypoxic, and while levels of sphingosine 1-phosphate were
below detectable limits in these samples, we speculate that HIF-1α regulated increases
in SK1 could be responsible for the decline of sphingosine during emergence. This could
mean that sphingosine abundance may represent a biomarker for the activation of
hypoxia-signaling pathways during low tide hypoxic episodes and may prompt further
investigations into the role that sphingolipid secondary messengers play in intertidal
adaptation.
There are limitations of global metabolomic approaches such as we employed here
because detection of many metabolites requires specific extraction and detection
protocols (14). This approach is unable to detect changes in flux through a particular
pathway and instead the data must be interpreted by detecting changes in one or more
intermediary metabolites or more often by changes in the levels of starting substrates
or end-products. Furthermore, this global profiling approach provides data on relative
metabolite levels and it does not report the absolute concentrations of compounds in
the tissue. The lack of absolute quantitative data restricts the conclusions that can be
91
drawn from the data because information regarding the proportion of a metabolite pool
that is subject to changes is absent, nor can changes in metabolite concentration be
interpreted within the context of enzyme Km’s (32). Ultimately, absolute quantification
of metabolite levels as well as metabolite flux measurements will be required to fully
elucidate metabolic reorganization in this model. Regardless of these shortcomings, we
demonstrate that mass-spectrometry based metabolomics can provide new insights into
the metabolic reprogramming that occurs in bivalves during the switch from aerobic to
anaerobic metabolism, despite the fact that this topic has been intensively studied over
the last 3 decades.
We suggest that the increased statistical rigor provided by profiling changes in
metabolite levels at high temporal resolution over 6 tidal cycles has led to the detection
of metabolites that cycle robustly through periods of tidal immersion and emergence.
Note that in nature, the duration and timing of bouts of emergence changes each day
because the tidal cycle advances by 25 min every tide, and the heights of each high tide
are typically different. In contrast, in this study, we employed a simulated tidal cycle,
consisting of equal periods of submergence and emergence that were repeated at the
same times each day. This experimental design was chosen because it allowed patterns
of metabolite abundance to be correlated accurately with episodes of immersion or
emergence, but further studies will be required to confirm that these patterns persist
under more variable conditions in the field. These data emphasize the overwhelming
92
effect that tidal cycles of emergence and immersion have on the metabolism of
intertidal mussels. While caution should be exercised in extrapolating these findings
directly to other species, a review of anaerobic metabolism in other species of bivalves
reveals that they deploy similar fermentation pathways during periods of reduced
oxygen availability (see review in (17)). Therefore, it will be interesting to investigate
the extent to which carnitine-conjugation is a ubiquitous strategy deployed in organisms
that regularly experience cycles of aerobic and anaerobic metabolism.
93
valine
isobutyryl-CoA
isoleucine
2-methylbutyryl-CoA
leucine
isovaleryl-CoA
3-methylcrotonyl-CoA
propionyl-CoA
succinyl-CoA
acetyl-CoA
isobutyrylcarnitine 2-methylbutyrylcarnitine isovalerylcarnitine
hydroxyisovaleryl
carnitine
propionylcarnitine
acetylcarnitine
transamination
oxidative decarboxylation
dehydrogenation
glucose
phosphoenolpyruvate
pyruvate
oxaloacetate
malate
aspartate
ketoglutaric
acid
glutamate
alanine
succinate
fumarate
TCA cycle
C A B
fatty acid
fatty acyl-CoA
stearoylcarnitine/
butyrylcarnitine
mitochondrial
β-oxidation
propionyl-CoA acetyl-CoA
propionylcarnitine acetylcarnitine
propionate
acetate
Fig. 10. Simplified metabolic schema showing the key pathways that are altered during low tide. A:
Consensus anaerobic pathways. B: Catabolism of fatty acids. C: Catabolism of branched-chain amino acids.
Metabolites in red and green text indicate compounds that were elevated or reduced during low tide,
respectively.
94
Chapter 2: References
1. Anelli V, Gault CR, Cheng AB, Obeid LM (2008). Sphingosine kinase 1 is up-
regulated during hypoxia in U87MG glioma cells. Journal of Biological Chemistry.
283, 3365-3375.
2. Bayne BL, Bayne CJ, Carefoot TC, Thompson RJ (1976). The physiological ecology
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Chapter 3: Molecular and biochemical observations of Mytilus
californianus under constant submergence
Chapter 3: Abstract
The mussel Mytilus californianus reside predominantly in the intertidal zone, a
fluctuating environment at the interface of the terrestrial and marine biomes. However,
cryptic populations have been found occupying subtidal regions offshore, which raise
questions about what physiological mechanisms that allow M. californianus to thrive in
both environments. As a sessile species M. californianus encounters hourly, daily and
seasonal fluctuations in oxygen, temperature, salinity and nutrient availability as a
consequence of tidal and climate processes; whereas, these same physical and
biological factors are comparatively more stable in subtidal environments. In order to
investigate the link between intertidal and subtidal physiology, we performed transcript
and metabolite screens of mussels held under constant submergence and compared the
results to our previously published screens of mussels in a simulated intertidal
environment. Submerged mussels were observed to exhibit either an open or closed
valve state corresponding to periods of active cardiac activity and bradycardia
respectively, and gill tissue was sampled from individuals exhibiting both states.
Enrichment analysis of significantly expressed genes revealed that genes up-regulated in
mussels exhibiting bradycardia and active activity were enriched for genes expressed
during the simulated low and high tide respectively. A metabolomics screen revealed
elevated levels of succinate, malate and alanine in mussels exhibiting bradycardia, which
100
suggested the activation of anaerobic pathways that are known to be induced during
aerial exposure. Additionally, we observed higher levels of carnitine-conjugate
intermediates of the fatty acid derivatives stearoyl-carnitine and butyryl-carnitine
carnitine, and branched-chain amino acid (BCKA) catabolism including,
isobutyrylcarnitine, 2-methylbutyroylcarnitine and isovalerylcarnitine. This is the first
study that unites patterns of bradycardia and valve state in intertidal and subtidal
mussels with conserved patterns of gene expression and metabolite abundance.
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Chapter 3: Introduction
The intertidal zone is one of the most variable environmental regions on earth because
it lies at the interface between the terrestrial and aquatic habitats, which are widely
contrasting in character. The environment of the intertidal zone is heavily governed by
the ebb and flood of the tides, which vary daily in time and space, due to the earth-
moon rotation and the sun. Along the contours of the shoreline surface, these
variations produce gradients in temperature, salinity, nutrients, and wave force (46, 59).
The ecological success (including growth, survival, and reproduction) of intertidal
organisms is determined by their physiological capacity to adjust to the stresses of a
fluctuating environment (33). Organisms that are well adapted to physical factors at
one end of a particular gradient may not thrive at the opposite end. As a result, vertical
distributions of species are a common feature of the intertidal landscape, due to the
various degrees of tolerance limits of the residing organisms along the tidally influenced
environmental gradients (32, 34). The traits that allow organisms to thrive in the
intertidal zone exist at all levels of biological organization, including molecular,
biochemical, morphological, and behavioral (63). Therefore, an integrative approach to
investigating adaptations to this habitat is important because it can provide insight to
how various traits contribute to an adaptive phenotype that ultimately supports
survival.
The mussel Mytilus californianus out-competes virtually all other invertebrate species
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for space in the mid-intertidal region along the rocky shoreline of the northeastern
Pacific Ocean. Individual mussels aggregate to form beds by attaching themselves to
the rocky substrate using secreted bysal threads. By general consensus, the upper limit
of the vertical distribution of M. californianus populations is set by temperature and
desiccation stress and the lower limit, by predation by sea star Pisaster ochraceous (14,
51). Over evolutionary time scales, mussels have acquired a suite of biological
mechanisms that allow them to endure the rigors of a constantly alternating
environment. During low tide aerial emergence, mussels are removed from the oxygen
and nutrient rich aquatic environment and subjected to the prevailing terrestrial
climate, in which they do not feed or respire aerobically. They respond to terrestrial
exposure by closing their valves to prevent desiccation (52), reducing rates of
metabolism to conserve energy (47, 73) and utilizing anaerobic metabolic pathways as
oxygen tension declines in the water retained between their shell valves (21). These
abilities allow mussels to endure prolonged bouts of hypoxia, starvation, and solar
irradiation that occur during periods of extended aerial emergence. During episodes of
high tide immersion, mussels have the opportunity to open their valves and utilize the
dissolved oxygen and suspended nutrients present in the prevailing aquatic
environment (16). Therefore, the presence of a valved shell and its utilization as a
regulated barrier between the interstitial environment of the shell cavity and that of the
prevailing surroundings, are adaptive traits that allow bivalves to flourish under
intertidal conditions.
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Intriguingly, small populations of M. californianus have been observed to reside in
subtidal habitats, below the boundaries of the intertidal zone either in shallow water or
offshore attached to seamounts at depths as low as -21 m (12, 52). The establishment
of these populations may be the end result of unique local near-shore ocean current
and settlement patterns, combined with reduced or nonexistent predation. They are
not only able to survive in the subtidal conditions but also to rather thrive. Mytilus
species that are cultivated on ropes below the sea surface exhibit exceptionally high
growth rates and meat content (45, 60). Therefore, while a large body of work suggests
that M. californianus is well adapted to the intertidal environment (see review in (29)),
implications of adaptability to constant submergence, is founded upon the discovery of
the highest species terminal sizes in wild subtidal populations (12).
Observations of bivalves in simulated subtidal conditions have revealed the prominence
of episodic behavior. For example, studies have shown spontaneous fluctuations in
valve movement (2), bradycardia (reduction of heart rate) (6) and oxygen consumption
(19). Valve gape in Mytilus has been shown to be mediated by extant environmental
factors including variation in food concentration (57), temperature (4), and pH (7), water
current velocity (49), and tide (61, 67). Interestingly, when maintained under constant
conditions, mussels continue to exhibit spontaneous bouts of valve closure and
bradycardia, sometimes on the order of hours (5). Instances of valve closure are
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associated with reduced filtration rate (25) and oxygen consumption (19), which
suggests the co-occurrence of limited feeding and anaerobic metabolism. Thus, mussels
exhibit substantial changes in their metabolism which appear to be unrelated to the
prevailing environmental conditions. The underlying molecular basis for these
spontaneous metabolic shifts is unknown.
The onset of bradycardia is tightly linked to valve gape state (open/closed) and mussels
with closed valves enter into bradycardia, which is quickly (on the order of minutes)
reversed following valve opening (18). Preliminary observation of M. californianus by
our laboratory revealed episodic bouts of bradycardia similar to observations reported
in studies of M. edulis (6, 18). The purpose of this study is to gain an understanding of
both the possible mechanisms that drive these episodic events, as well the associated
physiological changes, using an integrative approach that combines measurements of
heart rate, gene expression, and metabolite abundance.
The interpretation of data collected from mussels under subtidal conditions in the
context of those collected from intertidal studies can give us insights into the
mechanisms that trigger the observed endogenous episodic events. In the current study
we investigated differences in transcriptomic and metabolomic profiles between
constantly submerged mussels that have open valves/active cardiac activity or closed
valves/bradycardia. We then compared these differences with those that we observed
105
between mussels sampled during high and low tide in the simulated intertidal
environment (15, 16 (Chapters 1 & 2)), the results of which revealed sets of genes that
showed robust correlations with the ebb and flood tide. Genes up-regulated during
high tide were anti-correlated with genes induced during low tide. The metabolite
dataset revealed oscillations of compounds that are associated with core metabolic
pathways used by Mytilus during alternating tidal cycles. These pathways include those
that occur during high-tide oxygen consumption (citric acid cycle) and low tide
anaerobic fermentation (glucose-succinate and alanine-succinate pathways) (35). This is
the first transcriptomic and metabolomic combined analysis of mussels under constant
submergence. These observations provide data that will lead to defined biological
states in M. californianus and elucidate new insights and hypotheses regarding its
ancestral relationship to exclusively subtidal bivalve species that also show
endogenously mediated episodic behaviors (6, 50, 65).
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Chapter 3: Materials and Methods
Acclimation and Sampling Methods
Mytilus californianus of 4-5 cm length were collected at Zuma Beach, north of Los
Angeles, CA. Mussels were maintained in the dark under constant submergence in a
partially filled 50 gallon aquarium for two weeks before the start of the sampling. The
cold room was maintained at a constant temperature of 17 °C. Food as liquid algal
cultures (Shellfish Diet 1800, Reed Mariculture, Campbell, California) was continuously
added to the water daily. A total of 10% of the volume of seawater held in the aquaria
was replaced daily. Internal filters were turned on once a day to remove excess
particulate matter. Water color, as a proxy for food concentration, did not change
during the course of the experiment.
Cardiac activity is a robust indicator of valve gape status (18). We measured heart rates,
using an infra-red phototransducer system (CAPMON) (22). A transducer was attached
to each mussel at the beginning of the acclimation period, however, data collection did
not commence until after the two week acclimation period. After 3 days of data
collection, we identified and sampled mussels exhibiting different states of cardiac
activity by real time inspection of cardiac signatures over the course of 2 days between
the times of 8am until 5pm. Mussels that had active cardiac activity were sacrificed and
assigned to the “Active group” (Fig. 11A). Mussels that entered into bradycardia for
107
greater than 1 hour were sacrificed and assigned to the “Bradycardia” group (Fig. 11B).
A total of five pairs (Bradycardia/Active) of mussels plus an additional bradycardiac
mussel were sampled. Gill tissue was removed from each individual and immediately
frozen in a 2ml tube on dry ice, and stored at -80°C. In preparation for metabolite
analysis, an equal mass of gill tissue (50 mg) was isolated from each frozen sample on a
bed of dry ice, and restored at -80°C.
Long-term Monitoring of Cardiac Activity-A Preliminary Investigation
To examine the temporal significance of bradycardia we measured cardiac activity of
two mussels for six days. Mussels close their valves for greater periods under lowered
nutrient concentrations (58), therefore mussels were fed every 3 days compared to the
daily regime applied in the tissue sampling experiment. The two mussels were labeled
#1 (Fig. 12A) and #2. We then determined the lengths of active cardiac periods and
bradycardia intervals. In order to determine if a relationship exists between active
periods and bradycardia intervals, we performed regressions of active period lengths
before bradycardia versus bradycardia interval length; and active period lengths
following intervals of bradycardia versus bradycardia interval length for mussels #1 and
#2. Bradycardia intervals lasting <2 minutes were not considered in the analysis.
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RNA Isolation and Microarray Hybridization
Total RNA was isolated from gill tissue using TRIzol (Invitrogen) according to the
manufacturer’s instructions. The total RNA was purified further across glass-fiber filter
columns (Qiagen) according to the manufacturer’s instructions. An equal amount of
total RNA from each individual animal was amplified as described in (15 (Chapter 1)).
Briefly, double-stranded cDNA was prepared by reverse transcribing 2 μg pooled total
RNA in a 20-μL reaction containing 20 pmols of T7-dT15VN primer, 50 mM Tris·HCl (pH
8.3), 75 mM KCl, 3 mM MgCl2, 50 μMs dNTP, 20 units of RNaseOut (Invitrogen) and 100
units of M-MLV reverse transcriptase (Epicentre). The RNA and primer mixture was heat
denatured at 65 °C for 10 min, the remaining omponents added and the reaction
incubated at 40 °C for 2 h, and then stopped by heating to 65 °C for 15 min. Next, 60 μL
of in vitro transcription reaction mixture, containing 53 mM Tris·HCl (pH 7.5), 13 mM
NaCl, 8 mM MgCl2, 5.3% PEG 8000, 2.6 mM spermidine, 3.33 mM each (ATP, GTP, CTP),
2.5 mM UTP, 0.83 mM amino-allyl UTP (Epicentre), 0.12 units organic pyrophosphatase,
20 units RNaseOut, 500 units T7 RNA polymerase (Epicentre), was added and the
reaction incubated at 40 °C for 18 h. The resulting aRNA was purified using a Qiagen
RNeasy kit, and half the aRNA was labeled with Cy5 and the other half with Cy3.
Fluorescently labeled antisense RNA samples were hybridized to an in house
manufactured M. californianus cDNA array (15 (Chapter 1)) using an interwoven loop
design, which insured that each RNA sample was hybridized to either two or four arrays
109
with fluor reversal. This loop hybridization design yields improved statistical inference of
microarray data (39).
Gene Expression and Gene Enrichment Analysis
Tagged image file format images of hybridized arrays were captured with an Agilent
scanner (Agilent Technologies) and spot intensities quantified with Agilent Feature
Extraction software (version 9.5.1). Spot median pixel intensities without background
correction were collected, spatial intensity trends removed, and individual channels
normalized using joint lowess transformation. Relative expression of each gene in each
hybridization loop was estimated using MAANOVA version 0.98-7 for R using an ANOVA
model in which dye and sample were treated as fixed effects, and array was treated as
random effects (38). Gene expression data were centered by dividing the relative
expression of each gene by the median expression of that gene across the samples in
the dataset.
Gene Set Enrichment Analysis (GSEA) (64) was used to confirm if genes identified as tidal
in (15 (Chapter 1)) were over-represented in the bradycardia and/or active cardiac
functioning mussels. A ranked gene list was generated according to the differential
expression (MAANOVA) between the bradycardia and active phenotypes. The
phenotypes were correlated to prepared gene sets, including a low tide set containing
205 genes and a high tide set of containing 169 genes. Statistical significance was
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determined by 1000 permutations of the member genes of each gene set and re-
computation of a Kolmogorov–Smirnov-like statistic.
Metabolite Analysis
Gill samples were shipped on dry ice to Metabolon (Durham, NC) for metabolite
analysis. Samples were prepared for LC and GC separation and MS analysis at
Metabolon. The tissue samples were ground (Glen Mills Genogrinder 2000) in methanol
for 2 mins which served to dissociate small molecules bound to protein and to
precipitate proteins. The sample was centrifuged and the resulting supernatant was split
into equal volumes for analysis on the LC+, LC-, and GC platforms, and vacuum-dried.
The LC/MS portion of the platform incorporated a Waters Acquity UPLC system and a
Thermo-Finnigan LTQ mass spectrometer, including an electrospray ionization (ESI)
source and linear ion-trap (LIT) mass analyzer. Aliquots of the vacuum-dried sample
were reconstituted, one each in acidic or basic LC-compatible solvents containing 8 or
more injection standards at fixed concentrations (to both ensure injection and
chromatographic consistency). Extracts were loaded onto columns (Waters UPLC BEH
C18-2.1 x 100 mm, 1.7 μm) and gradient-eluted with water and 95% methanol
containing 0.1% formic acid (acidic extracts) or 6.5 mM ammonium bicarbonate (basic
extracts). Samples for GC/MS analysis were dried under vacuum desiccation for a
minimum of 18 hours prior to being derivatized under nitrogen using bistrimethyl-silyl-
trifluoroacetamide (BSTFA). The GC column was 5% phenyl dimethyl silicone and the
111
temperature ramp was from 60° to 340° C in a 17 minute period. All samples were then
analyzed on a Thermo-Finnigan Trace DSQ fast-scanning single-quadrupole mass
spectrometer using electron impact ionization. The instrument was tuned and
calibrated for mass resolution and mass accuracy daily. For monitoring of data quality
and process variation, multiple replicates of a pool of human plasma were injected
throughout the run, interspersed among the experimental samples in order to serve as
technical replicates for calculation of precision. Signatures for each metabolite were
identified by matching to a database of 1,205 authentic compound standards (24).
Quantitative comparisons of each compound in each sample were based on integrated
peak ion counts of the quantification ion peak and were adjusted for minor day-to-day
instrument gain drift by Metabolon as described (42). Null values were imputed with
the minimum value detected for that compound among all samples based on the
assumption that the values were below the level of detection. A Welch’s t-test was
performed to test the null-hypothesis that metabolite levels were equivalent between
the active and bradycardia samples.
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Chapter 3: Results
Observations of the cardiograms of fed mussels revealed that bradycardia periods
ranged between 7 m to 16 hrs (Fig. 11A & 11B). In preliminary observations of the
cardiograms of unfed mussels (Fig. 12A) bradycardia periods ranged between 1 h and 10
hrs. The regression analysis revealed that the period of cardiac activity following
bradycardia is strongly influenced by the preceding bradycardia interval in mussels #1
and #2, r
2
=0.82 (p<0.05) and 0.79 (p<0.05) respectively. Whereas, the influence of
bradycardia interval length on the cardiac activity period preceding the bradycardia
event was not substantial in mussels #1 and #2, r
2
=0.05 (p>0.05) and 0.17 (p>0.05)
respectively (Fig. 12B & 12C).
GSEA analysis revealed that genes up-regulated in bradycardia and active mussels were
enriched (p<0.01) in genes associated with the low tide (Fig. 13A) and high tide (Fig.
13B) gene sets, respectively. A subset of 99 and 80 genes (SI Table 5 & 6) contributed to
the nominal p-value of the analysis, thus these genes represent the core of the
enrichment signal. The core genes associated with bradycardia that were also
highlighted in our intertidal study (15), included genes belonging to several classes of
biological functions related to transcription such as early genes (JUN, FOSL2), anti-
proliferation (GADD45), transcription factors (CREBE, MAP4K3), and cell signaling, such
as ceramide kinase (CIRK1) and cAMP-responsive element binding protein-like 2
(CRBL2). Several stress related proteins were associated with bradycardia including
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heat shock protein (HSP 90), apoptosis-inhibiting baculoviral inhibitor of apoptosis (IAP)
repeat-containing proteins (BIRC2, BIRC7) and an inhibitor of NF-κB (IκBα). Consistent
with the association between anaerobic metabolism and tissue acidosis (26), we
observed 2 isoforms of carbonic anhydrase (CAZ, CA2) expressed in the bradycardia
group. Carbonic anhydrases counteracts acidosis by catalyzing the conversion of CO
2
to
bicarbonate.
Mussels undergoing bradycardia displayed significantly (p<0.05) higher levels of
succinate, malate and alanine consistent with active utilization of glucose-succinate and
alanine-succinate anaerobic pathways during bradycardia (Fig. 14A). Succinate
production was the dominant signal of anaerobiosis, with a more than 37-fold
difference in concentration. The change in succinate levels between bradycardia and
active mussels are similar to the observed variations between mussels undergoing
anaerobic and aerobic metabolism during aerial exposure and submergence respectively
(16).
Further agreement with tidal metabolite regulation is revealed by the identification of 7
carnitine-conjugated metabolites that accumulated in mussels subjected to constant
submergence. The concentrations of carnitine-conjugated metabolites were
significantly higher in bradycardia mussels, consistent with the hypothesis that
substrates involved in aerobic metabolism are stored as carnitine-conjugates during
114
anaerobic metabolism (16). For example, we identified the accumulation of the fatty
acid derivatives stearoyl-carnitine and butyryl-carnitine carnitine (Fig. 14B). The
mitochondrial membrane is impermeable to fatty acids because they contain bulky
polar molecules and the substitution of polar molecules with carnitine is necessary for
transport into the mitochondrial matrix and subsequent β-oxidation (23). The
accumulation of the fatty acid derivatives suggests that fatty acid catabolism ceased
during bradycardia. Similarly, mussels undergoing bradycardia show higher levels of
carnitine-conjugated intermediates of branched-chain amino acid (BCKA) catabolism,
including isobutyrylcarnitine, 2-methylbutyroylcarnitine and isovalerylcarnitine
carnitine (Fig. 14C), which suggests reduced activity of these pathways (see review in
(16) . Finally, the end products of both, fatty acid and branched-chain catabolism,
acetyl-CoA and propionyl-CoA (Fig. 14B) also accumulated as carnitine-conjugates
consistent with the relative shut-down of the TCA cycle and electron transport chain and
the induction of fermentative metabolism that occurs during tissue hypoxia.
In concordance with aerially exposed mussels, our results also revealed a decrease in
sphingosine in the bradycardia group (Fig. 14D). Sphingosine is a fatty acid that serves
as the backbone of sphingolipids, and plays multiple roles in cellular signaling cascades
by binding to extracellular G protein-coupled receptors and posing as secondary
messengers within the cell, affecting a variety of cellular processes such as cell growth,
differentiation, survival and motility (11).
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C
Bradycardia interval (hrs)
Cardiac period before interval (hrs)
R
2
= 0.05
R
2
= 0.17
R
2
= 0.82
R
2
= 0.79
2 4 6 8 10 12 14 2 4 6 8 10 12 14
5
10
15
20
25
5
10
15
20
25
5
10
15
20
25
5
10
15
20
25
Cardiac period after interval (hrs)
12 am 12 am 12 am 12 am 12 am 12 am 12 am
0
10
20
30
40
50
Cardiac activity (bpm)
0
10
20
30
40
2
R = 0.05
2
R = 0.82
R
2
= 0.17 R
2
= 0.79
> 0.05 p
< 0.05 p
> 0.05 p < 0.05 p
Fig. 12. Comparisons of periods of cardiac activity and intervals of bradycardia in mussels under low
nutrient conditions. (A) Heart rate of mussels #1 and #2 over a 6 day period showing cycles of periods
and intervals. (B) Mussel #1 and (C) #2 regression analyses of period before bradycardia intervals and
those that occur after the intervals.
117
>2.0
Relative expression
<0.5 1.0
Tidal cycle
High
Low
A
B
Enriched in emerged state
(99 transcripts)
Enriched in submerged
state
(80 transcripts)
Fig. 13. Concordance in gene expression between intertidal and subtidal mussels. (A) Heat map showing
the overlap between low tide genes and expression of enriched genes (GSEA p<0.05) in the Active and
Bradycardia groups. (B) Comparisons between high tide genes and the Active and Bradycardia groups.
118
Fig. 14. Metabolite screen comparisons of the Active and Bradycardia mussel groups by Welch’s T-test
*p<0.05 & **p<0.01. (A) Relative abundance of metabolites related to anaerobisis (B) carnitine-
conjugated fatty acids (C) BCKA catabolism and sphingosine.
0
1
3
6
9
40
50
60
0
2
4
6
8
10
0
1
2
3
4
0.0
0.5
1.0
1.5
2.0
Alanine Malate Succinate
Stearoyl-
carnitine
Butyryl-
carnitine
Acetyl-
carnitine
Propionyl-
carnitine
Sphingosine Isobutyryl-
carnitine
2-methyl-
butyryl-
carnitine
Isovaleryl
carnitine
A B
D C
**
**
**
**
*
*
*
*
*
*
*
Relative abundance Relative abundance
119
Chapter 3: Discussion
Concordance of Physiological Processes between Mussels from Simulated
Intertidal and Subtidal Environments
Mytilus californianus is a facultative anaerobe and can switch to anaerobic metabolism
even in the presence of available oxygen. Valve closure precedes anaerobiosis, making
it a good marker for the switch between aerobic and anaerobic metabolism (18). The
association between aerial exposure, valve closure, bradycardia, and anaerobic
metabolism in M. californianus was first confirmed in a study by Bayne (8), which
revealed that upon aerial exposure only a small proportion of mussels appeared to gape
open, with concomitant commencement of bradycardia and a decrease of oxygen
partial pressure of the water retained between closed shells to hypoxic levels within
minutes. Later, Curtis (18) demonstrated that under clean water conditions,
submerged M. edulis exhibited bradycardia simultaneously with valve closure on every
occasion. Thus, like measurement of valve gape, measurement of cardiac activity is a
reasonable technique to monitor the changes in metabolism that occur under constant
submergence. Our observations of mussels held under constant submergence revealed
random episodes of bradycardia lasting upwards of 31 hours; hence they voluntarily
adopt lengthy periods of anaerobiosis. This behavior has been shown in a number of
bivalve species including M. edulis (18), Isognomum alatus (68), Anodonta cygnea,
Anodonta anatine, Mya arenaria, and Scrobicularia plana (see (40)). To date, it is not
known what endogenous mechanisms regulate episodic valve closure and quiescence in
120
bivalves. The purpose of the current study was to investigate these unknown
mechanisms.
Transcriptomic and metabolomic screens revealed similarities in mRNA and metabolite
abundance between mussels displaying active or bradycardia cardiac activity under
constant submergence and mussels experiencing episodes of immersion and emergence
(16) in our tidal simulations. Observations of increased levels of succinate, malate, and
alanine in bradycardia mussels clearly showed that M. californianus undergoes
anaerobic metabolism in association with valve closure during submergence. The
observation of higher levels of carnitine-conjugated metabolites in mussels experiencing
bradycardia is consistent with the sequestration of metabolites that are normally
oxidized in the TCA cycle. During anaerobiosis the TCA cycle is effectively halted; thus
the observation of higher levels of carnitine-conjugated metabolites during the
induction of anaerobic pathways follows the logic of metabolic suppression that occurs
during functional hypoxia. These results demonstrate that mussels in a closed state, in
either subtidal or intertidal environments, have a common metabolic state that is
characterized by the accumulation of a specific set of metabolic end-products. In our
intertidal simulation study, the low tide gene set was rich in genes involved in
transcriptional pathways (15), and transcription-related genes were a similarly dominant
component of the list of genes expressed in mussels undergoing bradycardia in
submerged mussels. The similarity between the three datasets is that emergence leads
121
to valve closure and then bradycardia, and in submerged mussels, spontaneous valve
closure leads also leads to bradycardia. These data define biological states in mussels at
four levels, including organismal (open/closed), tissue (normoxic/hypoxic), metabolic
(aerobic/anaerobic), and mRNA expression (transcription). Together, the results
documented here, along with those of previous studies of physiology across the bivalve
taxa, can serve to broaden our understanding of the relationship between intertidal and
subtidal physiology in mussels.
Functionality of the Genome under Cellular Hypoxia
Mussels enter a hypoxic state during valve closure under constant submergence.
However, the precise nature of the signaling mechanisms that act to transduce
extracellular stimuli, such as oxygen
,
to the genome in bivalves is unknown. Well
documented in mammalian systems, the relationship between tissue hypoxia and gene
expression is governed by hypoxia-inducible factor-1 (HIF-1) (72). We identified a HIF-1
ortholog in our clone library which suggests that the HIF pathway is functional in M.
californianus. HIF-1 consists of two subunits, HIF-1α and HIF-β, which heterodimerize in
response to low cellular oxygen concentrations (36). After formation, HIF-1 binds to
target sequences upstream from promoter regions. These upstream targets are defined
as hypoxia response elements (HREs) and the genes they regulate are described as oxy-
genes (9). HIF-mediated transcription of oxy-genes has been shown to operate in the
invertebrate genetic models Daphnia, Drosophila, and Caenorhabditis (reviewed in (28)).
122
In endothermic vertebrates, oxy-genes serve to sustain ATP levels and restore
homeostasis through enhanced glycolysis and regulation of blood circulation processes
including angiogenesis, vasodilation, and erythropoiesis (28). Mussels have open
circulatory systems with non-pigmented blood cells, and they suppress glycolytic rate
when subjected to hypoxic challenges. Because of these physiological deviations from
mammalian systems, little is known about the role HIF-signaling may play in bivalves
(28). Nevertheless, our results revealed the induction of carbonic anhydrase (CA2)
during the closed state, which is an identified mammalian oxy-gene ortholog (44).
Furthermore, recent molecular studies of the Pacific oyster Crassostrea gigas (37)
revealed the presence of HREs flanking heat shock factor 1 (HSF-1), which is the master
regulator of the heat shock response. Heat shock factor 1 regulates the expression of
heat shock proteins (hsps), and we identified the expression of hsp genes in mussels
during bradycardia. Transcription factors other than HIF-1 have also been
demonstrated to be hypoxia responsive (reviewed in (17)). We found several of these
transcription factors expressed in our low tide phenotype, including CCAAT, AP-1, ETS-1,
and GADD153. These signaling molecules are associated with a large number of cellular
functions, including those associated with hypoxia-related adaptations such as anti-
inflammation, apoptosis, cell proliferation, and angiogenesis. At present, it is unknown
how the activities of these transcription factors function to enhance survival in bivalves.
123
Interpretation of Metabolic Oscillations under Submerged Conditions
The spontaneous bouts of bradycardia observed in mussels submerged in a constant
environment is an intriguing phenomenon in mussel behavior because it prompts
questions regarding the logic behind voluntary-adopted transitions to hypoxic states
while in the presence of environmental oxygen and food, two factors that drive high
yielding ATP synthesizing metabolic pathways. Answers to these questions may be
found in studies of rhythmic physiological processes that occur in another facultative
anaerobe, S. cerevisea (reviewed in (70)). Tu et al. (69) showed that yeast grown under
nutrient limited conditions exhibited biological cycles in the form of alternations
between oxidative and reductive phases of metabolism. These cycles were revealed by
oscillations in oxygen consumption by the yeast cultures. Microarray analysis showed
that half of the yeast genome exhibited periodic expression at a period equal to the
length of one respiratory cycle, and different genes were expressed maximally at
different times during the metabolic cycle. The investigators hypothesized that the
temporal expression of genes is a strategy yeast use to separate physiological processes
that may not be compatible with one another. For example, oxidative metabolism may
not be cooperative with DNA replication because of the possible damage to DNA that
may occur from exposure to the free-radicals produced (13). The shift between the
phases may be regulated by the buildup of metabolites from the respective
physiological processes in a putative negative feedback loop (71). An alternative
hypothesis is that cycles result from need to induce oxidative metabolism to supply
124
energy to the synthesis of proteins required for growing cells to progress beyond G1
phase (27). The logic of yeast metabolic cycles may be applicable in the characterization
of metabolic oscillations in mussels, because they also undergo cycles of oxidative and
reductive phases, produce relatively large changes in metabolites between phases, and
have an accompanying set of genes that are differentially expressed under the two
phases.
Our preliminary observations of mussels held for 7 days under constant submergence
revealed that interval length of normal cardiac activity was a response to period length
of the preceding episode of bradycardia. This association should not be confused with
oxygen debt, which refers to the increase in oxygen consumption and heart rate that
occurs briefly following periods of anaerobic metabolism and the commencement of
aerobic metabolism (62). The cardiac activity we describe in the present study strictly
refers to length of oxygen consumption as opposed to the intensity of consumption
(oxygen debt), which has been shown to be independent of the length of the previous
period of anaerobiosis (20). Similar findings of an association between the interval
length of bradycardia the following period of activity has only been shown in the
burrowing subtidal clam Arctica islandica, which exhibits periods of anaerobiosis on the
order of days (65). These cycles could be explained by the buildup and release of
specific metabolites that occur during periods of anaerobic and aerobic metabolism.
Our correlations suggest that valve closure and anaerobiosis represent the default state
125
in mussels in low nutrient conditions, and the induction of corresponding bouts of
aerobic metabolism is necessary to recover some aspect of physiology that occurs
during anaerobiosis. Because succinate is the most elevated metabolite during
anaerobic metabolism, it is possible that mussel cells have adapted sensitivities to
succinate and the switch between phases is succinate related. Recent studies of the
regulatory capacity of succinate revealed that it possesses hormone-like functions by
binding to G-protein-receptors (31, 54) and regulates the activation of HIF-1α by
inhibiting its degradation by prolyl 4-hydroxylases (41). Therefore, succinate is not
simply an end-product of anaerobic metabolism or intermediate of the TCA cycle, but
rather a critical compound in mammalian organisms that can produce broad changes in
cellular activity when participating as a ligand or allosteric inhibitor of enzyme activity.
Bivalves are known to have extraordinarily lengthy life spans compared to all other non-
colonial metazoans. For example, several species are reported to live beyond 100 years,
while Icelandic clam A. islandica is estimated to live over 400 years (11, 55). The rate-of-
living theory suggests that the maximal lifespan potential (MLSP) of a given organism is
negatively related to levels of oxidative damage over the course of its lifetime (10).
Researchers that study aging mechanisms have proposed that the relative longevity
exhibited by bivalves may be related to the low rates of metabolism and reactive
oxidative species formation (ROS) that occur during episodic bouts of anaerobisis (11,
55). Reactive oxidative species are a byproduct of mitochondrial respiration and include
126
hydrogen peroxide, alkyl peroxides, singlet oxide, and the hydroxyl radical. These
compounds can function as important components of signaling pathways involved in the
cell cycle, stress responses, and energy metabolism (3). However, overproduction of
ROS can lead to cellular damage because of their capacity to oxidize proteins, lipids, and
nucleic acids. Cells are protected from excess ROS by antioxidant enzymes such as
superoxide dismutase, catalase, and glutathione peroxidase (1). Oxidative stress occurs
when the rate of ROS production exceeds the rate of degradation by the various
antioxidants (1). Fermentative metabolism is a low energy output process because it
produces a relatively small amount of ATP per unit of glucose, in comparison to
oxidative metabolism. In order to maintain energy balance and ensure homeostasis
during aerial exposure, overall metabolism is reduced to align metabolic demand with
prevailing supply (73). Energy equilibrium ensures survival over the course of a single
low tide, which can be >17 hours in high shore habitats (30). Simultaneously, the
minimized synthesis and delivery of reducing equivalents to the electron transport chain
reduces the flux of ROS that occurs as a result of the incomplete reduction of molecular
oxygen during mitochondrial respiration. To date, no direct relationship between
metabolism and MLSP in bivalves has been confirmed (11). However, the rate-of-living
theory, which is supported by studies in mammals, birds, yeast, worms, flies, and mice
(11, 43), represents plausible explanation for the voluntarily-adopted anaerobiosis
observed in Mytilus.
127
Chapter 3: Conclusions
Mytilus californianus inhabit both intertidal and subtidal environments. However, they
are predominantly found residing in the intertidal zone and are typically studied within
that context. To gain insight into the strategies employed by M. californianus to persist
in two markedly different environments, we studied patterns of transcript and
metabolite abundance and compared them to those that arise from mussels subjected
to tidal fluctuations. Our observations revealed a strong metabolic and molecular,
physiological link between mussels from two environments that vary in their physical
and biological constituents. Our finding that all of the mussels studied displayed
voluntary-adopted intervals of anaerobiosis, on the scale of hours, in the presence of
oxygen is of particular interest because it suggests that bouts of anaerobiosis may
present a benefit over the perceived awards garnered from oxidative metabolism. For
example, the cycling of anaerobic and aerobic phases may allow for the temporal
compartmentalization of physiological processes or extend life through reductions of
ROS. Adaptations to subtidal conditions and tolerance to hypoxia shown in M.
californianus is consistent with its lineage to infaunal ancestors that arose during the
early Cambrian, a period of extremely limited atmospheric oxygen (57). One such
hypothesis regarding its ancestry and evolution is that Mytilus was originally confined to
the subtidal habitat, where it evolved episodic behaviors that served as exaptations for
intertidal life. Once confined to the intertidal zone by predators, further adaptations
that supported life in a fluctuating environment followed.
128
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71. Tu BP, Mohler RE, Liu JC, Dombek KM, Young ET, Synovec RE , McKnight SL
(2007). Cyclic changes in metabolic state during the life of a yeast cell.
Proceedings of the National Academy of Sciences. 104, 16886-16891.
72. Wang GL, Semenza GL (1995). Purification and Characterization of Hypoxia-
inducible Factor 1. Journal of Biological Chemistry. 270, 1230-1237.
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135
Chapter 3: Supplemental Information
SI Tables
SI Table 5. List of transcripts represented in heatmap shown in Fig. 13A.
USC ID Putative ID
Myt_31B24 Translocon-associated protein subunit gamma
Myt_12K13 Sodium- and chloride-dependent GABA transporter 1
Myt_23B16 TFIIH basal transcription factor complex helicase XPD subunit
Myt_28B23 DNA-binding protein inhibitor ID-2-A
Myt_22A21 Galactoside 2-alpha-L-fucosyltransferase 2
Myt_24L24 Chaperone protein dnaJ
Myt_50J03 Headcase protein
Myt_34A16 Ceramide kinase
Myt_20O16 Ribosome biogenesis regulatory protein homolog
Myt_21D19 Guanine nucleotide exchange factor DBS
Myt_26N05 Complement C1q-like protein 3
Myt_19K22 Unclassifiable EST
Myt_33L16 Protein toll
Myt_59L22 Zinc finger matrin-type protein 2
Myt_15M03 E3 ubiquitin-protein ligase RFWD2
Myt_36F22 Unclassifiable EST
Myt_12B08 ETS homologous factor
Myt_24A23 Homeobox protein SIX1
Myt_32B17 MRG-binding protein
Myt_32C04 Baculoviral IAP repeat-containing protein 7-A
Myt_33M24 Dystrophin
Myt_16O23 Protocadherin-9
Myt_54H08 von Willebrand factor D and EGF domain-containing protein
Myt_33F15 Uncharacterized protein C1orf161
Myt_09P07 Structural maintenance of chromosomes protein 1A
Myt_37J11 TBC1 domain family member 13
Myt_22O21 Fos-related antigen 2
Myt_47B22 PR domain zinc finger protein 16
Myt_20M04 Lysine-specific demethylase 3B
Myt_39D03 RNA polymerase II-associated factor 1 homolog
Myt_29A07 CCAAT/enhancer-binding protein epsilon
136
Myt_14P21 Unclassifiable EST
Myt_27A08 Heat shock protein HSP 90-alpha
Myt_47K03 cAMP-responsive element modulator
Myt_26A14 Unclassifiable EST
Myt_31P06 Band 4.1-like protein 3
Myt_19F06 Unclassifiable EST
Myt_06O22 Adapter molecule Crk
Myt_31C21 Phytanoyl-CoA dioxygenase, peroxisomal
Myt_34A21 Probable isocitrate dehydrogenase [NAD] subunit alpha,
mitochondrial
Myt_19K02 Clathrin light chain B
Myt_46F05 Unclassifiable EST
Myt_29C06 Feline leukemia virus subgroup C receptor-related protein 2
Myt_34M15 Unclassifiable EST
Myt_45O16 Dual specificity protein phosphatase 7
Myt_36H19 Unclassifiable EST
Myt_24A22 Baculoviral IAP repeat-containing protein 7
Myt_62M13 ETS-related transcription factor Elf-2
Myt_47N02 Photoreceptor-specific nuclear receptor
Myt_33N03 Unclassifiable EST
Myt_25B01 Unclassifiable EST
Myt_50L18 Unclassifiable EST
Myt_55I13 Unclassifiable EST
Myt_12I10 GPI transamidase component PIG-S
Myt_36D19 Myosin light chain kinase, smooth muscle
Myt_30C20 Protein quiver
Myt_33M03 Transcription factor AP-1
Myt_43N20 Serine/threonine-protein kinase pim-3
Myt_21B04 Iduronate 2-sulfatase
Myt_47O04 mTERF domain-containing protein 1, mitochondrial
Myt_30G23 Prolyl-tRNA synthetase
Myt_09E15 14-3-3 protein epsilon
Myt_16N01 Unclassifiable EST
Myt_38H19 Unclassifiable EST
Myt_16F16 Tropomyosin
Myt_15E22 Unclassifiable EST
Myt_33E23 T-complex protein 1 subunit epsilon
Myt_42P21 Tripartite motif-containing protein 2
Myt_27L14 Kinase D-interacting substrate of 220 kDa
137
Myt_55J12 Zinc finger protein 227
Myt_49B15 UPF0392 protein F13G3.3
Myt_15C18 Unclassifiable EST
Myt_64J04 Unclassifiable EST
Myt_30D02 Carbonic anhydrase 2
Myt_42O17 Angiopoietin-2
Myt_30A17 3-demethylubiquinone-9 3-methyltransferase
Myt_43H01 Transcription initiation protein SPT3 homolog
Myt_34K08 Universal stress protein YxiE
Myt_34J16 Unclassifiable EST
Myt_55C20 Protein SET
Myt_46A17 Tetratricopeptide repeat protein 26
Myt_12L09 Unclassifiable EST
Myt_42N16 Unclassifiable EST
Myt_34K10 Carbonic anhydrase
Myt_30C19 Transmembrane protein 47
Myt_51A11 Pleckstrin homology domain-containing family G member 5
Myt_30I10 Unclassifiable EST
Myt_46G07 Selenoprotein T2
Myt_39M08 Ring canal kelch homolog
Myt_42O15 Unclassifiable EST
Myt_34B19 Complement C1q-like protein 3
Myt_51D05 AAC-rich mRNA clone AAC4 protein
Myt_42F24 Y-box factor homolog
Myt_12K08 Unclassifiable EST
Myt_09G03 Mitogen-activated protein kinase kinase kinase kinase 3
Myt_42G24 Mdm2-binding protein
Myt_34K09 Cdc42 homolog
Myt_62O20 Unclassifiable EST
Myt_31M21 Unclassifiable EST
138
SI Table 6. List of transcripts represented in heatmap shown in Fig. 13B.
USC ID Putative ID
Myt_32O16 Unclassifiable EST
Myt_31J17 Unclassifiable EST
Myt_39M05 Unclassifiable EST
Myt_44D22 Unclassifiable EST
Myt_41G24 Unclassifiable EST
Myt_16A13 Unclassifiable EST
Myt_14P19 Serine palmitoyltransferase 1
Myt_39G18 Unclassifiable EST
Myt_50L14 Unclassifiable EST
Myt_43I24 S-adenosylmethionine synthase isoform type-2
Myt_15O14 RING finger and CHY zinc finger domain-containing protein 1
Myt_31P23 Metabotropic glutamate receptor 3
Myt_30P20 Magnesium-dependent phosphatase 1
Myt_43I04 Unclassifiable EST
Myt_32K09 Kinesin heavy chain
Myt_51F24 Unclassifiable EST
Myt_12D07 Homeobox protein Meis1
Myt_66F21 Unclassifiable EST
Myt_60D18 Unclassifiable EST
Myt_37C03 Golgi phosphoprotein 3
Myt_36B23 Unclassifiable EST
Myt_33O18 Unclassifiable EST
Myt_17N06 Unclassifiable EST
Myt_34G21 Unclassifiable EST
Myt_20I22 Splicing factor U2AF 50 kDa subunit
Myt_46M23 Unclassifiable EST
Myt_25C10 Serine/arginine-rich splicing factor 10
Myt_27L03 N-alpha-acetyltransferase 30, NatC catalytic subunit
Myt_40E01 Unclassifiable EST
Myt_29F18 Oncoprotein-induced transcript 3 protein
Myt_35E11 40S ribosomal protein S2
Myt_13O13 Leucine-rich repeat-containing protein 6
Myt_31E02 Methyl-CpG-binding domain protein 2
Myt_38A10 Ankyrin-1
Myt_26A19 Unclassifiable EST
139
Myt_19A19 Unclassifiable EST
Myt_23E12 Unclassifiable EST
Myt_25H09 Protein SMG8
Myt_22N17 Myotubularin-related protein 14
Myt_27D11 Unclassifiable EST
Myt_46E17 Bifunctional protein NCOAT
Myt_19C02 Methyl-CpG-binding domain protein 2
Myt_33H07 Unclassifiable EST
Myt_39O12 Regucalcin
Myt_44L05 Heparanase
Myt_07C15 Transmembrane protein 59
Myt_56L08 Heat shock protein 70 B2
Myt_62B15 Trithorax group protein osa
Myt_42M04 Centrosomal protein of 120 kDa
Myt_21D12 Unclassifiable EST
Myt_28C16 Unclassifiable EST
Myt_41B15 Unclassifiable EST
Myt_38L18 Fatty acyl-CoA reductase 1
Myt_31G04 Zinc finger protein 233
Myt_31C06 Unclassifiable EST
Myt_35B02 Unclassifiable EST
Myt_35D24 Unclassifiable EST
Myt_53B06 Vang-like protein 2-B
Myt_03N07 Tetratricopeptide repeat protein 25
Myt_24M20 Unclassifiable EST
Myt_34M18 Unclassifiable EST
Myt_34M13 Peroxisomal proliferator-activated receptor A-interacting complex 285
Myt_29G04 Peregrin
Myt_23G11 Tight junction protein ZO-1
Myt_33M08 Unclassifiable EST
Myt_31E15 Cell cycle checkpoint protein RAD1
Myt_35M12 Unclassifiable EST
Myt_48L20 Protein transport protein Sec23A
Myt_33M18 Leucine-rich repeat-containing protein 14
Myt_31J21 Ras-related protein Rab-32B
Myt_46I10 Succinyl-CoA ligase [GDP-forming] subunit beta, mitochondrial
Myt_01I19 Probable serine/threonine-protein kinase roco5
Myt_27G18 Unclassifiable EST
Myt_35D15 DnaJ homolog subfamily B member 4
140
Myt_20O18 C-myc promoter-binding protein
Myt_27H02 Unclassifiable EST
Myt_06G17 Ankyrin-2
Myt_40O06 Unclassifiable EST
Myt_10C14 AP-4 complex subunit mu-1
Myt_22I11 DnaJ homolog subfamily A member 2
141
Chapter 4: Transcriptome-wide gene expression in Mytilus
californianus under simulated combined stresses of aerial
exposure and solar radiation
Chapter 4: Abstract
The intertidal mussel Mytilus californianus is subjected to thermal and oxygen cycles
due to fluctuations of the tidal cycle. Within the intertidal habitat mussels that reside in
high shore environments experience even longer emergence times and greater thermal
perturbations during midday low tide. In this study, we investigated the combined
effect of daily long-term aerial exposure and midday thermal events attributed to solar
irradiance on the physiology of mussels, by monitoring transcriptome-wide gene
expression in a simulated tidal environment, and compared the results to data collected
previously from mussels subjected to more moderate tidal conditions and constant
temperature. Mussels were subjected to aerial exposure for 20 hours a day combined
with a simulated midday solar heating event of +8°C. Results revealed that 11% of the
transcriptome exhibited robust rhythmic patterns of gene expression. Two broad sets
of genes were induced over the fluctuating environment, including one that was up-
regulated during the combined low tide daytime thermal stress period (early-phase) and
another during the evening high tide (late-phase). The early-phase associated gene set
was enriched in genes related to transcription during the onset of low tide warming,
while no biological theme was detected during the late-phase period. Genes previously
shown to be up-regulated following a single low tide heat shock did not follow this
142
pattern under acclimation to repeated daily low tide heat shock conditions. Thus, we
conclude that environmental acclimation has a profound effect on transcription-wide
gene expression in M. californianus. These results indicate that M. californianus
displays phenotypic plasticity with respect to transcript expression and that this is likely
a necessary trait to promote homeostasis in the fluctuating environment of the
intertidal zone.
143
Chapter 4: Introduction
The intertidal zone is a highly variable environment because it lies at the interface of the
terrestrial and marine habitats. Largely influenced by the ebb and flood of tides, the
intertidal environment subjects its inhabitants to large variations in temperature,
oxygen availability, and feeding opportunities (16, 29). The competitive dominant
mussel, Mytilus californianus is highly adapted to the rigors of the fluctuating intertidal
environment (13) because as a sessile species, it must adjust its physiology in response
to prevailing environmental challenges. For example, during low tide, M. californianus
cannot feed, typically does not consume atmospheric oxygen (2), and compensates for
these constraints by reducing metabolism and utilizing anaerobic pathways to generate
ATP (6). During aerial exposure, this mussel is also subjected to fluctuations in
temperature due to complex interactions of intermittent wave splash, solar irradiance,
cloud cover and wind shear (17). As a eurythermal ectotherm, its body temperature
conforms to ambient temperature, which can vary on the order of minutes to hours.
Body temperature during aerial exposure often can be higher than the prevailing water
temperature, especially in temperate geographic regions (11). For example in
temperate zones during summer months particular Mytilus populations, such as those
that reside high on the shore can experience variations in temperatures of 25-30°C
during midday low tides (19). Large variations in temperature and long exposure times
to solar heating cause stress in mussels, which has been evaluated with measurements
of the heat shock response (20). The expression of heat shock proteins comprises part
144
of the heat shock response observed in nearly all major phylogenetic lineages in
response to thermal stress (11). The heat shock response is induced when
temperatures exceed the normal thermal ranges of protein stability in a particular
organism, causing denaturation and loss of function (26). Heat shock proteins (hsps) are
generally classified according to their molecular weights and the core set of hsps that
direct the activities of the heat shock response include hsp-40 kDa, hsp-70 kDa and hsp-
90 kDa (9). They function as molecular chaperones that identify proteins in non-native
conformation, resulting from protein denaturing or aberrant protein synthesis and assist
in folding processes that can restore protein function as well as preventing the
formation of cytotoxic protein aggregates (53). These processes are ATP-demanding,
consistent with the observation that hsps have high affinities for ATP (26). If proteins
are damaged beyond the repair capacity of hsps to restore function then they are
covalently bound to ubiquitin proteins that signal for their degradation by the
proteasome, which is also an ATP-demanding process (18). Therefore, thermally-
induced protein damage is energetically costly to the cell with respect to both the
restoration and refolding of damaged proteins and for the targeted degradation of
irreversibly damaged proteins.
Early work on effects of temperature stress on mussel physiology focused on
biochemical indices of thermal damage including relative levels of hsps and quantities of
ubiquitin conjugates (19, 20, 37). Field studies of thermal stress in M. californianus
145
revealed that individuals in relatively more stressful regions of the intertidal zone, such
as hotter micro-environments located high on the shore showed greater levels of hsp
than those in cooler, more moderate microhabitats such as those lower on shore (37).
Furthermore, mussels exhibit plasticity in their thermal response (15). For example, M.
californianus mussels reciprocally transplanted between low and high shore
microhabitats exhibited levels of hsp-70 and induction temperatures similar to the
mussels in the respective new habitats as opposed to those from their source locations.
Thus, M. californianus possess acute biochemical response mechanisms that serve to
counteract temperature increases during aerial exposure and promote cellular
homeostasis under an unpredictable environment. Indeed, growing evidence suggests
that the strong influence of temperature on physiological functions may play a role in
evolutionary mechanisms that determine distribution patterns of Mytilus populations
both locally and geographically (27, 33).
Advances in molecular techniques such as microarray-based gene expression profiling
allowed for a broader and more complex inspection of the impact of temperature stress
and aerial exposure during tidal emergence on mussel physiology. One of the first
assessments of transcriptome-wide gene expression in mussels provided data on which
genes were activated during low tide at several locations kilometers apart along the
west coast of North America (34). However, this snapshot approach did not offer
insights into how cells program their activities temporally over tidal driven changes in
146
temperature, oxygen and food availability. Our lab published the first temporal based
field analysis of the transcriptome for a bivalve over the course of tidal cycles (14). The
results of this study revealed that M. californianus mussels high on the shore and
experiencing once a day bouts of submergence temporally partitioned the expression of
genes involved in growth and metabolism, while mussels low on the shore did not show
this particular pattern of expression. Furthermore, the majority of thermal-responsive
genes were shown to be up-regulated during recovery at high tide. This discovery has
moved the characterization of stress that occurs in high shore populations beyond
rudimentary associations between temperature and heat shock protein expression, and
toward a more comprehensive and intricate interpretation of environmental influences
on cellular physiology. However, due to the stochastic nature of the field environment,
we were not able to find direct associations between the timing of tides and the most
dominant gene expression pattern. A deviation from these general findings was the
observed increase in transcript abundance of heat shock genes in response to increased
body temperatures. In order to resolve these questions, we designed a programmable
aquarium to simulate tidal cycle, midday low tide solar heating events and day/night
cycles. In initial laboratory simulations we acclimated mussels to a moderate, repeated
tidal cycle of 6 hr emergence and 6 hr submergence, which resulted in two high and low
tides per day, for three days and at a constant temperate of 17°C (4 (Chapter 1)). We
considered this a low-stress tidal simulation. On a fourth day, we subjected mussels to a
+7°C heat shock during midday low tide. We sampled mussels every two hours for gill
147
tissue and measured the expression of the transcriptome, which revealed sets of genes
that were up-regulated either during low tide (low-tide) genes, high tide (high-tide)
genes or daily (circadian) genes. The single low tide heating event produced a 24%
change of the transcriptome, consistent with the idea that the combined effect of tidal
emergence and warming has complex physiological consequences that are not well
understood.
In this study, we sought to broaden our understanding of the transcriptomic response in
mussels entrained to a stressful environmental regime (high-stress tidal simulation) that
consisted of a daily prolonged period of tidal emergence (20 hrs/day) combined with a
simulated midday solar heating event and a single 4 hr submergence period. The data
collected were interpreted in the context of results from our previously published low-
stress tidal simulation study which consisted of shorter exposure times and twice a day
periods of submergence. The direct comparisons between the high and low-stress tidal
simulation data sets allowed us to identify new phenotypes and biomarkers, which can
be used as indices of physiological state in mussels.
148
Chapter 4: Materials and Methods
Sampling methods
Mytilus californianus of 4-5 cm length were collected from a lower portion of sheltered
shoreline at Zuma Beach, north of Los Angeles, CA and placed in the intertidal
simulation aquarium (4 (Chapter 1)). The experiment was held in a cold room set to
15°C with a 12 hr cycle of light and dark starting at 6am. Mussels were acclimated for 4
weeks to a daily tidal cycle beginning with a 6am low tide, then a 6pm high tide,
followed by a 10pm low tide that lasted to the beginning of the sequence. Therefore,
mussels were aerially exposed for 20 hrs a day. Daily daytime solar heating simulations
began at 8am. Heating was simulated with computer controlled ceramic heat lamps
programmed to ramp at a rate of 2°C/hr until 23°C was reached, then the temperature
held constant until the time of submergence. Gill samples from three mussels per time
point were collected every 2 hrs for 48 hrs (Fig. 15A).
RNA isolation and microarray hybridization
Total RNA was extracted from gill tissue using Trizol (Invitrogen) according to the
manufacturer’s instructions. The total RNA was purified further across glass-fiber filter
columns (Qiagen) according to the manufacturer’s instructions. An equal amount of
total RNA from 3 individual animals sampled at each time-point was pooled and
amplified RNA was prepared as previously described (4, Chapter 1). Briefly, double-
149
stranded cDNA was prepared by reverse-transcribing 2 µ g pooled total RNA a 20 µ l
reaction containing 20 pmols of T7-dT
15
VN primer, 50 mM Tris-HCl (pH 8.3), 75 mM KCl,
3 mM MgCl
2
, 50 µMs dNTP, 20U of RNaseOut (Invitrogen) and 100U of MMLV-reverse
transcriptase (Epicentre). The RNA and primer mixture was heat-denatured at 65°C for
10 min, then the remaining components added, and the reaction incubated at 40°C for 2
hrs, and then stopped by heating to 65°C for 15 min. Next, 60 µ l of in vitro transcription
reaction mixture, containing 53 mM Tris-HCl (pH 7.5), 13 mM NaCl, 8 mM MgCl
2
, 5.3%
PEG 8000, 2.6 mM spermidine, 3.33 mM each (ATP, GTP, CTP), 2.5 mM UTP, 0.83 mM
amino-allyl UTP (Epicentre), 0.12U organic pyrophosphatase, 20U RNaseOut, 500U T7
RNA polymerase (Epicentre), was added and the reaction incubated at 40°C for 18hr.
The resulting aRNA was purified using a Qiagen RNeasy kit, and half the aRNA was
labeled with Cy5 and the other half with Cy3. Fluorescently-labeled aRNA samples were
hybridized to the M. californianus in house manufactured cDNA microarray (4, Chapter
1), containing 10,420 non-redundant genes, using an interwoven loop design, which
insured that each RNA sample was hybridized to either 2 or 4 arrays with fluor-reversal.
This loop hybridization design yields improved statistical inference of microarray data
(24).
150
Gene expression analysis
TIFF images of hybridized arrays were captured with an Agilent scanner (Agilent
technologies) and spot intensities quantified with Agilent Feature Extraction software
(ver. 9.5.1). Spot median pixel intensities without background correction were
collected and spatial intensity trends removed and individual channels normalized using
joint lowess transformation. Relative expression of each gene in each hybridization loop
was estimated using MAANOVA Version 0.98-7 for R using an ANOVA model in which
dye and sample were treated as fixed effects, and array was treated as random effects
(23).
Genes that exhibited rhythmic expression were identified using JTK_CYCLE (21), a
nonparametric statistical algorithm designed to identify and characterize cycling
variables in large datasets. The output data of the JTK_CYCLE includes a false discovery
rate and phase (start time of peak induction) for each gene. To identify patterns of peak
expression we performed a distribution analysis of the phases of genes exhibiting
rhythmic patterns of expression and having a p<0.05. Next, a ranked list of genes was
created in which genes spanning the transcriptome were ranked according to their
Pearson’s correlation to the expression pattern of the gene CEBPE which was chosen
because it showed a particularly robust rhythmic expression (p=0.004) (SI Fig. 4). The
expression pattern of CEBPE was shown to be up-regulated during daytime low tide and
down-regulated during evening high tide. This created two lists corresponding to genes
151
that were positively or negatively correlated to CEBPE. Gene Set Enrichment Analysis
(GSEA) (49) was used to confirm if the rhythmic identified gene sets were enriched in
Gene Ontology-biological processes gene sets within the database, which totaled 825.
Statistical significance was determined by 1000 permutations of the member genes of
each gene set and re-computation of a Kolmogorov–Smirnov statistic. A false discovery
rate (FDR) which represents the estimated probability that an associated gene set
represents a false positive finding is calculated for each gene set that is correlated to the
gene lists under analysis. An FDR<.25 is considered acceptable (49). We also performed
GSEA analysis to identify biological themes associated with gene sets that showed peak
expression during particular periods within the time course where biological variation is
likely to occur, including onset of thermal stress, which occurs during phase ranges (0-3
and 4-7) maximum thermal stress (8-11) and high tide (13-16). Ranked lists were
generated by ordering the average relative expression level of each gene within the
investigated phase ranges which created ordered lists for each 4 hr phase period. Genes
at the top of the list were those that were up-regulated during daytime low tide and
down-regulated during the evening submergence, whereas genes at the bottom were
those associated with peak expression during submergence. The ranked gene lists were
analyzed using the same parameters as the signature correlation based ranking which
was previously described.
152
Chapter 4: Results
Analysis of the transcriptome-wide gene expression data using the JTK_CYCLE algorithm
identified a total of 1,176 genes that followed a statistically significant (p<0.05),
rhythmic pattern of expression. Genes that exhibited periods of 17-30 h were
considered those that could possibly be associated with tidal, thermal or circadian cycles
and therefore included in a distribution analysis of phases. The distribution analysis of
rhythmically identified genes (Fig. 15B) discovered two discrete gene sets, the first
comprised of 432 genes that peaked in samples collected during daytime low tide which
spans phases 0-11 (corresponding to 7am-6pm), therefore we have categorized these
genes as early-phase genes. The bulk of these genes peaked between phases 1 and 6,
during the onset of thermal stress. A second set, which included 653 genes, peaked
during the night time high tide and subsequent low tide, spanning the phase range of
12-23 (7pm-6am), and were characterized as late-phase genes. The bulk of these genes
peaked between phases 13 and 17. Only 34 genes peaked between phases 8-11 which
was the period of low tide maximum temperature (23°C) and 378 genes peaked during
high tide phases 12-15 representing a >10-fold increase in peaking genes between these
two phase ranges. Comparisons between the lists of genes that were characterized as
tidal (i.e. peaking during emergence or submergence, respectively) or circadian in our
low-stress tidal simulation, with rhythmic genes discovered in the current dataset (Fig.
15C), identified 2 distinctive gene sets including 255 (SI Table 7) and 431 genes (SI Table
8) that show peak expression during early-phase and late-phase respectively. The
153
broader categorized early- and late-phase genes overlapped exclusively with low-tide
and high-tide genes, respectively. Therefore there is a level of biological congruence,
with respect to the effects of tide, between mussels acclimated under low and high-
stress tidal simulations.
The exclusive subset of early- and late-phase associated genes, indicated by Venn
analysis, showed that the bulk of peak expression occurred during the periods
associated with the onset of low tide thermal stress (phases 0-7) and submergence
(phases 12-15) (SI Fig. 5) in a pattern, similar fashion to the broader lists of early and
late-phase genes. Visual inspection of the exclusive gene sets using heatmaps revealed
clear differences in their respective patterns of gene expression (Fig. 16A and 16B).
Generally speaking, these particular early-phase related genes were up-regulated during
low tide with the bulk of genes peaking before maximum temperature heating episode
and down-regulated during high tide, while late-phase related genes were down-
regulated during low tide and up-regulated during high tide. Therefore, we have termed
the early-phase and late-phase exclusive subsets as aerial-exposure-radiation-stress
(AER-stress) genes and AER-stress-recovery genes respectively. Hence, mussels
subjected to the high-stress-tidal simulation exhibited rhythmic patterns of gene
expression and these associated genes belong to 5 gene subsets, including low-tide,
high-tide, circadian, AER-stress and AER-stress-recovery.
154
To determine whether genes that were induced during a single low tide thermal event
in our low-stress tidal simulation were affected by daily thermal events under the high-
stress tidal simulation, we evaluated the expression patterns of the top 100 members of
the thermally-responsive genes of a ranked list from the low-stress tidal simulation
study in the current data (Fig. 17). Visual inspection of the heatmap shows that the
expression patterns of the previously observed thermally-responsive genes (Fig. 17A)
are not co-expressed in the current dataset (Fig. 17B) and in general, do not conform to
any environmental variable. Furthermore, Venn analysis revealed that of these 100
transcripts evaluated, only 2 genes overlapped with the previously described AER-stress
gene and AER-stress-recovery gene sets.
Particular genes associated with the conserved heat shock response were, generally
speaking, induced during the periods of low tide heating events and during the onset of
submergence as well (Fig. 18). Heat shock 70 kDa protein 12A (HS12A) and 2 isoforms
of T-complex proteins (TCPA, TCPH) were induced during the low tide thermal events,
whereas 3 hsp-70 genes and 1 hsp-90 gene were up-regulated during evening high tide
and down-regulated daytime low tide. One point to note is that the expression of
inducible forms of hsp-70 that were ranked 1 and 2 in change of expression following a
+7°C heat shock in the low-stress simulation study, was abolished under acclimation to
thermal cycling.
155
Fig. 15. Gene expression profiling reveals rhythmically expressed genes in a high-stress tidal simulation.
(A) Representation of the environmental conditions used in the simulated tidal environment. Animals
were sampled every 2 hr starting at 7am. Animals were emerged during low tides, which occurred from
10pm-6pm the following day, while sunrise and sunset occurred at 6am and 6pm. Daytime irradiation
was simulated during low tide. Temperature was increased 2°C/hr to 23°C (indicated by the arrow) and
held constant until high tide. (B) Histogram showing the frequencies of 431 and 653 early and late phase
transcripts respectively. (C) Concordance of gene expression between low-stress and high stress tidal
simulations.
15
20
25
Frequency
B
C
Tide
7 am
6 am 6 pm 6 am 6 pm
Temperature (°C)
A
7 am
Phase hrs/Time of Day
2723 338
36 140
255
2687 328
46 176
431
50
100
150
200
250
15
25
Circadian
Genes
Tidal
Genes
Early-Phase
Genes
Circadian
Genes
Tidal
Genes
Late-Phase
Genes
Early-Phase
Genes
Late-Phase
Genes
Temp. (°C)
6 pm
0 3 6 9 12 15 18 21
Tidal cycle
High
Low
Light/Dark cycle
Sampling times
156
Fig. 16. Rhythmic expression of AER-genes under a high stress tidal simulation. (A) AER-stress
genes showing peak expression during daytime low tide and down-regulation following
nighttime high tide. (B) AER-stress-recovery genes showing peak expression following the
onset of high tide and down-regulation during daytime low tide.
A
AER-stress genes
15
20
25
Rhythmic transcripts over 48 hrs
15
20
25
B
AER-stress-recovery genes
Rhythmic transcripts over 48 hrs
>8.0
Relative expression
<0.8 0.0
157
Fig. 17. Pattern of expression of top 100 thermally-responsive genes, discovered following a single
thermal event under the low-stress tidal simulation. (A) The pattern of expression following a single low
tide thermal event. (B)The pattern of expression of these genes under repeated low tide thermal events.
15
20
25
Pattern of expression
under repeated low tide thermal events
A
Pattern of expression
following a single low
tide thermal event
15
20
25
Temperature (°C)
100 thermally-responsive genes
B
>8.0
Relative expression
<0.8 0.0
158
Fig. 18. Patterns of heat shock protein gene expression under repeated thermal events in
the high-stress tidal simulation.
15
20
25
15
20
25
Temperature (°C)
Heat shock 70-kDa protein 12A
T-complex protein 1 subunit alpha
T-complex protein 1 subunit eta
Heat shock 70 kDa protein cognate 4
Heat shock 70 kDa protein 12A(2)
Heat shock protein HSP 90-alpha
Heat shock 70 kDa protein 12A (3)
DnaJ homolog subfamily B member 11
>8.0
Relative expression
<0.8 0.0
159
Using a ranked gene list generated by correlating the expression pattern of JTK_CYCLE
identified rhythmic genes with that of the marker gene CEBPE, we found that a total of
74% of the member genes of the early-phase gene set were in the upper 20
th
percentile
of this gene list. Similarly, we found that 89% of the late-phase genes were at the lower
20
th
percentile of the ranked list which represents genes whose expression is anti-
correlated to that of the early-phase. Hence, this ranking method ensured that the
genes associated with early and late-phase were distributed at the top-end and bottom-
end of the list respectively. Using curated sets of genes that share a particular GO
biological process, GSEA was employed to correlate the gene sets to genes clustered at
either the top or bottom of the ranked gene list and yielded a FDR for significance of
correlated gene set and statistic of significance (p-value) based upon the degree of
enrichment. The GSEA analysis revealed that GO biological process gene sets were not
significantly (FDR<.25) correlated with the broad categories of early or late-phase. GSEA
analysis of particular ranges of phases revealed that phase range 0-3 (7am-10 am) and
onset of thermal stress, were enriched in genes associated with Negative Regulation of
Transcription (p=0.004, FDR=0.192), whereas no biological themes were detected for
genes up-regulated during phases 4-8 (11am-2pm), 9-12 (3pm-6pm) and 13-16 (7pm-
10pm). Only 3 genes associated with the Negative Regulation of Transcription gene set
where rhythmically expressed, thus the expression pattern of the remainder of genes in
the set is likely a form of pulsed expression at the daily onset of thermal stress.
160
The rhythmically expressed genes of Negative Regulation of Transcription included
Proliferation-associated protein 2G4 (PA2G4) (Fig. 19A), which is involved in the
repression of growth during bouts of stress (41), and BCL-6 co-repressor (BCOR) (Fig.
19A), which plays a role in anti-apoptosis and anti-inflammation (7), both of which are
represented in the AER-stress gene set. Also included in the Negative Regulation of
Transcription gene set is SIRT1 (Fig. 19A) which plays a pivotal role as a regulator of
energetics during bouts of starvation (38, 39) and is included in the circadian gene set.
Therefore, we conceptualize that gene members of different gene sets (e.g. circadian
and AER-stress) integrate to express a new phenotype (e.g. Negative Regulation of
Transcription) at some critical point within the daily environmental cycle (e.g. onset of
low tide thermal stress) as a possible strategy that enhances survival.
Other regulatory related genes that exhibit peak expression during the early-phase
included low-tide genes, immediate early gene-5 (IER5L) (Fig. 19B) which functions as a
first response following cues from extracellular stimuli (3) and the transcription factor
CCAAT/enhancer binding protein epsilon (CEBPE) which is considered a master regulator
of many cellular processes of growth and differentiation, immunity and inflammation
and disease (36) (Fig. 19B). Important regulatory AER-stress genes included Cyclic
AMP-dependent transcription factor ATF4 (ATF4), which suggests that cAMP-dependent
pathways may be activated during bouts of low tide thermal stress. The induction of
Rho-related GTP-binding protein (RHOQ), which is a critical membrane-bound
161
regulatory protein that forms the link between external stimuli and the synthesis of
cAMP further supports this. Furthermore, the up-regulation of genes related to cAMP is
consistent with their role in mobilizing glycogen reserves during bouts of increased
stress (31). Lastly, the calcium binding protein, Calmodulin (CALM) (SI Fig. 6), was
robustly expressed, consistent with its role of modulating an extensive array of protein
classes including calmodulin-dependent protein kinases, adenylate and guanylate
cylases, phophodiesterase and an ATP-dependent Ca
2+
pump (25). Furthermore, it plays
a critical role in the activation of HIF-1(46).
Interestingly, genes related to the TCA cycle, including Citrate synthase (ACLY) (Fig. 19C),
Aconitate hydratase (ACON), Iso-citrate dehydrogenase (IDH3) (Fig. 19C), and Malate
dehydrogenase (MDH) were co-expressed and peaked during the early-phase. An
isoform of Phosphoenolpyruvate carboxykinase (PCKG) which functions outside of the
mitochondria and a well-known component of gluconeogenesis is also an intermediate
in the glucose-succinate anaerobic pathway. Notably, AACS, PCKG and ACLY are AER-
stress genes, which indicates that their expression is required during periods of
combined low tide thermal stress. During low tide, mussels synthesize ATP by activating
anaerobic pathways including glucose-succinate and aspartate-succinate (22). These
pathways shuttle electrons removed from glucose in the direction of a reversed TCA
cycle that terminates at fumarate reductase. The enzymes ACLY, IDH3 and ACON are
active in the forward flux of the TCA cycle and therefore are not a component of
162
anaerobic pathways. PCKG and MDH are core components of the glucose-succinate
pathway by catalyzing the reduction of PEP to oxaloacetate and oxaloacetate to malate
respectively. Malate is eventually reduced to succinate and ATP is synthesized.
While a biological theme was not statistically associated with genes up-regulated during
high tide, a number of the submergence associated AER-stress-recovery genes are worth
mentioning, including 2 DNA repair genes; DNA repair and recombination protein
RAD54-like (RAD54), DNA ligase (DNLI1); and a class of genes involved with protein
degradation, Ubiquitin-conjugating enzyme E2-17 kDa (UBCD1), E3 ubiquitin-protein
ligase TRIM36 (TRI36) andE3 ubiquitin/ISG15 ligase TRIM25 (TRI25). Also induced were
genes that related to α-ketoglutarate metabolism included glutamate dehydrogenase 1
mitochondrial (DHE3) which reversibly converts α-ketoglutarate to glutamate, a non-
essential amino acid that plays important conserved roles in eukaryotic energetics and
α-ketoglutarate-dependent dioxygenase which plays a role in cellular oxygen sensing
(45). Lastly, there were several clock genes on the M. californianus microarray in the
current study which, were not on the array in the previous simulation study. Of these,
clock genes, PER1&2 and CRY1 were induced during high tide.
163
-0.8
-0.4
0.0
0.4
0.8
15
20
25
PA2G4
BCOR
SIRT 1
CEBPE
IER5L
A
B
C
IDH3
ACLY
-0.4
-0.2
0.0
0.2
0.4
0.6
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
0.6
0.4
0.2
0.0
-0.4
-0.2
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
0.8
0.4
0.0
-0.4
-0.8
6 pm 6 am 6 pm 6 am 6 am
mRNA expression (log
2
)
15
20
25
Temperature (°C)
Fig. 19. Gene expression patterns of particular genes under a high stress tidal simulation. (A) Genes
included in the Negative Regulation of Transcription gene set. (B) Regulatory genes peaking during
early-phase. (C) TCA cycle genes peaking during early-phase.
164
Chapter 4: Discussion
Cellular Regulation Dominates the Transcriptome Response during Early-
Phase
Gene expression in Mytilus californianus during bouts of tidal emergence, whether
combined with high temperatures, such as in our high-stress tidal simulation, or benign
temperatures, which were replicated in our low-stress tidal simulation (4 (Chapter 1)), is
dominated by genes associated with transcription regulation as evidenced by the
induction of numerous transcription factor and regulatory protein genes. Transcription
factors and the signaling molecules that can trigger their expression underpin cellular
coordination efforts which are time-dependent processes that affect homeostasis,
survival, growth, disease and many other related factors of cell physiology.
Transcriptional regulatory networks, which are comprised various interactions between
transcription factors and DNA, are staggeringly complex. In humans, 200-300
transcription factors bind to core promoters and another 1,400 have sequence-specific
DNA-binding properties (10). Transcription factor networks can be environmentally
sensitive, as exemplified in the proposed heat shock pathway model. Following the
sensing of a critical thermal perturbation, the trimer of heat shock factor 1 binds to heat
shock elements in hsp promoters (42), leading to the induction of hsp transcription (30).
On the scale of the transcriptome, the over-representation of genes related to
transcription regulation during episodes of environmental stress has also been observed
in the model species Arabidopsis thaliana, which is also a sessile organism that is
165
required to respond effectively to cyclical, broad shifts in the prevailing environment
(44). Furthermore, analyses of stress-regulatory networks in Escherichia coli revealed
that adjustments of sensing and signal transduction genes under stress conditions
confer survival fitness (47). Biochemical analyses of signaling activities in Mytilus
congeners revealed that the abundance of phosphorylated intermediates of key
signaling pathways, such as mitogen-activated protein kinases (MAPKs), cyclin-
dependent kinases (CDKs), protein kinase A (PKA) and protein kinase C (PKC), increased
with temperature and differences of phosphorylation between the examined species
was significant (8). Thus, signaling pathways regulated by either the induction of genes
or phosphorylation of signaling-enzymes may play a critical role in cellular
reprogramming during stressful events that typically occur during periods of midday low
tide in mussels. In this study, Negative Regulation of Transcription occurred at the onset
of low tide thermal stress, indicating that the cells may have reprogrammed the gene
expression networks to down-regulate transcription under unfavorable conditions and
conserve energy until favorable conditions return. Support for this idea is revealed by
the dramatic decrease in peaking genes following maximum low tide warming and a 10-
fold increase in the number of peaking genes following the onset of high tide.
166
Activation of Transcriptional Regulation of Metabolism
Anaerobic pathways dominate metabolism during tidal emergence; therefore, we found
it surprising that 3 forward TCA cycle genes were up-regulated during the low tide early
phase. While it is hard to reconcile the expression of these genes during emergence
when the forward TCA cycle is presumably inactive, we speculate that this may
represent an anticipatory response, brought upon by entrainment, which prepares
mussels for re-submergence either through increased levels of pre-submergence
translated enzymes or higher abundances of transcripts that are translated into proteins
after submergence occurs. The former mechanism is a matter of energetic balance
because the costs associated with the synthesis of proteins must not exceed the rate of
ATP production, which is relatively minimal during the low tide activation of anaerobic
metabolism. Whereas, the full potential of anticipatory gene expression can only be
achieved if the decay rate of mRNA is lowered to allow time for entry into the next
environmental phase (43) and/or mRNA is stored in cytoplasmic granules (1) for later
use (48).
A visual observation of TCA cycle gene expression patterns detected decreases in
transcript abundance following high tide consistent with idea that a high rate of
translation of the potentially stored or latent transcripts into the respective metabolic
enzymes occurred during re-submergence. The potential higher rate of ATP production
following high tide that can result from higher levels of pre-submergence transcripts can
167
provide necessary support to the energy demanding activities of heat shock proteins
and the associated chaperones. Specifically, increased abundances of isocitrate-
dehydrogenase enzymes may serve to increase the abundance of α-ketoglutarate,
which can possibly lead to greater amino acid synthesis via the subsequent conversion
of α-ketoglutarate to glutamate (28). Our findings of significant increase in glutamate
levels (5) and up-regulation of glutamate dehydrogenase transcripts, following the onset
high tide, lend support to this premise. The importance of glutamate level stability is
highlighted in a yeast study that revealed the acute activation of glutamate synthesis
genes when levels reach a critical lower limit (28). The possible role that isocitrate-
dehydrogenase may play in the regulation of glutamate synthesis has intrigued plant
biologists, however to date no direct associations between these processes have been
made (12). α-ketoglutarate also functions as an oxygen sensor by binding to
dioxygenases that play a role in the inhibition of HIF-1α during normoxia (45). The
rhythmic expression of α-ketoglutarate-dependent dioxygenase is consistent with
sensing of changes in cellular oxygen levels, which persist over alternating bouts of
emergence and submergence.
Because mussels reduce metabolism during low tide aerial emergence and likely reserve
the bulk of amino acid and proteins synthesis during periods of high tide submergence,
the activation of anticipatory gene expression programs may have been enforced, as a
result of the short 4 hour submergence period. Anticipatory transcriptomic control has
168
been observed in a study of baker’s yeast Saccharomyces cerevisiae (32) as it passes
through fluctuating environments. Yeast cultures were subjected to a series of stresses
sequentially in the order of heat, ethanol and oxidative stress. Survivors subjected to
the same sequence of stresses revealed that the genes that needed to be expressed in
order to cope in a particular stressful environment were in fact induced in the preceding
environment. Therefore anticipatory gene regulation, which is brought upon by
entrainment, is a conserved strategy amongst particular organisms and confers the
ability not only to respond to prevailing environmental signals by transcribing genes
that deal with challenges in real time (direct regulation) but also use those signals as
indicators of impending environmental change and alerts for preparedness
(asymmetrical anticiapatory regulation).
Thermal Stress Response
We observed the induction of well-studied heat shock response genes during both low
tide thermal events during the early-phase and the following high tide that occurs
during the late-phase, which highlights the complexity of the heat shock response. Our
findings in mussels sampled from the field (14) also revealed temporal partitioning in
the peak expression of heat shock response genes; for example, the expression of
inducible isoforms of hsp-70 peaked during low tide thermal stress periods, while
isoforms of t-complex protein peaked some time after low tide heating episodes.
Therefore, the heat shock response functions on a complex temporal basis and
169
understanding the timing and intensity of hsp related gene induction requires further
investigation, such as repeated experiments under various temperatures and higher
resolution sampling.
The regulation of the previously characterized thermally-responsive genes did not reveal
a robust, pattern of co-expression that would be consistent with thermal-induction
following a single heat shock in our low-stress tidal simulation study. Therefore, while
the expression of inducible hsps following thermal stress is a somewhat predictable
response in M. californianus, how changes in temperature affect transcriptomic-wide
reprogramming is a more complex issue that warrants additional detailed analyses.
Discordance between patterns of expression under infrequent versus frequent (daily)
episodes of thermal stress suggests that environmental acclimation, which in the
current study included a regime of 4 weeks of low tide thermal stress, has a profound
effect on environmentally-induced patterns of gene expression. Podrabsky and Somero
(35) thoroughly evaluated the diversity in transcriptome-wide patterns of gene
expression, under differing temperature based acclimations. Using the eurythermal
freshwater fish, Austrofundulus limnaeus, they subjected individuals to long-term
acclimation temperatures of 20-37°C under constant and fluctuating thermal regimes
and the results revealed complex differences in patterns of gene expression in fish
between the two regime types. Changes in patterns of gene expression as a result of
thermal cycling included alterations of phase and amplitude which suggest that thermal
170
acclimation in an aquatic organism can occur at the level of the transcriptome. Our
finding that genes that possibly allow mussels to cope with a single heat stress,
following weeks of no change in ambient temperature, are not regulated in a consistent
manner under cycles of thermal stress is consistent with Podrabsky’s observations of
diverse patterns of gene expression under differing thermal regimes. The observed
patterns may be the result of changes in the rate of expression of genes, as demands for
the synthesis of certain proteins diminish and some critical cellular concentration level is
reached following days of repeated exposure (52). Alternatively, changes in transcript
abundance may be due to thermal structural destabilization of mRNA (35).
The underlying molecular mechanisms that drive plasticity under stressful environments
are still not known, however Roelofs et al (40) provide a unique perspective on this
topic. They noted that TATA box core promoter structures are pervasive throughout
eukaryotic genomes and TATA-containing genes in plants and humans are enriched in
stress response genes. Furthermore, stress-related proteins expressed from genes with
a TATA box exhibit a high level of variability in their protein abundance in contrast to
TATA-less genes. They speculated that this acute plasticity enables cells to react to
environmental stresses in rapid fashion.
171
Circadian Patterns in a Sessile Bivalve
Circadian patterns of gene expression were robust in mussels subjected to our low-
stress tidal simulation, indicated by 80-90% of rhythmically expressed genes exhibiting a
period between 22-28 hours (4 (Chapter 1)). A large proportion of these genes were
induced at dawn and dusk, suggesting that light may have been the zeitgeber (cue) that
influenced the observed patterns of expression. Regulation of the circadian genes
identified in the previous study, under the high-stress tidal simulation, revealed clusters
of genes exhibiting peak expression during the onset of thermal stress and during
submergence which coincided with the onset of light and dark cycles. Therefore we
cannot determine if the environmental cues driving the expression of these particular
genes is consistent between both studies. Nonetheless, the rhythmic expression of
clock genes in the both the low and high-stress tidal simulations supports the notion
that part of the M. californianus transcriptome is regulated by clock oscillators. While
clock networks have not been well defined in any intertidal organism, rhythmic patterns
of physiology have been shown to occur in marine invertebrates over daily, tidal, simi-
lunar/lunar and seasonal time scales (50). However, only free-running phenotypes that
persist without the influence of zeitgebers (i.e. tide, sunlight and food) should be
regarded as being regulated by an endogenous clock. To date, free-running patterns of
gene expression have not been observed in bivalves.
172
Chapter 4: Conclusions
Mytilus californianus lives in a fluctuating environment and the coordinated expression
of genes may confer survival and fitness over cycles of aerial and thermal stress.
Thermal and hypoxia sensitive transcription factors, such as HIF-1 and HSF-1, may play
important roles in the coordinated transcriptomic response to environmental
fluctuations. We observed a cluster of expressed genes involved with transcriptional
processes at the onset of low tide associated thermal stress. HSF-1 is activated by
phosphorylation (30) thus conserved signaling pathways may provide the link between
environmental stimuli and transcription. The robust expression of Calmodulin and its
inclusion in the AER-stress gene set suggest that higher levels of stress require increased
levels of cell signaling activities. This correlation has also been revealed by direct
measurements of phosphorylation activity in Mytilus species under variations in
temperature (10).
Mytilus californianus is well adapted to bouts of hypoxia because the mussels can
initiate anaerobic metabolic pathways. The induction of Phosphoenolpyruvate
carboxykinase exclusively under the high-stress tidal simulation suggests that under
certain circumstances regulation of anaerobic metabolism is generated at the level of
transcription. Surprisingly we also observed the expression of TCA cycle genes during
bouts of tidal emergence which suggests an anticipatory mechanism of cellular
regulation. The observation of sharp decreases of TCA transcript abundance, following
173
re-submergence, suggests that rapid translation rates consistent with the constraints
the short 4 hr opportunity to feed, generate ATP and synthesize proteins.
The expression of heat shock proteins in response to thermal stress is a well conserved
biological process. However the complexity of the heat shock response lies in the fact
that there are many isoforms of hsps within a single species, networking pathways are
extensive and temporal factors are integrated in the response. Our observations of hsps
induced both during tidal emergence and following submergence stresses this point. On
the scale of the transcriptome we observed differences in expression pattern in genes
exposed to a one-time heat shock versus daily thermal perturbations. Thus acclimation
may produce a type of “heat hardening” that confers a degree of tolerance to low tide
heating events. Measurements of protein abundance may bring further understanding
of this biological phenomenon.
A circadian pattern of gene expression was the dominant feature under the low-stress
tidal simulation. In the current study we were unable to resolve whether the
rhythmically expressed genes were driven by light cues or tidal factors. Nevertheless,
the transcriptome may be driven by the discovered clock oscillators and the cues
(zeitgeber) driving the expression of these oscillators may be day/night or tidal cycles.
Future work in constant light or dark conditions will be necessary to elucidate the
complexity of endogenous rhythms in fluctuating environments.
174
Mytilus californianus is an ecologically important species and considered a bioindicator
of the prevailing near-shore environment. Understanding its physiology at several levels
of biological organization provides baseline knowledge that can be used to assess its
ecologic status. Understanding the physiology of mussels has become increasingly
important in light of climate change which will bring about higher terrestrial and sea
surface temperatures. This work is a first step toward understanding how the
transcriptome of M. californianus responds to low tide thermal perturbations and sheds
light on how it may respond to future changes in the environment.
175
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Chapter 4: Supplemental Information
SI Figures and Tables
SI Fig. 4. CEBPE pattern of gene expression. The expression of genes spanning the
transcriptome was correlated to the expression of CEBPE using Pearson’s correlation. A
ranked gene list was created based upon the correlation values.
CEBPE
15
20
25
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5 0.6
0.4
0.2
0.0
-0.4
-0.2
0.6
6 pm 6 am 6 pm 6 am 6 am
mRNA expression (log
2
)
Temperature (°C)
15
20
25
182
SI Fig. 5. Distribution of peak gene expression of subsets of the early and late-phase gene lists
over 48 phase hrs. AER genes represent genes that are distinctly expressed under the high-stress
tidal simulation. Circadian genes represent genes that showed peak expression daily under the
low-stress tidal simulation that are rhythmically expressed under the high-stress tidal simulation.
Tidal genes represent genes that showed peak expression during cycles of emergence and
submergence under the low-stress tidal simulation that are rhythmically expressed under the
high-stress tidal simulation.
0
20
40
60
80
100
120
140
160
1 3 5 7 9 11 13 15 17 19 21 23
AER genes
Circadian genes
Tidal genes
0 4 2 8 6 10 12 14 20 18 16 22
Frequency
Phase (hrs)
25
20
15
Tidal cycle
Light/Dark
cycle
Temperature (°C)
183
SI Fig. 6. The gene expression pattern of Calmodulin.
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
15
20
25
0.3
0.2
0.0
-0.2
-0.3
-0.1
0.1
CALM
6 pm 6 am 6 pm 6 am 6 am
mRNA expression (log
2
)
Temperature (°C)
15
20
25
184
SI Table 7. List of transcripts represented in heatmap shown in Fig. 16A.
Myt_21D17 Anaphase-promoting complex subunit 7
Myt_64E08 Unclassifiable EST
Myt_37G16 Lariat debranching enzyme A
Myt_65D14 Rho GDP-dissociation inhibitor 1
Myt_61I08 Unclassifiable EST
Myt_47K12 Mammalian ependymin-related protein 1
Myt_36C13 Uncharacterized protein DDB_G0287625
Myt_66G20 COP9 signalosome complex subunit 5
Myt_46H22 Cholecystokinin receptor type A
Myt_46N06 Unclassifiable EST
Myt_61P21 Unclassifiable EST
Myt_44D20 Insulin-degrading enzyme
Myt_17H15 UPF0392 protein F13G3.3
Myt_42I15 Progestin and adipoQ receptor family member 3
Myt_59I23 40S ribosomal protein S14
Myt_59F15 Uncharacterized methyltransferase WBSCR22
Myt_35F12 Alcohol dehydrogenase class-3 chain H
Myt_33K24 E3 ubiquitin/ISG15 ligase TRIM25
Myt_11M13 Coiled-coil and C2 domain-containing protein 1-like
Myt_42K11 Bardet-Biedl syndrome 5 protein homolog
Myt_12F15 Unclassifiable EST
Myt_25E18 Nucleolar protein 56
Myt_63O19 Unclassifiable EST
Myt_12G14 Unclassifiable EST
Myt_48C07 B9 domain-containing protein 2
Myt_59G23 Unclassifiable EST
Myt_62H07 Unclassifiable EST
Myt_42K15 Beta-galactosidase
Myt_40O22 TRAF3-interacting protein 1
Myt_35O06 Uncharacterized protein C9orf114 homolog
Myt_44H16 Protein FAM96A
Myt_53O11 DNA-binding protein P3A2
Myt_63G12 DENN domain-containing protein 1A
Myt_37H20 Malate dehydrogenase
Myt_35N24 Unclassifiable EST
Myt_34P12 Nuclear pore complex protein Nup98-Nup96
185
Myt_25D05 Short/branched chain specific acyl-CoA dehydrogenase, mitochondrial
Myt_33G17 Unclassifiable EST
Myt_51J23 Innexin unc-9
Myt_14F10 B9 domain-containing protein 2
Myt_48I23 DNA-directed RNA polymerases I and III subunit RPAC1
Myt_44I01 B-cell receptor-associated protein 31
Myt_24K16 Stress-70 protein, mitochondrial
Myt_38J15 Unclassifiable EST
Myt_32M23 Microsomal glutathione S-transferase 1
Myt_44O07 Unclassifiable EST
Myt_37G04 DnaJ homolog subfamily C member 2
Myt_21G23 Thioredoxin reductase 3 (Fragment)
Myt_16A19 Melanotransferrin
Myt_44N24 Unclassifiable EST
Myt_19M04 ADP-ribosylation factor-like protein 6
Myt_39L20 ADP-ribosylation factor 2
Myt_14C11 Unclassifiable EST
Myt_14M02 Midasin
Myt_46P10 COBW domain-containing protein 2
Myt_13L12 Eukaryotic translation initiation factor 1A, X-chromosomal
Myt_60K08 NADH-ubiquinone oxidoreductase chain 4
Myt_11K18 Unclassifiable EST
Myt_20E05 Oncoprotein-induced transcript 3 protein
Myt_26C06 28S ribosomal protein S35, mitochondrial
Myt_36H22 Protein asteroid homolog 1
Myt_61H18 Ankyrin-2
Myt_60O03 Unclassifiable EST
Myt_32B02 Ornithine aminotransferase, mitochondrial
Myt_34L05 Anoctamin-4
Myt_62P01 Lachesin
Myt_48D22 Neogenin (Fragment)
Myt_14F03 Unclassifiable EST
Myt_36D04 Oligoribonuclease, mitochondrial
Myt_30N22 PCI domain-containing protein 2
Myt_44B18 Protein max
Myt_62O20 Unclassifiable EST
Myt_59D22 Unclassifiable EST
Myt_23B24 Orexin receptor type 2
Myt_53N01 Leucine-rich repeat-containing protein 58
186
Myt_43I05 Dixin
Myt_37C21 AP2/ERF domain-containing protein PFD0985w
Myt_42M18 Importin-4
Myt_22F20 RING finger protein 160
Myt_28D07 Glutamate-rich WD repeat-containing protein 1
Myt_54K02 Unclassifiable EST
Myt_65J12 Unclassifiable EST
Myt_54A23 15-hydroxyprostaglandin dehydrogenase [NAD+]
Myt_60O04 Elongation factor 1-alpha (Fragment)
Myt_66K08 Unclassifiable EST
Myt_37K02 Sialin
Myt_25J13 Cathepsin F
Myt_16P11 Protocadherin-19
Myt_12G10 Unclassifiable EST
Myt_37L19 Glucose-6-phosphate isomerase
Myt_53O09 Collagen alpha-2(IV) chain
Myt_29K02 UPF0139 membrane protein C19orf56 homolog
Myt_51J17 Unclassifiable EST
Myt_38J06 UPF0568 protein C14orf166 homolog
Myt_22C09 39S ribosomal protein L39, mitochondrial
Myt_36J21 Calmodulin
Myt_44C14 Acetylcholine receptor subunit alpha-like
Myt_21I17 Unclassifiable EST
Myt_30J21 Solute carrier organic anion transporter family member 2B1
Myt_19C21 Unclassifiable EST
Myt_42G24 Mdm2-binding protein
Myt_34M19 Unclassifiable EST
Myt_59E06 40S ribosomal protein S17
Myt_29A02 Protein Mo25
Myt_21M08 Unclassifiable EST
Myt_20H15 Uncharacterized protein C6orf150
Myt_13L04 Selenoprotein S
Myt_30I20 Unclassifiable EST
Myt_30F07 CDGSH iron-sulfur domain-containing protein 3, mitochondrial
Myt_38L12 Cys-loop ligand-gated ion channel
Myt_62D15 Unclassifiable EST
Myt_14E04 Protein FAM100B-A
Myt_38K22 Probable low affinity copper uptake protein 2
Myt_33O11 Centrosomal protein of 41 kDa
187
Myt_26H05 Elongation factor Tu, mitochondrial
Myt_24K18 RNA polymerase-associated protein LEO1
Myt_30N02 Golgin subfamily B member 1
Myt_32P21 Unclassifiable EST
Myt_21H22 Torsin-1A-interacting protein 2
Myt_31K02 N-alpha-acetyltransferase 35, NatC auxiliary subunit
Myt_21H01 Cytosolic Fe-S cluster assembly factor NUBP1 homolog
Myt_28G13 Stathmin
Myt_30D11 Proliferation-associated protein 2G4
Myt_39L14 WW domain-containing oxidoreductase
Myt_34O02 CUB and sushi domain-containing protein 3
Myt_29P06 12 kDa FK506-binding protein
Myt_58L16 ATP-citrate synthase
Myt_53J10 Eukaryotic translation initiation factor 2 subunit 2
Myt_42P19 Melatonin-related receptor
Myt_64G17 T-complex protein 1 subunit alpha
Myt_48B17 RNA-binding protein Musashi homolog Rbp6
Myt_64J20 ADP-ribosylation factor 2
Myt_17K23 Unclassifiable EST
Myt_14D06 Alpha N-terminal protein methyltransferase 1A
Myt_42P13 Methylmalonic aciduria and homocystinuria type C protein homolog
Myt_17A11 Unclassifiable EST
Myt_37J14 Octopamine receptor beta-1R
Myt_15N06 Transcription elongation factor B polypeptide 1
Myt_16P23 Putative rRNA methyltransferase 3
Myt_40G16 Fibrinogen-like protein A
Myt_23B10 BCL-6 corepressor
Myt_16F12 Transmembrane protein 232
Myt_36D03 Peptidylprolyl isomerase domain and WD repeat-containing protein 1
Myt_30C04 Death-associated protein 1
Myt_23H20 Transcriptional regulator Myc-1
Myt_36M17 Protein PRRC1-A
Myt_12I10 GPI transamidase component PIG-S
Myt_34J16 Unclassifiable EST
Myt_30M07 Nitric oxide synthase, inducible
Myt_33J16 Cathepsin F
Myt_60P16 Unclassifiable EST
Myt_36H20 Monocarboxylate transporter 14
Myt_59M06 Protein MIS12 homolog
188
Myt_26B12 Unclassifiable EST
Myt_40O16 Phosphoenolpyruvate carboxykinase [GTP]
Myt_09I01 Cyclic AMP-dependent transcription factor ATF-4
Myt_67A09 Unclassifiable EST
Myt_35J12 Unclassifiable EST
Myt_64J04 Unclassifiable EST
Myt_28L02 NAD-dependent deacetylase sirtuin-1
Myt_34G12 Unclassifiable EST
Myt_64O16 Unclassifiable EST
Myt_65M19 GTPase KRas
Myt_34B21 Unclassifiable EST
Myt_43N01 V-type proton ATPase 21 kDa proteolipid subunit
Myt_41M20 Unclassifiable EST
Myt_28E06 FMRFamide receptor
Myt_21J13 Kelch-like protein 24
Myt_40F06 Organic cation transporter protein
Myt_41J05 Unclassifiable EST
Myt_38M23 Apical endosomal glycoprotein
Myt_22B24 Abhydrolase domain-containing protein 11
Myt_41M14 Polyubiquitin-C
Myt_38P17 UPF0764 protein C16orf89 homolog
Myt_26H17 Heat shock 70 kDa protein 12A
Myt_25D02 Unclassifiable EST
Myt_22P19 Sodium-dependent multivitamin transporter
Myt_42P02 Putative lipoyltransferase 2, mitochondrial
Myt_33C08 39S ribosomal protein L3, mitochondrial
Myt_30N06 Unclassifiable EST
Myt_51I10 Cytochrome P450 2C27
Myt_63A22 60S ribosomal protein L17
Myt_31B05 Bifunctional purine biosynthesis protein PURH
Myt_38M02 Unclassifiable EST
Myt_36N17 Prion-like-(Q/N-rich) domain-bearing protein 25
Myt_62J14 Ribosomal RNA processing protein 36 homolog
Myt_17P22 Suppressor of cytokine signaling 2
Myt_23A15 Intraflagellar transport protein 172 homolog
Myt_49I12 Unclassifiable EST
Myt_27M19 Transcriptional repressor CTCF
Myt_37F24 GTP-binding protein 128up
Myt_27B08 Unclassifiable EST
189
Myt_13B16 IQ domain-containing protein G
Myt_51I06 D-beta-hydroxybutyrate dehydrogenase, mitochondrial
Myt_38H14 GPN-loop GTPase 2
Myt_21B03 Ribonucleoside-diphosphate reductase small chain
Myt_45P10 Serine/threonine-protein kinase Nek8
Myt_30C18 SCO-spondin
Myt_12G08 F-box only protein 31
Myt_23B06 GTP-binding nuclear protein Ran
Myt_56G19 Unclassifiable EST
Myt_30B17 Cholesterol 7-alpha-monooxygenase
Myt_61F17 Mitochondrial import inner membrane translocase subunit Tim17-B
Myt_67B18 Elongation factor 1-alpha
Myt_31M21 Unclassifiable EST
Myt_34B19 Complement C1q-like protein 3
Myt_31K19 Unclassifiable EST
Myt_44P18 Plasma alpha-L-fucosidase
Myt_26O21 Tubulin alpha-1 chain
Myt_29D18 L-threonine 3-dehydrogenase
Myt_31J14 NAD-dependent alcohol dehydrogenase
Myt_21I07 Transaldolase
Myt_42O15 Unclassifiable EST
Myt_28J18 BTB/POZ domain-containing protein 17
Myt_45F05 Probable aconitate hydratase, mitochondrial
Myt_33J10 Monocarboxylate transporter 14
Myt_66O09 NADH dehydrogenase [ubiquinone] iron-sulfur protein 2, mitochondrial
Myt_37P18 DNA methyltransferase 1-associated protein 1
Myt_40O15 Unclassifiable EST
Myt_31K21 Unclassifiable EST
Myt_22L06 Unclassifiable EST
Myt_34C09 Eukaryotic peptide chain release factor subunit 1
Myt_30H18 TBC domain-containing protein kinase-like protein
Myt_44C01 Cytoglobin-1
Myt_29A22 40S ribosomal protein S23
Myt_26G06 Transcription intermediary factor 1-beta
Myt_39A14 Eukaryotic translation initiation factor 3 subunit G
Myt_40G18 Serine/threonine-protein phosphatase 6 regulatory ankyrin repeat
Myt_63N21 Caspase-3
Myt_30K23 Serine/arginine-rich splicing factor 4
Myt_37P04 Unclassifiable EST
190
Myt_21E05 Myb-like protein X
Myt_30J22 Protein trunk
Myt_36C04 Neuronal membrane glycoprotein M6-a
Myt_24J16 Ras-related protein Rab-8A
Myt_38H15 Unclassifiable EST
Myt_38H06 Calmodulin
Myt_51F10 Tripartite motif-containing protein 45
Myt_36N21 Succinyl-CoA ligase [ADP-forming] subunit beta, mitochondrial
Myt_15B24 ADP-ribosylation factor 4
Myt_23F07 Presenilin-1
Myt_32K15 Kelch domain-containing protein 1
Myt_36O04 Zinc finger protein 235
Myt_43C18 Carbonic anhydrase 1
Myt_23F01 Unclassifiable EST
Myt_27L23 Rho-related GTP-binding protein RhoQ
Myt_64N05 Unclassifiable EST
Myt_42D08 MAM and LDL-receptor class A domain-containing protein C10orf112
Myt_44P16 DNA repair protein XRCC1
Myt_60O13 Unclassifiable EST
Myt_31C19 Unclassifiable EST
Myt_44N01 Kelch-like protein 3
Myt_39N04 Voltage-dependent calcium channel gamma-3 subunit
Myt_12J20 Unclassifiable EST
Myt_11K19 DNA-directed RNA polymerase III subunit RPC7-like
Myt_27E11 T-complex protein 1 subunit eta
Myt_26G08 Structural maintenance of chromosomes protein 3
Myt_30N17 Unclassifiable EST
Myt_01M13 Unclassifiable EST
Myt_36A11 Anaphase-promoting complex subunit 4
Myt_36B09 Eukaryotic translation initiation factor 2 subunit 1
Myt_25O09 Ras-related protein Rab-6A
Myt_27F18 Latrophilin-2
Myt_17F05 Apoptosis inhibitor 1
Myt_29N01 Endo-1,4-beta-xylanase A
Myt_44N19 Unclassifiable EST
Myt_54D16 Heterogeneous nuclear ribonucleoprotein R
Myt_40J09 Elongation factor 1-alpha
Myt_45B09 Unclassifiable EST
Myt_40K14 Unclassifiable EST
191
Myt_67A11 Fasciclin-1
Myt_61B20 Protein BTG1
Myt_23J14 Nuclear receptor ROR-alpha
Myt_30C19 Transmembrane protein 47
Myt_39C12 Unclassifiable EST
Myt_47P07 Unclassifiable EST
Myt_45D10 Complement C1q tumor necrosis factor-related protein 3
Myt_15O12 SAM pointed domain-containing Ets transcription factor
Myt_62L11 Unclassifiable EST
Myt_43D24 Target of rapamycin complex 2 subunit MAPKAP1
Myt_24H04 Very low-density lipoprotein receptor
Myt_35J11 Immediate early response gene 5-like protein
Myt_20J17 Programmed cell death 6-interacting protein
Myt_21G20 IQ motif and SEC7 domain-containing protein 1
Myt_34P01 Palmitoyltransferase ZDHHC2
Myt_30L05 Histone deacetylase complex subunit SAP130
Myt_38M09 ERI1 exoribonuclease 3
Myt_17A08 Unclassifiable EST
Myt_62H03 Unclassifiable EST
Myt_30M08 Regulator of G-protein signaling 3
Myt_15B21 Leucine-rich repeat-containing protein 15
Myt_22C07 Katanin p60 ATPase-containing subunit
Myt_40J11 Neuronal calcium sensor 2
Myt_43L14 3-oxo-5-beta-steroid 4-dehydrogenase
Myt_25B01 Unclassifiable EST
Myt_27A24 Presenilin-1
Myt_29M08 Transcription intermediary factor 1-alpha
Myt_30L17 DNA repair protein XRCC3
Myt_41M03 Unclassifiable EST
Myt_30P05 Putative ankyrin repeat protein FPV162
Myt_59C06 High mobility group nucleosome-binding domain-containing protein 5
Myt_24G19 Protein tirA
Myt_29B11 Thymidine kinase 2, mitochondrial
Myt_32O01 Large subunit GTPase 1 homolog
Myt_46D17 NADH-ubiquinone oxidoreductase chain 4
Myt_67K17 Unclassifiable EST
Myt_59O18 Heterogeneous nuclear ribonucleoprotein K
Myt_30D02 Carbonic anhydrase 2
Myt_26E04 Histone-lysine N-methyltransferase, H3 lysine-9 specific 5
192
Myt_57M04 Cholecystokinin receptor type A
Myt_43N20 Serine/threonine-protein kinase pim-3
Myt_15C18 Unclassifiable EST
Myt_34A21 Probable isocitrate dehydrogenase [NAD] subunit alpha, mitochondrial
Myt_56L15 UPF0396 protein CG6066
Myt_49O01 Mitochondrial fission 1 protein
Myt_34O20 Carbonic anhydrase-related protein
Myt_21B04 Iduronate 2-sulfatase
Myt_42F24 Y-box factor homolog
Myt_27P23 rRNA 2'-O-methyltransferase fibrillarin
Myt_25P01 E3 ubiquitin-protein ligase TRIM33
Myt_33P18 Glyoxylate reductase
Myt_60K23 Unclassifiable EST
Myt_23J19 Toxin CfTX-2
Myt_37H02 Unclassifiable EST
Myt_38M16 ATPase family AAA domain-containing protein 1
Myt_45F19 Aplysianin-A
Myt_43C21 AB hydrolase superfamily protein C1039.03
Myt_19J16 Unclassifiable EST
Myt_38F24 Galectin-4
Myt_35I01 Vacuolar protein sorting-associated protein 28 homolog
Myt_29A08 Transmembrane protein 205
Myt_49K23 SWI/SNF-related matrix-associated actin-dependent regulator of
Myt_61G17 Ubiquitin-conjugating enzyme E2 G1
Myt_59N19 CD209 antigen-like protein E
Myt_37F01 Blastula protease 10
Myt_09P07 Structural maintenance of chromosomes protein 1A
Myt_04B22 Krueppel-like factor 5
Myt_68G08 Protein asteroid homolog 1
Myt_42D12 Disrupted in renal carcinoma protein 2
Myt_49M14 Septin-7
Myt_17N03 Inter-alpha-trypsin inhibitor heavy chain H4
Myt_20F23 Ankyrin-2
Myt_10B16 Unclassifiable EST
Myt_30C20 Protein quiver
Myt_55M12 Unclassifiable EST
Myt_15P11 Unclassifiable EST
Myt_42I14 AP-1 complex subunit mu-1
Myt_14O15 Unclassifiable EST
193
Myt_31L06 Heavy metal-binding protein HIP
Myt_41D04 Thrombospondin-1
Myt_29N18 Unclassifiable EST
Myt_48F15 Unclassifiable EST
Myt_20N17 Protein DDI1 homolog 2
Myt_29C06 Feline leukemia virus subgroup C receptor-related protein 2
Myt_64I03 Unclassifiable EST
Myt_24B08 Unclassifiable EST
Myt_14A02 Elongation factor 2
Myt_23J06 Arrestin domain-containing protein 2
Myt_68K24 Unclassifiable EST
Myt_21G24 Krueppel-like factor 11
Myt_22E24 Formin-binding protein 4
Myt_62N03 GMP synthase [glutamine-hydrolyzing]
Myt_59O17 tRNA-nucleotidyltransferase 1, mitochondrial
Myt_32J11 SAM domain-containing protein C14orf174
Myt_21A23 Acid ceramidase
Myt_42F20 Transcription cofactor vestigial-like protein 2
Myt_23O07 Unclassifiable EST
Myt_44C23 NAD-dependent deacetylase sirtuin-5
Myt_29C11 Unclassifiable EST
Myt_60K22 Probable ATP-dependent RNA helicase DDX4
Myt_63G18 Transcriptional repressor protein YY1
Myt_65M21 Mannose-1-phosphate guanyltransferase beta
Myt_29A09 SET and MYND domain-containing protein 5
Myt_48A21 Guanine nucleotide exchange factor DBS
Myt_44F22 Delta-aminolevulinic acid dehydratase
Myt_60A23 Protein CWC15 homolog
Myt_29A07 CCAAT/enhancer-binding protein epsilon
Myt_29D10 Unclassifiable EST
Myt_16O19 Unclassifiable EST
Myt_40B16 Calmodulin
Myt_38D06 Protein FAM40A
Myt_48H06 Intraflagellar transport protein 57 homolog
Myt_30L22 Unclassifiable EST
Myt_66O21 Sulfate transporter
Myt_66O20 Nucleolar GTP-binding protein 1
Myt_66P06 Unclassifiable EST
Myt_59G10 Unclassifiable EST
194
Myt_24N16 Probable proline racemase
Myt_35O03 Cardiolipin synthase
Myt_62N21 Acyl-CoA synthetase short-chain family member 3, mitochondrial
Myt_22B02 Unclassifiable EST
Myt_48J12 Collagen alpha-1(XII) chain
Myt_66O24 Myb-binding protein 1A
Myt_30F15 Unclassifiable EST
Myt_23I05 Apical endosomal glycoprotein
Myt_35P21 Neuromedin-U receptor 2
Myt_57E10 Aplycalcin
Myt_17M21 Probable peptidylglycine alpha-hydroxylating monooxygenase
Y71G12B.4
Myt_62F17 Unclassifiable EST
Myt_17A02 CUB and sushi domain-containing protein 3
Myt_31K12 BTB/POZ domain-containing protein KCTD7
Myt_41N01 Unclassifiable EST
Myt_14B03 Uncharacterized oxidoreductase ytbE
Myt_65H18 Unclassifiable EST
Myt_66N18 RNA-binding protein Musashi homolog Rbp6
Myt_66B09 Unclassifiable EST
Myt_37K18 Unclassifiable EST
Myt_41L22 Unclassifiable EST
Myt_44C24 Melatonin-related receptor
Myt_21O05 Unclassifiable EST
Myt_38I23 Ankyrin-1
Myt_43M16 NEDD8-conjugating enzyme Ubc12
Myt_33C22 Amiloride-sensitive amine oxidase [copper-containing]
Myt_50N22 Translocon-associated protein subunit alpha
Myt_30O02 Phosphatidylinositol-glycan biosynthesis class F protein
Myt_20M04 Lysine-specific demethylase 3B
Myt_17N14 VPS33B-interacting protein
Myt_36K15 Beta-1,3-galactosyltransferase brn
Myt_25A14 Galactoside 2-alpha-L-fucosyltransferase 2
Myt_64B24 Interferon-induced, double-stranded RNA-activated protein kinase
Myt_59J01 Zinc finger protein 622
Myt_14B12 Uncharacterized protein C20orf111 homolog
Myt_47L18 E3 ubiquitin-protein ligase LRSAM1
Myt_16O13 Probable ATP-dependent RNA helicase DDX46
Myt_43K24 Monomeric sarcosine oxidase
195
Myt_64M24 Unclassifiable EST
Myt_09E10 Unclassifiable EST
Myt_37D01 Unclassifiable EST
Myt_40P07 DnaJ homolog subfamily B member 4
Myt_09C20 Nucleoporin Nup37
Myt_12E24 Tyrosine-protein kinase receptor Tie-2
196
Myt_21D17 Anaphase-promoting complex subunit 7
Myt_64E08 Unclassifiable EST
Myt_37G16 Lariat debranching enzyme A
Myt_65D14 Rho GDP-dissociation inhibitor 1
Myt_61I08 Unclassifiable EST
Myt_47K12 Mammalian ependymin-related protein 1
Myt_36C13 Uncharacterized protein DDB_G0287625
Myt_66G20 COP9 signalosome complex subunit 5
Myt_46H22 Cholecystokinin receptor type A
Myt_46N06 Unclassifiable EST
Myt_61P21 Unclassifiable EST
Myt_44D20 Insulin-degrading enzyme
Myt_17H15 UPF0392 protein F13G3.3
Myt_42I15 Progestin and adipoQ receptor family member 3
Myt_59I23 40S ribosomal protein S14
Myt_59F15 Uncharacterized methyltransferase WBSCR22
Myt_35F12 Alcohol dehydrogenase class-3 chain H
Myt_33K24 E3 ubiquitin/ISG15 ligase TRIM25
Myt_11M13 Coiled-coil and C2 domain-containing protein 1-like
Myt_42K11 Bardet-Biedl syndrome 5 protein homolog
Myt_12F15 Unclassifiable EST
Myt_25E18 Nucleolar protein 56
Myt_63O19 Unclassifiable EST
Myt_12G14 Unclassifiable EST
Myt_48C07 B9 domain-containing protein 2
Myt_59G23 Unclassifiable EST
Myt_62H07 Unclassifiable EST
Myt_42K15 Beta-galactosidase
Myt_40O22 TRAF3-interacting protein 1
Myt_35O06 Uncharacterized protein C9orf114 homolog
Myt_44H16 Protein FAM96A
Myt_53O11 DNA-binding protein P3A2
Myt_63G12 DENN domain-containing protein 1A
Myt_37H20 Malate dehydrogenase
Myt_35N24 Unclassifiable EST
Myt_34P12 Nuclear pore complex protein Nup98-Nup96
Myt_25D05 Short/branched chain specific acyl-CoA dehydrogenase, mitochondrial
Myt_33G17 Unclassifiable EST
Myt_51J23 Innexin unc-9
197
Myt_14F10 B9 domain-containing protein 2
Myt_48I23 DNA-directed RNA polymerases I and III subunit RPAC1
Myt_44I01 B-cell receptor-associated protein 31
Myt_24K16 Stress-70 protein, mitochondrial
Myt_38J15 Unclassifiable EST
Myt_32M23 Microsomal glutathione S-transferase 1
Myt_44O07 Unclassifiable EST
Myt_37G04 DnaJ homolog subfamily C member 2
Myt_21G23 Thioredoxin reductase 3 (Fragment)
Myt_16A19 Melanotransferrin
Myt_44N24 Unclassifiable EST
Myt_19M04 ADP-ribosylation factor-like protein 6
Myt_39L20 ADP-ribosylation factor 2
Myt_14C11 Unclassifiable EST
Myt_14M02 Midasin
Myt_46P10 COBW domain-containing protein 2
Myt_13L12 Eukaryotic translation initiation factor 1A, X-chromosomal
Myt_60K08 NADH-ubiquinone oxidoreductase chain 4
Myt_11K18 Unclassifiable EST
Myt_20E05 Oncoprotein-induced transcript 3 protein
Myt_26C06 28S ribosomal protein S35, mitochondrial
Myt_36H22 Protein asteroid homolog 1
Myt_61H18 Ankyrin-2
Myt_60O03 Unclassifiable EST
Myt_32B02 Ornithine aminotransferase, mitochondrial
Myt_34L05 Anoctamin-4
Myt_62P01 Lachesin
Myt_48D22 Neogenin (Fragment)
Myt_14F03 Unclassifiable EST
Myt_36D04 Oligoribonuclease, mitochondrial
Myt_30N22 PCI domain-containing protein 2
Myt_44B18 Protein max
Myt_62O20 Unclassifiable EST
Myt_59D22 Unclassifiable EST
Myt_23B24 Orexin receptor type 2
Myt_53N01 Leucine-rich repeat-containing protein 58
Myt_43I05 Dixin
Myt_37C21 AP2/ERF domain-containing protein PFD0985w
Myt_42M18 Importin-4
198
Myt_22F20 RING finger protein 160
Myt_28D07 Glutamate-rich WD repeat-containing protein 1
Myt_54K02 Unclassifiable EST
Myt_65J12 Unclassifiable EST
Myt_54A23 15-hydroxyprostaglandin dehydrogenase [NAD+]
Myt_60O04 Elongation factor 1-alpha (Fragment)
Myt_66K08 Unclassifiable EST
Myt_37K02 Sialin
Myt_25J13 Cathepsin F
Myt_16P11 Protocadherin-19
Myt_12G10 Unclassifiable EST
Myt_37L19 Glucose-6-phosphate isomerase
Myt_53O09 Collagen alpha-2(IV) chain
Myt_29K02 UPF0139 membrane protein C19orf56 homolog
Myt_51J17 Unclassifiable EST
Myt_38J06 UPF0568 protein C14orf166 homolog
Myt_22C09 39S ribosomal protein L39, mitochondrial
Myt_36J21 Calmodulin
Myt_44C14 Acetylcholine receptor subunit alpha-like
Myt_21I17 Unclassifiable EST
Myt_30J21 Solute carrier organic anion transporter family member 2B1
Myt_19C21 Unclassifiable EST
Myt_42G24 Mdm2-binding protein
Myt_34M19 Unclassifiable EST
Myt_59E06 40S ribosomal protein S17
Myt_29A02 Protein Mo25
Myt_21M08 Unclassifiable EST
Myt_20H15 Uncharacterized protein C6orf150
Myt_13L04 Selenoprotein S
Myt_30I20 Unclassifiable EST
Myt_30F07 CDGSH iron-sulfur domain-containing protein 3, mitochondrial
Myt_38L12 Cys-loop ligand-gated ion channel
Myt_62D15 Unclassifiable EST
Myt_14E04 Protein FAM100B-A
Myt_38K22 Probable low affinity copper uptake protein 2
Myt_33O11 Centrosomal protein of 41 kDa
Myt_26H05 Elongation factor Tu, mitochondrial
Myt_24K18 RNA polymerase-associated protein LEO1
Myt_30N02 Golgin subfamily B member 1
199
Myt_32P21 Unclassifiable EST
Myt_21H22 Torsin-1A-interacting protein 2
Myt_31K02 N-alpha-acetyltransferase 35, NatC auxiliary subunit
Myt_21H01 Cytosolic Fe-S cluster assembly factor NUBP1 homolog
Myt_28G13 Stathmin
Myt_30D11 Proliferation-associated protein 2G4
Myt_39L14 WW domain-containing oxidoreductase
Myt_34O02 CUB and sushi domain-containing protein 3
Myt_29P06 12 kDa FK506-binding protein
Myt_58L16 ATP-citrate synthase
Myt_53J10 Eukaryotic translation initiation factor 2 subunit 2
Myt_42P19 Melatonin-related receptor
Myt_64G17 T-complex protein 1 subunit alpha
Myt_48B17 RNA-binding protein Musashi homolog Rbp6
Myt_64J20 ADP-ribosylation factor 2
Myt_17K23 Unclassifiable EST
Myt_14D06 Alpha N-terminal protein methyltransferase 1A
Myt_42P13 Methylmalonic aciduria and homocystinuria type C protein homolog
Myt_17A11 Unclassifiable EST
Myt_37J14 Octopamine receptor beta-1R
Myt_15N06 Transcription elongation factor B polypeptide 1
Myt_16P23 Putative rRNA methyltransferase 3
Myt_40G16 Fibrinogen-like protein A
Myt_23B10 BCL-6 corepressor
Myt_16F12 Transmembrane protein 232
Myt_36D03 Peptidylprolyl isomerase domain and WD repeat-containing protein 1
Myt_30C04 Death-associated protein 1
Myt_23H20 Transcriptional regulator Myc-1
Myt_36M17 Protein PRRC1-A
Myt_12I10 GPI transamidase component PIG-S
Myt_34J16 Unclassifiable EST
Myt_30M07 Nitric oxide synthase, inducible
Myt_33J16 Cathepsin F
Myt_60P16 Unclassifiable EST
Myt_36H20 Monocarboxylate transporter 14
Myt_59M06 Protein MIS12 homolog
Myt_26B12 Unclassifiable EST
Myt_40O16 Phosphoenolpyruvate carboxykinase [GTP]
Myt_09I01 Cyclic AMP-dependent transcription factor ATF-4
200
Myt_67A09 Unclassifiable EST
Myt_35J12 Unclassifiable EST
Myt_64J04 Unclassifiable EST
Myt_28L02 NAD-dependent deacetylase sirtuin-1
Myt_34G12 Unclassifiable EST
Myt_64O16 Unclassifiable EST
Myt_65M19 GTPase KRas
Myt_34B21 Unclassifiable EST
Myt_43N01 V-type proton ATPase 21 kDa proteolipid subunit
Myt_41M20 Unclassifiable EST
Myt_28E06 FMRFamide receptor
Myt_21J13 Kelch-like protein 24
Myt_40F06 Organic cation transporter protein
Myt_41J05 Unclassifiable EST
Myt_38M23 Apical endosomal glycoprotein
Myt_22B24 Abhydrolase domain-containing protein 11
Myt_41M14 Polyubiquitin-C
Myt_38P17 UPF0764 protein C16orf89 homolog
Myt_26H17 Heat shock 70 kDa protein 12A
Myt_25D02 Unclassifiable EST
Myt_22P19 Sodium-dependent multivitamin transporter
Myt_42P02 Putative lipoyltransferase 2, mitochondrial
Myt_33C08 39S ribosomal protein L3, mitochondrial
Myt_30N06 Unclassifiable EST
Myt_51I10 Cytochrome P450 2C27
Myt_63A22 60S ribosomal protein L17
Myt_31B05 Bifunctional purine biosynthesis protein PURH
Myt_38M02 Unclassifiable EST
Myt_36N17 Prion-like-(Q/N-rich) domain-bearing protein 25
Myt_62J14 Ribosomal RNA processing protein 36 homolog
Myt_17P22 Suppressor of cytokine signaling 2
Myt_23A15 Intraflagellar transport protein 172 homolog
Myt_49I12 Unclassifiable EST
Myt_27M19 Transcriptional repressor CTCF
Myt_37F24 GTP-binding protein 128up
Myt_27B08 Unclassifiable EST
Myt_13B16 IQ domain-containing protein G
Myt_51I06 D-beta-hydroxybutyrate dehydrogenase, mitochondrial
Myt_38H14 GPN-loop GTPase 2
201
Myt_21B03 Ribonucleoside-diphosphate reductase small chain
Myt_45P10 Serine/threonine-protein kinase Nek8
Myt_30C18 SCO-spondin
Myt_12G08 F-box only protein 31
Myt_23B06 GTP-binding nuclear protein Ran
Myt_56G19 Unclassifiable EST
Myt_30B17 Cholesterol 7-alpha-monooxygenase
Myt_61F17 Mitochondrial import inner membrane translocase subunit Tim17-B
Myt_67B18 Elongation factor 1-alpha
Myt_31M21 Unclassifiable EST
Myt_34B19 Complement C1q-like protein 3
Myt_31K19 Unclassifiable EST
Myt_44P18 Plasma alpha-L-fucosidase
Myt_26O21 Tubulin alpha-1 chain
Myt_29D18 L-threonine 3-dehydrogenase
Myt_31J14 NAD-dependent alcohol dehydrogenase
Myt_21I07 Transaldolase
Myt_42O15 Unclassifiable EST
Myt_28J18 BTB/POZ domain-containing protein 17
Myt_45F05 Probable aconitate hydratase, mitochondrial
Myt_33J10 Monocarboxylate transporter 14
Myt_66O09 NADH dehydrogenase [ubiquinone] iron-sulfur protein 2, mitochondrial
Myt_37P18 DNA methyltransferase 1-associated protein 1
Myt_40O15 Unclassifiable EST
Myt_31K21 Unclassifiable EST
Myt_22L06 Unclassifiable EST
Myt_34C09 Eukaryotic peptide chain release factor subunit 1
Myt_30H18 TBC domain-containing protein kinase-like protein
Myt_44C01 Cytoglobin-1
Myt_29A22 40S ribosomal protein S23
Myt_26G06 Transcription intermediary factor 1-beta
Myt_39A14 Eukaryotic translation initiation factor 3 subunit G
Myt_40G18 Serine/threonine-protein phosphatase 6 regulatory ankyrin repeat
Myt_63N21 Caspase-3
Myt_30K23 Serine/arginine-rich splicing factor 4
Myt_37P04 Unclassifiable EST
Myt_21E05 Myb-like protein X
Myt_30J22 Protein trunk
Myt_36C04 Neuronal membrane glycoprotein M6-a
202
Myt_24J16 Ras-related protein Rab-8A
Myt_38H15 Unclassifiable EST
Myt_38H06 Calmodulin
Myt_51F10 Tripartite motif-containing protein 45
Myt_36N21 Succinyl-CoA ligase [ADP-forming] subunit beta, mitochondrial
Myt_15B24 ADP-ribosylation factor 4
Myt_23F07 Presenilin-1
Myt_32K15 Kelch domain-containing protein 1
Myt_36O04 Zinc finger protein 235
Myt_43C18 Carbonic anhydrase 1
Myt_23F01 Unclassifiable EST
Myt_27L23 Rho-related GTP-binding protein RhoQ
Myt_64N05 Unclassifiable EST
Myt_42D08 MAM and LDL-receptor class A domain-containing protein C10orf112
Myt_44P16 DNA repair protein XRCC1
Myt_60O13 Unclassifiable EST
Myt_31C19 Unclassifiable EST
Myt_44N01 Kelch-like protein 3
Myt_39N04 Voltage-dependent calcium channel gamma-3 subunit
Myt_12J20 Unclassifiable EST
Myt_11K19 DNA-directed RNA polymerase III subunit RPC7-like
Myt_27E11 T-complex protein 1 subunit eta
Myt_26G08 Structural maintenance of chromosomes protein 3
Myt_30N17 Unclassifiable EST
Myt_01M13 Unclassifiable EST
Myt_36A11 Anaphase-promoting complex subunit 4
Myt_36B09 Eukaryotic translation initiation factor 2 subunit 1
Myt_25O09 Ras-related protein Rab-6A
Myt_27F18 Latrophilin-2
Myt_17F05 Apoptosis inhibitor 1
Myt_29N01 Endo-1,4-beta-xylanase A
Myt_44N19 Unclassifiable EST
Myt_54D16 Heterogeneous nuclear ribonucleoprotein R
Myt_40J09 Elongation factor 1-alpha
Myt_45B09 Unclassifiable EST
Myt_40K14 Unclassifiable EST
Myt_67A11 Fasciclin-1
Myt_61B20 Protein BTG1
Myt_23J14 Nuclear receptor ROR-alpha
203
Myt_30C19 Transmembrane protein 47
Myt_39C12 Unclassifiable EST
Myt_47P07 Unclassifiable EST
Myt_45D10 Complement C1q tumor necrosis factor-related protein 3
Myt_15O12 SAM pointed domain-containing Ets transcription factor
Myt_62L11 Unclassifiable EST
Myt_43D24 Target of rapamycin complex 2 subunit MAPKAP1
Myt_24H04 Very low-density lipoprotein receptor
Myt_35J11 Immediate early response gene 5-like protein
Myt_20J17 Programmed cell death 6-interacting protein
Myt_21G20 IQ motif and SEC7 domain-containing protein 1
Myt_34P01 Palmitoyltransferase ZDHHC2
Myt_30L05 Histone deacetylase complex subunit SAP130
Myt_38M09 ERI1 exoribonuclease 3
Myt_17A08 Unclassifiable EST
Myt_62H03 Unclassifiable EST
Myt_30M08 Regulator of G-protein signaling 3
Myt_15B21 Leucine-rich repeat-containing protein 15
Myt_22C07 Katanin p60 ATPase-containing subunit
Myt_40J11 Neuronal calcium sensor 2
Myt_43L14 3-oxo-5-beta-steroid 4-dehydrogenase
Myt_25B01 Unclassifiable EST
Myt_27A24 Presenilin-1
Myt_29M08 Transcription intermediary factor 1-alpha
Myt_30L17 DNA repair protein XRCC3
Myt_41M03 Unclassifiable EST
Myt_30P05 Putative ankyrin repeat protein FPV162
Myt_59C06 High mobility group nucleosome-binding domain-containing protein 5
Myt_24G19 Protein tirA
Myt_29B11 Thymidine kinase 2, mitochondrial
Myt_32O01 Large subunit GTPase 1 homolog
Myt_46D17 NADH-ubiquinone oxidoreductase chain 4
Myt_67K17 Unclassifiable EST
Myt_59O18 Heterogeneous nuclear ribonucleoprotein K
Myt_30D02 Carbonic anhydrase 2
Myt_26E04 Histone-lysine N-methyltransferase, H3 lysine-9 specific 5
Myt_57M04 Cholecystokinin receptor type A
Myt_43N20 Serine/threonine-protein kinase pim-3
Myt_15C18 Unclassifiable EST
204
Myt_34A21 Probable isocitrate dehydrogenase [NAD] subunit alpha, mitochondrial
Myt_56L15 UPF0396 protein CG6066
Myt_49O01 Mitochondrial fission 1 protein
Myt_34O20 Carbonic anhydrase-related protein
Myt_21B04 Iduronate 2-sulfatase
Myt_42F24 Y-box factor homolog
Myt_27P23 rRNA 2'-O-methyltransferase fibrillarin
Myt_25P01 E3 ubiquitin-protein ligase TRIM33
Myt_33P18 Glyoxylate reductase
Myt_60K23 Unclassifiable EST
Myt_23J19 Toxin CfTX-2
Myt_37H02 Unclassifiable EST
Myt_38M16 ATPase family AAA domain-containing protein 1
Myt_45F19 Aplysianin-A
Myt_43C21 AB hydrolase superfamily protein C1039.03
Myt_19J16 Unclassifiable EST
Myt_38F24 Galectin-4
Myt_35I01 Vacuolar protein sorting-associated protein 28 homolog
Myt_29A08 Transmembrane protein 205
Myt_49K23 SWI/SNF-related matrix-associated actin-dependent regulator of
Myt_61G17 Ubiquitin-conjugating enzyme E2 G1
Myt_59N19 CD209 antigen-like protein E
Myt_37F01 Blastula protease 10
Myt_09P07 Structural maintenance of chromosomes protein 1A
Myt_04B22 Krueppel-like factor 5
Myt_68G08 Protein asteroid homolog 1
Myt_42D12 Disrupted in renal carcinoma protein 2
Myt_49M14 Septin-7
Myt_17N03 Inter-alpha-trypsin inhibitor heavy chain H4
Myt_20F23 Ankyrin-2
Myt_10B16 Unclassifiable EST
Myt_30C20 Protein quiver
Myt_55M12 Unclassifiable EST
Myt_15P11 Unclassifiable EST
Myt_42I14 AP-1 complex subunit mu-1
Myt_14O15 Unclassifiable EST
Myt_31L06 Heavy metal-binding protein HIP
Myt_41D04 Thrombospondin-1
Myt_29N18 Unclassifiable EST
205
Myt_48F15 Unclassifiable EST
Myt_20N17 Protein DDI1 homolog 2
Myt_29C06 Feline leukemia virus subgroup C receptor-related protein 2
Myt_64I03 Unclassifiable EST
Myt_24B08 Unclassifiable EST
Myt_14A02 Elongation factor 2
Myt_23J06 Arrestin domain-containing protein 2
Myt_68K24 Unclassifiable EST
Myt_21G24 Krueppel-like factor 11
Myt_22E24 Formin-binding protein 4
Myt_62N03 GMP synthase [glutamine-hydrolyzing]
Myt_59O17 tRNA-nucleotidyltransferase 1, mitochondrial
Myt_32J11 SAM domain-containing protein C14orf174
Myt_21A23 Acid ceramidase
Myt_42F20 Transcription cofactor vestigial-like protein 2
Myt_23O07 Unclassifiable EST
Myt_44C23 NAD-dependent deacetylase sirtuin-5
Myt_29C11 Unclassifiable EST
Myt_60K22 Probable ATP-dependent RNA helicase DDX4
Myt_63G18 Transcriptional repressor protein YY1
Myt_65M21 Mannose-1-phosphate guanyltransferase beta
Myt_29A09 SET and MYND domain-containing protein 5
Myt_48A21 Guanine nucleotide exchange factor DBS
Myt_44F22 Delta-aminolevulinic acid dehydratase
Myt_60A23 Protein CWC15 homolog
Myt_29A07 CCAAT/enhancer-binding protein epsilon
Myt_29D10 Unclassifiable EST
Myt_16O19 Unclassifiable EST
Myt_40B16 Calmodulin
Myt_38D06 Protein FAM40A
Myt_48H06 Intraflagellar transport protein 57 homolog
Myt_30L22 Unclassifiable EST
Myt_66O21 Sulfate transporter
Myt_66O20 Nucleolar GTP-binding protein 1
Myt_66P06 Unclassifiable EST
Myt_59G10 Unclassifiable EST
Myt_24N16 Probable proline racemase
Myt_35O03 Cardiolipin synthase
Myt_62N21 Acyl-CoA synthetase short-chain family member 3, mitochondrial
206
Myt_22B02 Unclassifiable EST
Myt_48J12 Collagen alpha-1(XII) chain
Myt_66O24 Myb-binding protein 1A
Myt_30F15 Unclassifiable EST
Myt_23I05 Apical endosomal glycoprotein
Myt_35P21 Neuromedin-U receptor 2
Myt_57E10 Aplycalcin
Myt_17M21 Probable peptidylglycine alpha-hydroxylating monooxygenase
Y71G12B.4
Myt_62F17 Unclassifiable EST
Myt_17A02 CUB and sushi domain-containing protein 3
Myt_31K12 BTB/POZ domain-containing protein KCTD7
Myt_41N01 Unclassifiable EST
Myt_14B03 Uncharacterized oxidoreductase ytbE
Myt_65H18 Unclassifiable EST
Myt_66N18 RNA-binding protein Musashi homolog Rbp6
Myt_66B09 Unclassifiable EST
Myt_37K18 Unclassifiable EST
Myt_41L22 Unclassifiable EST
Myt_44C24 Melatonin-related receptor
Myt_21O05 Unclassifiable EST
Myt_38I23 Ankyrin-1
Myt_43M16 NEDD8-conjugating enzyme Ubc12
Myt_33C22 Amiloride-sensitive amine oxidase [copper-containing]
Myt_50N22 Translocon-associated protein subunit alpha
Myt_30O02 Phosphatidylinositol-glycan biosynthesis class F protein
Myt_20M04 Lysine-specific demethylase 3B
Myt_17N14 VPS33B-interacting protein
Myt_36K15 Beta-1,3-galactosyltransferase brn
Myt_25A14 Galactoside 2-alpha-L-fucosyltransferase 2
Myt_64B24 Interferon-induced, double-stranded RNA-activated protein kinase
Myt_59J01 Zinc finger protein 622
Myt_14B12 Uncharacterized protein C20orf111 homolog
Myt_47L18 E3 ubiquitin-protein ligase LRSAM1
Myt_16O13 Probable ATP-dependent RNA helicase DDX46
Myt_43K24 Monomeric sarcosine oxidase
Myt_64M24 Unclassifiable EST
Myt_09E10 Unclassifiable EST
Myt_37D01 Unclassifiable EST
207
Myt_40P07 DnaJ homolog subfamily B member 4
Myt_09C20 Nucleoporin Nup37
Myt_12E24 Tyrosine-protein kinase receptor Tie-2
208
SI Table 8. List of transcripts represented in heatmap shown in Fig. 16B.
Myt_31B21 Nudix hydrolase 24, chloroplastic
Myt_21O21 Leucine-rich repeat and coiled-coil domain-containing protein 1
Myt_16M02 Unclassifiable EST
Myt_28A07 Unclassifiable EST
Myt_41D21 Unclassifiable EST
Myt_62A18 Unclassifiable EST
Myt_28F14 Unclassifiable EST
Myt_19B11 Unclassifiable EST
Myt_33E17 Cell division protein kinase 20
Myt_17N06 Unclassifiable EST
Myt_12A20 Protein TBRG4
Myt_16O24 Uncharacterized protein C11orf42
Myt_16C15 Putative N-acetyltransferase C7orf52
Myt_17P11 Trafficking protein particle complex subunit 3
Myt_37I22 LisH domain-containing protein C1711.05
Myt_35E11 40S ribosomal protein S2
Myt_60I13 Unclassifiable EST
Myt_62M12 Unclassifiable EST
Myt_33I12 Tetraspanin-18
Myt_40P21 Unclassifiable EST
Myt_26D07 Epidermal differentiation-specific protein
Myt_29G19 Autophagy-related protein 16-1
Myt_15D15 Unclassifiable EST
Myt_38M05 Unclassifiable EST
Myt_33G11 Unclassifiable EST
Myt_40K20 Unclassifiable EST
Myt_23C22 Unclassifiable EST
Myt_51N16 Putative ferric-chelate reductase 1
Myt_61D22 Unclassifiable EST
Myt_54J21 Unclassifiable EST
Myt_33J12 Transcription intermediary factor 1-beta
Myt_29P09 Mediator of RNA polymerase II transcription subunit 4
Myt_29B19 Unclassifiable EST
Myt_46P21 BMP-binding endothelial regulator protein
Myt_33D16 Unclassifiable EST
Myt_22F10 Unclassifiable EST
209
Myt_66D05 Small acidic protein
Myt_36F16 Glutamate dehydrogenase 1, mitochondrial
Myt_45G21 Transcription elongation factor SPT4
Myt_40M20 Unclassifiable EST
Myt_28E04 Unclassifiable EST
Myt_67G17 Heat shock 70 kDa protein cognate 4
Myt_24M11 Unclassifiable EST
Myt_57H07 Uncharacterized protein C18H10.09
Myt_29N03
Myt_36L08 Unclassifiable EST
Myt_22P09 Unclassifiable EST
Myt_24K23 Protein phosphatase 1 regulatory subunit 11
Myt_34E14 Unclassifiable EST
Myt_64H15 Unclassifiable EST
Myt_43H08 Unclassifiable EST
Myt_12J15 Elongation factor 2
Myt_34P14 Transcription factor BTF3 homolog 4
Myt_13A22 Unclassifiable EST
Myt_16E02 DNA damage-regulated autophagy modulator protein 2
Myt_24K19 Unclassifiable EST
Myt_13O13 Leucine-rich repeat-containing protein 6
Myt_01K15 Baculoviral IAP repeat-containing protein 7-A
Myt_30B13 Unclassifiable EST
Myt_10H18 Unclassifiable EST
Myt_16K06 Unclassifiable EST
Myt_11A16 Unclassifiable EST
Myt_19L16 UPF0469 protein KIAA0907
Myt_33M18 Leucine-rich repeat-containing protein 14
Myt_66K03 Protein UXT
Myt_35J15 Hypoxia-inducible factor 1-alpha
Myt_11A13
Myt_29N15 Kv channel-interacting protein 4
Myt_40N15 Unclassifiable EST
Myt_19D01 Unclassifiable EST
Myt_33G24 Unclassifiable EST
Myt_03K15 F-box/WD repeat-containing protein 7
Myt_16G04 Unclassifiable EST
Myt_01K22 Unclassifiable EST
Myt_21I05 Unclassifiable EST
210
Myt_46C09 Probable alpha-ketoglutarate-dependent dioxygenase ABH7
Myt_41K09 Unclassifiable EST
Myt_39K01 Unclassifiable EST
Myt_46I07 Sialin
Myt_16H02 Unclassifiable EST
Myt_65N20 Unclassifiable EST
Myt_33M06 Uncharacterized protein C6orf168
Myt_31J08 Unclassifiable EST
Myt_34J06 Unclassifiable EST
Myt_24H07 Interferon-induced very large GTPase 1
Myt_53A15 Angiopoietin-1
Myt_61D04 Alpha-1,3-mannosyltransferase ALG2
Myt_39M23 Tensin-4
Myt_50M24 Unclassifiable EST
Myt_38I11 Unclassifiable EST
Myt_33N05 2-oxoglutarate dehydrogenase, mitochondrial
Myt_41D16 Unclassifiable EST
Myt_33I11 Unclassifiable EST
Myt_53I14 Dolichol-phosphate mannosyltransferase
Myt_36O03 Uncharacterized aminotransferase C660.12c
Myt_26A19 Unclassifiable EST
Myt_24A04 Lysine-specific demethylase 3B
Myt_30F16 Unclassifiable EST
Myt_14D21 Unclassifiable EST
Myt_44J02 Probable alpha-ketoglutarate-dependent dioxygenase ABH7
Myt_58A02
Myt_26D08 Unclassifiable EST
Myt_28I07 Peroxidasin homolog
Myt_21A02 Interferon alpha-inducible protein 27, mitochondrial
Myt_45P05 Caspase-3
Myt_35A16 Unclassifiable EST
Myt_06B06 Unclassifiable EST
Myt_39O21 Heavy metal-binding protein HIP
Myt_37C22 Unclassifiable EST
Myt_24A10 Ankyrin-3
Myt_45O23 Unclassifiable EST
Myt_34M04
Myt_49N20 C-type lectin domain family 4 member F
Myt_42D05 Unclassifiable EST
211
Myt_12P12 Unclassifiable EST
Myt_30C22 Unclassifiable EST
Myt_45J06 Probable ran guanine nucleotide release factor
Myt_62I14 Unclassifiable EST
Myt_26P02 Unclassifiable EST
Myt_43G15 Unclassifiable EST
Myt_24B06 Unclassifiable EST
Myt_17D10 Cytochrome P450 2B4
Myt_26D24 Unclassifiable EST
Myt_24A20 SH3 domain-binding protein 2
Myt_22N17 Myotubularin-related protein 14
Myt_33J05 Unclassifiable EST
Myt_40J13 G-protein coupled receptor 161
Myt_39N09 Unclassifiable EST
Myt_47D22 Unclassifiable EST
Myt_30B10 Unclassifiable EST
Myt_34C19 Unclassifiable EST
Myt_19L13 Unclassifiable EST
Myt_12D22 Unclassifiable EST
Myt_19K19
Myt_33D19 Titin
Myt_08M14 Unclassifiable EST
Myt_35P10 26S protease regulatory subunit 4
Myt_15B01 Monocarboxylate transporter 9
Myt_15K19 Class E basic helix-loop-helix protein 22
Myt_19L21 Unclassifiable EST
Myt_13L05 CD166 antigen homolog
Myt_22M08 Unclassifiable EST
Myt_20L18 Unclassifiable EST
Myt_01O07 Unclassifiable EST
Myt_35C03 Unclassifiable EST
Myt_43F01 Unclassifiable EST
Myt_38L18 Fatty acyl-CoA reductase 1
Myt_39D13 Unclassifiable EST
Myt_29B16 Prolyl endopeptidase
Myt_35A22 Heat shock 70 kDa protein 12A
Myt_34C18 Nesprin-1
Myt_25H13 Unclassifiable EST
Myt_39L22 Unclassifiable EST
212
Myt_02M07 Unclassifiable EST
Myt_63G09 Arginine/serine-rich coiled-coil protein 2
Myt_40O09 LIX1-like protein
Myt_33J24 Unclassifiable EST
Myt_32H16 Unclassifiable EST
Myt_23L22 Unclassifiable EST
Myt_27M12 E3 ubiquitin-protein ligase TRIM33
Myt_34I23 N-methyl-L-tryptophan oxidase
Myt_39J11 Unclassifiable EST
Myt_21I21 Unclassifiable EST
Myt_10A20 Unclassifiable EST
Myt_08M13 Unclassifiable EST
Myt_19G05 Rab proteins geranylgeranyltransferase component A 1
Myt_41N04 Unclassifiable EST
Myt_22F24 Unclassifiable EST
Myt_35E05 Tetratricopeptide repeat protein 36
Myt_62L07 Unclassifiable EST
Myt_33H06 Williams-Beuren syndrome chromosomal region 27 protein
Myt_41K11 Unclassifiable EST
Myt_46C16 Unclassifiable EST
Myt_52A22 Unclassifiable EST
Myt_19P23 Coiled-coil domain-containing protein 124
Myt_46P15 Unclassifiable EST
Myt_53C14 Unclassifiable EST
Myt_60D23 Unclassifiable EST
Myt_41K21 Unclassifiable EST
Myt_46I12 Orexin receptor type 2
Myt_38L07 Unclassifiable EST
Myt_61B24 Unclassifiable EST
Myt_48A11 Unclassifiable EST
Myt_32K12 Unclassifiable EST
Myt_58O05 Unclassifiable EST
Myt_29A16 Unclassifiable EST
Myt_26D18 60S ribosomal protein L7a
Myt_52L08 Unclassifiable EST
Myt_56H09 Unclassifiable EST
Myt_59N18 Uncharacterized protein C20orf26
Myt_28B06 Unclassifiable EST
Myt_55E12 Sacsin
213
Myt_02B21
Myt_47M04 Low-density lipoprotein receptor-related protein 8
Myt_07E14 Synapse-associated protein 1
Myt_08I02
Myt_66O10 Sodium- and chloride-dependent glycine transporter 1
Myt_23F21 Unclassifiable EST
Myt_43B23 Unclassifiable EST
Myt_35O05 Unclassifiable EST
Myt_32M12 Guanylate cyclase 32E
Myt_32E16 Unclassifiable EST
Myt_15O21 Formin-binding protein 4
Myt_64B10 Unclassifiable EST
Myt_14K12 Unclassifiable EST
Myt_21N22 Unclassifiable EST
Myt_46H04 Unclassifiable EST
Myt_50L14 Unclassifiable EST
Myt_21F01 Unclassifiable EST
Myt_01D19 Unclassifiable EST
Myt_42C18 Unclassifiable EST
Myt_42A24 Unclassifiable EST
Myt_35I24 Unclassifiable EST
Myt_27H02 Unclassifiable EST
Myt_01C09 Amiloride-sensitive amine oxidase [copper-containing]
Myt_43M15 NADH dehydrogenase [ubiquinone] iron-sulfur protein 4,
mitochondrial
Myt_22F07 Unclassifiable EST
Myt_46I06 Neuron navigator 2
Myt_26F09 Unclassifiable EST
Myt_26I01 Unclassifiable EST
Myt_19E03
Myt_65O22 Unclassifiable EST
Myt_63K18 Ficolin-1
Myt_60D06 Unclassifiable EST
Myt_36B12 Vesicle-associated membrane protein 7
Myt_46I10 Succinyl-CoA ligase [GDP-forming] subunit beta, mitochondrial
Myt_66J18 Unclassifiable EST
Myt_33J13 TAR DNA-binding protein 43
Myt_62C01 Unclassifiable EST
Myt_24C16 Inositol-trisphosphate 3-kinase B
214
Myt_22N07 Unclassifiable EST
Myt_36A01 Unclassifiable EST
Myt_24O04 Unclassifiable EST
Myt_01D22 E3 ubiquitin-protein ligase RNF13
Myt_49N03 Cell cycle checkpoint protein RAD1
Myt_33D07 Unclassifiable EST
Myt_48J23 Unclassifiable EST
Myt_17H24 Myophilin
Myt_31I15 Probable glutathione S-transferase 7
Myt_47F02 40S ribosomal protein S5
Myt_37D04 Transcription intermediary factor 1-beta
Myt_30B19 CDK5 regulatory subunit-associated protein 1-like 1
Myt_12H07 Unclassifiable EST
Myt_21B07 Unclassifiable EST
Myt_64O22 Unclassifiable EST
Myt_19F05 Unclassifiable EST
Myt_61I03 Probable serine/threonine-protein kinase pats1
Myt_29C19 Unclassifiable EST
Myt_40B07 UPF0249 protein ydjC homolog
Myt_54G11
Myt_32I14 Coiled-coil domain-containing protein 12
Myt_39E24 Unclassifiable EST
Myt_62C12 Unclassifiable EST
Myt_19F04 Unclassifiable EST
Myt_40J16 Unclassifiable EST
Myt_46I16 Actin-2
Myt_43E13 Unclassifiable EST
Myt_40J18 Unclassifiable EST
Myt_68K13 E3 ubiquitin-protein ligase LRSAM1
Myt_48L21 Unclassifiable EST
Myt_39G17 Unclassifiable EST
Myt_19C02 Methyl-CpG-binding domain protein 2
Myt_29P02 Unclassifiable EST
Myt_54M15 Ribosome production factor 2 homolog
Myt_28M22 Glutamine-rich protein 2
Myt_53K24 Cation transport regulator-like protein 2
Myt_10C14 AP-4 complex subunit mu-1
Myt_26B13 Unclassifiable EST
Myt_31G01 Unclassifiable EST
215
Myt_35M12 Unclassifiable EST
Myt_16I20 Unclassifiable EST
Myt_39B12 Unclassifiable EST
Myt_43P14 von Willebrand factor A domain-containing protein 3A
Myt_46J13 Unclassifiable EST
Myt_62F15 LisH domain-containing protein C1711.05
Myt_46J08 Unclassifiable EST
Myt_30B12 Unclassifiable EST
Myt_46F11 Friend of PRMT1 protein
Myt_37P01 Unclassifiable EST
Myt_29D04 Biorientation of chromosomes in cell division protein 1-like
Myt_65H17 Unclassifiable EST
Myt_25F06 Unclassifiable EST
Myt_27G18 Unclassifiable EST
Myt_39P11 Golgi to ER traffic protein 4 homolog
Myt_61P15 Unclassifiable EST
Myt_39H18 Myosin light chain kinase, smooth muscle
Myt_42M22 Unclassifiable EST
Myt_34F23 Unclassifiable EST
Myt_64E03 Unclassifiable EST
Myt_26H24 Unclassifiable EST
Myt_23H14 Unclassifiable EST
Myt_38C16 Biotin--protein ligase
Myt_39M11 Ribonuclease H1
Myt_40F12 Unclassifiable EST
Myt_43A15 Unclassifiable EST
Myt_17M01 Unclassifiable EST
Myt_38K06 Peroxisomal membrane protein PMP34
Myt_34M18 Unclassifiable EST
Myt_17D20 RuvB-like helicase 1
Myt_21O09 Unclassifiable EST
Myt_25G19 Copper chaperone for superoxide dismutase
Myt_44G07 Unclassifiable EST
Myt_38A13 Unclassifiable EST
Myt_45E06 Unclassifiable EST
Myt_35H07 Serpin B6
Myt_26F12 Unclassifiable EST
Myt_30B20 Unclassifiable EST
Myt_33H01 Unclassifiable EST
216
Myt_49E02 Unclassifiable EST
Myt_38O23 Low-density lipoprotein receptor-related protein 8
Myt_24I08 Unclassifiable EST
Myt_22M13 60 kDa SS-A/Ro ribonucleoprotein
Myt_63F23 Unclassifiable EST
Myt_17M02 Unclassifiable EST
Myt_16G13 Unclassifiable EST
Myt_33C06 Unclassifiable EST
Myt_47A07 Calcium-activated chloride channel regulator 4
Myt_14J05 Unclassifiable EST
Myt_20E21 Unclassifiable EST
Myt_46F24 Unclassifiable EST
Myt_42D11 Calmodulin
Myt_35C07 Unclassifiable EST
Myt_19B15 Unclassifiable EST
Myt_58J12 Unclassifiable EST
Myt_31E22 Poly(U)-specific endoribonuclease
Myt_58P13 Big defensin
Myt_38P04 Unclassifiable EST
Myt_68E04 Unclassifiable EST
Myt_63M22 Unclassifiable EST
Myt_29M10 Unclassifiable EST
Myt_17D13 Unclassifiable EST
Myt_35N10 Unclassifiable EST
Myt_34D14 Heat shock protein HSP 90-alpha
Myt_27L10 Unclassifiable EST
Myt_27C13 Unclassifiable EST
Myt_36H15 BTB/POZ domain-containing protein 2
Myt_06G17 Ankyrin-2
Myt_02B23 Unclassifiable EST
Myt_49H16 Unclassifiable EST
Myt_33H02 Unclassifiable EST
Myt_02A13 Unclassifiable EST
Myt_24C14 E3 ubiquitin-protein ligase TRIM33
Myt_33L21 Unclassifiable EST
Myt_12D07 Homeobox protein Meis1
Myt_53H04 Galactoside 2-alpha-L-fucosyltransferase 2
Myt_42I01 Unclassifiable EST
Myt_10O09 DNA damage-inducible transcript 4-like protein
217
Myt_26K07 Probable phospholipid-transporting ATPase IIB
Myt_26P15 Serum paraoxonase/arylesterase 1
Myt_17K03 Nitrogen permease regulator 2-like protein
Myt_13I19 Histone-lysine N-methyltransferase SETD2
Myt_16K07 DNA ligase 1
Myt_46J10 Unclassifiable EST
Myt_39F13 RNA-binding protein 25
Myt_23P15 Unclassifiable EST
Myt_36G17 Unclassifiable EST
Myt_27C08 Ankyrin-1
Myt_22O18 Unclassifiable EST
Myt_36H08 Unclassifiable EST
Myt_32L04 26S proteasome non-ATPase regulatory subunit 9
Myt_20A10 Unclassifiable EST
Myt_41I10 DNA repair and recombination protein RAD54-like (Fragment)
Myt_25O21 Unclassifiable EST
Myt_10I20 Unclassifiable EST
Myt_29L15 Kelch domain-containing protein 3
Myt_14E11 Unclassifiable EST
Myt_14F20 Unclassifiable EST
Myt_46E23 Unclassifiable EST
Myt_16M05 Unclassifiable EST
Myt_31O11 Unclassifiable EST
Myt_11A10 Peptidyl-prolyl cis-trans isomerase
Myt_39A01 Ras-related and estrogen-regulated growth inhibitor-like protein
Myt_36H16 Mitochondrial uncoupling protein 4
Myt_01I21 Unclassifiable EST
Myt_46H01 Heterogeneous nuclear ribonucleoprotein H2
Myt_31O10 Unclassifiable EST
Myt_04M21 Protein FAM164C
Myt_14I13 Zonadhesin
Myt_25C10 Serine/arginine-rich splicing factor 10
Myt_50I22 Unclassifiable EST
Myt_59A23 Probable splicing factor, arginine/serine-rich 7
Myt_46N18 Unclassifiable EST
Myt_26O07 Unclassifiable EST
Myt_38N09 Unclassifiable EST
Myt_55F21 Ankyrin-1
Myt_11A05 Unclassifiable EST
218
Myt_21J20 Unclassifiable EST
Myt_25E15 Probable serine/threonine-protein kinase pats1
Myt_33L22 Unclassifiable EST
Myt_31G14 Unclassifiable EST
Myt_31D20 Unclassifiable EST
Myt_20P07 Exosome complex exonuclease RRP42
Myt_40J24 Unclassifiable EST
Myt_38D22 Unclassifiable EST
Myt_16C02 Unclassifiable EST
Myt_23F24 Transcription intermediary factor 1-beta
Myt_34P11 Unclassifiable EST
Myt_25L17 Unclassifiable EST
Myt_25P05 Unclassifiable EST
Myt_53P15 Unclassifiable EST
Myt_68H21 Unclassifiable EST
Myt_01K23 2-oxoisovalerate dehydrogenase subunit alpha, mitochondrial
Myt_35J07 E3 ubiquitin-protein ligase LRSAM1
Myt_19K17 Unclassifiable EST
Myt_49K07 Unclassifiable EST
Myt_27D11 Unclassifiable EST
Myt_09A10 Unclassifiable EST
Myt_47O03 Poly(ADP-ribose) glycohydrolase
Myt_66F08 Unclassifiable EST
Myt_14D01 Tumor suppressor candidate 3
Myt_34G05 Unclassifiable EST
Myt_15B16 Golgin IMH1
Myt_34F06 Vigilin
Myt_55L04 Ubiquitin thioesterase OTU1
Myt_29I07 Zinc finger protein 207
Myt_58O15 Unclassifiable EST
Myt_40E04 Unclassifiable EST
Myt_22H22 Unclassifiable EST
Myt_14A10 Unclassifiable EST
Myt_22I10 Intracellular protein transport protein USO1
Myt_15B18 Unclassifiable EST
Myt_31A15 PAB-dependent poly(A)-specific ribonuclease subunit 3
Myt_26K14 Unclassifiable EST
Myt_67P24 Unclassifiable EST
Myt_07D02 Unclassifiable EST
219
Myt_67P21 Cytochrome P450 2D16
Myt_04K15 Peroxidasin
Myt_55J16 Unclassifiable EST
Myt_19A16 Poly [ADP-ribose] polymerase 14
Myt_23H10 Integumentary mucin C.1 (Fragment)
Myt_02J03 Unclassifiable EST
Myt_32G15 Unclassifiable EST
Myt_10P08 Leucine-rich repeat-containing protein C10orf11 homolog
Myt_67N21 NEDD8-conjugating enzyme Ubc12
Myt_16L04 Unclassifiable EST
Myt_48F19 Unclassifiable EST
Myt_47D02 von Willebrand factor A domain-containing protein 3A
Myt_29P03 Unclassifiable EST
Myt_67I15 Unclassifiable EST
Myt_63N05 Unclassifiable EST
Myt_33N19 Peptidyl-prolyl cis-trans isomerase FKBP8
Myt_48B07 Unclassifiable EST
Myt_12N24 DNA repair and recombination protein RAD54-like (Fragment)
Myt_50P20 Unclassifiable EST
Myt_57B05 DnaJ homolog subfamily B member 11
Myt_33M01 Unclassifiable EST
Myt_26N14 Unclassifiable EST
Myt_34M11 Unclassifiable EST
Myt_22P16 Unclassifiable EST
Myt_37H16 Hexokinase type 2
Myt_46P22 Unclassifiable EST
Myt_20L23 Mevalonate kinase
Myt_23L14 Transmembrane protein 220
Myt_66F20 Unclassifiable EST
Myt_33D17 Unclassifiable EST
Myt_62F19 Phosphoenolpyruvate carboxykinase [GTP]
Myt_61K16 Unclassifiable EST
Myt_23D05 Unclassifiable EST
Myt_27D10 Unclassifiable EST
Myt_26F10 Unclassifiable EST
Myt_37P08 Unclassifiable EST
Myt_28K10 Unclassifiable EST
Myt_15D16 Unclassifiable EST
Myt_26O01 Unclassifiable EST
220
Myt_31H05 PP2C-like domain-containing protein C3orf48
Myt_66I23 Cysteine-rich motor neuron 1 protein
Myt_34M13 Peroxisomal proliferator-activated receptor A-interacting complex
285
Myt_12D23 Glutamine synthetase
Myt_11K14 Pyruvate dehydrogenase protein X component, mitochondrial
Myt_36N19 CDK5 regulatory subunit-associated protein 1
Myt_40G04 Phytanoyl-CoA dioxygenase domain-containing protein 1 homolog
Myt_23A20 Probable dimethyladenosine transferase
Myt_25E16 Glucose dehydrogenase [acceptor]
Myt_22N03 Transcription intermediary factor 1-beta
Myt_16E10 Methyltransferase-like protein 14
Myt_27I07 Transmembrane protein 229A
Myt_25A24 Muscle M-line assembly protein unc-89
Myt_08K12
Myt_13P10 Dynactin subunit 6
Myt_46M21 Acyloxyacyl hydrolase
Myt_48K08 Unclassifiable EST
Myt_28O15 Zinc finger protein 431
Myt_58H15 GTPase IMAP family member 4
Myt_28J04 Transcription intermediary factor 1-alpha
Myt_46B21 Unclassifiable EST
Myt_50D23 UPF0587 protein C1orf123 homolog
Myt_42I20 Putative uncharacterized protein FLJ37770
Myt_15E18 Unclassifiable EST
Myt_22C04 Unclassifiable EST
Myt_16G15 Beta-1,3-galactosyltransferase 1
Myt_62I17 Unclassifiable EST
Myt_08H16 Unclassifiable EST
Myt_33M19 Unclassifiable EST
Myt_20L16 Pericentriolar material 1 protein
Myt_13C11 Unclassifiable EST
Myt_22M20 Uncharacterized threonine-rich GPI-anchored glycoprotein PJ4664.02
Myt_41I13
Myt_19J19 Unclassifiable EST
Myt_31A08 Unclassifiable EST
Myt_40D03 Unclassifiable EST
Myt_51C21 Unclassifiable EST
Myt_20P19 G protein-coupled receptor kinase 5
221
Myt_24L01 Unclassifiable EST
Myt_28H19 Transcription intermediary factor 1-beta
Myt_40M19 Membrane progestin receptor alpha
Myt_34H13 Unclassifiable EST
Myt_66P09 p21-activated protein kinase-interacting protein 1-like
Myt_44D21 Unclassifiable EST
Myt_21D12 Unclassifiable EST
Myt_27B11 Golgi-associated plant pathogenesis-related protein 1
Myt_19C03 Exportin-2
Myt_34N10 C-type lectin domain family 4 member F
Myt_15A15 Unclassifiable EST
Myt_20I22 Splicing factor U2AF 50 kDa subunit
Myt_04J07 Unclassifiable EST
Myt_26N01 Unclassifiable EST
Myt_13I08 GTPase IMAP family member 4
Myt_09C23 DNA fragmentation factor subunit alpha
Myt_13J18 Unclassifiable EST
Myt_16M20 Unclassifiable EST
Myt_01O17 Retrovirus-related Pol polyprotein from transposon 297
Myt_03E14 Unclassifiable EST
Myt_59P14 BTB/POZ domain-containing protein At5g48800
Myt_28N15 Unclassifiable EST
Myt_15O14 RING finger and CHY zinc finger domain-containing protein 1
Myt_30P04 Unclassifiable EST
Myt_28P16 Pyridoxal-dependent decarboxylase domain-containing protein 1
Myt_15J10 Unclassifiable EST
Myt_28A13 Unclassifiable EST
Myt_13D24 Unclassifiable EST
Myt_02F17 Unclassifiable EST
Myt_36H06 Fibrillin-1
Myt_12L18
Myt_32A21 Unclassifiable EST
Myt_04F09 WD repeat-containing protein 38
Myt_20D21 Kelch-like protein 28
Myt_39J20
Myt_21O11 Heat shock 70 kDa protein 12A
Myt_48L17 DAZ-associated protein 1
Myt_27M03 Uncharacterized protein 127L
Myt_34E02 Sorting nexin-11
222
Myt_35B08 Inositol 1,4,5-trisphosphate receptor type 1
Myt_26P23 BTB/POZ domain-containing protein 2
Myt_66D13 Unclassifiable EST
Myt_17P20 Unclassifiable EST
Myt_26B10 Unclassifiable EST
Myt_27B15 Unclassifiable EST
Myt_12E06 Unclassifiable EST
Myt_27C02 Long-chain fatty acid transport protein 1
Myt_12B04 Unclassifiable EST
Myt_19I23
Myt_17O19 Transcription initiation factor TFIID subunit 12
Myt_19H11 Transient receptor potential cation channel subfamily M member 2
Myt_25O05 Ankyrin repeat domain-containing protein 50
Myt_09E01 Mediator of RNA polymerase II transcription subunit 17
Myt_16E07 Spermatogenesis-associated protein 20
Myt_12A04 U4/U6.U5 tri-snRNP-associated protein 2
Myt_13P12 Unclassifiable EST
Myt_16N12 Unclassifiable EST
Myt_25B24 Unclassifiable EST
Myt_38I05 Unclassifiable EST
Myt_55P16
Myt_14F17 Presenilins-associated rhomboid-like protein, mitochondrial
Myt_45O10 Unclassifiable EST
Myt_12F08 Calmodulin
Myt_41F18 Unclassifiable EST
Myt_13D11 Hsp90 co-chaperone Cdc37
Myt_12D05 Unclassifiable EST
Myt_19K24 Unclassifiable EST
Myt_26A21 Unclassifiable EST
Myt_35I10 Uncharacterized protein R617
Myt_07C15 Transmembrane protein 59
Myt_42M15 Unclassifiable EST
Myt_11G09 Protein SET
Myt_22K08 Unclassifiable EST
Myt_63D12 UPF0480 protein C15orf24 homolog
Myt_06J21 E3 ubiquitin-protein ligase TRIM33
Myt_12L08 Neuronal acetylcholine receptor subunit alpha-7
Myt_14J15 60S ribosomal protein L13
Myt_41B06 14-3-3-like protein 1
223
Myt_34C13 Unclassifiable EST
Myt_20N13 Unclassifiable EST
Myt_20D01 Metallophosphoesterase MPPED2
Myt_20J03 Unclassifiable EST
Myt_11O02 Paired box protein Pax-6
Myt_63E17 Endonuclease domain-containing 1 protein
Myt_28J02 Protein phosphatase 1 regulatory subunit 3B
Myt_26L14 Syntaxin-18
Myt_45J03 Adenylate kinase domain-containing protein 1
Myt_42M16 A-kinase anchor protein 9
Myt_26E22 Unclassifiable EST
Myt_22K05 Lactation elevated protein 1
Myt_35H15 Unclassifiable EST
Myt_39C24 WD repeat-containing protein 91
Myt_20A15 Unclassifiable EST
Myt_61N21 Unclassifiable EST
Myt_19E21 Ubiquitin-conjugating enzyme E2-17 kDa
Myt_27P03 Dynein heavy chain 7, axonemal
Myt_24L14 Unclassifiable EST
Myt_50A08
Myt_22L12 Probable RING finger protein 207 homolog
Myt_32O23 Heterogeneous nuclear ribonucleoprotein G
Myt_34A06 Unclassifiable EST
Myt_13N20 F-box/LRR-repeat protein 17
Myt_11A24 GTPase IMAP family member 4
Myt_10C17
Myt_01E17 Unclassifiable EST
Myt_20H24 Unclassifiable EST
Myt_05B08 Unclassifiable EST
Myt_08B17 Cytoplasmic dynein 2 heavy chain 1
Myt_09D22 Lithostathine
Myt_28B16 26S proteasome non-ATPase regulatory subunit 10
Myt_58D22 Suppressor of fused homolog
Myt_24O10 Splicing factor, arginine/serine-rich 8
Myt_12B02 Unclassifiable EST
Myt_49P04 Cortactin-binding protein 2
Myt_46I21 E3 ubiquitin-protein ligase TRIM36
Myt_33H22 Interferon-induced, double-stranded RNA-activated protein kinase
Myt_15M12 Unclassifiable EST
224
Myt_50P06 Dual serine/threonine and tyrosine protein kinase
Myt_53B05 Unclassifiable EST
Myt_45K10 Cytochrome P450 2H1
Myt_22D10 RNA-binding protein Musashi homolog Rbp6
Myt_16M19 Unclassifiable EST
Myt_41N15 Unclassifiable EST
Myt_10C11 Zinc finger and BTB domain-containing protein 48
Myt_32I09 Dynein heavy chain 10, axonemal
Myt_63O13 Unclassifiable EST
Myt_25K04 Ankyrin-1
Myt_63I14 Exosome complex exonuclease RRP42
Myt_37N06 Proto-oncogene vav
Myt_16O04 Neuronal acetylcholine receptor subunit alpha-3
Myt_12E12 Unclassifiable EST
Myt_12D10 Conserved oligomeric Golgi complex subunit 6
Myt_63O17 Unclassifiable EST
Myt_15J22 Low-density lipoprotein receptor-related protein 1B
Myt_16D15 Elastin (Fragment)
Myt_13F19 Unclassifiable EST
Myt_50D11 E3 ubiquitin/ISG15 ligase TRIM25
Myt_20I15 Unclassifiable EST
Myt_16I09 Unclassifiable EST
Myt_52C03 Unclassifiable EST
Myt_23G03 Uncharacterized protein C14orf166B
Myt_49D10 Transmembrane protein 85
Myt_13N12 Unclassifiable EST
Myt_42E01 Splicing factor, proline- and glutamine-rich
Myt_28N07 Cytochrome c oxidase subunit 3
Myt_PER2 PER2
Myt_CRY2 CRY2
Myt_per1 per1
Myt_12K22 Cysteine synthase
Myt_43D08 Transcription factor 25
Myt_19K09 Protein FAM154B
225
Conclusion
Mytilus californianus is well adapted to life in the intertidal zone because during periods
of low tide aerial emergence they close their valves to avoid dessication, utilize
anaerobic pathways and reduce overall metabolism (1, 5). In order to understand the
molecular basis for survival under the harsh conditions of the intertidal zone, I
investigated the transcriptome-wide expression of genes in mussels under simulated
intertidal environments. My study comprised four interrelated experiments: an
examination of expression patterns over tidal cycles both in the laboratory and the field
(Chapter 1); a metabolomic screen and critical analysis of metabolism of the tidal
samples of Chapter 1 (Chapter 2); an investigation of the concordance, in gene
expression and metabolism, between mussels in subtidal and intertidal conditions
exhibiting alternating bouts of valve closure and opening (Chapter 3); and an
observation of gene expression over repeated simulated cycles of combined long term
aerial emergence and solar radiation (Chapter 4).
The ability to sense fluctuations in the prevailing environment and to respond
accordingly is a critical phenotype that allows mussels to flourish In the Intertidal zone.
These responses occur on various biological levels including behavioral (i.e. valve gape)
(4), organ system (i.e. heart rate) (3 (Chapter 2) , 4) and molecular (i.e. gene expression)
(9). Underpinning the various responses are the overarching strategies of energy
226
conservation and protection from the environmental. For example, metabolic
depression associated with low-tide exposure is a set of mechanisms that conserve
energy during a period of non-feeding (12) while activated hsps serve to rescue proteins
that are denatured during warm episodes. At the molecular level, environmental
regulation of genes may also serve to maintain homeostasis during tidal fluctuations.
The sensing of extracellular or intracellular cues, such as oxygen levels or thermally
denatured proteins (10), gives rise to signal transduction activity, including
phosphorylation, protein-protein interactions, and eventually the binding of
transcription factors to DNA regulatory elements to initiate coordinated transcription
(8). Monitoring of transcriptome-wide expression of genes and metabolites in high
resolution time-course experiments provides insight into the highly complex temporal
relationships between physiology and the environment. For example, our findings show
that during low tide emergence in the low-stress tidal simulation, mussels utilize
anaerobic metabolic pathways to synthesize ATP; however, the genes that correspond
to the core enzymes of these pathways are not up-regulated at the time of their activity.
On the other hand, under combined aerial and thermal stress, mussels express genes
related to anaerobic metabolism, possibly as a response to meet the energetic demands
of the simultaneous increases in hsp activity or to replace permanently damaged
proteins. Up-regulation of immediate early genes in response to tidal emergence is
another example of molecular-adaptation. These genes are well known first
responders to extracellular cues and can initiate transcriptional cascades (13). This
227
might confer mussels the opportunity to reprogram cellular pathways in a prompt and
coordinated fashion, which may be necessary under the constraints of intermittent
availability of food and oxygen. Furthermore, the onset of low tide warming leads to a
reprogramming of transcription and subsequent suppression of the transcriptome.
Suppression of transcription during stressful periods is consistent with the overall
capacity to down-regulate metabolism during episodes of stress (12). The
environmental coordination of gene expression has also been observed in the model
species Arabidopsis thaliana, which similarly undergoes drastic cyclical changes in
environment (11). Therefore such coordinated efforts may be conserved processes
amongst a variety of species that are not closely related.
Gene expression in M. californianus may also be regulated by endogenous mechanisms
such as clock genes. Clock genes are produced in a cyclical manner and are reset by an
environmental cue or zeitgeber (7). We observed a remarkable level of circadian-like
gene expression in mussels under the low-stress tidal simulation. Clusters of circadian
genes peaked at dawn and dusk implicating light as a possible zeitgeber (2 (Chapter 1),
4). However 17% of these genes either lost their rhythmicity following a heat shock,
which suggests that temperature has a profound effect on the putative clock apparatus
in M. californianus (2 (Chapter 1)). This observation brings forth a question of whether
disruptions of clock-controlled rhythms can be interpreted as an index of stress.
Because clock genes control the expression of cell cycle genes, disruption may have a
228
negative effect on proliferation and growth, consistent with reduced growth rates and
terminal sizes observed in mussels that reside in microhabitats subjected to chronic
thermal perturbations, such as those found at elevated portions of the shore (6).
Mussels live in putative biological states, which can be defined by behavior,
biochemistry and gene expression. Generally speaking, mussel valves can either be
open or closed. Regardless of whether mussels are in an intertidal setting or under
constant submergence, open valves are linked to aerobic metabolism while closed
valves indicate the presence of anaerobic metabolic activity (Chapter 3). Associated
with valve state is a suite of metabolic markers, most notably succinate, which strongly
reflect metabolic state (Chapter 3). Whereas the co-expression of genes (gene lists)
linked with different environments can be used to define molecular states. Our studies
revealed such gene sets including low-tide, high-tide, circadian, AER-stress and AER-
stress recovery. This represents the most comprehensive identification of molecular
states in Mytilus. Our High-resolution sampling technique enhanced the value of the
observed gene sets because they allow for more accurate assessment of the sensitivity
of the transcriptome to the prevailing environment. . Furthermore, the consistent,
environmentally-linked expression of particular genes such as CEBPE and IDH3, which
show a strong sensitivity to aerial emergence and submergence, support their
application as biomarkers of tidal status in field environments. Measuring the
expression of these particular genes could allow for associations to be made between
229
the prevailing field environment and other measurements of temporally sensitive
biology, such as enzyme activity and metabolites that are taken simultaneously.
This study represents a solid foundation in the knowledge of transcriptome-wide
variation over environmental cycles. Future simulation studies, such as those with
different temperature, tidal and light/dark parameters are needed to refine further
existing environmentally sensitive gene sets and to discover additional ones.
Furthermore, potential future studies of the oyster Crassostria gigas, which has a
sequenced genome, promise to push forward our understanding of the relationships
between the environment and the molluscan transcriptome. In this species, pedigreed
families and controlled crosses can be used to link phenotype to genotype, and
promoter sequences can be determined, which may help to identify the targets of
critical transcription factors, such as HIF-1 and HSF-1.
230
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243
The intertidal simulation system: A 50 gallon tank that simulates the intertidal environment by
control of a Neptune System © master aquarium controller. Tidal level is controlled by water
pumps. Low-tide thermal cycling was performed using ceramic heat lamps regulated by a
custom built computerized controller in conjunction with a temperature sensor (thermocouple)
was used to create precision-based temperature signatures. A salinity controller was used to
maintain salinity at 35 ppt. A feeding pump was used to introduce liquid food into the tank.
Chillers were used to maintain constant temperature in circulating water below the heat lamps.
Appendix
The Intertidal Simulation System
tide level
heat lamps
mussels
master
heat lamp control
salinity
chillers
feeder
thermocouple
Abstract (if available)
Abstract
Mussels of the genus Mytilus are distributed world-wide and are commercially cultured as a food source for humans. They are also an important ecological species that provide substrate for hundreds of invertebrate and vertebrate organisms as well as an energy source for a variety of marine species. Because of their commercial and ecological importance many studies have been conducted to understand aspects of their physiology. The dominant species on north-western rocky shorelines of North America is Mytilus californianus. As a sessile species M. californianus must endure fluctuations in temperature, salinity, food and oxygen due to the ebb and flood of the tide. During periods of low tide, mussels are exposed to the terrestrial environment where they cannot feed or breathe oxygen and are exposed to temperature fluctuations as a result of solar radiation, cloud cover, wave splash and wind shear. Mussels counteract these stresses by closing their valves to avoid dessication, and switching to anaerobic ATP-producing pathways as well as depressing their metabolism. Thus, M. californianus is well adapted to the highly variable environment of the intertidal zone. Using microarray-based gene expression profiling and metabolite screens, we performed a series of experiments aimed at understanding the fundamental mechanisms driving physiology in an intertidal marine mollusc. Experiments were performed in a custom built aquarium that simulated the intertidal zone, including precision control of tide, solar radiation, day:night cycles, and food levels. In our first experiment, we subjected mussels to balanced cycles of aerial emergence and submergence at constant temperature. Our findings revealed that >40% of the transcriptome exhibited rhythmic gene expression and that depending on the specific tidal conditions 80-90% of the rhythmic transcripts followed a circadian pattern of expression pattern with a period of 24-26 hr, while <2% followed a tidal pattern 10-14hr. Our data indicate that the circadian 24 hr cycle is the dominant driver of rhythmic gene expression in this intertidal inhabitant despite the profound environmental and physiological changes associated with aerial exposure during tidal emergence. Metabolite profiles of the same samples revealed that 24 metabolites oscillated significantly with a 12 hr period that was linked to the tidal cycle. These data confirmed the presence of alternating phases of fermentation and aerobic metabolism and highlight a role for carnitine conjugated metabolites during the anaerobic phase of this cycle. We also observed mussels that spontaneously open and close their valves in constant submerged conditions and a comparison of the expression and metabolite abundances revealed a close similarity in gene expression and utilization of metabolic pathways between subtidal and intertidal physiology as it relates to valve gape state. Lastly, we subjected mussels to an extreme environment that consisted of cycles of long aerial emergence periods combined with a daily heat stress. Surprisingly, the molecular phenotype was notably different from that observed under our more benign conditions, suggesting that M. californianus has a highly flexible physiology that allows it to make acute and complex cellular adjustments that allow it to buffer intense fluctuations in the often unpredictable environment within the intertidal zone. These experiments provide new insights and interpretations of intertidal physiology that can be used as a reference source for comparative studies of rhythmic biology in other organisms.
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University of Southern California Dissertations and Theses
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Asset Metadata
Creator
Connor, Kwasi M.
(author)
Core Title
Simulated and field environmental effects on the transcriptome and metabolome of mussel Mytilus californianus
School
College of Letters, Arts and Sciences
Degree
Doctor of Philosophy
Degree Program
Biology
Publication Date
09/22/2012
Defense Date
09/10/2012
Publisher
University of Southern California
(original),
University of Southern California. Libraries
(digital)
Tag
circadian,gene expression,Mytilus californianus,OAI-PMH Harvest
Language
English
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Electronically uploaded by the author
(provenance)
Advisor
Gracey, Andrew (
committee chair
), Edmands, Suzanne (
committee member
), Hedgecock, Dennis (
committee member
), Kiefer, Dale A. (
committee member
)
Creator Email
kmconnor@usc.edu,kwasiconnor@hotmail.com
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https://doi.org/10.25549/usctheses-c3-99295
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Tags
circadian
gene expression
Mytilus californianus