University of Nevada, Reno

Evolution of an Adaptive Trait: Phenotypic, Physiological, and Genetic Patterns of TTX Resistance in the Sierra Garter Snake Thamnophis couchii

A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Ecology, Evolution, and Conservation Biology

by

Jessica Summer Reimche

Dr. Chris Feldman/Dissertation Advisor Dr. Karen Schlauch/Dissertation Advisor

December 2020

Copyright © by Jessica Summer Reimche 2020 All Rights Reserved

THE GRADUATE SCHOOL

We recommend that the dissertation prepared under our supervision by

Jessica Summer Reimche

entitled

Evolution of an Adaptive Trait: Phenotypic, Physiological, and Genetic Patterns of TTX Resistance in the Sierra Garter Snake Thamnophis couchii

be accepted in partial fulfillment of the requirements for the degree of

DOCTOR OF PHILOSOPHY

Chris R. Feldman, Ph.D., Advisor

Karen Schlauch, Ph.D., Co-advisor

Thomas L. Parchman, Ph.D., Committee Member

Julie M. Allen Ph.D., Committee Member

Normand Leblanc, Ph.D., Graduate School Representative

David W. Zeh, Ph.D., Dean, Graduate School

December 2020

i

ABSTRACT

Understanding the molecular evolution of adaptive traits is central to advancing evolutionary biology. Thus, describing the genetic architecture of such traits is necessary to understand how adaptations arise, spread, and fix across populations. Where exactly these adaptive traits originate in the underlying genetic architecture remains a topic of controversy, with some evolutionary biologists arguing that origination of adaptive phenotypes occurs from changes in regulatory non-protein coding regions of the genome, while others claim they stem from structural mutations in protein coding regions.

Complex phenotypes such as adaptations are inherently difficult to study, typically involving multiple, potentially independent, genetic mechanisms that can be challenging to recognize. Therefore, the best approach to understanding these complex phenotypes is a layered approach, examining the connection between genotypes and phenotype at many levels. Here, we examine the complex phenotype, tetrodotoxin (TTX) resistance, at multiple scales (whole animal, physiological, and genetic), hoping to uncover both structural and regulatory changes responsible for this adaptation.

TTX resistance is an adaptive trait found in garter snakes (Thamnophis) that prey on toxic newts (Taricha spp.). Newts are defended by this lethal toxin (TTX) which binds to sodium channels, halts nerve impulses, and can end in death for those who ingest it.

Nevertheless, some garter snake species have evolved resistance to TTX, and prey on these newts. We examined an unstudied predator-prey interaction between toxic newts and a recently discovered TTX-resistant predator, the Sierra garter snake (Th. couchii).

We quantified phenotypic variation at the whole animal scale in both predator ii

(resistance) and prey (toxicity), identifying strong trait matching at sympatric sites, and high levels of phenotypic variation in predator TTX resistance both within and among populations. We then investigated whether this variation in predator traits is explained by the same physiological and genetic mechanisms underlying predator resistance in other

Thamnophis-Taricha systems. We confirmed that there is indeed a correlation between whole animal and skeletal muscle resistance and then sequenced three candidate genes and found that all individuals across the range of Th. couchii are fixed for resistance- conferring alleles despite phenotypic variation at both the whole animal and skeletal muscle levels. In the absence of structural variation in the sodium channel targets of

TTX, we investigated a potential avenue for TTX resistance from a transcriptomics perspective, exploring the role of gene expression in adaptive evolution. We found over

200 differentially expressed genes among low and high resistance snakes. This body of work examines the connections between genetic mechanisms and phenotype to better understand adaptive evolution and the potential molecular constraints that act upon it.

iii

DEDICATION

To all who have doubted themselves- don’t give up, we can do hard things

it has been one of the greatest and most difficult years of my life. i learned everything is temporary. moments. feelings. people. flowers. i learned love is about giving everything. and letting it hurt. i learned vulnerability is always the right choice because it is easy to be cold in a world that makes it so very difficult to remain soft. i learned all things come in twos. life and death. pain and joy. salt and sugar. me and you. it is the balance of the universe. it has been the year of hurting so bad but living so good. making friends out of strangers. making strangers out of friends. learning mint chocolate chip ice cream will fix just about everything. and for the pains it can’t there will always be my mother’s arms. we must learn to focus on warm energy. always. soak our limbs in it and become better lovers to the world. for if we can’t learn to be kind to each other how will we ever learn to be kind to the most desperate parts of ourselves.

-Rupi Kaur

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ACKNOWLEDGEMENTS

Thank you to Dr. Chris Feldman for welcoming me into this incredible system. He has granted me endless patience and helped develop an excitement and enthusiasm for science that I did not know before. I am honored to contribute to the amazing research that he and other collaborators have been conducting for almost twenty years.

Thank you to Dr. Karen Schlauch for her never ending support of me and my graduate career. Her mentorship is something I will carry with me for the rest of my life. I’m not sure she knows how important it was to hear the words “you can do this” from someone I admire so much. I am eternally grateful.

I owe much of this dissertation to the brilliant Robert del Carlo and Haley Moniz. For believing in me when I could not believe in myself, picking me up when I fell, and literally chopping off snake heads when I was emotionally taxed, they have held my hand through this entire journey. They have been role models since the moment I met them. Thank you for being examples of what it means to set high standards and continually strive for excellent science. I cannot wait to see where their careers lead them and support them however I can along the way.

I’m so appreciative of my committee for all of their guidance and support. I am lucky to have a wonderfully diverse and impressive group of scientists who welcomed all of my questions, offered critical feedback, and ended our conversations with “what can I do to help?” Thank you to Dr. Norm Leblanc, Dr. Tom Parchman, and Dr. Julie Allen, I am so grateful for their role in this dissertation.

I would like to sincerely thank the Evol Doers Lab Group for their mentorship, feedback, and inspiration throughout these five years. Thank you for enriching my graduate student career.

Thank you to the EECB and Biology Departments, and the UNR Graduate School. My education would not be possible without the financial and emotional support of their programs. Thank you for being advocates for graduate students and fighting to relieve the financial burden of higher education. Thank you for providing incredible courses that broadened my knowledge, challenged my abilities, and reminded me to never stop asking questions.

I would like to thank all of the funding agencies that supported this research: UNR Graduate Student Association, Nevada INBRE, Gans Collection and Charitable Fund, American Society of Ichthyologists and Herpetologists, Herpetologists League, and National Science Foundation.

I am indebted to my lab mates and a number of researchers that were vital to the completion of my dissertation. Thank you to Joshua Hallas, Erica Ely, Arielle Navarro, Kelly Robinson, Kenzie Wasley, Amber Durfree, Taylor Disbrow, Gabrielle Blaustein v for all of their help in the lab. Thank you to the Matocq Lab, Ferguson lab, Miura lab, and Singer lab for loaning equipment and offering assistance. Special thank you Vicki Thill for paving the way with her inspiring research. It is an honor to work along a biologist like her.

Thanks to my fellow graduate students for all of their camaraderie. I would like to specifically thank my roommate Devon Picklum for being a constant light in this frequently dark process. A special thank you to Angela Pitera, Nadya Muchoney, Anne Espeset, Anna Tataro, Jen Rippet, Chase Fiore, Sam Mann, and Levi Evans for their physical and emotional support throughout this process.

To my CrossFit community, who helped prepare me for this process more than I ever realized, thank you. For helping me build literal strength and confidence in myself, pushing me to do things I never thought possible, and preparing me for the discomfort and endurance of hard work and self-growth- they have changed my life in unimaginable ways.

Finally, thank you to my family, who never once let me believe I couldn’t do anything I put my mind to. To my father, who instilled in me a love for science so deep that it has carried me around the world, doing things I never thought possible. To my mother, who sacrificed so much to give me the entire world. And to my sister, who is the brightest light in my life. Thank you. With them on my team, this accomplishment was truly possible. Their unconditional love is the greatest gift.

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TABLE OF CONTENTS

Abstract ...... i

Dedication ...... iii

Acknowledgements ...... iv

Table of Contents ...... vi

List of Tables ...... vii

List of Figures ...... viii

Introduction ...... 1

Chapter 1: The geographic mosaic in parallel: Matching patterns of newt tetrodotoxin levels and snake resistance in multiple predator-prey pairs ...... 14

Chapter 2: On a convergent path? A multiscale approach shows the evolution of TTX resistance in the Sierra garter snake (Thamnophis couchii) is not entirely predictable ...... 53

Chapter 3: The role of gene expression in adaptive toxin resistance: Identifying novel genetic mechanisms underlying TTX resistance in the Sierra garter snake (Thamnophis couchii) ...... 125

Conclusion ...... 224

vii

LIST OF TABLES

Table 1-1 ...... 51

Table 2-1 ...... 100

Table 2-S1 ...... 102

Table 2-S2 ...... 122

Table 2-S3 ...... 124

Table 3-1 ...... 171

Table 3-2 ...... 173

Table 3-3 ...... 174

Table 3-4 ...... 180

Table 3-5 ...... 187

Table 3-S1 ...... 188

Table 3-S2 ...... 189

Table 3-S3 ...... 191

Table 3-S4 ...... 192

Table 3-S5 ...... 222

viii

LIST OF FIGURES

Figure 1-1 ...... 48

Figure 1-2 ...... 49

Figure 1-3 ...... 50

Figure 2-1 ...... 96

Figure 2-2 ...... 97

Figure 2-3 ...... 98

Figure 2-4 ...... 99

Figure 3-1 ...... 165

Figure 3-2 ...... 166

Figure 3-3 ...... 167

Figure 3-4 ...... 168

Figure 3-5 ...... 169

Figure 3-6 ...... 170

1

INTRODUCTION

Adaptation, the process by which organisms evolve to better fit their environment, is a central paradigm in evolutionary biology. Adaptive traits are ubiquitous across the natural world, making one of the main goals in biology to understand the molecular basis of adaptive evolution. This project presents an investigation of the molecular underpinnings of a single adaptive trait using the frameworks of three major evolutionary theories: coevolution, convergent evolution, and the genetic mechanisms of adaptive evolution.

Coevolution and adaptation

Coevolution is the process by which two or more interacting species cause reciprocal selection pressures on one another (Thompson, 2005). One of the central goals in coevolutionary studies is to understand the genetic and ecological conditions that enable interacting species to reciprocally evolve adaptive traits in response to the interaction. Because species interactions are complex relationships, there is high variability in strength and direction of coevolutionary interactions (Thompson, 2005).

The Geographic Mosaic Theory of Coevolution (GMTC) suggests that coevolution varies across time and space because populations are ecologically and genetically structured across the landscape (Thompson, 2005). This geographic structure can lead to diverse ecological dynamics and coevolutionary fates between interacting species, including the evolution of adaptive traits (Thompson, 1994, 1999, 2005)

Although much theoretical work has been performed to establish a framework for understanding coevolution, relatively few coevolutionary systems have been investigated at a landscape level to test the GMTC (Brodie et al., 2002; Thompson & Cunningham,

2002; Anderson & Johnson, 2008; Zangerl & Berenbaum, 2003; Mezquida & Benkman, 2

2005; Hanifin et al., 2008). Multiple independent coevolutionary systems are needed to examine whether geographic patterns occur in repeatable and predictable ways, and thereby allow greater inferences about the role of mechanisms responsible for coevolutionary patterns and adaptive evolution.

Convergent evolution and adaptation

Detailed coevolutionary and adaptive studies have revealed another remarkable adaptive evolutionary framework: convergent evolution. Patterns of convergent evolution occur when similar phenotypes arise independently in distinct lineages (Agrawal, 2017;

Losos, 2011), and we find comparable examples of convergence in every branch of life

(Conway-Morris, 2003; McGhee, 2011; Cowen, 2013). The independent and repeated evolution of similarity across space, time, and diversity demonstrates the ubiquity of convergence (Conway-Morris, 2003; McGhee, 2011; Cowen, 2013). What remains less clear, however, are the underlying causes of convergent evolution (Agrawal, 2017;

Losos, 2011; Stayton, 2015; Storz, 2016). Some hypotheses argue that convergent evolution is the outcome of natural selection fitting organisms optimally to similar environmental constraints/pressures (Christin et al., 2010; Losos 2011; Wake, 1991).

Others state that convergent traits are the result of functional and genetic constraints that limit the potential evolutionary avenues available to an organism (Feldman et al., 2012;

Stern & Orgorzo, 2008, 2009; Storz, 2016; Weinreich et al., 2006). While these hypotheses may not be mutually exclusive, determining the general principles underlying convergent evolution remains a primary goal of evolutionary biology (Agrawal, 2017;

Conway-Morris, 2003; Losos, 2011; McGhee, 2011). 3

One way to examine whether repeated evolutionary patterns are primarily due to selection or constraint is to compare the genetic pathways leading to convergent, adaptive phenotypes (Conte et al., 2012; Feldman et al., 2012; Miller et al., 2006; Stern, 2013;

Storz, 2016; Weinreich et al., 2006). Characterizing the genetic basis of adaptive traits can reveal whether species converge on similar phenotypes through diverse genetic mechanisms, or instead by constrained molecular responses (Brakefield, 2006; Christin et al., 2010; Losos, 2011; Maynard Smith et al., 1985; Stern & Orgorzo, 2008; Wake et al.,

2011).

Molecular basis of adaptation

Whether adaptive traits arise through convergence or novel pathways, the underlying genetic mechanisms are often intricate and difficult to resolve. Adaptive phenotypes arise through changes in protein coding regions of DNA or changes in non- coding regulatory regions, such as changes in gene expression (Orr, 2005; Hoekstra &

Coyne, 2007; Wray, 2007; Haygood et al., 2010, Wray, 2013; Necsulea & Kaessmann,

2014; Alvarez et al., 2015; Pardo-Diaz et al., 2015). Small nucleotide changes in a single or small number of genes under selection can lead to major phenotypic changes that lend organisms adaptive advantages in their environments (Nachman et al., 2003; Rosenblum et al., 2004, Hoekstra et al., 2006; Hoekstra & Coyne, 2007; Chan et al., 2010; Dobler et al., 2012, 2015, Linnen et al., 2013). While numerous examples of “small genes of major effect” have been discovered, changes in regulatory regions have been argued to play an even greater role in adaptive evolution (Carroll, 2008; Oleksiak et al., 2002; Wray, 2007).

This is due, in part, to the flexibility of gene expression changes in comparison to the typically constrained nature of protein evolution (Carroll, 2008; Oleksiak et al., 2002; 4

Wray, 2007). It is hypothesized that mutations in coding regions lead to limited changes in a gene’s activity while mutations in regulatory regions can lead to changes in more contexts (Carroll, 2005; Carrol, 2008; Haygood et al. 2010).

While we often think of these mechanisms independently, it is likely that complex adaptations involve both structural changes in protein coding regions in concert with changes in regulatory regions (Shapiro et al. 2004; Sartor et al., 2006; Chapman et al.,

2013; Brown et al., 2018, Rivas et al., 2018). This constitutes the next frontier in understanding how genetic mechanisms underlie adaptive traits. Modern high-throughput technology enables better understanding of how the genome functions to create complex, adaptive traits (Richards et al., 2009; Alvarez et al., 2015). Specifically, RNA-sequencing allows us to ask the question: how does gene expression affect adaptive phenotypes?

(Alvarez et al., 2015). By quantifying patterns of differential gene expression on a genome-wide scale, we can observe molecular mechanisms underlying phenotypes in response to external stimuli (Richards et al., 2009; Alvarez et al., 2015).

Garter snake-newt study system

The interactions between toxic newts (Taricha) and three resistant garter snake species (Thamnophis) provides a model system for the study of adaptive evolution

(Brodie & Brodie, 1999, Brodie et al., 2002, 2005; Feldman et al., 2009) and grants the unique opportunity to address adaptation from the three frameworks: coevolution, convergent evolution, and the genetic mechanisms of adaptive evolution. Pacific newts possess the poison, tetrodotoxin (TTX), a powerful neurotoxin (Brodie, 1968). TTX is concentrated in the granular glands of skin where it can be excreted for defense (Cardall

et al., 2004). TTX binds to the outer pore of voltage-gated sodium channels (Nav) and 5

blocks the transfer of Na+ across the membrane (Fozzard & Lipkin, 2010). The result is a halt of nerve impulses and muscle contractions (Fozzard & Lipkin, 2010) that leads to paralysis and even death (Brodie, 1968). Despite the fact that TTX is one of the most lethal natural toxins ever discovered, three species of Thamnophis engage in coevolutionary relationships and prey on sympatric newts and have independently evolved high resistance to TTX (Brodie et al., 2002, 2005; Feldman et al., 2009).

Phenotypic assays of TTX resistance in both snakes and newts reveal the evolution of dramatic phenotypes, making them an ideal system to investigate the molecular mechanisms of adaptive evolution (Feldman et al., 2009, Hanifin et al., 2008).

Research conducted on the mechanisms of TTX resistance suggests that resistance is conferred through changes in the specific proteins that TTX ligates to: voltage-gated

sodium channels (Nav). These Nav proteins are part of a multi-member gene family with

nine paralogs of α-subunit proteins (Nav 1.1- Nav 1.9) and four β-subunit proteins in amniotes (Goldin, 2001; Catterall, 2012; Zhang et al., 2013; Namadurai et al., 2015). The primary structure of the channels involves a single α-subunit that contains a membrane- spanning outer pore and allows selective permeation of Na+ ions (Goldin, 2001; Hille,

2001), and one or two β-subunits that remain extracellular (Catterall, 2012). The α- subunit consists of four domains (DI-DIV), each containing four pore forming segments

(P-loops). The genes coding for these subunits (SCN) are expressed in specific, excitable tissues including the central nervous system, peripheral nervous system, skeletal muscle, and cardiac tissue (Goldin, 2001, Catterall, 2012). The protein structures that line the outer pore and permit selectivity and permeability of Na+ through the channel are also the binding sites of TTX (Fozzard & Lipkin, 2010). 6

The genetic basis of TTX resistance in garter snake species is partially understood and presumed to be relatively simple: amino acid substitutions at the P-loop sites of the

skeletal muscle sodium channel (Nav1.4) result in structural changes that reduce the binding affinity of TTX to this membrane protein (Fig. 1) (Geffeney et al., 2005;

Feldman et al., 2009, 2010, 2012). In addition, similar amino acid substitutions in the

peripheral nerve channels (Nav1.6 and Nav1.7) appear to render these tissues resistant to

TTX (McGlothlin et al., 2014, 2016). In fact, many of the resistance conferring mutations seen in garter snakes are also shared by TTX-bearing puffer fish and newts (Jost et al.,

2008; Hanifin & Gilly, 2015). These convergent, genetic changes suggest remarkable constraint and predictability in the evolution of TTX resistance (Feldman et al., 2012,

2016). However, preliminary data on one Thamnophis species, the Sierra garter snake

(Th. couchii), suggests that simple point mutations in the SCN (Nav) loci may not fully explain TTX resistance and there may be undiscovered means that led to this adaption.

The aim of this research is to explore the adaptation of TTX resistance in Th. couchii on multiple scales, spanning whole animal phenotype down to genotype. First, we describe geographic patterns of TTX resistance across Th. couchii range and test for coevolutionary relationships of adaptive traits with sympatric newts. Then, we search for convergently evolved mechanisms in this system and investigate whether variation in resistance can be explained by the same physiological and genetic mechanisms found in other Thamnophis species. Lastly, we examine additional potential genetic mechanisms that lead to the widespread variation of TTX resistance in Th. couchii. Because this adaptive trait may be more complex and far less predictable than previously thought, identifying the connection between genetic mechanisms and expressed phenotypes is 7 critical, and will help us more broadly understand adaptive evolution and the potential molecular constraints that act upon it. The relatively unexplored Th. couchii system is parallel to that of Th. sirtalis allows us to create a comparative body of research with which to look at TTX resistance through the lenses of coevolution, convergent evolution, and the molecular basis of adaptive traits.

Chapter 1: Geographic patterns of coevolving TTX resistance and newt toxicity

We explore TTX resistance through the framework of coevolution by quantifying the adaptive traits in Th. couchii and sympatric prey, the rough-skinned newt (Ta. granulosa), Sierra newt (Ta. sierrae) and California newt (Ta. torosa) on a geographic scale. Our traits of interest are TTX resistance and newt toxicity. This system parallels the well-studied coevolution between predatory common garter snakes (Th. sirtalis) and their toxic newt prey exhibiting hotspots of newt tetrodotoxin (TTX) levels and matching snake TTX-resistance. We quantified predator and prey traits from hundreds of individuals across their distributions, and functional trait-matching at sympatric sites in the hopes of producing rare insights into the repeatability of landscape scale coevolutionary interactions that might structure geographic patterns of adaptive traits in this system. We hypothesize that there are high levels of variation in both traits across the landscape, as well as strong trait-matching that is indicative of a coevolutionary relationship between these taxa, on par with that seen in the Th. sirtalis system.

Chapter 2: Convergence of TTX resistance on physiological and genetic scales

We address the potential convergence of this adaptive trait by characterizing variation in this predatory adaptation at several biological scales and comparing results to 8 those found in other Thamnophis species. We assess the physiological scale of TTX resistance in skeletal muscle, and test for a correlation between muscle and whole animal

resistance. We then characterize functional genetic variation in three Nav loci known to confer whole animal resistance, and measure levels of gene expression for SCN4A:

Nav1.4 in skeletal muscle tissue. We hypothesize that TTX resistance is an example of convergent evolution and suspect to find variation in resistance in Th. couchii to be explained by the same physiological and genetic variation found in Th. sirtalis and Th. atratus.

Chapter 3: The role of gene expression in TTX resistance

We take a wide transcriptomics approach to answer questions about gene expression, and identify additional genes and pathways potentially involved in TTX resistance. We leverage the power of RNA-seq to compare gene expression in both muscle and liver tissue using a biologically relevant experimental design that asks questions about what genes are involved when TTX enters the digestive system of snakes with varying levels of resistance from a single population. We hypothesize that there will be differential gene expression among Th. couchii with low resistance and high resistance in both tissue types.

Taken together, this dissertation studies a remarkable adaptive trait through three unique lenses: how coevolutionary relationships drive adaptive traits, the potential convergent evolution underlying this trait across related taxa, and the role of genetic mechanisms in their involvement in adaptive traits. The combined results reveal a story about a complex trait that is both predictable and unpredictable: predictable at the whole 9

animal and physiological levels, while simultaneously unpredictable at the genetic level.

These projects contribute to a growing body of work that shows the complexity of

adaptive traits and pushes for future work in examining all scales of adaptive evolution

from genotype to phenotype.

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14

The geographic mosaic in parallel: matching patterns of newt tetrodotoxin levels and snake resistance in multiple predator-prey pairs

Jessica S. Reimche1,2 Edmund D. Brodie, Jr.3 Amber N. Stokes4 Erica J. Ely1,5 Haley A. Moniz1,2 Vicki L. Thill1,2 Joshua M. Hallas1,2 Michael E. Pfrender6 Edmund D. Brodie III7 Chris R. Feldman1,2

1Department of Biology, and 2Program in Ecology, Evolution, and Conservation Biology, University of Nevada, Reno, NV, USA 3Department of Biology, Utah State University, Logan, UT, USA 4Department of Biology, California State University Bakersfield, CA, USA 5Department of Herpetology, California Academy of Sciences, CA, USA 6Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA 7Mountain Lake Biological Station and Department of Biology, University of Virginia, Charlottesville, VA, USA

15

Abstract

1. The Geographic Mosaic Theory of Coevolution (GMTC) predicts that coevolutionary

arms races will vary over time and space because of the diverse ecological settings

and population histories of interacting species across the landscape. Thus,

understanding coevolution may require investigating broad sets of populations

sampled across the range of the interaction. In addition, comparing coevolutionary

dynamics between similar systems may reveal the importance of specific factors that

structure coevolution.

2. Here, we examine geographic patterns of prey traits and predator traits in the

relatively unstudied interaction between the Sierra garter snake (Thamnophis couchii)

and sympatric prey, the rough-skinned newt (Taricha granulosa), Sierra newt (Ta.

sierrae) and California newt (Ta. torosa). This system parallels, in space and

phenotypes, a classic example of coevolution between predatory common garter

snakes (Th. sirtalis) and their toxic newt prey exhibiting hotspots of newt tetrodotoxin

(TTX) levels and matching snake TTX-resistance.

3. We quantified prey and predator traits from hundreds of individuals across their

distributions, and functional trait-matching at sympatric sites.

4. We show strong regional patterns of trait covariation across the shared ranges of Th.

couchii and newt prey. Traits differ significantly among localities, with lower newt

TTX levels and snake TTX-resistance at the northern latitudes, and higher TTX levels

and snake resistance at southern latitudes. Newts and snakes in northern populations

show the highest degree of functional trait matching despite possessing the least

extreme traits. Conversely, newts and snakes in southern populations show the 16

greatest mismatch despite possessing exaggerated traits, with some snakes so resistant

to TTX they would be unaffected by any sympatric newt. Nevertheless, individual

variation was substantial, and appears to offer the opportunity for continued

reciprocal selection in most populations.

5. Overall, the three species of newts appear to be engaged in a TTX-mediated arms

race with Th. couchii. These patterns are congruent with those seen between newts

and Th. sirtalis, including the same latitudinal gradient in trait covariation, and the

potential “escape” from the arms race by snake predators. Such concordance in broad

scale patterns across two distinct systems suggests common phenomena might

structure geographic mosaics in similar ways.

KEYWORDS adaptation, arms race, coevolution, Taricha (Pacific newt), Thamnophis (garter snake), trait-matching

17

1 INTRODUCTION

The Geographic Mosaic Theory of Coevolution (GMTC) suggests that coevolution varies across time and space because populations are ecologically and genetically structured across the landscape (Thompson, 2005). This geographic structure can lead to diverse ecological dynamics and coevolutionary fates between interacting species (Thompson,

1994, 1999, 2005) because divergent processes (e.g., selection, gene flow, drift) can operate independently on ecologically distinct or subdivided populations, creating selection mosaics across landscapes (Thompson, 1994, 1997, 1999, 2005; Brodie,

Ridenhour, & Brodie, 2002; Thompson & Cunningham, 2002). Across a range of interacting species, there may be areas of intense reciprocal selection resulting in coevolutionary hotspots. Conversely, there may also be areas of weak or no reciprocal selection, resulting in coevolutionary coldspots (Thompson, 1994, 1999, 2005).

Hotspots and coldspots can be detected by quantifying geographic variation in the traits that mediate the coevolutionary interaction between species (i.e. the phenotypic interface of coevolution; Brodie & Ridenhour 2003) (summarized in Thompson, 1994,

2005). Areas where coevolved traits are well-matched indicate hotspots of reciprocal selection, while areas where traits are not well-matched (coldspots) are expected in regions where reciprocal selection is weak or absent, though directional selection may be intense for one member of the interaction (Thompson, 1994, 1997, 1999, 2005; Brodie et al., 2002; but also see Gomulkiewicz et al., 2007; Nuismer, Gomulkiewicz, & Ridenhour,

2010). Although much theoretical work has been done to establish a framework for understanding coevolution, relatively few coevolutionary systems have been investigated at a landscape level to test the GMTC (Brodie et al., 2002; Thompson & Cunningham, 18

2002; Anderson & Johnson, 2008; Zangerl & Berenbaum, 2003; Mezquida & Benkman,

2005; Hanifin, Brodie, & Brodie, 2008). In addition, historical contingency and other ecological or evolutionary forces might conspire to produce hotspots despite weak reciprocal selection or coldspots in the face of strong reciprocal selection (Gomulkiewicz,

Thompson, Holt, Nuismer, & Hochberg, 2000; Gomulkiewicz et al., 2007; Nuismer et al.,

2010). For example, the traits of ecological partners could match because of diffuse coevolution or selection from other members of a community, because of similar abiotic or other ecological conditions, or from historical selection pressures and genetic constraints (e.g. pleiotropy) that prevent trait change (Gomulkiewicz et al., 2000;

Gomulkiewicz et al., 2007; Nuismer et al., 2010). Likewise, mismatches are predicted under the GMTC, depending on patterns of gene flow and drift, selection intensity, the genetic architecture of coevolved traits, ecological constraints on adaptations, and even the temporal stage of the species interaction (Thompson, 1994, 1999; Nuismer,

Thompson, & Gomulkiewicz, 1999; Gomulkiewicz et al., 2000; Brodie et al., 2002;

Thompson, 2005; Kopp & Gavrilets, 2006; Gomulkiewicz et al., 2007; Nuismer,

Ridenhour, & Oswald, 2007; Hanifin et al., 2008; Feldman, Brodie, Brodie, & Pfrender,

2010; Nuismer et al., 2010). Thus, it may be difficult to infer the specific forces behind individual patterns in any one system. Multiple independent coevolutionary systems provide an opportunity to examine whether geographic patterns occur in repeatable and predictable ways, and thereby allow greater inferences about the role of ecological mechanisms responsible for coevolutionary patterns. We address this problem by investigating phenotypic matching between prey and predator traits in a largely unstudied predator-prey system involving poisonous newts and their resistant snake predators. We 19 then compare the geographic patterns to those from a parallel predator-prey system. By evaluating geographic patterns of covariation in two distinct systems, we can produce rare insights into the generality of landscape scale phenomena that might structure geographic mosaics.

The interaction between toxic newts (Taricha) and resistant garter snakes

(Thamnophis) provides a model system to investigate patterns of predator-prey coevolution across the landscape (Brodie & Brodie, 1999). Pacific newts possess a lethal neurotoxin, tetrodotoxin (TTX), that provides a nearly impenetrable chemical defense

(Brodie, 1968). TTX binds to the outer pore of voltage-gated sodium channels in nerves

and muscles (Nav proteins), blocking the movement of sodium ions across the cell membrane and halting action potentials (Hille, 2001; Fozzard & Limpkin, 2010). By arresting electrical impulses in muscles and nerves, TTX causes immobilization, respiratory failure, and often death (Brodie, 1968; Isbister & Kiernan 2005; Abal et al.,

2017). Despite the fact that TTX is one of the most potent natural toxins ever discovered, some species of Thamnophis prey on sympatric newts (Brodie et al., 2002, 2005;

Wiseman & Pool, 2007; Greene & Feldman, 2009), and these different species have independently evolved high tolerance of TTX (Feldman, Brodie, Brodie, & Pfrender,

2009).

Work on the coevolution between newts and snakes, largely centered around the interaction between the rough-skinned newt (Ta. granulosa) and the common garter snake (Th. sirtalis), demonstrates extensive geographic variation in both prey and predator traits in Western North America (Brodie & Brodie, 1990, 1991; Hanifin, Yotsu-

Yamashita, Yasumoto, Brodie, & Brodie, 1999; Brodie et al., 2002; Hanifin et al., 2008; 20

Hague et al., 2016; Hague, Feldman, Brodie, & Brodie, 2017). Newt TTX levels and snake resistance appear well-matched across much of the sympatric range of these two species. In localities where Ta. granulosa have low levels of TTX (or TTX is absent altogether), Th. sirtalis populations have low levels of resistance to TTX. In contrast, in areas where populations of Ta. granulosa possess higher levels of TTX, sympatric populations of Th. sirtalis display correspondingly elevated levels of TTX-resistance

(Brodie & Brodie, 1991; Hanifin et al., 1999; Brodie et al., 2002; Hanifin et al., 2008).

While the general pattern of phenotypic variation supports reciprocal selection in predator-prey interactions, a few regions (e.g., San Francisco Bay Area and middle Sierra

Nevada of California) show high levels of phenotypic mismatch, with extreme levels of

TTX-resistance in some snake populations. Snakes at these sites possess elevated TTX- resistance that far surpasses the levels required to withstand the amounts of TTX in sympatric newts, suggesting snake predators have “escaped” the arms race in these populations (Hanifin et al., 2008).

An independent predator-prey system was recently discovered in the Sierra Nevada

Mountains of California, involving the Sierra garter snake (Th. couchii) and sympatric species of Pacific newts (Taricha) (Brodie et al., 2005; Wiseman & Pool, 2007; Feldman et al., 2009). The Sierra garter snake is a highly aquatic predator that occupies a wide range of communities in the Lower Cascade and Sierra Nevada Mountains (Rossman,

Ford, & Seigel, 1996; Stebbins, 2003). This snake cohabits creeks, springs, ponds and lakes with three newt species: the rough-skinned newt (Ta. granulosa) in the Lower

Cascades; the Sierra newt (Ta. sierrae) along almost the entire Sierra Nevada Range; and the California newt (Ta. torosa) in the Southern Sierra. Though this snake is a voracious 21 predator of aquatic and semi-aquatic vertebrates (Fitch, 1949; Rossman et al., 1996;

Stebbins, 2003), it was only recently discovered preying on Sierra and California newts

(Brodie et al., 2005; Wiseman & Pool, 2007). This interaction provides an opportunity to investigate the extent to which independent predator and prey phenotypes covary across the landscape.

Here, we examine geographic variation in both prey and predator traits across the geographic range of this interaction, characterizing the phenotypic variation and correlation between prey and predator traits at sympatric locations. Our goals are to: 1) understand the spatial scale at which coevolution may be occurring; 2) evaluate the presence and extent of trait mismatches across populations, and; 3) determine whether phenotypes in Th. couchii and sympatric newts vary in a geographically parallel fashion to the well-established Th. sirtalis and Ta. granulosa system.

2 MATERIALS AND METHODS

2.1 Prey phenotype assays

To assess prey phenotypes, we quantified TTX in the skin of 108 newts (Ta. granulosa,

Ta. sierrae, and Ta. torosa) from 10 localities across the Lower Cascade and Sierra

Nevada mountain ranges, representing nine distinct watersheds that we used as our population-level samples (Table S1). We briefly housed newts (2-6 weeks) prior to TTX sampling by keeping animals from the same location together in 10 gallon tanks with 5 cm of chloride-free water, rocks and other features. We kept newts on a 12L:12D cycle with temperatures ranging from 15-20°C and fed newts blood worms every other day. 22

From each newt we took 3 mm diameter skin biopsies from the dorsal surface (the midback) between the pectoral and pelvic girdle (Hanifin, Brodie, & Brodie, 2002;

Lehman, 2007) and extracted TTX following Hanifin et al. (2002). We measured TTX concentrations with a Competitive Inhibition Enzymatic Immunoassay (Lehman, 2007;

Stokes, Williams, & French, 2012). We used standards from the linear range of the curve in concentrations of 10-500 ng/mL, and diluted all samples 1:1 in 1% Bovine Serum

Albumin in Phosphate Buffered Saline. We considered samples with less than 10 ng/mL of TTX to have no TTX (n = 1). We extrapolated measures of TTX in our skin samples to the whole animal using the calculation from Hanifin et al. (2004) to yield estimates of whole newt TTX levels for each individual (mg of TTX/newt).

We mapped the ranges of newt phenotypes across Ta. granulosa, Ta. sierrae and Ta. torosa distributions in the Sierra Nevada and Lower Cascade Ranges using the inverse distance weighted (IDW) interpolation in ArcMap (v10.3.1 ESRI). We used the IUCN database to obtain ranges for Ta. sierrae and Ta. torosa (which come from Stebbins,

2003, Kuchta & Tan, 2006). Because newt phenotypes did not appear normally distributed, we examined differences in mean phenotypes among populations using non- parametric Kruskal-Wallis tests in R v3.5.1 (R Core Team 2018). We also tested for correlations between phenotype and latitude using linear regression.

2.2 Predator phenotype assays

To assess predator phenotypes, we assayed TTX-resistance in 293 Th. couchii from 32 localities, representing 12 distinct watersheds used as our population-level samples for snakes (Table S2). Prior to phenotypic measures, we housed snakes individually in either 23

5 or 10 gallon tanks, depending on their size. We provided each tank with a water dish, hide box (Reptile Basics Inc), newspaper or sani-chip bedding (Harlan Teklad), full- spectrum lighting (Reptisun, 10.0 UVA/UVB, Exo Terra) and heat-tape placed under one end of the tank to generate a thermal gradient from roughly 24-30°C. We kept snakes in a room on a 12L:12D cycle with a constant temperature of 26°C, and fed snakes fish (live guppies or frozen trout) or feeder mice (frozen mice from a vendor) once per week.

We measured TTX-resistance using a well-established and highly repeatable bioassay of whole-animal performance (Brodie & Brodie, 1990; Ridenhour, Brodie, & Brodie,

2004). We placed snakes on a 4 m track lined with infrared sensors each 0.5 m (and a mounted video camera) to record sprint speed pre- and post-injection with TTX. We used the mean of the quickest two interval times as a snake’s speed, which appears to represent an individual’s maximal effort and yields repeatable measures over time. After measuring the pre-injection baseline speed of each snake, we rested snakes for 48 hours and then gave each snake an intraperitoneal (IP) injection of TTX diluted with Ringer’s solution, starting at 1 mass-adjusted mouse unit (MAMU), where 1 MAMU is the amount of TTX needed to kill a 20g mouse in 10 minutes, which corresponds to 0.01429 μg of TTX per gram of snake (Brown & Mosher, 1963; Brodie & Brodie, 1990; Ridenhour et al., 2004).

We measured post-injection speeds 30 min after TTX injections (Brodie et al., 2002;

Ridenhour et al., 2004). We then rested snakes for 48 hours and subsequently injected them with serially increasing doses (5, 10, 25, 50, 100, or higher MAMUs as needed) and recorded post-injection speeds. Note, however, that due to the prohibitive cost of TTX, we stopped increasing the doses of TTX before a 50% reduction in speed could be 24 estimated in 65 highly resistant snakes, resulting in measurements that underestimate true

TTX-resistance.

We scored resistance as the dose required to slow a snake to 50% of its pre-injection baseline speed (50% MAMU). We estimated this 50% dose using curvilinear regression on log-transformed dosages; doses equal to one we converted to 0.999, and those that were zero we converted to 0.001 (Brodie et al., 2002; Ridenhour et al., 2004). We calculated the curvilinear regression using the linear regression y’= α + βx’, where y is

TTX resistance and calculated as y’= ln(1/y-1), and x is the TTX dose calculated as x’=ln(x), α and β are the estimated regression parameters (Ridenhour et al., 2004).

Using this regression approach, we also calculated the doses required to slow a snake to 85% of its normal crawl speed, and to 15% of its normal speed. Between these values, the dose response curve appears linear, while above 85% and below 15%, TTX resistance appears asymptotic (Ridenhour et al., 2004; Hanifin et al., 2008). In other words, doses of

TTX above 85% have little to no effect on snake performance, while doses of TTX below

15% severely incapacitate snakes (Hanifin et al. 2008). We then used these 85% and 15% values as approximate thresholds for understanding phenotypic mismatches between newts and snakes (see below).

We mapped the distribution of predator phenotypes (50% doses) across the range of

Th. couchii in ArcMap as above; we obtained the distribution Th. couchii from the IUCN database (which comes from Rossman et al., 1996; Stebbins, 2003). As with newt phenotypes, we examined differences in mean predator phenotypes among populations using non-parametric, Kruskal-Wallis tests in R, and tested for correlations between phenotype and latitude using linear regression. 25

2.3 Phenotype matching and mismatching

To determine relationships between prey TTX levels and predator TTX-resistance phenotypes, we first adjusted predator TTX-resistance units from 50% MAMUs (based on IP injections) to mg of TTX in oral doses, because snakes in the wild ingest newts whole. We converted 50% MAMU to 50% IP Dose using the following equations

(Hanifin et al. 2008):

IPdose(mg) = (θ*0.00001429)*snake mass(g) where is the 50% MAMU and 0.0001429 is the conversion factor (1 MAMU= 0.01429μg

TTX per gram of snake) (Brown & Mosher, 1963; Brodie & Brodie, 1990; Brodie &

Brodie, 1991; Brodie et al., 2002; Ridenhour et al., 2004). The effects of TTX are dependent on body size (Brodie et al., 2002; Williams et al., 2002; Ridenhour et al.,

2004; Hanifin et al., 2008; Abal et al., 2017), therefore we estimated the oral dose of

TTX required to slow the average adult Th. couchii (mean adult mass = 38 g). We then converted IP dose (mg) to the oral dose required to achieve the same performance reduction by multiplying the IP dose by 40 (Williams, Brodie, & Brodie, 2002; Hanifin et al., 2008; Abal et al., 2017). By quantifying TTX-resistance as the estimated dose of orally ingested TTX (in mg) needed to reduce snake performance by 50%, we have an ecologically relevant metric to compare predator and prey phenotypes.

We examined the relationship between newt TTX levels (mg of TTX) and snake resistance (mg of TTX) for nine sympatric populations using linear regression in R. We then quantified the relationship of each sympatric interaction by calculating the functional mismatch of each point in our regression. Hanifin et al. (2008) defined a 26 functional mismatch as an ecological interaction between sympatric predator and prey that does not result in comparative fitness consequences. A matched population consists of a sympatric interaction expected to result in similar fitness outcomes (i.e., the average newt in a population contains enough TTX to reduce the average snake performance by

50% in that location) (Hanifin et al., 2008). We estimated population mismatches by calculating (d) which is simply the deviation of observed values from the expected matching values described by a linear regression between mean prey and mean 50% predator traits in sympatric populations. We calculated d by modifying the equation in

Hanifin et al. (2008) for measuring the shortest distance from a point to a line:

2 2 d = x1(A) + y1(B) + C/√(A + B )

Because the expected TTX-resistance assumes a matched interaction between snake and newt phenotypes (i.e. total TTX in newt skin = 50% dose of snakes in sympatric population), the line describing a perfect phenotypic match has a slope of 1 (A and B) and an intercept of 0 (C), assuming the traits occur on the same scale. Our equation then reduces to:

d = (xi -yi)/√2

where xi = average 50% dose of snakes from a given population and yi = average total

TTX in newt skin from the same population; i.e. linear match in phenotypes between predator and prey (dashed line in Figure 2b). Following Hanifin et al. (2008), we deemed predator-prey populations with d calculations greater than 0.6 or less than -0.6 (d < -0.6, d > 0.6) as showing high phenotypic mismatch (Hanifin et al., 2008); these values correspond to a 15% and 85% reduction in predator crawl speed, respectively, which suggest that reciprocal selection is low or absent. We considered locations where d ranges 27 between 0.6 and -0.6 as sites where both prey and predator populations are likely experiencing reciprocal selection.

Finally, we found substantial individual variation in prey and predator phenotypes within populations (see Results). Thus, we attempted to quantify phenotypic matching and mismatching at the level of individual newts and snakes from sympatric populations. We simulated chance encounters between prey and predator by randomly drawing one newt and one snake from the same locality and estimating their phenotypic mismatch: snake TTX resistance (50% oral dose in mg TTX) minus newt TTX (total mg of TTX). We then evaluated the distribution of estimated mismatches by randomly sampling newt-snake pairs 10000 times. Perfectly matched pairs result in no mismatch (0 mg of TTX), while a snake with a 50% dose of TTX that exceeds the amount of TTX in a sympatric newt results in a “predator mismatch” (excess TTX resistance, reported in + mg of TTX), and a snake with less TTX resistance than a sympatric newt yields a “prey mismatch” (deficient TTX resistance, reported in - mg of TTX). To put these values of

TTX into context, we plotted the 85% and 15% doses of TTX resistance for snakes.

These provide an indication of the proportion of sympatric newt-snake pairs that can potentially impose fitness costs on one another, and proportion of interactions that cannot result in reciprocal selection (mismatches that lie outside the zone of 85% and 15% doses).

We performed simulations on three populations of newts and snakes that span the geographic distribution and species involved in the interaction, as well as the range of phenotypes: Ta. granulosa (n = 13) v. Th. couchii (n = 11) from Battle Creek; Ta. sierrae

(n = 10) v. Th. couchii (n = 11) from Battle Creek; Ta. sierrae (n = 10) v. Th. couchii (n = 28

61) from Upper Yuba River; Ta. torosa (n = 15) v. Th. couchii (n = 94) from Upper Tule

River. We conducted all simulations and mismatch calculations in R.

3 RESULTS

3.1 Prey phenotypes

We found extensive variation in TTX levels among species, within species, and even within newt populations across the lower Cascade Range and Sierra Nevada Range

(Table 1). Among the three Taricha species, there are significant differences in levels of

TTX (Kruskal-Wallis = 18.597, df = 2, p < 0.001). The rough-skinned newt, Ta. granulosa possessed the lowest levels of TTX (0.06 mg of TTX/newt), significantly less

TTX than the sympatric Sierra newt, Ta. sierrae (Dunn’s post hoc test, p < 0.001), which contained nearly three times more TTX (0.17 mg of TTX/newt) at the same location in

North Battle Creek. On the other hand, the California newt, Ta. torosa, possessed the most toxic individuals of all three species, although average TTX levels for our single population were on par with those found in populations of Ta. sierrae across their range

(Dunn’s post hoc test, p = 0.23).

We also found significant differences in amounts of TTX between populations across the 9 sampled watersheds, with mean TTX levels ranging from 0.06 mg/newt to 1.25 mg/newt (Table 1, Kruskal-Wallis = 30.71, df = 7, p < 0.001); post-hoc comparisons revealed significant pairwise differences in TTX between the northernmost population

(Battle Creek) and three southern populations (Upper Tuolumne River, Upper King

River, and Upper Tule River). In addition, the variation in TTX levels appears to follow a latitudinal gradient, with populations at the higher latitudes possessing less TTX than 29

those at the southern latitudes (Figure 1a; r = -0.47, r2 = 0.25, F = 35.16, df = 106, p= <

0.001).

Lastly, we noted substantial variation in the amounts of TTX in the skin of individual

Ta. sierrae and Ta. torosa within populations (Table 1). Intrapopulation-level variation appears most pronounced in the southern half of the range; in the four southernmost locations (Upper Cosumnes, Upper Tuolumne, Upper King, Upper Tule), three newt populations have TTX levels above 1 mg, and the southernmost population (Ta. torosa,

Upper Tule watershed) contains tremendous TTX variation with individuals that possess low levels of TTX (0.02 mg) to those with 5.48 mg (Table 1).

3.2 Predator phenotypes

We documented extensive phenotypic variation in the predator Th. couchii, with significant differences in TTX-resistance across the 12 sampled watersheds (Table 1,

Kruskal-Wallis = 210.86, df = 11, p < 0.001). Post-hoc comparisons revealed significant pairwise differences between oral dose of TTX (mg) in 29 of the 66 watershed comparisons. Phenotypic variation in snakes follows a similar geographic trend as observed in sympatric newts, with populations of Th. couchii in the north displaying low

TTX-resistance, while those in the south show elevated TTX-resistance (Figure 1b), generating a strong correlation between TTX-resistance and latitude (r = -0.77, r2 = 0.59,

F = 429, df = 291, p < 0.001). Further, populations of Th. couchii at the southern end of the species range (Upper Tuolumne, Upper King, Upper Tule watersheds) possess individuals that function above 50% of their baseline sprint speed at well over 100

MAMUs. Multiple populations (especially in the middle Sierra Nevada) contain high 30 levels of phenotypic variation with 50% oral doses ranging from 0.40-2.21 mg of TTX

(18-102 MAMUs) in Upper Cosumnes, and 0.18-2.99 mg of TTX (8-137 MAMUs) in

Upper Tuolumne watersheds.

3.3 Phenotype matching and mismatching

We found a strong linear relationship between prey and predator phenotypes across their shared ranges (Figure 2; r = 0.86, r2 = 0.74, F = 25.59, df = 8, p < 0.001). Using our metric of functional trait matching (d), we found both areas of tight phenotypic matching (d <

0.6) and zones of mismatching (d > 0.6) across the landscape (Figures 1-2). A d of 0 indicates the average amount of TTX in a newt exactly matches the oral dose of TTX required to slow the average snake to 50% of its baseline speed in that locality. Negative values of d indicate the average amount of TTX in sympatric newts is higher than average 50% oral dose of TTX in sympatric snakes, and conversely, a positive d suggests that mean newt TTX levels are lower than the 50% dose of snakes. The four northernmost populations of predators and prey are especially well-matched, with low values of d (0 to 0.15) that reveal coevolutionary hotspots (Table 1; Figures 1-2). The five southernmost populations display relatively high phenotypic mismatch, with d values all greater than 0.6 that reveal coldspots (Table 1; Figures 1-2). In three populations, in particular (South Fork American River, Upper Cosumnes River, Upper King River), the average newt does not contain enough TTX to slow the average snake to even 85% of baseline speed. However, individual variation in prey and predator traits appears high in these southern populations (Table 1; Figure 2). 31

To further explore phenotypic mismatching within populations, we randomly paired newts and snakes (10000 times) from sympatric sites, and then calculated trait matching

(in units of mg of TTX), allowing us to create histograms of individual phenotypic mismatches expected to occur within populations. We conducted these simulations for newt-snake pairs at three sites that represent the full range of phenotypes, as well as the entire geographic and taxonomic breadth of the interaction. Simulations (Figure 3) reveal that even at a single site, the full range of predator-prey outcomes are possible, from newts that are too toxic for some sympatric snakes to survive, to predator-prey pairs that fall within a phenotypic interaction space in which both species could experience reciprocal selection (between 85% and 15% doses of TTX for snakes), to snakes that are so resistant to TTX that they can handle most sympatric newts with little or no ill effects

(snakes not slowed to even 85% of normal speed). The distributions of individual mismatches show that this range of potential outcomes is possible, even at sites where the mean values of newt and snake traits suggest that either the prey or predator are

“winning” (e.g. Upper Tule River).

4 DISCUSSION

We found strong patterns of trait matching between sympatric newt prey (Ta. granulosa,

Ta. sierrae, and Ta. torosa) and snake predators (Th. couchii), but also trait mismatching that suggests a potential “escape” from the arms race in some snake populations. These patterns largely mirror those seen in the well-characterized Ta. granulosa and Th. sirtalis system (Brodie et al., 2002; Hanifin et al., 2008), indicating these separate arms races between newts and their snake predators have experienced similar dynamics across time 32 and space. The next steps will be uncovering the ecological determinants and evolutionary constraints that lead to such repeatable patterns of coevolution.

4.1 Prey and predator phenotypes

Both prey and predator demonstrate substantial variation in phenotypes across their ranges in the Sierra Nevada and Lower Cascade, consistent with a latitudinal gradient.

The variation in TTX levels among populations of Ta. granulosa, Ta. sierrae and Ta. torosa is similar to that seen among coastal populations of Ta. granulosa and Ta. torosa

(Hanifin et al., 2008). Some populations of Sierran Ta. sierrae and Ta. torosa possess amounts of TTX that would be lethal to nearly any potential vertebrate predator, as is the case for coastal Ta. granulosa (Brodie, 1968, Hanifin et al., 2002). Our most toxic newt population (Ta. torosa, Upper Tule watershed) included individual newts with over 5 mg of TTX (i.e., animals weighing less than 15 g with enough poison to kill 2-5 humans).

Newts with higher TTX levels have only been found in a handful of sites (Hanifin et al.,

2008; Stokes et al., 2015). Surprisingly, these southern watersheds, which harbor our most toxic newts, also contain some of our least toxic individuals (range from 0.02-5.48 mg TTX). This extreme variability in newt phenotypes at a single location is rare, and has only been documented in a few other locations (Williams, Hanifin, Brodie, & Brodie,

2010; Stokes et al., 2015; Hague et al., 2016). Such within population variation in prey phenotypes warrants further investigation into the genetic and physiological underpinnings of TTX levels in newts (e.g. Bucciarelli, Shaffer, Green, & Kats, 2017;

Mailho-Fontana et al., 2019). 33

Similar to the variability across newt populations, we observed wide phenotypic variation in Th. couchii, with average oral doses of TTX-resistance (50% dose) ranging from 0.06 mg of TTX to 2.19 mg of TTX (2.6-100 MAMUs). Populations of Th. couchii in the southern end of their range are the most resistant to TTX, and possess individuals that function at 50% of their baseline sprint speed at oral doses well over 3 mg of TTX

(over 150 MAMUs). These measures of TTX-resistance place southern populations of

Th. couchii among the most resistant Thamnophis recorded (Brodie et al., 2002, 2005;

Feldman et al., 2009, 2010; Hague et al., 2017). In addition, the range of phenotypes seen within several populations of Th. couchii is uncommon, with only a few populations of

Th. atratus and Th. sirtalis possessing such extreme within-population variation (Brodie et al., 2002; Feldman et al., 2010, Hague et al., 2017).

4.2 Hotspots and coldspots

The strong linear relationship between TTX levels in Taricha and TTX-resistance in sympatric Th. couchii reveals hotspots of reciprocal selection (Figure 2). In other words, prey and predator traits appear functionally well-matched across the landscape (i.e. mean newt TTX level and dose of TTX required to slow the average sympatric snake to 50% of normal crawl speed), suggesting a history of ever-increasing phenotypic evolution between newts and Sierra garter snakes.

Despite overall trait matching between sympatric Taricha and Th. couchii, we also uncovered coevolutionary coldspots, or areas of weak (or absent) reciprocal selection.

Hanifin et al. (2008) classified roughly one-third of sympatric populations of Ta. granulosa and Th. sirtalis as functional mismatches, where predator resistance was so 34 extreme that the average newt TTX would have little to no effect on co-occurring snakes.

We observed similar relationships in the southern end of the Sierra Nevada between the newt prey Ta. sierrae and Ta. torosa, and sympatric Th. couchii predators. Areas of functional trait mismatch show exaggerated TTX-resistance levels in southern populations of Th. couchii that far exceed the resistance necessary to eat toxic newts at those locations (Figures 1-2). Thus, in roughly half the populations, Th. couchii may be imposing intense selection on Ta. sierrae and Ta. torosa, but this interaction may not be currently reciprocal. The mechanisms driving local patterns of hotspots and coldspots require investigation (see below), particularly the notable finding that two phylogenetically independent snake predators may have evolutionarily “escaped” the arms race with toxic newts.

4.3 Understanding trait matching and mismatching

Determining the ecological conditions and evolutionary forces that drive spatial patterns of phenotypic matching and mismatching is the next step in understanding the GMTC.

Differences in ecology among populations may explain the presence or absence of reciprocal selection (Thompson, 1994, 1997, 1999, 2005). Selection promoting predation on newts might vary across the Lower Cascade and Sierra Nevada. Differences in the abundance, availability, and seasonal importance of various prey items, especially newts, might permit northern populations of Th. couchii to generalize on other aquatic species, whereas southern populations of Th. couchii may be forced to specialize on Taricha and their larvae at certain times of year. If Th. couchii from southern populations are more likely to come across Taricha or are forced to rely on newts as a critical food resource, 35 then there may be stronger selection on those snake populations to exploit toxic prey.

Diet analysis of Th. couchii across their range could help clarify some of these questions and provide further hypotheses to explain why TTX-resistance is so elevated in the southern part of the range.

Besides quantifying the interactions between prey and predator, and obtaining a more complete view of the ecological communities they occupy, elucidating the genetic basis of the co-evolved traits would help us understand patterns of hotspots and coldspots.

Unfortunately, the genetic basis of TTX synthesis in newts remains unknown (Hanifin,

2010; Jal & Khora, 2015). On the other hand, TTX-resistance in garter snakes appears to have a relatively simple genetic basis, involving structural changes in outer pore (P- loops) of the sodium channels expressed in muscles and nerves that are the molecular targets of TTX (Geffeney, Fujimoto, Brodie, Brodie, & Ruben, 2005; Feldman, Brodie,

Brodie, & Pfrender, 2012; McGlothlin et al., 2014, 2016). Functional variation in the

skeletal muscle sodium channel (Nav1.4) appears especially important in contributing to whole animal TTX-resistance (Geffeney et al., 2005; Feldman et al., 2010; McGlothlin et al., 2016), and allelic variation in the gene that encodes this protein (SCN4a) appears responsible for drastic differences in phenotypes within and among populations of Th. sirtalis (Geffeney et al., 2005; Feldman et al., 2010; Hague et al., 2017) and Th. atratus

(Feldman et al., 2010). Geographic variation in TTX-resistance in Th. couchii may be due to similar genetic changes. In fact, the southernmost population of Th. couchii possess a mutation in SCN4a (Feldman et al., 2009) that is found in TTX-bearing pufferfish and in newts (Jost et al., 2008; Hanifin & Gilly, 2015), and is known to confer a 15-fold decrease of TTX ligation to the channel (Jost et al., 2008). Thus, phenotypic variation in 36

Th. couchii might be explained by allelic variation at this locus. Work is needed to characterize functional variation in SCN4a across the range of Th. couchii, and whether rates of gene flow might contribute to the high degree of phenotypic variation in snake populations.

Our results also stress the importance of understanding the biogeographic history and genetic structure of interacting species. The snakes and newts of this region may have distinct histories of colonization and fragmentation, and thus, different temporal depths of association across the range. In addition, prey and predator populations may have distinct spatial patterns of connectivity, which could contribute to hotpot and coldspot patterns.

Indeed, the Sierra Nevada and Lower Cascade Ranges possess a varied topography, geology, and climate (Schoenherr, 2017), as well as a complex history of glaciation and orogeny (Moore & Moring, 2013) well-known for fragmenting and re-connecting populations of vertebrates in this region through time (Feldman & Spicer, 2006; Kuchta

& Tan, 2006; Rissler, Hijmans, Graham, Moritz, & Wake, 2006; Kuchta, Parks, Mueller,

& Wake, 2009; Schierenbeck, 2014; Lavin, Wogan, McGuire, & Feldman, 2018). Thus, determining patterns of population structure and occupancy will aid in our interpretation of patterns of trait matching and mismatching.

Lastly, we found substantial variation in prey and predator phenotypes within populations. Intrapopulation variation is most pronounced in the southern watersheds, and includes over half the geographic range of the interaction (from the American River south). Notably, this variation is biologically meaningful, and appears to permit the full range of coevolutionary dynamics in a single population, even at sites where the average snake predator appears to be “winning” the arms race (Figure 3). How such variation in 37 adaptive traits is maintained within populations remains an open question. Specifically, why are there individual newts with low amounts of TTX and snakes with low TTX resistance in the same (southern) populations that harbor newts and snakes with extreme phenotypes? Presumably natural selection would have culled individuals with lower trait levels out of these populations, simply through generations of encounters with predators or prey possessing more extreme traits. Gene flow from the northern populations is one mechanism that could deliver allelic variation to create the range of phenotypes we observe. However, gene flow alone seems unlikely to connect these populations of small vertebrates across such broad expanses of space. An intriguing possibility is that newts and snakes with extreme trait values experience associated costs and lower fitness outside of the context of the predator-prey interaction (Brodie & Brodie 1999a, b).

The evidence of a tradeoff remains circumstantial in newts, but seems likely. Newts possess specialized structures in their skin to house and excrete TTX (Hanifin et al.,

2004; Mailho-Fontana et al., 2019), and after discharging TTX, it can take to months to regenerate TTX (Cardall, Brodie, Brodie, & Hanifin, 2004). Thus, the production, storage, and secretion of TTX is likely costly, regardless of whether TTX is the product of endogenous synthesis, bacterial symbionts, or bioaccumulation (Hanifin, 2010; Jal &

Khora, 2015; Mailho-Fontana et al., 2019). Not surprisingly, newts that are allopatric with garter snakes possess exceptionally low (often undetectable) levels of TTX (Hanifin et al., 2009; Hague et al., 2016; Mebs, Yotsu-Yamashita, Ream, Zajac, & Zehner, 2016).

The case for a tradeoff in garter snakes is clearer; the most TTX-resistant Th. sirtalis display slower crawl speeds (Brodie & Brodie 1999b; Hague et al., 2018). This

performance tradeoff is probably the direct result of amino acid replacements in Nav1.4 38 that reduce TTX ligation to the skeletal muscle sodium channel, but also impair ion channel function (Feldman et al., 2012; Hague et al., 2018). Understanding the physiological basis of TTX production in newts, and TTX-resistance in Th. couchii will be necessary to determine the costs associated with these adaptations, and whether countervailing selection puts the brakes on the evolution of extreme phenotypes in this system.

5 CONCLUSIONS

Our characterization of a phylogenetically independent newt-snake system displaying dramatic prey and predator phenotypes, as well as similar patterns of a latitudinal gradient in phenotypic matching and mismatching, suggests there may be common environmental or ecological determinants that structure the mosaics similar ways in both newt-snake systems. Such parallel phenotypic responses between newts and snakes, particularly the potential “escape” from the arms race in both Th. couchii and Th. sirtalis, call for additional work to understand the genetic architecture of resistance in Th. couchii and TTX production in species of Taricha. Investigating coevolutionary dynamics in these independent predator-prey systems may reveal common mechanisms that lead to parallel responses over space and time, creating some degree of predictability to the geographic mosaic.

ACKNOWLEDGEMENTS

We thank California Department of Fish & Wildlife for scientific collecting permits (to

CRF, EJE and EDB III), and K. Wiseman, D. Mulcahy and M. Edgehouse for assistance in the field and R. Hansen for field advice. We acknowledge USU and UNR IACUCs for 39 approval of live animal protocols (to EDB Jr. and CRF), and for aid with captive care and bioassays we thank A. Mortensen, J. Scoville, A. Wilkinson, J. Pluid (USU), and G.

Blaustein, S. Louden, T. Disbrow, A. Durfee, J. Gray, and W. Mandeville (UNR). We are grateful to J. Holland for invaluable contributions to track design and construction. We appreciate the use of photos from D. Picklum, J. Vindum, A. Pool, and G. Nafis. For feedback on analyses we thank M. Forister, K. Schlauch, J. Petereit, and P. Hurtado. We thank J. Vindum and M. Koo (CAS), and J. Campbell and C. Franklin (UTA) for help with the curation of specimens. We thank two anonymous reviewers for thoughtful comments, and we appreciate useful discussions and reviews from the UNR Evol Doers, particularly V. Alaasam, D. Baldan, J. DeBoer, J. Heppner, J. Jahner, M. Matocq, T.

May, J. Ouyang, and J. Voyles. This work was supported by National Science Foundation grants DEB0922251, DEB1034686 (to EDB Jr., EDB III., and MEP), and IOS1355221

(to CRF).

AUTHORS’ CONTRIBUTIONS

CRF, EDB Jr., EDB III, and MEP designed the study; CRF, EDB III, EJE and JSR field collected animals; EDB Jr., CRF, HAM, VLT, JSR, and ANS generated the data; JSR,

CRF, JMH, EDB III, and MEP analyzed the data; all authors helped interpret the results and draft the manuscript.

DATA ACCESSIBILITY

We deposited all animals as voucher specimens in museums: herpetology collections of the California Academy Sciences (CAS); University of Texas, Arlington (UTA); 40

University of Nevada, Reno (UNR). Data are available on the Open Science Framework

digital repository: https://osf.io/yp9nt/.

ORCID

Chris R Feldman https://orcid.org/0000-0003-2988-3145 Jessica S Reimche https://orcid.org/0000-0001-6536-7039 Edmund D Brodie, Jr. https://orcid.org/0000-0002-5739-474 Amber N Stokes https://orcid.org/0000-0001-6935-7794 Erica J Ely https://orcid.org/0000-0003-2457-0190 Haley A Moniz https://orcid.org/0000-0003-2838-511X Vicki L Thill https://orcid.org/0000-0002-6999-0909 Joshua M Hallas https://orcid.org/0000-0003-4147-4037 Michael E Pfrender https://orcid.org/0000-0001-6861-0655 Edmund D Brodie, III https://orcid.org/0000-0001-9231-8347

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McGlothlin, J. W., Chuckalovcak, J. P., Janes, D. E., Edwards, S. V., Feldman, C. R., Brodie, E. D., Jr., Pfrender, M. E. & Brodie, E. D., III (2014). Parallel evolution of tetrodotoxin resistance in three voltage-gated sodium channel genes in the garter snake Thamnophis sirtalis. Molecular Biology and Evolution 31, 2836-2846. McGlothlin, J. W., Kobiela, M. E., Feldman, C. R., Castoe, T. A., Geffeney, S. L., Hanifin, C. T., Toledo, G., Vonk, F. J., Richardson, M. K., Brodie, E. D., Jr., Pfrender, M. E. & Brodie, E. D. III. (2016). Historical contingency in a multigene family facilitates adaptive evolution of toxin resistance. Current Biology 26, 1616-1621. Mebs, D., Yotsu-Yamashita, M., Ream, J., Zajac, B. K., & Zehner, R. (2016). Tetrodotoxin concentrations in rough-skinned newts, Taricha granulosa, from populations of their northern distribution range. Salamandra 52, 255-260. Mezquida, E. T. & Benkman, C. W. (2005). The geographic selection mosaic for squirrels, crossbills and Aleppo pine. Journal of Evolutionary Biology 18, 348-357. Moore, J. G. & Moring, B. C. (2013). Rangewide glaciation in the Sierra Nevada, California. Geosphere 9, 1804-1818. Nuismer, S. L., Gomulkiewicz, R. & Ridenhour, B. J. (2010). When Is Correlation Coevolution? American Naturalist 175, 525-537. Nuismer, S. L., Thompson, J. N. & Gomulkiewicz, R. (1999). Gene flow and geographically structured coevolution. Proceedings of the Royal Society of London B Biological Sciences 266, 605-609. Nuismer, S. L., Ridenhour, B. J. & Oswald, B. P. (2007). Antagonistic coevolution mediated by phenotypic differences between quantitative traits. Evolution 61, 1823– 1834. R Core Team. (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/ Ridenhour, B. J., Brodie, E. D., III & Brodie, E. D., Jr. (2004). Resistance of neonates and field-collected garter snakes (Thamnophis spp.) to tetrodotoxin. Journal of Chemical Ecology 30, 143-154. Rissler, L. J., Hijmans, R. J., Graham, C. H., Moritz, C. & Wake, D. B. (2006). Phylogeographic lineages and species comparisons in conservation analyses: a case study of California herpetofauna. American Naturalist 167, 655-666. Rossman, D. A., Ford, N. B., & Seigel, R. A. (1996) The Garter Snakes: Evolution and Ecology. Norman, OK: University of Oklahoma Press. Schierenbeck, K. A. (2014). Phylogeography of California: An Introduction. Berkeley, CA: The University of California Press Schoenherr, A. A. (2017). A Natural History of California. (2nd ed.) Berkeley, CA: The University of California Press. Stebbins, R. C. (2003) Western Reptiles and Amphibians. (3rd ed.) Boston, MA: Houghton Mifflin Co. Stokes, A. N., Williams, B. L. & French, S. S. (2012). An improved competitive inhibition enzymatic immunoassay method for tetrodotoxin quantification. Biological Procedures Online 14, 3. Stokes, A. N., Ray, A. M., Buktenica, M. W., Gall, B. G., Paulson, E., Paulson, D., French, S. S., Brodie, E. D., III & Brodie, E. D., Jr. (2015). Tetrodotoxin levels in high elevation 44

populations of Taricha granulosa in Oregon and predation by otters. Northwestern Naturalist 96, 13-21. Thompson, J. N. (1994) The Coevolutionary Process. Chicago, IL: University of Chicago Press. Thompson, J. N. (1997). Evaluating the dynamics of coevolution among geographically structured populations. Ecology 78, 1619-1623. Thompson, J. N. (1999). Specific hypotheses on the geographic mosaic of coevolution. American Naturalist 153, S1-S14. Thompson, J. N. (2005) The Geographic Mosaic of Coevolution. Chicago, IL: University of Chicago Press. Thompson, J. N. & Cunningham, B. M. (2002). Geographic structure and dynamics of coevolutionary selection. Nature 417, 735-738. Williams, B. L., Brodie, E. D., Jr. & Brodie, E. D., III (2002). Comparisons between toxic effects of Tetrodotoxin administered orally and by intraperitoneal injection to the garter snake Thamnophis sirtalis. Journal of Herpetology 36, 112-115. Williams, B. L., Hanifin, C. T., Brodie, E. D., Jr. & Brodie, E.D., III. (2010). Tetrodotoxin affects survival probability of rough-skinned newts (Taricha granulosa) faced with TTX-resistant garter snake predators (Thamnophis sirtalis). Chemoecology 20, 285-290. Wiseman, K. D. & Pool, A. C. (2007). Thamnophis couchii (Sierra garter snake): Predator- prey interaction. Herpetological Review 38, 344. Zangerl, A. R. & Berenbaum, M. R. (2003). Phenotype matching in wild parsnip and parsnip webworms: causes and consequences. Evolution 57, 806-81

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FIGURE 1 Geographic distribution of phenotypes for newt prey (Ta. granulosa, Ta. sierrae and Ta. torosa) and snake predator (Th. couchii) in California, as well as hotspots and coldspots. (a) Distribution of TTX levels (TTX mg/newt) across newt range in the

Sierra Nevada, based on interpolation of samples from 10 localities (Table 1). (b)

Distribution of TTX-resistance (oral dose of TTX in mg required to slow a snake to 50% of its baseline speed) across Th. couchii range, based on interpolation of samples from 35 localities. (c) Phenotypic mismatch (d) among prey and predator phenotypes at paired locations representing hot and cold spots. Note that colors on panels (a) and (b) are on the same scale (in mg of TTX), while colors on panel (c) represent degree of prey-predator matching, with red corresponding to high phenotypic matching (hotspots) and blue representing phenotypic mismatch (coldspots). Photos: J. Vindum, G. Nafis, A. Pool.

FIGURE 2 Distribution of phenotypic matching between newt prey and garter snake predator at sympatric sites, as well as the relationship between prey and predator phenotypes. (a) Mean TTX levels of newts (Ta. granulosa, diamond; Ta. sierrae, circle;

Ta. torosa triangle) and mean TTX-resistance of snakes (Th. couchii, dashed square) of the nine paired populations. (b) Linear relationship (r = 0.94, r2 = 0.83, F = 39.17, df = 8, p < 0.001) between mean prey (TTX mg/newt) and mean predator phenotypes (50% oral dose of TTX in mg) at paired locations. Symbols represent newt species, and show mean and SE (vertical bars) of newt populations, while colors correspond to mean snake resistance values with SE (horizontal bars) in each matching population. Black dashed line shows the expected 1:1 relationship of perfectly matched phenotypes across 46 populations (i.e. mean total TTX in newt skin would reduce mean sympatric snake to

50% of its normal sprint speed); solid line shows best fit regression of actual mean newt

TTX levels and mean snake 50% doses of TTX; dashed gray lines represent the 15% and

85% doses of TTX-resistance estimated for each snake population. Populations that fall outside the 15% and 85% doses of TTX-resistance (gray areas) are considered mismatched; left of the 15% line, the average newt contains more TTX than the average sympatric snake can safely handle, while right of the 85% line the average snake would be unaffected by the TTX of the average sympatric newt. Regression performed on raw phenotypic values, but displayed on log scale (ln) with back-transformed units of TTX for ease of viewing. Photos: D. Picklum.

FIGURE 3 Histograms of simulated interaction mismatches for randomly drawn pairs of newts and snakes from three sympatric localities. (a) Distribution of mismatches between pairs of Th. couchii and Ta. granulosa from Battle Creek. (b) Distribution of mismatches between pairs of Th. couchii and Ta. sierrae from Battle Creek. (c) Distribution of mismatches between pairs of Th. couchii and Ta. sierrae from Upper Yuba River. (d)

Distribution of mismatches between pairs of Th. couchii and Ta. torosa from Upper Tule

River. Mismatches calculated by taking the difference in a newt phenotype (total newt

TTX in mg) from a paired snake phenotype (50% oral dose of TTX in mg); perfect matching between newt and snake yield no mismatch (0 mg TTX), while excess TTX resistance in a snake nets positive amounts of TTX, and deficient levels of TTX resistance in a snake results in negative amounts of TTX. Black dashed lines represent the expected 1:1 match between newt and snake phenotypes, and dashed gray lines 47 represent the 15% and 85% doses of TTX-resistance estimated for each snake population

(as in Figure 2). Pairs that fall outside these boundaries are considered mismatched; below the 15% line, a newt could severely incapacitate or kill its paired snake predator, while above the 85% line a snake is essentially unaffected by the TTX of its paired newt prey.

48

(a) (b) (c)

TTX Level Phenotypic (TTX mg) Mismatch (d) 0.06 - 0.16 -0.06 - 0.15 0.17 - 0.32 0.16 - 0.36 0.33 - 0.54 0.55 - 0.83 0.37 - 0.59 0.84 - 1.20 0.61 - 0.90 1.21 - 1.89 0.84 - 1.20 0.91 - 1.39 1.21 - 1.89 1.40 - 1.70 1.90 - 2.5+

Taricha sp Thamnophis couchii snake vs. newt

49

(a) (b)

Species SpeciesTh. couchii 1. Watershed Ta.Th. granulosa couchii 2.75 2. 1. Battle Creek Ta.Ta. sierrae granulosa 2. Honey Eagle Lakes 3. 3. North Fork Feather River Ta.Ta. torosa sierrae 4. Ta. torosa 4. Upper Yuba River 1. 5. 5. South Fork American River 6. Upper Cosumnes River 12 2. PhenotypesR6.esRisetsaisntacnece 3. Phenotypes 7. West Walker River ((TTX57.0 (%(TTX50 mg) %d omg) dsoe s ie n i nT TTTXX mg) 8. Upper Mokelumne River 9. Upper Stanislaus River 8. 0.004. 0-4 0 -. 106.16 1 4. 10. Upper Tuolumne River 9. 0.107. 1-7 0 -. 302.32 0.33 - 0.54 11. Upper King River 0.33 - 0.54 10 10. 0.55 - 0.83 12. Upper Tule River 5. 11. 0.55 - 0.83 15% 0.84 - 1.20 11 6. 0.814. 21- 1 -. 21.89 7. 12. 8. 1.31 -. 901 . 8- 2.5+ 1.9 - 2.5 9. 50% 10. 0.5 6

5 11. 4 1 12.

Prey TTX level (mg / newt) 2 0.15 85% 3

1 0.05

0.05 0.15 0.5 1 2.75 Predator TTX resistance (50% oral dose mg TTX) Ü

0 70 140 280 420 560 Miles 50

(a) (b) Th. couchii - Ta. granulosa: 1. Battle Creek Th. couchii - Ta. sierrae: 1. Battle Creek

15% 50% 85% 15% 50% 85% 20 20 15 15 10 10 Frequency (%) 5 5 0 0

-0.10 -0.05 0 0.05 0.10 -0.25 -0.20 -0.15 -0.10 -0.05 0 0.05 0.10 (c) (d) Th. couchii - Ta. sierrae: 4. Upper Yuba River Th. couchii - Ta. torosa: 12. Upper Tule River 15% 50% 85% 15% 50% 85% 30 30 25 25 20 20 15 15 10 10 Frequency (%) 5 5 0 0

-1.0 -0.5 0 0.5 1.0 1.5 2.0 -5 -4 -3 -2 -1 0 1 2 3 4 Phenotypic Mismatch (TTX mg) Phenotypic Mismatch (TTX mg) 51

TABLE 1 Sample locations and summary statistics for prey (Taricha sp.) and predator (Th. couchii) phenotypes, as well as degree to which phenotypes match (d). TTX-resistance is given in oral doses of TTX (mg) for ease of comparison to matching newt TTX levels, and Intraperitoneal injections (IP) of MAMUs (Mass Adjusted Mouse Units of TTX) to allow direct comparison to prior work on Thamnophis using the latter measure (e.g., Brodie et al., 2002).

Watershed Newt Species Newt Mean TTX; Range TTX; Snake Mean TTX-resist; Range TTX- Phenotypic (Latitude, Longitude) sample mg TTX / newt mg TTX / sample oral dose mg TTX resist; Mismatch (d) size (n) newt size (n) (IP 50% MAMU) oral dose mg TTX (IP 50% MAMU) 1. Battle Creek All newts: 23 All: 0.11 ± 0.08 All: 0.02-0.29 11 0.06 ± 0.02 0.03-0.08 All: -0.02 (40.44, -121.76) Ta. granulosa Ta. Ta. granulosa: Ta. granulosa: (2.58 ± 0.70) (1.28-3.81) Ta. granulosa: granulosa: 13 0.06 ± 0.03 0.02-1.20 0.01 Ta. sierrae Ta. sierrae: Ta. sierrae: Ta. sierrae: Ta. sierrae: 10 0.17 ± 0.08 0.07-0.28 -0.06 2. Honey Eagle Lakes Ta. sierrae 8 0.14 ± 0.10 0.01-0.31 20 0.11 ± 0.05 0.00-0.21 0.002 (40.31, -120.35) (4.83 ± 2.36) (0.1-9.5) 3. North Fork Feather River Ta. sierrae 5 0.13 ± 0.08 0.05-0.25 3 0.14 ± 0.04 0.10-0.18 0.05 (40.12, -121.27) (6.41 ± 1.70) (4.73-8.12) 4. Upper Yuba River Ta. sierrae 10 0.23 ± 0.17 0.08-0.57 60 0.32 ± 0.37 0.04-2.17 0.15 (39.44, -120.89) (14.49 ± 17.16) (1.7-82.5) 5. South Fork American Ta. sierrae 10 0.27 ± 0.13 0.09-0.44 11 1.14 ± 0.72 0.14-2.17 0.95 River (52.54 ± 32.97) (6.3-100) (38.79, -120.54) 6. Upper Cosumnes River Ta. sierrae 18 0.38 ± 0.47 0.01-1.56 47 1.18 ± 0.52 0.40-2.22 0.91 52

(38.53, -120.86) (54.20 ± 24.03 (18.6-102) 7. Upper Mokelumne River — — — — 10 0.40 ± 0.38 0.05-1.09 — (38.37, -120.72) (18.42 ± 17.36) (2.47-50.00) 8. West Walker River — — — — 1 0.07 — — (38.366, -19.48) (3.04) 9. Upper Stanislaus River — — — — 2 0.38 ± 0.44 0.07-0.70 — (38.20, -119.97) (17.64 ± 20.31) (3.28-32.0) 10. Upper Tuolumne River Ta. sierrae 9 0.69 ± 0.26 0.38-1.04 19 1.31 ± 0.86 0.18-2.99 0.82 (37.91, -120.02) (60.40 ± 39.68) (8.10-137.60) 11. Upper King River Ta. sierrae 10 0.72 ± 0.57 0.10-1.63 15 2.19 ± 0.52 1.09-3.80 1.68 (36.92, -118.84) (100.36 ± 24.40) (50.0-175.0) 12. Upper Tule River Ta. torosa 15 1.25 ± 1.58 0.02-5.48 94 1.846± 0.53 0.79-3.51 0.97 (36.17, -118.97) (85.63 ± 24.54) (36.5-161.5)

53

On a convergent path? A multiscale approach shows the evolution of TTX resistance in the Sierra garter snake (Thamnophis couchii) is not entirely predictable

Jessica S Reimche1,2*, Robert E del Carlo3,4, Edmund D Brodie Jr5, Joel W McGlothlin6, Karen Schlauch7, Michael E Pfrender8, Edmund D Brodie III9, Normand Leblanc3,4 and Chris R Feldman1,2

1Department of Biology and 2Program in Ecology, Evolution, and Conservation Biology, University of Nevada, Reno, NV, USA 3Department of Pharmacology and 4Program in Cellular and Molecular Pharmacology and Physiology, University of Nevada, Reno, NV, USA 5Department of Biology, Utah State University, Logan, UT, USA 6Department of Biological Sciences, Virginia Tech, Blacksburg, VA, USA 7Desert Research Institute, Reno, NV, USA 8Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA 9Department of Biology, University of Virginia, Charlottesville, VA, USA

54

ABSTRACT

The repeated evolution of tetrodotoxin (TTX) resistance provides a model system for testing hypotheses about the causes of convergent evolution. The neurotoxin TTX is employed by diverse animals as a potent chemical defense. The poison binds to voltage- gated sodium channels (Nav) in muscles and nerves, causing paralysis and even death in predators. Resistance to TTX in taxa bearing this defense, and a handful of predators, appears to come from coincidental replacements in the specific Nav residues that interact with TTX. This stereotyped genetic response suggests that molecular and phenotypic evolution may be constrained. Here, we investigate the extent of convergence in garter snakes (Thamnophis) that prey on TTX-bearing newts (Taricha) by examining the physiological and genetic basis of TTX resistance in the Sierra garter snake (Th. couchii).

We characterize variation in this predatory adaptation at several biological scales. First, we describe patterns of whole animal resistance across populations of Th. couchii. Next, we assess TTX resistance in skeletal muscle, and the correlation between muscle and whole animal resistance. We then characterize functional genetic variation in three Nav loci, and measure levels of gene expression for one of these loci. We found Th. couchii possess extensive geographic variation in resistance at the whole animal and skeletal muscle levels. As in other Thamnophis, resistance at both levels is highly correlated, suggesting convergence across biological scales from organism to organ.

However, Th. couchii shows no functional variation in Nav loci. Moreover, Th. couchii shows no difference in candidate gene expression across populations. Thus, TTX resistance in Th. couchii cannot be explained by the same relationship between genotype and phenotype seen in other taxa, suggesting additional mechanisms are responsible for

55 convergence at whole animal and organ levels, and molecular evolution in this system may not be as predictable as previously thought.

KEYWORDS: evolutionary genetics, adaptation, Tetrodotoxin (TTX), sodium channels

(Nav), muscle physiology, gene expression

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1 INTRODUCTION

The repeated evolution of forms across the tree of life is surely one of the most remarkable features of biodiversity. This pattern of convergent evolution occurs when similar phenotypes arise independently in distinct lineages (Agrawal, 2017; Losos, 2011).

Striking examples of convergence can be found on every branch of the tree, from replicated mutations in microbial lines (Bull et al., 1997; Cooper, Rozen, & Lenski, 2003;

Woods, Schneider, Winkworth, Riley, & Lenski, 2006), to the appearance succulent plant forms across diverse taxa (Arakaki et al., 2011; Ogburn & Edwards, 2010), to the repeated evolution of butterfly wing patterns (Joron et al., 2006; Li et al., 2019; Supple et al., 2013), and recurrent evolution of ecomorphologies in lizard (Losos, Jackman, Larson, de Queiroz, & Rodriguez-Schettino, 1998, Losos et al., 2003; Mahler, Ingram, Revell, &

Losos, 2013) and fish communities (Meyer, Kocher, Basasibwaki, & Wilson, 1990;

Wagner, Harmon, & Seehausen, 2012). The independent and repeated evolution of similarity across space, time, and diversity demonstrates the pervasiveness of convergence (Conway-Morris, 2003; McGhee, 2011; Cowen, 2013). What remains less clear, however, are the underlying causes of convergent evolution (Agrawal, 2017;

Losos, 2011; Stayton, 2015; Storz, 2016). Does convergent evolution demonstrate the primacy of natural selection in fitting organisms optimally to their environment? Or does convergence reveal that functional and genetic constraints canalize natural selection to predestined outcomes? While these hypotheses may not be mutually exclusive, determining the general principles surrounding convergent evolution remains a central challenge of evolutionary biology (Agrawal, 2017; Conway-Morris, 2003; Losos, 2011;

McGhee, 2011).

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One way to ascertain whether repeated evolutionary patterns are primarily due to selection or constraint is to compare the genetic pathways leading to convergent phenotypes (Conte, Arnegard, Peichel, & Schluter, 2012; Feldman, Brodie, Brodie, &

Pfrender, 2012; Miller, Lunzer, & Dean, 2006; Stern, 2013; Storz, 2016; Weinreich,

Delaney, DePristo, & Hartl, 2006). Given the genetic and developmental complexity of traits, the unique genomic histories of species, and the stochastic nature of mutation, we expect selection to produce convergent phenotypes through distinct genetic routes

(Christin, Weinreich, & Besnard, 2010; Losos 2011; Wake, 1991). Alternatively, if routes to convergent phenotypes are frequently constrained, then we expect to find a limited set of possible genetic mechanisms underlying convergent traits, suggesting some predictability in response to selection (Feldman et al., 2012; Stern & Orgorzo, 2008,

2009; Storz, 2016; Weinreich et al., 2006). Therefore, characterizing the genetic basis of convergent traits can reveal whether species converge on similar phenotypes through diverse genetic mechanisms, or instead by stereotyped molecular responses (Brakefield,

2006; Christin et al., 2010; Losos, 2011; Maynard Smith et al., 1985; Stern & Orgorzo,

2008; Wake, Wake, & Specht, 2011).

Progress on our understanding of convergent evolution has been made by examining the molecular basis of toxin resistance across diverse systems (Brodie & Brodie, 2015;

Dobler, Dalla, Wagschal, & Agrawal, 2012; Feldman et al., 2012; Petschenka, Wagschal, von Tschirnhaus, Donath, & Dobler, 2017). Chemically-mediated systems are ideally suited to the study of convergence because toxins provide a well-defined selection pressure, and toxin resistance is a readily observable adaptation. In addition, toxins typically have precise molecular targets, sometimes just a few amino acids in the toxin

58 binding site of a specific protein, providing a tractable genetic basis for the traits underlying resistance (Brodie & Brodie, 2015; Feldman et al., 2016; Petschenka et al.,

2017). Exploration of the genetic basis of repeated toxin resistance has demonstrated remarkable patterns of convergent molecular evolution in a wide array of taxa and systems. Stunning molecular convergence underpins the rapid appearance of identical pesticide resistance across diverse insects (ffrench-Constant, 1994; ffrench-Constant,

Daborn, & Le Goff, 2004; Rinkevich, Du, & Dong, 2013), the ability of various herbivorous insects to withstand cardiac glycosides in plants (Dobler et al., 2012, Dobler,

Petschenka, Wagschal, & Flacht, 2015; Petschenka et al., 2017), multiple vertebrate predators to resist nearly the same toxin in their toad prey (Mohammadi et al., 2016;

Ujvari et al., 2013, 2015), as well as the ability of several frog lineages to tolerate the dietary toxins they sequester for defense (Tarvin, Santos, O’Connell, Zakon, &

Cannatella, 2016; Tarvin et al., 2017), and even venom-combating molecules in mammal prey of snake predators (Drabeck, Dean, & Jansa, 2015). This growing body of work suggests that convergent toxin resistance frequently evolves through similar or even identical mutational steps (e.g., Dobler et al., 2012; Feldman et al., 2012; Geffeney et al.,

2019; Tarvin et al., 2016; Ujvari et al., 2015). Nevertheless, unique solutions to common selection pressures also arise (Dalla & Dobler, 2016; Feldman et al., 2016; Petschenka et al., 2017; Dobler et. al, 2019), and some systems appear nearly devoid of convergence at the biochemical or genetic levels (see Holding, Biardi, & Gibbs, 2016; McCabe &

Mackessy, 2017). Here we examine the genetic basis of convergent tetrodotoxin (TTX) resistance in a well-established predator-prey system. We take a multiscale approach to

59 studying convergence, examining the degree of similarity in the underlying traits that make up the adaptation at the organismal level.

The interaction between toxic newts (Taricha) and resistant garter snakes

(Thamnophis) has become a model system for understanding predator-prey coevolution and adaptive evolution (Brodie & Brodie 1999, 2015; Thompson, 2005). Pacific newts possess tetrodotoxin (TTX), a potent neurotoxin (Brodie, 1968; Mosher, Fuhrman,

Buchwald, & Fischer, 1964; Wakely, Fuhrman, Fuhrman, Fischer, & Mosher, 1966;

Hanifin, 2010; Lorentz, Stokes, Rossler, & Lotters, 2016) that can be excreted from the skin for defense (Cardall, Brodie, Brodie, & Hanifin, 2004). TTX binds to the outer pore

+ of voltage-gated sodium channels (Nav) and blocks the influx of Na across the cell membrane, halting nerve impulses and muscle contractions (Fozzard & Lipkin, 2010;

Hille, 2001), leading to paralysis and even death (Abal et al., 2017; Brodie, 1968).

Despite the fact that TTX is one of the most lethal natural toxins known (Lorentz et al.,

2016), Thamnophis from several populations in western North America are known to prey on sympatric newts with little to no ill effects (Brodie, 1968; Brodie & Brodie 1990;

Brodie, Ridenhour, & Brodie, 2002; Brodie et al., 2005; Greene & Feldman, 2009;

Wiseman & Pool, 2007). Furthermore, this ability to consume deadly newts appears to have evolved independently three or even four times among Thamnophis (Feldman,

Brodie, Brodie, & Pfrender, 2009; Hague, Feldman, Brodie, & Brodie, 2017).

Across diverse animal taxa, resistance to TTX is achieved through changes in the molecular targets of TTX blockade: voltage-gated sodium channels (Du, Nomura, Liu,

Huang, & Dong, 2009; Feldman et al., 2012; Geffeney, Fujimoto, Brodie, Brodie, &

Ruben, 2005; Geffeney et al., 2019; Hanifin & Gilly, 2015; Jost et al., 2008; McGlothlin

60 et al., 2014; Vaelli et al., 2020; Venkatesh et al., 2005; Yoshida, 1994). In amniotes, nine functional Nav channels are encoded by the SCN gene family of nine paralogs (Catterall,

2000; Goldin, 1999; Widmark, Sundstrom, Daza, & Larhammar, 2011). Each SCN gene codes for a unique α-subunit protein (Nav1.1- Nav1.9) expressed in a specific tissue type, including skeletal muscle, cardiac muscle, and the nerves of the central and peripheral nervous system (Catterall, 2012; Goldin, 2001; Zakon, 2012). These α-subunits consist of four domains (DI-DIV) that fold together to form a membrane-spanning channel with an inner and outer pore that allow selective permeation of Na+ ions (Goldin, 2001; Hille,

2001). The outer pore (P-loops) is lined with two rings of negatively charged amino acids that conduct and selectively filter Na+, but also serve as the binding site of TTX (Fozzard

& Lipkin, 2010; Goldin, 2001; Hille, 2001). TTX occludes the outer pore through steric attraction and several molecular bonds with residues of the P-loop, thereby blocking the passage of Na+ ions through the channel (Choudhary et al., 2003; Fozzard & Lipkin,

2010; Terlau et al., 1991). Three Nav are known to be natively resistant to TTX due to a single (ancestral) replacement at a critical P-loop residue in domain I: Nav1.5 in cardiac muscle, and Nav1.8 and Nav1.9 in sensory nerves (Akopian, Sivilotti, & Wood, 1996;

Backx, Yue, Lawrence, Marban, & Tomaselli, 1992; Benn, Costigan, Tate, Fitzgerald, &

Woolf, 2001). In addition, three other Nav are protected by the blood brain barrier, and thus sheltered from effects of TTX: Nav1.1-Nav1.3 of the central nervous system (Goldin,

2001, 2002; Zimmer, 2010). Thus, heightened resistance is expected to occur via substitutions in the remaining sensitive paralogs that are exposed to TTX: Nav1.4 in skeletal muscle, and Nav1.6 and 1.7 in peripheral nerves (Brodie & Brodie 2015; Jost et al., 2008; McGlothlin et al., 2014, 2016).

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The genetic basis of TTX resistance in garter snake species has been shown to be relatively simple: amino acid substitutions at the P-loop sites of the skeletal muscle sodium channel (gene SCN4A; protein Nav1.4) result in structural changes that reduce the binding affinity of TTX to this channel (Feldman et al., 2009, Feldman, Brodie, Brodie,

& Pfrender, 2010, Geffeney et al., 2005). Similar amino acid substitutions have been found in the peripheral nerve sodium channels (SCN8A and SCN9A; Nav1.6 and Nav1.7, respectively) thereby rendering these tissues resistant to TTX as well (Geffeney et al.,

2005; Jost et al., 2008; McGlothlin et al., 2014, 2016). In fact, many of the resistance- conferring mutations seen in multiple tissues of garter snakes are also shared by other

TTX-resistant snakes (Feldman et al., 2012), by TTX-defended newts (Hanifin & Gilly,

2015; Vaelli et al., 2020), and even by TTX-bearing puffer fish (Jost et al., 2008; Soong

& Venkatesh, 2006; Venkatesh et al., 2005). Thus, adaptation to a common selection pressure in predators and preys alike seems to have been accomplished by convergent responses, at the phenotypic and protein sequence levels. These coincidental genetic changes suggest remarkable constraint and predictability in the evolution of TTX resistance (Brodie & Brodie, 2015; Feldman et al., 2012).

To date, work on this system has largely centered around the common garter snake

(Th. sirtalis) and the rough-skinned newt (Ta. granulosa) (Brodie & Brodie, 1990, 1991;

Brodie et al., 2002; Hague et al., 2017; Hanifin, Yotsu-Yamashita, Yasumoto, Brodie, &

Brodie, 1999; Hanifin, Brodie, & Brodie, 2008). In the model Th. sirtalis system, point mutations in SCN4A explain the majority of variation in TTX resistance measured at the whole animal level (Feldman et al., 2010; Hague et al., 2017). Additionally, the physiological underpinnings of resistance are attributed to the structural changes in the

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Nav1.4 protein expressed in skeletal muscle tissue (Geffeney et al., 2005). In Th. sirtalis whole animal measures of TTX resistance are tightly correlated to measures of resistance in isolated skeletal muscle (Geffeney, Brodie, Ruben, & Brodie, 2002). Thus, changes in skeletal muscle electrophysiology through the mutations in Nav channels targeted by TTX appear to be the major component of TTX resistance in Th. sirtalis (Geffeney et al., 2002,

2005). This connection allows us to address questions about convergence at several biological scales: the whole animal, organ (muscle tissue), gene expression, and genotype.

Here, we examine a parallel predator-prey system involving the Sierra garter snake

(Th. couchii) and three species of Pacific newts (Ta. granulosa, Ta. sierrae, Ta. torosa) inhabiting the Sierra Nevada Mountains and Cascade Ranges of California (Brodie et al.,

2005; Reimche et al., 2020). These species appear to be involved in a coevolutionary arms race similar to the Th. sirtalis and Ta. granulosa system, with strong regional patterns of trait covariation across the shared range of these species (Reimche et al.,

2020). Variation in TTX resistance within and among populations of Th. couchii is especially pronounced (Reimche et al., 2020). Our goal is to determine if the same mechanisms underpinning the predatory adaptation in Th. sirtalis are responsible for variation in TTX resistance in Th. couchii.

We assess the extent of convergence between Th. sirtalis and Th. couchii by examining TTX resistance (and plausible resistance-conferring mechanisms) in Th. couchii at multiple biological scales: whole animal, muscle tissue, genotype, and gene expression. First, we describe patterns of phenotypic TTX resistance in populations of

Th. couchii. Second, we examine TTX resistance in skeletal muscle, as well as the

63 correlation between variation in muscle and whole animal TTX resistance. We then examine sequence variation in the three SCN genes (SCN4A, SCN8A, SCN9A) encoding the TTX-sensitive Nav proteins expected to be under selection from TTX poisoning

(Nav1.4 in skeletal muscle, and Nav1.6 and 1.7 in peripheral nerves), and known to provide TTX resistance in other taxa (Jost et al., 2008; McGlothlin et al., 2016). Lastly, we test whether differential expression of SCN4A (skeletal muscle sodium channel gene) is associated with TTX resistance in muscle tissue and at the whole animal level.

Changes in gene expression have not been explicitly addressed in this system but could provide an additional mechanism of TTX resistance. By identifying the physiological and genetic underpinnings of TTX resistance in a parallel Thamnophis system, we hope to shed light on the potential predictability of adaptive toxin resistance and convergent evolution.

2 MATERIALS AND METHODS

2.1 Field collections and captive care

We field collected 339 snakes from 44 localities representing 19 watersheds from across the entire distribution of Th. couchii in the Sierra Nevada Mountains and Lower Cascade

Ranges of California (Table S1). Prior to phenotypic measures and physiological assays, we housed snakes individually in either 5 or 10-gallon tanks, depending on snake size.

We provided each tank with a water dish, hide box (Reptile Basics Inc), newspaper or sani-chip bedding (Harlan Teklad), full-spectrum lighting (Reptisun, 10.0 UVA/UVB,

Exo Terra) and heat-tape placed under one end of the tank to generate a thermal gradient from roughly 24-30°C. We kept snakes in a room on a 12L:12D cycle with a constant

64 temperature of 26°C and fed snakes either fish (live guppies or frozen trout) or feeder mice (frozen mice from a vendor) once or twice per week.

We followed the protocols approved by the Utah State University (USU) and

University of Nevada Reno (UNR) Institutional Animal Care and Use Committees

(IACUC) for all care, handling, and work on live snakes.

2.2 Phenotype Bioassays

2.2.1 Whole animal TTX resistance assay

Our final dataset of whole animal phenotypes includes TTX resistance data from 332 Th. couchii, which includes 39 new samples in addition to previous data collected from

Brodie et al. (2005), Feldman et al. (2009), and Reimche et al. (2020). All data were generated with the same methods. We assessed the ability of live snakes to function under ecologically relevant amounts of TTX they might ingest when consuming a toxic newt. We used a well-established bioassay of whole animal performance that compares the reduction in locomotor ability of individual snakes when subjected to increasing doses of TTX (Brodie & Brodie, 1990; Brodie et al., 2002; Ridenhour et al., 2004).

Specific details of the whole animal procedure can be found in Brodie et al. (2002) and

Ridenhour et al. (2004). Briefly, we placed snakes on a 4 m track lined with infrared sensors each 0.5 m (and a mounted video camera) to record sprint speeds. We used the mean of the quickest two interval times as a snake’s speed. After measuring the baseline speed of each snake (pre-injection), we rested snakes for 48 hours and then gave each snake an intraperitoneal (IP) injection of TTX, starting at 1 mass-adjusted mouse unit

(MAMU). One MAMU is the amount of TTX needed to kill a 20g mouse in 10 minutes,

65 which corresponds to 0.01429 μg of TTX per gram of snake (Brodie & Brodie 1990;

Brown & Mosher, 1963; Ridenhour et al., 2004). We then recorded post-injection speeds

30 min after IP injection (Brodie et al., 2002, Ridenhour et al., 2004). We then repeated the process: after 48 hours of rest the snakes were injected snakes with serially increasing doses of TTX (5, 10, 25, 50, 100, and 150 MAMUs), and recorded post-injection speeds.

We scored resistance as the dose required to slow a snake to 50% of its pre-injection baseline speed (50% MAMU). We estimated this 50% dose using linear regression on log-transformed dosages (see Brodie et al., 2002; Reimche et al., 2020; Ridenhour et al.,

2004). We also report measures of TTX resistance in oral doses of mg of TTX (Table 1) following the conversion of Williams, Brodie, & Brodie, (2002). Because these values are not mass-adjusted, we simply used the mean body size of a Th. couchii (38 g) to calculate oral doses (see Reimche et al., 2020).

2.2.2 Skeletal muscle TTX resistance assay

In Th. sirtalis, whole animal TTX resistance is tightly correlated with the ability of skeletal muscles to withstand TTX (Geffeney et al., 2002). Resistance to TTX in the skeletal muscles, in turn, appears to be a function of mutations in the skeletal muscle sodium channels (Nav1.4) that alter the ability TTX to ligate to the outer pore of these channels (Geffeney et al., 2005). To determine if Th. couchii and Th. sirtalis possess the same physiological mechanism of TTX resistance, we characterized skeletal muscle TTX resistance in Th. couchii. We collected muscle TTX resistance data from a subset of the same animals for which we obtained whole animal TTX resistance data, sampling 25 Th. couchii from 10 localities spanning seven distinct watersheds to capture the geographic

66 breadth and phenotypic variation seen across the range of the species. We subjected isolated muscle tissue to a dose response protocol analogous to that of the whole animal resistance assay; serially increasing doses of TTX to measure the concentrations of TTX that reduce muscle performance.

Following euthanasia, we immediately submerged snakes in physiologic Krebs Buffer perfused with 95%O2 / 5%CO2. We dissected and extracted 2-4 cm of the iliocostalis muscle (Fig. 2b), part of the dorsolateral muscle group involved in snake locomotion

(Jayne, 1988). We then performed myography on a Contraction System Chamber 800A which we operated using the Dynamic Muscle Control (DMC) v4 software (Aurora

Scientific Inc., Aurora, Ontario, Canada). Before suspending the muscle, we calibrated the force transducer with a 40g load, well above the expected biological outputs. For all

Th. couchii, we suspended the iliocostalis from the force transducer and secured it to the chamber platform by vessel clips or tied with nylon surgical sutures. We filled the chamber with the same Krebs buffer, continuously bubbled with 95%O2 / 5%CO2 and maintained at 25 °C. The entire length of the muscle was situated between equidistant parallel electric field stimulation (EFS) platinum electrodes. To optimize the protocol, we applied a 500 μs pulse of direct current in increasing magnitude from 1 to 300 mA under baseline tension of 0.1 g to suspend the muscle. The EFS stimulus was set to 1.5 times the smallest magnitude current that provided the greatest muscle contraction. At this optimum electrical stimulus, we optimized the length-tension relationship for each muscle by increasing the baseline tension until the force output peaked. We recorded all trials at 10 kHz sampling frequency.

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We applied a pulse to induce muscle contraction at baseline (absent TTX) and at serially increasing doses. We added known doses of TTX in citrate buffer pH 6.8 to the bath of known volume. Doses began as low as 1nM and increased until the transient force peak magnitude decayed. In many cases, we increased the concentration of TTX until the abolishment of muscular activity was achieved, though this was not possible for every experiment. We kept the volume of TTX in citrate buffer in the bath below 0.01% of the total bath volume to minimize effects of diluting the Krebs buffer and pH alterations.

We generated dose response curves from the peak transient contraction force magnitude, and we fitted responses to a sigmoidal curve described by the following equation:

Force (N/g) = A2 + (A1-A2)/(1+e^((log([TTX] (nM))-log(x0))/dx)) where A1 and A2 are the upper- and lower-bounds, dx is the rate of decline in force with increasing [TTX], and x0 is the center of the curve (toxin concentration producing half- maximal inhibition, IC50). We estimated this 50% concentration using the above sigmoidal fit on log-transformed dosages; concentrations equal to zero were set to 0.1. If the contraction recorded at 0 nM TTX was unusable, then we used the maximum force produced throughout the routine (frequently occurring within the first testing with 10 nM

TTX).

Data were non-normally distributed, and we determined the relationship between whole animal TTX resistance and skeletal muscle TTX resistance using simple linear regression and Spearman’s rank correlation coefficient in R Studio v1.2.1335 (R Core

Team, 2019). We also compared the levels of TTX resistance at the whole animal level between individuals from two populations (the Honey-Eagle Lakes watershed versus

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Upper Tuolumne River Watershed), and compared levels of TTX resistance at muscle level between individuals from these populations using the non-parametric t-test,

Wilcoxon-Mann-Whitney test in R v3.6.2 (R Core Team, 2019).

2.3 Sodium channel sequencing

Specific amino acid replacements in the outer pore (P-loop) of voltage-gated sodium channels (Nav proteins) alter TTX ligation to these proteins and are thought to confer physiological resistance to TTX in snakes (Feldman et al., 2009, 2010, 2012; Geffeney et al., 2005; Hague et al., 2017). Three sodium channels, in particular, are normally sensitive to TTX, and must be protected or altered to provide TTX resistance at the whole animal level (McGlothlin et al., 2014, 2016): skeletal muscle sodium channels (NaV1.4), motor neuron sodium channels (NaV1.6), and sensory neuron sodium channels (NaV1.7).

Indeed, TTX resistant Th. sirtalis possess resistance-conferring mutations in these three sodium channels (McGlothlin et al., 2014). We therefore examined functional variation in the candidate genes that encode these three proteins: SCN4A (NaV1.4), SCN8A (NaV1.6), and SCN9A (NaV1.7). We focused on DNA sequence variation in portions of the four domains (DI–DIV) that code for the P-loops of these voltage-gated sodium channels.

We extracted genomic DNA from tail-snips, skeletal muscle, or liver tissue with the

DNeasy Blood & Tissue Kit (Qiagen Inc., Germantown, MD, USA) and amplified fragments of genomic DNA corresponding to the four P-loops by PCR using

Thamnophis-specific primers (Table S2). We Sanger-sequenced fragments in both directions using amplification primers and used an ABI Prism 3730 DNA Analyzer

(Thermo Fisher Scientific, Waltham, MA, USA) at the Nevada Genomics Center

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(University of Nevada, Reno, NV, USA) to resolve sequences. We edited raw chromatograms and aligned nucleotide sequences in Geneious v9.1.4 (Biomatters Ltd.,

Auckland, New Zealand; Kearse et al., 2012) using a full SCN4A contig of Th. couchii

(Feldman et al., 2009; GenBank FJ570812.1) and contigs of SCN8A and SCN9A from Th. sirtalis (McGlothlin et al., 2014; GenBank BK008864.1, BK008865.1). We then translated to amino acid sequences in Geneious to examine functional variation

(structural changes) in the P-loops. We aligned all Th. couchii sequences to reference sequences of Th. Sirtalis, Th. elegans, and the king cobra, Ophiophagus hannah (from

McGlothlin et al., 2016; GenBank KJ908928.1, KJ908891.1, KJ908908.1, KJ908933.1,

KJ908935.1, KJ908913.1, KJ908932.1, KJ908918.1, KJ908938.1, KJ908927.1,

KX063605.1, KX079432.1, KX079373.1, KX079433.1, BK009416.1, BK009417.1,

BK009418.1, BK009419.1). Our final dataset includes P-loop sequences of all four domains of SCN4A from 109 snakes from 23 localities, P-loop sequences of SCN8A from

28 snakes from eight localities, and P-loop sequences of SCN9A from 27 snakes from eight localities (Table 1), spanning the entire range of whole animal and muscle TTX resistance. We submitted all sequences to GenBank (accession numbers pending).

Finally, we sought to identify other novel genetic changes (e.g., splice variation) that might provide TTX resistance by examining the entire coding region (CDS) of SCN4A from two Th. couchii. We compared sequences of the entire SCN4A from a highly resistant Th. couchii from Upper Tule River (Feldman et al., 2009; GenBank FJ570812.1) to that from a Th. couchii with low TTX resistance from Battle Creek. We isolated and purified mRNA from fresh skeletal muscle with the RNeasy Mini Plus Kit (Qiagen, Inc.).

We reverse transcribed total mRNA to cDNA with the iScript Select cDNA Synthesis Kit

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(BioRad) and oligo (dT) primers. We then amplified and sequenced a series of overlapping pieces of SCN4A to construct a complete contig of the locus using primers designed for Thamnophis SCN4A (Feldman et al., 2009). We edited raw reads and aligned sequences in Geneious. We then translated the entire contig to amino acid sequences in Geneious. We deposited this contig in GenBank (accession number pending).

2.4 Gene expression assay

In addition to examining structural variation in the Nav proteins, we sought to quantify variation in the expression of SCN4A (Nav1.4) using quantitative PCR (qPCR). Allelic variation at this locus predicts skeletal muscle resistance in Th. sirtalis (Geffeney et al.,

2005) and explains a large proportion of whole animal TTX resistance in Th. sirtalis

(Feldman et al., 2010; Hague et al., 2017) and Th. atratus (Feldman et al., 2010). Thus, differences in the expression of SCN4A might also be associated with differences in TTX resistance in both muscle and whole animal. We expect to observe increased levels of

SCN4A expression in Th. couchii with high TTX resistance compared to those with low resistance.

2.4.1 Tissue collection

We dissected a segment of skeletal muscle (iliocostalis) immediately following euthanasia. We stored all tissue samples in RNAlaterTM (Thermo Fisher Scientific,

Waltham, MA, USA) at -80°C. We collected tissues from 23 Th. couchii from eight localities with varying levels of TTX resistance: 10 individuals with low TTX resistance

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(≤ 5 MAMU) from Battle Creek and Honey-Eagle Lakes watersheds, 13 individuals with high TTX resistance (20-140 MAMU) from Upper Tuolumne River, Upper Kings River, and Upper Tule River watersheds. We extracted RNA from snake skeletal muscle tissue using the same protocol above with the exception of an additional cDNA conversion step; we used both SuperScript III reverse transcriptase (Invitrogen, Thermo Fisher Scientific) and random hexamers, and iScript Select cDNA Synthesis kit (BioRad, Hercules, CA,

USA) with oligo (dT) primers.

2.4.2 Quantitative polymerase chain reaction

To measure gene expression profiles in SCN4A, we conducted qPCR in two different labs and different approaches to verify robustness of results. For both experiments, we amplified a fragment of SCN4A corresponding to a P-loop region (Table S3) and normalized SCN4A expression means to a housekeeping gene, 18S ribosomal RNA (18S), known to have even expression patterns across tissue types and species of vertebrates

(Currier et al., 2012; Vandesompele et al., 2002). For samples collected from Honey-

Eagle Lakes and Upper Tuolumne River watersheds, we performed qPCR in triplicate on all cDNA samples using custom designed TaqMan gene expression assays and mastermix

(Applied Biosystems Inc, Foster City, CA, USA) on an ABI 2720 real-time thermocycler according to the recommended cycling parameters. We also normalized mean SCN4A expression to an additional housekeeping gene, glyceraldehyde 3-phosphate dehydrogenase (GAPDH). For samples collected from Battle Creek, Upper Kings River, and Upper Tule River watersheds, we performed qPCR in duplicate on all cDNA samples using the SYBR green chemistry assay (Applied Biosystems Inc) on a MJ-Research

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Chromo 4 real-time thermocycler (BioRad) according to the recommended cycling parameters.

We determined relative gene expression using the comparative Ct method (ΔΔCt) detailed in the ABI Guide to Performing Relative Quantitation of Gene Expression using

Real-Time Quantitative PCR (Applied Biosystems, 2008). We used the average value from triplicate and duplicate runs for analysis. To account for batch effects among our two datasets, we applied a non-parametric empirical Bayes framework that adjusts normalized Ct values using the ComBat function in the sva package in R (Johnson, Li, &

Rabinovic, 2007; Leek, Johnson, Parker, Jaffe, & Storey, 2012). Using the combined data, we normalized SCN4A (Nav1.4) to 18S and a control snake (Th. sirtalis with the ancestral, non-resistant SCN4A genotype). Data were not normally distributed, so we compared relative expression among low and high resistant individuals using a non- parametric t-test, Wilcoxon-Mann-Whitney test in R.

3 RESULTS

3.1 Phenotype bioassays

3.1.1 Whole animal TTX resistance

Our comprehensive sample of snakes (n = 332) reveals extensive phenotypic variation in whole animal TTX resistance across the geographic range of Th. couchii (Figure 1; Table

1). These data (39 new samples, collected in 2017-2019, and data from Brodie et al.,

2005; Feldman et al., 2009; Reimche et al., 2020) are discussed at length in Reimche et al., (2020). Briefly, populations of Th. couchii in the north display low TTX resistance, those in the middle of the range show increasing levels of resistance, and those in the

73 south show high to extreme levels of TTX resistance. Northern populations include snakes with resistance as low as 1-2 MAMUs, while southern populations contain snakes that can maintain sprint performance at doses exceeding 100 MAMU (i.e., snakes can function at mass-adjusted doses of TTX that would kill over 100 mice). These highly resistant Th. couchii possess phenotypes that exceed the TTX resistance of Oregon Th. sirtalis (Brodie et al., 2002), but are roughly an order of magnitude lower than the most

TTX-resistant Th. sirtalis (Brodie et al., 2002) and Th. atratus (Feldman et al., 2010) from central California. Interestingly, multiple populations, especially in the central

Sierra Nevada, contain high levels of phenotypic variation in resistance (10-100 MAMU in Upper Cosumnes River, 6-100 MAMU in South Fork American River, and 3-135

MAMU in Upper Tuolumne River watersheds). Phenotypic variation among populations is similar to that found in Th. sirtalis (Brodie et al., 2002). However, only a few Th. sirtalis populations display the magnitude of within population variation we see in Th. couchii populations (Brodie et al., 2002).

3.1.2 Skeletal muscle resistance

Myography assays from a subset of Th. couchii (n = 25) from seven watersheds also shows broad variation in skeletal muscle TTX resistance (Table 1). Individual TTX resistance ranged from 16-2400 nM of TTX. Northern and southern populations of Th. couchii display significantly different resistance levels (Wilcoxon rank sum test W = 1, p

< 0.001; Figure 4); muscles from Th. couchii of the Honey-Eagle Lakes watershed in

Lassen County have mean TTX resistance levels of 200 nM TTX (max = 540 nM TTX), while muscles from snakes of the Upper Tuolumne River watershed in Tuolumne County

74 have mean TTX resistance levels of 1500 nM (min = 450 nM). These numbers are comparable to TTX resistance in the skeletal muscles of Th. sirtalis. However, as with whole animal measures, the most resistant muscles from Th. sirtalis are an order of magnitude higher than those seen in Th. couchii, with muscles from some central

California Th. sirtalis displaying IC50 values as high as 42000 nM of TTX (Geffeney et al., 2002). As in Th. sirtalis (Geffeney et al., 2002), whole animal TTX resistance (50%

MAMU) and skeletal muscle TTX resistance (IC50) are highly correlated (Spearman’s

2 rank correlation; r = 0.71, R = 0.72, F24 = 59.93, p < 0.001). In general, skeletal muscle

TTX resistance predicts whole animal TTX resistance and is responsible for a large portion of variation in whole animal resistance (Figure 2).

3.2 Sodium channel sequencing

Sequence data reveal that the three TTX-sensitive sodium channels of Th. couchii contain resistance-conferring mutations in the pore-forming segments (P-loops) that interact with

TTX. However, each channel paralog is fixed for a single resistance-conferring allele across the entire species. Thus, functional sequence variation in sodium channels appears to be lacking at the individual and population levels. First, all Th. couchii contain the same mutation in the skeletal muscle channel gene, SCN4A (encoding Nav1.4): a single amino acid substitution (M1276T) in the DIII P-loop of Nav1.4 at a critical TTX binding site (detailed in Feldman et al., 2009) (Figure 3, Table 1). This mutation appears to be unique among Thamnophis, but is seen in a range of other TTX-resistant taxa (Geffeney et al., 2019; Hanifin & Gilly, 2015; Jost et al., 2008; Vaelli et al., 2020) and is known to confer a 15-fold reduction in TTX ligation to the pore (Jost et al., 2008). Additionally, we

75 found that all Th. couchii are fixed for the ancestral P-loop substitutions in the motor nerve channel gene SCN8A (Nav1.6) and sensory nerve channel gene SCN9A (Nav1.7)

(Fig. 3, Table 1). In SCN8A (Nav1.6), there is a single amino acid substitution in the DIV

P-loop (I1709V). In SCN9A, there are four P-loop substitutions, one in DIII (D1393E) and three in DIV (A1681G, D1684N, G1685Y). These are the same P-loop substitutions seen across the phylogeny of advanced snakes (which includes all of Thamnophis), including both TTX-resistant and TTX-sensitive snakes (McGlothlin et al., 2016).

When comparing the entire coding region (CDS) of SCN4A between two Th. couchii, one with high TTX resistance (from Upper Tule River watershed), one with low TTX resistance (Battle Creek watershed), we found just a single amino acid difference

(G984W). This substitution occurs in the intracellular region between the second and third domain of the P-loop and likely has no influence on TTX resistance (Fozzard &

Lipkin, 2010).

3.3 Gene expression qPCR was able to identify the presence and quantity of both housekeeping genes

(GAPDHand 18S), and the gene of interest (SCN4A) in muscle tissue. When we compare the expression of SCN4A by phenotype, we find no significant differences in mean relative gene expression (Figure 4d). Relative to 18S, TTX-resistant snakes express

SCN4A with a 15-fold increase (± 14.5) compared to a 10-fold increase (± 9.9) in snakes with low TTX resistance. Relative to GAPDH, TTX-resistant snakes express SCN4A with a 4-fold increase (± 4.5) compared to a 2-fold increase (± 2.0) in those with low TTX resistance. While this trend is interesting, variance in each group is large, and statistical

76 analyses indicate no significant differences in relative gene expression of SCN4A between TTX-resistant and TTX-sensitive snakes, as normalized to 18S (Wilcoxon rank sum test W = 72, p = 0.69). Additionally, there is no linear relationship between relative expression of SCN4A and whole animal phenotype (50% MAMU: Spearman’s rank

2 correlation; r = 0.06, R = 0.01, F = 1.22, p = 0.28) or skeletal muscle phenotype (IC50:

Spearman’s rank correlation; r = -0.05, R2 = -0.08, F = 0.12, p = 0.73). Thus, SCN4A appears to be expressed similarly across individuals (and populations) regardless of organismal and tissue level TTX resistance.

4 DISCUSSION

Convergent evolution may arise from intense natural selection, from bias in the production of genetic variation, or from constraints on the genetic architecture, developmental programs, or phenotypic structures of adaptations (Agrawal, 2017;

Arnold, 1992; Brakefield, 2006; Brodie & Brodie, 2015; Christin et al., 2010; Feldman et al., 2012; Losos, 2011; Maynard Smith et al., 1985; Stern & Orgogozo, 2009; Ujvari et al., 2015; Wake, 1991; Weinreich et al., 2006). Determining the relative roles of these possible causes of convergence requires identifying similar adaptive traits that have arisen independently in separate lineages, and then determining the molecular mechanisms underlying those ecologically relevant traits. Here, we examine the degree of convergent evolution in toxin resistance in two distinct garter snake species (Th. couchii and Th. sirtalis) by contrasting the underlying physiological and genetic mechanisms of

TTX resistance that have arisen in parallel. This multiscale approach allows us to determine the levels of biological organization at which convergent evolution has

77 occurred, and the extent to which convergence may or may not be predictable in this system.

It is apparent that Th. couchii demonstrate wide variation in TTX resistance at the whole animal level (Figure 1a); some populations have evolved resistance to the most toxic newts recorded (Reimche et al., 2020). As in Th. sirtalis (Geffeney et al., 2002), this variation in whole animal TTX resistance is largely predicted by variation in muscle TTX resistance (Figure 2c). These results suggest that TTX resistance in skeletal muscle is the primary physiological mechanism underlying this predatory adaptation in both Th. couchii and Th. sirtalis. If Th. atratus (a third species with TTX-resistant populations) also shows the same correlation between whole animal and muscle resistance, then all three TTX resistant garter snake species have converged on the same physiological path of poison resistance. The next question is whether these phenotypic patterns are explained by the same genetic mechanism.

Functional variations in the P-loops of voltage-gated sodium channels (Nav loci) is thought to control TTX resistance in Th. sirtalis and Th. atratus (Feldman et al., 2010;

Geffeney et al., 2005; Hague et al., 2017; McGlothlin et al., 2014), most other TTX- resistant snakes (Feldman et al., 2012, 2016; McGlothlin et al., 2016), and TTX-bearing newts (Hanifin & Gilly, 2015; Vaelli et al., 2020) and pufferfish (Jost et al., 2008; Soong

& Venkatesh, 2006). We first screened variation in SCN4A, the gene that encodes the sodium channel expressed in skeletal muscle (Nav1.4), as variations at this locus appear to explain whole animal phenotypes in Th. sirtalis (Feldman et al., 2010; Geffeney et al.,

2005; Hague et al., 2017). Indeed, SCN4A contains a TTX resistant replacement in Th. couchii (Feldman et al., 2009). This mutation, an M->T at a critical site in the P-loop of

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DIII leads to a 15-fold increase in TTX resistance (Jost et al., 2008), and is also seen in

Pacific newts (Hanifin & Gilly, 2015; Vaelli et al., 2020), pufferfish (Jost et al., 2008), and even TTX resistant invertebrates such as blue-ringed octopus (Geffeney et al., 2019).

Oddly, however, all Th. couchii possess this same substitution in SCN4A. Thus, despite large differences in TTX resistance at both the whole animal and muscle level, all individuals are fixed for this mutation. These surprising results lead to the questions that if all snakes have the same resistance-conferring mutation in their skeletal muscle, then how are some animals (and their muscles) so sensitive to low levels of TTX and others so resistant? The wide variation in phenotypes, in the absence of allelic variation in SCN4A, suggests that other mechanisms must contribute to TTX resistance and TTX sensitivity in

Th. couchii.

Because TTX resistance in Th. couchii cannot be explained by allelic variations in

SCN4A, we screened variations in two other candidate genes (SCN8A and SCN9A) that encode TTX sensitive sodium channels (Nav1.6 and Nav1.7) known to contain TTX resistant replacements in Th. sirtalis (McGlothlin et al., 2014), as well as in their equivalent paralogs in newts (Vaelli et al., 2020) and pufferfish (Jost et al., 2008). Again,

Th. couchii display resistance-conferring mutations in their sodium channels, but no functional sequence variation in these paralogs. In fact, all Th. couchii individuals possess the same resistance-conferring mutations in SCN8A and SCN9A that are seen in

Th. sirtalis (McGlothlin et al., 2014) and all advanced snakes (McGlothlin et al., 2016).

Some are ancient mutations hypothesized to be present in the common ancestor of all living reptiles, while others are present in the common ancestors of all snakes

(McGlothlin et al., 2016). Thus, all Th. couchii appear fixed for the same ancestral

79 replacements in the sodium channels of peripheral nerves that appear to provide low levels of TTX resistance in colubrid snakes (McGlothlin et al., 2016).

Because structural differences (amino acid replacements) in the SCN genes do not explain variation in phenotypic resistance, we explored whether patterns of SCN gene expression might be correlated with phenotypic variation. Differential gene expression of the SCN gene family could provide an alternate or additional mode of achieving TTX resistance (Feldman et al., 2016), as up-regulation of certain SCN genes may increase production of resistant channels in the skeletal muscle and nerve tissue affected by TTX.

However, we found no significant differences in the relative expression of SCN4A in skeletal muscle among populations exhibiting resistance extremes. We did note that

SCN4A is expressed at higher levels than the housekeeping genes (GADPH and 18S) in all snakes, but it remains to be seen whether other Thamnophis species express SCN4A at elevated levels, and whether changes in expression might provide some measure of TTX resistance in snakes. Unfortunately, this study could not address questions about the expression and function of neuronal Nav channels in peripheral nerves (Nav1.6: SCN8A,

Nav1.7: SCN9A) nor the expression of the Nav protein itself. Future work is needed to develop a protocol to successfully isolate peripheral nerve tissue in these animals so we can fully explore the role these other candidate genes may play in TTX resistance.

There are at least three non-mutually exclusive hypotheses that could explain TTX resistance in the absence of allelic variation and expression differences in SCN4A. The first involves changes in the expression of other sodium channel genes. While we did not observe significant differences in gene expression of our single candidate gene, SCN4A,

TTX resistance may still be at least partly conferred by differential gene expression of

80

other targets of TTX, loci of the SCN gene family (Nav1.1-Nav1.9). For example, in mammals, multiple Nav paralogs are expressed in early development (Rogart, Cribbs,

Muglia, Kephart, & Kaiser, 1989) and different Nav paralogs have even been isolated from skeletal muscle and cardiac tissue (Krause et al., 2015; Maier et al., 2004; Rogart et al., 1989). The differential expression of insensitive Nav channels (e.g. Nav 1.5, Nav1.8, and Nav 1.9) in muscle tissue could contribute to variable levels of muscle sensitivity to

TTX, and therefore a causal factor underlying resistant phenotypes (Feldman et al.,

2016). Second, there may be novel genes and regulatory regions involved in the production of TTX-resistant phenotypes. To date, work on understanding the genetic basis of TTX resistance in Thamnophis has centered on functional variation in the α- subunits of Nav proteins (Feldman et al., 2009, 2010, 2012; Geffeney et al., 2005; Hague et al., 2017; McGlothlin et al., 2014, 2016). These large subunits create the membrane spanning channel and the outer pore that is the target of TTX. However, each Nav α- subunit possesses one or two accessory β-subunits that are responsible for modulating channel gating, regulating channel expression in the cell membrane, and controlling neuron excitability (Isom, 2001; Namadurai et al., 2015). It seems plausible that variation in the β-subunits on TTX targeted channels could contribute to resistance.

Lastly, there may be other TTX resistance-conferring mechanisms working in tandem with the expression of resistant Nav1.4 channels in skeletal muscle tissue. One reasonable hypothesis is the involvement of neuromuscular processes both up and downstream of

Nav channels including the sensitivity of voltage sensor (DHPR) and its coupling to

Cav1.1, the quantity of acetylcholine released by the presynaptic neuron, and the structure of the neuromuscular junction controlling the activation of Nav channels. Another

81 plausible hypothesis involves the presence of a TTX binding glycoprotein in the blood plasma, as seen in various pufferfish species (Matsui, Yamamori, Furukawa, & Kono,

2000; Yotsu-Yamashita, Nagaoka, Muramoto, Cho, & Konoki, 2018). Investigating these hypotheses will be crucial in undercovering the full complement of mechanisms of TTX resistance. Previous work on this system has suggested that TTX resistance in snakes is influenced by a small number of genes of large effect (Feldman et al., 2010, Geffeney et al., 2005; Hague et al., 2017; McGlothlin et al., 2016). As shown in Th. couchii, TTX resistance may be more complex than previously recognized, possibly involving a large number of genes or regulatory regions of small effect. Determining the various modes of

TTX resistance in Th. couchii, and across multiple scales of organization, will require more sophisticated techniques that provide higher resolution at the genome and transcriptome level.

5 CONCLUSIONS

The evolution of TTX resistance in garter snakes (Thamnophis) provides an excellent opportunity to test hypotheses regarding the extent and causes of convergent evolution because multiple species appear to have independently evolved resistance to potent toxin

(Feldman et al., 2009). In addition, the genetic basis of TTX resistance in vertebrates is well established and appears to have a narrow response; point mutations in a few genes that encode the sodium channel (Nav) targets of TTX (Brodie & Brodie, 2015; Jost et al.,

2008; McGlothlin et al., 2016; Soong & Venkatesh, 2006; Vaelli et al., 2020). These same point mutations are present in both TTX-resistant predators and TTX-resistant preys, demonstrating remarkable convergence in this adaptive trait (Brodie & Brodie,

82

2015). Furthermore, closely related taxa are expected to display convergence at the molecular level, if those lineages share genetic lines of least resistance (Arnold, 1992;

McGlothlin et al., 2018; Schluter, 1996).

Here, we show that Th. couchii possesses variation in TTX resistance at the whole animal and muscle levels, but those phenotypes are not readily explained by any apparent variation in sodium channels hypothesized to contribute to resistance in almost all other snake species (Feldman et al., 2009, 2010, 2012; Geffeney et al., 2005; Hague et al.,

2017), as well as in TTX-bearing newts (Hanifin & Gilly, 2015; Vaelli et al., 2020) and pufferfish (Jost et al., 2008; Soong & Venkatesh, 2006). In fact, the eastern hog-nosed snake (Heterodon platirhinos) is the only other vertebrate to display variation in whole animal TTX resistance without variation in SCN4A genotype (Feldman et al., 2016). The discovery of such a phenotype-genotype mismatch in two distantly related snake species suggests that the evolution of TTX resistance may not be as predictable as previously thought. Though convergent evolution may be difficult to study, taking a multiscale approach may provide a deeper and more nuanced view of the seemingly limitless combinations of genetic and developmental mechanisms at play in adaptive evolution

(e.g., Cordero et al., 2018; Dobler et al., 2019).

ACKNOWLEDGMENTS

We thank CAF&W for permits to CRF, and USU and UNR IACUCs for approval of live animal protocols. We thank Mike Edgehouse, Kevin Wiseman and Bob Hansen for field advice, and Erica Ely, Rowan Feldman-Matocq and Matt Forister for aid with field collections. We are grateful to Gabrielle Blaustein, Haley Moniz, Vicki Thill, Amber

83

Durfee, Kenzie Wasley, Taylor Disbrow, Sage Kruleski and Aubrey Smith for aid live animal care at UNR, and Haley Moniz, Vicki Thill, Kenzie Wasley, Taylor Disbrow and

Jake Holland for invaluable work with the race track. We thank Josh Hallas, Jen Rippert,

Mike Hague, and the UNR Genomics Center for guidance on sequencing, Arielle

Navarro for assistance with sequencing and myography, Daphne Cooper and Jacqueline

Lopez for advice on RNA protocols, and Miriam Ba, Karla Hernández, and Chase Fiore for help with qPCR protocols. We thank Angela Pitera for help with analyses and Josh

Hallas for map aid. Lastly, we thank the UNR Evol Doers, especially Marjorie Matocq,

Mike Logan, Jenny Ouyang, Josh Jahner, Valentina Alaasam, Haley Moniz, Danny

Nielsen, and Vicki Thill, for useful feedback and thoughtful discussions. This work was supported by an NSF grant to CRF and NL (IOS-1355221), an NSF grant to MEP, EDB

III and EDB Jr (DEB-1034686), and an NIH grant to NL (R01 HL146054).

DATA ACCESSIBILITY

We deposited all animals as voucher specimens in museums: herpetology collections of the California Academy Sciences (CAS); University of Texas, Arlington (UTA);

University of Nevada, Reno (UNR). Phenotypic data are available on the Open Science

Framework digital repository: https://osf.io/yp9nt/. All DNA sequences data are available on GenBank (accession numbers pending).

AUTHOR CONTRIBUTIONS

CRF, EDB Jr., EDB III, MEP and NL designed the study; CRF, EDB III and JSR field collected animals; EDB Jr. and CRF collected whole animal data; JSR and REDC

84 collected muscle data; JSR, CRF, REDC and JWM collected sequence data; JSR and

CRF collected qPCR data; JSR, REDC, CRF, EDB Jr., JWM and KS analyzed the data;

JSR and CRF produced an initial draft, all authors contributed text and approved the final manuscript.

ORCID:

Jessica S Reimche https://orcid.org/0000-0001-6536-7039 Robert E del Carlo https://orcid.org/ 0000-0003-4845-7037 Edmund D Brodie Jr https://orcid.org/0000-0002-5739-474 Joel W McGlothlin http://orcid.org/0000-0003-3645-6264 Karen Schlauch https://orcid.org/ 00000001-6916-8571 Michael E Pfrender https://orcid.org/0000-0001-6861-0655 Edmund D Brodie III https://orcid.org/0000-0001-9231-8347 Normand Leblanc https://orcid.org/ 0000-0002-1090-9432 Chris R Feldman https://orcid.org/0000-0003-2988-3145

85

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FIGURE 1 Geographic distribution of TTX resistance phenotypes for the Sierra garter snake (Th. couchii) in California, and latitudinal gradient in phenotypes. (a) Map of whole animal TTX resistance, scored as the amount of TTX required to slow a snake to

50% of its baseline speed, given in mass-adjusted mouse units (50% MAMU).

Distribution of phenotypes based on interpolation of nearly 340 samples from 44 localities, sampled across 19 different watersheds in the Sierra Nevada and Cascade

Ranges. Symbols denote the type of data collected from animals from the 19 watersheds: circles: whole animal TTX phenotypes and DNA sequences from the skeletal muscle sodium channel gene (SCN4A); dashed circle, whole animal TTX resistance, skeletal muscle TTX resistance, and SCN4A sequences; long-short dashed circle: whole animal

TTX resistance, SCN4A sequences, and relative SCN4A expression data (qSCN4A); dashed square: whole animal TTX resistance, skeletal muscle TTX resistance, SCN4A sequences, SCN8A sequences (motor neuron sodium channel gene), SCN9A sequences

(sensory neuron sodium channel gene), and qSCN4A. (b) Linear relationship between

2 TTX resistance and latitude (r = 0.79, R = 0.63, F1,330 = 39.17, p < 0.001); based on mean TTX resistance (50% MAMU) of animals from 44 sites. Whole animal data from

Brodie et al., (2005), Feldman et al., (2009), Reimche et al., (2020) and this study; map modified from Reimche et al., (2020).

FIGURE 2 Quantification of TTX resistance in skeletal muscles and relationship between tissue and organismal resistance. (a) Th. couchii showing the dorsal region (red area) used for myography work. (b) Pinned section of snake (dorsal view), with skin removed, revealing the iliocostalis muscle (dashed outline) isolated, dissected, and then

94 suspended from force transducer to measure contractile force and skeletal muscle TTX resistance. (c) linear relationship between whole animal TTX resistance and skeletal muscle TTX resistance in Th. couchii sampled from multiple sites across seven watersheds. Whole animal TTX resistance reported as the amount of TTX required to slow a snake to 50% of its baseline speed, in mass adjusted mouse units (50% MAMU).

Skeletal muscle resistance is measured as the concentration of TTX required to reduce a skeletal muscle to 50% its original contraction force (IC50; 50% Inhibition

Concentration). Skeletal muscle phenotypes strongly predict whole animal phenotypes (r

2 = 0.71, R = 0.72, F1,24 = 59.93, p < 0.001). Figure shown on log scale for ease of viewing, but regression performed on raw (untransformed) data. Photo credit: D.A.

Picklum.

FIGURE 3 Functional genetic variation in SCN genes encoding the Nav proteins that

TTX targets. (a) Structural model of the Nav1.4 α-subunit outer pore showing all four domains that fold together to form a membrane-spanning channel. (b) Pore loop regions

(P-loop) that display TTX resistant amino acid substitutions in the three TTX sensitive proteins required to provide whole-animal resistance in Th. couchii: skeletal muscle sodium channel, Nav1.4 (SCN4A: green); motor neuron sodium channel, Nav1.6 (SCN8A: pink); sensory nerve sodium channel, Nav1.7 (SCN9A: blue). (c) Nine SCNparalogs.

Colored circles denote resistance conferring mutations in Nav sodium channels. (d)

Amino acid sequences of the P-loops of domains III and IV highlighting the residues known to confer TTX resistance across taxa. Homo sapien, eastern racer (Coluber constrictor), and nonresistant garter snake (Th. elegans) are provided for comparison.

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Note that Th. couchii is fixed for each of these three SCNA genes, despite variation in whole animal and skeletal muscle phenotype.

FIGURE 4 Comparison of aspects of TTX resistance at three different biological scales in two Th. couchii populations. (a) Geographic variation of whole animal TTX resistance across Th. couchii. Green circle shows all populations are fixed for a single allele for

SCN4A (Nav1.4). (b) Whole animal TTX resistance shown in TTX required to slow a snake to 50% of its baseline speed, in mass adjusted mouse units (50% MAMU), show significant differences between northern (Honey Eagle Lakes watershed: blue) and southern (Upper Tuolumne River watershed: orange) populations (Wilcoxon rank sum test, W = 43, p < 0.001). (c) Skeletal muscle resistance, shown in dose of TTX (nM) required to reduce muscles to 50% of normal contraction (IC50), also shows a significant difference across populations (W = 1, p < 0.001). (d) There are no differences in the expression of SCN4A, the candidate gene that codes for skeletal muscle sodium channels

(Nav1.4), using two different housekeeping markers as controls (18S: W = 23, p =

0.38;GAPDH: W = 27, p = 0.65).

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(a) (b) Watershed TTXResi sresistancetance 150 1. Lower Pit River (50%(50% MAMU)dose in TTX mg) 2. Battle Creek <1 - 5 3. Honey / Eagle Lakes 6 - 15 4. Thomes Creek 1 16 - 25 5. North Fork Feather River 26 - 39 6. Middle Fork Feather River 2 40 - 55 7. Upper Yuba River 4 3 56 - 80 125 8. South Fork American River 81 - 150+ 9. Upper Carson River 5 10. Upper Cosumnes River 6 Samples 11. Upper Mokelumne River 7 animal, SCN4A 12. West Walker River animal, muscle, 13. Upper Stanislaus River 8 SCN4A 100 14. Upper Tuolumne River animal, SCN4A, 10 9 15. Upper San Joaquin River qSCN4A 16. Upper Kings River 11 12 animal, muscle, 17. Tulare Lake Bed SCN4A, qSCN4A 13 18. Upper Tule River 14 75 19. Upper Deer Creek 15

16 17 50 18 19

Whole Animal TTX Resistance (50% MAMU) 25

R2 = 0.625 0 p < 0.001 40o 39o 38o 37o 36o Lattude (N S)

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(a) (c) 6

4

2 (b)

0

2

Whole Animal TTX Resistance (log 50% MAMU) R = 0.71 -2 p < 0.001

3 4 5 6 7 8 Skeletal Muscle TTX Resistance (log nM TTX)

98

99

100

TABLE 1 Major watershed (source) ordered North to South, with mean phenotypic measures and sample sizes (n), and genotypes for Th. couchii populations sampled for whole animal TTX resistance, muscle physiology, sodium channel sequencing (SCNA loci) and qPCR work (SCN4A). Mean whole animal TTX resistance is given in both intraperitoneal injections (IP) of MAMUs (Mass Adjusted Mouse Units) of TTX and oral doses of TTX (mg) required to reduce the average snake (38 g) to 50% of its baseline performance speed, to allow direct comparison to prior work on Thamnophis and Taricha using those measures (e.g. Brodie et al., 2002). P-loop mutations that define the SCNA alleles are provided and shown in in Fig 3. SCN4A expression levels reported as fold change relative to housekeeping gene (18S).

Watershed Mean whole animal Mean skeletal muscle SCN4A SCN8A SCN9A SCN4A copy TTX resistance; TTX resistance; IC50 allele (n) allele (n) allele (n) number; expression MAMU, oral dose mg nM TTX (n) relative to 18S (n) TTX (n) 1. Lower Pit River 1.70, 0.04 (4) – – – – – ENGY 2. Battle Creek 2.58, 0.06 (13) – T (8) V (4) 19.51 (2) (3) 3. Honey Eagle Lakes 4.41, 0.10 (27) 197.13 (8) T (18) V (7) – 8.63 (8) 4. Thomes Creek 1.32, 0.03 (1) – – – – – 5. North Fork Feather River 18.27, 0.40 (5) – – – – – 6. Middle Fork Feather River 1.10, 0.02 (1) – T (1) – – – 7. Upper Yuba River 14.56, 0.32 (60) 115.43 (4) T (32) – – – 8. South Fork American River 52.54, 1.14 (11) – – – – – 9. Upper Carson River 17.40, 0.38 (1) – – – – – 10. Upper Cosumnes River 54.20, 1.17 (47) – T (12) – – – 11. Upper Mokelumne River 18.43, 0.40 (10) 321.32 (1) T (1) – – – 12. West Walker River 3.10, 0.07 (1) 430.65 (1) T (1) – – – 13. Upper Stanislaus River 15.70, 0.34 (2) – T (2) – – – EGNY 14. Upper Tuolumne River 52.15, 1.13 (20) 1497.14 (9) T (18) V (8) 8.83 (7) (8) 15. Upper San Joaquin River 3.10, 0.07 (1) 197.40 (1) – – – – 16. Upper Kings River 100.74, 2.19 (16) – T (10) – – 32.95 (4) 17. Tulare Lake Bed 40.74, 0.88 (1) 366.12 (1) – – – – EGNY 18. Upper Tule River 87.26, 1.89 (107) – T (6) V (8) 3.01 (2) (5)

101

19. Upper Deer Creek 130.00, 2.82 (4) – – – – –

102

SUPPLEMENTAL TABLE 1 Collector and locality information for all snakes used Museum Collector voucher ID ID Locality Watershed County State Latitude Longitude El Dorado National Forest, confluence of Panther Creek and North Fork Mokelumne Upper 38.4736027 - no voucher CRF2335 River Mokelumne Amador California 8 120.4158278 El Dorado National Forest, confluence of Panther Creek and North Fork Mokelumne Upper 38.4736027 - CAS 253222 CRF2336 River Mokelumne Amador California 8 120.4158278 El Dorado National Forest, confluence of Panther Creek and North Fork Mokelumne Upper 38.4736027 - UNR:Herp:8303 CRF2337 River Mokelumne Amador California 8 120.4158278 El Dorado National Forest, confluence of Panther Creek and North Fork Mokelumne Upper 38.4736027 - UNR:Herp:8301 CRF2338 River Mokelumne Amador California 8 120.4158278 El Dorado National Forest, confluence of Panther Creek and North Fork Mokelumne Upper 38.4736027 - no voucher CRF2339 River Mokelumne Amador California 8 120.4158278 El Dorado National Forest, confluence of Panther Creek and North Fork Mokelumne Upper 38.4736027 - no voucher CRF2340 River Mokelumne Amador California 8 120.4158278 Plumas National Forest, un- named tributary of the N Fork of Feather River, ca 0.25 mi S of Barbees Bar Rd, and ca North Fork 39.7453694 UNR:Herp:8326 CRF2341 0.35 mi E of Concow Feather Butte California 4 -121.478075 Plumas National Forest, un- named tributary of the N Fork North Fork 39.7453694 no voucher CRF2342 of Feather River, ca 0.25 mi S Feather Butte California 4 -121.478075

103

of Barbees Bar Rd, and ca 0.35 mi E of Concow Upper UNR:Herp:8343 CRF2346 North Fork Mokelumne River Mokelumne Amador California 38.462794 -120.374062 no voucher CRF2630 Deep Creek Upper King Fresno California 36.934489 -119.247356 no voucher CRF2631 Deep Creek Upper King Fresno California 36.934489 -119.247356 no voucher CRF2633 Deep Creek Upper King Fresno California 36.934489 -119.247356 Canyon Creek, El Dorado Co., Upper no voucher CRF2669 CA Yuba Nevada California 39.442691 -120.658378 Canyon Creek, El Dorado Co., Upper no voucher CRF2670 CA Yuba Nevada California 39.442691 -120.658378 Canyon Creek, El Dorado Co., Upper no voucher CRF2671 CA Yuba Nevada California 39.442691 -120.658378 Canyon Creek, El Dorado Co., Upper no voucher CRF2672 CA Yuba Nevada California 39.442691 -120.658378 Canyon Creek, El Dorado Co., Upper no voucher CRF2673 CA Yuba Nevada California 39.442691 -120.658378 Canyon Creek, El Dorado Co., Upper no voucher CRF2674 CA Yuba Nevada California 39.442691 -120.658378 Canyon Creek, El Dorado Co., Upper no voucher CRF2675 CA Yuba Nevada California 39.442691 -120.658378 Canyon Creek, El Dorado Co., Upper no voucher CRF2676 CA Yuba Nevada California 39.442691 -120.658378 Canyon Creek, El Dorado Co., Upper no voucher CRF2677 CA Yuba Nevada California 39.442691 -120.658378 Canyon Creek, El Dorado Co., Upper no voucher CRF2678 CA Yuba Nevada California 39.442691 -120.658378 Canyon Creek, El Dorado Co., Upper no voucher CRF2679 CA Yuba Nevada California 39.442691 -120.658378 Canyon Creek, El Dorado Co., Upper no voucher CRF2680 CA Yuba Nevada California 39.442691 -120.658378 Canyon Creek, El Dorado Co., Upper no voucher CRF2681 CA Yuba Nevada California 39.442691 -120.658378 UNR:Herp:0998 Honey 2 CRF3051 Williams Creek Eagle Lakes Lassen California 40.39627 -120.77225

104

UNR:Herp:1004 Honey 2 CRF3052 Williams Creek Eagle Lakes Lassen California 40.39627 -120.77225 Honey no voucher CRF3053 Williams Creek Eagle Lakes Lassen California 40.39627 -120.77225 Honey no voucher CRF3054 Williams Creek Eagle Lakes Lassen California 40.39627 -120.77225 UNR:Herp:0997 Honey 8 CRF3055 Williams Creek Eagle Lakes Lassen California 40.39627 -120.77225 UNR:Herp:0995 Honey 8 CRF3056 Williams Creek Eagle Lakes Lassen California 40.39627 -120.77225 Honey no voucher CRF3057 Willard Creek Eagle Lakes Lassen California 40.36777 -120.77225 UNR:Herp:1005 Honey 3 CRF3058 Willard Creek Eagle Lakes Lassen California 40.36777 -120.80296 UNR:Herp:1003 Honey 8 CRF3059 Willard Creek Eagle Lakes Lassen California 40.36777 -120.80296 UNR:Herp:0995 Honey 7 CRF3060 Willard Creek Eagle Lakes Lassen California 40.36777 -120.80296 UNR:Herp:1003 Honey 5 CRF3061 Willard Creek Eagle Lakes Lassen California 40.36777 -120.80296 UNR:Herp:0995 Honey 3 CRF3062 Willard Creek Eagle Lakes Lassen California 40.36777 -120.80296 UNR:Herp:0999 Upper 5 CRF3063 Cherry Creek Tuolumne Tuolumne California 38.04029 -119.90284 UNR:Herp:1001 Upper 0 CRF3064 Cherry Creek Tuolumne Tuolumne California 38.04029 -119.90284 UNR:Herp:0998 Upper 3 CRF3065 Cherry Creek Tuolumne Tuolumne California 38.04029 -119.90284 UNR:Herp:1006 Upper 5 CRF3066 Cherry Creek Tuolumne Tuolumne California 38.04029 -119.90284 UNR:Herp:1000 Upper 1 CRF3067 Cherry Creek Tuolumne Tuolumne California 38.04029 -119.90284 UNR:Herp:1005 Upper 4 CRF3069 Cherry Creek Tuolumne Tuolumne California 38.04301 -119.902411 UNR:Herp:1002 Upper 1 CRF3070 Cherry Creek Tuolumne Tuolumne California 38.04301 -119.902411

105

UNR:Herp:0996 Upper 8 CRF3072 Cherry Creek Tuolumne Tuolumne California 38.04301 -119.902411 UNR:Herp:1003 Upper 7 CRF3074 Cherry Creek Tuolumne Tuolumne California 38.04301 -119.902411 UNR:Herp:0997 Upper San 3 CRF3211 Cherry Creek Joaquin Madera California 37.627199 -119.075909 Hughes Creek, ~1 mi upstream (N) of Kings River UNR:Herp:0996 and Choinumni County Park, Tulare Lake 1 CRF3217 Fresno Co., CA Bed Fresno California 36.83633 -119.36788 UNR:Herp:0997 Upper 4 CRF3220 Cherry Creek Tuolumne Tuolumne California 38.06843 -119.864053 UNR:Herp:0995 Upper 1 CRF3221 Cherry Creek Tuolumne Tuolumne California 38.06843 -119.864053 UNR:Herp:0995 Upper 6 CRF3293 North Fork Mokelumne River Mokelumne Amador California 38.42339 -120.54173 UNR:Herp:0998 Upper 6 CRF3306 Cherry Creek Tuolumne Tuolumne California 38.06843 -119.864053 UNR:Herp:0995 Upper 2 CRF3307 Cherry Creek Tuolumne Tuolomne California 38.06843 -119.864053 UNR:Herp:1002 Upper 7 CRF3308 Cherry Creek Tuolumne Tuolomne California 38.06843 -119.864053 West Walker River, at crossing of Hwy 108, just west of Sonora Bridge Campground, 2 mi west of Hwy 395 via Hwy 108, West UNR:Herp:1004 Humboldt-Toiyabe National Walker 1 CRF3309 Forest, Mono Co., CA River Mono California 38.3663 -119.48172 Tamarack Lake, ~1 mi S of Packer Lake, Tahoe National Honey no voucher CRF3312 Forest, Sierra Co., CA Eagle Lakes Sierra California 39.61058 -120.654868 Grassy Lake, ~0.1 mi S. of Lakes Basin Campground, Middle Plumas National Forest, Fork no voucher CRF3324 Plumas Co., CA Feather Plumas California 39.70167 -120.6637

106 no voucher DGM583 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher DGM584 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher DGM585 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher DGM586 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher DGM594 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher DGM595 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher DGM596 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher DGM598 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 Upper Deer EDBjr2010 Upper no voucher 4 Tyler Creek White Tulare California 35.902583 -118.6425 Upper Deer EDBjr2010 Upper no voucher 5 Tyler Creek White Tulare California 35.902583 -118.6425 Upper Deer EDBjr2010 Upper no voucher 6 Tyler Creek White Tulare California 35.902583 -118.6425 Upper Deer EDBjr2010 Upper no voucher 7 Tyler Creek White Tulare California 35.902583 -118.6425 EDBjr2070 no voucher 2 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 EDBjr2070 no voucher 3 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 EDBjr2070 no voucher 4 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 EDBjr2133 Battle UTA 56597 8 Antelope Creek Creek Shasta California 40.368056 -121.990278 EDBjr2133 Battle UTA 56598 9 Antelope Creek Creek Shasta California 40.368056 -121.990278 Thomes EDBjr2134 Creek Sac 40.2328333 - UTA 57029 0 Indian Creek River Tehama California 3 121.8757778 EDBjr2134 Battle UTA 57030 1 Deer Creek Creek Shasta California 40.702222 -121.716667

107

EDBjr2134 Battle UTA 57031 2 Deer Creek Creek Shasta California 40.702222 -121.716667 EDBjr2134 no voucher 3 Hat Creek Lower Pit Shasta California 40.8175 -121.491389 EDBjr2134 no voucher 3 Hat Creek Lower Pit Shasta California 40.8175 -121.491389 EDBjr2134 no voucher 3 Hat Creek Lower Pit Shasta California 40.8175 -121.491389 EDBjr2134 no voucher 3 Hat Creek Lower Pit Shasta California 40.8175 -121.491389 EDBjr2136 Battle CAS 236826 5 Battle_Ck_Shasta Creek Shasta California 40.44692 -121.86821 EDBjr2136 Battle CAS 236827 6 Battle_Ck_Shasta Creek Shasta California 40.44692 -121.86821 EDBjr2136 Battle CAS 236828 7 Battle_Ck_Shasta Creek Shasta California 40.44692 -121.86821 EDBjr2136 Battle CAS 236829 8 Battle_Ck_Shasta Creek Shasta California 40.44692 -121.86821 EDBjr2136 Battle CAS 236830 9 Battle_Ck_Shasta Creek Shasta California 40.44692 -121.86821 EDBjr2137 Battle CAS 236831 0 Battle_Ck_Shasta Creek Shasta California 40.44692 -121.86821 EDBjr2137 Battle CAS 236832 1 Rock_Ck_Shasta Creek Shasta California 40.455156 -121.857494 EDBjr2137 Battle no voucher 2 Rock_Ck_Shasta Creek Shasta California 40.455156 -121.857494 EDBjr2138 Keleher_SF_Yuba_Riv_Neva Upper CAS 236849 8 da Yuba Nevada California 39.44289 -120.65839 EDBjr2138 Keleher_SF_Yuba_Riv_Neva Upper CAS 236850 9 da Yuba Nevada California 39.44289 -120.65839 EDBjr2139 Keleher_SF_Yuba_Riv_Neva Upper CAS 236851 0 da Yuba Nevada California 39.44289 -120.65839 EDBjr2139 Keleher_SF_Yuba_Riv_Neva Upper CAS 236852 1 da Yuba Nevada California 39.44289 -120.65839 EDBjr2139 Keleher_SF_Yuba_Riv_Neva Upper CAS 236853 2 da Yuba Nevada California 39.44289 -120.65839

108

EDBjr2139 Keleher_SF_Yuba_Riv_Neva Upper CAS 236854 3 da Yuba Nevada California 39.44289 -120.65839 EDBjr2139 Keleher_SF_Yuba_Riv_Neva Upper CAS 236855 4 da Yuba Nevada California 39.44289 -120.65839 EDBjr2139 Keleher_SF_Yuba_Riv_Neva Upper CAS 236856 5 da Yuba Nevada California 39.44289 -120.65839 EDBjr2139 Keleher_SF_Yuba_Riv_Neva Upper CAS 236857 6 da Yuba Nevada California 39.44289 -120.65839 EDBjr2139 Keleher_SF_Yuba_Riv_Neva Upper CAS 236858 7 da Yuba Nevada California 39.44289 -120.65839 EDBjr2141 CAS 236872 4 Wishon Upper Tule Tulare California 36.169444 -118.704167 EDBjr2141 CAS 236873 5 Wishon Upper Tule Tulare California 36.169444 -118.704167 EDBjr2141 CAS 236874 6 Coy_Flat_Tulare Upper Tule Tulare California 36.128836 -118.620091 EDBjr2141 Upper El CAS 236875 7 Grizzly_Flat_ElDorado Cosumnes Dorado California 38.662778 -120.615 EDBjr2141 Upper El CAS 236876 8 Grizzly_Flat_ElDorado Cosumnes Dorado California 38.662778 -120.615 EDBjr2141 Upper El CAS 236877 9 Grizzly_Flat_ElDorado Cosumnes Dorado California 38.662778 -120.615 EDBjr2142 Upper El CAS 236878 0 Grizzly_Flat_ElDorado Cosumnes Dorado California 38.662778 -120.615 EDBjr2148 no voucher 9 Wishon Upper Tule Tulare California 36.169444 -118.704167 EDBjr2149 no voucher 0 Wishon Upper Tule Tulare California 36.169444 -118.704167 EDBjr2149 no voucher 1 Wishon Upper Tule Tulare California 36.169444 -118.704167 EDBjr2149 no voucher 2 Wishon Upper Tule Tulare California 36.169444 -118.704167 EDBjr2149 no voucher 3 Wishon Upper Tule Tulare California 36.169444 -118.704167 EDBjr2149 no voucher 5 Wishon Upper Tule Tulare California 36.169444 -118.704167

109

EDBjr2149 no voucher 6 Wishon Upper Tule Tulare California 36.169444 -118.704167 EDBjr2149 no voucher 7 Wishon Upper Tule Tulare California 36.169444 -118.704167 EDBjr2149 no voucher 8 Wishon Upper Tule Tulare California 36.169444 -118.704167 EDBjr2149 no voucher 9 Wishon Upper Tule Tulare California 36.169444 -118.704167 EDBjr2150 no voucher 0 Wishon Upper Tule Tulare California 36.169444 -118.704167 EDBjr2158 CAS 237198 7 Wishon Upper Tule Tulare California 36.169444 -118.704167 EDBjr2158 no voucher 8 Deep Creek Upper King Fresno California 36.93449 -119.24736 EDBjr2158 - no voucher 9 Deep Creek Upper King Fresno California 36.933333 119.2443889 EDBjr2159 - no voucher 0 Deep Creek Upper King Fresno California 36.933333 119.2443889 EDBjr2159 - no voucher 1 Deep Creek Upper King Fresno California 36.933333 119.2443889 EDBjr2159 - no voucher 2 Deep Creek Upper King Fresno California 36.933333 119.2443889 EDBjr2159 no voucher 4 Deep Creek Upper King Fresno California 36.93449 -119.24736 EDBjr2159 no voucher 5 Deep Creek Upper King Fresno California 36.93449 -119.24736 EDBjr2159 no voucher 6 Deep Creek Upper King Fresno California 36.93449 -119.24736 EDBjr2159 no voucher 7 Deep Creek Upper King Fresno California 36.93449 -119.24736 EDBjr2159 no voucher 8 Deep Creek Upper King Fresno California 36.93449 -119.24736 EDBjr2159 no voucher 9 Deep Creek Upper King Fresno California 36.93449 -119.24736 EDBjr2160 no voucher 0 Deep Creek Upper King Fresno California 36.93449 -119.24736

110

EDBjr2160 no voucher 1 Deep Creek Upper King Fresno California 36.93449 -119.24736 EDBjr2160 no voucher 2 Deep Creek Upper King Fresno California 36.93449 -119.24736 EDBjr2160 no voucher 3 Deep Creek Upper King Fresno California 36.93449 -119.24736 EDBjr2160 no voucher 4 Deep Creek Upper King Fresno California 36.93449 -119.24736 EDBjr2162 Upper El no voucher 8 Dogtown Creek Cosumnes Dorado California 38.606222 -120.43375 EDBjr2162 Upper El no voucher 9 Dogtown Creek Cosumnes Dorado California 38.606222 -120.43375 EDBjr2163 Upper El no voucher 0 Dogtown Creek Cosumnes Dorado California 38.606222 -120.43375 EDBjr2195 Honey CAS 237608 1 Devils_Corral_Lassen Eagle Lakes Lassen California 40.570833 -120.959167 EDBjr2195 Honey CAS 237609 2 Devils_Corral_Lassen Eagle Lakes Lassen California 40.36777 -120.80296 EDBjr2195 Honey CAS 237610 3 Devils_Corral_Lassen Eagle Lakes Lassen California 40.36777 -120.80296 EDBjr2195 Honey CAS 237611 4 Devils_Corral_Lassen Eagle Lakes Lassen California 40.36777 -120.80296 EDBjr2195 Honey CAS 237612 5 Devils_Corral_Lassen Eagle Lakes Lassen California 40.36777 -120.80296 EDBjr2195 Honey CAS 237613 6 Devils_Corral_Lassen Eagle Lakes Lassen California 40.36777 -120.80296 EDBjr2195 Honey CAS 237614 7 Devils_Corral_Lassen Eagle Lakes Lassen California 40.36777 -120.80296 EDBjr2195 Honey CAS 237615 8 Devils_Corral_Lassen Eagle Lakes Lassen California 40.36777 -120.80296 EDBjr2206 Upper El no voucher 3 Leoni Meadows Cosumnes Dorado California 38.61036 -120.50472 EDBjr2206 Upper El no voucher 7 Leoni Meadows Cosumnes Dorado California 38.61036 -120.50472 EDBjr2206 Upper El no voucher 8 Leoni Meadows Cosumnes Dorado California 38.61036 -120.50472

111

EDBjr2206 Upper El no voucher 9 Dogtown Creek Cosumnes Dorado California 38.606222 -120.43375 EDBjr2207 Upper El no voucher 0 Dogtown Creek Cosumnes Dorado California 38.606222 -120.43375 EDBjr2207 Upper El no voucher 1 Dogtown Creek Cosumnes Dorado California 38.606222 -120.43375 EDBjr2207 Upper El no voucher 2 Dogtown Creek Cosumnes Dorado California 38.606222 -120.43375 EDBjr2207 Upper El no voucher 3 Dogtown Creek Cosumnes Dorado California 38.606222 -120.43375 EDBjr2207 Upper El no voucher 4 Dogtown Creek Cosumnes Dorado California 38.606222 -120.43375 EDBjr2232 South Fork El CAS 238411 9 Pollock Pines American Dorado California 38.606222 -120.43375 EDBjr2235 South Fork El no voucher 0 Pollock Pines American Dorado California 38.606222 -120.43375 EDBjr2273 Upper El UTA 57316 6 Leoni Meadows Cosumnes Dorado California 38.61036 -120.50472 EDBjr2273 Upper El UTA 57317 7 Leoni Meadows Cosumnes Dorado California 38.61036 -120.50472 UTA EDBjr2273 Upper El 57318/57319 8 Leoni Meadows Cosumnes Dorado California 38.61036 -120.50472 UTA 57320/ EDBjr2273 Upper El CAS 241819 9 Leoni Meadows Cosumnes Dorado California 38.61036 -120.50472 EDBjr2274 Upper El CAS 241820 0 Leoni Meadows Cosumnes Dorado California 38.61036 -120.50472 EDBjr2278 Upper El CAS 238411 6 Leoni Meadows Cosumnes Dorado California 38.61036 -120.50472 EDBjr2278 Upper El no voucher 7 Leoni Meadows Cosumnes Dorado California 38.610361 -120.504722 EDBjr2278 Upper El no voucher 8 Leoni Meadows Cosumnes Dorado California 38.61036 -120.50472 EDBjr2286 Upper El no voucher 6 Dogtown Creek Cosumnes Dorado California 38.606222 -120.43375 EDBjr2286 Upper El no voucher 7 Dogtown Creek Cosumnes Dorado California 38.606222 -120.43375

112

EDBjr2294 South Fork El no voucher 4 Beauty Lake American Dorado California 38.856555 -120.23972 EDBjr2294 South Fork El no voucher 5 Beauty Lake American Dorado California 38.856555 -120.23972 EDBjr2329 Upper El UTA 57185 4 Dogtown Creek Cosumnes Dorado California 38.606222 -120.43375 EDBjr2329 Upper El UTA 57186 5 Dogtown Creek Cosumnes Dorado California 38.606222 -120.43375 EDBjr2329 Upper El UTA 57187 6 Dogtown Creek Cosumnes Dorado California 38.606222 -120.43375 EDBjr2329 Upper El UTA 57188 7 Dogtown Creek Cosumnes Dorado California 38.606222 -120.43375 EDBjr2329 Upper El UTA 57189 8 Dogtown Creek Cosumnes Dorado California 38.606222 -120.43375 EDBjr2329 Upper El UTA 57190 9 Dogtown Creek Cosumnes Dorado California 38.606222 -120.43375 EDBjr2330 Upper El UTA 57191 0 Dogtown Creek Cosumnes Dorado California 38.606222 -120.43375 EDBjr2330 Upper El UTA 57192 1 Dogtown Creek Cosumnes Dorado California 38.606222 -120.43375 EDBjr2330 Upper El UTA 57193 2 Dogtown Creek Cosumnes Dorado California 38.606222 -120.43375 EDBjr2330 Upper El UTA 57194 3 Dogtown Creek Cosumnes Dorado California 38.606222 -120.43375 EDBjr2330 Upper El UTA 57195 4 Dogtown Creek Cosumnes Dorado California 38.606222 -120.43375 EDBjr2330 Upper El UTA 57196 5 Dogtown Creek Cosumnes Dorado California 38.606222 -120.43375 EDBjr2330 Upper El UTA 57197 6 Dogtown Creek Cosumnes Dorado California 38.606222 -120.43375 EDBjr2330 Upper El UTA 57198 7 Dogtown Creek Cosumnes Dorado California 38.606222 -120.43375 EDBjr2330 Upper El UTA 57199 8 Dogtown Creek Cosumnes Dorado California 38.606222 -120.43375 EDBjr2330 Upper El no voucher 9 Dogtown Creek Cosumnes Dorado California 38.606222 -120.43375

113

EDBjr2331 Upper El UTA 57303 0 Dogtown Creek Cosumnes Dorado California 38.606222 -120.43375 EDBjr2331 Upper El UTA 57304 1 Dogtown Creek Cosumnes Dorado California 38.606222 -120.43375 EDBjr2331 Upper El UTA 57305 2 Dogtown Creek Cosumnes Dorado California 38.606222 -120.43375 EDBjr2350 North Fork UTA 57306 6 Indian Jim Feather Butte California 39.9446 -121.3075 EDBjr2351 South Fork El UTA 57307 3 Oligby Creek American Dorado California 38.7639 -120.4781 EDBjr2351 South Fork El UTA 57308 5 South Fork American River American Dorado California 38.7661 -120.3801 EDBjr2351 South Fork El UTA 57310 7 Oligby Creek American Dorado California 38.7639 -120.4781 EDBjr2351 South Fork El UTA 57311 8 Oligby Creek American Dorado California 38.7639 -120.4781 EDBjr2351 South Fork El UTA 57312 9 South Fork American River American Dorado California 38.7896 -120.5964 EDBjr2352 South Fork El UTA 57313 0 South Fork American River American Dorado California 38.7661 -120.3801 EDBjr2352 North Fork UTA 57315 6 North Fork Feather River Feather Butte California 39.7441 -121.4728 Upper no voucher EJE138 Sawmill Lake Yuba Nevada California 39.444264 -120.60304 Upper no voucher EJE139 Sawmill Lake Yuba Nevada California 39.444264 -120.60304 Upper no voucher EJE140 Prairie Creek Yuba Nevada California 39.4563 -120.606897 Upper no voucher EJE141 Prairie Creek Yuba Nevada California 39.4563 -120.606897 Upper no voucher EJE142 Prairie Creek Yuba Nevada California 39.4563 -120.606897 UNR:Herp:0997 Upper 0 EJE143 Prairie Creek Yuba Nevada California 39.4563 -120.606897 Upper no voucher EJE144 Prairie Creek Yuba Nevada California 39.4563 -120.606897

114

Upper no voucher EJE145 Bowman Lake Yuba Nevada California 39.456712 -120.609758 Upper CASï_260887 EJE147 Bowman Lake Yuba Nevada California 39.456712 -120.609758 Upper CASï_260888 EJE148 Bowman Lake Yuba Nevada California 39.456712 -120.609758 Upper CASï_260889 EJE149 Bowman Lake Yuba Nevada California 39.456712 -120.609758 Upper CASï_260890 EJE150 Bowman Lake Yuba Nevada California 39.456712 -120.609758 Upper no voucher EJE152 Faucherie Lake Yuba Nevada California 39.427506 -120.57256 Upper no voucher EJE154 Faucherie Lake Yuba Nevada California 39.427506 -120.57256 Upper no voucher EJE155 Faucherie Lake Yuba Nevada California 39.427506 -120.57256 Upper CASï_260892 EJE156 Faucherie Lake Yuba Nevada California 39.427506 -120.57256 Upper no voucher EJE158 Faucherie Lake Yuba Nevada California 39.428902 -120.568987 Upper CASï_260894 EJE159 Faucherie Lake Yuba Nevada California 39.428902 -120.568987 Upper CASï_260895 EJE160 Faucherie Lake Yuba Nevada California 39.428902 -120.568987 Upper no voucher EJE161 Jackson Meadows Res Yuba Sierra California 39.511051 -120.554053 UNR:Herp:0999 Upper 1 EJE163 Jackson Meadows Res Yuba Sierra California 39.511051 -120.554053 UNR:Herp:1000 Upper 5 EJE164 Carr Lake Yuba Nevada California 39.511051 -120.554053 Canyon Creek, El Dorado Co., Upper no voucher EJE165 CA Yuba Nevada California 39.442981 -120.658114 UNR:Herp:0996 Canyon Creek, El Dorado Co., Upper 0 EJE166 CA Yuba Nevada California 39.44289 -120.65839 Canyon Creek, El Dorado Co., Upper no voucher EJE167 CA Yuba Nevada California 39.44289 -120.65839

115

Canyon Creek, El Dorado Co., Upper no voucher EJE169 CA Yuba Nevada California 39.44289 -120.65839 Canyon Creek, El Dorado Co., Upper no voucher EJE170 CA Yuba Nevada California 39.44289 -120.65839 UNR:Herp:0994 Canyon Creek, El Dorado Co., Upper 6 EJE171 CA Yuba Nevada California 39.44289 -120.65839 Canyon Creek, El Dorado Co., Upper no voucher EJE172 CA Yuba Nevada California 39.44289 -120.65839 Canyon Creek, El Dorado Co., Upper no voucher EJE173 CA Yuba Nevada California 39.44289 -120.65839 UNR:Herp:1002 Upper 0 EJE174 Milton Res Yuba Nevada California 39.52261 -120.51649 Upper no voucher EJE175 Sawmill Lake Yuba Nevada California 39.444264 -120.60304 Upper no voucher EJE176 Sawmill Lake Yuba Nevada California 39.444264 -120.60304 Upper no voucher EJE177 Milton Res Yuba Nevada California 39.52261 -120.51649 Upper no voucher EJE179 Jackson Meadows Res Yuba Sierra California 39.511051 -120.554053 Upper no voucher EJE180 Jackson Meadows Res Yuba Sierra California 39.511051 -120.554053 UNR:Herp:1000 Upper 9 EJE181 South Fork Tuolumne River Tuolumne Tuolumne California 37.820742 -119.91915 UNR:Herp:1005 Upper 9 EJE182 South Fork Tuolumne River Tuolumne Tuolumne California 37.817948 -119.919969 UNR:Herp:0999 Upper 8 EJE184 South Fork Tuolumne River Tuolumne Tuolumne California 37.821434 -119.918561 UNR:Herp:1001 Upper 3 EJE186 South Fork Tuolumne River Tuolumne Tuolumne California 37.821434 -119.918561 UNR:Herp:0996 Upper 6 EJE187 South Fork Tuolumne River Tuolumne Tuolumne California 37.819352 -119.919625 UNR:Herp:0999 Upper 3 EJE188 South Fork Tuolumne River Tuolumne Tuolumne California 37.821224 -119.91888 Canyon Creek, El Dorado Co., Upper no voucher EJE189 CA Yuba Nevada California 39.44289 -120.65839

116

UNR:Herp:1000 Upper 8 JSR007 Pinecrest Lake Stanislaus Tuolumne California 38.198119 -119.973055 UNR:Herp:0999 Upper 0 JSR009 Pinecrest Lake Stanislaus Tuolumne California 38.198119 -119.973055 Upper no voucher KDW598 East Fork Carson River Carson Alpine California 38.65819 -119.72597 no collector Battle no voucher ID Antelope Creek Creek Shasta California 40.368056 -121.990278 no collector South Fork El no voucher id North Fork Feather River American Dorado California 39.7441 -121.4728 no collector North Fork 39.7453694 no voucher id North Fork Feather River Feather Butte California 4 -121.478075 no collector Upper El no voucher id North Fork Consumnes River Cosumnes Dorado California 38.64969 -120.40815 no collector Upper El no voucher id North Fork Consumnes River Cosumnes Dorado California 38.64969 -120.40815 no collector Upper El no voucher ID Leoni Meadows Cosumnes Dorado California 38.61036 -120.50472 no collector Upper no voucher id North Fork Mokelumne River Mokelumne Amador California 38.466954 -120.362456 no collector Upper no voucher id North Fork Mokelumne River Mokelumne Amador California 38.466954 -120.362456 no collector Upper no voucher id North Fork Mokelumne River Mokelumne Amador California 38.466954 -120.362456 no collector no voucher ID Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no collector no voucher ID Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no collector no voucher ID Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no collector no voucher ID Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no collector no voucher ID Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no collector no voucher ID Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667

117

no collector no voucher ID Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no collector no voucher ID Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no collector no voucher ID Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no collector no voucher ID Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no collector no voucher ID Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no collector no voucher ID Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no collector no voucher ID Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no collector no voucher ID Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no collector no voucher ID Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no collector no voucher ID Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher SE1 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher SE10 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher SE11 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher SE12 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher SE13 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher SE14 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher SE15 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher SE2 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher SE3 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher SE4 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher SE5 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher SE6 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher SE7 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher SE8 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher SE9 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher SS1 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667

118 no voucher SS2 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher SS3 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher SS4 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher SS5 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher SS6 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher SS7 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher SS8 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher SS9 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher SZ1 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher SZ10 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher SZ11 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher SZ12 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher SZ2 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher SZ3 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher SZ4 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher SZ5 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher SZ7 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher SZ8 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher SZ9 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 Honey no voucher TcDUC Devils_Corral_Lassen Eagle Lakes Lassen California 40.570833 -120.959167 no voucher TT1 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher TT2 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher TT3 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher TT4 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher TT5 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher TT6 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher TT7 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 Honey no voucher UC1 Willard Creek Eagle Lakes Lassen California 40.36777 -120.80296 Honey no voucher UC10 Willard Creek Eagle Lakes Lassen California 40.36777 -120.80296 Honey no voucher UC2 Willard Creek Eagle Lakes Lassen California 40.36777 -120.80296 Honey no voucher UC3 Willard Creek Eagle Lakes Lassen California 40.36777 -120.80296

119

Honey no voucher UC4 Willard Creek Eagle Lakes Lassen California 40.36777 -120.80296 Honey no voucher UC6 Willard Creek Eagle Lakes Lassen California 40.36777 -120.80296 Honey no voucher UC8 Willard Creek Eagle Lakes Lassen California 40.36777 -120.80296 no voucher UG1 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher UG10 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher UG11 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher UG13 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher UG2 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher UG4 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher UG5 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher UG6 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher UG7 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher UG8 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher UG9 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher UM1 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher UM10 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher UM11 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher UM12 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher UM2 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher UM3 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher UM4 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher UM5 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher UM6 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher UM7 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher UM8 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 no voucher UM9 Cold Springs Creek Upper Tule Tulare California 36.006667 -118.701667 El Dorado National Forest, confluence of Panther Creek and North Fork Mokelumne Upper 38.4736027 - no voucher CRF2335 River Mokelumne Amador California 8 120.4158278 El Dorado National Forest, Upper 38.4736027 - CAS 253222 CRF2336 confluence of Panther Creek Mokelumne Amador California 8 120.4158278

120

and North Fork Mokelumne River El Dorado National Forest, confluence of Panther Creek and North Fork Mokelumne Upper 38.4736027 - UNR:Herp:8303 CRF2337 River Mokelumne Amador California 8 120.4158278 El Dorado National Forest, confluence of Panther Creek and North Fork Mokelumne Upper 38.4736027 - UNR:Herp:8301 CRF2338 River Mokelumne Amador California 8 120.4158278 El Dorado National Forest, confluence of Panther Creek and North Fork Mokelumne Upper 38.4736027 - no voucher CRF2339 River Mokelumne Amador California 8 120.4158278 El Dorado National Forest, confluence of Panther Creek and North Fork Mokelumne Upper 38.4736027 - no voucher CRF2340 River Mokelumne Amador California 8 120.4158278 Plumas National Forest, un- named tributary of the N Fork of Feather River, ca 0.25 mi S of Barbees Bar Rd, and ca North Fork 39.7453694 UNR:Herp:8326 CRF2341 0.35 mi E of Concow Feather Butte California 4 -121.478075 Plumas National Forest, un- named tributary of the N Fork of Feather River, ca 0.25 mi S of Barbees Bar Rd, and ca North Fork 39.7453694 no voucher CRF2342 0.35 mi E of Concow Feather Butte California 4 -121.478075 Upper UNR:Herp:8343 CRF2346 North Fork Mokelumne River Mokelumne Amador California 38.462794 -120.374062 no voucher CRF2630 Deep Creek Upper King Fresno California 36.934489 -119.247356 no voucher CRF2631 Deep Creek Upper King Fresno California 36.934489 -119.247356 no voucher CRF2633 Deep Creek Upper King Fresno California 36.934489 -119.247356 Canyon Creek, El Dorado Co., Upper no voucher CRF2669 CA Yuba Nevada California 39.442691 -120.658378

121

Canyon Creek, El Dorado Co., Upper no voucher CRF2670 CA Yuba Nevada California 39.442691 -120.658378 Canyon Creek, El Dorado Co., Upper no voucher CRF2671 CA Yuba Nevada California 39.442691 -120.658378 Canyon Creek, El Dorado Co., Upper no voucher CRF2672 CA Yuba Nevada California 39.442691 -120.658378 Canyon Creek, El Dorado Co., Upper no voucher CRF2673 CA Yuba Nevada California 39.442691 -120.658378 Canyon Creek, El Dorado Co., Upper no voucher CRF2674 CA Yuba Nevada California 39.442691 -120.658378 Canyon Creek, El Dorado Co., Upper no voucher CRF2675 CA Yuba Nevada California 39.442691 -120.658378

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SUPPLEMENTAL TABLE 2 Primers used on Th. couchii gDNA for PCR amplification and Sanger sequencing of the three sodium channel loci (SCN4A, SCN8A, SCN9A) involved in TTX resistance.

Locus (protein) Forward primer (5’-3’) Reverse primer (5’-3’) Size Ta Source P-loop region (bp) (Co) SCN4A (Nav1.4) Domain I e8F: GTGTCCAGAAGGATTTCTCTGC e9R: CCACCACAGCCAGGATTAAA 900 65 Feldman et al., 2009 Domain II e13F: GCATGCAGCTATTTGGGAAG e13R: CCCGATGACCATGACCATTA 900 65 Feldman et al., 2009 Domain III Ex21.F: Ex21.R: 600 63 Hague et al., 2017 AGCAAATGAACCCACATATTGGCAC GGTCAAAGGGCAAGCTGAGAAGGA T Ex22-23.F: Ex22-23.R: 500 63 Hague et al., 2017 CCCAAATCCCACTCATGGCT GCTGGAAAGGCAAAGGAAGC Domain IV Ex26a.F: Ex26a.R: 800 63 Hague et al., 2017 GCACCTTTTTGTATCCTTTCTGC TGCTTCAGGGCATCCATTTCTCCA SCN8A (Nav1.6) Domain I 8F: AGCCTACCTGACTGCAAGCCT 8R: AGGGCAGGACAGGACAGGGC 500 65 McGlothlin et al., 2014 Domain II 15F: TGTGTCCCTCCCCACCCACC 15R: 500 65 McGlothlin et al., 2014 ACCCCAAACCTCTTGCTAGTTCAGA Domain III 21F: TCAAGGCAGGAACCCCCTTCT 21R: 500 63 McGlothlin et al., 2014 AGCGAAAATGTTTGGCTGATCCATCT 22F1: 22R1: GGGGCCATTGCTGAATATTCTG 400 63 McGlothlin et al., 2014 ACAAATGAGTATTCCTAGGCCTAA 22R2: GCCTTGTTTGGGCTCAGAAG Domain IV 26aF: GTGCAGTCAGGTGGCGGTGA 26aR: 500 63 McGlothlin et al., 2014 TCCCAACGGAAGGATTCCCACA 26bF: ACCCATCCTCAACCGTCCTCCA 26bR: 700 63 McGlothlin et al., 2014 ACAGGTGGTGGATCACTGCTTTG 26DIVPloopF: 26DIVPloopR: 400 63 McGlothlin et al., 2014 CCGCCTGGCCCGTATTGGTC ACTGGGTAGCGTCGGGGTCA SCN9A (Nav1.7) McGlothlin et al., 2014 Domain I 8F: AGCCTACCTGACTGCAAGCCT 8R: AGGGCAGGACAGGACAGGGC 400 65 McGlothlin et al., 2014 Domain II 15F: TGTGTCCCTCCCCACCCACC 15R: 500 65 McGlothlin et al., 2014 ACCCCAAACCTCTTGCTAGTTCAGA

123

Domain III 21F1: TGCTTTTAGGTGGGCCACAT 21R1: TCAGTCCAATGGCTTTCAT, 500 63 McGlothlin et al., 2014 21R2: AGCATTGCCTGGATGGACTT 22F: 22R: TGGGGCTCTCCCCAACATGGA 300 63 McGlothlin et al., 2014 TCTGTTAATGGAACAGCGTCTGCC Domain IV 26aF: 26aR: 500 63 McGlothlin et al., 2014 AGGGGGATAGAGCCAATTTCGGA TCCCAACCGAAGGATTGCCACA 26bF: 26bR: 500 63 McGlothlin et al., 2014 ACAAGGAGCCAGACTGTGACCC TGGCATAAGCTTTCAGTGTGTGTGGT

124

SUPPLEMENTAL TABLE 3 Primer pairs used on Th. couchii cDNA for qPCR transcript quantification. The two housekeeping genes 18S ribosomal RNA (18S) and Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) were used to normalize transcript numbers in our gene of interest, the skeletal muscle sodium channel (SCN4A).

o Locus Forward primer (5’-3’) Reverse primer (5’-3’) Size (bp) Ta (C ) Assay Source 18S TGCGGAAAGCAGACATCGA GCGCTCGACCTCATCCT 65 60 TaqMan This study GAPDH GTCAGCAATGCTTCTTGTACT AGACCTTCCACAATGCCATAGTT 88 60 TaqMan This study ACCA G SCN4A TCAACCACATGGACAATCTTA AGTTGCTCGTTTTTTACTATTCC 83 60 TaqMan This study ACCA ATCCA 18S CCGATGCTCTTAACTGAGTGT GGTCCAAGAATTTCACCTCTAGC 211 63 SYBR Feldman et al., 2009 CT Green SCN4A GCATGCAGCTATTTGGGAAG CCCGATGACCATGACCATTA 215 63 SYBR Feldman et al., 2009 Green SCN4A CGCTGTGTCAATACCACCAC TATTGAGGCTGTTCCTCCTG 248 63 SYBR Feldman et al., 2009 Green

125

The role of gene expression in adaptive toxin resistance: Identifying novel genetic mechanisms underlying TTX resistance in the Sierra garter snake (Thamnophis couchii)

Jessica S Reimche1,2, Robert E del Carlo3,4, Haley A Moniz1,2, Edmund D. Brodie, Jr.5, Normand Leblanc3,4, Karen A. Schlauch6,7 and Chris R Feldman1,2

1Department of Biology and 2Program in Ecology, Evolution, and Conservation Biology, University of Nevada, Reno, NV, USA 3Department of Pharmacology and 4Program in Cellular and Molecular Pharmacology and Physiology, University of Nevada, Reno, NV, USA 5Department of Biology, Utah State University, Logan, UT, USA 6Department of Biochemistry and Molecular Biology, University of Nevada, Reno, NV, USA 7Desert Research Institute, Reno, NV, USA

126

Abstract

Uncovering the genetic mechanisms underlying adaptive traits and understanding how much each mechanism contributes to adaptation is a long sought-after objective of evolutionary biology. While progress has been made, more studies are needed to clarify whether adaptive traits arise through changes in protein coding regions of DNA or changes in non-coding regulatory regions, or rather, if these two mechanisms work in tandem to produce complex adaptations. Toxin resistance in predators is an adaptive trait that lends itself well to this discussion because many toxins have specific molecular targets which narrow the potential genetic pool underlying resistance. The resistance of predatory garter snakes (Thamnophis) to toxic newts (Taricha) is no exception. Poisonous Taricha exude a lethal neurotoxin (tetrodotoxin; TTX), that binds to voltage-gated sodium channels (Nav in muscle and nerves, causing paralysis and even death to predators. Remarkably, various

Thamnophis species have evolved resistance to TTX and feed on these toxic newts.

Resistance to TTX in Thamnophis is thought to be explained by mutations in the protein coding regions of the specific Nav proteins that interact with TTX. However, in one species,

Th. couchii, variation in TTX resistance is not explained by variation in Nav sequences. If structural changes in these candidate genes cannot explain these patterns, perhaps changes in regulatory regions, such as gene expression, contribute to this trait. Our study takes a genome-wide approach to answer questions about gene expression, and identify additional genes and pathways potentially involved in TTX resistance. These results shed light on the complexity of adaptive traits in a natural system and may provide evidence that TTX

127 resistance can be explained by both changes in protein coding regions as well as changes in gene expression.

Keywords: adaptation, evolutionary genetics, RNA-sequencing, tetrodotoxin

128

Introduction

One of the fundamental questions in evolutionary biology is whether adaptive phenotypes arise through changes in protein coding regions of DNA or changes in non-coding regulatory regions, such as changes in gene expression (Orr, 2005; Hoekstra and Coyne,

2007; Wray, 2007; Haygood et al., 2010, Wray, 2013; Necsulea and Kaessmann, 2014;

Alvarez et al., 2015; Pardo-Diaz et al., 2015). Small nucleotide changes in a single or small number of genes under selection can lead to major phenotypic changes that lend organisms adaptive advantages in their environments (Nachman et al., 2003; Rosenblum et al., 2004,

Hoekstra et al., 2006; Hoekstra and Coyne, 2007; Chan et al., 2010; Dobler et al., 2012,

2015, Linnen et al., 2013)). For example, polymorphisms in a single locus are responsible for adaptive color differences in populations of both reptiles and mammals (Nachman et al., 2003; Rosenblum et al., 2004, Hoekstra et al., 2006; Rosenblum et al., 2004).

Nonsynonymous changes to the gene encoding the sodium-potassium ATPase (Na+-K+-

ATPase) alpha-subunit gene confer insects the ability to feed on cardiac glycoside producing plants across five separate insect orders (Dobler et al., 2012, 2015). Moreover, the deletion of a gene region gives rise to adaptive pelvic girdle reductions in stickleback fish (Chan et al., 2010). While numerous examples of “few genes of major effect” have been discovered, changes in regulatory regions have been argued to play an even greater role in adaptive evolution due to the flexibility of gene expression changes in comparison to the typically constrained nature of protein evolution (Carroll, 2008; Oleksiak et al., 2002;

Wray, 2007). It is hypothesized that changes in coding regions lead to limited changes in a gene’s activity while changes in regulatory regions can lead to changes in much more contexts than a point mutation in a single gene (Carroll, 2005; Carrol, 2008; Haygood et

129 al. 2010). For example, the expression changes of a handful of genes are correlated with adaptive beak morphologies in Darwin’s finches (Abzhanov et al., 2004, 2006; Mallarino et al., 2011). Similarly, changes in the regulation of a single gene are involved in wing size and shape variation among wasp species (Loehlin and Werren, 2012), and the changes in expression patterns of Hox genes lead to the modification of limbs into feeding appendages in crustaceans (Averof and Patel, 1997).

It is likely that complex adaptations are polygenic and involve both structural changes in protein coding regions in concert with changes in regulatory regions (Shapiro et al. 2004; Sartor et al., 2006; Chapman et al., 2013; Brown et al., 2018, Rivas et al., 2018,

Crawford et al., 2020). This is even more likely in physiological traits because they are often quantitative traits with continuous variation across individuals and populations

(Crawford et al., 2020). The remarkable adaptation of some fish populations to toxic environments demonstrates how both molecular mechanisms contribute to an adaptive phenotype. Researchers studying fish populations in hydrogen-sulfide polluted water found changes in both gene expression and DNA sequence across multiple genes each partially explain adaptive phenotypes permitting fish to live in toxic environments (Brown et al.,

2018). Specifically, Brown et al. (2018) found significant overlap between differentially expressed genes and genes under selection. This constitutes an apparent call to action to further study the influence of genetic mechanisms on adaptive traits.

To partially answer this call, the present study capitalizes on a well-known natural system of predatory adaptations by garter snakes (Thamnophis) to toxic Pacific newts

(Taricha). Pacific newts are defended by one of the most lethal natural toxins ever discovered, tetrodotoxin (TTX) (Brodie, 1968). The molecular targets of TTX are voltage-

130

gated sodium channels (Nav) in muscles and nerves (Fozzard and Lipkin, 2010). TTX binds to these channels, halting electrical impulses in these tissues, causing paralysis and sometimes even death (Brodie, 1968; Fozzard and Lipkin, 2010). Despite this incredible defense strategy, multiple garter snake species have evolved resistance to TTX and prey on sympatric newts (Brodie et al., 2002, 2005; Feldman et al., 2009, Reimche et al., 2020a).

To date, studies have shown that TTX resistance in snakes appears to come from a few changes in genes of major effect. Specific replacements in the outer pore of three sodium channels alter TTX ligation to these proteins, conferring resistance to snakes (Nav:SCN gene family) (Geffeney et al., 2005; Feldman et al., 2009, 2010, 2012; McGlothlin et al.,

2014, 2016; Hague et al., 2017). Mutations in the skeletal muscle and peripheral nerve sodium channels (SCN4A: Nav1.4, SCN8A: Nav1.6, SCN9A:Nav1.7) appear to be especially important in producing extreme TTX resistance in some garter snake species (Geffeney et al., 2005; Feldman et al., 2009, 2010, 2012; McGlothlin et al., 2014, 2016; Hague et al.,

2017). However, organism-level variation in TTX resistance cannot be fully explained by the structural variation in these few genes (Feldman et al., 2010, 2016; Hague et al., 2017;

Reimche et al., 2020b), suggesting that TTX resistance may be a polygenic trait; there may be other additional genes involved, and that patterns of gene expression may be important in generating the phenotype.

Moreover, this pattern is especially evident in the Sierra garter snake (Th. couchii), which displays substantial variation in TTX resistance (Reimche et al., 2020a) despite little or no sequence variation in Nav candidate genes: SCN4A: Nav1.4, SCN8A: Nav1.6, and

SCN9A: Nav1.7 (Reimche et al., 2020b). Additionally, quantitative PCR experiments show no statistically significant change in gene expression of Nav1.4 between Th. couchii snakes

131 with different TTX resistance (Reimche et al., 2020b). The differential gene expression of other TTX-resistance targets, such as the genes in the SCN gene family (Nav1.1-Nav1.9), have yet to be investigated. Thamnophis couchii is the only garter snake species to display variation in resistance despite no variation in Nav candidate genes, suggesting that the genetic basis of TTX resistance may be much more complex than previously thought. In the absence of allelic variation and expression differences in SCN4A (Nav1.4), we hypothesize that there must be additional genes and/or regulatory regions that explain TTX resistance.

Our understanding of the genetic mechanisms underlying TTX resistance has been made with limited molecular data sets, centering on variation in a few genes, and largely focusing on a single locus (SCN4A, Nav1.4) (Geffeney et al., 2005; Feldman et al., 2009,

2010, 2012, 2016; Hague et al., 2017, 2020). Exploring other potential molecular mechanisms underpinning phenotypic resistance can be greatly improved by using more recent advances in genome-wide investigations. Here, we leverage the power of RNA- sequencing to understand TTX resistance by identifying areas of the transcriptome contributing to resistance. We test the hypothesis that differential gene expression in muscle and liver tissue is responsible for phenotypic variation in TTX resistance in Th. couchii. Muscle tissue was investigated because of the known expression of TTX resistant sodium ion channels (Nav1.4) in this specific population of snakes from Tuolumne County,

California (Feldman et al., 2009; Reimche et al., 2020b), and liver tissue was investigated because of the liver’s role in detoxification (Lamb et al., 2004; Orr et al., 2020). To best answer questions about the physiological response to ingesting newts with TTX in the wild, we created a novel assay to quantify the most biologically relevant gene expression profiles

132 by directly feeding snakes food containing TTX and waiting until digestion processes had begun to harvest tissues. All snakes were from a single population with varying levels of phenotypic TTX resistance but identical Nav (SCN) sequences (Reimche et al., 2020b). Our analysis includes both a targeted and discovery approach. To answer questions about the role of sodium channel gene (SCN) expression in TTX-resistant phenotypes, we examine these candidate genes and their expression levels across resistance phenotypes in both tissue types post-ingestion. The discovery approach allows us to identify novel candidate genes potentially involved in resistance by investigating the function of genes that are differentially expressed across phenotypes. This type of population-level exploration of gene expression has never been performed in this snake-newt system and provides a completely novel dataset for the system that allows us to better answer questions about the genetic mechanisms involved in TTX resistance across multiple species. Our transcriptional examination provides insight into long-standing questions over the role of gene expression in contributing to major adaptive phenotypes.

Materials and Methods

Field collection

We field-collected 12 Th. couchii individuals from three localities in Tuolumne County,

California (Table S1). In attempt to create as homogenous a sample as possible with wild caught organisms, all snakes were collected from the same watershed (Upper Tuolumne

River). All but one individual were female adults. Snakes were housed individually in either five or 10-gallon tanks, depending on their size. We provided each tank with a water dish, hide box (Reptile Basics Inc), newspaper or sani-chip bedding (Harlan Teklad), full-

133 spectrum lighting (Reptisun, 10.0 UVA/UVB, Exo Terra) and heat-tape placed under one end of the tank to generate a thermal gradient from roughly 24-30°C. Snakes were kept in a room on a 12L:12D cycle with a constant temperature of 26±1°C and fed either fish (live guppies or frozen trout or tilapia) or feeder mice (frozen mice from a vendor) once or twice per week. We followed the protocols approved by the University of Nevada Reno (UNR)

Institutional Animal Care and Use Committees (IACUC) for all care, handling, and work on live snakes.

Phenotypic Resistance

Whole-animal resistance phenotypes were collected and reported in an earlier study

(Reimche et al., 2020b). We measure phenotypic resistance using a well-established bioassay of whole animal performance that compares the reduction in locomotor ability of individual snakes when subjected to increasing doses of TTX (Brodie and Brodie, 1990;

Brodie et al., 2002; Ridenhour et al., 2004). Specific details of the procedure can be found in Brodie et al. (2002) and Ridenhour et al. (2004). To summarize, we recorded the baseline sprint speed of snakes moving down a track lined with infrared sensors. After measuring this pre-injection speed, we then gave each snake an intraperitoneal (IP) injection of TTX, starting at 1 mass-adjusted mouse unit (MAMU), where 1 MAMU is the amount of TTX needed to kill a 20g mouse in 10 minutes, which corresponds to 0.01429 mg of TTX per gram of snake (Brodie & Brodie 1990; Brown & Mosher, 1963; Ridenhour et al., 2004).

We then recorded post-injection speeds after IP injection (Brodie et al., 2002, Ridenhour et al., 2004) and repeated the process with serially increasing doses. Resistance is quantified as the dose required to reduce a snake to 50% of its pre-injection locomotor

134 performance (50% MAMU). In our sample of 12 individuals, three snakes showed low levels of TTX resistance (< 15MAMU), five had moderate levels of TTX resistance (20-

50MAMU), and four had high TTX resistance (> 50MAMU).

SCN4A (Nav1.4) genotypes

Genotypes were also collected and reported in an earlier study (Reimche et al., 2020b). To confirm genotype, we searched for specific amino acid replacements in the outer pore (P- loop) of the third and fourth domains (DIII, DIV) of the gene encoding skeletal muscle sodium channels, SCN4A. These structural changes alter TTX ligation to these proteins and are thought to confer physiological resistance to TTX in snakes (Feldman et al., 2009,

2010, 2012; Geffeney et al., 2005; Hague et al., 2017). All snakes in this species are fixed for the same allele, each possess a single amino acid substitution (M1276T) in the DIII P- loop of Nav1.4 at a TTX binding site (as detailed in Feldman et al., 2009; Reimche et al.,

2020b).

Experimental Protocol

Current resistance assays involve a subcutaneous injection of TTX, but in the wild, snakes interact with this toxin by ingesting it. Ultimately, these assays cannot examine the mechanisms snakes may have in place to handle this toxin once ingested and throughout the digestion and detoxification process. To mimic this interaction in vivo, we created a controlled feeding trial that mimics a snake ingesting and digesting toxic newt prey (Fig.

1). This allowed us to control for factors such TTX concentration to snake body mass ratio, snake behavior, snake regurgitation upon TTX digestion, etc. Before the feeding trials, we

135 collected the body mass (g) of each snake and calculated a 5MAMU intraperitoneal (IP) injection dose of TTX diluted with Ringer’s solution (1MAMU= 0.01429ug TTX per gram of snake; Brown & Mosher, 1963; Brodie & Brodie, 1990; Ridenhour et al. 2004). This dose is within the range of TTX concentrations found in newt populations (Hanifin et al.,

2008; Reimche et al., 2020) and is equivalent to a dose astonishingly equal to five times the lethal does for a mammal. We then converted IP dose (mg) to the oral dose required to achieve the same performance reduction by multiplying the IP dose by 40 (Williams et al.,

2002; Hanifin et al., 2008; Abal et al., 2017). Snakes were fed a piece of thawed tilapia that weighed 5% of snake body mass. We left snakes to digest for exactly 20 minutes, after which there was an obvious food bolus in the stomach. We then injected the 5MAMU oral dose directly into the food bolus. We let snakes digest food with TTX for exactly 60 minutes. Preliminary trials on Th. elegans and Th. couchii with 3-5MAMUs demonstrated that our procedures were effective; snakes were clearly impacted and incapacitated in the same manner as wild snakes that have ingested toxic newts (Brodie et al., 2002; Williams et al., 2004; Feldman et al., 2020). We then euthanized snakes and immediately collected liver and muscle tissue. Tissues were stored samples in RNAlaterTM (Thermo Fisher

Scientific, Waltham, MA, USA) at -80°C.

RNA Isolation and RNA Sequencing Library Construction

We isolated total RNA from both skeletal muscle and liver tissues utilizing Trizol reagent

(Invitrogen, Carlsbad, CA) according to the manufacturer’s protocol, and further processed as described by the Qiagen RNA clean-up protocol (Qiagen Inc., Germantown, MD, USA).

Aliquots of all RNA samples were submitted to the Nevada Genomics Center, where RNA

136 quality was assessed using an Agilent 2100 BioAnalyzer (Santa Clara, CA) and RNA quantity was determined using a Ribogreen quantification assay (Thermo Fisher Scientific,

Waltham, MA).

We sent samples to UC Davis DNA Technologies and Expression Analysis Core for RNA-seq library construction and sequencing. The UC Davis Core generated strand- specific and barcode-indexed RNA-seq libraries from 1 ug total RNA each after poly-A enrichment using the Kapa mRNA-seq Hyper kit (Kapa Biosystems, Cape Town, South

Africa) per the manufacturer’s instruction. They verified the fragment size distribution of the libraries via micro-capillary gel electrophoresis on a Bioanalyzer 2100 (Agilent).

Libraries were quantified by fluorometry on a Qubit fluorometer (LifeTechnologies) and pooled in equimolar ratios. They quantified the pool by qPCR with a Kapa Library Quant kit (Kapa Biosystems) and sequenced on a partial lane of an Illumina NovaSeq (Illumina,

San Diego, CA) with paired-end 150 bp reads.

Sequence Quality Control and Correction

The UNR Bioinformatics Core processed the raw RNA-sequencing reads. Paired-end reads were sequenced from the 24 libraries. Library sizes ranged from 13,501,548 to 20,585,413 reads. Raw reads went through three separate trimming steps to remove adapters, rRNAs, and decrease overall all duplication percentages. The UNR Core identified sequencing errors in the raw FASTQ-format reads by k-mer rarity and corrected them using rCorrector, version 1.0.4. (Song & Florea, 2015). Sequences were further filtered and read pairs with one read flagged as unfixable by rCorrector using TranscriptomeAssemblyTools

(https://github.com/harvardinformatics/Transcriptome AssemblyTools, commit md5

137 e2df226) were removed. The Core trimmed Illumina Truseq adapted and low-quality bases from the corrected FASTQ read pairs with TrimGalore, version 0.6.4, CutAdapt version.

1.18, and these trimming parameters: maximum error rate 10%, minimum trimmed length

36 nt, adapter overlap stringency of >1 bp, and 2-color chemistry polyG filter setting – nextseq=5 (Martin, 2011).

Additionally, processing steps included the removal of residual ribosomal RNAs in the prepared libraries by alignment of the trimmed corrected read pairs to the SILVA SSURef and LSUParc rRNA databases, release 132 (Quast et al., 2012) using bowtie2, version 2.35

(Langmead & Salzberg, 2012). They retrieved SILVA SSURef and LSUParc FASTA flat files from https://www.arb-silva.de/no_cache/download/archive/release_132/Exports/ and modified them to replace all uracil nucleotides with thymines and then concatenated then to construct a rRNA bowtie2 index. Trimmed read pairs were aligned to the index with bowtie2 --very-sensitive-local sensitivity presets and collected rRNA-mapping and non- mapping fractions using the --al-conc-gz and --un-conc-gz options, respectively.

Following rRNA removal, the Core re-trimmed read pairs to remove any remaining adapter & polyG contaminants using Trimmomatic, version 0.39 with settings

ILLUMINACLIP::2:30:10 LEADING:3 TRAILING:3

SLIDINGWINDOW:4:15 MINLEN:36 (Bolger et al., 2014). Sequence and library quality metrics were measured with FastQC version 0.11.8

(https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and then summarized using

MultiQC version 1.7 (Ewels et al., 2016).

De Novo Transcript Assembly

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Transcripts were assembled using Trinity version 2.9.0 (Grabherr et al., 2011). Multiple assemblies were generated from single individuals (liver and muscle tissue), sets of individuals, and from all samples. In all cases Trinity was executed with default parameters, including in-silico input normalization to 50x read coverage, with --

SS_lib_type RF, --include_supertranscripts, and maximum memory of 210 GB, and limited to 50 CPUs keep parallel bfly operations within stated memory constraints. The

Trinity assembly with the highest BUSCO score (> 87%) and lowest duplication ratio was the de novo assembly using the reads from both muscle and liver tissue in a single individual snake with high TTX resistance. First, k-mers (k-25) were extracted and counted from the trimmed reads and were then assembled in contigs. de Bruijn graphs are then generated for each cluster of overlapping contigs. Transcript IDs were assigned to contigs and alternatively spliced transcripts were resolved by receiving unique transcript IDs.

Assemblies were filtered to remove poorly supported transcripts and probable assembly artifacts by Transrate, version 1.0.3 (Smith-Unna et al., 2016) and the respective in silico normalized transcripts provided for Transrate mapping tests. Lowly expressed transcripts were also filtered by minimum transcript-per-million thresholds after pseudoalignment of corrected reads by kallisto, version 0.46.2 (Bray et al., 2016).

Assembly completeness was evaluated, and filter impacts thereon, for each assembly and after each filtering method using the identification of the near-universal single-copy orthologs of BUSCO, version 4.0.2, in transcriptome mode, eukaryote auto-lineage selection (Seppy et al., 2019).

Transcriptome Annotation by Homology

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Assemblies were annotated by sequence homologies both to NCBI nr, release 2019-12

(Sayers et al., 2020) and the Thamnophis sirtalis 6.0 RefSeq proteome (Accession

GCF_001077635; Perry et al., 2018), using DIAMOND, version 0.9.29 (Buchfink et al.,

2015). To compare assembled transcripts with the protein sets, DIAMOND was used as follows: in "blastx"-like mode, with the BLOSUM62 matrix, compositional-based statistics

(Hauser et al., 2016), e-value cutoff set to < 1e-8, and only alignments scoring at worst 10% lower than the best alignment reported via the --top 10 option. For the comparison of assemblies vs. the nr database, the lowest-common ancestor (LCA) taxonomic classifications were generated using DIAMOND.

Quantifying Gene Expression

Samples were grouped by tissue and phenotype (i.e. samples collected from muscle tissue in high resistance snakes make up the group MuscleHigh, Table S1). MuscleLow and

LiverLow both contain three samples, MuscleMid and LiverMid have five, and

MuscleHigh and LiverHigh include four. Overall transcript abundance (expression values) was estimated using kallisto count matrix (Bray et al., 2016). Transcript isoforms were then summarized to the gene level using DESeq2 (Love et al., 2014). The raw data included a total of 91,244 potentially unique transcripts, with the majority being expressed in liver tissue.

Transcripts underwent novel filtering steps similar to those in Nadeau et al. (2017).

First, transcripts with counts less than 10 in all 24 samples were excluded. Then, to address outliers, we flagged all genes in which more than half of samples within a phenotypic group had counts less than 10 and a single sample with a count greater than 100. For these genes,

140 the single value greater than 100 was replaced with the average of the other counts in the group. As an example, the transcript TRINITY_DN773_c1_g2 has the following counts series for the experiments MuscleHigh1, MuscleHigh2, MuscleHigh3, MuscleHigh4: 5,

12, 10, 307. The count 307 was replaced by 9 (average of 5, 12, 10).

After filtering, a total of 49,114 high-quality transcripts remained. These data were normalized with respect to total library size using the standard median ratio method for

RNA-Seq data (Love et al., 2014, Nadeau et al., 2017). Principal components analysis

(PCA) was then conducted on a covariance matrix of the filtered and normalized expression counts to observe whether variance of the samples could be described by tissue type and resistance phenotype. PCA was performed in R (R Core Team, 2019) using the mixOmics package (Rohart et al., 2017).

Differential Gene Expression Analysis

After filtering and normalization, differential gene expression between tissues and phenotypes was examined using the DESeq2 package (Love et al., 2014) in R. Data were non-normally distributed and fitted to a negative binomial distribution (Love et al., 2014).

Two main comparisons were considered among muscle and liver tissues across the two phenotypes: MuscleHigh vs. MuscleLow and LiverHigh vs. LiverLow. DESeq2 incorporates a multiple hypothesis testing correction to adjust for the false discovery rate:

Benjamini and Hochberg method was used to control for false discovery rate and adjust p- values (Benjamini and Hochberg, 1995). Genes with both |log2(fold-change)| >1 and adjusted p-value p< 0.05 were deemed statistically significantly differentially expressed and explored further. Genes were removed if the difference in average expression (absolute

141 value of the difference between high resistance average counts and low resistance average counts) was less than 20 because they fell within the bottom 10 percent of the distribution of expression differences. These cutoff values remain generous as this is the first step to thoroughly explore all potential mechanisms associated with TTX resistance in this system.

Sodium channel transcripts of interest (SCN gene family) were explored regardless of log2(fold-change) and p-value for the targeted analysis.

Gene Ontology (GO) Analysis

All differentially expressed genes (DEG) were annotated with orthologous human gene

Ensembl identifiers, as justified in Andrew et al. (2017). PANTHER was used to determine the function of these genes and assess any potential adaptive changes (PANTHER version

14; Mi et al., 2019). Differentially expressed genes and their associated proteins from our two main comparisons (MuscleHigh vs MuscleLow and LiverHigh vs LiverLow) were then categorized by molecular function and biological processes using GO functional classifications (Mi et al. 2019). PANTHER assigns these categories using all of the experimental annotations available in the Gene Ontology Consortium which includes over

45,000 specific GO terms (http://geneontology.org). Genes are often assigned to multiple categories because there are many GO terms that describe different aspects of its function

(Mi et al., 2019). These outputs allow for a more accurate inference of gene function for the hundreds of DEGs identified in the discovery analysis. Genes that were not classified by PANTHER were assigned a molecular function and biological process via manual literature searches of scientific articles on human orthologs.

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The DEGs were then used to conduct additional statistical tests in PANTHER.

Using the overrepresentation tool, we compared our list of DEGs to a reference gene list and determined whether a particular category (molecular function or biological process) of genes was over- or under-represented (Mi et al., 2013). The human reference genome

(GRCh38.p13) and the Ensembl genes 100 database) (http://enseml.org) were used as reference for this examination, following the protocol of Andrew et al. [LIST

REFERENCE HERE]. The overrepresentation test is a simple hypothesis test: a simple binomial comparison of expected number of genes per category vs. the observed number of genes per category (Mi et al., 2013). The expected value is the number of genes in a given PANTHER category from the reference dataset. The expected probability of a category is the number of genes in that category divided by the total number of genes in the reference dataset. Similarly, the observed number is simply the number of significantly expressed genes that are placed in that category. The percentage of observed genes is the number of observed genes divided by the total number of statistically significant genes under observation.

As an example, if 20% of the DEGs are involved in binding, and 10% of the human reference genome is involved in binding, the difference between those expected probabilities can be tested using a binomial test. A p-value is then calculated to determine whether this over-representation is statistically significant (Mi et al., 2013). We conducted these tests with both the GO Molecular Function Complete Annotation and the

GO Biological Process Complete Annotation datasets, using the false discovery rate correction and a significance level of alpha= 0.05. Figures were produced in both R and

OriginPro 2020b (OriginLab Corporation, Northampton, Massachusetts, USA). The GO

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Molecular Function Annotation dataset is a database of genes categorized by the molecular activities of individual gene products; the GO Biological Process Complete dataset contains genes categorized by the pathways and larger processes to which the gene product’s activity contributes (Mi et al., 2013; http//:pantherdb.org).

Results

The PCA of the normalized counts showed a clear separation between muscle and liver tissue, with 93% of the variance explained by PC1 (the first principal component), which separates tissue type (Fig. 2).

TTX-resistant vs TTX-sensitive muscle tissue

The MuscleHigh vs. MuscleLow comparison generated 33 differentially expressed transcripts at our desired threshold (|log2(fold-change)| >1) with adjusted p-values < 0.05.

To expand this analysis, we included four additional transcripts with p- values < 0.066. Of these 37 transcripts, 17 did not fall within the DIAMOND E-value cutoffs during BLAST and were removed from further analysis. Thirteen of the genes were upregulated in TTX- resistant snakes, while the other seven are downregulated in resistant individuals (Fig. 3,

Table 1). PANTHER gene ontology software assigned genes and associated proteins to specific molecular functions and biological processes (Table 2) as well as more general parent categories (Table S2). The 20 genes were classified into seven separate parent molecular function categories (Fig. 4) and 12 parent biological processes (Fig. 5). The majority of genes fell under the binding category for molecular function and cellular process and metabolic process categories for biological processes. Of these parent

144 categories, both binding and catalytic activity were overrepresented when compared to the categorical breakdown of genes in the human genome. Specifically, we found an overrepresented amount of protein binding, nucleosome binding, proteins involved in cytoskeleton structure, and proteins involved in hydrolase activity, although these results lie just outside statistical significance (p < 0.056, Table S3).

TTX-resistant vs TTX-sensitive liver tissue

Within the comparison of liver tissue from animals with high and low TTX resistance, 532 transcripts were differentially expressed with adjusted p-values < 0.05 and |log2(fold- change)| >1. Of these, 114 had differences in average expression less than 20 and were removed from further analysis. In addition, 229 transcripts did not fall within the

DIAMOND cutoffs when blasting the NCBI nr database. Thus 189 genes were examined further. Ninety-seven of these genes were upregulated in TTX-resistant snakes, while the other 92 were downregulated in resistant individuals (Fig. 3, Table 3). PANTHER assigned genes and associated proteins into eight separate parent molecular function categories and

17 parent biological processes (Fig. 4, Fig. 5, Table 4). More specific classifications can be found in the supplement (Table S4). Similar to our muscle analysis, the majority of genes fall under the binding category for molecular function and cellular process and metabolic process categories for biological processes. Of these parent categories, molecular functions binding and catalytic activity were significantly overrepresented (p <

0.001) and biological processes metabolic process and cellular process are significantly overrepresented (p < 0.001, p < 0.05; Table S5.).

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Six genes were differentially expressed in both tissue types. Nuclease-sensitive element-binding protein (YBOX1), polyadenylate-binding protein-interacting protein

(PAIP2B), beta-actin (ACTB), and major histocompatibility complex class I-related gene protein (MR1) were upregulated in snakes with high TTX resistance while exopolyphosphatase (PRUNE1) and tigger transposable element-derived protein (TIGD3) were downregulated in snakes with high TTX-resistance.

Sodium channel candidate genes

Five different sodium channel genes were expressed in both muscle and liver tissue but none of these five were differentially expressed among phenotypic groups, all with

|log2(fold-change)| <1 and p-values greater than 0.6 (Fig. 6, Table 5). The genes include

SCN4A (Nav1.4, voltage-gated sodium channel alpha subunit in skeletal muscle), SCN2B

(beta subunit 2), SCN4B (beta subunit 4), and two transcripts for SCN5A (Nav1.5, voltage- gated sodium channel alpha subunit in cardiac tissue). SCN4A was expressed in relatively high levels in muscle tissue and extremely low levels in liver tissue. SCN2B was expressed in high amounts in both muscle and liver tissue while SCN4B was found in high amounts in muscle and low amounts in liver. Interestingly, SCN5A, a natively resistant sodium ion channel expressed in cardiac tissue, was expressed in both muscle and liver tissue.

Discussion

Our approach takes a wide lens to view the potential genetic mechanisms underlying an adaptive trait, TTX resistance in garter snakes. Nearly twenty years of research have focused on a single family of genes (Nav: SCN) that have reliably predicted variation in

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TTX resistance in other Thamnophis species (Geffeney et al., 2005; Feldman et al., 2009,

2010, 2012; McGlothlin et al., 2014, 2016; Hague et al., 2017, 2020). The investigation of

TTX resistance in Th. couchii suggests wide trait variation (Reimche et al., 2020a) but also an exception to this genetic pattern (Reimche et al., 2020b), and has led to the genome- wide exploration of this trait. Here, we used a biologically relevant experimental design that asks questions the genes involved when TTX enters the digestive system of snakes.

We focused on a single population of snakes with varying levels of TTX resistance to minimize differences expected to result from other processes in subdivided populations

(e.g. neutral drift or selection due to different sets of environmental and ecological pressures). Analysis reveals hundreds of differentially expressed genes in muscle and liver tissue and shows that the genetic mechanisms underlying TTX resistance may be much more complex than previously thought.

Muscle tissue response

In the model Th. sirtalis system, point mutations in sodium ion channels in skeletal muscle

(SCN4A: Nav1.4) explain the majority of variation in TTX resistance at the organismal level (Feldman et al., 2010; Hague et al., 2017). In addition, the physiological underpinnings of resistance are attributed to the structural changes in the Nav1.4 protein

(Geffeney et al., 2005) as whole animal measures of TTX resistance are tightly correlated to measures of resistance in isolated skeletal muscle (Geffeney, Brodie, Ruben, & Brodie,

2002). While we find evidence for convergence on the physiological level in Th. couchii, the substantial variation in TTX resistance, however, cannot be fully explained by the structural variation in SCN4A (Feldman et al., 2009; Reimche et al., 2020b), as all

147 individuals are fixed for a single point mutation that should lead to high levels of TTX resistance (Jost et al., 2008; Feldman et al., 2009). In the absence of structural variation in

SCN4A, we hypothesized that there may be expression level differences in skeletal muscle tissue that could potentially explain differences in TTX resistance (Reimche et al., 2020b).

We identified 20 genes that were differentially expressed among high and low resistance snakes that were categorized into seven molecular functions: binding activity, catalytic activity, transcription, structural activity, molecular function regulator, translation, and transportation. Genes associated with binding, transcription, translation, molecular function regulator, transportation, and structure were mainly upregulated in high resistance snakes and genes associated with catalytic activity were downregulated in high resistance snakes. These categories are broad and all encompassing. For example,

PANTHER defines binding activity as “the selective, non-covalent, often stoichiometric interaction of a molecule with one or more specific sites on another molecule” (PANTHER, version 14). In order to determine how these genes may be involved in TTX resistance we present a detailed investigation of a select few genes, describing function and the physiologically pathways of that they are a part.

Binding activity

The majority of differentially expressed genes fell into the binding category. One pattern that might suggest a mechanism for TTX resistance is the upregulation of immunity proteins major histocompatibility class I (MR1) and coagulation factor V (F5) in snakes with high TTX resistance. While TTX is likely too small for snakes to develop antibodies

(Bane et al., 2014), TTX is excreted by granular glands in newts (Mailho-Fontanta et al.,

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2019) that exude compounds other than TTX (Brodie et al., 1974; Shimizu & Kobakashi,

1983). It is possible that snakes have developed an immune response that detects accessory proteins and other macromolecules that are excreted along with TTX. The upregulated immune proteins in resistant snakes may indicate that these snakes have a secondary immune response that may buffer the effects of TTX through non-specific molecular interactions.

Another interesting result is the upregulation of genes involved in muscle growth and performance in snakes with high TTX resistance. The upregulation of growth- hormone receptor (GHR), tropomyosin (TPM1), and beta-actin (ACTB) could all result in larger muscles with enhanced contractile activity. Growth hormone is involved in hypertrophy and muscle repair (Velloso, 2008), while beta-actin and tropomyosin are involved in better regulation of contractile machinery (Huxley & Niedergerke, 1954;

Lehman et al., 1994). The growth hormone receptor itself is only one of several transcripts which either directly or indirectly influence growth in these muscle cells but taken together, these transcripts could suggest that there is muscle remodeling at play in resistant snakes. This hypothesis is particularly interesting when considering the tradeoff of TTX resistance at the physiological level (Hague et al., 2018, del Carlo et al., in prep).

The same beneficial mutations in Nav1.4 (SCN4A) that confer resistance have an antagonistic role for muscle physiology; snakes with resistant sodium channels in their skeletal muscle experience biophysical tradeoffs in the form of slower, weaker muscles that may cause negative fitness tradeoffs at the whole animal level (Hague et al., 2018, del Carlo et al., in prep). It is possible that resistant Th. couchii are compensating for these tradeoffs by increasing the expression of genes involved in muscle growth and

149 repair. Theoretically, the stronger a muscle is in the absence of TTX, the more likely it is to delay paralysis in the presence of the toxin. Such strength can be modified by neuromuscular and calcium handling mechanisms. For example, a snake could simply deliver more action potentials to a neuromuscular junction, compensating weaker contractions with more frequent contractions. Conversely, when sodium conductance is reduced by the presence of the toxin, a more sensitive voltage sensor (DHPR) downstream of the sodium channel could still engage contraction.

Catalytic activity

All of the genes in this category are downregulated in high resistant individuals and have functions associated with cellular metabolism (PIK3G, PRKAG2, ACOT). One compelling idea is the hypothesis that high resistance snakes have adapted to lower energy and oxygen demands. This may allow them to mitigate the consequences of higher doses of TTX, while the effects may still be paralytic, they may not be as harmful to snakes that are adapted to lower oxygen requirements in their cells and tissues.

Liver tissue response

Liver plays a large role in detoxification (Lamb et al., 2004; Orr et al., 2020) and thus we hypothesized that there would be differentially expressed genes among low and high resistant snakes. Past research on Th. sirtalis has shown that TTX is detected in liver tissue up to 7 weeks after ingesting a single toxic newt (Williams et al., 2004). This may suggest that the liver exhibits mechanisms for concentrating TTX in liver tissue and shield muscle and nervous tissue from high concentrations of TTX (William et al., 2004, 2012).

Additionally, liver appears to be an appropriate candidate tissue because TTX producing

150 animals exhibit extremely high levels of TTX in their liver tissue (Noguchi and Arakawa,

2008; Bane et al., 2014).

We identified 189 differentially expressed genes among low and high resistance snakes in liver tissue that were categorized into eight separate molecular functions: binding activity, catalytic activity, transcription, structural activity, molecular function regulator, translation, transportation, and molecular transducer activity. In all eight categories, there are genes that are both upregulated and downregulated. In the absence of a clear pattern that correlates gene expression with TTX resistance, a candidate gene approach within these categories is necessary to learn more about their potential role in TTX resistance.

Binding activity

Similar to muscle, the majority of genes fell into the binding category. A number of these genes are associated with the immune system. Of those that are upregulated in high resistant snakes, a handful of them have roles in response to toxic substances (EPHX1,

MAOB, MR1). Additionally, we see an upregulation of an immunoglobulin protein

(IGDCC4). The immunoglobulin supergene protein family are natural anti-toxins found in animals resistant to snake venoms and often act as venom binding protein (Bastos et al.,

2016; Gibbs et al., 2020). While we did not find expression of a TTX-specific binding protein (that we know of), perhaps the upregulation of these immune response genes is involved in a natural immune response to TTX operating through non-specific molecular mechanisms

Catalytic activity

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Genes involved in oxygenation could lead to some sort of involvement in TTX resistance.

Oxygenation is a compelling story for a system involving a neurotoxin which kills by paralysis of the ventilatory muscles. Genes specifically involved in angiogenesis

(ANGPTL4, HSPG2, PIK3CA, PIK3CG, PIK3R6, PML, PRKCB, ZC3H12A) are upregulated in high resistant individuals. Animals that regularly face respiratory arrest throughout their natural history and diet might very well increase their blood flow to a major organ, and this may highlight a potential pathway involved in resistance.

Expression of sodium channel genes (SCN)

Results of SCN genes in this study verify the results found in Reimche et al. (2020b); there are no significant changes in expression of SCN4A (Nav1.4). Interestingly, we do see the expression of natively resistant SCN5A (Nav1.5) in both tissue types, regardless of phenotype. Previous research on mammals found multiple Nav paralogs expressed in isolated cardiac tissue (Maier et al., 2004, Zakon et al., 2010). The expression of insensitive

Nav channels such as Nav1.5 in muscle and liver tissue could influence the heightened baseline resistance that Th. couchii and most Thamnophis demonstrate relative to other snakes (Motychak et al., 1999; Feldman et al., 2012). The same can be said for the expression of auxiliary protein partners of the sodium channels (SCN2B and SCN4B). The non-significant differences in expression among these sodium channel genes suggests that there are other genes involved in TTX resistance.

Conclusions & Future Work

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While some of these results may have straightforward impacts on the phenotype, which would lend them to a model of genome-based resistance, we cannot ignore the possibility that some of these genes may have relatively neutral effects on muscle and liver function.

Both neutral and adaptive processes contribute to variation in gene expression found within and among individuals and populations (Oleksiak et al., 2002; Whitehead & Crawford,

2005; Crawford & Oleksiak, 2007; Whittkopp, 2007; Alvarez et al., 2015). A necessary next-step experiment would be to examine and identify variation in the Thamnophis genus to provide the community with a basis of naturally occurring gene expression variation in this group. We recognize that this study does not highlight a single, leading mechanism for

TTX resistance. This could lead to a possible hypothesis of TTX resistance being a polygenic trait that is composed of many genes. While a handful of intriguing patterns emerge from the vast amounts of data this project produced, not all of these genes may have a causal effect on TTX resistance in this system. Rather, our results detect differences in hundreds of genes that may contribute to this complex trait. A much deeper investigation into the genes and the pathways to which they belong must be conducted to provide evidence for additional mechanisms that underlie increased TTX resistance in Th. couchii.

This research supports the hypothesis that genomes are vast and complex, as are organismal traits and their adaptations. It can be expected that many genes are required for this adaptive trait and may be influenced by the environment and phenotypic plasticity.

Even in species as well studied as humans, we may have a robustly mapped genome, names and sequences for every gene, but knowing which phenotypic trait or traits those genes underlie is as of yet unknown. To better understand complex traits, research should expand to include a detailed investigation of the genome as well as its interactions with the

153 environment. Future work can focus on building genomic resources to compare the genomes of snakes with varying levels of TTX resistance and identify additional regions of differentiation in this system. Provided that the majority of novel phenotypes are supported by changes in regulatory regions, the data presented in this project can serve as a guide, highlighting potential regions of the genome that should be investigated. Once we know more about gene function, and the pathways and processes they are involved in, it may be important to confirm expression level differences with isolated qPCR studies. It may also be important to investigate the role of alternative splicing in this system. In addition, a whole genome resequencing project, within a single species of snakes, would be an ideal complement to the transcriptomics detailed here and will contribute to a growing body of work that investigates patterns in both protein coding and regulatory regions that likely underpin complex, adaptive traits such as TTX resistance.

Acknowledgements

We thank CAF&W for permits to CRF, and UNR and USU IACUCs to CRF and EDB Jr. for approval of live animal protocols. We thank Erica Ely for aid with field collections. We thank John Gray, Gabrielle Blaustein, Vicki Thill, Amber Durfee, Kenzie Wasley, Taylor

Disbrow, Sage Kruleski and Aubrey Smith for live animal care at UNR. We thank Josh

Hallas for valuable help and insight with experimental design. We thank Marjorie Matocq and especially Brad Ferguson and Levi Evans for their aid and equipment for RNA extraction. We thank the UNR Genomics Center for RNA quantification, and UC Davis

DNA Technologies and Expression Analysis Core for prepping the libraries and running

154 the sequencing. We thank Richard Tillet and the UNR Bioinformatics Core for their great contributions to this project with the use of a partial INBRE award. The authors would also like to acknowledge the support of Research & Innovation and the Office of Information

Technology at the University of Nevada, Reno for computing time on the Pronghorn High-

Performance Computing Cluster. This work was supported by an NSF grant to CRF and

NL (IOS-1355221) and a Nevada INBRE award to JSR and CRF from the National

Institute of General Medical Sciences (GM103440).

Author Contributions

JSR and CRF designed the study; CRF and NL provided lab resources, and CRF, NL and

JSR funded the project; CRF, JSR, and REdC field collected animals; CRF, HAM, JSR, and EDB Jr. collected whole animal phenotypic data; JSR, REdC, and HAM conducted feeding trials and dissections; JSR performed molecular genetic lab work to isolate RNA;

JSR and KAS analyzed RNA-seq data; JSR and KAS produced the initial draft, and all authors contributed text and approved the final manuscript.

ORCID:

Jessica S Reimche https://orcid.org/0000-0001-6536-7039 Robert E del Carlo https://orcid.org/ 0000-0003-4845-7037 Haley A Moniz https://orcid.org/0000-0003-2838-511X Edmund D Brodie Jr. https://orcid.org/0000-0002-5739-474 Normand Leblanc https://orcid.org/ 0000-0002-1090-9432 Karen Schlauch https://orcid.org/ 00000001-6916-8571 Chris R Feldman https://orcid.org/0000-0003-2988-3145

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Figure 1. Experimental design showing 12 Th. couchii snakes in three distinct phenotypic groups

(low resistance, mid resistance, high resistance). All snakes were fed fish injected with a 5MAMU dose of TTX. Both fish and dose were adjusted for mass and body size. Snakes were anesthetized after digestion processes had begun and muscle and liver tissue were collected from all individuals.

Figure 2. Principal Components Analysis (PCA) of normalized and filtered RNAseq count data.

PCA1 shows that 93% of variance in count data is explained by tissue type. Colors correspond to phenotypic groups: blue- low TTX resistance, green- mid TTX resistance, orange- high TTX resistance. Triangles indicate samples from muscle tissue and circles indicate liver tissue.

Figure 3. Patterns of average gene expression in each molecular function category. The top row shows differentially expressed genes in liver tissue and the bottom row shows differentially expressed genes from muscle tissue. Each column represents a molecular function category assigned via PANTHER. Average transcript counts of each phenotypic group were scaled to the low resistance group and then log transformed. Red lines indicate the upregulation of a gene in the high resistance phenotype and blue lines indicate the downregulation of a gene in the high resistance phenotype.

Figure 4. Categorical breakdown of differentially expressed genes among high resistance and low resistance phenotypes by molecular function. Genes were assigned molecular functions via

PANTHER gene ontology software. There were 20 differentially expressed genes in muscle tissue, and 189 in liver tissue.

* signifies a category that is significantly overrepresented when compared to the human genome

164

Figure 5. Categorical breakdown of differentially expressed genes among high resistance and low resistance phenotypes by biological process. Genes were assigned biological processes via

PANTHER gene ontology software. There were 20 differentially expressed genes in muscle tissue, and 189 in liver tissue.

* signifies a category that is significantly overrepresented when compared to the human genome

Figure 6. Average measure of expression (transcript counts) of all sodium channel (SCN) genes expressed in both muscle and liver tissue among all three resistance phenotypes. Shape indicates tissue type and color indicates the various SCN genes. SCN4A (Nav1.4) is found in skeletal muscle tissue and has been a candidate gene of interest in this system, SCN5A (Nav1.5) is a natively TTX- resistant channel found in cardiac tissue, and SCN2B and SCN4B and genes coding for the beta subunit of select sodium channels. No statistically significant differences were found in average expression measures among low and high resistance snakes.

165

Low TTX-resistant T. couchii (0-15MAMU)

TTX 3 snakes

Mid TTX-resistant T. couchii (15-50MAMU)

5 MAMU extract RNA from muscle, and liver tissue 5 snakes

High TTX-resistant T. couchii (>50MAMU)

4 snakes

166

PlotIndiv

Muscle Low 1e+06 Muscle Mid

Muscle High

0e+00 Liver Low

Liver Mid

-1e+06 Liver High PC2: 18% expl. var expl. PC2: 18% PC2: 18% explained variance explained 18% PC2: -2e+06

-2e+06 -1e+06 0e+00 1e+06 2e+06

PC1: 75%PC1: explained75% expl. var variance

167

168

Molecular Function (number of genes) Molecular Function (number of genes)

Binding (15) Binding (120) * Catalytic Activity (4) Catalytic Acitvity (67) * Structural Molecule Activity (2) Transcription Regulator Activity (17) Transcription Regulatory Activity (2) Transporter Activity (14) Molecular Function Regulator (1) Structural Molecule Activity (7) Translation Regulator Activity (1) Molecular Function Regulator (6) Transporter Activity (1) Translation Regulator Activity (6)

Muscle Liver

169

Biological Process (number of genes)

Cellular Process (99) * Metabolic Process (89) * Biological Process (number of genes) Biological Regulation (44)

Cellular Process (11) Localization (33) Metabolic Process (10) Response to Stimuli (33) Biological Regulation (6) Signaling (30) Cellular Component Organization (6) Cellular Component Organization (22) Localization (4) Developmental Process (13) Response to Stimuli (4) Immune System Process (13) Signaling (4) Growth (12) Developmental Process (3) Multicellular Organismal Process (8) Locomotion (3) Biological Adhesion (6) Multicellular Organismal Process (3) Reproductive Process (6) Growth (2) Locomotion (5) Immune Response (1) Cell Population Proliferation (3) Multi-organism process (3) Muscle Liver Behavior (1) Reproduction (1)

170

6000

Tissue Type

4000 Liver Muscle

Sodium Channel Genes (SCN)

SCN4A, Nav1.4 alpha subunit SCN5A, Nav1.5 alpha subunit 2000 SCN2B, beta 2 subunit SCN4B, beta 4 subunit

0 Average Measure of Expression (TranscriptAverage Counts) Measure of Expression Low Mid High

Phenotype

171

Table 1. Average counts, fold-change, and associated p value for all differentially expressed genes in the muscle tissue discovery analysis between MuscleHigh and MuscleLow samples. Shaded rows highlight genes that are upregulated in snakes with high TTX resistance.

Muscle Low Muscle Mid Muscle High log2 Fold- Fold- Gene ID Protein Average Counts Average Counts Average Counts Change Change Adj. p-value nuclease-sensitive element-binding protein YBX1 1 0.00 1.67 7498.07 15.11 35364.15 0.0000*** polyadenylate-binding protein-interacting PAIP2B protein 0.00 60.26 88.70 8.71 418.77 0.0002*** SEMA4G semaphorin-4G 3.40 4.16 55.23 4.00 16.00 0.0004*** ROHU_015292 (TMEM200A/B ) transmembrane protein 200A/200B 7.23 13.93 44.80 2.16 4.47 0.0027** ACTB beta-actin 0.00 0.00 20.01 6.56 94.35 0.0039** TPM1 tropomyosin alpha-1 chain 6264.64 2958.85 28040.98 2.16 4.48 0.0200* TBCEL tubulin-specific chaperone E 59.53 62.01 131.73 1.15 2.21 0.0200* F5 coagulation factor V 8.78 13.88 39.02 2.14 4.41 0.0416* major histocompatibility complex class I- MR1 related gene protein 0.00 19.53 25.61 6.91 120.26 0.0490* N-terminal EF-hand calcium-binding NECAB2 protein 2 1.93 7.62 104.12 5.75 53.89 0.0490* NOC2L nucleolar complex protein 2 homolog 231.86 395.37 456.18 0.98 1.97 0.0536 LOC10658520 0 (GHR) growth hormone receptor 878.05 1278.74 2103.66 1.26 2.40 0.0536 FAM83G FAM83G protein 61.07 87.95 167.33 1.45 2.74 0.0581 tigger transposable element-derived TIGD3 protein 470.13 338.49 174.34 -1.43 0.37 0.0001*** 5'-AMP-activated protein kinase subunit Prkag2 gamma-2 369.81 217.58 89.09 -2.06 0.24 0.0128* ACOT13 acyl-coenzyme A thioesterase 13 485.36 332.13 147.05 -1.72 0.30 0.0406* zinc finger SWIM domain-containing ZSWIM7 protein 7 98.62 86.93 38.85 -1.35 0.39 0.0481* MRPL11 39S ribosomal protein L11, mitochondrial 222.19 164.51 111.99 -0.99 0.50 0.0490* PRUNE1 exopolyphosphatase PRUNE1 261.18 235.60 116.74 -1.17 0.44 0.0581 phosphatidylinositol 4,5-bisphosphate 3- Pik3cg kinase catalytic subunit gamma 24.90 16.84 5.22 -2.19 0.22 0.0623

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*** p<0.001, ** p<0.01, * p<0.05

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Table 2. List of differentially expressed genes and proteins in the muscle tissue discovery analysis between MuscleHigh and MuscleLow samples.. Molecular function and biological process were categorized by PANTHER classification system software (Version 14).

Gene ID Protein Molecular Function Biological Process ACOT13 acyl-coenzyme A thioesterase 13 catalytic activity metabolic process⧪ cellular component organization, cellular process, developmental process, ACTB beta-actin binding, structural molecule activity localization, locomotion, multicellular organismal process F5 coagulation factor V binding⧪ metabolic process⧪, localization⧪ FAM83G FAM83G protein binding⧪ signaling⧪, developmental process⧪ LOC106585200 (GHR) growth hormone receptor binding⧪ growth⧪ major histocompatibility complex MR1 class I-related gene protein binding⧪ immune response⧪ 39S ribosomal protein L11, MRPL11 mitochondrial binding, structural molecule activity cellular component organization, cellular process, metabolic process N-terminal EF-hand calcium- NECAB2 binding protein 2 binding⧪ biological regulation, cellular process, metabolic process nucleolar complex protein 2 biological regulation, cellular component organization, cellular process, NOC2L homolog binding, transcription regulator activity metabolic process polyadenylate-binding protein- binding⧪, translation regulator PAIP2B interacting protein activity⧪ biological regulation⧪, metabolic process⧪, cellular process⧪ phosphatidylinositol 4,5- bisphosphate 3-kinase catalytic biological regulation, cellular process, localization, locomotion, metabolic Pik3cg subunit gamma catalytic activity process, response to stimuli, signaling 5'-AMP-activated protein kinase Prkag2 subunit gamma-2 catalytic activity⧪ metabolic process⧪, biological regulation⧪ PRUNE1 exopolyphosphatase PRUNE1 catalytic activity cellular process, metabolic process ROHU_015292 transmembrane protein (TMEM200A/B) 200A/200B transporter activity⧪ signaling⧪, cellular process⧪ biological regulation, cellular component organization, cellular process, developmental process, growth, localization, locomotion, multicellular SEMA4G semaphorin-4G binding, molecular function regulator organismal process, response to stimuli, signaling TBCEL tubulin-specific chaperone E binding cellular component organization tigger transposable element- TIGD3 derived protein binding⧪ cellular component organization, cellular process, multicellular organismal TPM1 tropomyosin alpha-1 chain binding process nuclease-sensitive element- binding⧪, transcription regulator YBX1 binding protein 1 activity response to stimuli⧪ zinc finger SWIM domain- ZSWIM7 containing protein 7 binding⧪ cellular process, metabolic process, response to stimuli ⧪ estimated molecular function and biological process not formally assigned via PANTHER

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Table 3. Average counts, fold-change, and associated p value for all differentially expressed genes in the liver tissue discovery analysis between LiverHigh and LiverLow samples. Shaded rows highlight genes that are upregulated in snakes with high TTX resistance.

Liver Low Average Liver Mid Liver High Average log2 Fold- Gene ID Protein Counts Average Counts Counts Change Fold-Change Adj. p-value LOC100401646 (YBX1) nuclease-sensitive element-binding protein 1 0.00 0.00 1122.43 12.82 164.30 5.40E-21*** STON2 stonin-2 4.63 19.26 33.02 2.70 7.27 4.00E-05*** LOC106556117 (IGDCC4) immunoglobulin superfamily DCC subclass member 4-like 7.72 54.00 331.69 5.41 29.26 6.87E-05*** FBXW8 F-box only protein 8 48.47 56.03 112.80 1.23 1.51 8.66E-05*** LOC106554312 LON peptidase N-terminal domain and RING finger protein (LONRF1) 1-like 70.28 155.09 449.91 2.67 7.13 0.00010683*** LOC113428608 major histocompatibility complex class I-related gene (MR1) protein-like isoform X3 0.00 14.60 80.60 9.02 81.34 0.00010683*** SOD3 SOD3 0.00 9.39 28.09 7.50 56.22 0.00014295*** FAM169A soluble lamin-associated protein of 75 kDa 0.00 2.95 35.26 7.82 61.23 0.00017914*** Actb beta-actin 0.19 0.14 37.27 6.95 48.31 0.000221*** CCDC88A girdin isoform X2 15.80 37.26 73.09 2.23 4.99 0.00036713*** LOC113429913 (H2-Q9) H-2 class I histocompatibility antigen, Q9 alpha chain-like 5.18 30.44 78.03 4.02 16.13 0.00038669*** STX3 syntaxin-3 101.32 116.04 224.92 1.15 1.32 0.00060274*** PAIP2B polyadenylate-binding protein-interacting protein 2B 0.00 23.87 30.77 7.63 58.20 0.00095385*** ACER2 alkaline ceramidase 2 44.63 63.83 125.38 1.49 2.23 0.00102929** isocitrate dehydrogenase [NAD] subunit gamma, IDH3G mitochondrial 54.01 56.01 158.16 1.55 2.39 0.00107554** LOC113428546 (A2ML1) alpha-2-macroglobulin-like 32.42 220.60 135.98 2.07 4.28 0.00107554** MTRF1L peptide chain release factor 1, mitochondrial isoform X1 8.14 19.38 34.17 1.96 3.86 0.00152923** TMPRSS15 enteropeptidase 0.32 2.09 39.98 7.03 49.47 0.00226321** GM2A ganglioside GM2 activator 630.55 587.18 1480.71 1.23 1.52 0.00271088** LOC107287648 (ANGPTL4) angiopoietin-4-like 3.46 4.29 28.11 3.07 9.44 0.00337296** LOC113452151 (LBP) lipopolysaccharide-binding protein-like 42.68 90.32 200.80 2.23 4.99 0.00381086** CFLAR CASP8 and FADD-like apoptosis regulator 109.92 150.85 234.41 1.10 1.21 0.00440281** LOC113418988 bombesin receptor-activated protein C6orf89 homolog 228.82 297.50 538.97 1.24 1.53 0.00467585** LOC103063682 phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic (PIK3CA) subunit alpha isoform X1 26.41 67.62 84.08 1.65 2.71 0.005005** SLC16A9 monocarboxylate transporter 9 1.38 10.62 22.43 3.89 15.10 0.00524984** H2AZ2 Histone H2A.V 33.14 36.88 188.87 2.51 6.31 0.00602766** KDM6B Lysine-specific demethylase 6B 1201.40 1687.59 4483.65 1.90 3.61 0.00737548** LOC113445334 (HMCN1) hemicentin-1-like 8.27 27.48 75.96 3.17 10.04 0.00834607** RNPEP aminopeptidase B 19.17 36.70 62.99 1.72 2.95 0.00834607** ECE1 endothelin-converting enzyme 1 605.89 634.81 1248.00 1.04 1.09 0.0101785* KLF2 Krueppel-like factor 2 138.02 263.43 420.88 1.61 2.59 0.0103465* - cytochrome protein 4.23 7.59 68.12 4.07 16.53 0.01082531*

175

PRSS23 serine protease 23 21.07 50.31 148.35 2.80 7.85 0.01082531* SHMT1 serine hydroxymethyltransferase, cytosolic 9116.07 11827.36 25738.65 1.50 2.24 0.01096384* cyclic AMP-dependent transcription factor ATF-6 alpha ATF6 isoform X1 140.25 183.87 329.95 1.23 1.52 0.01195556* CCDC80 coiled-coil domain-containing protein 80 30.92 68.04 145.80 2.21 4.90 0.01195556* SLC16A7 Monocarboxylate transporter 2 8.80 24.99 47.91 2.44 5.98 0.01210389* SMAD3 mothers against decapentaplegic homolog 3 22.35 30.51 61.21 1.43 2.04 0.01229652* PEX13 peroxisome biogenesis factor 13 isoform X2 179.92 183.80 439.93 1.29 1.66 0.01336476* L1-RT L1-encoded reverse transcriptase-like protein 5.01 16.72 58.94 3.50 12.25 0.01357432* AEBP1 adipocyte enhancer-binding protein 1 isoform X2 240.36 281.78 760.70 1.66 2.75 0.01416903* LOC107300858 (PML) protein PML-like 34.24 128.07 297.13 3.12 9.71 0.01449793* unnamed (CVF) cobra venom factor 5.15 23.90 88.15 4.10 16.78 0.01511957* ZC3H12A ribonuclease 124.88 142.49 284.60 1.19 1.43 0.01559925* HSD11B1 corticosteroid 11-beta-dehydrogenase isozyme 1 21.11 112.20 243.74 3.54 12.51 0.01653231* unnamed (CVF) cobra venom factor 1 41.89 138.75 297.12 2.82 7.98 0.01682376* INPP1 inositol polyphosphate 1-phosphatase 14.98 25.11 45.82 1.56 2.43 0.01682376* LOC113412554 (PHLDA2) pleckstrin homology-like domain family A member 2 31.72 34.31 91.95 1.54 2.36 0.01722235* CYTH1 cytohesin-1 isoform X1 58.61 92.30 134.62 1.18 1.40 0.01764883* RGS11 regulator of G-protein signaling 11 3.95 12.05 28.66 2.80 7.85 0.01764883* CYTH3 cytohesin-3 87.76 117.32 266.18 1.60 2.57 0.01765123* LOC106556978 (Mrgprh) mas-related G-protein coupled receptor member H-like 57.12 106.09 348.35 2.61 6.79 0.01824903* CLN8 protein CLN8 158.39 217.46 389.28 1.29 1.66 0.01871966* CTSS cathepsin S 20.42 46.98 87.46 2.07 4.28 0.01928044* LOC103062262 (CYB561D2) cytochrome b561 domain-containing protein 2 53.19 67.17 168.93 1.67 2.78 0.01988534* SEPTIN5 septin-5 isoform X2 16.36 30.20 45.93 1.44 2.07 0.01988534* NFAT5 nuclear factor of activated T-cells 5 isoform X1 34.25 41.98 83.25 1.28 1.64 0.0200805* EBAG9 receptor-binding cancer antigen expressed on SiSo cells 11.48 17.87 43.99 1.91 3.65 0.02017282* SPRY1 protein sprouty homolog 1 30.31 55.20 84.05 1.48 2.18 0.02226688* RELB transcription factor RelB 13.88 31.62 54.40 1.96 3.83 0.02257798* TP63 tumor protein 63 isoform X1 1.31 0.81 56.05 5.25 27.61 0.02257798* CHD2 chromodomain-helicase-DNA-binding protein 2 306.55 351.65 619.75 1.02 1.03 0.02395601* LOC106548010 (TNFRSF10A) tumor necrosis factor receptor superfamily member 10A-like 24.70 40.15 70.31 1.48 2.18 0.02408096* SHROOM4 protein Shroom4 17.41 26.07 52.60 1.60 2.55 0.02468083* HCK tyrosine-protein kinase HCK isoform X1 68.59 204.77 358.89 2.39 5.70 0.02475002* LOC106555327 patr class I histocompatibility antigen, A-5 alpha chain-like 41.71 52.72 323.86 2.95 8.73 0.02475002* SPATA1 spermatogenesis-associated protein 1 isoform X2 2.57 35.40 80.20 4.92 24.21 0.02475002* WW domain-containing transcription regulator protein 1 WWTR1 isoform X1 40.81 53.76 103.64 1.32 1.74 0.02592087* ANK2 ankyrin-2 23.19 27.78 71.22 1.62 2.61 0.02627164* KEAP1 Kelch-like ECH-associated protein 1 177.14 191.39 440.12 1.31 1.72 0.02690564* PAPLN papilin isoform X1 87.50 184.22 476.75 2.45 6.00 0.02823313* HSD17B12 very-long-chain 3-oxoacyl-CoA reductase 1086.21 1252.37 2458.62 1.18 1.39 0.02898882*

176

LOC103064438 (ALOXE3) hydroperoxide isomerase 5.79 4.16 33.23 2.53 6.42 0.03016414* POGLUT1 protein O-glucosyltransferase 1 7.15 10.77 28.24 1.93 3.72 0.03111405* PIK3R6 phosphoinositide 3-kinase regulatory subunit 6 11.85 31.06 39.03 1.68 2.83 0.03126991* SLC43A2 large neutral amino acids transporter small subunit 4 31.77 46.11 118.22 1.89 3.58 0.03227367* MANBA beta-mannosidase isoform X1 26.48 23.90 65.06 1.30 1.70 0.03271269* PRDX6 peroxiredoxin-6 2296.73 3013.03 4884.22 1.09 1.19 0.03435817* GRK3 beta-adrenergic receptor kinase 2 1.56 4.77 21.80 3.68 13.51 0.03549604* COX3 cytochrome c oxidase subunit III 13.49 13.92 64.60 2.24 5.03 0.03845894* FGF7 Keratinocyte growth factor 48.69 62.24 126.44 1.35 1.84 0.03845894* LOC100093145 (H2AC20) histone H2A type 2-C 3.88 15.43 35.66 3.13 9.79 0.03882968* LOC106557325 (PLA2) phospholipase A2 inhibitor-like 128.80 159.68 345.89 1.43 2.05 0.04093008* EPHX1 epoxide hydrolase 1 66.74 382.27 509.93 2.93 8.60 0.04152756* LOC106557311 (SLIT3) slit homolog 3 protein-like 123.51 179.02 291.41 1.25 1.55 0.04152756* basement membrane-specific heparan sulfate proteoglycan HSPG2 core protein isoform X1 127.07 129.46 333.30 1.39 1.94 0.04192795* AKAP13 A-kinase anchor protein 13 155.17 188.42 321.30 1.04 1.09 0.04289775* ACOD1 cis-aconitate decarboxylase 53.45 150.17 485.06 3.18 10.11 0.0429821* UGP2 UTP--glucose-1-phosphate uridylyltransferase 868.29 1117.15 2036.14 1.23 1.51 0.0429821* LOC106550339 (MAOB) amine oxidase [flavin-containing] B-like 91.32 163.45 337.17 1.88 3.55 0.04354219* IL17REL putative interleukin-17 receptor E-like isoform X1 5.13 6.35 65.81 3.65 13.33 0.04387481* FYB1 FYN-binding protein isoform X1 115.90 213.80 459.82 1.99 3.95 0.0440682* EVI2B protein EVI2B 5.83 18.72 40.87 2.77 7.69 0.04615662* SLC30A1 zinc transporter 1 177.54 430.24 528.78 1.57 2.47 0.04713912* DLGAP4 disks large-associated protein 4 isoform X4 25.83 37.22 59.72 1.17 1.36 0.04735602* HMBOX1 homeobox-containing protein 1 isoform X5 49.56 78.30 139.91 1.48 2.21 0.04735602* LOC106548973 (BCAM) basal cell adhesion molecule-like 672.32 550.76 1830.62 1.45 2.09 0.04748818* LOC106554257 (TIGD3) tigger transposable element-derived protein 3-like 210.20 156.19 66.14 -1.66 2.76 1.49E-06*** LOC107300410 (ZNF260) zinc finger protein 260-like 153.70 97.87 67.78 -1.18 1.39 3.59E-06*** Srst T2 Octapeptide-repeat protein T2 304.64 229.32 115.76 -1.39 1.94 8.66E-05*** LOC106545890 (RNF19A) E3 ubiquitin-protein ligase RNF19A-like 3870.10 1936.41 577.07 -2.75 7.54 0.00010683*** SLC46A1 proton-coupled folate transporter 410.54 369.65 167.90 -1.28 1.65 0.00010683*** TSNARE1 t-SNARE domain-containing protein 1 80.09 60.10 37.05 -1.12 1.24 0.00010683*** LOC106548570 (RNF145) RING finger protein 145-like 731.56 662.28 364.57 -1.00 1.01 0.00015214*** LOC107286984 calcium release-activated calcium channel protein 1 isoform (ORAI1) X2 364.66 261.51 167.12 -1.13 1.28 0.0001828*** SWI/SNF-related matrix-associated actin-dependent regulator SMARCD3 of chromatin subfamily D member 3 isoform X1 272.93 222.08 116.92 -1.22 1.49 0.00046104*** MT-CYB cytochrome b (mitochondrion) 402194.07 267570.38 86578.52 -2.22 4.91 0.00051384***

177

ND3 NADH dehydrogenase subunit 3 (mitochondrion) 47035.79 27873.56 6738.29 -2.80 7.86 0.00057691*** COX3 cytochrome c oxidase subunit III 416645.80 267361.40 95666.96 -2.12 4.51 0.00095385*** THOP1 thimet oligopeptidase 811.10 717.41 315.52 -1.36 1.85 0.00101211** LOC106540558 (CFH) complement factor H-like isoform X1 48.32 35.61 4.98 -3.28 10.77 0.00107554** amyloid-beta A4 precursor protein-binding family B member APBB1 1 295.68 364.36 95.03 -1.63 2.67 0.00107601** LOC113449258 (TARS1) threonine--tRNA ligase, cytoplasmic-like 223.98 180.07 94.05 -1.25 1.57 0.00133446** LOC106540868 (Dennd1a) DENN domain-containing protein 1A-like 495.42 462.47 243.20 -1.03 1.06 0.00164656** VPS4A vacuolar protein sorting-associated protein 4A 740.84 592.41 355.69 -1.06 1.11 0.00179261** COX1 cytochrome c oxidase subunit I 1773543.89 1208596.13 459323.80 -1.95 3.80 0.0018474** LRRC47 leucine-rich repeat-containing protein 47 isoform X2 30.60 15.76 3.91 -2.93 8.60 0.0018474** STX12 syntaxin-12 67.88 57.13 29.84 -1.16 1.35 0.00215272** ND5 NADH dehydrogenase subunit 5 672780.61 435475.04 137101.22 -2.29 5.27 0.00215728** LOC114582726 (ATG16L1) autophagy-related protein 16-like isoform X7 152.01 137.13 44.89 -1.75 3.07 0.00222429** CCT7 T-complex protein 1 subunit eta 1126.00 922.83 561.48 -1.00 1.01 0.00233266** OPA3 optic atrophy 3 protein 150.78 134.63 46.98 -1.68 2.81 0.00233266** AUH methylglutaconyl-CoA hydratase, mitochondrial 123.45 56.88 48.93 -1.32 1.74 0.00271088** TCF25 transcription factor 25 536.67 565.91 253.71 -1.08 1.16 0.00271088** PRUNE1 exopolyphosphatase PRUNE1 643.05 544.84 272.86 -1.24 1.53 0.00295644** RPS19 ribosomal protein S19 874.56 714.75 262.21 -1.74 3.02 0.00295644** LOC106545435 glutathione S-transferase-like 5714.42 529.51 202.69 -4.82 23.21 0.00337296** BOLA1 bolA-like protein 1 728.73 554.11 304.22 -1.26 1.58 0.00367225** DPH1 diphthamide biosynthesis protein 1 179.36 145.77 61.83 -1.53 2.35 0.00375172** ATP5F1C ATP synthase subunit gamma, mitochondrial isoform X2 1412.43 1021.34 675.83 -1.06 1.13 0.00403268** - endonuclease-reverse transcriptase 310.56 275.24 93.51 -1.73 2.99 0.00440281** glutamyl-tRNA(Gln) amidotransferase subunit A, QRSL1 mitochondrial 158.02 128.97 70.51 -1.15 1.32 0.00462552** ND5 NADH dehydrogenase subunit 5 1099242.65 853388.74 305971.89 -1.85 3.40 0.00524984** AATF protein AATF 564.20 654.21 204.70 -1.46 2.14 0.00585784** DHX30 putative ATP-dependent RNA helicase DHX30 304.98 257.59 48.04 -2.66 7.10 0.00591751** NXN nucleoredoxin 118.44 84.99 54.59 -1.11 1.22 0.00737548** MYO19 unconventional myosin-XIX 318.90 247.36 132.21 -1.27 1.61 0.00831725** mitochondrial import inner membrane translocase subunit TIMM50 TIM50 607.18 507.46 255.67 -1.25 1.56 0.008661** HSP90AB1 heat shock protein HSP 90-beta 1402.32 850.89 334.66 -2.07 4.27 0.00911704** PELP1 proline-, glutamic acid- and leucine-rich protein 1 376.94 333.97 109.11 -1.79 3.19 0.00985026** PEX19 peroxisomal biogenesis factor 19 2245.13 2219.03 1087.54 -1.05 1.09 0.00998102** LOC106549491 (COX5A) cytochrome c oxidase subunit 5A, mitochondrial 3056.15 2745.37 1413.25 -1.11 1.24 0.0103465* DDX56 probable ATP-dependent RNA helicase DDX56 isoform X2 483.10 455.37 225.85 -1.10 1.20 0.01082531* LOC106553357 (ZNF585A) zinc finger protein 585A-like 365.02 363.18 151.92 -1.27 1.61 0.01082531* NDUFA1 NADH dehydrogenase subunit 1, partial (mitochondrion) 65290.66 40341.26 10813.88 -2.59 6.73 0.01082531* TRIM3 tripartite motif-containing protein 3 176.75 158.85 81.88 -1.11 1.22 0.01082531*

178

UBAP2L ubiquitin-associated protein 2-like 793.88 685.04 350.04 -1.18 1.39 0.01109983* RHBDD3 rhomboid domain-containing protein 3 38.54 29.28 10.21 -1.93 3.71 0.01126031* LOC106540358 (DNAJA1) dnaJ homolog subfamily A member 1-like 70.52 52.98 28.04 -1.33 1.77 0.01195555* LOC113450753 (TIGD1) tigger transposable element-derived protein 1-like 404.15 373.78 160.94 -1.33 1.76 0.01204879* LOC113448783 (DHRS4) dehydrogenase/reductase SDR family member 4-like 1149.31 851.86 481.02 -1.26 1.58 0.01205197* ABLIM2 actin-binding LIM protein 2 770.09 645.14 376.21 -1.03 1.07 0.01210389* RABL2A rab-like protein 2A 202.50 200.36 77.02 -1.39 1.93 0.01372889* PDCD2L programmed cell death protein 2 332.33 240.71 150.16 -1.14 1.31 0.01378666* OLA1 obg-like ATPase 1 isoform X1 267.42 221.27 129.21 -1.04 1.09 0.01425351* MRPS33 28S ribosomal protein S33, mitochondrial 86.97 65.68 35.93 -1.26 1.59 0.0157745* HGH1 protein HGH1 homolog 162.88 146.16 62.12 -1.39 1.92 0.01669279* H/ACA ribonucleoprotein complex non-core subunit NAF1 NAF1 isoform X2 125.85 113.74 55.51 -1.19 1.41 0.01871965* LOC106556569 (Bap18) chromatin complexes subunit BAP18 isoform X1 635.64 536.99 261.65 -1.28 1.64 0.01988534* NTAQ1 protein N-terminal glutamine amidohydrolase 121.42 114.28 42.50 -1.50 2.26 0.02225095 ZBTB48 telomere zinc finger-associated protein 37.92 41.14 11.15 -1.76 3.10 0.02225095* NDUFA1 NADH dehydrogenase subunit 1 (mitochondrion) 36033.36 19877.30 4955.96 -2.86 8.19 0.02257798* IFT46 intraflagellar transport protein 46 homolog isoform X2 164.67 154.45 74.99 -1.13 1.28 0.02417354* EXOSC4 exosome complex component RRP41 418.44 339.88 158.68 -1.40 1.95 0.02475002* LOC106547299 general transcription factor II-I repeat domain-containing (GTF2IRD1) protein 2-like 715.44 491.56 251.43 -1.51 2.28 0.02475002* WDR46 WD repeat-containing protein 46 444.37 398.82 192.03 -1.21 1.46 0.02475002* PPOX protoporphyrinogen oxidase isoform X1 95.69 88.83 36.79 -1.36 1.86 0.02594235* CDC7 cell division cycle 7-related protein kinase 240.60 189.14 112.55 -1.10 1.20 0.02613454* LOC106547150 (ZNF250) zinc finger protein 250 468.34 367.76 162.00 -1.53 2.34 0.02746511* IAH1 isoamyl acetate-hydrolyzing esterase 1 homolog 134.40 90.45 51.96 -1.36 1.85 0.02785361* TMEM159 promethin 159.30 153.69 35.99 -2.14 4.58 0.02785361* INO80 DNA helicase 56.86 51.16 20.95 -1.43 2.04 0.02870783* TAF3 transcription initiation factor TFIID subunit 3 237.48 189.77 113.06 -1.07 1.14 0.03096236* PPP1R13L relA-associated inhibitor 74.84 61.12 28.31 -1.40 1.95 0.03389346* SAC3D1 SAC3 domain-containing protein 1 31.40 30.90 9.32 -1.74 3.04 0.03421929* SPAG7 sperm-associated antigen 7 861.44 842.70 425.82 -1.02 1.03 0.03462573* PLPP1 phospholipid phosphatase 1 isoform X1 34.40 35.45 8.41 -2.03 4.10 0.03565982* NOP56 nucleolar protein 56 1281.37 1119.68 530.62 -1.27 1.62 0.03795258* RASSF2 ras association domain-containing protein 2 isoform 61.70 49.22 30.34 -1.02 1.05 0.03826536* LOC106544999 (Prkcb) protein kinase C beta type 1091.89 1180.57 467.12 -1.23 1.50 0.0383459* LOC112541713 (MCT10) monocarboxylate transporter 10-like 55.24 39.62 7.52 -2.87 8.21 0.04045973* DGAT1 diacylglycerol O-acyltransferase 1 407.20 350.22 185.16 -1.14 1.29 0.04152756* STARD10 PCTP-like protein 94.19 71.11 31.55 -1.57 2.47 0.04152756* UFL1 E3 UFM1-protein ligase 1 52.25 50.79 2.66 -4.26 18.17 0.04152756* ZBTB22 zinc finger and BTB domain-containing protein 22 78.46 66.81 28.55 -1.45 2.09 0.0429821*

179

INTS13 integrator complex subunit 13 isoform X1 222.98 193.16 106.21 -1.06 1.13 0.04354219* LOC113430508 (SH2B3) SH2B adapter protein 3-like 133.64 107.60 64.49 -1.05 1.11 0.0440682* EEF1G Elongation factor 1-gamma 1199.29 966.41 468.45 -1.36 1.84 0.04430459* TATA box-binding protein-associated factor RNA TAF1C polymerase I subunit C 99.62 99.44 26.81 -1.89 3.58 0.04615662* *** p<0.001, ** p<0.01, * p<0.05

180

Table 4. List of differentially expressed genes and proteins in the liver tissue discovery analysis between LiverHigh and LiverLow samples. Molecular function and biological process were categorized by PANTHER classification system software (Version 14).

GeneID Protein Molecular Function Biological Process - cytochrome protein catalytic activity⧪, transporter activity⧪ cellular process⧪, localization⧪, metabolic process⧪ - endonuclease-reverse transcriptase catalytic activity⧪ metabolic process⧪, cellular process* binding, catalytic activity, molecular function A2ML1 Alpha-2-macroglobulin-like protein 1 regulator metabolic process⧪, biological regulation⧪ AATF Protein AATF binding⧪ response to stimuli⧪, ABLIM2 Actin-binding LIM protein 2 binding cellular component organization or biogenesis, cellular process ACER2 Alkaline ceramidase 2 binding⧪, catalytic activity⧪ biological adhesion⧪, reproductive process⧪ ACOD1 Cis-aconitate decarboxylase catalytic activity response to stimuli cellular component organization or biogenesis, cellular process, developmental process, localization, locomotion, multicellular Actb Actin, cytoplasmic 1 binding, structural molecule activity organismal process AEBP1 Adipocyte enhancer-binding protein 1 catalytic activity, transcription regulator activity cellular process, metabolic process structural molecule activity⧪, molecular function AKAP13 A-kinase anchor protein 13 regulator⧪ signaling⧪ ALOXE3 Hydroperoxide isomerase ALOXE3 binding⧪, catalytic activity⧪ metabolic process⧪ ANGPTL4 Angiopoietin-related protein 4 binding⧪ developmental process⧪, growth⧪ ANK2 Ankyrin-2 binding localization Amyloid-beta A4 precursor protein-binding APBB1 family B member 1 binding biological regulation, cellular process, metabolic process Cyclic AMP-dependent transcription factor ATF- ATF6 6 alpha transcription regulator activity⧪, binding⧪ biological regulation⧪ multi-organism process, cellular component organization or biogenesis, ATG16L1 Autophagy-related protein 16-1 binding⧪, catalytic activity⧪ metabolic process, cellular process ATP5F1C ATP synthase subunit gamma, mitochondrial catalytic activity, transporter activity localization, metabolic process, cellular process AUH Methylglutaconyl-CoA hydratase, mitochondrial catalytic activity cellular process, metabolic process Bap18 Chromatin complexes subunit BAP18 binding⧪ cellular process⧪ BCAM Basal cell adhesion molecule binding⧪ biological adhesion⧪ BOLA1 BolA-like protein 1 binding⧪ CCDC80 Coiled-coil domain-containing protein 80 binding⧪ cellular component organization or biogenesis, cellular process, CCDC88A Girdin binding localization CCT7 T-complex protein 1 subunit eta binding⧪ cellular process⧪ CDC7 Cell division cycle 7-related protein kinase catalytic activity cellular process, metabolic process, response to stimuli CFH Complement factor H binding⧪ immune system process⧪ CFLAR CASP8 and FADD-like apoptosis regulator catalytic activity biological regulation, cellular process, metabolic process biological adhesion, cellular component organization or biogenesis, cellular process, developmental process, locomotion, multicellular CHD2 Chromodomain-helicase-DNA-binding protein 2 binding organismal process, response to stimuli CLN8 Protein CLN8 binding⧪ metabolic process⧪

181

COX1 Cytochrome c oxidase subunit 1 catalytic activity, transporter activity cellular process, localization, metabolic process COX3 Cytochrome c oxidase subunit 3 catalytic activity*⧪, transporter activity cellular process⧪, localization⧪, metabolic process⧪ COX5A Cytochrome c oxidase subunit 5A, mitochondrial catalytic activity, transporter activity response to stimuli, localization, metabolic process, cellular process immune system process, response to stimuli, metabolic process, CTSS Cathepsin S catalytic activity cellular process CYB561D2 Cytochrome b561 domain-containing protein 2 binding, catalytic activity cellular process⧪, localization⧪, metabolic process⧪ CYTH1 Cytohesin-1 binding⧪ biological adhesion⧪, signaling⧪ CYTH3 Cytohesin-3 binding⧪ biological adhesion⧪, signaling⧪ DDX56 Probable ATP-dependent RNA helicase DDX56 binding⧪, translation regulator activity⧪ biological regulation⧪, cellular process⧪ cellular component organization or biogenesis, localization, cellular Dennd1a DENN domain-containing protein 1A binding, molecular function regulator process DGAT1 Diacylglycerol O-acyltransferase 1 catalytic activity metabolic process, cellular process DHRS4 Dehydrogenase/reductase SDR family member 4 catalytic activity⧪ metabolic process⧪ DHX30 ATP-dependent RNA helicase DHX30 binding biological regulation⧪, cellular process⧪ DLGAP4 Disks large-associated protein 4 binding⧪ signaling⧪ DNAJA1 DnaJ homolog subfamily A member 1 binding⧪ signaling⧪ 2-(3-amino-3-carboxypropyl)histidine synthase DPH1 subunit 1 binding⧪ biological regulation, cellular process, metabolic process Receptor-binding cancer antigen expressed on EBAG9 SiSo cells binding⧪ cellular process⧪, immune system process⧪ ECE1 Endothelin-converting enzyme 1 binding⧪, catalytic activity⧪ signaling⧪, response to stimuli⧪, developmental process⧪ EEF1G Elongation factor 1-gamma binding⧪, translation regulator activity⧪ metabolic process, cellular process EPHX1 Epoxide hydrolase 1 catalytic activity⧪ metabolic process⧪ EVI2B Protein EVI2B binding⧪ immune system process⧪ EXOSC4 Exosome complex component RRP41 binding⧪, molecular function regulator⧪ biological regulation, cellular process, metabolic process FAM169A Soluble lamin-associated protein of 75 kDa binding⧪ FBXW8 F-box/WD repeat-containing protein 8 binding⧪ metabolic process⧪ FGF7 Fibroblast growth factor 7 binding⧪ growth⧪, signaling⧪ biological regulation, cellular process, immune system process, FYB1 FYN-binding protein 1 binding⧪ localization, response to stimuli, signaling GM2A Ganglioside GM2 activator catalytic activity⧪ signaling⧪, metabolic process⧪ GRK3 Beta-adrenergic receptor kinase 2 binding⧪ signaling⧪ General transcription factor II-I repeat domain- GTF2IRD1 containing protein 1 binding⧪, transcription regulator activity⧪ cellular process⧪, biological regulation⧪ H-2 class I histocompatibility antigen, Q9 alpha H2-Q9 chain-like binding⧪ immune system process⧪ H2AC20 histone H2A type 2-C binding⧪ growth⧪, biological regulation⧪, cellular process⧪ H2AZ2 Histone H2A.V binding⧪ growth⧪, biological regulation⧪, cellular process⧪ biological regulation, cell population proliferation, developmental process, response to stimuli, signaling, metabolic process, cellular HCK Tyrosine-protein kinase HCK binding, catalytic activity process transcription regulator activity⧪, molecular HGH1 Protein HGH1 homolog transducer activity⧪ growth⧪, signaling⧪

182

biological regulation, cellular component organization or biogenesis, HMBOX1 Homeobox-containing protein 1 binding metabolic process, cellular process HMCN1 Hemicentin-1 binding⧪, structural molecule activity⧪ biological adhesion⧪, growth⧪, cell population proliferation⧪ HSD11B1 Corticosteroid 11-beta-dehydrogenase isozyme 1 binding, catalytic activity metabolic process HSD17B12 Very-long-chain 3-oxoacyl-CoA reductase binding⧪, catalytic activity⧪ metabolic process⧪ HSP90AB1 Heat shock protein HSP 90-beta binding biological regulation, cellular process, response to stimuli Basement membrane-specific heparan sulfate HSPG2 proteoglycan core protein binding⧪, structural molecule activity⧪ developmental process, multicellular organismal process IAH1 Isoamyl acetate-hydrolyzing esterase 1 homolog binding⧪, catalytic activity⧪ metabolic process⧪ Isocitrate dehydrogenase [NAD] subunit gamma, IDH3G mitochondrial catalytic activity cellular process, metabolic process cellular component organization or biogenesis, cellular process, IFT46 Intraflagellar transport protein 46 homolog transporter activity⧪, binding⧪ localization immunoglobulin superfamily DCC subclass IGDCC4 member 4-like binding⧪ immune system process⧪ IL17REL Putative interleukin-17 receptor E-like binding, molecular transducer activity signaling⧪ biological regulation, cellular component organization or biogenesis, INO80 Chromatin-remodeling ATPase INO80 binding, catalytic activity cellular process, metabolic process, response to stimuli INPP1 Inositol polyphosphate 1-phosphatase catalytic activity cellular process, metabolic process INTS13 Integrator complex subunit 13 binding⧪ localization⧪, reproductive process⧪, developmental process⧪ KDM6B Lysine-specific demethylase 6B binding, catalytic activity biological regulation⧪ KEAP1 Kelch-like ECH-associated protein 1 binding⧪ cellular process⧪, metabolic process⧪, developmental process⧪ KLF2 Krueppel-like factor 2 transcription regulator activity biological regulation, cellular process, metabolic process L1-RT L1-encoded reverse transcriptase-like protein catalytic activity⧪ metabolic process⧪, cellular process⧪ immune system process, multi-organism process, multicellular organismal process, biological regulation, signaling, response to LBP Lipopolysaccharide-binding protein binding stimuli, metabolic process, cellular process LOC10654543 5 glutathione S-transferase-like catalytic activity⧪ metabolic process⧪ LOC10655532 patr class I histocompatibility antigen, A-5 alpha 7 chain-like binding immune system process⧪ LOC11341898 bombesin receptor-activated protein C6orf89 8 homolog binding⧪ biological regulation⧪, cellular process⧪, growth⧪ LON peptidase N-terminal domain and RING LONRF1 finger protein 1 catalytic activity⧪ cellular process⧪, metabolic process⧪ LRRC47 Leucine-rich repeat-containing protein 47 catalytic activity metabolic process, cellular process MANBA Beta-mannosidase catalytic activity cellular process, metabolic process MAOB Amine oxidase [flavin-containing] B catalytic activity⧪ metabolic process⧪ MCT10 Monocarboxylate transporter 10 transporter activity⧪ localization⧪ Major histocompatibility complex class I-related MR1 gene protein binding⧪ immune system process⧪ mas-related G-protein coupled receptor member Mrgprh H-like binding⧪ signaling⧪

183

MRPS33 28S ribosomal protein S33, mitochondrial binding⧪, translation regulator activity⧪ biological regulation⧪ MT-CYB Cytochrome b catalytic activity⧪, transporter activity⧪ cellular process⧪, localization⧪, metabolic process⧪ cellular component organization or biogenesis, metabolic process, MTRF1L Peptide chain release factor 1-like, mitochondrial binding⧪, translation regulatory activity* cellular process MYO19 Unconventional myosin-XIX structural molecule activity⧪ locomotion⧪, localization⧪ H/ACA ribonucleoprotein complex non-core NAF1 subunit NAF1 binding cellular component organization or biogenesis, cellular process ND3 NADH-ubiquinone oxidoreductase chain 3 catalytic activity⧪ metabolic process⧪ ND5 NADH-ubiquinone oxidoreductase chain 5 catalytic activity⧪ metabolic process⧪ NADH dehydrogenase [ubiquinone] 1 alpha NDUFA1 subcomplex subunit 1 catalytic activity⧪ metabolic process⧪ biological regulation, multicellular organismal process, response to NFAT5 Nuclear factor of activated T-cells 5 binding, transcription regulator activity stimuli, signaling, metabolic process, cellular process NOP56 Nucleolar protein 56 binding NTAQ1 Protein N-terminal glutamine amidohydrolase catalytic activity metabolic process, cellular process NXN Nucleoredoxin catalytic activity⧪ cellular process⧪, metabolic process⧪, signaling⧪ OLA1 Obg-like ATPase 1 catalytic activity metabolic process⧪ OPA3 Optic atrophy 3 protein biological regulation, metabolic process Calcium release-activated calcium channel protein ORAI1 1 transporter activity localization Polyadenylate-binding protein-interacting protein PAIP2B 2B binding⧪ cellular process⧪ PAPLN Papilin structural molecule activity⧪ PDCD2L Programmed cell death protein 2-like binding⧪ cellular process⧪ PELP1 Proline-, glutamic acid- and leucine-rich protein 1 binding*⧪ cellular process⧪ cellular component organization or biogenesis, localization, cellular PEX13 Peroxisomal membrane protein PEX13 binding⧪ process cellular component organization or biogenesis⧪, localization⧪, cellular PEX19 Peroxisomal biogenesis factor 19 binding⧪ process⧪ Pleckstrin homology-like domain family A reproduction, reproductive process, developmental process, PHLDA2 member 2 binding⧪ multicellular organismal process Phosphatidylinositol 4,5-bisphosphate 3-kinase locomotion, signaling, response to stimuli, localization, metabolic PIK3CA catalytic subunit alpha isoform catalytic activity process, cellular process binding, catalytic activity, molecular function biological regulation, cellular process, metabolic process, response to PIK3R6 Phosphoinositide 3-kinase regulatory subunit 6 regulation stimuli, signaling PLA2 Phospholipase A2 catalytic activity⧪ metabolic process⧪, cellular process⧪ PLPP1 Phospholipid phosphatase 1 catalytic activity metabolic process⧪, cellular process⧪ PML Protein PML binding⧪, transcription regulator activity⧪ growth⧪, cellular process⧪ POGLUT1 Protein O-glucosyltransferase 1 catalytic activity⧪ metabolic process⧪ binding, catalytic activity, molecular function behavior, biological regulation, localization, multicellular organismal PPOX Protoporphyrinogen oxidase regulation process, response to stimuli, signaling, cellular process PPP1R13L RelA-associated inhibitor binding⧪, transcription regulator activity⧪ biological regulation⧪, cellular process⧪ PRDX6 Peroxiredoxin-6 binding⧪, catalytic activity⧪ biological regulation, cellular process

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biological regulation, signaling, response to stimuli, metabolic process, Prkcb Protein kinase C beta type catalytic activity cellular process PRSS23 Serine protease 23 binding⧪, catalytic activity⧪ metabolic process⧪ PRUNE1 Exopolyphosphatase PRUNE1 catalytic activity cellular process, metabolic process Glutamyl-tRNA(Gln) amidotransferase subunit A, QRSL1 mitochondrial binding⧪, catalytic activity⧪ biological regulation⧪, cellular process⧪, metabolic activity⧪ biological regulation, localization, response to stimuli, signaling, RABL2A Rab-like protein 2A catalytic activity cellular process RASSF2 Ras association domain-containing protein 2 binding⧪, catalytic activity⧪ metabolic process⧪, growth* biological regulation, immune system process, response to stimuli, RELB Transcription factor RelB binding, transcription regulator activity signaling, multi-organism process, metabolic process, cellular process RGS11 Regulator of G-protein signaling 11 catalytic activity⧪ signaling⧪ RHBDD3 Rhomboid domain-containing protein 3 catalytic activity metabolic process⧪ RNF145 RING finger protein 145 catalytic activity cellular process⧪, metabolic process⧪ RNF19A E3 ubiquitin-protein ligase RNF19A binding, catalytic activity biological regulation, metabolic process, cellular process RNPEP Aminopeptidase B catalytic activity⧪, binding⧪ metabolic process⧪ RPS19 40S ribosomal protein S19 binding, structural molecule activity cellular component organization or biogenesis, cellular process SAC3D1 SAC3 domain-containing protein 1 binding⧪ localization, metabolic process SH2B3 SH2B adapter protein 3 binding biological regulation, signaling, response to stimuli, cellular process binding, catalytic activity, translation regulator biological regulation, cellular component organization or biogenesis, SHMT1 Serine hydroxymethyltransferase, cytosolic activity metabolic process, response to stimuli, cellular process SHROOM4 Protein Shroom4 binding cellular component organization or biogenesis, cellular process SLC16A7 Monocarboxylate transporter 2 transporter activity localization SLC16A9 Monocarboxylate transporter 9 transporter activity localization SLC30A1 Zinc transporter 1 transporter activity⧪ localization⧪ Large neutral amino acids transporter small SLC43A2 subunit 4 transporter activity localization SLC46A1 Proton-coupled folate transporter transporter activity⧪ localization⧪ locomotion, developmental process, multicellular organismal process, SLIT3 Slit homolog 3 protein binding response to stimuli, cellular process SMAD3 Mothers against decapentaplegic homolog 3 binding⧪, transcription regulator activity⧪ cell population proliferation⧪, growth⧪, SWI/SNF-related matrix-associated actin- dependent regulator of chromatin subfamily D SMARCD3 member 3 binding⧪, transcription regulator activity⧪ metabolic process⧪, growth⧪, developmental process⧪ SOD3 Extracellular superoxide dismutase [Cu-Zn] binding, catalytic activity cellular process, metabolic process, response to stimuli SPAG7 Sperm-associated antigen 7 binding⧪ reproductive process⧪ SPATA1 spermatogenesis-associated protein 1 isoform X2 binding⧪ reproductive process⧪ biological regulation, cellular process, developmental process, metabolic process, multicellular organismal process, response to SPRY1 Protein sprouty homolog 1 binding⧪ stimuli, signaling STARD10 START domain-containing protein 10 binding⧪ signaling⧪ biological regulation, cellular component organization or biogenesis, STON2 Stonin-2 binding⧪ cellular process, localization

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cellular component organization or biogenesis, localization, cellular STX12 Syntaxin-12 binding process biological regulation, cellular component organization or biogenesis, STX3 Syntaxin-3 binding localization, signaling, cellular process TATA box-binding protein-associated factor RNA TAF1C polymerase I subunit C binding biological regulation⧪ TAF3 Transcription initiation factor TFIID subunit 3 transcription regulator activity⧪ biological regulation⧪ TARS1 threonine--tRNA ligase, cytoplasmic-like translation regulatory activity⧪ biological regulation⧪ TCF25 Transcription factor 25 transcription regulator activity⧪ biological regulation⧪ THOP1 Thimet oligopeptidase catalytic activity metabolic process, cellular process TIGD1 Tigger transposable element-derived protein 1 binding⧪ TIGD3 Tigger transposable element-derived protein 3 binding⧪ Mitochondrial import inner membrane translocase cellular component organization or biogenesis, localization, cellular TIMM50 subunit TIM50 catalytic activity process TMEM159 Promethin binding⧪ metabolic process⧪ TMPRSS15 Enteropeptidase binding⧪ metabolic process⧪ Tumor necrosis factor receptor superfamily TNFRSF10A member 10A biding⧪, transcription regulator activity⧪ biological regulation, signaling, response to stimuli, cellular process growth⧪, metabolic process⧪, reproductive process⧪, developmental TP63 Tumor protein 63 biding⧪, transcription regulator activity⧪ process⧪ TRIM3 Tripartite motif-containing protein 3 catalytic activity cellular process, metabolic process cellular component organization or biogenesis, cellular process, TSNARE1 t-SNARE domain-containing protein 1 binding localization UBAP2L Ubiquitin-associated protein 2-like binding⧪ metabolic process⧪ biological regulation, cellular process, metabolic process, response to UFL1 E3 UFM1-protein ligase 1 catalytic activity stimuli UGP2 UTP--glucose-1-phosphate uridylyltransferase binding⧪ metabolic process⧪ unnamed (CVF) cobra venom factor binding⧪ immune system process⧪ unnamed (CVF) cobra venom factor 1 binding⧪ immune system process⧪ VPS4A Vacuolar protein sorting-associated protein 4A binding⧪ metabolic process⧪ cellular component organization or biogenesis, cellular process, WDR46 WD repeat-containing protein 46 binding⧪ metabolic process WW domain-containing transcription regulator biological regulation, metabolic process, response to stimuli, signaling, WWTR1 protein 1 transcription regulator activity cellular process YBX1 Nuclease-sensitive element-binding protein 1 binding⧪, transcription regulator activity⧪ response to stimuli⧪ Zinc finger and BTB domain-containing protein ZBTB22 22 binding⧪ cellular process⧪, metabolic process⧪, response to stimuli⧪ ZBTB48 Telomere zinc finger-associated protein binding⧪ cellular process⧪, metabolic process⧪, response to stimuli⧪ ZC3H12A Endoribonuclease ZC3H12A binding⧪ cellular process⧪, metabolic process⧪, response to stimuli⧪ ZNF250 Zinc finger protein 250 binding⧪ cellular process⧪, metabolic process⧪, response to stimuli⧪ ZNF260 Zinc finger protein 260 binding⧪ cellular process⧪, metabolic process⧪, response to stimuli⧪

186

ZNF585A Zinc finger protein 585A binding⧪ cellular process⧪, metabolic process⧪, response to stimuli⧪ ⧪ estimated molecular function and biological process not formally assigned via PANTHER

187

Table 5. Sodium channel genes (SCN) expressed in muscle and liver tissue across all three phenotypic groups (low resistance, middle resistance, and high resistance).

Muscle High vs. Muscle Low Muscle Low Muscle Mid Muscle High log2 Fold- Adj. p- Gene ID Protein Average Counts Average Counts Average Counts Change Fold-Change value

Nav1.4 alpha SCN4A subunit 3281.88 2989.97 2757.75 -0.25 0.84 1.0000 Nav1.5 alpha subunit SCN5A transcript1 270.01 291.55 205.64 -0.39 0.76 1.0000 Nav1.5 alpha subunit SCN5A transcript2 81.80 123.74 144.81 0.83 1.78 1.0000 sodium channel SCN2B beta 2 subunit 1664.87 1586.46 1163.20 -0.52 0.70 1.0000 sodium channel SCN4B beta 4 subunit 528.38 603.91 268.19 -0.98 0.51 1.0000

Liver High vs. Liver Low Liver Low Liver Mid Liver High log2 Fold- Adj. p- Gene ID Protein Average Counts Average Counts Average Counts Change Fold-Change value

Nav1.4 alpha SCN4A subunit 0.95 8.22 0.56 -0.69 0.62 0.9599 Nav1.5 alpha subunit SCN5A transcript1 5031.08 3501.13 4595.76 -0.13 0.91 0.9542 Nav1.5 alpha subunit SCN5A transcript2 22.39 10.57 37.38 0.75 1.68 0.6448 sodium channel SCN2B beta 2 subunit 3418.63 3077.04 2773.71 -0.30 0.81 0.8108 sodium channel SCN4B beta 4 subunit 1.26 0.44 1.54 0.47 1.38 0.9574

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Supplemental Table 1. Collection and phenotypic information on 12 Thamnophis couchii snakes used in experiment

RNA-seq Collector ID, Museum TTX Resistance Phenotypic Experimental Voucher Locality County, State Sex SVL (cm) Mass (g) (MAMU) Group* Design JSR007, MuscleLow, UNR:Herp:10008 Pinecrest Lake Tuolumne, CA F 53 36 2.9 low LiverLow CRF3220, Kibbie Ridge, above MuscleLow, UNR:Herp:09974 Cherry Creek Tuolumne, CA F 55 75 8.1 low LiverLow CRF3308, Kibbie Ridge, above MuscleLow, UNR:Herp:10027 Cherry Creek Tuolumne, CA F 54 40 12.2 low LiverLow CRF3063, MuscleMid, UNR:Herp:09995 Cherry Creek Tuolumne, CA F 60 101 17.2 mid LiverMid CRF3307, Kibbie Ridge, above MuscleMid, UNR:Herp:09952 Cherry Creek Tuolumne, CA F 52 45 21.3 mid LiverMid JSR009, MuscleMid, UNR:Herp:09990 Pinecrest Lake Tuolumne, CA M 39 24 27 mid LiverMid CRF3067, MuscleMid, UNR:Herp:10001 Cherry Creek Tuolumne, CA F 48 42 28.8 mid LiverMid CRF3306, Kibbie Ridge, above MuscleMid, UNR:Herp:09986 Cherry Creek Tuolumne, CA F 51 33 36.40 mid LiverMid EJE181, MuscleHigh, UNR:Herp:10009 S. Fork Tuolumne River Tuolumne, CA F 29 9 56 high LiverHigh EJE187, MuscleHigh, UNR:Herp:09966 S. Fork Tuolumne River Tuolumne, CA F 36 20 66.6 high LiverHigh EJE184, MuscleHigh, UNR:Herp:09998 S. Fork Tuolumne River Tuolumne, CA F 35 24 85.1 high LiverHigh EJE188, MuscleHigh, UNR:Herp:09993 S. Fork Tuolumne River Tuolumne, CA F 39 31 101.4 high LiverHigh *Low 0-15 MAMU, Mid 15-50 MAMU, High >50 MAMU

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Supplemental Table 2. List of differentially expressed genes in the muscle tissue discovery analysis. Specific molecular functions and biological processes were categorized using the GO complete datasets in PANTHER (Version 14).

Gene ID Protein Molecular Function Biological Process acyl-coenzyme A acyl-CoA metabolic process; protein homotetramerization; negative regulation of cold-induced ACOT13 thioesterase 13 protein binding; acyl-CoA hydrolase activity thermogenesis regulation of cyclin-dependent protein serine/threonine kinase activity; morphogenesis of a polarized epithelium; retina homeostasis; establishment or maintenance of cell polarity); axonogenesis; protein deubiquitination; substantia nigra development; regulation of transmembrane transporter activity; negative regulation of protein binding; cell junction assembly; adherens junction assembly; maintenance of blood- RNA polymerase II cis-regulatory region sequence-specific brain barrier; Fc-gamma receptor signaling pathway involved in phagocytosis; ATP-dependent chromatin DNA binding; structural constituent of cytoskeleton; protein remodeling; apical protein localization; positive regulation of gene expression, epigenetic; ephrin receptor binding; ATP binding; kinesin binding; protein kinase binding; signaling pathway; synaptic vesicle endocytosis; cell motility; regulation of norepinephrine uptake; Tat protein binding; nucleosomal DNA binding; identical protein positive regulation of norepinephrine uptake; membrane organization; platelet aggregation; protein binding; tau protein binding; nitric-oxide synthase binding; localization to adherens junction; cellular response to cytochalasin B; postsynaptic actin cytoskeleton ACTB beta-actin structural constituent of postsynaptic actin cytoskeleton organization; regulation of transepithelial transport; regulation of protein localization to plasma membrane platelet degranulation; endoplasmic reticulum to Golgi vesicle-mediated transport; blood coagulation; blood circulation; post-translational protein modification; cellular protein metabolic process; COPII vesicle F5 coagulation factor V copper ion binding; protein binding coating FAM83G FAM83G protein protein binding; protein kinase binding signal transduction; BMP signaling pathway cytokine production involved in immune response; antigen processing and presentation of peptide antigen via MHC class I; positive regulation of T cell mediated cytotoxicity directed against tumor cell target; major histocompatibility immune response; antigen processing and presentation of exogenous antigen;interleukin-1 beta complex class I-related protein binding; beta-2-microglobulin binding; MHC class I production;interleukin-17 production; T cell differentiation in thymus; innate immune response; defense MR1 gene protein receptor activity; T cell receptor binding response to Gram-negative bacterium; defense response to Gram-positive bacterium 39S ribosomal protein RNA binding; structural constituent of ribosome; protein ribosomal large subunit assembly; translation; mitochondrial translational elongation; mitochondrial MRPL11 L11, mitochondrial binding; rRNA binding; large ribosomal subunit rRNA binding translational termination regulation of amyloid precursor protein biosynthetic process; positive regulation of adenosine receptor calcium ion binding; protein binding; A2A adenosine receptor signaling pathway; positive regulation of ERK1 and ERK2 cascade; positive regulation of glutamate N-terminal EF-hand binding; type 5 metabotropic glutamate receptor binding; receptor signaling pathway; negative regulation of G protein-coupled receptor internalization; positive NECAB2 calcium-binding protein 2 identical protein binding regulation of protein localization to membrane negative regulation of transcription by RNA polymerase II; negative regulation of B cell apoptotic process; chromatin binding; transcription corepressor activity; RNA apoptotic process; chromatin assembly; cellular response to UV; negative regulation of histone acetylation; nucleolar complex binding; protein binding; nucleosome binding; histone binding; ribosomal large subunit biogenesis; regulation of signal transduction by p53 class mediator; negative NOC2L protein 2 homolog repressing transcription factor binding regulation of intrinsic apoptotic signaling pathway polyadenylate-binding protein-interacting translation repressor activity; mRNA regulatory element binding; PAIP2B protein protein binding negative regulation of translation; negative regulation of translational initiation angiogenesis; cytokine production; adaptive immune response; dendritic cell chemotaxis; positive regulation of acute inflammatory response; respiratory burst involved in defense response; protein phosphorylation; phosphatidylinositol biosynthetic process; endocytosis; inflammatory response; G protein kinase activity; protein serine/threonine kinase activity; protein-coupled receptor signaling pathway; positive regulation of cytosolic calcium ion concentration; T protein binding; ATP binding; kinase activity; 1- cell chemotaxis; negative regulation of triglyceride catabolic process; phosphatidylinositol 3-kinase phosphatidylinositol-3-kinase activity; phosphatidylinositol 3- signaling; positive regulation of phosphatidylinositol 3-kinase signaling; phosphorylation; cell migration; kinase activity; 1-phosphatidylinositol-4-phosphate 3-kinase platelet activation; neutrophil chemotaxis; secretory granule localization; regulation of cell adhesion activity; identical protein binding; ephrin receptor binding; mediated by integrin; natural killer cell chemotaxis;phosphatidylinositol-3-phosphate biosynthetic process; phosphatidylinositol 4,5- phosphatidylinositol-4,5-bisphosphate 3-kinase activity; T cell proliferation; T cell activation; mast cell degranulation; positive regulation of MAP kinase activity; bisphosphate 3-kinase phosphatidylinositol kinase activity; phosphatidylinositol-3,4- innate immune response; phosphatidylinositol phosphorylation; phosphatidylinositol-mediated signaling; Pik3cg catalytic subunit gamma bisphosphate 5-kinase activity positive regulation of protein kinase B signaling; negative regulation of cardiac muscle contraction; platelet

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aggregation; cellular response to cAMP; neutrophil extravasation; hepatocyte apoptotic process; regulation of calcium ion transmembrane transport; negative regulation of fibroblast apoptotic process glycogen metabolic process; regulation of glycolytic process; protein phosphorylation; negative regulation of protein kinase activity; fatty acid biosynthetic process; ATP biosynthetic process; carnitine shuttle; cell cycle arrest; positive regulation of peptidyl-threonine phosphorylation; sterol biosynthetic process; AMP-activated protein kinase activity; cAMP-dependent protein macroautophagy; regulation of macroautophagy; regulation of fatty acid metabolic process; activation of kinase inhibitor activity; ATP binding; cAMP-dependent protein protein kinase activity; intracellular signal transduction; cellular response to glucose starvation; regulation kinase regulator activity; phosphorylase kinase regulator activity; of fatty acid biosynthetic process; positive regulation of protein kinase activity; regulation of fatty acid AMP binding; protein kinase regulator activity; protein kinase oxidation; regulation of glucose import; regulation of catalytic activity; regulation of protein 5'-AMP-activated protein binding; protein kinase activator activity; adenyl ribonucleotide serine/threonine kinase activity; regulation of signal transduction by p53 class mediator; negative Prkag2 kinase subunit gamma-2 binding; ADP binding regulation of cAMP-dependent protein kinase activity exopolyphosphatase activity; inorganic diphosphatase activity; exopolyphosphatase protein binding; tubulin binding; pyrophosphatase activity; polyphosphate catabolic process; dephosphorylation; regulation of microtubule polymerization; regulation PRUNE1 PRUNE1 phosphatase activity; metal ion binding of neurogenesis protein binding; semaphorin receptor binding; chemorepellent neural crest cell migration; axon guidance; positive regulation of cell migration; negative regulation of SEMA4G semaphorin-4G activity axon extension involved in axon guidance; negative chemotaxis; semaphorin-plexin signaling pathway tubulin-specific microtubule cytoskeleton organization; tubulin complex assembly; post-chaperonin tubulin folding TBCEL chaperone E protein binding; alpha-tubulin binding pathway tigger transposable TIGD3 element-derived protein DNA binding; protein binding in utero embryonic development; positive regulation of heart rate by epinephrine; muscle contraction; regulation of muscle contraction; cytoskeleton organization; actin filament organization; regulation of heart contraction; regulation of cell shape; muscle filament sliding; negative regulation of cell migration; ruffle actin binding; structural constituent of cytoskeleton; protein organization; positive regulation of ATPase activity; cellular response to reactive oxygen species; wound binding; cytoskeletal protein binding; structural constituent of healing; sarcomere organization; positive regulation of cell adhesion; positive regulation of stress fiber muscle; identical protein binding; protein homodimerization assembly; ventricular cardiac muscle tissue morphogenesis; cardiac muscle contraction; negative tropomyosin alpha-1 activity; protein heterodimerization activity; actin filament regulation of vascular associated smooth muscle cell proliferation; negative regulation of vascular TPM1 chain binding associated smooth muscle cell migration RNA polymerase II cis-regulatory region sequence-specific DNA binding; DNA-binding transcription factor activity, RNA polymerase II-specific; DNA-binding transcription activator negative regulation of transcription by RNA polymerase II; mRNA splicing, via spliceosome; in utero activity, RNA polymerase II-specific; nucleic acid binding; DNA embryonic development; regulation of transcription, DNA-templated; Notch signaling pathway; epidermis binding; chromatin binding; double-stranded DNA binding; development; regulation of gene expression; negative regulation of translation; positive regulation of single-stranded DNA binding; RNA binding; mRNA binding; transcription by RNA polymerase II; mRNA stabilization; embryonic morphogenesis; RNA transport; protein binding; miRNA binding; GTPase binding;C5- tRNA transport; negative regulation of striated muscle cell differentiation; positive regulation of cell nuclease-sensitive methylcytidine-containing RNA binding; sequence-specific division; CRD-mediated mRNA stabilization; cellular response to interleukin-7; protein localization to YBX1 element-binding protein 1 double-stranded DNA binding cytoplasmic stress granule; miRNA transport; negative regulation of cellular senescence zinc finger SWIM domain-containing ZSWIM7 protein 7 protein binding; zinc ion binding double-strand break repair via homologous recombination; protein stabilization

191

Supplemental Table 3. Results of PANTHER overrepresentation statistical test on differentially expressed genes in muscle tissue. Comparisons are made using the GO Molecular Function Complete dataset. Fischer exact test conducted with a False Discovery Rate (FDR) correction. All genes were compared to Homo sapien dataset (GRCH38.p13).

Number of Expected number H.sapien genes of genes in Observed number Molecular Function per function analysis of genes in analysis Over/Underrepresented fold Enrichment raw p-value FDR correction

protein binding (GO:0005515) 14109 12.86 19 + 1.48 0.000862 0.05559

thiolester hydrolase activity (GO:0016790) 42 0.04 2 + 52.26 0.000726 0.05559

nucleosome binding (GO:0031491) 69 0.06 2 + 31.81 0.00188 0.05559

structural constituent of cytoskeleton (GO:0005200) 105 0.1 2 + 20.9 0.00421 0.05559

cytoskeletal protein binding (GO:0008092) 998 0.91 4 + 4.4 0.0115 0.06225517

chromatin binding (GO:0003682) 607 0.55 3 + 5.42 0.017 0.07225

binding (GO:0005488) 16469 15.01 19 + 1.27 0.0206 0.0791775

protein kinase binding (GO:0019901) 687 0.63 3 + 4.79 0.0235 0.08560714

protein-containing complex binding (GO:0044877) 1280 1.17 4 + 3.43 0.0263 0.08747609

structural molecule activity (GO:0005198) 711 0.65 3 + 4.63 0.0257 0.08747609

kinase binding (GO:0019900) 774 0.71 3 + 4.25 0.0319 0.0972

hydrolase activity, acting on ester bonds (GO:0016788) 778 0.71 3 + 4.23 0.0324 0.0972

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Supplemental Table 4. List of differentially expressed genes in the liver tissue discovery analysis. Specific molecular functions and biological processes were categorized using the GO complete datasets in PANTHER (Version 14). GeneID Protein Molecular Function Biological Process protease binding(GO:0002020); endopeptidase inhibitor activity(GO:0004866); serine-type endopeptidase inhibitor Alpha-2-macroglobulin- activity(GO:0004867); peptidase inhibitor negative regulation of endopeptidase activity(GO:0010951); regulation of A2ML1 like protein 1 activity(GO:0030414) endopeptidase activity(GO:0052548) regulation of transcription by RNA polymerase II(GO:0006357); cellular response to DNA damage stimulus(GO:0006974); regulation of mitotic cell cycle(GO:0007346); negative regulation of superoxide anion generation(GO:0032929); ribosome biogenesis(GO:0042254); negative regulation of amyloid precursor protein biosynthetic process(GO:0042985); negative regulation of apoptotic process(GO:0043066); positive regulation of transcription by RNA polymerase RNA binding(GO:0003723); protein binding(GO:0005515); II(GO:0045944); negative regulation of reactive oxygen species metabolic protein kinase binding(GO:0019901); leucine zipper domain process(GO:2000378); negative regulation of apoptotic signaling AATF Protein AATF binding(GO:0043522); tau protein binding(GO:0048156) pathway(GO:2001234) actin binding(GO:0003779); protein binding(GO:0005515); Actin-binding LIM protein metal ion binding(GO:0046872); actin filament transcription, DNA-templated(GO:0006351); lamellipodium assembly(GO:0030032); ABLIM2 2 binding(GO:0051015) actin cytoskeleton organization(GO:0030036) negative regulation of cell-matrix adhesion(GO:0001953); activation of cysteine-type endopeptidase activity involved in apoptotic process(GO:0006919); cellular response to DNA damage stimulus(GO:0006974); positive regulation of cell population proliferation(GO:0008284); regulation of autophagy(GO:0010506); positive regulation of cell death(GO:0010942); sphingolipid biosynthetic process(GO:0030148); DNA damage response, signal transduction by p53 class mediator(GO:0030330); response to retinoic acid(GO:0032526); negative regulation protein binding(GO:0005515); N-acylsphingosine of cell adhesion mediated by integrin(GO:0033629); cellular response to amidohydrolase activity(GO:0017040); metal ion drug(GO:0035690); regulation of apoptotic process(GO:0042981); sphingosine binding(GO:0046872); dihydroceramidase biosynthetic process(GO:0046512); ceramide catabolic process(GO:0046514); ACER2 Alkaline ceramidase 2 activity(GO:0071633); ceramidase activity(GO:0102121) negative regulation of protein glycosylation in Golgi(GO:0090285) positive regulation of antimicrobial humoral response(GO:0002760); 'de novo' pyrimidine nucleobase biosynthetic process(GO:0006207); cellular amino acid metabolic process(GO:0006520); glutamine metabolic process(GO:0006541); defense response(GO:0006952); inflammatory response(GO:0006954); embryo implantation(GO:0007566); negative regulation of NF-kappaB transcription factor activity(GO:0032088); negative regulation of type I interferon production(GO:0032480); negative regulation of toll-like receptor 2 signaling aspartate carbamoyltransferase activity(GO:0004070); pathway(GO:0034136); negative regulation of toll-like receptor 4 signaling carbamoyl-phosphate synthase (glutamine-hydrolyzing) pathway(GO:0034144); cellular response to interferon-beta(GO:0035458); negative activity(GO:0004088); ATP binding(GO:0005524); amino regulation of innate immune response(GO:0045824); negative regulation of acid binding(GO:0016597); carboxyl- or carbamoyltransferase inflammatory response(GO:0050728); defense response to virus(GO:0051607); activity(GO:0016743); hydrolase activity, acting on carbon- cellular response to molecule of bacterial origin(GO:0071219); cellular response to nitrogen (but not peptide) bonds(GO:0016810); hydrolase lipopolysaccharide(GO:0071222); cellular response to interferon- activity, acting on carbon-nitrogen (but not peptide) bonds, in gamma(GO:0071346); cellular response to interleukin-1(GO:0071347); cellular cyclic amides(GO:0016812); lyase activity(GO:0016829); response to tumor necrosis factor(GO:0071356); cellular response to progesterone Cis-aconitate metal ion binding(GO:0046872); aconitate decarboxylase stimulus(GO:0071393); tolerance induction to lipopolysaccharide(GO:0072573); ACOD1 decarboxylase activity(GO:0047613) positive regulation of reactive oxygen species metabolic process(GO:2000379) RNA polymerase II cis-regulatory region sequence-specific regulation of cyclin-dependent protein serine/threonine kinase activity(GO:0000079); Actb Actin, cytoplasmic 1 DNA binding(GO:0000978); structural constituent of morphogenesis of a polarized epithelium(GO:0001738); retina

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cytoskeleton(GO:0005200); protein binding(GO:0005515); homeostasis(GO:0001895); establishment or maintenance of cell ATP binding(GO:0005524); kinesin binding(GO:0019894); polarity(GO:0007163); axonogenesis(GO:0007409); protein protein kinase binding(GO:0019901); Tat protein deubiquitination(GO:0016579); substantia nigra development(GO:0021762); binding(GO:0030957); nucleosomal DNA regulation of transmembrane transporter activity(GO:0022898); negative regulation of binding(GO:0031492); identical protein protein binding(GO:0032091); cell junction assembly(GO:0034329); adherens binding(GO:0042802); tau protein binding(GO:0048156); junction assembly(GO:0034333); maintenance of blood-brain barrier(GO:0035633); nitric-oxide synthase binding(GO:0050998); structural Fc-gamma receptor signaling pathway involved in phagocytosis(GO:0038096); ATP- constituent of postsynaptic actin cytoskeleton(GO:0098973) dependent chromatin remodeling(GO:0043044); apical protein localization(GO:0045176); positive regulation of gene expression, epigenetic(GO:0045815); ephrin receptor signaling pathway(GO:0048013); synaptic vesicle endocytosis(GO:0048488); cell motility(GO:0048870); regulation of norepinephrine uptake(GO:0051621); positive regulation of norepinephrine uptake(GO:0051623); membrane organization(GO:0061024); platelet aggregation(GO:0070527); protein localization to adherens junction(GO:0071896); cellular response to cytochalasin B(GO:0072749); postsynaptic actin cytoskeleton organization(GO:0098974); regulation of transepithelial transport(GO:0150111); regulation of protein localization to plasma membrane(GO:1903076) RNA polymerase II transcription regulatory region sequence- specific DNA binding(GO:0000977); DNA-binding transcription repressor activity, RNA polymerase II- specific(GO:0001227); metallocarboxypeptidase activity(GO:0004181); extracellular matrix structural constituent(GO:0005201); calmodulin binding(GO:0005516); negative regulation of transcription by RNA polymerase II(GO:0000122); Adipocyte enhancer- collagen binding(GO:0005518); zinc ion proteolysis(GO:0006508); peptide metabolic process(GO:0006518); protein AEBP1 binding protein 1 binding(GO:0008270) processing(GO:0016485); regulation of collagen fibril organization(GO:1904026) protein phosphorylation(GO:0006468); G protein-coupled receptor signaling pathway(GO:0007186); heart development(GO:0007507); regulation of Rho protein signal transduction(GO:0035023); positive regulation of Rho protein signal transduction(GO:0035025); intracellular signal transduction(GO:0035556); positive regulation of apoptotic process(GO:0043065); positive regulation of I-kappaB kinase/NF-kappaB signaling(GO:0043123); positive regulation of MAP kinase cAMP-dependent protein kinase activity(GO:0004691); MAP- activity(GO:0043406); regulation of small GTPase mediated signal kinase scaffold activity(GO:0005078); guanyl-nucleotide transduction(GO:0051056); nuclear export(GO:0051168); cardiac muscle cell exchange factor activity(GO:0005085); Rho guanyl-nucleotide differentiation(GO:0055007); regulation of sarcomere organization(GO:0060297); exchange factor activity(GO:0005089); protein bone development(GO:0060348); cell growth involved in cardiac muscle cell binding(GO:0005515); Rho GTPase binding(GO:0017048); development(GO:0061049); adrenergic receptor signaling pathway(GO:0071875); metal ion binding(GO:0046872); protein kinase A adenylate cyclase-activating adrenergic receptor signaling pathway involved in heart binding(GO:0051018); molecular adaptor process(GO:0086023); regulation of glucocorticoid mediated signaling AKAP13 A-kinase anchor protein 13 activity(GO:0060090) pathway(GO:1900169) iron ion binding(GO:0005506); protein binding(GO:0005515); oxidoreductase activity, acting on single donors with incorporation of molecular oxygen, incorporation of two atoms sphingolipid metabolic process(GO:0006665); sensory perception of of oxygen(GO:0016702); metal ion binding(GO:0046872); pain(GO:0019233); arachidonic acid metabolic process(GO:0019369); lipoxygenase intramolecular transferase activity, transferring hydroxy pathway(GO:0019372); peroxisome proliferator activated receptor signaling groups(GO:0050486); hepoxilin A3 synthase pathway(GO:0035357); linoleic acid metabolic process(GO:0043651); fat cell activity(GO:0051120); hydroperoxy icosatetraenoate differentiation(GO:0045444); ceramide biosynthetic process(GO:0046513); hepoxilin Hydroperoxide isomerase isomerase activity(GO:0106255); hydroperoxy biosynthetic process(GO:0051122); oxidation-reduction process(GO:0055114); ALOXE3 ALOXE3 icosatetraenoate dehydratase activity(GO:0106256) establishment of skin barrier(GO:0061436)

194

angiogenesis(GO:0001525); response to hypoxia(GO:0001666); lipid metabolic process(GO:0006629); regulation of lipid metabolic process(GO:0019216); negative regulation of apoptotic process(GO:0043066); protein unfolding(GO:0043335); enzyme inhibitor activity(GO:0004857); signaling receptor positive regulation of angiogenesis(GO:0045766); negative regulation of lipoprotein Angiopoietin-related binding(GO:0005102); protein binding(GO:0005515); lipase activity(GO:0051005); triglyceride homeostasis(GO:0070328); negative ANGPTL4 protein 4 identical protein binding(GO:0042802) regulation of endothelial cell apoptotic process(GO:2000352) regulation of heart rate(GO:0002027); atrial septum development(GO:0003283); cellular calcium ion homeostasis(GO:0006874); endoplasmic reticulum to Golgi vesicle-mediated transport(GO:0006888); endocytosis(GO:0006897); cytoskeleton organization(GO:0007010); signal transduction(GO:0007165); positive regulation of gene expression(GO:0010628); regulation of cardiac muscle contraction by regulation of the release of sequestered calcium ion(GO:0010881); regulation of cardiac muscle contraction by calcium ion signaling(GO:0010882); protein transport(GO:0015031); paranodal junction assembly(GO:0030913); regulation of protein stability(GO:0031647); T-tubule organization(GO:0033292); protein localization to organelle(GO:0033365); protein localization to cell surface(GO:0034394); cellular protein localization(GO:0034613); protein localization to M-band(GO:0036309); protein localization to T-tubule(GO:0036371); positive regulation of potassium ion transport(GO:0043268); protein stabilization(GO:0050821); regulation of release of sequestered calcium ion into cytosol(GO:0051279); response to methylmercury(GO:0051597); regulation of calcium ion transport(GO:0051924); positive regulation of calcium ion transport(GO:0051928); regulation of cardiac muscle contraction(GO:0055117); regulation of ventricular cardiac muscle cell membrane repolarization(GO:0060307); sarcoplasmic reticulum calcium ion transport(GO:0070296); protein localization to endoplasmic reticulum(GO:0070972); protein localization to plasma membrane(GO:0072659); regulation of cardiac muscle cell contraction(GO:0086004); ventricular cardiac muscle cell action potential(GO:0086005); atrial cardiac muscle cell action potential(GO:0086014); SA node cell action potential(GO:0086015); membrane depolarization during SA node cell action potential(GO:0086046); atrial cardiac muscle cell to AV node cell structural constituent of cytoskeleton(GO:0005200); protein communication(GO:0086066); SA node cell to atrial cardiac muscle cell binding(GO:0005515); cytoskeletal anchor communication(GO:0086070); regulation of heart rate by cardiac activity(GO:0008093); enzyme binding(GO:0019899); protein conduction(GO:0086091); regulation of SA node cell action potential(GO:0098907); kinase binding(GO:0019901); spectrin binding(GO:0030507); regulation of atrial cardiac muscle cell action potential(GO:0098910); positive protein-macromolecule adaptor activity(GO:0030674); ion regulation of potassium ion transmembrane transporter activity(GO:1901018); channel binding(GO:0044325); ATPase regulation of calcium ion transmembrane transporter activity(GO:1901019); positive binding(GO:0051117); phosphorylation-dependent protein regulation of calcium ion transmembrane transporter activity(GO:1901021); positive ANK2 Ankyrin-2 binding(GO:0140031) regulation of cation channel activity(GO:2001259) negative regulation of transcription by RNA polymerase II(GO:0000122); double- strand break repair(GO:0006302); regulation of transcription, DNA- templated(GO:0006355); apoptotic process(GO:0006915); cellular response to DNA damage stimulus(GO:0006974); cell cycle arrest(GO:0007050); signal amyloid-beta binding(GO:0001540); chromatin transduction(GO:0007165); axonogenesis(GO:0007409); response to iron binding(GO:0003682); protein binding(GO:0005515); ion(GO:0010039); positive regulation of neuron projection transcription factor binding(GO:0008134); ubiquitin protein development(GO:0010976); negative regulation of cell growth(GO:0030308); ligase binding(GO:0031625); histone binding(GO:0042393); positive regulation of apoptotic process(GO:0043065); histone H4 Amyloid-beta A4 protein-containing complex binding(GO:0044877); tau protein acetylation(GO:0043967); positive regulation of DNA repair(GO:0045739); positive precursor protein-binding binding(GO:0048156); proline-rich region regulation of transcription, DNA-templated(GO:0045893); positive regulation of APBB1 family B member 1 binding(GO:0070064) transcription by RNA polymerase II(GO:0045944); positive regulation of protein

195

secretion(GO:0050714); negative regulation of thymidylate synthase biosynthetic process(GO:0050760) transcription regulatory region sequence-specific DNA binding(GO:0000976); RNA polymerase II transcription regulatory region sequence-specific DNA binding(GO:0000977); RNA polymerase II cis-regulatory region sequence-specific DNA binding(GO:0000978); DNA- binding transcription factor activity, RNA polymerase II- specific(GO:0000981); DNA-binding transcription activator activity, RNA polymerase II-specific(GO:0001228); DNA- eye development(GO:0001654); regulation of transcription by RNA polymerase binding transcription factor activity(GO:0003700); protein II(GO:0006357); protein folding(GO:0006457); signal transduction(GO:0007165); binding(GO:0005515); ubiquitin protein ligase visual perception(GO:0007601); endoplasmic reticulum unfolded protein binding(GO:0031625); cAMP response element response(GO:0030968); ATF6-mediated unfolded protein response(GO:0036500); binding(GO:0035497); identical protein positive regulation of apoptotic process(GO:0043065); positive regulation of binding(GO:0042802); sequence-specific DNA transcription by RNA polymerase II(GO:0045944); positive regulation of ATF6- Cyclic AMP-dependent binding(GO:0043565); protein heterodimerization mediated unfolded protein response(GO:1903893); positive regulation of transcription transcription factor ATF-6 activity(GO:0046982); sequence-specific double-stranded from RNA polymerase II promoter in response to endoplasmic reticulum ATF6 alpha DNA binding(GO:1990837) stress(GO:1990440) autophagosome assembly(GO:0000045); positive regulation of autophagy(GO:0010508); protein transport(GO:0015031); macroautophagy(GO:0016236); cerebellar cortex development(GO:0021695); hippocampus development(GO:0021766); corpus callosum development(GO:0022038); protein localization to phagophore assembly protein binding(GO:0005515); ubiquitin-like protein site(GO:0034497); negative stranded viral RNA replication(GO:0039689); protein Autophagy-related protein transferase activity(GO:0019787); identical protein lipidation involved in autophagosome assembly(GO:0061739); ATG16L1 16-1 binding(GO:0042802); GTPase binding(GO:0051020) xenophagy(GO:0098792) oxidative phosphorylation(GO:0006119); ATP biosynthetic process(GO:0006754); RNA binding(GO:0003723); protein binding(GO:0005515); ATP synthesis coupled proton transport(GO:0015986); cristae ATP synthase subunit ATPase activity(GO:0016887); proton-transporting ATP formation(GO:0042407); mitochondrial ATP synthesis coupled proton ATP5F1C gamma, mitochondrial synthase activity, rotational mechanism(GO:0046933) transport(GO:0042776) spermidine biosynthetic process(GO:0008295); putrescine biosynthetic process from agmatinase activity(GO:0008783); metal ion arginine, using agmatinase(GO:0033389); agmatine biosynthetic AUH Agmatinase, mitochondrial binding(GO:0046872) process(GO:0097055) mRNA 3'-UTR binding(GO:0003730); enoyl-CoA hydratase activity(GO:0004300); methylglutaconyl-CoA hydratase Methylglutaconyl-CoA activity(GO:0004490); itaconyl-CoA hydratase leucine catabolic process(GO:0006552); fatty acid beta-oxidation(GO:0006635); AUH hydratase, mitochondrial activity(GO:0050011) branched-chain amino acid catabolic process(GO:0009083) Chromatin complexes DNA binding(GO:0003677); protein binding(GO:0005515); Bap18 subunit BAP18 identical protein binding(GO:0042802) chromatin organization(GO:0006325) transmembrane signaling receptor activity(GO:0004888); laminin receptor activity(GO:0005055); protein Basal cell adhesion binding(GO:0005515); protein C-terminus cell adhesion(GO:0007155); cell-matrix adhesion(GO:0007160); signal BCAM molecule binding(GO:0008022); laminin binding(GO:0043236) transduction(GO:0007165) BOLA1 BolA-like protein 1 protein binding(GO:0005515) Coiled-coil domain- fibronectin binding(GO:0001968); heparin response to bacterium(GO:0009617); positive regulation of cell-substrate CCDC80 containing protein 80 binding(GO:0008201) adhesion(GO:0010811); extracellular matrix organization(GO:0030198) G-protein alpha-subunit binding(GO:0001965); actin regulation of protein phosphorylation(GO:0001932); DNA replication(GO:0006260); binding(GO:0003779); protein kinase C regulation of DNA replication(GO:0006275); small GTPase mediated signal binding(GO:0005080); guanyl-nucleotide exchange factor transduction(GO:0007264); regulation of neuron projection CCDC88A Girdin activity(GO:0005085); GDP-dissociation inhibitor development(GO:0010975); cell migration(GO:0016477); lamellipodium

196

activity(GO:0005092); epidermal growth factor receptor assembly(GO:0030032); cytoskeleton-dependent intracellular binding(GO:0005154); insulin receptor binding(GO:0005158); transport(GO:0030705); cytoplasmic microtubule organization(GO:0031122); TOR protein binding(GO:0005515); microtubule signaling(GO:0031929); activation of protein kinase activity(GO:0032147); activation binding(GO:0008017); G-protein gamma-subunit of protein kinase B activity(GO:0032148); regulation of actin cytoskeleton binding(GO:0031682); phosphatidylinositol organization(GO:0032956); regulation of cell population proliferation(GO:0042127); binding(GO:0035091); SH2 domain binding(GO:0042169); positive regulation of cilium assembly(GO:0045724); positive regulation of epidermal protein homodimerization activity(GO:0042803); vascular growth factor receptor signaling pathway(GO:0045742); positive regulation of stress endothelial growth factor receptor 2 binding(GO:0043184); fiber assembly(GO:0051496); membrane organization(GO:0061024); maintenance of protein kinase B binding(GO:0043422); dynein light protein location in plasma membrane(GO:0072660); positive regulation of protein intermediate chain binding(GO:0051959) localization to cilium(GO:1903566) protein folding(GO:0006457); binding of sperm to zona pellucida(GO:0007339); positive regulation of telomere maintenance via telomerase(GO:0032212); protein stabilization(GO:0050821); toxin transport(GO:1901998); positive regulation of protein binding(GO:0005515); ATP binding(GO:0005524); establishment of protein localization to telomere(GO:1904851); positive regulation of T-complex protein 1 identical protein binding(GO:0042802); unfolded protein protein localization to Cajal body(GO:1904871); positive regulation of telomerase CCT7 subunit eta binding(GO:0051082) RNA localization to Cajal body(GO:1904874) G1/S transition of mitotic cell cycle(GO:0000082); double-strand break repair via break-induced replication(GO:0000727); DNA replication(GO:0006260); protein phosphorylation(GO:0006468); positive regulation of cell population proliferation(GO:0008284); positive regulation of nuclear cell cycle DNA protein kinase activity(GO:0004672); protein serine/threonine replication(GO:0010571); positive regulation of G2/M transition of mitotic cell kinase activity(GO:0004674); protein binding(GO:0005515); cycle(GO:0010971); peptidyl-serine phosphorylation(GO:0018105); cell cycle phase Cell division cycle 7- ATP binding(GO:0005524); kinase activity(GO:0016301); transition(GO:0044770); cell division(GO:0051301); negative regulation of G0 to G1 CDC7 related protein kinase metal ion binding(GO:0046872) transition(GO:0070317) complement activation(GO:0006956); complement activation, alternative protein binding(GO:0005515); heparin binding(GO:0008201); pathway(GO:0006957); viral process(GO:0016032); regulation of complement identical protein binding(GO:0042802); heparan sulfate activation(GO:0030449); regulation of complement-dependent CFH Complement factor H proteoglycan binding(GO:0043395) cytotoxicity(GO:1903659) proteolysis(GO:0006508); apoptotic process(GO:0006915); activation of cysteine- type endopeptidase activity involved in apoptotic process(GO:0006919); skeletal muscle tissue development(GO:0007519); response to bacterium(GO:0009617); negative regulation of cardiac muscle cell apoptotic process(GO:0010667); positive regulation of neuron projection development(GO:0010976); skeletal muscle atrophy(GO:0014732); regulation of skeletal muscle satellite cell proliferation(GO:0014842); skeletal myofibril assembly(GO:0014866); viral process(GO:0016032); cellular response to insulin stimulus(GO:0032869); response to testosterone(GO:0033574); wound healing(GO:0042060); regulation of apoptotic process(GO:0042981); negative regulation of apoptotic process(GO:0043066); positive regulation of I-kappaB kinase/NF-kappaB signaling(GO:0043123); negative regulation of cysteine-type endopeptidase activity involved in apoptotic protease binding(GO:0002020); cysteine-type endopeptidase process(GO:0043154); skeletal muscle tissue regeneration(GO:0043403); positive activity(GO:0004197); death receptor binding(GO:0005123); regulation of NF-kappaB transcription factor activity(GO:0051092); regulation of protein binding(GO:0005515); enzyme activator necroptotic process(GO:0060544); negative regulation of necroptotic activity(GO:0008047); peptidase activator process(GO:0060546); positive regulation of ERK1 and ERK2 activity(GO:0016504); protein-containing complex cascade(GO:0070374); cellular response to epidermal growth factor binding(GO:0044877); cysteine-type endopeptidase activity stimulus(GO:0071364); cellular response to estradiol stimulus(GO:0071392); cellular involved in apoptotic process(GO:0097153); cysteine-type response to hypoxia(GO:0071456); cellular response to dexamethasone endopeptidase activity involved in apoptotic signaling stimulus(GO:0071549); cellular response to nitric oxide(GO:0071732); positive CASP8 and FADD-like pathway(GO:0097199); cysteine-type endopeptidase activity regulation of glomerular mesangial cell proliferation(GO:0072126); apoptotic CFLAR apoptosis regulator involved in execution phase of apoptosis(GO:0097200) signaling pathway(GO:0097190); execution phase of apoptosis(GO:0097194);

197

negative regulation of myoblast fusion(GO:1901740); regulation of extrinsic apoptotic signaling pathway via death domain receptors(GO:1902041); negative regulation of extrinsic apoptotic signaling pathway via death domain receptors(GO:1902042); positive regulation of extracellular matrix organization(GO:1903055); negative regulation of reactive oxygen species biosynthetic process(GO:1903427); negative regulation of cellular response to transforming growth factor beta stimulus(GO:1903845); negative regulation of hepatocyte apoptotic process(GO:1903944); positive regulation of hepatocyte proliferation(GO:2000347); negative regulation of extrinsic apoptotic signaling pathway(GO:2001237) RNA polymerase II cis-regulatory region sequence-specific DNA binding(GO:0000978); DNA binding(GO:0003677); chromatin organization(GO:0006325); regulation of transcription by RNA DNA helicase activity(GO:0003678); RNA polymerase II(GO:0006357); cellular response to DNA damage Chromodomain-helicase- binding(GO:0003723); protein binding(GO:0005515); ATP stimulus(GO:0006974); muscle organ development(GO:0007517); DNA duplex CHD2 DNA-binding protein 2 binding(GO:0005524); histone binding(GO:0042393) unwinding(GO:0032508); hematopoietic stem cell differentiation(GO:0060218) phospholipid metabolic process(GO:0006644); ceramide metabolic process(GO:0006672); lipid transport(GO:0006869); nervous system development(GO:0007399); cholesterol metabolic process(GO:0008203); lipid protein binding(GO:0005515); ceramide biosynthetic process(GO:0008610); protein catabolic process(GO:0030163); negative CLN8 Protein CLN8 binding(GO:0097001) regulation of proteolysis(GO:0045861); ceramide biosynthetic process(GO:0046513) mitochondrial electron transport, cytochrome c to oxygen(GO:0006123); response to oxidative stress(GO:0006979); aging(GO:0007568); aerobic respiration(GO:0009060); electron transport coupled proton transport(GO:0015990); cytochrome-c oxidase activity(GO:0004129); protein cerebellum development(GO:0021549); respiratory electron transport Cytochrome c oxidase binding(GO:0005515); heme binding(GO:0020037); metal ion chain(GO:0022904); response to copper ion(GO:0046688); response to electrical COX1 subunit 1 binding(GO:0046872) stimulus(GO:0051602) cytochrome-c oxidase activity(GO:0004129); protein binding(GO:0005515); electron transfer mitochondrial electron transport, cytochrome c to oxygen(GO:0006123); respiratory Cytochrome c oxidase activity(GO:0009055); oxidoreduction-driven active chain complex IV assembly(GO:0008535); aerobic respiration(GO:0009060); proton COX3 subunit 3 transmembrane transporter activity(GO:0015453) transmembrane transport(GO:1902600) cytochrome-c oxidase activity(GO:0004129); protein Cytochrome c oxidase binding(GO:0005515); electron transfer mitochondrial electron transport, cytochrome c to oxygen(GO:0006123); electron COX5A subunit 5A, mitochondrial activity(GO:0009055); metal ion binding(GO:0046872) transport chain(GO:0022900); proton transmembrane transport(GO:1902600) toll-like receptor signaling pathway(GO:0002224); adaptive immune response(GO:0002250); proteolysis(GO:0006508); immune response(GO:0006955); response to acidic pH(GO:0010447); protein processing(GO:0016485); antigen processing and presentation(GO:0019882); antigen processing and presentation of exogenous peptide antigen via MHC class II(GO:0019886); extracellular matrix disassembly(GO:0022617); collagen catabolic process(GO:0030574); basement fibronectin binding(GO:0001968); cysteine-type membrane disassembly(GO:0034769); neutrophil degranulation(GO:0043312); endopeptidase activity(GO:0004197); collagen antigen processing and presentation of peptide antigen(GO:0048002); proteolysis binding(GO:0005518); cysteine-type peptidase involved in cellular protein catabolic process(GO:0051603); cellular response to activity(GO:0008234); laminin binding(GO:0043236); thyroid hormone stimulus(GO:0097067); positive regulation of cation channel CTSS Cathepsin S proteoglycan binding(GO:0043394) activity(GO:2001259) ferroxidase activity(GO:0004322); protein Cytochrome b561 domain- binding(GO:0005515); oxidoreductase activity(GO:0016491); CYB561D2 containing protein 2 heme binding(GO:0020037); metal ion binding(GO:0046872) oxidation-reduction process(GO:0055114) ARF guanyl-nucleotide exchange factor vesicle-mediated transport(GO:0016192); regulation of cell adhesion(GO:0030155); activity(GO:0005086); protein binding(GO:0005515); lipid regulation of ARF protein signal transduction(GO:0032012); establishment of CYTH1 Cytohesin-1 binding(GO:0008289) epithelial cell polarity(GO:0090162)

198

ARF guanyl-nucleotide exchange factor activity(GO:0005086); protein binding(GO:0005515); regulation of ARF protein signal transduction(GO:0032012); positive regulation of phosphatidylinositol-3,4,5-trisphosphate cell adhesion(GO:0045785); Golgi vesicle transport(GO:0048193); establishment of CYTH3 Cytohesin-3 binding(GO:0005547) epithelial cell polarity(GO:0090162) nucleic acid binding(GO:0003676); RNA Probable ATP-dependent binding(GO:0003723); RNA helicase activity(GO:0003724); rRNA processing(GO:0006364); positive regulation of neuron projection DDX56 RNA helicase DDX56 protein binding(GO:0005515); ATP binding(GO:0005524) development(GO:0010976) Rab guanyl-nucleotide exchange factor activity(GO:0017112); SH3 domain binding(GO:0017124); Rab GTPase endocytosis(GO:0006897); protein transport(GO:0015031); endocytic binding(GO:0017137); phosphatidylinositol-3-phosphate recycling(GO:0032456); regulation of Rab protein signal transduction(GO:0032483); DENN domain-containing binding(GO:0032266); phosphatidylinositol phosphate positive regulation of GTPase activity(GO:0043547); synaptic vesicle Dennd1a protein 1A binding(GO:1901981) endocytosis(GO:0048488) monoacylglycerol biosynthetic process(GO:0006640); triglyceride metabolic 2-acylglycerol O-acyltransferase activity(GO:0003846); process(GO:0006641); triglyceride biosynthetic process(GO:0019432); lipid diacylglycerol O-acyltransferase activity(GO:0004144); storage(GO:0019915); very-low-density lipoprotein particle assembly(GO:0034379); protein binding(GO:0005515); O-acyltransferase long-chain fatty-acyl-CoA metabolic process(GO:0035336); acylglycerol acyl-chain activity(GO:0008374); transferase activity, transferring acyl remodeling(GO:0036155); retinol metabolic process(GO:0042572); neutrophil Diacylglycerol O- groups(GO:0016746); retinol O-fatty-acyltransferase degranulation(GO:0043312); diacylglycerol metabolic process(GO:0046339); fatty DGAT1 acyltransferase 1 activity(GO:0050252) acid homeostasis(GO:0055089) 3-keto sterol reductase activity(GO:0000253); carbonyl reductase (NADPH) activity(GO:0004090); oxidoreductase alcohol metabolic process(GO:0006066); protein targeting to activity, acting on NAD(P)H, quinone or similar compound as peroxisome(GO:0006625); protein localization(GO:0008104); steroid metabolic Dehydrogenase/reductase acceptor(GO:0016655); alcohol dehydrogenase [NAD(P)+] process(GO:0008202); cellular ketone metabolic process(GO:0042180); oxidation- DHRS4 SDR family member 4 activity(GO:0018455); identical protein binding(GO:0042802) reduction process(GO:0055114) nucleic acid binding(GO:0003676); chromatin binding(GO:0003682); RNA binding(GO:0003723); RNA helicase activity(GO:0003724); double-stranded RNA ATP-dependent RNA binding(GO:0003725); helicase activity(GO:0004386); protein central nervous system development(GO:0007417); mitochondrial large ribosomal DHX30 helicase DHX30 binding(GO:0005515); ATP binding(GO:0005524) subunit assembly(GO:1902775) Disks large-associated protein binding(GO:0005515); molecular adaptor signaling(GO:0023052); regulation of postsynaptic neurotransmitter receptor DLGAP4 protein 4 activity(GO:0060090) activity(GO:0098962) G protein-coupled receptor binding(GO:0001664); ATPase activator activity(GO:0001671); protein protein folding(GO:0006457); response to unfolded protein(GO:0006986); response binding(GO:0005515); ATP binding(GO:0005524); Hsp70 to heat(GO:0009408); negative regulation of protein ubiquitination(GO:0031397); protein binding(GO:0030544); Tat protein positive regulation of ATPase activity(GO:0032781); cellular response to binding(GO:0030957); ubiquitin protein ligase stress(GO:0033554); positive regulation of apoptotic process(GO:0043065); negative binding(GO:0031625); metal ion binding(GO:0046872); low- regulation of apoptotic process(GO:0043066); negative regulation of JUN kinase density lipoprotein particle receptor binding(GO:0050750); activity(GO:0043508); regulation of protein transport(GO:0051223); protein unfolded protein binding(GO:0051082); chaperone localization to mitochondrion(GO:0070585); negative regulation of establishment of DnaJ homolog subfamily binding(GO:0051087); C3HC4-type RING finger domain protein localization to mitochondrion(GO:1903748); negative regulation of nitrosative DNAJA1 A member 1 binding(GO:0055131) stress-induced intrinsic apoptotic signaling pathway(GO:1905259) 2-(3-amino-3- carboxypropyl)histidine protein binding(GO:0005515); transferase cell population proliferation(GO:0008283); peptidyl-diphthamide biosynthetic DPH1 synthase subunit 1 activity(GO:0016740) process from peptidyl-histidine(GO:0017183) Receptor-binding cancer antigen expressed on SiSo protein binding(GO:0005515); peptidase activator activity EBAG9 cells involved in apoptotic process(GO:0016505) regulation of cell growth(GO:0001558); apoptotic process(GO:0006915) endopeptidase activity(GO:0004175); metalloendopeptidase positive regulation of receptor recycling(GO:0001921); regulation of systemic arterial Endothelin-converting activity(GO:0004222); protein binding(GO:0005515); zinc ion blood pressure by endothelin(GO:0003100); proteolysis(GO:0006508); heart ECE1 enzyme 1 binding(GO:0008270); peptide hormone development(GO:0007507); substance P catabolic process(GO:0010814); bradykinin

199

binding(GO:0017046); protein homodimerization catabolic process(GO:0010815); calcitonin catabolic process(GO:0010816); protein activity(GO:0042803) processing(GO:0016485); peptide hormone processing(GO:0016486); regulation of vasoconstriction(GO:0019229); endothelin maturation(GO:0034959); hormone catabolic process(GO:0042447); embryonic digit morphogenesis(GO:0042733); ear development(GO:0043583); pharyngeal system development(GO:0060037) Elongation factor 1- translation elongation factor activity(GO:0003746); protein EEF1G gamma binding(GO:0005515); cadherin binding(GO:0045296) translational elongation(GO:0006414); response to virus(GO:0009615) xenobiotic metabolic process(GO:0006805); response to toxic substance(GO:0009636); response to organic cyclic compound(GO:0014070); epoxide hydrolase activity(GO:0004301); cis-stilbene-oxide arachidonic acid metabolic process(GO:0019369); aromatic compound catabolic EPHX1 Epoxide hydrolase 1 hydrolase activity(GO:0033961) process(GO:0019439); epoxide metabolic process(GO:0097176) positive regulation of granulocyte differentiation(GO:0030854); negative regulation of apoptotic process(GO:0043066); positive regulation of neutrophil differentiation(GO:0045660); myeloid cell development(GO:0061515); negative regulation of cell cycle arrest(GO:0071157); regulation of stem cell EVI2B Protein EVI2B protein binding(GO:0005515) division(GO:2000035) maturation of 5.8S rRNA(GO:0000460); nuclear-transcribed mRNA catabolic process(GO:0000956); rRNA processing(GO:0006364); rRNA catabolic process(GO:0016075); positive regulation of cell growth(GO:0030307); nuclear- transcribed mRNA catabolic process, exonucleolytic, 3'-5'(GO:0034427); U4 snRNA 3'-end processing(GO:0034475); regulation of mRNA stability(GO:0043488); exonucleolytic catabolism of deadenylated mRNA(GO:0043928); DNA deamination(GO:0045006); defense response to virus(GO:0051607); nuclear mRNA 3'-5'-exoribonuclease activity(GO:0000175); protein surveillance(GO:0071028); histone mRNA catabolic process(GO:0071044); Exosome complex binding(GO:0005515); mRNA 3'-UTR AU-rich region polyadenylation-dependent snoRNA 3'-end processing(GO:0071051); RNA EXOSC4 component RRP41 binding(GO:0035925) phosphodiester bond hydrolysis, exonucleolytic(GO:0090503) Soluble lamin-associated FAM169A protein of 75 kDa protein binding(GO:0005515) protein polyubiquitination(GO:0000209); Golgi organization(GO:0007030); cell population proliferation(GO:0008283); protein ubiquitination(GO:0016567); post- translational protein modification(GO:0043687); positive regulation of dendrite morphogenesis(GO:0050775); labyrinthine layer blood vessel F-box/WD repeat- ubiquitin-protein transferase activity(GO:0004842); protein development(GO:0060716); positive regulation of transcription factor catabolic FBXW8 containing protein 8 binding(GO:0005515) process(GO:1901485) MAPK cascade(GO:0000165); positive regulation of protein phosphorylation(GO:0001934); signal transduction(GO:0007165); positive regulation of cell population proliferation(GO:0008284); fibroblast growth factor receptor signaling pathway(GO:0008543); epidermis development(GO:0008544); response to wounding(GO:0009611); animal organ morphogenesis(GO:0009887); mesenchymal cell proliferation(GO:0010463); positive regulation of gene expression(GO:0010628); positive regulation of keratinocyte proliferation(GO:0010838); cytokine-mediated signaling pathway(GO:0019221); cell differentiation(GO:0030154); lung development(GO:0030324); regulation of cell migration(GO:0030334); hair follicle morphogenesis(GO:0031069); actin cytoskeleton reorganization(GO:0031532); protein localization to cell surface(GO:0034394); positive regulation of transcription, fibroblast growth factor receptor binding(GO:0005104); type 2 DNA-templated(GO:0045893); positive regulation of epithelial cell fibroblast growth factor receptor binding(GO:0005111); proliferation(GO:0050679); positive regulation of peptidyl-tyrosine protein binding(GO:0005515); growth factor phosphorylation(GO:0050731); positive chemotaxis(GO:0050918); positive activity(GO:0008083); heparin binding(GO:0008201); regulation of keratinocyte migration(GO:0051549); positive regulation of cell FGF7 Fibroblast growth factor 7 chemoattractant activity(GO:0042056) division(GO:0051781); positive regulation of protein kinase B

200

signaling(GO:0051897); branching involved in salivary gland morphogenesis(GO:0060445); positive regulation of epithelial cell proliferation involved in lung morphogenesis(GO:0060501); regulation of branching involved in salivary gland morphogenesis by mesenchymal-epithelial signaling(GO:0060665); secretion by lung epithelial cell involved in lung growth(GO:0061033) signaling receptor binding(GO:0005102); protein immune response(GO:0006955); integrin-mediated signaling pathway(GO:0007229); binding(GO:0005515); lipid binding(GO:0008289); protein- biological_process(GO:0008150); T cell receptor signaling pathway(GO:0050852); FYB1 FYN-binding protein 1 containing complex binding(GO:0044877) protein localization to plasma membrane(GO:0072659) glycosphingolipid metabolic process(GO:0006687); ganglioside catabolic lipid transporter activity(GO:0005319); enzyme activator process(GO:0006689); lipid transport(GO:0006869); learning or activity(GO:0008047); phospholipase activator memory(GO:0007611); oligosaccharide catabolic process(GO:0009313); lipid activity(GO:0016004); sphingolipid activator protein storage(GO:0019915); positive regulation of catalytic activity(GO:0043085); activity(GO:0030290); beta-N-acetylgalactosaminidase neutrophil degranulation(GO:0043312); neuromuscular process controlling GM2A Ganglioside GM2 activator activity(GO:0032428) balance(GO:0050885); positive regulation of hydrolase activity(GO:0051345) protein kinase activity(GO:0004672); G protein-coupled receptor kinase activity(GO:0004703); protein protein phosphorylation(GO:0006468); signal transduction(GO:0007165); G protein- Beta-adrenergic receptor binding(GO:0005515); ATP binding(GO:0005524); beta- coupled receptor signaling pathway(GO:0007186); receptor GRK3 kinase 2 adrenergic receptor kinase activity(GO:0047696) internalization(GO:0031623) RNA polymerase II cis-regulatory region sequence-specific DNA binding(GO:0000978); DNA-binding transcription factor activity, RNA polymerase II-specific(GO:0000981); DNA-binding transcription repressor activity, RNA negative regulation of transcription by RNA polymerase II(GO:0000122); regulation General transcription polymerase II-specific(GO:0001227); DNA-binding of transcription, DNA-templated(GO:0006355); transcription by RNA polymerase factor II-I repeat domain- transcription factor activity(GO:0003700); protein II(GO:0006366); multicellular organism development(GO:0007275); transition GTF2IRD1 containing protein 1 binding(GO:0005515) between slow and fast fiber(GO:0014886) leukocyte migration involved in immune response(GO:0002522); innate immune response-activating signal transduction(GO:0002758); negative regulation of inflammatory response to antigenic stimulus(GO:0002862); protein phosphorylation(GO:0006468); inflammatory response(GO:0006954); cell adhesion(GO:0007155); transmembrane receptor protein tyrosine kinase signaling pathway(GO:0007169); integrin-mediated signaling pathway(GO:0007229); mesoderm development(GO:0007498); positive regulation of cell population proliferation(GO:0008284); regulation of cell shape(GO:0008360); peptidyl-tyrosine phosphorylation(GO:0018108); cytokine-mediated signaling pathway(GO:0019221); cell differentiation(GO:0030154); positive regulation of actin filament polymerization(GO:0030838); lipopolysaccharide-mediated signaling pathway(GO:0031663); peptidyl-tyrosine autophosphorylation(GO:0038083); Fc- gamma receptor signaling pathway involved in phagocytosis(GO:0038096); regulation of cell population proliferation(GO:0042127); negative regulation of apoptotic process(GO:0043066); leukocyte degranulation(GO:0043299); innate immune response(GO:0045087); respiratory burst after phagocytosis(GO:0045728); protein autophosphorylation(GO:0046777); regulation of defense response to virus by phosphotyrosine residue binding(GO:0001784); protein virus(GO:0050690); regulation of inflammatory response(GO:0050727); regulation of tyrosine kinase activity(GO:0004713); non-membrane phagocytosis(GO:0050764); regulation of DNA-binding transcription factor spanning protein tyrosine kinase activity(GO:0004715); activity(GO:0051090); interferon-gamma-mediated signaling pathway(GO:0060333); Tyrosine-protein kinase signaling receptor binding(GO:0005102); protein regulation of podosome assembly(GO:0071801); positive regulation of actin HCK HCK binding(GO:0005515); ATP binding(GO:0005524) cytoskeleton reorganization(GO:2000251) HGH1 Protein HGH1 homolog molecular_function(GO:0003674) biological_process(GO:0008150) Homeobox-containing DNA-binding transcription factor activity, RNA polymerase negative regulation of transcription by RNA polymerase II(GO:0000122); positive HMBOX1 protein 1 II-specific(GO:0000981); DNA-binding transcription repressor regulation of telomere maintenance via telomerase(GO:0032212); positive regulation

201

activity, RNA polymerase II-specific(GO:0001227); double- of chromatin binding(GO:0035563); negative regulation of transcription, DNA- stranded telomeric DNA binding(GO:0003691); protein templated(GO:0045892); positive regulation of transcription, DNA- binding(GO:0005515); telomeric DNA binding(GO:0042162); templated(GO:0045893); regulation of telomerase activity(GO:0051972); positive identical protein binding(GO:0042802); sequence-specific regulation of telomerase activity(GO:0051973) DNA binding(GO:0043565); protein-containing complex binding(GO:0044877); sequence-specific double-stranded DNA binding(GO:1990837) cell cycle(GO:0007049); homophilic cell adhesion via plasma membrane adhesion extracellular matrix structural constituent(GO:0005201); molecules(GO:0007156); heterophilic cell-cell adhesion via plasma membrane cell calcium ion binding(GO:0005509); protein adhesion molecules(GO:0007157); visual perception(GO:0007601); response to HMCN1 Hemicentin-1 binding(GO:0005515) bacterium(GO:0009617); cell division(GO:0051301) 11-beta-hydroxysteroid dehydrogenase [NAD(P)] activity(GO:0003845); steroid binding(GO:0005496); oxidoreductase activity(GO:0016491); protein homodimerization activity(GO:0042803); NADP glucocorticoid biosynthetic process(GO:0006704); steroid catabolic Corticosteroid 11-beta- binding(GO:0050661); 11-beta-hydroxysteroid dehydrogenase process(GO:0006706); lung development(GO:0030324); oxidation-reduction HSD11B1 dehydrogenase isozyme 1 (NADP+) activity(GO:0070524) process(GO:0055114) fibronectin binding(GO:0001968); estradiol 17-beta- dehydrogenase activity(GO:0004303); protein binding(GO:0005515); collagen binding(GO:0005518); heparin binding(GO:0008201); oxidoreductase activity(GO:0016491); 3-oxo-arachidoyl-CoA reductase activity(GO:0102339); 3-oxo-behenoyl-CoA reductase fatty acid biosynthetic process(GO:0006633); estrogen biosynthetic activity(GO:0102340); 3-oxo-lignoceroyl-CoA reductase process(GO:0006703); positive regulation of cell-substrate adhesion(GO:0010811); Very-long-chain 3- activity(GO:0102341); 3-oxo-cerotoyl-CoA reductase extracellular matrix organization(GO:0030198); long-chain fatty-acyl-CoA HSD17B12 oxoacyl-CoA reductase activity(GO:0102342) biosynthetic process(GO:0035338); oxidation-reduction process(GO:0055114) placenta development(GO:0001890); protein folding(GO:0006457); xenobiotic UTP binding(GO:0002134); CTP binding(GO:0002135); RNA metabolic process(GO:0006805); response to unfolded protein(GO:0006986); binding(GO:0003723); double-stranded RNA telomere maintenance via telomerase(GO:0007004); response to salt binding(GO:0003725); protein binding(GO:0005515); ATP stress(GO:0009651); virion attachment to host cell(GO:0019062); central nervous binding(GO:0005524); GTP binding(GO:0005525); drug system neuron axonogenesis(GO:0021955); establishment of cell binding(GO:0008144); ATPase activity(GO:0016887); polarity(GO:0030010); positive regulation of transforming growth factor beta receptor sulfonylurea receptor binding(GO:0017098); protein kinase signaling pathway(GO:0030511); regulation of protein ubiquitination(GO:0031396); regulator activity(GO:0019887); kinase binding(GO:0019900); positive regulation of protein binding(GO:0032092); negative regulation of protein kinase binding(GO:0019901); MHC class II protein proteasomal ubiquitin-dependent protein catabolic process(GO:0032435); positive complex binding(GO:0023026); nitric-oxide synthase regulation of phosphoprotein phosphatase activity(GO:0032516); positive regulation regulator activity(GO:0030235); TPR domain of peptidyl-serine phosphorylation(GO:0033138); cellular response to binding(GO:0030911); heat shock protein stress(GO:0033554); cellular response to heat(GO:0034605); purinergic nucleotide binding(GO:0031072); ubiquitin protein ligase receptor signaling pathway(GO:0035590); cellular response to drug(GO:0035690); binding(GO:0031625); dATP binding(GO:0032564); peptide Fc-gamma receptor signaling pathway involved in phagocytosis(GO:0038096); binding(GO:0042277); identical protein response to cocaine(GO:0042220); positive regulation of protein import into binding(GO:0042802); protein homodimerization nucleus(GO:0042307); neutrophil degranulation(GO:0043312); negative regulation of activity(GO:0042803); histone deacetylase neuron apoptotic process(GO:0043524); positive regulation of nitric oxide binding(GO:0042826); ATP-dependent protein biosynthetic process(GO:0045429); positive regulation of cell binding(GO:0043008); protein folding differentiation(GO:0045597); positive regulation of cell size(GO:0045793); axon chaperone(GO:0044183); ion channel binding(GO:0044325); extension(GO:0048675); protein stabilization(GO:0050821); chaperone-mediated cadherin binding(GO:0045296); protein dimerization protein complex assembly(GO:0051131); negative regulation of protein metabolic activity(GO:0046983); tau protein binding(GO:0048156); process(GO:0051248); positive regulation of protein kinase B Heat shock protein HSP unfolded protein binding(GO:0051082); DNA polymerase signaling(GO:0051897); positive regulation of telomerase activity(GO:0051973); HSP90AB1 90-beta binding(GO:0070182); disordered domain specific regulation of interferon-gamma-mediated signaling pathway(GO:0060334);

202

binding(GO:0097718); histone methyltransferase regulation of type I interferon-mediated signaling pathway(GO:0060338); negative binding(GO:1990226) regulation of cell cycle arrest(GO:0071157); cellular response to interleukin- 4(GO:0071353); cellular response to organic cyclic compound(GO:0071407); positive regulation of protein serine/threonine kinase activity(GO:0071902); supramolecular fiber organization(GO:0097435); regulation of cellular response to heat(GO:1900034); negative regulation of transforming growth factor beta activation(GO:1901389); negative regulation of proteasomal protein catabolic process(GO:1901799); positive regulation of tau-protein kinase activity(GO:1902949); negative regulation of complement-dependent cytotoxicity(GO:1903660); regulation of cellular protein localization(GO:1903827); positive regulation of cyclin-dependent protein kinase activity(GO:1904031); telomerase holoenzyme complex assembly(GO:1905323); positive regulation of protein localization to cell surface(GO:2000010) retinoid metabolic process(GO:0001523); angiogenesis(GO:0001525); glycosaminoglycan biosynthetic process(GO:0006024); glycosaminoglycan catabolic process(GO:0006027); lipid metabolic process(GO:0006629); receptor-mediated amyloid-beta binding(GO:0001540); integrin endocytosis(GO:0006898); inflammatory response(GO:0006954); brain binding(GO:0005178); calcium ion binding(GO:0005509); development(GO:0007420); animal organ morphogenesis(GO:0009887); tissue protein binding(GO:0005515); protein C-terminus development(GO:0009888); negative regulation of angiogenesis(GO:0016525); cell binding(GO:0008022); extracellular matrix structural differentiation(GO:0030154); extracellular matrix organization(GO:0030198); Basement membrane- constituent conferring compression resistance(GO:0030021); cellular protein metabolic process(GO:0044267); negative regulation of cell specific heparan sulfate low-density lipoprotein particle receptor death(GO:0060548); circulatory system development(GO:0072359); negative HSPG2 proteoglycan core protein binding(GO:0050750) regulation of amyloid fibril formation(GO:1905907) Isoamyl acetate- hydrolyzing esterase 1 hydrolase activity(GO:0016787); identical protein IAH1 homolog binding(GO:0042802) lipid catabolic process(GO:0016042) magnesium ion binding(GO:0000287); isocitrate dehydrogenase (NAD+) activity(GO:0004449); ATP Isocitrate dehydrogenase binding(GO:0005524); oxidoreductase activity, acting on the carbohydrate metabolic process(GO:0005975); tricarboxylic acid [NAD] subunit gamma, CH-OH group of donors, NAD or NADP as cycle(GO:0006099); isocitrate metabolic process(GO:0006102); oxidation-reduction IDH3G mitochondrial acceptor(GO:0016616); NAD binding(GO:0051287) process(GO:0055114) smoothened signaling pathway(GO:0007224); biological_process(GO:0008150); intraciliary transport involved in cilium assembly(GO:0035735); intraciliary Intraflagellar transport molecular_function(GO:0003674); protein C-terminus transport(GO:0042073); protein stabilization(GO:0050821); cilium IFT46 protein 46 homolog binding(GO:0008022) assembly(GO:0060271) Putative interleukin-17 IL17REL receptor E-like interleukin-17 receptor activity(GO:0030368) cytokine-mediated signaling pathway(GO:0019221) mitotic sister chromatid segregation(GO:0000070); double-strand break repair via homologous recombination(GO:0000724); DNA repair(GO:0006281); double-strand break repair(GO:0006302); chromatin remodeling(GO:0006338); transcription, DNA- templated(GO:0006351); positive regulation of nuclear cell cycle DNA replication(GO:0010571); protein deubiquitination(GO:0016579); positive regulation of cell growth(GO:0030307); cellular response to UV(GO:0034644); nucleosome mobilization(GO:0042766); ATP-dependent chromatin remodeling(GO:0043044); DNA binding(GO:0003677); actin binding(GO:0003779); regulation of transcription from RNA polymerase II promoter in response to protein binding(GO:0005515); ATP binding(GO:0005524); stress(GO:0043618); positive regulation of transcription by RNA polymerase DNA-dependent ATPase activity(GO:0008094); ATPase II(GO:0045944); spindle assembly(GO:0051225); cell division(GO:0051301); UV- activity(GO:0016887); histone binding(GO:0042393); alpha- damage excision repair(GO:0070914); cellular response to ionizing Chromatin-remodeling tubulin binding(GO:0043014); 3'-5' DNA helicase radiation(GO:0071479); regulation of G1/S transition of mitotic cell INO80 ATPase INO80 activity(GO:0043138) cycle(GO:2000045)

203

phosphate-containing compound metabolic process(GO:0006796); signal inositol-1,4-bisphosphate 1-phosphatase transduction(GO:0007165); inositol phosphate metabolic process(GO:0043647); Inositol polyphosphate 1- activity(GO:0004441); protein binding(GO:0005515); metal phosphatidylinositol phosphorylation(GO:0046854); inositol phosphate INPP1 phosphatase ion binding(GO:0046872) dephosphorylation(GO:0046855) mitotic spindle organization(GO:0007052); regulation of mitotic cell cycle(GO:0007346); flagellated sperm motility(GO:0030317); snRNA transcription by RNA polymerase II(GO:0042795); cell division(GO:0051301); centrosome Integrator complex subunit localization(GO:0051642); regulation of fertilization(GO:0080154); protein INTS13 13 protein binding(GO:0005515) localization to nuclear envelope(GO:0090435) inflammatory response to antigenic stimulus(GO:0002437); chromatin RNA polymerase II cis-regulatory region sequence-specific remodeling(GO:0006338); response to activity(GO:0014823); hippocampus DNA binding(GO:0000978); chromatin development(GO:0021766); cell fate commitment(GO:0045165); endothelial cell binding(GO:0003682); protein binding(GO:0005515); beta- differentiation(GO:0045446); positive regulation of transcription by RNA polymerase catenin binding(GO:0008013); chromatin DNA II(GO:0045944); mesodermal cell differentiation(GO:0048333); cardiac muscle cell binding(GO:0031490); sequence-specific DNA differentiation(GO:0055007); oxidation-reduction process(GO:0055114); response to binding(GO:0043565); metal ion binding(GO:0046872); fungicide(GO:0060992); cellular response to hydrogen peroxide(GO:0070301); Lysine-specific dioxygenase activity(GO:0051213); histone demethylase histone H3-K27 demethylation(GO:0071557); positive regulation of cold-induced KDM6B demethylase 6B activity (H3-K27 specific)(GO:0071558) thermogenesis(GO:0120162) in utero embryonic development(GO:0001701); ubiquitin-dependent protein catabolic process(GO:0006511); regulation of autophagy(GO:0010506); viral process(GO:0016032); protein ubiquitination(GO:0016567); protein deubiquitination(GO:0016579); positive regulation of proteasomal ubiquitin- dependent protein catabolic process(GO:0032436); cellular response to oxidative protein binding(GO:0005515); transcription factor stress(GO:0034599); cytoplasmic sequestering of transcription factor(GO:0042994); binding(GO:0008134); identical protein negative regulation of DNA-binding transcription factor activity(GO:0043433); post- Kelch-like ECH-associated binding(GO:0042802); disordered domain specific translational protein modification(GO:0043687); regulation of epidermal cell KEAP1 protein 1 binding(GO:0097718) differentiation(GO:0045604); cellular response to interleukin-4(GO:0071353) negative regulation of transcription by RNA polymerase II(GO:0000122); cell morphogenesis(GO:0000902); in utero embryonic development(GO:0001701); regulation of transcription by RNA polymerase II(GO:0006357); negative regulation of interleukin-6 production(GO:0032715); multicellular organism growth(GO:0035264); positive regulation of transcription from RNA polymerase II promoter in response to stress(GO:0036003); regulation of gene expression, epigenetic(GO:0040029); erythrocyte maturation(GO:0043249); positive regulation of nitric oxide biosynthetic process(GO:0045429); positive regulation of transcription, DNA-templated(GO:0045893); positive regulation of transcription by RNA polymerase II(GO:0045944); positive regulation of retinoic acid receptor signaling pathway(GO:0048386); positive regulation of protein metabolic RNA polymerase II cis-regulatory region sequence-specific process(GO:0051247); type I pneumocyte differentiation(GO:0060509); cellular DNA binding(GO:0000978); DNA-binding transcription response to hydrogen peroxide(GO:0070301); cellular response to interleukin- factor activity, RNA polymerase II-specific(GO:0000981); 1(GO:0071347); cellular response to tumor necrosis factor(GO:0071356); cellular DNA binding(GO:0003677); DNA-binding transcription response to cycloheximide(GO:0071409); cellular response to fluid shear factor activity(GO:0003700); protein binding(GO:0005515); stress(GO:0071498); cellular response to laminar fluid shear stress(GO:0071499); metal ion binding(GO:0046872); sequence-specific double- cellular stress response to acid chemical(GO:0097533); cellular response to KLF2 Krueppel-like factor 2 stranded DNA binding(GO:1990837) peptide(GO:1901653); negative regulation of sprouting angiogenesis(GO:1903671) toll-like receptor signaling pathway(GO:0002224); leukocyte chemotaxis involved in lipopolysaccharide binding(GO:0001530); signaling receptor inflammatory response(GO:0002232); macrophage activation involved in immune binding(GO:0005102); protein binding(GO:0005515); response(GO:0002281); acute-phase response(GO:0006953); cellular defense Lipopolysaccharide- lipoteichoic acid binding(GO:0070891); lipopeptide response(GO:0006968); opsonization(GO:0008228); lipopolysaccharide LBP binding protein binding(GO:0071723) transport(GO:0015920); cytokine-mediated signaling pathway(GO:0019221);

204

lipopolysaccharide-mediated signaling pathway(GO:0031663); detection of molecule of bacterial origin(GO:0032490); response to lipopolysaccharide(GO:0032496); negative regulation of tumor necrosis factor production(GO:0032720); positive regulation of chemokine production(GO:0032722); positive regulation of interleukin- 6 production(GO:0032755); positive regulation of interleukin-8 production(GO:0032757); positive regulation of tumor necrosis factor production(GO:0032760); macromolecule localization(GO:0033036); toll-like receptor 4 signaling pathway(GO:0034142); positive regulation of toll-like receptor 4 signaling pathway(GO:0034145); positive regulation of tumor necrosis factor biosynthetic process(GO:0042535); positive regulation of macrophage activation(GO:0043032); innate immune response(GO:0045087); positive regulation of cytolysis(GO:0045919); defense response to Gram-negative bacterium(GO:0050829); defense response to Gram-positive bacterium(GO:0050830); positive regulation of respiratory burst involved in inflammatory response(GO:0060265); cellular response to lipopolysaccharide(GO:0071222); cellular response to lipoteichoic acid(GO:0071223); positive regulation of neutrophil chemotaxis(GO:0090023) LON peptidase N-terminal domain and RING finger protein binding(GO:0005515); metal ion LONRF1 protein 1 binding(GO:0046872) protein polyubiquitination(GO:0000209) Leucine-rich repeat- RNA binding(GO:0003723); phenylalanine-tRNA ligase LRRC47 containing protein 47 activity(GO:0004826); protein binding(GO:0005515) hydrolase activity, hydrolyzing O-glycosyl carbohydrate metabolic process(GO:0005975); cellular protein modification compounds(GO:0004553); beta-mannosidase process(GO:0006464); glycoprotein catabolic process(GO:0006516); oligosaccharide MANBA Beta-mannosidase activity(GO:0004567); mannose binding(GO:0005537) catabolic process(GO:0009313); neutrophil degranulation(GO:0043312) response to toxic substance(GO:0009636); response to aluminum ion(GO:0010044); response to selenium ion(GO:0010269); negative regulation of serotonin secretion(GO:0014063); substantia nigra development(GO:0021762); electron transport chain(GO:0022900); response to lipopolysaccharide(GO:0032496); protein binding(GO:0005515); primary amine oxidase neurotransmitter catabolic process(GO:0042135); dopamine catabolic activity(GO:0008131); electron transfer process(GO:0042420); response to drug(GO:0042493); response to activity(GO:0009055); identical protein ethanol(GO:0045471); positive regulation of dopamine metabolic Amine oxidase [flavin- binding(GO:0042802); flavin adenine dinucleotide process(GO:0045964); hydrogen peroxide biosynthetic process(GO:0050665); MAOB containing] B binding(GO:0050660) response to corticosterone(GO:0051412) protein binding(GO:0005515); amino acid transmembrane amino acid transmembrane transport(GO:0003333); thyroid hormone transporter activity(GO:0015171); aromatic amino acid generation(GO:0006590); amino acid transport(GO:0006865); aromatic amino acid Monocarboxylate transmembrane transporter activity(GO:0015173); thyroid transport(GO:0015801); transmembrane transport(GO:0055085); thyroid hormone MCT10 transporter 10 hormone transmembrane transporter activity(GO:0015349) transport(GO:0070327); thyroid-stimulating hormone secretion(GO:0070460) cytokine production involved in immune response(GO:0002367); antigen processing and presentation of peptide antigen via MHC class I(GO:0002474); positive regulation of T cell mediated cytotoxicity directed against tumor cell target(GO:0002854); immune response(GO:0006955); antigen processing and presentation of exogenous antigen(GO:0019884); interleukin-1 beta production(GO:0032611); interleukin-17 production(GO:0032620); T cell Major histocompatibility protein binding(GO:0005515); beta-2-microglobulin differentiation in thymus(GO:0033077); innate immune response(GO:0045087); complex class I-related binding(GO:0030881); MHC class I receptor defense response to Gram-negative bacterium(GO:0050829); defense response to MR1 gene protein activity(GO:0032393); T cell receptor binding(GO:0042608) Gram-positive bacterium(GO:0050830) 28S ribosomal protein S33, translation(GO:0006412); mitochondrial translational elongation(GO:0070125); MRPS33 mitochondrial structural constituent of ribosome(GO:0003735) mitochondrial translational termination(GO:0070126)

205

response to hypoxia(GO:0001666); mitochondrial electron transport, ubiquinol to cytochrome c(GO:0006122); response to heat(GO:0009408); response to toxic substance(GO:0009636); electron transport coupled proton transport(GO:0015990); animal organ regeneration(GO:0031100); response to cobalamin(GO:0033590); response to glucagon(GO:0033762); response to drug(GO:0042493); hyperosmotic salinity response(GO:0042538); response to ethanol(GO:0045471); response to ubiquinol-cytochrome-c reductase activity(GO:0008121); cadmium ion(GO:0046686); response to copper ion(GO:0046688); response to protein-containing complex binding(GO:0044877); metal ion mercury ion(GO:0046689); response to calcium ion(GO:0051592); response to MT-CYB Cytochrome b binding(GO:0046872) hyperoxia(GO:0055093) Peptide chain release factor 1-like, translational termination(GO:0006415); mitochondrial translational MTRF1L mitochondrial translation release factor activity(GO:0003747) termination(GO:0070126) microfilament motor activity(GO:0000146); motor activity(GO:0003774); actin binding(GO:0003779); ATP binding(GO:0005524); ATPase activity(GO:0016887); actin- actin filament organization(GO:0007015); vesicle transport along actin dependent ATPase activity(GO:0030898); myosin light chain filament(GO:0030050); regulation of cytokinesis(GO:0032465); mitochondrion Unconventional myosin- binding(GO:0032027); actin filament binding(GO:0051015); migration along actin filament(GO:0034642); regulation of mitochondrial MYO19 XIX plus-end directed microfilament motor activity(GO:0060002) fission(GO:0090140) snoRNA guided rRNA pseudouridine synthesis(GO:0000454); box H/ACA snoRNP assembly(GO:0000493); pseudouridine synthesis(GO:0001522); rRNA processing(GO:0006364); positive regulation of telomere maintenance via telomerase(GO:0032212); ribosome biogenesis(GO:0042254); RNA stabilization(GO:0043489); positive regulation of telomerase activity(GO:0051973); telomerase RNA stabilization(GO:0090669); positive regulation of telomere H/ACA ribonucleoprotein RNA binding(GO:0003723); protein binding(GO:0005515); maintenance via telomere lengthening(GO:1904358); positive regulation of complex non-core subunit identical protein binding(GO:0042802); telomerase RNA telomerase RNA localization to Cajal body(GO:1904874); telomerase holoenzyme NAF1 NAF1 binding(GO:0070034) complex assembly(GO:1905323) mitochondrial electron transport, NADH to ubiquinone(GO:0006120); response to oxidative stress(GO:0006979); response to light intensity(GO:0009642); NADH-ubiquinone protein binding(GO:0005515); NADH dehydrogenase mitochondrial respiratory chain complex I assembly(GO:0032981); cellular response ND3 oxidoreductase chain 3 (ubiquinone) activity(GO:0008137) to glucocorticoid stimulus(GO:0071385) response to hypoxia(GO:0001666); mitochondrial electron transport, NADH to ubiquinone(GO:0006120); response to organonitrogen compound(GO:0010243); electron transport coupled proton transport(GO:0015990); mitochondrial respiratory NADH-ubiquinone NADH dehydrogenase activity(GO:0003954); NADH chain complex I assembly(GO:0032981); response to hydrogen ND5 oxidoreductase chain 5 dehydrogenase (ubiquinone) activity(GO:0008137) peroxide(GO:0042542) NADH dehydrogenase [ubiquinone] 1 alpha mitochondrial electron transport, NADH to ubiquinone(GO:0006120); mitochondrial NDUFA1 subcomplex subunit 1 NADH dehydrogenase (ubiquinone) activity(GO:0008137) respiratory chain complex I assembly(GO:0032981) RNA polymerase II cis-regulatory region sequence-specific cytokine production(GO:0001816); regulation of transcription, DNA- DNA binding(GO:0000978); DNA-binding transcription templated(GO:0006355); transcription by RNA polymerase II(GO:0006366); factor activity, RNA polymerase II-specific(GO:0000981); response to osmotic stress(GO:0006970); signal transduction(GO:0007165); DNA-binding transcription activator activity, RNA excretion(GO:0007588); calcineurin-NFAT signaling cascade(GO:0033173); positive polymerase II-specific(GO:0001228); chromatin regulation of transcription by RNA polymerase II(GO:0045944); regulation of binding(GO:0003682); DNA-binding transcription factor calcineurin-NFAT signaling cascade(GO:0070884); cellular response to cytokine activity(GO:0003700); protein binding(GO:0005515); stimulus(GO:0071345); positive regulation of NIK/NF-kappaB Nuclear factor of activated transcription factor binding(GO:0008134); sequence-specific signaling(GO:1901224); positive regulation of leukocyte adhesion to vascular NFAT5 T-cells 5 double-stranded DNA binding(GO:1990837) endothelial cell(GO:1904996) RNA binding(GO:0003723); protein binding(GO:0005515); NOP56 Nucleolar protein 56 snoRNA binding(GO:0030515); cadherin rRNA processing(GO:0006364)

206

binding(GO:0045296); histone methyltransferase binding(GO:1990226) protein binding(GO:0005515); protein-N-terminal asparagine Protein N-terminal amidohydrolase activity(GO:0008418); protein-N-terminal NTAQ1 glutamine amidohydrolase glutamine amidohydrolase activity(GO:0070773) cellular protein modification process(GO:0006464) in utero embryonic development(GO:0001701); Wnt signaling pathway(GO:0016055); cell differentiation(GO:0030154); negative regulation of Wnt signaling pathway(GO:0030178); negative regulation of protein thioredoxin-disulfide reductase activity(GO:0004791); protein- ubiquitination(GO:0031397); oxidation-reduction process(GO:0055114); circulatory NXN Nucleoredoxin disulfide reductase activity(GO:0047134) system development(GO:0072359); cellular oxidant detoxification(GO:0098869) protein binding(GO:0005515); ATP binding(GO:0005524); GTP binding(GO:0005525); ATPase activity(GO:0016887); ribosome binding(GO:0043022); ribosomal large subunit binding(GO:0043023); cadherin binding(GO:0045296); metal OLA1 Obg-like ATPase 1 ion binding(GO:0046872) platelet degranulation(GO:0002576); ATP metabolic process(GO:0046034) visual perception(GO:0007601); regulation of lipid metabolic process(GO:0019216); regulation of growth(GO:0040008); response to stimulus(GO:0050896); OPA3 Optic atrophy 3 protein neuromuscular process(GO:0050905); mitochondrion morphogenesis(GO:0070584) store-operated calcium entry(GO:0002115); adaptive immune response(GO:0002250); positive regulation of adenylate cyclase calcium channel activity(GO:0005262); protein activity(GO:0045762); regulation of calcium ion transport(GO:0051924); positive binding(GO:0005515); calmodulin binding(GO:0005516); regulation of calcium ion transport(GO:0051928); mammary gland epithelium Calcium release-activated store-operated calcium channel activity(GO:0015279); development(GO:0061180); calcium ion import(GO:0070509); calcium ion ORAI1 calcium channel protein 1 identical protein binding(GO:0042802) transmembrane transport(GO:0070588) Polyadenylate-binding translation repressor activity, mRNA regulatory element protein-interacting protein binding(GO:0000900); protein binding(GO:0005515); negative regulation of translation(GO:0017148); negative regulation of translational PAIP2B 2B translation repressor activity(GO:0030371) initiation(GO:0045947) metalloendopeptidase activity(GO:0004222); serine-type proteolysis(GO:0006508); negative regulation of endopeptidase PAPLN Papilin endopeptidase inhibitor activity(GO:0004867) activity(GO:0010951); extracellular matrix organization(GO:0030198) Programmed cell death PDCD2L protein 2-like protein binding(GO:0005515) cell cycle(GO:0007049) chromatin binding(GO:0003682); RNA Proline-, glutamic acid- binding(GO:0003723); protein binding(GO:0005515); rRNA processing(GO:0006364); positive regulation of transcription by RNA PELP1 and leucine-rich protein 1 transcription factor binding(GO:0008134) polymerase II(GO:0045944); cellular response to estrogen stimulus(GO:0071391) fatty acid alpha-oxidation(GO:0001561); neuron migration(GO:0001764); suckling behavior(GO:0001967); protein targeting to peroxisome(GO:0006625); locomotory behavior(GO:0007626); protein localization(GO:0008104); protein import into peroxisome matrix, docking(GO:0016560); protein ubiquitination(GO:0016567); Peroxisomal membrane cerebral cortex cell migration(GO:0021795); protein import into peroxisome PEX13 protein PEX13 protein binding(GO:0005515) membrane(GO:0045046); microtubule-based peroxisome localization(GO:0060152) protein targeting to peroxisome(GO:0006625); peroxisome organization(GO:0007031); peroxisome membrane biogenesis(GO:0016557); peroxisome fission(GO:0016559); protein import into peroxisome protein binding(GO:0005515); peroxisome membrane membrane(GO:0045046); protein stabilization(GO:0050821); transmembrane targeting sequence binding(GO:0033328); peroxisome transport(GO:0055085); chaperone-mediated protein folding(GO:0061077); membrane class-1 targeting sequence binding(GO:0036105); chaperone-mediated protein transport(GO:0072321); establishment of protein Peroxisomal biogenesis protein N-terminus binding(GO:0047485); ATPase localization to peroxisome(GO:0072663); negative regulation of lipid PEX19 factor 19 binding(GO:0051117) binding(GO:1900131)

207

placenta development(GO:0001890); apoptotic process(GO:0006915); animal organ morphogenesis(GO:0009887); regulation of gene expression(GO:0010468); regulation of cell migration(GO:0030334); positive regulation of apoptotic Pleckstrin homology-like process(GO:0043065); regulation of spongiotrophoblast cell domain family A member protein binding(GO:0005515); phosphatidylinositol phosphate proliferation(GO:0060721); regulation of glycogen metabolic process(GO:0070873); PHLDA2 2 binding(GO:1901981) regulation of growth hormone activity(GO:1903547) MAPK cascade(GO:0000165); angiogenesis(GO:0001525); liver development(GO:0001889); vasculature development(GO:0001944); glucose metabolic process(GO:0006006); protein phosphorylation(GO:0006468); phosphatidylinositol biosynthetic process(GO:0006661); epidermal growth factor receptor signaling pathway(GO:0007173); G protein-coupled receptor signaling pathway(GO:0007186); axon guidance(GO:0007411); regulation of gene expression(GO:0010468); phosphatidylinositol 3-kinase signaling(GO:0014065); positive regulation of phosphatidylinositol 3-kinase signaling(GO:0014068); negative regulation of macroautophagy(GO:0016242); phosphorylation(GO:0016310); cell migration(GO:0016477); cytokine-mediated signaling pathway(GO:0019221); platelet activation(GO:0030168); T cell costimulation(GO:0031295); positive regulation of TOR signaling(GO:0032008); activation of protein kinase activity(GO:0032147); positive regulation of peptidyl-serine phosphorylation(GO:0033138); phosphatidylinositol-3-phosphate biosynthetic process(GO:0036092); insulin receptor signaling pathway via phosphatidylinositol 3- kinase(GO:0038028); Fc-epsilon receptor signaling pathway(GO:0038095); Fc- gamma receptor signaling pathway involved in phagocytosis(GO:0038096); ERBB2 signaling pathway(GO:0038128); regulation of multicellular organism growth(GO:0040014); anoikis(GO:0043276); regulation of cellular respiration(GO:0043457); protein kinase B signaling(GO:0043491); negative protein serine/threonine kinase activity(GO:0004674); protein regulation of neuron apoptotic process(GO:0043524); endothelial cell binding(GO:0005515); ATP binding(GO:0005524); kinase migration(GO:0043542); hypomethylation of CpG island(GO:0044029); activity(GO:0016301); 1-phosphatidylinositol-3-kinase phosphatidylinositol phosphorylation(GO:0046854); vascular endothelial growth activity(GO:0016303); protein kinase activator factor receptor signaling pathway(GO:0048010); phosphatidylinositol-mediated activity(GO:0030295); phosphatidylinositol 3-kinase signaling(GO:0048015); T cell receptor signaling pathway(GO:0050852); leukocyte activity(GO:0035004); 1-phosphatidylinositol-4-phosphate 3- migration(GO:0050900); positive regulation of protein kinase B kinase activity(GO:0035005); insulin receptor substrate signaling(GO:0051897); cardiac muscle contraction(GO:0060048); adipose tissue Phosphatidylinositol 4,5- binding(GO:0043560); phosphatidylinositol-4,5-bisphosphate development(GO:0060612); cellular response to glucose stimulus(GO:0071333); bisphosphate 3-kinase 3-kinase activity(GO:0046934); phosphatidylinositol kinase energy homeostasis(GO:0097009); negative regulation of fibroblast apoptotic catalytic subunit alpha activity(GO:0052742); phosphatidylinositol-3,4-bisphosphate process(GO:2000270); regulation of genetic imprinting(GO:2000653); negative PIK3CA isoform 5-kinase activity(GO:0052812) regulation of anoikis(GO:2000811) angiogenesis(GO:0001525); phosphatidylinositol biosynthetic process(GO:0006661); G protein-coupled receptor signaling pathway(GO:0007186); platelet activation(GO:0030168); regulation of natural killer cell mediated cytotoxicity(GO:0042269); positive regulation of MAP kinase activity(GO:0043406); protein binding(GO:0005515); phosphatidylinositol-4,5- regulation of phosphatidylinositol 3-kinase activity(GO:0043551); positive regulation Phosphoinositide 3-kinase bisphosphate 3-kinase activity(GO:0046934); 1- of T cell differentiation(GO:0045582); positive regulation of PIK3R6 regulatory subunit 6 phosphatidylinositol-3-kinase regulator activity(GO:0046935) angiogenesis(GO:0045766); phosphatidylinositol phosphorylation(GO:0046854) phospholipase A2 activity(GO:0004623); signaling receptor activation of MAPK activity(GO:0000187); innate immune response in binding(GO:0005102); calcium ion binding(GO:0005509); mucosa(GO:0002227); neutrophil mediated immunity(GO:0002446); fatty acid bile acid binding(GO:0032052); calcium-dependent biosynthetic process(GO:0006633); phospholipid metabolic process(GO:0006644); phospholipase A2 activity(GO:0047498); phospholipase A2 phosphatidic acid biosynthetic process(GO:0006654); actin filament activity (consuming 1,2- organization(GO:0007015); signal transduction(GO:0007165); positive regulation of PLA2 Phospholipase A2 dipalmitoylphosphatidylcholine)(GO:0102567); phospholipase cell population proliferation(GO:0008284); positive regulation of calcium ion

208

A2 activity consuming 1,2- transport into cytosol(GO:0010524); lipid catabolic process(GO:0016042); dioleoylphosphatidylethanolamine)(GO:0102568) leukotriene biosynthetic process(GO:0019370); antibacterial humoral response(GO:0019731); neutrophil chemotaxis(GO:0030593); activation of phospholipase A2 activity(GO:0032431); interleukin-8 production(GO:0032637); cellular response to insulin stimulus(GO:0032869); intracellular signal transduction(GO:0035556); phosphatidylglycerol acyl-chain remodeling(GO:0036148); phosphatidylinositol acyl-chain remodeling(GO:0036149); phosphatidylserine acyl-chain remodeling(GO:0036150); phosphatidylcholine acyl- chain remodeling(GO:0036151); phosphatidylethanolamine acyl-chain remodeling(GO:0036152); positive regulation of transcription by RNA polymerase II(GO:0045944); regulation of glucose import(GO:0046324); phosphatidylcholine metabolic process(GO:0046470); phosphatidylglycerol metabolic process(GO:0046471); positive regulation of fibroblast proliferation(GO:0048146); arachidonic acid secretion(GO:0050482); positive regulation of protein secretion(GO:0050714); positive regulation of immune response(GO:0050778); defense response to Gram-positive bacterium(GO:0050830); positive regulation of NF-kappaB transcription factor activity(GO:0051092); antimicrobial humoral immune response mediated by antimicrobial peptide(GO:0061844); positive regulation of glomerular visceral epithelial cell apoptotic process(GO:1904635) phospholipid metabolic process(GO:0006644); sphingosine metabolic process(GO:0006670); ceramide metabolic process(GO:0006672); signal diacylglycerol diphosphate phosphatase transduction(GO:0007165); protein kinase C-activating G protein-coupled receptor activity(GO:0000810); protein binding(GO:0005515); signaling pathway(GO:0007205); negative regulation of cell population phosphatidate phosphatase activity(GO:0008195); phosphatase proliferation(GO:0008285); dephosphorylation(GO:0016311); regulation of lipid activity(GO:0016791); sphingosine-1-phosphate phosphatase metabolic process(GO:0019216); sphingolipid biosynthetic process(GO:0030148); activity(GO:0042392); lipid phosphatase intracellular steroid hormone receptor signaling pathway(GO:0030518); androgen Phospholipid phosphatase activity(GO:0042577); ceramide-1-phosphate phosphatase receptor signaling pathway(GO:0030521); phospholipid PLPP1 1 activity(GO:0106235) dephosphorylation(GO:0046839) response to hypoxia(GO:0001666); regulation of protein phosphorylation(GO:0001932); positive regulation of defense response to virus by host(GO:0002230); regulation of transcription, DNA-templated(GO:0006355); protein targeting(GO:0006605); protein import into nucleus(GO:0006606); apoptotic process(GO:0006915); activation of cysteine-type endopeptidase activity involved in apoptotic process(GO:0006919); DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest(GO:0006977); cell cycle arrest(GO:0007050); transforming growth factor beta receptor signaling pathway(GO:0007179); common-partner SMAD protein phosphorylation(GO:0007182); negative regulation of cell population proliferation(GO:0008285); intrinsic apoptotic signaling pathway in response to DNA damage(GO:0008630); intrinsic apoptotic signaling pathway in response to oxidative DNA binding(GO:0003677); transcription coactivator stress(GO:0008631); response to UV(GO:0009411); response to gamma activity(GO:0003713); ubiquitin-protein transferase radiation(GO:0010332); regulation of calcium ion transport into activity(GO:0004842); protein binding(GO:0005515); zinc ion cytosol(GO:0010522); fibroblast migration(GO:0010761); viral binding(GO:0008270); ubiquitin protein ligase process(GO:0016032); negative regulation of angiogenesis(GO:0016525); protein binding(GO:0031625); SUMO binding(GO:0032183); ubiquitination(GO:0016567); viral life cycle(GO:0019058); myeloid cell identical protein binding(GO:0042802); protein differentiation(GO:0030099); regulation of cell adhesion(GO:0030155); negative homodimerization activity(GO:0042803); SMAD regulation of cell growth(GO:0030308); PML body organization(GO:0030578); binding(GO:0046332); protein heterodimerization granulocyte differentiation(GO:0030851); positive regulation of histone activity(GO:0046982); cobalt ion binding(GO:0050897); deacetylation(GO:0031065); positive regulation of telomere PML Protein PML sumo-dependent protein binding(GO:0140037) maintenance(GO:0032206); negative regulation of telomere maintenance via

209

telomerase(GO:0032211); endoplasmic reticulum calcium ion homeostasis(GO:0032469); circadian regulation of gene expression(GO:0032922); negative regulation of translation in response to oxidative stress(GO:0032938); response to cytokine(GO:0034097); regulation of circadian rhythm(GO:0042752); intrinsic apoptotic signaling pathway in response to DNA damage by p53 class mediator(GO:0042771); entrainment of circadian clock by photoperiod(GO:0043153); proteasome-mediated ubiquitin-dependent protein catabolic process(GO:0043161); innate immune response(GO:0045087); cell fate commitment(GO:0045165); negative regulation of transcription, DNA- templated(GO:0045892); positive regulation of transcription, DNA- templated(GO:0045893); negative regulation of mitotic cell cycle(GO:0045930); positive regulation of fibroblast proliferation(GO:0048146); retinoic acid receptor signaling pathway(GO:0048384); protein stabilization(GO:0050821); maintenance of protein location in nucleus(GO:0051457); defense response to virus(GO:0051607); negative regulation of telomerase activity(GO:0051974); positive regulation of apoptotic process involved in mammary gland involution(GO:0060058); interferon- gamma-mediated signaling pathway(GO:0060333); branching involved in mammary gland duct morphogenesis(GO:0060444); protein-containing complex assembly(GO:0065003); intrinsic apoptotic signaling pathway in response to endoplasmic reticulum stress(GO:0070059); cellular response to interleukin- 4(GO:0071353); cellular senescence(GO:0090398); extrinsic apoptotic signaling pathway(GO:0097191); regulation of signal transduction by p53 class mediator(GO:1901796); negative regulation of viral release from host cell(GO:1902187); positive regulation of nucleic acid-templated transcription(GO:1903508); positive regulation of protein localization to chromosome, telomeric region(GO:1904816); cellular response to leukemia inhibitory factor(GO:1990830); negative regulation of ubiquitin-dependent protein catabolic process(GO:2000059); regulation of double-strand break repair(GO:2000779); positive regulation of extrinsic apoptotic signaling pathway(GO:2001238) somitogenesis(GO:0001756); protein O-linked glycosylation(GO:0006493); gastrulation(GO:0007369); regulation of gastrulation(GO:0010470); protein O-linked glycosylation via serine(GO:0018242); positive regulation of Notch signaling UDP-glucosyltransferase activity(GO:0035251); UDP- pathway(GO:0045747); axial mesoderm development(GO:0048318); paraxial Protein O- xylosyltransferase activity(GO:0035252); glucosyltransferase mesoderm development(GO:0048339); muscle tissue development(GO:0060537); POGLUT1 glucosyltransferase 1 activity(GO:0046527) circulatory system development(GO:0072359) temperature homeostasis(GO:0001659); G protein-coupled receptor signaling pathway(GO:0007186); phospholipase C-activating G protein-coupled receptor signaling pathway(GO:0007200); positive regulation of cytosolic calcium ion concentration(GO:0007204); protein kinase C-activating G protein-coupled receptor signaling pathway(GO:0007205); neuropeptide signaling pathway(GO:0007218); chemical synaptic transmission(GO:0007268); negative regulation of DNA replication(GO:0008156); sleep(GO:0030431); response to starvation(GO:0042594); eating behavior(GO:0042755); negative regulation of potassium ion transport(GO:0043267); regulation of neurotransmitter secretion(GO:0046928); positive regulation of calcium ion transport(GO:0051928); negative regulation of neuropeptide hormone activity(GO:0005184); type 1 transmission of nerve impulse(GO:0051970); positive regulation of transmission of hypocretin receptor binding(GO:0031771); type 2 hypocretin nerve impulse(GO:0051971); excitatory postsynaptic potential(GO:0060079); PPOX Orexin receptor binding(GO:0031772) positive regulation of cold-induced thermogenesis(GO:0120162)

210

porphyrin-containing compound biosynthetic process(GO:0006779); oxygen-dependent protoporphyrinogen oxidase protoporphyrinogen IX biosynthetic process(GO:0006782); heme biosynthetic Protoporphyrinogen activity(GO:0004729); oxidoreductase activity(GO:0016491); process(GO:0006783); response to drug(GO:0042493); oxidation-reduction PPOX oxidase flavin adenine dinucleotide binding(GO:0050660) process(GO:0055114) negative regulation of transcription by RNA polymerase II(GO:0000122); cardiac right ventricle morphogenesis(GO:0003215); ventricular cardiac muscle tissue development(GO:0003229); transcription, DNA-templated(GO:0006351); regulation of transcription by RNA polymerase II(GO:0006357); apoptotic process(GO:0006915); post-embryonic development(GO:0009791); embryonic camera-type eye development(GO:0031076); multicellular organism transcription corepressor activity(GO:0003714); protein growth(GO:0035264); hair cycle(GO:0042633); positive regulation of cell binding(GO:0005515); transcription factor differentiation(GO:0045597); multicellular organismal homeostasis(GO:0048871); binding(GO:0008134); identical protein cardiac muscle contraction(GO:0060048); regulation of signal transduction by p53 PPP1R13L RelA-associated inhibitor binding(GO:0042802); cadherin binding(GO:0045296) class mediator(GO:1901796) glutathione peroxidase activity(GO:0004602); protein binding(GO:0005515); ubiquitin protein ligase binding(GO:0031625); identical protein binding(GO:0042802); cadherin binding(GO:0045296); calcium-independent phospholipase A2 activity(GO:0047499); response to oxidative stress(GO:0006979); cellular response to oxidative peroxiredoxin activity(GO:0051920); phospholipase A2 stress(GO:0034599); hydrogen peroxide catabolic process(GO:0042744); neutrophil activity (consuming 1,2- degranulation(GO:0043312); cell redox homeostasis(GO:0045454); dipalmitoylphosphatidylcholine)(GO:0102567); phospholipase glycerophospholipid catabolic process(GO:0046475); positive regulation of mRNA A2 activity consuming 1,2- splicing, via spliceosome(GO:0048026); oxidation-reduction process(GO:0055114); PRDX6 Peroxiredoxin-6 dioleoylphosphatidylethanolamine)(GO:0102568) cellular oxidant detoxification(GO:0098869) adaptive immune response(GO:0002250); regulation of transcription by RNA polymerase II(GO:0006357); protein phosphorylation(GO:0006468); calcium ion transport(GO:0006816); cellular calcium ion homeostasis(GO:0006874); apoptotic process(GO:0006915); mitotic nuclear envelope disassembly(GO:0007077); signal transduction(GO:0007165); regulation of glucose transmembrane transport(GO:0010827); negative regulation of glucose transmembrane transport(GO:0010829); peptidyl-serine phosphorylation(GO:0018105); platelet activation(GO:0030168); positive regulation of vascular endothelial growth factor receptor signaling pathway(GO:0030949); positive regulation of vesicle fusion(GO:0031340); positive regulation of insulin secretion(GO:0032024); histone chromatin binding(GO:0003682); protein kinase H3-T6 phosphorylation(GO:0035408); intracellular signal activity(GO:0004672); protein serine/threonine kinase transduction(GO:0035556); B cell activation(GO:0042113); lipoprotein activity(GO:0004674); protein kinase C transport(GO:0042953); positive regulation of I-kappaB kinase/NF-kappaB activity(GO:0004697); calcium-dependent protein kinase C signaling(GO:0043123); regulation of myeloid cell differentiation(GO:0045637); activity(GO:0004698); protein kinase C positive regulation of angiogenesis(GO:0045766); positive regulation of calcium ion- binding(GO:0005080); calcium channel regulator dependent exocytosis(GO:0045956); negative regulation of insulin receptor signaling activity(GO:0005246); calcium ion binding(GO:0005509); pathway(GO:0046627); calcium ion-regulated exocytosis of protein binding(GO:0005515); ATP binding(GO:0005524); neurotransmitter(GO:0048791); B cell receptor signaling pathway(GO:0050853); calcium-dependent phospholipid binding(GO:0005544); zinc positive regulation of B cell receptor signaling pathway(GO:0050861); positive ion binding(GO:0008270); nuclear receptor transcription regulation of NF-kappaB transcription factor activity(GO:0051092); cellular response coactivator activity(GO:0030374); histone kinase activity (H3- to carbohydrate stimulus(GO:0071322); presynaptic modulation of chemical synaptic T6 specific)(GO:0035403); histone binding(GO:0042393); transmission(GO:0099171); positive regulation of nucleic acid-templated Prkcb Protein kinase C beta type androgen receptor binding(GO:0050681) transcription(GO:1903508); regulation of synaptic vesicle exocytosis(GO:2000300) serine-type endopeptidase activity(GO:0004252); protein proteolysis(GO:0006508); post-translational protein modification(GO:0043687); PRSS23 Serine protease 23 binding(GO:0005515) cellular protein metabolic process(GO:0044267)

211

exopolyphosphatase activity(GO:0004309); inorganic diphosphatase activity(GO:0004427); protein binding(GO:0005515); tubulin binding(GO:0015631); polyphosphate catabolic process(GO:0006798); dephosphorylation(GO:0016311); Exopolyphosphatase pyrophosphatase activity(GO:0016462); phosphatase regulation of microtubule polymerization(GO:0031113); regulation of PRUNE1 PRUNE1 activity(GO:0016791); metal ion binding(GO:0046872) neurogenesis(GO:0050767) Glutamyl-tRNA(Gln) protein binding(GO:0005515); ATP binding(GO:0005524); amidotransferase subunit hydrolase activity(GO:0016787); glutaminyl-tRNA synthase regulation of protein stability(GO:0031647); mitochondrial translation(GO:0032543); QRSL1 A, mitochondrial (glutamine-hydrolyzing) activity(GO:0050567) glutaminyl-tRNAGln biosynthesis via transamidation(GO:0070681) GTPase activity(GO:0003924); protein binding(GO:0005515); RABL2A Rab-like protein 2A GTP binding(GO:0005525) intracellular protein transport(GO:0006886) skeletal system development(GO:0001501); ossification(GO:0001503); protein phosphorylation(GO:0006468); cell cycle(GO:0007049); positive regulation of protein autophosphorylation(GO:0031954); negative regulation of peptidyl-serine phosphorylation(GO:0033137); epidermal growth factor receptor signaling pathway via I-kappaB kinase/NF-kappaB cascade(GO:0038168); positive regulation of apoptotic process(GO:0043065); regulation of osteoblast differentiation(GO:0045667); regulation of osteoclast differentiation(GO:0045670); positive regulation of protein kinase activity(GO:0045860); positive regulation of JNK cascade(GO:0046330); bone remodeling(GO:0046849); homeostasis of number Ras association domain- protein kinase activity(GO:0004672); protein of cells(GO:0048872); protein stabilization(GO:0050821); negative regulation of RASSF2 containing protein 2 binding(GO:0005515) NIK/NF-kappaB signaling(GO:1901223) negative regulation of transcription by RNA polymerase II(GO:0000122); stimulatory C-type lectin receptor signaling pathway(GO:0002223); regulation of transcription by RNA polymerase II transcription regulatory region sequence- RNA polymerase II(GO:0006357); inflammatory response(GO:0006954); I-kappaB specific DNA binding(GO:0000977); RNA polymerase II cis- kinase/NF-kappaB signaling(GO:0007249); antigen processing and regulatory region sequence-specific DNA presentation(GO:0019882); lymphocyte differentiation(GO:0030098); negative binding(GO:0000978); DNA-binding transcription factor regulation of interferon-beta production(GO:0032688); circadian regulation of gene activity, RNA polymerase II-specific(GO:0000981); DNA expression(GO:0032922); cellular response to stress(GO:0033554); response to binding(GO:0003677); chromatin binding(GO:0003682); cytokine(GO:0034097); NIK/NF-kappaB signaling(GO:0038061); myeloid dendritic DNA-binding transcription factor activity(GO:0003700); cell differentiation(GO:0043011); T-helper 1 cell differentiation(GO:0045063); innate protein binding(GO:0005515); protein kinase immune response(GO:0045087); positive regulation of transcription by RNA RELB Transcription factor RelB binding(GO:0019901); identical protein binding(GO:0042802) polymerase II(GO:0045944); cellular response to osmotic stress(GO:0071470) G protein-coupled receptor signaling pathway(GO:0007186); regulation of G protein- GTPase activity(GO:0003924); GTPase activator coupled receptor signaling pathway(GO:0008277); negative regulation of signal Regulator of G-protein activity(GO:0005096); G-protein beta-subunit transduction(GO:0009968); intracellular signal transduction(GO:0035556); positive RGS11 signaling 11 binding(GO:0031681) regulation of GTPase activity(GO:0043547) MAPK cascade(GO:0000165); liver development(GO:0001889); regulation of acute inflammatory response(GO:0002673); proteolysis(GO:0006508); response to xenobiotic stimulus(GO:0009410); negative regulation of natural killer cell Rhomboid domain- endopeptidase activity(GO:0004175); serine-type activation(GO:0032815); positive regulation of protein catabolic RHBDD3 containing protein 3 endopeptidase activity(GO:0004252) process(GO:0045732); regulation of protein secretion(GO:0050708) zinc ion binding(GO:0008270); ubiquitin protein ligase RNF145 RING finger protein 145 activity(GO:0061630) protein ubiquitination(GO:0016567) protein polyubiquitination(GO:0000209); microtubule cytoskeleton ubiquitin-protein transferase activity(GO:0004842); organization(GO:0000226); ubiquitin-dependent protein catabolic transcription factor binding(GO:0008134); ubiquitin process(GO:0006511); protein ubiquitination(GO:0016567); positive regulation of conjugating enzyme binding(GO:0031624); metal ion proteasomal ubiquitin-dependent protein catabolic process(GO:0032436); regulation E3 ubiquitin-protein ligase binding(GO:0046872); ubiquitin protein ligase of protein catabolic process at postsynapse, modulating synaptic RNF19A RNF19A activity(GO:0061630) transmission(GO:0099576)

212

aminopeptidase activity(GO:0004177); epoxide hydrolase activity(GO:0004301); metalloexopeptidase activity(GO:0008235); metallopeptidase RNPEP Aminopeptidase B activity(GO:0008237); zinc ion binding(GO:0008270) proteolysis(GO:0006508); negative regulation of blood pressure(GO:0045776) ribosomal small subunit assembly(GO:0000028); nuclear-transcribed mRNA catabolic process, nonsense-mediated decay(GO:0000184); maturation of SSU-rRNA from tricistronic rRNA transcript (SSU-rRNA, 5.8S rRNA, LSU- rRNA)(GO:0000462); monocyte chemotaxis(GO:0002548); rRNA processing(GO:0006364); translation(GO:0006412); translational initiation(GO:0006413); SRP-dependent cotranslational protein targeting to membrane(GO:0006614); nucleolus organization(GO:0007000); Notch signaling pathway(GO:0007219); response to extracellular stimulus(GO:0009991); viral transcription(GO:0019083); erythrocyte differentiation(GO:0030218); maturation of SSU-rRNA(GO:0030490); killing of cells of other organism(GO:0031640); ribosomal small subunit biogenesis(GO:0042274); defense response to Gram-negative bacterium(GO:0050829); positive regulation of cellular component RNA binding(GO:0003723); structural constituent of movement(GO:0051272); positive regulation of respiratory burst involved in ribosome(GO:0003735); protein binding(GO:0005515); inflammatory response(GO:0060265); negative regulation of respiratory burst fibroblast growth factor binding(GO:0017134); protein kinase involved in inflammatory response(GO:0060266); antimicrobial humoral immune RPS19 40S ribosomal protein S19 binding(GO:0019901); identical protein binding(GO:0042802) response mediated by antimicrobial peptide(GO:0061844) SAC3 domain-containing spindle assembly(GO:0051225); centrosome duplication(GO:0051298); cell SAC3D1 protein 1 protein binding(GO:0005515) division(GO:0051301) neutrophil homeostasis(GO:0001780); signal transduction(GO:0007165); transmembrane receptor protein tyrosine kinase signaling pathway(GO:0007169); blood coagulation(GO:0007596); negative regulation of cell population proliferation(GO:0008285); hemopoiesis(GO:0030097); embryonic hemopoiesis(GO:0035162); intracellular signal transduction(GO:0035556); monocyte homeostasis(GO:0035702); megakaryocyte development(GO:0035855); cellular response to interleukin-3(GO:0036016); thrombopoietin-mediated signaling pathway(GO:0038163); negative regulation of tyrosine phosphorylation of STAT protein(GO:0042532); negative regulation of MAP kinase activity(GO:0043407); negative regulation of receptor signaling pathway via JAK-STAT(GO:0046426); transmembrane receptor protein tyrosine kinase adaptor erythrocyte development(GO:0048821); negative regulation of protein kinase B activity(GO:0005068); stem cell factor receptor signaling(GO:0051898); negative regulation of response to cytokine binding(GO:0005173); protein binding(GO:0005515); stimulus(GO:0060761); negative regulation of chemokine-mediated signaling signaling receptor complex adaptor activity(GO:0030159); pathway(GO:0070100); negative regulation of platelet aggregation(GO:0090331); signaling adaptor activity(GO:0035591); protein tyrosine negative regulation of Kit signaling pathway(GO:1900235); cellular response to SH2B3 SH2B adapter protein 3 kinase binding(GO:1990782) chemokine(GO:1990869) translation repressor activity, mRNA regulatory element binding(GO:0000900); glycine hydroxymethyltransferase dTMP biosynthetic process(GO:0006231); glycine metabolic process(GO:0006544); activity(GO:0004372); thiol-dependent ubiquitin-specific L-serine metabolic process(GO:0006563); L-serine catabolic process(GO:0006565); protease activity(GO:0004843); protein binding(GO:0005515); one-carbon metabolic process(GO:0006730); purine nucleobase biosynthetic zinc ion binding(GO:0008270); L-allo-threonine aldolase process(GO:0009113); protein deubiquitination(GO:0016579); negative regulation of activity(GO:0008732); amino acid binding(GO:0016597); translation(GO:0017148); glycine biosynthetic process from serine(GO:0019264); pyridoxal phosphate binding(GO:0030170); small molecule tetrahydrofolate interconversion(GO:0035999); carnitine biosynthetic binding(GO:0036094); identical protein process(GO:0045329); tetrahydrofolate metabolic process(GO:0046653); folic acid Serine binding(GO:0042802); protein homodimerization metabolic process(GO:0046655); protein homotetramerization(GO:0051289); cellular hydroxymethyltransferase, activity(GO:0042803); mRNA 5'-UTR binding(GO:0048027); response to tetrahydrofolate(GO:1904482); cellular response to leukemia inhibitory SHMT1 cytosolic cobalt ion binding(GO:0050897); serine binding(GO:0070905) factor(GO:1990830)

213

myosin II binding(GO:0045159); actin filament actin filament organization(GO:0007015); brain development(GO:0007420); actin SHROOM4 Protein Shroom4 binding(GO:0051015) cytoskeleton organization(GO:0030036); cognition(GO:0050890) pyruvate secondary active transmembrane transporter activity(GO:0005477); protein binding(GO:0005515); monocarboxylic acid transmembrane transporter activity(GO:0008028); lactate transmembrane transporter monocarboxylic acid transport(GO:0015718); lactate transmembrane Monocarboxylate activity(GO:0015129); symporter activity(GO:0015293); transport(GO:0035873); transport across blood-brain barrier(GO:0150104); pyruvate SLC16A7 transporter 2 pyruvate transmembrane transporter activity(GO:0050833) transmembrane transport(GO:1901475) protein binding(GO:0005515); monocarboxylic acid Monocarboxylate transmembrane transporter activity(GO:0008028); symporter monocarboxylic acid transport(GO:0015718); urate metabolic process(GO:0046415); SLC16A9 transporter 9 activity(GO:0015293) transmembrane transport(GO:0055085) in utero embryonic development(GO:0001701); zinc ion transport(GO:0006829); cellular calcium ion homeostasis(GO:0006874); cellular zinc ion homeostasis(GO:0006882); negative regulation of neurotransmitter secretion(GO:0046929); calcium ion import(GO:0070509); cadmium ion transmembrane transport(GO:0070574); zinc ion transmembrane zinc ion transmembrane transporter activity(GO:0005385); transport(GO:0071577); negative regulation of zinc ion transmembrane protein binding(GO:0005515); calcium channel inhibitor import(GO:0071584); detoxification of cadmium ion(GO:0071585); negative SLC30A1 Zinc transporter 1 activity(GO:0019855) regulation of calcium ion import(GO:0090281) protein binding(GO:0005515); amino acid transmembrane amino acid transport(GO:0006865); neutral amino acid transport(GO:0015804); transporter activity(GO:0015171); neutral amino acid negative regulation of amino acid transport(GO:0051956); negative regulation of Large neutral amino acids transmembrane transporter activity(GO:0015175); L-amino leucine import(GO:0060358); L-alpha-amino acid transmembrane SLC43A2 transporter small subunit 4 acid transmembrane transporter activity(GO:0015179) transport(GO:1902475) folic acid binding(GO:0005542); folic acid transmembrane transporter activity(GO:0008517); proton transmembrane cellular iron ion homeostasis(GO:0006879); folic acid transport(GO:0015884); heme transporter activity(GO:0015078); heme transmembrane transport(GO:0015886); folic acid metabolic process(GO:0046655); methotrexate transporter activity(GO:0015232); methotrexate transport(GO:0051958); transmembrane transport(GO:0055085); intestinal folate Proton-coupled folate transmembrane transporter activity(GO:0015350); absorption(GO:0098829); proton transmembrane transport(GO:1902600); folate SLC46A1 transporter transmembrane transporter activity(GO:0022857) import across plasma membrane(GO:1904447) aortic valve morphogenesis(GO:0003180); atrioventricular valve morphogenesis(GO:0003181); axon guidance(GO:0007411); negative regulation of cell population proliferation(GO:0008285); negative regulation of gene expression(GO:0010629); negative regulation of cell growth(GO:0030308); cellular response to hormone stimulus(GO:0032870); Roundabout signaling pathway(GO:0035385); axon extension involved in axon guidance(GO:0048846); negative chemotaxis(GO:0050919); response to cortisol(GO:0051414); ventricular septum morphogenesis(GO:0060412); apoptotic process involved in calcium ion binding(GO:0005509); heparin luteolysis(GO:0061364); negative regulation of chemokine-mediated signaling SLIT3 Slit homolog 3 protein binding(GO:0008201); Roundabout binding(GO:0048495) pathway(GO:0070100) transcription regulatory region sequence-specific DNA negative regulation of transcription by RNA polymerase II(GO:0000122); ureteric binding(GO:0000976); RNA polymerase II cis-regulatory bud development(GO:0001657); response to hypoxia(GO:0001666); in utero region sequence-specific DNA binding(GO:0000978); DNA- embryonic development(GO:0001701); mesoderm formation(GO:0001707); binding transcription factor activity, RNA polymerase II- somitogenesis(GO:0001756); liver development(GO:0001889); heart specific(GO:0000981); cis-regulatory region sequence-specific looping(GO:0001947); osteoblast development(GO:0002076); immune system DNA binding(GO:0000987); RNA polymerase II activating development(GO:0002520); regulation of transcription, DNA- transcription factor binding(GO:0001102); transcription templated(GO:0006355); regulation of transcription by RNA polymerase coactivator binding(GO:0001223); DNA-binding transcription II(GO:0006357); activation of cysteine-type endopeptidase activity involved in Mothers against activator activity, RNA polymerase II-specific(GO:0001228); apoptotic process(GO:0006919); immune response(GO:0006955); cell cycle decapentaplegic homolog DNA binding(GO:0003677); DNA-binding transcription arrest(GO:0007050); transforming growth factor beta receptor signaling SMAD3 3 factor activity(GO:0003700); transforming growth factor beta pathway(GO:0007179); SMAD protein complex assembly(GO:0007183); endoderm

214

receptor binding(GO:0005160); protein binding(GO:0005515); development(GO:0007492); anatomical structure morphogenesis(GO:0009653); collagen binding(GO:0005518); beta-catenin embryonic pattern specification(GO:0009880); positive regulation of gene binding(GO:0008013); transcription factor expression(GO:0010628); positive regulation of alkaline phosphatase binding(GO:0008134); zinc ion binding(GO:0008270); activity(GO:0010694); positive regulation of epithelial to mesenchymal DEAD/H-box RNA helicase binding(GO:0017151); protein transition(GO:0010718); viral process(GO:0016032); regulation of striated muscle kinase binding(GO:0019901); phosphatase tissue development(GO:0016202); protein deubiquitination(GO:0016579); regulation binding(GO:0019902); chromatin DNA of transforming growth factor beta receptor signaling pathway(GO:0017015); signal binding(GO:0031490); ubiquitin protein ligase transduction involved in regulation of gene expression(GO:0023019); cell binding(GO:0031625); mineralocorticoid receptor differentiation(GO:0030154); negative regulation of cell growth(GO:0030308); binding(GO:0031962); glucocorticoid receptor adrenal gland development(GO:0030325); positive regulation of cell binding(GO:0035259); identical protein migration(GO:0030335); positive regulation of bone mineralization(GO:0030501); binding(GO:0042802); protein homodimerization BMP signaling pathway(GO:0030509); negative regulation of transforming growth activity(GO:0042803); ubiquitin binding(GO:0043130); bHLH factor beta receptor signaling pathway(GO:0030512); thyroid gland transcription factor binding(GO:0043425); sequence-specific development(GO:0030878); primary miRNA processing(GO:0031053); positive DNA binding(GO:0043565); co-SMAD regulation of chondrocyte differentiation(GO:0032332); positive regulation of binding(GO:0070410); I-SMAD binding(GO:0070411); R- interleukin-1 beta production(GO:0032731); regulation of transforming growth factor SMAD binding(GO:0070412) beta2 production(GO:0032909); positive regulation of transforming growth factor beta3 production(GO:0032916); activin receptor signaling pathway(GO:0032924); negative regulation of osteoblast proliferation(GO:0033689); nodal signaling pathway(GO:0038092); wound healing(GO:0042060); T cell activation(GO:0042110); negative regulation of protein catabolic process(GO:0042177); positive regulation of protein import into nucleus(GO:0042307); negative regulation of apoptotic process(GO:0043066); cell- cell junction organization(GO:0045216); positive regulation of nitric oxide biosynthetic process(GO:0045429); negative regulation of fat cell differentiation(GO:0045599); negative regulation of osteoblast differentiation(GO:0045668); positive regulation of transcription, DNA- templated(GO:0045893); negative regulation of mitotic cell cycle(GO:0045930); positive regulation of transcription by RNA polymerase II(GO:0045944); paraxial mesoderm morphogenesis(GO:0048340); developmental growth(GO:0048589); embryonic foregut morphogenesis(GO:0048617); embryonic cranial skeleton morphogenesis(GO:0048701); regulation of epithelial cell proliferation(GO:0050678); negative regulation of inflammatory response(GO:0050728); regulation of immune response(GO:0050776); protein stabilization(GO:0050821); positive regulation of positive chemotaxis(GO:0050927); positive regulation of DNA-binding transcription factor activity(GO:0051091); regulation of binding(GO:0051098); negative regulation of cytosolic calcium ion concentration(GO:0051481); positive regulation of stress fiber assembly(GO:0051496); positive regulation of focal adhesion assembly(GO:0051894); pericardium development(GO:0060039); transdifferentiation(GO:0060290); SMAD protein signal transduction(GO:0060395); negative regulation of wound healing(GO:0061045); negative regulation of lung blood pressure(GO:0061767); lens fiber cell differentiation(GO:0070306); cellular response to cytokine stimulus(GO:0071345); cellular response to transforming growth factor beta stimulus(GO:0071560); positive regulation of canonical Wnt signaling pathway(GO:0090263); extrinsic apoptotic signaling pathway(GO:0097191); activation of cysteine-type endopeptidase activity involved in apoptotic signaling pathway(GO:0097296); positive regulation of extracellular matrix assembly(GO:1901203); positive regulation of pri-miRNA transcription by RNA

215

polymerase II(GO:1902895); negative regulation of cardiac muscle hypertrophy in response to stress(GO:1903243) positive regulation of neuroblast proliferation(GO:0002052); secondary heart field specification(GO:0003139); cardiac right ventricle formation(GO:0003219); neural retina development(GO:0003407); nucleosome disassembly(GO:0006337); chromatin DNA-binding transcription activator activity, RNA remodeling(GO:0006338); transcription, DNA-templated(GO:0006351); regulation of polymerase II-specific(GO:0001228); chromatin transcription by RNA polymerase II(GO:0006357); positive regulation of G2/M binding(GO:0003682); transcription coactivator transition of mitotic cell cycle(GO:0010971); regulation of lipid metabolic activity(GO:0003713); signaling receptor process(GO:0019216); muscle cell differentiation(GO:0042692); regulation of protein binding(GO:0005102); protein binding(GO:0005515); binding(GO:0043393); positive regulation of transcription, DNA- SWI/SNF-related matrix- transcription factor binding(GO:0008134); nuclear receptor templated(GO:0045893); positive regulation of transcription by RNA polymerase associated actin-dependent binding(GO:0016922); nuclear receptor transcription II(GO:0045944); positive regulation of smooth muscle cell regulator of chromatin coactivator activity(GO:0030374); nuclear hormone receptor differentiation(GO:0051152); positive regulation of nucleic acid-templated SMARCD3 subfamily D member 3 binding(GO:0035257) transcription(GO:1903508) superoxide dismutase activity(GO:0004784); copper ion response to hypoxia(GO:0001666); removal of superoxide radicals(GO:0019430); Extracellular superoxide binding(GO:0005507); protein binding(GO:0005515); heparin cellular response to oxidative stress(GO:0034599); response to copper SOD3 dismutase [Cu-Zn] binding(GO:0008201) ion(GO:0046688); oxidation-reduction process(GO:0055114) Sperm-associated antigen nucleic acid binding(GO:0003676); protein SPAG7 7 binding(GO:0005515) establishment of mitotic spindle orientation(GO:0000132); metanephros development(GO:0001656); ureteric bud development(GO:0001657); organ induction(GO:0001759); negative regulation of cell population proliferation(GO:0008285); negative regulation of epithelial to mesenchymal transition(GO:0010719); negative regulation of transforming growth factor beta receptor signaling pathway(GO:0030512); negative regulation of GTPase activity(GO:0034260); negative regulation of fibroblast growth factor receptor signaling pathway(GO:0040037); negative regulation of epidermal growth factor receptor signaling pathway(GO:0042059); negative regulation of MAP kinase activity(GO:0043407); negative regulation of Ras protein signal transduction(GO:0046580); animal organ development(GO:0048513); negative regulation of neurotrophin TRK receptor signaling pathway(GO:0051387); bud elongation involved in lung branching(GO:0060449); epithelial to mesenchymal transition involved in cardiac fibroblast development(GO:0060940); negative regulation of ERK1 and ERK2 cascade(GO:0070373); negative regulation of lens SPRY1 Protein sprouty homolog 1 protein binding(GO:0005515) fiber cell differentiation(GO:1902747) phosphatidylcholine biosynthetic process(GO:0006656); bile acid START domain- secretion(GO:0032782); positive regulation of peroxisome proliferator activated STARD10 containing protein 10 protein binding(GO:0005515); lipid binding(GO:0008289) receptor signaling pathway(GO:0035360) hematopoietic progenitor cell differentiation(GO:0002244); endocytosis(GO:0006897); vesicle-mediated transport(GO:0016192); regulation of protein binding(GO:0005515); clathrin adaptor endocytosis(GO:0030100); synaptic vesicle recycling(GO:0036465); synaptic vesicle STON2 Stonin-2 activity(GO:0035615) endocytosis(GO:0048488); membrane organization(GO:0061024) autophagosome assembly(GO:0000045); intracellular protein transport(GO:0006886); vesicle fusion(GO:0006906); vesicle-mediated transport(GO:0016192); cholesterol SNARE binding(GO:0000149); SNAP receptor efflux(GO:0033344); vesicle docking(GO:0048278); protein STX12 Syntaxin-12 activity(GO:0005484); protein binding(GO:0005515) stabilization(GO:0050821) intracellular protein transport(GO:0006886); exocytosis(GO:0006887); vesicle SNARE binding(GO:0000149); SNAP receptor fusion(GO:0006906); positive regulation of cell population activity(GO:0005484); protein binding(GO:0005515); proliferation(GO:0008284); synaptic vesicle docking(GO:0016081); cytokine- STX3 Syntaxin-3 arachidonic acid binding(GO:0050544) mediated signaling pathway(GO:0019221); neuron projection

216

development(GO:0031175); synaptic vesicle fusion to presynaptic active zone membrane(GO:0031629); positive regulation of cell adhesion(GO:0045785); vesicle docking(GO:0048278); positive regulation of chemotaxis(GO:0050921); long-term synaptic potentiation(GO:0060291); membrane fusion(GO:0061025); exocytic insertion of neurotransmitter receptor to postsynaptic membrane(GO:0098967); positive regulation of protein localization to plasma membrane(GO:1903078); positive regulation of protein localization to cell surface(GO:2000010) RNA polymerase I preinitiation complex assembly(GO:0001188); transcription by RNA polymerase I(GO:0006360); transcription initiation from RNA polymerase I TATA box-binding RNA polymerase I core promoter sequence-specific DNA promoter(GO:0006361); transcription elongation from RNA polymerase I protein-associated factor binding(GO:0001164); RNA polymerase I general promoter(GO:0006362); termination of RNA polymerase I RNA polymerase I subunit transcription initiation factor activity(GO:0001181); protein transcription(GO:0006363); transcription by RNA polymerase II(GO:0006366); TAF1C C binding(GO:0005515) positive regulation of gene expression, epigenetic(GO:0045815) negative regulation of transcription by RNA polymerase II(GO:0000122); transcription by RNA polymerase II(GO:0006366); transcription initiation from RNA p53 binding(GO:0002039); protein binding(GO:0005515); polymerase II promoter(GO:0006367); negative regulation of DNA-binding RNA polymerase II general transcription initiation factor transcription factor activity(GO:0043433); maintenance of protein location in Transcription initiation activity(GO:0016251); metal ion binding(GO:0046872); nucleus(GO:0051457); regulation of signal transduction by p53 class TAF3 factor TFIID subunit 3 protein heterodimerization activity(GO:0046982) mediator(GO:1901796) DNA binding(GO:0003677); DNA-binding transcription negative regulation of transcription by RNA polymerase II(GO:0000122); heart TCF25 Transcription factor 25 factor activity(GO:0003700); protein binding(GO:0005515) development(GO:0007507) metalloendopeptidase activity(GO:0004222); protein binding(GO:0005515); peptide binding(GO:0042277); metal protein polyubiquitination(GO:0000209); proteolysis(GO:0006508); peptide THOP1 Thimet oligopeptidase ion binding(GO:0046872) metabolic process(GO:0006518) Tigger transposable TIGD1 element-derived protein 1 DNA binding(GO:0003677) Tigger transposable TIGD3 element-derived protein 3 DNA binding(GO:0003677); protein binding(GO:0005515) RNA binding(GO:0003723); phosphoprotein phosphatase activity(GO:0004721); protein serine/threonine phosphatase release of cytochrome c from mitochondria(GO:0001836); protein activity(GO:0004722); protein tyrosine phosphatase dephosphorylation(GO:0006470); mitochondrial membrane Mitochondrial import inner activity(GO:0004725); interleukin-2 receptor organization(GO:0007006); protein transport(GO:0015031); protein import into membrane translocase binding(GO:0005134); protein binding(GO:0005515); mitochondrial matrix(GO:0030150); peptidyl-tyrosine TIMM50 subunit TIM50 ribonucleoprotein complex binding(GO:0043021) dephosphorylation(GO:0035335) TMEM159 Promethin protein binding(GO:0005515) lipid droplet formation(GO:0140042) serine-type endopeptidase activity(GO:0004252); scavenger TMPRSS15 Enteropeptidase receptor activity(GO:0005044); protein binding(GO:0005515) proteolysis(GO:0006508); endocytosis(GO:0006897) apoptotic process(GO:0006915); activation of cysteine-type endopeptidase activity involved in apoptotic process(GO:0006919); signal transduction(GO:0007165); cell surface receptor signaling pathway(GO:0007166); activation of NF-kappaB-inducing kinase activity(GO:0007250); extrinsic apoptotic signaling pathway via death domain receptors(GO:0008625); TRAIL-activated apoptotic signaling pathway(GO:0036462); regulation of apoptotic process(GO:0042981); positive protease binding(GO:0002020); death receptor regulation of apoptotic process(GO:0043065); leukocyte migration(GO:0050900); activity(GO:0005035); protein binding(GO:0005515); cellular response to mechanical stimulus(GO:0071260); extrinsic apoptotic signaling Tumor necrosis factor transcription factor binding(GO:0008134); signaling receptor pathway(GO:0097191); regulation of extrinsic apoptotic signaling pathway via death TNFRSF10 receptor superfamily activity(GO:0038023); identical protein domain receptors(GO:1902041); negative regulation of extrinsic apoptotic signaling A member 10A binding(GO:0042802); TRAIL binding(GO:0045569) pathway via death domain receptors(GO:1902042) transcription regulatory region sequence-specific DNA negative regulation of transcription by RNA polymerase II(GO:0000122); replicative TP63 Tumor protein 63 binding(GO:0000976); RNA polymerase II transcription cell aging(GO:0001302); skeletal system development(GO:0001501); establishment

217

regulatory region sequence-specific DNA of planar polarity(GO:0001736); epithelial cell development(GO:0002064); binding(GO:0000977); DNA-binding transcription factor chromatin remodeling(GO:0006338); regulation of transcription, DNA- activity, RNA polymerase II-specific(GO:0000981); DNA- templated(GO:0006355); apoptotic process(GO:0006915); cellular response to DNA binding transcription activator activity, RNA polymerase II- damage stimulus(GO:0006974); Notch signaling pathway(GO:0007219); specific(GO:0001228); p53 binding(GO:0002039); DNA spermatogenesis(GO:0007283); ectoderm and mesoderm interaction(GO:0007499); binding(GO:0003677); chromatin binding(GO:0003682); cell population proliferation(GO:0008283); proximal/distal pattern damaged DNA binding(GO:0003684); DNA-binding formation(GO:0009954); multicellular organism aging(GO:0010259); epidermal cell transcription factor activity(GO:0003700); protein division(GO:0010481); regulation of epidermal cell division(GO:0010482); positive binding(GO:0005515); identical protein regulation of keratinocyte proliferation(GO:0010838); keratinocyte binding(GO:0042802); metal ion binding(GO:0046872); WW differentiation(GO:0030216); polarized epithelial cell differentiation(GO:0030859); domain binding(GO:0050699); MDM2/MDM4 family protein hair follicle morphogenesis(GO:0031069); developmental process(GO:0032502); binding(GO:0097371) negative regulation of intracellular estrogen receptor signaling pathway(GO:0033147); embryonic forelimb morphogenesis(GO:0035115); embryonic hindlimb morphogenesis(GO:0035116); post-anal tail morphogenesis(GO:0036342); odontogenesis of dentin-containing tooth(GO:0042475); intrinsic apoptotic signaling pathway in response to DNA damage by p53 class mediator(GO:0042771); regulation of apoptotic process(GO:0042981); regulation of cysteine-type endopeptidase activity involved in apoptotic process(GO:0043281); skin morphogenesis(GO:0043589); negative regulation of keratinocyte differentiation(GO:0045617); positive regulation of osteoblast differentiation(GO:0045669); positive regulation of Notch signaling pathway(GO:0045747); negative regulation of transcription, DNA- templated(GO:0045892); positive regulation of transcription, DNA- templated(GO:0045893); positive regulation of transcription by RNA polymerase II(GO:0045944); sympathetic nervous system development(GO:0048485); female genitalia morphogenesis(GO:0048807); protein tetramerization(GO:0051262); neuron apoptotic process(GO:0051402); cloacal septation(GO:0060197); prostatic bud formation(GO:0060513); squamous basal epithelial stem cell differentiation involved in prostate gland acinus development(GO:0060529); establishment of skin barrier(GO:0061436); positive regulation of protein insertion into mitochondrial membrane involved in apoptotic signaling pathway(GO:1900740); regulation of signal transduction by p53 class mediator(GO:1901796); positive regulation of cell cycle G1/S phase transition(GO:1902808); positive regulation of somatic stem cell population maintenance(GO:1904674); cranial skeletal system development(GO:1904888); positive regulation of fibroblast apoptotic process(GO:2000271); negative regulation of mesoderm development(GO:2000381); negative regulation of cellular senescence(GO:2000773) protein binding(GO:0005515); protein C-terminus binding(GO:0008022); zinc ion binding(GO:0008270); identical protein binding(GO:0042802); metal ion protein polyubiquitination(GO:0000209); nervous system Tripartite motif-containing binding(GO:0046872); ubiquitin protein ligase development(GO:0007399); protein transport(GO:0015031); proteasome-mediated TRIM3 protein 3 activity(GO:0061630) ubiquitin-dependent protein catabolic process(GO:0043161) t-SNARE domain- SNARE binding(GO:0000149); SNAP receptor intracellular protein transport(GO:0006886); vesicle fusion(GO:0006906); vesicle- TSNARE1 containing protein 1 activity(GO:0005484); protein binding(GO:0005515) mediated transport(GO:0016192); vesicle docking(GO:0048278) Ubiquitin-associated binding of sperm to zona pellucida(GO:0007339); stress granule UBAP2L protein 2-like RNA binding(GO:0003723); protein binding(GO:0005515) assembly(GO:0034063); hematopoietic stem cell homeostasis(GO:0061484) osteoblast differentiation(GO:0001649); cellular response to DNA damage protein binding(GO:0005515); protein kinase stimulus(GO:0006974); positive regulation of cell population binding(GO:0019901); UFM1 ligase activity(GO:0061666); proliferation(GO:0008284); positive regulation of autophagy(GO:0010508); histone UFL1 E3 UFM1-protein ligase 1 UFM1 transferase activity(GO:0071568) modification(GO:0016570); erythrocyte differentiation(GO:0030218); negative

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regulation of protein ubiquitination(GO:0031397); negative regulation of NF-kappaB transcription factor activity(GO:0032088); regulation of proteasomal ubiquitin- dependent protein catabolic process(GO:0032434); regulation of protein localization(GO:0032880); regulation of intracellular estrogen receptor signaling pathway(GO:0033146); response to endoplasmic reticulum stress(GO:0034976); negative regulation of apoptotic process(GO:0043066); regulation of I-kappaB kinase/NF-kappaB signaling(GO:0043122); regulation of inflammatory response(GO:0050727); hematopoietic stem cell differentiation(GO:0060218); positive regulation of glial cell proliferation(GO:0060252); reticulophagy(GO:0061709); protein ufmylation(GO:0071569); response to L- glutamate(GO:1902065); negative regulation of IRE1-mediated unfolded protein response(GO:1903895); protein K69-linked ufmylation(GO:1990592) UTP:glucose-1-phosphate uridylyltransferase activity(GO:0003983); protein binding(GO:0005515); glucose binding(GO:0005536); pyrimidine ribonucleotide glycogen metabolic process(GO:0005977); glycogen biosynthetic binding(GO:0032557); identical protein process(GO:0005978); UDP-glucose metabolic process(GO:0006011); UDP- UTP--glucose-1-phosphate binding(GO:0042802); metal ion binding(GO:0046872); glucuronate biosynthetic process(GO:0006065); brain development(GO:0007420); UGP2 uridylyltransferase uridylyltransferase activity(GO:0070569) glucose 1-phosphate metabolic process(GO:0019255) actomyosin contractile ring contraction(GO:0000916); protein targeting to lysosome(GO:0006622); vesicle budding from membrane(GO:0006900); nucleus organization(GO:0006997); nuclear envelope organization(GO:0006998); vacuole organization(GO:0007033); mitotic metaphase plate congression(GO:0007080); mitotic nuclear envelope reassembly(GO:0007084); abscission(GO:0009838); viral process(GO:0016032); vesicle-mediated transport(GO:0016192); endosomal transport(GO:0016197); macroautophagy(GO:0016236); viral life cycle(GO:0019058); viral release from host cell(GO:0019076); nuclear envelope reassembly(GO:0031468); intracellular cholesterol transport(GO:0032367); negative regulation of cytokinesis(GO:0032466); regulation of protein localization(GO:0032880); endosomal vesicle fusion(GO:0034058); multivesicular body assembly(GO:0036258); viral budding via host ESCRT complex(GO:0039702); ubiquitin-dependent protein catabolic process via the multivesicular body sorting pathway(GO:0043162); mitotic cytokinesis checkpoint(GO:0044878); cell division(GO:0051301); cytoskeleton-dependent cytokinesis(GO:0061640); late endosomal microautophagy(GO:0061738); midbody abscission(GO:0061952); vesicle uncoating(GO:0072319); ubiquitin-independent protein catabolic process via the multivesicular body sorting pathway(GO:0090611); positive regulation of viral protein binding(GO:0005515); ATP binding(GO:0005524); release from host cell(GO:1902188); regulation of protein localization to plasma protein C-terminus binding(GO:0008022); ATPase membrane(GO:1903076); positive regulation of exosomal secretion(GO:1903543); activity(GO:0016887); protein domain specific positive regulation of viral budding via host ESCRT complex(GO:1903774); positive Vacuolar protein sorting- binding(GO:0019904); protein-containing complex regulation of viral life cycle(GO:1903902); ESCRT complex VPS4A associated protein 4A binding(GO:0044877) disassembly(GO:1904896); ESCRT III complex disassembly(GO:1904903) WD repeat-containing maturation of SSU-rRNA from tricistronic rRNA transcript (SSU-rRNA, 5.8S rRNA, WDR46 protein 46 RNA binding(GO:0003723) LSU-rRNA)(GO:0000462); rRNA processing(GO:0006364) negative regulation of transcription by RNA polymerase II(GO:0000122); osteoblast differentiation(GO:0001649); tissue homeostasis(GO:0001894); negative regulation of protein phosphorylation(GO:0001933); heart process(GO:0003015); regulation of transcription coactivator activity(GO:0003713); transcription transcription, DNA-templated(GO:0006355); transcription initiation from RNA WW domain-containing corepressor activity(GO:0003714); protein polymerase II promoter(GO:0006367); negative regulation of protein kinase transcription regulator binding(GO:0005515); protein homodimerization activity(GO:0006469); nervous system development(GO:0007399); positive WWTR1 protein 1 activity(GO:0042803) regulation of cell population proliferation(GO:0008284); positive regulation of

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epithelial to mesenchymal transition(GO:0010718); protein ubiquitination(GO:0016567); stem cell division(GO:0017145); SCF-dependent proteasomal ubiquitin-dependent protein catabolic process(GO:0031146); glomerulus development(GO:0032835); multicellular organism growth(GO:0035264); hippo signaling(GO:0035329); negative regulation of fat cell differentiation(GO:0045599); positive regulation of transcription by RNA polymerase II(GO:0045944); mesenchymal cell differentiation(GO:0048762); cilium assembly(GO:0060271); regulation of SMAD protein signal transduction(GO:0060390); kidney morphogenesis(GO:0060993); regulation of metanephric nephron tubule epithelial cell differentiation(GO:0072307); negative regulation of canonical Wnt signaling pathway(GO:0090090) RNA polymerase II cis-regulatory region sequence-specific negative regulation of transcription by RNA polymerase II(GO:0000122); mRNA DNA binding(GO:0000978); DNA-binding transcription splicing, via spliceosome(GO:0000398); in utero embryonic factor activity, RNA polymerase II-specific(GO:0000981); development(GO:0001701); regulation of transcription, DNA- DNA-binding transcription activator activity, RNA templated(GO:0006355); Notch signaling pathway(GO:0007219); epidermis polymerase II-specific(GO:0001228); nucleic acid development(GO:0008544); regulation of gene expression(GO:0010468); negative binding(GO:0003676); DNA binding(GO:0003677); regulation of translation(GO:0017148); positive regulation of transcription by RNA chromatin binding(GO:0003682); double-stranded DNA polymerase II(GO:0045944); mRNA stabilization(GO:0048255); embryonic binding(GO:0003690); single-stranded DNA morphogenesis(GO:0048598); RNA transport(GO:0050658); tRNA binding(GO:0003697); RNA binding(GO:0003723); mRNA transport(GO:0051031); negative regulation of striated muscle cell binding(GO:0003729); protein binding(GO:0005515); miRNA differentiation(GO:0051154); positive regulation of cell division(GO:0051781); binding(GO:0035198); GTPase binding(GO:0051020); C5- CRD-mediated mRNA stabilization(GO:0070934); cellular response to interleukin- methylcytidine-containing RNA binding(GO:0062153); 7(GO:0098761); protein localization to cytoplasmic stress granule(GO:1903608); Nuclease-sensitive sequence-specific double-stranded DNA miRNA transport(GO:1990428); negative regulation of cellular YBX1 element-binding protein 1 binding(GO:1990837) senescence(GO:2000773) RNA polymerase II transcription regulatory region sequence- specific DNA binding(GO:0000977); DNA-binding transcription factor activity, RNA polymerase II- specific(GO:0000981); DNA binding(GO:0003677); protein Zinc finger and BTB binding(GO:0005515); metal ion binding(GO:0046872); domain-containing protein sequence-specific double-stranded DNA ZBTB22 22 binding(GO:1990837) regulation of transcription by RNA polymerase II(GO:0006357) transcription regulatory region sequence-specific DNA binding(GO:0000976); RNA polymerase II cis-regulatory region sequence-specific DNA binding(GO:0000978); double- stranded telomeric DNA binding(GO:0003691); DNA-binding transcription factor activity(GO:0003700); protein regulation of transcription by RNA polymerase II(GO:0006357); telomere Telomere zinc finger- binding(GO:0005515); identical protein maintenance via telomere lengthening(GO:0010833); positive regulation of ZBTB48 associated protein binding(GO:0042802); metal ion binding(GO:0046872) transcription, DNA-templated(GO:0045893) DNA binding(GO:0003677); chromatin nuclear-transcribed mRNA catabolic process, endonucleolytic cleavage-dependent binding(GO:0003682); RNA binding(GO:0003723); mRNA decay(GO:0000294); angiogenesis(GO:0001525); negative regulation of protein binding(GO:0003729); mRNA 3'-UTR binding(GO:0003730); phosphorylation(GO:0001933); positive regulation of defense response to virus by endoribonuclease activity(GO:0004521); exoribonuclease host(GO:0002230); immune response-activating signal transduction(GO:0002757); activity(GO:0004532); ribonuclease activity(GO:0004540); apoptotic process(GO:0006915); inflammatory response(GO:0006954); cellular thiol-dependent ubiquitin-specific protease response to DNA damage stimulus(GO:0006974); nervous system activity(GO:0004843); protein binding(GO:0005515); miRNA development(GO:0007399); regulation of gene expression(GO:0010468); positive binding(GO:0035198); RNA stem-loop binding(GO:0035613); regulation of autophagy(GO:0010508); positive regulation of endothelial cell mRNA 3'-UTR AU-rich region binding(GO:0035925); migration(GO:0010595); positive regulation of gene expression(GO:0010628); Endoribonuclease ribosome binding(GO:0043022); metal ion negative regulation of gene expression(GO:0010629); negative regulation of muscle ZC3H12A ZC3H12A binding(GO:0046872) cell apoptotic process(GO:0010656); positive regulation of lipid

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storage(GO:0010884); positive regulation of cell death(GO:0010942); viral process(GO:0016032); protein deubiquitination(GO:0016579); cell differentiation(GO:0030154); negative regulation of NF-kappaB transcription factor activity(GO:0032088); negative regulation of interferon-gamma production(GO:0032689); negative regulation of interleukin-1 beta production(GO:0032691); negative regulation of interleukin-6 production(GO:0032715); negative regulation of tumor necrosis factor production(GO:0032720); cellular response to oxidative stress(GO:0034599); cellular response to glucose starvation(GO:0042149); positive regulation of protein import into nucleus(GO:0042307); negative regulation of macrophage activation(GO:0043031); negative regulation of I-kappaB kinase/NF-kappaB signaling(GO:0043124); negative regulation by host of viral genome replication(GO:0044828); negative regulation of nitric oxide biosynthetic process(GO:0045019); positive regulation of fat cell differentiation(GO:0045600); positive regulation of angiogenesis(GO:0045766); positive regulation of transcription by RNA polymerase II(GO:0045944); T cell receptor signaling pathway(GO:0050852); protein complex oligomerization(GO:0051259); defense response to virus(GO:0051607); negative regulation of cardiac muscle contraction(GO:0055118); positive regulation of mRNA catabolic process(GO:0061014); 3'-UTR-mediated mRNA destabilization(GO:0061158); cellular response to lipopolysaccharide(GO:0071222); cellular response to interleukin-1(GO:0071347); cellular response to tumor necrosis factor(GO:0071356); RNA phosphodiester bond hydrolysis(GO:0090501); RNA phosphodiester bond hydrolysis, endonucleolytic(GO:0090502); RNA phosphodiester bond hydrolysis, exonucleolytic(GO:0090503); cellular response to virus(GO:0098586); negative regulation of cytokine production involved in inflammatory response(GO:1900016); positive regulation of execution phase of apoptosis(GO:1900119); positive regulation of p38MAPK cascade(GO:1900745); negative regulation of NIK/NF-kappaB signaling(GO:1901223); positive regulation of protein deubiquitination(GO:1903003); negative regulation of production of miRNAs involved in gene silencing by miRNA(GO:1903799); cellular response to sodium arsenite(GO:1903936); negative regulation of tumor necrosis factor secretion(GO:1904468); cellular response to ionomycin(GO:1904637); cellular response to chemokine(GO:1990869); negative regulation of T-helper 17 cell differentiation(GO:2000320); positive regulation of reactive oxygen species metabolic process(GO:2000379); positive regulation of miRNA catabolic process(GO:2000627) RNA polymerase II cis-regulatory region sequence-specific DNA binding(GO:0000978); DNA-binding transcription factor activity, RNA polymerase II-specific(GO:0000981); nucleic acid binding(GO:0003676); protein binding(GO:0005515); identical protein binding(GO:0042802); metal ion binding(GO:0046872); sequence-specific double-stranded DNA regulation of transcription, DNA-templated(GO:0006355); regulation of transcription ZNF250 Zinc finger protein 250 binding(GO:1990837) by RNA polymerase II(GO:0006357) RNA polymerase II cis-regulatory region sequence-specific DNA binding(GO:0000978); DNA-binding transcription factor activity, RNA polymerase II-specific(GO:0000981); DNA binding(GO:0003677); protein binding(GO:0005515); regulation of transcription by RNA polymerase II(GO:0006357); multicellular ZNF260 Zinc finger protein 260 metal ion binding(GO:0046872) organism development(GO:0007275)

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RNA polymerase II cis-regulatory region sequence-specific DNA binding(GO:0000978); DNA-binding transcription activator activity, RNA polymerase II-specific(GO:0001228); nucleic acid binding(GO:0003676); metal ion regulation of transcription, DNA-templated(GO:0006355); regulation of transcription ZNF585A Zinc finger protein 585A binding(GO:0046872) by RNA polymerase II(GO:0006357)

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Supplemental Table 5. Statistically significantly overrepresented categories of differentially expressed genes in liver tissue. Comparisons are made using the GO Molecular Function Complete and GO Biological Process Complete datasets. Fischer exact test conducted with a False Discovery Rate (FDR) correction. All genes were compared to Homo sapien dataset (GRCH38.p13).

Expected Number of Observed number of fold raw p- FDR H.sapien genes number of genes Over/Underrepresented genes in Enrichment value correction per category in analysis analysis protein binding (GO:0005515) 14109 147 118.42 + 1.24 1.31E-06 0.00021779 catalytic activity (GO:0003824) 5869 80 49.26 + 1.62 1.13E-06 0.00021779 binding (GO:0005488) 16469 161 138.22 + 1.16 5.60E-06 0.0007448 oxidoreductase activity Molecular (GO:0016491) 774 19 6.5 + 2.92 3.58E-05 0.00396783 hydrolase activity Function (GO:0016787) 2576 41 21.62 + 1.9 7.01E-05 0.0066595 tau protein binding (GO:0048156) 45 4 0.38 + 10.59 0.000734 0.05423444 pyrimidine ribonucleotide binding (GO:0032557) 3 2 0.03 + 79.43 0.000678 0.05423444 identical protein binding (GO:0042802) 2072 32 17.39 + 1.84 0.000841 0.0559265

metabolic process (GO:0008152) 8590 109 72.09 + 1.51 2.85E-08 9.88E-05 cellular metabolic process (GO:0044237) 7790 98 65.38 + 1.5 8.74E-07 0.00151508 organic substance metabolic process (GO:0071704) 8009 97 67.22 + 1.44 7.22E-06 0.00834391 oxidative phosphorylation (GO:0006119) 120 8 1.01 + 7.94 1.20E-05 0.00907198 ATP synthesis coupled Biological electron transport Process (GO:0042773) 89 7 0.75 + 9.37 1.57E-05 0.00907198 mitochondrial ATP synthesis coupled electron transport (GO:0042775) 88 7 0.74 + 9.48 1.46E-05 0.00907198 oxidation-reduction process (GO:0055114) 968 22 8.12 + 2.71 2.65E-05 0.01186484 electron transport coupled proton transport (GO:0015990) 5 3 0.04 + 71.49 3.08E-05 0.01186484 energy coupled proton transmembrane transport, 5 3 0.04 + 71.49 3.08E-05 0.01186484

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against electrochemical gradient (GO:0015988) protein stabilization (GO:0050821) 191 9 1.6 + 5.61 4.71E-05 0.01591668 respiratory electron transport chain (GO:0022904) 108 7 0.91 + 7.72 5.05E-05 0.01591668 cellular respiration (GO:0045333) 158 8 1.33 + 6.03 7.74E-05 0.02236215 ATP metabolic process (GO:0046034) 208 9 1.75 + 5.16 8.83E-05 0.02354893 biological_process (GO:0008150) 17984 167 150.94 + 1.11 0.00014 0.03043256 cellular process (GO:0009987) 15625 152 131.14 + 1.16 0.000147 0.03043256 nitrogen compound metabolic process (GO:0006807) 7092 84 59.52 + 1.41 0.000154 0.03043256 electron transport chain (GO:0022900) 176 8 1.48 + 5.42 0.000158 0.03043256 energy derivation by oxidation of organic compounds (GO:0015980) 228 9 1.91 + 4.7 0.000172 0.03138547 primary metabolic process (GO:0044238) 7575 88 63.58 + 1.38 0.000193 0.03345655 proton transmembrane transport (GO:1902600) 137 7 1.15 + 6.09 0.000207 0.03417471 negative regulation of protein metabolic process (GO:0051248) 1129 22 9.48 + 2.32 0.000287 0.04476952 regulation of protein stability (GO:0031647) 302 10 2.53 + 3.95 0.000297 0.04476952 multicellular organism development (GO:0007275) 5115 64 42.93 + 1.49 0.000398 0.05672012 positive regulation of respiratory burst involved in inflammatory response (GO:0060265) 2 2 0.02 + > 100 0.000409 0.05672012 system development (GO:0048731) 4516 58 37.9 + 1.53 0.000444 0.05920569

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CONCLUSION

This study addressed the molecular basis of TTX resistance in Th. couchii by studying the adaptive trait at multiple scales: whole animal, physiological, and genetic.

In Chapter 1, it is demonstrated that Th. couchii displays wide variation in TTX resistance at the whole animal level (Chapter 1), similar to the variation found in Th. sirtalis.

Even more interesting is the geographic pattern of trait matching between sympatric newt prey

(Ta. granulosa, Ta. sierrae, and Ta. torosa) and snake predators (Th. couchii). In the northern part of Th. couchii range, we found newts with low levels of toxicity and snakes with low levels of resistance, and these two adaptive traits increased in intensity in Th. couchii samples moving southward. The southernmost populations exhibit the highest levels of trait mismatching. These results indicate a coevolutionary relationship between Th. couchii and sympatric Taricha that is parallel to that found in Th. sirtalis. Additionally, the areas of trait mismatch may suggest that snakes have “won” the coevolutionary arms race in those populations, but more studies are needed to address the complexities of these patterns and interactions. For example, garter snakes are generalist predators and newts are thought to make up a small part of their diet, leading to questions about how strong of a selection pressure these two taxa actually pose on each other.

Testing for isolated reciprocal selection is extremely challenging as these species are interacting with many more organisms throughout their life history. But in general, these patterns largely mirror those seen in the well-characterized Ta. granulosa and Th. sirtalis system, indicating these separate arms races between newts and their snake predators have experienced similar dynamics across time and space.

The next steps in examining this adaptive trait was to uncover the more complex physiological determinants and evolutionary constraints that lead to such repeatable patterns of

225 coevolution. Our exploration of the convergent evolution of TTX resistance in Th. couchii

(Chapter 2) confirmed that there are similar physiological mechanisms underlying this adaptation in both Th. sirtalis and Th. couchii. Both species demonstrate a correlation between whole animal and skeletal muscle resistance suggesting that patterns of variation in whole animal TTX resistance follow patterns of variation in muscle TTX resistance. This correlation should be explored in a third species with known TTX-resistant populations, Th. atratus, to strengthen the evidence for the convergence of the same physiological path of resistance in this system.

While we found evidence for convergence on the physiological scale of this adaptation, we did not confirm similar genetic mechanisms to explain TTX resistance in Th. couchii. Th.

couchii show no functional variation in voltage-gated sodium channel (Nav) l oci and no statistically significant difference in SCN4A (Nav1.4) gene expression across populations with varying levels of whole animal and skeletal muscle resistance. To bolster this research further, we need to test for expression differences of the other Nav candidate genes expressed in nerve

tissue (SCN8A: Nav1.6, SCN9A: Nav1.7). If nerve tissue is more sensitive to TTX than muscle, it could arguably serve greater importance in determining the phenotypic outcome at the whole animal level. Given our results, we concluded that TTX resistance in Th. couchii cannot be explained by the same relationship between genotype and phenotype seen in other taxa, suggesting additional mechanisms are responsible for convergence at whole animal and organ levels, and molecular evolution in this system may not be as predictable as previously thought.

This could be taken as evidence for the hypothesis of TTX resistance being a polygenic trait that is composed of many genes. This surprising exception led to the genome-wide exploration of this trait.

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In lieu of robust genomic resources for Thamnophis, we assembled a hybrid de novo transcriptome of Th. couchii and identified differentially expressed transcripts among snakes with varying TTX resistance (Chapter 3). We created an experimental design that mimics the digestive and immune response that may take place when snakes eat a toxic newt. Our samples focused on a single population of snakes with varying levels of TTX resistance to minimize differences expected to result from other processes in subdivided populations. Results revealed hundreds of differentially expressed genes in muscle and liver tissue and support the hypothesis that the genetic mechanisms underlying TTX resistance are complex and potentially polygenic. Some genes found to be differentially expressed in this experiment lie in biochemical pathways that may contribute to TTX resistance, such as muscle growth and performance, oxygenation, and immune response.

But with a lack of detailed knowledge on the function and processes these genes and proteins are a part of, we cannot provide a confident conclusion as to which genes directly relate to resistance in Th. couchii.

To further this research, it is imperative to move beyond correlating patterns of gene expression and investigate the potentially causal relationships of these genes and pathways. A first step would be to gain a deeper understanding of gene function and biological process and confirm expression level differences of potential candidate genes with isolated qPCR studies. The results derived in this project are extremely unique to this system. No other research program has investigated the entire transcriptome of Thamnophis with the hopes of identifying the polygenic origins of TTX resistance. Due to the novelty of these data types, it will be useful to collaborate with experts in physiology and immunology to unravel to relevant details about the genes that were statistically differentially expressed among phenotypes. Increasing sample sizes and tissue types of snakes with varying phenotypes could also be useful to better elucidate critical genes and

227 transcripts. Including control samples from snakes with no TTX in their system is also necessary to adequately measure biological replication of non-stress organisms. Future researchers should be certain to include samples from the same population and account for sequencing batch effects.

The muscle and liver tissue analyzed in this project likely only tell part of the story of how resistant snakes handle a lethal neurotoxin like TTX. TTX is first introduced to snakes through gut tissue before it ends up in kidney and liver tissue through transport via the blood steam. Because of this, the gene expression profiles from blood, kidney, and gut should be also considered in additional sequencing projects.

Another direction worthy of exploring is understanding the role of alternative splicing in this system. Alternative splicing can be a key mechanism for generating functional phenotypic diversity as it allows genes to express multiple mRNAs and encode multiple proteins. It is possible that alternative splicing of candidate genes or other unknown genes may be responsible for some of the variation seen in TTX resistance. Using bioinformatic tools, the data generated in Chapter

3 can be mined for alternative splice variants, or isoforms, and the biological relevance of the potential variation can be addressed.

The future directions of this system should continue to build upon the genomic resources originated in Chapter 3. Specifically, experiments should be performed to quantify standing variation in gene expression among individuals and populations. With this knowledge we can make more educated inferences about which differentially expressed genes actually influence TTX resistance. In addition, we should investigate the genomes of Th. couchii. Techniques such as genome selection scans and genome-wide association studies (GWAS) can be used to find (or verify) additional regions of differentiation among snakes with varying resistance levels.

Specifically, genome selection scans can identify additional adaptive loci, and GWAS can help

228 link genotype to phenotype. The combination of these techniques can offer great insight into the evolutionary forces shaping variation in this trait. By supplementing the transcriptome with whole genome resequencing, we can better answer questions about the molecular basis of TTX resistance.

Taken together, the combined results of this study begin to form a systems-level understanding of TTX resistance in Th. couchii. While we are left with many unanswered questions about the genetic mechanisms underlying resistance, by addressing an adaptive trait from multiple scales (whole animal, physiological, and genetic), we highlight the complexity of adaptation and shed light about the powerful roles coevolution and convergent evolution play in the origin of these adaptive traits.