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Transcriptional and morphological impacts of copper on Mytilus californianus larval development in current and future ocean conditions

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Transcriptional and morphological impacts of copper on Mytilus californianus larval development in current and future ocean conditions

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TRANSCRIPTIONAL AND MORPHOLOGICAL IMPACTS OF COPPER
ON MYTILUS CALIFORNIANUS LARVAL DEVELOPMENT IN
CURRENT AND FUTURE OCEAN CONDITIONS

by

Megan R. Hall

Department of Biological Sciences
Marine Environmental Biology

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

Dissertation Committee:
Dr. Andrew Y. Gracey (Chair)
Dr. James W. Moffett
Dr. Sergey Nuzhdin
Dr. Kelvin J.A. Davies

August 2018

ii
Acknowledgements

I would first and foremost like to thank my adviser, Andrew Gracey, for his continued support
and encouragement throughout my graduate studies. I would also like to thank James Moffett for
his steadfast enthusiasm about this research, and his intellectual and scientific contributions to
the work. I thank the other members of my dissertation and qualifying committees, Sergey
Nuzhdin, Kelvin Davies, Dennis Hedgecock, Suzanne Edmands, Dale Kiefer, Dave Hutchins,
and Frank Gilliland for their invaluable feedback and commentary.

I am extremely grateful for my lab mates, past and present, specifically Jacqueline Lin and
Kwasi Connor, for their unwavering support and their smiles and laughter which will remain one
of my fondest memories of graduate school. I would like to thank all of the undergraduate and
master’s students who have been integral to the success of this research: Kelly Biro, Jennifer
Ferraro, Jennifer Imm, Lauren Kircher, and Michelle Nguyen.

I would like to thank the faculty of MEB and USC for their contributions to all that I have
learned, including Donal Manahan, Dennis Hedgecock, Jed Fuhrman, Laura Gomez-Consarnau,
Dave Hutchins, Dave Caron, John Heidelberg, Karla Heidelberg, Linda Duguay, and Jim
Fawcett. I would also like to thank the staff of the Wrigley Institute for Environmental Studies,
specifically Jessica Dutton and Diane Kim, and the staff of USC Sea Grant, specifically Phyllis
Griffman, for their encouragement of my interests in conducting applied science and exploring
the world of ocean policy.

I am grateful for the kind and tireless staff of MEB and the Wrigley Marine Science Center, who
have been an invaluable resource, and also patient and kind people, throughout my time at USC,
especially Don Bingham, Linda Bazilian, Doug Burleson, Adolfo de la Rosa, Kelly Spafford,
Lauren Czarnecki-Oudin, and Sean Conner.

I thank my fellow MEB students and colleagues who have contributed invaluable ideas,
assistance, and friendship, especially Zhi Zhu, Nate Churches, Johanna Holm, Sarah Hu,
Michael Morando, Ben Tully, David Needham, Barret Phillips, Victoria Campbell, and Paige
iii
Connell. I further thank all of my friends near and far for their support throughout the course of
my PhD.

Finally, I would like to thank my family and those that I have come to consider my family:
Thanks to my mother, Jill Hall, my father, Joseph H. Hall IV, and my sister, Alexandra Hall, for
their steadfast confidence in me and my endeavors, and for inspiring me to love the natural world
and to seek a career that protects it. Their love, support, and inspiration have been essential to all
that I have accomplished and all that I will continue to pursue. I thank Rekha Sachdeva for her
unconditional kindness and generosity, and for her support and companionship in the final
months of my Ph.D.

Finally, I thank Rohan Sachdeva—who is my rock, my inspiration, and my best friend. I thank
Rohan not only for his intellectual and scientific contributions, but for his endless
encouragement, patience, and generosity. Rohan has the biggest heart of anyone I know, and his
passion for science continuously inspires me. Rohan has helped me to become my best self, and
for that I am eternally grateful.

iv

Table of Contents

Acknowledgements………………………………………………………………………………..ii

Abstract……………………………………………………………………………………………v

Introduction ……………………………………………………………………………………….1

Chapter 1: Concentration-dependent transcriptional response of Mytilus californianus
larvae to copper ………………………………………………………………………………….14

Chapter 2: Concentration-dependent impacts of ocean acidification on copper toxicity to
mussel larvae.…………………………………………………………………………………....133

Chapter 3: Identification of markers of copper toxicity and exposure in early Mytilus
californianus larvae ……………………………………………………………………………..199

Conclusion……………………………………………………………………………………….296

v

Abstract
Copper contamination is a long-standing problem in urban areas such as Southern California.
Water quality criteria are determined by toxicity testing with live organisms, and limits are often
set to protect the most sensitive member of an ecosystem. Mussels of the genus Mytilus are key
members of intertidal ecosystems, and are also particularly sensitive to copper. We incorporated
whole-transcriptome sequencing into traditional embryo-larval development ecotoxicology
assays to comprehensively assess sensitive, concentration-dependent gene expression changes in
response to copper. Specifically, we focused on identifying biomarkers for which expression
changes preceded morphological changes. For all experiments, Mytilus californianus embryos
were exposed to a range of copper concentrations, and larval survival, morphology, and
transcriptional profiles were assessed at 48 hours post-fertilization. In Chapter 1, we identified
candidate sensitive transcriptional biomarkers. Biomarkers were primarily all downregulated in
response to copper. Key functional categories that were identified among these genes include
biomineralization/shell formation, metal binding, and development. Concentration responsive
transcripts were also compared between adult and larval mussels. While there was some overlap
in adult and larval copper-responsive genes, many of the makers were unique to each life history
stage. In Chapter 2, we assessed the impacts of simulated ocean acidification (OA) on
transcriptional markers of copper exposure. Larval assays revealed that simulated OA impacts
copper toxicity in a dose-dependent manner, and may in fact reduce copper toxicity to M.
californianus larvae at intermediate copper doses (6-9 µg/L) . Copper responsive transcripts were
identified under both high and low CO2 conditions, and exhibited a range of response patterns.
Observed patterns suggest that larvae may be modulating certain pathways to reduce copper
vi
uptake and/or negative physiological impacts of copper. In Chapter 3, we linked transcriptional
markers to whole-organism phenotype to distinguish markers of exposure and markers of
effect. Normal and abnormal larvae from a control (0 µg/L) and two copper treatments (3 and 6
µg/L) were sorted into separate groups, and expression of each phenotypic group was measured.
Differential expression analysis of morphology- and copper concentration-specific expression
signatures revealed putative markers of copper exposure and effects. Markers of copper exposure
and copper-induced abnormality were involved in many of the same pathways, yet unique genes
were detected in each gene set. Cumulatively, this work reveals sensitive novel biomarkers of
copper exposure and copper toxicity on an important marine invertebrate. This information could
be used by regulators to determine copper quality criteria both now and as climate-induced
changes in coastal waters progress.

Introduction

Copper contamination in coastal and marine waters
Heavy metal contamination of freshwater and marine water bodies is a long-recognized problem,
especially in urban regions where industrial byproducts are high. In California, metal pollution is
especially concerning as there are many highly populated urban centers where industrial activity
and non-point source pollution release substantial amounts of heavy metals (Schiff, James Allen,
Zeng, & Bay, 2000). Copper is a particularly well-studied contaminant in California and in
coastal waters worldwide (Sadiq, 1992). Copper contamination in marine coastal waters is
derived primarily from two sources: runoff of urban non-point source pollution, and antifouling
paints. Copper is released as a product or byproduct in several industries, including mining,
leather production, metal product fabrication, and manufacturing of electrical equipment
(Patterson et al. 1998). It is also used in pesticides, anti-fouling products for aquatic systems, and
fertilizers, and is released during the burning of fossil fuels (EPA, 2016). Copper derived from
all of these sources can enter watersheds and eventually coastal waters via urban runoff.
Stormwater runoff plumes during rainfall events can have copper concentrations as high as 66
ug/L (Schiff, Bay, & Diehl, 2003), a concentration which is nearly 14x the national
recommended acute water quality criteria for copper in marine water (U.S. EPA, 2018). In San
Francisco Bay, it was found that ~53% of copper is derived from urban runoff (TDC
Environmental, 2004).

The other major source of copper in marine systems is copper-based antifouling paints. Marine
antifouling paints are used to deter the settlement of fouling organisms (e.g. mussels, barnacles,
polychaetes) on ships and boats. Copper, specifically cuprous oxide, is the active ingredient in
many antifouling paints. Copper slowly leaches into water from these paints and can become
concentrated in enclosed water bodies that do not have significant exchange with open coastal
water. While the extent of copper contamination derived from antifouling paints varies, in some
harbors, such as Lower Newport Bay, paint-derived copper was calculated to account for ~80%
of the total copper input to the bay (U.S. EPA, 2002).

Coastal and marine ecosystems that are susceptible to copper contamination consist of many
economically and ecologically important organisms. While copper is a micronutrient that serves
1

essential biological roles, it becomes toxic to marine and aquatic life at higher concentrations.
Thus, excessive copper pollution poses a threat to the well-being of organisms and ecosystems.
Regulatory agencies determine allowable concentrations of metals and other contaminants in
coastal waters (water quality criteria) in order to protect the most sensitive organisms within the
ecosystem. Water quality criteria are determined by toxicity tests on sensitive representative
organisms, combined with assessment of the influence of other chemical parameters on
bioavailability and toxicity (Chadwick et al., 2008). Assessments of susceptibility to acute
copper exposure across many taxa have shown that larval mussels of the genus Mytilus are the
most sensitive genus to copper (EPA, 2016). Mytilus spp. are an ecologically and economically
important group of organisms along the west coast, and in many other parts of the world, with
multiple species found in California waters. Mytilus galloprovincialis and Mytilus trossulus,
which can hybridize, are found in bays and other sheltered water bodies (Hilbish et al., 2000),
and the native California mussel, Mytilus californianus, grows on rocky outcrops along open
ocean coastal sites. Mussels are ecosystem engineers that grow in thick beds, providing
important habitat and settlement sites for other smaller organisms. They also can play an
important role in enhancing water quality, as they clear excess nutrients and algae while they
feed. Mussels are a common prey item of intertidal organisms and are important for human food
production as well. They are an increasingly popular taxon for aquaculture cultivation.

Copper toxicity assays for marine waters
Because of larval mussels’ sensitivity to copper, as well as their ecological and economic
importance, they and other sensitive larval bivalves are frequently used as test organisms in
toxicity assays for the determination of water quality criteria. Acute toxicity assays are
conducted by exposing embryos to a toxin at a range of concentrations for 48-96 hours. Embryos
are added to the solutions soon after fertilization, left unperturbed for the duration of the
experiment, and then sampled. Larval survivorship and developmental status (stage and normal
development) are quantified. Normal development of Mytilus larvae at 48 hours is characterized
by larvae reaching the D-hinge veliger stage, in which larvae have developed two symmetrical
shells, each which resembles a “D”. Normal larvae at this stage are capable of swimming,
filtering particles, and closing and opening their shells in response to environmental stimuli
(Helm, Bourne, & Lovatelli, 2004). Abnormal animals are characterized by velum protrusions,
2

misshapen shells, and failure to form shells (E50 Committee, 2013; His, Seaman, & Beiras,
1997). Abnormal animals have severely decreased chances of survival due to increased predatory
pressure (Martin, Osborn, Billig, & Glickstein, 1981a). They also cannot contribute to the
reproductive adult population, as they will not settle and metamorphose into juveniles. Because
of the potentially drastic impacts of abnormal development on mussel populations, this is a
useful endpoint to determine toxicity. Copper causes abnormal development at relatively low
concentrations for marine bivalves (Martin, Osborn, Billig, & Glickstein, 1981a).

Developmental toxicity tests are rapid and relatively cost-effective (D. Johnson, 1988), but this
approach does not consider the toxic impacts of copper at the biochemical and molecular level.
Copper-induced damage to cellular structures or disruption of physiological processes may occur
at concentrations below those that induce whole-organism developmental abnormality. These
subtler changes could be detrimental to the population in the long run as well, but cannot be
detected via morphological assessment alone. Changes in gene expression in response to
environmental stimuli provide insight into the most fundamental level of biological regulation,
and can reveal key pathways and processes that are targeted by environmental changes and
ultimately result in altered phenotypes (Gracey, 2007). Transcriptional changes can also serve as
biomarkers, i.e. biological endpoints that provide information on organism health or
physiological state in response to a contaminant (Hook, Gallagher, & Batley, 2014).

Ecotoxicological studies have gained enormous new impetus with the invention and increasing
accessibility of high-throughput genomic and transcriptomic sequencing (Ankley et al., 2006).
The incorporation of molecular “omics” data into traditional ecotoxiciology, or
“ecotoxicogenomics”, has become increasingly common. Ecotoxicogenomics has been proposed
as an approach to better address mechanisms of toxicity, and to understand how toxins cause
detrimental whole-organism phenotypic outcomes (Snape, Maund, Pickford, & Hutchinson,
2004). Specifically, changes in the transcriptome or proteome in response to toxin exposure can
reveal sensitive molecular changes that occur at relatively low concentrations of the toxin, before
any negative whole-organism outcomes are apparent. These molecular changes can thus serve as
sensitive, low-concentration biomarkers of exposure or effects (Daston, 2008; Hook et al., 2014).
Sensitive molecular changes are not necessarily associated with toxic effects, and could simply
3

be markers of exposure to a toxin. Markers of exposure and effect are distinguished by
phenotypic anchoring, i.e. connecting sublethal molecular changes to higher level whole
organism, population, or ecological outcomes (Daston, 2008; Hook et al., 2014; Paules, 2003;
Tennant, 2002). In this way, biomarkers that are associated with negative outcomes can be
identified.

In this dissertation, we identify copper-responsive changes in whole-transcriptome gene
expression, identify genes that could serve as sensitive biomarkers of copper exposure and/or
toxicity, and link gene expression patterns with negative outcomes at the whole organism level to
distinguish markers of exposure and effect.

Mechanisms of copper toxicity
Copper is a required micronutrient at low concentrations and is a cofactor of essential enzymes
including cytochrome c oxidase and superoxide dismutase. Copper is primarily toxic in its
divalent form as Cu
2+
(cupric ion) (Moffett, 2005; Sunda, 1994). Thus, reduction of Cu
2+
or
complexation by other molecules typically results in reduced copper toxicity. Once excess
copper enters the body of an organism, detoxification mechanisms exist to maintain metal ion
homeostasis. Mechanisms include binding to metallothioneins, glutathiones, superoxide
dismutase, and other metal-scavenging proteins (Stohs & Bagchi, 1995; Valko, Morris, &
Cronin, 2005; Viarengo, 1985), after which copper is no longer reactive and can be stored in
cells in a non-toxic form. Induction of antioxidants and antioxidant enzymes are additional
detoxification mechanisms to protect against excess copper. However, these defenses can
become overwhelmed, at which point elevated copper concentrations can have toxic effects
(Viarengo, 1985).

One of the best recognized mechanisms of copper toxicity is oxidative stress-induced damage to
cellular components and processes. DNA damage, specifically breaks in the double helix, can
result (Stohs & Bagchi, 1995; Valko et al., 2005). Copper also initiates the process of lipid
peroxidation (Bus & Gibson, 1979; Valko et al., 2005), which can destabilize membranes of
lysosomes and other organelles (Viarengo et al., 1984; Viarengo, Zanicchi, Moore, & Orunesu,
4

1981). Copper-induced oxidative stress can damage proteins (Cervantes-Cervantes, Calderón-
Salinas, Albores, & Muñoz-Sánchez, 2005), often leading to irreversible damage and
ubiquitination/proteasome degradation. Oxidative stress can have negative impacts on
proteasome activity as well, and copper-induced oxidative stress was found to inhibit
components of the 20s proteasome (Götze, Matoo, Beniash, Saborowski, & Sokolova, 2014).

Impacts on immune function, particularly hemocytes of bivalve mollusks, are also well-studied
mechanisms of copper toxicity (Foster, Grewal, Graves, Hughes, & Sokolova, 2011; Ivanina,
Hawkins, & Sokolova, 2016 and sources therein; Pipe, Coles, Carrisan, & Ramanathan, 1999).
Copper can impact number, integrity, and function of hemocytes, and can impact other immune
functions including phagocytosis, superoxide production, and peroxidase activity. However, the
impacts of copper on these functions are not always inhibitory, so it seems that other factors
control the relative effects of copper on immune function (Ivanina et al., 2016). Copper can also
impact enzyme activity in unpredictable ways. Copper toxicity to enzymes typically results from
displacement of the standard metal cofactor from an enzyme, or binding to a deactivating site on
the enzyme (Viarengo, 1985). Metal replacement or binding to other parts of enzymes could also
stimulate enzyme activity, however. Copper and other metals often accumulate in mitochondria
(Sokolova, Ringwood, & Johnson, 2005), where they negatively impact oxidative
phosphorylation and other mitochondrial processes (Ivanina & Sokolova, 2013 and sources
therein; Viarengo, 1985). Finally, copper can disrupt ion (Ca+2, Na, K) homeostasis, and
specifically disruption of Na/K ATPase activity in bivalves and fish (EPA, 2016).

While most of the above mechanisms of toxicity were primarily studied in adult organisms,
copper is known to have significant impacts on larval development as well. Exposure of marine
invertebrate embryos and early larvae to relatively low concentrations of copper results in
abnormal development and mortality (His et al., 1997; Hoare, Beaumont, & Davenport, 1995;
Martin, Osborn, Billig, & Glickstein, 1981b). However, investigations into the molecular
pathways underlying developmental abnormalities are limited.

Copper-responsive transcriptional patterns in adult and larval mollusks reflect many of the above
pathways and mechanisms. Genes involved in metal binding and sequestration, such as
5

metallothioneins and ferritin, were often induced in response to copper (Dondero et al., 2006;
Navarro, Faria, Barata, & Piña, 2011; Silva-Aciares, Zapata, Tournois, Moraga, & Riquelme,
2011; Varotto et al., 2013). Many genes involved in protein turnover are copper responsive as
well. This includes genes related to protein synthesis (ribosomes, translation, protein folding)
(Negri et al., 2013; Varotto et al., 2013); protein regulation (Silva-Aciares et al., 2011; Zapata,
Tanguy, David, Moraga, & Riquelme, 2009); and chaperoning, ubiquitination and degradation of
damaged proteins, such as heat shock proteins and sequestosome-1 (Dondero et al., 2006;
Navarro et al., 2011; Varotto et al., 2013; Zapata et al., 2009). While induction of protein
turnover genes is likely a response to oxidative stress, specific markers of oxidative stress have
been copper-responsive as well, particularly glutathione s-transferase, catalase, glutathione
peroxidase, and superoxide dismutase (Dondero et al., 2006; Navarro et al., 2011; Varotto et al.,
2013; Zapata et al., 2009). Other well-recognized physiological responses that were represented
among copper-responsive transcripts include respiratory chain (Silva-Aciares et al., 2011; Zapata
et al., 2009); immunity, specifically lysozymes, anti-microbial peptides, and C1q domain
containing proteins (Dondero et al., 2006; Varotto et al., 2013; Xu et al., 2016); apoptosis
(Varotto et al., 2013; Xu et al., 2016); and DNA repair (Xu et al., 2016).

Transcripts related to other functions that are not typically recognized as pathways involved in
copper toxicity appeared as well. These include DNA synthesis (Dondero et al., 2006); RNA
processing and nuclear mRNA splicing (Negri et al., 2013); development, including organ
development, post-embryonic development, nervous system development, and growth (Negri et
al., 2013; Silva-Aciares et al., 2011; Sussarellu, Lebreton, Rouxel, Akcha, & Rivière, 2018;
Zapata et al., 2009); cytoskeleton (Silva-Aciares et al., 2011; Zapata et al., 2009); cell signaling
(Silva-Aciares et al., 2011); cell adhesion/ extracellular matrix (Varotto et al., 2013); and shell
proteins (Negri et al., 2013; Sussarellu et al., 2018). In general, stress genes were upregulated,
but many of the other expression patterns for a given functional group were relatively
inconsistent. Direction and magnitude of expression changes varied based on copper
concentration, duration of exposure, life history stage, and even individual animal. The only
study which simultaneously compared adult and larval transcriptional responses found that the
larval response did not appear to be as extensive as the adult response (Navarro et al., 2011).
However, this could be explained by the use of a limited set of biomarkers in this study.
6

While these studies provide important insights, only one study has investigated gene expression
responses associated with metal exposure in marine bivalve larvae. Sussarellu et al. (2018)
sought to elucidate the mechanisms underlying copper-induced abnormal development in early
larvae of the oyster, Crassostrea gigas. Larvae were exposed to 4 copper concentrations, and the
expression of selected genes were assessed with RT-qPCR. Here we expand upon findings from
this work by considering more fine-scale concentration specific responses, and by analyzing
responses of the whole transcriptome to identify potentially unexpected pathways involved in the
copper response.

Impacts of a changing ocean on copper toxicity
As anthropogenic influences on the global environment continue to increase, the impacts of
multiple stressors on populations and ecosystems become an increasing concern. The presence of
multiple environmental changes must be taken into account in ecotoxicology testing as well. It is
becoming widely recognized that toxicants can no longer be tested in isolation, and assays must
account for many factors in dynamic ecosystems (Hahn, 2011). In marine settings, ocean
acidification in particular is a great concern because of its potential impacts on water chemistry
as well as the biology of marine organisms (Doney, Fabry, Feely, & Kleypas, 2009). For
calcifying organisms especially (Gattuso & Buddemeier, 2000), ocean acidification poses an
unprecedented risk (Honisch et al., 2012) as calcium carbonate saturation constants are expected
to decrease, thus challenging the integrity of calcium carbonate shells and exoskeletons.

In the California Current system, an area with naturally slightly acidified waters, ocean
acidification is predicted to have especially strong effects. By 2050, many surface waters are
predicted to be under-saturated for aragonite year-round (Gruber et al., 2012). California coastal
waters, which are home to mussels and many other calcifying marine invertebrates, also
experience chronic copper pollution. Based on water chemistry alone, it is unclear how free
bioavailable copper concentrations (Cu
2+
) will be affected by ocean acidification. While most
models predict that decreased organic complexation will result in large Cu
2+
increases (Millero,
Woosley, DiTrolio, & Waters, 2009; Richards, Chaloupka, Sanò, & Tomlinson, 2011), others
7

predict that changes in redox potential will result in reduction of Cu
2+
to Cu
+
, and thus less toxic
copper (Hoffmann, Breitbarth, Boyd, & Hunter, 2012).

The introduction of biological factors complicates the story even more, and the combined effects
of metals and ocean acidification on organismal health have been variable (reviewed in Ivanina
& Sokolova, 2015). Many studies suggest that metal toxicity will increase with ocean
acidification (Han, Wu, Wu, Lv, & Liu, 2013; Lewis, Clemow, & Holt, 2012; Lewis et al., 2016;
Roberts et al., 2012). Other studies reveal a contrasting pattern, whereby divalent metal ions
become less toxic with increasing ocean acidification (Li, Wang, & Wang, 2017; Pascal, Fleeger,
Galvez, & Carman, 2010), or OA alleviates the negative impacts of metal exposure alone
(Ivanina & Sokolova, 2013; Ivanina et al., 2013). Such unpredictable responses to metals and
OA are observed even within well-studied taxa such as bivalve mollusks (Götze et al., 2014; Han
et al., 2013; Ivanina et al., 2013; Ivanina & Sokolova, 2013; Lewis et al., 2016).

The impacts of ocean acidification and copper toxicity on bivalve larval development have not
been previously assessed. This interaction could have important implications for water quality
criteria in a changing ocean, as changes in metal toxicity could require criteria to become more
stringent. In this dissertation we measure the interaction of copper and ocean acidification in the
48-hour Mytilus embryo-larval development assay, and assess transcriptional responses to these
stressors.

Investigating concentration-specific transcriptional responses to copper in Mytilus larvae
This dissertation explores the copper concentration-responsive transcriptional profiles of mussel
larvae at 48 hours post fertilization (hpf). Concentration-dependent whole-transcriptome
responses to metals have not been studied in bivalve larvae, yet expression profiles could reveal
sensitive biomarkers of copper exposure and effects, and provide insight into the molecular
pathways involved in the onset of abnormal development in copper-exposed larvae. In the first
chapter, we measure transcriptional responses to 10 copper concentrations in two mussel
families. In the second chapter, we examine the impacts of copper combined with simulated
ocean acidification on survival and normal development, and the associated changes in gene
8

expression responses. In the third chapter, we link transcriptional responses with phenotype at
low copper concentrations in order to distinguish biomarkers of exposure and effect.

References

Ankley, G. T., Daston, G. P., Degitz, S. J., Denslow, N. D., Hoke, R. A., Kennedy, S. W., et al.
(2006). Toxicogenomics in Regulatory Ecotoxicology. Environmental Science &
Technology, 1–11.
Bus, J. S. & Gibson, J. E. (1979). Lipid peroxidation and its role in toxicology. In: Reviews in
Biochemical Toxicology. (Hodgson, E., Bend, J. R. & Philpot, R. M., eds.), pp. 125-149.
Elsevier, Amsterdam.
Cervantes-Cervantes, M. P., Calderón-Salinas, J. V., Albores, A., & Muñoz-Sánchez, J. L.
(2005). Copper increases the damage to DNA and proteins caused by reactive oxygen
species. Biological Trace Element Research, 103(3), 229–248.
http://doi.org/10.1385/BTER:103:3:229
Chadwick, D.B., I. Rivera-Duarte, P.F. Wang, G. Rosen, R. Santore, P. Paquin and A. Ryan.
(2008). Demonstration of an integrated compliance model for predicting copper fate and
effects in DoD Harbors, Environmental Security Technology Certification Program
(ESTCP). Project ER- 0523, Final Report, November 1, 2008. Available online at:
http://www.dtic.mil/dtic/tr/fulltext/u2/a499564.pdf.
Daston, G. P. (2008). Gene expression, dose-response, and phenotypic anchoring: applications
for toxicogenomics in risk assessment. Toxicological Sciences : an Official Journal of the
Society of Toxicology, 105(2), 233–234. http://doi.org/10.1093/toxsci/kfn138
Dondero, F., Dagnino, A., Jonsson, H., Caprì, F., Gastaldi, L., & Viarengo, A. (2006). Assessing
the occurrence of a stress syndrome in mussels (Mytilus edulis) using a combined
biomarker/gene expression approach. Aquatic Toxicology, 78, S13–S24.
http://doi.org/10.1016/j.aquatox.2006.02.025
Doney, S. C., Fabry, V. J., Feely, R. A., & Kleypas, J. A. (2009). Ocean Acidification: The Other
CO 2Problem. Annual Review of Marine Science, 1(1), 169–192.
http://doi.org/10.1146/annurev.marine.010908.163834
E50 Committee. (2013). Standard Guide for Conducting Static Acute Toxicity Tests Starting with
Embryos of Four Species of Saltwater Bivalve Molluscs (pp. 1–22). West Conshohocken,
PA: ASTM International.
EPA, U. S. (2016). Draft Aquatic Life Ambient Estuarine/Marine Water Quality Criteria for
Copper - 2016, 1–257.
Foster, B., Grewal, S., Graves, O., Hughes, F. M., & Sokolova, I. M. (2011). Copper exposure
affects hemocyte apoptosis and Perkinsus marinus infection in eastern oysters Crassostrea
virginica (Gmelin). Fish & Shellfish Immunology, 31(2), 341–349.
http://doi.org/10.1016/j.fsi.2011.05.024
Gattuso, J. P., & Buddemeier, R. W. (2000). Ocean biogeochemistry. Calcification and CO2.
Nature, 407(6802), 311–313. http://doi.org/10.1038/35030280
Götze, S., Matoo, O. B., Beniash, E., Saborowski, R., & Sokolova, I. M. (2014). Interactive
effects of CO2 and trace metals on the proteasome activity and cellular stress response of
9

marine bivalves Crassostrea virginica and Mercenaria mercenaria. Aquatic Toxicology, 149,
65–82. http://doi.org/10.1016/j.aquatox.2014.01.027
Gracey, A. Y. (2007). Interpreting physiological responses to environmental change through
gene expression profiling. Journal of Experimental Biology, 210(9), 1584–1592.
http://doi.org/10.1242/jeb.004333
Gruber, N., Hauri, C., Lachkar, Z., Loher, D., Frolicher, T. L., & Plattner, G. K. (2012). Rapid
Progression of Ocean Acidification in the California Current System. Science, 337(6091),
220–223. http://doi.org/10.1126/science.1216773
Hahn, M. E. (2011). Mechanistic research in aquatic toxicology: Perspectives and future
directions. Aquatic Toxicology, 105(3-4), 67–71.
http://doi.org/10.1016/j.aquatox.2011.06.001
Han, Z.-X., Wu, D.-D., Wu, J., Lv, C.-X., & Liu, Y.-R. (2013). Effects of Ocean Acidification
on Toxicity of Heavy Metals in the Bivalve Mytilus edulisL. Synthesis and Reactivity in
Inorganic, Metal-Organic, and Nano-Metal Chemistry, 44(1), 133–139.
http://doi.org/10.1080/15533174.2013.770753
Helm, M. M., Bourne, N., & Lovatelli, A. (2004). Hatchery culture of bivalves: a practical
manual. FAO Fisheries Technical Paper, 1–177.
Hilbish, T. J., Mullinax, A., Dolven, S. I., Meyer, A., Koehn, R. K., & Rawson, P. D. (2000).
Origin of the antitropical distribution pattern in marine mussels (Mytilus spp.): routes and
timing of transequatorial migration. Marine Biology, (136), 69–77.
His, E., Seaman, M. N. L., & Beiras, R. (1997). A simplification the bivalve embryogenesis and
larval development bioassay method for water quality assessment. Water Research, 31(2),
351–355. http://doi.org/10.1016/S0043-1354(96)00244-8
Hoare, K., Beaumont, A. R., & Davenport, J. (1995). Variation among populations in the
resistance of Mytilus edulis embryos to copper:adaptation to pollution? Marine Ecology
Progress Series, 120, 155–161. http://doi.org/10.3354/meps120155
Hoffmann, L. J., Breitbarth, E., Boyd, P. W., & Hunter, K. A. (2012). Influence of ocean
warming and acidification on trace metal biogeochemistry. Marine Ecology Progress Series,
470, 191–205. http://doi.org/10.3354/meps10082
Honisch, B., Ridgwell, A., Schmidt, D. N., Thomas, E., Gibbs, S. J., Sluijs, A., et al. (2012). The
Geological Record of Ocean Acidification. Science, 335(6072), 1058–1063.
http://doi.org/10.1126/science.1208277
Hook, S. E., Gallagher, E. P., & Batley, G. E. (2014). The role of biomarkers in the assessment
of aquatic ecosystem health. Integrated Environmental Assessment and Management, 10(3),
327–341. http://doi.org/10.1002/ieam.1530
Ivanina, A. V., & Sokolova, I. M. (2013). Interactive effects of pH and metals on mitochondrial
functions of intertidal bivalves Crassostrea virginica and Mercenaria mercenaria. Aquatic
Toxicology, 144-145, 303–309. http://doi.org/10.1016/j.aquatox.2013.10.019
Ivanina, A. V., & Sokolova, I. M. (2015). Interactive effects of metal pollution and ocean
acidification on physiology of marine organisms. Current Zoology, 61(4), 653–668.
http://doi.org/10.1093/czoolo/61.4.653
Ivanina, A. V., Beniash, E., Etzkorn, M., Meyers, T. B., Ringwood, A. H., & Sokolova, I. M.
(2013). Short-term acute hypercapnia affects cellular responses to trace metals in the hard
clams Mercenaria mercenaria. Aquatic Toxicology (Amsterdam, Netherlands), 140-141, 123–
133. http://doi.org/10.1016/j.aquatox.2013.05.019
10

Ivanina, A. V., Hawkins, C., & Sokolova, I. M. (2016). Interactive effects of copper exposure
and environmental hypercapnia on immune functions of marine bivalves Crassostrea
virginica and Mercenaria mercenaria. Fish & Shellfish Immunology, 49, 54–65.
http://doi.org/10.1016/j.fsi.2015.12.011
Johnson, D. (1988). Development of Mytilus edulis embryos: a bio-assay for polluted waters.
Marine Ecology Progress Series, 46, 135–138. http://doi.org/10.3354/meps046135
Lewis, C., Clemow, K., & Holt, W. V. (2012). Metal contamination increases the sensitivity of
larvae but not gametes to ocean acidification in the polychaete Pomatoceros lamarckii
(Quatrefages). Marine Biology, 160(8), 2089–2101. http://doi.org/10.1007/s00227-012-
2081-8
Lewis, C., Ellis, R. P., Vernon, E., Elliot, K., Newbatt, S., & Wilson, R. W. (2016). Ocean
acidification increases copper toxicity differentially in two key marine invertebrates with
distinct acid-base responses. Scientific Reports, 6, 21554. http://doi.org/10.1038/srep21554
Li, Y., Wang, W.-X., & Wang, M. (2017). Alleviation of mercury toxicity to a marine copepod
under multigenerational exposure by ocean acidification. Scientific Reports, 7(1), 324.
http://doi.org/10.1038/s41598-017-00423-1
Martin, M., Osborn, K. E., Billig, P., & Glickstein, N. (1981a). Toxicities of ten metals to
Crassostrea gigas and Mytilus edulis embryos and Cancer magister larvae. Marine Pollution
Bulletin, 12(9), 305–308. http://doi.org/10.1016/0025-326X(81)90081-3
Martin, M., Osborn, K. E., Billig, P., & Glickstein, N. (1981b). Toxicities of ten metals to
Crassostrea gigas and Mytilus edulis embryos and Cancer magister larvae. Marine Pollution
Bulletin, 12(9), 305–308. http://doi.org/10.1016/0025-326X(81)90081-3
Millero, F., Woosley, R., DiTrolio, B., & Waters, J. (2009). Effect of Ocean Acidification on the
Speciation of Metals in Seawater. Oceanography, 22(4), 72–85.
http://doi.org/10.5670/oceanog.2009.98
Moffett, J. W. (2005). The Relationship Between Copper Speciation and Toxicity for Larvae of
the Blue Mussel Mytilus edulis: Regulatory Implications and Determination of Water Effect
Ratios. Fort Belvoir, VA: Defense Technical Information Center.
Navarro, A., Faria, M., Barata, C., & Piña, B. (2011). Transcriptional response of stress genes to
metal exposure in zebra mussel larvae and adults. Environmental Pollution (Barking, Essex :
1987), 159(1), 100–107. http://doi.org/10.1016/j.envpol.2010.09.018
Negri, A., Oliveri, C., Sforzini, S., Mignione, F., Viarengo, A., & Banni, M. (2013).
Transcriptional Response of the Mussel Mytilus galloprovincialis (Lam.) following
Exposure to Heat Stress and Copper. PLoS ONE, 8(6), e66802–11.
http://doi.org/10.1371/journal.pone.0066802
Pascal, P.-Y., Fleeger, J. W., Galvez, F., & Carman, K. R. (2010). The toxicological interaction
between ocean acidity and metals in coastal meiobenthic copepods. Marine Pollution
Bulletin, 60(12), 2201–2208. http://doi.org/10.1016/j.marpolbul.2010.08.018
Patterson, J.W., R.A. Minear, E. Gasca and C. Petropoulou. (1998). Industrial discharges of
metals to water. In: H.E. Allen, A.W. Garrison and G.W. Luther III (Eds.). Metals in
Surface Waters. Ann Arbor Press, Chelsea, MI. pp. 37-66.
Paules, R. (2003). Phenotypic Anchoring: Linking Cause and Effect. Environmental Health
Perspectives, 111, 1–2.
Pipe, R. K., Coles, J. A., Carrisan, F. M. M., & Ramanathan, K. (1999). Copper induced
immunomodulation in the marine mussel, Mytilus edulis. Aquatic Toxicology, (46), 43–54.
11

Richards, R., Chaloupka, M., Sanò, M., & Tomlinson, R. (2011). Modelling the effects of
â€˜coastalâ€™ acidification on copper speciation. Ecological Modelling, 222(19), 3559–
3567. http://doi.org/10.1016/j.ecolmodel.2011.08.017
Roberts, D. A., Birchenough, S. N. R., Lewis, C., Sanders, M. B., Bolam, T., & Sheahan, D.
(2012). Ocean acidification increases the toxicity of contaminated sediments. Global Change
Biology, 19(2), 340–351. http://doi.org/10.1111/gcb.12048
Sadiq. (1992). Toxic Metal Chemistry in Marine Environments. CRC Press.
Schiff, K. C., Bay, S. M., & Diehl, D. (2003). Stormwater toxicity in Chollas Creek and San
Diego Bay, California. Environmental Monitoring and Assessment, 81, 119–132.
Schiff, K. C., James Allen, M., Zeng, E. Y., & Bay, S. M. (2000). Southern California. Marine
Pollution Bulletin, 41(1-6), 76–93. http://doi.org/10.1016/S0025-326X(00)00103-X
Silva-Aciares, F., Zapata, M., Tournois, J., Moraga, D., & Riquelme, C. (2011). Identification of
genes expressed in juvenile Haliotis rufescens in response to different copper concentrations
in the north of Chile under controlled conditions. Marine Pollution Bulletin, 62(12), 2671–
2680. http://doi.org/10.1016/j.marpolbul.2011.09.023
Snape, J. R., Maund, S. J., Pickford, D. B., & Hutchinson, T. H. (2004). Ecotoxicogenomics: the
challenge of integrating genomics into aquatic and terrestrial ecotoxicology. Aquatic
Toxicology, 67(2), 143–154. http://doi.org/10.1016/j.aquatox.2003.11.011
Sokolova, I. M., Ringwood, A. H., & Johnson, C. (2005). Tissue-specific accumulation of
cadmium in subcellular compartments of eastern oysters Crassostrea virginica Gmelin
(Bivalvia: Ostreidae). Aquatic Toxicology, 74(3), 218–228.
http://doi.org/10.1016/j.aquatox.2005.05.012
Stohs, S. J., & Bagchi, D. (1995). Oxidative mechanisms in the toxicity of metal ions. Free
Radical Biology & Medicine, 18(2), 321–336.
Sunda, W. G. (1994). Trace Metal/Phytoplankton Interactions in the Sea. In Chemistry of
Aquatic Systems: Local and Global Perspectives (pp. 213–247). Dordrecht: Springer
Netherlands. http://doi.org/10.1007/978-94-017-1024-4_9
Sussarellu, R., Lebreton, M., Rouxel, J., Akcha, F., & Rivière, G. (2018). Copper induces
expression and methylation changes of early development genes in Crassostrea gigas
embryos. Aquatic Toxicology (Amsterdam, Netherlands), 196, 70–78.
http://doi.org/10.1016/j.aquatox.2018.01.001
TDC Environmental. (2004). Copper sources in urban runoff and shoreline activities:
Information update. Prepared for the Clean Estuary Partnership. 72 pp.
Tennant, R. W. (2002). The National Center for Toxicogenomics: using new technologies to
inform mechanistic toxicology. Environmental Health Perspectives, 110(1), A8–10.
U.S. EPA. (2002). Total maximum daily loads for toxic pollutants, San Diego Creek and
Newport Bay, California. EPA Region IX. San Francisco, CA.
U.S. EPA (2018) National Recommended Water Quality Criteria—Aquatic Life Criteria Table.
https://www.epa.gov/wqc/national-recommended-water-quality-criteria-aquatic-life-criteria-
table#table
Valko, M., Morris, H., & Cronin, M. T. D. (2005). Metals, toxicity and oxidative stress. Current
Medicinal Chemistry, 12(10), 1161–1208.
Varotto, L., Domeneghetti, S., Rosani, U., Manfrin, C., Cajaraville, M. P., Raccanelli, S., et al.
(2013). DNA Damage and Transcriptional Changes in the Gills of Mytilus galloprovincialis
Exposed to Nanomolar Doses of Combined Metal Salts (Cd, Cu, Hg). PLoS ONE, 8(1),
e54602–18. http://doi.org/10.1371/journal.pone.0054602
12

Viarengo, A. (1985). Biochemical Effects of Trace Metals. Marine Pollution Bulletin, 16, 153–
158.
Viarengo, A., Pertica, M., Mancinelli, G., Orunesu, M., Zanicchi, G., Moore, M. N., & Pipe, R.
K. (1984). Possible role of lysosomes in the detoxication of copper in the digestive gland
cells of metal-exposed mussels. Marine Environmental Research, 14(1-4), 469–470.
http://doi.org/10.1016/0141-1136(84)90118-1
Viarengo, A., Zanicchi, G., Moore, M. N., & Orunesu, M. (1981). Accumulation and
detoxication of copper by the mussel mytilus galloprovincialis Lam: A study of the
subcellular distribution in the digestive gland cells. Aquatic Toxicology, 1(3-4), 147–157.
http://doi.org/10.1016/0166-445X(81)90011-4
Xu, M., Jiang, L., Shen, K.-N., Wu, C., He, G., & Hsiao, C.-D. (2016). Transcriptome response
to copper heavy metal stress in hard-shelled mussel (Mytilus coruscus). Genomics Data, 7,
152–154. http://doi.org/10.1016/j.gdata.2015.12.010
Zapata, M., Tanguy, A., David, E., Moraga, D., & Riquelme, C. (2009). Transcriptomic response
of Argopecten purpuratus post-larvae to copper exposure under experimental conditions.
Gene, 442(1-2), 37–46. http://doi.org/10.1016/j.gene.2009.04.019

13

Chapter 1: Concentration-dependent transcriptional response of Mytilus californianus
larvae to copper

Abstract
Copper contamination of coastal waters is a long-standing problem in many regions. Copper
toxicity in marine waters is determined with bivalve embryo-larval toxicity tests which measure
survival and normal development as endpoints. Gene expression data is increasingly
incorporated into such assays as a more sensitive and comprehensive marker of contaminant
exposure or toxicity. Here we measured the impacts of copper on Mytilus californianus larval
transcriptional profiles, and identified sensitive biomarkers of copper exposure. Sensitive
biomarkers were primarily all downregulated in response to copper. Key functional categories
that were identified among these genes include biomineralization/shell formation, metal binding,
and development. Concentration responsive transcripts were also identified in adult mussels, and
compared with larval biomarkers of exposure. While there was some overlap in adult and larval
copper-responsive genes, many of the makers were unique to each life history stage.

Introduction
In marine environments, short-term bivalve embryo-larval toxicity assays are standard to
determine the toxicity of metals, effluent, and numerous other chemicals (E50 Committee, 2013;
EPA, 1995). Bivalve toxicity assays currently use abnormal development as the most sensitive
endpoint, and fail to consider detrimental molecular changes that may occur at low contaminant
concentrations. Despite the recognized utility of applying molecular data to traditional
toxicology assays, many established model marine systems are still not well-characterized at the
molecular level.

Over the past two decades, advances in high-throughput sequencing have created enormous
potential for studies in ecotoxicology. Large-scale gene expression analysis has the potential to
provide more sensitive indicators of toxicity, while simultaneously characterizing biochemical
and physiological regulation in response to a toxin (Ankley et al., 2006; Calzolai et al., 2007;
Hahn, 2011; Schirmer, Fischer, Madureira, & Pillai, 2010). Transcriptional characterization may
14

also reveal unexpected pathways involved in the toxicity of a chemical (e.g. Poynton et al.,
2007). Ultimately from these datasets, interesting biomarker genes can be identified that are
highly correlated with toxin exposure (Nordberg, 2010), or with some negative outcome at the
whole-organism or population level (Ankley et al., 2010; Connon et al., 2010). Here we chose a
well-established marine model for toxicity tests, Mytilus spp. larvae, to study the transcriptional
response to a range of environmentally relevant copper concentrations.

Copper is a common environmental pollutant in coastal waters globally (Sadiq, 1992), but
Southern California has been a hotspot for copper contamination (Rivera-Duarte et al., 2005;
Schiff, Bay, & Diehl, 2003; Schiff, Brown, Diehl, & Greenstein, 2007). Mytilus mussels are a
key component of ecosystems in this region, and thus provide an environmentally relevant model
for copper research. The effects of copper on larval Mytilus spp. survival and development are
relatively well studied (e.g. Arnold, Cotsifas, Smith, Le Page, & Gruenthal, 2009; Hoare,
Beaumont, & Davenport, 1995a; Hoare, Davenport, & Beaumont, 1995b; Johnson, 1988), yet
there has been little investigation into the biochemical effects of copper on this sensitive stage,
nor on the potential molecular drivers of developmental abnormality. All investigations of
Mytilus spp. global transcriptome responses to toxins have been conducted in adults (e.g. Negri
et al., 2013; Varotto et al., 2013; Venier et al., 2006; Xu et al., 2016), so we know little about
how early life history stages respond to toxins at the transcriptional level, especially at low,
sublethal concentrations.

Traditional endpoints of toxicity assays are often measured in concentration-response
experiments, which allow for modeling of the response to a toxin and for consistent
determination of biologically relevant concentration thresholds, such as the lowest observed
effect concentration (LOEC) (Walker, Sibly, and Peakall 2001). Transcriptional response to a
drug or toxin can also be analyzed in a dose-responsive fashion (Daston, 2008; Ji et al., 2009; Ji,
Siemers, Lei, Schweizer, & Bruccoleri, 2011). Concentration-responsive transcriptional analysis
has been used to some extent in ecotoxicological investigations (e.g. Brulle, Morgan,
Cocquerelle, & Vandenbulcke, 2010; Poynton et al., 2008; Whitehead, Triant, Champlin, &
Nacci, 2010), yet many transcriptomic studies still focus predominantly on a low number of
concentrations, if more than one is even used. Transcriptional dose-response or concentration-
15

response experiments allow for a more complete characterization of the discrete physiological
responses to different levels of a toxin (Denslow, Garcia-Reyero, & Barber, 2007). More
specifically, concentration-specific biomarkers can be identified, which could provide a high-
resolution metric of toxin-exposure level in environmental monitoring. Biomarkers that respond
to low concentrations of a contaminant can be especially useful for identifying sublethal
physiological changes that may not be evident at the whole-organism level.

This work presents an analysis of concentration-responsive transcription in Mytilus californianus
larvae and adults after short-term (24-48 hour) exposures to copper. We aimed to identify low-
concentration transcriptional biomarkers of short-term copper exposure, and to characterize
functional pathways associated with a range of copper exposure concentrations. We also
compared the concentration-dependent copper response of adults and larvae, and identified
transcriptional biomarkers of copper exposure and toxicity. The biomarkers identified in this
study represent robust indicators of copper stress that could be incorporated into environmental
monitoring and toxicity testing.

Methods
Broodstock collection and fertilization
Adult M. californianus were collected from Santa Monica, California and transported to the
Wrigley Marine Science Center (WMSC) on Catalina Island, where they were held in a subtidal
cage hanging from the dock for 1.5 years prior to spawning. Animals were collected for two trial
spawns during May-June of 2013. During both experiments, 15-20 animals were removed from
the subtidal cage, and placed directly on ice for one hour. To induce spawning, mussels were
then rinsed in fresh DI water, scraped clean of any fouling organisms, and transferred to a tank of
filtered seawater at 23-25˚C. Mussels were sometimes fed a small quantity of Shellfish Diet 1800
(Reed Mariculture) to induce spawning if thermal shock alone was not sufficient.

Once an adult mussel started spawning, it was removed from the main tank, rinsed thoroughly
with filtered seawater, and transferred to a separate beaker containing 0.2 um filtered seawater.
Each individual that spawned was held alone in a beaker to prevent cross-contamination of
gametes. Spawning mussels were identified as male or female based on the appearance of the
16

gametes. When spawning was complete, the adult mussels were removed from the beakers, and
the appearance of eggs and motility of sperm were examined under low power on a compound
microscope. Once eggs had transformed from club-shaped to round, sperm from a single male
was added ad libitum to eggs of a single female to reach an average density of ~5 sperm per egg.
Fertilization for most eggs, evidenced by the formation of a polar body and first division of the
zygote, was complete after ~30 minutes. The number of fertilized embryos was counted to
determine density of the embryo mixture. Embryos were then stocked into 1L treatment
containers at a density of 10 per mL (total count of 10,000 per container).

Copper solution preparation

Copper solutions were prepared in 0.2 um filtered seawater using a 1 mM stock solution of
copper sulfate (CuSO4). Each replicate experiment consisted of one control container and 9
containers with increasing copper concentrations—2, 3.1, 4, 6, 8, 10, 15, 20, and 25 µg/L Cu.
After spiking copper into each container, jars were mixed thoroughly and allowed to equilibrate
for 1 hour.

Experimental conditions and sample processing

Once larvae were added, experimental containers were incubated at 20˚C with a 16:8 hr L:D
cycle. The total exposure time was 48 hr. At the end of this time, all containers were removed
from the incubator. Five 10 mL samples were taken from each culture and preserved with EtOH
for subsequent counting and morphological analysis. The remainder of the culture was filtered
through a 20 um sieve to collect larvae. Larvae were then concentrated into 15 mL centrifuge
tubes, pelleted at 2000g for 4 minutes, and concentrated again into 1.5 mL centrifuge tubes. The
seawater supernatant was removed and the larval pellet was frozen at -80*C.

RNA extraction and Library Preparation for Illumina HiSeq

Larval samples for both trials were prepared for sequencing. This resulted in a total of 20
samples (10 copper concentrations per experiment) that were sequenced. RNA was extracted
17

from all samples using the Trizol extraction protocol (Ambion). MaxTract columns (Qiagen)
were used during the phase separation step to ensure maximum retrieval of the aqueous phase
containing RNA. A total RNA yield of ~7.5 ug was obtained for each sample. cDNA libraries
were prepared for next generation sequencing on the Illumina HiSeq 2500 platform using a
modified version of the Illumina TruSeq protocol developed in house. One library was prepared
per experimental sample, producing 20 libraries total. These libraries were multiplexed and run
over two lanes with 50bp SR reads.

Two additional libraries were prepared for transcriptome assembly. The first was prepared using
RNA from a larval sample from the first experimental trial, which had been exposed to 8 µg/L
Cu. We used a modified version of the Illumina TruSeq protocol to prepare the library, and
sequenced on one lane of Illumina HiSeq 2500 with 150 bp SR reads. The second library was
prepared using RNA from a pool of ~3000 48-hr M. californianus larvae reared in the lab under
control conditions. Samples were prepared using the NEBNext Ultra Directional RNA Library
Prep Kit for Illumina, and run over 10% of an Illumina NextSeq lane, with 75bp PE reads.

M. californianus de novo transcriptome assembly
Reads collected in the two assembly sequencing runs (described above) were quality trimmed
and vetted for adapter sequence contamination in Trimmomatic v0.33 {Bolger:2014ek;
ILLUMINACLIP:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36).
Paired end reads were merged using fq2fa, part of IDBA v1.1.1(--merge --filter){Peng:2013eu}.
Both sets of reads were then assembled individually with IDBA-tran (maxk 124). For the longer
read library (150 bp), the short.sequence.h script in IDBA-tran was edited to run on longer
sequences by changing the kMaxShortSequence parameter to 200. Both resulting assemblies
were merged into one file with a previously published Expressed Sequence Tag (EST) library
(Gracey et al., 2008), and further consolidated by running cd-hit-est v4.6.5 (-c 0.98) (Fu, Niu,
Zhu, Wu, & Li, 2012; Li & Godzik, 2006) once, and CAP3 three times (–o 50 –p 98) (Huang &
Madan, 1999). Cd-hit-est was then run once more (-c 0.95).

Peptides were predicted for the resulting assembly, and the contigs and predicted peptides were
annotated using Trinotate v 2.0.2 with default parameters (Haas et al., 2013). Full taxonomic
18

paths for hits from the nt database were attained using a custom script, localgiTax.py. Uniprot
annotations from Trinotate were filtered to only include hits with E-values lower than 1e-15, and
the nr hits were filtered to only include best hits with E-values lower than 1e-20. All annotations
were searched and only those containing the term ‘Metazoan’ in the taxonomic path were
retained. Contigs with a ‘Metazoan’ annotation from either Uniprot or nt were retained, and
annotations were consolidated.

BLASTn was then run on the assembly (-perc_identity 90 –outfmt 6), with the M. californianus
mitochondrial genome as the reference. Contigs that matched with an alignment length greater
than 100 bp were removed from the assembly. Finally, contigs that had ribosomal RNA
annotations in the Trinotate output were also removed. The final assembly consisted of 23,025
contigs with an average length of 1905.38 bp.

Data Filtering and Analysis
Microscope counts of larvae were converted to proportion of animals surviving and developing
normally in each replicate. Each proportion was then divided by the mean control proportion to
calculate control-normalized survival and normal development. These normalized counts were
analyzed in the r package ‘drc’ (Ritz, Baty, Streibig, & Gerhard, 2015). Four-parameter log-
logistic curves were fitted to each dataset to calculate 50% lethal concentration (LC50) and 50%
normal development effective concentration (EC50) values.

Raw RNAseq reads were quality trimmed and contaminating adapter sequence was removed
using Trimmomatic v0.33 (Bolger, Lohse, & Usadel, 2014) with default parameter settings. The
trimmed reads were then mapped to the M. californianus mitochondrial genome using BBMap
v34 (minid=0.95 ambiguous=all sssr=1.0)(Bushnell 2014) to separate mitochondrial transcripts
from nuclear genes. All reads that did not map to the mitochondrial genome were used for
subsequent analysis. Remaining sequences were mapped to our M. californinaus transcriptome
assembly with BBMap v34 (minid=0.95, ambiguous=all, sssr=1.0, nhtag=t). Read counts were
obtained using FeatureCounts v1.5.0-p3 (Liao, Smyth, & Shi, 2014), counting on the feature
level and only allowing for uniquely mapping reads to be counted (-T 8 -O -F). The two samples
from the highest copper concentrations (25 µg/L) were removed at this point, as the
19

overwhelming majority of read count values were 0, and the samples had consisted of very few
living larvae. The resulting count table was further processed in EdgeR (McCarthy, Chen, &
Smyth, 2012; Robinson, McCarthy, & Smyth, 2010) to generate normalized trimmed mean of
M-values (TMM) counts.

Concentration-responsive gene expression was analyzed with Sigmoidal Dose Response Search
(SDRS) v0.04 (Ji et al., 2011)to identify genes with sigmoidal expression, and to parameterize
the expression dose-response curve of each gene. For genes with significant (p<0.05) sigmoidal
expression, the program calculates an expression EC50, as well as a low and high dose for the
95% confidence interval (CI) of the expression EC50. The fold expression change from
minimum expression to maximum expression is also calculated. Calculation of an expression
EC50 value and the associated confidence interval allowed us to assign a quantitative
concentration threshold that could be related to negative morphological outcomes for biomarker
detection. With this data, we compared expression EC50 values and confidence intervals with
morphological EC50 values for normal development.

WGCNA (Langfelder & Horvath, 2007)was used to create gene co-expression networks and
identify modules of genes with similar expression patterns. The threshold for merging modules
was lowered to 0.15, which proved a more appropriate cutoff to characterize unique expression
patterns in this dataset. Color values were assigned to each of these modules by the program, and
module membership of significant sigmoidal genes was determined. To analyze functional
enrichment of interesting gene sets obtained with SDRS and WGCNA, we conducted Gene
Ontology (GO) (Ashburner et al., 2000) enrichment analysis in the Cytoscape (Shannon et al.,
2003) plugin program, BINGO (Maere, Heymans, & Kuiper, 2005). Enrichment was calculated
using a hypergeometric test for overrepresentation (p<0.05), with a Benjamini and Hochberg
False Discovery rate correction.

Comparison with Adult Expression

Data from an unpublished experiment was used to compare larval concentration-responsive
transcriptional patterns with those of adult mussels. In summary, adult M. californianus were
20

exposed to 10 copper concentrations ranging from 0-120 µg/L (Femrite et al., in prep).
Incubations lasted 24 hours, with fresh copper-spiked seawater added every 4 hours to ensure
that nominal copper concentrations were maintained in each treatment. Four adult mussels were
exposed to each treatment. Animals were sacrificed at the end of the assay, and tissue was flash-
frozen for RNA extraction. RNA was then reverse transcribed and hybridized to custom cDNA
microarrays in an interwoven loop design. Differentially expressed genes were analyzed for
sigmoidal concentration-responsive expression using SDRS. Copper concentration response of
adults and larvae was compared. The Mytilus Sanger EST assembly that was used on adult
arrays was aligned with the new Mytilus assembly presented in this study using a reciprocal
BLASTn search (once using the ESTs as target and again using our new assembly as the target).
Only one match with the best bit score was selected for each BLAST query. These matches were
used to align SDRS analyses from adults and larvae, and to compare the relative expression
patterns of highly similar sequences.

Results
Normal Development and Survival
The proportion of larvae surviving (survival) and the proportion of larvae reaching a normal D-
hinge shape (normal development) at the end of the 48-hour period in each trial was calculated.
Both endpoints exhibited significant sigmoidal concentration-responsive patterns (Figure 1).
Based on the modeled curves, LC50 and EC50 values were calculated for survival and normal
development in each trial. In trial 1, the EC50 was 7.26 µg/L Cu and the LC50 was 6.5 µg/L Cu.
The larvae appeared to be more sensitive in trial 2, with an EC50 of 4.03 µg/L, and an LC50 of
3.96 µg/L. Although the LC50 was lower than EC50 in both trials, total mortality did not occur
until higher concentrations, whereas total population abnormality occurred at relatively low
concentrations.

Evaluation of Transcript Sensitivity to Copper
Genes with copper-responsive sigmoidal expression patterns were both up and down-regulated.
1483 genes exhibited sigmoidal expression in at least one trial, but only 317 had sigmoidal
expression in both trials. Of these, 248 were consistently upregulated (Figure 2A), and 61 were
consistently downregulated (Figure 2B). Expression EC50 values were calculated, representing
21

the concentration at which the gene’s expression was induced (or repressed) by 50% of its
maximum expression value. The lower threshold of the 95% confidence interval for the EC50
value was also identified, which is the lowest concentration of significant induction or
suppression of the gene (Figure 2C). We will refer to this value as the lowest observed
transcriptional effect concentration (LOTEC) for genes. Although most genes were either up- or
down-regulated consistently in both trials, the LOTEC and EC50 values of specific genes were
not in the same relative order between both trials. The EC50 ranks (r = 0.46, p<0.001) and
LOTEC ranks (r = 0.42, p<0.001) between the two trials were moderately correlated, but there
was clearly some discrepancy between trials in the exact pattern of gene modulation.

We compared transcriptional LOTEC and EC50 values of downregulated and upregulated
concentration-responsive genes in each trial with normal development curves and normal
development EC50 values, and found that nearly all downregulated genes exhibit transcriptional
LOTECs and EC50s lower than the normal development EC50 (Figures 3 and 4). Interestingly,
in both trials, each with a distinct normal development EC50, the median EC50 value of
downregulated genes (3.29 and 2.02 in trials 1 and 2, respectively) was located immediately
before the precipitous drop in population normal development (Figure 4A and C). Upregulated
genes had much higher transcriptional LOTECs and EC50s, with most genes reaching 50%
induction at copper concentrations that caused developmental abnormality in the whole
population (Figures 3 and 4, B and D). Because downregulation of genes predominantly
occurred at low copper concentrations in larvae, these genes could prove useful as early warning
signs of copper exposure. On the other hand, up-regulated genes responded to copper at high
concentrations, once most of the population was already abnormal. Thus, up-regulated genes are
better indicators of copper toxicity and negative phenotypic outcomes.

GO enrichment analysis revealed that most functional enrichment occurred in upregulated genes.
Downregulated genes were enriched for two categories—polysaccharide binding and
metabolism. Enriched categories in upregulated genes included the proteasome core complex,
proteasome regulation, the ESCRT III complex, ribonucleotide binding, oxidoreductase activity,
hydrolase activity, peptidase activity, and copper ion binding.

22

Down-regulated genes as sensitive indicators of copper exposure
To identify concentration-responsive gene sets that did not necessarily exhibit a sigmoidal
expression pattern, genes were clustered into modules using WGCNA. Clustering co-expressed
genes into modules has proven a good way to classify gene sets that respond in the same way to
external stimuli, and often share involvement in the same functional pathways (Langfelder and
Horvath 2007). We sought to identify modules that were regulated at low copper concentrations,
and could prove more sensitive indicators of copper exposure than normal development. Module
membership of the genes recognized by SDRS was also examined, and used to better understand
the functional importance of sigmoidally responsive genes within the larger context of a
biological network.

79% of all expressed genes in our assembly (genes with a summed TMM across all samples >
300) had some modular identity, and these genes were classified in 23 distinct modules. Several
modules exhibited significant correlations with phenotypic data and/or copper concentration.
Significant modules represented both up- and down-regulated genes, and showed expression
changes at a range of copper concentrations. We focused on the significant modules that
represented down-regulated genes (Figure 5, p<0.05), as suppressed transcripts had proven to be
more sensitive indicators in the sigmoidal expression analysis. 87% of sigmoidal down-regulated
genes belonged within the three down-regulated modules, represented by the colors white,
yellow, and steelblue (Table 1). There was a clear progression of EC50 and LOTEC range
associated with each module, with steelblue the most sensitive and yellow the least sensitive
(Table 1). There was slight disagreement of EC50 and LOTEC concentration range between
trials which, based on the Spearman’s rho of 0.46, is not surprising. The modular identities of
sigmoidal genes were used to conduct a more in-depth functional analysis of sensitive down-
regulated transcripts.

Several functional or structural categories were represented in all three modules. The most
prominent group of genes was related to development (Table 2; Table S1; Figure 6A). Although
neither the blue nor white modules were significantly enriched for development genes, the
yellow module exhibited overrepresentation of genes related to cell fate commitment, regulation
of organ formation, and cell differentiation (p<0.05). Specifically, neural development genes
23

and genes related to nervous system function were common across the three modules (Figure 6B)
The yellow module again was enriched for a related category—negative regulation of glial cell
formation (p<0.05). Two other categories common to the significant down-regulated modules
were biomineralization/shell matrix formation and enzymes containing divalent metal ion
cofactors (Figures 6C and 6D). The metal-binding enzymes coded by these genes were primarily
associated with zinc, iron, and molybdenum.

Two additional categories were specific to only one or two of the three modules. The first is
muscle and muscle development-related genes, many of which were involved in regulation of
twichin/actin/myosin interactions (Table 2; Table S1; Figure 6E). These genes were only evident
in the white and yellow modules. Furthermore, cell projection genes were overrepresented in the
yellow module, but weren’t present in the other two modules. Many of these genes were related
to axon/dendrite cell projections (Table2; Table S1), highlighting again the theme of nervous
system function. Cell projection genes were also related to axoneme or cilial projections.
Finally, two copies of Metallothionein 10 (III and IV), a well-studied bivalve marker that is
typically induced by metal exposure, were downregulated in the white and steelblue modules
respectively.

A handful of upregulated sigmoidal genes exhibited EC50 values below or coincident with the
steep drop in normal development in one of the two trials, although no particular module was
specifically representative of this set of more sensitive induced genes. The first of these genes
was Protein SSUH2 homolog, a gene involved in damaged protein transport and purportedly
tumor suppression (Reinartz et al., 2010). The other three genes—runt-related transcription
factor, Annexin A11, and Transmembrane protease serine 3 (TMPS3)—are all related to
development and cell cycle. TMPS3 expression has also been associated with tumors in humans
(Sawasaki et al. 2004).

Molecular markers of copper toxicity across life-history stages
ANOVA analysis of adult M. californianus transcription revealed 1,012 genes that were
differentially expressed in response to copper. 88% of these genes were identified as sigmoidal
24

by SDRS. 572 genes were upregulated, and 323 were downregulated. As in larvae, adult genes
were repressed at lower copper concentrations, and induced at higher copper concentrations. The
mode EC50 value for downregulated transcripts in adults was 12.5 µg/L, while the mode EC50
for upregulated transcripts was 22.5. Only 48 genes were identified as significantly sigmoidal in
both life history stages (Table S2). This included 5 downregulated genes and 43 upregulated
genes. The low number of shared genes indicates that the larval and adult responses to copper
largely consist of different specific genes, yet some similar functional patterns were observed for
upregulated genes in both life stages.

Examination of upregulated sigmoidal genes’ function in adults revealed protein chaperone and
protein folding genes, genes involved in apoptosis and cell-stress response, oxidative
stress/redox genes, and signaling genes as main groups that exhibited monotonic, sigmoidal up-
regulation in response to copper. Some protein chaperone genes—six HSP-70 isoforms and
binding proteins, and Sequestosome-1—were up-regulated in response to copper in larvae as
well (Table S2). Two subunits of the T-complex protein, and four isoforms of cAMP-responsive
element-binding protein-like 2, an oxidative stress gene, were also upregulated in both larvae and
adults.

Larval induced transcription patterns differed from adult transcriptional profiles in the categories
of signaling genes, metallothioneins, and most oxidative stress genes. Signaling genes identified
in adults were CCAAT/enhancer binding protein, MYC, and PIM-1 (Table S3), none of which
exhibited significant modulation in larvae (Table S4). While larvae did also exhibit a notable
response of signaling pathways, the specific concentration-responsive signaling genes in larvae
differed from those in adult animals. Larval upregulated transcripts were enriched for genes in
numerous signaling pathways, including Ras protein signaling, several toll-like receptor
signaling pathways, immune response signal transduction, and cytokine-mediated signaling
(p<0.05). Likewise, most adult markers of oxidative stress were not consistently upregulated in
larvae. Adult-specific genes include glutamate cysteine ligase, several subunits of glutathione-s-
transferase, peroxiredoxin, and thioredoxin reductase (Table S3). Markers of redox activity that
were sigmoidal in larvae, but not adults, include superoxide dismutase, tyramine beta-
hydroxylase, DBH-like monooxygenease protein 1 homolog, phylloquinone omega-hydroxylase,
25

and quinone oxidoreductase (Table S4). All of these but superoxide dismutase were upregulated.
While larvae also did exhibit induction of a single glutathione-s-transferase subunit, it was not a
subunit that was copper-responsive in adults.

Another gene family that exhibited a difference between adults and larvae was metallothioneins,
classic biomarkers of metal exposure. In adults, metallothionein (MT) was not a significant
marker of copper exposure, but in larvae, several copies of MT-10 and MT-20IV exhibit
significant sigmoidal response to copper in both trials. The response was not consistent,
however, and MT-10 genes were downregulated, while MT-20IV was upregulated.

Downregulated transcripts exhibited very little overlap between adult and larval mussels. As in
larvae, adult genes were suppressed at lower copper concentrations relative to upregulated
transcripts, but functionally the responsive transcripts were quite different. In adults, functional
categories observed were cell cycle transcripts and genes involved in adenylate metabolism
(Table S3). Sigmoidal copper-suppressed larval genes, on the other hand, were responsible for
biomineralization, neural and muscular function, and divalent metal ion binding (Table S1; Table
S4). The five genes that were commonly downregulated in adults and larvae did not exhibit a
consistent functional theme (Table S2).

Discussion

Consistency of M. californianus larval response to copper
Normal development EC50s observed in this experiment were consistent with EC50s of Mytilus
galloprovincialis and Mytilus edulis from previous studies (Figure 1; Arnold et al., 2009; Martin,
Osborn, Billig, & Glickstein, 1981, Barnes 1989). The EC50s of 7.26 and 4.03 in the first and
second trial, respectively, were also higher than the current U.S. federal chronic copper limit of
3.1 µg/L, although only slightly in the second trial. Comparable normal development EC50
values in this study relative to other Mytilus spp. EC50s indicate that M. californianus could
serve as a good supplement, or even substitute, for M. galloprovincialis or M. edulis larvae in
bioassays for west coast waters. This might be an especially useful transition, as M. californinaus
26

is a morphologically unique, native species, and thus its use may avoid some challenges of
working with the M. edulis species complex (Arnold et al. 2009).

There was a slight shift in the LC50 and EC50 between trials, with the larval population
appearing more sensitive to copper in the second trial (Figure 1). Bivalve populations exhibit
natural high-standing variation in genotypes (Curole & Hedgecock, 2007), and different
populations of mussels have exhibited differences in copper tolerance (Hoare et al., 1995a).
Despite the differences between trials, many consistent expression patterns were evident.

Sensitive Indicators of Larval Copper Exposure
We aimed to identify sensitive markers of copper exposure, or genes that responded to copper at
low concentrations that did not induce abnormal development. Both sigmoidal concentration
response modeling and network analysis revealed two broad patterns of modulation—down-
regulated genes were indicative of exposure to lower copper concentrations, while upregulation
was usually induced by higher copper levels (Figure 2A and B; Figure 3; Figure 4). The fact that
downregulated genes’ EC50 values were nearly all lower than the normal development EC50
indicates that these genes are highly sensitive, as transcriptional EC50 is a less sensitive indicator
of expression changes than LOTEC (Figure 4). This pattern indicates that primarily down-
regulated genes should be considered as candidate biomarkers of exposure. Biomarkers of
exposure exhibit concentration-dependent expression patterns, yet are not directly correlated
with any detrimental outcome at the whole-organism or population level. These markers can
serve as sensitive indicators that copper concentrations are approaching toxic levels.

Sensitive biomarkers of exposure consisted of genes involved in three broad functional groups:
development and cell differentiation, biomineralization, and metal ion binding. The first group
was genes related to cell differentiation and organogenesis (Table S1, Figure 6A). These genes
were enriched in the yellow module, yet were present across all three modules and had LOTEC
values ranging from 0.001-6.8 in the first trial, and 0.001-2.7 in the second trial (Table S1).
Downregulation of development genes has been observed before in two studies of post-larval
and juvenile mollusks exposed to low copper concentrations (Silva-Aciares, Zapata, Tournois,
Moraga, & Riquelme, 2011; Zapata, Tanguy, David, Moraga, & Riquelme, 2009). Adult mussels
27

exposed to copper in other studies have also demonstrated down-regulation of organ
development and post-embryonic development genes (Negri et al., 2013), suggesting that this
seemingly larval-specific response to copper may actually persist later in life. However, in the
same study, development genes were also down-regulated in response to heat stress, which
provides evidence that developmental modulation may not be a copper-specific response.

Of the development genes suppressed in our study, many were specifically involved in nervous
system and muscular development (Table S1; Figure 6B). Two nervous system components and
processes were enriched in the yellow module. These were cell projection, which refers to the
formation of cellular extensions such as axons, and negative regulation of glial cell formation.
Nervous system genes exhibited a LOTEC range of 0.7-4.8 µg/L in the first trial, and 0.1-2.4
µg/L in the second trial. Fundamental neuronal and muscular developmental pathways are
activated early in development, generally during or immediately after the trochophore stage
(Dyachuk & Odintsova, 2009; Voronezhskaya, Nezlin, Odintsova, Plummer, & Croll, 2008), and
continue to progress through the early veliger stage at 48 hours post-fertilization.

While metals such as mercury and lead are well known for inhibiting nervous system function
(Weis, 2014), the effects of copper on neural development have been studied very little in
aquatic organisms. Studies that have investigated this system have generally found that copper
does have consequences, however. Zebrafish embryos were exposed to copper, and exhibited
reduced formation of neuroblasts, sensory organs of the nervous system meant to orient fish
within currents (Johnson, Carew, & Sloman, 2007). In scallop post-larvae, cellular
communication and membrane receptor genes, which are often central components of
neurological function, were downregulated in response to 10 µg/L copper (Zapata et al., 2009).
Our data reflected this pattern, with numerous calcium binding proteins also downregulated as
part of the yellow module. Additionally, adult mussels exposed to copper did show
downregulation of two genes related to nervous system development, although another three
were also up-regulated (Negri et al., 2013).

Muscle formation, another integral developmental pathway, was also modulated at low copper
concentrations (0.04-6.2 µg/L). Three key genes in this process—twitchin, myosin, and
28

paramyosin—were downregulated primarily as part of the yellow module (Table S1, Figure 6E).
Several genes with regulatory roles in this system, such as calponin, were down-regulated in
response to copper, a trend that has been observed in juvenile red abalone (Silva-Aciares et al.,
2011). Muscle and neuronal development are closely integrated in early larval stages (Dyachuk
& Odintsova, 2009), thus it is understandable why the two systems should show a similar
response to copper toxicity.

Although developmental pathways are not commonly recognized biochemical targets of copper
toxicity in invertebrates, altered expression of these genes is reasonable considering the well-
studied effects of copper on morphological development and behavior. Drastic structural
malformation and erratic swimming behavior of abnormal larvae could be explained by the
systemic down-regulation of developmental programs and neurological function. Here we have
discovered some potential drivers of this phenomenon which precede developmental
abnormality, and may be indicative of the early traces of failure. The exact mechanism by which
copper causes developmental and neurological malfunction, and whether copper generally
affects these genes systematically or targets primarily a few regulators, is still unclear.

The second key functional group, represented primarily by the white and yellow modules, was
biomineralization and shell matrix protein genes (Table S1, Figure 6C). LOTEC values of these
genes ranged from 0.3-5.9 µg/L in the first trial, and 0.001-3.29 pbp in the second trial. Known
and purported shell formation genes were down-regulated as part of this group, including protein
PIF (Suzuki et al., 2009), chitin synthase (Schönitzer & Weiss, 2007), several tyrosine and
tyrosinase genes (Aguilera, McDougall, & Degnan, 2014), and components of the tyrosine
metabolic pathway (Liu et al., 2015). Copper suppression of shell-formation pathways in larvae
has not been observed, yet this offers additional potential explanation of the eventual failure of
abnormal larvae to develop a normal D-shaped shell in the early veliger stage. The suppression
of calcium binding proteins, which was mentioned as a pathway involved in nervous system
development, is also likely associated with failure in shell development.

Finally, genes coding for divalent metal-containing enzymes were down-regulated across a range
of low copper concentrations (0.001-6.5 µg/L), although they had the strongest representation in
29

the white module (Table S1, Figure 6D). Interestingly, most of these genes were bound to
divalent cations other than copper, including Zn
2+
, Mo
2+
, and Fe
2+
. Copper-induced
downregulation of iron and zinc binding stress-protein transcripts was observed previously in
juvenile abalone (Silva-Aciares et al. 2011). Metals often bind organic ligands without much
specificity (Williams, 1981; 1984), and copper is the most likely to replace almost any other
divalent metal at a binding site (Irving & Williams, 1953). Copper is already known to replace
zinc in some proteins (Viarengo, 1985), so as copper concentration increases relative to other
metal ions, enzymes may exhibit even more binding promiscuity. Considering this information,
downregulation of metal-binding enzymes that have become functionally disrupted by erroneous
copper binding may be a necessary and protective response mechanism.

The toxic response across life history stages
The overarching pattern of the mussel larval transcriptional response reflected adult
transcriptional patterns. In both life stages, gene down-regulation began at relatively low copper
concentrations. This conserved pattern is consistent with what we might expect from an energetic
model of stress tolerance. In such a framework, the earliest phases of stress response involve
limitation of growth, development, and reproduction to divert energy to maintenance and
homeostasis (Sokolova, 2013). As stress intensifies, pathways involved in oxidative stress and
protein chaperoning are activated to reduce further harm to vital metabolic and respiratory
processes.

The functions of up-regulated genes were somewhat consistent between larvae and adults. Genes
involved in oxidative stress, damaged protein turnover, and signaling were up-regulated in both
life history stages. As many of these are generic cellular stress response pathways, which are
highly conserved across taxa as well (Kültz, 2005), it is not surprising that these pathways are
common in larvae and adults. The specific genes in each of these categories were quite different,
however (Tables S2-S4), which indicates that larvae and adults may be using different means to
reach the same end of cellular defense and protein turnover. A previous study of larval and adult
zebra mussel responses to metals also showed some overlap in stress responsive gene
induction/suppression in both life history stages, but larvae did not exhibit transcriptional
changes in the complete suite of genes induced in adults (Navarro, Faria, Barata, & Piña, 2011).
30

Numerous other studies on adult and larval mollusks have observed induction of the same genes
that were identified here in adults, larvae, and both life stages (Navarro et al., 2011; Negri et al.,
2013; Varotto et al., 2013; Xu et al., 2016; Zapata et al., 2009), including Heat-shock Protein 70,
sequestosome-1, and Glutathione-S-transferase. Previous research also reflects the discrepancies
we observed in metallothionein expression. Metallothioneins in our study showed either no
response in adults, or induction and down-regulation, depending on the isoform, in larvae. In
zebra mussel larvae, metallothionein exhibited an inconsistent response to copper (Navarro et al.,
2011), and adult gill in the same study also showed no response of metallothionein to copper. In
our study of larvae, MT 10-III and IV were downregulated in response to copper, while MT 20-
IV was upregulated. Previous research shows that MT-10 is a constitutive isoform, while MT-20
is a metal-inducible isoform, and that each isoform exhibits differential copper binding (Vergani,
Grattarola, Grasselli, Dondero, & Viarengo, 2007). These differences could explain the opposing
expression patterns of the two MT isoforms observed in our study.

Conclusions
Through this work, we have discovered that M. californianus larvae exhibit concentration-
dependent transcriptional responses to environmentally relevant copper concentrations.
Transcriptomes indicated that copper-induced abnormality in larvae is preceded by negative
modulation of biomineralization and developmental genes. Network analysis and concentration-
response modeling of gene expression proved to be effective methods for identifying copper-
responsive biomarkers of exposure and effect, and for discovering pathways that are activated in
response to both low and high copper concentrations. Finally, the toxic copper response is
somewhat conserved across life history stages, although the specific genes involved differ. On
the other hand, the functionality of transcripts suppressed in response to low copper
concentrations are life-history dependent. Marker genes discovered in this study should be re-
tested and confirmed using different populations, and perhaps different species, of Mytilus prior
to implementation or integration into testing protocols. Further studies should also more
carefully analyze these biomarkers of exposure to determine whether any negative physiological
consequences arise at induction/suppression concentrations of these markers, even if they may
not become apparent until later in development. We conclude that genes identified in this study,
31

with some further testing, could be utilized to increase the sensitivity of copper exposure
assessments, and potentially reduce the time and subjectivity involved in processing embryo-
larval toxicity assays.

References

Aguilera, F., McDougall, C., & Degnan, B. M. (2014). Evolution of the tyrosinase gene family in
bivalve molluscs: independent expansion of the mantle gene repertoire. Acta Biomaterialia,
10(9), 3855–3865. http://doi.org/10.1016/j.actbio.2014.03.031
Ankley, G. T., Bennett, R. S., Erickson, R. J., Hoff, D. J., Hornung, M. W., Johnson, R. D., et al.
(2010). Adverse outcome pathways: A conceptual framework to support ecotoxicology
research and risk assessment. Environmental Toxicology and Chemistry, 29(3), 730–741.
http://doi.org/10.1002/etc.34
Ankley, G. T., Daston, G. P., Degitz, S. J., Denslow, N. D., Hoke, R. A., Kennedy, S. W., et al.
(2006). Toxicogenomics in Regulatory Ecotoxicology. Environmental Science &
Technology, 1–11.
Arnold, W. R., Cotsifas, J. S., Smith, D. S., Le Page, S., & Gruenthal, K. M. (2009). A
comparison of the copper sensitivity of two economically important saltwater mussel species
and a review of previously reported copper toxicity data for mussels: Important implications
for determining future ambient copper saltwater criteria in the USA. Environmental
Toxicology, 24(6), 618–628. http://doi.org/10.1002/tox.20452
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., et al. (2000).
Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nature
Genetics, 25(1), 25–29. http://doi.org/10.1038/75556
Barnes, D.A. (1989) The effects of initial egg density and added copper concentration on the
development of early larval stages of the mussel Mytilus edulis. M.Sc. Thesis. Univeristy of
Wales, Bangor
Bolger, A. M., Lohse, M., & Usadel, B. (2014). Trimmomatic: a flexible trimmer for Illumina
sequence data. Bioinformatics, 30(15), 2114–2120.
http://doi.org/10.1093/bioinformatics/btu170
Brulle, F., Morgan, A. J., Cocquerelle, C., & Vandenbulcke, F. (2010). Transcriptomic
underpinning of toxicant-mediated physiological function alterations in three terrestrial
invertebrate taxa: a review. Environmental Pollution (Barking, Essex : 1987), 158(9), 2793–
2808. http://doi.org/10.1016/j.envpol.2010.06.019
Calzolai, L., Ansorge, W., Calabrese, E., Denslow, N., Part, P., & Lettieri, T. (2007).
Transcriptomics and proteomics. Applications to ecotoxicology. Comparative Biochemistry
and Physiology Part D: Genomics and Proteomics, 2(3), 245–249.
http://doi.org/10.1016/j.cbd.2007.04.007
Connon, R. E., Beggel, S., D'Abronzo, L. S., Geist, J. P., Pfeiff, J., Loguinov, A. V., et al.
(2010). Linking molecular biomarkers with higher level condition indicators to identify
effects of copper exposures on the endangered delta smelt (Hypomesus transpacificus).
Environmental Toxicology and Chemistry, 30(2), 290–300. http://doi.org/10.1002/etc.400
32

Curole, J. P., & Hedgecock, D. (2007). Bivalve Genomics: Complications, Challenges, and
Future Perspectives. In Aquaculture Genome Technologies (pp. 525–544). Oxford, UK:
Blackwell Publishing Ltd. http://doi.org/10.1002/9780470277560.ch29
Daston, G. P. (2008). Gene expression, dose-response, and phenotypic anchoring: applications
for toxicogenomics in risk assessment. Toxicological Sciences : an Official Journal of the
Society of Toxicology, 105(2), 233–234. http://doi.org/10.1093/toxsci/kfn138
Denslow, N. D., Garcia-Reyero, N., & Barber, D. S. (2007). Fish “n” chips: the use of
microarrays for aquatic toxicology. Mol. BioSyst., 3(3), 172–177.
http://doi.org/10.1039/B612802P
Dyachuk, V., & Odintsova, N. (2009). Development of the larval muscle system in the mussel
Mytilus trossulus (Mollusca, Bivalvia). Development, Growth & Differentiation, 51(2), 69–
79. http://doi.org/10.1111/j.1440-169X.2008.01081.x
E50 Committee. (2013). Standard Guide for Conducting Static Acute Toxicity Tests Starting with
Embryos of Four Species of Saltwater Bivalve Molluscs (pp. 1–22). West Conshohocken,
PA: ASTM International.
EPA, U. S. (1995). Short-term Methods for Estimating the Chronic Toxicity of Effluents and
Receiving Waters to West Coast Marine and Estuarine Organisms, 1–666.
Femrite. C., Moffett, J.W., and Gracey, A.Y. In prep. Dose-response effects of copper on gene
expression in the mussel Mytilus californianus.
Fu, L., Niu, B., Zhu, Z., Wu, S., & Li, W. (2012). CD-HIT: accelerated for clustering the next-
generation sequencing data. Bioinformatics, 28(23), 3150–3152.
http://doi.org/10.1093/bioinformatics/bts565
GRACEY, A. Y., CHANEY, M. L., Boomhower, J. P., Tyburczy, W. R., Connor, K., & Somero,
G. N. (2008). Rhythms of Gene Expression in a Fluctuating Intertidal Environment. Current
Biology, 18(19), 1501–1507. http://doi.org/10.1016/j.cub.2008.08.049
Haas, B. J., Papanicolaou, A., Yassour, M., Grabherr, M., Blood, P. D., Bowden, J., et al. (2013).
De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for
reference generation and analysis. Nature Protocols, 8(8), 1494–1512.
http://doi.org/10.1038/nprot.2013.084
Hahn, M. E. (2011). Mechanistic research in aquatic toxicology: Perspectives and future
directions. Aquatic Toxicology, 105(3-4), 67–71.
http://doi.org/10.1016/j.aquatox.2011.06.001
Hoare, K., Beaumont, A. R., & Davenport, J. (1995a). Variation among populations in the
resistance of Mytilus edulis embryos to copper:adaptation to pollution? Marine Ecology
Progress Series, 120, 155–161. http://doi.org/10.3354/meps120155
Hoare, K., Davenport, J., & Beaumont, A. R. (1995b). Effects of exposure and previous exposure
to copper on growth of veliger larvae and survivorship of Mytilus edulis juveniles. Marine
Ecology Progress Series, 120, 163–168. http://doi.org/10.3354/meps120163
Huang, X., & Madan, A. (1999). CAP3: A DNA sequence assembly program. Genome
Research, 9(9), 868–877.
Irving, H., & Williams, R. J. P. (1953). 637. The stability of transition-metal complexes. Journal
of the Chemical Society (Resumed), (0), 3192–3210. http://doi.org/10.1039/jr9530003192
Ji, R.-R., de Silva, H., Jin, Y., Bruccoleri, R. E., Cao, J., He, A., et al. (2009). Transcriptional
Profiling of the Dose Response: A More Powerful Approach for Characterizing Drug
Activities. PLoS Computational Biology, 5(9), e1000512–12.
http://doi.org/10.1371/journal.pcbi.1000512
33

Ji, R.-R., Siemers, N. O., Lei, M., Schweizer, L., & Bruccoleri, R. E. (2011). SDRS--an
algorithm for analyzing large-scale dose-response data. Bioinformatics, 27(20), 2921–2923.
http://doi.org/10.1093/bioinformatics/btr489
JOHNSON, A., CAREW, E., & SLOMAN, K. (2007). The effects of copper on the
morphological and functional development of zebrafish embryos. Aquatic Toxicology, 84(4),
431–438. http://doi.org/10.1016/j.aquatox.2007.07.003
Johnson, D. (1988). Development of Mytilus edulis embryos: a bio-assay for polluted waters.
Marine Ecology Progress Series, 46, 135–138. http://doi.org/10.3354/meps046135
Kültz, D. (2005). MOLECULAR AND EVOLUTIONARY BASIS OF THE CELLULAR
STRESS RESPONSE. Annual Review of Physiology, 67(1), 225–257.
http://doi.org/10.1146/annurev.physiol.67.040403.103635
Langfelder, P., & Horvath, S. (2007). Eigengene networks for studying the relationships between
co-expression modules. BMC Systems Biology, 1(1), 54. http://doi.org/10.1186/1752-0509-1-
54
Li, W., & Godzik, A. (2006). Cd-hit: a fast program for clustering and comparing large sets of
protein or nucleotide sequences. Bioinformatics, 22(13), 1658–1659.
http://doi.org/10.1093/bioinformatics/btl158
Liao, Y., Smyth, G. K., & Shi, W. (2014). featureCounts: an efficient general purpose program
for assigning sequence reads to genomic features. Bioinformatics, 30(7), 923–930.
http://doi.org/10.1093/bioinformatics/btt656
Liu, J., Yang, D., Liu, S., Li, S., Xu, G., Zheng, G., et al. (2015). Microarray: a global analysis of
biomineralization-related gene expression profiles during larval development in the pearl
oyster, Pinctada fucata. BMC Genomics, 16(1), 980–15. http://doi.org/10.1186/s12864-015-
1524-2
Maere, S., Heymans, K., & Kuiper, M. (2005). BiNGO: a Cytoscape plugin to assess
overrepresentation of Gene Ontology categories in Biological Networks. Bioinformatics,
21(16), 3448–3449. http://doi.org/10.1093/bioinformatics/bti551
Martin, M., Osborn, K. E., Billig, P., & Glickstein, N. (1981). Toxicities of ten metals to
Crassostrea gigas and Mytilus edulis embryos and Cancer magister larvae. Marine Pollution
Bulletin, 12(9), 305–308. http://doi.org/10.1016/0025-326X(81)90081-3
McCarthy, D. J., Chen, Y., & Smyth, G. K. (2012). Differential expression analysis of
multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids
Research, 40(10), 4288–4297. http://doi.org/10.1093/nar/gks042
Navarro, A., Faria, M., Barata, C., & Piña, B. (2011). Transcriptional response of stress genes to
metal exposure in zebra mussel larvae and adults. Environmental Pollution (Barking, Essex :
1987), 159(1), 100–107. http://doi.org/10.1016/j.envpol.2010.09.018
Negri, A., Oliveri, C., Sforzini, S., Mignione, F., Viarengo, A., & Banni, M. (2013).
Transcriptional Response of the Mussel Mytilus galloprovincialis (Lam.) following
Exposure to Heat Stress and Copper. PLoS ONE, 8(6), e66802–11.
http://doi.org/10.1371/journal.pone.0066802
Nordberg, G. F. (2010). Biomarkers of exposure, effects and susceptibility in humans and their
application in studies of interactions among metals in China. Toxicology Letters, 192(1), 45–
49. http://doi.org/10.1016/j.toxlet.2009.06.859
Poynton, H. C., Loguinov, A. V., Varshavsky, J. R., Chan, S., Perkins, E. J., & Vulpe, C. D.
(2008). Gene expression profiling in Daphnia magna part I: concentration-dependent profiles
34

provide support for the No Observed Transcriptional Effect Level. Environmental Science &
Technology, 42(16), 6250–6256.
Poynton, H. C., Varshavsky, J. R., Chang, B., Cavigiolio, G., Chan, S., Holman, P. S., et al.
(2007). Daphnia magna ecotoxicogenomics provides mechanistic insights into metal
toxicity. Environmental Science & Technology, 41(3), 1044–1050.
Reinartz, A., Ehling, J., Franz, S., Simon, V., Bravo, I. G., Tessmer, C., et al. (2010). Small
intestinal mucosa expression of putative chaperone fls485. BMC Gastroenterology, 10(1),
27. http://doi.org/10.1186/1471-230X-10-27
Ritz, C., Baty, F., Streibig, J. C., & Gerhard, D. (2015). Dose-Response Analysis Using R. PLoS
ONE, 10(12), e0146021. http://doi.org/10.1371/journal.pone.0146021
Rivera-Duarte, I., Rosen, G., Lapota, D., Chadwick, D. B., Kear-Padilla, L., & Zirino, A. (2005).
Copper Toxicity to Larval Stages of Three Marine Invertebrates and Copper Complexation
Capacity in San Diego Bay, California. Environmental Science & Technology, 39(6), 1542–
1546. http://doi.org/10.1021/es040545j
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010). edgeR: a Bioconductor package for
differential expression analysis of digital gene expression data. Bioinformatics, 26(1), 139–
140. http://doi.org/10.1093/bioinformatics/btp616
Sadiq. (1992). Toxic Metal Chemistry in Marine Environments. CRC Press.
Sawasaki, T., Shigemasa, K., Gu, L., Beard, J. B., & O’Brien, T. J. (2004). The transmembrane
protease serine (TMPRSS3/TADG-12) D variant: a potential candidate for diagnosis and
therapeutic intervention in ovarian cancer. Tumor Biology, 25(3), 141-148.
Schiff, K. C., Bay, S. M., & Diehl, D. (2003). Stormwater toxicity in Chollas Creek and San
Diego Bay, California. Environmental Monitoring and Assessment, 81, 119–132.
Schiff, K., Brown, J., Diehl, D., & Greenstein, D. (2007). Extent and magnitude of copper
contamination in marinas of the San Diego region, California, USA. Marine Pollution
Bulletin, 54(3), 322–328. http://doi.org/10.1016/j.marpolbul.2006.10.013
Schirmer, K., Fischer, B. B., Madureira, D. J., & Pillai, S. (2010). Transcriptomics in
ecotoxicology. Analytical and Bioanalytical Chemistry, 397(3), 917–923.
http://doi.org/10.1007/s00216-010-3662-3
Schönitzer, V., & Weiss, I. M. (2007). The structure of mollusc larval shells formed in the
presence of the chitin synthase inhibitor Nikkomycin Z. BMC Structural Biology, 7(1), 71.
http://doi.org/10.1186/1472-6807-7-71
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., et al. (2003).
Cytoscape: a software environment for integrated models of biomolecular interaction
networks. Genome Research, 13(11), 2498–2504. http://doi.org/10.1101/gr.1239303
Silva-Aciares, F., Zapata, M., Tournois, J., Moraga, D., & Riquelme, C. (2011). Identification of
genes expressed in juvenile Haliotis rufescens in response to different copper concentrations
in the north of Chile under controlled conditions. Marine Pollution Bulletin, 62(12), 2671–
2680. http://doi.org/10.1016/j.marpolbul.2011.09.023
Sokolova, I. M. (2013). Energy-Limited Tolerance to Stress as a Conceptual Framework to
Integrate the Effects of Multiple Stressors. Integrative and Comparative Biology, 53(4),
597–608. http://doi.org/10.1093/icb/ict028
Suzuki, M., Saruwatari, K., Kogure, T., Yamamoto, Y., Nishimura, T., Kato, T., & Nagasawa, H.
(2009). An acidic matrix protein, Pif, is a key macromolecule for nacre formation. Science,
325(5946), 1388–1390. http://doi.org/10.1126/science.1173793
35

Varotto, L., Domeneghetti, S., Rosani, U., Manfrin, C., Cajaraville, M. P., Raccanelli, S., et al.
(2013). DNA Damage and Transcriptional Changes in the Gills of Mytilus galloprovincialis
Exposed to Nanomolar Doses of Combined Metal Salts (Cd, Cu, Hg). PLoS ONE, 8(1),
e54602–18. http://doi.org/10.1371/journal.pone.0054602
Venier, P., De Pittà, C., Pallavicini, A., Marsano, F., Varotto, L., Romualdi, C., et al. (2006).
Development of mussel mRNA profiling: Can gene expression trends reveal coastal water
pollution? Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis,
602(1-2), 121–134. http://doi.org/10.1016/j.mrfmmm.2006.08.007
Vergani, L., Grattarola, M., Grasselli, E., Dondero, F., & Viarengo, A. (2007). Molecular
characterization and function analysis of MT-10 and MT-20 metallothionein isoforms from
Mytilus galloprovincialis. Archives of Biochemistry and Biophysics, 465(1), 247–253.
http://doi.org/10.1016/j.abb.2007.05.023
Viarengo, A. (1985). Biochemical Effects of Trace Metals. Marine Pollution Bulletin, 16, 153–
158.
Voronezhskaya, E. E., Nezlin, L. P., Odintsova, N. A., Plummer, J. T., & Croll, R. P. (2008).
Neuronal development in larval mussel Mytilus trossulus (Mollusca: Bivalvia).
Zoomorphology, 127(2), 97–110. http://doi.org/10.1007/s00435-007-0055-z
Walker, C.H., Hopkin, S.P., Sibly, R.M., and Peakall, D.B. (2001). Principles of Ecotoxicology,
Second Edition. Taylor & Francis. ISBN-13: 978-0748409402
Weis, J. (2014). Delayed Behavioral Effects of Early Life Toxicant Exposures in Aquatic Biota.
Toxics, 2(2), 165–187. http://doi.org/10.3390/toxics2020165
Whitehead, A., Triant, D. A., Champlin, D., & Nacci, D. (2010). Comparative transcriptomics
implicates mechanisms of evolved pollution tolerance in a killifish population. Molecular
Ecology, 19(23), 5186–5203. http://doi.org/10.1111/j.1365-294X.2010.04829.x
Williams, R. J. P. (1981). The Bakerian Lecture, 1981: Natural Selection of the Chemical
Elements. Proceedings of the Royal Society B: Biological Sciences, 213(1193), 361–397.
http://doi.org/10.1098/rspb.1981.0071
Williams, R. J. P. (1984). Zinc: what is its role in biology? Endeavour, 8(2), 65–70.
http://doi.org/10.1016/0160-9327(84)90040-1
Xu, M., Jiang, L., Shen, K.-N., Wu, C., He, G., & Hsiao, C.-D. (2016). Transcriptome response
to copper heavy metal stress in hard-shelled mussel (Mytilus coruscus). Genomics Data, 7,
152–154. http://doi.org/10.1016/j.gdata.2015.12.010
Zapata, M., Tanguy, A., David, E., Moraga, D., & Riquelme, C. (2009). Transcriptomic response
of Argopecten purpuratus post-larvae to copper exposure under experimental conditions.
Gene, 442(1-2), 37–46. http://doi.org/10.1016/j.gene.2009.04.019

36
Proportion (Control Normalized)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
0 5 10 15 20 25 30
Normal Development
Survival
Proportion (Control Normalized)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Copper Concentration (ug/L)
0 5 10 15 20 25 30
LC50 = 6.5 ug/L
EC50 = 7.26 ug/L
LC50 = 3.96 ug/L
EC50 = 4.03 ug/L
Figure 1
A
B
Normal development and survival were plotted against copper concentration for
trial 1 (A) and trial 2 (B). LC50 for survival and EC50 for normal development in
each trial are indicated on the curves. Data indicate that larvae were slightly
more sensitive to copper in trial 2.
37
0 ppb
2 ppb
3.1
ppb
4 ppb
6 ppb
8 ppb
10 ppb
15 ppb
0 ppb
2 ppb
3.1 ppb
4 ppb
6 ppb
8 ppb
15 ppb
20 ppb
0 ppb
2 ppb
3.1 ppb
4 ppb
6 ppb
8 ppb
10 ppb
15 ppb
20 ppb
0 ppb
2 ppb
3.1 ppb
4 ppb
6 ppb
8 ppb
10 ppb
15 ppb
20 ppb
-3.0 0 3.0
Figure 2
A B
-3.0 0 3.0
C
0
0.2
0.4
0.6
0.8
1
1.2
0 5 10 15 20
Proportion Normal Development
Copper Concentration (ug/L)
Calponin homolog OV9M
EC50 Low Threshold = 1.92 ug/L
EC50 = 3.29 ug/L
Calponin homolog OV9M
Heatmaps depict expression of sigmoidal genes that were upregulated (A) and
downregulated (B) in both trials 1 and 2 (left and right panels). Count values are control-
normalized and log2 transformed. For each gene, a low threshold for EC50 and the
EC50 were calculated. Those values are illustrated for an example gene (C). The EC50
low threshold was used to order genes in the heat maps (A and B).
38
Figure 3
A B
D
Proportion Normal Development
Frequency of low EC50 threshold
Copper Concentration
The frequency of sigmoidal genes’ low transcriptional EC50 thresholds
(LOTEC) were plotted as a yellow histogram for downregulated genes in trials 1
(A) and 2 (C), and upregulated genes in trials 1 (B) and 2 (D). These values
were compared to normal development (dark red line) in each trial. LOTEC
thresholds are more sensitive indicators of copper-induced expression changes
than EC50 values, which is evidenced by the shift toward lower copper doses
compared to frequency histograms of EC50 values (Figure 4).
C
39
Figure 4
A B
C D
Proportion Normal Development
Frequency of transcriptional EC50s
Copper Concentration
The frequency of sigmoidal genes’ transcriptional EC50s were plotted as a yellow
histogram for down regulated genes in trials 1 (A) and 2 (C), and upregulated genes in
trials 1 (B) and 2 (D). These values were compared to normal development (dark red
line) in each trial. Downregulated genes had EC50s primarily at lower copper
concentrations, while upregulated genes had EC50s at higher copper concentrations.
40
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−0.2 0.2 0.6
0.0 0.4 0.8
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−0.4 0.0 0.4
0.0 0.4 0.8
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−0.2 0.2 0.6
0.0 0.4 0.8
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●
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−0.2 0.2 0.6
0.2 0.6 1.0
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−0.4 0.0 0.4
0.2 0.6 1.0
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●
●
●
●
●
●
−0.2 0.2 0.6
0.2 0.6 1.0
Copper Concentration
(ug/L)
Proportion Survival
Proportion Normal
Development
Eigengene
White Yellow Steelblue
A B
−1
−0.5
0
0.5
1
White
Yellow
Steelblue
−0.54
(0.03)
−0.66
(0.006)
−0.72
(0.002)
0.65
(0.007)
0.81
(1e−04)
0.69
(0.003)
0.65
(0.007)
0.77
(4e−04)
0.68
(0.004)
Copper concentration
(μg/L)
Normal Development
Survival
White Yellow Steelblue
Figure 5--The eigengenes of co-expression modules were correlated with three
experimental/phenotypic traits—copper concentration, normal development, and
survival. There are 83 genes in the white module; 207 in the yellow; and 36 in the
steelblue. In the heatmap (A), Pearson’s correlation values are listed in each box,
and the associated p-value is listed in parentheses below. Box color corresponds to
the correlation value, and the correlation values associated with each color is
depicted on the scale bar to the right. Eigengenes for each module were also
plotted against the three experimental/phenotypic traits (B). Points represent
eigengene values, and red trendlines were added with locally weighted scatterplot
smoothing (LOWESS).
41
A. Development B. Nervous System C. Biomineralization D. Metal-ion binding E. Muscle
Figure 6
Gene expression of major functional groups present in downregulated genes, with gene
name and module color listed for each plot. Curves represent expression of the gene in
Trial 1 (black), and Trial 2 (red). For each graph, copper concentration (ug/L) is on the x
axis, with expression level (TMM) on the left (Trial 1) and right (Trial 2) axes. Five
representative genes are shown for five major functional categories, development (A),
nervous system and neural development (B), biomineralization and shell matrix
formation (C), divalent metal ion binding (D), and muscle/muscle development (E).
42
Table 1
Descriptive statistics assigned by SDRS to genes in three sensitive
downregulated modules. The % of all sigmoidal downregulated genes that
belonged to each module is listed as well.
Table 2
The number of genes from each module that fall within notable functional
categories that were well-represented among sigmoidal and modular genes.
Steelblue White Yellow
% Sigmoidal Genes in Module 12 50 25
EC50 Range T1 (ug/L) 2.02 - 3.81 1.83 - 6.84 4.00 - 8.73
EC50 Median T1 (ug/L) 2.71 3.29 6.52
EC50 Range T2 (ug/L) 0.80 - 3.81 0.24 - 4.86 1.58 - 3.29
EC50 Median T2 (ug/L) 1.44 2.45 2.58
LOTEC Range T1 (ug/L) 0.04 - 2.99 0.001 - 5.63 2.23 - 6.84
LOTEC Median T1 (ug/L) 1.13 1.88 4.20
LOTEC Range T2 (ug/L) 0.001 - 2.02 0.001 - 3.45 0.001 - 3.29
LOTEC Median T2 (ug/L) 0.001 1.42 1.44
Steelblue White Yellow
Biomineralization; Shell matrix formation 3 9 5
Development; Cell fate commitment 4 2 17
Muscle; Muscle development - 5 11
Nervous system; Neural development 4 8 9
Cell projection—axon, dendrite - - 10
Cell projection—axoneme, cilia - - 16
Metal ion binding 5 12 7
43
Blastx_swissprot_gene
name
Blastn_nt_geneprediction Module Functional Group LOTEC EC50 T1 P-value Direction LOTEC EC50 T2 P-value Direction
Mytilus galloprovincialis LKD-
rich protein-1 mRNA,
complete cds steelblue Biomineralization 0.34891 2.33937 0.0012627 Down 0.0011 0.79971 6.16E-05 Down
Temptin; steelblue Biomineralization 6.20704 0.067636 Down 0.02628 2.02083 9.80E-06 Down
Lectin BRA-3;
Mytilus galloprovincialis C-
type lectin 8 mRNA,
complete cds steelblue Biomineralization 2.98569 3.81058 0.0050346 Down 0.69082 2.02083 0.023161 Down
Peroxidasin homolog; white Biomineralization 2.98569 0.55285 Down 18.1572 0.62806 Up
Fibrinogen-like protein
A; white Biomineralization 0.3014 2.12188 0.006499 Down 4.00111 0.067199 Down
Mantle protein;
Mytilus coruscus shell
protein-3 mRNA, complete
cds white Biomineralization 3.29173 0.073871 Down 6.20704 0.13977 Down
Temptin; white Biomineralization 6.20704 0.22237 Down 2.84352 3.13498 0.039192 Down
Perlucin-like protein
{ECO:0000303|PubMe
d:21643827};
Mytilus galloprovincialis C-
type lectin 2 mRNA,
complete cds white Biomineralization 1.50798 4.00111 0.0060207 Down 0.0011 0.23616 0.0001043 Down
Protein PIF; white Biomineralization 5.62997 6.84326 0.039361 Down 3.29173 4.63179 0.035227 Down
Temptin; white Biomineralization 1.50798 2.70811 0.0058697 Down 1.83296 2.12188 0.033784 Down
Chitin synthase 6;
PREDICTED: Cimex
lectularius uncharacterized
LOC106666781
(LOC106666781), transcript
variant X2, mRNA yellow Biomineralization 7.5447 0.38461 Down 2.33937 2.98569 0.015979 Down
Myosin-IIIb;
Lottia gigantea hypothetical
protein partial mRNA yellow Biomineralization 5.91147 7.18542 0.013925 Down 8.31803 0.15597 Down
Protein PIF; yellow Biomineralization 4.20117 7.18542 0.0097231 Down 0.1136 2.22797 4.58E-05 Down
SDRS Trial 1 SDRS Trial 2 Transcript information
Table S1--Annotated sensitive biomarkers classified by module and broad functional category. SDRS information for these genes (if
available) is provided as well
44
Galactosylgalactosylxyl
osylprotein 3-beta-
glucuronosyltransferas
e 2; yellow Biomineralization 2.45634 0.2773 Down 2.45634 0.1286 Down
Dynein beta chain,
ciliary;
PREDICTED: Crassostrea
gigas dynein beta chain,
ciliary-like (LOC105328728),
mRNA yellow
Cell projection:
axoneme, cilia,
flagella 9.62916 0.09848 Down 14.2266 0.44314 Down
Dynein heavy chain 7,
axonemal;
PREDICTED: Crassostrea
gigas dynein heavy chain
12, axonemal-like
(LOC105340285), mRNA yellow
Cell projection:
axoneme, cilia,
flagella 8.73393 0.45069 Down 20.0183 0.75127 Down
Dynein heavy chain 5,
axonemal;
Lottia gigantea hypothetical
protein mRNA yellow
Cell projection:
axoneme, cilia,
flagella 7.18542 0.051637 Down 15.6849 0.45418 Down
Lebercilin; yellow
Cell projection:
axoneme, cilia,
flagella 7.18542 0.38744 Down 3.29173 0.15906 Down
Cilia- and flagella-
associated protein 43
{ECO:0000312|HGNC:
HGNC:26684}; yellow
Cell projection:
axoneme, cilia,
flagella 7.5447 0.19954 Down 3.13498 0.23992 Down
Cilia- and flagella-
associated protein 45
{ECO:0000312|HGNC:
HGNC:17229};
PREDICTED: Saccoglossus
kowalevskii coiled-coil
domain-containing protein
19, mitochondrial-like
(LOC100367739), mRNA yellow
Cell projection:
axoneme, cilia,
flagella 6.84326 0.10414 Down 3.13498 0.57588 Down
Alstrom syndrome
protein 1; yellow
Cell projection:
axoneme, cilia,
flagella 7.18542 0.12006 Down 7.5447 0.13339 Down
Dynein heavy chain 5,
axonemal;
Lottia gigantea hypothetical
protein partial mRNA yellow
Cell projection:
axoneme, cilia,
flagella 7.92193 0.16128 Down 8.31803 0.474 Down
Transmembrane
protein 231; yellow
Cell projection:
axoneme, cilia,
flagella 4.86338 0.098132 Down 2.22797 2.22797 0.04927 Down
45
Cilia- and flagella-
associated protein 54
{ECO:0000305}; yellow
Cell projection:
axoneme, cilia,
flagella 6.20704 0.21997 Down 12.2895 0.38405 Down
Polyamine-modulated
factor 1-binding protein
1; yellow
Cell projection:
axoneme, cilia,
flagella 8.31803 0.18692 Down 6.51739 0.28735 Down
Fibrocystin-L; yellow
Cell projection:
axoneme, cilia,
flagella 6.51739 6.84326 0.038127 Down 3.13498 0.050229 Down
IQ domain-containing
protein G;
PREDICTED: Crassostrea
gigas IQ domain-containing
protein G-like
(LOC105333470), transcript
variant X2, mRNA yellow
Cell projection:
axoneme, cilia,
flagella 6.51739 0.12109 Down 1.9246 0.22714 Down
Tctex1 domain-
containing protein 4; yellow
Cell projection:
axoneme, cilia,
flagella 6.20704 6.84326 0.039999 Down 0.00122 1.58338 0.0003029 Down
Radial spoke head 1
homolog; yellow
Cell projection:
axoneme, cilia,
flagella 5.91147 0.15898 Down 3.62913 0.64828 Down
Kinesin-like protein
KIF19;
PREDICTED: Crassostrea
gigas kinesin-like protein
KIF19 (LOC105325781),
transcript variant X2, mRNA yellow
Cell projection:
axoneme, cilia,
flagella 7.5447 0.44246 Down 19.0651 0.88004 Down
Stimulated by retinoic
acid gene 6 protein
homolog; steelblue Development 1.12528 2.57915 0.022168 Down 0.00122 1.43617 0.0007344 Down
Retinoic acid receptor
beta; steelblue Development 2.33937 2.70811 0.025453 Down 0.00116 1.02066 0.0035881 Down
Calcitonin gene-related
peptide type 1
receptor; steelblue Development 6.20704 0.058864 Down 1.58338 3.13498 0.0066773 Down
Armadillo repeat-
containing protein 4; steelblue Development 4.41123 0.56978 Down 0.34891 0.8397 0.042229 Down
Fibropellin-3; white Development 3.81058 0.17259 Down 1.74567 2.57915 0.0001595 Down
Notch-regulated
ankyrin repeat-
containing protein;
Mytilus edulis gene for endo-
1,4-mannanase, exons 1-6 white Development 4.00111 0.067895 Down 3.45631 0.11328 Down
46
Protein muscleblind; yellow Development 6.51739 0.3818 Down 16.4691 0.5515 Down
Nuclear receptor ROR-
beta yellow Development 7.18542 0.19931 Down 9.17062 0.93244 Down
Protein Wnt-5a; yellow Development 4.20117 0.27694 Down 5.36187 0.055988 Down
FRAS1-related
extracellular matrix
protein 1; yellow Development 7.5447 0.43943 Down 8.31803 0.092795 Down
Muscarinic
acetylcholine receptor
DM1; yellow Development 6.84326 0.2358 Down 8.73393 0.71625 Down
Homeobox protein
orthopedia;
PREDICTED: Nicrophorus
vespilloides homeobox
protein orthopedia-like
(LOC108562940), transcript
variant X3, mRNA yellow Development 7.92193 0.07195 Down 2.02083 3.29173 0.02822 Down
Homeobox protein
XHOX-3; yellow Development 6.51739 0.85027 Down 11.147 0.25646 Down
Armadillo repeat-
containing protein 4;
PREDICTED: Saccoglossus
kowalevskii armadillo
repeat-containing protein 4-
like (LOC100368358), mRNA yellow Development 9.17062 0.62131 Down 1.58338 2.02083 0.044048 Down
Cdc42 homolog; yellow Development 3.81058 0.070234 Down 5.91147 0.077337 Down
Homeobox protein
Meis1;
Schistosoma haematobium
Putative homeobox protein
Meis3-like 1 mRNA yellow
Cell Fate
Commitment;
Regulation of organ
formation 7.92193 0.11338 Down 7.5447 0.4577 Down
Homeobox protein
abdominal-B; yellow
Cell Fate
Commitment;
Regulation of organ
formation 8.73393 0.29295 Down 10.1106 0.2096 Down
Homeobox protein Hox-
C11;
Mizuhopecten yessoensis
Post1 gene for Post1
homeodomain protein P1,
partial cds yellow
Cell Fate
Commitment;
Regulation of organ
formation 6.84326 7.18542 0.047254 Down 2.70811 3.29173 0.017163 Down
47
One cut domain family
member 2;
PREDICTED: Crassostrea
gigas one cut domain family
member 2-like
(LOC105341544), transcript
variant X7, mRNA yellow
Cell Fate
Commitment;
Regulation of organ
formation 6.84326 0.61925 Down 0.00116 0.81538 Up
Protein patched
homolog 1; yellow
Cell Fate
Commitment;
Regulation of organ
formation 8.73393 0.41391 Down 0.76163 2.33937 0.013837 Down
Zinc finger protein ZIC
1;
PREDICTED: Cyprinodon
variegatus Zic family
member 2 (zic2), mRNA yellow
Cell Fate
Commitment;
Regulation of organ
formation 2.22797 0.87599 Down 5.10655 0.34257 Down Protein Wnt-11
{ECO:0000303|PubMe yellow
Cell Fate
Commitment; 15.6849 0.46234 Down 3.62913 0.3818 Down Hairy/enhancer-of-split
related with YRPW yellow
Cell Fate
Commitment; 7.18542 0.060314 Down 9.17062 0.26277 Down
Protein FAM161A; yellow
Cell projection:
axon/dendrite 7.92193 0.51955 Down 16.4691 0.68798 Down Microtubule-associated
protein 4; yellow
Cell projection:
axon/dendrite 6.84326 0.33897 Down 9.62916 0.68914 Down
Glutamate receptor
ionotropic, kainate 3; yellow
Cell projection:
axon/dendrite
related 3.45631 4.86338 0.032478 Down 4.00111 0.20105 Down
Adenylate cyclase type
9
{ECO:0000303|PubMe
d:9628827}; yellow
Cell projection:
axon/dendrite
related 7.18542 0.56055 Down 10.1106 0.65263 Down
Bile salt-activated
lipase; yellow
Cell projection:
axon/dendrite
related 5.10655 0.10242 Down 2.12188 3.29173 0.0067304 Down
Excitatory amino acid
transporter 2; yellow
Cell projection:
axon/dendrite
related 6.84326 0.10096 Down 1.36778 3.29173 6.82E-05 Down
Calcium-activated
potassium channel
slowpoke;
PREDICTED: Biomphalaria
glabrata calcium-activated
potassium channel
slowpoke-like
(LOC106056545), transcript
variant X10, mRNA yellow
Cell projection:
axon/dendrite
related 6.20704 0.19509 Down 6.84326 0.39817 Down
48
Sodium-dependent
dopamine transporter
{ECO:0000303|PubMe
d:11125028}; yellow
Cell projection:
axon/dendrite
related 7.5447 0.14894 Down 2.84352 0.59025 Down
Lipoxygenase
homology domain-
containing protein 1; yellow
Cell projection:
axon/dendrite
related 7.5447 0.38292 Down 9.62916 0.7936 Down
Ileal sodium/bile acid
cotransporter; yellow
Cell projection:
axon/dendrite
related 10.1106 0.365 Down 2.45634 2.57915 0.044233 Down
Protein Wnt-2b; yellow
Negative
Regulation of Glial
Cell Differentiation 4.86338 7.92193 0.016244 Down 1.43617 2.70811 0.0079873 Down
Nuclear receptor
subfamily 2 group E
member 1;
Euperipatoides
kanangrensis partial mRNA
for tll (tailless gene) yellow
Negative
Regulation of Glial
Cell Differentiation 8.31803 0.28245 Down 3.45631 0.30688 Down
Neurogenic locus notch
homolog protein 1; yellow
Negative
Regulation of Glial
Cell Differentiation 8.31803 0.3598 Down 8.73393 0.34695 Down
Sodium- and chloride-
dependent GABA
transporter 3; steelblue Nervous System 7.18542 0.56147 Down 0.3323 2.57915 0.0001108 Down
5-hydroxytryptamine
receptor 4; steelblue Nervous System 0.72536 3.62913 0.019267 Down 2.02083 2.57915 0.023606 Down Dyslexia susceptibility 1
candidate gene 1 steelblue Nervous System 20.0183 0.94194 Down 1.12528 2.84352 0.0006547 Down Neuronal acetylcholine
receptor subunit alpha- steelblue Nervous System 12.904 0.4186 Down 3.13498 0.067533 Down
Glutamine synthetase; white Nervous System 2.98569 0.089509 Down 1.36778 2.84352 0.019926 Down
Omega-amidase NIT2; white Nervous System 7.5447 0.29354 Down 20.0183 0.96528 Down Neuronal acetylcholine
receptor subunit alpha- white Nervous System 2.33937 3.13498 0.0064766 Down 6.84326 0.35067 Down Neuronal acetylcholine
receptor subunit alpha- white Nervous System 2.84352 0.36187 Down 0.00116 1.30265 0.0021148 Down
Cathepsin L2; white Nervous System 2.22797 2.84352 0.012471 Down 5.91147 0.72654 Up Pituitary homeobox x
{ECO:0000250|UniProt white Nervous System 9.62916 0.2069 Down 1.74567 3.62913 0.0035903 Down T-cell leukemia
homeobox protein 3; white Nervous System 1.9246 0.061587 Down 0.14498 1.58338 0.021352 Down Glutamate receptor 1
{ECO:0000312|WormB white Nervous System 1.58338 2.33937 0.022354 Down 2.98569 0.090045 Down
Arylsulfatase B; yellow Nervous System 3.45631 5.10655 0.02375 Down 1.43617 2.84352 0.0001092 Down Probable G-protein
coupled receptor 75; yellow Nervous System 7.18542 0.1136 Down 9.17062 0.72776 Down
49
Frizzled-4;
PREDICTED: Lingula anatina
frizzled-4-like yellow Nervous System 8.31803 0.094336 Down 13.5492 0.13312 Down Dorsal root ganglia
homeobox protein; yellow Nervous System 7.92193 0.17269 Down 2.12188 2.45634 0.013964 Down Homeobox protein
TGIF2;
Lottia gigantea hypothetical
protein mRNA yellow Nervous System 14.2266 0.30939 Down 2.22797 0.24323 Down Excitatory amino acid
transporter;
PREDICTED: Cyprinodon
variegatus excitatory amino yellow Nervous System 7.18542 0.40173 Down 18.1572 0.39905 Up Mytilus coruscus transgelin-
like protein-3 mRNA, white Nervous System 1.66255 2.98569 0.0003945 Down 2.45634 0.58734 Down
ADAMTS-like protein
3; steelblue Metal Ion Binding 6.51739 0.078184 Down 1.9246 2.84352 0.0005995 Down
Translin-associated
factor X-interacting
protein 1 steelblue Metal Ion Binding 0.49095 0.19287 Down 1.50798 2.12188 0.014437 Down
Zinc finger protein 227; steelblue Metal Ion Binding 0.00704 0.63384 Up 0.17622 2.12188 0.0064814 Down
Peptidoglycan
recognition protein 3;
Mytilus galloprovincialis
peptidoglycan recognition
protein 2 mRNA, complete
cds steelblue Metal Ion Binding 0.04495 2.02083 0.0010341 Down 0.0011 0.8397 8.17E-05 Down
Mytilus edulis MT-10 gene
for metallothionein steelblue Metal Ion Binding 3.62913 0.34088 Down 0.09813 1.58338 0.010512 Down
Dipeptidase 1; white Metal Ion Binding 3.29173 5.10655 0.01389 Down 4.20117 0.062249 Down
Zinc metalloproteinase
nas-13; white Metal Ion Binding 2.84352 5.62997 0.020039 Down 3.45631 4.86338 0.037519 Down
Carboxypeptidase B; white Metal Ion Binding 0.22491 2.22797 0.0006779 Down 0.0011 1.24062 7.27E-05 Down
Mitochondrial
amidoxime-reducing
component 1; white Metal Ion Binding 2.33937 2.84352 0.025159 Down 4.63179 0.090161 Down
Carbonic anhydrase 4; white Metal Ion Binding 2.33937 4.00111 0.0019707 Down 0.0011 1.66255 3.39E-05 Down
Carbonic anhydrase 14;
Mytilus edulis partial mRNA
for putative carbonic
anhydrase (CA9 gene) white Metal Ion Binding 1.50798 2.98569 0.000763 Down 5.36187 0.97028 Up
Mitochondrial
amidoxime-reducing
component 1; white Metal Ion Binding 2.84352 0.15385 Down 0.00279 0.50244 Down
Guanylate cyclase
soluble subunit beta-2; white Metal Ion Binding 3.29173 0.32373 Down 4.20117 0.15066 Down
Mytilus edulis mRNA for
metallothionein MT 10 III white Metal Ion Binding 3.29173 0.059185 Down 2.12188 2.70811 0.0032589 Down
50
Mytilus galloprovincialis
metallothionein 10-III (MT-
10-III) mRNA, MT-10-III-a
allele, complete cds white Metal Ion Binding 2.45634 0.13025 Down 1.30265 2.70811 0.036528 Down
Interferon regulatory
factor 2-binding protein-
like B; yellow Metal Ion Binding 0.00307 0.58976 Up 6.20704 0.3472 Down
Zinc finger protein 226; yellow Metal Ion Binding 6.51739 0.085054 Down 9.17062 0.76646 Down
Glutamyl
aminopeptidase;
PREDICTED: Gallus gallus
glutamyl aminopeptidase
(aminopeptidase A)
(ENPEP), mRNA yellow Metal Ion Binding 6.51739 7.92193 0.016922 Down 12.2895 0.30813 Down
E3 SUMO-protein
ligase RanBP2; yellow Metal Ion Binding 0.00229 0.7601 Up 6.20704 0.32485 Down
Carboxypeptidase M; yellow Metal Ion Binding 7.18542 0.2973 Down 0.00525 0.50565 Up Acid phosphatase type
7 {ECO:0000305}; yellow Metal Ion Binding 16.4691 0.63915 Up 8.31803 0.57165 Down Tyrosine 3-
monooxygenase; white
Metal Ion Binding;
Biomineralization 1.74567 2.45634 0.0040691 Down 0.54128 2.02083 0.017494 Down Tyrosinase-like protein
1; white
Metal Ion Binding;
Biomineralization 2.57915 4.20117 0.000605 Down 1.83296 2.84352 7.74E-05 Down Tyrosinase-like protein
2; yellow
Metal Ion Binding;
Biomineralization 6.51739 0.20023 Down 2.84352 0.22618 Down Calponin homolog
OV9M;
Mytilus coruscus calponin-
like protein-1 mRNA, white Muscle 1.9246 3.29173 0.0013752 Down 0.1136 2.12188 0.0005701 Down Myosin heavy chain,
striated muscle;
Mytilus galloprovincialis
partial mRNA for myosin white Muscle 2.45634 3.62913 0.001302 Down 0.00171 1.9246 0.0002314 Down
Calmodulin;
Campanularia hincksii
voucher 37IT calmodulin white Muscle 2.84352 0.1083 Down 2.98569 0.16276 Down
Paramyosin;
Mytilus galloprovincialis
mRNA for paramyosin,
complete cds white Muscle 2.22797 3.29173 0.0036337 Down 2.98569 0.056972 Down
Calponin-3;
Mytilus coruscus calponin-
like protein-2 mRNA,
complete cds white Muscle 2.33937 3.29173 0.01315 Down 0.72536 2.45634 0.0070507 Down
Myosin light chain
kinase, smooth muscle; yellow Muscle 2.22797 0.59562 Down 4.20117 0.23667 Down
Myosin heavy chain,
striated muscle;
Mytilus galloprovincialis
partial mRNA for myosin
heavy chain (MHC gene),
from pedal retractor muscle yellow Muscle 2.70811 4.00111 0.0047806 Down 0.02898 1.74567 0.0044422 Down
51
Myosin heavy chain,
striated muscle;
Mytilus galloprovincialis
partial mRNA for myosin
heavy chain (MHC gene),
from catch muscle yellow Muscle 5.91147 0.051743 Down 2.57915 2.70811 0.048159 Down
Mytilus galloprovincialis
gene for twitchin, partial cds,
clone: TOPOXL_tw_D1_11f-
3_12r yellow Muscle 7.18542 0.21984 Down 9.62916 0.65263 Down
Mytilus galloprovincialis
gene for twitchin, partial cds,
clone: TOPOXL_tw_D1_11f-
3_12r yellow Muscle 7.18542 0.51254 Down 9.17062 0.47922 Down
Mytilus galloprovincialis
gene for twitchin, partial cds,
clone: TOPOXL_tw_D1_11f-
3_12r yellow Muscle 7.5447 0.30193 Down 3.13498 0.1969 Down
Calmodulin; yellow Muscle 6.20704 0.087556 Down 0.04495 1.83296 0.0035251 Down
Mytilus galloprovincialis
gene for twitchin, partial cds,
clone: TOPOXL_tw_D1_11f-
3_12r yellow Muscle 6.20704 6.51739 0.041462 Down 3.29173 3.29173 0.048095 Down
Calmodulin;
Cupiennius salei partial
mRNA for Csa-calmodulin 6
(Csa-calmodulin 6 gene),
isolate Cs3 yellow Muscle 4.63179 7.18542 0.0001621 Down 1.9246 3.13498 0.0025035 Down
Calmodulin; yellow Muscle 3.62913 5.36187 0.0076285 Down 0.34891 1.83296 0.017432 Down
Myosin-IIIb;
Lottia gigantea hypothetical
protein partial mRNA yellow Muscle 5.91147 7.18542 0.013925 Down 8.31803 0.15597 Down
52
Adult Annotation
Gene Name
ANOVA
P-value
LOTEC EC50 P-value Direction
Swissprot Gene
Name
nt Gene Name LOTEC EC50 P-value Direction LOTEC EC50 P-value Direction
26S proteasome non-
ATPase regulatory
subunit 2 0.001 14.6 50.6 0.0046 Down
26S proteasome
non-ATPase
regulatory subunit
2;
PREDICTED:
Lingula anatina
26S proteasome
non-ATPase
regulatory subunit
2-like
(LOC106163869),
mRNA 6.51739 11.7043 0.00019 Up 11.147 12.29 0.0379 Up
26S proteasome non-
ATPase regulatory
subunit 7 0.003 42.6 82.6 0.0098 Up
26S proteasome
non-ATPase
regulatory subunit
7;
Lottia gigantea
hypothetical
protein mRNA 7.18542 10.6161 0.00113 Up 11.704 14.938 0.0089 Up
Unclassifiable EST 0.000 30.6 70.6 2E-05 Up
97 kDa heat shock
protein;
Mytilus
californianus gene
for cysteine
peptidase,
complete cds 10.6161 12.2895 0.02972 Up 9.1706 10.616 0.0387 Up
AN1-type zinc finger
protein 2B 0.000 36.6 82.6 0.0003 Up
AN1-type zinc
finger protein 2B;
PREDICTED:
Lingula anatina
AN1-type zinc
finger protein 2A-
like
(LOC106168849),
transcript variant
X2, mRNA 10.1106 11.7043 0.02422 Up 8.318 12.904 0.0009 Up
AN1-type zinc finger
protein 6 0.000 30.6 74.6 5E-05 Up
AN1-type zinc
finger protein 6; 8.73393 13.5492 0.00139 Up 12.29 15.685 0.0127 Up
Adult SDRS Larval Annotation Larval SDRS Trial 1 Larval SDRS Trial 2
Table S2--SDRS parameters for genes identified as sigmoidal in both adults and larvae. For each panel, gene IDs and gene names are
on the left, and on the right are LOTEC, EC50, significance values, and direction of change in response to copper.
53
Unclassifiable EST 0.000 48.6 88.6 0.0033 Up
AN1-type zinc
finger protein 6; 8.73393 13.5492 0.00139 Up 12.29 15.685 0.0127 Up
Calmodulin 0.000 7.7 26.6 0.0011 Down Calmodulin;
Cupiennius salei
partial mRNA for
Csa-calmodulin 6
(Csa-calmodulin 6
gene), isolate Cs3 4.63179 7.18542 0.00016 Down 1.9246 3.135 0.0025 Down
cAMP-responsive
element-binding
protein-like 2 0.000 50.6 88.6 0.0025 Up
cAMP-responsive
element-binding
protein-like 2; 9.62916 14.2266 ####### Up 9.6292 13.549 0.0004 Up
cAMP-responsive
element-binding
protein-like 2 0.000 28.6 74.6 5E-05 Up
cAMP-responsive
element-binding
protein-like 2; 8.31803 12.904 ####### Up 9.1706 12.29 0.0122 Up
cAMP-responsive
element-binding
protein-like 2 0.000 28.6 74.6 5E-05 Up
cAMP-responsive
element-binding
protein-like 2; 9.17062 12.2895 0.00084 Up 9.1706 12.29 0.0076 Up
Unclassifiable EST 0.000 42.6 82.6 0.0001 Up
cAMP-responsive
element-binding
protein-like 2; 8.31803 12.904 ####### Up 9.1706 12.29 0.0122 Up
Carbonic anhydrase
4 0.001 14.6 30.6 0.0053 Down
Carbonic
anhydrase 4; 2.33937 4.00111 0.00197 Down 0.0011 1.6626 ###### Down
Rho-related GTP-
binding protein RhoJ 0.005 74.6 94.6 0.0062 Up Cdc42 homolog; 7.92193 9.62916 0.0158 Up 10.616 15.685 0.0099 Up
DNA repair protein
XRCC1 0.002 46.6 84.6 0.0233 Up
Dynein assembly
factor 5, axonemal
{ECO:0000250|Un
iProtKB:Q86Y56};
PREDICTED:
Crassostrea gigas
dynein assembly
factor 5, axonemal-
like
(LOC105339120),
mRNA 9.62916 12.2895 0.0023 Up 10.616 11.704 0.0395 Up
E3 ubiquitin-protein
ligase RNF13 0.000 30.6 72.6 0.0011 Up
E3 ubiquitin-
protein ligase
RNF167; 5.62997 8.73393 0.00024 Up 14.227 14.938 0.0331 Up
54
Heat shock-related
70 kDa protein 2 0.000 40.6 80.6 ###### Up
Heat shock 70 kDa
protein IV;
Mytilus
galloprovincialis
hsp70 mRNA for
heat shock protein
70, complete cds 9.62916 14.2266 ####### Up 10.616 14.938 0.0007 Up
Heat shock-related
70 kDa protein 2 0.000 34.6 76.6 ###### Up
Heat shock 70 kDa
protein;
Mytilus
galloprovincialis
hsp70 mRNA for
heat shock protein
70, complete cds 9.62916 12.2895 0.004 Up 10.111 12.29 0.0125 Up
Heat shock cognate
71 kDa protein 0.003 40.6 80.6 ###### Up
Heat shock 70 kDa
protein;
Mytilus
galloprovincialis
hsp70-4 gene for
heat shock protein
70 9.62916 12.2895 0.00632 Up 7.1854 9.1706 0.003 Up
HSP70B2 0.000 36.6 78.6 ###### Up
Heat shock protein
68;
Mytilus
galloprovincialis
hsp70-4 gene for
heat shock protein
70 9.62916 13.5492 0.00016 Up 9.1706 13.549 0.0001 Up
Heat shock-related
70 kDa protein 2 0.000 40.6 80.6 ###### Up
Heat shock protein
68;
Mytilus
galloprovincialis
hsp70-4 gene for
heat shock protein
70 9.17062 14.2266 ####### Up 9.6292 13.549 ###### Up
Heat shock-related
70 kDa protein 2 0.000 34.6 76.6 ###### Up
Heat shock protein
70 B2;
Mytilus
galloprovincialis
hsp70-4 gene for
heat shock protein
70 9.62916 12.2895 0.00312 Up 10.111 12.29 0.0258 Up
HSP70B2 0.000 36.6 78.6 ###### Up
Heat shock protein
70 B2;
Mytilus
galloprovincialis
hsp70-2 gene for
heat shock protein
70 9.62916 14.2266 ####### Up 9.1706 14.227 ###### Up
55
Hsp70-binding
protein 1 0.000 50.6 86.6 0.0002 Up
Hsp70-binding
protein 1; 9.62916 12.2895 0.00691 Up 10.111 14.227 0.0001 Up
Leucine-rich repeat-
containing protein 7 0.000 20.6 28.6 0.0166 Down
Leucine-rich repeat
and death domain-
containing protein
1; 5.91147 8.73393 0.00275 Down 2.7081 3.135 0.0369 Down
Unclassifiable EST 0.000 24.6 54.6 0.0003 Up
MICOS complex
subunit MIC19; 6.84326 7.18542 0.04073 Up 15.685 16.469 0.0391 Up
Unclassifiable EST 0.000 26.6 56.6 0.0008 Up
MICOS complex
subunit MIC19; 6.84326 7.18542 0.04073 Up 15.685 16.469 0.0391 Up
Proteasome subunit
alpha type-1 0.002 44.6 84.6 0.0011 Up
Proteasome
subunit alpha type-
1;
PREDICTED:
Biomphalaria
glabrata
proteasome
subunit alpha type-
1-like
(LOC106064573),
mRNA 7.18542 10.1106 0.00163 Up 10.111 13.549 0.0051 Up
UPF0474 protein
C5orf41 0.000 32.6 72.6 0.0003 Up
Protein CREBRF
homolog; 10.1106 12.2895 0.02224 Up 5.3619 10.616 0.0005 Up
UPF0474 protein
C5orf41 0.000 26.6 68.6 0.0007 Up
Protein CREBRF
homolog; 10.1106 12.2895 0.02224 Up 5.3619 10.616 0.0005 Up
Unclassifiable EST 0.009 18.6 64.6 2E-05 Up
Ras-related protein
Rab-43; 9.62916 11.7043 0.01009 Up 12.29 14.938 0.0106 Up
RuvB-like 1 0.000 50.6 80.6 0.0006 Up RuvB-like 1;
PREDICTED:
Parasteatoda
tepidariorum ruvB-
like 1
(LOC107442391),
transcript variant
X2, mRNA 9.17062 11.7043 0.00266 Up 9.6292 13.549 0.0013 Up
Hematopoietic
prostaglandin D
synthase 0.000 34.6 80.6 0.0048 Up S-crystallin SL11; 10.1106 11.7043 0.02598 Up 0.5968 1.9246 0.0158 Down
Unclassifiable EST 0.000 44.6 82.6 ###### Up Sequestosome-1; 9.62916 14.2266 ####### Up 10.616 14.938 0.0007 Up
Sequestosome-1 0.000 40.6 82.6 3E-05 Up Sequestosome-1; 9.62916 14.2266 ####### Up 10.616 14.938 0.0007 Up
Sequestosome-1 0.000 42.6 82.6 0.0001 Up Sequestosome-1; 9.62916 14.2266 ####### Up 10.616 14.938 0.0007 Up
56
Stress-induced-
phosphoprotein 1 0.000 42.6 84.6 9E-05 Up
Stress-induced-
phosphoprotein 1; 9.17062 12.2895 0.00053 Up 9.6292 13.549 0.0147 Up
Syntenin-1 0.000 30.6 72.6 0.0001 Up Syntenin-1; 8.73393 11.147 0.01586 Up 11.147 14.938 0.0057 Up
T-complex protein 1
subunit beta 0.006 58.6 88.6 0.002 Up
T-complex protein
1 subunit beta;
PREDICTED:
Trichogramma
pretiosum T-
complex protein 1
subunit beta
(LOC106659770),
transcript variant
X2, mRNA 9.62916 12.2895 0.00346 Up 10.111 14.227 0.0039 Up
T-complex protein 1
subunit eta 0.000 40.6 80.6 0.002 Up
T-complex protein
1 subunit eta; 6.84326 10.1106 0.00207 Up 8.7339 13.549 0.0017 Up
Tubulin beta-2C
chain 0.000 26.6 80.6 0.0213 Down Tubulin beta chain;
Chlamys farreri
beta tubulin
mRNA, complete
cds 2.33937 2.84352 0.01885 Down 1.0717 3.8106 0.0051 Down
Tumor necrosis
factor receptor
superfamily member
11B 0.001 18.6 62.6 1E-05 Up
Tumor necrosis
factor receptor
superfamily
member 11B; 8.73393 11.7043 0.00066 Up 11.704 12.29 0.035 Up
Unclassifiable EST 0.005 22.6 68.6 0.0002 Up
Tyramine beta-
hydroxylase
{ECO:0000312|W
ormBase:H13N06.
6b}; 8.73393 10.1106 0.02009 Up 11.704 14.227 0.0231 Up
Unclassifiable EST 0.005 22.6 68.6 0.0002 Up
Tyramine beta-
hydroxylase; 7.18542 8.73393 0.0186 Up 9.1706 11.704 0.0235 Up
Ubiquitin-conjugating
enzyme E2 D3 0.000 50.6 86.6 0.0009 Up
Ubiquitin-
conjugating
enzyme E2 D3; 10.1106 14.2266 ####### Up 12.29 15.685 0.0042 Up
WW domain-binding
protein 2 0.009 16.6 42.6 0.0001 Up
WW domain-
binding protein 2; 7.18542 8.73393 0.01611 Up 14.938 16.469 0.0339 Up
57
Unclassifiable EST 0.000 32.6 66.6 0.0004 Up
Mytilus
galloprovincialis
transcription
factor fos-like 2
mRNA, complete
cds 8.31803 12.904 0.00695 Up 7.9219 12.904 0.0055 Up
Unclassifiable EST 0.000 18.6 52.6 0.0038 Up
Ciona intestinalis
cDNA,
clone:ciad010a08,
full insert
sequence 10.1106 11.7043 0.03612 Up 7.5447 12.904 0.0036 Up
Unclassifiable EST 0.005 36.6 66.6 0.004 Up
M.edulis gene for
polyphenolic
adhesive protein 10.1106 12.2895 0.00683 Up 11.704 12.29 0.0437 Up
58
Gene Name 5' Acc # 3' Acc #
ANOVA P-
value
LOTEC EC50 p-value Direction
Glycoprotein-N-
acetylgalactosamine 3-beta-
galactosyltransferase 1 GE757517 GE763190 0.00944568 0.2 6.5 0.0031368 Down
Heat shock 70 kDa protein
12B ES738254 0.00465374 0.4 9 0.00031441 Down
Receptor-type tyrosine-
protein phosphatase zeta GE763770 GE757414 0.00261899 0.6 7.7 0.016744 Down
Leucine-rich repeat-
containing protein 70 ES394998 ES395043 0.00091686 2.8 12.6 0.015445 Down
E3 ubiquitin-protein ligase
TRIM33 ES394206 ES392967 0.00942632 2.8 2.8 0.045649 Down
E3 ubiquitin-protein ligase
TRIM36 GE758792 GE765018 2.67E-05 3.3 30.6 0.0050689 Down
Unclassifiable EST ES738468 2.98E-05 3.9 20.6 0.00065806 Down
Tripartite motif-containing
protein 71 ES389288 ES388969 0.00044682 3.9 20.6 0.012868 Down
Tripartite motif-containing
protein 71 GE759238 GE754745 0.00189304 3.9 18.6 0.018259 Down
Metallophosphoesterase
MPPED2 ES395397 ES393454 0.00026564 4.7 16.6 0.018251 Down
Unclassifiable EST ES388170 ES388603 0.00575445 4.7 7.7 0.03445 Down
Heat shock 70 kDa protein
12A ES392521 ES395102 7.15E-05 4.7 14.6 0.027597 Down
Unclassifiable EST ES405670 ES401781 0.00630052 4.7 26.6 0.00030002 Down
Unclassifiable EST ES736434 0.00225067 5.5 20.6 0.025887 Down
Table S3--SDRS parameters for genes identified as sigmoidal in adults. Gene IDs and gene names are on
the left, and on the right are LOTEC, EC50, significance values, and direction of change in response to
copper.
Annotation SDRS
59
Homeobox protein SIX1 ES396331 ES387840 0.00016388 5.5 28.6 0.0028186 Down
Alpha-1,3-mannosyl-
glycoprotein ES394717 ES394031 0.00037568 5.5 20.6 0.0012233 Down
Nicotinamide
phosphoribosyltransferase ES391372 ES391686 0.00107588 5.5 24.6 1.9191E-05 Down
Unclassifiable EST GE764183 GE757288 0.00020842 5.5 26.6 0.00011435 Down
Inter-alpha-trypsin inhibitor
heavy chain H3 GE758688 GE765098 0.00022622 5.5 28.6 0.0045218 Down
Unclassifiable EST GE752732 GE752557 0.0016959 5.5 26.6 0.0067893 Down
Unclassifiable EST GE747090 GE754183 0.00980981 5.5 28.6 0.012252 Down
Leucine-rich repeat
serine/threonine-protein
kinase 2 0.00081107 6.5 32.6 0.0073858 Down
Unclassifiable EST ES738427 0.00566127 6.5 16.6 0.0033013 Down
A-kinase anchor protein 9 ES391812 ES395162 0.00093628 6.5 12.6 0.034591 Down
Transcription intermediary
factor 1-alpha ES396692 ES391246 0.0001087 6.5 26.6 0.009114 Down
Unclassifiable EST ES388814 ES393189 0.00689373 6.5 58.6 0.0030368 Down
Scavenger receptor cysteine-
rich domain-containing
group B protein ES399301 ES402435 0.00261899 6.5 26.6 0.00027114 Down
Midline-1 GE762709 GE755188 3.68E-05 6.5 30.6 0.006331 Down
Transmembrane protein 145 GE762543 GE761095 0.00079267 6.5 28.6 0.0044972 Down
UPF0468 protein C16orf80 ES736692 4.56E-05 7.7 44.6 0.0011764 Down
1,25-dihydroxyvitamin D(3)
24-hydroxylase,
mitochondrial ES393460 ES391898 0.00245609 7.7 12.6 0.0039683 Down
Slit homolog 2 protein ES395075 ES395648 0.00020707 7.7 26.6 0.0018906 Down
Unclassifiable EST ES387604 ES387933 0.00021775 7.7 28.6 0.0027957 Down
Aminopeptidase N ES737978 0.00030643 7.7 38.6 0.0054138 Down
Unclassifiable EST ES736829 0.00749068 7.7 38.6 0.0023634 Down
60
Uncharacterized protein
C14orf166B ES393279 ES393975 0.00048128 7.7 38.6 0.0018744 Down
Unclassifiable EST ES389589 ES389759 8.82E-06 7.7 42.6 0.0043057 Down
Cytochrome P450 2J2 GE755499 GE755998 0.00463392 7.7 26.6 0.016803 Down
Glucose-6-phosphate 1-
dehydrogenase ES396708 0.00081938 9 40.6 0.012153 Down
PC4 and SFRS1-interacting
protein ES390687 ES392692 0.00486456 9 34.6 0.00018053 Down
Unclassifiable EST ES396524 ES389536 6.44E-05 9 40.6 0.0022358 Down
Unclassifiable EST ES403171 ES397096 0.00179767 9 30.6 1.8791E-05 Down
Unclassifiable EST ES402473 ES407955 4.61E-08 9 32.6 0.0022787 Down
Unclassifiable EST ES399597 ES407134 0.00026929 9 22.6 0.0015408 Down
Headcase protein homolog GE764656 GE756923 7.28E-08 9 32.6 0.0018312 Down
Unclassifiable EST ES388811 ES394026 9.13E-07 10.7 30.6 0.0014989 Down
Unclassifiable EST ES390378 ES389675 3.05E-06 10.7 30.6 4.91E-06 Down
cGMP-gated cation channel
alpha-1 ES394615 ES395023 0.00123178 10.7 38.6 0.015302 Down
Unclassifiable EST ES394428 ES391453 0.00465374 10.7 52.6 0.00029355 Down
Unclassifiable EST ES390807 ES394537 6.63E-06 10.7 32.6 0.0045183 Down
Unclassifiable EST ES390576 ES391764 8.16E-05 10.7 24.6 0.00023527 Down
Protein FAM124A ES394240 ES396044 0.00016438 10.7 40.6 0.0044797 Down
Unclassifiable EST ES736301 0.00419822 10.7 48.6 0.0033647 Down
Unclassifiable EST ES393283 ES395934 8.51E-06 10.7 26.6 0.01348 Down
Unclassifiable EST ES393539 ES389230 0.00316653 10.7 28.6 0.0012021 Down
Unclassifiable EST ES389678 ES388235 0.00841571 10.7 30.6 0.010559 Down
MYCBP-associated protein ES389946 ES395523 0.00172332 10.7 30.6 0.022778 Down
Gelsolin ES404743 ES399669 2.37E-05 10.7 24.6 0.00014269 Down
Zinc finger protein 140 ES400436 ES399242 0.0034183 10.7 30.6 0.0021789 Down
Hippocalcin-like protein 1 ES400883 ES404044 0.00191471 10.7 34.6 0.00086843 Down
PR domain zinc finger
protein 16 GE761378 GE762508 1.67E-08 10.7 44.6 0.00089013 Down
Actin-related protein 2 GE751519 GE749763 0.0027074 10.7 12.6 0.03191 Down
Unclassifiable EST GE747159 GE751756 0.00750624 10.7 30.6 0.0054343 Down
61
Tripartite motif-containing
protein 2 GE750136 GE750428 1.15E-05 10.7 34.6 0.0021717 Down
Coiled-coil domain-
containing protein 147 ES736356 0.00107116 12.6 40.6 0.014603 Down
Unclassifiable EST ES738680 0.00247803 12.6 30.6 0.019537 Down
Titin ES736349 0.00694025 12.6 24.6 0.030901 Down
Unclassifiable EST ES387622 ES389196 4.18E-05 12.6 48.6 0.008045 Down
Unclassifiable EST ES389353 ES390238 3.87E-05 12.6 34.6 0.00016019 Down
Unclassifiable EST ES388970 ES391484 2.84E-05 12.6 46.6 0.00049275 Down
Unclassifiable EST ES396092 ES391749 8.02E-06 12.6 36.6 0.0040103 Down
Unclassifiable EST ES393224 ES393820 0.00010476 12.6 24.6 0.016807 Down
Calpain-11 ES388226 ES396942 0.0027074 12.6 32.6 0.016967 Down
Unclassifiable EST ES390943 ES394937 0.00170251 12.6 48.6 0.00017108 Down
Unclassifiable EST ES388227 ES393679 2.84E-05 12.6 28.6 0.0022175 Down
Unclassifiable EST ES391617 ES395170 0.00074885 12.6 26.6 0.0098567 Down
Unclassifiable EST ES393264 ES395429 0.00016438 12.6 32.6 0.00076267 Down
Unclassifiable EST ES401144 ES406654 0.0013624 12.6 26.6 0.00076277 Down
Unclassifiable EST ES406162 ES398173 0.00041644 12.6 46.6 0.0016464 Down
Unclassifiable EST ES405982 ES404904 0.00738065 12.6 42.6 0.0028092 Down
RNA-binding protein 38 ES398207 ES404240 0.00082346 12.6 28.6 0.0033246 Down
Unclassifiable EST ES403298 ES402806 5.68E-05 12.6 42.6 0.0026143 Down
Cubilin ES407472 ES398942 0.00232743 12.6 38.6 0.0052931 Down
Polypeptide N-
acetylgalactosaminyltransfer
ase 13 ES397827 ES400811 0.0002079 12.6 44.6 0.001594 Down
Unclassifiable EST ES401989 ES399402 0.00072561 12.6 42.6 8.2705E-05 Down
Unclassifiable EST ES407545 ES398956 7.90E-06 12.6 36.6 0.0012319 Down
Collagen alpha-1(IX) chain GE762769 GE761664 2.30E-05 12.6 26.6 0.00042888 Down
Tripartite motif-containing
protein 3 GE760958 GE757868 0.00020311 12.6 40.6 0.0080635 Down
Unclassifiable EST GE760245 GE763119 2.74E-06 12.6 48.6 6.0723E-05 Down
Transcription intermediary
factor 1-alpha GE757776 GE761381 1.15E-05 12.6 28.6 0.00044886 Down
62
Paired box protein Pax-6 GE759949 GE762864 0.00099889 12.6 48.6 0.0011809 Down
Myosin-10 ES738781 3.67E-05 14.6 34.6 0.00049426 Down
Unclassifiable EST ES737702 0.00012531 14.6 46.6 0.017644 Down
Unclassifiable EST ES388963 ES394473 0.00018237 14.6 52.6 0.0024147 Down
Unclassifiable EST ES395423 ES393857 0.00014645 14.6 54.6 0.0020288 Down
Unclassifiable EST ES393934 ES392462 9.47E-05 14.6 44.6 0.004649 Down
Transcription intermediary
factor 1-alpha ES737711 0.00011986 14.6 40.6 0.0096923 Down
Unclassifiable EST ES737380 0.00059975 14.6 42.6 0.0048813 Down
Unclassifiable EST ES394895 ES393636 0.00019212 14.6 46.6 0.0068753 Down
Unclassifiable EST ES390720 ES390079 2.37E-05 14.6 34.6 0.00099691 Down
Unclassifiable EST 0.00890046 14.6 28.6 0.0022657 Down
Unclassifiable EST ES388324 ES395849 2.18E-05 14.6 36.6 0.0010618 Down
Centrosomal protein of 135
kDa ES389450 ES395389 1.14E-05 14.6 38.6 0.00011246 Down
Src kinase-associated
phosphoprotein 2 ES390351 4.21E-05 14.6 22.6 0.0030998 Down
Unclassifiable EST ES389257 ES389893 0.00698864 14.6 24.6 0.0024984 Down
Unclassifiable EST ES395876 ES390973 0.00269443 14.6 28.6 0.00092838 Down
Unclassifiable EST ES389323 ES391047 0.00135048 14.6 20.6 0.037439 Down
Unclassifiable EST ES405600 ES404767 0.00982235 14.6 24.6 0.0021759 Down
Immunoglobulin superfamily
DCC subclass member 3 ES402725 ES405598 0.00106127 14.6 30.6 0.033434 Down
Unclassifiable EST ES406609 ES399428 0.00566127 14.6 52.6 0.0028086 Down
G1/S-specific cyclin-D2 ES397669 ES401728 3.18E-07 14.6 28.6 0.00015314 Down
Unclassifiable EST ES406012 ES401069 0.00077198 14.6 36.6 0.012266 Down
Leucine-rich repeat-
containing protein 70 ES402176 ES406477 0.00059975 14.6 34.6 0.0034958 Down
Unclassifiable EST ES405219 ES402781 5.74E-05 14.6 50.6 0.010789 Down
Uncharacterized protein
C2orf73 ES399283 ES401488 5.21E-05 14.6 24.6 0.0050359 Down
Unclassifiable EST ES402623 1.01E-05 14.6 44.6 0.0074045 Down
63
Uncharacterized protein
KIAA0753 GE756562 GE765316 0.00967917 14.6 36.6 0.0088616 Down
Leucine-rich repeat and
immunoglobulin-like domain-
containing nogo GE755957 GE763200 0.00049866 14.6 24.6 0.00020049 Down
Homeobox protein DLX-1 GE757420 GE756425 1.13E-05 14.6 34.6 0.0026631 Down
Tetratricopeptide repeat
protein 17 GE756990 GE756949 5.64E-05 14.6 52.6 0.001628 Down
Zinc finger and BTB domain-
containing protein 46 GE750936 GE747916 0.0055128 14.6 48.6 0.0056038 Down
Unclassifiable EST GE751379 GE753527 0.00117388 14.6 30.6 0.0082584 Down
Arginase-1 GE747329 GE750272 3.84E-05 14.6 56.6 0.00036428 Down
E3 ubiquitin-protein ligase
TRIM33 GE750372 GE747221 2.37E-05 14.6 32.6 0.0026936 Down
WD repeat-containing
protein 96 ES390167 ES392909 0.00301661 16.6 48.6 0.019204 Down
Cytochrome P450 2C8 ES394253 ES393169 0.0075621 16.6 36.6 0.019478 Down
T-box transcription factor
TBX20 ES391202 ES389269 9.37E-07 16.6 46.6 0.00047014 Down
Unclassifiable EST ES395684 ES390870 0.00038273 16.6 44.6 0.0017125 Down
Unclassifiable EST ES389283 ES387918 0.00627702 16.6 50.6 0.02103 Down
Unclassifiable EST ES389042 ES390390 2.09E-05 16.6 32.6 0.012427 Down
Tissue alpha-L-fucosidase ES390967 ES392559 0.00068846 16.6 26.6 0.014691 Down
Unclassifiable EST ES390376 ES394875 0.00457973 16.6 62.6 0.0047819 Down
Unclassifiable EST ES396902 ES392276 2.47E-05 16.6 56.6 0.00057556 Down
Unclassifiable EST ES394664 ES394295 0.00284443 16.6 50.6 0.0035573 Down
Cytosolic 5'-nucleotidase 3 ES399690 ES401304 0.00710219 16.6 34.6 0.027304 Down
Unclassifiable EST ES397233 ES401496 0.00559078 16.6 54.6 0.0050414 Down
Unclassifiable EST ES404106 ES407297 4.73E-05 16.6 28.6 0.00067361 Down
Unclassifiable EST ES404505 ES402281 0.00583318 16.6 22.6 0.019178 Down
Unclassifiable EST ES401025 ES405534 2.30E-05 16.6 36.6 0.0032169 Down
Unclassifiable EST GE760991 GE758459 0.00199559 16.6 24.6 0.015839 Down
64
Centrosome-associated
protein CEP250 GE761567 GE763687 0.0001087 16.6 28.6 0.008577 Down
Unclassifiable EST GE765142 GE761491 0.0004473 16.6 26.6 0.0012681 Down
Aspartate aminotransferase,
cytoplasmic ES394447 ES388395 0.00383923 18.6 44.6 0.0052341 Down
Unclassifiable EST ES387583 ES394256 0.00239726 18.6 54.6 0.0054244 Down
Unclassifiable EST ES388452 ES394678 0.00123178 18.6 58.6 0.0005241 Down
1,25-dihydroxyvitamin D(3)
24-hydroxylase,
mitochondrial ES388354 ES390832 0.00447479 18.6 36.6 0.034642 Down
E3 ubiquitin-protein ligase
TRIM33 ES391834 ES396154 7.15E-05 18.6 20.6 0.029928 Down
Caspase-3 ES387665 ES396404 5.41E-06 18.6 38.6 0.00045582 Down
Unclassifiable EST ES392818 ES390319 0.0042268 18.6 44.6 0.00027931 Down
YLP motif-containing protein
1 ES405853 ES403961 0.0045667 18.6 36.6 0.021936 Down
Thrombospondin-1 ES402703 ES406748 0.00043259 18.6 42.6 0.010766 Down
Unclassifiable EST ES404910 ES397816 0.00404029 18.6 52.6 0.010048 Down
Unclassifiable EST ES402635 1.63E-06 18.6 44.6 0.00037551 Down
Unclassifiable EST ES402090 ES405631 2.84E-06 18.6 60.6 0.00049818 Down
Unclassifiable EST ES400804 ES399011 2.87E-05 18.6 60.6 0.0056784 Down
Unclassifiable EST ES405303 ES407941 0.00057374 18.6 36.6 0.0017302 Down
Uncharacterized protein
C8orf74 ES397317 ES397276 0.0060796 18.6 46.6 0.01208 Down
Arylsulfatase B GE761364 GE759628 0.00083491 18.6 26.6 0.014065 Down
Titin GE755648 GE764731 5.45E-05 18.6 52.6 0.0089101 Down
Unclassifiable EST 0.00540214 18.6 26.6 0.024997 Down
Unclassifiable EST ES394680 ES392842 0.00580948 20.6 32.6 0.012268 Down
Unclassifiable EST ES388611 ES391189 1.74E-05 20.6 38.6 0.0010707 Down
Unclassifiable EST ES388075 ES393545 0.00374673 20.6 44.6 0.00070254 Down
Coiled-coil domain-
containing protein 81 ES736642 0.00287858 20.6 62.6 0.0065235 Down
65
Unclassifiable EST ES391953 ES396024 0.00732379 20.6 32.6 0.040374 Down
Unclassifiable EST ES394884 0.00218732 20.6 46.6 0.0041358 Down
Alpha-(1,3)-
fucosyltransferase ES390657 ES389253 6.84E-06 20.6 52.6 0.019634 Down
Transcription intermediary
factor 1-beta ES388285 ES391060 0.00078894 20.6 28.6 0.014016 Down
Heat shock 70 kDa protein
12B ES405284 ES400953 0.00292826 20.6 58.6 0.0044913 Down
Protein NDRG1 ES405298 ES400712 0.00022935 20.6 38.6 0.0045537 Down
Unclassifiable EST ES398534 ES401253 0.00151731 20.6 24.6 0.033089 Down
Unclassifiable EST GE757673 GE759296 0.00221839 20.6 42.6 0.0025499 Down
Unclassifiable EST GE762117 GE757384 0.00144255 20.6 60.6 0.0039241 Down
Wiskott-Aldrich syndrome
protein family member 3 GE750562 GE752213 0.00011027 20.6 38.6 0.0033573 Down
Unclassifiable EST GE754144 GE750245 0.00689373 20.6 54.6 0.0124 Down
Kelch-like protein 28 GE749844 GE752117 0.00144237 20.6 30.6 0.0039294 Down
Neurocalcin-delta ES396898 ES391653 0.0093224 22.6 38.6 0.0097507 Down
Ankyrin-1 ES738940 0.0004054 22.6 50.6 0.0024008 Down
Spermatogenesis-associated
protein 6 ES394231 ES393913 0.00047646 22.6 52.6 0.029284 Down
Thyroglobulin ES393158 ES392740 0.00697133 22.6 50.6 0.02992 Down
Unclassifiable EST ES390337 ES394303 0.00631441 22.6 26.6 0.036094 Down
Transcription intermediary
factor 1-beta ES391176 ES389367 0.00032299 22.6 40.6 0.023512 Down
Unclassifiable EST ES738578 7.54E-05 22.6 36.6 0.016101 Down
Sulfite oxidase,
mitochondrial ES393610 ES391613 0.00585326 22.6 48.6 0.00072628 Down
DnaJ homolog subfamily B
member 4 ES389145 0.00486421 22.6 46.6 0.021321 Down
Unclassifiable EST ES738659 0.00059975 22.6 44.6 0.013667 Down
E3 ubiquitin-protein ligase
DZIP3 ES391332 ES389577 0.00571393 22.6 64.6 0.011235 Down
66
Fibroblast growth factor
receptor 2 ES388584 ES394073 0.00203937 22.6 34.6 0.024471 Down
Forkhead box protein K2 ES397021 ES401241 4.76E-05 22.6 60.6 0.0060507 Down
Phytanoyl-CoA hydroxylase-
interacting protein-like ES397658 ES407980 0.00418594 22.6 48.6 0.027058 Down
Unclassifiable EST ES407634 ES397593 0.00097612 22.6 52.6 0.0060987 Down
Heat shock 70 kDa protein
12B ES397471 ES400785 0.0034424 22.6 48.6 0.015541 Down
Unclassifiable EST ES403026 ES404183 1.63E-05 22.6 28.6 0.015458 Down
Unclassifiable EST GE764591 GE762964 0.00950569 22.6 52.6 0.013002 Down
5'-AMP-activated protein
kinase subunit beta-1 GE761512 GE756886 0.00258795 22.6 42.6 0.012326 Down
Unclassifiable EST ES738872 0.0033067 24.6 54.6 0.020006 Down
Unclassifiable EST ES395912 ES396925 0.00024991 24.6 72.6 0.0019358 Down
Unclassifiable EST ES395412 ES390598 0.00329276 24.6 58.6 0.027166 Down
Unclassifiable EST ES396679 ES390118 5.71E-05 24.6 56.6 0.015432 Down
Radial spoke head 10
homolog B2 ES392538 ES393418 0.00763201 24.6 52.6 0.038868 Down
Uncharacterized protein
C14orf145 ES395854 ES387768 5.95E-06 24.6 54.6 0.013221 Down
Trichohyalin ES390900 ES391328 0.00038759 24.6 56.6 0.010499 Down
Cell adhesion molecule 2 ES398607 ES402616 5.68E-05 24.6 60.6 0.00082818 Down
Unclassifiable EST ES399039 ES403915 0.00034555 24.6 56.6 0.027716 Down
Armadillo repeat-containing
protein 4 ES398293 ES407862 0.00019307 24.6 66.6 0.0013217 Down
Rho GTPase-activating
protein 6 ES398924 ES407380 0.00048067 24.6 60.6 0.0013244 Down
Sperm surface protein Sp17 ES397923 ES401750 0.00020049 24.6 66.6 0.0025289 Down
Enhanced at puberty protein
1 GE761379 GE764780 0.00097623 24.6 62.6 0.0093634 Down
PP2C-like domain-containing
protein C3orf48 GE755914 GE763958 0.00085414 24.6 64.6 0.010194 Down
67
Tripartite motif-containing
protein 29 GE753910 GE750178 0.00211478 24.6 44.6 0.0030087 Down
Vacuolar fusion protein CCZ1
homolog ES388477 ES391785 0.00172332 26.6 42.6 0.0077887 Down
Unclassifiable EST ES396663 ES394108 0.00073571 26.6 66.6 0.0068247 Down
Caspase-3 ES394750 ES395952 0.00132702 26.6 38.6 0.041228 Down
Centriolin ES393074 ES389224 5.67E-06 26.6 60.6 0.006993 Down
Unclassifiable EST ES390568 ES392505 0.00139846 26.6 64.6 0.007293 Down
Transcription intermediary
factor 1-beta ES393959 ES387743 0.00670463 26.6 56.6 0.0074932 Down
Unclassifiable EST ES391002 ES396176 0.00459042 26.6 64.6 0.0088421 Down
Unclassifiable EST ES400369 ES406775 0.00124092 26.6 42.6 0.018057 Down
Unclassifiable EST ES399675 ES402060 0.00268691 26.6 52.6 0.022994 Down
Unclassifiable EST ES402346 ES397305 0.00551702 26.6 44.6 0.015017 Down
Unclassifiable EST ES403084 ES405685 0.00034054 26.6 56.6 0.010432 Down
Unclassifiable EST GE763755 GE760059 0.0091368 26.6 54.6 0.0062258 Down
Microsomal glutathione S-
transferase 1 GE750194 GE752722 9.36E-05 26.6 64.6 0.0038569 Down
Apoptosis-inducing factor 3 GE751746 GE753054 1.71E-05 26.6 50.6 0.033922 Down
Myosin-10 ES736684 0.00053628 28.6 54.6 0.01405 Down
Unclassifiable EST ES390271 ES395782 0.00287686 28.6 48.6 0.03405 Down
Epidermal growth factor-like
protein 7 ES390272 ES396534 0.00075827 28.6 48.6 0.03414 Down
Leucine-rich repeat-
containing protein C10orf92 ES736082 0.00052307 28.6 60.6 0.0016987 Down
Uncharacterized protein
C2orf63 ES738242 0.00572685 28.6 72.6 0.013981 Down
Programmed cell death
protein 4 ES389349 ES388401 0.00027774 28.6 54.6 0.00020769 Down
Adenylate kinase isoenzyme
5 ES738667 0.00041644 28.6 68.6 0.0069638 Down
Unclassifiable EST ES392549 ES396633 0.00024991 28.6 42.6 0.040211 Down
Transcription factor HES-1 ES403991 ES397366 1.74E-05 28.6 64.6 0.00015999 Down
68
Peptidyl-prolyl cis-trans
isomerase-like 6 ES401647 0.00380719 28.6 66.6 0.0055408 Down
Unclassifiable EST GE757081 GE758895 0.00026564 28.6 48.6 0.0056905 Down
Unclassifiable EST GE754817 GE762844 0.00587037 28.6 58.6 0.0080069 Down
Unclassifiable EST GE747181 GE751228 0.00038273 28.6 68.6 0.0013254 Down
Unclassifiable EST ES738096 3.01E-05 30.6 42.6 0.033108 Down
Unclassifiable EST ES392450 ES394182 0.00668015 30.6 62.6 0.021101 Down
Unclassifiable EST ES388559 ES389493 2.71E-06 30.6 46.6 0.040715 Down
Unclassifiable EST ES396892 ES396029 0.00224339 30.6 54.6 0.027835 Down
Unclassifiable EST ES391567 ES396129 0.00306421 30.6 58.6 0.0094421 Down
Unclassifiable EST ES392460 ES396371 0.00132702 30.6 48.6 0.018914 Down
Radial spoke head 1
homolog ES388613 0.00950569 30.6 72.6 0.0084262 Down
Microtubule-associated
protein tau ES388686 ES393854 0.00420146 30.6 38.6 0.041962 Down
NADH-cytochrome b5
reductase 3 ES397853 ES405035 0.00375993 30.6 68.6 0.0081878 Down
Adenylosuccinate synthetase
isozyme 2 ES405371 ES402386 0.00269443 30.6 68.6 0.0046368 Down
Uncharacterized protein
C2orf61 GE755485 GE758522 0.00011943 30.6 60.6 0.0017814 Down
Unclassifiable EST ES394400 ES388411 0.00291959 32.6 56.6 0.032348 Down
Unclassifiable EST ES738056 0.00454036 32.6 72.6 0.017412 Down
Unclassifiable EST ES389930 0.00097838 32.6 52.6 0.034877 Down
Homeobox protein SIX4 ES404549 ES404388 0.00103777 32.6 52.6 0.0074552 Down
Sterile alpha motif domain-
containing protein 15 ES405428 ES401792 0.00892515 32.6 72.6 0.001743 Down
Coiled-coil domain-
containing protein 109A ES399475 ES403407 0.00483305 32.6 46.6 0.0092295 Down
Sperm-associated antigen 8 ES399202 ES404691 0.0001435 32.6 74.6 0.0035632 Down
UPF0536 protein C12orf66 ES406749 ES401500 0.00967917 32.6 70.6 0.0026135 Down
Protein-glutamine gamma-
glutamyltransferase K GE756313 GE764356 0.00038182 32.6 48.6 0.022288 Down
69
Adenosine deaminase GE755415 GE755664 0.00569366 32.6 66.6 0.02431 Down
Toll-like receptor 6 GE763098 GE760195 0.00872876 32.6 48.6 0.008751 Down
T-lymphoma invasion and
metastasis-inducing protein
2 GE760483 GE765084 0.00040156 32.6 50.6 0.013742 Down
Unclassifiable EST GE757083 GE764556 0.00447751 32.6 54.6 0.0053159 Down
Unclassifiable EST ES405275 0.00889851 34.6 70.6 0.023295 Down
SPRY domain-containing
protein 3 GE760885 GE759988 1.82E-05 34.6 40.6 0.045456 Down
Uncharacterized protein
CXorf65 GE750688 GE747765 0.00812004 34.6 70.6 0.01706 Down
Unclassifiable EST ES394196 0.00059975 36.6 66.6 0.0033721 Down
C-terminal-binding protein 2 ES394090 ES394818 0.00114298 36.6 68.6 0.012338 Down
Putative golgin subfamily A
member 6-like protein 6 ES737843 0.00085328 36.6 70.6 0.015445 Down
Unclassifiable EST ES737603 0.0092541 36.6 68.6 0.0051591 Down
Proline-serine-threonine
phosphatase-interacting
protein 1 ES401285 ES398668 0.00485186 36.6 48.6 0.026737 Down
39S ribosomal protein L28,
mitochondrial ES395815 ES395633 0.00751774 38.6 54.6 0.009883 Down
Unclassifiable EST ES405876 ES407802 0.00340855 38.6 70.6 0.019293 Down
Unclassifiable EST GE752044 GE747228 0.00486456 38.6 56.6 0.037147 Down
Unclassifiable EST ES390201 ES388655 0.00967064 40.6 68.6 0.037701 Down
Putative protein TPRXL ES401552 ES400197 6.19E-05 40.6 52.6 0.025626 Down
Uncharacterized protein
C12orf56 ES398527 ES401690 0.00309793 40.6 74.6 0.01316 Down
Unclassifiable EST GE756199 GE764270 0.00278801 40.6 68.6 0.018927 Down
Neurogenic locus notch
homolog protein 3 GE752030 GE752212 0.00077807 40.6 74.6 0.013322 Down
Tektin-2 ES394649 ES394913 0.00752943 42.6 56.6 0.045293 Down
Unclassifiable EST ES403053 ES405410 0.00117166 42.6 56.6 0.020592 Down
Unclassifiable EST ES399066 ES402972 0.00290687 42.6 72.6 0.028816 Down
70
Structural maintenance of
chromosomes protein 1A ES405803 ES397399 0.00127507 42.6 62.6 0.014096 Down
Unclassifiable EST ES407661 ES407943 0.00418594 42.6 60.6 0.030413 Down
Unclassifiable EST GE764007 GE764611 0.00258001 42.6 64.6 0.042633 Down
Elongator complex protein 3 GE758197 GE758809 0.00914151 42.6 66.6 0.0092487 Down
Early endosome antigen 1 ES735894 0.00950912 44.6 76.6 0.0093325 Down
Kelch-like protein 24 ES393961 ES391518 0.00116656 44.6 60.6 0.038654 Down
Protein disulfide-isomerase
A5 ES394223 ES394197 0.00099517 44.6 50.6 0.04604 Down
Unclassifiable EST ES407385 ES397743 0.00355272 44.6 52.6 0.040868 Down
Tektin-1 ES387718 ES395791 0.00625718 46.6 72.6 0.029812 Down
Spermatogenesis-associated
protein 17 ES394564 ES395044 0.0004473 46.6 76.6 0.021518 Down
Unclassifiable EST ES400159 ES405788 0.00291375 46.6 80.6 0.02074 Down
DnaJ homolog subfamily B
member 13 GE751494 GE751242 0.00463462 46.6 62.6 0.025355 Down
Tudor domain-containing
protein 3 ES399232 ES403125 0.00826362 48.6 60.6 0.043695 Down
Zinc finger CCCH-type with G
patch domain-containing
protein GE752389 GE748928 0.00498383 48.6 56.6 0.03408 Down
Unclassifiable EST GE754107 GE749442 0.0022819 48.6 68.6 0.028504 Down
Parkin coregulated gene
protein ES393684 ES395901 0.00018863 50.6 80.6 0.011021 Down
Unclassifiable EST ES393447 ES392461 0.00274735 50.6 52.6 0.049154 Down
Matrix-remodeling-
associated protein 7 ES393280 ES389980 0.00045493 52.6 56.6 0.043878 Down
Ankyrin repeat domain-
containing protein 36C ES393978 ES390268 0.00770311 52.6 66.6 0.046511 Down
Unclassifiable EST GE764134 GE754425 0.00278801 52.6 62.6 0.049038 Down
Corepressor interacting with
RBPJ 1 ES389377 ES389504 0.00034131 56.6 86.6 0.021835 Down
Unclassifiable EST GE761755 0.00087251 56.6 66.6 0.046176 Down
71
Unclassifiable EST GE750864 GE751693 0.00183104 56.6 78.6 0.024421 Down
Unclassifiable EST ES394830 ES394979 0.00316881 58.6 72.6 0.017604 Down
Midline-1 ES403630 ES403141 0.00713308 58.6 74.6 0.034011 Down
Heat shock 70 kDa protein
12A ES402481 ES401453 0.00172332 58.6 68.6 0.031101 Down
Unclassifiable EST GE759776 GE760810 0.00495858 58.6 80.6 0.03793 Down
Unclassifiable EST ES395811 ES389057 0.00116925 60.6 80.6 0.016104 Down
Unclassifiable EST ES387727 ES396654 0.00419552 62.6 68.6 0.04083 Down
Tubulin beta-2B chain ES390835 ES392974 0.00788104 62.6 74.6 0.043346 Down
Unclassifiable EST ES392378 ES388621 0.00404029 62.6 80.6 0.032739 Down
Caspase-7 GE749855 GE749862 0.00267554 62.6 66.6 0.04481 Down
Tetraspanin-9 GE758140 GE763202 0.00891318 66.6 82.6 0.030892 Down
Unclassifiable EST ES388130 ES392239 0.00250147 70.6 86.6 0.027568 Down
Tubulin alpha-3C/D chain ES394718 ES395148 0.00270375 72.6 88.6 0.016569 Down
Tubulin alpha-3C/D chain GE750405 GE750219 0.00054567 76.6 92.6 0.013967 Down
Alanyl-tRNA editing protein
Aarsd1 ES403517 ES403102 0.00127507 78.6 82.6 0.046974 Down
Coiled-coil domain-
containing protein 42A ES404863 ES407423 0.00815608 82.6 86.6 0.048288 Down
Unclassifiable EST GE756081 GE763106 0.00658117 3.9 18.6 0.0006411 Up
Eukaryotic peptide chain
release factor subunit 1 ES398701 ES406217 0.00262752 6.5 24.6 0.0011485 Up
Cysteine protease ATG4D GE762743 GE761496 0.0066213 7.7 32.6 0.017604 Up
Myc proto-oncogene protein ES394134 ES389703 0.00072561 9 24.6 0.0026527 Up
ATP-binding cassette sub-
family F member 2 ES387687 ES390824 1.48E-05 9 36.6 0.00070659 Up
Unclassifiable EST ES403138 ES400765 0.00333497 9 26.6 0.0074398 Up
ATP-dependent zinc
metalloprotease YME1L1 GE761051 GE763839 0.00357191 9 26.6 0.0029182 Up
Unclassifiable EST ES737448 0.00682693 10.7 20.6 0.00098065 Up
Inositol-3-phosphate
synthase 1 ES738125 0.00369419 10.7 42.6 0.012618 Up
72
WD repeat-containing
protein 74 ES396248 ES388505 0.00034555 10.7 24.6 0.001302 Up
Histidyl-tRNA synthetase,
cytoplasmic ES399546 ES401760 0.00072218 10.7 52.6 0.00030592 Up
ATPase family AAA domain-
containing protein 3B ES406658 ES404274 0.00752943 10.7 24.6 0.00051927 Up
Growth hormone
secretagogue receptor type 1 ES404823 ES406322 0.0045351 10.7 28.6 0.0012469 Up
Krueppel-like factor 12 GE758885 GE762882 5.36E-06 10.7 40.6 0.00025918 Up
Pituitary tumor-transforming
gene 1 protein-interacting
protein GE749220 GE747966 0.00914242 10.7 12.6 0.031215 Up
Kelch-like protein 31 ES736030 0.0010434 12.6 56.6 0.00032043 Up
Transgelin-3 ES388114 ES391269 0.00067172 12.6 36.6 0.0001374 Up
Unclassifiable EST ES396214 ES391756 6.04E-05 12.6 54.6 0.0039039 Up
Thioredoxin reductase 3
(Fragment) ES736456 4.96E-06 12.6 48.6 0.00021758 Up
Unclassifiable EST ES738083 0.00073542 12.6 30.6 0.0023466 Up
Retinoic acid receptor
gamma ES391546 ES396516 0.0024157 12.6 32.6 0.0072371 Up
Ankyrin-1 ES394357 ES388573 0.00217 12.6 32.6 0.0030558 Up
Unclassifiable EST ES390532 ES391252 6.8549E-05 12.6 38.6 0.00012836 Up
Unclassifiable EST ES408089 ES407764 2.84E-05 12.6 48.6 0.0001942 Up
HEAT repeat-containing
protein 3 ES405548 ES405078 0.00379875 12.6 18.6 0.010661 Up
Cell adhesion molecule 3 ES397618 ES403128 0.00486456 12.6 34.6 0.022537 Up
Mdm2-binding protein ES406834 0.00074341 12.6 44.6 0.00050955 Up
Unclassifiable EST ES405748 ES401537 1.90E-05 12.6 48.6 1.4822E-05 Up
Unclassifiable EST ES738541 1.13E-05 14.6 48.6 0.00053658 Up
Unclassifiable EST ES396157 ES394494 0.00019466 14.6 32.6 0.0045118 Up
Cell division control protein
42 homolog ES736451 1.06E-05 14.6 52.6 4.05E-06 Up
73
Unclassifiable EST ES388593 ES394945 0.00277227 14.6 18.6 0.026632 Up
Deformed epidermal
autoregulatory factor 1
homolog ES394072 ES388414 5.21E-05 14.6 44.6 9.0823E-05 Up
Unclassifiable EST ES387928 0.00022622 14.6 40.6 0.00025009 Up
Unclassifiable EST ES399978 0.00309212 14.6 58.6 0.010671 Up
Carbonic anhydrase 13 ES404598 ES398694 0.00192183 14.6 48.6 0.0099341 Up
Unclassifiable EST ES402397 ES404808 0.00817914 14.6 54.6 0.0013452 Up
Complement C1q-like protein
3 ES402562 ES405580 1.21E-06 14.6 40.6 0.00064136 Up
Unclassifiable EST ES400230 ES398965 0.00928708 14.6 24.6 0.023242 Up
Unclassifiable EST ES407552 ES403951 0.00262312 14.6 56.6 0.0028784 Up
Unclassifiable EST ES401143 ES406612 0.00018957 14.6 60.6 0.0036871 Up
Unclassifiable EST ES407506 ES400641 0.00046787 14.6 22.6 0.0088647 Up
Unclassifiable EST GE757839 GE759043 0.00764 14.6 54.6 0.0014316 Up
Unclassifiable EST 3.82E-05 14.6 48.6 0.00098957 Up
Unclassifiable EST GE747655 GE748896 5.64E-05 14.6 38.6 0.00025676 Up
Unclassifiable EST 0.00179767 16.6 40.6 0.0075436 Up
Phospholipase A2,
membrane associated ES738494 9.50E-06 16.6 42.6 1.667E-06 Up
SAM pointed domain-
containing Ets transcription
factor ES389830 ES387975 7.83E-05 16.6 40.6 0.0013635 Up
Unclassifiable EST ES391077 ES393059 4.98E-06 16.6 50.6 0.00032376 Up
ATP-dependent RNA helicase
DDX54 ES738489 0.00716154 16.6 40.6 0.0008703 Up
Neuronal PAS domain-
containing protein 4 ES392456 ES395260 0.00013369 16.6 40.6 0.0006904 Up
Unclassifiable EST ES395490 ES395897 5.21E-05 16.6 58.6 0.00095248 Up
Friend leukemia integration
1 transcription factor ES391647 ES395642 9.3E-08 16.6 54.6 0.00016924 Up
Vacuolar protein sorting-
associated protein 4B ES401450 ES402807 0.00700335 16.6 38.6 0.0015052 Up
74
Complement C1q tumor
necrosis factor-related
protein 9B ES403729 ES400587 0.00245609 16.6 34.6 0.002565 Up
Unclassifiable EST ES407868 ES406802 0.00049191 16.6 30.6 0.011896 Up
Unclassifiable EST ES399323 ES405095 0.00267554 16.6 38.6 0.00090767 Up
Unclassifiable EST ES401722 ES402360 2.81E-05 16.6 62.6 0.0010109 Up
Signal recognition particle
receptor subunit alpha ES399204 ES406416 0.00589915 16.6 56.6 0.010691 Up
Alpha-1,3-mannosyl-
glycoprotein ES404337 ES407459 0.00335095 16.6 42.6 1.0748E-05 Up
Gamma-secretase subunit
PEN-2 ES408003 ES405527 0.00022622 16.6 26.6 0.011459 Up
Unclassifiable EST GE759932 GE762495 1.87E-05 16.6 48.6 2.0682E-05 Up
Slit homolog 1 protein GE757138 GE760784 0.00920497 16.6 64.6 4.6016E-05 Up
DnaJ homolog subfamily C
member 12 GE759387 GE763615 0.00794272 16.6 32.6 0.0016048 Up
Otoconin-90 GE759995 GE758145 0.00904345 16.6 56.6 0.00014345 Up
Unclassifiable EST ES735936 0.00103777 18.6 62.6 1.2141E-05 Up
Unclassifiable EST ES736570 0.00689373 18.6 60.6 0.0016521 Up
Unclassifiable EST ES388954 ES389213 2.03E-06 18.6 64.6 8.2012E-05 Up
Glycyl-tRNA synthetase ES738115 ES393391 0.00163146 18.6 30.6 0.0071604 Up
Unclassifiable EST ES736157 ES391503 7.07E-07 18.6 64.6 0.00054721 Up
CCAAT/enhancer-binding
protein epsilon ES407426 ES400247 1.14E-06 18.6 48.6 0.00007078 Up
Unclassifiable EST ES398297 ES397200 0.00254055 18.6 24.6 0.030447 Up
Zinc transporter ZIP12 ES406615 ES403258 7.22E-08 18.6 62.6 4.21E-07 Up
Unclassifiable EST ES400249 ES402931 0.00118028 18.6 26.6 0.028543 Up
Unclassifiable EST ES404521 ES402716 0.00081839 18.6 44.6 7.99E-07 Up
Transmembrane protein 47 ES397849 ES403259 3.41E-06 18.6 46.6 0.00025941 Up
Regulator of G-protein
signaling 3 ES404584 ES402957 0.00338186 18.6 60.6 0.0041012 Up
Unclassifiable EST ES405942 ES403539 2.58E-07 18.6 44.6 2.67E-06 Up
75
Monocarboxylate transporter
9 ES399317 ES399930 0.00841571 18.6 40.6 0.0034063 Up
Unclassifiable EST ES399051 ES405454 0.00129563 18.6 38.6 0.00035524 Up
Solute carrier family 17
member 9 ES404734 ES407759 0.00069962 18.6 62.6 0.0011656 Up
Unclassifiable EST ES404926 ES397094 0.00558476 18.6 48.6 0.00069163 Up
Unclassifiable EST ES398180 ES400550 3.90E-05 18.6 48.6 0.00045561 Up
Unclassifiable EST ES406470 ES404098 0.00904345 18.6 40.6 0.0027852 Up
Unclassifiable EST ES400990 ES405684 4.38E-08 18.6 46.6 1.23E-06 Up
Transcription initiation
protein SPT3 homolog ES403797 ES398973 3.98E-07 18.6 54.6 0.00014155 Up
Proto-oncogene
serine/threonine-protein
kinase pim-1 ES400777 ES401187 3.84E-05 18.6 34.6 0.0046921 Up
Aconitate hydratase,
mitochondrial GE763756 GE762262 4.07E-06 18.6 64.6 6.5254E-05 Up
Cyclic AMP-responsive
element-binding protein 1 GE763700 GE759250 3.76E-07 18.6 54.6 0.028504 Up
Unclassifiable EST GE755768 GE759681 0.00078395 18.6 44.6 0.023985 Up
Beta-1,3-
galactosyltransferase 1 GE761443 GE758644 0.00078844 18.6 60.6 0.00016986 Up
Signal recognition particle 68
kDa protein GE762367 GE762291 7.35E-05 18.6 54.6 0.021127 Up
Unclassifiable EST GE758828 GE756398 0.00096601 18.6 28.6 0.0047132 Up
Krueppel-like factor 6 ES738439 7.17E-06 20.6 44.6 1.0757E-05 Up
Unclassifiable EST ES735897 0.00179767 20.6 66.6 0.00092896 Up
Unclassifiable EST ES736193 6.63E-06 20.6 48.6 3.1627E-05 Up
Unclassifiable EST ES389831 ES390705 7.79E-05 20.6 58.6 0.001622 Up
Protocadherin gamma-A4 ES392101 ES394434 0.00375082 20.6 44.6 0.0011219 Up
Unclassifiable EST ES387617 ES392406 0.00712251 20.6 30.6 0.0062378 Up
Galactokinase ES391223 ES388059 0.0024481 20.6 34.6 0.007401 Up
2',5'-phosphodiesterase 12 ES389951 ES387520 0.0017553 20.6 48.6 0.0042534 Up
76
Low-density lipoprotein
receptor ES396454 ES390770 2.37E-05 20.6 56.6 7.1711E-05 Up
Unclassifiable EST ES392042 ES388892 0.000271 20.6 46.6 7.9868E-05 Up
Feline leukemia virus
subgroup C receptor-related
protein 2 ES400276 ES400573 9.93E-05 20.6 50.6 0.00007464 Up
Unclassifiable EST ES404860 ES401860 1.06E-05 20.6 58.6 3.5846E-05 Up
Unclassifiable EST ES406041 ES400575 0.00014699 20.6 40.6 0.0011189 Up
Tetratricopeptide repeat
protein 35 ES405241 ES402993 0.00079153 20.6 62.6 0.0080437 Up
Unclassifiable EST ES397470 ES403905 1.84E-08 20.6 46.6 4.38E-08 Up
Unclassifiable EST ES399986 ES406116 0.00076555 20.6 56.6 0.00055826 Up
Unclassifiable EST ES398639 ES398941 0.00805027 20.6 34.6 0.0045998 Up
Unclassifiable EST ES403173 ES399471 0.003969 20.6 24.6 0.029583 Up
Transcription factor p65 ES400192 ES407493 0.00041276 20.6 54.6 0.00016647 Up
Unclassifiable EST ES402381 ES397427 0.00141564 20.6 70.6 0.00023344 Up
Unclassifiable EST ES402987 ES405384 0.00315755 20.6 68.6 6.4761E-05 Up
Unclassifiable EST ES397971 ES403958 0.00088339 20.6 72.6 0.00024723 Up
Unclassifiable EST ES402168 7.24E-06 20.6 46.6 5.6239E-05 Up
Unclassifiable EST ES400022 ES398628 0.00024713 20.6 48.6 7.94E-06 Up
Putative rRNA
methyltransferase 3 ES404933 ES406452 8.81E-05 20.6 56.6 0.0081407 Up
Baculoviral IAP repeat-
containing protein 3 ES404824 ES406769 0.00320746 20.6 42.6 0.00073736 Up
Unclassifiable EST ES401458 ES404845 4.76E-05 20.6 56.6 0.00010451 Up
Unclassifiable EST ES397266 ES404788 0.00160219 20.6 58.6 3.0328E-05 Up
Unclassifiable EST GE756355 GE764123 0.00457973 20.6 60.6 0.00019912 Up
Unclassifiable EST GE760458 GE755054 0.00098347 20.6 30.6 0.025068 Up
Uncharacterized protein
C21orf63 GE764040 GE762103 2.98E-05 20.6 48.6 0.00072274 Up
Unclassifiable EST ES738259 1.14E-05 22.6 44.6 0.0058301 Up
Unclassifiable EST ES738400 0.00098072 22.6 68.6 0.0011245 Up
77
78 kDa glucose-regulated
protein ES737050 0.0014573 22.6 60.6 0.014627 Up
Unclassifiable EST ES738765 0.00960349 22.6 68.6 0.0043617 Up
Myosin light polypeptide 6 ES738196 0.00291375 22.6 52.6 0.0043411 Up
Glutaredoxin-1 ES389121 ES389586 4.96E-06 22.6 66.6 0.0036121 Up
DNA damage-regulated
autophagy modulator protein
2 ES395926 ES395005 0.00765217 22.6 42.6 0.0069941 Up
Suppressor of cytokine
signaling 2 ES389869 ES392617 0.00036427 22.6 60.6 4.74E-06 Up
Glutathione S-transferase
Mu 4 ES388437 ES394560 7.36E-06 22.6 68.6 0.00001131 Up
Tumor necrosis factor
receptor superfamily
member 19 ES393049 6.34E-06 22.6 64.6 0.0010688 Up
Unclassifiable EST ES389567 ES388056 0.00011027 22.6 72.6 8.4315E-05 Up
Unclassifiable EST ES392798 ES390841 0.00214847 22.6 66.6 0.00034392 Up
Osteoclast-stimulating
factor 1 ES393145 ES396009 0.00057197 22.6 68.6 0.00033278 Up
Unclassifiable EST ES396225 ES396648 0.00830944 22.6 44.6 0.0023806 Up
Unclassifiable EST ES387995 ES389782 0.00297811 22.6 70.6 3.6668E-05 Up
Mesencephalic astrocyte-
derived neurotrophic factor ES401990 ES406508 4.35E-07 22.6 74.6 0.00085714 Up
Tumor necrosis factor
receptor superfamily
member 27 ES406014 ES397335 1.33E-07 22.6 66.6 2.48E-06 Up
Unclassifiable EST ES401242 ES397510 5.46E-06 22.6 46.6 0.00022986 Up
Unclassifiable EST ES399015 ES404590 1.73E-06 22.6 44.6 0.011511 Up
Leucine-rich repeat and
immunoglobulin-like domain-
containing nogo ES404362 ES399850 0.00589915 22.6 68.6 0.00090922 Up
Unclassifiable EST ES404621 0.00135048 22.6 54.6 0.0024069 Up
78
Eukaryotic translation
initiation factor 2 subunit 1 ES400371 ES399814 0.00070909 22.6 40.6 0.0014147 Up
Unclassifiable EST ES403759 ES398396 0.00615862 22.6 64.6 0.0010902 Up
Cartilage matrix protein ES405850 ES398875 2.09E-05 22.6 64.6 0.00071717 Up
Unclassifiable EST ES405253 ES404543 4.43E-06 22.6 64.6 0.00092941 Up
Protein ETHE1, mitochondrial ES399891 ES398675 4.78E-06 22.6 70.6 0.0012524 Up
Growth arrest and DNA
damage-inducible protein
GADD45 alpha ES401752 ES406290 6.8549E-05 22.6 48.6 0.00058882 Up
Unclassifiable EST ES403231 ES402191 0.00174205 22.6 58.6 0.0053152 Up
BTB/POZ domain-containing
protein 3 GE756567 GE762635 0.00019098 22.6 40.6 0.0030207 Up
Ganglioside GM2 activator GE762569 GE759823 1.14E-06 22.6 68.6 0.00001495 Up
Peptide methionine sulfoxide
reductase GE759615 GE760365 0.00078395 22.6 64.6 0.0050359 Up
Ubiquitin-fold modifier 1 GE755377 GE755848 3.38E-05 22.6 72.6 2.6245E-05 Up
Unclassifiable EST GE751066 2.54E-05 22.6 46.6 4.6632E-05 Up
T-complex protein 1 subunit
alpha GE749699 GE750319 0.00619088 22.6 66.6 0.0083577 Up
Unclassifiable EST GE748866 GE750110 0.00571393 22.6 42.6 0.004649 Up
Unclassifiable EST ES737021 1.99E-05 24.6 60.6 7.6392E-05 Up
Unclassifiable EST ES736143 4.43E-06 24.6 62.6 2.3828E-05 Up
Unclassifiable EST ES736113 1.87E-05 24.6 50.6 0.00095991 Up
Unclassifiable EST ES736225 0.00123179 24.6 56.6 0.004754 Up
Unclassifiable EST ES736814 0.00744822 24.6 68.6 0.00067079 Up
Calcium and integrin-binding
protein 1 ES395295 ES390731 0.00061376 24.6 70.6 0.00094186 Up
Unclassifiable EST ES388687 ES391591 0.00781926 24.6 56.6 0.0083133 Up
Unclassifiable EST ES738572 1.94E-05 24.6 50.6 0.024291 Up
Unclassifiable EST ES387658 ES395390 0.00161207 24.6 60.6 0.002796 Up
T-complex protein 1 subunit
epsilon ES736258 1.11E-07 24.6 70.6 0.0003398 Up
79
Unclassifiable EST ES388616 ES396365 0.00957422 24.6 64.6 0.00062588 Up
Unclassifiable EST ES391029 ES392256 0.000685 24.6 64.6 1.4033E-05 Up
Unclassifiable EST ES389851 ES393035 0.00973683 24.6 54.6 0.00032712 Up
Unclassifiable EST ES387686 ES388204 1.29E-05 24.6 68.6 0.0016244 Up
Unclassifiable EST ES737077 ES387586 4.05E-05 24.6 66.6 0.0017424 Up
Signal recognition particle 14
kDa protein ES387877 ES394032 0.00083961 24.6 62.6 0.021813 Up
Unclassifiable EST ES392514 ES394882 0.00016358 24.6 50.6 0.0019468 Up
Unclassifiable EST ES393662 0.00841571 24.6 72.6 0.00036938 Up
Unclassifiable EST ES396896 ES395270 2.12E-05 24.6 70.6 3.6046E-05 Up
Unclassifiable EST ES388432 ES392533 7.13E-07 24.6 62.6 7.931E-06 Up
Unclassifiable EST ES401814 ES401999 3.83E-05 24.6 58.6 0.00018181 Up
Unclassifiable EST ES401162 ES406590 0.00384123 24.6 36.6 0.0064417 Up
Unclassifiable EST ES404999 ES399477 5.93E-05 24.6 68.6 7.58E-06 Up
Unclassifiable EST ES399624 ES402487 4.78E-06 24.6 62.6 3.1572E-05 Up
Unclassifiable EST ES406900 ES403669 5.60E-05 24.6 60.6 0.0001496 Up
Unclassifiable EST ES399721 ES397698 0.00333497 24.6 58.6 0.00034571 Up
Unclassifiable EST ES399131 ES406964 0.00034001 24.6 70.6 0.00019931 Up
Unclassifiable EST ES406358 ES403884 0.00242206 24.6 70.6 0.00091429 Up
ATP-binding cassette sub-
family F member 3 ES400217 ES401679 7.78E-06 24.6 68.6 0.0082135 Up
Unclassifiable EST ES399744 ES405763 7.50E-05 24.6 54.6 7.35E-06 Up
Unclassifiable EST ES403001 ES398089 0.00369419 24.6 56.6 0.0068753 Up
Angiopoietin-related protein
2 ES406013 4.15E-05 24.6 68.6 1.5895E-05 Up
Unclassifiable EST ES398777 ES397203 7.07E-07 24.6 74.6 0.00093749 Up
Vacuolar protein sorting-
associated protein 37A ES408022 ES405221 2.07E-05 24.6 76.6 0.00048246 Up
Unclassifiable EST ES404154 ES400190 0.00141694 24.6 52.6 0.0016886 Up
F-box/WD repeat-containing
protein 4 ES403123 ES407762 9.09E-06 24.6 64.6 9.07E-06 Up
Unclassifiable EST ES404159 ES398749 0.00021775 24.6 70.6 0.0001145 Up
Unclassifiable EST GE756926 GE756482 0.0003903 24.6 62.6 0.00032794 Up
80
Leukotriene-B(4) omega-
hydroxylase 1 GE755217 GE756132 0.00182296 24.6 72.6 1.6382E-05 Up
Unclassifiable EST GE761909 GE762395 2.11E-05 24.6 70.6 0.00099042 Up
Unclassifiable EST 1.98E-05 24.6 68.6 2.41E-06 Up
Unclassifiable EST GE747432 GE748331 2.27E-07 24.6 64.6 4.44E-06 Up
Unclassifiable EST GE747515 GE751573 0.00177015 24.6 38.6 0.0050772 Up
Unclassifiable EST GE751476 GE749429 6.63E-06 24.6 70.6 0.0003482 Up
Unclassifiable EST ES738278 0.0091405 26.6 64.6 0.016955 Up
Tribbles homolog 2 ES736382 3.05E-06 26.6 60.6 0.00033204 Up
Unclassifiable EST ES394136 ES393492 5.21E-05 26.6 62.6 5.26E-06 Up
Unclassifiable EST ES395675 ES396323 0.00221436 26.6 44.6 0.0025791 Up
Unclassifiable EST ES395217 ES389956 0.00210962 26.6 62.6 0.00017023 Up
Unclassifiable EST ES395558 ES392332 0.00027418 26.6 66.6 0.005599 Up
Lathosterol oxidase ES393198 ES390508 2.45E-05 26.6 68.6 0.0067089 Up
Unclassifiable EST ES395779 ES396400 8.1606E-05 26.6 62.6 0.00033759 Up
Iduronate 2-sulfatase ES395775 ES390564 2.80E-05 26.6 58.6 0.0011484 Up
Unclassifiable EST ES738291 0.00840419 26.6 64.6 0.00016424 Up
Unclassifiable EST ES396065 ES393178 7.17E-05 26.6 66.6 0.0026522 Up
Unclassifiable EST ES392281 ES392380 8.4893E-05 26.6 54.6 0.00063991 Up
Peroxisome proliferator-
activated receptor alpha ES396750 ES389115 0.0001087 26.6 70.6 6.6033E-05 Up
Unclassifiable EST ES393846 ES396736 1.16E-06 26.6 70.6 0.00002732 Up
Unclassifiable EST ES393756 ES391318 0.00059496 26.6 64.6 2.2631E-05 Up
Flotillin-2 ES404430 ES407619 0.00096625 26.6 70.6 0.00036643 Up
Long-chain fatty acid
transport protein 6 ES407376 ES401548 0.0004473 26.6 68.6 0.002151 Up
Unclassifiable EST ES405764 ES398418 0.00074341 26.6 70.6 0.0001061 Up
Unclassifiable EST ES406392 ES401158 2.55E-07 26.6 72.6 3.7594E-05 Up
Unclassifiable EST ES399594 ES399801 0.00324582 26.6 58.6 0.0013815 Up
Unclassifiable EST ES407743 ES400702 0.00169382 26.6 46.6 0.00096267 Up
Uncharacterized protein
C9orf135 ES402764 ES404479 0.00291959 26.6 46.6 0.0075219 Up
81
S-adenosylmethionine
synthase isoform type-1 ES404687 ES402170 0.00041644 26.6 70.6 0.0010815 Up
Myosin light chain kinase,
smooth muscle ES402241 ES406573 0.00132226 26.6 62.6 0.0019216 Up
Exportin-T ES397702 ES407048 0.00818129 26.6 52.6 0.001257 Up
Unclassifiable EST ES404462 ES398970 1.58E-05 26.6 52.6 0.00025787 Up
Unclassifiable EST ES402125 ES399523 8.74E-08 26.6 72.6 1.1175E-05 Up
Glyoxylate
reductase/hydroxypyruvate
reductase ES404331 ES406831 0.00018863 26.6 38.6 0.0074363 Up
Protein tweety homolog 2 GE758680 GE764008 1.98E-05 26.6 74.6 4.6301E-05 Up
Cat eye syndrome critical
region protein 5 GE760450 GE759675 0.00308754 26.6 60.6 0.00015998 Up
Unclassifiable EST GE762358 GE760002 0.00902729 26.6 54.6 0.00022825 Up
Transmembrane 9
superfamily member 3 GE762699 GE758185 1.86E-06 26.6 72.6 2.6165E-05 Up
Multidrug resistance protein
1 GE761521 GE756038 1.06E-06 26.6 72.6 0.00010372 Up
Transcription intermediary
factor 1-beta GE760348 GE760758 9.11E-07 26.6 72.6 0.00024707 Up
Serologically defined colon
cancer antigen 1 GE761106 GE762383 0.00156109 26.6 74.6 0.0018922 Up
Peroxisomal membrane
protein 2 GE763937 GE762079 0.00156556 26.6 72.6 0.0013854 Up
Lysosomal acid phosphatase GE760674 GE762004 0.00292826 26.6 58.6 0.021877 Up
Unclassifiable EST GE749705 GE752478 0.00155574 26.6 72.6 0.0093977 Up
Unclassifiable EST GE752549 GE748149 7.90E-06 26.6 62.6 0.00011748 Up
Unclassifiable EST GE753076 GE750510 0.00026564 26.6 66.6 0.0098165 Up
Baculoviral IAP repeat-
containing protein 2 GE751353 GE749074 0.00529208 26.6 36.6 0.010251 Up
Gamma-aminobutyric acid
receptor-associated protein-
like 2 GE750898 GE747860 0.00487768 26.6 48.6 0.00036481 Up
82
Unclassifiable EST ES389445 ES390244 6.19E-05 28.6 76.6 0.00044835 Up
Isoleucyl-tRNA synthetase,
cytoplasmic ES391821 ES393527 0.00054694 28.6 36.6 0.024156 Up
IQ domain-containing
protein G ES391092 ES396022 0.00166096 28.6 62.6 0.0014652 Up
Unclassifiable EST ES390512 ES396101 6.6044E-05 28.6 74.6 6.9385E-05 Up
Unclassifiable EST ES392262 ES393580 1.98E-05 28.6 72.6 0.00044988 Up
Unclassifiable EST ES737038 2.76E-05 28.6 64.6 0.0014049 Up
Cholecystokinin receptor type
A ES736479 8.1606E-05 28.6 72.6 0.00065632 Up
Unclassifiable EST ES392777 ES396468 6.19E-05 28.6 62.6 4.2224E-05 Up
Unclassifiable EST ES388562 ES389391 0.00024055 28.6 60.6 0.00089031 Up
Unclassifiable EST ES388375 ES395957 2.55E-07 28.6 64.6 1.15E-06 Up
RING finger protein 141 ES396408 ES391738 0.00357191 28.6 74.6 0.0030856 Up
Rho-related GTP-binding
protein RhoQ ES391369 ES389049 1.63E-06 28.6 66.6 1.11E-06 Up
Cat eye syndrome critical
region protein 5 ES398793 ES406524 0.0051911 28.6 34.6 0.025178 Up
Unclassifiable EST ES406135 ES405839 0.00944568 28.6 62.6 0.016301 Up
Unclassifiable EST ES398247 ES397985 0.0051305 28.6 46.6 0.0034146 Up
Unclassifiable EST ES402847 ES400715 0.000434 28.6 68.6 0.0013105 Up
Rab GDP dissociation
inhibitor beta ES401980 ES406072 0.00011264 28.6 74.6 7.8166E-05 Up
Unclassifiable EST ES405797 ES405018 5.00E-06 28.6 74.6 3.8143E-05 Up
Cell division cycle protein
123 homolog ES399812 ES400395 6.44E-05 28.6 58.6 0.0093846 Up
Unclassifiable EST ES406997 ES399417 1.37E-05 28.6 62.6 1.3015E-05 Up
Immediate early response
gene 5-like protein ES407208 ES399791 4.38E-05 28.6 46.6 0.0019164 Up
cAMP-specific 3',5'-cyclic
phosphodiesterase 4B ES404600 ES400864 0.0099164 28.6 64.6 8.6649E-05 Up
Unclassifiable EST ES399894 ES403868 2.77E-05 28.6 72.6 0.0027136 Up
Unclassifiable EST ES400015 ES403947 9.37E-07 28.6 72.6 2.63E-06 Up
83
Unclassifiable EST GE765016 GE761310 3.39E-05 28.6 68.6 0.00051964 Up
Dual specificity protein
phosphatase 7 GE765349 GE764386 2.97E-05 28.6 74.6 0.00052066 Up
Protein FAM48A GE764170 GE761519 2.98E-07 28.6 72.6 0.00015931 Up
Unclassifiable EST GE757533 GE762330 0.00065725 28.6 66.6 4.5575E-05 Up
Unclassifiable EST GE764119 GE763726 3.75E-05 28.6 72.6 0.0032604 Up
Charged multivesicular body
protein 5 GE758726 GE759940 0.00097037 28.6 74.6 0.0007502 Up
Chitotriosidase-1 GE760025 GE757193 6.78E-05 28.6 80.6 0.0026704 Up
Unclassifiable EST GE753958 GE749057 0.00321325 28.6 58.6 0.020627 Up
Growth arrest and DNA
damage-inducible protein
GADD45 gamma GE753109 GE750465 5.4827E-05 28.6 68.6 0.0074552 Up
Unclassifiable EST GE753863 GE752892 0.00231417 28.6 74.6 0.00034129 Up
Unclassifiable EST ES736184 0.00294203 30.6 74.6 0.0014765 Up
Unclassifiable EST ES736917 8.37E-08 30.6 74.6 7.0259E-05 Up
Unclassifiable EST ES738544 0.00078844 30.6 38.6 0.023942 Up
Unclassifiable EST ES736325 0.00021775 30.6 74.6 2.45E-06 Up
Unclassifiable EST ES737967 0.00232682 30.6 74.6 0.0025853 Up
Unclassifiable EST ES394274 ES392165 0.00993849 30.6 84.6 0.0095334 Up
Unclassifiable EST ES738622 0.0001327 30.6 60.6 0.002217 Up
T-complex protein 1 subunit
epsilon ES393656 0.00326033 30.6 70.6 0.0012036 Up
Unclassifiable EST ES390113 ES394492 0.00562945 30.6 70.6 0.0037779 Up
Kinesin-like protein KIF26A ES390228 ES396111 0.00750624 30.6 70.6 0.0031803 Up
Tryptophanyl-tRNA
synthetase, cytoplasmic ES389805 ES395058 0.0033484 30.6 34.6 0.031232 Up
Unclassifiable EST 2.66E-06 30.6 64.6 5.5556E-05 Up
Unclassifiable EST ES392063 ES387925 0.00296959 30.6 74.6 0.0026184 Up
Unclassifiable EST ES392216 ES395574 0.00109313 30.6 40.6 0.018625 Up
Ras-related protein Rab-32 ES395940 ES391870 1.24E-05 30.6 74.6 0.00047757 Up
Unclassifiable EST ES403096 ES402330 1.29E-05 30.6 76.6 0.00068021 Up
Unclassifiable EST ES400735 ES399363 3.12E-07 30.6 66.6 0.00008383 Up
84
Unclassifiable EST ES400595 ES398205 2.82E-05 30.6 54.6 0.0017453 Up
UPF0598 protein C8orf82 ES401853 ES401527 0.0001435 30.6 74.6 0.0014091 Up
Cholecystokinin receptor type
A ES405328 ES403769 0.00909827 30.6 68.6 0.00013101 Up
Unclassifiable EST ES399710 ES406166 2.74E-06 30.6 78.6 0.00011015 Up
Unclassifiable EST ES398791 ES400272 4.68E-05 30.6 64.6 0.00011186 Up
GTPase IMAP family
member 4 ES404563 ES403085 5.46E-06 30.6 80.6 0.0070234 Up
Galectin-4 ES398988 ES402467 1.29E-06 30.6 74.6 0.0025401 Up
Golgi to ER traffic protein 4
homolog ES397645 ES405790 0.00558476 30.6 66.6 0.00069891 Up
GTP-binding protein Rheb ES406812 ES402044 2.30E-05 30.6 62.6 0.00024594 Up
Adenosine monophosphate-
protein transferase FICD GE757506 GE763728 0.00038182 30.6 68.6 0.0033948 Up
Prolyl endopeptidase GE757226 GE757361 0.00016268 30.6 70.6 0.0034426 Up
AFG3-like protein 2 GE763178 GE758571 3.00E-05 30.6 78.6 0.00039991 Up
Unclassifiable EST GE752061 GE750573 0.00048128 30.6 40.6 0.02127 Up
Vesicle-fusing ATPase GE749096 GE753634 0.003239 30.6 58.6 0.017557 Up
Unclassifiable EST GE753580 GE752350 0.0002862 30.6 62.6 0.0011778 Up
Unclassifiable EST GE751283 GE749996 0.00979641 30.6 64.6 0.0077836 Up
ETS homologous factor ES736115 0.00070909 32.6 64.6 0.00019424 Up
Unclassifiable EST ES736827 0.00141684 32.6 38.6 0.042403 Up
Lipid phosphate
phosphohydrolase 3 ES391034 ES390859 0.00019212 32.6 62.6 0.0011829 Up
Uncharacterized protein
C20orf111 ES396335 ES394939 0.0004473 32.6 76.6 0.0014658 Up
Unclassifiable EST ES390923 ES387744 0.00020425 32.6 74.6 7.2932E-05 Up
Unclassifiable EST ES387705 ES389959 3.37E-06 32.6 68.6 1.2054E-05 Up
Unclassifiable EST ES388717 ES396606 0.00116837 32.6 66.6 0.00002729 Up
Unclassifiable EST ES388302 ES388682 0.00179957 32.6 70.6 0.0098339 Up
Unclassifiable EST ES736036 0.00425339 32.6 70.6 0.01599 Up
Unclassifiable EST ES393819 ES393762 0.00559078 32.6 50.6 0.0082502 Up
85
Ubiquitin-conjugating
enzyme E2 variant 2 ES389239 ES393606 2.03E-06 32.6 68.6 4.24E-07 Up
Unclassifiable EST ES737579 4.82E-07 32.6 76.6 0.00076998 Up
Unclassifiable EST ES738094 1.39E-05 32.6 76.6 0.00012488 Up
Unclassifiable EST ES388156 0.00280977 32.6 80.6 0.0090593 Up
Heat shock factor protein 1 ES392109 ES392823 0.0060525 32.6 64.6 0.0034888 Up
Unclassifiable EST ES396016 ES389161 0.00251306 32.6 72.6 0.00041529 Up
Synaptopodin 2-like protein ES396305 ES389627 2.44E-05 32.6 78.6 2.3009E-05 Up
Unclassifiable EST ES400583 ES401004 0.00115355 32.6 56.6 0.0060684 Up
Unclassifiable EST ES400951 ES398217 0.00011027 32.6 76.6 0.00047668 Up
Sushi domain-containing
protein 2 ES399293 ES397926 0.00071174 32.6 76.6 9.4634E-05 Up
Cyclin-dependent kinase 9 ES399818 ES398933 5.88E-05 32.6 74.6 0.0048228 Up
Muskelin ES400085 ES407652 2.839E-06 32.6 78.6 0.00027788 Up
Unclassifiable EST ES400598 ES404474 4.66E-05 32.6 54.6 0.0023892 Up
Zinc finger protein ZPR1 ES407257 ES400748 0.00342408 32.6 62.6 0.035933 Up
Serine racemase ES405849 ES402825 0.00030333 32.6 74.6 0.00036722 Up
Unclassifiable EST ES408018 ES404539 0.00055428 32.6 76.6 0.00020948 Up
Unclassifiable EST ES405187 ES407213 0.00470219 32.6 44.6 0.015691 Up
Unclassifiable EST ES402065 ES397607 8.30E-05 32.6 74.6 0.0051989 Up
Unclassifiable EST GE758552 GE760044 0.0082345 32.6 64.6 0.0024548 Up
Uncharacterized protein
C12orf29 GE754643 GE759031 2.83E-06 32.6 78.6 0.00017744 Up
Unclassifiable EST GE763528 GE756727 0.00230152 32.6 42.6 0.016357 Up
Estrogen-related receptor
gamma GE754308 GE756913 0.00026583 32.6 50.6 0.030858 Up
Unclassifiable EST GE753682 GE751116 0.0001958 32.6 76.6 0.0010464 Up
Erythrocyte band 7 integral
membrane protein GE747864 GE750054 8.1606E-05 32.6 74.6 0.00099365 Up
Unclassifiable EST GE753920 GE748163 9.93E-05 32.6 70.6 0.0024866 Up
T-complex protein 1 subunit
delta GE749869 GE750631 2.93E-06 32.6 80.6 0.00015692 Up
Unclassifiable EST GE753414 GE749683 0.00068871 32.6 74.6 0.0007584 Up
86
Unclassifiable EST ES396584 ES393343 0.00312019 34.6 66.6 4.0618E-05 Up
Unclassifiable EST ES389870 ES396906 0.0004473 34.6 78.6 3.4468E-05 Up
Unclassifiable EST ES388567 ES390150 0.00738065 34.6 40.6 0.032385 Up
Unclassifiable EST ES396213 ES388190 0.00303811 34.6 76.6 0.0004254 Up
Cholecystokinin receptor type
A ES738358 7.07E-07 34.6 76.6 4.65E-06 Up
Archaemetzincin-2 ES392452 ES395394 0.00369419 34.6 76.6 0.0022431 Up
Unclassifiable EST ES399542 ES398254 0.00022622 34.6 78.6 0.0017178 Up
Unclassifiable EST ES400446 ES400021 0.00036874 34.6 58.6 0.0023933 Up
Unclassifiable EST ES402598 ES405820 0.00853367 34.6 74.6 0.010045 Up
Unclassifiable EST ES399162 ES405798 3.05E-06 34.6 78.6 0.00011255 Up
Unclassifiable EST ES397915 0.00559078 34.6 70.6 0.011643 Up
Ubiquitin-conjugating
enzyme E2 D3 ES407492 ES400611 1.05E-07 34.6 80.6 0.00042834 Up
Unclassifiable EST 0.00166096 34.6 74.6 0.014148 Up
F-box only protein 4 ES404040 ES397155 1.12E-06 34.6 80.6 0.00018231 Up
Unclassifiable EST ES405148 ES407558 1.73E-06 34.6 78.6 0.00078035 Up
Unclassifiable EST ES404463 ES399827 0.00010945 34.6 78.6 5.4958E-05 Up
Tetratricopeptide repeat
protein 26 GE764986 GE762754 5.62E-05 34.6 78.6 0.00090074 Up
Baculoviral IAP repeat-
containing protein 2 GE762537 GE758210 0.00246078 34.6 78.6 0.002051 Up
Unclassifiable EST GE762219 GE763294 0.00070909 34.6 66.6 0.00010571 Up
Collagen alpha-1(XXII) chain GE751241 GE753783 0.00123179 34.6 74.6 0.023714 Up
Unclassifiable EST GE753472 GE753987 3.42E-06 34.6 78.6 0.000946 Up
RNA-binding protein NOB1 ES392982 ES389539 0.00183104 36.6 78.6 0.0039688 Up
Unclassifiable EST ES390490 ES389953 0.00292971 36.6 74.6 0.00062807 Up
Cytochrome P450 2U1 ES389558 ES391249 4.28E-05 36.6 68.6 0.00015266 Up
Unclassifiable EST ES394252 ES396908 8.1606E-05 36.6 78.6 9.0169E-05 Up
Unclassifiable EST ES400802 ES407781 0.00940661 36.6 66.6 0.003733 Up
Unclassifiable EST ES398226 ES398059 0.00841571 36.6 70.6 0.036148 Up
Unclassifiable EST ES406860 ES397303 1.88E-07 36.6 78.6 5.6413E-05 Up
Actin, alpha skeletal muscle ES408116 ES405968 2.43E-05 36.6 76.6 0.0001259 Up
87
Unclassifiable EST ES406643 ES397578 0.00337978 36.6 66.6 0.0021089 Up
Receptor-interacting
serine/threonine-protein
kinase 1 ES398587 ES405463 0.0072706 36.6 76.6 9.3047E-05 Up
Unclassifiable EST ES398593 ES405715 0.00420146 36.6 64.6 0.0066844 Up
Alpha-1D adrenergic
receptor ES400670 ES407334 0.00925975 36.6 74.6 0.0042405 Up
D site-binding protein GE761529 GE761864 0.00254055 36.6 62.6 0.0031001 Up
cAMP-responsive element-
binding protein-like 2 GE757206 GE762611 0.00077455 36.6 78.6 0.0001678 Up
T-complex protein 1 subunit
theta GE747539 GE748024 2.03E-06 36.6 72.6 0.0019283 Up
Unclassifiable EST GE751949 GE747258 1.99E-06 36.6 84.6 0.0012149 Up
Multiple inositol
polyphosphate phosphatase
1 GE749272 GE751245 4.72E-05 36.6 78.6 0.00048553 Up
Unclassifiable EST ES736799 0.00306476 38.6 76.6 0.00060626 Up
Unclassifiable EST ES736339 0.00217628 38.6 80.6 0.00040712 Up
Unclassifiable EST ES393202 ES396991 2.66E-05 38.6 78.6 0.00039622 Up
Unclassifiable EST ES738314 1.98E-05 38.6 78.6 7.75E-06 Up
Serine/arginine-rich splicing
factor 10 ES388931 ES392814 0.00281355 38.6 82.6 0.020105 Up
Unclassifiable EST ES389303 ES394121 8.37E-08 38.6 82.6 9.1766E-05 Up
Unclassifiable EST ES394483 ES396229 0.00535631 38.6 66.6 0.0013717 Up
Unclassifiable EST ES406302 ES405856 0.00192183 38.6 76.6 0.00021248 Up
Unclassifiable EST ES397495 0.00231194 38.6 64.6 0.0065541 Up
Unclassifiable EST ES401949 ES406870 0.00950912 38.6 70.6 0.00033525 Up
Unclassifiable EST ES402104 ES404949 0.00016148 38.6 78.6 1.3596E-05 Up
Unclassifiable EST ES399439 ES405648 9.53E-05 38.6 80.6 0.010708 Up
Peroxisomal acyl-coenzyme
A oxidase 1 ES407620 ES400182 0.00794272 38.6 70.6 0.022272 Up
Serine racemase ES406793 ES400056 0.00091699 38.6 78.6 0.0001472 Up
Transcription factor SOX-4 ES402468 ES402630 0.00037848 38.6 80.6 0.0000248 Up
88
Unclassifiable EST ES405958 ES399344 0.00033511 38.6 80.6 0.0040088 Up
Slit homolog 2 protein GE756431 GE755211 0.00517714 38.6 68.6 0.00063112 Up
Unclassifiable EST ES737161 1.67E-08 40.6 82.6 2.3298E-05 Up
Unclassifiable EST ES394591 ES387479 0.00020049 40.6 80.6 0.0010135 Up
Netrin-1 ES393933 ES396170 0.00380719 40.6 84.6 0.0075653 Up
Unclassifiable EST ES395890 ES394318 0.0002121 40.6 78.6 3.4008E-05 Up
Unclassifiable EST ES736581 7E-09 40.6 82.6 2.9753E-05 Up
Eukaryotic initiation factor
4A-II ES396875 ES388999 0.00122356 40.6 66.6 0.010476 Up
Carbohydrate
sulfotransferase 9 ES396284 ES390801 0.00162422 40.6 76.6 0.00010237 Up
Unclassifiable EST ES394163 ES396019 1.39E-06 40.6 70.6 0.00058369 Up
Galactosylgalactosylxylosylpr
otein 3-beta-
glucuronosyltransferase 2 ES395958 ES389819 1.88E-07 40.6 80.6 3.3432E-05 Up
Eukaryotic translation
initiation factor 6 ES400980 ES401813 0.00694025 40.6 78.6 0.012925 Up
Unclassifiable EST ES400621 ES403864 0.0022775 40.6 80.6 0.0053619 Up
Unclassifiable EST ES736781 0.00229622 42.6 80.6 5.8746E-05 Up
Unclassifiable EST ES737108 0.0001104 42.6 70.6 0.0011268 Up
Unclassifiable EST ES736652 0.00978541 42.6 78.6 0.003218 Up
Unclassifiable EST ES738606 1.58E-05 42.6 84.6 0.0012221 Up
Unclassifiable EST ES389977 ES388768 0.00330251 42.6 66.6 0.0033492 Up
Unclassifiable EST 0.00239447 42.6 84.6 0.0074292 Up
Unclassifiable EST ES390397 ES391839 0.00664842 42.6 56.6 0.025062 Up
Inter-alpha-trypsin inhibitor
heavy chain H3 ES390670 ES389952 0.00052003 42.6 80.6 0.00039496 Up
Membrane metallo-
endopeptidase-like 1 ES400844 ES403250 0.00000006 42.6 82.6 4.34E-06 Up
Unclassifiable EST ES406280 ES401908 3.84E-05 42.6 78.6 0.00066256 Up
cAMP-responsive element-
binding protein-like 2 GE760632 GE763165 7.07E-07 42.6 82.6 2.0256E-05 Up
89
Protein transport protein
Sec23A GE760303 GE763132 2.14E-08 42.6 84.6 3.4746E-05 Up
Unclassifiable EST GE761403 0.000391 42.6 80.6 1.07E-06 Up
Unclassifiable EST GE750600 GE747703 3.82E-05 42.6 82.6 0.0054949 Up
Protein phosphatase 1
regulatory inhibitor subunit
16B GE750799 GE747482 0.00558475 42.6 82.6 0.0001487 Up
Unclassifiable EST GE752165 GE749752 9.86E-05 42.6 82.6 0.0016881 Up
Unclassifiable EST ES395341 ES393823 0.00179957 44.6 78.6 0.012195 Up
CCR4-NOT transcription
complex subunit 1 ES394397 ES388196 0.0001087 44.6 84.6 0.00021067 Up
Unclassifiable EST ES400686 ES407306 0.00308285 44.6 64.6 0.017612 Up
Unclassifiable EST ES402118 ES401780 9.01E-07 44.6 84.6 0.0013431 Up
Unclassifiable EST GE761701 GE758524 0.00303909 44.6 74.6 0.0093147 Up
Ferric-chelate reductase 1 GE757854 GE759734 0.00459042 44.6 80.6 0.012099 Up
Cysteine and histidine-rich
domain-containing protein 1 GE765178 GE759105 0.00043647 44.6 84.6 0.00063589 Up
Unclassifiable EST GE753114 GE748714 1.31E-05 44.6 86.6 0.00032029 Up
GRIP and coiled-coil domain-
containing protein 2 ES738276 2.37E-05 46.6 88.6 0.0022321 Up
ADAMTS-like protein 1 ES394684 ES392706 3.89E-05 46.6 86.6 0.0012397 Up
Protein RMD5 homolog A ES391800 ES391218 0.00036468 46.6 78.6 0.0036406 Up
Unclassifiable EST ES390567 ES391529 0.00030356 46.6 84.6 0.00010837 Up
Unclassifiable EST ES401319 ES400391 0.00032053 46.6 80.6 0.0019907 Up
Unclassifiable EST ES397320 ES402430 4.28E-08 46.6 82.6 5.18E-07 Up
Unclassifiable EST ES404624 ES407226 0.00389014 46.6 84.6 0.0048036 Up
Kyphoscoliosis peptidase ES397175 ES398542 0.00954651 46.6 86.6 0.005219 Up
Formin-1 ES403740 ES407141 0.00016388 46.6 78.6 0.0033937 Up
Uncharacterized protein
C8orf41 ES406525 ES398908 0.00016875 46.6 90.6 0.021829 Up
Unclassifiable EST ES402053 ES405899 3.95E-07 46.6 80.6 0.00010961 Up
Glutathione S-transferase
omega-1 ES404769 ES402747 2.94E-05 46.6 88.6 0.0030794 Up
90
Peroxiredoxin-1 ES400640 ES399010 0.00034098 46.6 84.6 0.0063908 Up
Proline-rich transmembrane
protein 1 GE764670 GE759336 0.00114298 46.6 78.6 0.0024446 Up
10 kDa heat shock protein,
mitochondrial GE754466 GE762981 6.62E-08 46.6 86.6 0.0011858 Up
Unclassifiable EST ES738224 4.99E-08 48.6 84.6 5.2002E-05 Up
Unclassifiable EST ES396219 ES391795 0.00279229 48.6 86.6 0.014833 Up
UDP-glucose 6-
dehydrogenase ES388697 ES390714 2.17E-05 48.6 80.6 0.0057052 Up
Unclassifiable EST ES402194 ES405400 0.00430925 48.6 84.6 0.00034613 Up
Surfeit locus protein 4 ES405972 ES399531 0.00011803 48.6 86.6 0.0009345 Up
Glutamate--cysteine ligase
catalytic subunit GE756334 GE761065 4.55E-07 48.6 86.6 0.00030325 Up
Unclassifiable EST GE759797 GE759273 0.00016145 48.6 84.6 0.00036419 Up
Unclassifiable EST GE756954 GE754639 0.00033511 48.6 82.6 0.002191 Up
Unclassifiable EST GE755717 GE765010 0.00939698 48.6 78.6 0.013025 Up
Unclassifiable EST ES738712 0.00033639 50.6 84.6 0.00071748 Up
Unclassifiable EST ES738702 0.00023297 50.6 86.6 0.00057652 Up
Unclassifiable EST ES394769 ES391219 5.62E-05 50.6 84.6 4.4377E-05 Up
Unclassifiable EST ES407555 ES406855 0.00486456 50.6 88.6 0.0097818 Up
Unclassifiable EST ES390779 ES396296 0.0045667 52.6 84.6 0.023494 Up
Coatomer subunit epsilon ES391671 ES391364 0.00335502 52.6 84.6 0.011756 Up
Unclassifiable EST ES390263 ES388673 0.00080848 52.6 88.6 0.0022575 Up
Unclassifiable EST ES738786 0.00014592 52.6 78.6 0.022693 Up
5-hydroxytryptamine
receptor 2A ES387469 0.00010028 52.6 86.6 0.0004727 Up
Unclassifiable EST ES387924 ES388492 0.00027768 52.6 84.6 0.0016731 Up
Cyclic AMP-dependent
transcription factor ATF-3 ES390403 ES390523 0.00014645 52.6 88.6 0.0028351 Up
Unclassifiable EST ES391570 ES387827 0.00100656 52.6 78.6 0.006432 Up
Acyl-coenzyme A
thioesterase 2,
mitochondrial ES406689 ES399396 0.00029574 52.6 84.6 0.012539 Up
91
Unclassifiable EST ES407331 ES403438 0.00598157 52.6 82.6 0.0016388 Up
Unclassifiable EST GE764738 GE758762 0.00088339 52.6 82.6 0.00065593 Up
Unclassifiable EST GE753006 GE750450 0.00384123 52.6 84.6 0.00041297 Up
Unclassifiable EST ES738720 0.00000222 54.6 86.6 0.00012972 Up
Unclassifiable EST ES737368 0.00566127 54.6 90.6 0.0039929 Up
Unclassifiable EST ES738594 0.00082555 54.6 88.6 0.0031008 Up
Poly [ADP-ribose]
polymerase 12 ES396110 ES393925 0.00960475 54.6 68.6 0.019334 Up
Unclassifiable EST ES400422 ES402675 0.00071972 54.6 84.6 0.0042708 Up
Unclassifiable EST ES407278 ES405528 0.00043537 54.6 84.6 0.0025832 Up
Myosin-1 GE759702 GE760928 7.29E-05 54.6 86.6 0.0022468 Up
Unclassifiable EST GE748080 GE748107 5.36E-06 54.6 88.6 0.00045133 Up
Unclassifiable EST GE750741 GE753467 0.00838319 54.6 76.6 0.037013 Up
Unclassifiable EST ES396062 ES397004 0.00203914 56.6 78.6 0.0084614 Up
JmjC domain-containing
protein 8 ES397062 ES406214 0.00068922 56.6 84.6 0.01447 Up
Putative tyrosine-protein
phosphatase auxilin ES401858 ES398545 0.00013616 56.6 86.6 0.0031032 Up
Unclassifiable EST ES407302 ES397345 0.00375082 56.6 82.6 0.0017195 Up
Unclassifiable EST GE751436 GE752949 0.00087752 56.6 82.6 0.004652 Up
Unclassifiable EST ES396295 ES393493 0.00435012 58.6 86.6 0.0011292 Up
Unclassifiable EST ES392693 ES390438 0.00218541 58.6 88.6 0.0092407 Up
F-box only protein 21 ES391708 ES395017 0.00075518 58.6 78.6 0.022492 Up
Unclassifiable EST GE751298 GE749015 1.84E-08 58.6 88.6 0.00058557 Up
Signal recognition particle 54
kDa protein ES391041 ES389390 0.00034209 60.6 88.6 0.0025994 Up
Dentin sialophosphoprotein ES388921 ES392284 0.00907307 60.6 86.6 0.025942 Up
Alpha-crystallin B chain ES401526 ES406058 1.50E-09 60.6 90.6 0.0027467 Up
Unclassifiable EST ES401985 ES407918 0.00243635 60.6 86.6 0.0076555 Up
Zinc finger protein 836 ES399734 ES402588 0.00356922 60.6 88.6 0.0038748 Up
Unclassifiable EST GE752492 0.00114298 60.6 70.6 0.031382 Up
Unclassifiable EST ES738620 0.00023682 62.6 88.6 0.0010726 Up
Unclassifiable EST ES395762 ES391237 7.79E-05 62.6 90.6 0.0068059 Up
92
Glutathione S-transferase A2 ES396279 2.94E-07 62.6 92.6 0.0047743 Up
Unclassifiable EST ES404311 ES403860 0.00391392 62.6 88.6 0.0037811 Up
Protein YIPF4 ES405101 ES404366 0.00040368 62.6 86.6 0.014879 Up
Unclassifiable EST GE756553 GE765159 0.00442554 62.6 82.6 0.0084671 Up
Unclassifiable EST GE764529 GE761513 0.0063705 62.6 90.6 0.0065284 Up
Eukaryotic translation
initiation factor 3 subunit C GE763090 GE754896 0.0075621 62.6 88.6 0.0010173 Up
Unclassifiable EST GE748613 GE749004 0.00251414 62.6 88.6 0.0025872 Up
Unclassifiable EST ES738101 0.00045724 64.6 90.6 0.012638 Up
Signal recognition particle
receptor subunit beta ES397601 ES399570 0.00019671 64.6 90.6 0.015478 Up
Unclassifiable EST ES399292 ES398661 0.0072706 64.6 94.6 0.0026496 Up
Unclassifiable EST ES399846 ES407527 6.84E-06 64.6 92.6 0.0088496 Up
Unclassifiable EST GE750705 GE747973 0.00035005 64.6 90.6 0.0015925 Up
Alpha-crystallin A chain ES737047 8.35E-07 66.6 92.6 0.0021024 Up
Unclassifiable EST ES392674 ES391465 0.00749251 66.6 90.6 0.0028098 Up
Unclassifiable EST ES738595 0.00210343 66.6 82.6 0.019842 Up
Unclassifiable EST ES401916 ES402525 0.00291375 66.6 90.6 0.0029316 Up
Glutamate--cysteine ligase
regulatory subunit ES407895 ES404927 4.00E-06 66.6 92.6 0.0077849 Up
Unclassifiable EST ES394997 ES388333 0.00217628 68.6 84.6 0.028698 Up
Unclassifiable EST ES396277 ES393064 0.00350535 68.6 86.6 0.020072 Up
Uncharacterized protein
C4orf29 ES408086 ES397756 0.00570342 68.6 88.6 0.01807 Up
Unclassifiable EST GE757952 0.0066213 68.6 94.6 0.010586 Up
Transcriptional repressor NF-
X1 ES393563 ES393237 0.00101463 70.6 86.6 0.033725 Up
Liver carboxylesterase 1 ES401719 ES400837 6.78E-05 72.6 94.6 0.024876 Up
Unclassifiable EST GE764719 GE762823 0.00783995 72.6 90.6 0.0060006 Up
Unclassifiable EST ES399735 ES407565 0.00082092 74.6 94.6 0.012787 Up
NEDD8-conjugating enzyme
Ubc12 GE749863 GE749908 0.00070909 76.6 94.6 0.016104 Up
93
Unclassifiable EST ES400256 ES397197 0.0067099 80.6 90.6 0.031417 Up
Arsenite methyltransferase ES400347 ES403449 0.00521643 80.6 92.6 0.023494 Up
C-type lectin domain family
4 member K GE750613 GE753739 0.00268774 84.6 94.6 0.030011 Up
Unclassifiable EST ES737140 0.00697133 Not significant 10.7 0.068971 Down
Guanine nucleotide-binding
protein G(i) subunit alpha-1 ES735886 0.00558476 Not significant 20.6 0.11259 Up
Dynein heavy chain 9,
axonemal ES738803 0.009685 Not significant 76.6 0.068155 Down
Piwi-like protein 1 ES390335 0.00435012 Not significant 60.6 0.12854 Down
Unclassifiable EST ES390462 ES390509 0.00053655 Not significant 50.6 0.08044 Down
28S ribosomal protein S14,
mitochondrial ES391003 ES391726 0.00357191 Not significant 76.6 0.057835 Up
NudC domain-containing
protein 3 ES394448 ES390185 0.00698636 Not significant 0.6 0.082481 Down
Unclassifiable EST ES396186 ES393538 7.12E-05 Not significant 26.6 0.077058 Down
Unclassifiable EST ES396155 ES392635 0.00034054 Not significant 26.6 0.13214 Down
HCLS1-binding protein 3 ES395035 ES394908 0.0017377 Not significant 60.6 0.30337 Down
CAP-Gly domain-containing
linker protein 4 ES391180 ES389219 0.00226576 Not significant 62.6 0.052443 Down
Unclassifiable EST ES390751 ES388311 0.00494212 Not significant 56.6 0.067688 Down
Unclassifiable EST ES392408 ES394426 0.00069753 Not significant 46.6 0.11047 Up
ARF GTPase-activating
protein GIT2 ES389895 ES389547 0.00610237 Not significant 82.6 0.11354 Up
Kyphoscoliosis peptidase ES394912 ES392988 0.00018492 Not significant 62.6 0.05992 Down
Unclassifiable EST ES396372 ES394496 0.0013221 Not significant 78.6 0.16259 Down
Unclassifiable EST ES390243 ES387973 0.00420759 Not significant 36.6 0.24293 Up
Kyphoscoliosis peptidase ES395909 ES392048 0.00072561 Not significant 24.6 0.050778 Down
Unclassifiable EST ES393174 ES388667 0.00962895 Not significant 74.6 0.07712 Down
Unclassifiable EST ES738072 0.00751098 Not significant 36.6 0.05447 Down
Unclassifiable EST ES737163 0.00357191 Not significant 14.6 0.090858 Down
ADP-ribosylation factor-like
protein 6 ES737706 0.00843415 Not significant 54.6 0.051337 Up
94
Unclassifiable EST ES387818 ES394751 0.0099164 Not significant 6.5 0.18265 Down
Unclassifiable EST ES389459 ES388173 0.00375993 Not significant 86.6 0.12238 Down
Shootin-1 ES395677 ES391786 0.00452423 Not significant 30.6 0.093197 Down
Unclassifiable EST ES390202 0.00333497 Not significant 18.6 0.053008 Down
Arachidonate 5-lipoxygenase ES388399 ES395606 0.00723796 Not significant 28.6 0.0774 Down
Microspherule protein 1 ES393024 ES393779 2.71E-05 Not significant 44.6 0.12835 Up
Transcription intermediary
factor 1-beta ES396964 ES388347 0.00397632 Not significant 30.6 0.15556 Down
E3 ubiquitin-protein ligase
TRIM63 ES737157 0.00116837 Not significant 66.6 0.1664 Down
Unclassifiable EST ES738941 0.00950954 Not significant 60.6 0.13359 Down
Titin ES735889 0.00413884 Not significant 44.6 0.48035 Down
Unclassifiable EST ES737397 0.00228479 Not significant 36.6 0.058716 Down
Receptor-type tyrosine-
protein phosphatase delta ES388721 ES390683 0.0099164 Not significant 36.6 0.11591 Down
Unclassifiable EST ES393000 ES395317 0.00420146 Not significant 0.8 0.35595 Down
Centrin-3 ES395698 ES392339 0.004923 Not significant 84.6 0.11197 Down
Unclassifiable EST ES738751 ES388169 0.00286522 Not significant 32.6 0.084313 Up
Retinoic acid receptor
gamma ES390148 ES392492 0.00692869 Not significant 84.6 0.066211 Up
Tripartite motif-containing
protein 66 ES388007 0.00092469 Not significant 48.6 0.055652 Down
Cell division cycle protein 20
homolog ES388827 ES388500 0.00569272 Not significant 30.6 0.27549 Down
Unclassifiable EST ES391535 ES388502 0.00242206 Not significant 46.6 0.093359 Down
Sialin ES389293 ES389789 0.00027722 Not significant 88.6 0.10644 Down
Cell adhesion molecule 1 ES391303 ES392568 0.00147622 Not significant 0.6 0.74502 Down
UPF0573 protein C2orf70 ES389197 ES389922 7.79E-05 Not significant 20.6 0.18545 Down
Unclassifiable EST ES391448 0.00909159 Not significant 12.6 0.5524 Down
Transcription intermediary
factor 1-beta ES391487 ES390626 0.00029389 Not significant 48.6 0.072176 Down
Unclassifiable EST ES394988 ES388048 0.00904345 Not significant 46.6 0.24491 Down
95
Unclassifiable EST ES391039 ES396808 0.00632402 Not significant 72.6 0.059035 Down
Unclassifiable EST ES393712 ES389704 0.00681434 Not significant 84.6 0.052262 Up
Fatty acid-binding protein,
liver ES389194 ES392058 0.00697133 Not significant 14.6 0.25548 Down
Unclassifiable EST ES393551 ES390505 0.00144255 Not significant 66.6 0.11937 Down
Trichohyalin ES389053 ES388586 0.00783995 Not significant 54.6 0.12215 Down
Ras-like protein family
member 12 ES398442 ES398825 0.00984473 Not significant 100.6 0.071894 Up
Unclassifiable EST ES397093 ES402361 0.00293988 Not significant 106.6 0.3832 Down
Unclassifiable EST ES407353 ES407627 0.00096601 Not significant 34.6 0.13718 Up
Unclassifiable EST ES400727 ES406766 0.00082555 Not significant 54.6 0.087667 Down
Solute carrier family 23
member 2 ES398868 ES400522 0.0051911 Not significant 66.6 0.055475 Up
Unclassifiable EST ES398983 ES403902 0.00773287 Not significant 38.6 0.36369 Up
L-xylulose reductase ES407739 ES399040 0.00156741 Not significant 74.6 0.053304 Down
Cytochrome P450 1A1 ES398625 ES407986 0.00267554 Not significant 9 0.48035 Down
Unclassifiable EST ES401233 ES402753 0.00659911 Not significant 9 0.51914 Down
Dihydrolipoyllysine-residue
succinyltransferase
component of ES399783 ES403753 0.00909827 Not significant 9 0.4969 Down
Unclassifiable EST ES407262 ES402952 0.00035005 Not significant 28.6 0.91222 Down
Unclassifiable EST ES401872 ES402121 0.00214595 Not significant 28.6 0.85365 Down
Nucleoside diphosphate-
linked moiety X motif 19,
mitochondrial ES402739 ES405980 0.00597631 Not significant 9 0.059271 Down
Unclassifiable EST ES403674 ES402496 0.00369419 Not significant 110.6 0.47623 Down
Unclassifiable EST ES407098 ES398446 0.00607736 Not significant 100.6 0.13794 Up
Unclassifiable EST ES404654 ES400628 0.00084815 Not significant 44.6 0.073754 Down
BMP and activin membrane-
bound inhibitor homolog ES399613 ES399993 0.00245609 Not significant 32.6 0.075803 Down
Unclassifiable EST ES402193 ES403955 0.00209571 Not significant 34.6 0.057835 Down
Caspase-2 ES403011 ES405654 0.00123178 Not significant 16.6 0.51132 Down
96
Adenylate kinase isoenzyme
1 ES404537 ES405491 0.00251414 Not significant 46.6 0.13399 Down
Uncharacterized protein
C1orf194 ES407901 ES401009 0.00486456 Not significant 90.6 0.11835 Down
Unclassifiable EST ES401245 ES402172 0.00106127 Not significant 4.7 0.65811 Down
Unclassifiable EST ES401937 ES404321 0.00187688 Not significant 70.6 0.05681 Up
Unclassifiable EST ES402795 ES406903 0.00283326 Not significant 90.6 0.32418 Down
Transcription factor E2F8 ES402411 ES402377 0.00124737 Not significant 16.6 0.064853 Up
U3 small nucleolar RNA-
associated protein 15
homolog ES401641 ES400014 0.00479305 Not significant 24.6 0.077869 Up
Uncharacterized protein
C20orf96 ES404476 ES398785 0.009685 Not significant 34.6 0.44382 Down
WD repeat-containing
protein 67 ES404113 ES402477 0.00044576 Not significant 22.6 0.074163 Down
Unclassifiable EST ES404556 ES405691 0.00655794 Not significant 42.6 0.050126 Down
Ropporin-1-like protein GE762104 GE760114 0.00568492 Not significant 86.6 0.06377 Down
Unclassifiable EST GE755753 0.00024627 Not significant 64.6 0.071613 Down
Unclassifiable EST GE759562 GE760929 0.00039711 Not significant 50.6 0.054527 Down
N-acetylated-alpha-linked
acidic dipeptidase 2 GE757496 GE757029 0.004451 Not significant 54.6 0.2205 Down
Unclassifiable EST GE757115 GE758930 0.00582163 Not significant 28.6 0.093927 Down
Transient receptor potential
cation channel subfamily M
member 2 GE755938 GE765163 0.00302789 Not significant 60.6 0.091014 Down
Uncharacterized protein
C14orf166B GE756097 GE763330 0.00068922 Not significant 78.6 0.068312 Down
Unclassifiable EST GE759656 GE764707 0.00521437 Not significant 70.6 0.054413 Down
Centrosomal protein of 63
kDa GE759983 GE760382 0.00414004 Not significant 40.6 0.063603 Down
Unclassifiable EST GE755066 2.19E-05 Not significant 26.6 0.079398 Down
Laminin subunit beta-1 GE764403 GE755943 0.00542295 Not significant 56.6 0.077995 Down
97
Proline-serine-threonine
phosphatase-interacting
protein 1 GE764021 GE754955 0.00565515 Not significant 16.6 0.11207 Up
Unclassifiable EST GE758733 GE754628 0.00097838 Not significant 98.6 0.098176 Up
Unclassifiable EST GE757896 GE764475 0.0034424 Not significant 34.6 0.15564 Down
Unclassifiable EST GE765296 GE762249 0.00929174 Not significant 40.6 0.075022 Up
26S proteasome non-ATPase
regulatory subunit 6 GE756627 GE761382 0.00076617 Not significant 54.6 0.071335 Up
Unclassifiable EST GE762945 GE761471 0.00104826 Not significant 80.6 0.083028 Down
Optic atrophy 3 protein GE764068 GE756947 0.00723796 Not significant 38.6 0.061701 Up
Leucine-rich repeat-
containing protein 58 GE758314 GE755730 0.00011325 Not significant 42.6 0.050727 Up
Unclassifiable EST GE761043 GE757857 0.00973977 Not significant 74.6 0.11488 Up
Unclassifiable EST 0.00192572 Not significant 54.6 0.068998 Down
Tissue alpha-L-fucosidase GE758654 GE763555 0.0055128 Not significant 46.6 0.092395 Down
Niemann-Pick C1 protein GE749439 GE748328 0.00068922 Not significant 24.6 0.11397 Down
Unclassifiable EST 0.00396247 Not significant 60.6 0.0509 Down
Nucleolar pre-ribosomal-
associated protein 1 GE749718 GE752282 0.00337978 Not significant 68.6 0.066287 Up
Protein FAM183A GE750777 0.00344249 Not significant 88.6 0.051584 Down
Transcription intermediary
factor 1-beta GE754239 GE748917 0.00122516 Not significant 58.6 0.097959 Down
Dolichyl-
diphosphooligosaccharide--
protein glycosyltransferase
48 kDa GE753108 GE752493 0.0054219 Not significant 94.6 0.135 Up
Unclassifiable EST GE752686 GE748028 0.00348498 Not significant 18.6 0.072601 Down
Unclassifiable EST GE747313 GE753222 0.00764012 Not significant 16.6 0.87869 Down
Spectrin alpha chain, brain GE750225 GE751444 0.00434757 Not significant 50.6 0.14455 Down
Unclassifiable EST GE752797 GE752142 0.00036878 Not significant 66.6 0.090277 Down
Unclassifiable EST GE747092 GE753079 0.00214847 Not significant 22.6 0.1549 Down
Poly [ADP-ribose]
polymerase 1 GE747467 GE751344 0.00466601 Not significant 52.6 0.082583 Down
98
Transcription intermediary
factor 1-alpha GE754110 GE753010 0.00029652 Not significant 26.6 0.058631 Down
Unclassifiable EST GE752825 GE749399 0.00662098 Not significant 26.6 0.41422 Down
99
Swissprot Gene
Name
nt Gene Name LOTEC EC50 p-value Direction LOTEC EC50 p-value Direction
Calponin homolog
OV9M;
Mytilus coruscus
calponin-like
protein-1 mRNA,
complete cds 1.9246 3.29173 0.001375 Down 0.1136 2.12188 0.00057 Down
UV excision repair
protein RAD23
homolog B; 6.51739 11.7043 0.001446 Up 14.2266 14.938 0.04612 Up
Paramyosin;
Mytilus
galloprovincialis
mRNA for
paramyosin,
complete cds 4.20117 5.36187 0.013944 Down 11.147 14.2266 0.00405 Up
Actin-5C;
Mytilus
galloprovincialis
actin mRNA,
complete cds 7.18542 8.31803 0.021765 Up 10.1106 13.5492 0.01785 Up
Mytilus
californianus gene
for cysteine
peptidase,
complete cds 5.62997 7.18542 0.013882 Up 4.20117 4.86338 0.03441 Up
Leukocyte
receptor cluster
member 9; 9.62916 11.7043 0.007425 Up 9.62916 14.2266 0.00622 Up
Annotation SDRS Trial1 SDRS Trial2
Table S4--SDRS parameters for genes identified as sigmoidal in larvae in both Trial 1 and Trial 2. Gene IDs and
gene names are on the left, and on the right are LOTEC, EC50, significance values, and direction of change in
response to copper.
100
Cytochrome P450
10; 1.66255 1.74567 0.048939 Down 11.147 14.2266 0.00433 Up
GTPase IMAP
family member 7; 9.62916 13.5492 0.005776 Up 9.62916 13.5492 0.00483 Up
GTPase IMAP
family member 7; 8.31803 10.1106 0.016418 Up 8.73393 12.904 0.01992 Up
26S protease
regulatory subunit
6B;
Lottia gigantea
hypothetical protein
mRNA 6.84326 10.6161 0.000227 Up 9.62916 12.904 0.0103 Up
Protein Wnt-2b; 4.86338 7.92193 0.016244 Down 1.43617 2.70811 0.00799 Down
26S protease
regulatory subunit
6A;
PREDICTED:
Saccoglossus
kowalevskii 26S
protease regulatory
subunit 6A-like
(LOC100376932),
mRNA 10.1106 11.7043 0.027094 Up 9.62916 12.2895 0.01127 Up
Protein ABHD4
{ECO:0000305}; 8.73393 11.7043 0.000534 Up 10.6161 14.2266 0.00751 Up
Glutathione S-
transferase 1;
Mytilus
galloprovincialis
glutathione S-
transferase sigma 3
mRNA, complete
cds 7.18542 12.2895 0.000146 Up 12.904 15.6849 0.00773 Up
Matrix
metalloproteinase-
19; 5.91147 8.31803 0.003555 Up 11.7043 14.938 0.00418 Up
101
Tetratricopeptide
repeat protein 38;
PREDICTED:
Crassostrea gigas
tetratricopeptide
repeat protein 38-
like
(LOC105329719),
transcript variant
X2, mRNA 5.10655 7.18542 0.002622 Up 5.91147 7.92193 0.03092 Up
Inactive
carboxypeptidase-
like protein X2; 3.45631 5.10655 0.02576 Down 2.02083 2.57915 0.01476 Down
Caveolin-1; 5.10655 5.36187 0.04795 Down 1.83296 2.84352 0.00014 Down
Vacuolar protein
sorting-associated
protein 28
homolog; 6.20704 10.6161 0.004072 Up 12.904 14.938 0.02874 Up
Glutaredoxin-3;
PREDICTED: Limulus
polyphemus
glutaredoxin-3-like
(LOC106470626),
mRNA 7.5447 11.147 3.70E-05 Up 8.73393 13.5492 0.00034 Up
Poly [ADP-ribose]
polymerase 15; 7.5447 11.147 1.56E-05 Up 9.17062 13.5492 0.01052 Up
Protein mab-21-
like 1; 4.63179 7.92193 0.005212 Down 1.83296 2.12188 0.04598 Down
102
Cathepsin L;
Mytilus
galloprovincialis
shell fibrous
prismatic cathepsin-
like protein 1
mRNA, partial cds 8.31803 10.1106 0.010977 Up 7.92193 13.5492 0.00659 Up
Proteasomal
ubiquitin receptor
ADRM1; 6.20704 9.17062 7.11E-05 Up 9.17062 13.5492 0.00515 Up
Neural/ectoderm
al development
factor IMP-L2; 6.20704 9.62916 5.29E-05 Up 6.84326 11.7043 0.01666 Up
Uncharacterized
protein C1orf50
homolog; 7.18542 9.62916 0.002156 Up 7.92193 11.147 0.03716 Up
Protein PIF; 5.62997 6.84326 0.039361 Down 3.29173 4.63179 0.03523 Down
26S proteasome
non-ATPase
regulatory subunit
8; 5.91147 8.73393 0.000689 Up 13.5492 14.2266 0.04161 Up
Pyridoxal 5'-
phosphate
synthase subunit
SNZERR; 6.84326 9.62916 0.000443 Up 12.2895 15.6849 0.00515 Up
Myosin heavy
chain, striated
muscle;
Mytilus
galloprovincialis
partial mRNA for
myosin heavy chain
(MHC gene), from
pedal retractor
muscle 2.45634 3.62913 0.001302 Down 0.00171 1.9246 0.00023 Down
103
Bcl-2-like protein
1; 6.84326 8.31803 0.023092 Up 11.7043 14.2266 0.01741 Up
Annexin A11; 5.91147 7.18542 0.01261 Up 4.20117 5.62997 0.00658 Up
26S protease
regulatory subunit
10B;
PREDICTED: Apis
mellifera 26S
protease regulatory
subunit 10B
(LOC551386),
mRNA 8.31803 11.147 0.002503 Up 8.31803 13.5492 0.00189 Up
Ubiquitin
thioesterase
OTUB1
{ECO:0000250|Un
iProtKB:Q7TQI3}; 6.84326 9.17062 0.006998 Up 10.6161 14.2266 0.01151 Up
Proteasome
subunit beta type-
3;
Lottia gigantea
hypothetical protein
mRNA 8.31803 10.1106 0.012642 Up 7.5447 12.904 0.00131 Up
Probable
nucleoredoxin 1-2; 8.31803 11.7043 0.001473 Up 11.7043 14.938 0.00378 Up
DBH-like
monooxygenase
protein 1
homolog; 8.73393 10.1106 0.032019 Up 9.62916 14.2266 7.08E-05 Up
Guanine
nucleotide
exchange factor
MSS4; 10.1106 12.2895 0.042552 Up 4.86338 7.5447 0.03263 Up
Temptin; 1.50798 2.70811 0.00587 Down 1.83296 2.12188 0.03378 Down
104
Toxocara canis
genome assembly
T_canis_Equador
,scaffold
TCNE_scaffold0008
094 0.0011 0.11928 6.41E-05 Down 12.2895 15.6849 0.00405 Up
Proteasome
subunit alpha type-
6; 7.18542 9.62916 0.002315 Up 9.17062 14.2266 0.00247 Up
Proteasome
maturation
protein; 9.17062 12.904 0.028243 Up 11.147 14.2266 0.00595 Up
Major vault
protein
{ECO:0000312|E
MBL:DAA05661.1}
; 7.5447 8.73393 0.024477 Up 7.5447 12.904 0.00223 Up
Transitional
endoplasmic
reticulum ATPase;
Lottia gigantea
hypothetical protein
partial mRNA 9.17062 11.7043 0.007593 Up 7.18542 12.904 0.00154 Up
39S ribosomal
protein L14,
mitochondrial; 9.17062 12.904 0.020219 Up 13.5492 15.6849 0.01104 Up
Ubiquitin domain-
containing protein
UBFD1;
PREDICTED:
Coturnix japonica
ubiquitin family
domain containing
1 (UBFD1), mRNA 8.31803 11.7043 0.000383 Up 12.2895 14.2266 0.02105 Up
Cystatin-B; 5.91147 8.31803 0.004333 Up 14.2266 14.938 0.03628 Up
105
N6-adenosine-
methyltransferase
subunit METTL14; 7.92193 9.62916 0.013308 Up 5.36187 7.18542 0.01037 Up
Proteasome
subunit beta type-
6;
Mytilus trossulus
clone BM102A
proteasome subunit
beta type-6-like
protein mRNA,
partial cds 6.84326 9.17062 0.001306 Up 10.6161 14.938 0.00218 Up
Angiopoietin-
related protein 6;
Mytilus
galloprovincialis
fibrinogen-related
protein 8 mRNA,
complete cds 8.31803 11.7043 0.004603 Up 9.62916 14.938 0.00137 Up
Tubulin-specific
chaperone C; 8.31803 11.7043 0.000308 Up 5.91147 7.5447 0.01646 Up
Protoporphyrinoge
n oxidase; 9.17062 11.147 0.02101 Up 10.6161 14.2266 0.00191 Up
Polyubiquitin-B;
Mytilus edulis
mitochondrial
partial ub gene for
ubiquitin 8.31803 11.7043 4.60E-05 Up 10.6161 14.2266 0.00298 Up
Protein MAK16
homolog A;
PREDICTED:
Saccoglossus
kowalevskii protein
MAK16 homolog
(LOC100377801),
mRNA 9.17062 12.904 0.011812 Up 10.6161 11.147 0.0485 Up
106
Heat shock
cognate 71 kDa
protein;
Mytilus
galloprovincialis
HSP70 (HSP70)
mRNA, complete
cds 7.5447 12.2895 3.40E-05 Up 8.73393 12.2895 0.00676 Up
Desumoylating
isopeptidase 1; 10.1106 14.2266 0.000892 Up 13.5492 15.6849 0.02048 Up
T-complex protein
1 subunit zeta;
PREDICTED:
Crassostrea gigas T-
complex protein 1
subunit zeta-like
(LOC105327904),
mRNA 10.6161 12.2895 0.03289 Up 11.7043 13.5492 0.03093 Up
Hsc70-interacting
protein; 4.41123 8.73393 0.00021 Up 6.84326 10.6161 0.02453 Up
26S proteasome
non-ATPase
regulatory subunit
14;
PREDICTED:
Ictidomys
tridecemlineatus
proteasome
(prosome,
macropain) 26S
subunit, non-
ATPase, 14
(Psmd14), mRNA 7.5447 11.147 0.000613 Up 9.62916 14.2266 0.00068 Up
Tripartite motif-
containing protein
45;
Mytilus
californianus gene
for cysteine
peptidase,
complete cds 5.91147 10.6161 5.03E-06 Up 6.84326 9.17062 0.04123 Up
107
26S proteasome
non-ATPase
regulatory subunit
1;
Homo sapiens
mRNA for
proteasome subunit
p112, complete cds 6.51739 11.7043 0.000669 Up 7.92193 11.7043 0.01067 Up
BTB/POZ domain-
containing protein
2; 10.1106 13.5492 0.000834 Up 12.904 14.938 0.02477 Up
Filamin-A; 0.69082 2.45634 0.003731 Down 1.9246 2.84352 0.00213 Down
Ubiquitin carboxyl-
terminal
hydrolase 13; 10.6161 12.2895 0.033589 Up 8.73393 10.1106 0.03125 Up
Runt-related
transcription
factor 1; 5.62997 6.84326 0.024647 Up 4.86338 5.10655 0.04906 Up
Myosin heavy
chain, striated
muscle;
Mytilus
galloprovincialis
partial mRNA for
myosin heavy chain
(MHC gene), from
pedal retractor
muscle 2.70811 4.00111 0.004781 Down 0.02898 1.74567 0.00444 Down
Homeobox protein
Hox-C11;
Mizuhopecten
yessoensis Post1
gene for Post1
homeodomain
protein P1, partial
cds 6.84326 7.18542 0.047254 Down 2.70811 3.29173 0.01716 Down
108
26S proteasome
non-ATPase
regulatory subunit
11;
PREDICTED: Limulus
polyphemus 26S
proteasome non-
ATPase regulatory
subunit 11-like
(LOC106473515),
mRNA 6.51739 9.17062 0.000524 Up 8.73393 12.2895 0.00303 Up
Major vault
protein
{ECO:0000312|E
MBL:DAA05661.1}
;
Mytilus
galloprovincialis
major vault protein
mRNA, partial cds 5.62997 10.1106 5.55E-05 Up 7.18542 11.7043 0.02219 Up
Proteasome
subunit beta type-
1; 6.20704 8.73393 0.000804 Up 10.1106 14.2266 0.00119 Up
Heat shock
protein 70 B2;
Mytilus
galloprovincialis
hsp70 mRNA for
heat shock protein
70, complete cds 9.62916 13.5492 8.32E-05 Up 10.1106 14.938 0.00015 Up
Endoglucanase;
Mytilus edulis eg
gene for endo-1,4-
beta-D-glucanase,
exons 1-3 1.24062 2.70811 0.008377 Down 0.06642 2.12188 0.00052 Down
Proteasome
subunit beta type-
2; 7.92193 10.1106 0.01209 Up 7.92193 13.5492 0.00059 Up
Mytilus
galloprovincialis
LKD-rich protein-1
mRNA, complete
cds 0.34891 2.33937 0.001263 Down 0.0011 0.79971 6.16E-05 Down
109
Dihydrofolate
reductase; 8.31803 11.147 0.006927 Up 5.62997 6.51739 0.03744 Up
Growth hormone-
inducible
transmembrane
protein; 6.84326 7.92193 0.02471 Up 11.147 14.2266 0.00761 Up
GTP-binding
protein GEM; 6.51739 9.17062 0.024742 Up 3.62913 4.41123 0.0156 Up
Diphthine methyl
ester synthase; 8.31803 13.5492 0.000863 Up 5.36187 11.147 0.00329 Up
Mytilus
galloprovincialis
small heat shock
protein 24.1 mRNA,
complete cds 9.62916 12.2895 0.008576 Up 9.62916 12.2895 0.02142 Up
Gamma-
interferon-
inducible
lysosomal thiol
reductase; 6.51739 9.62916 0.000403 Up 9.17062 13.5492 0.00571 Up
Aquaporin-4; 0.3014 2.12188 0.005038 Down 1.66255 3.81058 0.02243 Down
Polyubiquitin;
Mytilus edulis
mitochondrial
partial ub gene for
ubiquitin 9.62916 13.5492 2.29E-05 Up 11.147 14.2266 0.00812 Up
Heat shock 70 kDa
protein;
Mytilus
galloprovincialis
hsp70-4 gene for
heat shock protein
70 9.62916 12.2895 0.001986 Up 9.62916 13.5492 0.00356 Up
110
Heat shock
protein 68;
Mytilus
galloprovincialis
hsp70 mRNA for
heat shock protein
70, complete cds 9.62916 14.2266 3.09E-05 Up 10.1106 14.2266 0.00014 Up
Polyubiquitin;
Biomphalaria
glabrata poly-
ubiquitin gene,
complete cds 9.62916 12.2895 0.004702 Up 12.2895 12.904 0.0449 Up
Lipopolysaccharid
e-induced tumor
necrosis factor-
alpha factor
homolog; 8.31803 9.17062 0.036835 Up 9.17062 13.5492 0.00101 Up
Alpha-crystallin A
chain; 9.62916 14.2266 0.000101 Up 11.147 14.938 0.00145 Up
Homo sapiens 12p
BAC RP11-318G8
(Roswell Park
Cancer Institute
Human BAC Library)
complete sequence 3.45631 5.62997 0.001855 Down 0.00128 1.83296 0.00307 Down
1.50798 3.29173 1.11E-05 Down 0.00116 1.66255 3.96E-06 Down
Probable
phospholipid-
transporting
ATPase IIB;
Lottia gigantea
hypothetical protein
partial mRNA 10.1106 12.2895 0.009723 Up 6.51739 10.1106 0.03523 Up
Chromobox
protein homolog
7; 9.62916 11.7043 0.017444 Up 9.62916 14.2266 0.00299 Up
111
Mytilus
californianus gene
for cysteine
peptidase,
complete cds 11.147 14.938 0.003526 Up 4.00111 9.17062 0.01209 Up
Epoxide hydrolase
4; 3.81058 6.20704 0.023173 Down 3.29173 3.45631 0.04046 Down
AP-2 complex
subunit beta;
PREDICTED:
Crassostrea gigas
AP-1 complex
subunit beta-1-like
(LOC105331028),
mRNA 9.62916 10.6161 0.035093 Up 10.1106 12.2895 0.03808 Up
Mytilus edulis gene
for endo-1,4-
mannanase, exons
1-6 9.17062 13.5492 2.83E-06 Up 10.1106 14.2266 0.00067 Up
Probable Bax
inhibitor 1;
Mytilus
galloprovincialis
Bax inhibitor-1
protein (BI1)
mRNA, complete
cds 9.62916 13.5492 0.000796 Up 11.147 14.938 0.00486 Up
Importin-4; 11.147 11.147 0.049822 Up 11.7043 12.904 0.03663 Up
Zinc finger protein
564; 7.5447 10.6161 0.000784 Up 10.1106 14.938 0.0015 Up
Transmembrane
protein 33; 8.73393 10.1106 0.022943 Up 6.51739 11.7043 0.00038 Up
112
Medium-chain
specific acyl-CoA
dehydrogenase,
mitochondrial;
PREDICTED:
Lepidothrix
coronata acyl-CoA
dehydrogenase, C-4
to C-12 straight
chain (ACADM),
mRNA 9.17062 11.7043 0.012424 Up 12.904 14.2266 0.03266 Up
Mytilus
californianus gene
for cysteine
peptidase,
complete cds 9.62916 12.2895 0.002575 Up 9.17062 13.5492 0.00085 Up
Nose resistant to
fluoxetine protein
6; 7.18542 9.17062 0.004032 Up 10.1106 14.2266 0.00047 Up
Heme-binding
protein 2; 9.62916 13.5492 0.00109 Up 14.2266 16.4691 0.016 Up
Hepatocyte
growth factor-
regulated tyrosine
kinase substrate; 8.73393 11.7043 0.000689 Up 5.91147 8.73393 0.0011 Up
Calponin-3;
Mytilus coruscus
calponin-like
protein-2 mRNA,
complete cds 2.33937 3.29173 0.01315 Down 0.72536 2.45634 0.00705 Down
Phospholipase A-2-
activating protein; 8.31803 11.7043 0.001135 Up 11.7043 12.904 0.03698 Up
Protein MB21D2; 7.18542 11.147 0.001425 Up 11.147 12.904 0.02735 Up
113
Mytilus edulis
mRNA for
metallothionein
10IV 0.46758 2.02083 0.011776 Down 0.31647 2.33937 0.00262 Down
Tether containing
UBX domain for
GLUT4; 8.31803 12.904 0.000149 Up 7.18542 8.73393 0.03057 Up
Cyclic nucleotide-
binding domain-
containing protein
2; 4.00111 8.73393 0.004889 Down 1.18154 2.22797 0.01865 Down
Mytilus
galloprovincialis
hsp90-2 gene for
heat shock protein
90 11.7043 12.2895 0.038725 Up 14.2266 14.2266 0.04967 Up
7.18542 9.17062 0.011636 Up 12.2895 14.2266 0.02535 Up
Interferon-
inducible GTPase
5; 10.6161 10.6161 0.047364 Up 0.00148 1.50798 0.03611 Down
Cleft lip and
palate
transmembrane
protein 1
homolog;
PREDICTED:
Xiphophorus
maculatus cleft lip
and palate
associated
transmembrane
protein 1 (clptm1),
mRNA 9.17062 11.7043 0.002323 Up 11.147 12.2895 0.03708 Up
114
Alpha-soluble NSF
attachment
protein;
Mytilus
galloprovincialis
partial hsc70 gene
for heat shock
cognate 70, exons 4-
5 5.91147 8.73393 0.004149 Up 12.904 15.6849 0.01836 Up
Vacuolar protein
sorting-associated
protein 4B; 9.62916 11.7043 0.011474 Up 6.84326 8.31803 0.03655 Up
Vacuolar protein
sorting-associated
protein 4B; 9.62916 11.7043 0.019684 Up 6.84326 7.5447 0.03936 Up
Superoxide
dismutase [Cu-
Zn]; 5.91147 11.147 0.004017 Up 4.20117 9.17062 0.00074 Up
Kyphoscoliosis
peptidase; 2.22797 4.00111 0.003072 Down 2.12188 2.98569 0.00209 Down
Meteorin-like
protein; 5.91147 8.31803 0.011237 Down 1.74567 2.57915 0.03284 Down
Heat shock
protein HSP 90-
alpha;
Mytilus coruscus
HSP90 (HSP90)
mRNA, complete
cds 7.5447 10.6161 0.002552 Up 11.147 12.904 0.03783 Up
Three prime
repair
exonuclease 2; 9.62916 12.2895 0.007362 Up 12.2895 14.2266 0.01711 Up
BTB/POZ domain-
containing protein
3; 5.91147 11.147 0.001536 Up 10.6161 14.2266 0.00655 Up
Hemicentin-1; 0.00128 1.83296 1.56E-05 Down 2.12188 3.13498 0.00116 Down
Hemicentin-1; 0.02059 1.83296 0.000397 Down 0.01464 2.02083 1.47E-05 Down
115
Phylloquinone
omega-
hydroxylase
CYP4F2
{ECO:0000305}; 7.92193 10.6161 0.01666 Down 2.45634 2.84352 0.03184 Down
KIF1-binding
protein homolog; 9.62916 12.2895 0.013489 Up 10.6161 11.7043 0.03875 Up
tRNA:m(4)X
modification
enzyme TRM13
homolog; 10.6161 13.5492 0.007794 Up 3.45631 4.86338 0.00697 Up
Zinc finger protein
471; 10.1106 14.2266 0.000582 Up 10.6161 12.2895 0.024 Up
NSFL1 cofactor
p47; 10.6161 10.6161 0.046434 Up 11.7043 14.2266 0.02539 Up
5-
hydroxytryptamin
e receptor 4; 0.72536 3.62913 0.019267 Down 2.02083 2.57915 0.02361 Down
CD63 antigen; 7.18542 11.147 0.000814 Up 11.7043 14.2266 0.02726 Up
Actin,
cytoplasmic;
Lepeophtheirus
salmonis Atlantic
form clone lsaA-evu-
513-353 Actin
putative mRNA,
complete cds 7.18542 12.904 5.21E-05 Up 10.6161 14.938 0.00027 Up
Phospholipase B-
like 1; 9.17062 11.7043 0.012273 Up 13.5492 15.6849 0.01549 Up
Opine
dehydrogenase
{ECO:0000303|Pu
bMed:15565272}; 1.43617 4.20117 0.00203 Down 2.70811 3.13498 0.02533 Down
FAS-associated
factor 2; 8.73393 12.2895 0.001532 Up 9.17062 10.6161 0.04869 Up
116
Hexosaminidase
D; 6.51739 8.31803 0.019579 Up 5.62997 8.73393 0.00565 Up
Mytilus
californianus gene
for cysteine
peptidase,
complete cds 8.31803 12.904 9.16E-05 Up 7.18542 10.6161 0.00677 Up
Ankyrin-1; 10.6161 12.2895 0.022846 Up 4.63179 7.5447 0.00193 Up
MAM and LDL-
receptor class A
domain-
containing protein
2; 3.62913 6.20704 0.008204 Down 1.9246 2.12188 0.04544 Down
Charged
multivesicular
body protein 7; 10.6161 12.2895 0.025023 Up 11.7043 14.938 0.00784 Up
RING finger
protein 44; 7.5447 12.904 0.000261 Up 11.147 14.938 0.00079 Up
Tubulin alpha-1
chain;
Lottia gigantea
hypothetical protein
mRNA 7.5447 12.2895 0.009383 Up 10.6161 14.938 0.00087 Up
Armadillo repeat-
containing protein
6; 7.92193 12.904 6.04E-05 Up 13.5492 14.2266 0.03768 Up
Group XV
phospholipase A2;
Mytilus
californianus gene
for cysteine
peptidase,
complete cds 10.6161 13.5492 0.011254 Up 9.17062 14.938 0.00367 Up
Neuronal
acetylcholine
receptor subunit
alpha-3; 6.84326 7.92193 0.028824 Up 16.4691 16.4691 0.04934 Up
117
Sialin; 6.20704 7.5447 0.017444 Up 10.1106 13.5492 0.00295 Up
Thiamine
transporter 1; 4.86338 7.18542 0.007518 Down 0.0011 0.56834 6.37E-05 Down
Glutathione S-
transferase; 6.84326 8.73393 0.006698 Up 8.31803 13.5492 0.00379 Up
Actin,
cytoplasmic;
Meretrix meretrix
beta-actin mRNA,
complete cds 7.18542 7.92193 0.034561 Up 10.6161 14.2266 0.00799 Up
Quinone
oxidoreductase
PIG3; 6.51739 10.6161 2.17E-06 Up 11.147 14.2266 0.00339 Up
NEDD8-
conjugating
enzyme Ubc12; 9.62916 14.2266 9.36E-05 Up 11.147 14.938 0.00224 Up
Mytilus
galloprovincialis
microsatellite ISSR
sequence, clone
17.617 5.91147 8.31803 0.013098 Up 8.31803 13.5492 0.01267 Up
Mytilus chilensis
microsatellite Mch-
UCH14 sequence 6.51739 7.5447 0.021633 Up 8.31803 12.904 0.00585 Up
Solute carrier
family 22
member 3; 10.6161 11.7043 0.040061 Up 5.62997 7.92193 0.00417 Up
Protein-glutamine
gamma-
glutamyltransfera
se K; 7.5447 8.73393 0.032229 Down 0.00134 1.02066 0.01486 Down
Synaptotagmin-
11; 10.6161 12.2895 0.026485 Up 3.81058 4.86338 0.00985 Up
118
X-box-binding
protein 1
{ECO:0000250|Un
iProtKB:P17861};
Mytilus edulis X-box
binding protein 1
mRNA, partial cds 7.18542 8.73393 0.018057 Up 5.36187 10.6161 0.00118 Up
Obg-like ATPase 1
{ECO:0000255|HA
MAP-
Rule:MF_03167};
PREDICTED: Sorex
araneus Obg-like
ATPase 1 (OLA1),
transcript variant
X2, mRNA 7.18542 11.147 0.000103 Up 10.6161 14.938 0.01065 Up
Methylthioribose
kinase
{ECO:0000255|HA
MAP-
Rule:MF_01683};
Mytilus
galloprovincialis
microsatellite ISSR
sequence, clone
14.715 5.10655 7.18542 0.00815 Up 12.904 14.938 0.01626 Up
Ras-related
protein Rab-20; 7.5447 10.1106 0.007832 Up 7.18542 10.6161 0.01187 Up
Arylsulfatase B; 3.45631 5.10655 0.02375 Down 1.43617 2.84352 0.00011 Down
E3 ubiquitin-
protein ligase
RNF181; 9.62916 12.2895 0.019058 Up 10.6161 14.938 0.00373 Up
26S protease
regulatory subunit
7;
Lottia gigantea
hypothetical protein
mRNA 6.84326 9.62916 0.003429 Up 9.62916 14.2266 0.00685 Up
Hemicentin-1; 5.36187 9.62916 6.56E-05 Up 3.62913 5.62997 0.00516 Up
Zinc
metalloproteinase
nas-13; 2.84352 5.62997 0.020039 Down 3.45631 4.86338 0.03752 Down
Heat shock 70 kDa
protein 12A; 8.31803 11.7043 1.86E-05 Up 9.62916 13.5492 0.00444 Up
119
UPF0565 protein
C2orf69 homolog;
Mytilus
californianus gene
for cysteine
peptidase,
complete cds 12.904 14.938 0.020785 Up 11.7043 14.938 0.00743 Up
Actin;
Euagrus chisoseus
clone E143 actin
mRNA, partial cds 9.62916 14.2266 4.83E-06 Up 10.1106 14.2266 9.10E-05 Up
Baculoviral IAP
repeat-containing
protein 7; 4.86338 9.17062 0.004715 Up 6.84326 12.2895 5.35E-05 Up
Probable
palmitoyltransfer
ase ZDHHC12; 9.17062 11.147 0.014758 Up 14.938 15.6849 0.04501 Up
IST1 homolog; 7.5447 9.17062 0.029053 Up 11.147 11.147 0.0496 Up
Circularly
permutated Ras
protein 1;
Mytilus
californianus gene
for cysteine
peptidase,
complete cds 9.17062 12.2895 0.000673 Up 6.84326 12.2895 0.00085 Up
UPF0046 protein
C25E10.12; 7.5447 12.904 0.002237 Up 10.1106 14.938 0.00127 Up
Protein SSUH2
homolog; 5.10655 6.20704 0.03181 Up 3.62913 5.62997 0.03544 Up
Farnesyl
pyrophosphate
synthase; 7.92193 10.6161 0.015253 Up 6.51739 11.7043 0.01155 Up
Actin;
Euagrus chisoseus
clone E143 actin
mRNA, partial cds 10.1106 14.938 0.000349 Up 12.904 15.6849 0.00573 Up
Tctex1 domain-
containing protein
4; 6.20704 6.84326 0.039999 Down 0.00122 1.58338 0.0003 Down
120
Geranylgeranyl
pyrophosphate
synthase;
PREDICTED:
Pteropus alecto
geranylgeranyl
diphosphate
synthase 1
(GGPS1), transcript
variant X4, mRNA 5.36187 10.6161 0.000246 Up 7.5447 11.147 0.03195 Up
26S proteasome
non-ATPase
regulatory subunit
5; 10.1106 11.147 0.035279 Up 8.73393 13.5492 0.00051 Up
Stimulated by
retinoic acid gene
6 protein
homolog; 1.12528 2.57915 0.022168 Down 0.00122 1.43617 0.00073 Down
5-methylcytosine
rRNA
methyltransferase
NSUN4; 6.51739 9.17062 0.001002 Up 16.4691 16.4691 0.04471 Up
Mytilus
galloprovincialis
MACPF domain-
containing protein 1
mRNA, complete
cds 6.51739 8.31803 0.014533 Up 2.98569 3.29173 0.03037 Down
Transmembrane
protein 145; 1.83296 2.57915 0.006122 Down 0.16783 2.02083 0.0032 Down
Anamorsin
homolog
{ECO:0000255|HA
MAP-
Rule:MF_03115}; 8.31803 11.147 0.012221 Up 7.5447 12.2895 0.00073 Up
121
Carboxypeptidase
B; 0.22491 2.22797 0.000678 Down 0.0011 1.24062 7.27E-05 Down
Mytilus chilensis
microsatellite Mch-
UCH48 sequence 9.62916 12.2895 0.002789 Up 13.5492 14.938 0.03904 Up
Major vault
protein
{ECO:0000312|E
MBL:DAA05661.1}
; 7.92193 10.6161 0.004755 Up 14.938 15.6849 0.03768 Up
26S proteasome
non-ATPase
regulatory subunit
12;
PREDICTED:
Biomphalaria
glabrata 26S
proteasome non-
ATPase regulatory
subunit 12-like
(LOC106051538),
mRNA 9.17062 11.7043 0.00848 Up 6.20704 11.7043 0.00059 Up
Mytilus
galloprovincialis
small heat shock
protein 24.1 mRNA,
complete cds 9.62916 12.2895 0.007796 Up 9.62916 13.5492 0.00108 Up
Serine
incorporator 3; 6.84326 11.7043 0.002485 Up 11.7043 14.2266 0.01842 Up
Metabotropic
glutamate
receptor 2; 10.1106 10.6161 0.04481 Up 0.00155 0.27338 0.0466 Down
122
Actin,
cytoplasmic;
PREDICTED:
Parasteatoda
tepidariorum actin,
clone 403-like
(LOC107443493),
transcript variant
X2, mRNA 9.62916 14.2266 0.00212 Up 11.147 14.938 0.00103 Up
Actin,
cytoplasmic;
Schistosoma
mansoni actin
mRNA, complete
cds 11.7043 15.6849 0.020157 Up 12.2895 15.6849 0.00603 Up
Retinoic acid
receptor beta; 2.33937 2.70811 0.025453 Down 0.00116 1.02066 0.00359 Down
Ribonucleoside-
diphosphate
reductase small
chain;
PREDICTED:
Parasteatoda
tepidariorum
ribonucleoside-
diphosphate
reductase subunit
M2-like
(LOC107449543),
mRNA 8.73393 10.6161 0.014706 Up 12.2895 14.938 0.01556 Up
Sulfotransferase
1C4; 9.62916 13.5492 0.019129 Up 9.62916 14.2266 0.00575 Up
DBH-like
monooxygenase
protein 1
homolog; 11.147 14.938 0.00138 Up 12.904 15.6849 0.00865 Up
GTPase IMAP
family member 4; 9.62916 14.2266 2.76E-05 Up 13.5492 15.6849 0.01497 Up
Spartin; 4.63179 6.51739 0.005377 Up 6.20704 11.147 0.01465 Up
GTPase IMAP
family member 4; 10.1106 14.938 0.000463 Up 16.4691 16.4691 0.04398 Up
123
Mytilus chilensis
microsatellite Mch-
UCH25 sequence 11.7043 15.6849 0.006815 Up 0.00122 1.83296 0.00034 Down
Tyrosine 3-
monooxygenase; 1.74567 2.45634 0.004069 Down 0.54128 2.02083 0.01749 Down
Proteasome
subunit alpha type-
3;
PREDICTED:
Erinaceus
europaeus
proteasome subunit
alpha 3 (PSMA3),
transcript variant
X1, mRNA 10.6161 11.147 0.044955 Up 9.62916 14.2266 0.00173 Up
Peptidoglycan
recognition
protein 3;
Mytilus
galloprovincialis
peptidoglycan
recognition protein
2 mRNA, complete
cds 0.04495 2.02083 0.001034 Down 0.0011 0.8397 8.17E-05 Down
Tctex1 domain-
containing protein
1; 2.02083 3.81058 0.016978 Down 2.22797 2.45634 0.02448 Down
Ubiquitin fusion
degradation
protein 1 homolog
{ECO:0000250|Un
iProtKB:Q92890};
PREDICTED:
Dufourea
novaeangliae
ubiquitin fusion
degradation protein
1 homolog
(LOC107187841),
mRNA 7.92193 10.1106 0.007157 Up 6.84326 12.2895 0.00973 Up
124
Cytochrome c
oxidase assembly
protein COX11,
mitochondrial;
PREDICTED: Calypte
anna cytochrome c
oxidase assembly
protein COX11,
mitochondrial
(LOC103529157),
partial mRNA 7.18542 9.17062 0.006173 Up 12.904 13.5492 0.04403 Up
Lysozyme;
Mytilus
galloprovincialis C-
type lysozyme 3
mRNA, complete
cds 6.51739 8.73393 0.005476 Up 13.5492 15.6849 0.01539 Up
Lipopolysaccharid
e-induced tumor
necrosis factor-
alpha factor
homolog; 7.5447 9.62916 0.005917 Up 6.84326 8.73393 0.02882 Up
GTPase IMAP
family member 7; 11.147 14.938 0.000807 Up 13.5492 15.6849 0.01184 Up
Uncharacterized
protein C11orf70
homolog; 7.18542 9.17062 0.009851 Up 11.7043 14.2266 0.01418 Up
Electron transfer
flavoprotein
subunit alpha,
mitochondrial;
PREDICTED:
Camponotus
floridanus electron
transfer
flavoprotein subunit
alpha,
mitochondrial
(LOC105250553),
mRNA 6.84326 9.17062 0.004095 Up 12.2895 14.938 0.01055 Up
125
Peptidyl-prolyl cis-
trans isomerase
FKBP7; 5.10655 7.18542 0.00507 Up 15.6849 16.4691 0.0334 Up
Vacuolar protein
sorting-associated
protein VTA1
homolog; 9.62916 11.7043 0.022931 Up 10.1106 14.938 0.00652 Up
Ribonuclease Oy; 7.92193 11.147 0.001316 Up 7.92193 12.2895 0.00252 Up
Mytilus chilensis
microsatellite Mch-
UCH36 sequence 6.51739 9.17062 0.001671 Up 10.6161 14.2266 0.00905 Up
GTPase IMAP
family member 4; 9.62916 13.5492 0.000777 Up 6.20704 9.62916 0.00375 Up
Proteasome
subunit beta type-
4; 8.31803 9.62916 0.020735 Up 10.6161 14.2266 0.01307 Up
Xenopus tropicalis
split hand/foot
malformation
(ectrodactyly) type
1, mRNA (cDNA
clone MGC:147791
IMAGE:7531611),
complete cds 7.5447 12.2895 0.025539 Up 10.6161 14.938 0.00478 Up
Proteasome
subunit beta type-
7;
PREDICTED:
Eufriesea mexicana
proteasome subunit
beta type-7
(LOC108552053),
mRNA 5.91147 8.73393 0.000134 Up 9.62916 13.5492 0.01185 Up
Annexin A11; 11.147 12.2895 0.04465 Up 5.91147 5.91147 0.04855 Up
126
Cdc42 homolog; 9.17062 12.904 0.00055 Up 6.20704 11.147 0.02072 Up
GTPase IMAP
family member 4; 10.1106 14.2266 8.15E-05 Up 10.1106 14.2266 0.00011 Up
Ras-related C3
botulinum toxin
substrate 1; 5.91147 8.73393 0.000327 Up 5.36187 10.6161 0.00297 Up
Periostin; 0.00128 0.92577 0.0083 Down 1.18154 2.98569 0.00039 Down
Apolipoprotein D; 5.36187 7.92193 0.002406 Up 12.904 14.938 0.01982 Up
Protein CutA
homolog; 5.91147 7.92193 0.003082 Up 14.2266 15.6849 0.02942 Up
Proteasome
inhibitor PI31
subunit; 7.5447 12.2895 0.004454 Up 5.91147 9.17062 0.00021 Up
Kyphoscoliosis
peptidase; 9.17062 12.904 0.00032 Up 14.2266 14.938 0.0424 Up
Transmembrane
protease serine 3; 6.84326 8.73393 0.004527 Up 3.62913 5.10655 0.00232 Up
NAD-dependent
protein
deacetylase
sirtuin-7; 9.17062 12.2895 0.000897 Up 9.17062 14.2266 0.00079 Up
Tyrosinase-like
protein 1; 2.57915 4.20117 0.000605 Down 1.83296 2.84352 7.74E-05 Down
Tubulin alpha
chain;
Mytilus
galloprovincialis
alpha-tubulin
mRNA, partial cds 7.5447 7.5447 0.049419 Up 11.7043 13.5492 0.02733 Up
Antistasin; 1.66255 2.98569 0.000186 Down 1.83296 2.57915 0.00152 Down
Proteasome
subunit alpha type-
7;
Lottia gigantea
hypothetical protein
mRNA 8.31803 11.147 0.007726 Up 11.7043 14.938 0.00545 Up
Dipeptidyl
peptidase 1; 7.92193 8.31803 0.045352 Up 7.92193 12.904 0.00607 Up
127
Mytilus
californianus gene
for cysteine
peptidase,
complete cds 5.36187 8.31803 0.000257 Up 10.1106 14.2266 0.00085 Up
Protein mab-21-
like 3; 10.6161 14.938 0.001023 Up 10.1106 13.5492 0.00063 Up
Mytilus
galloprovincialis
MgC1q22 gene for
putative C1q
domain containing
protein MgC1q22 4.20117 4.63179 0.045382 Down 1.18154 3.13498 0.01049 Down
Peptidyl-prolyl cis-
trans isomerase;
PREDICTED:
Neodiprion lecontei
peptidyl-prolyl cis-
trans isomerase E
(LOC107227753),
mRNA 8.31803 8.31803 0.048304 Up 9.62916 13.5492 0.01114 Up
Mytilus edulis eg
gene for endo-1,4-
beta-D-glucanase,
exons 1-3 2.45634 2.84352 0.030228 Down 2.57915 3.45631 0.01147 Down
Protein SYS1
homolog; 6.84326 9.17062 0.002937 Up 12.904 14.2266 0.03119 Up
Protein PIF; 4.20117 7.18542 0.009723 Down 0.1136 2.22797 4.58E-05 Down
Mytilus
californianus gene
for cysteine
peptidase,
complete cds 8.73393 13.5492 2.08E-05 Up 9.62916 13.5492 0.0005 Up
7.92193 10.6161 0.006612 Up 11.7043 14.2266 0.01039 Up
128
Mytilus
californianus gene
for cysteine
peptidase,
complete cds 11.147 12.2895 0.037804 Up 8.73393 9.62916 0.02763 Up
Cathepsin Z;
Lottia gigantea
hypothetical protein
partial mRNA 6.20704 9.17062 0.000795 Up 5.62997 10.6161 0.0093 Up
Mitochondrial
import inner
membrane
translocase
subunit Tim17-B; 6.20704 7.18542 0.01928 Up 8.73393 13.5492 0.00064 Up
Charged
multivesicular
body protein 2b; 10.1106 11.7043 0.027152 Up 7.92193 12.904 0.00621 Up
Mytilus
galloprovincialis
gene for twitchin,
partial cds, clone:
TOPOXL_tw_D1_11
f-3_12r 6.20704 6.51739 0.041462 Down 3.29173 3.29173 0.0481 Down
Mytilus
galloprovincialis
serine protease A
mRNA, complete
cds 0.56834 2.02083 0.01828 Down 3.13498 3.45631 0.0342 Down
GTPase IMAP
family member 4; 9.62916 13.5492 0.000503 Up 11.7043 12.904 0.04343 Up
129
Mytilus
galloprovincialis
serine protease C
mRNA, complete
cds 2.12188 3.29173 0.029437 Down 0.00122 1.43617 0.00116 Down
Mytilus
californianus gene
for cysteine
peptidase,
complete cds 8.73393 11.7043 0.003465 Up 8.73393 12.2895 0.01481 Up
FUN14 domain-
containing protein
1; 7.18542 10.6161 0.000334 Up 8.73393 11.7043 0.0364 Up
Anamorsin
homolog
{ECO:0000255|HA
MAP-
Rule:MF_03115}; 7.18542 12.2895 0.000112 Up 10.1106 14.938 0.00034 Up
Histone
deacetylase 6; 10.1106 14.938 0.000263 Up 12.2895 15.6849 0.00336 Up
Perlucin-like
protein
{ECO:0000303|Pu
bMed:21643827};
Mytilus
galloprovincialis C-
type lectin 2 mRNA,
complete cds 1.50798 4.00111 0.006021 Down 0.0011 0.23616 0.0001 Down
AP-4 complex
subunit beta-1; 6.84326 7.5447 0.03502 Up 0.00122 1.74567 0.00326 Down
Apolipoprotein D;
Mytilus edulis APO-
D mRNA, partial
cds 7.18542 9.62916 0.001726 Up 9.62916 13.5492 0.00676 Up
Ubiquitin-
conjugating
enzyme E2 D2; 9.62916 14.938 0.000709 Up 11.147 14.938 0.00117 Up
130
Calmodulin; 3.62913 5.36187 0.007629 Down 0.34891 1.83296 0.01743 Down
Lectin BRA-3;
Mytilus
galloprovincialis C-
type lectin 8 mRNA,
complete cds 2.98569 3.81058 0.005035 Down 0.69082 2.02083 0.02316 Down
Elongator
complex protein
6; 7.92193 11.147 0.001459 Up 6.84326 10.6161 0.0007 Up
Lipopolysaccharid
e-induced tumor
necrosis factor-
alpha factor
homolog; 7.18542 12.2895 1.21E-05 Up 7.5447 12.904 0.0003 Up
Dynein heavy
chain 7,
axonemal; 8.31803 12.904 0.007466 Up 11.147 14.2266 0.00726 Up
GLIPR1-like
protein 1; 1.24062 2.98569 0.000492 Down 2.45634 3.29173 0.0052 Down
Antistasin; 2.22797 3.29173 0.003582 Down 0.54128 2.22797 0.01436 Down
Hemicentin-1; 10.6161 14.2266 0.01987 Up 11.147 14.938 0.00146 Up
Leucine-rich
repeat-containing
protein 74A
{ECO:0000312|HG
NC:HGNC:23346}; 4.00111 5.10655 0.024341 Down 1.36778 2.12188 0.01277 Down
Cytochrome P450
26A1; 8.73393 10.6161 0.020925 Up 8.73393 11.147 0.04208 Up
131
Mytilus
galloprovincialis
metallothionein 20-
IV (MT-20-IV)
mRNA, complete
cds 11.7043 12.904 0.03924 Up 8.31803 9.62916 0.04413 Up
E3 ubiquitin-
protein ligase
TRAF7; 3.45631 5.10655 0.033677 Down 0.27338 2.57915 0.00029 Down
132
Chapter 2: Concentration-dependent impacts of ocean acidification on copper toxicity to
mussel larvae

Abstract
Coastal ecosystems have faced compounding challenges in recent decades, including metal
pollution and ocean acidification (OA). Calcifying organisms, often essential components of
coastal ecosystems, are particularly susceptible to OA. In order to model and predict impending
changes in coastal ecosystems, it is essential to understand how key species are impacted by
novel stressor combinations. In particular, these interactions have not been well-studied in
sensitive early life history stages. Here we test the hypothesis that exposure to OA and copper
will have detrimental impacts on larval development in the California mussel (Mytilus
californianus). We sought to detect effects of OA at a range of copper doses, and to probe
underlying mechanisms of these effects using transcriptome profiling. We examined early larval
(48 h post-fertilization) survival, development, and transcriptional profiles, of M. californianus
in response to 6 copper concentrations (0-15 µg/L) at two CO2 concentrations (400 and 800
ppm). Larval development assays revealed that there was little consistent impact of elevated CO2
on healthy larval development at 0-3 µg/L copper. However, at higher copper concentrations (6-9
µg/L), elevated CO2 increased the proportion of animals developing normally. Transcriptional
markers reflected this trend as well. Copper responsive transcripts were identified under both
high and low CO2 conditions, many with sigmoidal response patterns, but many with other
response patterns as well. Many GO terms were enriched only among transcripts that were
copper responsive at elevated CO2, and were involved in neurological function/behavior, fatty
acid synthesis, DNA repair, and symporter activity. Observed patterns suggest that larvae may be
modulating certain pathways to reduce copper uptake and/or negative physiological impacts of
copper. We conclude that simulated OA impacts copper toxicity in a dose-dependent manner,
and may in fact reduce copper toxicity to M. californianus larvae at intermediate copper doses.

Introduction
Marine organisms in coastal ecosystems face an increasing diversity of environmental stressors
(Doney et al., 2012). Environmental stressors often interact in complex ways, sometimes
resulting in unpredictable effects on organism and ecosystem health (reviewed in Gunderson,
Armstrong, & Stillman, 2016; reviewed in Ivanina & Sokolova, 2015)). This makes it imperative
133

to better understand the effects of environmentally relevant stressor combinations, and to
investigate the mechanisms driving complex stress responses. In coastal waters, enduring
contaminants such as metals (Walker and Livingstone 1992), and the more recent phenomenon
of ocean acidification (OA) (Doney et al., 2012; Doney, Fabry, Feely, & Kleypas, 2009), create
unprecedented stress combinations for marine organisms.

There is a growing body of research on interactions between metals and OA (Ivanina &
Sokolova, 2015; Lewis et al., 2016; Y. Li, Wang, & Wang, 2017; Nardi et al., 2017). Chemistry
models predict that some metals, e.g. copper, should become more toxic with ocean acidification
(Hoffmann, Breitbarth, Boyd, & Hunter, 2012; Millero, Woosley, DiTrolio, & Waters, 2009),
which has been confirmed in several studies (Han, Wu, Wu, Lv, & Liu, 2013; Lewis et al., 2016;
Lewis, Clemow, & Holt, 2012; Roberts et al., 2012). Other studies reveal a contrasting pattern,
whereby divalent metal ions become less toxic with increasing ocean acidification (Y. Li et al.,
2017; Pascal, Fleeger, Galvez, & Carman, 2010), or OA alleviates the negative impacts of metal
exposure alone(Ivanina & Sokolova, 2013; Ivanina et al., 2013). Such unpredictable responses to
metals and OA are observed even within well-studied taxa such as bivalve mollusks (e.g. Götze,
Matoo, Beniash, Saborowski, & Sokolova, 2014; Han et al., 2013; Ivanina et al., 2013; Ivanina
& Sokolova, 2013; Lewis et al., 2016). Calcifying marine organisms primarily reside in coastal
waters where contaminant loads are high. Accordingly, bivalves may be particularly susceptible
to interactive effects of stressors in a changing ocean. Bivalves also play important roles in
coastal and intertidal ecosystems, and many species comprise valuable fisheries or aquaculture
operations. The field is still limited in the scope of existing data and lacks consensus on
outcomes, suggesting that more research is necessary to better understand the interactive effects
of metal and OA stressors.

Very few studies have examined the effects of metals and ocean acidification on organismal
development (Lewis et al., 2012; Y. Li et al., 2017). While there is a rich body of research on the
impacts of metals (His, Beiras, & Seaman, 1999; His, Seaman, & Beiras, 1997; Martin, Osborn,
Billig, & Glickstein, 1981; Wood, Farrell, & Brauner, 2011) and OA (Dupont, Havenhand,
Thorndyke, Peck, & Thorndyke, 2008; Dupont, Ortega-Martínez, & Thorndyke, 2010; Kurihara,
Asai, Kato, & Ishimatsu, 2008; Kurihara, Kato, & Ishimatsu, 2007; Pan, Applebaum, &
134

Manahan, 2015; Pespeni et al., 2013; Stumpp, Wren, Melzner, Thorndyke, & Dupont, 2011;
Waldbusser et al., 2014) on marine larvae as individual stressors, little is known about their
combined effects on this sensitive life history stage (Ivanina & Sokolova, 2015). Multiple
stressor studies with metals and OA are also generally devoid of research on concentration-
dependent interactions. Focusing on only one or two metal concentrations may limit our
understanding of the stress level dependent physiological responses that an organism may exhibit
(Sokolova, 2013). To understand the complex impacts of OA and metal on early life stage
organisms, we exposed Mytilus californianus embryos to a range of 7 copper concentrations and
two CO2 concentrations for 48 hours, and measured survival, developmental morphology, and
whole genome expression profiles.

Mussels have long been used as indicator organisms (Farrington et al., 2016). Bivalve larvae are
also regularly the subjects of toxicity assays for determination of metal water quality criteria, as
they are highly sensitive to metals such as copper(Arnold, Cotsifas, Smith, Le Page, &
Gruenthal, 2009; EPA, 2016). Despite the importance and vulnerability of bivalve larvae, no
studies have examined their response to simultaneous metal exposure and ocean acidification.
Larval toxicity assays typically measure survival and abnormal development as endpoints, but
there has been substantial research on transcriptional markers of metal exposure and/or toxicity
in adult mussels (Dondero et al., 2006; Gomes et al., 2011; Negri et al., 2013; Varotto et al.,
2013; Venier et al., 2006), and some work in the larvae of mussels and other bivalves as well
(Chapter 1; Silva-Aciares, Zapata, Tournois, Moraga, & Riquelme, 2011; Zapata, Tanguy,
David, Moraga, & Riquelme, 2009; Navarro, Faria, Barata, & Piña, 2011). We examined
transcriptional responses to combined copper and OA to detect sensitive markers of exposure,
and to examine potential pathways targeted by co-exposure to these stressors. We also assessed
the relationship between copper concentration-dependent transcriptional responses and
concentration-dependent survival and normal development. Based on previous research, we
hypothesized that copper would become more toxic to mussel larvae under simulated OA,
evidenced by shifts in the sensitivity of normal development and survival concentration-response
curves, and increased modulation of transcripts involved in metal toxicity and OA-induced stress
at lower copper concentrations.

135

Methods
Broodstock collection and fertilization
On four dates in 2016 (June, July, October, December), adult Mytilus californianus were
collected from jetties at Will Rogers State Beach in Santa Monica, CA. To create thermal shock,
animals were refrigerated at 4° C for approximately 12 hours, and then rinsed with freshwater
and scraped clean prior to spawning induction. Mussels were then added to a tank of aerated,
filtered seawater collected from Big Fisherman’s Cove on Santa Catalina Island, CA. Water was
heated to 23-25˚C.

Once an adult mussel started spawning, it was removed from the collective tank, rinsed
thoroughly with filtered seawater, and transferred to a separate beaker containing 0.2 um filtered
seawater. Each mussel that spawned was held alone in a beaker to prevent cross-contamination
of gametes. Spawning mussels were identified as male or female based on the appearance of the
gametes. When spawning was complete, the adult mussels were removed from the beakers, and
the appearance of eggs and motility of sperm were examined under low power on a compound
microscope. Once eggs had transformed from club-shaped to round, sperm from a single male
was added to eggs of a single female to reach an average density of ~5 sperm per egg.
Fertilization for most eggs, evidenced by the formation of a polar body and first division of the
zygote, was complete after ~30 minutes. Mussel larvae from each collection date were stocked
into 42 1 L treatment containers at a density of 10-12 larvae mL
-1

Experimental design and water chemistry manipulation
The 42 treatment containers consisted of two CO2 concentration exposures (400 and 800 ppm)
and seven copper concentrations at each CO2 concentration (0, 3, 6, 9, 12, 15, and 20 µg/L).
Three triplicate containers were prepared for each treatment. Bottles were triple rinsed first with
5% HCl (prior to the day of experiment), then with ultrapure water (18.2 MΩ), and finally with
experimental seawater. One liter of 0.2 um filtered seawater collected from Big Fisherman’s
Cove, Santa Catalina Island, CA was added to each bottle. Bottles were bubbled with either 800
ppm CO2-air mix, or with an air pump for the 400 ppm CO2 treatment. The pH of a subset of
bottles was checked prior to addition of copper. Sample pH was measured using an Orion 5 star
pH meter with an NBS buffer system and 3 point calibration. Copper was then added to
136

containers using a 1 mM stock solution of copper sulfate (CuSO4). After spiking copper,
containers were bubbled for ~1.5 hours prior to the addition of larvae. Once larvae were added to
experimental containers, they were incubated with bubbling at 17*C for 48 hours, with a 12:8 hr
L:D cycle.

Carbonate chemistry measurements
Immediately after larval addition (T=0 hr), and again immediately prior to larval collection
(T=48 hr), two water chemistry samples were taken from each container—one 25 ml sample was
added to a borosilicate glass scintillation vial, poisoned with HgCl for DIC measurements, and
sealed with an air-tight lid; another 15 mL sample was taken for pH measurements. pH was
measured within one hour of collection using an Orion 5 star pH meter with an NBS buffer
system and 3 point calibration at pH of 4, 7, and 10. DIC samples were stored at 4° C, and later
brought to room temperature and analyzed on a CM140 total inorganic carbon analyzer (UIC).
DIC data presented here are only for the first trial. CO2Sys excel v2.3 (Pierrot et al. 2006) was
used to calculate pCO2 in sample seawater, using equilibrium constants (K1 and K2) from
Mehrbach et al. 1973, an NBS pH scale, Input temperature = 23° C, Output temperature=17° C,
salinity = 35 PSU, and pressure = 0 dbars. Average pH values for the low and high CO2
treatments were 8.08 and 7.88, and average DIC values were 2049.49 and 2100.13 umol/kg
(Supplemental Table 1). The average calculated pCO2 was 419.0 ppm for present-day seawater,
and 694.7 ppm for future seawater (bubbled with 800 ppm CO2/air mix). Average pCO2
concentrations were rounded to 420 ppm and 700 ppm for future reference throughout the paper.

Larval sample collection
Once water chemistry samples had been collected, each larval culture was filtered through a 20
um sieve, and concentrated into a 15 mL Falcon tube with filtered seawater. The total
concentrate volume was recorded. From each concentrated culture, five to seven 500 ul samples
were taken, and mixed with 500 ul 60% EtOH. These samples were later counted to determine
survival and proportions of abnormal development. Samples were also photographed to better
characterize distinct morphologies of abnormal and normal larvae. The remainder of each culture
was then spun down at 2000 g for 2 minutes, and the larval pellet was transferred to a 1.5 mL
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centrifuge tube. Tubes were again spun at 4000 rcf for 1 minute, the seawater supernatant was
removed and the larval pellet was frozen at -80° C.

Larval counts
For each experiment, 3-7 samples were counted and inspected for survival and proportion of
animals that had reached the D-veliger stage of development, and those that had developed
abnormally. The proportion of animals surviving and developing normally in each replicate was
calculated using microscope counts of larvae and drop/sample volume. Each proportion was then
divided by the mean control proportion at 400 ppm CO2 to calculate control-normalized survival
and normal development. Survival and normal development data were further analyzed in the R
package ‘drc’ (Ritz, Baty, Streibig, & Gerhard, 2015). For each experiment, four-parameter log-
logistic curves (LL.4 model in the drc package) were fitted to each dataset to calculate 50%
lethal concentration (LC50) and 50% normal development effective concentration (EC50)
values. A lack of fit test was applied for each model to determine whether the LL.4 model was
significantly different than a basic linear model (anova). Significant shifts in curve shape and
EC50 between CO2 concentrations were also detected using anova in drc. Most survival log-
logistic models were not significantly different than an anova, so survival curves were also
analyzed using a two-way ANOVA (r packages aov and anova). Specific differences were
further detected using a Tukey’s post-hoc test (R command TukeyHSD).

Assembly and Annotation of de novo Transcriptome
Three M. californianus libraries were integrated to generate a de novo transcriptome assembly.
M. californianus sanger sequences (Gracey et al., 2008) were assembled using a custom
workflow (A. Gracey, pers comm 2018) using IDBA-tran (Peng et al., 2013). Two additional
libraries were used for assembly as well. For both libraries, RNA was extracted from larval
samples using TRIzol (Thermo Fisher) according to the manufacturer’s protocol. The first was
prepared from 210 ng of RNA of 48-hour M. californianus larvae. The sample was poly-A
selected using the NEBNext poly-A mRNA Magnetic Isolation Module, according to the
manufacturer’s protocol. Samples were further processed with the NEB Next Ultra RNA kit,
according to the manufacturer’s protocol with some modifications. Samples were fragmented for
12 minutes (instead of 15) prior to cDNA synthesis, and the first strand synthesis reaction was
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run for 50 minutes at 42° C. PCR enrichment was visualized using a BioRad qPCR
Thermocycler, and the reaction was terminated shortly after entering the exponential
amplification stage. The amplification was run for 13 cycles. The resulting libraries had an insert
size of ~250 bp.The library was sequenced 75 bp paired end on an Illumina NextSeq. The second
library was created from the RNA of 48-hour M. californianus larvae exposed to 8 µg/L copper,
prepared using a modified version of the Illumina TruSeq RNA prep protocol (described in
Chapter 1 methods), and sequenced 150 bp single end over one lane of Illumina HiSeq 2500.
Raw Illumina reads were quality trimmed and contaminating adapter sequence was removed
using Trimmomatic v0.33 (Bolger, Lohse, & Usadel, 2014) with default parameter settings.
Paired end reads were merged where possible using bbmerge.sh (BBMap v34; minid=0.95
ambiguous=all sssr=1.0; Bushnell 2014) with default parameters. Common contaminating
sequences were filtered from SE reads, merged PE reads, and unmerged PE reads using
bbmap.sh by mapping to the DH10B E. coli genome and the NCBI UniVec database
(minid=0.85, idfilter=0.90). The sanger assembly was also filtered using BLAST (blastn,
perc_identity=90), and only contigs with an alignment length greater than 100 bp with a
contaminant database target were removed.
Illumina libraries were mapped to the sanger assembly with bbmap.sh (minid=0.85,
idfilter=0.90), and unmapped reads were written as output. Unmapped reads were assembled
using idba_tran (IDBA v1.1.1, --merge --filter, Peng et al. 2013) with maxk=124. The two
resulting assemblies were combined with the sanger assembly, and redundant sequences were
merged with CD-HIT-est v4.6.5, with c=0.95 (Fu, Niu, Zhu, Wu, & Li, 2012; W. Li & Godzik,
2006), two rounds of CAP3 (o=50, p=98, (Huang & Madan, 1999)), one round of minimus2
(OVERLAP=50, MINID=98, (Treangen, Sommer, Angly, Koren, & Pop, 2011)), and one final
round of CD-HIT-est. Mitochondrial sequences were filtered from the assembly by running
blastn (Altschul, Gish, Miller, Myers, & Lipman, 1990) against the M. californianus
mitochondrial genome (perc_identity=90, alignment length >=100), and contigs shorter than 200
bp were removed using seqmagick (convert, min-length=200, Matsen Group 2017).

ORFs were detected and peptides were predicted in the assembly using the Transdecoder v-3.0.1
pipeline, including searches for peptide homology with known proteins in the PFAM (Finn et al.,
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2016) and UniProt databases . The Trinotate pipeline (Haas et al., 2013) was then used for
annotation of contigs and predicted peptides, according to the standard workflow
(https://trinotate.github.io/). Additional annotations were retrieved using blastn (outfmt '6 std
stitle staxids') against the NCBI EST and nt databases, and diamond blastx and blastp (
taxonmap ~/prot.accession2taxid.gz --taxonnodes ~/nodes.dmp --more-sensitive --outfmt 102 --
max-target-seqs s10 --evalue 1e-5; (Buchfink, Xie, & Huson, 2015)) against the NCBI nr
database. The full taxonomic path was retrieved for BLAST and diamond BLAST output by
joining taxon IDs with a parsed file that joined taxon ID and taxonomic path (R. Sachdeva,
pers.comm. 2017). All taxonomy annotations were combined into one file, and the number of
metazoan annotations per contig were counted. Contigs that were metazoan for all of the
annotations were kept. Additionally, predicted peptides with metazoan taxonomy in blastp
results against UniProt and nt were kept. Finally, contigs that annotated as metazoan for all
BLAST searches, but could not be resolved below “root”, “cellular organism”, “Eukaryota”, or
“Opisthokonta” for diamond blast taxonomy searches, were kept as well.

The final metazoan-only assembly was further verified by mapping paired end Sanger reads to
the assembly. Reads were mapped using mapPacBio.sh (BBMap v34, minid=95), only allowing
paired reads to map together (pairedonly=t). 70% of paired Sanger reads successfully mapped
together to the final assembly.

Sample RNA extraction and Library Preparation for Illumina HiSeq
Larval samples from the first trial were prepared for sequencing. Survival was low in the 15 and
20 µg/L treatments, so only 0, 3, 6, 9, and 12 µg/L samples were sequenced. This resulted in a
total of 30 samples (3 replicates x 5 copper concentrations x 2 CO2 concentrations) that were
sequenced. RNA was extracted from all samples using the Trizol extraction protocol (Ambion).
MaxTract columns (Qiagen) were used during the phase separation step to ensure maximum
retrieval of the aqueous phase containing RNA. cDNA libraries were prepared for next
generation sequencing on the Illumina HiSeq 4000 platform using the NEBNext
®
Ultra™ RNA
Library Prep Kit for Illumina
®
, according to the protocol described above, except with a starting
quantity of 200 ng RNA, and that samples were PCR amplified for 15 cycles. The libraries were
multiplexed, and run over two lanes with 50bp SR reads.
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RNAseq Processing and Analysis
Raw RNAseq reads were quality trimmed and contaminating adapter sequence was removed
using Trimmomatic v0.33 (Bolger et al. 2014) with default parameter settings. The trimmed
reads were then mapped to the M. californianus mitochondrial genome using BBMap v34
(minid=0.95 ambiguous=all sssr=1.0)(Bushnell 2014) to separate mitochondrial transcripts from
nuclear genes. All reads that did not map to the mitochondrial genome were used for subsequent
analysis.

Mapped Read Counts and Gene Expression Analysis
Larval reads from the copper-ocean acidification experiment were mapped to the de novo
assembly described above with bbmap.sh (minid=0.95, ambiguous=random, sssr=1.0, nhtag=t,
minlength=40). The resulting bam files were counted and summarized with featureCounts (Liao,
Smyth, & Shi, 2014), allowing for multimapping reads (-M), and allowing for mapped reads
overlapping two contigs to be counted toward those contigs (-O).
Count tables were loaded into R v 3.3.1 (R Core Team 2016) and normalized TMM count tables
were generated with package edgeR (McCarthy, Chen, & Smyth, 2012; Robinson, McCarthy, &
Smyth, 2010). Only contigs with edgeR-calculated counts per million (cpm) greater than 5 in 6
or more samples were kept, resulting in 20,024 contigs used in downstream analysis. Quasi-
likelihood dispersions were estimated with the glmQLFit function (robust=TRUE), and
significant differential expression was detected using a quasi-likelihood F-test (edgeR function
glmQLFTest). Significant differentially expressed genes with a fold change greater than 1.2 were
further detected (edgeR function glmTreat). Differential expression (DE) was assessed between
high and low CO2 concentrations at each copper concentration. For example, genes were detected
that were differentially expressed between 800 ppm CO2 and 400 ppm CO2 at 6 µg/L copper,
then also at 9 µg/L copper, and so on. The same was done for all copper concentrations. The
filtered cpm table generated in edgeR was further used for analysis with Sigmoidal Dose
Response Search (SDRS) v0.04 (Ji, Siemers, Lei, Schweizer, & Bruccoleri, 2011). 400 and 800
ppm CO2 treated samples were analyzed separately with SDRS to distinctly consider
transcriptional responses to copper. Settings used were: -multiple=1.05, -ldose=0.001, -
hdose=12, =step=3, -trim=0.2, -significance=0.05. Significant genes were detected by applying
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the Benjamini & Hochberg FDR correction to p-values produced by the program. SDRS output
includes the effective concentration at which a transcript is induced or repressed by 50% (EC50),
and the low and high 95% confidence interval of the EC50. The low 95% confidence interval of
the EC50 essentially equates to the lowest dose at which transcriptional changes are statistically
detected, so we also used this value in analysis and henceforth refer to it as the Lowest Observed
Effective Concentration (LOEC).

Functional analysis
Functional enrichment analysis was conducted using gene ontology (GO) (Ashburner et al.,
2000) terms using the Cytoscape (Shannon et al., 2003) plug-in, BiNGO (Maere, Heymans, &
Kuiper, 2005). Gene lists were pasted into the program, and overrepresentation was tested using
a hypergeometric test with Benjamini & Hochberg FDR correction (p < 0.05). The GO
annotation file was generated using GO annotations produced by Trinotate, and only annotations
for the 20,024 filtered contigs were included. The core ontology file (go.obo) was downloaded
from the gene ontology website in October 2017 (http://geneontology.org/page/download-
ontology).

Figure creation
All figures were generated in R. Bar plots were created with package ggplot2 (Wickham 2009),
venn diagrams with package VennDiagram (Chen 2017), histograms with the hist() function, and
line graphs with the plot() function. Heatmaps were generated with the heatmap() function, using
default parameters for clustering, row normalization, and distance calculations.

Results
Impact of copper and OA on larval survival and development
At 400 ppm CO2, survival and normal development copper-response curves all exhibited classic
sigmoidal shape (Figure 1, Figure 2), with LC50 and EC50 values ranging from 5.7-10.3 µg/L
and 5.1-7.1 µg/L, respectively (Table 1). Exposure to 800 or 1200 ppm CO2 had seemingly
positive effects on survival and normal development. In all four trials, survival was higher across
most of the concentration range at 800 ppm CO2 relative to 400 ppm CO2. Survival was
significantly higher in 800 ppm CO2 at 6, 9, and 15 µg/L copper in trial 1 (p < 0.001), and in
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1200 ppm CO2 at 6,7, and 8 µg/L copper in trial 4 (p < 0.01)(Figure 1). ANOVA demonstrated
that copper and CO2 had significant effects on larval survival in all trials (p<<0.001 and p < 0.01,
respectively) , and the interaction of copper and CO2 had significant effects on larval survival in
all but one trial (p < 0.05) (Table 1).

Higher CO2 conditions likewise altered the normal development dose-response curve under
control or low copper concentration (0-3 µg/L), normal development was slightly lower in 800
ppm CO2; however, at slightly higher copper concentrations (6-9 µg/L), normal development
was higher in 800 ppm CO2 (Figure 2). This trend was reflected by a significant increase in
EC50 in three out of four trials (p < 0.001) (Table 1; Figure 2). Concentration-response curve
shape also changed significantly in three out of four trials, detected by shifts in the minimum,
maximum, and slope parameters of the log-logistic model (p < 0.05). Taken together, these
results indicate that exposure to projected future OA conditions somewhat ameliorates the
detrimental effects of copper on mussel larvae, and that the onset of detrimental effects occurs at
higher copper concentrations (mortality and abnormal development).

Copper and CO2 responsive expression profiles
Overall transcriptional profiles were very similar for all low copper-concentration samples
(Figure 3A). However, at higher copper concentrations (6-12 µg/L), samples diverged based on
CO2 treatment. At these higher copper concentrations, samples from the two CO2 treatments
clustered at a lagged copper concentration, with samples from 9 µg/L copper at 800 ppm CO2
more similar to samples from 6 µg/L copper at 400 ppm CO2; and samples from 12 µg/L copper
at 800 ppm CO2 more similar to samples from 9 µg/L copper at 400 ppm CO2. To explore
specific patterns of altered gene expression that could explain the morphological differences
between animals at 400 and 800 ppm CO2, we searched for sigmoidal concentration-responsive
transcripts using SDRS, and searched for CO2-induced changes in the magnitude of expression
response for SDRS transcripts and others using edgeR.

We first scanned for copper concentration-responsive transcripts using SDRS. In 400 ppm CO2,
5,463 genes were identified with a sigmoidal concentration response, whereas in 800 ppm CO2,
only 1,516 genes were identified with a sigmoidal concentration response (Table 2). Transcripts
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were identified that were uniquely responsive in 400 ppm, uniquely responsive in 800 ppm, and
responsive in both 400 and 800 ppm (Table 2). A heatmap of the transcripts that were responsive
in both CO2 conditions revealed that transcripts were responsive at higher copper concentrations
at 800 ppm CO2 (Figure 3B), and hierarchical clustering of samples again indicated that samples
from slightly higher copper concentrations at 800 ppm CO2 appeared to be the most similar to
samples from lower copper concentrations at 400 ppm CO2 (Figure 3C).

Sensitivity of the expression response was measured in SDRS with predicted EC50 values and
the lowest 95% confidence intervals of EC50 values, henceforth called the lowest observed
effective concentration, or “LOEC”. For the gene set commonly responsive at 400 and 800 ppm
CO2, the average EC50 and LOEC of genes were higher at 800 ppm CO2, whether considering
the whole gene set, upregulated genes, or downregulated genes (Table 2). Genes that were
uniquely responsive at 400 ppm similarly had lower average EC50 values (7.62 µg/L) than those
uniquely responsive at 800 ppm (9.25 µg/L), but average LOEC values were similar for the two
gene sets (4.05 µg/L in the 400 ppm only gene set; 4.25 µg/L in the 800 ppm only gene set).

Spearman’s rank correlation of EC50 and LOEC values at low and high CO2 concentrations
revealed that values were significantly correlated (p < 2 x 10
-16
), although the Spearman’s rho
was relatively low (EC50 rho = 0.45, and LOEC rho = 0.43). Thus, genes responded to copper in
a similar order at both CO2 concentrations, but there were still some substantial shifts in
sensitivity. These shifts in gene expression sensitivity are hardly surprising considering the
change in the normal development concentration response curve by ~2 µg/L Cu at 800 ppm.

On average the genes that were copper responsive at both CO2 concentrations were less sensitive
to copper at the higher CO2 condition, but we also sought to identify genes that did become more
copper sensitive at the higher CO2 concentration. Specifically, we examined shifts in transcript
sensitivity relative to the whole-organism endpoint of abnormal development. Genes with LOEC
values at copper concentrations below the normal development EC50 (NDEC50) were compared
at the two CO2 concentrations (Figure 4). At 800 ppm CO2, 95% of the total genes identified by
SDRS had expression LOEC values lower than the 800 ppm NDEC50 (Table 2). At 400 ppm
CO2, 77% of the total genes identified by SDRS had a 400 ppm expression LOEC lower than the
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400 ppm NDEC50 (Table 2). The number of genes that were uniquely modulated below the
NDEC50 varied between the downregulated and upregulated gene groups—only 7% of
downregulated genes were suppressed below NDEC50 at only 800 ppm, whereas 46% of
upregulated genes were induced below NDEC50 at only 800 ppm (Figure 4 C-D). This indicates
that the gene set downregulated below NDEC50 at 800 ppm is largely the same as those
downregulated at 400 ppm, but approximately half of the gene set upregulated below the
NDEC50 at 800 ppm consists of “novel” sensitive markers.

Amplitude of expression change—another metric of transcript sensitivity—was also compared
between CO2 concentrations. DE analysis was performed between low and high CO2 treatments
at each copper concentration (Table S2). DE genes included many of the genes that were
identified by SDRS in one or both concentrations, but also included genes that were not
classified as having a sigmoidal expression response (Table S2). Genes with higher expression
at 800 ppm CO2 were typically downregulated in response to copper, while many of the genes
with lower expression at 800 ppm CO2 relative to 400 ppm CO2were in fact upregulated over the
range of copper concentrations (Supplemental Figure 1A). Thus these genes responded in the
same way and direction at 800 ppm CO2 as at 400 ppm CO2, but the response at 800 ppm
CO2was significantly reduced. Rather than becoming copper responsive, many of these genes
actually became less copper responsive at the higher CO2 concentration. Again, this trend
revealed that transcripts were less sensitive to copper at the higher CO2 concentration.

In addition to transcripts that shift in copper sensitivity, we also considered transcripts with
altered expression patterns at the higher CO2 concentration. There were genes that were not
identified as sigmoidal by SDRS in both conditions, but many of these genes still exhibited
sigmoidal expression patterns in response to copper in both CO2 concentrations, and were likely
not statistically identified because of muted responses (Supplemental Figure 2). However, some
interesting non-sigmoidal trends did appear as well (Supplemental Figure 1B; Supplemental
Figure 2), including genes that exhibited a non-monotonic response in which they were
upregulated at lower copper concentrations at 800 ppm CO2, then downregulated at higher
copper concentrations. Further exploration of these gene sets is required to understand the
nuances of copper and CO2 interactions in these expression patterns.
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Functional GO characterization of gene sets
GO term enrichment was performed for the gene sets that exhibited altered copper sensitivity or
expression patterns at 800 ppm CO2. Enriched gene ontology categories were compared among
copper/CO2-responsive gene sets identified by SDRS to further explore the extent of shared and
unique mechanisms involved in the copper/OA interaction

First the set of sigmoidal genes with LOEC values lower than the normal development EC50 was
considered. These genes became more sensitive to copper, and were responsive at concentrations
coincident with or below major morphological changes. These pathways could thus be indicative
of the drivers of morphological differences in low and high CO2 treatments, potentially including
mechanisms that led to reduced copper uptake and/or toxicity. Genes that were upregulated and
downregulated at 400 and 800 ppm CO2 were analyzed. Many GO terms were enriched in each
of the four categories (Figure 4E). GO terms enriched at only 800 ppm CO2 were of particular
interest, as these could be indicative of the processes conferring higher copper tolerance to larvae
under acidified conditions. At 800 ppm CO2, 45 terms were uniquely enriched among
downregulated genes, and 99 terms were uniquely enriched among upregulated genes (Figure
4E).

GO categories that were prominent among downregulated genes at only 800 ppm CO2 were
related to glutamine/glutamate processes, sodium:carboxylic acid transmembrane transport, and
synaptic signaling and neurotransmission (Supplemental Table 3). Similar categories were
enriched in downregulated genes at both 400 and 800 ppm CO2, but the categories were not as
specific. This shared set of enriched GO terms included many genes related generally to
transmembrane transporter and channel activity, and neurotransmitters/neurological signaling.
Notably, the neurological signaling categories enriched at both 400 and 800 ppm CO2 were
primarily related to post-synaptic processes, whereas enriched categories at 800 ppm CO2 alone
were synaptic/trans-synaptic (Supplemental Table 3).

GO terms uniquely enriched at 800 ppm among upregulated genes were primarily related to acyl
carrier protein transferase, reductase, and hydrolase activity; cell cycle regulation; and DNA
organization, recombination, replication, and repair. GO terms associated with acyl carrier
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protein activity were not enriched at all in the gene set upregulated at 400 ppm CO2. Many terms
related to cell cycle process and DNA recombination and replication, as well as transcription,
were enriched in upregulated genes at both 400 and 800 ppm CO2. However, GO terms related to
DNA repair and response to DNA damage were primarily enriched among genes upregulated at
800 ppm CO2 (Supplemental Table 4).

Discussion
We examined the impacts of copper and CO2 on early mussel larval survival, normal
development, and gene expression. We discovered that simulated OA has complex
concentration-dependent impacts on copper toxicity. Copper-responsive larval survival and
normal development LC50 and EC50 values fell within a typical concentration range for M.
californianus (Chapter 1) and for other Mytilus species (Arnold et al., 2009). EC50 and LC50
values were surprisingly higher under future CO2 conditions than current CO2 conditions (Figure
1, Figure 2, Table 1), indicating that copper toxicity to mussel larvae was reduced by simulated
ocean acidification. However, the impacts of ocean acidification on larval health did appear to be
dependent on copper concentration. Animals typically fared slightly worse under future CO2
conditions at lower copper concentrations (0-3 µg/L), but exhibited a relative increase in survival
and normal development at higher copper concentrations (6-9 µg/L).

Co-exposure to metals and CO2 has primarily proven to be detrimental to animal health,
measured by endpoints including increased DNA damage and lipid peroxidation (Lewis et al.,
2016; Nardi et al., 2017; Roberts et al., 2012), diminished immune response (Han et al., 2013),
reduced survival (Lewis et al., 2012), reduced reproductive output (Fitzer, Caldwell, Clare,
Upstill-Goddard, & Bentley, 2013), increased metal accumulation (Götze et al., 2014; Ivanina et
al., 2013), energy deficiency (Götze et al., 2014), and extracellular acidosis (Lewis et al., 2016).
However, there is also a growing number of cases in which co-exposure to metals and lower pH
conditions or simulated OA alleviates some of the negative effects of either stressor alone, or in
some cases even proves beneficial. For example, isolated mitochondria of adult clams
(Mercenaria mercenaria) exposed to copper and lower pH alleviated the suppression of ADP-
stimulated mitochondrial respiration that was caused by copper exposure alone (Ivanina &
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Sokolova, 2013). Similarly, isolated clam cells exposed to copper and cadmium alone exhibited
significant increases in reactive oxygen species (ROS) production, but elevated pCO2 led to
diminished ROS production in response to these metals(Ivanina et al., 2013). In two studies with
copepods, metals and OA had antagonistic effects leading to increased survival (Pascal et al.,
2010), shorter development times, increased reproductive output, and reduced metal
accumulation (Y. Li et al., 2017). There is not a consistent pattern across these studies in terms
of taxa, CO2 concentration, metal, or endpoints that gives rise to any pattern of conditions that
confer greater tolerance. In fact, several studies that directly compare taxa observed notable
differences in responses to metal and CO2 interactions (Götze et al., 2014; Ivanina & Sokolova,
2013; Lewis et al., 2016).

We also sought to explore mechanisms that could explain reduced copper toxicity, and to
determine whether those mechanisms were consistent with other studies that discovered reduced
metal toxicity associated with ocean acidification. To address potential mechanisms, we
examined transcriptional profiles of larval samples. Altered patterns of gene expression revealed
several processes that potentially contribute to this observation.

Overall expression patterns reflected the morphological trends observed: the response to copper
at 800 ppm CO2 was not as sensitive as the response to copper at 400 ppm CO2. This was
evidenced by sample clustering patterns for the entire gene set (Figure 3A), and hierarchical
clustering specifically for the set of genes that were copper-responsive at both CO2
concentrations (Figure 3B). A lower mean expression EC50 at 400 ppm CO2 relative to 800 ppm
(Table 2) confirmed that significant transcriptional responses to copper did not occur until higher
copper concentrations at 800 ppm CO2. Similarly, the amplitude of the expression response to
copper was muted for transcripts at 800 ppm relative to transcripts at 400 ppm CO2
(Supplemental Table 2; Supplemental Figure 1), also indicating that transcripts were less
sensitive at higher CO2 concentrations. The lack of a significant transcriptional response to CO2
alone (Supplemental Table 2) aligns with a previous study on OA impacts on mussel larval
transcription (Kelly, Padilla-Gamiño, & Hofmann, 2016).

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Analysis of expression profiles indicated that few unique genes were involved in the copper
response at 800 ppm CO2, although the relative sensitivity of shared genes changed in somewhat
variable ways. The clustering of samples on an MDS plot reflect the generally similar copper-
responsive expression patterns of samples, with copper concentration as the main explanatory
variable on the leading dimension 1 (Figure 3A). Similarly, of copper-responsive genes at 800
ppm CO2, only a small fraction was unique as identified by SDRS (Table 2). However, the
copper response pattern of individual shared genes did not always simply shift to higher
concentrations, as evidenced by Spearman’s rank coefficients of EC50 and LOEC values for
genes at 400 and 800 ppm CO2. Although significant, the relatively low Spearman’s rank
correlations of 0.45 for EC50 values and 0.43 for LOEC values indicate that there were some
notable shifts in the relative concentration response patterns of genes. These results indicate that
even when the same genes were involved, the relative sensitivity was not consistently the same.

The trend of similar genes shifting expression induction or suppression to slightly higher copper
concentrations suggests that larvae are effectively exposed to less copper at the higher CO2
concentration, and that many of the same molecular mechanisms that precede and coincide with
the onset of abnormal development under normal CO2 conditions are eventually stimulated at the
higher CO2 concentration. The reasons for this apparent lower exposure could be a result of
reduced copper uptake via chemical competition at uptake sites (De Schamphelaere & Janssen,
2004; Y. Li et al., 2017), or a result of muted internal effects via enhanced sequestration or
cellular damage defense mechanisms (Ivanina et al., 2013). A combination of these phenomena
may have also led to the observed result. We examined genes with significantly different
expression patterns at the high CO2 concentration to assess likely explanations.

Despite the predominant trend of the same suite of genes responding at higher copper
concentrations in conjunction with shifted normal development, there were some gene sets that
became more sensitive markers of copper exposure at the higher CO2 concentration (Figure 4).
Genes and pathways that become more sensitive to copper at 800 ppm CO2, or that exhibited
significantly altered expression patterns at 800 ppm CO2, are indicative of unique CO2-induced
defenses, and provide more insight into the molecular mechanisms that may be driving reduced
toxicity. Genes that exhibit expression changes at copper concentrations below the EC50 for
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normal development (NDEC50) can serve as sensitive biomarkers of impending morphological
damage, and can be indicative of sub-lethal stress occurring prior to the onset of whole organism
abnormality (Broeg, Westernhagen, Zander, Körting, & Koehler, 2005; Daston, 2008; Smit et al.,
2009). Genes regulated in this concentration range may also reveal initial coping mechanisms
that organisms mount in response to copper. About half of upregulated genes with LOEC values
below the NDEC50 at 800 ppm CO2 were uniquely expressed in this condition (Figure 4D).
While very few downregulated genes were unique to 800 ppm CO2 (Figure 4C), GO enrichment
revealed a somewhat different story (Figure 4E). For both upregulated and downregulated genes,
numerous GO categories were only enriched at 800 ppm CO2, a trend that indicates that a
somewhat novel response is occurring in response to combined copper and CO2 exposure at low
copper concentrations.

GO terms enriched in downregulated genes were related to neurotransmission, including genes
involved in glutamine and glutamate transmembrane transport (sodium/amino acid symporters—
e.g. C. Zander, Zhang, Albers, & Grewer, 2014), cycling of gamma-Aminobutyric acid (GABA)
precursors glutamine and glutamate, and pre-synaptic neurotransmission and activity
(Supplemental Table 3). Several GO terms related sodium and cation transmembrane/symporter
activity were also uniquely enriched at 800 ppm, and closer inspection of the genes involved in
these categories revealed that they were related to the glutamine/glutamate/GABA cycle and/or
pre-synaptic processes as well. These genes included Sodium- and chloride-dependent glycine
transporter 2; Sodium-and chloride-dependent GABA transporter 3; Electroneutral sodium
bicarbonate exchanger 1; Excitatory amino acid transporter 2; and Sodium-dependent proline
transporter.

Copper alone does not have any significant effect on early mussel larval swimming frequency
(Hall M, Foley B, Cheung E, Abbasi M, Churches ND, 2016), but it is possible that OA has
physiological impacts that alter behavior patterns. OA has been demonstrated to alter behavioral
patterns of marine invertebrates, making them less responsive to external stimuli (Charpentier &
Cohen, 2016; Watson et al., 2014). The study conducted in mollusks (Watson et al., 2014) found
that GABA played an important role in reduced predator avoidance behavior, and that restoration
of GABA to normal conditions restored normal behavior patterns as well. Research in larval sea
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urchins has proven that the GABA system becomes active early in larval development, and that
inhibition of GABA receptors significantly inhibits larval swimming (H. Katow, Abe, Katow,
Zamani, & Abe, 2013). Therefore if OA reduces GABA production in mussel larvae, it could
reduce larval swimming, and thus reduce copper uptake as well. Larval movement and behavior
were not measured in this experiment, but it would be informative to measure these endpoints in
future studies.

Upregulated GO terms that were unique to sensitive markers at 800 ppm CO2 were involved in
DNA repair and fatty acid production (Supplemental Table 4). Specific genes involved in the
regulation of DNA strand breaks included Replication protein A 32 kDa subunit; Fidgetin-like
protein 1; Regulator of telomere elongation helicase 1; Serine/threonine-protein kinase Chk1;
and Telomeric repeat-binding factor 2-interacting protein 1. Fatty acid production genes were
two copies of fatty acid synthase. Damage to key cellular components by copper-induced
oxidative stress, especially lipids and DNA, are well-known mechanisms of copper toxicity
(Lewis et al., 2016; Nardi et al., 2017; Stohs & Bagchi, 1995).

Increased fatty acid synthesis under OA conditions (Díaz-Gil, Catalán, Palmer, Faulk, & Fuiman,
2015) could reduce copper-induced damage to lipids that constitute vital components of cells.
Increased DNA repair mechanisms under OA conditions could also reduce copper-induced
damage. Reduced copper-induced damage to DNA and lipids at 800 ppm CO2 could be a key
contributing factor to higher levels of normal development. All of these trends suggest that
pathways that are responsive to CO2, either via pH alterations or altered carbonate chemistry
parameters, could also incidentally reduce negative impacts of copper toxicity. In this scenario,
activation of those pathways by CO2 could have the added benefit of conferring defense against
copper toxicity, even if the pathways are not directly copper responsive, per se. A similar theory
has been suggested by Ivanina et al. (2013), in that intertidal invertebrates regularly exposed to
large fluctuations in pH could exhibit substantial pH-driven induction of antioxidant defense
mechanisms, which could potentially lead to the observed result of attenuated ROS production in
animals co-exposed to hypercapnia (i.e. abnormally elevated CO2 concentrations) and metals.

151

We conclude that copper and ocean acidification interact in a concentration-dependent way, and
that ocean acidification in fact attenuates the negative effects of copper, shifting the onset of
toxic responses (reduced survival and normal development) to higher copper concentrations.
Overall, most of the same genes were involved in the copper response at both CO2
concentrations, and were likewise induced or repressed at higher copper concentrations.
However several gene sets exhibit different trends, and highlight that some distinct functional
pathways are also activated at low vs high CO2. The patterns suggest that mussel larvae are
likely effectively exposed to less copper, either via modulation of transporters and movement,
chemical interactions at metal uptake sites, or a combination of these factors. Some enhanced
defense mechanisms (e.g. increased lipid and DNA repair) may also function to increase relative
normal development in organisms co-exposed to copper and CO2. These findings underscore the
fact that metals and OA have complex and so far unpredictable impacts on marine invertebrates,
and highlight the need to better understand the underlying chemical and physiological drivers of
the trends observed.

References

Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990). Basic local
alignment search tool. Journal of Molecular Biology, 215(3), 403–410.
http://doi.org/10.1016/S0022-2836(05)80360-2
Arnold, W. R., Cotsifas, J. S., Smith, D. S., Le Page, S., & Gruenthal, K. M. (2009). A
comparison of the copper sensitivity of two economically important saltwater mussel species
and a review of previously reported copper toxicity data for mussels: Important implications
for determining future ambient copper saltwater criteria in the USA. Environmental
Toxicology, 24(6), 618–628. http://doi.org/10.1002/tox.20452
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., et al. (2000).
Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nature
Genetics, 25(1), 25–29. http://doi.org/10.1038/75556
Bolger, A. M., Lohse, M., & Usadel, B. (2014). Trimmomatic: a flexible trimmer for Illumina
sequence data. Bioinformatics, 30(15), 2114–2120.
http://doi.org/10.1093/bioinformatics/btu170
Broeg, K., Westernhagen, H. V., Zander, S., Körting, W., & Koehler, A. (2005). The “bioeffect
assessment index” (BAI). A concept for the quantification of effects of marine pollution by
an integrated biomarker approach. Marine Pollution Bulletin, 50(5), 495–503.
http://doi.org/10.1016/j.marpolbul.2005.02.042
152

Buchfink, B., Xie, C., & Huson, D. H. (2015). Fast and sensitive protein alignment using
DIAMOND. Nature Methods, 12(1), 59–60. http://doi.org/10.1038/nmeth.3176
Bushnell, B. (2016). BBMap short read aligner. URL http://sourceforge.net/projects/bbmap
Charpentier, C. L., & Cohen, J. H. (2016). Acidification and γ-aminobutyric acid independently
alter kairomone-induced behaviour. Royal Society Open Science, 3(9), 160311.
http://doi.org/10.1098/rsos.160311
Chen, H. (2017). VennDiagram: Generate High-Resolution Venn and Euler Plots. R package
version 1.6.18. https://CRAN.R-project.org/package=VennDiagram
Daston, G. P. (2008). Gene expression, dose-response, and phenotypic anchoring: applications
for toxicogenomics in risk assessment. Toxicological Sciences : an Official Journal of the
Society of Toxicology, 105(2), 233–234. http://doi.org/10.1093/toxsci/kfn138
De Schamphelaere, K. A. C., & Janssen, C. R. (2004). Bioavailability and chronic toxicity of
zinc to juvenile rainbow trout (Oncorhynchus mykiss): comparison with other fish species
and development of a biotic ligand model. Environmental Science & Technology, 38(23),
6201–6209.
Díaz-Gil, C., Catalán, I. A., Palmer, M., Faulk, C. K., & Fuiman, L. A. (2015). Ocean
acidification increases fatty acids levels of larval fish. Biology Letters, 11(7), 20150331.
http://doi.org/10.1098/rsbl.2015.0331
Dondero, F., Dagnino, A., Jonsson, H., Caprì, F., Gastaldi, L., & Viarengo, A. (2006). Assessing
the occurrence of a stress syndrome in mussels (Mytilus edulis) using a combined
biomarker/gene expression approach. Aquatic Toxicology, 78, S13–S24.
http://doi.org/10.1016/j.aquatox.2006.02.025
Doney, S. C., Fabry, V. J., Feely, R. A., & Kleypas, J. A. (2009). Ocean Acidification: The Other
CO 2Problem. Annual Review of Marine Science, 1(1), 169–192.
http://doi.org/10.1146/annurev.marine.010908.163834
Doney, S. C., Ruckelshaus, M., Duffy, J. E., Barry, J. P., Chan, F., English, C. A., et al. (2012).
Climate change impacts on marine ecosystems. Annual Review of Marine Science, 4(1), 11–
37. http://doi.org/10.1146/annurev-marine-041911-111611
Dupont, S., Havenhand, J., Thorndyke, W., Peck, L., & Thorndyke, M. (2008). Near-future level
of CO2-driven ocean acidification radically affects larval survival and development in the
brittlestar Ophiothrix fragilis. Marine Ecology Progress Series, 373, 285–294.
http://doi.org/10.3354/meps07800
Dupont, S., Ortega-Martínez, O., & Thorndyke, M. (2010). Impact of near-future ocean
acidification on echinoderms. Ecotoxicology, 19(3), 449–462. http://doi.org/10.1007/s10646-
010-0463-6
EPA, U. S. (2016). Draft Aquatic Life Ambient Estuarine/Marine Water Quality Criteria for
Copper - 2016, 1–257.
Farrington, J. W., Tripp, B. W., Tanabe, S., Subramanian, A., Sericano, J. L., Wade, T. L., &
Knap, A. H. (2016). Edward D. Goldberg's proposal of ‘the Mussel Watch’: Reflections after
40 years. Marine Pollution Bulletin, 110(1), 501–510.
http://doi.org/10.1016/j.marpolbul.2016.05.074
Finn, R. D., Coggill, P., Eberhardt, R. Y., Eddy, S. R., Mistry, J., Mitchell, A. L., et al. (2016).
The Pfam protein families database: towards a more sustainable future. Nucleic Acids
Research, 44(D1), D279–85. http://doi.org/10.1093/nar/gkv1344
Fitzer, S. C., Caldwell, G. S., Clare, A. S., Upstill-Goddard, R. C., & Bentley, M. G. (2013).
Response of Copepods to Elevated pCO2 and Environmental Copper as Co-Stressors – A
153

Multigenerational Study. PLoS ONE, 8(8), e71257–9.
http://doi.org/10.1371/journal.pone.0071257
Fu, L., Niu, B., Zhu, Z., Wu, S., & Li, W. (2012). CD-HIT: accelerated for clustering the next-
generation sequencing data. Bioinformatics, 28(23), 3150–3152.
http://doi.org/10.1093/bioinformatics/bts565
Gomes, T., Pinheiro, J. P., Cancio, I., Pereira, C. G., Cardoso, C., & Bebianno, M. J. (2011).
Effects of copper nanoparticles exposure in the mussel Mytilus galloprovincialis.
Environmental Science & Technology, 45(21), 9356–9362. http://doi.org/10.1021/es200955s
Götze, S., Matoo, O. B., Beniash, E., Saborowski, R., & Sokolova, I. M. (2014). Interactive
effects of CO2 and trace metals on the proteasome activity and cellular stress response of
marine bivalves Crassostrea virginica and Mercenaria mercenaria. Aquatic Toxicology, 149,
65–82. http://doi.org/10.1016/j.aquatox.2014.01.027
Gracey, A. Y., Chaney, M. L., Boomhower, J. P., Tyburczy, W. R., Connor, K., & Somero, G.
N. (2008). Rhythms of Gene Expression in a Fluctuating Intertidal Environment. Current
Biology, 18(19), 1501–1507. http://doi.org/10.1016/j.cub.2008.08.049
Gunderson, A. R., Armstrong, E. J., & Stillman, J. H. (2016). Multiple Stressors in a Changing
World: The Need for an Improved Perspective on Physiological Responses to the Dynamic
Marine Environment. Annual Review of Marine Science, 8(1), 357–378.
http://doi.org/10.1146/annurev-marine-122414-033953
Haas, B. J., Papanicolaou, A., Yassour, M., Grabherr, M., Blood, P. D., Bowden, J., et al. (2013).
De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for
reference generation and analysis. Nature Protocols, 8(8), 1494–1512.
http://doi.org/10.1038/nprot.2013.084
Hall M, Foley B, Cheung E, Abbasi M, Churches ND (2016) A New Behavioral Phenotyping
Strategy for Pacific Oyster (Crassostrea gigas) Larvae Reveals CohortLevel Effects on
Copper Toxicity Swimming Response. Ann Aquac Res 3(3): 1025.
Han, Z.-X., Wu, D.-D., Wu, J., Lv, C.-X., & Liu, Y.-R. (2013). Effects of Ocean Acidification
on Toxicity of Heavy Metals in the Bivalve Mytilus edulisL. Synthesis and Reactivity in
Inorganic, Metal-Organic, and Nano-Metal Chemistry, 44(1), 133–139.
http://doi.org/10.1080/15533174.2013.770753
His, E., Beiras, R., & Seaman, M. N. L. (1999). The Assessment of Marine Pollution - Bioassays
with Bivalve Embryos and Larvae (Vol. 37, pp. 1–178). Elsevier.
http://doi.org/10.1016/S0065-2881(08)60428-9
His, E., Seaman, M. N. L., & Beiras, R. (1997). A simplification the bivalve embryogenesis and
larval development bioassay method for water quality assessment. Water Research, 31(2),
351–355. http://doi.org/10.1016/S0043-1354(96)00244-8
Hoffmann, L. J., Breitbarth, E., Boyd, P. W., & Hunter, K. A. (2012). Influence of ocean
warming and acidification on trace metal biogeochemistry. Marine Ecology Progress Series,
470, 191–205. http://doi.org/10.3354/meps10082
Huang, X., & Madan, A. (1999). CAP3: A DNA sequence assembly program. Genome
Research, 9(9), 868–877.
Ivanina, A. V., & Sokolova, I. M. (2013). Interactive effects of pH and metals on mitochondrial
functions of intertidal bivalves Crassostrea virginica and Mercenaria mercenaria. Aquatic
Toxicology, 144-145, 303–309. http://doi.org/10.1016/j.aquatox.2013.10.019
154

Ivanina, A. V., & Sokolova, I. M. (2015). Interactive effects of metal pollution and ocean
acidification on physiology of marine organisms. Current Zoology, 61(4), 653–668.
http://doi.org/10.1093/czoolo/61.4.653
Ivanina, A. V., Beniash, E., Etzkorn, M., Meyers, T. B., Ringwood, A. H., & Sokolova, I. M.
(2013). Short-term acute hypercapnia affects cellular responses to trace metals in the hard
clams Mercenaria mercenaria. Aquatic Toxicology (Amsterdam, Netherlands), 140-141, 123–
133. http://doi.org/10.1016/j.aquatox.2013.05.019
Ji, R.-R., Siemers, N. O., Lei, M., Schweizer, L., & Bruccoleri, R. E. (2011). SDRS--an
algorithm for analyzing large-scale dose-response data. Bioinformatics, 27(20), 2921–2923.
http://doi.org/10.1093/bioinformatics/btr489
Katow, H., Abe, K., Katow, T., Zamani, A., & Abe, H. (2013). Development of the GABA-ergic
signaling system and its role in larval swimming in sea urchin. The Journal of Experimental
Biology, 216(Pt 9), 1704–1716. http://doi.org/10.1242/jeb.074856
Kelly, M. W., Padilla-Gamiño, J. L., & Hofmann, G. E. (2016). High pCO 2affects body size,
but not gene expression in larvae of the California mussel ( Mytilus californianus). ICES
Journal of Marine Science: Journal Du Conseil, 73(3), 962–969.
http://doi.org/10.1093/icesjms/fsv184
Kurihara, H., Asai, T., Kato, S., & Ishimatsu, A. (2008). Effects of elevated pCO2 on early
development in the mussel Mytilus galloprovincialis. Aquatic Biology, 4, 225–233.
http://doi.org/10.3354/ab00109
Kurihara, H., Kato, S., & Ishimatsu, A. (2007). Effects of increased seawater pCO2 on early
development of the oyster Crassostrea gigas. Aquatic Biology, 1, 91–98.
http://doi.org/10.3354/ab00009
Lewis, C., Clemow, K., & Holt, W. V. (2012). Metal contamination increases the sensitivity of
larvae but not gametes to ocean acidification in the polychaete Pomatoceros lamarckii
(Quatrefages). Marine Biology, 160(8), 2089–2101. http://doi.org/10.1007/s00227-012-
2081-8
Lewis, C., Ellis, R. P., Vernon, E., Elliot, K., Newbatt, S., & Wilson, R. W. (2016). Ocean
acidification increases copper toxicity differentially in two key marine invertebrates with
distinct acid-base responses. Scientific Reports, 6, 21554. http://doi.org/10.1038/srep21554
Li, W., & Godzik, A. (2006). Cd-hit: a fast program for clustering and comparing large sets of
protein or nucleotide sequences. Bioinformatics, 22(13), 1658–1659.
http://doi.org/10.1093/bioinformatics/btl158
Li, Y., Wang, W.-X., & Wang, M. (2017). Alleviation of mercury toxicity to a marine copepod
under multigenerational exposure by ocean acidification. Scientific Reports, 7(1), 324.
http://doi.org/10.1038/s41598-017-00423-1
Liao, Y., Smyth, G. K., & Shi, W. (2014). featureCounts: an efficient general purpose program
for assigning sequence reads to genomic features. Bioinformatics, 30(7), 923–930.
http://doi.org/10.1093/bioinformatics/btt656
Maere, S., Heymans, K., & Kuiper, M. (2005). BiNGO: a Cytoscape plugin to assess
overrepresentation of Gene Ontology categories in Biological Networks. Bioinformatics,
21(16), 3448–3449. http://doi.org/10.1093/bioinformatics/bti551
Martin, M., Osborn, K. E., Billig, P., & Glickstein, N. (1981). Toxicities of ten metals to
Crassostrea gigas and Mytilus edulis embryos and Cancer magister larvae. Marine Pollution
Bulletin, 12(9), 305–308. http://doi.org/10.1016/0025-326X(81)90081-3
Matsen Group (2017) Seqmagick. https://fhcrc.github.io/seqmagick/#
155

McCarthy, D. J., Chen, Y., & Smyth, G. K. (2012). Differential expression analysis of
multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids
Research, 40(10), 4288–4297. http://doi.org/10.1093/nar/gks042
Millero, F., Woosley, R., DiTrolio, B., & Waters, J. (2009). Effect of Ocean Acidification on the
Speciation of Metals in Seawater. Oceanography, 22(4), 72–85.
http://doi.org/10.5670/oceanog.2009.98
Nardi, A., Mincarelli, L. F., Benedetti, M., Fattorini, D., d'Errico, G., & Regoli, F. (2017).
Indirect effects of climate changes on cadmium bioavailability and biological effects in the
Mediterranean mussel Mytilus galloprovincialis. Chemosphere, 169, 493–502.
http://doi.org/10.1016/j.chemosphere.2016.11.093
Navarro, A., Faria, M., Barata, C., & Piña, B. (2011). Transcriptional response of stress genes to
metal exposure in zebra mussel larvae and adults. Environmental Pollution (Barking, Essex :
1987), 159(1), 100–107. http://doi.org/10.1016/j.envpol.2010.09.018
Negri, A., Oliveri, C., Sforzini, S., Mignione, F., Viarengo, A., & Banni, M. (2013).
Transcriptional Response of the Mussel Mytilus galloprovincialis (Lam.) following
Exposure to Heat Stress and Copper. PLoS ONE, 8(6), e66802–11.
http://doi.org/10.1371/journal.pone.0066802
Pan, T. C. F., Applebaum, S. L., & Manahan, D. T. (2015). Experimental ocean acidification
alters the allocation of metabolic energy. Proceedings of the National Academy of Sciences,
112(15), 4696–4701. http://doi.org/10.1073/pnas.1416967112
Pascal, P.-Y., Fleeger, J. W., Galvez, F., & Carman, K. R. (2010). The toxicological interaction
between ocean acidity and metals in coastal meiobenthic copepods. Marine Pollution
Bulletin, 60(12), 2201–2208. http://doi.org/10.1016/j.marpolbul.2010.08.018
Peng, Y., Leung, H. C. M., Yiu, S.-M., Lv, M.-J., Zhu, X.-G., & Chin, F. Y. L. (2013). IDBA-
tran: a more robust de novo de Bruijn graph assembler for transcriptomes with uneven
expression levels. Bioinformatics, 29(13), i326–34.
http://doi.org/10.1093/bioinformatics/btt219
Pespeni, M. H., Sanford, E., Gaylord, B., Hill, T. M., Hosfelt, J. D., Jaris, H. K., et al. (2013).
Evolutionary change during experimental ocean acidification. Proceedings of the National
Academy of Sciences, 110(17), 6937–6942. http://doi.org/10.1073/pnas.1220673110
Pierrot, D. E. Lewis,and D. W. R. Wallace. 2006. MS Excel Program Developed for CO2
System Calculations. ORNL/CDIAC-105a. Carbon Dioxide Information Analysis Center,
Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tennessee. doi:
10.3334/CDIAC/otg.CO2SYS_XLS_CDIAC105a
R Core Team (2016). R: A language and environment for statistical computing. R Foundation for
Statistical Computing, Vienna, Austria. URL http://www.R-project.org/
Ritz, C., Baty, F., Streibig, J. C., & Gerhard, D. (2015). Dose-Response Analysis Using R. PLoS
ONE, 10(12), e0146021. http://doi.org/10.1371/journal.pone.0146021
Roberts, D. A., Birchenough, S. N. R., Lewis, C., Sanders, M. B., Bolam, T., & Sheahan, D.
(2012). Ocean acidification increases the toxicity of contaminated sediments. Global Change
Biology, 19(2), 340–351. http://doi.org/10.1111/gcb.12048
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010). edgeR: a Bioconductor package for
differential expression analysis of digital gene expression data. Bioinformatics, 26(1), 139–
140. http://doi.org/10.1093/bioinformatics/btp616
156

Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., et al. (2003).
Cytoscape: a software environment for integrated models of biomolecular interaction
networks. Genome Research, 13(11), 2498–2504. http://doi.org/10.1101/gr.1239303
Silva-Aciares, F., Zapata, M., Tournois, J., Moraga, D., & Riquelme, C. (2011). Identification of
genes expressed in juvenile Haliotis rufescens in response to different copper concentrations
in the north of Chile under controlled conditions. Marine Pollution Bulletin, 62(12), 2671–
2680. http://doi.org/10.1016/j.marpolbul.2011.09.023
Smit, M. G. D., Bechmann, R. K., Hendriks, A. J., Skadsheim, A., Larsen, B. K., Baussant, T., et
al. (2009). Relating biomarkers to whole-organism effects using species sensitivity
distributions: a pilot study for marine species exposed to oil. Environmental Toxicology and
Chemistry, 28(5), 1104–1109. http://doi.org/10.1897/08-464.1
Sokolova, I. M. (2013). Energy-Limited Tolerance to Stress as a Conceptual Framework to
Integrate the Effects of Multiple Stressors. Integrative and Comparative Biology, 53(4),
597–608. http://doi.org/10.1093/icb/ict028
Stohs, S. J., & Bagchi, D. (1995). Oxidative mechanisms in the toxicity of metal ions. Free
Radical Biology & Medicine, 18(2), 321–336.
Stumpp, M., Wren, J., Melzner, F., Thorndyke, M. C., & Dupont, S. T. (2011). CO2 induced
seawater acidification impacts sea urchin larval development I: Elevated metabolic rates
decrease scope for growth and induce developmental delay. Comparative Biochemistry and
Physiology, Part A, 160(3), 331–340. http://doi.org/10.1016/j.cbpa.2011.06.022
Treangen, T. J., Sommer, D. D., Angly, F. E., Koren, S., & Pop, M. (2011). Next generation
sequence assembly with AMOS. Current Protocols in Bioinformatics, Chapter 11, Unit
11.8–11.8.18. http://doi.org/10.1002/0471250953.bi1108s33
Varotto, L., Domeneghetti, S., Rosani, U., Manfrin, C., Cajaraville, M. P., Raccanelli, S., et al.
(2013). DNA Damage and Transcriptional Changes in the Gills of Mytilus galloprovincialis
Exposed to Nanomolar Doses of Combined Metal Salts (Cd, Cu, Hg). PLoS ONE, 8(1),
e54602–18. http://doi.org/10.1371/journal.pone.0054602
Venier, P., De Pittà, C., Pallavicini, A., Marsano, F., Varotto, L., Romualdi, C., et al. (2006).
Development of mussel mRNA profiling: Can gene expression trends reveal coastal water
pollution? Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis,
602(1-2), 121–134. http://doi.org/10.1016/j.mrfmmm.2006.08.007
Waldbusser, G. G., Hales, B., Langdon, C. J., Haley, B. A., Schrader, P., Brunner, E. L., et al.
(2014). Saturation-state sensitivity of marine bivalve larvae to ocean acidification. Nature
Climate Change, 5(3), 273–280. http://doi.org/10.1038/nclimate2479
Walker, C.H., and Livingstone, D.R., eds. (1992). Persistent Pollutants in Marine Ecosystems.
Oxford, UK: Pergamon
Watson, S.-A., Lefevre, S., McCormick, M. I., Domenici, P., Nilsson, G. E., & Munday, P. L.
(2014). Marine mollusc predator-escape behaviour altered by near-future carbon dioxide
levels. Proceedings. Biological Sciences / the Royal Society, 281(1774), 20132377–
20132377. http://doi.org/10.1098/rspb.2013.2377
Wickham, H. (2009). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York
Wood, C. M., Farrell, A. P., & Brauner, C. J. (2011). Homeostasis and Toxicology of Essential
Metals (Vol. 31). Academic Press.
Zander, C., Zhang, Z., Albers, T., & Grewer, C. (2014). Amino Acid Transporters and
Glutamine. In Glutamine in Clinical Nutrition (pp. 21–36). New York, NY: Springer New
York. http://doi.org/10.1007/978-1-4939-1932-1_2
157

Zapata, M., Tanguy, A., David, E., Moraga, D., & Riquelme, C. (2009). Transcriptomic response
of Argopecten purpuratus post-larvae to copper exposure under experimental conditions.
Gene, 442(1-2), 37–46. http://doi.org/10.1016/j.gene.2009.04.019

158

Figure 1—Survival dose response curves. Survival is plotted against copper concentration for
four trials (A, B, C, and D). Points represent mean control-normalized survival at each copper
concentration. Curves are modeled four-parameter log-logistic curves, and error bars represent
the standard error of each curve. Survival is depicted at 400 ppm CO2 (blue points and curves)
and 800 ppm CO2, or at 1200 ppm CO2 in trial 4 (D) (black points and curves). Asterisks
represent copper concentrations for which survival was significantly different between CO2
concentrations.
Copper
Survival_controlnormalized
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159
Figure 2—Normal Development dose response curves. Proportion of normal development is
plotted against copper concentration for four trials (A, B, C, and D). Points represent mean
control-normalized normal development at each copper concentration. Curves are modeled
four-parameter log-logistic curves, and error bars represent the standard error of each curve.
Normal development is depicted at 400 ppm CO2 (blue points and curves) and 800 ppm CO2,
or at 1200 ppm CO2 in trial 4 (D) (black points and curves). Shapes in the upper right hand
corner of each box indicate significant shifts in curve shape (triangle) or EC50 (asterisk)
between the two CO2 treatments.
Proportion Normal Development
Copper Concentration(ug/L)
* *
*
Copper
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0 5 10 15 20
0.0
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400 ppm CO
2
800 ppm CO
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160
400−12
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800−12
800−9
400−6
800−0
800−6
800−3
400−3
400−0
400−0
400−3
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400−3
400−6
400−9
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800−3
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YlOrRd (sequential)
Downregulated Genes Upregulated Genes
Low Expression High Expression
A B
C
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800−3
800−6
800−9
800−12
400−0
400−3
400−6
400−9
400−12
Figure 3—Sequencing of Trial 1 revealed copper and OA-responsive
transcriptional patterns (A) Multidimensional Scaling Plot clustering
samples by transcriptional patterns. All transcripts with a mean cpm >
5 in 6 or more samples were included. Each treatment was
conducted in triplicate. Larger, darker points are indicative of higher
copper concentrations; red points are samples from 400 ppm CO2;
blue points are samples from 800 ppm CO2. (B) Heatmaps of genes
that were identified by SDRS at both 400 and 800 ppm CO2.(C)
Hierarchical clustering of samples from heatmaps in (B). Survival and
Normal Development curves from Trial 1 are included for reference
(D).
Copper Concentration
% Survival
% Normal Development
D
161
0 5 10 15
0 200 400 600 800
0.0 0.2 0.4 0.6 0.8 1.0 1.2 Frequency
0 5 10 15
0 50 100 150 200 250 300
0.0 0.2 0.4 0.6 0.8 1.0 1.2
Copper Concentration
Frequency of LOEC
400 ppm
400 ppm
800 ppm
800 ppm
C D
A B E
Upregulated 800 ppm
Upregulated 400 ppm Downregulated 400 ppm
Downregulated 800 ppm
Figure 4—Analysis of the gene set with LOEC values below the
Normal Development EC50 at 400 ppm and 800 ppm CO2.
Histograms demonstrate the numbers of up- and down-regulated
genes, and the distribution of LOEC values, at 400 ppm CO2 (A)
and 800 ppm CO2 (B). Venn diagrams were constructed for this
gene set, for genes that were downregulated (C) and upregulated
(D) in response to copper. Venn diagrams demonstrate unique and
overlapping GO terms identified in each gene set (E).
Proportion Normal Development
162
Supplemental Figure 1—Example gene expression dose-response profiles of genes with
CO2-induced differential expression (DE). Most DE genes were expressed in the same direction
at both 400 ppm CO2 and 800 ppm CO2, but with a reduced magnitude of response, as
depicted by several examples in A. However, some DE genes (primarily all with higher
expression at 800 ppm CO2 than 400 ppm CO2) exhibited different expression patterns at 400
ppm and 800 ppm CO2, often consisting of upregulation at low copper doses, followed by
downregulation at higher copper doses, as depicted by several examples in B. Nonetheless,
the ultimate trend for these genes was to change in the same direction relative to copper.

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Gene Expression Count (cpm)
Gene Expression Count (cpm)
Copper Concentration
Copper Concentration
DE at 6 ug/L
DE at 9 ug/L
DE at 12 ug/L
A
B
800 ppm CO2
400 ppm CO2
Expression significantly higher
at 400 ppm CO2
Expression significantly higher
at 800 ppm CO2
163
Supplemental Figure 2— Gene expression dose response curves of several example SDRS-
identified genes. Gene expression counts (cpm) were plotted against copper concentration for
genes that were only sigmoidal at 400 ppm CO2 (A), and only sigmoidal at 800 ppm CO2 (B). In
both cases, a range of expression patterns were identified among genes from the CO2
treatment that did not elicit a sigmoidal response.
Downreg400 Only Downreg800 Only
Upreg400 Only Upreg800 Only
Copper Concentration
Gene Expression Count (cpm)
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800 ppm CO2
400 ppm CO2
A B
164
DRC Output
Effect of variables on survival (p-
value)
LC50 Low
CO2
LC50 High
CO2
Normal
Devel EC50
Low CO2
Normal
Devel EC50
High CO2
Survival
Lack of Fit
test
Normal
Devel Lack
of Fit test CO2 Copper
CO2 x
copper
Experiment 1 5.6952 11.8537 5.6798 7.063 0 0.0078 3.64 E-15 2.00 E-16 3.40 E-08
Experiment 2 10.3368 9.3762 7.1481 9.2662 0.4034 0 3.03 E-06 2.00 E-16 0.594
Experiment 3 8.0443 N/A 5.1174 4.4724 0.2241 0.0006 0.00833 2.00 E-16 0.02072
Experiment 4 5.76 N/A 5.3279 6.3225 0.2206 0.2012 3.04 E-10 2.00 E-16 1.75 E-05
Table 1—Analysis of survival and normal development in response to copper and CO2. In the first pane, results of the
sigmoidal dose response curve analysis in R program drc are shown. LC50 and EC50 values for low CO2 (400 ppm)
and high CO2 (800 ppm for experiments 1-3; 1200 ppm for experiment 4) were calculated for each experiment. P-values
for ANOVA lack of fit tests were also calculated for survival and normal development in each experiment. In the second
pane, the significance of effects of copper, CO2, and the interaction on survival were calculated using ANOVA. The
numbers shown are p-values.
165
400 ppm 800 ppm 400 ppm Only 800 ppm Only
Both 400 and 800
ppm
Counts
Genes identified by
SDRS 5463 1516 4326 379 1137
Upregulated genes 2396 897 1838 339 558
Downregulated
genes 3067 619 2488 40 579
Mean EC50 (ug/L Cu)
All genes--400 ppm 7.50 7.62 7.07
All genes--800 ppm 9.16 9.25 9.13
Mean LOEC (ug/L Cu)
All genes--400 ppm 3.82 4.05 2.93
All genes--800 ppm 4.15 4.25 4.12
Counts
Downreg Expression
LOEC < ND EC50 2659 607 2096 44 563
Upreg Expression
LOEC < ND EC50 1523 839 1072 388 451
Expression LOEC <
ND EC50 4182 1446 3168 432 1014
Table 2—SDRS results. The number of genes (counts) for various sets of genes identified by SDRS are shown for the
whole gene set, and for genes with a LOEC value lower than the normal development EC50 at that CO2 concentration.
Mean EC50 and LOEC values are also shown for gene sets.
166
Experiment
Number
Nominal CO2
Concentration pH mean
pH
standard
error
DIC (ug/L) mean
DIC (ug/L) Std
Error
Calculated
pCO2
1 400 8.069 0.022 2049.492 21.696 419.03
1 800 7.883 0.027 2100.135 15.543 695.745
2 400 8.109 0.037 - - -
2 800 7.877 0.037 - - -
3 400 8.006 0.008 - - -
3 800 7.850 0.011 - - -
4 400 7.969 0.005 - - -
4 1200 7.619 0.003 - - -
Supplemental Table 1—Carbonate Chemistry Measurements. pH was measured for all 4 experiments. Mean and
standard error of pH across all samples is shown. DIC was also measured for experiment 1. Mean and standard error of
DIC were calculated. pCO2 was calculated for each measured sample using CO2Sys version 2.3, and the mean pCO2
was calculated.
167
Higher
Expression 800
Higher
Expression 400
Total DE
Shared with
SDRS 400 ppm
Shared with
SDRS 800 ppm
0:400-800 0 0 0 0 0
3:400-800 0 0 0 0 0
6:400-800 15 40 55 29 34
9:400-800 1876 1314 3190 1621 641
12:400-800 427 170 597 459 140
Supplemental Table 2-- Number of genes differentially expressed between CO
2
concentrations. All genes were
detected with differential expression analysis in edgeR (padj < 0.05). Many of these genes were also identified by SDRS
at either 400 ppm CO
2
, 800 ppm CO
2
, or both.
168
GO ID
Adjusted p-
value
GO term GO ID
Adjusted p-
value
GO term GO ID
Adjusted p-
value
GO term
1525 2.36E-02 angiogenesis 4351 5.51E-03
glutamate decarboxylase
activity 4175 4.63E-02
endopeptidase
activity
1568 1.56E-02
blood vessel
development 4356 5.51E-03
glutamate-ammonia
ligase activity 4497 2.96E-02
monooxygenase
activity
1944 1.07E-02
vasculature
development 6541 3.85E-02
glutamine metabolic
process 5576 1.17E-05 extracellular region
48514 8.95E-03
blood vessel
morphogenesis 6542 5.51E-03
glutamine biosynthetic
process 5578 4.89E-03
proteinaceous
extracellular matrix
48662 9.68E-03
negative regulation of
smooth muscle cell
proliferation 4871 3.45E-03
signal transducer
activity 5604 2.18E-03 basement membrane
72358 9.66E-03
cardiovascular system
development 15849 1.28E-03 organic acid transport 5615 1.36E-02 extracellular space
48678 6.21E-03 response to axon injury 46942 1.28E-03
carboxylic acid
transport 6690 8.77E-03
icosanoid metabolic
process
31102 2.71E-03
neuron projection
regeneration 46943 1.07E-03
carboxylic acid
transmembrane
transporter activity 8233 9.70E-03 peptidase activity
31103 2.98E-02 axon regeneration 5342 1.17E-03
organic acid
transmembrane
transporter activity 9986 3.54E-04 cell surface
42136 3.12E-02
neurotransmitter
biosynthetic process 5343 8.77E-03
organic acid:sodium
symporter activity 16491 9.83E-03
oxidoreductase
activity
42401 9.68E-03
cellular biogenic amine
biosynthetic process 1903825 2.73E-03
organic acid
transmembrane
transport 19825 7.88E-03 oxygen binding
Only enriched in genes downregulated at
400 ppm CO2
Only enriched in genes downregulated at 800
ppm CO2
Enriched in genes downregulated at 400
ppm and genes downregulated at 800
ppm
Supplemental Table 3--GO terms enriched for downregulated SDRS genes with Lowest Observed Effective
Concentration less than the Normal Development EC50
169
42415 8.88E-03
norepinephrine
metabolic process 1905039 2.73E-03
carboxylic acid
transmembrane
transport 20037 1.91E-02 heme binding
42417 2.73E-05
dopamine metabolic
process 5416 2.63E-02
cation:amino acid
symporter activity 31012 2.47E-03 extracellular matrix
42420 1.06E-02
dopamine catabolic
process 5283 2.63E-02
sodium:amino acid
symporter activity 5887 1.29E-05
integral component
of plasma membrane
42421 8.88E-03
norepinephrine
biosynthetic process 15081 2.18E-03
sodium ion
transmembrane
transporter activity 16021 3.54E-04
integral component
of membrane
42445 5.19E-03
hormone metabolic
process 15291 2.35E-03
secondary active
transmembrane
transporter activity 31224 1.69E-04
intrinsic component
of membrane
1872 1.56E-02
(1->3)-beta-D-glucan
binding 15293 1.07E-03 symporter activity 31226 1.00E-05
intrinsic component
of plasma membrane
3810 3.12E-02
protein-glutamine
gamma-
glutamyltransferase
activity 15294 4.79E-04
solute:cation symporter
activity 32154 3.54E-04 cleavage furrow
3824 2.98E-02 catalytic activity 15296 2.58E-03
anion:cation symporter
activity 32642 3.58E-02
regulation of
chemokine
production
4252 3.30E-04
serine-type
endopeptidase activity 15370 5.08E-04
solute:sodium
symporter activity 33559 3.95E-02
unsaturated fatty
acid metabolic
process
4383 6.21E-03
guanylate cyclase
activity 98655 1.01E-02
cation transmembrane
transport 35094 1.37E-05 response to nicotine
8074 1.81E-02
guanylate cyclase
complex, soluble 6812 3.58E-02 cation transport 38023 9.09E-05
signaling receptor
activity
4500 8.88E-03
dopamine beta-
monooxygenase
activity 6814 9.83E-03 sodium ion transport 43235 6.26E-03 receptor complex
4553 1.68E-03
hydrolase activity,
hydrolyzing O-glycosyl
compounds 6820 5.89E-04 anion transport 44420 6.00E-03
extracellular matrix
component
170
4806 3.65E-02
triglyceride lipase
activity 35725 2.18E-03
sodium ion
transmembrane
transport 44421 2.42E-02
extracellular region
part
4857 2.59E-03
enzyme inhibitor
activity 22853 1.25E-02
active ion
transmembrane
transporter activity 44425 1.89E-02 membrane part
4866 3.73E-03
endopeptidase
inhibitor activity 6972 2.93E-02 hyperosmotic response 44459 6.89E-04
plasma membrane
part
4867 7.50E-04
serine-type
endopeptidase
inhibitor activity 16211 5.51E-03 ammonia ligase activity 46906 8.77E-03 tetrapyrrole binding
5044 1.81E-02
scavenger receptor
activity 16880 5.51E-03
acid-ammonia (or
amide) ligase activity 46872 1.03E-02 metal ion binding
5344 1.56E-02 oxygen carrier activity 32153 7.34E-04 cell division site 46914 2.41E-02
transition metal ion
binding
5261 1.48E-02 cation channel activity 32155 7.34E-04 cell division site part 50840 4.81E-02
extracellular matrix
binding
55067 1.84E-02
monovalent inorganic
cation homeostasis 7601 3.85E-02 visual perception 70011 2.59E-02
peptidase activity,
acting on L-amino
acid peptides
5506 3.67E-05 iron ion binding 7218 3.58E-02
neuropeptide signaling
pathway 71944 1.19E-02 cell periphery
5537 1.10E-03 mannose binding 7268 2.26E-03
chemical synaptic
transmission 97267 1.91E-02
omega-hydroxylase
P450 pathway
5539 1.08E-02
glycosaminoglycan
binding 7271 9.83E-03
synaptic transmission,
cholinergic 97610 3.54E-04 cell surface furrow
5581 1.19E-08 collagen trimer 7274 4.12E-02
neuromuscular synaptic
transmission 98590 3.10E-02
plasma membrane
region
5788 1.63E-04
endoplasmic reticulum
lumen 42165 1.41E-02
neurotransmitter
binding 1901568 3.10E-02
fatty acid derivative
metabolic process
5886 2.71E-03 plasma membrane 45211 9.43E-03 postsynaptic membrane 60089 4.61E-05
molecular transducer
activity
5902 2.44E-02 microvillus 98916 2.26E-03
anterograde trans-
synaptic signaling 5509 1.52E-02 calcium ion binding
171
5201 4.20E-02
extracellular matrix
structural constituent 99536 2.35E-03 synaptic signaling 22803 9.43E-03
passive
transmembrane
transporter activity
22610 3.23E-03 biological adhesion 99537 2.35E-03 trans-synaptic signaling 22804 1.59E-03
active
transmembrane
transporter activity
31589 3.83E-02 cell-substrate adhesion 97060 2.85E-02 synaptic membrane 22824 4.92E-06
transmitter-gated ion
channel activity
7155 6.46E-03 cell adhesion 51180 3.85E-02 vitamin transport 22834 1.00E-05
ligand-gated channel
activity
7160 1.08E-02 cell-matrix adhesion 51183 1.41E-02
vitamin transporter
activity 22835 4.92E-06
transmitter-gated
channel activity
8061 4.57E-04 chitin binding 140103 2.78E-03
catalytic activity, acting
on a glycoprotein 22836 7.34E-04
gated channel
activity
6030 2.46E-04
chitin metabolic
process 22838 4.76E-03
substrate-specific
channel activity
7168 4.73E-02
receptor guanylyl
cyclase signaling
pathway 4872 1.29E-05 receptor activity
7186 3.36E-02
G-protein coupled
receptor signaling
pathway 4888 1.07E-03
transmembrane
signaling receptor
activity
7565 6.21E-03 female pregnancy 5215 1.41E-06 transporter activity
8172 3.12E-02
S-methyltransferase
activity 5216 1.01E-02 ion channel activity
8236 4.68E-04
serine-type peptidase
activity 5230 1.41E-06
extracellular ligand-
gated ion channel
activity
8237 3.84E-02
metallopeptidase
activity 5231 1.37E-05
excitatory
extracellular ligand-
gated ion channel
activity
8395 1.11E-03
steroid hydroxylase
activity 98656 6.26E-04
anion
transmembrane
transport
172
8645 2.04E-02 hexose transport 22857 1.54E-05
transmembrane
transporter activity
9636 2.36E-02
response to toxic
substance 22890 1.31E-03
inorganic cation
transmembrane
transporter activity
9897 1.39E-02
external side of plasma
membrane 22891 7.63E-05
substrate-specific
transmembrane
transporter activity
9975 1.06E-02 cyclase activity 22892 1.36E-03
substrate-specific
transporter activity
10466 4.67E-03
negative regulation of
peptidase activity 1990351 4.79E-03 transporter complex
10817 7.95E-03
regulation of hormone
levels 51119 8.40E-03
sugar
transmembrane
transporter activity
10951 2.36E-02
negative regulation of
endopeptidase activity 55085 2.13E-04
transmembrane
transport
15149 6.21E-03
hexose transmembrane
transporter activity 1902495 3.29E-03
transmembrane
transporter complex
15578 1.56E-02
mannose
transmembrane
transporter activity 34219 9.83E-03
carbohydrate
transmembrane
transport
15669 3.78E-03 gas transport 34220 2.94E-04
ion transmembrane
transport
15671 1.56E-02 oxygen transport 34702 2.83E-03 ion channel complex
15721 1.77E-02
bile acid and bile salt
transport 43169 4.68E-03 cation binding
15761 1.56E-02 mannose transport 98802 8.55E-03
plasma membrane
receptor complex
15850 4.73E-02
organic hydroxy
compound transport 6811 3.54E-04 ion transport
1905950 6.21E-03
monosaccharide
transmembrane
transport 8028 3.58E-02
monocarboxylic acid
transmembrane
transporter activity
173
2000191 4.73E-02
regulation of fatty acid
transport 8324 4.79E-03
cation
transmembrane
transporter activity
2000193 1.56E-02
positive regulation of
fatty acid transport 8509 2.98E-04
anion
transmembrane
transporter activity
6869 3.46E-02 lipid transport 8514 2.74E-04
organic anion
transmembrane
transporter activity
16486 2.98E-02
peptide hormone
processing 15075 1.07E-04
ion transmembrane
transporter activity
4364 2.88E-03
glutathione transferase
activity 15144 8.40E-03
carbohydrate
transmembrane
transporter activity
16209 4.73E-02 antioxidant activity 15145 6.45E-03
monosaccharide
transmembrane
transporter activity
16614 1.24E-06
oxidoreductase
activity, acting on CH-
OH group of donors 15267 8.55E-03 channel activity
16616 2.83E-05
oxidoreductase
activity, acting on the
CH-OH group of donors,
NAD or NADP as
acceptor 15276 1.00E-05
ligand-gated ion
channel activity
16620 4.08E-02
oxidoreductase
activity, acting on the
aldehyde or oxo group
of donors, NAD or
NADP as acceptor 15711 8.14E-04
organic anion
transport
16628 1.48E-02
oxidoreductase
activity, acting on the
CH-CH group of donors,
NAD or NADP as
acceptor 15718 1.53E-02
monocarboxylic acid
transport
174
16684 1.73E-02
oxidoreductase
activity, acting on
peroxide as acceptor 15749 2.23E-02
monosaccharide
transport
16705 9.45E-05
oxidoreductase
activity, acting on
paired donors, with
incorporation or
reduction of molecular
oxygen 99600 6.54E-04
transmembrane
receptor activity
16712 1.20E-04
oxidoreductase
activity, acting on
paired donors, with
incorporation or
reduction of molecular
oxygen, reduced flavin
or flavoprotein as one
donor, and
incorporation of one
atom of oxygen 15464 1.07E-04
acetylcholine
receptor activity
16715 2.98E-02
oxidoreductase
activity, acting on
paired donors, with
incorporation or
reduction of molecular
oxygen, reduced
ascorbate as one donor,
and incorporation of
one atom of oxygen 5892 3.06E-04
acetylcholine-gated
channel complex
16860 2.34E-02
intramolecular
oxidoreductase activity 22848 9.09E-05
acetylcholine-gated
cation-selective
channel activity
55114 3.24E-11
oxidation-reduction
process 98960 1.36E-04
postsynaptic
neurotransmitter
receptor activity
175
4601 2.96E-02 peroxidase activity 99094 1.17E-03
ligand-gated cation
channel activity
4602 4.08E-02
glutathione peroxidase
activity 99529 9.09E-05
neurotransmitter
receptor activity
involved in
regulation of
postsynaptic
membrane potential
16755 2.98E-02
transferase activity,
transferring amino-acyl
groups 99565 9.09E-05
chemical synaptic
transmission,
postsynaptic
16798 7.93E-03
hydrolase activity,
acting on glycosyl
bonds 1904315 9.09E-05
transmitter-gated ion
channel activity
involved in
regulation of
postsynaptic
membrane potential
16829 3.25E-04 lyase activity 60078 1.69E-04
regulation of
postsynaptic
membrane potential
16830 4.71E-02
carbon-carbon lyase
activity 60079 7.63E-05
excitatory
postsynaptic
potential
16849 1.06E-02
phosphorus-oxygen
lyase activity 30594 1.00E-05
neurotransmitter
receptor activity
16941 4.73E-02
natriuretic peptide
receptor activity
17144 1.06E-02 drug metabolic process
17171 2.43E-04
serine hydrolase
activity
30246 2.49E-12 carbohydrate binding
30414 4.11E-04
peptidase inhibitor
activity
30552 4.56E-02 cAMP binding
176
30802 2.04E-02
regulation of cyclic
nucleotide
biosynthetic process
30823 4.73E-02
regulation of cGMP
metabolic process
30826 4.73E-02
regulation of cGMP
biosynthetic process
46068 1.65E-02
cGMP metabolic
process
6182 6.21E-03
cGMP biosynthetic
process
52652 1.06E-02
cyclic purine
nucleotide metabolic
process
31225 3.02E-02
anchored component
of membrane
31348 3.12E-02
negative regulation of
defense response
31960 1.48E-02
response to
corticosteroid
32101 4.73E-02
regulation of response
to external stimulus
32303 4.73E-02
regulation of icosanoid
secretion
32305 1.56E-02
positive regulation of
icosanoid secretion
32675 2.34E-02
regulation of
interleukin-6
production
35356 4.73E-02
cellular triglyceride
homeostasis
35428 6.21E-03
hexose transmembrane
transport
38024 8.81E-03 cargo receptor activity
43102 3.12E-02 amino acid salvage
177
43230 4.20E-02 extracellular organelle
46394 1.06E-03
carboxylic acid
biosynthetic process
46395 2.08E-04
carboxylic acid
catabolic process
72329 1.56E-02
monocarboxylic acid
catabolic process
43177 2.73E-05 organic acid binding
52689 1.20E-04
carboxylic ester
hydrolase activity
32787 7.83E-07
monocarboxylic acid
metabolic process
16053 1.06E-03
organic acid
biosynthetic process
16054 2.08E-04
organic acid catabolic
process
43436 2.05E-08
oxoacid metabolic
process
43648 3.51E-02
dicarboxylic acid
metabolic process
19752 7.21E-08
carboxylic acid
metabolic process
19369 2.98E-02
arachidonic acid
metabolic process
31406 2.73E-05 carboxylic acid binding
8391 3.78E-03
arachidonic acid
monooxygenase
activity
8392 1.56E-02
arachidonic acid
epoxygenase activity
5540 1.58E-03 hyaluronic acid binding
6082 2.15E-08
organic acid metabolic
process
178
1900015 4.56E-02
regulation of cytokine
production involved in
inflammatory response
50707 6.21E-03
regulation of cytokine
secretion
32682 3.49E-03
negative regulation of
chemokine production
50715 6.88E-03
positive regulation of
cytokine secretion
50727 4.67E-03
regulation of
inflammatory response
50728 1.62E-03
negative regulation of
inflammatory response
51384 1.84E-02
response to
glucocorticoid
61134 4.58E-03
peptidase regulator
activity
61135 6.21E-03
endopeptidase
regulator activity
70062 4.64E-02 extracellular exosome
70163 1.56E-02
regulation of
adiponectin secretion
70165 1.56E-02
positive regulation of
adiponectin secretion
70206 3.12E-02 protein trimerization
70330 3.33E-04 aromatase activity
70372 1.98E-02
regulation of ERK1 and
ERK2 cascade
71265 3.12E-02
L-methionine
biosynthetic process
71267 3.12E-02 L-methionine salvage
71637 1.10E-03
regulation of monocyte
chemotactic protein-1
production
179
71638 1.10E-03
negative regulation of
monocyte chemotactic
protein-1 production
97164 1.22E-02
ammonium ion
metabolic process
98754 1.68E-02 detoxification
98869 4.73E-02
cellular oxidant
detoxification
1900165 1.33E-02
negative regulation of
interleukin-6 secretion
1903561 4.20E-02 extracellular vesicle
5975 6.97E-07
carbohydrate
metabolic process
5996 4.90E-02
monosaccharide
metabolic process
6022 3.46E-04
aminoglycan metabolic
process
6040 1.90E-04
amino sugar metabolic
process
6066 6.21E-03
alcohol metabolic
process
6520 4.74E-02
cellular amino acid
metabolic process
6555 2.98E-02
methionine metabolic
process
9712 6.89E-06
catechol-containing
compound metabolic
process
9713 3.89E-04
catechol-containing
compound
biosynthetic process
18958 8.83E-03
phenol-containing
compound metabolic
process
180
19336 1.06E-02
phenol-containing
compound catabolic
process
19614 1.06E-02
catechol-containing
compound catabolic
process
46189 6.21E-03
phenol-containing
compound
biosynthetic process
6584 6.89E-06
catecholamine
metabolic process
42423 3.89E-04
catecholamine
biosynthetic process
42424 1.06E-02
catecholamine
catabolic process
42439 3.12E-02
ethanolamine-
containing compound
metabolic process
6589 8.88E-03
octopamine
biosynthetic process
6629 2.35E-04 lipid metabolic process
6631 2.69E-05
fatty acid metabolic
process
6691 1.77E-02
leukotriene metabolic
process
6692 2.98E-02
prostanoid metabolic
process
6693 2.98E-02
prostaglandin
metabolic process
6790 5.66E-03
sulfur compound
metabolic process
8202 1.28E-02
steroid metabolic
process
181
1901071 1.30E-03
glucosamine-
containing compound
metabolic process
1901135 2.98E-02
carbohydrate
derivative metabolic
process
1901136 3.32E-02
carbohydrate
derivative catabolic
process
1901605 6.79E-04
alpha-amino acid
metabolic process
1901607 1.68E-03
alpha-amino acid
biosynthetic process
1901615 4.09E-05
organic hydroxy
compound metabolic
process
1901616 2.88E-03
organic hydroxy
compound catabolic
process
1901617 2.01E-02
organic hydroxy
compound
biosynthetic process
97 6.21E-03
sulfur amino acid
biosynthetic process
44106 3.12E-02
cellular amine
metabolic process
44242 4.54E-02
cellular lipid catabolic
process
44255 2.00E-02
cellular lipid metabolic
process
44272 4.43E-03
sulfur compound
biosynthetic process
44281 2.33E-07
small molecule
metabolic process
182
44282 2.14E-04
small molecule
catabolic process
44283 4.57E-04
small molecule
biosynthetic process
44706 1.84E-02
multi-multicellular
organism process
46333 3.12E-02
octopamine metabolic
process
8652 1.73E-03
cellular amino acid
biosynthetic process
9067 1.48E-02
aspartate family amino
acid biosynthetic
process
9086 1.81E-02
methionine
biosynthetic process
9187 2.98E-02
cyclic nucleotide
metabolic process
9190 1.65E-02
cyclic nucleotide
biosynthetic process
9308 6.21E-03
amine metabolic
process
9309 9.68E-03
amine biosynthetic
process
16042 6.88E-03 lipid catabolic process
16052 2.20E-02
carbohydrate catabolic
process
183
GO ID
Adjusted
p-value
GO term GO ID
Adjusted p-
value
GO term GO ID
Adjusted p-
value
GO term
151 1.74E-02
ubiquitin ligase
complex 4313 3.00E-02
[acyl-carrier-protein] S-
acetyltransferase activity 70 1.71E-03
mitotic sister chromatid
segregation
166 1.88E-02 nucleotide binding 4314 3.00E-02
[acyl-carrier-protein] S-
malonyltransferase activity 75 2.14E-02 cell cycle checkpoint
209 4.83E-04
protein
polyubiquitination 4316 3.00E-02
3-oxoacyl-[acyl-carrier-
protein] reductase (NADPH)
activity 7049 4.02E-12 cell cycle
932 7.73E-04 P-body 4317 3.00E-02
3-hydroxypalmitoyl-[acyl-
carrier-protein] dehydratase
activity 22402 4.70E-07 cell cycle process
2199 4.13E-02
zona pellucida
receptor complex 4319 3.00E-02
enoyl-[acyl-carrier-protein]
reductase (NADPH, B-specific)
activity 45786 6.93E-05
negative regulation of cell
cycle
3254 1.96E-02
regulation of
membrane
depolarization 4320 3.00E-02
oleoyl-[acyl-carrier-protein]
hydrolase activity 278 5.65E-07 mitotic cell cycle
3724 1.35E-02 RNA helicase activity 47117 3.00E-02
enoyl-[acyl-carrier-protein]
reductase (NADPH, A-specific)
activity 10972 4.99E-04
negative regulation of G2/M
transition of mitotic cell
cycle
3916 2.88E-02
DNA topoisomerase
activity 102131 3.00E-02
3-oxo-glutaryl-[acp] methyl
ester reductase activity 10389 8.79E-04
regulation of G2/M
transition of mitotic cell
cycle
4004 2.26E-02
ATP-dependent RNA
helicase activity 102132 3.00E-02
3-oxo-pimeloyl-[acp] methyl
ester reductase activity 45132 4.13E-02
meiotic chromosome
segregation
Only enriched in genes upregulated at
400 ppm CO2
Only enriched in genes upregulated at 800 ppm
CO2
Enriched in genes upregulated at 400 ppm
and genes upregulated at 800 ppm
Supplemental Table 4--GO terms enriched for upregulated SDRS genes with Lowest Observed Effective Concentration
less than the Normal Development EC50
184
4298 2.34E-09
threonine-type
endopeptidase
activity 47451 3.00E-02
3-hydroxyoctanoyl-[acyl-
carrier-protein] dehydratase
activity 2E+06 3.23E-03
negative regulation of
mitotic cell cycle phase
transition
4357 7.49E-03
glutamate-cysteine
ligase activity 16295 3.00E-02
myristoyl-[acyl-carrier-
protein] hydrolase activity 45930 3.13E-04
negative regulation of
mitotic cell cycle
4842 6.20E-03
ubiquitin-protein
transferase activity 16296 3.00E-02
palmitoyl-[acyl-carrier-
protein] hydrolase activity 51321 3.13E-04 meiotic cell cycle
5524 3.70E-04 ATP binding 16297 3.00E-02
acyl-[acyl-carrier-protein]
hydrolase activity 51436 1.06E-04
negative regulation of
ubiquitin-protein ligase
activity involved in mitotic
cell cycle
5681 7.49E-03 spliceosomal complex 16419 3.00E-02 S-malonyltransferase activity 51437 1.70E-04
positive regulation of
ubiquitin-protein ligase
activity involved in
regulation of mitotic cell
cycle transition
5721 2.68E-02
pericentric
heterochromatin 16420 3.00E-02 malonyltransferase activity 98687 6.35E-06 chromosomal region
5832 1.74E-02
chaperonin-
containing T-complex 6471 3.00E-02 protein ADP-ribosylation 98813 3.13E-04
nuclear chromosome
segregation
5839 9.92E-09
proteasome core
complex 6723 3.00E-02
cuticle hydrocarbon
biosynthetic process 140013 9.37E-03 meiotic nuclear division
6401 4.45E-02 RNA catabolic process 6996 1.10E-02 organelle organization 140014 7.40E-04 mitotic nuclear division
6409 1.10E-02
tRNA export from
nucleus 10565 2.58E-02
regulation of cellular ketone
metabolic process 2E+06 6.02E-04
negative regulation of cell
cycle G2/M phase transition
6458 3.56E-03
'de novo' protein
folding 17025 1.35E-02 TBP-class protein binding 2E+06 1.15E-02 meiotic cell cycle process
6464 4.00E-03
cellular protein
modification process 18024 3.69E-02
histone-lysine N-
methyltransferase activity 2E+06 1.05E-05 mitotic cell cycle process
6508 3.20E-07 proteolysis 19171 3.00E-02
3-hydroxyacyl-[acyl-carrier-
protein] dehydratase activity 280 6.34E-06 nuclear division
6513 1.74E-02
protein
monoubiquitination 19828 3.00E-02
aspartic-type endopeptidase
inhibitor activity 775 2.15E-05
chromosome, centromeric
region
185
6534 4.64E-02
cysteine metabolic
process 22850 3.86E-02
serotonin-gated cation-
selective channel activity 779 7.50E-05
condensed chromosome,
centromeric region
6606 3.28E-02
protein import into
nucleus 2E+06 8.06E-03
positive regulation of protein
modification by small protein
conjugation or removal 793 2.03E-06 condensed chromosome
6610 4.64E-02
ribosomal protein
import into nucleus 2E+06 5.52E-03
regulation of protein
ubiquitination involved in
ubiquitin-dependent protein
catabolic process 796 2.52E-03 condensin complex
6750 1.63E-02
glutathione
biosynthetic process 31398 3.23E-03
positive regulation of protein
ubiquitination 819 4.99E-04 sister chromatid segregation
6913 9.09E-03
nucleocytoplasmic
transport 31519 3.87E-02 PcG protein complex 3676 7.83E-06 nucleic acid binding
6998 4.08E-02
nuclear envelope
organization 45934 4.96E-02
negative regulation of
nucleobase-containing
compound metabolic process 3677 6.08E-10 DNA binding
7062 4.35E-02
sister chromatid
cohesion 50852 2.65E-02
T cell receptor signaling
pathway 3678 3.02E-08 DNA helicase activity
7077 1.10E-02
mitotic nuclear
envelope disassembly 61135 3.10E-02
endopeptidase regulator
activity 4003 8.98E-03
ATP-dependent DNA
helicase activity
8026 2.82E-03
ATP-dependent
helicase activity 65004 1.05E-02
protein-DNA complex
assembly 4386 1.57E-04 helicase activity
8186 2.26E-02
RNA-dependent
ATPase activity 70652 2.74E-02 HAUS complex 4518 1.78E-02 nuclease activity
8270 9.23E-03 zinc ion binding 4520 3.00E-02
endodeoxyribonuclease
activity 5634 1.99E-15 nucleus
9056 3.91E-03 catabolic process 4536 3.15E-02 deoxyribonuclease activity 5654 5.08E-05 nucleoplasm
9889 6.05E-04
regulation of
biosynthetic process 51301 1.36E-03 cell division 5694 4.41E-20 chromosome
9891 3.76E-02
positive regulation of
biosynthetic process 51726 8.03E-04 regulation of cell cycle 7059 7.31E-07 chromosome segregation
9892 1.74E-02
negative regulation of
metabolic process 10948 3.16E-03
negative regulation of cell
cycle process 7076 1.33E-03
mitotic chromosome
condensation
186
9893 3.72E-03
positive regulation of
metabolic process 33301 3.00E-02
cell cycle comprising mitosis
without cytokinesis 502 9.54E-08 proteasome complex
9987 2.93E-02 cellular process 60260 2.55E-02
regulation of transcription
initiation from RNA
polymerase II promoter 6139 2.89E-07
nucleobase-containing
compound metabolic
process
10468 4.76E-03
regulation of gene
expression 2E+06 7.43E-04
regulation of cell cycle phase
transition 6259 2.73E-16 DNA metabolic process
10556 2.99E-04
regulation of
macromolecule
biosynthetic process 2E+06 4.68E-03
negative regulation of cell
cycle phase transition 6260 3.60E-15 DNA replication
10557 4.45E-02
positive regulation of
macromolecule
biosynthetic process 2E+06 8.03E-04
regulation of mitotic cell
cycle phase transition 6281 1.77E-08 DNA repair
10604 1.93E-03
positive regulation of
macromolecule
metabolic process 45448 3.00E-02 mitotic cell cycle, embryonic 6974 1.29E-08
cellular response to DNA
damage stimulus
10608 1.88E-02
posttranscriptional
regulation of gene
expression 5680 4.93E-02 anaphase-promoting complex 6310 1.28E-08 DNA recombination
10827 3.27E-02
regulation of glucose
transport 2E+06 4.99E-04
regulation of cell cycle G2/M
phase transition 6355 9.24E-03
regulation of transcription,
DNA-templated
10951 4.69E-02
negative regulation of
endopeptidase
activity 7127 3.00E-02 meiosis I 31570 3.00E-02 DNA integrity checkpoint
16070 1.65E-02
RNA metabolic
process 7131 1.74E-02
reciprocal meiotic
recombination 32392 3.31E-08 DNA geometric change
16595 1.63E-02 glutamate binding 7346 3.64E-04
regulation of mitotic cell
cycle 32508 5.65E-07 DNA duplex unwinding
16604 2.79E-02 nuclear body 10564 4.29E-03
regulation of cell cycle
process 32774 1.83E-02 RNA biosynthetic process
16925 7.51E-03 protein sumoylation 35186 3.00E-02
syncytial blastoderm mitotic
cell cycle 43618 8.98E-03
regulation of transcription
from RNA polymerase II
promoter in response to
stress
187
17056 4.26E-03
structural constituent
of nuclear pore 2E+06 2.91E-03
regulation of double-strand
break repair 43620 1.08E-02
regulation of DNA-
templated transcription in
response to stress
17069 4.84E-02 snRNA binding 10569 3.62E-04
regulation of double-strand
break repair via homologous
recombination 44427 1.06E-14 chromosomal part
17076 4.76E-03
purine nucleotide
binding 2E+06 2.03E-02
regulation of response to DNA
damage stimulus 44428 4.70E-07 nuclear part
17109 7.49E-03
glutamate-cysteine
ligase complex 3684 4.58E-03 damaged DNA binding 45898 3.23E-03
regulation of RNA
polymerase II
transcriptional preinitiation
complex assembly
19184 2.88E-02
nonribosomal peptide
biosynthetic process 6282 7.43E-03 regulation of DNA repair 45899 2.29E-04
positive regulation of RNA
polymerase II
transcriptional preinitiation
complex assembly
19538 2.24E-03
protein metabolic
process 6284 6.02E-04 base-excision repair 51171 3.13E-04
regulation of nitrogen
compound metabolic
process
19773 1.85E-04
proteasome core
complex, alpha-
subunit complex 6302 2.81E-03 double-strand break repair 51172 4.33E-02
negative regulation of
nitrogen compound
metabolic process
19787 5.12E-03
ubiquitin-like protein
transferase activity 3697 3.78E-04 single-stranded DNA binding 51252 1.43E-02
regulation of RNA metabolic
process
30397 1.74E-02
membrane
disassembly 152 3.15E-02
nuclear ubiquitin ligase
complex 60261 3.23E-03
positive regulation of
transcription initiation
from RNA polymerase II
promoter
30433 3.74E-02
ubiquitin-dependent
ERAD pathway 217 3.03E-02
DNA secondary structure
binding 61418 6.02E-03
regulation of transcription
from RNA polymerase II
promoter in response to
hypoxia
188
30521 3.84E-02
androgen receptor
signaling pathway 228 1.28E-08 nuclear chromosome 51276 1.17E-10 chromosome organization
30554 4.66E-04
adenyl nucleotide
binding 405 1.61E-02 bubble DNA binding 71103 1.12E-11 DNA conformation change
31324 3.46E-02
negative regulation of
cellular metabolic
process 723 1.10E-02 telomere maintenance 2E+06 5.90E-03
positive regulation of DNA-
templated transcription,
initiation
31325 1.33E-03
positive regulation of
cellular metabolic
process 724 4.14E-04
double-strand break repair via
homologous recombination 2E+06 1.01E-02
regulation of nucleic acid-
templated transcription
31326 3.39E-04
regulation of cellular
biosynthetic process 725 4.14E-04 recombinational repair 2E+06 1.05E-02
regulation of RNA
biosynthetic process
31328 4.14E-02
positive regulation of
cellular biosynthetic
process 18 4.58E-03
regulation of DNA
recombination 2220 1.07E-02
innate immune response
activating cell surface
receptor signaling pathway
31329 2.12E-02
regulation of cellular
catabolic process 35825 1.74E-02 homologous recombination 2223 1.07E-02
stimulatory C-type lectin
receptor signaling pathway
31647 9.53E-03
regulation of protein
stability 776 6.75E-04 kinetochore 2474 4.29E-03
antigen processing and
presentation of peptide
antigen via MHC class I
32182 3.35E-02
ubiquitin-like protein
binding 777 5.80E-04
condensed chromosome
kinetochore 2479 4.14E-04
antigen processing and
presentation of exogenous
peptide antigen via MHC
class I, TAP-dependent
32268 1.81E-05
regulation of cellular
protein metabolic
process 785 5.86E-05 chromatin 5838 6.52E-10
proteasome regulatory
particle
32269 5.49E-03
negative regulation of
cellular protein
metabolic process 790 6.95E-04 nuclear chromatin 6511 5.81E-04
ubiquitin-dependent
protein catabolic process
32270 4.21E-02
positive regulation of
cellular protein
metabolic process 808 3.23E-03 origin recognition complex 6521 4.63E-05
regulation of cellular amino
acid metabolic process
189
32388 3.08E-02
positive regulation of
intracellular transport 6261 1.57E-02
DNA-dependent DNA
replication 6725 2.03E-06
cellular aromatic
compound metabolic
process
32434 3.15E-04
regulation of
proteasomal ubiquitin-
dependent protein
catabolic process 5657 1.33E-03 replication fork 6807 3.00E-02
nitrogen compound
metabolic process
32436 1.29E-03
positive regulation of
proteasomal ubiquitin-
dependent protein
catabolic process 51052 4.07E-02
regulation of DNA metabolic
process 6950 2.39E-02 response to stress
32553 6.97E-03
ribonucleotide
binding 5664 2.74E-02
nuclear origin of replication
recognition complex 8094 1.57E-04
DNA-dependent ATPase
activity
32555 5.92E-03
purine ribonucleotide
binding 2E+06 3.87E-02
regulation of DNA-templated
transcription, initiation 8540 4.63E-05
proteasome regulatory
particle, base subcomplex
32559 6.06E-04
adenyl ribonucleotide
binding 6335 3.23E-03
DNA replication-dependent
nucleosome assembly 8541 6.95E-04
proteasome regulatory
particle, lid subcomplex
32991 6.84E-03
macromolecular
complex 6351 3.14E-02 transcription, DNA-templated 9057 2.40E-02
macromolecule catabolic
process
33143 4.64E-02
regulation of
intracellular steroid
hormone receptor
signaling pathway 98847 1.61E-02
sequence-specific single
stranded DNA binding 9058 3.66E-02 biosynthetic process
33145 4.64E-02
positive regulation of
intracellular steroid
hormone receptor
signaling pathway 30894 7.29E-03 replisome 9059 4.52E-04
macromolecule
biosynthetic process
33148 1.63E-02
positive regulation of
intracellular estrogen
receptor signaling
pathway 6323 1.61E-03 DNA packaging 9378 1.07E-03
four-way junction helicase
activity
190
33157 3.26E-02
regulation of
intracellular protein
transport 31572 8.98E-03 G2 DNA damage checkpoint 9896 5.91E-03
positive regulation of
catabolic process
34097 4.13E-02 response to cytokine 32200 1.46E-02 telomere organization 10498 1.33E-03
proteasomal protein
catabolic process
34336 7.49E-03
misfolded RNA
binding 71824 2.53E-02
protein-DNA complex subunit
organization 10605 4.73E-02
negative regulation of
macromolecule metabolic
process
34399 2.41E-03 nuclear periphery 97659 3.14E-02
nucleic acid-templated
transcription 16567 1.75E-03 protein ubiquitination
35500 1.54E-02 MH2 domain binding 98505 7.94E-03
G-rich strand telomeric DNA
binding 16579 3.00E-02 protein deubiquitination
35639 4.00E-03
purine ribonucleoside
triphosphate binding 43596 1.88E-02 nuclear replication fork 16887 3.00E-02 ATPase activity
35770 4.94E-03
ribonucleoprotein
granule 43601 7.29E-03 nuclear replisome 18130 3.68E-02
heterocycle biosynthetic
process
36211 4.00E-03
protein modification
process 44454 3.31E-08 nuclear chromosome part 19219 1.36E-03
regulation of nucleobase-
containing compound
metabolic process
36464 3.22E-03
cytoplasmic
ribonucleoprotein
granule 44815 2.65E-02 DNA packaging complex 19222 2.12E-03
regulation of metabolic
process
36503 6.76E-03 ERAD pathway 42393 3.90E-02 histone binding 19438 3.73E-02
aromatic compound
biosynthetic process
42787 4.96E-02
protein
ubiquitination
involved in ubiquitin-
dependent protein
catabolic process 43047 1.61E-02
single-stranded telomeric
DNA binding 19941 6.89E-04
modification-dependent
protein catabolic process
43086 1.72E-02
negative regulation of
catalytic activity 45008 3.00E-02 depyrimidination 22624 3.12E-10
proteasome accessory
complex
43141 4.13E-02
ATP-dependent 5'-3'
DNA helicase activity 32993 1.97E-04 protein-DNA complex 30162 6.09E-04 regulation of proteolysis
191
43154 4.35E-02
negative regulation of
cysteine-type
endopeptidase
activity involved in
apoptotic process 34723 3.23E-03
DNA replication-dependent
nucleosome organization 30163 5.91E-03 protein catabolic process
43200 1.20E-02
response to amino
acid 35098 3.86E-02 ESC/E(Z) complex 30261 2.31E-03 chromosome condensation
43412 1.51E-02
macromolecule
modification 43234 1.93E-02 protein complex 31145 3.99E-04
anaphase-promoting
complex-dependent
catabolic process
43517 1.54E-02
positive regulation of
DNA damage response,
signal transduction by
p53 class mediator 43623 4.33E-02
cellular protein complex
assembly 31146 1.88E-02
SCF-dependent proteasomal
ubiquitin-dependent
protein catabolic process
44092 4.45E-02
negative regulation of
molecular function 31323 1.69E-03
regulation of cellular
metabolic process
44183 6.98E-03
protein binding
involved in protein
folding 31331 6.62E-03
positive regulation of
cellular catabolic process
44238 1.44E-04
primary metabolic
process 31396 1.08E-02
regulation of protein
ubiquitination
44248 6.33E-04
cellular catabolic
process 31397 6.43E-03
negative regulation of
protein ubiquitination
44267 4.10E-04
cellular protein
metabolic process 31461 2.89E-02
cullin-RING ubiquitin ligase
complex
44451 2.15E-02 nucleoplasm part 31595 1.55E-05
nuclear proteasome
complex
44611 1.54E-02
nuclear pore inner
ring 31597 4.14E-04
cytosolic proteasome
complex
44752 1.63E-02
response to human
chorionic
gonadotropin 31974 8.03E-04 membrane-enclosed lumen
44766 2.57E-02
multi-organism
transport 31981 2.70E-07 nuclear lumen
192
45861 4.13E-02
negative regulation of
proteolysis 32446 1.82E-03
protein modification by
small protein conjugation
45935 1.74E-02
positive regulation of
nucleobase-
containing compound
metabolic process 33209 1.38E-02
tumor necrosis factor-
mediated signaling pathway
46686 2.04E-02
response to cadmium
ion 33238 2.52E-03
regulation of cellular amine
metabolic process
46931 1.74E-02
pore complex
assembly 33554 4.49E-05 cellular response to stress
48096 2.88E-02
chromatin-mediated
maintenance of
transcription 34641 5.80E-04
cellular nitrogen compound
metabolic process
50773 2.04E-02
regulation of dendrite
development 34645 3.65E-04
cellular macromolecule
biosynthetic process
50821 4.96E-02 protein stabilization 34654 1.78E-02
nucleobase-containing
compound biosynthetic
process
51031 1.10E-02 tRNA transport 35567 4.13E-02
non-canonical Wnt
signaling pathway
51081 1.74E-02
nuclear envelope
disassembly 36402 2.29E-04
proteasome-activating
ATPase activity
51169 1.09E-02 nuclear transport 38061 2.15E-02 NIK/NF-kappaB signaling
51170 1.95E-02 nuclear import 38093 1.74E-02
Fc receptor signaling
pathway
51173 1.10E-03
positive regulation of
nitrogen compound
metabolic process 38095 1.07E-02
Fc-epsilon receptor
signaling pathway
51246 4.99E-05
regulation of protein
metabolic process 42176 1.08E-02
regulation of protein
catabolic process
51248 1.54E-02
negative regulation of
protein metabolic
process 42590 6.95E-04
antigen processing and
presentation of exogenous
peptide antigen via MHC
class I
193
51254 3.63E-02
positive regulation of
RNA metabolic
process 42623 1.12E-02 ATPase activity, coupled
51409 1.63E-02
response to
nitrosative stress 43161 5.66E-04
proteasome-mediated
ubiquitin-dependent
protein catabolic process
51900 4.53E-03
regulation of
mitochondrial
depolarization 43170 2.10E-04
macromolecule metabolic
process
60147 6.20E-04
regulation of
posttranscriptional
gene silencing 43227 2.02E-02
membrane-bounded
organelle
60148 2.88E-02
positive regulation of
posttranscriptional
gene silencing 43231 7.33E-05
intracellular membrane-
bounded organelle
60765 1.63E-02
regulation of
androgen receptor
signaling pathway 43233 8.03E-04 organelle lumen
60964 4.51E-04
regulation of gene
silencing by miRNA 43248 1.33E-03 proteasome assembly
60966 6.20E-04
regulation of gene
silencing by RNA 43632 1.10E-03
modification-dependent
macromolecule catabolic
process
60968 1.01E-02
regulation of gene
silencing 44237 3.88E-02 cellular metabolic process
61077 1.01E-02
chaperone-mediated
protein folding 44249 1.57E-02
cellular biosynthetic
process
61136 2.47E-04
regulation of
proteasomal protein
catabolic process 44257 1.10E-02
cellular protein catabolic
process
61614 4.13E-02
pri-miRNA
transcription from
RNA polymerase II
promoter 44260 1.01E-05
cellular macromolecule
metabolic process
194
70003 2.34E-09
threonine-type
peptidase activity 44265 3.00E-02
cellular macromolecule
catabolic process
70035 2.82E-03
purine NTP-dependent
helicase activity 45732 4.14E-04
positive regulation of
protein catabolic process
70646 1.07E-03
protein modification
by small protein
removal 45862 5.10E-04
positive regulation of
proteolysis
71230 3.22E-02
cellular response to
amino acid stimulus 46483 1.89E-06
heterocycle metabolic
process
71371 4.64E-02
cellular response to
gonadotropin
stimulus 48285 8.94E-07 organelle fission
71372 4.64E-02
cellular response to
follicle-stimulating
hormone stimulus 51438 1.18E-03
regulation of ubiquitin-
protein transferase activity
71431 1.10E-02
tRNA-containing
ribonucleoprotein
complex export from
nucleus 51439 1.06E-04
regulation of ubiquitin-
protein ligase activity
involved in mitotic cell
cycle
71704 5.88E-04
organic substance
metabolic process 51443 6.02E-04
positive regulation of
ubiquitin-protein
transferase activity
75733 2.68E-02
intracellular transport
of virus 51444 2.47E-04
negative regulation of
ubiquitin-protein
transferase activity
80134 2.05E-02
regulation of response
to stress 51603 7.43E-03
proteolysis involved in
cellular protein catabolic
process
80135 2.12E-02
regulation of cellular
response to stress 60071 8.16E-03
Wnt signaling pathway,
planar cell polarity pathway
90263 4.64E-02
positive regulation of
canonical Wnt
signaling pathway 60255 5.66E-04
regulation of
macromolecule metabolic
process
195
90305 4.33E-02
nucleic acid
phosphodiester bond
hydrolysis 70013 8.03E-04
intracellular organelle
lumen
90316 2.30E-02
positive regulation of
intracellular protein
transport 70647 5.37E-03
protein modification by
small protein conjugation
or removal
97067 2.88E-02
cellular response to
thyroid hormone
stimulus 80090 4.99E-04
regulation of primary
metabolic process
97068 7.49E-03 response to thyroxine 90175 1.74E-02
regulation of establishment
of planar polarity
97069 7.49E-03
cellular response to
thyroxine stimulus 90304 7.09E-10
nucleic acid metabolic
process
97367 1.59E-02
carbohydrate
derivative binding 97159 2.40E-03
organic cyclic compound
binding
101031 1.74E-02 chaperone complex 140097 6.91E-10
catalytic activity, acting on
DNA
140098 3.02E-02
catalytic activity,
acting on RNA 2E+06 1.01E-05
organic cyclic compound
metabolic process
2E+06 6.20E-03
regulation of cellular
response to heat 2E+06 4.66E-02
organic cyclic compound
biosynthetic process
2E+06 2.65E-02
positive regulation of
protein localization to
nucleus 2E+06 1.49E-03
heterocyclic compound
binding
2E+06 1.88E-02
nucleoside phosphate
binding 2E+06 2.39E-02
organic substance
biosynthetic process
2E+06 1.93E-05
organonitrogen
compound catabolic
process 2E+06 3.81E-02
positive regulation of
proteasomal protein
catabolic process
2E+06 3.02E-04
organic substance
catabolic process 2E+06 2.36E-02 catalytic complex
2E+06 2.57E-02
multi-organism
localization 2E+06 8.03E-04
regulation of proteolysis
involved in cellular protein
catabolic process
196
2E+06 4.88E-02
positive regulation of
RNA biosynthetic
process 2E+06 6.65E-05
positive regulation of
proteolysis involved in
cellular protein catabolic
process
2E+06 8.19E-03
regulation of protein
targeting to
mitochondrion 2E+06 1.96E-02
regulation of protein
modification by small
protein conjugation or
removal
2E+06 6.16E-03
regulation of protein
targeting 2E+06 7.35E-03
negative regulation of
protein modification by
small protein conjugation
or removal
2E+06 6.76E-03
regulation of
establishment of
protein localization to
mitochondrion 2E+06 2.34E-03
regulation of cellular
protein catabolic process
2E+06 2.04E-02
positive regulation of
establishment of
protein localization to
mitochondrion 2E+06 1.97E-04
positive regulation of
cellular protein catabolic
process
2E+06 1.54E-02
positive regulation of
production of miRNAs
involved in gene
silencing by miRNA 2E+06 1.18E-04
regulation of ubiquitin
protein ligase activity
2E+06 3.46E-02
regulation of cellular
protein localization 2E+06 2.47E-04
negative regulation of
ubiquitin protein ligase
activity
2E+06 7.49E-03
positive regulation of
cellular protein
localization 2E+06 5.55E-05
positive regulation of
ubiquitin protein ligase
activity
2E+06 1.21E-02
positive regulation of
protein targeting to
mitochondrion 2E+06 8.91E-07 peptidase complex
197
2E+06 7.49E-03
response to L-
phenylalanine
derivative 2E+06 9.54E-08 endopeptidase complex
2E+06 7.49E-03
cellular response to L-
phenylalanine
derivative 2E+06 1.36E-03
positive regulation of
protein ubiquitination
involved in ubiquitin-
dependent protein
catabolic process
2E+06 4.13E-02
regulation of protein
localization to Cajal
body
2E+06 4.13E-02
positive regulation of
protein localization to
Cajal body
2E+06 2.89E-04
regulation of cellular
macromolecule
biosynthetic process
2E+06 2.40E-03
regulation of hepatic
stellate cell activation
2E+06 2.40E-03
negative regulation of
hepatic stellate cell
activation
2E+06 1.63E-02
positive regulation of
gene silencing by
miRNA
198
Chapter 3: Identification of markers of copper toxicity and exposure in early Mytilus
californianus larvae

Abstract
Molecular markers of toxin exposure or effects should ultimately be validated with phenotypic
anchoring. Phenotypic anchoring can also distinguish markers of exposure and effect. In this
study, copper-responsive gene expression profiles were linked to larval morphology to identify
sensitive markers of copper exposure and effects. Normal and abnormal larvae from a control (0
µg/L) and two copper treatments (3 and 6 µg/L) were sorted into separate groups, and RNA was
sequenced with whole transcriptome shotgun sequencing (RNA-Seq). Differential expression
analysis of morphology- and copper concentration-specific expression signatures revealed
putative markers of copper exposure and effects. Markers of copper exposure and copper-
induced abnormality were involved in many of the same pathways, yet unique genes were
detected in each gene set. Markers of effect appeared to be more resolving between phenotypes
at the lower copper concentration, while markers of exposure were informative at both copper
concentrations.

Introduction
Heavy metal contamination of freshwater and marine water bodies is a long-recognized problem,
especially in urban regions where industrial byproducts are high (Livingstone, Donkin, & Walker
1992). In California, metal pollution is an especially important problem as there are many highly
populated urban centers where industrial and non-point source pollution release substantial
amounts of heavy metals (Schiff, Bay, & Diehl, 2003; Schiff, James Allen, Zeng, & Bay, 2000;
Schiff, Brown, Diehl, & Greenstein, 2007). Total maximum daily loads (TMDLs) and other
contaminant concentration thresholds are determined by regulatory assessment of the toxicity of
contaminants to common organisms in the affected ecosystem (E50 Committee, 2013; EPA,
1995; 2016). Toxicity assays may be performed with a variety of taxa and life history stages,
depending on the contaminant and the ecosystem in question. The standard assay for metal
toxicity in coastal or marine waters assesses early larval development of marine mollusks, often
Mytilus mussels, and determines the proportion of survival and normal development as the
primary endpoints (E50 Committee, 2013; EPA, 2016).

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Advances in “-omics” technology over the past two decades have introduced powerful tools that
have vastly enhanced the sensitivity and utility of toxicity testing (Calzolai et al., 2007; Hahn,
2011; Kim, Koedrith, & Seo, 2015; Nuwaysir, Bittner, Trent, Barrett, & Afshari, 1999; Schirmer,
Fischer, Madureira, & Pillai, 2010; Waters & Fostel, 2004). Changes in the transcriptome or
proteome in response to a toxicant can reveal sensitive biomarkers that respond to toxin exposure
at relatively low concentrations, before any negative whole-organism outcomes are apparent
(Daston, 2008; Hook, Gallagher, & Batley, 2014). However, these sensitive molecular changes
are not necessarily associated with toxic effects, and could simply be markers of exposure to a
toxin. Markers of exposure and effect are distinguished by phenotypic anchoring, i.e. connecting
sublethal molecular changes to higher level whole organism, population, or ecological outcomes
(Daston, 2008; Hook et al., 2014; Paules, 2003; Tennant, 2002). Frameworks such as adverse
outcome pathways (Ankley et al., 2010; OECD, 2013) attempt to use phenotypic anchoring to
link molecular events to detrimental effects at the whole-organism level, thus identifying
markers of effect (rather than exposure).

In marine bivalve embryo-larval development tests, abnormal development is the best-
recognized effect of metal toxicity at the whole-organism level (EPA, 1995; Johnson, 1988;
Sussarellu, Lebreton, Rouxel, Akcha, & Rivière, 2018). Abnormal development is especially
apparent at 48 hours post fertilization (hpf), when normal larvae reach the D-veliger stage. At
this point, abnormal animals exhibit gross morphological deformities, including velum
protrusions, misshapen shells, and failure to form shells (E50 Committee, 2013; His, Seaman, &
Beiras, 1997). However, few studies have investigated the underlying molecular changes that
could be driving abnormal development in this and similar systems ( Chapter 1, Chapter 2,
Sussarellu et al., 2018). In these studies, gene expression and other molecular responses are
determined for a pool of organisms, which often include both normal and diseased or deformed
phenotypes (e.g. normal and abnormal larvae). To better understand the molecular patterns
associated with copper-induced abnormal development in marine bivalve larvae, we analyzed
whole transcriptomes to examine the distinct gene expression profiles of control and copper-
exposed normal and abnormal Mytilus californianus larvae at 48 hpf.

200

Mechanisms of copper toxicity in marine invertebrates are fairly well studied, and include
disruption of ion regulation, inhibition of enzymes and ATP-driven pumps, and cellular damage
caused by oxidative stress, all of which can lead to disruption of normal cellular function
(reviewed in EPA, 2016). We hypothesized that many of these same pathways would be affected
by low copper concentrations in this experiment, and would also be primarily associated with
abnormal development. Similarly, we anticipated that genes involved in larval development
and/or shell formation would be affected, as abnormal phenotypes are most frequently
characterized by malformed body plans and failure to produce a larval shell.

In concentration or dose response experiments that measure development as an endpoint,
organisms are exposed to a range of concentrations and an effective concentration at which 50%
of the population becomes abnormal (EC50) is determined (E50 Committee, 2013; EPA, 2016).
Inherent to this experimental design, animals sampled at concentrations that yield 50%
abnormality within the population will be comprised of some individuals that exhibit signs of
toxicity and others that do not. Examining these co-occurring normal and abnormal phenotypes
provides an opportunity to understand differences in the underlying molecular pathways driving
these different morphological outcomes. To this end, larvae were exposed to 6 copper
concentrations, and RNA Seq was performed on pools of animals from the control and two low
copper concentrations that contained both normal and abnormal larvae. We sought to identify
and further examine previously identified markers of copper toxicity and exposure by linking
transcriptional profiles with toxic and non-toxic phenotypes. This work could also strengthen the
adverse outcome pathway for copper toxicity in mussel larval development.

Methods
Broodstock collection and embryo copper exposure
Adult Mytilus californianus were collected from an intertidal site at Will Rogers State Beach,
Santa Monica, CA. Animals were refrigerated for approximately 6 hours in preparation for
spawning induced by thermal shock. Mussels were then added to a tank of filtered seawater
maintained at 23° C. Once spawning commenced, individuals were removed, rinsed with 0.2 µm
filtered seawater, and isolated in separate beakers. Gametes were examined for quality, and after
201

eggs transformed into a spherical shape, sperm was added to reach a density of ~5 sperm per
egg. Successful fertilization was identified by the production of a polar body. After 95% of eggs
exhibited successful fertilization, embryos were stocked into treatment containers at a density of
~13 larvae/mL.

Six 1-L containers were prepared, including one control and five copper treatments (3, 6, 9, 12
and 15 µg/l). All containers were filled with one liter of 0.2 µm filtered seawater, collected from
Big Fisherman’s Cove on Santa Catalina Island, CA. A 0.1 mM solution of copper sulfate was
used to spike containers with the appropriate amount of copper. After copper addition, containers
were mixed by gentle shaking. Once embryos were added to containers, they were incubated at
17°C with a 12 h D: 12 h L cycle for 48 hours.

Larval counts and count analysis
At the end of the 48 hour incubation period, the control and treatment containers were filtered
through an 80 µm sieve to concentrate larvae. Larvae were then rinsed from the sieve into 50 mL
Falcon tubes. The volume of each Falcon tube was recorded, and for each tube 3-5 100 µl drops
were added to a Sedgewick rafter, and examined under a compound microscope. The number of
normal and abnormal larvae in each drop were recorded in order to determine the proportion of
survival and abnormal development. Each proportion was further divided by the mean control
proportion 0 µg/l copper to calculate control-normalized survival and normal development.
Normal development data were further analyzed in the R package ‘drc’ (Ritz, Baty, Streibig, &
Gerhard, 2015).A four-parameter log-logistic curve (LL.4 model in the drc package) was fit to
the dataset to calculate 50% normal development effective concentration (EC50) values. The
survival curve was not sigmoidal, as the concentration range used in this experiment did not
capture the entire scope of the survival curve. Survival was analyzed using ANOVA (r packages
aov and anova). Specific differences between concentrations were further detected using a
Tukey’s post-hoc test (R command TukeyHSD).

Sample preservation and sorting
Once all counts had been taken, tubes were centrifuged at 5000 g for 5 minutes; the supernatant
was removed, and the remaining 1 ml of seawater containing larvae from each Falcon tube was
202

transferred to a 2 ml tube. Approximately 500 µl of RNAlater® (Ambion) was mixed thoroughly
into each centrifuge tube. Samples were refrigerated overnight to allow for infiltration of RNA
into larval tissues, and then stored at -80°C, according to the RNAlater® Tissue Collection
protocol.

Preserved larval samples from the control and 3 and 6 µg/l copper treatments were removed from
the freezer and brought to room temperature. Small subsamples were removed from the tube
using a Pasteur pipette, and placed in a glass dish for sorting. Because samples were highly
concentrated, 1x PBS was added to facilitate visual inspection of different larval types and
accurate separation. The dish was placed under a compound microscope, and larvae were
separated into Normal and Abnormal pools using a mouth pipetting system (V. Campbell and
D.Caron, pers.comm., April 2014). Three replicate pools were taken for each condition (0 ppb
abnormal, 0 ppb normal, 3 ppb abnormal, 3 ppb normal, 6 ppb abnormal, and 6 ppb normal),
with about 50 animals in each pool. Photographs were taken of ~25 larvae in each pool using a
digital camera attached to a dissecting scope. The camera was set to manual focus, set at the
maximum optical zoom, and fixed in this position. Similarly, the microscope was set at 40x
magnification. A 1 cm stage micrometer was used to calibrate pixel to micron conversion for
subsequent image analysis. Larval pools were then spun down quickly and excess liquid was
removed. Tubes were then re-frozen at -80° C until RNA extraction.

RNA extraction, library preparation, and sequencing
Samples were homogenized in a tissue lyser with metal ball bearings, and then RNA was
extracted using a modified Trizol protocol (Ambion). MaxTract columns (QIAGEN) were used
to maximize phase separation and supernatant removal after chloroform addition. RNA was
quantified with the Qubit HS RNA Assay Kit (Thermo Fisher), and 40 ng of each sample was
used for library preparation. Prior to library preparation, each sample was combined with 4 µl of
External RNA Controls Consortium (ERCC) RNA spike in mix 1 (Thermo Fisher) at a 1:10,000
dilution. Samples were poly-A selected using the NEB Next Poly(A) mRNA Magnetic Isolation
Module. This step was integrated into the library preparation workflow using the NEB Next
Ultra RNA Library Prep Kit for Illumina, with some modifications. Samples were fragmented
for 12 minutes (instead of 15) prior to cDNA synthesis, and the first strand synthesis reaction
203

was run for 50 minutes at 42° C. PCR enrichment was visualized using a BioRad qPCR
Thermocycler, and the reaction was terminated shortly after entering the exponential
amplification stage. PCR amplification of libraries was run for 18 cycles. Library sizes and
quantity were analyzed on a Bioanalyzer, and quantity was additionally measured with qubit.
Samples were pooled and sequenced over one lane of Illumina HiSeq 4000 with 50bp SR reads.

Downstream data analysis
Raw RNAseq reads were quality trimmed and contaminating adapter sequence was removed
using Trimmomatic v0.33 (Bolger et al. 2014) with default parameter settings. The trimmed
reads were then mapped to the M. californianus mitochondrial genome using BBMap v34
(minid=0.95 ambiguous=all sssr=1.0)(Bushnell 2016) to separate mitochondrial transcripts from
nuclear genes. All reads that did not map to the mitochondrial genome were used for subsequent
analysis. Larval reads were mapped to the de novo assembly described in Chapter 2 with
bbmap.sh (minid=0.95, ambiguous=random, sssr=1.0, nhtag=t, minlength=40). The resulting
bam files were counted and summarized with featureCounts (Liao, Smyth, & Shi, 2014),
allowing for multimapping reads (-M), and allowing for mapped reads overlapping two contigs
to be counted toward those contigs (-O).

Count tables were loaded into R (R Core Team, 2016) and processed in DESeq2 (Love, Huber,
& Anders, 2014). Initial inspection of a PCA plot of normalized transcriptional counts revealed
that there were two outliers, one replicate of normal animals at 0 µg/l copper, and one normal
animal replicate at 3 µg/l copper. These two samples also proved to be outliers in a PCA of only
the ERCC reads, which one would expect to be relatively consistent across samples after
normalization. Therefore, these samples were removed from downstream analysis. For the
remaining 17 samples, reads with counts higher than 40 were removed in the initial filtration.
DESeq2 was used to further process the data, according to the standard workflow, and significant
differentially expressed (DE) genes were detected between group pairs. The entire process was
run twice with different grouping assignments—the first, which was used to identify markers of
exposure, grouped all 0 µg/l, all 3 µg/l, and all 6 µg/l copper samples (as opposed to grouping by
morphology in addition to copper), and compared 0 µg/l with 3 µg/l , and 0 µg/l with 6 µg/l. The
204

second grouping assignment used factors that distinguished samples by both copper
concentration and morphology, and compared normal and abnormal animals at 0 µg/l, 3 µg/l, and
6 µg/l. DE genes identified by each of these approaches were further filtered to retain only those
that demonstrated significant changes in expression padj < 0.1 (according to the DESeq2
protocol), and an absolute fold change greater than 1.2.

Functional analysis
Functional enrichment analysis was conducted using Gene Ontology (GO) (Ashburner et al.,
2000) terms using the Cytoscape (Shannon et al., 2003) plug-in, BiNGO (Maere, Heymans, &
Kuiper, 2005). Overrepresentation was tested using a hypergeometric test with Benjamini &
Hochberg FDR correction (p < 0.05). The GO annotation file was generated using GO
annotations produced by Trinotate, and only annotations for the 27,642 filtered contigs were
included. The most recent core ontology file (go.obo) was used for analysis.
(http://geneontology.org/page/download-ontology, October 2017)

Figures
All figures were generated in R studio (version 3.3.1—RStudio Team 2017). Survival was
plotted with ggplot2 (Wickham 2009); normal development was plotted using the drc function
plot.drm; and venn diagrams were plotted with the package VennDiagram (Chen 2017). PCA
plots were generated in DESeq2, and heatmaps were created using the pheatmap package (Kolde
2015). Transformed counts for heatmaps and PCA plots were calculated with Variance
Stabilizing Transformation, using the DESeq2 function vst. This method is recommended for
normalizing data for visualization according to the DESeq2 protocol. Average counts were taken
for replicates, and averages were divided by control counts so the control count would be 1 for
all samples.

Results
Survival and Normal Development
Mean larval survival was 100% in the control. Survival rates were relatively high in this
experiment, so our concentration range did not capture the full survival response curve (Figure
205

1A). It appears that the LC50 is higher than 15 µg/l copper, as mean survival at the highest
concentration is 55% of the control. Slight hormesis was observed at 3 and 6 µg/l copper,
resulting in higher survival rates at these two concentrations. Survival was significantly lower
than the control at 15 µg/l copper (p < 0.005). Normal development in the control was on
average 69% of the total population (Figure 1B). Normal development exhibited a classic
sigmoidal dose response curve (Figure 1B), and the EC50 was 5.87 µg/l. Larvae from 0, 3, and 6
µg/l copper were sorted and sequenced for transcriptional analysis and biomarker identification.

Transcriptional patterns and morphology
Principal Component Analysis (PCA) of transcriptional profiles revealed that replicate samples
clustered by copper concentration and morphological condition (Figure 2). Three broad clusters
of samples were apparent. The first included normal animals from the control (0 µg/l copper) and
3 µg/l copper treatments. The second consisted of abnormal animals from the 3 µg/l copper
treatment, and normal and abnormal animals exposed to 6 µg/l copper. The third was abnormal
animals in the control (0 µg/l copper). The majority of variation along PC1 was driven by the
distinction between samples 0-Normal, 3-Normal, and 0-Abnormal; and samples 3-Abnormal, 6-
Normal, and 6-Abnormal. Thus position along PC1 cannot be explained directly by copper
concentration or morphology, and appears to be driven by some combination of these factors.
The variance along PC2 is primarily associated with the difference between control normal and
abnormal conditions, and little variation is seen along this axis for samples at higher copper
concentrations. Inspection of photographs for each group of animals confirmed that abnormal
and normal animals did exhibit distinct morphologies at each copper concentration (Figure 3).
Even at 6 µg/l copper, where transcriptional profiles were very similar for normal and abnormal
animals, the two groups were quite different morphologically.

To explore the genes driving the observed differences in morphology (Figure 3) and
transcriptional patterns (Figure 2), differential expression (DE) was assessed between conditions.
Specifically, we identified markers of copper exposure and markers of copper toxicity by
extracting unique and overlapping groups of DE genes (Figure 4; Figure 5A-B). Markers of
copper exposure were defined as genes that were DE between all control animals (0 µg/l) and
animals at both copper concentrations (3 and 6 µg/l), as exposure markers should be evident in
206

all animals exposed to a toxin. Markers of toxicity were defined as genes that were DE between
normal and abnormal animals at 3 µg/l copper, 6 µg/l copper, or at both copper concentrations
(Figure 4). Abnormal development is the detrimental phenotype that was used to anchor markers
of effect/ toxicity. Natural markers of abnormality (as opposed to copper-induced abnormality)
were ruled out by excluding genes DE between normal and abnormal animals at 0 µg/l copper.

Markers of Exposure
564 genes were differentially expressed between all control animals and all copper-exposed
animals at both concentrations (Figure 5; Table 1). 230 additional genes were only DE between
control and 3 µg/l samples, yet 746 genes were uniquely expressed between control and 6 µg/l
samples (Figure 5). Of the common set of 564 DE genes, 469 were upregulated in response to
copper, and 95 were downregulated (Figure 5C-D; Table 1).

Many of the identified markers of exposure were related to cell adhesion, extracellular
proteinaceous matrix, and shell formation (Figure 6; Table 1). We identified several shell
formation markers that have appeared in previous larval investigations, including Temptin,
Perlucin, and Acidic mammalian chitinase (Chapters 1 and 2). Additional markers related to
proteinaceous matrix, adhesion, and shell formation were identified, including Insoluble matrix
shell protein 5, Matrix metalloproteinase-16, Junctional adhesion molecule C, Periostin, Neural-
cadherin, and A disintegrin and metalloproteinase with thrombospondin motifs 13. Other
markers included several well-recognized markers of oxidative stress, including Glutathione-s-
transferase P, mitochondrial Glutathione reductase, and Glutathione peroxidase, as well as
Putative DBH-like monooxygenase protein 2, which has oxidoreductase activity. All of these
markers were upregulated relative to the control in copper conditions. Downregulated markers of
exposure did not exhibit any specific trends in functional category, and included genes such as
Chromobox protein homolog 5, Cytochrome c oxidase subunits 1 and 3, Cytochrome b,
Metalloprotease TIK12, Amine sulfotransferase, and Antistasin.

GO terms enriched in these common biomarkers of exposure were primarily related to the same
processes described above. There were two chitin-related terms: chitin binding and chitin
metabolic process. Several terms were involved in development, including blood vessel
207

morphogenesis, neuron projection extension, chondrocyte differentiation, cartilage development,
and negative regulation of cell development; while there were also terms related to healing and
tissue regeneration. Finally, several terms were related to peptidase/hydrolase activity and
regulation, as well as chemokine and cytokine secretion.

Markers of Effect
To identify markers of effect, we investigated transcriptional markers associated with abnormal
development in low to mid-range copper concentrations (Figure 3). In these treatments, some
organisms exhibited normal development at the end of 48 hours, while others became abnormal,
despite exposure to identical conditions. Markers of effect (or copper-induced abnormal
development) were identified for both 3 µg/l and 6 µg/l as genes that were only DE between
normal animals at 3 µg/l, at 6 µg/l, and the common set between these two concentrations
(Figure 4). First, to account for natural abnormality, we identified 1240 genes as DE between
normal and abnormal animals at 0 µg/l copper (Figure 5B). Any genes that were DE between
normal and abnormal animals at 0 µg/l were not considered for further analysis. After subtracting
the genes that were associated with natural abnormality under control conditions, there were 735
genes that appeared to be markers of copper induced abnormality. The number of DE genes
between copper-exposed normal and abnormal animals was 909 at 3 µg/l copper, and 70 at 6 µg/l
copper. In general, abnormal phenotypes were associated with induction of transcripts relative to
normal phenotypes, with 90% of transcripts more highly expressed in abnormal animals at 3
µg/l, and 76% expressed more highly in abnormal animals at 6 µg/l (Figure 5E-F; Table 2).

Many notable genes were DE between normal and abnormal animals at 3 µg/l copper (Figure 7;
Table 2). Prominent categories that were evident in this group were similar to those that appeared
in the markers of exposure. Genes related to oxidative stress and redox cycling were again
evident, including Probable glutathione S-transferase 8, Putative ferric-chelate reductase 1
homolog, Peroxidasin, Glutathione S-transferase, Peroxidase-like protein, Superoxide
dismutase[Cu-Zn], Microsomal glutathione-S transferase 3, Cytochrome P450 1A1, 3A29, 4V2,
and 2B4, and Ferric chelate reductase 1. Several protein matrix/shell formation genes appeared
again as well, including Matrix metalloproteinase-17, Protein PIF, Peroxidasin, and Carbonic
anhydrase 12. Genes involved in apoptosis which have appeared regularly as markers of high
208

copper concentrations (or whole population abnormal development) in previous experiments
(Chapter 1, Chapter 2) were also more highly expressed in abnormal animals at 3 µg/l. These
included Baculoviral IAP repeat-containing protein 7-A , Ferritin heavy chain, and
Sequestosome-1.

Other markers were involved in development and neuron function, including
Sodium/potassium/calcium exchanger 4, Neuronal acetylcholine receptor subunits alpha-3,
alpha-10, and alpha-6; Pituitary homeobox x, Homeobox protein extradenticle, and Membrane
metallo-endopeptidase-like 1 (Figure 7; Table 2). Finally, several unique genes related to cell
adhesion belonged to this set as well. These genes were Protocadherin-16, A disintegrin and
metalloproteinase with thrombospondin motifs 16, and A disintegrin and metalloproteinase with
thrombospondin motifs 3. The above genes were upregulated, but genes that were
downregulated also included several cell adhesion genes (A disintegrin and metalloproteinase
with thrombospondin motifs 3 and Stereocilin ), as well as calcium and zinc binding genes
(Calmodulin, Aspartyl/asparaginyl beta-hydroxylase, Carbonic anhydrase 12, Zinc finger and
BTB domain-containing protein 44, MORC family CW-type zinc finger protein 2A,
Synaptotagmin-like protein 5, and PHD finger protein 14). Again, no notable trends were
apparent among downregulated genes. Five GO terms were enriched in the markers of effects at
3 µg/l copper: chitin binding, chitin metabolic process, amino sugar metabolic process,
glucosamine-containing compound metabolic process, and extracellular region.

Amplitude-dependent markers of exposure and effect
Comparison of the biomarkers of effect at 3 µg/l with biomarkers of exposure revealed that 59
genes were shared between the two gene sets (Table 3). These markers predominantly consisted
of genes that are DE in copper-exposed larvae, but whose expression was amplified in abnormal
larvae. The expression of 97% of genes was amplified in abnormal larvae, whereas expression
was reduced for only 3% of genes(Figure 8; Table 3). These markers again were related to
oxidative stress and/or oxidoreductase activity (Apolipoprotein D, Putative ferric chelate
reductase 1 homolog, Cytochrome P450 3A29, and DBH-like monooxygenase protein 1
homolog); extracellular/ proteinaceous matrix formation (Putative tyrosinase-like protein tyr-3
209

and Cartilage matrix protein); and cell adhesion (Junctional adhesion molecule B, Periostin,
Protocadherin-9, Lactadherin). For several additional genes related to cell adhesion, two
separate copies of the gene appeared in each set of markers, respectively. These genes included
Integrin beta-5; Cadherin 99C; and Protocadherin Fat 1. Two other notable genes that were
identified as amplitude-dependent markers were Zinc transporter ZIP12, and Serine/threonine-
protein phosphatase 2A, both of which bind divalent metals. There was 1 overlapping gene
between markers of exposure and markers of effect at 6 µg/l, and 4 genes overlapping between
overall markers of exposure and effect. All of these overlapping genes were unannotated.

Discussion
Phenotypic anchoring of transcriptional biomarkers is a common and necessary approach to
ultimately distinguish biomarkers of exposure from those of effect (Daston, 2008; Hook et al.,
2014; Paules, 2003). In this study, we used larval morphology to anchor gene expression
profiles. Copper concentration-response experiments were performed with embryos of Mytilus
californianus to assess markers of copper exposure and effects. Normal development, which
served as the phenotypic anchor, exhibited a sigmoidal response curve, and 100% of larvae were
abnormal by 9 µg/l copper. The normal development EC50 of 5.87 µg/l copper agreed with
previous work on Mytilus larvae (Chapter 1; Chapter 2; Arnold, Cotsifas, Smith, Le Page, &
Gruenthal, 2009; Martin, Osborn, Billig, & Glickstein, 1981) . At 3 µg/l and 6 µg/l copper, both
normal and abnormal animals were present (Figure 1; Figure 3). Animals from these two
concentrations were therefore used to distinguish morphology-dependent biomarkers. The
control concentration (0 µg/l) also contained abnormal animals, which was important as a control
to detect markers of copper-induced abnormality, as opposed to natural abnormality caused by
other factors.

Expression trends in all copper-exposed animals were used to anchor biomarkers of exposure,
while expression in abnormal copper-exposed animals was used to anchor biomarkers of effect
(Figure 4). The number of DE genes between treatments made sense in light of the overall
transcriptional profiles (Figure 2). Many genes were differentially expressed between all control
and all copper-exposed larvae, and DE analysis revealed 564 common markers of exposure at
210

both copper concentrations (Figure 5A, C-D; Table 1). Overall, many genes were also DE
between normal and abnormal animals, and 735 total putative markers of effect were identified
(Figure 5B, E-F; Table 2). However, there were very few genes DE between normal and
abnormal animals at 6 µg/l copper, reflecting the overall expression pattern (Figure 2). The
transcriptional similarity between normal and abnormal animals at 6 µg/l was somewhat
surprising, as these animals did have distinct morphologies (Figure 3). The PCA and lack of DE
between normal and abnormal animals at this copper concentration (Figure 5B) indicate that
transcriptional changes are not as informative of morphological differences at 6 µg/l copper. The
fact that transcriptional profiles are significantly different for normal and abnormal animals at 0
and 3 µg/l copper, but not at 6 µg/l, suggests that as copper concentrations get higher, the
transcriptional signature of toxicity becomes the dominant expression signature, even in
seemingly normally developing animals. Thus markers of effect are the most useful at low
copper concentrations.

Markers of exposure and effect were involved in many similar functions. In general, genes were
related to oxidative stress or redox reactions, cell adhesion, and shell formation/extracellular
proteinaceous matrix. These categories were prominent among larval copper-responsive genes in
previous work as well (Chapter1, Chapter2, Sussarellu 2018).

Several genes related to oxidative stress or oxidoreductase activity were identified as markers of
effect at 3 µg/l (Figure 6; Table 1). Cu/Zn Superoxide Dismutase uses copper ions to oxidize
superoxide molecules (Valentine & Mota de Freitas, 1985) and is a well-known component of
the oxidative stress response (Finkel & Holbrook, 2000) that appeared only in the markers of
effect. Four Cytochrome P450 subunits were identified in this group as well, three of which were
unique to the markers of effect. Cytochrome P450s are iron-bound monooxygenases that have
been implicated in the generation of reactive oxygen species (Lewis, 2002). Another iron-
binding molecule, Ferritin, is important for sequestering and oxidizing excess ferrous ions to
prevent oxidative stress (Orino et al., 2001). We found that Ferritin heavy chain was a marker of
abnormal development at 3 µg/l copper, and other ferritin-related genes were identified as
copper-responsive in Chapter 1. Glutathione-related markers appeared in both gene sets (Figure
6; Figure 7; Table 1; Table 2, but unique Glutathione S-transferases were identified as markers of
211

effect. Glutathione S-transferase P, mitochondrial Glutathione reductase, and Glutathione
peroxidase were all identified as markers of exposure, while Glutathione S-transferase,
Microsomal Glutathione S-transferase, and Glutathione S-transferase 8 were identified as
markers of effect. Glutathione S-transferases are known to play distinct roles in the oxidative
stress response (Veal, Toone, Jones, & Morgan, 2002) and in xenobiotic detoxification in general
(Salinas & Wong, 1999), as are Glutathione reductase and Glutathione peroxidase (Freedman,
Ciriolo, & Peisach, 1989). Thus, activation of these genes could be indicative of additional
detoxification functions necessary in abnormal animals, but not in all copper-exposed animals.
Additional genes involved in oxidative stress or redox cycling were identified in the amplitude-
dependent markers of exposure as well (Table 3; Figure 8), suggesting that the oxidative stress
response is more strongly induced in abnormal animals than normal animals, and that higher
expression levels of these genes can be considered markers of effects at 3 µg/l copper.

Several previously identified indicators of damaged protein turnover and cellular damage
appeared in the markers of effect (Figure 7; Table 2). Sequestosome-1, a zinc-binding gene
involved in protein degradation (Seibenhener et al., 2004), Chapter 1), appeared in the markers
of effect. Sequestosome-1 has been a consistent biomarker of copper exposure across all
experiments, and it is highly induced in response to copper. Baculoviral IAP repeat-containing
protein 7 is likewise a zinc-binding gene, and it is essential to the regulation of apoptosis and cell
proliferation. Other genes coding for Baculoviral IAP repeat-containing proteins were identified
among copper responsive markers in adult and larval mussels (Chapter 1). These and other
markers of cellular damage have been primarily upregulated in previous work as well (Chapter 1,
Chapter 2).

Genes related to larval shell proteinaceous matrix were prominent in both gene sets (Figures 6-8;
Tables 1-3). Chitin is known to be a core component of the molluscan shell proteinaceous matrix
(Furuhashi, Schwarzinger, Miksik, Smrz, & Beran, 2009; Weiner, Traub, & Parker, 1984). The
chitin binding and chitin metabolic process GO terms were enriched in markers of exposure and
low concentration markers of effect. The markers of exposure included Chitinase 3-like protein
2, Acidic mammalian chitinase, Collagen alpha-1(XII) chain, and Lactase-phlorizin hydrolase,
and the markers of effect included Chitotriosidase-1, Collagen alpha-4(VI) chain, Protein PIF,
212

Inactive carboxypeptidase-like protein X2, and Beta-hexosaminidase. Chitin-related genes also
responded to copper at relatively low concentrations in previous work (Chapter 1, Chapter 2), so
this indicates that these could be good early markers of copper effects. Chitin-related genes have
been identified as markers of zinc exposure in Daphnia magna (Poynton et al., 2007), and of
copper exposure in adult mussels (Negri et al., 2013). Other markers of exposure or effects were
also involved in the formation of the proteinaceous matrix that is integral to mollusk shell
structure development. Temptin, a component of the tyrosinase metabolic pathway which is
involved in larval shell formation (Liu et al., 2015); Perlucin (Weiss, Kaufmann, Mann, & Fritz,
2000); and Insoluble shell matrix protein 5 appeared in the markers of exposure (Table 1). They
were not identified as markers of effect, so they are likely not directly involved in the abnormal
development of larvae. Protein PIF (Suzuki et al., 2009), on the other hand, was unique to the
copper effects genes (Table 2; Figure 7). All of these results indicate that shell matrix pathways
are targeted by copper, and copper-induced abnormality may be associated with additional
modulation of shell matrix protein forming genes.

While the cell adhesion GO term was only enriched among the markers of exposure, there were
still many genes related to cell adhesion in both markers of exposure and effect (Table 1; Table
2). Cell adhesion is known to play an essential role in metazoan development, especially in
nervous system development (Hynes & Lander, 1992), and a lack of proper cell adhesion
mechanisms can lead to abnormal developmental patterns or embryo death (Gurdon, 1992). The
prominence of cell adhesion genes among the markers of exposure is somewhat unexpected, as
the literature suggests that disruption of cell adhesion often leads to abnormal development.
However, there were unique cell adhesion genes that were identified as markers of effects, and
some of the cell-adhesion-related markers of exposure (e.g. Periostin, Junctional cell adhesion
molecule B, and Protocadherin-9) were also shared amplitude-dependent markers of exposure or
effect (Table 3). For these genes, higher expression was associated with abnormal development
(Table 3; Figure 8). Therefore, it does appear that certain aspects of cell adhesion are involved in
abnormal development induced at low copper concentrations, and that some cell adhesion genes
can serve as good markers of effect.

213

We did not detect transcriptional changes in metallothionein genes in this experiment, and have
only detected metallothionein as a transcriptional marker of copper exposure sporadically in
previous experiments (Chapter 1). Metallothioneins are well-recognized markers of metal
exposure in marine invertebrates (Khati, Ouali, Mouneyrac, & Banaoui, 2012). However,
expression changes in metallothionein genes in response to copper have been variable in
mussels, with metallothioneins sometimes induced (Negri et al., 2013) and sometimes not
induced (Lemoine, Bigot, Sellos, Cosson, & Laulier, 2000). Therefore, metallothionein
expression does not appear to be a consistent transcriptional marker of copper exposure or
effects. Other metals, such as cadmium, induce and bind to metallothionein (MT) with more
consistency (Lemoine 2000, Vergani et al. 2007, Poynton et al. 2007; Jenny et al. 2006).
Metallothionein induction is in general less consistent for essential metals like copper and zinc
relative to non-essential metals like cadmium. Additionally, metallothionein induction may not
be as important in the toxic response to copper, as copper is redox active, so the induction of
reactive oxygen species is likely a more predominant mode of action in its toxicity.

One notable trend that emerged from the expression patterns of exposure and effects biomarkers
is that many upregulated genes were sensitive markers in this experiment (Figure 5C-F), whereas
upregulated genes were not always detected as sensitive biomarkers in previous studies (Chapter
1). More specifically, some of the sensitive upregulated markers in this experiment were only
expressed at higher concentrations in previous experiments (Chapter 1, Chapter 2). This shift in
sensitivity can likely be attributed to differences in the nature of bulk pooled sequencing vs
sequencing of specific morphological groups. At 3 µg/l copper, there were clear transcriptional
differences associated with distinct morphologies. However, if those samples had been
sequenced together, the nuances of morphology-specific expression would have been impossible
to detect. On the other hand, at 6 µg/l copper, the transcriptional profiles of normal and abnormal
animals coalesced, and were both indicative of toxic effects. Previous work at the cellular level
has found similar patterns of phenotype-associated alterations in gene expression when cells or
tissues are sequenced individually rather than in a pool (reviewed in Gawad et al., 2016), but this
is a novel experimental approach and finding in the field of ecotoxicology. In this scenario,
pooled sequencing of normal and abnormal animals would be adequate to capture the
transcriptional effects of copper. Thus, it seems that pooled sequencing would be effective to
214

detect biomarkers at higher concentrations, but that morphology-specific gene expression is more
sensitive and informative at lower copper concentrations.

In conclusion, we have identified robust transcriptional markers of copper exposure and effect in
Mytilus californianus larvae. Markers of effect were the most informative at lower copper
concentrations, as the expression of these genes in both normal and abnormal animals is similar
at higher copper concentrations. We have confirmed that many transcriptional markers of
exposure or toxicity that were previously identified in adult animals are likewise markers in
larval mussels. We have also identified some biomarkers of copper exposure and effects that
have not been previously identified in mussels. Markers of exposure exhibited similar functional
categories to markers of effect, which suggests that abnormal animals exercise similar yet
amplified responses to copper, rather than modulating different responses and pathways. Markers
of copper exposure are characterized by genes involved in oxidoreductase activity, oxidative
stress, cell adhesion, and extracellular proteinaceous matrix. Additional genes involved in these
processes are associated with copper-induced abnormal development at low concentrations (3
µg/l), as well as notable genes involved in protein degradation/apoptosis. The exact mechanisms
of copper-induced abnormal development remain unclear, but these results highlight pathways
that should be further explored at the enzymatic and cellular level.

References

Ankley, G. T., Bennett, R. S., Erickson, R. J., Hoff, D. J., Hornung, M. W., Johnson, R. D., et al.
(2010). Adverse outcome pathways: A conceptual framework to support ecotoxicology
research and risk assessment. Environmental Toxicology and Chemistry, 29(3), 730–741.
http://doi.org/10.1002/etc.34
Arnold, W. R., Cotsifas, J. S., Smith, D. S., Le Page, S., & Gruenthal, K. M. (2009). A
comparison of the copper sensitivity of two economically important saltwater mussel species
and a review of previously reported copper toxicity data for mussels: Important implications
for determining future ambient copper saltwater criteria in the USA. Environmental
Toxicology, 24(6), 618–628. http://doi.org/10.1002/tox.20452
215

Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., et al. (2000).
Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nature
Genetics, 25(1), 25–29. http://doi.org/10.1038/75556
Bushnell, B. (2016). BBMap short read aligner. URL http://sourceforge.net/projects/bbmap
Calzolai, L., Ansorge, W., Calabrese, E., Denslow, N., Part, P., & Lettieri, T. (2007).
Transcriptomics and proteomics. Applications to ecotoxicology. Comparative Biochemistry
and Physiology Part D: Genomics and Proteomics, 2(3), 245–249.
http://doi.org/10.1016/j.cbd.2007.04.007
Chen, H. (2017). VennDiagram: Generate High-Resolution Venn and Euler Plots. R package
version 1.6.18. https://CRAN.R-project.org/package=VennDiagram
Daston, G. P. (2008). Gene expression, dose-response, and phenotypic anchoring: applications
for toxicogenomics in risk assessment. Toxicological Sciences : an Official Journal of the
Society of Toxicology, 105(2), 233–234. http://doi.org/10.1093/toxsci/kfn138
E50 Committee. (2013). Standard Guide for Conducting Static Acute Toxicity Tests Starting with
Embryos of Four Species of Saltwater Bivalve Molluscs (pp. 1–22). West Conshohocken,
PA: ASTM International.
EPA, U. S. (1995). Short-term Methods for Estimating the Chronic Toxicity of Effluents and
Receiving Waters to West Coast Marine and Estuarine Organisms, 1–666.
EPA, U. S. (2016). Draft Aquatic Life Ambient Estuarine/Marine Water Quality Criteria for
Copper - 2016, 1–257.
Finkel, T., & Holbrook, N. J. (2000). Oxidants, oxidative stress and the biology of ageing.
Nature, 408(6809), 239–247. http://doi.org/10.1038/35041687
Freedman, J. H., Ciriolo, M. R., & Peisach, J. (1989). The role of glutathione in copper
metabolism and toxicity. The Journal of Biological Chemistry, 264(10), 5598–5605.
Furuhashi, T., Schwarzinger, C., Miksik, I., Smrz, M., & Beran, A. (2009). Molluscan shell
evolution with review of shell calcification hypothesis. Comparative Biochemistry and
Physiology. Part B, Biochemistry & Molecular Biology, 154(3), 351–371.
http://doi.org/10.1016/j.cbpb.2009.07.011
Gawad, C., Koh, W., & Quake, S.R. (2016). Single-cell genome sequencing: current state of the
science. Nature Reviews Genetics, 17, 175-188.
Gurdon, J. B. (1992). The generation of diversity and pattern in animal development. Cell, 68(2),
185–199. http://doi.org/10.1016/0092-8674(92)90465-O
Hahn, M. E. (2011). Mechanistic research in aquatic toxicology: Perspectives and future
directions. Aquatic Toxicology, 105(3-4), 67–71.
http://doi.org/10.1016/j.aquatox.2011.06.001
His, E., Seaman, M. N. L., & Beiras, R. (1997). A simplification the bivalve embryogenesis and
larval development bioassay method for water quality assessment. Water Research, 31(2),
351–355. http://doi.org/10.1016/S0043-1354(96)00244-8
Hook, S. E., Gallagher, E. P., & Batley, G. E. (2014). The role of biomarkers in the assessment
of aquatic ecosystem health. Integrated Environmental Assessment and Management, 10(3),
327–341. http://doi.org/10.1002/ieam.1530
Hynes, R. O., & Lander, A. D. (1992). Contact and adhesive specificities in the associations,
migrations, and targeting of cells and axons. Cell, 68(2), 303–322.
Johnson, D. (1988). Development of Mytilus edulis embryos: a bio-assay for polluted waters.
Marine Ecology Progress Series, 46, 135–138. http://doi.org/10.3354/meps046135
216

Khati, W., Ouali, K., Mouneyrac, C., & Banaoui, A. (2012). Metallothioneins in Aquatic
Invertebrates:Their Role in Metal Detoxification and their Use in Biomonitoring. Energy
Procedia, 18, 784–794. http://doi.org/10.1016/j.egypro.2012.05.094
Kim, H. J., Koedrith, P., & Seo, Y. R. (2015). Ecotoxicogenomic approaches for understanding
molecular mechanisms of environmental chemical toxicity using aquatic invertebrate,
Daphnia model organism. International Journal of Molecular Sciences, 16(6), 12261–12287.
http://doi.org/10.3390/ijms160612261
Kolde, R. (2015). pheatmap: Pretty Heatmaps. R package version 1.0.8. https://CRAN.R-
project.org/package=pheatmap
Lemoine, S., Bigot, Y., Sellos, D., Cosson, R., & Laulier, M. (2000). Metallothionein Isoforms
in Mytilus edulis (Mollusca, Bivalvia): Complementary DNA Characterization and
Quantification of Expression in Different Organs after Exposure to Cadmium, Zinc, and
Copper. Marine Biotechnology (New York, N.Y.), 2(2), 195–203.
Lewis, D. F. V. (2002). Oxidative stress: the role of cytochromes P450 in oxygen activation.
Journal of Chemical Technology & Biotechnology, 77(10), 1095–1100.
http://doi.org/10.1002/jctb.648
Liao, Y., Smyth, G. K., & Shi, W. (2014). featureCounts: an efficient general purpose program
for assigning sequence reads to genomic features. Bioinformatics, 30(7), 923–930.
http://doi.org/10.1093/bioinformatics/btt656
Liu, J., Yang, D., Liu, S., Li, S., Xu, G., Zheng, G., et al. (2015). Microarray: a global analysis of
biomineralization-related gene expression profiles during larval development in the pearl
oyster, Pinctada fucata. BMC Genomics, 16(1), 980–15. http://doi.org/10.1186/s12864-015-
1524-2
LIVINGSTONE, D. R., DONKIN, P., & WALKER, C. H. (1992). Pollutants in Marine
Ecosystems: an Overview. In Persistent Pollutants in Marine Ecosystems (pp. 235–263).
Elsevier. http://doi.org/10.1016/B978-0-08-041874-2.50017-3
Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and
dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550.
http://doi.org/10.1186/s13059-014-0550-8
Maere, S., Heymans, K., & Kuiper, M. (2005). BiNGO: a Cytoscape plugin to assess
overrepresentation of Gene Ontology categories in Biological Networks. Bioinformatics,
21(16), 3448–3449. http://doi.org/10.1093/bioinformatics/bti551
Martin, M., Osborn, K. E., Billig, P., & Glickstein, N. (1981). Toxicities of ten metals to
Crassostrea gigas and Mytilus edulis embryos and Cancer magister larvae. Marine Pollution
Bulletin, 12(9), 305–308. http://doi.org/10.1016/0025-326X(81)90081-3
Negri, A., Oliveri, C., Sforzini, S., Mignione, F., Viarengo, A., & Banni, M. (2013).
Transcriptional Response of the Mussel Mytilus galloprovincialis (Lam.) following
Exposure to Heat Stress and Copper. PLoS ONE, 8(6), e66802–11.
http://doi.org/10.1371/journal.pone.0066802
Nuwaysir, E. F., Bittner, M., Trent, J., Barrett, J. C., & Afshari, C. A. (1999). Microarrays and
toxicology: the advent of toxicogenomics. Molecular Carcinogenesis, 24(3), 153–159.
OECD. (2013). Guidance Document on Developing and Assessing Adverse Outcome Pathways,
1–45.
Orino, K., Lehman, L., Tsuji, Y., Ayaki, H., Torti, S. V., & Torti, F. M. (2001). Ferritin and the
response to oxidative stress. The Biochemical Journal, 357(Pt 1), 241–247.
217

Paules, R. (2003). Phenotypic Anchoring: Linking Cause and Effect. Environmental Health
Perspectives, 111, 1–2.
Poynton, H. C., Varshavsky, J. R., Chang, B., Cavigiolio, G., Chan, S., Holman, P. S., et al.
(2007). Daphnia magna ecotoxicogenomics provides mechanistic insights into metal
toxicity. Environmental Science & Technology, 41(3), 1044–1050.
R Core Team (2016). R: A language and environment for statistical computing. R Foundation for
Statistical Computing, Vienna, Austria. URL http://www.R-project.org/
Ritz, C., Baty, F., Streibig, J. C., & Gerhard, D. (2015). Dose-Response Analysis Using R. PLoS
ONE, 10(12), e0146021. http://doi.org/10.1371/journal.pone.0146021
RStudio Team (2015). RStduio: Inttegrated Development for R. RStudio, Inc., Boston, MA.
URL http://www.rstudio.com
Salinas, A. E., & Wong, M. G. (1999). Glutathione S-transferases--a review. Current Medicinal
Chemistry, 6(4), 279–309.
Schiff, K. C., Bay, S. M., & Diehl, D. (2003). Stormwater toxicity in Chollas Creek and San
Diego Bay, California. Environmental Monitoring and Assessment, 81, 119–132.
Schiff, K. C., James Allen, M., Zeng, E. Y., & Bay, S. M. (2000). Southern California. Marine
Pollution Bulletin, 41(1-6), 76–93. http://doi.org/10.1016/S0025-326X(00)00103-X
Schiff, K., Brown, J., Diehl, D., & Greenstein, D. (2007). Extent and magnitude of copper
contamination in marinas of the San Diego region, California, USA. Marine Pollution
Bulletin, 54(3), 322–328. http://doi.org/10.1016/j.marpolbul.2006.10.013
Schirmer, K., Fischer, B. B., Madureira, D. J., & Pillai, S. (2010). Transcriptomics in
ecotoxicology. Analytical and Bioanalytical Chemistry, 397(3), 917–923.
http://doi.org/10.1007/s00216-010-3662-3
Seibenhener, M. L., Babu, J. R., Geetha, T., Wong, H. C., Krishna, N. R., & Wooten, M. W.
(2004). Sequestosome 1/p62 is a polyubiquitin chain binding protein involved in ubiquitin
proteasome degradation. Molecular and Cellular Biology, 24(18), 8055–8068.
http://doi.org/10.1128/MCB.24.18.8055-8068.2004
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., et al. (2003).
Cytoscape: a software environment for integrated models of biomolecular interaction
networks. Genome Research, 13(11), 2498–2504. http://doi.org/10.1101/gr.1239303
Sussarellu, R., Lebreton, M., Rouxel, J., Akcha, F., & Rivière, G. (2018). Copper induces
expression and methylation changes of early development genes in Crassostrea gigas
embryos. Aquatic Toxicology (Amsterdam, Netherlands), 196, 70–78.
http://doi.org/10.1016/j.aquatox.2018.01.001
Suzuki, M., Saruwatari, K., Kogure, T., Yamamoto, Y., Nishimura, T., Kato, T., & Nagasawa, H.
(2009). An acidic matrix protein, Pif, is a key macromolecule for nacre formation. Science,
325(5946), 1388–1390. http://doi.org/10.1126/science.1173793
Tennant, R. W. (2002). The National Center for Toxicogenomics: using new technologies to
inform mechanistic toxicology. Environmental Health Perspectives, 110(1), A8–10.
Valentine, J. S., & Mota de Freitas, D. (1985). Copper-zinc superoxide dismutase: A unique
biological “ligand” for bioinorganic studies. Journal of Chemical Education, 62(11), 990.
http://doi.org/10.1021/ed062p990
Veal, E. A., Toone, W. M., Jones, N., & Morgan, B. A. (2002). Distinct roles for glutathione S-
transferases in the oxidative stress response in Schizosaccharomyces pombe. The Journal of
Biological Chemistry, 277(38), 35523–35531. http://doi.org/10.1074/jbc.M111548200
218

Waters, M. D., & Fostel, J. M. (2004). Toxicogenomics and systems toxicology: aims and
prospects. Nature Reviews. Genetics, 5(12), 936–948. http://doi.org/10.1038/nrg1493
Weiner, S., Traub, W., & Parker, S. B. (1984). Macromolecules in Mollusc Shells and Their
Functions in Biomineralization [and Discussion]. Philosophical Transactions of the Royal
Society B: Biological Sciences, 304(1121), 425–434. http://doi.org/10.1098/rstb.1984.0036
Weiss, I. M., Kaufmann, S., Mann, K., & Fritz, M. (2000). Purification and characterization of
perlucin and perlustrin, two new proteins from the shell of the mollusc Haliotis laevigata.
Biochemical and Biophysical Research Communications, 267(1), 17–21.
http://doi.org/10.1006/bbrc.1999.1907
Wickham, H. (2009) ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York,

219
0 5 10 15
0.0
0.2
0.4
0.6
0.8
1.0
1.2
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●
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●
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●
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0.0
0.3
0.6
0.9
1.2
0 5 10 15
*
*
*
* *
Figure 1: Proportion of control-normalized survival (A) and normal development (B) plotted against copper concentration. Mean
survival with standard error (A) and mean normal development with standard error and modeled 4-parameter log-logistic curves (B)
are plotted. Blue points and lines represent control-normalized survival (A) and normal development (B), while the black dashed line
represents non-normalized normal development. Asterisks indicate concentrations that exhibited significantly different proportions
from the control (p < 0.005). The normal development EC50 was 5.87 ug/L.
Copper Concentration (ug/L)
Proportion Survival
Proportion Normal Development
A
B
220
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−10
0
10
−20 −10 0 10 20
PC1: 62% variance
PC2: 16% variance
group
●
●
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●
●
0 Abnormal
0 Normal
3 Abnormal
3 Normal
6 Abnormal
6 Normal
Figure 2—A PCA plot was created of filtered larval transcriptomes (total
gene count > 40 across all samples). Point colors are unique to copper
concentrations and morphologies. Counts were normalized in DESeq2, and
transformed with variance stabilizing transformation (vst) prior to plotting.
221
0 ug/L 3 ug/L
6 ug/L
Normal Abnormal
Figure 3--Images of normal and abnormal larvae at each copper concentration.
Normal animals are morphologically very similar for all copper concentrations,
while there was more variation observed in abnormal larval profiles.
222
Markers of Exposure
A
Differentially Expressed Genes
Control Normal and Abnormal
Copper-Exposed Normal and Abnormal
Figure 4—Markers of effect and markers of exposure were
detected by isolating gene sets that were differentially expressed
between animals exposed to different copper concentrations and
that exhibited different morphologies. Markers of exposure were
considered genes that were differentially expressed between all
animals (normal and abnormal) at the control copper concentration
and all animals at each copper concentration (A). Markers of effect
were considered genes that were differentially expressed between
normal and abnormal animals in copper samples, but not in control
samples (B-C).
Copper-Exposed
Normal
Control Normal
Differentially Expressed Genes
B C
Markers of Effect/ Toxicity
Control Abnormal
Copper-Exposed
Abnormal
B
Markers of Effect/Toxicity
C
223
6 ug/L Normal
3 ug/L Abnormal
6 ug/L Abnormal
3 ug/L Normal
0 ug/L Normal
0 ug/L Abnormal
0.8
0.9
1
1.1
1.2
1.3
0 ug/L Normal
0 ug/L Abnormal
3 ug/L Normal
3 ug/L Abnormal
6 ug/L Normal
6 ug/L Abnormal
0.8
0.9
1
1.1
1.2
1.3
0 ug/L Normal
0 ug/L Abnormal
3 ug/L Normal
6 ug/L Normal
3 ug/L Abnormal
6 ug/L Abnormal
0.8
0.9
1
1.1
1.2
1.3
A B
C D
Figure 5—Venn diagrams illustrate gene sets that were chosen as
markers of exposure (A) and markers of effect (B). Heatmaps depict
expression patterns of shared markers of exposure (C-D) and all
markers of effect (E-F). Counts were transformed using Variance
Stabilizing Transformation in DESeq2. Each column represents the
control-normalized mean count for all replicates in a given condition.
Yellow coloration represents higher expression values, and blue
coloration represents lower expression values.
0 ug/L Normal
0 ug/L Abnormal
3 ug/L Normal
3 ug/L Abnormal
6 ug/L Normal
6 ug/L Abnormal
0.8
0.9
1
1.1
1.2
1.3
E F
0 vs 6
0 vs 3
0 Normal vs 0 Abnormal
6 Normal vs 6 Abnormal
3 Normal vs 3 Abnormal
224
Normal Abnormal
Glutathione S-transferase P Glutathione peroxidase
Glutathione reductase;
mitochondrial
Acidic mammalian chitinase Chitinase-3-like protein 2 Temptin
Perlucin Perlucin-like protein Cartilage matrix protein Temptin Junctional adhesion molecule C Neural-cadherin
Chromobox protein homolog 5
Cytochrome c oxidase
subunit 3
Cytochrome c oxidase
subunit 1
Cytochrome b
Metalloprotease TIKI1 Antistasin
Figure 6—Example profiles of markers of exposure. Genes are related to oxidative stress, shell formation, cell
adhesion, and other processes. Red lines depict expression of abnormal animals, at black lines depict expression
of normal animals.
Expression Level (variance stabilized transformation)
Copper Concentration (μg/L)
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0 3 6
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0 3 6
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0 3 6
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0 3 6
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0 3 6
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0 3 6
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0 3 6
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225
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0 3 6
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0 3 6
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0 3 6
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0 3 6
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0 3 6
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0 3 6
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0 3 6
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0 3 6
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0 3 6
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0 3 6
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6.7 7.1
0 3 6
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Sequestosome-1 Superoxide dismutase [Cu-Zn] Cytochrome P450 4V2 Cytochrome P450 1A1
Baculoviral IAP repeat-
containing protein 7-A
Ferritin heavy chain
Probable glutathione S-
transferase 8
Microsomal glutathione S-
transferase 3
Protein PIF Glutathione S-transferase Pituitary Homeobox x
Neuronal acetylcholine
receptor subunit alpha-6
A disintegrin and
metalloproteinase with
throm bospondin m otifs 3
Stereocilin Calmodulin
Aspartyl/asparaginyl beta-
hydroxylase
Carbonic anhydrase 12
Zinc finger and BTB
domain- containing
protein 44
Figure 7—Example profiles of markers of effects at 3 ug/L copper. Genes are related to apoptosis, oxidative
stress, shell formation, development, cell adhesion, and divalent cation binding. Red lines depict expression of
abnormal animals, at black lines depict expression of normal animals.
Copper Concentration (μg/L)
Expression Level (variance stabilized transformation)
Normal Abnormal
226
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8.5 9.5
0 3 6
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● ●
Apolipoprotein D Apolipoprotein D Zinc transporter ZIP-12
Putative ferric-chelate
reductase 1 homolog
DBH-like monooxygenase
protein 1 homolog
Junctional adhesion molecule B Periostin Protocadherin-9 Cartilage matrix protein Lactadherin
Figure 8—Example profiles of a subset of the genes that were identified as both markers of exposure and effect.
Genes are related to apoptosis, oxidative stress, shell formation, development, cell adhesion, and divalent cation
binding. Red lines depict expression of abnormal animals, at black lines depict expression of normal animals.
Expression Level (variance stabilized transformation)
Copper Concentration (μg/L)
Normal Abnormal
227
Blastx against Uniprot Blastp against Uniprot log2FoldChange
Adjusted p-
value
log2FoldChange
Adjusted p-
value
. . -4.082559191 1.13E-18 -3.33727683 6.38E-13
. . -3.79688634 8.45E-10 -3.774438156 3.66E-10
. . -3.646322139 7.20E-12 -3.518646911 1.28E-11
Transforming growth factor-
beta-induced protein ig-h3;
Transforming growth factor-
beta-induced protein ig-h3; -3.645509125 1.94E-11 -3.178849002 5.32E-09
Nose resistant to fluoxetine
protein 6;
Nose resistant to fluoxetine
protein 6; -3.542920808 3.46E-07 -2.597967883 0.000389073
. . -3.536875286 3.38E-08 -2.371894132 0.000610217
Vitelline membrane outer
layer protein 1;
Vitelline membrane outer
layer protein 1; -3.506524029 2.15E-06 -3.052052341 4.24E-05
. . -3.455934362 2.90E-07 -2.919317178 1.67E-05
. . -3.418492991 9.97E-07 -3.073755842 1.00E-05
Transforming growth factor-
beta-induced protein ig-h3;
Transforming growth factor-
beta-induced protein ig-h3; -3.391788635 2.19E-09 -2.981039247 1.03E-07
.
Insoluble matrix shell protein
5
{ECO:0000303|PubMed:2122
1694}; -3.336359479 3.20E-10 -2.848012861 8.08E-08
Ganglioside GM2 activator; Ganglioside GM2 activator; -3.325558047 7.46E-08 -3.249247966 6.61E-08
Lactase-phlorizin hydrolase; . -3.323587537 3.90E-12 -2.75153848 8.41E-09
Differential Expression
between 0 and 3 ug/L
copper
Differential Expression between
0 and 6 ug/L copper
Transcript Annotation
Table 1--Markers of Exposure in both 3 and 6 ug/L copper. Genes that were DE between all
control and all copper exposed animals at each copper concentration were identified.
228
. . -3.310414144 1.45E-05 -2.871001317 0.000194483
Ependymin-related protein 2; Ependymin-related protein 2; -3.275876997 2.89E-22 -3.080648879 9.62E-21
.
Mammalian ependymin-
related protein 1; -3.196255804 3.50E-25 -2.799712746 2.27E-20
. . -3.185088691 2.31E-05 -3.161935323 1.36E-05
Junctional adhesion molecule
B;
Junctional adhesion molecule
B; -3.181701707 3.20E-10 -3.651768095 2.42E-14
Nose resistant to fluoxetine
protein 6;
Nose resistant to fluoxetine
protein 6; -3.175863759 1.92E-05 -2.376737551 0.002448989
.
C-type lectin domain family
17, member A; -3.156228497 2.29E-05 -2.167432422 0.007296753
Junctional adhesion molecule
C;
Junctional adhesion molecule
C; -3.148229234 2.76E-08 -3.602702088 1.41E-11
. . -3.138949483 0.0001033 -2.871962727 0.000339642
. . -3.113081518 2.46E-08 -2.999923524 3.60E-08
. . -3.102253677 7.99E-12 -2.901643639 3.37E-11
. . -3.055613638 7.36E-09 -2.561321898 1.61E-06
. . -3.04388041 2.52E-07 -2.57844046 1.23E-05
Beta-1,3-glucan-binding
protein
{ECO:0000312|EMBL:AAM21
213.1};
Beta-1,3-glucan-binding
protein
{ECO:0000312|EMBL:AAM21
213.1}; -3.032275898 3.75E-06 -2.473029178 0.000202033
Periostin; Periostin; -3.011454163 8.07E-14 -2.646574853 1.80E-11
. . -2.982325904 1.41E-10 -2.935266513 3.99E-11
Lactase-phlorizin hydrolase; . -2.958553764 2.12E-10 -2.439694528 1.44E-07
. . -2.939582383 0.0006075 -2.526096659 0.003574229
. . -2.915803636 0.0001833 -2.612287069 0.000710671
Alpha-amylase; Alpha-amylase 1; -2.906267704 0.0005195 -2.193022426 0.012624014
Collagen alpha-6(VI) chain; Collagen alpha-6(VI) chain; -2.899774347 1.59E-10 -2.539936111 1.14E-08
. . -2.896671477 7.32E-08 -2.528172647 2.28E-06
. . -2.88688806 0.0003715 -2.5944729 0.001193154
229
. . -2.87837326 4.30E-09 -2.487722983 3.23E-07
Multiple inositol
polyphosphate phosphatase 1
{ECO:0000250|UniProtKB:Q9
UNW1};
Multiple inositol
polyphosphate phosphatase 1
{ECO:0000250|UniProtKB:Q9
UNW1}; -2.876536495 0.0009792 -2.860956107 0.000625796
Cyclic nucleotide-binding
domain-containing protein 2;
Cyclic nucleotide-binding
domain-containing protein 2; -2.875479723 0.0005093 -2.711295907 0.000762063
Temptin; . -2.832881764 5.75E-07 -2.556334204 4.70E-06
. . -2.828891577 1.83E-05 -2.424173111 0.000221798
. . -2.823795978 4.01E-12 -2.080192121 4.32E-07
. Suprabasin; -2.81524227 0.001239 -1.983949676 0.035134838
Inactive pancreatic lipase-
related protein 1;
Inactive pancreatic lipase-
related protein 1; -2.812697872 0.0001308 -2.963036529 1.95E-05
. . -2.810341105 2.46E-05 -2.708421381 2.49E-05
. . -2.794315804 2.52E-07 -2.328466103 1.56E-05
Temptin; Temptin; -2.788343808 6.98E-07 -2.305225265 4.17E-05
Cytochrome P450 4Z1; Cytochrome P450 4Z1; -2.775347537 0.0005869 -2.432305616 0.002447584
. . -2.77517849 4.91E-07 -2.289892615 3.28E-05
Perlucin;
Perlucin-like protein
{ECO:0000303|PubMed:2164
3827}; -2.761131537 3.47E-07 -2.691647364 2.25E-07
. . -2.761059993 1.62E-14 -2.013126774 2.91E-08
. . -2.757641232 0.0009886 -2.225230814 0.009329275
Zinc transporter ZIP12; Zinc transporter ZIP12; -2.756470936 0.0023216 -2.386795033 0.008294497
. . -2.751209435 3.51E-05 -2.416064356 0.000239435
. . -2.750719784 4.91E-15 -2.236172837 6.68E-11
. . -2.748119113 0.0017974 -2.902543101 0.00042166
. . -2.739191427 0.0009122 -3.929327954 4.27E-08
Contactin-5; Contactin-5; -2.729385794 0.0005375 -2.773552485 0.000202109
. . -2.727602989 0.0005301 -3.849652614 2.33E-08
230
Transforming growth factor-
beta-induced protein ig-h3;
Transforming growth factor-
beta-induced protein ig-h3; -2.723236652 0.0007269 -2.478741787 0.001721635
. . -2.722703477 0.0011721 -2.548140177 0.001743758
. . -2.720333575 0.0005753 -3.859421474 2.33E-08
Ovochymase-1; Ovochymase-1; -2.719880348 5.94E-06 -2.308215875 0.000113324
. . -2.719201258 3.93E-05 -2.536612993 8.77E-05
Periostin; Periostin; -2.703213448 3.45E-09 -2.325787571 2.25E-07
Transmembrane protease
serine 3;
Transmembrane protease
serine 3; -2.702360186 6.99E-15 -2.507091685 8.83E-14
Transforming growth factor-
beta-induced protein ig-h3;
Transforming growth factor-
beta-induced protein ig-h3; -2.698335982 4.06E-10 -2.836126949 5.90E-12
Calcitonin receptor; Calcitonin receptor; -2.684152936 0.0019006 -2.090296375 0.019314784
. . -2.674916821 9.50E-05 -2.965511085 3.66E-06
. Suprabasin; -2.673313363 0.0003372 -2.75913054 7.93E-05
Complement C1q-like protein
4;
Complement C1q-like protein
4; -2.667908567 5.64E-08 -2.756218544 4.16E-09
Deleted in malignant brain
tumors 1 protein;
Deleted in malignant brain
tumors 1 protein; -2.64109722 1.64E-29 -2.089637746 1.57E-19
Sodium/hydrogen exchanger
9B2;
Sodium/hydrogen exchanger
9B2; -2.633419969 0.0027265 -2.682147351 0.001147097
. . -2.628744058 5.34E-05 -2.905981665 1.79E-06
. . -2.622457435 0.003654 -2.847697961 0.000595281
. . -2.610358053 0.0007807 -2.562001198 0.000542151
. . -2.608232086 0.000519 -2.592836233 0.000277849
Low-density lipoprotein
receptor-related protein 5;
Low-density lipoprotein
receptor-related protein 5; -2.60450206 7.38E-08 -2.004803477 4.64E-05
Ovochymase-1; Ovochymase-1; -2.585935597 0.0024621 -2.289337091 0.006330076
Zwei Ig domain protein zig-4
{ECO:0000303|PubMed:1180
9975};
Zwei Ig domain protein zig-4
{ECO:0000303|PubMed:1180
9975}; -2.575715874 0.0035315 -2.189398269 0.012624014
. . -2.5699933 0.0042805 -2.7662285 0.000822572
231
Aggrecan core protein; Aggrecan core protein; -2.552475061 0.0013987 -2.11530431 0.008620471
. . -2.55208778 1.14E-06 -2.261957358 1.16E-05
. . -2.550210256 0.0045211 -2.406123816 0.00539227
Peptidyl-prolyl cis-trans
isomerase B; Ganglioside GM2 activator; -2.545857567 0.0001308 -2.40982174 0.000174561
. . -2.538421172 0.0052887 -2.202562356 0.014382605
Periostin; Periostin; -2.524216485 6.62E-06 -2.43383177 6.25E-06
Kynurenine--oxoglutarate
transaminase 3;
Kynurenine--oxoglutarate
transaminase 3; -2.515134969 0.0069576 -2.179173507 0.018068493
. . -2.510804172 0.003816 -2.737981464 0.000556115
.
Myosin light chain kinase,
smooth muscle; -2.506002905 4.62E-05 -2.706469514 2.69E-06
Four-domain proteases
inhibitor;
Four-domain proteases
inhibitor; -2.486367172 4.91E-07 -2.073261206 2.17E-05
. . -2.464904464 0.0038546 -2.533447996 0.001396187
. . -2.454716558 4.65E-06 -2.272689705 1.19E-05
Kielin/chordin-like protein; Kielin/chordin-like protein; -2.452608453 0.0028864 -1.755761713 0.041791146
. . -2.451140086 0.0026067 -2.83747927 0.000100753
Temptin; Temptin; -2.45025019 1.88E-08 -2.435512322 5.32E-09
Periostin; Periostin; -2.440861998 1.77E-10 -2.165184589 6.30E-09
. . -2.436424922 0.0081428 -2.421946277 0.005199151
. . -2.435176556 0.0008213 -2.408231033 0.000453839
Cleavage stimulation factor
subunit 1;
Cleavage stimulation factor
subunit 1; -2.43428728 0.004663 -1.920135064 0.028239364
. . -2.432720937 0.0038599 -2.036845107 0.015094054
. . -2.425312718 0.0003719 -2.331955538 0.000316893
. . -2.423013827 0.0029987 -2.179856987 0.005953446
. . -2.408549001 0.0105478 -2.183103279 0.016670173
. Agrin; -2.404652311 0.0001237 -1.973524609 0.001678601
. . -2.40337288 0.001619 -2.680478348 0.000111965
. . -2.39581952 0.003816 -2.094978791 0.009853679
232
Epididymal secretory protein
E1; . -2.391183783 2.52E-09 -2.248536361 6.78E-09
Pre-mRNA-splicing factor ATP-
dependent RNA helicase
DHX15
{ECO:0000250|UniProtKB:O4
3143};
Pre-mRNA-splicing factor ATP-
dependent RNA helicase
DHX15
{ECO:0000250|UniProtKB:O4
3143}; -2.390000247 0.0050077 -2.519685207 0.001262639
A disintegrin and
metalloproteinase with
thrombospondin motifs 13;
A disintegrin and
metalloproteinase with
thrombospondin motifs 13; -2.389090292 0.0146508 -1.942459967 0.048746245
Perlwapin-like protein
{ECO:0000303|PubMed:2164
3827};
Perlwapin-like protein
{ECO:0000303|PubMed:2164
3827}; -2.385456796 0.000313 -2.1745661 0.000665241
. . -2.383895972 0.009227 -1.728080489 0.070408494
. . -2.382073499 3.19E-13 -1.889496047 5.05E-09
. . -2.368256314 0.008839 -2.616060431 0.001344386
. . -2.367417336 8.95E-06 -2.063646477 8.47E-05
. . -2.367362618 1.73E-07 -2.494426338 5.32E-09
. . -2.353869782 0.016451 -1.863906423 0.062071768
Sphingomyelin
phosphodiesterase;
Sphingomyelin
phosphodiesterase; -2.35270976 0.0035315 -2.525827092 0.000592586
. . -2.341437029 0.0110603 -1.877592929 0.042464373
Collagen alpha-4(VI) chain; Collagen alpha-1(XII) chain; -2.333939025 0.0115447 -1.929962156 0.035795595
. . -2.328318249 0.0055246 -1.528377701 0.091315201
. . -2.327823433 0.0077377 -1.691671949 0.062071768
. Ganglioside GM2 activator; -2.326414785 9.73E-13 -2.104135293 2.91E-11
Prostaglandin E2 receptor EP4
subtype;
Prostaglandin E2 receptor EP4
subtype; -2.31911083 0.0082644 -1.652457373 0.071481839
Perlucin-like protein
{ECO:0000303|PubMed:2164
3827};
Perlucin-like protein
{ECO:0000303|PubMed:2164
3827}; -2.306634571 9.92E-05 -2.431729498 1.16E-05
233
Coatomer subunit gamma-2; Coatomer subunit gamma-2; -2.305389342 0.012585 -1.843279054 0.047003968
. . -2.303628848 0.0095546 -1.988548977 0.021907148
Sodium- and chloride-
dependent glycine transporter
2;
Sodium- and chloride-
dependent glycine transporter
2; -2.290403357 0.0115447 -1.794304107 0.049904761
D-galactoside-specific lectin; D-galactoside-specific lectin; -2.290330981 0.0084195 -2.106508646 0.011378408
Basement membrane-
specific heparan sulfate
proteoglycan core protein;
Basement membrane-
specific heparan sulfate
proteoglycan core protein; -2.290106219 0.0100274 -2.530164191 0.001516817
. . -2.276489446 0.0134078 -1.743046042 0.063336702
Ryncolin-4; Ryncolin-4; -2.273434078 0.0005312 -3.126082486 4.23E-08
Carbonic anhydrase-related
protein;
Carbonic anhydrase-related
protein; -2.269668509 0.012585 -2.086649692 0.016366141
Periostin; Periostin; -2.264735023 0.0082506 -2.136332393 0.008274867
Ovochymase-1; Ovochymase-1; -2.26292495 2.28E-05 -1.690152599 0.002076967
Complement C1q-like protein
4;
Complement C1q-like protein
4; -2.257462967 0.0072213 -2.736662857 0.000192524
. . -2.252340077 0.0147587 -1.879089049 0.039549565
Collectin-12; Collectin-12; -2.250824015 0.0231884 -2.163665038 0.020931607
Mesenchyme-specific cell
surface glycoprotein;
Mesenchyme-specific cell
surface glycoprotein; -2.244105957 1.14E-07 -1.910504593 5.06E-06
Niemann-Pick C1 protein; Niemann-Pick C1 protein; -2.23645527 0.012091 -2.141344838 0.010641121
Ganglioside GM2 activator; Ganglioside GM2 activator; -2.224867142 0.0004398 -2.060167139 0.00066212
Syntenin-1; Syntenin-1; -2.222643211 0.0245131 -1.897603429 0.050614413
. . -2.220506278 0.0057186 -1.82426365 0.021870763
Agrin; Agrin; -2.215475687 3.93E-05 -2.229443765 1.20E-05
Chitinase-3-like protein 2; Chitinase-3-like protein 2; -2.21473286 0.0089178 -2.182056948 0.005716602
. . -2.211281401 0.0205383 -2.324184279 0.007521827
. . -2.21017641 0.0169222 -2.07290815 0.017802781
. . -2.209203914 0.001239 -2.199097563 0.000586796
. . -2.209118209 0.0160228 -1.937126496 0.029240345
234
Complement C1q-like protein
3;
Complement C1q-like protein
3; -2.203793915 0.0249469 -2.266457044 0.01209293
. . -2.200155464 0.0231557 -2.163750141 0.01655411
. . -2.193351714 0.0051202 -2.313999905 0.001185531
Glutathione S-transferase P; Glutathione S-transferase P; -2.192849517 0.0042757 -2.277232765 0.001204785
. . -2.191362305 0.0060369 -1.477535007 0.082869673
. . -2.188890578 0.0003556 -1.721358295 0.005408347
Galactose-specific lectin
nattectin; Hepatic lectin; -2.188255728 0.0009378 -1.929344085 0.002772965
CD209 antigen-like protein E; . -2.17864154 0.0140624 -2.312657893 0.003998406
Serine incorporator 3; Serine incorporator 3; -2.177919286 0.0269079 -2.123920554 0.020905031
Lysozyme 3
{ECO:0000250|UniProtKB:P83
673,
ECO:0000312|EMBL:BAG419
79.1};
Lysozyme 3
{ECO:0000250|UniProtKB:P83
673,
ECO:0000312|EMBL:BAG419
79.1}; -2.177847279 0.0042757 -1.771188381 0.019443646
Ankyrin repeat domain-
containing protein 66;
Ankyrin repeat domain-
containing protein 66; -2.177217157 0.0148626 -1.868941831 0.031700035
C-type lectin 1;
C-type lectin domain family
10 member A; -2.176595823 0.0026067 -1.933032527 0.005716602
NF-kappa-B inhibitor cactus; NF-kappa-B inhibitor cactus; -2.175621247 0.0252248 -2.241261557 0.0118886
Neurocan core protein; Neurocan core protein; -2.174260599 1.94E-11 -2.113920245 1.18E-11
. . -2.169178403 0.0198458 -2.25828288 0.00777495
Temptin; Temptin; -2.168229812 5.13E-13 -1.985707702 9.14E-12
. . -2.168222776 0.0230935 -2.829823317 0.00045698
Aggrecan core protein; Aggrecan core protein; -2.165123645 0.0084334 -1.939057578 0.014064686
Serine/threonine-protein
kinase SMG1;
Serine/threonine-protein
kinase SMG1; -2.151924792 0.0305908 -1.805765585 0.064970836
Collagen alpha-1(XII) chain; Collagen alpha-1(XII) chain; -2.142351301 3.10E-06 -1.911888875 1.95E-05
Vesicle transport protein
GOT1B;
Vesicle transport protein
GOT1B; -2.1381137 0.034723 -2.440809975 0.006627373
235
Protein CASP; Protein CASP; -2.134941413 0.0266342 -1.953930764 0.033288978
F-box/WD repeat-containing
protein 2;
F-box/WD repeat-containing
protein 2; -2.133421732 0.0139983 -2.129825761 0.007865066
Cathepsin L; Cathepsin L; -2.131665068 0.0001153 -2.031619067 0.000117669
. . -2.126509798 0.0346101 -2.818305627 0.000854746
. . -2.122042656 0.0003624 -2.202311731 6.57E-05
Zinc metalloproteinase nas-
13; Meprin A subunit beta; -2.119095893 2.89E-07 -2.200652268 1.56E-08
. . -2.116374112 0.0139305 -1.892606642 0.021274536
Neural-cadherin; Neural-cadherin; -2.105198957 0.0280967 -2.382515717 0.004986585
. . -2.1045034 0.0010414 -1.767940682 0.005341685
Epoxide hydrolase 4; Epoxide hydrolase 4; -2.09491189 0.0111048 -2.098165168 0.005716602
Four-domain proteases
inhibitor;
Four-domain proteases
inhibitor; -2.086542979 0.0078686 -2.591166544 0.000123647
DBH-like monooxygenase
protein 1 homolog;
DBH-like monooxygenase
protein 1 homolog; -2.083315705 0.00703 -1.83216355 0.013710966
Cathepsin L; Cathepsin L; -2.079733059 0.0361885 -1.791679539 0.062711253
. . -2.069609825 0.0003575 -1.940283604 0.000434036
Brorin; Brorin; -2.062276768 0.0159409 -2.45061122 0.000909958
Epidermal growth factor-like
protein 7;
Epidermal growth factor-like
protein 7; -2.060765049 0.045004 -2.678100078 0.00192265
Sodium- and chloride-
dependent glycine transporter
2;
Sodium- and chloride-
dependent glycine transporter
2; -2.060490725 0.0215528 -1.719797045 0.049615173
Sodium-dependent
multivitamin transporter;
Sodium-dependent
multivitamin transporter; -2.060485396 0.0496652 -1.970812475 0.045659123
. . -2.060370455 4.23E-05 -2.020030865 2.40E-05
. . -2.056284986 0.0101037 -2.124774351 0.003502854
Collagen alpha-1(VIII) chain; Collagen alpha-1(VIII) chain; -2.055281083 0.0063458 -2.521137766 0.00010537
Collagen alpha-4(VI) chain; Collagen alpha-4(VI) chain; -2.04655529 0.0346101 -1.730255281 0.066652815
236
DNA repair protein RAD51
homolog 4;
DNA repair protein RAD51
homolog 4; -2.042081411 0.0418952 -2.262298284 0.010808672
. . -2.039310642 0.0497064 -1.787712437 0.073868845
THO complex subunit 1; THO complex subunit 1; -2.03817221 0.033107 -1.89264233 0.035981533
Phospholipase A2 isozymes
PA3A/PA3B/PA5;
Phospholipase A2 isozymes
PA3A/PA3B/PA5; -2.034262755 0.0440247 -2.204732015 0.014067086
V-type proton ATPase 21 kDa
proteolipid subunit;
V-type proton ATPase 21 kDa
proteolipid subunit; -2.034073181 0.0178901 -1.665388615 0.048782674
Mesenchyme-specific cell
surface glycoprotein;
Mesenchyme-specific cell
surface glycoprotein; -2.028065782 0.0069576 -1.71990046 0.018450308
. . -2.019546635 0.0003103 -2.405976891 1.61E-06
. . -2.016826384 0.0251267 -1.797628355 0.03653623
Mesenchyme-specific cell
surface glycoprotein;
Mesenchyme-specific cell
surface glycoprotein; -2.016240967 0.0097416 -1.92610558 0.008022243
Plasma kallikrein;
Transmembrane protease
serine 6; -2.014632599 0.0340011 -2.024492759 0.019771076
. . -2.012822116 0.0255775 -1.636129435 0.066062836
Complement C1q-like protein
4;
Complement C1q-like protein
4; -2.00784985 0.0346101 -2.129355647 0.012615957
Periostin; Periostin; -2.002706249 3.17E-09 -2.203386745 4.12E-12
. . -2.002599665 0.0444333 -1.989851502 0.029213104
WD repeat-containing protein
90;
WD repeat-containing protein
90; -2.002014891 0.0300598 -1.966424584 0.020498573
Neurogenic locus notch
homolog protein 2;
Neurogenic locus notch
homolog protein 2; -2.001727621 0.0400731 -2.54839155 0.001875242
Proteasomal ubiquitin
receptor ADRM1;
Proteasomal ubiquitin
receptor ADRM1; -2.001632193 0.0540142 -1.848667934 0.059717207
Runt-related transcription
factor 1;
Runt-related transcription
factor 1; -2.001202882 0.0068837 -1.651277236 0.022768967
. . -2.00077146 0.03184 -1.830917796 0.038019226
237
Integrator complex subunit
10;
Integrator complex subunit
10; -2.000726285 0.0493614 -2.118017798 0.020141226
. . -1.995632331 0.0558112 -2.951940994 0.000434036
Solute carrier organic anion
transporter family member
4C1;
Solute carrier organic anion
transporter family member
4C1; -1.993909867 0.0535039 -2.450461867 0.005487704
UPF0462 protein C4orf33
homolog;
UPF0462 protein C4orf33
homolog; -1.990175595 0.0282448 -1.739175053 0.045914788
Temptin; Temptin; -1.989092552 0.0002492 -1.91348762 0.000192524
DBH-like monooxygenase
protein 1 homolog;
DBH-like monooxygenase
protein 1 homolog; -1.988927722 0.0155996 -1.894006236 0.013065448
Cathepsin Z; Cathepsin Z; -1.986721557 0.0347945 -1.817080451 0.0404317
Alkyldihydroxyacetonephosph
ate synthase, peroxisomal;
Alkyldihydroxyacetonephosph
ate synthase, peroxisomal; -1.981941475 0.0538722 -1.693811248 0.088744488
Protocadherin-9; Protocadherin-9; -1.981126282 0.0512466 -1.800265361 0.06109429
Protein mom-5
{ECO:0000303|PubMed:9288
750};
Protein mom-5
{ECO:0000303|PubMed:9288
750}; -1.981014718 0.027444 -1.672890897 0.055414205
Glutathione reductase,
mitochondrial;
Glutathione reductase,
mitochondrial; -1.980415381 0.0265659 -2.082393992 0.00944694
Minor histocompatibility
antigen H13;
Minor histocompatibility
antigen H13; -1.978850403 0.0030022 -1.836932259 0.003615013
Protein disulfide-isomerase; Protein disulfide-isomerase; -1.974862043 0.0055246 -1.303983825 0.084601413
. . -1.972152824 0.0163749 -2.063429843 0.005343133
Fucose-1-phosphate
guanylyltransferase
{ECO:0000312|MGI:MGI:1922
790};
Fucose-1-phosphate
guanylyltransferase
{ECO:0000312|MGI:MGI:1922
790}; -1.969795027 0.0234211 -1.865375971 0.021405766
. Mucin-1; -1.963355721 0.0178901 -2.460772948 0.000467141
238
Serine/threonine-protein
phosphatase 2A 65 kDa
regulatory subunit A alpha
isoform;
Serine/threonine-protein
phosphatase 2A 65 kDa
regulatory subunit A alpha
isoform; -1.961637074 0.0490289 -2.017838714 0.024707836
Glutamyl-tRNA(Gln)
amidotransferase subunit B,
mitochondrial
{ECO:0000255|HAMAP-
Rule:MF_03147};
Glutamyl-tRNA(Gln)
amidotransferase subunit B,
mitochondrial
{ECO:0000255|HAMAP-
Rule:MF_03147}; -1.960993334 0.0466929 -2.344555817 0.005528267
Neurogenic locus Notch
protein;
Neurogenic locus Notch
protein; -1.958074515 0.0080054 -1.295777853 0.09767564
Hemicentin-1; Hemicentin-1; -1.957974616 0.0216559 -2.478944491 0.000584246
Macrophage-expressed gene
1 protein;
Macrophage-expressed gene
1 protein; -1.957260285 0.0169222 -1.775914746 0.021761476
. . -1.95638519 0.0205383 -2.043367323 0.007305133
Barrier-to-autointegration
factor 1
{ECO:0000250|UniProtKB:Q0
3565}; . -1.953184166 0.0325983 -1.788799762 0.037488801
. . -1.952384749 0.0496706 -2.366924421 0.005341685
. . -1.950830086 2.28E-05 -1.974990291 5.65E-06
. . -1.949530394 0.0232018 -1.724617751 0.035566958
. . -1.948042624 0.0254967 -1.899963887 0.018156903
Carboxylesterase 5A; Carboxylesterase 5A; -1.946619671 0.0204528 -1.465938741 0.083039932
Inactive pancreatic lipase-
related protein 1;
Inactive pancreatic lipase-
related protein 1; -1.945123451 0.0012438 -1.726825526 0.003076224
Putative defense protein 1; Ferric-chelate reductase 1; -1.943634381 0.0299856 -1.777693461 0.03515609
. . -1.942038547 0.0606137 -2.169961567 0.016585074
Sacsin; Sacsin; -1.938618266 0.0726233 -2.175005393 0.021968405
V-type proton ATPase subunit
C 1-A;
V-type proton ATPase subunit
C 1-A; -1.935643037 0.0648354 -1.89960387 0.047847912
239
F-box only protein 7; F-box only protein 7; -1.933926097 0.0199963 -2.414281551 0.000608553
. . -1.933820864 0.0282448 -2.523216746 0.000599105
Acidic mammalian chitinase; Acidic mammalian chitinase; -1.932435786 0.0003372 -1.441125066 0.008849653
Putative ferric-chelate
reductase 1 homolog;
Putative ferric-chelate
reductase 1 homolog; -1.928993768 0.0720375 -1.868079472 0.059402623
. . -1.927882896 0.0105317 -1.777936813 0.012268562
. . -1.926682659 0.0100197 -1.4247888 0.061002899
Synaptotagmin-11; Synaptotagmin-11; -1.923780719 0.016413 -1.794266516 0.016585074
. . -1.915770726 0.0005867 -1.722322781 0.001396187
. . -1.915205456 0.0009122 -1.946800045 0.00025696
Wee1-like protein kinase 1-B; Wee1-like protein kinase; -1.913460325 0.0654356 -1.862870753 0.050481361
. . -1.911771662 0.0326691 -2.479836294 0.000866934
Beta-amyloid-like protein
{ECO:0000305};
Beta-amyloid-like protein
{ECO:0000305}; -1.910827184 0.0566434 -2.542382853 0.002073295
. . -1.907877735 0.0745914 -2.137085363 0.022124142
. . -1.907225261 0.0218425 -1.870537123 0.014324151
Perlucin-like protein
{ECO:0000303|PubMed:2164
3827};
Perlucin-like protein
{ECO:0000303|PubMed:2164
3827}; -1.905658696 0.0720375 -1.962812771 0.039407662
Sodium- and chloride-
dependent glycine transporter
2;
Sodium- and chloride-
dependent glycine transporter
2; -1.905577602 0.0567469 -1.683062535 0.075577729
. . -1.904871269 0.0218655 -1.942639069 0.009890213
. . -1.89810096 0.03076 -1.837522381 0.022730564
. . -1.896981306 0.0002645 -1.683459156 0.000778622
. . -1.895097543 4.57E-06 -1.696513067 2.31E-05
Pro-neuropeptide Y; . -1.893600403 0.0127305 -1.453683382 0.057275894
. . -1.890973395 0.0268726 -1.694933291 0.036496833
Uncharacterized protein
sll1483;
Transforming growth factor-
beta-induced protein ig-h3; -1.890769509 6.62E-06 -2.031914766 1.82E-07
240
Complement C1q tumor
necrosis factor-related
protein 3;
Complement C1q tumor
necrosis factor-related
protein 3; -1.889981952 0.0756404 -1.728136701 0.082760905
Laminin-like protein epi-1; Laminin-like protein epi-1; -1.887914669 0.0802719 -1.980598177 0.040173843
. . -1.887489183 0.0198364 -1.841640914 0.013379555
Tctex1 domain-containing
protein 1;
Tctex1 domain-containing
protein 1; -1.886774528 0.0039549 -2.148494919 0.000209853
Macrophage-expressed gene
1 protein;
Macrophage-expressed gene
1 protein; -1.88590984 0.0325312 -1.955538409 0.01329554
Protein rolling stone; Protein rolling stone; -1.885454004 0.0038993 -2.309169604 4.69E-05
T-complex protein 1 subunit
zeta;
T-complex protein 1 subunit
zeta; -1.883208157 0.005687 -1.677708737 0.009890213
. . -1.881425394 6.28E-05 -1.962057746 7.67E-06
Plancitoxin-1; Plancitoxin-1; -1.881008573 0.0683963 -1.915121728 0.039406127
Putative DBH-like
monooxygenase protein 2;
Putative DBH-like
monooxygenase protein 2; -1.88087839 0.0257233 -1.553710833 0.058166614
. . -1.877336842 0.0159409 -1.846875472 0.009517503
General transcription factor
IIF subunit 2;
General transcription factor
IIF subunit 2; -1.874974104 0.075226 -1.841394688 0.055491672
. . -1.874531526 0.0172759 -1.538795127 0.045927741
Farnesyl pyrophosphate
synthase;
Farnesyl pyrophosphate
synthase; -1.8719131 0.0462032 -2.264282622 0.0045273
Hemicentin-1
{ECO:0000312|MGI:MGI:2685
047};
Hemicentin-1
{ECO:0000312|MGI:MGI:2685
047}; -1.871670289 0.0720375 -2.757816873 0.000800956
. . -1.871331215 0.0720375 -2.21278076 0.012628738
. . -1.871282749 0.0720375 -1.942097461 0.036700653
Lactase-phlorizin hydrolase; Lactase-phlorizin hydrolase; -1.870135957 0.0264966 -1.775596782 0.022597908
. . -1.864717224 0.003816 -1.856989484 0.001754394
Hepatic lectin; Hepatic lectin; -1.85598455 0.0282448 -1.821643683 0.018522362
. . -1.855107821 0.0265793 -1.849472801 0.015044093
241
. . -1.852038072 0.0606153 -1.571575763 0.0973864
. . -1.85049355 0.0580081 -2.174782362 0.009179168
Cytochrome P450 3A29; Cytochrome P450 3A29; -1.848009668 0.0613702 -2.09842839 0.014017328
Glucose-6-phosphate
exchanger SLC37A2
{ECO:0000305};
Glucose-6-phosphate
exchanger SLC37A2
{ECO:0000305}; -1.847016454 0.0426203 -1.637236126 0.057305988
Sulfotransferase family
cytosolic 1B member 1;
Sulfotransferase family
cytosolic 1B member 1; -1.84668294 0.0613164 -1.830844025 0.040472912
C-type lectin domain family 4
member M; Lectin BRA-3; -1.831128256 0.0068578 -1.285066535 0.065457325
. . -1.830219998 0.0630281 -1.849782623 0.03720777
. . -1.830174298 0.091661 -1.738313335 0.081038981
Receptor expression-
enhancing protein 5;
Receptor expression-
enhancing protein 5; -1.827926891 0.0989107 -2.402330582 0.008516299
. . -1.826883864 0.0720375 -1.618127644 0.09106095
DNA mismatch repair protein
Mlh1;
DNA mismatch repair protein
Mlh1; -1.826170829 0.0736481 -1.824631678 0.047376649
Atrial natriuretic peptide
receptor 2;
Atrial natriuretic peptide
receptor 1; -1.825481263 0.0801734 -1.811309912 0.055292985
Protein FAM234B; Protein FAM234B; -1.825448006 0.0139983 -1.620360115 0.021162403
Apolipoprotein D; Apolipoprotein D; -1.823912317 0.0028393 -1.928074083 0.000467141
. . -1.823114771 3.51E-05 -1.34484865 0.003125783
Zinc finger SWIM domain-
containing protein 8;
Zinc finger SWIM domain-
containing protein 8; -1.820680574 0.0930473 -2.408082442 0.006722024
. . -1.820473674 0.0720375 -2.027409537 0.020931607
. . -1.820253671 0.0001366 -1.757400445 0.000101507
. . -1.820087251 0.0891207 -1.935516928 0.040092384
. . -1.819609994 0.0001408 -2.117525179 1.02E-06
tRNA-splicing endonuclease
subunit Sen54;
tRNA-splicing endonuclease
subunit Sen54; -1.819592145 0.0201712 -1.643731529 0.026104142
242
Condensin complex subunit 1; Condensin complex subunit 1; -1.81926743 0.0981232 -1.810774273 0.068025126
. . -1.818763777 0.0530811 -1.890248671 0.024116529
. . -1.815096223 0.0010137 -2.433164265 3.13E-07
. . -1.811402984 0.0496652 -2.269929034 0.003272923
Glutathione peroxidase
{ECO:0000303|PubMed:2356
7855}; . -1.805660486 0.0479989 -1.760702834 0.034676642
. . -1.804209481 0.0129927 -1.536122506 0.029071364
. . -1.803805463 0.0773582 -1.738654949 0.062067965
Serine protease inhibitor Cvsi-
2
{ECO:0000303|PubMed:1946
4375}; . -1.803793032 9.04E-06 -1.485007045 0.000228822
Dynein heavy chain 5,
axonemal;
Dynein heavy chain 5,
axonemal; -1.798594958 0.0720375 -1.916492341 0.029521431
. . -1.795638615 0.0697073 -1.695301029 0.062071768
Putative epidermal cell
surface receptor;
Putative epidermal cell
surface receptor; -1.779869512 0.0040536 -1.481087711 0.014067086
Ankyrin repeat and SOCS box
protein 11; Ankyrin-3; -1.778024214 0.0250704 -1.584153658 0.035117958
Mitochondrial import inner
membrane translocase
subunit Tim23;
Mitochondrial import inner
membrane translocase
subunit Tim23; -1.777129796 0.0774408 -1.670300487 0.070296697
Tctex1 domain-containing
protein 1-B;
Tctex1 domain-containing
protein 1-B; -1.77292501 0.0683203 -1.870689528 0.029415032
UBA-like domain-containing
protein 2-A;
UBA-like domain-containing
protein 2-A; -1.767734152 0.073191 -1.601290492 0.080705466
. . -1.761270862 0.0316806 -1.952898101 0.006311067
Collagen alpha-3(VI) chain; Collagen alpha-3(VI) chain; -1.761037418 1.10E-05 -1.419968298 0.00036471
243
Titin;
Basement membrane-
specific heparan sulfate
proteoglycan core protein; -1.756804176 0.0445269 -1.804686996 0.020390341
Acidic phospholipase A2 S3-
24;
Acidic phospholipase A2 S3-
24; -1.753317711 0.0978643 -2.252001898 0.009456295
Epididymal secretory protein
E1;
Epididymal secretory protein
E1; -1.753061278 0.003391 -1.589173555 0.005131291
. . -1.752723597 0.0503063 -1.633337579 0.047705562
. . -1.751979534 0.0991743 -1.754640843 0.065110385
Acylamino-acid-releasing
enzyme;
Acylamino-acid-releasing
enzyme; -1.747299623 0.0683203 -1.608764596 0.068820014
Protocadherin Fat 1; Protocadherin Fat 1; -1.745437465 0.0530025 -1.537605127 0.069238209
. . -1.745373961 0.0180737 -2.014477901 0.001612055
. . -1.741379483 0.0016114 -1.449514701 0.007796162
ATP-dependent RNA helicase
DHX8;
ATP-dependent RNA helicase
DHX8; -1.740899525 0.0455225 -1.598039929 0.047706746
. . -1.739470305 0.0172051 -1.982471086 0.001780005
Toxin CrTX-A; Toxin CrTX-A; -1.738504306 0.0454192 -2.682065967 7.81E-05
. . -1.737533956 0.0620619 -1.522713911 0.082391639
. . -1.725050329 0.0924038 -1.565213747 0.099183309
. . -1.716746845 0.0116558 -1.580890795 0.013032608
. . -1.713342951 0.0692985 -1.774969184 0.033505039
Tetratricopeptide repeat
protein 8;
Tetratricopeptide repeat
protein 8; -1.70502802 0.0974642 -1.820757006 0.041790054
Nucleolar protein 6; Nucleolar protein 6; -1.70293882 0.0160197 -1.973368056 0.00119948
. . -1.70276996 0.0194969 -1.673626818 0.011925727
Dipeptidyl peptidase 2; Dipeptidyl peptidase 2; -1.702277218 0.0573379 -2.237188456 0.002233959
Tubulin alpha-1 chain; . -1.701818975 0.0068837 -1.621216575 0.005701343
Putative tyrosinase-like
protein tyr-3;
Putative tyrosinase-like
protein tyr-3; -1.701310448 0.0919372 -1.773080255 0.045225867
Dipeptidyl peptidase 1; Dipeptidyl peptidase 1; -1.698423777 0.0210209 -2.03696035 0.001169502
244
. . -1.689122321 0.0653752 -1.975094994 0.010939032
. . -1.686054849 0.0267654 -2.169158106 0.000595281
. . -1.676383744 0.052675 -1.653995237 0.034229004
. . -1.676047208 0.0901097 -1.637326607 0.064874302
.
MAM domain-containing
protein 2; -1.671154755 1.65E-05 -1.693135099 3.45E-06
E3 ubiquitin-protein ligase
TRIM71;
E3 ubiquitin-protein ligase
TRIM71; -1.669956994 0.0077621 -1.403302885 0.020491776
. . -1.66784757 0.0409102 -1.567458079 0.036578276
Probable Dol-P-
Man:Man(7)GlcNAc(2)-PP-Dol
alpha-1,6-
mannosyltransferase;
Probable Dol-P-
Man:Man(7)GlcNAc(2)-PP-Dol
alpha-1,6-
mannosyltransferase; -1.659444599 0.0771922 -1.562037619 0.067377878
. . -1.658680357 0.087192 -1.661194596 0.054663827
Chymotrypsin-like serine
proteinase;
Chymotrypsin-like serine
proteinase; -1.652490855 0.0517709 -1.600624041 0.038019226
. . -1.652456346 0.053298 -1.774162845 0.017566674
N-acyl-
phosphatidylethanolamine-
hydrolyzing phospholipase D;
N-acyl-
phosphatidylethanolamine-
hydrolyzing phospholipase D; -1.648489282 0.0346101 -1.66702862 0.017535434
Tubulin--tyrosine ligase-like
protein 12;
Tubulin--tyrosine ligase-like
protein 12; -1.646593846 0.0346101 -1.457915902 0.046476175
. . -1.64251801 0.0326691 -1.445342912 0.046075095
. . -1.641745345 0.0580081 -1.507340281 0.059422355
. . -1.64007418 9.92E-05 -1.766325767 5.11E-06
Integrin beta-5; Integrin beta-7; -1.635233175 0.0029987 -2.045505441 1.56E-05
. . -1.634908349 0.0190084 -1.614692353 0.010750095
. . -1.633756502 0.0161862 -1.414888219 0.028195658
. . -1.630342777 0.0503006 -1.340765087 0.094386316
Cornifelin homolog; Cornifelin homolog B; -1.627026763 5.05E-05 -1.841011418 5.29E-07
Cathepsin B; Cathepsin B; -1.626984076 3.46E-06 -1.824932068 1.45E-08
245
. . -1.625245983 0.0412069 -1.316278242 0.086955879
. . -1.61825765 3.90E-06 -1.721600977 1.21E-07
. . -1.615611724 0.0035315 -1.465367381 0.005120846
Probable G-protein coupled
receptor 75;
Probable G-protein coupled
receptor 75; -1.614733248 0.0440247 -1.482338579 0.045112447
Aquaporin-4; Aquaporin-4; -1.604356309 0.075799 -2.074306862 0.004861766
. . -1.598058858 0.0351566 -1.602738311 0.018507653
Exocyst complex component
3;
Exocyst complex component
3; -1.59403715 0.0911258 -2.04967427 0.007422644
Neurogenic locus notch
homolog protein 1;
Neurogenic locus notch
homolog protein 1; -1.592832998 0.049248 -1.348451686 0.077447285
. . -1.59029063 0.003004 -1.410545444 0.005713129
. . -1.589876652 0.0048268 -1.708960032 0.000663033
. . -1.589285359 0.0805433 -1.926415433 0.010800176
. . -1.587003233 0.0923161 -1.741528975 0.031479856
. . -1.586223113 0.0024602 -1.297784372 0.011530954
. . -1.585260683 4.30E-09 -1.451023143 2.76E-08
Apolipoprotein D; Apolipoprotein D; -1.579667387 0.0272177 -2.112107875 0.000307347
G2/mitotic-specific cyclin-A; G2/mitotic-specific cyclin-A; -1.575634797 0.0720375 -1.434211872 0.074063387
Choline transporter-like
protein 1;
Choline transporter-like
protein 1; -1.572508844 0.0325929 -1.454847002 0.032473828
WASH complex subunit 3
{ECO:0000312|MGI:MGI:1914
532};
WASH complex subunit 3
{ECO:0000250|UniProtKB:Q9
Y3C0}; -1.569898067 0.0567469 -1.683885047 0.019067943
. . -1.56975613 0.0470217 -1.794370578 0.007796162
Ankyrin repeat and SOCS box
protein 3;
Ankyrin repeat and SOCS box
protein 3; -1.567565288 0.073191 -1.641970686 0.032169919
. . -1.566264928 0.0002145 -1.856224806 9.52E-07
246
Solute carrier family 23
member 2
{ECO:0000250|UniProtKB:Q9
UGH3};
Solute carrier family 23
member 2
{ECO:0000250|UniProtKB:Q9
UGH3}; -1.560975422 0.0785746 -1.931996273 0.008183759
. . -1.55521518 0.0512466 -1.794925433 0.008047991
. . -1.554718828 0.0338904 -1.319189318 0.060334733
. . -1.553876308 1.15E-09 -1.386283607 2.33E-08
Isocitrate dehydrogenase
[NAD] subunit beta,
mitochondrial;
Isocitrate dehydrogenase
[NAD] subunit beta,
mitochondrial; -1.54294142 0.0981232 -1.574897158 0.053936712
. . -1.54277186 0.0319049 -2.010088104 0.0006523
. . -1.539135825 0.0543425 -1.624567092 0.020491561
DBH-like monooxygenase
protein 1 homolog;
DBH-like monooxygenase
protein 1 homolog; -1.533826128 0.0543291 -1.457544256 0.043518552
. . -1.53151789 0.0681485 -1.410670107 0.065943924
. . -1.526733851 0.0962459 -1.530035465 0.059048786
Cartilage matrix protein; Cartilage matrix protein; -1.523029624 0.0079891 -1.48976734 0.004504488
. . -1.522487672 0.0095875 -1.953157455 7.61E-05
. . -1.520574555 0.0180737 -1.480837785 0.011478093
Beta-1,3-galactosyl-O-
glycosyl-glycoprotein beta-1,6-
N-
acetylglucosaminyltransferas
e;
Beta-1,3-galactosyl-O-
glycosyl-glycoprotein beta-1,6-
N-
acetylglucosaminyltransferas
e; -1.518739677 0.0530811 -1.705210409 0.011223075
Alpha-L-fucosidase; Alpha-L-fucosidase; -1.516012846 0.0019006 -1.836280094 1.54E-05
. . -1.514037805 0.0720375 -2.000951091 0.003207723
. . -1.512864928 0.0829597 -1.421106522 0.071500816
Myelin P2 protein; Agrin; -1.509087962 0.0048268 -1.226173405 0.01963205
. . -1.506431196 0.071162 -1.726184324 0.014461291
. . -1.4879311 0.0102647 -2.12160759 7.66E-06
. . -1.477035891 0.0022246 -1.825641108 1.20E-05
247
Turripeptide Gsg9.2; . -1.476308023 0.0919372 -1.584333466 0.035709656
. . -1.470265781 0.0997724 -1.346365449 0.098176502
Transmembrane protein 256
homolog;
Transmembrane protein 256
homolog; -1.469113704 0.0150529 -1.561677966 0.003502854
. . -1.461444496 0.0235544 -1.436427172 0.013963536
. . -1.454152267 0.0789647 -1.369395045 0.066298178
Eukaryotic translation
initiation factor 3 subunit L
{ECO:0000255|HAMAP-
Rule:MF_03011};
Eukaryotic translation
initiation factor 3 subunit L
{ECO:0000255|HAMAP-
Rule:MF_03011}; -1.450386062 0.0013925 -1.218648761 0.005953446
. . -1.446891375 0.0189936 -1.73938168 0.000862783
. . -1.446272181 0.0011926 -1.252438833 0.003854858
Dolichyl-
diphosphooligosaccharide--
protein glycosyltransferase
subunit 1;
Dolichyl-
diphosphooligosaccharide--
protein glycosyltransferase
subunit 1; -1.443809913 0.0433695 -1.209239353 0.073274854
. . -1.438209074 0.0886279 -1.792378187 0.009178242
. . -1.436818064 0.0989107 -1.38341045 0.073888774
. Cystatin; -1.431911777 3.61E-06 -1.648194033 5.58E-09
Dolichyl-
diphosphooligosaccharide--
protein glycosyltransferase
subunit 1;
Dolichyl-
diphosphooligosaccharide--
protein glycosyltransferase
subunit 1; -1.430157456 0.0609929 -1.303064796 0.062071768
. . -1.421289505 0.0426203 -1.479572961 0.015977503
Histidine ammonia-lyase; Histidine ammonia-lyase; -1.411364907 0.0974955 -1.544328042 0.032828753
. . -1.402752794 0.0147587 -1.399452878 0.006864636
Lactadherin; Lactadherin; -1.401930588 0.0021709 -1.675378043 2.40E-05
. . -1.391112489 0.0155996 -2.432982676 4.18E-08
Transforming growth factor-
beta-induced protein ig-h3;
Transforming growth factor-
beta-induced protein ig-h3; -1.38966884 0.0230935 -1.40996985 0.009972161
. . -1.386208126 0.0136882 -1.621816084 0.000731279
248
Dolichyl-
diphosphooligosaccharide--
protein glycosyltransferase
subunit 2;
Dolichyl-
diphosphooligosaccharide--
protein glycosyltransferase
subunit 2; -1.385150686 0.0035087 -1.259470463 0.004703523
Titin; Titin; -1.382957663 0.0838619 -1.434584942 0.039354777
Vascular endothelial growth
factor A;
Vascular endothelial growth
factor A; -1.381815979 0.0650852 -1.635882549 0.008798294
Cathepsin L; Cathepsin L2; -1.38105994 0.0002645 -1.403620097 5.80E-05
. . -1.380541623 0.0770286 -1.397511327 0.04129331
. . -1.374654142 1.71E-05 -2.467457587 1.21E-19
Lysosomal aspartic protease; Lysosomal aspartic protease; -1.374479685 5.41E-05 -1.419819205 7.66E-06
. . -1.371215108 0.0429197 -1.214848881 0.053936712
Ceramide synthase 4; Ceramide synthase 4; -1.363053477 0.0606137 -1.43578434 0.022730564
. . -1.351335898 0.0035097 -1.930218996 4.64E-07
Matrix metalloproteinase-19; Matrix metalloproteinase-16; -1.348534561 0.0790622 -1.333087208 0.048715443
. . -1.345761283 0.074911 -1.462994797 0.023800976
Cathepsin L1; Cathepsin L1; -1.340580474 0.0066321 -1.338329472 0.002781538
Proteasome subunit alpha
type-3;
Proteasome subunit alpha
type-3; -1.334087924 5.56E-06 -1.839730165 2.14E-12
. . -1.331931439 0.0809647 -1.428900841 0.029647348
Cytoplasmic FMR1-
interacting protein 2;
Cytoplasmic FMR1-
interacting protein 2; -1.325990602 0.053298 -1.328671848 0.029472955
. . -1.313608566 0.0004492 -2.600141188 8.08E-18
Hepatic lectin; CD209 antigen; -1.305708566 0.0805433 -1.522533258 0.014354218
. . -1.305173435 0.0942739 -1.417364539 0.032835976
. . -1.301687441 0.0001408 -1.781915954 2.41E-09
Tubulin alpha-4A chain; Tubulin alpha-4A chain; -1.300238398 0.0848629 -1.252880236 0.062058841
. . -1.283196481 3.09E-05 -2.28585868 1.24E-18
Putative aminopeptidase
W07G4.4;
Putative aminopeptidase
W07G4.4; -1.279970924 0.0800014 -1.428192729 0.020775209
249
Hemocytin; Mucin-5AC {ECO:0000305}; -1.27557799 0.0067146 -1.637849682 3.29E-05
. . -1.273700456 0.0189639 -1.491316582 0.001204785
Ganglioside GM2 activator; Ganglioside GM2 activator; -1.273608189 0.0407364 -1.25784467 0.023552925
Carboxypeptidase B; Carboxypeptidase B; -1.272013418 0.0189936 -1.663625095 0.000194657
Neuronal acetylcholine
receptor subunit alpha-10;
Neuronal acetylcholine
receptor subunit alpha-10; -1.266079282 0.0182944 -1.327749175 0.005119084
. . -1.263938123 0.0016219 -1.356855659 0.00016927
Innexin unc-9; Innexin unc-9; -1.262311354 0.0560887 -1.561943644 0.004094388
. . -1.247257786 0.0005716 -1.633491929 1.82E-07
. . -1.24716285 0.0170752 -1.385753598 0.002299954
. . -1.234886098 0.0037466 -1.428108398 0.000112862
Sperm-associated antigen 16
protein;
Sperm-associated antigen 16
protein; -1.230294057 0.0838619 -1.257300967 0.042251539
. . -1.223098385 0.0078594 -1.324334755 0.00104116
. . 1.221654556 0.0213951 1.258102143 0.007372616
. . 1.234246607 0.0211861 1.323290814 0.004761716
14-3-3 protein epsilon; 14-3-3 protein epsilon; 1.236150469 0.0017994 1.329714035 0.000171659
. . 1.242467358 0.0683203 2.291239205 5.06E-06
. . 1.289395884 0.0102832 1.638924055 0.000100234
. . 1.294517682 0.0326691 1.229051744 0.024738953
Profilin; Profilin; 1.301799087 1.23E-05 1.212899384 1.81E-05
. . 1.305220433 0.0105317 1.450984713 0.001077588
. . 1.30729287 0.0402581 2.072801385 2.85E-05
Dynein heavy chain 7,
axonemal;
Dynein heavy chain 7,
axonemal; 1.311454842 0.0619707 1.611917839 0.005478581
. . 1.318813858 0.0632438 1.769911347 0.00192265
. . 1.33157779 0.0006277 1.510798271 1.42E-05
AT-rich interactive domain-
containing protein 3A;
AT-rich interactive domain-
containing protein 3A; 1.344288696 0.0605412 1.281948342 0.045112447
. . 1.362622505 0.0407364 1.509942392 0.008183759
Tropomodulin; Tropomodulin-1; 1.372370396 0.0048078 1.204464634 0.008860886
250
. . 1.372628133 0.0255037 1.455550563 0.006864636
. . 1.382158784 0.0622639 1.505749124 0.017907785
Protein SSUH2 homolog; . 1.393418404 0.0948476 1.974699916 0.002539606
. . 1.402242008 0.0013678 1.639606745 2.07E-05
RNA polymerase II-associated
protein 3;
RNA polymerase II-associated
protein 3; 1.406336278 0.0893086 1.333073437 0.070380886
. . 1.418564845 0.0230935 2.139519947 1.67E-05
Probable RNA-directed DNA
polymerase from transposon
X-element;
Nucleic-acid-binding protein
from mobile element jockey; 1.434847025 9.97E-07 1.364456043 9.52E-07
. . 1.447088097 0.0216559 1.53237656 0.005732936
. . 1.448498895 0.045004 1.993750882 0.000573302
. . 1.457910381 0.0004432 1.288687043 0.001086735
. . 1.459430614 0.0981767 1.749722342 0.016196476
Regulator of G-protein
signaling 22;
Regulator of G-protein
signaling 22; 1.470227731 0.0501157 1.426846468 0.033604787
Histone H4; Histone H4; 1.480025987 0.0440247 1.560406389 0.014839034
. . 1.482285145 0.00875 1.608188706 0.00122744
. . 1.504801776 0.0512466 1.332005806 0.062067965
. . 1.519304581 1.00E-05 1.449877635 9.13E-06
. . 1.530607713 1.02E-06 1.367957171 5.89E-06
Double-stranded RNA-binding
protein Staufen homolog
{ECO:0000305}; . 1.536418944 0.0004398 2.001691037 1.55E-07
. . 1.538328425 0.0262555 1.414110392 0.026639191
Probable RNA-directed DNA
polymerase from transposon
X-element;
Probable RNA-directed DNA
polymerase from transposon
X-element; 1.538654294 7.97E-05 1.301776876 0.000592586
. . 1.540628608 0.0711687 2.238393398 0.000914405
. . 1.545539616 0.0619662 1.669154733 0.019768027
. . 1.552938883 0.0293007 1.260756147 0.062086057
251
. . 1.556370842 4.03E-07 1.345690333 6.65E-06
Fatty acid-binding protein
homolog 6;
Fatty acid-binding protein
homolog 6; 1.559181643 5.08E-06 2.034890392 2.91E-11
. . 1.560933678 0.0010811 1.315405665 0.004330377
. . 1.564407957 0.0113425 1.693132595 0.001831987
. . 1.587655609 2.69E-06 1.510535401 2.62E-06
Cytochrome c oxidase subunit
3; . 1.591698604 0.000496 1.774982238 1.56E-05
. . 1.599309118 0.0433516 1.339515315 0.072496432
. . 1.600176441 0.0522737 1.607318314 0.028425668
Metalloprotease TIKI1; Metalloprotease TIKI2; 1.614039744 0.0760456 1.963509421 0.009456295
. . 1.623565722 0.0013033 1.993903576 6.66E-06
. . 1.624605776 0.0167414 1.355523429 0.036569188
ATP synthase subunit a; . 1.626488008 1.21E-05 1.771803092 2.25E-07
CTD small phosphatase-like
protein 2;
CTD small phosphatase-like
protein 2; 1.633125324 0.0687099 1.638643689 0.039576776
Cytochrome c oxidase subunit
1; . 1.634689705 1.27E-08 1.869462753 2.83E-12
. . 1.643385142 0.0475148 1.403182072 0.070341755
. . 1.656020148 0.0189095 1.816203157 0.00328305
. . 1.695878723 0.0738285 1.478141757 0.094713766
. . 1.707920185 0.0887146 1.659911127 0.065135062
. . 1.781927104 1.33E-06 1.856317592 6.65E-08
. . 1.781991351 0.0101871 1.761525577 0.005442244
. . 1.797278215 0.0373829 2.079787871 0.005255424
. . 1.805791256 0.0949172 3.220950059 8.46E-05
. . 1.823454192 7.36E-09 1.422666976 6.23E-06
Protein FAM107A; Protein FAM107A; 1.83245746 0.0288318 1.477780525 0.068450238
. . 1.839466694 5.58E-05 2.010137737 1.79E-06
. . 1.84868235 0.0040205 2.155063193 0.000132687
Antistasin; Antistasin; 1.862787482 0.0031525 1.518565281 0.01394537
252
Amine sulfotransferase; Amine sulfotransferase; 1.871075049 0.0050077 2.343958106 4.47E-05
Chromobox protein homolog
5; . 1.874932903 4.51E-08 1.91034679 3.78E-09
CTTNBP2 N-terminal-like
protein;
CTTNBP2 N-terminal-like
protein; 1.892208236 0.0230747 1.424556397 0.085157674
. . 1.894485783 0.0494991 1.647155919 0.070296697
Cytochrome b; . 1.914105806 7.94E-11 1.836827045 5.17E-11
Fatty acid-binding protein
homolog 5;
14 kDa fatty acid-binding
protein; 1.933664366 0.0178366 1.443902758 0.075035416
. . 1.947529429 0.0661628 3.256115026 8.51E-05
Phosphatidylinositol transfer
protein alpha isoform;
Phosphatidylinositol transfer
protein alpha isoform; 1.94836897 0.0015777 1.489758806 0.015406181
. . 1.964690816 0.0219461 2.228099289 0.003021628
. . 1.978871217 9.59E-06 1.576237229 0.000397556
. . 1.985993301 2.50E-05 1.599403506 0.000631264
GTPase IMAP family member
4;
GTPase IMAP family member
4; 1.993976409 0.0372656 1.648755271 0.075035416
. . 2.007533863 0.0163768 2.07074637 0.006159996
. . 2.009679453 0.0212476 2.252394732 0.003447771
. . 2.035168626 0.0097428 1.628819855 0.035430684
. . 2.039359657 0.0206533 1.776617072 0.034945147
Nuclear factor 1 X-type; Nuclear factor 1 X-type; 2.059531501 0.0391437 1.655663965 0.092960687
. . 2.135470195 0.0384983 1.816324817 0.073737172
. . 2.162659528 0.0153497 2.711454682 0.000378349
. . 2.181141449 0.0009402 3.077093629 6.93E-08
. . 2.188872462 0.005271 2.051245213 0.00539227
. . 2.201525259 0.0002471 2.34972778 1.99E-05
. . 2.216392106 3.20E-10 2.306216271 5.22E-12
. . 2.243004442 3.92E-11 1.564771981 4.28E-06
. . 2.286352368 0.0013678 1.702341308 0.01836311
. . 2.302673875 0.0206533 2.028980569 0.038525858
253
. . 2.316528589 0.0004398 1.716104783 0.010168394
. . 2.364058761 0.0163372 1.9629746 0.046346985
. . 2.384032422 0.0054275 1.574873096 0.085157674
. . 2.563379451 3.93E-05 1.593810085 0.01655411
. . 2.565409145 4.34E-05 2.643646312 7.67E-06
254
Blastx against Uniprot Blastp against Uniprot log2FoldChange Adjusted p-value
. . -4.655157338 7.91E-06
GTP-binding protein GEM; GTP-binding protein GEM; -4.54294277 1.77E-05
Cathepsin L; Cathepsin L; -4.529318999 3.42E-05
Zinc transporter ZIP12; Zinc transporter ZIP12; -4.484622322 8.33E-05
Vitelline membrane outer
layer protein 1;
Vitelline membrane outer
layer protein 1; -4.125633382 0.000158581
. . -4.110180675 4.92E-05
Proton myo-inositol
cotransporter;
Proton myo-inositol
cotransporter; -4.062889181 0.000250405
Ras-related C3 botulinum
toxin substrate 1;
Ras-related C3 botulinum
toxin substrate 1; -3.974561786 0.000223903
DNA replication licensing
factor mcm7;
DNA replication licensing
factor mcm7; -3.895736238 0.001423745
. . -3.81672875 0.000927872
. . -3.795866331 2.43E-05
Transitional endoplasmic
reticulum ATPase;
Transitional endoplasmic
reticulum ATPase; -3.783794861 0.000918542
Cation-dependent mannose-6-
phosphate receptor;
Cation-dependent
mannose-6-phosphate
receptor; -3.776066802 0.001516865
. . -3.771129442 0.00464838
. . -3.747176774 4.73E-05
Krueppel-like factor 1; Krueppel-like factor 1; -3.73172285 0.001447869
Transcript Annotation Differential expression
Table 2--Markers of Effect at 3 ug/L copper. Genes that were DE between
normal and abnormal animals at only 3 ug/L copper were identified.
255
DNA-directed RNA
polymerase III subunit RPC6;
DNA-directed RNA
polymerase III subunit
RPC6; -3.728452597 0.00130538
3-hydroxy-3-methylglutaryl-
coenzyme A reductase;
3-hydroxy-3-methylglutaryl-
coenzyme A reductase; -3.724382878 0.000643924
V-type proton ATPase subunit
C 1-A;
V-type proton ATPase
subunit C 1-A; -3.716636632 0.002003759
Dihydrolipoyllysine-residue
succinyltransferase
component of 2-oxoglutarate
dehydrogenase complex,
mitochondrial;
Dihydrolipoyllysine-residue
succinyltransferase
component of 2-
oxoglutarate
dehydrogenase complex,
mitochondrial; -3.711920342 0.004897733
Melatonin-related receptor;
Alpha-1A adrenergic
receptor; -3.659726265 0.000728992
Beta-hexosaminidase;
Putative beta-
hexosaminidase
{ECO:0000250|UniProtKB:
Q04786}; -3.644980777 5.14E-08
CD63 antigen; CD63 antigen; -3.631799877 1.57E-07
. . -3.630917452 0.001257312
. . -3.62943062 0.002105263
. . -3.600974641 0.001014223
RNA-binding protein 4.1; RNA-binding protein 4.1; -3.595507625 0.003846014
TLC domain-containing
protein 2;
TLC domain-containing
protein 2; -3.585393223 0.003407502
Galactose-3-O-
sulfotransferase 3;
Galactose-3-O-
sulfotransferase 3; -3.57881184 0.003397275
256
Dynein heavy chain 3,
axonemal;
Dynein heavy chain 3,
axonemal; -3.578753964 0.003628352
. . -3.576159324 5.56E-05
Uncharacterized protein
C8orf34 homolog;
Uncharacterized protein
C8orf34; -3.571714734 0.006541546
. . -3.559304666 0.005405816
. . -3.558280234 0.010896292
. . -3.537807454 0.000651357
Vesicle transport protein
GOT1B;
Vesicle transport protein
GOT1B; -3.533819002 0.006832625
Complement C1q-like protein
3;
Complement C1q-like
protein 3; -3.521648781 0.004028591
. . -3.520062754 0.001058783
Transposon TX1
uncharacterized 149 kDa
protein; . -3.499827739 0.002054996
. . -3.487830204 0.012523462
Ladderlectin; . -3.466007837 0.012692933
. . -3.462367159 0.000538901
. . -3.451913575 0.002175628
. . -3.415922713 0.010558901
Delta-like protein 1; Delta-like protein C; -3.41080367 0.006505206
Active breakpoint cluster
region-related protein;
Active breakpoint cluster
region-related protein; -3.402054978 0.000766548
Neuroblastoma-amplified
sequence;
Neuroblastoma-amplified
sequence; -3.396411828 0.000875126
. . -3.393250072 0.004388701
Cadherin-99C
{ECO:0000303|PubMed:15708
564};
Cadherin-99C
{ECO:0000303|PubMed:15
708564}; -3.39035982 0.010088412
. . -3.388429289 0.01169044
257
Matrix metalloproteinase-17;
Matrix metalloproteinase-
17; -3.373105392 0.010063493
Protein arginine N-
methyltransferase 1-B
{ECO:0000303|PubMed:16214
893};
Protein arginine N-
methyltransferase 1-B
{ECO:0000303|PubMed:16
214893}; -3.37133627 0.011463002
Threonine synthase-like 2; Threonine synthase-like 2; -3.37066531 0.0045555
. . -3.367330021 0.000677556
Cathepsin Z; Cathepsin Z; -3.364381598 0.001494371
N-acetylserotonin O-
methyltransferase-like
protein;
N-acetylserotonin O-
methyltransferase-like
protein; -3.362123484 0.001198336
. . -3.335661107 0.015756462
. . -3.317521916 0.0111598
Maltase 2; Maltase 2; -3.313116825 0.024007725
. . -3.305113992 0.018381322
. . -3.292982664 0.017064715
Dendritic arbor reduction
protein 1
{ECO:0000312|EMBL:AAF477
85.2};
Dendritic arbor reduction
protein 1
{ECO:0000312|EMBL:AAF4
7785.2}; -3.286182662 4.86E-05
. . -3.285984845 0.008572167
. . -3.284531943 0.011895647
. . -3.260248704 0.011944447
Tubulin polyglutamylase
complex subunit 2;
Tubulin polyglutamylase
complex subunit 2; -3.24624958 0.011895647
F-box/LRR-repeat protein 18;
F-box/LRR-repeat protein
18; -3.243553299 0.005090185
. . -3.243078019 0.011117507
Sodium-dependent
multivitamin transporter;
Sodium-dependent
multivitamin transporter; -3.234074743 0.026938873
258
Growth arrest and DNA
damage-inducible protein
GADD45 alpha;
Growth arrest and DNA
damage-inducible protein
GADD45 alpha; -3.232344676 0.02083501
Cyclin-C; Cyclin-C; -3.229665857 0.014149637
. . -3.208279294 0.021135027
Carnosine N-
methyltransferase
{ECO:0000303|PubMed:26001
783, ECO:0000305,
ECO:0000312|RGD:1311863};
Carnosine N-
methyltransferase
{ECO:0000303|PubMed:26
001783, ECO:0000305,
ECO:0000312|RGD:131186
3}; -3.205192975 0.010896292
Atrial natriuretic peptide
receptor 2;
Atrial natriuretic peptide
receptor 2; -3.199973681 0.027596629
. . -3.199768287 0.0021955
. . -3.197054211 0.012845469
. . -3.196112663 0.03074935
.
Transmembrane protein
266
{ECO:0000250|UniProtKB:
Q2M3C6}; -3.191055616 0.024228467
Probable glutathione S-
transferase 8;
Probable glutathione S-
transferase 8; -3.186184142 0.000824854
Fructose-bisphosphate
aldolase;
Fructose-bisphosphate
aldolase; -3.186139714 0.024490015
. . -3.174816781 0.010896292
Otoconin-90; Otoconin-90; -3.169310558 0.007852283
Forkhead box protein D4; Forkhead box protein D4; -3.167063721 0.027545556
. . -3.158772242 0.029564146
. . -3.156062773 0.03074935
259
Ankyrin repeat domain-
containing protein 63
{ECO:0000250|UniProtKB:C9J
TQ0};
Ankyrin repeat domain-
containing protein 63
{ECO:0000250|UniProtKB:
C9JTQ0}; -3.15340628 0.016709994
Ferritin heavy chain;
Ferritin, heavy subunit
{ECO:0000303|PubMed:18
625196}; -3.135400809 0.014151865
. . -3.131931729 0.000357815
CD109 antigen; CD109 antigen; -3.126471817 0.018899561
. . -3.123717456 0.027596629
. . -3.123675628 0.017309095
Sodium/potassium/calcium
exchanger 4;
Sodium/potassium/calciu
m exchanger 4; -3.122159845 0.027716048
L-rhamnose-binding lectin
ELEL-1
{ECO:0000303|PubMed:25881
955};
L-rhamnose-binding lectin
ELEL-1
{ECO:0000303|PubMed:25
881955}; -3.121700385 0.001447343
Tubulin-specific chaperone D;
Tubulin-specific chaperone
D; -3.120170812 0.019148265
. . -3.119319387 0.004028591
. . -3.109078593 0.018844407
Dipeptidase 1; Dipeptidase 1; -3.104609785 0.011972354
SWI/SNF-related matrix-
associated actin-dependent
regulator of chromatin
subfamily B member 1;
SWI/SNF-related matrix-
associated actin-
dependent regulator of
chromatin subfamily B
member 1; -3.102641496 0.036291455
. DAZ-associated protein 2; -3.094704274 0.026798979
Putative ferric-chelate
reductase 1 homolog;
Putative ferric-chelate
reductase 1 homolog; -3.09204675 0.028847704
260
Mitochondrial folate
transporter/carrier;
Mitochondrial folate
transporter/carrier; -3.088553137 0.033293514
Carbohydrate sulfotransferase
15;
Carbohydrate
sulfotransferase 15; -3.088412163 0.001001215
Mannose-P-dolichol utilization
defect 1 protein;
Mannose-P-dolichol
utilization defect 1 protein; -3.086159867 0.04250141
. . -3.076198528 0.033044875
. . -3.067456552 0.032454637
Protein arginine N-
methyltransferase 9;
Protein arginine N-
methyltransferase 9; -3.053587465 0.024083521
. . -3.053448624 0.032105345
. . -3.052956671 0.03169037
Multiple inositol
polyphosphate phosphatase 1
{ECO:0000312|HGNC:HGNC:7
102};
Multiple inositol
polyphosphate
phosphatase 1
{ECO:0000312|HGNC:HGN
C:7102}; -3.048505018 0.043782973
T-box transcription factor
TBX2b;
T-box transcription factor
TBX2b; -3.041044702 0.037041965
Niemann-Pick C1 protein; Niemann-Pick C1 protein; -3.035691781 0.041997861
. . -3.031354601 4.91E-05
Succinate dehydrogenase
[ubiquinone] cytochrome b
small subunit, mitochondrial;
Succinate dehydrogenase
[ubiquinone] cytochrome b
small subunit,
mitochondrial; -3.023652063 0.028847704
. . -3.02177677 0.044005318
. . -3.021109593 0.038767923
Tyrosine-protein phosphatase
non-receptor type 4; . -3.020460764 0.028704505
261
Uncharacterized protein
C56G2.4;
Uncharacterized protein
C56G2.4; -3.012950404 0.038336757
. . -3.012370534 0.028031966
. . -3.009919198 0.013625066
. . -3.006115928 0.036337561
Nose resistant to fluoxetine
protein 6;
Nose resistant to
fluoxetine protein 6; -3.003280916 0.037051894
. . -3.003010835 0.024902021
. . -3.002659893 0.011895647
. . -3.000477524 0.042639447
tRNA:m(4)X modification
enzyme TRM13 homolog;
tRNA:m(4)X modification
enzyme TRM13 homolog; -2.997881136 0.044327963
Xaa-Pro aminopeptidase 1; Xaa-Pro aminopeptidase 1; -2.996183238 0.04779653
. . -2.993911681 0.046380903
PAX3- and PAX7-binding
protein 1;
PAX3- and PAX7-binding
protein 1; -2.993760659 0.024228467
Uncharacterized protein
C11orf70 homolog;
Uncharacterized protein
C11orf70 homolog; -2.979666585 0.005235692
Multiple coagulation factor
deficiency protein 2 homolog;
Multiple coagulation factor
deficiency protein 2
homolog; -2.974569338 0.051871527
. . -2.971633772 0.047048865
Derlin-1
{ECO:0000303|PubMed:15215
855};
Derlin-1
{ECO:0000303|PubMed:15
215855}; -2.967737841 0.047321824
Probable tubulin
polyglutamylase TTLL9;
Probable tubulin
polyglutamylase TTLL9; -2.965727132 0.032462304
Nardilysin; Nardilysin; -2.963775723 0.01018946
Cytoglobin-1; Cytoglobin-1; -2.958376396 0.036222667
Peroxidasin; Peroxidasin; -2.954618766 0.046429687
262
. Tetraspanin-9; -2.952419988 0.003191881
. . -2.952359135 0.037694183
HAUS augmin-like complex
subunit 3;
HAUS augmin-like complex
subunit 3; -2.951813181 0.039281694
Heat shock 70 kDa protein
12B;
Heat shock 70 kDa protein
12B; -2.94987906 0.027967055
. . -2.949760139 0.051871527
Solute carrier organic anion
transporter family member
1B1;
Solute carrier organic
anion transporter family
member 1B1; -2.949153666 0.028758314
Fibrillin-2; Fibrillin-1; -2.948226816 0.03169037
Lysosomal alpha-
mannosidase;
Lysosomal alpha-
mannosidase; -2.948088987 0.04634171
. . -2.946832422 0.020401472
. . -2.944631862 0.003303965
Receptor expression-
enhancing protein 5;
Receptor expression-
enhancing protein 5; -2.940117397 0.04709923
Pre-mRNA cleavage complex
2 protein Pcf11; . -2.940011654 0.044327963
. . -2.939536074 0.031949015
Protocadherin-16; Protocadherin-16; -2.931659185 0.056673155
Cysteine-rich secretory protein
LCCL domain-containing 2;
Cysteine-rich secretory
protein LCCL domain-
containing 2; -2.931377361 0.036291455
. . -2.930010484 0.010063493
Liver carboxylesterase 1; Liver carboxylesterase 1; -2.925483336 0.056573843
Succinate dehydrogenase
[ubiquinone] iron-sulfur
subunit, mitochondrial;
Succinate dehydrogenase
[ubiquinone] iron-sulfur
subunit, mitochondrial; -2.914401293 0.028473358
. . -2.91331762 0.047666518
. . -2.911588326 0.018524163
263
. . -2.908108358 0.030602336
. . -2.902730511 0.038044336
. . -2.902118125 0.020454241
T-box transcription factor
TBX2-B;
T-box transcription factor
TBX2-B; -2.900030161 0.04250141
Cadherin-99C
{ECO:0000303|PubMed:15708
564};
Cadherin-99C
{ECO:0000303|PubMed:15
708564}; -2.899045028 0.04779653
Glycerol-3-phosphate
phosphatase {ECO:0000305};
Glycerol-3-phosphate
phosphatase
{ECO:0000305}; -2.894131108 0.056673155
. . -2.893831494 0.044150454
V-type proton ATPase subunit
d;
V-type proton ATPase
subunit d; -2.890866259 0.05379558
Neuronal acetylcholine
receptor subunit alpha-6;
Neuronal acetylcholine
receptor subunit alpha-6; -2.888131579 0.059110228
.
Uncharacterized protein
C12orf45 homolog; -2.883311096 0.040059063
D-3-phosphoglycerate
dehydrogenase;
D-3-phosphoglycerate
dehydrogenase; -2.882816438 0.024416394
Glutathione S-transferase; Glutathione S-transferase; -2.882150843 0.027481394
Protein archease; Protein archease; -2.877576039 0.000546027
. . -2.875035653 0.040566055
. . -2.874744091 0.010659196
Integrator complex subunit 4;
Integrator complex subunit
4; -2.871238199 0.055586296
Trafficking protein particle
complex subunit 2;
Trafficking protein particle
complex subunit 2; -2.870645898 0.03623557
. . -2.869094229 0.03169037
264
Synaptopodin 2-like protein;
Synaptopodin 2-like
protein; -2.867351359 0.044041899
Protein FAM107A; Protein FAM107A; -2.865718677 0.017518987
Baculoviral IAP repeat-
containing protein 7-A;
Baculoviral IAP repeat-
containing protein 7-A; -2.865694539 0.057597128
Dystonin; Dystonin; -2.864656168 0.066758613
. . -2.859148712 0.044005318
. . -2.858729694 0.04887538
. . -2.85828911 0.03169037
Nuclear envelope integral
membrane protein 1;
Nuclear envelope integral
membrane protein 1; -2.857023143 0.046337288
Neuronal acetylcholine
receptor subunit alpha-3;
Neuronal acetylcholine
receptor subunit alpha-3; -2.856771484 0.03213114
Neuronal acetylcholine
receptor subunit alpha-10;
Neuronal acetylcholine
receptor subunit alpha-7; -2.854905896 0.052773486
. . -2.852403157 0.049703066
Adenylosuccinate synthetase
isozyme 1
{ECO:0000255|HAMAP-
Rule:MF_03126};
Adenylosuccinate
synthetase isozyme 1
{ECO:0000255|HAMAP-
Rule:MF_03126}; -2.8523347 0.024228467
. . -2.851863823 0.037051894
Alpha-latroinsectotoxin-Lt1a;
Alpha-latroinsectotoxin-
Lt1a; -2.851391105 0.058553838
U5 small nuclear
ribonucleoprotein 200 kDa
helicase;
U5 small nuclear
ribonucleoprotein 200 kDa
helicase; -2.846548661 0.061751803
Zwei Ig domain protein zig-4
{ECO:0000303|PubMed:11809
975};
Zwei Ig domain protein zig-
4
{ECO:0000303|PubMed:11
809975}; -2.846314778 0.021743945
. . -2.845004456 0.028847704
265
Protein TFG; Protein TFG; -2.841050649 0.062692146
Fructose-bisphosphate
aldolase; . -2.83963805 0.057597128
Arylacetamide deacetylase;
Arylacetamide
deacetylase; -2.835543838 0.040566055
Collagen alpha-2(IV) chain; Collagen alpha-1(IV) chain; -2.833481913 0.039853435
. . -2.831745842 0.061085582
Rho-related GTP-binding
protein RhoE;
Rho-related GTP-binding
protein RhoE; -2.831677039 0.068580802
Dehydrodolichyl diphosphate
synthase complex subunit
Nus1 {ECO:0000305};
Dehydrodolichyl
diphosphate synthase
complex subunit Nus1
{ECO:0000305}; -2.830548726 0.058553838
PI-PLC X domain-containing
protein 3;
PI-PLC X domain-
containing protein 3; -2.82908549 0.03074935
. . -2.828745828 0.044005318
. . -2.828446651 0.046826609
. . -2.826879265 0.056957635
Protein Wnt-10a; Protein Wnt-10a; -2.824851089 0.073212864
Transforming growth factor-
beta-induced protein ig-h3;
Transforming growth
factor-beta-induced
protein ig-h3; -2.823892344 0.024083521
Aldose 1-epimerase; Aldose 1-epimerase; -2.823192074 0.050903844
Cytochrome P450 4V2; Cytochrome P450 4V2; -2.820628002 0.058553838
. . -2.820357155 0.068950361
. . -2.817814801 0.037622999
. . -2.817506411 0.024481083
. . -2.815358129 0.063228359
. . -2.813352667 0.058731081
. . -2.813145051 0.027716048
266
Membrane metallo-
endopeptidase-like 1;
Membrane metallo-
endopeptidase-like 1; -2.807441298 0.012692933
Cdc42 homolog; Cdc42 homolog; -2.807067244 0.034600672
. . -2.806355107 0.062107281
. . -2.803523569 0.034484978
Alpha-1,3/1,6-
mannosyltransferase ALG2;
Alpha-1,3/1,6-
mannosyltransferase
ALG2; -2.79886246 0.050106379
Paired box protein Pax-6; Paired box protein Pax-6; -2.795228783 0.072542663
Kunitz-type serine protease
inhibitor LmKTT-1a;
Kunitz-type serine
protease inhibitor LmKTT-
1a; -2.792932533 0.076000501
. . -2.79171107 0.055681202
DNA-directed RNA
polymerase II subunit RPB2;
DNA-directed RNA
polymerase II subunit
RPB2; -2.790498578 0.049394502
Uveal autoantigen with coiled-
coil domains and ankyrin
repeats; Ankycorbin; -2.789492278 0.045554608
Putative tyrosinase-like
protein tyr-3;
Putative tyrosinase-like
protein tyr-3; -2.785711364 0.033293514
CCR4-NOT transcription
complex subunit 11;
CCR4-NOT transcription
complex subunit 11; -2.782768497 0.067368396
Kyphoscoliosis peptidase; Kyphoscoliosis peptidase; -2.780820844 0.049357743
Phospholipid scramblase 1; Phospholipid scramblase 2; -2.780180629 0.076729671
. . -2.778710475 0.011463002
Inactive pancreatic lipase-
related protein 1;
Inactive pancreatic lipase-
related protein 1; -2.775825026 0.000686386
. . -2.774810375 0.069690479
267
Transmembrane protein 214-
A;
Transmembrane protein
214-B; -2.772399085 0.050903844
Serine protease inhibitor
dipetalogastin;
Serine protease inhibitor
dipetalogastin; -2.770031211 0.00862019
. . -2.767998218 0.069500033
Kielin/chordin-like protein; Kielin/chordin-like protein; -2.76371891 0.008572167
Tyrosyl-DNA
phosphodiesterase 1;
Tyrosyl-DNA
phosphodiesterase 1; -2.760640836 0.05258197
NmrA-like family domain-
containing protein 1;
NmrA-like family domain-
containing protein 1; -2.760342656 0.058047742
Phylloquinone omega-
hydroxylase CYP4F2
{ECO:0000305};
Phylloquinone omega-
hydroxylase CYP4F2
{ECO:0000305}; -2.757226357 0.069446903
Frataxin, mitochondrial; Frataxin, mitochondrial; -2.756870061 0.059110228
. . -2.756443739 0.079020628
. . -2.756272288 0.061762376
. . -2.752688838 0.067727875
. . -2.750786326 0.079020628
. . -2.747910982 0.036337561
. . -2.745790791 0.080226699
. . -2.744548416 0.076271889
116 kDa U5 small nuclear
ribonucleoprotein component;
116 kDa U5 small nuclear
ribonucleoprotein
component; -2.742315707 0.081674804
Mitochondria-eating protein;
Mitochondria-eating
protein; -2.740856019 0.062838807
Four-domain proteases
inhibitor;
Four-domain proteases
inhibitor; -2.737859692 0.089938799
. . -2.734335265 0.002789283
. . -2.734102534 0.047800996
268
. . -2.733602073 0.081995877
V-type proton ATPase subunit
d;
V-type proton ATPase
subunit d; -2.729408441 0.079020628
. . -2.720597225 0.043145558
Ras-related protein Rab-5C;
Ras-related protein Rab-
5C; -2.716668508 0.086766284
. . -2.713350651 0.065681837
. . -2.711598247 8.67E-06
. . -2.701065223 0.078146552
. . -2.699283499 0.078004516
. . -2.699061075 0.064832876
. . -2.69841394 0.058553838
. . -2.698394398 0.025858229
Neurotrypsin; Neurotrypsin; -2.697355842 0.067675796
. . -2.696840929 0.062550052
. . -2.694869475 0.026307861
Leucine-rich repeat-containing
protein 74A
{ECO:0000250|UniProtKB:Q0V
AA2};
Leucine-rich repeat-
containing protein 74A
{ECO:0000250|UniProtKB:
Q0VAA2}; -2.693591398 0.076000501
E3 SUMO-protein ligase
RanBP2;
E3 SUMO-protein ligase
RanBP2; -2.693200353 0.085064773
. . -2.690251766 0.027481394
Phospholipid scramblase 2; Phospholipid scramblase 2; -2.689058092 0.084762754
Relaxin receptor 2; Relaxin receptor 2; -2.687686635 0.032462304
. . -2.687577869 1.51E-09
. . -2.687339974 0.020853365
269
Cytoplasmic tRNA 2-thiolation
protein 1
{ECO:0000255|HAMAP-
Rule:MF_03053};
Cytoplasmic tRNA 2-
thiolation protein 1
{ECO:0000255|HAMAP-
Rule:MF_03053}; -2.686817077 0.071467635
DNA-directed RNA
polymerases I, II, and III
subunit RPABC1;
DNA-directed RNA
polymerases I, II, and III
subunit RPABC1; -2.682249693 0.07495783
. . -2.681201952 0.055486066
. . -2.680066805 0.054631854
Lysozyme; Lysozyme; -2.677614308 0.086890986
Protocadherin Fat 1;
Cadherin EGF LAG seven-
pass G-type receptor 3; -2.677215904 0.093007754
. . -2.676509564 0.009572283
. . -2.673234336 0.066701622
. . -2.672264422 0.099869121
. . -2.67173142 0.058553838
Josephin-1; Josephin-1; -2.669646215 0.053195592
Cytochrome P450 1A1; Cytochrome P450 1A1; -2.666712954 0.062107281
Sodium-dependent phosphate
transport protein 2B;
Sodium-dependent
phosphate transport
protein 2A; -2.666334174 0.096815195
. . -2.66571871 0.098065333
Microsomal glutathione S-
transferase 3;
Microsomal glutathione S-
transferase 3; -2.663021065 0.072542663
Fibronectin type III domain-
containing protein;
Fibronectin type III domain-
containing protein 2; -2.662338511 2.76E-05
. . -2.661632984 0.099486875
. . -2.659616662 9.03E-07
270
Gamma-interferon-inducible
lysosomal thiol reductase;
Gamma-interferon-
inducible lysosomal thiol
reductase; -2.659516425 3.04E-05
Probable peptidylglycine alpha-
hydroxylating monooxygenase
1
{ECO:0000312|WormBase:Y7
1G12B.4};
Probable peptidylglycine
alpha-hydroxylating
monooxygenase 1
{ECO:0000312|WormBase:
Y71G12B.4}; -2.658230159 0.007295562
G protein-activated inward
rectifier potassium channel 4;
G protein-activated inward
rectifier potassium
channel 2; -2.656456317 0.002453739
. . -2.654628113 0.041640522
Insulin-induced gene 2
protein;
Insulin-induced gene 2
protein; -2.654042862 0.09932486
. . -2.650134176 0.054745319
. . -2.649713961 0.09052387
. . -2.645624635 0.052026232
. . -2.638978783 0.099093478
Transmembrane protein
FAM155A;
Transmembrane protein
FAM155A; -2.637075113 0.074812227
. . -2.63696494 0.076000501
. . -2.636238447 0.074299294
. . -2.63574478 0.086766284
. . -2.635571305 0.062838807
60 kDa SS-A/Ro
ribonucleoprotein;
60 kDa SS-A/Ro
ribonucleoprotein; -2.632264636 0.094798971
LIM domain-binding protein 2;
LIM domain-binding
protein 2; -2.630897842 0.09932486
. . -2.630273169 0.045963781
. . -2.630259119 0.028870291
. . -2.624433353 0.099869121
271
. . -2.623992616 0.063882026
. . -2.622708414 0.063465396
U4/U6.U5 tri-snRNP-
associated protein 2; . -2.622570879 0.067138471
Dolichyl pyrophosphate
Man9GlcNAc2 alpha-1,3-
glucosyltransferase;
Dolichyl pyrophosphate
Man9GlcNAc2 alpha-1,3-
glucosyltransferase; -2.620603345 0.079020628
Transforming growth factor-
beta-induced protein ig-h3;
Transforming growth
factor-beta-induced
protein ig-h3; -2.616600334 4.14E-05
. . -2.615647169 0.087561162
Lachesin; Lachesin; -2.613072325 0.094798971
Transmembrane protein 218;
Transmembrane protein
218; -2.612054736 0.029178292
. . -2.60738445 0.079020628
Peptidase inhibitor 16; Peptidase inhibitor 16; -2.605035612 0.081674804
. . -2.603157378 0.078634928
. . -2.59882057 0.068723929
Serine/threonine-protein
phosphatase 2A 65 kDa
regulatory subunit A alpha
isoform;
Serine/threonine-protein
phosphatase 2A 65 kDa
regulatory subunit A alpha
isoform; -2.597420594 0.068485241
. . -2.595987379 0.085475028
Zinc finger protein 26; Zinc finger protein 26; -2.595373983 0.099056212
. . -2.593892757 0.093758217
. . -2.593888701 0.059031879
Neuroendocrine convertase 2;
Neuroendocrine
convertase 2; -2.593477526 0.092699889
. . -2.59121807 8.12E-05
. . -2.589345491 0.061751803
272
Glutamate receptor
ionotropic, delta-2;
Glutamate receptor
ionotropic, kainate 1; -2.588890846 0.09932486
Mannose-1-phosphate
guanyltransferase alpha-A;
Mannose-1-phosphate
guanyltransferase alpha-A; -2.587676868 0.083415072
. . -2.587222561 0.04601215
Leukocyte receptor cluster
member 9;
Leukocyte receptor cluster
member 9; -2.585529312 0.060359174
. . -2.584189908 0.067069655
. . -2.582053967 0.058731081
.
Low-density lipoprotein
receptor-related protein
12; -2.572744828 0.035948419
Putative transcription factor
SOX-14;
Putative transcription
factor SOX-14; -2.567503436 0.000918542
Hepatic lectin; Hepatic lectin; -2.566746579 0.020611556
Prestin;
Solute carrier family 26
member 9; -2.566253073 0.080735539
. . -2.559932214 0.048079952
. . -2.559494076 0.09962083
. . -2.558020199 0.072985754
. . -2.554416271 0.040089937
. . -2.548896927 0.043399506
. . -2.548355829 0.095410238
. . -2.546203806 0.024826314
. . -2.545891918 0.058731081
. . -2.544066177 0.07601814
. . -2.541587147 0.02066311
Phenylalanine--tRNA ligase
alpha subunit;
Phenylalanine--tRNA
ligase alpha subunit; -2.540612356 0.097394271
273
Ferric-chelate reductase 1; Ferric-chelate reductase 1; -2.538907474 0.036715327
. . -2.534202405 0.098286176
. . -2.529353563 0.035170341
. . -2.529227414 0.095113032
Mitochondria-eating protein;
Mitochondria-eating
protein; -2.525763526 0.078146552
. . -2.52417236 0.012523462
. . -2.522445024 0.04779653
. . -2.520998107 0.094798971
. . -2.516852069 0.090338327
. . -2.513907122 0.000871314
. . -2.513549052 0.017485613
. . -2.513456908 0.017697238
. . -2.513336048 0.063803032
. . -2.510743511 0.07344971
. . -2.510073394 0.079742571
Nuclear pore complex protein
Nup98-Nup96;
Nuclear pore complex
protein Nup98-Nup96; -2.507801729 0.085487446
. . -2.503009694 0.007293902
Pregnancy zone protein; Pregnancy zone protein; -2.502945275 0.07495783
. . -2.501622659 0.09932486
Protocadherin-9; Protocadherin-9; -2.5015924 0.09052387
Perivitellin-2 67 kDa subunit;
Perivitellin-2 67 kDa
subunit; -2.496782354 0.044327963
Calcitonin gene-related
peptide type 1 receptor;
Calcitonin gene-related
peptide type 1 receptor; -2.494414256 0.052114946
. . -2.479932255 9.07E-07
Splicing factor 3B subunit 4;
Splicing factor 3B subunit
4; -2.479554613 0.084555328
. . -2.474268417 0.032083951
274
. . -2.470239591 0.095868807
. . -2.468809399 0.032462304
. . -2.465581499 0.085064773
Electron transfer flavoprotein
regulatory factor 1
{ECO:0000250|UniProtKB:Q6I
PR1}; . -2.459665295 0.032462304
BTB/POZ domain-containing
protein KCTD7;
BTB/POZ domain-
containing protein KCTD7; -2.453960989 0.079626361
Cytochrome P450 3A29; Cytochrome P450 3A29; -2.432736441 0.077086306
Actin, cytoplasmic; Actin, cytoplasmic; -2.4322587 0.049394502
Aquaporin-4; Aquaporin-4; -2.429441165 0.002054996
Pituitary homeobox x
{ECO:0000250|UniProtKB:Q9
W5Z2};
Pituitary homeobox x
{ECO:0000250|UniProtKB:
Q9W5Z2}; -2.428584863 0.06353297
. . -2.425685519 0.079020628
. . -2.423138908 0.055646329
Peptidyl-prolyl cis-trans
isomerase FKBP14;
Peptidyl-prolyl cis-trans
isomerase FKBP14; -2.422140707 1.05E-05
Actin-related protein 2/3
complex subunit 3;
Actin-related protein 2/3
complex subunit 3; -2.410112412 0.038336757
Galectin-6; Galectin-6; -2.409298941 0.030426071
. . -2.408802156 0.027625421
. . -2.408048523 0.064563583
. . -2.406567923 0.003191881
. . -2.394524897 9.41E-05
.
Cell death specification
protein 2; -2.394466775 0.000546027
. . -2.386268002 0.055264269
. . -2.380952063 0.089665291
275
Protein-glutamine gamma-
glutamyltransferase K;
Protein-glutamine gamma-
glutamyltransferase K; -2.351573164 0.055486066
. . -2.349778737 0.016958899
Nose resistant to fluoxetine
protein 6;
Nose resistant to
fluoxetine protein 6; -2.348755399 0.067138471
. . -2.331064584 0.039003472
. . -2.31611968 0.079020628
Short-chain specific acyl-CoA
dehydrogenase,
mitochondrial;
Short-chain specific acyl-
CoA dehydrogenase,
mitochondrial; -2.310098297 0.047321824
F-box only protein 4; F-box only protein 4; -2.307500265 0.023907929
Intraflagellar transport
protein 27 homolog;
Intraflagellar transport
protein 27 homolog; -2.306544786 0.000215398
. . -2.30420585 0.045331947
Tubulin alpha-5 chain; Tubulin alpha-5 chain; -2.297574071 0.033251106
Neuronal PAS domain-
containing protein 4
{ECO:0000305};
Neuronal PAS domain-
containing protein 4
{ECO:0000305}; -2.294448113 0.026094485
. . -2.284621648 0.047341065
Phospholipase A-2-activating
protein;
Phospholipase A-2-
activating protein; -2.278455892 0.041718561
Endoplasmic reticulum-Golgi
intermediate compartment
protein 2;
Endoplasmic reticulum-
Golgi intermediate
compartment protein 2; -2.278361619 0.05598414
. . -2.277780394 0.055732433
. . -2.270828196 1.71E-10
. . -2.269361683 1.16E-05
SPARC; SPARC; -2.267072899 0.003768072
Fascin; Fascin; -2.253186281 0.000249007
. . -2.248177835 0.055817284
276
E3 ubiquitin-protein ligase
arih1;
E3 ubiquitin-protein ligase
arih1; -2.245324718 0.000677556
. . -2.23707403 0.042639447
. . -2.230831459 0.03681854
. . -2.228346868 0.038048808
Proline-rich transmembrane
protein 1;
Proline-rich
transmembrane protein 1; -2.223933145 0.094919224
Usher syndrome type-1G
protein;
Usher syndrome type-1G
protein; -2.220531171 0.062107281
. . -2.219530818 0.022692346
Dolichyl-
diphosphooligosaccharide--
protein glycosyltransferase
subunit STT3A;
Dolichyl-
diphosphooligosaccharide--
protein glycosyltransferase
subunit STT3A; -2.216435914 0.016959344
. . -2.216175154 0.021803264
. . -2.211631655 0.019552738
. . -2.2090863 0.079427328
. . -2.199113172 0.018346022
Tetratricopeptide repeat
protein 30A;
Tetratricopeptide repeat
protein 30A; -2.198247227 0.057970526
. . -2.188314079 0.040059063
Innexin unc-9; Innexin unc-9; -2.188251466 0.000629609
EKC/KEOPS complex subunit
LAGE3; . -2.186488126 0.051871527
Glutaminyl-peptide
cyclotransferase;
Glutaminyl-peptide
cyclotransferase; -2.173338923 0.04394338
Cartilage matrix protein; Cartilage matrix protein; -2.165676044 0.012523462
Putative defense protein 1; Putative defense protein 1; -2.139242745 0.006791211
277
Putative defense protein 3; Putative defense protein 3; -2.134004209 0.025858229
. . -2.128869189 0.000338893
. . -2.111953201 0.095410238
. . -2.104169688 5.39E-06
Multidrug resistance-
associated protein 1;
Multidrug resistance-
associated protein 1; -2.103417772 0.050738359
. . -2.102402758 0.052679589
. . -2.097739998 0.092851343
Dihydrolipoyl dehydrogenase,
mitochondrial;
Dihydrolipoyl
dehydrogenase,
mitochondrial; -2.095993582 0.089437722
Major facilitator superfamily
domain-containing protein 10;
Major facilitator
superfamily domain-
containing protein 10; -2.09517192 0.048994174
. . -2.083785345 5.18E-08
. . -2.083534921 3.65E-05
DBH-like monooxygenase
protein 1 homolog;
DBH-like monooxygenase
protein 1 homolog; -2.081542996 0.047800996
. . -2.075590407 0.000215398
Superoxide dismutase [Cu-
Zn];
Superoxide dismutase [Cu-
Zn]; -2.067764545 9.16E-09
Fibropellin-1; Fibropellin-1; -2.053367607 0.081259761
. . -2.028373314 9.03E-07
Electron transfer flavoprotein
subunit alpha, mitochondrial;
Electron transfer
flavoprotein subunit alpha,
mitochondrial; -2.023319339 0.027310247
.
Tumor necrosis factor
receptor superfamily
member 5; -2.022181715 0.007345903
278
Ferric-chelate reductase 1; Ferric-chelate reductase 1; -2.010171592 1.45E-11
. . -1.991986945 0.008228912
Ubiquitin carboxyl-terminal
hydrolase 14;
Ubiquitin carboxyl-
terminal hydrolase 14; -1.99158302 0.039723472
Solute carrier family 13
member 5;
Solute carrier family 13
member 3; -1.986818887 0.026024155
Putative defense protein 3; Putative defense protein 3; -1.984330535 0.03058008
. Protein AMBP; -1.978957366 0.026270794
Sequestosome-1; Sequestosome-1; -1.976819942 0.01762589
Peroxidase-like protein; Peroxidase-like protein; -1.974701034 0.066701622
Probable 39S ribosomal
protein L45, mitochondrial;
Probable 39S ribosomal
protein L45, mitochondrial; -1.967875507 0.063465396
. . -1.953467606 0.09052387
. . -1.950347194 0.049334527
. . -1.938926789 0.059502638
Myosin light chain kinase,
smooth muscle; . -1.935385008 0.00023923
. . -1.930302399 0.065755938
Collagen alpha-1(IV) chain; Collagen alpha-1(IV) chain; -1.930066507 0.085409496
. . -1.926941408 0.089437722
Splicing factor, proline- and
glutamine-rich;
Non-POU domain-
containing octamer-
binding protein; -1.924837094 0.066058566
. . -1.924773356 0.066701622
DBH-like monooxygenase
protein 1 homolog;
DBH-like monooxygenase
protein 1 homolog; -1.923881671 0.008308293
279
Hemicentin-2;
Hemicentin-2
{ECO:0000305}; -1.922838751 0.046662208
T-complex protein 1 subunit
zeta;
T-complex protein 1
subunit zeta; -1.920009803 0.050066159
Threonylcarbamoyladenosine
tRNA methylthiotransferase;
Threonylcarbamoyladenosi
ne tRNA
methylthiotransferase; -1.918665383 0.092699889
Rho guanine nucleotide
exchange factor 12;
Rho guanine nucleotide
exchange factor 12; -1.91552999 0.020600901
Runt-related transcription
factor 1;
Runt-related transcription
factor 1; -1.905177337 0.066701622
Mitogen-activated protein
kinase kinase kinase 8;
Serine/threonine-protein
kinase Tao {ECO:0000305}; -1.902232332 0.044327963
Hemocytin; Mucin-5AC {ECO:0000305}; -1.894299443 1.05E-06
. . -1.886735733 0.002140475
Transcriptional regulator ERG
homolog;
Transcriptional regulator
ERG homolog; -1.88612213 0.099869121
. . -1.884419245 0.030905441
. . -1.884002425 0.026090764
. . -1.877708832 0.06153006
Cytosolic phospholipase A2;
Cytosolic phospholipase
A2; -1.873641677 0.079020628
. . -1.86655333 2.19E-09
A disintegrin and
metalloproteinase with
thrombospondin motifs 16;
A disintegrin and
metalloproteinase with
thrombospondin motifs 16; -1.864351715 0.028758314
BTB/POZ domain-containing
protein 3;
BTB/POZ domain-
containing protein 3; -1.857824955 0.012845469
280
Junctional adhesion molecule
B;
Junctional adhesion
molecule B; -1.852951446 0.007852283
. . -1.849429915 0.061585288
Minor histocompatibility
antigen H13;
Minor histocompatibility
antigen H13; -1.847831556 0.035864291
. . -1.845557163 0.001439588
. . -1.844734752 0.001834909
. . -1.844167556 0.012845469
SWI/SNF-related matrix-
associated actin-dependent
regulator of chromatin
subfamily D member 1;
SWI/SNF-related matrix-
associated actin-
dependent regulator of
chromatin subfamily D
member 1; -1.821495385 0.017697238
. . -1.818036354 0.09778625
PDZ and LIM domain protein
3;
LIM domain-binding
protein 3; -1.810324616 0.000840381
Endoplasmin
{ECO:0000250|UniProtKB:P08
113};
Endoplasmin
{ECO:0000250|UniProtKB:
P08113}; -1.807830564 0.011077422
Tubulin alpha-1 chain; . -1.802339021 0.024306257
. . -1.796123805 0.005144978
Sperm-associated antigen 8
{ECO:0000312|MGI:MGI:3056
295};
Sperm-associated antigen
8
{ECO:0000312|MGI:MGI:3
056295}; -1.791586779 0.031302181
. . -1.790209943 0.00274731
Ectonucleoside triphosphate
diphosphohydrolase 1;
Ectonucleoside
triphosphate
diphosphohydrolase 1; -1.785908172 0.069486974
Integrin beta-5; Integrin beta-7; -1.784769619 0.006762473
281
Mammalian ependymin-
related protein 1;
Mammalian ependymin-
related protein 1; -1.7825031 3.27E-06
Periostin; Periostin; -1.776315808 0.048079952
. . -1.77560606 0.066701622
. . -1.772945165 0.063465396
Neuronal acetylcholine
receptor subunit alpha-10;
Neuronal acetylcholine
receptor subunit alpha-10; -1.771351566 0.037476907
. . -1.760311658 3.41E-05
Protein disulfide-isomerase
A5;
Protein disulfide-
isomerase A5; -1.759734941 0.061505752
. . -1.757000364 0.079626361
Tubulin beta-4B chain; . -1.753721882 0.032462304
. . -1.745857774 0.008239604
. . -1.743993378 0.011150978
Sterol regulatory element-
binding protein 1;
Sterol regulatory element-
binding protein 1; -1.735711052 0.014151865
. . -1.732452059 0.020988855
ER membrane protein
complex subunit 3;
ER membrane protein
complex subunit 3; -1.731720804 0.000692112
. . -1.728719731 0.000559024
Homeobox protein
extradenticle;
Homeobox protein
extradenticle; -1.726466092 0.012441193
. . -1.725162994 0.051165917
. . -1.721561484 0.099835661
Spermidine synthase; Spermidine synthase; -1.721423288 0.072785694
Cytochrome P450 2B4; Cytochrome P450 2B4; -1.711676846 0.001447869
Excitatory amino acid
transporter; . -1.708444319 0.031302181
Cysteine and glycine-rich
protein 2;
Cysteine and glycine-rich
protein 2; -1.695502832 0.041869017
282
Inward rectifier potassium
channel 2;
ATP-sensitive inward
rectifier potassium
channel 12; -1.694264839 0.058845007
Prosaposin; Prosaposin; -1.691059996 0.000677254
. . -1.681397889 0.046722966
Transcription factor Sp5; Transcription factor Sp5; -1.681013369 0.065141057
. . -1.67203192 0.026024155
. . -1.666425369 0.006360383
. . -1.664831866 0.052114282
Dynein beta chain, ciliary; Dynein beta chain, ciliary; -1.663573097 0.001862216
. . -1.660688894 0.000706501
Fibropellin-1; Fibropellin-1; -1.657874949 0.086766284
78 kDa glucose-regulated
protein;
78 kDa glucose-regulated
protein; -1.639942542 0.0004034
. . -1.632883208 0.023744504
Major vault protein
{ECO:0000312|EMBL:DAA056
61.1};
Major vault protein
{ECO:0000312|EMBL:DAA0
5661.1}; -1.632374574 0.024968503
. . -1.627455821 0.000521165
Innexin unc-9; Innexin unc-9; -1.624831112 0.062107281
. . -1.622284434 0.059110228
Solute carrier family 25
member 38-A
{ECO:0000255|HAMAP-
Rule:MF_03064};
Solute carrier family 25
member 38-B
{ECO:0000255|HAMAP-
Rule:MF_03064}; -1.613268074 0.053099379
Serine/threonine-protein
kinase PAK 3;
Serine/threonine-protein
kinase PAK 1; -1.607194883 0.000217739
. . -1.596849354 0.080315082
Chitotriosidase-1; Chitotriosidase-1; -1.585621614 0.034600672
. . -1.580296079 0.021138687
. . -1.575102109 0.081900052
283
. . -1.571301889 0.037434049
Lactadherin; Lactadherin; -1.56665731 0.002186869
. . -1.562457148 0.07575335
E3 ubiquitin-protein ligase
NEURL1;
E3 ubiquitin-protein ligase
NEURL1; -1.562433756 0.009373138
. . -1.557338672 0.036733997
Aspartate--tRNA ligase,
cytoplasmic;
Aspartate--tRNA ligase,
cytoplasmic; -1.555676322 0.026938873
Collagen alpha-4(VI) chain; Collagen alpha-4(VI) chain; -1.553687332 0.002661619
.
MAM domain-containing
protein 2; -1.546638153 0.000214703
Myosin light chain kinase,
smooth muscle;
Myosin light chain kinase,
smooth muscle; -1.54516041 0.067675796
Adhesion G-protein coupled
receptor D1
{ECO:0000303|PubMed:25713
288};
Adhesion G-protein
coupled receptor D1
{ECO:0000303|PubMed:25
713288}; -1.544098649 0.048341139
. . -1.542944772 0.000968598
Protein FAM111A; Protein FAM111A; -1.541638562 0.069690479
Galectin-9C; Galectin-4; -1.527503393 0.066701622
. . -1.526931914 0.008097498
Dual specificity protein
phosphatase 7;
Dual specificity protein
phosphatase 7; -1.520482824 0.024228467
Calponin-2; Calponin-2; -1.517995743 0.027310247
CCAAT/enhancer-binding
protein epsilon;
CCAAT/enhancer-binding
protein epsilon; -1.509420993 0.015077049
Zwei Ig domain protein zig-4
{ECO:0000303|PubMed:11809
975}; . -1.508957897 0.000643924
284
Beta-1,3-glucan-binding
protein
{ECO:0000312|EMBL:AAM212
13.1};
Beta-1,3-glucan-binding
protein
{ECO:0000312|EMBL:AAM
21213.1}; -1.505669645 0.084661428
. . -1.499702766 0.006366864
Junctophilin-1; Junctophilin-1; -1.492678162 0.034600672
Myosin regulatory light chain,
smooth muscle;
Myosin regulatory light
chain, smooth muscle; -1.492030111 3.57E-08
. . -1.486624231 0.001372747
MAM domain-containing
glycosylphosphatidylinositol
anchor protein 2;
Synaptogenesis protein syg-
2 {ECO:0000305}; -1.482897032 0.001439588
. . -1.47942272 0.001518689
Tubulin alpha-3 chain; . -1.478535305 0.029171037
Pre-mRNA-splicing factor
RBM22;
Pre-mRNA-splicing factor
RBM22; -1.472958159 0.080713559
Eukaryotic initiation factor 4A-
I;
Eukaryotic initiation factor
4A-I; -1.470880192 0.007852283
T-complex protein 1 subunit
alpha;
T-complex protein 1
subunit alpha; -1.469898983 0.024033954
Apolipoprotein D; Apolipoprotein D; -1.463566067 0.079020628
. . -1.460021769 6.68E-06
. . -1.459206353 0.028606466
. . -1.457604422 0.00115153
. . -1.45734463 0.028947225
Sulfotransferase family
cytosolic 1B member 1;
Sulfotransferase family
cytosolic 1B member 1; -1.444997747 0.04779653
X-box-binding protein 1
{ECO:0000250|UniProtKB:P17
861};
X-box-binding protein 1
{ECO:0000250|UniProtKB:
P17861}; -1.444867778 0.000643924
. . -1.437979913 0.030110773
285
Dynein heavy chain 5,
axonemal;
Dynein heavy chain 5,
axonemal; -1.434258148 0.043129345
Filamin-A; Filamin-A; -1.428094931 0.022267015
Annexin A11; Annexin A11; -1.426840809 0.003552947
Inactive carboxypeptidase-like
protein X2;
Inactive carboxypeptidase-
like protein X2; -1.423062456 0.012845469
. . -1.413873806 0.000766548
RNA-binding protein 39; RNA-binding protein 39; -1.411931302 0.00045644
. . -1.405601604 0.000110971
Cell surface hyaluronidase
{ECO:0000303|PubMed:10767
548};
Cell surface hyaluronidase
{ECO:0000303|PubMed:10
767548}; -1.392446284 0.046117425
Low-density lipoprotein
receptor-related protein 1B;
Low-density lipoprotein
receptor-related protein
1B; -1.387989925 0.000766548
. . -1.384202156 0.004028591
T-complex protein 1 subunit
eta;
T-complex protein 1
subunit eta; -1.384179859 0.089437722
. . -1.382293964 0.016998182
Filamin-A; Filamin-A; -1.381132804 0.023475575
Glyoxylate
reductase/hydroxypyruvate
reductase;
Glyoxylate
reductase/hydroxypyruvate
reductase; -1.380981918 0.016709994
Thimet oligopeptidase; Thimet oligopeptidase; -1.370983734 0.036027235
Chitotriosidase-1; Chitotriosidase-1; -1.369824818 0.071113596
Tubulin alpha-3 chain; Tubulin alpha-3 chain; -1.361046913 0.053618457
Putative defense protein 3; Putative defense protein 3; -1.36103217 0.036992725
Patched domain-containing
protein 3;
Patched domain-
containing protein 3; -1.355474281 0.099093478
Alpha-L-fucosidase; Alpha-L-fucosidase; -1.348410302 0.03074935
286
Tropomyosin; Tropomyosin; -1.346974533 0.050445015
. . -1.338846742 0.079020628
. . -1.338432039 0.04779653
Malate dehydrogenase,
cytoplasmic;
Malate dehydrogenase,
cytoplasmic; -1.337598866 0.073910183
Frizzled-4; Frizzled-4; -1.333215835 0.024481083
Myosin heavy chain, striated
muscle;
Myosin heavy chain,
striated muscle; -1.332918181 6.67E-05
Calponin homolog OV9M; Calponin homolog OV9M; -1.33269322 0.000390331
. . -1.332101087 0.073231603
Protein PIF; Protein PIF; -1.325955516 0.09962083
. . -1.322774739 3.89E-12
. . -1.320271277 8.37E-06
. . -1.316130117 0.048995098
. . -1.300110439 0.017278467
Actin-like protein 6B; Actin-like protein 6B; -1.299781573 0.09932486
Lysosomal aspartic protease; . -1.286840375 0.014227695
. . -1.277839855 0.059110228
Peptidase inhibitor 16; Peptidase inhibitor 16; -1.274403497 2.32E-06
. . -1.270628766 0.006563842
Mammalian ependymin-
related protein 1;
Mammalian ependymin-
related protein 1; -1.27060166 1.05E-05
Myosin heavy chain, striated
muscle;
Myosin heavy chain,
striated muscle; -1.264575047 0.050903844
Stomatin-2; Stomatin-2; -1.245918256 0.047881367
Trithorax group protein osa;
Trithorax group protein
osa; -1.243088425 0.04779653
. . -1.240835964 0.01873378
. . -1.235178183 0.005144978
14-3-3 protein zeta/delta; 14-3-3 protein zeta/delta; -1.226214192 0.040918013
AP-2 complex subunit mu; AP-2 complex subunit mu; -1.223672444 0.025808577
287
Periostin; Periostin; -1.219369698 0.001610361
Apolipoprotein D; Apolipoprotein D; -1.20923888 0.054337532
Ankyrin repeat domain-
containing protein 7;
Ankyrin repeat domain-
containing protein 7; 1.203356187 0.046104935
cGMP-specific 3',5'-cyclic
phosphodiesterase
{ECO:0000250|UniProtKB:Q9V
FI9};
cGMP-specific 3',5'-cyclic
phosphodiesterase
{ECO:0000250|UniProtKB:
Q9VFI9}; 1.206401363 0.01588695
Basal body-orientation factor
1
{ECO:0000250|UniProtKB:Q8
ND07};
Basal body-orientation
factor 1
{ECO:0000250|UniProtKB:
Q8ND07}; 1.209221695 0.006754672
Tudor domain-containing
protein 1;
Tudor domain-containing
protein 1; 1.210123605 0.058054264
. . 1.217764759 0.006306768
.
Uncharacterized protein
C6orf163 homolog; 1.218200716 0.024481083
. . 1.231789375 0.078758997
Transcription initiation factor
TFIID subunit 1-like;
Transcription initiation
factor TFIID subunit 1; 1.234611534 0.021148992
Melanoma-derived growth
regulatory protein;
Melanoma inhibitory
activity protein 3; 1.242341985 0.094798971
. . 1.245011777 0.020554131
. . 1.248865135 0.052825009
von Willebrand factor D and
EGF domain-containing
protein;
von Willebrand factor D
and EGF domain-
containing protein; 1.266463088 0.008149924
Protein DEK; Protein DEK; 1.271664085 0.005869116
. . 1.280777423 0.013128939
. . 1.28351452 0.02083501
288
RE1-silencing transcription
factor B;
RE1-silencing transcription
factor A; 1.29213983 0.040906741
UPF0430 protein CG31712;
Arginine and glutamate-
rich protein 1-A; 1.310116582 0.045963781
. . 1.315528653 0.095196631
PHD finger protein 14; PHD finger protein 14; 1.315881923 0.026798979
. . 1.323253718 0.033293514
Neuroguidin-A; Neuroguidin; 1.323540986 0.092851343
. . 1.326761386 0.027596629
. . 1.327221735 0.000559024
Protein SCAF8; Protein SCAF8; 1.329708722 0.01850819
Aspartyl/asparaginyl beta-
hydroxylase;
Aspartyl/asparaginyl beta-
hydroxylase; 1.34201858 0.014910953
. . 1.359700199 0.000537863
UPF0430 protein CG31712;
Arginine and glutamate-
rich protein 1; 1.367666678 0.021298953
Dynamin-binding protein; Dynamin-binding protein; 1.37220847 0.036291455
.
Uncharacterized protein
C6orf163 homolog; 1.376117166 0.018346022
Synaptotagmin-like protein 5;
Synaptotagmin-like protein
5; 1.393754515 0.026112862
. . 1.419420915 0.058054636
. . 1.426903232 0.039387866
Mblk-1-related factor 1
{ECO:0000303|PubMed:16139
210};
Mblk-1-related factor 1
{ECO:0000303|PubMed:16
139210}; 1.429426349 0.058640738
Coiled-coil domain-containing
protein 83;
Coiled-coil domain-
containing protein 83; 1.430085464 0.049394502
Ubiquitin carboxyl-terminal
hydrolase 8;
Ubiquitin carboxyl-
terminal hydrolase 8; 1.433418669 0.041437427
289
MORC family CW-type zinc
finger protein 2A;
MORC family CW-type zinc
finger protein 2A; 1.435253266 0.002248038
. . 1.451883781 0.0955722
Zinc finger and BTB domain-
containing protein 44;
Zinc finger and BTB
domain-containing protein
44; 1.460807392 0.089050608
. . 1.46713146 0.000169569
WW domain-binding protein
11;
WW domain-binding
protein 11; 1.469418649 0.047853799
. . 1.477452356 0.000521165
. . 1.490660008 0.064517988
. . 1.491940999 0.071038399
Protein HGV2;
Nuclear autoantigenic
sperm protein; 1.501029388 0.050738359
. Collagen alpha-1(IX) chain; 1.526238282 0.098286176
Epithelial cell-transforming
sequence 2 oncogene-like;
Epithelial cell-
transforming sequence 2
oncogene-like; 1.553334295 0.014148265
Serine/threonine-protein
phosphatase 6 regulatory
ankyrin repeat subunit B;
Ankyrin-3
{ECO:0000303|PubMed:78
36469}; 1.576470825 1.59E-05
. . 1.591108054 0.07486182
. . 1.609400982 0.036992725
WD repeat-containing protein
87;
WD repeat-containing
protein 87; 1.612332687 0.008550859
Leucine-rich repeat-containing
protein 49;
Leucine-rich repeat-
containing protein 49; 1.668438016 0.055646329
. . 1.706550775 0.09052387
Tetratricopeptide repeat
protein 32;
Tetratricopeptide repeat
protein 32; 1.721016434 0.091744954
290
. . 1.745665417 0.027967055
. . 1.761949456 0.041640522
Protein HEXIM1; Protein HEXIM1; 1.869720718 0.032894056
. . 1.875177318 4.92E-05
. . 1.87697364 0.071587008
. . 1.948296019 0.079626361
. . 1.958528351 0.075501557
. . 1.982331027 0.012195112
. . 2.006921087 0.010663513
A disintegrin and
metalloproteinase with
thrombospondin motifs 3;
A disintegrin and
metalloproteinase with
thrombospondin motifs 3; 2.011685208 0.015756462
Stereocilin; Stereocilin; 2.127814239 0.015756462
. . 2.195545901 0.047321824
. . 2.345495591 0.058553838
. . 2.352126488 0.091744954
. . 2.370542825 0.036667278
Carbonic anhydrase 12; Carbonic anhydrase 2; 2.373890786 0.041640522
Protein FAM117B; Protein FAM117B; 2.471623257 0.018937463
. . 2.476546064 0.078146552
. . 2.5181772 0.059110228
Calmodulin; Calmodulin; 2.615864111 0.026835598
. . 2.616575595 0.027625421
. . 2.63007132 0.091744954
. . 2.635800239 0.095868807
. . 2.76224296 0.089437722
Leucine-rich repeat-containing
protein 74A
{ECO:0000250|UniProtKB:Q0V
AA2};
Leucine-rich repeat-
containing protein 74A
{ECO:0000250|UniProtKB:
Q0VAA2}; 3.008715793 0.052440432
. . 3.401840974 9.98E-05
291
Blastx against
Uniprot
Blastp against
Uniprot log2FoldChange Adjusted p-value log2FoldChange Adjusted p-value log2FoldChange Adjusted p-value
. . -1.795638615 0.069707253 -1.695301029 0.062071768 -2.522445024 0.04779653
Periostin; Periostin; -2.002706249 3.17E-09 -2.203386745 4.12E-12 -1.219369698 0.001610361
Minor
histocompatibili
ty antigen H13;
Minor
histocompatibilit
y antigen H13; -1.978850403 0.003002222 -1.836932259 0.003615013 -1.847831556 0.035864291
Periostin; Periostin; -2.524216485 6.62E-06 -2.43383177 6.25E-06 -1.776315808 0.048079952
Cathepsin L; Cathepsin L; -2.079733059 0.036188498 -1.791679539 0.062711253 -4.529318999 3.42E-05
Runt-related
transcription
factor 1;
Runt-related
transcription
factor 1; -2.001202882 0.006883675 -1.651277236 0.022768967 -1.905177337 0.066701622
T-complex
protein 1
subunit zeta;
T-complex
protein 1 subunit
zeta; -1.883208157 0.005686952 -1.677708737 0.009890213 -1.920009803 0.050066159
V-type proton
ATPase subunit C
1-A;
V-type proton
ATPase subunit C
1-A; -1.935643037 0.064835407 -1.89960387 0.047847912 -3.716636632 0.002003759
Lactadherin; Lactadherin; -1.401930588 0.002170912 -1.675378043 2.40E-05 -1.56665731 0.002186869
. . -2.77517849 4.91E-07 -2.289892615 3.28E-05 -1.664831866 0.052114282
Junctional
adhesion
molecule B;
Junctional
adhesion
molecule B; -3.181701707 3.20E-10 -3.651768095 2.42E-14 -1.852951446 0.007852283
Differential Expression between
0 and 3 ug/L copper
Differential Expression between 0
and 6 ug/L copper
Differential Expression between
normal and abnormal animals at
3 ug/L copper
Transcript Annotation
Table 3--Amplitude-dependent markers of exposure and effect. These genes that were identified as
common markers of exposure, and as markers of effect at 3 ug/L copper. All but two exhibited
significantly higher expression in abnormal animals at 3 ug/L copper than normal animals.
292
Hemocytin;
Mucin-5AC
{ECO:0000305}; -1.27557799 0.006714598 -1.637849682 3.29E-05 -1.894299443 1.05E-06
. . -1.949530394 0.023201777 -1.724617751 0.035566958 -2.408802156 0.027625421
. . -2.188890578 0.000355595 -1.721358295 0.005408347 -1.884419245 0.030905441
.
MAM domain-
containing
protein 2; -1.671154755 1.65E-05 -1.693135099 3.45E-06 -1.546638153 0.000214703
Cathepsin Z; Cathepsin Z; -1.986721557 0.034794501 -1.817080451 0.0404317 -3.364381598 0.001494371
. . -2.720333575 0.000575335 -3.859421474 2.33E-08 -2.474268417 0.032083951
. . -2.727602989 0.00053011 -3.849652614 2.33E-08 -2.468809399 0.032462304
Innexin unc-9; Innexin unc-9; -1.262311354 0.056088738 -1.561943644 0.004094388 -2.188251466 0.000629609
. . -1.223098385 0.007859419 -1.324334755 0.00104116 -1.270628766 0.006563842
. . -1.598058858 0.035156574 -1.602738311 0.018507653 -1.938926789 0.059502638
Serine/threonin
e-protein
phosphatase 2A
65 kDa
regulatory
subunit A alpha
isoform;
Serine/threonine-
protein
phosphatase 2A
65 kDa regulatory
subunit A alpha
isoform; -1.961637074 0.049028931 -2.017838714 0.024707836 -2.597420594 0.068485241
Receptor
expression-
enhancing
protein 5;
Receptor
expression-
enhancing
protein 5; -1.827926891 0.098910715 -2.402330582 0.008516299 -2.940117397 0.04709923
Putative ferric-
chelate
reductase 1
homolog;
Putative ferric-
chelate reductase
1 homolog; -1.928993768 0.072037527 -1.868079472 0.059402623 -3.09204675 0.028847704
DBH-like
monooxygenase
protein 1
homolog;
DBH-like
monooxygenase
protein 1
homolog; -1.533826128 0.054329114 -1.457544256 0.043518552 -2.081542996 0.047800996
293
. . -1.59029063 0.003004006 -1.410545444 0.005713129 -1.338432039 0.04779653
Kielin/chordin-
like protein;
Kielin/chordin-
like protein; -2.452608453 0.002886423 -1.755761713 0.041791146 -2.76371891 0.008572167
Sodium-
dependent
multivitamin
transporter;
Sodium-
dependent
multivitamin
transporter; -2.060485396 0.049665208 -1.970812475 0.045659123 -3.234074743 0.026938873
. . -1.273700456 0.018963864 -1.491316582 0.001204785 -1.844734752 0.001834909
Cartilage matrix
protein;
Cartilage matrix
protein; -1.523029624 0.007989132 -1.48976734 0.004504488 -2.165676044 0.012523462
Hepatic lectin; Hepatic lectin; -1.85598455 0.028244819 -1.821643683 0.018522362 -2.566746579 0.020611556
Cytochrome
P450 3A29;
Cytochrome P450
3A29; -1.848009668 0.061370213 -2.09842839 0.014017328 -2.432736441 0.077086306
Putative
tyrosinase-like
protein tyr-3;
Putative
tyrosinase-like
protein tyr-3; -1.701310448 0.091937179 -1.773080255 0.045225867 -2.785711364 0.033293514
Integrin beta-5; Integrin beta-7; -1.635233175 0.002998719 -2.045505441 1.56E-05 -1.784769619 0.006762473
Protein
FAM107A;
Protein
FAM107A; 1.83245746 0.028831786 1.477780525 0.068450238 -2.865718677 0.017518987
. . -2.510804172 0.003816038 -2.737981464 0.000556115 -3.009919198 0.013625066
. . -1.4879311 0.01026466 -2.12160759 7.66E-06 -1.557338672 0.036733997
Aquaporin-4; Aquaporin-4; -1.604356309 0.075798955 -2.074306862 0.004861766 -2.429441165 0.002054996
. . -2.5699933 0.004280506 -2.7662285 0.000822572 -3.62943062 0.002105263
. . -2.823795978 4.01E-12 -2.080192121 4.32E-07 -1.316130117 0.048995098
Tubulin alpha-1
chain; . -1.701818975 0.006883675 -1.621216575 0.005701343 -1.802339021 0.024306257
. . -1.461444496 0.023554421 -1.436427172 0.013963536 -1.562457148 0.07575335
Alpha-L-
fucosidase;
Alpha-L-
fucosidase; -1.516012846 0.001900582 -1.836280094 1.54E-05 -1.348410302 0.03074935
. . -1.615611724 0.003531466 -1.465367381 0.005120846 -2.075590407 0.000215398
294
Vesicle transport
protein GOT1B;
Vesicle transport
protein GOT1B; -2.1381137 0.034722955 -2.440809975 0.006627373 -3.533819002 0.006832625
. . -1.907877735 0.07459137 -2.137085363 0.022124142 -3.292982664 0.017064715
Complement
C1q-like protein
3;
Complement C1q-
like protein 3; -2.203793915 0.02494688 -2.266457044 0.01209293 -3.521648781 0.004028591
. . 1.600176441 0.052273736 1.607318314 0.028425668 1.948296019 0.079626361
. . -2.368256314 0.008838993 -2.616060431 0.001344386 -3.520062754 0.001058783
Protocadherin-
9; Protocadherin-9; -1.981126282 0.051246569 -1.800265361 0.06109429 -2.5015924 0.09052387
. . -1.633756502 0.016186237 -1.414888219 0.028195658 -1.772945165 0.063465396
Zwei Ig domain
protein zig-4
{ECO:0000303|
PubMed:11809
975};
Zwei Ig domain
protein zig-4
{ECO:0000303|P
ubMed:1180997
5}; -2.575715874 0.003531466 -2.189398269 0.012624014 -2.846314778 0.021743945
. . -1.64007418 9.92E-05 -1.766325767 5.11E-06 -1.627455821 0.000521165
. . -2.810341105 2.46E-05 -2.708421381 2.49E-05 -2.406567923 0.003191881
Zinc transporter
ZIP12;
Zinc transporter
ZIP12; -2.756470936 0.00232159 -2.386795033 0.008294497 -4.484622322 8.33E-05
. . -1.446891375 0.018993636 -1.73938168 0.000862783 -1.338846742 0.079020628
. . -1.752723597 0.050306349 -1.633337579 0.047705562 -3.747176774 4.73E-05
. . 2.162659528 0.015349714 2.711454682 0.000378349 3.401840974 9.98E-05
. . -1.234886098 0.003746607 -1.428108398 0.000112862 -1.300110439 0.017278467
295
Conclusion

This dissertation presents an investigation into the concentration dependent effects of copper
exposure on mussel larval development and whole transcriptome gene expression. In this work
we identified markers of copper exposure and toxicity in the mussel embryo-larval development
toxicity assay, and provided insight on the mechanisms of copper-induced toxicity in marine
mussel larvae. We further investigated how ocean acidification could impact mussel larval
development in conjunction with copper exposure, and the implications of this interaction for
larval development toxicity assays.

Mytilus californianus 48 hour embryo-larval development assay
The sensitivity of M. californianus larvae to copper has not been as thoroughly explored as it has
for other congeners, Mytilus edulis and Mytilus galloprovincialis (Arnold, Cotsifas, Smith, Le
Page, & Gruenthal, 2009; His, Beiras, & Seaman, 1999; Hoare, Davenport, & Beaumont, 1995).
M. californianus has been suggested as a more appropriate species for water quality testing in
California, as it is morphologically distinct, and is the only remaining native mussel species in
Southern California. There has also been some thought that M. californianus may be more
sensitive to copper than M. edulis and M. galloprovincialis. Water quality criteria are set to
protect the most sensitive organism in an ecosystem, particularly if that organism is an essential
component of the ecosystem. Thus, it is useful to know more about M. californianus larval
sensitivity to copper.

Experiments were conducted on 7 different mussel families over 3 years. All experiments
measured survival and normal development of mussel larval populations at a range of copper
concentrations at 48 hours post fertilization (hpf). Test copper concentrations ranged from 0-25
µg/L. Normal development consistently exhibited a sigmoidal concentration response. Survival
responses had less consistent patterns, yet in each experiment survival eventually declined over
the concentration range. Normal development and survival both displayed hormesis in some
experiments. Observation of sigmoidal dose-response (or concentration response) relationships
for a variety of endpoints, including survival and normal development, is common in
toxicological experiments (Bliss, 1935; Landis & Yu, 1995). U-shaped, or hormetic curves, are
also common (Calabrese & Baldwin, 1999). The endpoints in these experiments appear to
296

display a combination of these two response patterns, with sigmoidal curves often exhibiting
mild hormesis at low concentrations. Concentration response curves were modeled to calculate
concentrations that exhibit 50% population lethality (LC50) and normal development (EC50),
and the values were compared across all experiments (Table 1). Normal development EC50s at
400 ppm CO2 were quite consistent, and ranged from 4.03-7.26 µg/l copper, with a mean of 5.77
µg/l. At 800 ppm CO2 , normal development EC50s were more variable, yet were higher than
corresponding 400 ppm EC50s in 3 out of 4 experiments (Table 1). LC50s were less consistent,
and ranged from 3.96 to 11.85 µg/l copper, with LC50s again higher at higher CO2
concentrations.

In all experiments described herein, M. californianus sensitivity is lower than the national
recommended chronic copper water quality criteria for (3.1 µg/l copper)(US EPA, 2018),
indicating that the current limit is adequate, at least to maintain high rates of normal
development. Normal development EC50s in experiments with Mytilus edulis and Mytilus
galloprovincialis larvae ranged from 3.52-30.6 µg/l copper (Arnold et al. 2009), but values were
primarily dependent on dissolved organic carbon (DOC) concentration, rather than mussel
species or the laboratory that tested larvae. Without DOC measurements for the present study, it
is difficult to directly compare EC50s with those of other species from previous research.
However, EC50 values in this dissertation all fell within the concentration range of previously
observed EC50s, which indicates that M. californianus is not particularly sensitive to copper
relative to other Mytilus species. Therefore, our data suggest that M. californianus could be used
in water quality testing, but that limits derived from development assays of other Mytilus species
are comparable.

Identification of common transcriptional biomarkers of exposure and effect
While development assays are informative of toxic copper concentrations, these whole-organism
morphological assessments may not capture the full story. Sublethal molecular changes occur
within organisms at lower concentrations, at which morphological changes may not be apparent.
To address these sublethal molecular changes, we identified transcriptional biomarkers of
copper. Samples were sequenced from four of the seven experiments to assess copper-responsive
transcriptional patterns. Two experiments from Chapter 1 were sequenced; one experiment from
297

Chapter 2; and one experiment from Chapter 3. Each experiment represented results from a
different mussel “family”, or a unique cross of two parents. No two experiments shared the same
parents.

Chapters one and two investigated the transcriptional response at concentrations that spanned the
entirety of the normal development curve (0-25, and 0-12 µg/l, respectively). Chapter 3 focused
on low and mid-range copper concentrations (0-6 µg/l) to dissect the association of gene
expression with specific morphologies. In all experiments, the normal development curve was
used to anchor transcriptional patterns, and all patterns were considered in light of the associated
morphological changes. Because we were searching for sensitive, sublethal changes, we focused
primarily on low-concentration markers of copper exposure and/or toxicity, or those that
exhibited notable expression changes at concentrations lower than the normal development
EC50.

In chapters one and two, genes with copper-responsive sigmoidal expression patterns were used
as biomarkers. Genes identified by Sigmoidal Dose Response Search (SDRS) (Ji, Siemers, Lei,
Schweizer, & Bruccoleri, 2011) were assigned transcriptional EC50 values, as well as lowest
observed transcriptional EC50 values (LOTEC/LOEC), i.e. the lowest concentration for which
changes in expression level were detected. LOTEC and transcriptional EC50s were compared
with the normal development curve to assess the relative sensitivity of transcripts, and genes
with LOTECs that were less than the normal development EC50 were used as sensitive
biomarkers. While genes did display other non-sigmoidal expression patterns in chapters one and
two, identification with SDRS was useful to detect genes with monotonic responses, which were
typically less noisy, and to assign concentration-specific information to the expression profile.
Some additional biomarkers were detected with weighted gene coexpression network analysis
(WGCNA) in Chapter 1. In Chapter 3, significant changes in expression were detected with
differential expression (DE) analysis, and we only sequenced samples from copper
concentrations that were lower than or coincident with the normal development EC50.

In Chapter 1, the majority of genes that were identified by SDRS in both trials were upregulated
(Table 2; Chapter 1, Figure 2), but the downregulated genes proved to be more sensitive
298

biomarkers (Chapter 1, Figure 3). However, there were still many upregulated sensitive
biomarkers (Table 2; Chapter 1, Figure 3B) in Trial 1 of Chapter 1. Sensitive markers identified
by WGCNA were primarily downregulated (Chapter 1, Figures 5-6). In Chapter 2, the majority
of genes identified by SDRS at 400 ppm CO2 were downregulated in response to copper,
although many genes were upregulated as well (Table 2). Sensitive markers in Chapter 2
consisted of both upregulated and downregulated genes, and again downregulated genes were
more prominent (Table 2). In Chapter 3, the majority of copper responsive genes (or genes that
were DE between control and copper exposed animals) were upregulated (Table 2), as they had
been in Chapter 1. These trends indicate that both upregulated and downregulated genes can
serve as sensitive markers of copper exposure, although there is some family-dependent variation
in the overall expression pattern of sensitive markers.

There was substantial variation in the genes that were detected as sensitive markers of copper
exposure across the four families. Besides potential genetic variation among parent pairs, other
factors could have likewise contributed to the differences in genes identified as sensitive
biomarkers across all trials. These include different library preparation methods, different
sequencing cores, and different experiment locations. Despite this variability, 19 markers of
copper-induced effects and 20 markers of copper exposure were found to be sensitive markers
across all 4 trials (Figure 1, Table 3). Consistent sensitive markers of exposure and effect were
involved in many of the same functions, including cytoskeleton, cell adhesion, development,
shell proteinaceous matrix, neurological development or signaling, vascular system, and calcium
and zinc binding (Table 3). Impacts of copper on these functions has been recognized in previous
work (Curtis, Williamson, & Depledge, 2001; Gómez-Mendikute & Cajaraville, 2003;
Narahashi, Ma, Arakawa, Reuveny, & Nakahiro, 1994; Silva-Aciares, Zapata, Tournois, Moraga,
& Riquelme, 2011; Sussarellu, Lebreton, Rouxel, Akcha, & Rivière, 2018; Young, Adee,
Piscopo, & Buschbom, 1981; Zapata, Tanguy, David, Moraga, & Riquelme, 2009). However,
only a handful of studies have identified these pathways as copper-responsive in developing
mollusks, and in these studies different genes from the ones described here were targeted to
address each function (Silva-Aciares et al., 2011; Sussarellu et al., 2018; Zapata et al., 2009).

299

Other commonly recognized markers of cellular stress, and specifically oxidative stress and
metal-induced stress, including Superoxide Dismutase, HSP 70, Glutathione-S-transferase,
Glutathione Peroxidase, Metallothionein, Ferritin, and Sequestosome-1 were identified as
sensitive markers sporadically throughout our studies. While these genes were not consistently
responsive at low copper concentrations, many of them were directly linked to abnormal
development at copper concentrations as low as 3 µg/l in the third chapter. The appearance of
these genes as phenotype-dependent sensitive markers suggests that pooled sequencing of all
phenotypes may obfuscate some of the nuanced transcriptional response to copper toxicity.
Research on single-cell resistance to cancer treatment has demonstrated that even single cells can
display notable transcriptional differences which are predictive of phenotype (Shaffer et al.,
2017). Similarly, it appears that larvae exhibit phenotype-dependent expression profiles at lower
copper concentrations, and that bulk sequencing may prevent detection of phenotype-specific
transcriptional biomarkers.

Impacts of a changing ocean on biomarkers
For one of the four family crosses, the impact of simulated ocean acidification on larval
development and transcription was examined (Chapter 2). In this experiment, the normal
development curve shifted to the right when exposed to a higher CO2 concentration, meaning
that larvae became less sensitive to copper at a seawater pCO2 of 700 ppm (the actual measured
seawater pCO2). The transcriptional pattern corresponded with shifts in normal development, and
resulted in gene expression profiles at high CO2 that were very similar to expression profiles at
lower copper concentrations at low CO2 (Chapter 2, Figure 3). Most of the sensitive
downregulated markers at 700 ppm CO2 were shared with downregulated markers at 400 ppm
CO2, but there were some unique upregulated sensitive biomarkers (Chapter 2, Figure 4).
Comparison of enriched gene ontology terms between the two CO2 concentrations also indicated
that sensitive biomarkers at 700 ppm CO2 were involved in some unique pathways, primarily
related to fatty acid processes and pre-synaptic neurological processes and signaling (Chapter 2,
Supplemental Tables 3-4). A subset of the common markers of copper exposure and effects at
400 ppm CO2 (Table 3) were identified among sensitive markers at 700 ppm CO2 (Table 4)—11
of the 20 common markers of exposure were identified, and 10 of the 19 common markers of
effect. These patterns indicate that many of the same sensitive biomarkers will be informative
300

under future ocean conditions, but some of them may become less sensitive. On the other hand,
some new biomarkers and functional pathways may be activated, providing potential new
biomarkers that are indicative of combined OA and copper exposure.

Next steps/Future work
This work serves as a substantial starting point for future studies, which will be important to
confirm many of our findings and expand upon our understanding of copper toxicity in bivalve
larvae. Biomarkers identified in this study suggest several pathways that are likely involved in
transcriptional responses to copper and copper-induced abnormal development. Biochemical and
enzymatic investigations into these pathways are necessary to confirm that modulation of
putative pathways is actually driving abnormal development. Genetic manipulation with RNAi
or CRISPR would be an interesting way to determine whether abnormal development proceeds
normally in the absence of putative biomarker expression. Findings of the OA simulation
experiments should be confirmed, and experiments should be scaled up so that sufficient larval
biomass is available to measure copper uptake. Measurement of copper uptake in larvae would
clarify whether reduced copper toxicity was simply a result of reduced uptake, or of enhanced
internal responses. Additional markers of effect should be identified using other phenotypic
anchors, especially longer-term whole organism or population-level outcomes. Even among
normal animals at low copper concentrations, impacts of copper exposure may ultimately result
in reduced fitness, including reduced survival or failure of metamorphosis and settlement.
Finally, biomarkers identified here will be the most informative if they are verified to be copper-
specific. Thus, similar studies should be conducted with other metals, and metal-specific
expression profiles and biomarkers should be identified for marine bivalve larvae.

References

Arnold, W. R., Cotsifas, J. S., Smith, D. S., Le Page, S., & Gruenthal, K. M. (2009). A
comparison of the copper sensitivity of two economically important saltwater mussel species
and a review of previously reported copper toxicity data for mussels: Important implications
for determining future ambient copper saltwater criteria in the USA. Environmental
Toxicology, 24(6), 618–628. http://doi.org/10.1002/tox.20452
BLISS, C. I. (1935). THE CALCULATION OF THE DOSAGE-MORTALITY CURVE. Annals
of Applied Biology, 22(1), 134–167. http://doi.org/10.1111/j.1744-7348.1935.tb07713.x
301

Calabrese, E. J., & Baldwin, L. A. (1999). Reevaluation of the Fundamental Dose–Response
Relationship. BioScience, 49(9), 725–732. http://doi.org/10.2307/1313596
Curtis, T. M., Williamson, R., & Depledge, M. H. (2001). The initial mode of action of copper
on the cardiac physiology of the blue mussel, Mytilus edulis. Aquatic Toxicology, 52(1), 29–
38.
Gómez-Mendikute, A., & Cajaraville, M. P. (2003). Comparative effects of cadmium, copper,
paraquat and benzo[a]pyrene on the actin cytoskeleton and production of reactive oxygen
species (ROS) in mussel haemocytes. Toxicology in Vitro : an International Journal
Published in Association with BIBRA, 17(5-6), 539–546.
His, E., Beiras, R., & Seaman, M. N. L. (1999). The Assessment of Marine Pollution - Bioassays
with Bivalve Embryos and Larvae (Vol. 37, pp. 1–178). Elsevier.
http://doi.org/10.1016/S0065-2881(08)60428-9
Hoare, K., Davenport, J., & Beaumont, A. R. (1995). Effects of exposure and previous exposure
to copper on growth of veliger larvae and survivorship of Mytilus edulis juveniles. Marine
Ecology Progress Series, 120, 163–168. http://doi.org/10.3354/meps120163
Ji, R.-R., Siemers, N. O., Lei, M., Schweizer, L., & Bruccoleri, R. E. (2011). SDRS--an
algorithm for analyzing large-scale dose-response data. Bioinformatics, 27(20), 2921–2923.
http://doi.org/10.1093/bioinformatics/btr489
Landis, W. G., & Yu, M.-H. (1995). Introduction to environmental toxicology. CRC Press.
Narahashi, T., Ma, J. Y., Arakawa, O., Reuveny, E., & Nakahiro, M. (1994). GABA receptor-
channel complex as a target site of mercury, copper, zinc, and lanthanides. Cellular and
Molecular Neurobiology, 14(6), 599–621.
Shaffer, S. M., Dunagin, M. C., Torborg, S. R., Torre, E. A., Emert, B., Krepler, C., et al. (2017).
Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance.
Nature, 546(7658), 431–435. http://doi.org/10.1038/nature22794
Silva-Aciares, F., Zapata, M., Tournois, J., Moraga, D., & Riquelme, C. (2011). Identification of
genes expressed in juvenile Haliotis rufescens in response to different copper concentrations
in the north of Chile under controlled conditions. Marine Pollution Bulletin, 62(12), 2671–
2680. http://doi.org/10.1016/j.marpolbul.2011.09.023
Sussarellu, R., Lebreton, M., Rouxel, J., Akcha, F., & Rivière, G. (2018). Copper induces
expression and methylation changes of early development genes in Crassostrea gigas
embryos. Aquatic Toxicology (Amsterdam, Netherlands), 196, 70–78.
http://doi.org/10.1016/j.aquatox.2018.01.001
Young, J. S., Adee, R. R., Piscopo, I., & Buschbom, R. L. (1981). Effects of copper on the
sabellid polychaete,Eudistylia vancouveri. II. Copper accumulation and tissue injury in the
branchial crown. Archives of Environmental Contamination and Toxicology, 10(1), 87–104.
http://doi.org/10.1007/BF01057578
Zapata, M., Tanguy, A., David, E., Moraga, D., & Riquelme, C. (2009). Transcriptomic response
of Argopecten purpuratus post-larvae to copper exposure under experimental conditions.
Gene, 442(1-2), 37–46. http://doi.org/10.1016/j.gene.2009.04.019
US EPA (2018) National Recommended Water Quality Criteria—Aquatic Life Criteria Table.
https://www.epa.gov/wqc/national-recommended-water-quality-criteria-aquatic-life-criteria-
table#table

302
Chapter 1 T1
Chapter 2:
400 ppm CO
2
Chapter 3:
Markers of Exposure
Chapter 1 T2
Chapter 1 T1
Chapter 1 T2
Chapter 3:
Markers of Effect
Figure 1: Venn diagrams of sensitive copper-responsive biomarkers identified in each of the
four family crosses. The red text represents genes that were common to all trials. Common
markers of exposure (A) and effect (B) were identified.
A
B
Chapter 2:
400 ppm CO
2
303
Chapter Nominal pCO
2
Number of Copper
Concentrations tested
Normal
Development EC50
(ug/L)
Survival LC50
(ug/L)
1 400 10 7.26 6.5
1 400 10 4.03 3.96
2 400 7 5.68 5.70
2 400 7 7.15 10.34
2 400 7 5.11 8.04
2 400 10 5.33 5.76
3 400 6 5.87 N/A
2 800 7 7.06 11.85
2 800 7 9.26 9.37
2 800 7 4.47 N/A
2 1200 10 6.35 N/A
Table 1: Normal development EC50s and survival LC50s are shown for each experiment. N/A is written for curves
that could not be modeled due to an incomplete response.
304
Chapter Nominal
pCO
2
Detection
Method
Number
Upregulated
Genes
Number
Downregulated
Genes
Number
Upregulated
Genes with
LOTEC < ND
EC50
Number
Downregulated
Genes with
LOTEC < ND
EC50
1 400 SDRS 899 166 335 161
1 400 SDRS 431 302 17 295
2 400 SDRS 2396 3067 1523 2659
3 400 DESeq2 472 96 N/A N/A
2 800 SDRS 897 619 839 607
Table 2: The number of upregulated and downregulated biomarkers for each experiment are
shown. Sensitive biomarker counts, or sigmoidal genes with lowest observed transcriptional
effect concentrations (LOTEC) lower than the normal development EC50, are also listed.
305
Table 3--Markers of copper exposure and copper-induced effects that appeared as
sensitive markers in all four transcriptomes

Markers of Exposure Markers of Effect
Aquaporin-4; Aquaporin-4;
Hemicentin-1; Neuronal acetylcholine receptor subunit alpha-6;
Perlucin-like protein
{ECO:0000303|PubMed:21643827}; Filamin-A;
Antistasin; Periostin;
Tctex1 domain-containing protein 1; Kyphoscoliosis peptidase;
Periostin; Calmodulin;
Carboxypeptidase B;
Leucine-rich repeat-containing protein 74A
{ECO:0000250|UniProtKB:Q0VAA2};
Temptin; Inactive carboxypeptidase-like protein X2;
Perlucin; Myosin heavy chain, striated muscle;
Epoxide hydrolase 4; Protein PIF;
Zinc metalloproteinase nas-13; Dipeptidase 1;
Cyclic nucleotide-binding domain-containing
protein 2; Calcitonin gene-related peptide type 1 receptor;
Dynein heavy chain 7, axonemal; Dynein beta chain, ciliary;
Neurogenic locus notch homolog protein 1; Cdc42 homolog;
Deleted in malignant brain tumors 1 protein;
Pituitary homeobox x
{ECO:0000250|UniProtKB:Q9W5Z2};
Sodium- and chloride-dependent glycine
transporter 2; Myosin light chain kinase, smooth muscle;
Transmembrane protease serine 3; Frizzled-4;
Alpha-L-fucosidase; Alpha-L-fucosidase;
Apolipoprotein D; Apolipoprotein D;
Protein SSUH2 homolog;

306

Table 4--Common markers of copper exposure and copper-induced effects
that appeared as sensitive markers at 800 ppm CO2

Markers of Exposure Markers of Effect
Antistasin; Apolipoprotein D;
Apolipoprotein D; Aquaporin-4;
Aquaporin-4; Calmodulin;
Carboxypeptidase B; Dipeptidase 1;
Cyclic nucleotide-binding domain-containing
protein 2; Kyphoscoliosis peptidase;
Hemicentin-1;
Leucine-rich repeat-containing protein 74A
{ECO:0000250|UniProtKB:Q0VAA2};
Perlucin-like protein
{ECO:0000303|PubMed:21643827};
Neuronal acetylcholine receptor subunit
alpha-6;
Sodium- and chloride-dependent glycine
transporter 2; Protein PIF;
Tctex1 domain-containing protein 1; Alpha-L-fucosidase;
Temptin; Cdc42 homolog;
Zinc metalloproteinase nas-13; Dynein beta chain, ciliary;
Alpha-L-fucosidase;

307

Abstract (if available)

Abstract Copper contamination is a long-standing problem in urban areas such as Southern California. Water quality criteria are determined by toxicity testing with live organisms, and limits are often set to protect the most sensitive member of an ecosystem. Mussels of the genus Mytilus are key members of intertidal ecosystems, and are also particularly sensitive to copper. We incorporated whole-transcriptome sequencing into traditional embryo-larval development ecotoxicology assays to comprehensively assess sensitive, concentration-dependent gene expression changes in response to copper. Specifically, we focused on identifying biomarkers for which expression changes preceded morphological changes. For all experiments, Mytilus californianus embryos were exposed to a range of copper concentrations, and larval survival, morphology, and transcriptional profiles were assessed at 48 hours post-fertilization. ❧ In Chapter 1, we identified candidate sensitive transcriptional biomarkers. Biomarkers were primarily all downregulated in response to copper. Key functional categories that were identified among these genes include biomineralization / shell formation, metal binding, and development. Concentration responsive transcripts were also compared between adult and larval mussels. While there was some overlap in adult and larval copper-responsive genes, many of the makers were unique to each life history stage. In Chapter 2, we assessed the impacts of simulated ocean acidification (OA) on transcriptional markers of copper exposure. Larval assays revealed that simulated OA impacts copper toxicity in a concentration-dependent manner, and may in fact reduce copper toxicity to M. californianus larvae at intermediate copper doses (6-9 μg/L) . Copper responsive transcripts were identified under both high and low CO₂ conditions, and exhibited a range of response patterns. Observed patterns suggest that larvae may be modulating certain pathways to reduce copper uptake and/or negative physiological impacts of copper. In Chapter 3, we linked transcriptional markers to whole-organism phenotype to distinguish markers of exposure and markers of effect. Normal and abnormal larvae from a control (0 μg/L) and two copper treatments (3 and 6 μg/L) were sorted into separate groups, and expression of each phenotypic group was measured. Differential expression analysis of morphology- and copper concentration-specific expression signatures revealed putative markers of copper exposure and effects. Markers of copper exposure and copper-induced abnormality were involved in many of the same pathways, yet unique genes were detected in each gene set. Cumulatively, this work reveals sensitive novel biomarkers of copper exposure and copper toxicity on an important marine invertebrate. This information could be used by regulators to determine copper quality criteria both now and as climate-induced changes in coastal waters progress.

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University of Southern California Dissertations and Theses

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Asset Metadata

Creator Hall, Megan Rebecca (author)

Core Title Transcriptional and morphological impacts of copper on Mytilus californianus larval development in current and future ocean conditions

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Marine and Environmental Biology

Publication Date 01/26/2020

Defense Date 04/11/2018

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag biomarkers,Copper,ecotoxicology,gene expression,larvae,morphology,mussel,Mytilus californianus,normal development,OAI-PMH Harvest,ocean acidification,toxicity,transcriptomics

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Gracey, Andrew Y (committee chair), Davies, Kelvin J. A. (committee member), Moffett, James W (committee member), Nuzhdin, Sergey (committee member)

Creator Email meganhal@usc.edu,meganrh19@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-28595

Unique identifier UC11668698

Identifier etd-HallMeganR-6500.pdf (filename),usctheses-c89-28595 (legacy record id)

Legacy Identifier etd-HallMeganR-6500.pdf

Dmrecord 28595

Document Type Dissertation

Format application/pdf (imt)

Rights Hall, Megan Rebecca

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA