City University of New York (CUNY) CUNY Academic Works

All Dissertations, Theses, and Capstone Projects Dissertations, Theses, and Capstone Projects

9-2018

Characterizing Venom Gene Expression, Function and Diversity in Predatory Marine Snails of the

Juliette Gorson The Graduate Center, City University of New York

How does access to this work benefit ou?y Let us know!

More information about this work at: https://academicworks.cuny.edu/gc_etds/2887 Discover additional works at: https://academicworks.cuny.edu

This work is made publicly available by the City University of New York (CUNY). Contact: [email protected] CHARACTERIZING VENOM GENE EXPRESSION, FUNCTION AND SPECIES

DIVERSITY IN PREDATORY MARINE SNAILS OF THE TEREBRIDAE

by

JULIETTE GORSON

A dissertation submitted to the Graduate Faculty in Biology in partial fulfillment of the requirements for the degree of Doctor of Philosophy, The City University of New York

2018

© 2018

JULIETTE GORSON

All Rights Reserved

ii

Characterizing venom gene expression, function and species diversity in predatory marine snails of the Terebridae by Juliette Gorson

This manuscript has been read and accepted for the Graduate Faculty in Biology in satisfaction of the dissertation requirement for the degree of Doctor of Philosophy.

______Date Mandë Holford, Hunter College Chair of Examining Committee

______Date Cathy Savage-Dunn Executive Officer

Supervising Committee

Weigang Qiu, Hunter College

Ntino Krampis, Hunter College

Mark Siddall, American Museum of Natural History

Nicolas Puillandre, Muséum National d'Histoire Naturelle

Lisle Gibbs, Ohio State University

THE CITY UNIVERSITY OF NEW YORK

iii

ABSTRACT Characterizing venom gene expression, function and species diversity in predatory marine snails of the Terebridae by Juliette Gorson

Advisor: Mandë Holford

The Terebridae is a group of predatory marine snails that use their venom to feed on marine annelids. Similar to other venomous organisms, the Terebridae have evolved over millions of years, diverging from their closest relative in the Paleocene era. This thesis investigates what is driving terebrid evolution and species diversification by applying a multidimensional approach to examine terebrid phylogeny, diet, and venom gene expression. The Terebridae are an ideal group for a case study in venom evolution and species diversification, as the ~ 400 described terebrid species are globally distributed, are highly representative of a broad range of foregut anatomies and produce venom peptides not found in any other venomous lineage to date. To provide robust hypotheses for terebrid evolution it is imperative to reconstruct a reliable phylogeny. In this thesis, an interdisciplinary approach using molecular and morphological characters was applied to reconstruct a valid working phylogeny of the Terebridae (Chapter 2). The phylogeny constructed was then used as a road map to inform questions about terebrid evolution and speciation. We identified planktotrophy as the ancestral larval state in terebrids, while lecithotrophy has evolved on at least 18 occasions and is moving towards a (small) size optimum. We also found that there is no difference in across clade diversification rates, but there is a slow increase in diversification rates over time. We determined that foregut anatomy, or the evolution of a venom apparatus, is not

iv influencing diversification rates in the Terebridae. While the venom of other venomous snails, such as cone snails, have been extensively characterized, the venom of terebrids is relatively unknown and this thesis has provided the groundwork to elucidate Terebridae venom evolution and variation using next generation transcriptomic sequencing (Chapter 3). Specifically, it was discovered that the venom of the Terebridae is highly divergent from other mollusks and contains venom peptides that are unique in their structure and function. Like their venom, the diet of the

Terebridae is poorly studied and this thesis presents the first molecular analyses of terebrid diet in relation to venom variation using gut metabarcoding (Chapter 4). We identified 18 species of worms in the gut of 17 terebrids, providing a foundation for exploring diet as a driver for terebrid species diversification. Finally, novel terebrid venom peptides (teretoxins) were examined by a series of functional assays to determine bioactivity (Chapter 5). This work provides the first functional bioactivity of two new teretoxins in relation to pain and diet, suggesting that these peptides may act as antinociceptive and orexigenic agents. The integrated methods applied allowed for the investigation of terebrid venom gene evolution and variation at a macro- and microevolutionary level. Terebrids are an understudied group in venom research and this multidimensional approach thoroughly advanced the knowledge of terebrid venom evolution, clarified questions about what drives venom variation in the Terebridae, and provided comparative analyses for the convergence of venom throughout the animal kingdom.

v

ACKNOWLEDGEMENTS I can’t believe that five years have already passed and I am publishing my dissertation as well as multiple papers about my research. When I first started at the Graduate Center, the end seemed so far away, something that I would achieve, but couldn’t even imagine. I am here today because of the dedication and passion I have for science as well as for the support that has surrounded me throughout this journey.

My journey started at Hofstra University where I was getting my Master’s in marine biology. It was while at Hofstra University that I learned about cone snails. Though no one at the university studied these amazing creatures, my persistence landed me a grant to Hawaii where I was able to collect these snails and bring them back to the lab for my Master’s thesis research. It was also around this time that I landed an awesome internship at the NY aquarium and couldn’t believe that I would be creating educational programs for students to learn about marine life- it was a dream-come true. My boss at the aquarium, Chanda, put me in touch with Dr. Mandë

Holford, who quickly accepted my invitation to bring the whole lab to the aquarium for a tour. It was at this time that Mandë and I first bonded and shortly thereafter, she would invite me on a collection trip to Papua New Guinea. Oh Mandë! What a ride we have been through together on this journey to explore the venom genes of the Terebridae. Mandë and I have experienced a lot together- journals rejecting our paper, arguing with the journals, the journals accepting our paper and Mandë becoming an editor at the journal. We presented our research to the Prince of Monaco, we created an exhibit at the American Museum of Natural History, and I cried with her after being hit in the head with a brick in Madang, Papua New Guinea. Mandë, I can’t imagine being mentored by anyone other than you, a social butterfly that has taught me the importance of science communication, writing, and outreach.

vi

I would also like to thank my wonderful committee members: Ntino, Weigang, Mark,

Nico, and Lisle. Without all of your input throughout these past five years, I wouldn’t be here, defending, today. Nico, you have been especially amazing, hosting me on multiple occasions at the museum in Paris and working with me so closely on the Phylogeny project. I will always remember our trip to the AMERICAN diner in PARIS- where we stuffed our faces.

I am grateful for everyone that has come through the lab and has helped me with research, given me advice, or just talked to me (for 1 min, 10 min, or 1 hour). Prachi, the post-doc of the lab, you have been an amazing friend to me since the first time we ate lunch together at Hunter

North and I couldn’t stop asking you questions about your 2,000 person wedding. You have been such a huge part of my life for the past five years and I am not sure I would have made it to the end without you. Not only have you provided me with comfort over the years, you have invited me into your family. I have feasted on Mukesh’s wonderful cooking and I have gotten to watch

Vrishti grow up into a mini-me! You have such an amazing daughter because you are such an amazing person. Aida, you have always been my partner in crime in the lab. We have experienced a lot together, from riding around in an Emirate’s Bentley to running around the museum creating activities 24 hours before an event! You have always provided me with someone to talk to (sorry for talking to you too much) and you have helped me understand the importance of being a woman in science. Tainya, what a kid! You have brought so much fun to the lab, with your (your mom’s) crazy stories and your upbeat personality. Let’s get chicken wings in Flushing soon. Patrick, thanks for dealing with my antics and providing me with some crazy stories. A special thank you to Emily, an amazing undergraduate who joined our lab right after being in my second ever intro chemistry class. I have gotten to see Emily grow as a person, from a girl that would cry at the smallest amount of stress to a young woman who was awarded an NSF grant and will be attending

vii

UCSB for a PhD program in the Fall. I am so proud of you! Also, a special thank you to Justin for being a crazy nut-ball and constantly keeping me entertained- honestly, the lab wouldn’t run without you.

Although I am a scientist, I have been supported by so many others that have nothing to do with science. My family and friends have been incredible throughout my entire ride. Jan Peterson and Melissa Marquino have been there through it all. Melissa has jumped on the snail train and continues to buy me all things snail (and all things small). Jan, the fact curator, you have supported my endeavors and have always been there for me, whether it be watching me present a 3-minute dissertation or just to tell me some amazing (sometimes untrue) facts about the world. Katie, you have been an amazing person that has tremendously helped me over these last few months. Thank you for listening to my complaints and helping me cross the finish line. Nick (and Arthur), duuuude! From video gaming to crazy ass arguments in the woods. You have been a terrific friend to me, Andrew, and Maverick! So happy to have met you in Queens and I hope you never move away (screw NH). BAFO later? Anne Marie and Brittany, although you were not born into my family, you sure have become a part of my family. Thanks for allowing me to be my whacky self and not getting too embarrassed by all of my antics. You two have been through a lot with me and there is only more to come. Yes Brittany, you will eventually be forced to go on a camping trip with me, that means no showering!

Sophie, my older sister, I am pretty sure there is no one else on this planet that is as proud of me as you are. You have supported me from day 1 and that support has continued even as our interests have diverged. You spent 3 weeks with me on the Incan trail, where we made up a weird talk show and I got to quiz you on science facts. I got out of my comfort zone a little to plan your amazing bachelorette party in Miami- I know I only brought boots, but they were a hit ;-) You

viii have showed me off to all of your friends and have given me your clothing so that I can show off at my new job. You’re the best sister and I can’t wait to continue going on adventures with you.

Oh yeah, I also love your husband, Andrew. Andrew thanks for making me a part of your family, inviting me to work for your company, and believing in me! Best brother-in-law ever! Mom and

Dad, holy crap, you guys are incredible. The best parents a kid like me could ask for. You have always supported me and my crazy ideas and you have given me the freedom to explore. From that first trip I took to Madagascar by myself as a 16 year-old (how did you guys let me do that) to being OK with me having a 6-foot long iguana named Fudge in the house. You have supported my decisions, which haven’t always been the best, but they have brought me to this point, which isn’t too shabby. I watched the two of you cringe as you watched me play rugby and I will never forget how mad the two of you were at me when I did ZERO packing of my dorm room after freshman year. But, I have made it this far, so we must be doing something right! Also, you have both been an amazing example of what a happy couple looks like. Yes, I have seen you fight, I will never forget that fight at the pool (robe vs. dad’s shirt), but you are both honest, respectful, and in love. You have provided me with an example of how I should live my life.

Which brings me to the last person that deserves a thank you…Andrew! I won’t include everything I want to say to you in this dissertation because I have most likely already told you in person (let’s be real we all know I like to talk). Andrew, you have been with me for over ten years and have watched me go from an 18 year-old crazy, beer drinking girl to a 28 year-old…doctor!

We have experienced A LOT together, from driving on the nub all the way to Canada for our first camping trip together to driving 5,000 miles in 2.5 weeks to go fly fishing in Montana. You are the man of my dreams because you respect me and love me. You are open minded: you never say no to me, without at least having a conversation first (example, we have two dogs, a cat, and a

ix snake). You are sensitive: you will never forget Jack. You are honest: you don’t sugar coat anything, but give it to me straight (especially when we are talking about looks). You are funny: one word for you, syncing! You are my best friend: when I think about home or family I only picture you. I constantly want to be listening to you, talking to you, and around you. Thank you for always supporting me and believing in me.

Thank you to everyone for letting me be me.

x

PUBLICATIONS

Fedosov, A.; Malcolm, G.; Terryn, Y.; Gorson, J.; Modica, M.V.; Holford, M.; Puillandre, N. Phylogenetic classification of the family Terebridae (: ). 2018. Journal of Molluscan Research (Accepted).

Eriksson, A.; Anand, P.; Gorson, J.; Stewart, J.; Holford, M.; Cladrige-Chang, A. 2018. Characterizing food intake and pain in Drosophila using venom peptides from terebrids. Scientific Reports (under review).

Leffler, A; Kuryatove, A; Zebroski, H.; Powell, S.; Filipenko, P; Hussein, A.; Gorson, J; Heizmann, A; Lyskov, S; Tsein, R.; Poget, S.; Nicke, A; Lindstrom, J; Rudy, B; Bonneau, R; Holford, M. 2017. Discovery of peptide ligands through docking and virtual screening at a nicotinic acetylcholine receptor homology models. PNAS 114 (38): E8100-E8109.

Gorson, J and Holford, M. 2016. Small packages, big returns: Uncovering the venom diversity of small invertebrate Conoidean snails. Integrative and Comparative Biology 56 (5): 962-972.

Verdes, A; Anand, P; Gorson, J; Jannetti, S; Kelly, P; Leffler, A; Simpson, D; Ramrattan, G; Holford, M. 2016. From Mollusks to Medicine: A Venomics Approach for the Discovery and Characterization of Therapeutics from Terebridae Peptide Toxins. Toxins 8(4): 117.

Moon, J; Gorson, J*; Wright, ME; Yee, L; Khawaja, S; Shin, HY; Karma, Y; Musunri, RL; Yun, M; Holford, M. 2016. Characterization and Recombinant Expression of Terebrid Venom Peptide from guttata. Toxins 8 (3): 63.

Gorson, J; Ramrattan, G; Verdes, A; Wright, ME; Kantor, Y; Srinivasan, R; Musunuri, RL; Packer, D; Albano, G; Qiu, WG; Holford, M. 2015. Molecular Diversity and Gene Evolution of the Venom Arsenal of Terebridae Predatory Marine Snails. Genome Biology and Evolution 7 (6): 1761-78. --

Funding for the research presented here came from the National Science Foundation NSF

CHE#1247550 and CHE#1228921, Camille and Henry Dreyfus Foundation, and the City

University of New York Doctoral Student Research Grant.

xi

TABLE OF CONTENTS Abstract……………………………………………………………………………………………4 Acknowledgements………………………………………………………………………………..6 Table of contents…………………………………………………………………………………12 List of tables and figures…………………………………………………………………………14 Chapter 1. An introduction to venom research…………………………………………………..15 Chapter 2. Identifying macroevolutionary patterns of species diversity in venomous terebridae marine snails

Introduction………………………………………………………………………………28 Results…………………………………………………………………………………....31 Discussion………………………………………………………………………………..43 Methods…………………………………………………………………………………..49

Chapter 3. Investigating gene structure and variation in the venom arsenal of the Terebridae

Introduction………………………………………………………………………………58 Results……………………………………………………………………………………61 Discussion………………………………………………………………………………..73 Methods…………………………………………………………………………………..77 Chapter 4. Determining drivers of Terebridae venom diversity as inferred by diet

Introduction………………………………………………………………………………81 Results……………………………………………………………………………………84 Discussion………………………………………………………………………………..89 Methods………………………………………………………………………………….92 Chapter 5. Deciphering Nature’s arsenal: Using Drosophila behavioral assays to characterize venom-peptide bioactivity

Introduction……………………………………………………………………………....97 Results…………………………………………………………………………………..101 Discussion………………………………………………………………………………108 Methods…………………………………………………………………………………111

xii

Chapter 6. Concluding remarks and future directions………………………………………….120 Appendixes……………………………………………………………………………………..124 Bibliography……………………………………………………………………………………131

xiii

LIST OF TABLES AND FIGURES Figure 1.1. Biodiversity of venomous taxa ...... 15 Figure 1.2. Venomics: an integrated NGS and proteomic strategy ...... 18 Figure 1.3. Conoidean venom characterization ...... 21 Table 1.1. Snail venom peptides to drugs ...... 26 Figure 2.1. Terebridae Bayesian phylogenetic tree with trait mapping ...... 32 Table 2.1. Newly defined anatomy types ...... 36 Figure 2.2. Diversification rates of the Terebridae across clades and time ...... 38 Figure 2.3. BAMM results for depth rate over time ...... 40 Figure 3.1. From venom to drugs...... 60 Figure 3.2. Venomics pipeline for the identification of toxin homologs ...... 62 Figure 3.3. Comparison of peptide toxin frameworks ...... 63 Figure 3.4. Comparison of peptide toxin frameworks within species ...... 65 Figure 3.5. Phylogenetic tree of Kunitz-type serine protease inhibitors ...... 67 Figure 3.6. Multiple sequence alignment of turripeptide-like sequences ...... 68 Figure 3.7. Phylogenetic tree of actinoporins ...... 70 Figure 3.8. Multiple sequence alignment of conopressin sequences ...... 71 Figure 3.9. Teretoxin gene superfamilies ...... 73 Figure 4.1. Representative terebrids and their annelid prey ...... 84 Figure 4.2. Terebridae phylogeny and annelid genera identified in the gut ...... 85 Figure 4.3. Comparison of gut contents in terebrids with and without a venom gland (VG) ...... 86 Figure 4.4. Correlation of terebrid diet breadth and venom complexity ...... 87 Figure 4.5. Comparison of peptide toxin frameworks and gut content of terebrids ...... 88 Figure 5.1. Schematic representation showing the pipeline used in this study ...... 100 Figure 5.2. Comparison of Tv1 and Tsu1.1 ...... 102 Figure 5.3. Effects of Tv1 and Tsu1.1 on activity and motor coordination of male Drosophila 104 Figure 5.4. Thermal nociception in adult Drosophila is diminished by Tv1 ...... 106 Figure 5.5. Tsu1.1 has effects on food intake by increasing meal frequency ...... 107 Figure 5.6. Tsu1.1 has effects on food intake by increasing meal frequency ...... 110

xiv

Chapter One An introduction to venom research (This chapter has been published as a review article: Gorson, J. and Holford, M. 2016. Small Packages, Big Returns: Uncovering the Venom Diversity of Small Invertebrate Conoidean Snails. Integrative and Comparative Biology. 56(5): 962-972.)

Venom is defined as any exogenous substance that is used to elicit an adverse effect in its target, and as a result a wide range of organisms from notorious snakes to lesser known leeches and bees are considered venomous (Figure 1.1) (Casewell et al. 2013a; Escoubas & King 2009;

Petras et al. 2015; King 2015). Historically, organisms used in venom research were chosen

Fig. 1.1. Biodiversity of venomous taxa. Phylogenetic reconstruction of the tree of life highlighting venomous organisms. Grey bars represent the clades that include venomous organisms. Highlighted clades represent the traditionally studied venomous taxa (scorpions, spiders, snakes, and lizards).

- 1 - opportunistically, based on size and ease of collection, which largely focused on vertebrates, specifically snakes. Two genera of snakes account for almost 40% of all published venom toxin sequences in elapid snake venom research (Fry et al. 2008). Remarkably, one easy to collect

(Naja) has been used to identify 40% of all three-finger snake venom toxins (3FTxs) sequenced.

Only three studies have used harder to milk and less studied, non front-fanged snakes to investigate

3FTx bioactivity (Fry et al. 2003; Pawlak et al. 2009, 2006; King 2015). The venom research strategy of size and accessibility can neglect the ecology, morphology, or evolutionary relatedness between organisms, resulting in a diversity of venomous , such as invertebrates, being effectively ignored (Puillandre & Holford 2010; Bjoern von Reumont et al. 2014; Modica &

Holford 2010).

Invertebrates are underrepresented in venom research. Spiders, which are the most diverse group of venomous animals, with about 45,000 species, make up less than five percent of all venom research studies (Nentwig 2013). Analogous to the within taxa bias seen in snakes, of the 17,000 species of scorpions described, only ~50 species have had their venom investigated (King 2015).

It can be argued that the venom research bias existed largely due to lack of technological methods for effectively collecting and characterizing small quantities of venom. The deficiency of invertebrates has led to a dearth of information that has hindered venom research. However, recent technological advancements in the field of molecular biology and proteomics has increased the representation of marine cone snails, sea anemones, bees and ants in venom studies (Norton &

Olivera 2006; Moran et al. 2008; Barlow et al. 2009; Zhang et al. 2015; Sanggaard et al. 2014;

Casewell et al. 2013a). Extensive research on a broad range of organisms is imperative in order to effectively derive and test hypotheses about venom as it relates to species diversification, predator- prey interactions, and to describe the immense biodiversity of animals found on Earth (Figure 1.1).

- 2 -

Rise of -omics

Early research on venom relied heavily on identifying proteins using Edman Degradation and mass spectrometry (MS) (J R Perkins, Smith, et al. 1993; J R Perkins, Parker, et al. 1993). In conjunction with fractionation, MS allowed for the separation and identification of individual venom components. Development of soft ionization methods in the late 1980's, such as electrospray ionization (ESI) (Fenn 2003)and matrix-assisted laser desorption (Tanaka 2003;

Karas & Hillenkamp 1988) have revolutionized biological research. In particular, the ability to identify proteins directly from mass spectrometry data, is a powerful capability that soon demonstrated to be crucial for analyzing venoms. Quickly thereafter, research groups started to implement MS techniques to characterize snake venom (John R Perkins et al. 1993; J R Perkins,

Smith, et al. 1993). MS approaches also enable the identification of isoforms of venom peptides, slight variations in sequences, and post translational modifications (Safavi-Hemami et al. 2014;

Craig et al. 1999; Escoubas et al. 2008; Petras et al. 2015). Recent advancements in MS protocols have produced what are referred to as Top-Down methods, in which whole intact venom components can be identified (Anand et al. 2014; Ueberheide et al. 2009; Breuker et al. 2008;

Sunagar et al. 2015). Although groundbreaking, in some organisms a proteome-only MS approach can be problematic. MS requires extraction of venom, which is impractical for organisms that do not readily store venom for delivery and other organisms that have hard-to-access venom delivery systems, which prevents stimulation or extraction (B M von Reumont et al. 2014). Additionally,

MS methods for determining primary venom peptide sequences are largely dependent on downstream data analyses of source databases, such as Mascot or Genbank (Perkins et al. 1999;

Bhatia et al. 2012). For model organisms with a rich complement of sequence databases, such as

- 3 - humans, mice, or drosophila, this is not an issue. In the case of non-model organisms, the application of MS methods for primary sequencing is severely limited by the database used. Non- model systems generally require de novo sequence assembly and source databases that are either missing or deficient. As a result, an integrated strategy, termed venomics (Calvete et al. 2007;

Calvete 2014; Eichberg et al. 2015), in which MS proteomics is combined with next generation transcriptomic or genomic sequencing and bioinformatic methods is necessary to validate characterization of de novo venom peptides found in non-model organisms and to paint the full canvas of venom evolution and variation (Figure 1.2) (Fry et al. 2013; Sunagar et al. 2015). Using the multi-omic integrated venomic strategy, venom research has become more accessible to

Fig. 1.2. Venomics: an integrated NGS and proteomic strategy. An integrated multi -omics approach using genomic, transcriptomic, bioinformatic, and proteomic protocols to identify venom proteins and peptides. Application of a combined -omics strategy validates de novo venom peptide/protein identification and provides robust data to test hypotheses related to venom evolution and ecology. The sequences shown at the bottom are an example of a validated peptide database obtained from NGS and proteomics. - 4 - smaller, harder to collect, and understudied venomous taxa. The integrated venomic strategy has also broadened the scientific community engaged in venom research from traditional chemists and pharmacologists looking for bioactive compounds for drug discovery and development, to evolutionary biologists looking for anatomical and molecular characters to understand venom evolution through various taxa over time (Otvos et al. 2013; Favreau & Stöcklin 2009; Koh & Kini

2012; Elmer et al. 2010; Jouiaei et al. 2015; Duda & Palumbi 1999; Gorson et al. 2015; Moran et al. 2008; Zhang et al. 2015).

The honey bee, Apis mellifera, was the first venomous organism to have a fully sequenced genome using Sanger sequencing (Weinstock et al. 2006). Since then, the development of Next

Generation Sequencing (NGS) high throughput techniques has allowed rapid sequencing of other venomous organisms. The genomes of tarantula Acanthoscurria geniculate (Sanggaard et al.

2014), scorpion Mesobuthus martensii (Cao et al. 2013), velvet spider Stegodyphus mimosarum

(Sanggaard et al. 2014), fire ant Soenopsis invicta (Wurm et al. 2011), and king cobra Ophiophagus hannah (Vonk et al. 2013) have all been sequenced using NGS technologies. With multiple platforms available, such as Illumina (Illumina, Inc.), 454 (Roche Applied Science), SOLiD

(ThermoFisher Scientific), and Ion Torrent (ThermoFisher Scientific), genome sequencing of venomous organisms is becoming both accessible and affordable. However, genomics alone does not provide enough information for determining the exact mode and tempo of gene expression and does not give significant insight into differential gene expression within various tissue types

(Sunagar et al. 2015).

While genomics is the study of the complete DNA composition of an organism, venom gland transcriptomics is the sequencing of mRNA specific to the venom gland or secretory tissue of a venomous organism and therefore a glimpse at the specific venom cocktail being used at the

- 5 - time by the animal (Durban et al. 2011; Dutertre et al. 2014; Gorson et al. 2015; Sunagar et al.

2015). Both transcriptomics and genomics enable the identification of certain domains of a venom protein, such as the signal and pre-pro regions that are rarely identified on the proteomic level as they are cleaved off after translation (Kaas et al. 2010; Espiritu et al. 2001; Robinson & Norton

2014; Duda & Palumbi 1999; Sunagar et al. 2015). Employing an integrated venomic strategy has enabled researchers to resolve previously unanswerable questions, such as identifying a correlation between varying venom compositions and differences in ecological and environmental factors.

Several studies employing a combined genomics and transcriptomics approach have looked at venom variation between different developmental stages in snakes (Durban et al. 2013; Gibbs et al. 2013; Durban et al. 2011). Specifically, proteomics and transcriptomics were used to show venom variation in various populations of the Southern Pacific Rattlesnake, Crotalus oreganus helleri in the United States (Sunagar et al. 2014). Lectin β-chains, which are generally undergoing positive selection, were found to be evolving under negative selection in the C. oreganus helleri rattlesnake population found on Catalina Island (in the Pacific Ocean) (Sunagar et al. 2014). The integrated venomics approach used in this study revealed that there can be different evolutionary selection pressures acting on different venom classes depending on the population site. In another study that used an integrative venomics approach, it was found that there were significant differences in the mature peptides being produced in different samples of venom from consors (Biass et al. 2015). Proteomics and transcriptomics were used to analyze C. consors venom at three different stages: venom milked from the snail, venom extracted from the venom gland, and venom expressed in the transcriptome, effectively tracing the venom production and

- 6 - delivery process from the venom glad tissue to the point of venom envenomation of the prey.

The surprising result was that the cocktail of venom peptides identified in the transcriptome, in the venom produced within the venom gland, and in the venom injected into the prey were not heavily correlated. Each venom compartment was distinctive in terms of peptide and protein content. This study emphasizes the complexity of the venom mechanism of Conodiean snails and indicates what is being secreted in the venom is Fig. 1.3. Conoidean venom characterization. (A) A generic representation of the Conoidean not necessarily the same as what is being venom apparatus, which includes: a venom bulb that is contracted to push the venom through the venom gland, where the venom is being produced, produced in the gland (Biass et al. 2015). a radular sac that contains hollowed teeth (harpoons) that are used to inject venom into the Advances in –omic technologies have prey, and a proboscis that extends several times beyond the snails body size to deliver venom- increased the breadth of research being done on filled radula to a prey target. Scissors shown represent the dissection of the venom duct for all organisms and have advanced research of downstream analysis by transcriptomic, genomic or proteomic methods. (B) Identification of Conus venom peptide superfamilies and non-model organisms. cysteine frameworks. (C) Conoidean venom peptides selected for bioactivity characterization. MVIIA is a peptide from Conus magus venom that produced the (Prialt®) drug that is commercially available. MVIIA is in the O1 gene superfamily and has a VI/VII Characterizing conoidean venom evolution cysteine framework. Tv1 is a peptide from Terebra variegata that has a cysteine pattern and variation similar to the M-superfamily in cone snails and has an III cysteine framework but has a peptide fold of antiparallel beta hairpins that is unique to The technological –omics advancements known venom peptides. Tv1 and MVIIA are very distinct peptides illustrating the disparate removed the barrier requiring large amounts of complexity of Conoidean venom peptides.

- 7 - crude venom extracts and smaller-sized taxa such as centipedes, certain sea anemones, ants, and small scorpions have become the focal point of an ever increasing number of venom studies

(Calvete et al. 2009; Xu et al. 2014; Putnam et al. 2007; Cao et al. 2013; Sanggaard et al. 2014;

Moran et al. 2008). One non-model organism that has received a lot of attention using venomic technologies are the venomous marine snails in the Conoidean superfamily.

Conoidean snails are slow moving predators and therefore rely heavily on the efficacy of their venom (Azam et al. 2005; Yao et al. 2008; Kendel et al. 2013; King 2015). The dependence on venom for prey capture has led conoidean venom peptides to achieve incredible molecular specificity (Olivera 2002; Olivera et al. 2014). Conoideans subdue their prey using a venom apparatus made up of a proboscis, radular tooth, a radular sac, venom gland, and venom bulb

(Figure 1.3A) (Modica & Holford 2010; Kantor & Puillandre 2012; Kantor & Taylor 2000; Taylor

1990). Cone snails (Conus) are the most studied in the (Puillandre et al. 2014, 2015), however, Conus comprises only ~5% of the biodiverse group of venomous marine snails (Holford et al. 2009; Olivera et al. 1999; King 2015). Other non-Conus Conoideans, such as the

(s.l.) family, which has more recently been divided into seven family groups (Bouchet et al. 2011;

Tucker & Tenorio 2009), and the Terebridae family, also produce venom (Aguilar et al. 2009;

Gonzales & Saloma 2014; Heralde et al. 2008; Moon et al. 2016; Gorson et al. 2015). Cone snails and terebrids dwell in shallow-water tropical marine habitats, while the majority of turrids can be found at greater depths (>200m) (Taylor 1977). Terebrids and turrids (some less than 3mm in length) have incredibly small venom ducts, producing limited amounts of venom, which initially inhibited their characterization. Using an integrated venomics strategy, venom research of terebrids and turrids has become more feasible (Castelin et al. 2012; Gorson et al. 2015; Kendel et al. 2013; Gonzales & Saloma 2014; Moon et al. 2016). Conoidean venoms are a complex

- 8 - mixture of small molecules, peptides and proteins (Neves et al. 2015; Norton & Olivera 2006;

Gorson et al. 2015; Gonzales & Saloma 2014). Each Conoidean venom consists of >100 different peptides which contain a signal sequence, followed by a pro region, and a mature peptide at the C- terminus (Lavergne et al. 2015; Olivera et al. 1999). As cone snails are well studied, 3,000 different peptides () have been identified from Conus venoms since the 1970’s (Conoserver.org).

The majority of conotoxins have been classified into venom gene superfamilies by examining the sequence identity of the signal sequence (Jacob & McDougal 2010; Robinson et al. 2014;

Robinson & Norton 2014). A similar process is being used to characterize turrid and terebrid venom peptide superfamilies (Figure 1.3B) (Gorson et al. 2015; Heralde et al. 2008; Gonzales &

Saloma 2014). Different venom peptide superfamilies generally have distinct physiological targets and high specificity for those targets (Olivera et al. 1999). While there are similarities between the venoms of Conus, inter- and intraspecific variation exists, such as differences in the proportions of cysteine frameworks or venom gene superfamilies (Jakubowski et al. 2005; Abdel-Rahman et al. 2011; Romeo et al. 2008; Duda & Palumbi 1999; Olivera et al. 1999). Due to the high rates of non-synonymous mutations and the early divergence of Conoidean families, the venoms of terebrids and turrids (s.l.) vary significantly from the venoms of cone snails (Powell 1966;

Puillandre & Holford 2010; Duda & Palumbi 1999; Gorson et al. 2015). The disparity in Conus and terebrid venom was recently revealed by looking at the variation in venom peptide superfamilies between Triplostephanus anilis and (Gorson et al. 2015). Fourteen terebrid venom gene superfamilies were identified in the two terebrid species (TA, TB, TC, TD,

TE, TF, TG, TH, TI, TJ, TK, TL, TM). Of the fourteen terebrid superfamilies described, only one,

TM, is similar to a superfamily found in Conus marmoreus (H superfamily) (Robinson & Norton

2014). The divergence of Conus and terebrid venom gene superfamilies suggests that terebrid

- 9 - venom peptides will have different structural features and physiological targets from Conus, thereby increasing the pool of bioactive compounds that can be explored for discovery of novel therapeutic drugs. Conoidean venoms are exceedingly effective candidates for drug discovery as they are: (i) rapid acting, (ii) highly selective, and (iii) very potent (Figure 1.3C).

Potential of conoidean venom to increase drug discovery and development

Many bioactive peptides have evolved as a means of or defense, especially in venomous animals (Olivera et al. 1985; Gray et al. 1988; Dutertre et al. 2014; Olivera et al. 1999).

The wide variety of biologically active venom peptides are a promising resource for drug discovery

(Twede et al. 2009; Ortiz et al. 2015; Lewis & Garcia 2003; Favreau & Stöcklin 2009; Koh & Kini

2012; King 2015). The constant selective pressures acting on venom, due to the effects of the predator-prey arms race (Dawkins & Krebs 1979; Van Valen 1973; Holding et al. 2016; Casewell et al. 2013a), enabled venom peptides to develop features to increase stability and prey molecular target affinity. Venom peptides tend to interfere with transmissions of ions in and out of cells, suggesting they would be effective tools for manipulating ion channel driven cell disorders such as pain or cancer (Vetter & Lewis 2012; Lang et al. 2014; Miljanich 2004). These properties make venom peptides more appealing than artificially or chemically conceived peptide-like compounds for which bioactivity is not guaranteed (Uhlig et al. 2014).

The past two and a half decades have seen an increase in the number of projects that are taking advantage of the Earth’s amazingly biodiverse group of venomous organisms to develop novel drugs, to create tools for diagnosing human diseases, and to create probes to help advance the study of molecular receptors and physiological pathways (King 2011; Nisani et al. 2007;

Diochot et al. 2012; Casewell et al. 2013a). As reptiles were the most accessible venomous

- 10 - organisms for quite some time, the majority of approved venom drugs were discovered from snake venom. Specifically, snake venom proteins targeting thrombin, integrin, and fibrinogen receptors were discovered (Koh & Kini 2012; King 2011). Captopril®, an angiotensin-converting enzyme

(ACE) inhibitor synthesized to mimic a venom peptide from Brazilian lancehead snakes, is a breakthrough drug that validates venom-based drug discovery research (Cushman & Ondetti

1991). The venomic strategy has made it more affordable and practical to examine non-model venomous organisms for peptide or protein components that can lead to new therapeutics.

Investigating more venomous organisms will greatly increase the amount of compounds available for drug discovery and development (Puillandre & Holford 2010; Casewell et al. 2013a; King

2015).

The >1 million estimated venom peptides expressed in conoidean venom are an immense resource for discovering novel compounds for therapeutic drug development. The majority of conoidean venom peptides are disulfide-rich molecules that have been shown to manipulate voltage and ligand gated potassium (K+), calcium (Ca2+), and sodium (Na+) channels, as well as nicotinic acetylcholine receptors, and noradrenaline transporters (Becker & Terlau 2008; Terlau

& Olivera 2004; Olivera 2002) (Table 1). As mentioned previously, ion channels and receptors are the molecular targets for cancer, pain, and other debilitating human disorders (Dave & Lahiry

2012; Gkika & Prevarskaya 2011; Wood et al. 2004; Veiseh et al. 2007; Lang et al. 2014).

Ziconotide (Prialt®), a peptide from the venom of Conus magus, was approved for commercial use by the FDA in 2004 and is the first non-opiod analgesic (Schmidtko et al. 2010; Olivera 2000;

Miljanich 2004). Similar to MVIIA, CVID, MrIA, Vc1.1, RgIA, Contulakin-G, and Conantokin-

T are peptides synthesized from the natural secretions of Conus that are currently undergoing clinical trials to determine of their potential as pain therapeutics (Table 1) (Armishaw & Alewood

- 11 -

2005; McIntosh et al. 2009; Vincler et al. 2006; Han et al. 2008; Malmberg et al. 2003; Miljanich

2004). Although most Conus peptides in pharmaceutical development are being used as analgesics,

PVIIA shows promise as a therapy for myocardial infarction, and Conantokin-G for epilepsy

(Table 1.1) (Koch et al. 2004; Armishaw & Alewood 2005; Twede et al. 2009).

Conclusions

The progress of –omics technologies triggered a domino effect in venom research. An integrated venomics strategy has enabled a broader range of venomous organisms, many of which are non-model organisms, difficult to acquire, and contain limited amounts of venom, to be

Table 1.1. Snail venom peptides to drugs. Conoidean venom peptides under drug development.

examined and ultimately contribute to the understanding of venom evolution in biodiversity.

Without the vast technological molecular improvements in the last decade, studies would most

- 12 - likely still revolve around snakes, spiders, and scorpions. The increasing amount of venom peptides identified from Conoideans that are now in clinical trials, demonstrates the importance of expanding the diversity of venomous species examined (Table 1). As -omics technology continues to improve, it will be easier and cheaper to add more species to the pool of venomous organisms under investigation, enabling researchers to resolve questions about venom convergence across the animal kingdom and increase the quantity of peptides available for drug discovery and development for the benefit of human health.

- 13 -

Chapter Two Identifying macroevolutionary patterns of species diversity in venomous Terebridae marine snails

Introduction

Explaining the amazing biodiversity of species that inhabit our planet remains a significant challenge. Several hypotheses have been proposed to describe species diversity, including sea level changes (Chakona et al. 2013), phenotypic plasticity (Pfennig et al. 2010), and quality of diet

(Codron et al. 2007). In the case of venomous organisms, drivers of diversification have included the acquisition of a venom apparatus, which is a specific gland for producing venom, and a mechanism, such as beaks, fangs, harpoons, spines, or pinchers, for delivering it. Possession of a venom apparatus is considered an opportunistic innovation that favors speciation by enabling the exploitation of new ecological niches characterized by various prey (Vidal & Hedges 2005; Fry et al. 2006; Castelin et al. 2012). Venomous predatory animals are highly dependent on their venoms for survival and as a result venom is believed to be a key driver of species diversification that affects animal development strategies, optimal size ranges, body plans and habitat ranges

(Casewell et al. 2013b; Phuong et al. 2016; Chang et al. 2016). However, this hypothesis has only been examined in a few cases, mostly for vertebrates or terrestrial invertebrates and generally at the species level with indirect evidence (Fry et al. 2008; Daltry et al. 1996). For example, a large- scale phylogenetic study in reptiles revealed a novel clade containing snakes, anguimorphs, and iguanians. Anguimorphs and iguanians were considered to be non-venomous (Vidal & Hedges

2005), but then several toxins were found in these two taxa (Fry et al. 2006), in agreement with the phylogenetic pattern. In this case study, the ability to produce venom appeared to be linked to reptilian speciation and was corroborated in the most recent reptilian phylogeny (Vidal and Hedges

2005; Fry et al. 2006).

- 14 -

Here we examine diversification patterns in the predatory marine snails of the family

Terebridae (auger snails). Terebridae are an ideal group for a case study investigating diversification driven by evolutionary innovations because the > 400 described terebrid species, which are highly representative of a broad range of anatomical feeding strategies, more so than any other taxon of venomous marine organisms (Mills 1979; Miller 1971; Castelin et al. 2012).

Additionally, this group has several features that would potentially impact diversification rates as they are highly variable with respect to size, depth, biogeographical distribution, and life-history traits. Terebrids belong to the venomous marine snail superfamily Conoidea, which includes cone snails (family ) and ‘‘turrids’’ (a complex of families) (Puillandre et al. 2011; Holford et al. 2009; Castelin et al. 2012). Terebrids can range in size from 15-230mm and are distributed globally in subtropical and tropical oceans (Taylor 1990; Terryn 2007; Terryn & Holford 2008).

In a previous study, a molecular phylogeny of the Terebridae was generated using 89 terebrid species (~400 samples), belonging to 12 out of the 15 accepted genera, and identified Euterebra and , two previously unrecorded lineages (Castelin et al. 2012). It was also discovered that with the exception of , Pellifronia, and Terenolla, all other terebrid genera are non- monophyletic. The non-monophyly of most genera that have been analyzed in the past, indicates that the current genus-level classification of the Terebridae group is overrun by homoplasy and will require a greater extent of taxonomic investigations (Castelin et al. 2012). In the 2012 terebrid phylogenetic reconstruction, six related characteristics were mapped (proboscis, venom gland, odontophore, accessory proboscis structure, radula, and salivary glands) onto the terebrid phylogeny to assess the evolution of anatomy in the Terebridae. It was revealed that the terebrid hypodermic radula, used to inject venom into prey, likely evolved independently on at least three occasions, and could be correlated to a variety of functionalities (Castelin et al. 2012).

- 15 -

Additionally, the proboscis was lost six times in the Terebridae and the venom gland was lost eight. Indeed, the wide diversity of foregut anatomies in the Terebridae is equivalent to the diversity found in the entire Conoidea superfamily. Given these findings, terebrids are an ideal model system to test: (1) if the venomous apparatus actually influences diversification rates by comparing closely related lineages that both have a venom apparatus present and absent; (2) if any other feature is independently influencing diversification rates; or (3) if terebrid diversification is not driven by any single factor, but rather a cooperative relationship between key features.

To examine these three hypotheses, we applied an integrated approach combining molecular phylogeny, anatomical classification, and phylogenetic comparative methods (PCM). As prior studies have demonstrated, producing robust species hypotheses and phylogenies is a fundamental prerequisite to define lineage-specific disparity in the Terebridae (Holford et al. 2009; Castelin et al. 2012). Prior studies applying PCMs to investigate diversification include investigating the early diversification of flowers (Sauquet et al. 2017), studying the link between biogeography and rattlesnake diversification and dispersal (Chen et al. 2017), and examining how differing diversification rates and colonization times in amphibians can determine differences in species richness (Hutter et al. 2017). Extensive research on a broad range of organisms is imperative in order to derive and test robust hypotheses about venom as it relates to species diversification, predator-prey interactions, and to describe the immense biodiversity of animals found on Earth

(Gorson & Holford 2016). Neglected predatory invertebrates, such as terebrids, provide a unique perspective for investigating the evolution of venomous animals. This work increases the amount of terebrid specimens previously applied in a molecular phylogeny by 4-fold and uses their phylogeny to investigate shell size, depth, larval development, and foregut anatomy as drivers of terebrid diversification.

- 16 -

Results

Reconstruction of Terebridae molecular phylogeny

A dataset of 1761 samples was used to reconstruct the molecular phylogeny of the

Terebridae family. A multigene approach was applied using cytochrome oxidase I (COI: 1152 samples), 16S (717 samples), 12S (817 samples), and 28S (259 samples) genes. Analyses of each individual gene was performed using RAxML and no supported conflicts were found between the four gene trees. The four genes were concatenated to produce a consensus tree (Figure 2.1).

- 17 -

Figure 2.1. Terebridae Bayesian phylogenetic tree with trait mapping. Posterior Probabilities (pp) are marked with dots on the nodes, where black dots represent a pp of 1 and grey dots represent a pp between .9 and 1. Blue dots represent a multispiral protoconch,- 18 - while red dots represent a paucispiral protoconch. Roman numerals represent newly defined anatomy types. Shells represent 12 of the 17 cryptic species identified. Only samples with ≥ 2 genes successfully sequenced were used in the combined gene dataset, a total of 898 samples. Our new terebrid phylogeny has a total of 161 described species including approximately 30 new species, plus many potential species arising within species complexes

(Fedosov, et. al. 2018, submitted). Interestingly, even though the species representation doubled from the previous reported terebrid molecular phylogenies, the overall topology of the terebrid tree is consistent with what was previously published and the family has remained monophyletic as described originally in the first molecular phylogeny of the group (Holford et al. 2009).

Our current terebrid phylogenetic reconstruction groups the family into six clades (A-F), similar to previously published reports. However, Clade A (Pellifronia) is no longer a sister group to all other terebrids and is now divided into subclade A1 with a single species represented,

Pellifronia jungi, and subclade A2, represented by Bathyterebra coriolisi (Fig. 2.1). The genera represented by Clades B (Oxymeris), Clade C (Terebra), and Clade D () are as previously described (Castelin et al. 2012; Holford et al. 2009). The largest clade E, which contains the

Myurella genus, is again divided into subclades E1-E5B, with the corresponding genera E1

() E2 (Punctoterebra), E3 (Profunditerebra), E4 (Neoterebra), E5A (Maculauger), and

E5B (Myurellopsis). Additionally, it was revealed that Clade F, previously unreported and consisting of 6 species, is now the sister group to all other terebrids and has been further divided into two subclades F1 and F2. Clade F1 corresponds to the revised genus Duplicaria and Clade F2 is recognized as genus Partecosta.

The origin of the Terebridae is estimated at 50.6 Ma [95% highest probability density (HPD):

44.1-51.2], matching the well documented Terebridae found in the Early Eocene period

(stage Ypresian: 47.8-56 Ma). The six main lineages of terebrids all appeared before the end of the

Eocene. It is also remarkable that the diversifications of each of the main lineages, including the

- 19 - subgroups within the clades A, E and F, all started concomitantly, between the mid-Oligocene (30

Ma) and the early Miocene (20 Ma).

Discovery of cryptic terebrid species

Using nominative species recognized in WoRMS as a baseline, multiple cryptic species were revealed in at least 14 independent lineages in the genera Duplicaria, Hastula, Terebra, Myurella,

Punctoterebra, Profunditerebra, Neoterebra and Maculauger. Our phylogenetic analysis suggests that at least 17 nominative species can be regarded as putative cryptic species complexes, encompassing in total as many as 47 molecular operational taxonomic units (MOTUs) in the COI tree (Fedosov, et. al. 2018, submitted).

Two and up to six distinct phylogenetic lineages (K2P genetic distance ≥ 0.025 for sympatric lineages and above 0.04 for allopatric ones) referred to by a certain species name either comprise a monophyletic lineage (4 complexes), or more often, do not show immediate affinity to each other. Nevertheless, in all but one case MOTUs belonging to some morphological species complex fall within one major Terebridae clade (i.e. within one genus). The remarkable exception is the

Profunditerebra orientalis complex, in which two MOTUs cluster within the genus

Profunditerebra and a morphologically strikingly similar form is found in Maculauger (Figure

2.1).

Three clusters comprise pairs of lineages with allopatric distribution, and in three clusters comprising three or more divergent lineages (Punctoterebra textilis, T. fenestrata and

Punctoterebra trismacaria) at least one of them does not overlap in distribution with others.

Furthermore, our data suggest difference in bathymetric distribution in at least four putative

- 20 - species complexes (Terebra cumingii, Myurella burchi, Punctoterebra trismacaria, and

Profunditerebra orientalis); however, such differences do not exist between sister-lineages.

In most cases we managed to ascribe with certainty based on morphological grounds, one among the genetically divergent MOTUs in a cluster, to a current species name (i.e. identify which of the lineages corresponds to the described species) (Fedosov, et. al. 2018, submitted). In addition, we could assign some other putative MOTUs of each cluster to names which were considered junior synonyms of the species. Most of the morphologically similar MOTUs are distinguishable if examined thoroughly enough, thus they can comprise complexes of pseudo-cryptic species.

Nevertheless, the extent of the revealed cryptic diversity suggests that a considerable fraction of the Terebridae diversity still awaits formal description.

Identifying patterns in terebrid larval ecology, morphology and bathymetric data

Larval ecology

We examined the protoconch in a total of 638 intact specimens belonging to 116 Terebridae species. In our dataset, multispiral (m) protoconchs ranged from 1 whorl up to 5 whorls, and displayed a small nucleus, a general conical shape, and an evident ‘sinusigera mark’ (the thin sigmoid sinus marking the protoconch-teleoconch boundary, clearly indicating a planktotrophic development). Paucispiral (p) protoconchs ranged from 1 to 2.25 whorls, and possessed a broad nucleus, often a cylindrical shape and no sinusugera, indicating a lecithotrophic development. A number of specimens displayed an intermediate protoconch, with 2.25 whorls, indicating either a lecithotrophic larva with a longer dispersive stage, or a short-lived planktotrophic larva. In those cases, the shell was attributed to one of the two developmental types based on the general protoconch appearance rather than on whorl number alone. Of the 116 species examined in the

- 21 - study, 71% are planktotrophic and 28% are lecithotrophic (Figure 2.1).

Table 2.1. Newly defined anatomy types. Twelve anatomy types were defined by looking at the presence or absence of a proboscis, venom gland, salivary gland, or accessory proboscis structure (APS), as well as looking at the type of marginal tooth. Species listed do not encompass all species with the anatomy type, but rather a subset, while clades represent all of the clades that contain each of the anatomy types.

Foregut anatomy

The current study evaluated the presence or absence of a proboscis (PR), venom gland (VG), odontophore (OD), accessory proboscis structure (APS), and salivary glands (SG) as well as ranked the type of marginal teeth (RadT) (radula absent, duplex, solid recurved, flat, semi-enrolled, or hypodermic) to redefine the feeding types present in 51 of the 161 terebrid species used in this study. In contrast to the 3 feeding types defined by Miller, this study identified 12 unique foregut anatomies (Fig 2.1, Table 2.1). We can see that certain anatomy types are clade specific, such as type XII which is only found in the C clade, while other anatomy types can be found in multiple clades such as type I which can be found in the B clade and in multiple E subclades. A full summary of all 12 foregut anatomies is provided in Table 2.1.

- 22 -

Shell Size and Depth

In addition to foregut anatomy we examined shell size and depth range of our terebrid dataset and found that these were consistent with what was previously reported. Shell sizes were determined using 325 specimens that represented at least one individual from each of the 161 terebrid species used in this study. Measurements were checked for reasonability against size ranges published by Bratcher & Cernohorsky (Bratcher & Cernohorsky 1987) or in the original descriptions of the shells. The sample group ranged in length from 10mm (Partecosta trilineata) to 274mm (Oxymeris maculata), with an average length of 61mm. The Terebridae can be found at varying depths in all tropical marine habitats. From the specimens used in our dataset, certain species, such as Bathyterebra coriolis, remain in deep waters (minimum of 390m and maximum of 400m), while other species, such as , remain in shallow waters (minimum of 0m and maximum of 3m). Though most species have a narrow depth range, certain terebrid species have a broad depth range, such as Myurella nebulosa (minimum of 1m and maximum of 762m) or

Myurellopsis joserosadoi (minimum of 5m and maximum of 287m).

Diversification Across-Clade and Time

The BAMM analysis supported a model with a single evolutionary regime for the Terebridae, as reflected in the mean phylorate plot (Figure 2.2A), regardless of the value attributed to the prior probability of a rate shift. We do see a clade with a slightly higher diversification rate, but it is not statistically significant. We found evidence for a slow increase of diversification rates when a realistic sampling fraction of 26% was considered (Figure 2.2B). These results are congruent with the RPANDA analyses, which identified a model with a rate- constant speciation (lambda = 0.134 lineages/mya) and variable extinction rate model as best describing Terebridae evolutionary

- 23 - pattern (Figure 2.2C). From these analyses, the decrease in terebrid extinction rate can be used to explain an increase in global diversification rate beginning around 25 mya (Figure 2.2D).

Figure 2.2. Diversification rates of the Terebridae across clades and time. A. BAMM phylorate plot showing diversification rate across clades of the Terebridae where rates increase from blue to red. Rate values represent new lineages per million years. B. The blue curve indicates the net diversification rates-through-time trajectory as analyzed by BAMM. C. RPANDA plot showing the estimated speciation (blue), extinction (red) and net diversification (purple) rates through time for the Terebridae phylogeny. D. RPANDA plow showing the estimated accumulation of species richness through time for the Terebridae phylogeny. Despite the absence of across-clade heterogeneity in diversification rates, the best shift configurations retrieved by BAMM analysis for continuous traits displayed evidence of shifts in terebrid traits’ diversification rates. For example, for size, we retrieved two likely evolutionary rate shifts: one for the single species Myurella pertusa belonging to clade E1 and the other for clades B and C, corresponding to the Terebra and Oxymeris genera (Figure S2.1). Size appeared to have undergone a fast divergence at the beginning of the Terebridae evolutionary history,

- 24 - followed by several oscillations between 35 and 15mya, with the evolutionary rate still increasing towards the present (Figure S2.2). Depth apparently underwent 7 shifts in evolutionary rates: one for clade C including Terebra n.sp. aff. cumingii 1, Terebra n.sp. aff. cumingii 2, Terebra n.sp. 27 and Terebra cumingii; one for the subset of clade E1 including Myurella brunneobandata, M. pseudofortunei and M. n. sp. aff. fortunei; three for subsets of clade E2, including respectively

Punctoterebra teramachii and P. baileyi; P. polygirata, P. trismacaria and P.textilis; P. sp. aff. textilis 1 and P. n. sp. aff. trismacaria 1; the last two in the E5B clade for the single species

Myurellopsis joserosadoi and M. guphilae. The lineage through time plot for depth emphasizes a constant, very low diversification rate at the beginning of Terebridae evolutionary history, followed by a steep increase at ca. 40 mya, a marked decrease at 30 mya, and a second rapid increase from ca. 25 mya to the present (Figure 2.3).

- 25 -

Figure 2.3. BAMM results for depth rate over time. Rate vs. time plot from the depth trait analysis, where “trait rate” is given as change per million years, and “time before present” is in millions of years.

Key innovations in Terebridae diversification

We tested in BiSSE (Binary State Speciation and Extinction) several models of trait evolution.

For depth, size and larval development we detected no significant departure from the null model

(assuming the same speciation, extinction and transition rates for the two trait states considered).

For presence/absence of venom gland, the two models selected on the basis of their equal AICc values had irreversible transition rates (equal in one of the two) and equal speciation- extinction rates. These results do not support any of the tested traits as drivers of diversification.

- 26 -

Phylogenetic Diversity of larval development, size, foregut anatomy, and bathymetric data

A phylogenetic diversity analysis on the planktotrophic vs. lecithotrophic communities, carried out with the R package picante, retrieved negative standardized effect size (SES) values and low quantiles for the mean nearest taxon distance (MNTD) of the lecithotrophic community only, while the values obtained for mean pairwise distance (MPD) were negative with low quantiles for the planktotrophic community, and positive with high quantiles for the lecithotrophic community. The negative MNTD values for the lecithotrophic community indicate phylogenetic clustering closer to the tips of the phylogeny, meaning that the phylogenetic distance among lecithotrophic species is smaller than expected. Lecithotrophy is a trait shared by closely related species, indicating that it has evolved before separation of the lineages and that reversal is rare. On the other hand, the obtained MPD values suggest that phylogenetic diversity is high for planktotrophic developers, indicating an ancient phylogenetic clustering. This data indicates that planktotrophy is the ancestral larval ecology in the Terebridae.

With regards to the presence/absence of a venom gland, phylogenetic diversity analyses were carried out on a subset of 51 species, due to unavailability of anatomical data for most the species included in the phylogeny. Negative SES values and low quantiles were obtained both for the

MNTD and for the MPD of the species without a venom gland, indicating that their phylogenetic distance is smaller than expected. These results confirm that the loss of a venom gland happened in phylogenetically clustered terminal taxa, and that when the venom gland is lost in the ancestor, the reversal is extremely unlikely.

The phylogenetic diversity analysis on the shallow water vs. deep water communities, retrieved negative standardized effect size (SES) values and low quantiles for the three metrics

- 27 -

(PD, MNTD, and MPD) for the deep-water community. For shallow water community we obtained a negative PD (but with a higher value than in the deep-water), while the MPD had a positive value with a high quantile. Negative values indicate a low phylogenetic diversity and by converse a phylogenetic clustering higher than expected in the deep-water species. Shallow species are more phylogenetically diversified, and the high MPD value supports an ancient origin of such overdispersion.

Evolutionary Modeling

In our analyses the rate of size evolution is more than five times higher in planktotrophic species than in lecithotrophic species, while the strength of pull towards a size optimum is slightly higher for lecithotrophic species. This finding is based on the best fitting model for adult size in

Terebridae species included in our dataset, which is the OUMVA, according to the Akaike weights, with a delta AICc>5 with respect to the second-best fitting model OUMA. This model allows the larval ecology to influence the optimal size, the rate of size evolution and the strength of pull towards the optima across our Terebridae dataset. The optimal size value itself has a value of 70

(±18) mm for planktotrophic and 21 (±7) mm for lecithotrophic species. Our results indicate that species with long-living pelagic larvae tend to be generally larger, but also have a wider size range than lecithotrophic species, whose size tends to evolve faster. The best fitting model for depth was a simple Brownian model (BM), not supporting any correlation between depth and larval development.

Discussion

- 28 -

A robust phylogenetic context was used to clarify phylogenetic relationships of the

Terebridae and to provide a framework for investigating several characteristics such as morphology, larval ecology, anatomical features linked to the venom apparatus, and multiple abiotic factors. The molecular phylogeny presented here was based on a significant increase in the dataset compared to previously published phylogenies for the group, quadrupling the number of specimens used and almost doubling the number of species. Macroevolutionary analyses, ancestral state reconstruction, phylogenetic diversity analyses and evolutionary modeling identified unexpected evolutionary traits within the Terebridae, with implications for the whole Conoidean superfamily.

To improve the current working terebrid phylogeny, a series of field expeditions were conducted from 2012 to 2016 to increase the species coverage and geographical distribution of terebrid specimens. As a result, the species coverage increased from 406 samples in 2012 to 1761 samples in the current study, a 1355 sample increase. This sampling increase represents 40% of the >400 described species (26% of the estimated species diversity) and further confirmed the monophyly of the Terebridae family and the existence of 6 major clades (Clades A-F) (Holford et al. 2009; Castelin et al. 2012). A novel result for the terebrid molecular analysis is the discovery of a new sister group to all terebrids, Clade F. In prior publications, Clade A was described as the sister group to all terebrids (Castelin et al. 2012; Modica et al. 2014). Our results highlight the importance of diverse sampling to accurately reconstruct phylogenies, which has also been reported for other taxa (Pyron & Wiens 2011; Regier et al. 2013; Philippe & Telford 2006; Nagy et al. 2012; Marin & Hedges 2018). With the revised phylogeny, we also identified a number of

MOTUs that comprise complexes of pseudo-cryptic species, which suggests that a considerable fraction of the diversity in the Terebridae still needs formal description (Fedosov, et. al. 2018,

- 29 - submitted). The molecular phylogeny generated in this study enabled us to examine several factors of terebrid evolution and we were able to robustly map several characteristics onto the tree to investigate phenotypic patterns in terebrid macroevolution (Figure 2.1).

Across-clade diversification is constant in Terebridae

The results obtained by BAMM analysis outlined the absence of any significant shift in diversification rates across different evolutionary lineages in the Terebridae. While a concerted effort was made to increase our sampling size, a significant amount of terebrid species are unrepresented in this study and the lack of a dense taxon sampling, which is required for accurate detection of rate shifts for taxa spanning broad geographical areas, might explain the observed pattern (Ackerly et al. 2006; Moore & Donoghue 2007; Marazzi & Sanderson 2010; Drummond et al. 2012). However, decoupling of phyletic diversification and morphological divergence is a widespread phenomenon that may have multiple causes (Zelditch et al. 2015). Dependence of diversity and disparity on time can be weakened by variation in rate among clades (Zelditch et al.

2015) or by temporal changes in rates such as in the case of density-dependent evolutionary decelerations (Alfaro et al. 2009; Rabosky 2012; Etienne & Haegeman 2012) or adaptation to a stable adaptive peak that leads to a decrease in speciation rates over time (Hansen 1997).

Additionally, diversification rates may remain constant even in the presence of key innovations conferring ecological opportunity as is the case with pharyngeal jaws in labrid fishes or locomotor strategies in triggerfish (Claramunt et al. 2012; Dornburg et al. 2011; Alfaro et al. 2009). In some circumstances, diversification rates have even been shown to decrease after the acquisition of such key innovations, as evidenced by the development of foregut fermentation in colobine monkeys

(Tran 2014).

- 30 -

Across-time diversification is slowly increasing in Terebridae

The divergence rate through time plot obtained in BAMM suggests that the diversification rates are slowly increasing in the Terebridae, when using a sampling fraction of 26% of total extant diversity. Interestingly, results generated by RPANDA corroborate these results and show that the slow increase in diversification rates can be attributed to a decrease in extinction rate. The slow increase is observed across the entire Terebridae and not in a single group, which shows that it is not a specific trait found in a terebrid lineage that is driving this increase, but rather a factor, or combination of factors, found across the family. A constant increase in diversification rates was identified in Grebes (Aves) and was hypothesized to be caused by fragmentation of habitat, a factor that effected the entire family (Ogawa et al. 2015). Similarly, a study looking at freshwater snails showed an increase in speciation rates after experiencing ecological opportunity through dispersal to new locations (Delicado et al. 2018).

A potential hypothesis to explain the slow increase in diversification rates across the entire

Terebridae family is an ecological release initiated by the colonization of deep waters, while deep waters were still warm and a part of the shallow water community. As terebrids moved deeper and occupied new niches, less competition existed, which enabled a slow, but steady increase of diversification. By removing competitors, a strong increase in fitness as well as niche variation and directional selection for niche evolution exists (Emery & Ackerly 2014). In addition, a decrease in extinction rates may have been caused by an increase in sea levels, which leads to an increase in the continental shelf and therefore an increase in molluscan habitat (Orzechowski et al.

2015). More molluscan habitat would lead to an ecological release due to a decrease in competition for resources on the shelf.

- 31 -

Foregut diversity is not a driver for terebrid diversification

In the Terebridae, an accelerated evolutionary rate for size (in clades B and C) and the generally observed morphological disparity of the foregut is not accompanied by shifts in diversification rates. Instead, size appears to follow a complex history of diversification in the Terebridae. While size might not actually be a key trait allowing the exploitation of a new ecological niche, and thus not expected to lead to increased diversification rates, foregut morphology is considered tightly linked to feeding specialization, which is a fundamental trait in predatory groups. Therefore, it was very surprising that diversification rates were stable across the entire Terebridae phylogeny, even with the vast diversity of terebrid species. Generally, the acquisition of new predatory abilities can constitute a great ecological opportunity, allowing access to new sets of prey and thus new ecological niches (Fry et al. 2006; Vidal & Hedges 2005; Castelin et al. 2012). For our Terebridae dataset, although morphological data was available for only a subset of species that is exceedingly reduced for statistical analyses, it is evident that in at least Clade E, the level of morphological diversification is extremely high with 9 foregut types, but this did not correlate to any shift in diversification rates. As a result, contrary to expectations, we did not retrieve any evidence of foregut anatomy influencing the diversification of the Terebridae. BiSSE best-fit model supported that the loss of the venom gland is irreversible and this was also corroborated by the phylogenetic diversity results. This result is especially surprising given that it has long been proposed that a venom apparatus is considered an opportunistic innovation that favors speciation in the Conoidea.

Further sampling is required to confirm this finding, but given the foregut diversity of Clade E, it would appear our initial findings will be substantiated.

- 32 -

Larval development plays a role in terebrid shell size

Planktotrophic larvae are able to be carried across long distances by ocean currents, while feeding, and are characterized by increased distribution rates (Pechenik 1999; Bradbury et al.

2008). Lecithotrophy, on the other hand, is characterized by abbreviated larval periods and reduced dispersal (Selkoe & Toonen 2011). Larval development evolution trends are unidirectional, moving from planktotrophy to lecithotrophy (Gould & Eldredge 1986; Rouse 2000; Collin et al.

2007). Past studies suggest that reversals are very rare due to constraints on re-evolving complex feeding structures that are generally reduced when transitioning from planktotrophy to lecithotrophy (e.g. loss of the velum structure) (Strathmann 1978; Wray 1995). A pelagic larval development is displayed by the vast majority (ca 70%) of marine invertebrate species, and is considered the ancestral larval ecology in gastropods (Nielsen 2009; Thorson 1950), including most lineages of (Haszprunar 1988). The dichotomy between the two contrasting larval ecologies has been well studied in marine invertebrates, planktotrophic species have smaller egg sizes and high female fertility and lecithotrophic species possess lower female fertility and larger egg sizes, and they can therefore be placed at the two edges of an r-K continuum (Thorson

1950; Vance 1973; Strathmann 1977; Todd & Doyle 1981; Pianka 1970). In some circumstances e.g. where phytoplankton abundance is low or not constant throughout the year, lecithotrophy may be more advantageous even if it requires more maternal investment, as it relies on yolk reserves that are not subject to fluctuations. Lecithotrophic organisms have elevated rates of both speciation and extinction and have even been seen to accumulate faster over time in certain neogastropod clades (Shuto 1974; Hansen 1978; Lutz et al. 1986; Jablonski 1986; Jablonski & Lutz 1983).

Our study further corroborates past publications that planktotrophy is ancestral to lecithotrophy and that lecithotrophy has evolved at least 18 times in the Terebridae. Our findings

- 33 - also imply the existence of a strong evolutionary link between larval ecology and adult body size in the Terebridae. Across our entire dataset the best-fitting model with strong support, according to Akaike weights, estimates a different optimal size for the two divergent larval ecologies, but with a higher strength of pull toward a size optimum in the lecithotrophic species. In detail, this model consistently estimates that size in lecithotrophic species is significantly smaller than in planktotrophic species.

The slower evolutionary rate together with the strength of the pull towards the optimum observed for size in lecithotrophic species indicates that size is highly constrained in this group.

These results are congruent with the previous general framework that considers planktotrophic and lecithotrophic species at the two extremes of an r-K continuum, the r-strategist being characterized by fast growth, shorter longevity, semelparity, high fecundity, and small eggs. We can therefore hypothesize that since lecithotrophic species rely on yolk reserves that are fixed at the moment of egg production, their size at the time of hatching is fixed, while in planktotrophic species it may vary according to the food intake.

Conclusions

The possession of venom has been considered a driver for species diversification that influences predator-prey interactions, and macroevolutionary patterns. Here we examined the

Terebridae, an understudied group of venomous sea snails, to illustrate the complexity of molluscan venom arsenals as revealed in recent characterizations of terebrid venom peptides

(Gorson et al. 2015; Kendel et al. 2013; Imperial et al. 2007). Surprisingly, we did not find a correlation between venom and terebrid diversification, however using an interdisciplinary approach of molecular and morphological techniques we identified that adaptation to deep water,

- 34 - small size and loss of planktotrophy are key innovations driving Terebridae diversification. While our sample size is limited, our results suggest that with regards to terebrids and potentially other

Conoidean mollusks such as cone snails and turrids, the ability to express and use venom for predation and defense plays a diminished role in species diversification.

Methods

Sample Collection

All of the material used in this study was collected during several expeditions conducted by the Museum National d’Histoire Naturelle de Paris (MNHN) and the Holford Laboratory. The dataset includes 1,761 specimens assigned to 161 species collected from New Caledonia (56),

Philippine Islands (93), Chesterfield Islands (5), Australia (9), Vanuatu (135), Panama (79),

Madagascar (135), Mozambique (165), New Zealand (1), Fiji (4), Florida, United States (3),

French Guyana (1), Guadeloupe (88), Papua New Guinea (846), United Arab Emirates (3),

Vietnam (7), Marquesas Islands (20), French Polynesia (26), Solomon Islands (36), Sao Tome (2),

Tahiti (14), Thailand (10), Tasmania (6), and Taiwan (17). These samples originate from depths ranging from 0m to ~ 800 m. All specimens were specifically fixed for molecular analysis in the field. Live specimens were anesthetized using magnesium chloride (MgCl2) and a piece of tissue was cut from the foot and fixed in 95% ethanol. Specimens collected after 2012 were processed with a microwave oven (Galindo et al. 2014). Shells were kept intact for identification and deposited as vouchers in MNHN. All specimens were identified, and the classification used here follows that of the jointly published paper, including the new species and genus names (Fedosov et al. submitted).

- 35 -

DNA Sequencing and Molecular Phylogenetic Analyses

Total genomic DNA was extracted from foot tissue using NucleoSpin® 96 Tissues

(Macherey-Nagel) or the Epmotion 5075 robot (Eppendorf), following the manufacturer’s protocol. Fragments of three mitochondrial genes (Cytochrome Oxidase I (COI), 16S rRNA and

12S rRNA) and one nuclear gene (28S rRNA) were amplified. PCR reactions were performed as described in Holford, et. al 2009. (Holford et al. 2009) successfully amplified products were sent to Genewiz (South Plainfield, NJ) or to the Eurofins sequencing facility (France) for bidirectional

Sanger sequencing.

Sequences were aligned for each gene independently using MUSCLE version 3.2 (Edgar

2004). The accuracy of these alignments was manually inspected using BioEdit version 7.0.0.0

(Hall 1999). Best-fit substitution models were identified for each gene separately using jModelTest2 version 2.1.6 (Posada 2008). Best-scoring Maximum Likelihood (ML) trees were estimated using RAxML (Stamatakis 2014, 2006a). Each gene, and each codon position within the

COI gene, were considered as independent, each following its best-fit substitution model.

Robustness of the nodes was assessed using the thorough bootstrapping algorithm (Felsenstein

1985) with 1000 replicates. Phylogenies were jointly estimated using the Bayesian Markov Chain

Monte Carlo method implemented in BEAST version 1.8.4 (Drummond & Rambaut 2007). The program BEAUti version 1.8.4 (Drummond & Rambaut 2007) was used to generate the file used in BEAST. A birth-death process speciation prior was implemented and the substitution models identified in jModelTest2 version 2.1.6 were applied to each gene partitioned separately, with base frequencies estimated during the analysis. An uncorrelated lognormal clock was applied to estimate the relaxed molecular clock. The analysis was run for 75 million generations and sampled every 1,000 generations. The oldest known Terebridae, Mirula plicata (Lamarck, 1803) from the

- 36 - lower Eocene (56-47.7Ma) was used to constrain the stem node of Terebridae with a normal distribution mean of 50.7 Ma and a standard deviation (SD) of 1.48. A burn-in of 10% was removed after convergence analysis was evaluated using Tracer version 1.7 (Drummond &

Rambaut 2007) to check that all ESS values were greater than 200. Analyses were performed on the Cipres Science Gateway (http://www.hylo.org/portal2), using the RAxML-HPC2 on XSEDE tool for ML and the BEAST on XSEDE tool for BA.

Larval Ecology, Morphology and Bathymetric Data

Larval ecology

In Terebridae, as in many other families of marine gastropods, larval ecology can be easily inferred from the appearance of the protoconch, the larval shell that is often maintained at the tip of adult shell (Jablonski & Lutz 1983; Lima & Lutz 1990; Eldredge et al. 2005). The protoconch of intact terebrid shells was examined and categorized as multi- or paucispiral and the number of whorls present was measured to the nearest quarter whorl. Depending on the protoconch appearance, species were defined as planktotrophic, when the protoconch is multispiral, or lecithotrophic, when the protoconch is paucispiral. The protoconch of 638 intact terebrid shells were examined under a microscope and categorized as multi- or paucispiral and the number of whorls present was counted to the nearest quarter whorl.

Foregut anatomy

The anatomy of the terebrids was studied by manual dissections – when possible, on the same specimens sequenced for phylogeny. As most informative morphological characters in Conoidea are related to feeding, we specifically focused on the anterior alimentary channel structures to infer

- 37 - ability of the Terebridae lineages to envenomate their preys. Manual dissections were complemented by SEM studies of radular morphology, known to be extremely diverse in the

Terebridae. When present, radular sacs were isolated, and soft tissues immersed in a 3-5% solution of commercially available bleach. The radulae were then rinsed several times in cleaned distilled water, mounted on a 12 mm SEM stub, air-dried and coated for the SEM study.

Depth

To calculate species bathymetric ranges, all depth records were summarized for each species, with the deeper station record added to one sequence, and the shallower – to another. If only one depth record was provided (i.e. a station was sampled at constant depth), this depth record was added to both sequences. Subsequently, a confirmed species bathymetric range was calculated as the range between the minimal depth in ‘deeper’ sequence to maximal depth in ‘shallower’ sequence. This algorithm was implemented in an in-house Python script to quickly analyze large datasets of species occurrences.

Species number estimations

Species number estimations were made by scaling up the number of described species on the World Register of Marine Species (WoRMS) database, in which 416 described terebrid species are recorded. The sampling from this study includes 127 species that overlap with the WoRMS database. In addition to these 127 species, we identified 83 potential new species (6 new species described in a joint paper – REF, 10 molecular operational unites (MOTUs) amongst cryptic species which will be raised to nominative species, 32 MOTUs which after more detailed study are expected to be described as new species, plus 35 MOTUs which have an affinity to existing species but have distinct morphological features which may infer new species). Only one current

- 38 - species was found to be a synonym and not valid. The field studies covered thoroughly both shallow and deep waters and we could expect future expeditions to find a similar ratio between new and described species. If we extrapolate this pattern to the 416 described species in WORMS, then estimates of Terebridae species range from 625 to 675 species as a minimum.

Diversification rates through time and across clades

Macroevolutionary dynamics of diversification were modelled across Terebridae phylogeny

(after outgroup removal) using the software BAMM v.2.5.0 (Bayesian Analysis of

Macroevolutionary Mixtures, (Rabosky et al. 2013; Rabosky 2014), on the Maximum Clade

Credibility tree obtained in BEAST. BAMM explores models of lineage diversification implementing a Metropolis Coupled Markov Chain Monte Carlo (MC3) to improve the efficiency in simulating the posterior probability distribution. We ran 10 million generations of reversible jump Markov Chain Monte Carlo sampling, drawing samples from the posterior every 10000 generations. Priors were chosen using the R package BAMMtools (Rabosky et al. 2014), except for the prior probability of rate shift, which has been shown to affect BAMM results (Moore et al.

2016, Rabosky et al. 2017). For this prior we tested values ranging from 0.1 to 50 and we choose the value leading to the highest ESS values for LogLikelihood and NumberOfShifts. We accounted for incomplete taxon sampling using a sampling fraction of 26%, estimated using a total Terebridae diversity of 625 species. We processed the output data using BAMMtools to obtain summary statistics after removing a 10% burn-in, including mean phylorate plot that shows rates shifts in diversification along the phylogenetic tree, and to plot diversification rate through time. BAMM was used both to estimate diversification rates through time and among/within clades, and to define diversification rates for continuous traits (depth and size).

- 39 -

To corroborate BAMM results we used the time-dependent diversification approach implemented in the R package RPANDA (Morlon et al. 2016). This approach allows both speciation and extinction to change continuously through time, while in BAMM the extinction rate is assumed to be constant, thus including scenarios in which diversification rates are negative

(Morlon et al. 2011). For the whole Terebridae tree (with a 26% sampling fraction) we tested six nested diversification models with RPANDA: i) a Yule model, with a constant speciation rate and null extinction; (ii) a constant birth-death model, with constant speciation and extinction rates; (iii) a variable speciation rate model without extinction; (iv) a variable speciation rate model with constant extinction; (v) a rate- constant speciation and variable extinction rate model; and (vi) a model in which both speciation and extinction rates vary (Legendre & Condamine 2018). To select the best fitting model, ML scores of each model and the resulting corrected Akaike information criterion (AICc) were compared.

Trait-Dependent diversification

To model simultaneously the evolution of discrete traits and their impact on diversification, we used trait-dependent diversification models, in which species are characterized by an evolving trait and their diversification follows a birth-death process in which speciation and extinction rates may depend on the trait state. We used four characters: 1) Larval ecology, where species were defined by having either a planktotrophic (0) or non-planktotrophic (1) ecology; 2) Venom gland, where species were defined according to either the presence (0) or the absence (1) of the venom apparatus;

3) depth, where species were defined as shallow (0) when found above 100m or deep-water (1) below 100m; and 4) size, where species were identified as either small (0) for shell length lower than 25mm or large (1) for lengths exceeding 25mm. As described above, continuous traits were

- 40 - transformed into categorical two-state traits using a threshold generally corresponding to the photic zone for the depth, and to a size banding proposed by Terebridae specialists. We applied the Binary

State Speciation and Extinction model (BiSSE, Maddison et al. 2007) for four two-state datasets, accounting for state-specific incomplete taxon sampling. The BiSSE model has six distinct parameters: two speciation rates, two extinction rates and two transition rates (i.e. anagenetic change) between the trait states. Analyses were performed using the R-package diversitree

(FitzJohn 2012) on the MCC tree obtained from BEAST, using the functions make.bisse to construct the likelihood functions for each model based on the data, and the functions constrain and find.mle to apply different diversification scenarios. We used AIC to select among different models: the scenario supported with the lowest AIC was considered the best when ∆AIC>2 against other models.

Phylogenetic Diversity

We used a phylogenetic diversity approach to measure how functional and ecological discrete traits are distributed along Terebridae phylogeny. This approach depicts how different ecological processes interact with the evolutionary processes to influence the distribution of species and traits in the communities.

Phylogenetic diversity (PD) was calculated for four different pairs of clusters: 1) the planktotrophic vs. lecithotrophic developers; 2) the shallow vs. deep-living species; 3) the species with venom gland vs. species that had lost it and 4) shell size, small vs. large. In all cases, phylogenetic diversity was calculated using different metrics, standardized for unequal richness samples, using the R package picante (Kembel et al. 2013). First we calculated Faith’s

Phylogenetic Diversity (PD), corresponding to the sum of the total phylogenetic branch length for

- 41 - one or multiple samples (Faith 1992). Then we measured beta diversity both as the Mean Nearest

Taxon Distance (MNTD) separating taxa into two communities (corresponding to the average phylogenetic distance to the most similar taxon or individual in the other community) and as the

Mean Pairwise Distance (MPD) separating taxa in two communities (Webb et al. 2002; Helmus et al. 2007; Gotelli & Colwell 2001).

Evolutionary Modeling

To test whether shifts in larval development have been associated with selective constraints on the evolution of shell size and bathymetric distribution, we fitted two Brownian Motion (BM) models and five different Ornstein-Uhlenbeck (OU) models using the R package OUwie (Beaulieu

2016) to 100 trees reconstructed with stochastic character mapping of the trait “larval development” using the make.simmap function available in the R package phytools. For the parametrization of make.simmap, we used the estimated ancestral state, and a transition matrix with equal rates estimated from our empirical data with an MCMC search, and we performed 100 replicates then summarized in a consensus tree, to account for the inherent stochasticity of the process. BM models are processes where phenotypic variation, which accumulates with time, as is the case with random variation, neutral genetic drift, or drift-mutation equilibrium (Felsenstein

2001; Beaulieu et al. 2012). Here we fitted BM1 and BMS models, respectively with a single rate and different rate parameters for each state in the tree. The OU models, in addition to the stochastic displacement described by BM models, consider an optimal trait value and a tendency towards that optimum (Hansen 1997; Beaulieu et al. 2012). The simplest OU model (OU1) has a single optimum (θ) applied to all branches regardless of the developmental state. The remaining four OU models differ in how the rate parameters are allowed to vary in the model. In the first (OUM

- 42 - model) phenotypic optima means (θx) are different while both the strengths of selection (αx) and

2 the rate of stochastic motion around the optima (σ x) acting on all selective regimes are identical.

Then we also fitted a model that only allowed strengths of selection to vary among selective regimes (α1, α2: OUMA model), as well as one that only allowed the rates of stochastic evolution

2 2 away from the optimum to vary (σ A, σ B: OUMV model). Eventually, we fitted a model

(OUMVA) that allowed all three parameters (θ, α, σ) to vary among different selective regimes.

To choose the best-fitting model we used a model-averaging approach, where we calculated the

Akaike weights for each model (i.e. the relative likelihood of each model) (Burnham & Anderson

2002) by means of the second-order Akaike information criteria (AICc) that includes a correction for reduced sample sizes (Hurvich & Tsai 1989). We ensured that the eigenvalues of the Hessian matrix calculated in our OUwie analysis were positive, since this is an indication of the reliability of parameters estimation (Beaulieu et al. 2012).

- 43 -

Chapter Three Investigating gene structure and variation in the venom arsenal of the Terebridae

Introduction

Animal venoms are among the most complex biochemical natural secretions known and comprise a mixture of bioactive compounds (Norton & Olivera 2006; Vonk et al. 2013; Bjoern von Reumont et al. 2014). Though complex, there is a high degree of venom peptide convergence throughout the animal kingdom in the basic molecular structure and targets of venom toxins

(Escoubas & King 2009; Casewell et al. 2013b). These features make venom and venomous animals an ideal model system for investigating several biological phenomena from evolution to function (Fry et al. 2008; Escoubas & King 2009; Casewell et al. 2013b).

Venom research has primarily focused on certain groups, such as snakes, spiders, and scorpions, but much less is known about predatory marine snails. Venomous marine snails of the superfamily Conoidea include cone snails (family Conidae), auger snails (family Terebridae), and turrids (a complex of families). venom was first analyzed in the 1970’s, since then, researchers have extracted over 3,000 different peptides from Conus venom (conoserver.org).

Cone snail peptides (conotoxins) are disulfide-rich molecules shown to target sodium (Na+), potassium (K+), or calcium (Ca2+) channels, nicotinic acetylcholine receptors, and noradrenaline transporters (Becker & Terlau 2008; Olivera 2002; Terlau & Olivera 2004). In 2004, the Food and

Drug Administration (FDA) approved the analgesic drug Prialt® (ziconotide), which is a 23 amino acid peptide produced by Conus magus (Onofrei et al. 2004; McGivern 2007; Olivera 2000).

Ziconotide is a breakthrough drug and proof-of-concept for the potential of snail venom research.

Ziconotide also identified a new pathway for treating pain, namely inhibiting N-type Ca2+ channels. The efficiency in which peptide toxins from cone snails can be used to manipulate ion

- 44 - channels and receptors has led to their extensive use in biomedical research; however, peptide toxins from other members of the Conoidean superfamily are vastly under investigated.

The Terebridae family is a relatively understudied group closely related to cone snails.

While all cone snails use a venom apparatus to subdue their prey, not all terebrids have a venom apparatus (Puillandre & Holford 2010; Holford et al. 2009; Castelin et al. 2012). Recent efforts in the Holford group to describe the , phylogeny, and venom diversity of terebrid snails has demonstrated that teretoxin peptides have similar structural characteristics as cone snail peptides, which would make them just as important to biochemists and neurologists as tools for investigating cell signaling. Despite their structural similarities, cone snails and terebrids of the Conoidean lineages diverged in the early Paleocene (Powell 1966), and this early separation has resulted in peptide toxins that are unique and highly divergent from each other (Puillandre & Holford 2010).

Conotoxin gene families are also characterized by high rates of non-synonymous substitutions

(predicted to be 10 to 20 times faster than rates reported for genes of other metazoans) and gene duplication events (Duda & Palumbi 1999). This rapid divergence in gene families has led to significant differences in the functional gene products expressed, even between closely related species (Olivera 2006). In 2007, Imperial et al. analyzed the gene superfamilies in Hastula hectica

(family Terebridae) and found very little overlap with gene superfamilies expressed in Conus venom ducts (Imperial et al. 2007). In 2015, Gorson et al. analyzed the gene superfamilies in

Terebra subulata and Triplostephanus anilis and classified fourteen gene superfamilies of which thirteen are unique to terebrids. The significant difference between terebrid and cone snails indicates that the Terebridae family is a new resource for studying venom evolution and is a new source for biomedical drug discovery and development.

- 45 -

Figure 3.1. From venom to drugs. Depiction of strategy for identifying venom peptides in Conoidean snails using next generation sequencing. The venom duct is dissected from Conoidean snails and mRNA is extracted. Extracted mRNA is sequenced and de novo assembled. Assembled sequences are then annotated for a variety of comparative analyses such as phylogenetic reconstruction, identification of putative toxins, and assignment of GO function. When combined, comparative analyses can identify lineages that produce bioactive compounds with potential biomedical application for drug development (the pill at the bottom illustrates therapeutic potential). From Gorson et al. 2015.

This study provides the first comprehensive comparative NGS transcriptome analysis of

Terebridae venom ducts to investigate terebrid venom composition and diversification (Figure

3.1). Terebrids express a diverse array of hypervariable, disulfide-rich venom peptides, known as teretoxins, which have a wide variety of molecular scaffolds and molecular targets (Puillandre &

Holford 2010; Anand et al. 2014; Gorson et al. 2015; Imperial et al. 2007; Kendel et al. 2013).

Several novel putative teretoxin are identified using a validated in-house bioinformatics pipeline.

The evolutionary relationships and possible origins of several venom toxin families (e.g., Kunitz- type serine protease inhibitors, turripeptides, conopressins, actinoporins, etc.) with terebrid homologs are examined through phylogenetic methodologies. Additionally, a more thorough

- 46 - classification of teretoxin gene superfamilies is proposed using 24 transcriptomes compared to the two transcriptomes that were used in the first analysis (Gorson et al. 2015). The increase in transcriptome number in turn increased the inventory of putative teretoxins by 20-fold and has shed light on both the intra- and inter- specific variation that exists within terebrid snail venom.

The putative teretoxins identified do not only enhance our understanding of venom gene evolution in a group of predatory marine snails, but the identified venom peptides have also increased the pool of peptides for discovering new drug therapies.

Results

Putative teretoxin identification

An in-house bioinformatics pipeline was used to identify 2299 putative teretoxins from venom gland transcriptomes of 24 terebrid specimens (Figure 3.2). De novo assembly of the 24 transcriptomes had an average assembly size of 162,599 and an average N50 of 733 (Table S3.1).

The 24 transcriptomes belong to 17 distinct species, representing several clades across the

Terebridae phylogeny. There is 1 transcriptome from the Oxymeris clade (B), 15 transcriptomes from the Terebra clade (C), 5 transcriptomes from the Hastula clade (D), and 3 transcriptomes from the Myurella clade (E). In the Terebra and Hastula clades, the clades for which all of the species have a venom gland, replicate specimens were sequenced to investigate intraspecific teretoxin variation. Specifically, there are 5 Terebra subulata transcriptomes, 2 transcriptomes, and 2 Hastula matheroniana transcriptomes.

- 47 -

Figure 3.2 Venomics pipeline for the identification of toxin homologs. Diagram of the bioinformatics pipeline followed to identify putative venom components in terebrids. The different steps are highlighted with colored ovals and the corresponding software used indicated below each step. After RNA extraction and sequencing, the first step is data processing (blue) and de novo assembly of the transcriptomes. Subsequently, a gene prediction and filtering step (purple) is performed, in which contigs are translated into amino acids, and open reading frames and signal sequences are predicted. The following step is a homology search (red) using BLAST. The results are validated (green) through BLAST and phylogenetic reconstructions to generate a final list of candidate toxins (orange). To quantitatively measure the quality of the transcriptome assemblies, the program

BUSCO (Benchmarking Universal Single-Copy Orthologs) was used to determine the amount of eukaryote core genes ((n=429) evolutionarily conserved orthologs) present in the assemblies. All transcriptome assemblies had BUSCO percentages >50%, which is not unusual since all transcriptomes were assembled from specific venom gland tissues (Figure S3.1).

All transcriptomes had on average 135 putative teretoxins. Differences in venom peptide toxin composition are commonly found among different species (Colinet et al. 2013; Barghi et al.

2015; Phuong et al. 2016), but the variation in putative teretoxins recovered from each of the terebrid transcriptomes may also be explained by differences in library construction methods and sequencing depth. To ensure the bioinformatics pipeline was in fact identifying putative venom peptides, one of the transcriptomes comes from the tissue of the accessory feeding organ of

- 48 -

Oxymeris areolata, a terebrid lacking a traditional venom gland. In the Oxymeris areolata transcriptome, only 11 venom peptides were identified, 4 of which are similar to turripeptides, 3 of which are similar to conotoxins, 2 of which are similar to Kunitz-type serine protease inhibitors and only 2 of which are similar to teretoxins. The identification of some teretoxin venom peptides in O. areolata is expected as the venom apparatus was ancestral to terebrids and it is not surprising to find venom peptide homologs in “nonvenomous” animals, as was done by Fry et al. (2003) in a

“nonvenomous” colubrid (Fry et al. 2003). Therefore, the benchmarking activity demonstrates the accuracy of the bioinformatics pipeline (Figure 3.2).

Inter- and Intraspecific comparison of putative teretoxins

To determine the composition of putative teretoxins identified we classified them based on their cysteine framework, which is the pattern of cysteines found in the mature teretoxin

Figure 3.3. Comparison of peptide toxin frameworks. A. Heatmap showing the interspecific variation of relative cysteine frameworks in 17 terebrid transcriptomes and all published Conus peptides. B. Pearson correlation showing the similarities/differences between the distribution of cysteine frameworks in terebrid and Conus venom. peptides. We used the same cysteine framework Roman numeral nomenclature as applied to cone

- 49 - snail conotoxins and compared the quantity of teretoxins found in each cysteine framework to what is found in conotoxins. The 2299 putative teretoxins identified from our bioinformatics pipeline displayed a wide array of cysteine frameworks (Figure 3.2). Specifically, terebrids have a substantial proportion of putative teretoxin venom peptides with cysteine framework VI/VII, VIII,

IX, and XXII (Fig. 3.3A). It should be noted that cone snails also have a sizeable proportion of venom peptides that fall into framework VI/VII, however, they have a very small proportion of framework VIII, IX, and XXII putative peptides. Similarly, cone snails also have a high proportion of framework I and III toxins, while terebrids do not. In fact, there was not a single framework III teretoxin found in the terebrids, while 19% of all cone snail conotoxins are framework III. Most striking, is the large amount (on average 22%) of venom peptides in terebrids that are identified as having unique frameworks. This finding confirms that the divergence of terebrids and cone snails have resulted in each having distinct venom arsenals. The novel putative teretoxin peptides will be classified with new cysteine frameworks, distinct from those found in conotoxins. It is expected that these putative peptides will likely have novel molecular targets to those associated with cone snail peptides.

Along with family differences between cone snails and terebrids, we also found interspecific differences within the Terebridae family (Figure 3.3). As made evident by the Pearson correlation heatmap (Figure 3.2B), differences in cysteine distribution can be most seen when comparing Hastula albula, Myurella kilburni, and Terebra amanda with the rest of the species. In all three of these species, less than 10% of the putative teretoxins are framework VIII, while on average framework VIII peptides represent 15% of all peptides in the other terebrid species. M. kilburni also has the lowest percentage of framework IX putative teretoxins and H. albula is the only species where a framework XXV teretoxin peptide was found. Though differences exist, we

- 50 - also find putative venom peptides with 100% sequence identity in multiple terebrid species. For example, mature venom peptide

DTKLFFDDCTACEKGCVYSLTCNCFDRSDYCFCDCPSQFY, which is a framework XXII and has 8 cysteines, can be found in Terebra argus, T. babylonia, T. funiculata, T. guttata-2,

Cinguloterebra jenningsi, Hastula matheroniana, T. straminea, and T. textilis. Similarly, mature venom peptide VALPCPYGCPLRCCHMTDGVCLRNKQGC, which is a framework VI/VII and has 6 cysteines, can be found in Hastula albula, Terebra cingulifera, H. hectica, Cinguloterebr jenningsi, H. matheroniana, Myurella paucistriata, T. subulata-3, T. subulata-4, and T. textilis.

In addition, we find intraspecific variation within the species Terebra subulata, Terebra guttata, and Hastula matheroniana, where we had replicate transcriptomes (Figure 3.4). In all

Figure 3.4. Comparison of peptide toxin frameworks within species. Heatmap showing the intraspecific variation of relative cysteine frameworks in 3 terebrid species and 9 terebrid transcriptomes. three species we see the same general pattern, high percentages of frameworks VI/VII, VIII, IX, and novel, while having relatively low percentages of the remaining frameworks. T. subulata-1 has only 7% framework VIII peptides, while the rest of the T. subulata samples have >12%

- 51 - framework VIII peptides. Similarly, in H. matheroniana-2, there is a combined 7% of framework

XI and XII peptides, while H. matheroniana had no framework XI an XII identified. A longstanding question in venom research is why the venom arsenals of predatory animals have so many peptides. By first characterizing the teretoxins in several species of terebrids we have provided a foundation to begin to examine how terebrid venom variation may be linked to evolutionary patterns such as diet or speciation.

Comparison of venom protein toxin classes found in terebrid venom

While teretoxins were the vast majority of all venom peptides identified, venom proteins similar to other toxin classes were also identified. Phylogenetic trees of aligned mature peptide toxins were constructed to evaluate evolutionary relationships of selected kunitz-type serine protease inhibitor, turriptpetide, actinoporin, and conopressin venom toxin classes. For example, a total of 55 Kunitz-type serine protease inhibitor peptides were found in our in house teretoxin database (Figure 3.5). Kunitz-type serine protease inhibitors have a broad range of activities, which include inhibiting the activities of serine protease genes that have roles in blood clotting, signaling pathways, and enzyme activation/degradation (Page & Di Cera 2008). Kunitz-type serine protease inhibitors have been identified in the venoms of snakes and bats (Francischetti et al. 2013;

Fernández et al. 2016; Low et al. 2013), jellyfish (Liu et al. 2015), leeches and bloodworms (Kvist et al. 2014; von Reumont et al. 2014b) and a plethora of insects (Negulescu et al. 2015; Kim et al.

2013; Qian et al. 2015; Takác et al. 2006; Veiga et al. 2005; Sigle & Ramalho-Ortigão 2013;

- 52 -

Ribeiro et al. 2007). The terebrid Kunitz-type serine protease inhibitors identified were validated using InterProScan (Jones et al. 2014; Zdobnov & Apweiler 2001) and only kept if the Kunitz domain was identified. Interestingly, the terebrid Kunitz-type serine protease inhibitors diverge

Figure 3.5. Phylogenetic tree of Kunitz-type serine protease inhibitors. Kunitz-type serine protease inhibitor phylogeny includes sequences from venomous and non-venomous taxa and terebrid sequences. Terebrid sequences are highlighted in bold. Tree was reconstructed with RAxML v8.2.10. Numbers at the nodes represent bootstrap values, estimated through the bootstopping algorithm. into two separate groups, one similar to Conus toxins and snakes and another similar to Conus toxins and scorpions (Figure 3.5). This could have arisen by an ancestral duplication of these venom genes in the ancestor of the family followed up divergence and the acquisition of distinct amino acids, which led to two different terebrid Kunitz toxin subgroups.

- 53 -

With regards to turripeptides, a total of 14 teretoxin peptides similar to turripeptides were found in our teretoxin database (Figure 3.6). Turripeptides are short peptides, rich in cysteines, that were found in the venoms of turrids, a diverse group of Conoidean snails that are related to

Figure 3.6. Multiple sequence alignment of turripeptide-like sequences. Multiple sequence alignment of turripeptides and terebrid sequences generated by MAFFT v7.310. Conserved residues are colored, including the conserved cysteines, which are highlighted in red. The first 14 peptides are from terebrids and were identified in this study. cone snails and terebrids (Watkins et al. 2006). Turripeptide-like toxins have been identified in bloodworms, jellyfish and zoanthids (von Reumont et al. 2014b; Brinkman et al. 2015; Huang et al. 2016). Similar to conotoxins, turripeptides function as neurotoxins that modulate ion channels, which can have an array of different functions (Gonzales & Saloma 2014; Aguilar et al. 2009).

The tereteoxins identified have the same cysteine framework (C-C-C-C-C-C) as three turripeptides previously identified as well as five sequences identified as turripeptides (Figure 3.6).

A total of 16 teretoxin peptides similar to actinoporins were found in our inhouse database (Figure 3.7). Actinoporins are a highly conserved cytolytic toxin class that lack cysteines and act by forming pores within biological membranes that can lead to cell death (García-Ortega et al. 2011) and have been found in sea anemones (Bjoern von Reumont et al. 2014; García-Ortega et al. 2011), mollusks (Shiomi et al. 2002; Gorson et al. 2015), annelids (Björn M von Reumont et

- 54 - al. 2014) and chordates (Warren WC et. al. et al. 2008). The actinoporin peptides are similar to

Conus peptides as well as Monoplex peptides, which further validates the identification of these sequences (Figure 3.7).

- 55 -

Figure 3.7. Phylogenetic tree of actinoporins. Actinoporin phylogeny includes sequences from venomous and non-venomous taxa and terebrid sequences. Terebrid sequences are highlighted in bold. Tree was reconstructed with RAxML v8.2.10. Numbers at the nodes represent bootstrap values, estimated through the bootstopping algorithm.

- 56 -

Finally, 12 teretoxin peptides similar to conopressins were found (Figure 3.8).

Conopressins are short, nine residue peptides with two cysteines that were first discovered in the venoms of Conus geographus and Conus striatus and are similar to vasopressin, which are hormonal neurotransmitters (Cruz et al. 1987). All conopressins identified in the terebrids start with the same seven residue sequence, CFIRNCP, identified in C. geographus and C. striatus

(Figure 3.8).

Figure 3.8. Multiple sequence alignment of conopressin sequences. Multiple sequence alignment of conopressins and terebrid sequences generated by MAFFT v7.310. Conserved residues are colored, including the conserved cysteines, which are highlighted in red. The first 12 sequences are from terebrids and were identified in this study.

Teretoxin venom gene superfamily identification

Having identified and characterized >2000 teretoxins it is now possible to determine teretoxin venom gene superfamilies. All terebrid venom peptides are expressed as a single gene with a signal sequence, a pro-region, and the mature peptide (Terlau & Olivera 2004). The signal sequence of each gene has been used in cone snails to group mature peptides into functional gene superfamilies (Robinson et al. 2014). Similarly, it is expected that teretoxins can also be grouped into gene superfamilies using their signal sequences. In fact, the first two terebrid transcriptomes from Triplostephanus anilis and Terebra subulata were used to propose 14 teretoxin gene superfamilies, of which 13 out of the 14 superfamilies were distinct from cone snail superfamilies

(Gorson et al. 2015).

Three important criteria are used to establish a gene superfamily classification that is phylogenetically relevant and reflects the evolution of teretoxins and enables the identification of

- 57 - terebrid gene superfamilies: 1) A new teretoxin superfamily should be an independent lineage, not be nested within another clade, and have strong support values (bootstrap ≥70). 2) Sequence identity within the potential superfamily should be at least 60%. And 3) The cysteine pattern should be different from the one found in the sister clades. The signal sequences from the 24 terebrid transcriptomes that were assembled were used to classify 27 terebrid venom gene superfamilies.

A pairwise distance matrix for all 2299 putative toxins was analyzed through Automatic

Barcode Gap Discovery (ABGD) (Puillandre et al. 2012), and signal sequences were assigned into hypothetical groups. Signal sequences that were alone in a group were removed for further analysis. Of the 2299 sequences identified, 1995 sequences were used to generate a tree to identify superfamilies. Twenty-seven superfamilies were identified following the criteria outlined above

(Figure 3.9). Each superfamily, by definition, is characterized by a conserved signal sequence and generally by a unique conserved cysteine framework. For example, superfamily T1 is characterized by framework IX, while superfamily T2 is characterized by framework VI/VII.

There are some superfamilies that are characterized by more than one cysteine framework

(superfamilies T22, T23, and T26), which is acceptable as reported in conotoxins because of possible gene duplication events and other gene expression mechanisms (Robinson & Norton

2014). All 27 superfamilies are unique from those superfamilies identified in cone snails and are different from those previously identified by Gorson et al. in 2015 (Gorson et al. 2015).

- 58 -

Fig. 3.9. Teretoxin gene superfamilies. Circular phylogeny of terebrid gene superfamilies (midpoint root) obtained from MAFFT alignment of teretoxin signal sequences under ML criteria with JTT + I + G and 1,000 pseudoreplicates. Clades highlighted in red have strong support values but only those that meet additional criteria (see details in text) are designated as teretoxin superfamilies. Inner ring shows superfamily name and outer ring shows cysteine framework of the superfamily. Discussion

The Unique Venom of Terebrids

This study applied Next Generation Sequencing and state of the art bioinformatics to accomplish a large scale, comprehensive analysis of 24 transcriptomes with a focus on identifying

- 59 - disulfide rich venom peptides. Two thousand two hundred twenty nine teretoxins were successfully identified using a validated in-house bioinformatics pipeline. The assembled teretoxin database was used to characterize convergent evolution of venom peptides across multiple toxin classes with a focus on teretoxins and identified 27 teretoxin gene superfamilies and associated cysteine frameworks.

This study demonstrates the differences between teretoxins and conotoxins. The distribution of cysteine frameworks in cone snails and terebrids varies greatly, with the exception of framework VI/VII peptides, which are abundantly found in both cone snails and terebrids.

Although the biggest differences were found between cone snails and terebrids, we did find some differences within the family as well as within species. Inter- and intraspecific variation in venom composition have been documented as a result of diverse factors such as sex, age, diet or biogeography (Cipriani et al. 2017; Sunagar et al. 2014; Colinet et al. 2013; Phuong et al. 2016;

Barghi et al. 2015). We attempted to analyze venom variation due to diet and the results of this study is discussed in Chapter 4.

To date, the best way to identify putative toxins is through homology searching such as

BLAST. However, this method continues to leave unique venom peptides undiscoverable. Even with advancements in computational technologies, machine learning for example, a “known” dataset must be used to train a program to identify putative toxins, which biases the search results.

Therefore, proteomic analysis of the venom duct is an incredibly valuable tool that must be utilized in future studies. Proteomics will also allow for the identification of post-translational modifications, which are abundant in cono- and teretoxins (Craig et al. 1999; Safavi-Hemami et al. 2010; Buczek et al. 2005; Gorson et al. 2015).

- 60 -

Terebrid species express a wide array of putative toxins that represent at least five known venom toxin classes. These toxin classes have been convergently recruited into a diversity of animal venoms. The recovered Kunitz-type serine protease inhibitors, actinoporins, conopressins, and turripeptides confirm that terebrid venom is a complex cocktail of venom peptides used for predation and possibly for defense. It is proposed that proteins/peptides found in venoms are the result of toxin recruitment events, by which an ordinary protein gene, generally involved in some key regulatory process, is duplicated and the new gene is expressed in the venom gland under strong selection pressures (Fry et al. 2009; Casewell et al. 2013b). Once recruited, these new genes may undergo additional gene duplication events coupled with neofunctionalization, which results in gene families that encode peptide toxins with innumerous molecular targets and functional activities (Casewell et al. 2013b). Therefore, it is not surprising to find similar toxins in very distantly related organisms as the venom genes have evolved convergently under strong predator- prey pressures. It is important to note that there are several clades with low support values.

Terebrid Superfamilies

Twenty-seven unique superfamilies (T1-27) were identified mimicking the criteria outlined in Gorson et al. (2015). Interestingly, all 27 superfamilies are unique from cone snail superfamilies. This lack of similarity supports prior hypotheses regarding the potential to identify novel and undescribed venom peptide superfamilies and mature toxins in the Terebridae

(Puillandre & Holford 2010; Gorson et al. 2015). Though 27 new gene superfamilies were identified in this study, there are concerns for two reasons: 1) None of the superfamilies identified in 2015 were recovered in this analysis and 2) No superfamily had greater than ten sequences. As the number of sequences to be analyzed grows, the topology of the superfamily phylogeny

- 61 - changes, which causes a discrepancy in superfamily identification when using just two specimens

(done in 2015) and twenty-four specimens (done in this study). Therefore, it is expected that as the number of terebrid transcriptomes assembled increases we may see additional teretoxin gene superfamilies emerge until we have a threshold of teretoxins that is representative of the whole terebrid venom arsenal. Additionally, as signal sequences are very short sequences and generally do not exceed 30 amino acids, support values remain low throughout the tree. Therefore, using programs to compute pairwise differences between sequences followed by a program to group sequences into hypothetical groups, such as ABGD (Puillandre et al. 2012), helped to remove sequences that are unique in the dataset and increase support for the phylogenetic analyses.

Conclusions

This study lays a foundation for the continued investigation of terebrid venom peptides using cutting edge technology. A new benchmarked bioinformatics pipeline was created to aid in the ongoing challenge of assembling non-model organism transcriptomes de novo. The expansive number of terebrid venom gland transcriptomes assembled allowed for a high depth of coverage that is reflected in the number of teretoxins identified and in the non-teretoxin identified for the first time in terebrids. The identification of more than two-thousand putative teretoxins enabled a comprehensive comparison of cone snail and terebrid venom peptides, which led to the conclusion that while teretoxins share organizational features with conotoxins, they differ substantially, namely in terms of cysteine framework distributions and gene superfamilies. This study shed light on the importance of studying non-model organisms to investigate fundamental questions about venom gene evolution and increased the pool of peptides available for examining the structure and function of ion channels.

- 62 -

Methods

Collection, Extraction, and Sequencing

Samples used in this study were collected on a 2011 expedition to Inhaca, Mozambique, a

2014 expedition to Madang, Papua New Guinea, and a 2015 expedition to Madang, Papua New

Guinea. Venom glands from each specimen was dissected in the field and stored in RNAlater and frozen at -80°C until used.

Total RNA was extracted using a Qiagen RNeasy Micro kit, from individual samples, except in two cases where venom gland tissues were combined from four individuals because of their extremely small sizes (Myurella kilburni and Triplostephanus anilis). The integrity of total

RNA was confirmed using agarose gel electrophoresis. Total RNA was used as a template to perform polyA enriched first strand cDNA synthesis using the HiSeq RNA sample preparation kit for Illumina Sequencing (Illumina Inc., CA) following manufacturer’s instructions. The Agilent

2100 BioAnalyzer was used to assess quality in the resulting cDNA libraries.

The cDNA libraries were sequenced using Illumina HiSeq 1000 technology using a paired end flow cell and 80 x 2 cycle sequencing at the New York Genome Center or at the Weill Cornell

Medical College genomics resources core facility.

Read Processing, De Novo Assembly, and Putative Toxin Identification

Raw reads were quality checked with FastQC v0.11.5

(www.bioinformatics.babraham.ac.uk). Adapter sequences and low-quality reads (Phred score

<33) were removed using Trimmomatic v0.36 (Bolger et al. 2014) and trimmed reads were re- evaluated with FastQC to ensure the high quality of the data after the trimming process. Due to the

- 63 - lack of a reference genome, the processed reads were de novo assembled using Trinity v2.4.0 (Haas et al. 2013; Grabherr et al. 2011b).

De novo assembled transcriptomes were analyzed to identify putative toxins and venom components following a custom in silico venomics pipeline modified from Gorson et al. (2015)

(Gorson et al. 2015). Transcriptomes were translated with EMBOSS’ GetORF

(http://emboss.sourceforge.net/apps/cvs/emboss/apps/getorf.html). As venom is mainly composed of secreted peptides, SignalP v4.1 (Petersen et al. 2011) was used to predict signal peptide sequences in the putative coding regions. Only putative peptides with a signal sequence were analyzed using homology search strategies. Transcripts were searched using the BLASTP tool

(Altschul 1997; Johnson et al. 2008) against an in-house database that includes all known toxins available in public databases such as ConoServer (Kaas et al. 2008, 2012) or Tox-Prot (Jungo &

Bairoch 2005; Jungo et al. 2012) and additional putative toxins identified by the Holford group.

Hits with an e-value of 1x10-5 or less were cross validated by being searched against the

UniProtKB/Swiss-Prot database using BLASTP (Altschul 1997; Johnson et al. 2008). Transcripts were considered putative toxins if 1) the best hit from the UniProtKB/Swiss-Prot database had a higher e-value than the initial hit from the venom database, or 2) the best hit from the

UniProtKB/Swiss-Prot database was labeled as a toxin. If this criteria was not met, transcripts were considered false positive and discarded from further analysis. (Figure 3.1)

Phylogenetic Reconstruction of Putative Venom Toxin Homologs

Phylogenetic analyses of individual toxin families including terebrid homologs were performed to validate the homology searches predicted using BLAST to investigate evolutionary relationships across taxa. Selected putative toxins (i.e. non-redundant contigs that were identified

- 64 - as a toxin by BLAST search methods) and similar sequences from venomous and non-venomous taxa spanning the Metazoa were downloaded from Uniprot and provided by Verdes et al. (2018) and aligned with MAFFT v7.310 (Katohs & Standley 2013). The best model of protein evolution for each family was selected with ProtTest 3 (Darriba et al. 2011) and used for phylogenetic tree reconstruction on the Cipres Science Gateway (http://www.hylo.org/portal2), using the RAxML-

HPC Blackbox tool version 8.2.10 (Stamatakis 2006b, 2006a), with support values estimated through a bootstopping algorithm. All trees were rooted with non-venomous homologs.

Identification of Teretoxin Superfamilies

Following the superfamily classification outlined in Gorson et al. (2015), only signal sequences were analyzed using phylogenetic methods to define super families (Gorson et al. 2015).

Signal sequences of all putative teretoxins were identified using SignalP (Petersen et al. 2011) and

ConoPrec on the Conoserver website (Kaas et al. 2008, 2012). Corresponding nucleotide sequences were used to group sequences into hypothetical groups. Evolutionary divergence was calculated between sequences (pairwise differences) using the MEGA 4 algorithm (Tamura et al.

2007) and the MEGA distance matrix was analyzed using ABGD (Puillandre et al. 2012) to predict hypothetical groups based on the distribution of pairwise differences. Sequences that were grouped alone were removed from the dataset. Peptide signal sequences were aligned using MAFFT (Katoh et al. 2002; Katoh & Standley 2013) and the best fitting model of protein evolution (JTT + I + G) was selected using ProtTest 3 (Darriba et al. 2011) following the AIC and used for phylogenetic tree reconstruction. Maximum likelihood analyses were conducted using RAxML version 8.2.10

(Stamatakis 2006b, 2006a) and the bootstopping algorithm on the CIPRES portal (Miller et al.

2010). There has been no gene identified to be an appropriate outgroup for this analysis.

- 65 -

- 66 -

Chapter Four Determining drivers of Terebridae venom diversity as inferred by diet

Introduction

Across the animal kingdom, interspecific variation and speciation are driven by factors such as diet (Lobato et al. 2014), resource partitioning and niche separation (Knudsen et al. 2006), climate change (Carstens & Knowles 2007), phenotypic plasticity (Stauffer & van Snik Gray

2004), and hybridization (Peterson et al. 2009). Diversification, specifically in organisms using venom for predation, is proposed to be driven by the acquisition of a venom apparatus, which enables proliferation into new niches and opportunities to diversify prey, which is favorable for speciation (Fry et al. 2012, 2006). This hypothesis is supported by the fact that venom for predation has evolved several times across the tree of life (Fry et al. 2009). As venoms are considered to be a key driver of species diversification, size ranges, body plans, habitat ranges, and developmental strategies can vary greatly among related venomous organisms (Casewell et al. 2013b; Phuong et al. 2016; Fry et al. 2006). The variations in mechanism of delivery (beaks, fangs, harpoons, spines, etc.) as well as variations in venom composition (proteins, peptides, small molecules, etc.) make venomous organisms an excellent case for studying theories of convergent evolution.

Understanding the selective forces that drive diversification is critical in understanding evolution in venomous organisms.

Diet has always been thought to be a major driver of venom composition patterns because of a perceived link between venom composition and prey acquisition (Vonk et al. 2011; Casewell et al. 2013b). There are two main hypotheses to explain the link between diet and venom composition: 1) prey preference can determine venom components (types of compounds in venom)

(Daltry et al. 1996) and 2) there should be a positive correlation between dietary breadth and

- 67 - venom complexity (number of compounds in venom) (Pahari et al. 2007; Li et al. 2005). Studies that show that venoms from different species are most effective on their preferred prey help support the first hypothesis that prey preference can drive venom composition (Silva & Aird 2001;

Richards et al. 2012). Similarly, to support the second hypothesis (number of compounds in venom) studies have shown that increased prey breadth is correlated to venom complexity. For example Phuong et al. (2016) found that cone snails with a more generalized diet tend to have more complex venoms (Phuong et al. 2016) or Li et al. (2005) who found a 50- to 100- fold decrease in toxicity of venom in a group of sea snakes that have shifted to eating only fish eggs

(Li et al. 2005).

The Terebridae (auger snails) is a family of carnivorous marine snails that capture their prey with a variety of different techniques, exemplified by structural variations in their foregut anatomy. Although some members of the Terebridae family produce venoms using a venom gland similar to that which is found in their close relative (cone snails), many terebrids have lost a structured venom apparatus (Holford et al. 2009; Holford et al. 2009; Castelin et al. 2012). In the

1970’s, based on analysis of the foregut, Miller identified the fascinating degree of anatomical variability and described three different types of terebrid physiology: (1) Type I has salivary glands present, a shrunken buccal tube, but lack a radular sac, a venom duct, and a venom bulb; (2) Type

II has a venom apparatus similar to that found in Conus, which includes a radular sac, a venom duct, venom bulb, and a proboscis, as well as salivary glands; (3) Type III lacks salivary glands and a venom apparatus, but an accessory feeding organ is present (Miller 1970; Miller 1970). In

2012, Castelin et al. mapped the six characteristics that define these 3 different feeding types

(proboscis, venom gland, odontophore, accessory proboscis structure, radula, and salivary glands) on to the terebrid phylogeny to assess the evolution of anatomy in the Terebridae (Castelin et al.

- 68 -

2012). Their results indicate the hypodermic radula found in the Terebridae used to inject venom into prey likely evolved independently on at least three occasions, the proboscis was lost six times in the Terebridae, while the venom gland was lost eight times (Castelin et al. 2012). Additionally, it was revealed that the diversity of foregut anatomies found in the entire Conoidean superfamily was present in just the Terebridae family. As shown in Chapter 2 of this dissertation, we expanded the number of feeding types from the traditional 3 Miller types to 12 distinct foregut anatomies

(Gorson et. al 2018). In our investigation, foregut anatomy types were not restricted by phylogenetic relatedness, but rather, one anatomy type could be associated with species in multiple different clades.

Although diet is widely accepted as a major force in shaping venom evolution across a plethora of taxa, there have been few multi-species studies that specifically look at the role of diet on venom composition and species diversity. Repeated innovations in the foregut anatomy and variations in venom composition suggest that the Terebridae adapted to eating different diets and to date, this hypothesis remains untested. Here, we perform the first molecular study of the

Terebridae diet using DNA-barcoding of the gut contents of terebrids, to better understand the link between terebrid foregut anatomies, venom diversity, and prey diversity. Specifically, we employ a gut metabarcoding approach, using tailored primers and Next Generation Sequencing (NGS), to investigate the prey of 17 species across the Terebridae phylogeny and examine the correlation between terebrid gut content and venom gland transcriptomes.

- 69 -

Figure 4.1. Representative terebrids and their annelid prey. (1) Hastula hectica. (2) Myurella nebulosa. (3) Terebra subulata. (4) Terebra argus. (5) Myurellopsis undulata. (6) Terebra guttata. (7) Scolelepis. (8) Neanthes. (9) Pygospio. (10) Odontosyllis. (11) Syllis. (12) Worm pulled from the mouth of Terebra guttata (6), identified as Scolelepis. Results

Terebrid gut content characterization reveals a diversity of annelid diet

A select group of terebrid specimens was used to investigate diet. Specifically, the gut content of 17 species from across the six terebrid clades was sequenced and the range of worm prey genera detected in each predator specimens was between 1 and 7 (M=3.06, SD=1.37), and for each species was between 2 and 11 (M=6.18, SD=2.58) (Fig. 4.2). Overall, eighteen different worm genera, belonging to nine families and two phyla (Annelida and Sipuncula) were identified as terebrid prey. The most represented prey was the genus Scolelepis Blainville, 1828

(Spionidae:Polychaeta), found in 16 species out of 17 terebrid species and in 89% of the analyzed specimens (n=71). Three other annelid genera, Pseudopolydora Czerniavsky, 1881

(Spionidae:Polychaeta), Spio Fabricius, 1785 (Spionidae:Polychaeta) and Neanthes Kinberg, 1865

(Nereididae:Polychaeta) were largely represented as well (13-15 species and 30-48% of the specimens). DNA of each of these four prey genera were detected in terebrid species collected in all macro sampling areas, having or not having a venom apparatus and belonging to all the considered phylogenetic clades. One third of the total prey genera detected, including the only sipunculid record, were instead found in only one terebrid species and in 1 to 3 specimens. In

- 70 -

Figure 4.2. Terebridae phylogeny and annelid genera identified in the gut. Smaller pie charts represent the diet of each adjacent taxon. Larger pie charts represent the aggregate diet for terebrids in the same clade. Terebrids with a venom gland are highlighted in red. general, the Annelid genera found as terebrid prey belong to two classes: Errantia (7 genera) and

Sedentaria (10 genera). In all specimens but one, we found at least one Sedentaria prey and all other samples had between 1 and 5 Sedentaria prey genera, while in only half of the dataset (n=36) we detected at least one Errantia prey, with the highest number being 4 (Figure 4.1). Interestingly, a Terebra guttata sample collected in the field was found with a worm in its mouth (Figure 4.1).

The 16S gene of the worm was sequenced and BLASTed against nr and was identified as

Scolelepis eltaninae with a reported e-value of 7x10-94. S. eltaninae belongs to the Spionidae family, which was highly prevalent in all terebrid species analyzed in this study.

Correlation between presence/absence of a venom apparatus and diet diversity

- 71 -

The Kruskal Wallace non-parametric test detected a significant difference of diet breadth between terebrid specimens with and without venom apparatus (p<.01). Specifically, terebrid specimens with a venom gland present had a more restricted diet (12 annelid genera recorded) than terebrid species without a venom gland (16 annelid genera recorded) (Figure

4.3A). A randomization test was preformed to further corroborate the

Figure 4.3. Comparison of gut contents in terebrids with results of the Kruskal Wallace non- and without a venom gland (VG). (A) Diet diversity in terebrids with a VG present and absent. (B) Histogram showing parametric test. The difference between the frequency of Shannon index differences with randomized data in blue and with study data in red. the Shannon diversity indexes observed for the samples with the presence (1.91) and absence of a venom apparatus (2.26) was calculated and found to be 0.352. Observations were then randomized by column 100 times and the differences in Shannon indexes was recalculated. It was found that our data produced the greatest difference in Shannon indexes (p<.01), supporting the finding that terebrid species without a venom gland had a greater diversity of annelid diet (Figure 4.3B).

- 72 -

Venom variation and diet diversity

The transcriptomes from six terebrid specimens (Hastula hectica, Hastula matheroniana,

Terebra subulata, Terebra guttata, Terebra argus, and Terebra straminea) were examined with the results from their gut content sequencing to investigate correlations between terebrid venom variation and diet diversity. To examine venom complexity and diet diversity, venom complexity was defined by the number of cysteine frameworks identified and the number of total putative

Figure 4.4. Correlation of terebrid diet breadth and venom complexity. Correlation between diet breadth and the number of putative toxins in six terebrids (A) and the correlation between diet breadth and the number of cysteine frameworks (B). toxins identified. A small variation in number of cysteine framework between examined species was detected, a range of 8 to 10 different cysteine frameworks were found in each species. On the other hand, the number of mature terebrid venom peptide toxins resulted to be more variable, total number of venom peptides ranged from 41 to 128 in our analyzed species. Hastula hectica showed the lowest venom diversity while Hastula matheroniana showed the highest. Plots of the average species Shannon index against the number of mature toxins and cysteine frameworks did not produce any significant patterns (Figure 4.4).

- 73 -

Although no statistical results were uncovered, there are descriptive results worth noting.

For example, Hastula hectica has a venom composition different from the other five individuals analyzed in this study (Figure 4.5). H. hectica has a higher proportion of framework I and XI putative teretoxins and surprisingly has no framework XXII putative teretoxins, which on average account for approximately 7% of all teretoxins. Interestingly, of the six individuals analyzed, H. hectica was the only individual that consumed Neanthes and did not consume Scolelepis, which was found as prey in the other terebrid species (Figure 4.5). Likewise, T. subulata, T. argus, and

T. straminea all consume the same genera of annelids (Scolelepis and Pseudopolydora) but their venom composition is not the same. A similar pattern is shown with H. matheroniana and T. guttata, suggesting venom variation may not be linked to diet preferences.

Figure 4.5. Comparison of peptide toxin frameworks and gut content of terebrids. Heat map showing the interspecific variation of relative cysteine frameworks in terebrid toxins. Adjacent pie charts represent the identified diet for each specimen.

No correlation between abiotic factors and diet diversity

To investigate other drivers of terebrid diversification that might not be tied to venom, we investigated shell length, geographic location, and depth. The Jaccard heat map did not identify

- 74 - any similarity between the diet of different terebrids, except between Myurella undulata and

Terebra dislocata. H' index distributions showed nonsignificant differences between phylogenetic clades, macro collecting areas and shell lengths (Figure S4.1). No significant differences were detected when comparing H' distributions and maximum, minimum, average and range of collecting depth. A significant negative correlation emerged between shell length and number of prey genera for each specimen (Figure S4.1B).

Discussion

The degree of anatomical variation found in terebrids, including presence/absence of a traditional venom apparatus, was previously thought to be correlated with venom diversification and prey preference. This study applied a next generation sequencing approach to investigate this hypothesis and was the first to molecularly identify annelid prey genera in the gut contents of terebrids to determine diet variation among terebrid species. We identified a total of 18 annelid prey genera being consumed by terebrids, of which Scolelepis and Spio (both in the Spionidae family) were the most prevalent (Figure 4.1 and 4.2).

Our findings indicate there is a statistical difference between the dietary breadth of terebrid species with and without a venom apparatus, where those with a venom apparatus consume fewer annelid prey types than those without a venom apparatus. These results were further corroborated with a randomization test and are not an artifact of the sampling group. Our results correspond with the current literature that the evolution of venom production can cause prey specificity (

Barlow et al. 2009; Fry et al. 2008; Phuong et al. 2016; Casewell et al. 2013b). In a recent study in snakes it was found that venom diversity was a result of adaptations to specific diets and the complexity of venom in snakes is due to the fact that venoms are under strong natural selection

- 75 - pressures, to combat selection pressures in prey cellular targets (Daltry et al. 1996; Kordis et al.

2000; Barlow et al. 2009). Similarly, for terebrids with a venom gland, venom variation might be explained by an evolutionary arms race between predator and specific annelid prey.

Contrary to what was found for cone snails, we did not find a statistical correlation between venom complexity, either the type of compounds or the number of compounds, and diet diversity

(Phuong et al. 2016). The lack of statistical significance is most likely do to: 1) the low samples size (only 6 of the 17 terebrid species were used in the venom variation analysis) and 2) a need to redefine venom complexity. In Phuong et al.’s paper (2016), it was determined that total number of mature toxins was the best indicator of venom complexity, while the number of gene superfamilies and the number of cysteine frameworks were less ideal indicators (Phuong et al.

2016). Here we used both total number of mature teretoxins and the cysteine framework but did not find a statistical correlation. Although there was no statistical correlation between genera of prey consumed and distribution of cysteine frameworks in the venom, there is a quantitative relationship wherein Hastula hectica had a different suite of putative teretoxins than the other terebrids analyzed and it was the only snail that was found to consume Neanthes and not consume

Scolelepis. From our analyses we cannot definitively confirm if H. hectica diet preference can be explained by the different putative teretoxins expressed in its venom gland. These results highlight how important it is to examine teretoxins and diet within the context of a single individual. Our findings and those of Phoung and colleagues both suggest the current method of classifying cono- or teretoxins is not simple and venom complexity might need to be redefined.

Overall, we determined that the terebrid species used in this study are consuming the same prey genera regardless of their species, foregut anatomy, shell size, and biogeography. Importantly, apart from H. hectica, terebrids with and without a venom apparatus overlap in the prey genera

- 76 - that they consume. These findings highlight several unknowns about terebrid venom evolution that require further examination to clarify, specifically: 1) Are the terebrid species that lack a venom gland still producing venom using a different tissue, allowing them to capture and consume the same genera of annelid prey? In several of the terebrid species used in our study, while they did not have a venom gland they did have salivary glands, and it has been reported that the salivary gland of Conoidean species as well of leeches, ticks and other animals can produce venom toxins

(Biggs et al. 2008; Min et al. 2010; Bose et al. 2017; Stafford-Banks et al. 2014; Mans et al. 2003);

2) Is venom unnecessary for prey capture in terebrids that do not have any components of the venom apparatus (venom gland, proboscis, radular sac)? If this is true, it could explain why the traditional venom gland has been lost on eight occasions in the Terebridae (Castelin et al. 2012).

While several questions are raised from our findings, our main result supports that the tremendous amount of variation in terebrid foregut anatomy can possibly be explained by the niche variation hypothesis. The niche variation hypothesis suggests that “populations with wider niches are more variable than populations with narrower niches” (Van Valen 1965). The terebrids, which are globally distributed and found at various bathymetric sea levels, have broadened their niches and their diet so that they can overlap in geographical location and possibly even niche, but still not have to over compete for food. In summary, this study illuminates the importance of examining non-model systems to investigate questions about venom evolution, and specifically introduces new questions about the feeding behavior of terebrid species that lack a venom gland.

Methods

DNA extraction and amplification for Next Generation Sequencing

- 77 -

Seventy-one terebrid species (N= 71) collected in Kavieng, Papua New Guinea (N= 60),

Tampa, Florida (N= 4), and Fujairah, United Arab Emirates (N= 7) were chosen from five phylogenetic clades to represent the diverse foreguts found in the Terebridae family. DNA was extracted following a phenol chloroform extraction. Dissected guts were lysed overnight in lysis buffer and proteinase K at 56ºC. After centrifuging the samples post incubation, 200 µL phenol was added. Samples were centrifuged at 13,000 rpm and the supernatant was added to 100 µL phenol and 100 µL chloroform. Following centrifugation, the supernatant was added to 200 µL chloroform and centrifuged. The supernatant was added to 20 µL sodium acetate (3M, pH 5.2) and

400 µL 95% ethanol. After 2 hours resting at -20ºC, samples were centrifuged, and the resulting pellet was suspended in 500 µL 70% ethanol. The pellet was collected and suspended in 50 µL

Tris EDTA buffer (pH 8) and stored at -20ºC. DNA was quantified using Invitrogen’s Qubit fluorometer.

PCR primers and amplification

We established a set of PCR primers and a PCR protocol that optimized the amplification of the 16S rDNA gene in marine worms. The PCR primers should ideally find a balance between the short, degraded prey DNA found in the guts and a viable sequence length necessary for identifying the prey. We aimed to optimize primers that would amplify a wide range of marine worm DNA, the known prey of terebrids, without amplifying the terebrid DNA. For this study, we selected a primer, which spans a fragment length of ~350 bp. The efficiency of primers was tested by amplifying a mix of DNA containing terebrid and marine worm DNA in the ratios 100:1 and

1000:1, respectively. PCR products were confirmed on a 1.3% agarose gel stained with GelRed.

- 78 -

PCR was carried out in 25 µL volumes with a final concentration of 1x of the supplied

PCR buffer (Invitrogen Platinum Hot Start PCR Master Mix 2X) and 0.2 µM of each forward and reverse primer. For each reaction, 1µL of the extracted DNA was added. The thermocycler protocol was 94ºC, followed by 40 cycles of 94ºC for 20 seconds, 60ºC for 20 seconds, and 72ºC for a minute. The extension period lasted for 5 minutes at 72ºC and the PCR products were cooled at 17ºC and stored at -20ºC following PCR product confirmation. Confirmation of PCR products was based on the presence of a band in a 1.3% agarose gel stained with GelRed.

Library Preparation and Sequencing

Libraries were prepared using the Nextera XT DNA Sample Preparation Kit according to the manufacturer’s protocol (Illumina 2014), unless otherwise stated. Following Illumina’s

(Illumina, USA) technical note for cluster optimization and the resultant quantity of data each library was normalized for sequencing to 8 pM according to the manufacturer’s protocol. The libraries were quantified using both a Qbit Fluorometer and Agilent 2100 Bioanalyzer and equimolar amounts of DNA were pooled from each sample. Three pools were created with approximately 30 samples per pool, to increase the amount of reads per sample. The three pooled samples were each sequenced at the Hunter College/CTBR Bioinformatics sequencing facility, on an Illumina MiSeq, using the Nano Kit V2 chemistry with 500 cycles and a PhiX spike in of 5%, resulting in 300 bp paired end reads (Illumina, USA).

Bioinformatics Pipeline

Samples were demultiplexed using CASAVA (Illumina, USA) allowing no mismatch per barcode. Remaining adapters were trimmed from the reads with Trimmomatic using the provided

- 79 -

Truseq adapter sequences (Bolger et al. 2014). Paired reads were merged using FLASH (Magoč

& Salzberg 2011) with a minimum overlap of 250. FastX Toolkit (HannonLab 2014) was used to quality filter reads with a minimum of 60% of bases ≥ Q30 and a minimum length of 150bp and was used to trim PCR primer sequences. Trimmed sequences were then clustered at 97% sequence identity using the cd-hit-est algorithm available from CD-HIT (Li & Godzik 2006). All resulting sequence clusters were screened for chimeras using cd-hit-dup in CD-HIT (Li & Godzik 2006).

Reads identification

Obtained sequences were BLASTed against nr database using an e-value cutoff of 1x10-5 to discard eventual predator sequences and contaminations. For each sequenced specimen, retained prey sequences were added to a database of ~400 16S rDNA sequences retrieved from NCBI coming from a range of polychaete and sipuncula families. Sequences were aligned using MAFFT

(Katoh et al. 2002) and maximum likelihood trees were produced using RAxML (Stamatakis

2006b, 2006a) on the CIPRES portal (Miller et al. 2010). Due to the incompleteness of the 16S rDNA NCBI database at the species level, we considered it more reliable to identify prey sequences at genus level. All sequences included in each phylogenetic clade were manually inspected and a representative one was chosen and identified with the nearest genus in the tree.

Because of the inhomogeneity of our sequencings, we only considered our data to represent the presence/absence of annelid genera.

Diet data analysis

- 80 -

For each prey genus detected, we reported the number of predator specimens and species

(absolute number and percentage frequency) and the sampling localities, the presence/absence of the venom apparatus, and the phylogenetic clade of the predators.

For estimating the prey width, the Shannon's diversity index (H') was calculated at the specimen and species level. The Jaccard similarity coefficient was used to graphically compare predator diets and the results were displayed as heat matrices. Specimen diversity values were then categorized by species, presence/absence of venom apparatus, phylogenetic clade, collecting macro area (Florida, UAE, and Papua New Guinea) and shell length, and obtained distributions were compared with the Kruskal Wallis non-parametric test. Using our in-house Terebridae database, we recovered the maximum, minimum, and average collection depth of each species as well as the depth range, and we plotted these data against the species Shannon's index. All analyses were made with Past3.

Transcriptomics and identification of putative toxins

Putative toxins were identified using a transcriptomics and bioinformatics pipeline. RNA was extracted from the venom duct of six terebrid species using a RNeasy Micro kit (Qiagen), before storage at -80°C. RNA was sequenced using the Illumina HiSeq 2500 platform with a multiplexed sample run in a single lane, using paired end clustering and 101x2 cycle sequencing.

Raw reads were quality filtered using FastQC (Andrews 2010) to retain sequences with a phred score ≥30. Low quality reads and adapters were trimmed using Trimmomatic (Bolger et al. 2014).

The de novo assembly program, Trinity (Grabherr et al. 2011a; Haas et al. 2013), was used with default parameters to assemble the six transcriptomes. Once assembled, EMBOSS’s GetORF (Ison et al. 2011) was used to translate transcripts into Open Reading Frames (ORFs). SignalP (Petersen

- 81 - et al. 2011) was used to predict signal sequences in the translated ORFs, as teretoxins are secretory peptides and are all associated with a signal sequences that is cleaved off before envenomation.

ORFs with a signal sequence were analyzed using the basic local algorithm search tool (BLAST)

(Altschup et al. 1990) and BLASTed against an in-house toxin database to identify putative toxins similar to already known toxins. Sequences that hit to a peptide in the database with an evalue ≤

1x10-4 were then BLASTed against nr to ensure they were still BLASTing with a strong evalue to a toxin. The list of putative toxins was finally run through ConoPrec (Kaas et al. 2008, 2012) to predict pre- and post-cleavage regions and cysteine frameworks. The number of mature toxins and cysteine frameworks obtained from the transcriptomics analyses were then plotted against the average Shannon index calculated for each species.

- 82 -

Chapter Five Deciphering Nature’s arsenal: Using Drosophila behavioral assays to characterize venom- peptide bioactivity (This paper has been submitted as an article: Eriksson, A.; Anand, P.; Gorson, J.; Grijuc, C.; Hadeliya, O.; Stewart, J.; Holford, M.; Claridge-Chang, A. Submitted. Deciphering Nature’s arsenal: Using Drosophila behavioral assays to characterize venom-peptide bioactivity. Scientific Reports.

Introduction

Venomous animals use their expansive venom arsenal to disrupt the physiology of other animals, for both defensive and predatory purposes. Due to the energetic cost of venom and the need for a fast-acting biological effect, venoms have evolved into cocktails of molecules with potent neurotoxic, hemophilic, and cytotoxic activities (Dutertre et al. 2014). Specifically, venoms contain numerous, diverse peptides, many with highly specific bioactive properties that have proven useful as pharmacological therapeutics (Harvey 2014; Erak et al. 2018; Puillandre &

Holford 2010). Currently, six venom-derived peptides are commercially available drugs: ziconotide for pain (Miljanich 2004); exenatide for diabetes (Fosgerau & Hoffmann 2015); bivalirudin for anticoagulation (Shammas 2005); captopril for hypertension (Cushman & Ondetti

1991); and eptifibatide and tirofiban for coronary syndrome (Scarborough et al. 1999). Venom- peptide research and drug discovery has increased exponentially with the advance of genomic– transcriptomic sequencing and proteomic mass-spectrometry (Prashanth et al. 2012). However, translating newly discovered venom peptides into commercial therapeutics is challenging

(Prashanth et al. 2017). A range of bioassays (including high-throughput electrophysiology, fluorescence, and radioactivity-based assays) have been used to accelerate characterization of novel venom peptides (Koh & Kini 2012). Lacking from these is the ability to efficiently characterize the physiological properties of the enormous diversity of venom peptides being identified. There is a

- 83 - need for new whole-animal in vivo methods that are applicable to scant quantities of venom peptides.

A striking example of the discovery-to-function gap in venom peptides is the identification and characterization of peptides from marine snail venom. Predatory marine snails of the conoidean family, which includes cone snails (Conidae), terebrids (Terebridae), and turrids (Turridae), use venom peptides to prey on fish, worms, and other mollusks (Craik et al. 2013; Fosgerau &

Hoffmann 2015; Prashanth et al. 2017). Conodiean snail venom peptides molecularly target ion channels and receptors, and in the case of cone snails, have shown remarkably selectivity for certain channels and receptors (Jacob & McDougal 2010; Puillandre et al. 2012; Robinson & Norton

2014). Additionally, the compact size of conoidean venom peptides and their enhanced disulfide- bridged stability make them good candidates for pharmaceutical drug discovery and development

(Layer & Mcintosh 2006). There are over 15,000 species of conoideans, with an estimated venom- peptide reservoir of over a million compounds (Davis et al. 2009). However, less than 2% of conoidean venom peptides have been functionally characterized to date. Originally identified with assays in mice, Prialt (ziconotide) is the first conoidean venom drug, and it is used to treat chronic pain in HIV and cancer patients (Wallace 2006; Miljanich 2004). Ziconotide is chemically identical to the naturally occurring Conus magus peptide (MVIIA), and has illuminated a new molecular target for treating pain, namely N-type calcium channels (Miljanich & Ramachandran 1995;

Olivera et al. 1987). Ziconotide’s success has peaked the interest of pharmaceutical companies and there are several other cone snail venom peptides, conotoxins, in various stages of pharmaceutical drug development (Eisapoor et al. 2016; Adams et al. 2003; Craig et al. 1999; Miljanich 2004).

Given this, devising new cost-effective strategies to identify the bioactivities of snail-venom

- 84 - peptides could further advance their use as biomedical physiological tools and development as drug candidates.

While conotoxins have received a lot of attention, the venom peptides from terebrid snails are less studied, representing an untapped resource. Terebrid transcriptomes and proteomic data reveal that similar to conotoxins, the venom peptides from terebrid snails—teretoxins—are highly structured and disulfide-rich; two features that benefit stability and pharmacokinetics (Verdes et al.

2016; Gorson et al. 2015; Kendel et al. 2013). However, it is important to note that while evolutionary similar, teretoxin and conotoxin differ in size, structural integrity and complexity, suggesting that they might be applicable to different molecular applications (Gorson et al. 2015).

The potential of novel attributes of teretoxins for combating human disease and disorder demands efficient strategies to characterize these promising bioactive peptides (Essack et al. 2012; Layer &

Mcintosh 2006).

Pain and obesity are two major therapeutic areas for which an improved comprehensive pipeline that includes high-throughput physiological screening of teretoxin venom peptides could have an impact. In the U.S., the over-prescription of opioid pain medications has led to an epidemic of addiction and overdose for which treatment remains elusive (Wachholtz & Gonzalez 2014; Rudd et al. 2016). Remarkably, a potential solution to nonaddictive pain therapies is the snail venom drug

Ziconotide. As ziconotide’s molecular site of action is N-type calcium channels and not opioid receptors, it is not addictive (McGivern 2007). However, Ziconotide does not cross the blood brain barrier and has to be delivered to the central nervous system via intrathecal injection, an invasive delivery method that limits its use (Pope & Deer 2013; Sanford 2013). Even with its limitations, ziconotide is a paradigm shifting breakthrough that demonstrates there is an alternative

- 85 - nonaddictive strategy for treating pain. As a result, here we examined teretoxins to identify new snail venom peptides that may have nociceptive properties and are peripherally active.

In a similar manner, in the past 30 years

the prevalence of obesity has reached epidemic

proportions where the morbid obesity rates

continue to rise (Sturm & Hattori 2013). Obesity

is recognized as a major public health concern

and is associated with numerous complications

such as diabetes, cardiovascular diseases and

various forms of cancer (Gallagher & LeRoith

2015; Park et al. 2014; Allott & Hursting 2015).

Obesity is a complex disorder caused by an

imbalance between energy intake and the

expenditure and the interaction between

predisposing genetic and environmental factors

(Hebebrand & Hinney 2009; Faith & Kral 2006;

Qi & Cho 2008). Several peptidergic systems Figure 5.1. Schematic representation showing the pipeline used in this study. Venom glands are within the central nervous system and the dissected from terebrids in the field. RNA is extracted in the lab and sequenced using next- periphery are known to regulate energy generation sequencing. Transcriptomic data is assembled and searched for peptide candidates. homeostasis. However, the specific gene-gene Selected peptides are chemically synthesized, oxidized and injected into flies. Bioactivity is and gene-environment interactions involved in measured in several behavioral assays. this process remain unclear. A proposed treatment to combat obesity is being able to restore the energy homeostasis as a result of a

- 86 - dysregulation in the peptidergic system (Gale et al. 2004). However, despite several efforts, the cause of obesity still remains inconclusive and the only effective treatment against obesity currently is gastric bypass surgery (Bandstein 2016). New compounds that can be used to investigate the cellular physiology of pain and obesity are needed in order to develop effective treatment.

In the search for new bioactive compounds, we developed a physiological approach using vinegar flies (Drosophila melanogaster) to screen biologically active teretoxin peptides on thermosensation and feeding, behaviors that have been used to model pain and obesity. Our approach uses bioinformatic analyses of transcriptomic and proteomic data coupled with in vivo pain and diet fly assays to identify and functionally characterize peripherally active venom peptides from terebrid snails (Figure 5.1). The strategy requires minimal amounts of venom peptide, by using the gold-standard genetically tractable fly small-animal model. Flies are abundant, affordable, and amenable to automated behavioral testing, making them suitable for testing venom- peptide bioactivity. To establish the viability of this approach, we used several fly assays to characterize two teretoxins: Tv1 from Terebra variegata and Tsu1.1 from Terebra subulata. The two peptides had distinct properties: Tv1 displayed reduced avoidance towards noxious heat;

Tsu1.1 increased the number of feeding bouts. The disparate bioactivity of Tv1 and Tsu1.1 supports the robust nature of our systematic fly behavioral assays for identifying new venom peptides that may have therapeutic relevance.

Results

Tsu1.1 is a novel teretoxin peptide

We used a bioinformatic pipeline to sequence, assemble and search the Terebra subulata venom-gland transcriptome for novel-peptide candidates (Figure 5.2A). One of these candidates was a sequence with 21 amino acid residues (SAVEECCENPVCKHTSGCPTT), which we called

- 87 -

Terebra subulata teretoxin 1.1

(Tsu1.1) (Figure 5.2B). We compared Tsu1.1 with another peptide, Terebra variegata teretoxin 1 (Tv1) (Anand et al.

2014). For the two teretoxins, the cysteine frameworks—a feature that is used to group conotoxins into functional groups—are distinct (Halai et al. 2009; Essack et al. 2012). Tv1 is arranged CC-C-

C-CC (Framework III) while

Tsu1.1 is arranged CC-C-C Figure 5.2. Comparison of Tv1 and Tsu1.1. A. The (Framework I) (Figure 5.2C). In bioinformatic pipeline used to identify Tsu1.1. The orange box indicates the initial steps to generate reads through next- cone snails, Framework III generation sequencing (NGS), the green shows the process to assemble the terebrid venom gland transcriptome, and the conotoxins are in the M purple represents the final steps that lead to the discovery of teretoxins. B. Shell images of Terebra variegata (left) and superfamily, a group of peptides Terebra subulata (right), scale bar represents ~1 cm, followed by the sequence of each venom peptide, with cysteines that target either sodium (Na+) or highlighted in red, and finally the NMR structure of Tv146 and the predicted I-TASSER structure of Tsu1.1. C. An alignment potassium (K+) channels (Akondi of Tv1 and conotoxins in the M-superfamily, followed by an alignment of Tsu1.1 and conotoxins in the A-superfamily. et al. 2014). By contrast, Green represents shared amino acids, while red represents shared cysteine framework Framework I conotoxins are in the

A superfamily, which target either adrenergic receptors or nicotinic acetylcholine receptors

(nAChR) (Akondi et al. 2014). An alignment of Tv1 and Tsu1.1 with known conotoxins confirms

- 88 - that, beyond their cysteine scaffold, Tv1 and Tsu1.1 are not homologous to members of their respective conotoxin superfamilies (Figure 5.2C) (Anand et al. 2014).

Tv1 and Tsu1.1 have minimal effects on mortality

We comparatively examined the bioactivity of Tsu1.1 and Tv1 teretoxin peptides. In order to investigate the physiological activity of the two peptides, we determined the levels at which the two peptides had lethal toxicity. Initial concentration was decided based on previous studies investigating the bioactivity of Tv1 in polychaete worms, where it was found that Tv1 caused a partial paralysis at a concentration of 20 μM. However, an initial test with injections of 100 µM peptide (the highest dosage tested) found that—even at this concentration—neither peptide had major mortality effects in flies (Figure S1A–B). Based on these results, we used injections of either

100 µM or 20 µM in subsequent experiments to explore Tv1 and Tsu1.1’s non-lethal bioactivities.

Tsu1.1 injections induce mild hypoactivity

While Tv1 had only trivial effects on activity, Tsu1.1 injections had noticeable, though small, effects on multi-day daytime and nighttime activity (Figure 5.3A, C). Over a five-day assay,

Tsu1.1-injected flies walked 27% less (–226.93 mm [95CI –472.1, 29.29], g = –0.34, P = 0.04) than controls during the daytime (Figure 5.3B), or a ∆distance of –226.93 mm ([95CI –472.1,

29.29], g = –0.34, P = 0.04) (Figure 5.3B). We investigated if the Tsu1.1 effect was associated with functional motor deficits using a climbing assay (Bartholomew et al. 2015). Upon injection

- 89 - with Tsu1.1, the flies exhibited only small or negligible differences in climbing index (Figure 5.3C

Figure 5.3. Effects of Tv1 and Tsu1.1 on activity and motor coordination of male Drosophila. Activity was recorded for 5 consecutive days using automated video tracking and measured as the distance travelled per hour (mm/h/fly). Controls were maintained during the same conditions as the teretoxin-injected flies with the only difference that they were injected with PBS. A. Activity assay of Tsu1.1; B. Activity assay of Tv1; C. Climbing index measured the short term effects of the toxins as well as possible disruptions in their motor coordination. The climbing assays of Tsu1.1 revealed only trivial effects on climbing ability. D. Climbing assay of Tv1, all at 20 μM and 100 μM. All error bars represent 95% CI. The numbers indicated below each bar denote the standardized effect size (g). The climbing index was done with at least 15–20 male flies in each vial. and 5.3D); this result suggests that Tsu1.1 does not diminish motor coordination.

Injection of Tv1 reduces heat avoidance

Teretoxin Tv1 has the same cysteine framework as known M superfamily conotoxins, members of which have been characterized for targeting voltage-gated sodium channel (VGSC) subtypes NaV1.8 and NaV 1.9. These channels have been associated with controlling neuropathic

- 90 - pain (Wood et al. 2004; Kalso 2005; Lewis et al. 2012), and some M-superfamily peptides are potential analgesics (Jacob & McDougal 2010; Robinson & Norton 2014). Based on its similarity to M-superfamily conotoxins, we examined if Tv1 had analgesic activity. We used an assay that measures Drosophila heat avoidance, that has been applied as a model of acute pain (Neely et al.

2010). The assay monitors flies’ innate avoidance of noxious 46°C surfaces (Figure 5.4A). The heat-avoidance assay was performed on flies injected with 100 µM of Tv1 and Tsu1.1 and analyzed at 1 and 2h time intervals after injection). One hour after injection, control flies injected with PBS had a heat avoidance of 70.9 [95CI 65.4, 76.4]; Tv1-injected flies exhibited a markedly reduced avoidance response of 58.6 [95CI 53.0, 64.2]. This represents a 12.33% reduction in avoidance

([95CI -20.31, -4.39], g = -1.02, p = 0.005) after one hour; in terms of standardized effect sizes, this is a large effect (Cumming & Calin-Jageman 2016). By the second hour, this response diminished to –4.97% ([95CI –14.34, 4.7], g = -0.43, p = 0.33) (Figure 5.4B). For Tsu1.1, injected flies displayed heat avoidance that was similar to controls: 70.5% ([95CI 65.5, 75.5]), or an avoidance change of –2.3% ([95CI -10.4, 5.8], g = -0.26, p = 0.58v). These findings indicate that

Tv1 reduces fly sensitivity to noxious heat.

- 91 -

Figure 5.4. Thermal nociception in adult Drosophila is diminished by Tv1. A thermal heat assay used to measure the analgesic effect of Tsu1.1 and Tv1 on adult flies. A. Schematic representation of the assay measuring thermal nociception. The assay measures the avoidance from a noxious (46°C) surface. B. Injection with the higher concentration of Tv1 produced a large reduction in heat avoidance in the first hour: ∆ = –12.33% [95CI - 20.31, -4.39], g = -1.02, p = 0.005, N=15. Hedges’ g is a standardized effect size. C. Avoidance behavior in flies injected with two different concentrations of Tsu1.1: the control flies and the experimental flies had a similar heat avoidance.

Tsu1.1 administration increases food intake

Feeding and foraging behaviors strongly rely on a neuronal and endocrinological connectivity through the gut–brain axis that also includes a peripheral regulation (Oury & Karsenty

- 92 -

2011; Belgardt & Brüning 2010; Schoofs et al. 2014). Thus, while we do not expect the teretoxins

Tv1 and Tsu1.1 to cross the fly blood-brain barrier, the injected peptides could affect feeding via peripheral systems. Consequently, we assayed feedings over a 6 h time period using capillary- feeder assay (Ja et al. 2007) that was adapted for video tracking of single-fly feeding. In the Tv1 experiment, almost no difference in food intake was seen after injection: food intake decreased only –6.13 nl ([95 CI –23.10, 35.08 ], g = –0.07, P = 0.7) (Figure 5.5E). In the Tsu1.1 experiment, while control flies injected with PBS drank an average of 214.0 nl [95 CI 180.12, 247,88] the

Tsu1.1 injected flies drank 307.4 nl [95 CI 270, 344.8]: 46% more liquid: an increase of +93.34 nl

[95 CI 41.61, 141.485 ], g = +0.55, P = 0.0002 (Figure 5.5A).

Figure 5.5. Tsu1.1 has effects on food intake by increasing meal frequency. A single-fly feeding assay was used to assess total food intake , the average number of meals (meal duration , and meal size (over a 6 h period in 5–7 day old adult Canton-S males. A. Flies injected with Tsu1.1 toxin consumed more: + 93.3 nl [95 CI 41.6, 141.49 ], g = +0.55, P = 0.0002, N = 106, 112. B. Average number of feed bouts where Tsu 1.1 injected flies displayed a dramatic increase: ∆feeding bouts +8.64 [95 CI 0.53, 17.23], g = +0.53, p = 0.047, N = 106, 112. C. and D. Mean meal duration and volume per feed showed only minor alterations after injection of Tsu1.1. E. - H. The feeding behavioral effect of Tv1 dissected into volume intake, feed count, feed duration and volume per feed. All metrics showed only trivial differences. The data represent the mean differences with their 95% CI. Control data is depicted in grey and injected-fly data in orange. The numbers below each column denote the effect sizes for the individual driver lines. The numbers at the base of each column denote the sample size (N). Tsu1.1 increases meal frequency

- 93 -

To determine the behavior that produces the increased consumption, we examined the aspects of foraging behavior (Figure 5.5B). Consummatory behaviors in individual flies were analyzed over a 6 h period, allowing us to determine three additional feeding parameters: number of meals, the average meal size, and the meal duration (Figure 5.5B–D). Injection of Tv1 had no effect on any of the parameters analyzed (Figure 5.5E–H). However, Tsu1.1 injection elicited an increase in meal count: while control flies had 31.56 meals per fly [95 CI 27.16, 35.96], flies injected with Tsu1.1 consumed 40.20 meals [95 CI 36.64, 43.76]. This was a +8.64 meals ([95 CI

0.53, 17.23], g = +0.53, p = 0.047) or an increase of 27%. Feed volume and the total feed duration was also measured, both of which showed very small increases (Figure 5.5C–D). Thus, the increased feed volume appears to arise largely as a result of an increased number of meals, with very minor contributions from increased meal duration and volume.

Discussion

This work demonstrates the potential of Drosophila behavioral assays to be used to analyze the physiological bioactivity of venom peptides. Specifically, we characterized the physiological effects of two teretoxins, Tv1 and Tsu1.1 from Terebra variegata and Terebra subulata respectively on thermosensation and feeding, behaviors that have been used to model pain and obesity. Pain and diet-related disorders are two areas for which new bioactive compounds are needed to advance therapeutic development.

Previously, Tv1 injected into Nereis virens, a polychaete worm, has been shown to induce partial paralysis (Anand et al. 2014). This effect was absent in Drosophila (Figure 5.3A and C), suggesting that the Tv1 binds to targets which are not conserved between polychaetes and insect.

However, our findings did show that injected Tv1 reduces fly heat avoidance (Figure 5.4). This is

- 94 - the first demonstration that teretoxins can have an antinociceptive effect. A possible mechanism of action for Tv1-induced analgesia in flies is by modulation of TRP (Transient Receptor Potential) channels. TRP channels are like physiological canaries in the mine, they sense external stimuli and serve as an early warning system for the organism. TRP channels are found throughout animal species including humans, mice, worms and zebrafish (Figure 5.6). It has been previously shown that Drosophila TrpA1 is a thermoreceptor and a critical mediator of nociception in flies and mammals (Hamada et al. 2008; Neely et al. 2011). Prior reports suggest Drosophila TRP is required for preserving light response, whereas external noxious heat stimuli (>400 C) activates dTRPA1, Pyrexia, and Painless channels (Lee et al. 2005; Rosenzweig et al. 2005; Sokabe et al.

2008; Venkatachalam & Montell 2007). The decreased sensitivity to noxious heat observed in our heat avoidance assay for Tv1 treated flies suggests it is possible that Tv1 inhibits one of the three heat-activated TRP channels. While the Tv1-induced decrease was small, it is a significant finding in that Tv1 was peripherally administered; therefore, Tv1 could serve as a scaffold for designing novel peripherally active analgesic peptide compounds.

Alternative to Tv1, Tsu1.1 has an orexigenic effect, increasing food consumption by 46%.

Using a single-fly feeding assay with automated food-consumption tracking allowed us to dissect several aspects of feeding: intake, meal count, meal volume and meal duration. Of these, increased meal count appeared to account for some of the food intake increase, while meal volume and duration underwent only small changes. This increase in feed count does not account for the entire increase in the total consumption. As Tsu1.1 was administered by injection, it is possible Tsu1.1 affects the peripheral system of the fly and regulates feeding via a neuroendocrinological pathway.

Several studies have been tried to isolate the effect of neuromodulators on feeding (Pool et al. 2014;

Eriksson et al. 2017). A majority of these neuromodulators have a negative effect on feeding most

- 95 - likely due to disruptions of its motor coordination. However, previous studies indicate activation of serotonin, octopamine and dopamine causes an increase in feeding (Marella et al. 2012; Inagaki et al. 2012; Williams et al. 2014; Eriksson et al. 2017). Further analyses are needed to determine which, if any, of these systems are affected by Tsu1.1.

Conclusions

We developed a sequence driven:activity-based hybrid approach using venom transcriptomic analysis and behavioral characterization in Drosophila melanogaster to identify

Figure 5.6. Comparison of TRP channels across the tree of life. Maximum-likelihood phylogeny showing the relationship between members of the human TRP-channel superfamily and members of Caenorhabditis elegans (worm), Drosophila melanogaster (fly), Danio rerio (zebrafish) and Mus musculus (mouse) TRP channels. venom-peptide bioactivity. Application of our method found that Tv1 reduces heat avoidance in fly, suggesting that it has an antinociceptive effect, and also indicated that Tsu1.1, a novel teretoxin, modulates feeding behavior. The orexigenic effect of Tsu1.1 arose primarily from increased meal frequency, with additional effects from meal duration and volume. Taken together, our findings

- 96 - illustrate the utility of Drosophila assays to identify the bioactivity of novel venomics-derived peptides. This phenotypic-screening approach can help to realize the enormous potential that terebrid snail venom peptides have as compounds for characterizing cellular physiology, and as candidate therapeutics.

Methods

Identification of venom peptides

Tsu1.1 was discovered using a transcriptomics and bioinformatics pipeline (Figure 5.2A).

RNA was extracted from the venom duct of Terebra subulata using an RNeasy Micro kit (Qiagen).

RNA was sequenced with an Illumina HiSeq 2500 with a multiplexed sample run in a single lane, using paired end clustering and 101 x 2 cycle sequencing. Raw reads were first processed on Fast

QC to assess the quality, looking for a phred score ≥30. Low quality reads and adapters were trimmed using Trimmomatic. The de novo assembly program Trinity (Grabherr et al. 2011a; Haas et al. 2013) was used with default parameters to assemble the transcriptome. Once a transcriptome was assembled, EMBOSS’s GetORF (Rice et al. 2000) was used to translate transcripts into Open

Reading Frames (ORFs). SignalP (Petersen et al. 2011) was used to predict signal sequences in the translated ORFs. As teretoxins are secretory peptides, they are all associated with a signal sequence that is cleaved off from the mature toxin sequence before envenomation. Only those

ORFs with signal sequences were analyzed using the basic local algorithm search tool (BLAST)

(Altschup et al. 1990) and an in-house toxin database to identify putative toxins homologous to already known toxins. The sequence of Tsu1.1 was found to be SAVEECCENPVCKHTSGCPTT.

- 97 -

Synthesis and oxidative folding of Tv1

As teretoxins are present only in miniscule quantities in the venom, a greater quantity of the linear peptide Tv1 was chemically synthesized and purified by previously described methods and further oxidatively folded into biologically active form (Anand et al. 2014).

Synthesis and purification of Tsu1.1

Tsu1.1 peptide with a sequence of SAVEECCENPVCKHTSGCPTT was synthesized by microwave assisted Fmoc solid–phase peptide synthesis on a CEM Liberty synthesizer using standard side chain protection. Following treatment of peptidyl resin with Reagent K [92.5% TFA

(Trifluoroacetic acid), 2.5% TIS (Triisopropylsilane), 2.5% EDT (1,2 Ethanedithiol) and 2.5% water, 4h)] and methyl tertiary butyl ether (MTBE) precipitation, crude Tsu1.1 checked for purity on Agilent UHPLC machine and eluted using a linear gradient from 0% to 55% buffer B (80%

Acetonitrile with 20% water) in 3.5 min. The identity of synthesized peptide was confirmed by molecular mass measurement of purified peptide using 6520 Agilent Q-TOF LC-MS (Figure S5.2 and S5.3).

Oxidative folding of Tsu1.1

A one-step thiol-assisted oxidation was used to prepare folded Tsu1.1 peptide. The linear peptide (20 µM) was incubated in 0.1M Tris –HCl, 0.1MNaCl,100μM EDTA,1 mM GSH (reduced glutathione), 1mM GSSG (oxidized glutathione), pH 7.5. The folding reaction was terminated by acidification with 8% formic acid at 15min, 30min, 1, 2, 3, 4, and 24 h and the folding yield monitored using UHPLC. A preparative scale folding reaction was then conducted at an optimized time of 1h, and the folded peptide was purified using X-Bridge semipreparative column (waters).

- 98 -

Elution was carried out at 5 ml/min with 0% buffer B and 100% buffer A for the first 5min, then increasing buffer B to 35% in 35min. The purity was confirmed using Agilent poroshell UHPLC column and the molecular mass of the oxidized peptide was confirmed by LC-MS. The oxidatively folded peptide was used in all the experiments (Figure S5.4 and S5.5).

Structure prediction

The membrane protein topology and signal peptide prediction was made using TOPCONS according to the online instructions (http://http://topcons.cbr.su.se) (Tsirigos et al. 2015). Model structure was made by using the I-TASSER web application (Roy et al. 2010; Zhang 2007). The quality of the model predicted by I-TASSER is verified using two different score systems: TM- score and a root mean square deviation. These two scoring systems gives an estimate how close the model is to the native structure and both of these values are computed from a confidence score

(C-Score) (Yang & Zhang 2015; Roy et al. 2010). For the TM-score a value above 0.5 usually implies a correct topology for the model (Xu & Zhang 2010; Zhang & Skolnick 2004). The top five high scoring structures based on C-score from the I-TASSER prediction were superimposed using PyMol (http://www.pymol.org).

Multiple sequence alignment

The ConoPrec tool, which is available on the ConoServer website was used to identify similar sequences recorded in the ConoServer database (Kaas et al. 2008, 2012). The multiple sequence alignment was performed using ClustalW software (http://clustal.org) (Thompson et al.

1994).

- 99 -

Fly stocks

All Drosophila melanogaster flies used in the experiments were male Canton-Special aged

5–9 days at the onset of the experiment. Experimental flies were maintained at 25°C, 60–70% relative humidity, under 12:12 h light and dark cycles for 4 to 7 days before the experiment day.

If starvation was required, flies were wet starved for 24 h before experiments. Wet starvation was performed by keeping flies in a plastic vial with 2% agar.

Peptide injection

Concentration of the toxins was determined based on previous studies investigating the bioactivity of Tv1 (Anand et al. 2014) which concluded Tv1 to cause partial paralysis in polychaete worms at a concentration of 20 μM. A low (20 μM) and high (100 μM) concentration was used for both peptides to obtain a range for bioactivity. Injection glass micropipettes (Nanoliter2000 borosilicate glass capillaries, World Precision Instruments, Sarasota, FL, U.S.A.) were pulled with a horizontal pipette puller (model P-1000, Sutter Instrument Co., Novoto, CA, USA) creating a

11–17 µm wide opening. Freshly pulled needle was blunted by pressing the tip through a paper tissue. The injection micropipette was then mounted on a microinjector (Eppendorf Femtojet

Express 5248) at an injection pressure of 300 kPa. Flies were anesthetized using CO2 and kept sedated throughout the entire injection process (maximally for 5 minutes). Anesthesia was maintained by keeping the flies on a pad consisting of porous polyethylene. A volume of 0.2 μl was injection by a pulse pressure of 300 kPa under stereomicroscope. Typically, the injection site was made intra-thoracically beneath the supraalar bristles (SA1, SA2) and the presutural bristle, into the area between the mesopleura and pteropleura.

- 100 -

Locomotion assay

Flies were tested using an automated video tracking software measuring the distance traveled of the flies. Flies were placed in 5 mm × 65 mm glass tubes with food placed at one end of the tube and a cotton plug in the other, allowing proper respiration of the flies. The food was allowed to air dry for one hour (to prevent flies from drowning in wet bait) before the flies were added. A total of 56 flies were used for each bioactive peptide with the same number of control flies injected with the vehicle. To determine phenotypic defects in the flies upon injection of Tv1 and Tsu1.1, we examined their activity using an automated video-tracking assay. The activity assay employs video tracking to measure the total distance (mm/h/fly) over five consecutive days and excludes a 24 h habituation period. The tracking assay measured the long term and short term effects of distance travelled and circadian rhythms of the flies. The flies were tracked with computer vision code in LabVIEW using standard background subtraction and centroid methods.

Behavior data was analyzed and plotted with Python using Jupyter and the scikits-bootstrap, seaborn and SciPy packages. Data was collected for five consecutive days with the first day subtracted from the assay, a habituation period. The data was then averaged by hour over the entire duration of the experiment.

Thermosensation assay

A thermosensation pain assay for adult Drosophila was performed as previously described53. Approximately 20 flies, 5 to 7 days old were placed in sealed behavioral chamber

(Petri dish measuring 35mm x 11mm; Nunclon). Flies were allowed to acclimatize to the environment for 60 min at room temperature. Using a water bath, the bottom of the chamber was

- 101 - heated to 46°C. The chambers were heated for 4 min where after they were removed from the water and immobilized flies were counted. The percentage avoidance was calculated by counting the number of flies that failed to avoid the noxious temperature compared to the total number of flies in the chamber. The distance from the heated bottom for each fly was not taken into consideration.

Climbing assay

Twenty-four hours before the experiment, male flies were separated from the females and transferred to a newly prepared food vial. No more than 20 flies were kept in the same vial. After the habituation period, five flies were transferred to 50 ml serological pipette tubes (Falcon, USA) that was cut to 50 mm in length. The top and bottom of the tube were sealed with parafilm with three small holes to provide ventilation. Flies were habituated to the new environment by lying flat on the surface for one hour at 25°C. The tube was tapped against a hard surface at the beginning of the experiment to place all the flies at the bottom of the vial. The time for each fly to reach the top marked point was recorded. Any flies that could not reach the top mark within 60 seconds were marked as failure to climb. Climbing index was calculated as a range from 0 to 1 for the number of flies that managed to reach the top mark within a certain time. A failure to reach the top mark gave them a score of 0 and a success a score of 1. An average for all flies was calculated for all the trials.

Feeding assay

Male flies were anesthetized by cooling and placed in chambers for the feeding assay, where capillaries delivered liquid food (5% sucrose, 10% red color food dye in deionized water)

- 102 - to the fly. The experiment was conducted within an incubator that was maintained at 22°C (Ja et al. 2007). The individual toxins were tested on different days; all experimental conditions were kept constant in between and during each experiment with respect to: temperature, humidity, circadian time, days after eclosion and the sex of the flies. The level of the fluid was monitored using video tracking for 6 h. Each capillary was accessible by a single fly kept in a 12mm × 12mm

× 2mm chamber cut from acrylic. The food intake assays for the individual fly lines were not performed concurrently but with identical duration, start time for the experiment, age and sex of flies, temperature and starvation time. For experiments involving starvation, flies were wet starved for 24 h before the start of the experiment; during wet starvation, flies were deprived of food but not water in a vial containing 1% agarose dissolved in deionized water. Different sets of flies were used for the vehicle and toxin injections, as well as for the fed and the food-deprivation.

Determination of effects on activity and motor coordination

Flies aged for 5–7 days after eclosion were anesthetized using CO2 and single flies were transferred to glass tube (5 mm x 665 mm) containing food in one end and sealed with a plastic cap with the opposing end sealed with a cotton wool plug. Standard fly food was used for both the experimental and negative control. One hour prior to the start of the experiment flies were anaesthetized and injected with the toxin. The flies were allowed to recover for one hour before being transferred to the glass tubes and inserted into the arena within a light and temperature regulated incubator. The temperature was set to 22°C with a 60–70% relative humidity, under

12:12 h light and dark cycles. When comparing experimental and control flies, they were distributed with 56 control flies and 56 experimental flies on each side of the arena. The flies were habituated for 24 h before commencing the experiment. The activity was automatically measured

- 103 - by tracking the path of each individual fly by means of video tracking and calculated as the numbers of crosses over the midline of the glass tube along with distance travelled. To assess sleep duration a threshold for sleep was used and is according to previously published protocols for sleep assessment in Drosophila: a period of inactivity lasting for at least five consecutive minutes

(Cirelli & Bushey 2008) sleep duration was measured as the cumulative amount of sleep in a 24 h period measured in minutes.

Statistics and analysis

The data was analyzed with estimation methods to calculate mean, mean differences, confidence intervals (Claridge-Chang & Assam 2016), and Hedges’ g where appropriate (Cumming & Calin-

Jageman 2016). Data is presented for each individual fly as well as the mean difference in estimation plots. 95% confidence intervals for the mean differences were calculated using bootstrap methods (resampled 5000 times, bias-corrected, and accelerated) and are displayed with the bootstrap distribution of the mean. Effect size was measured using Hedges’ g and are as per standard practice referred to as either ‘trivial’ (g < 0.2), ‘small’ (0.2 < g < 0.5), ‘moderate’ (0.5 < g < 0.8) or ‘large’ (g > 0.8) (Cumming 2012). Hedges’ is a quantitative measurement for the difference between means and is an indication of how much two groups differ from each other where a Hedges’ g of 1 shows that the two groups differ by 1 standard deviation. Commonly used significance testing and power calculations were avoided following recommended practice

(Altman et al. 2000; Cumming 2012) the Mann Whitney U statistic was used to calculate P values for pro forma reporting exclusively. To indicate estimate precision, 95% confidence intervals

(95CI) were calculated using bootstrap methods and reported in text and/or as error bars (Cumming

2012). The data for behavioral data is presented as mean-difference estimation plots. Confidence

- 104 - intervals were bias-corrected and accelerated and are displayed with the bootstrap distribution of the mean; resampling was performed 5,000 times. While P values reported are related to two-tailed

Student’s t-test statistics, significance testing was not performed (Claridge-Chang & Assam 2016).

- 105 -

Chapter Six

Concluding remarks and research implications

The research presented in this dissertation examines the terebrid family using a multidisciplinary approach to examine terebrid taxonomy and phylogeny, and terebrid diversification and function due to venom variation. Terebrids are an ideal group for a case study investigating diversification driven by evolutionary innovations because of the more than 400 described terebrid species, which are highly representative of a broad range of anatomical feeding strategies, more so than any other taxon of venomous marine organism (Castelin et al. 2012). This group has several features that are potentially impactful to diversification rates as they are highly variable with respect to size, depth ranges, biogeographical distribution, and larval development.

Terebrids are also closely related to cone snails, which have been researched since the 1970’s and have thus provided a foundation for terebrid investigations.

Our results shed light into the evolution of the Terebridae family and identified possible drivers for the vast diversity that exists within this family. Specifically, an updated phylogeny with over 1,500 samples was constructed and used to analyze key traits of the Terebridae (Chapter 2).

We have generated a significant amount of transcriptomic resources that have allowed us to identify putative venom peptides in seventeen species of the Terebridae (Chapter 3). A thorough examination of the gut content of terebrids was used to help explain the gut diversity and venom variation seen in the Terebridae (Chapter 4). And finally, a high throughput bioassay was created to test two putative teretoxin venom peptides identified via transcriptomics (Chapter 5). The general conclusions of this dissertation research are the following:

1. Across-clade diversification is constant in the Terebridae, while across-time terebrid

diversification is slowly increasing due to decreased extinction rates. Our results indicate

- 106 -

that the slow increase observed across the entire Terebridae family cannot be attributed to

a specific trait found in a terebrid lineage, but rather a trait found across the entire family.

This finding is possibly caused by an ecological release initiated by colonization of deep

waters in conjunction with a rise in sea levels (Chapter 2).

2. Twelve unique feeding types were identified in the Terebridae, which is an increase from

the three feeding types previously identified by Miller in 1970. Surprisingly, these feeding

types are not influencing diversification rates in the Terebridae (Chapter 2).

3. Over two-thousand putative toxins have been identified, the majority of which are

teretoxins, but some belong to other toxin classes, such as kunitz-type serine protease

inhibitors, conopressins, and actinoporins, which demonstrates the convergent evolution of

venom peptide genes across distantly related taxa. Twenty-seven teretoxin gene

superfamilies were identified, all of which are unique from known conotoxin gene

superfamilies. Additionally, there is a vast discrepancy between the venom peptides

identified in terebrids and those identified in cone snails, which highlights the importance

of continuously increasing the pool of venomous organisms being investigated (Chapter

3).

4. For the first time terebrid diet was molecularly identified to be comprise of marine annelids,

the most predominant genera being Spio and Scolelepis. Though terebrids have varying

foregut anatomies, sizes, and habitats, we did not find a correlation of these traits and diet

preferences for different annelid genera. However, it appears that terebrids follow the niche

variation hypothesis that populations with wider niches are more variable than those with

a narrower niche. Terebrids have a wide niche and are found globally and across

bathymetric ranges and are extremely variable in their foregut anatomy, size, and larval

- 107 -

development. Additionally, there is a statistical difference explaining the disparity in the

diversity of prey being consumed, where those species lacking a venom apparatus consume

a more diverse prey than those with a venom apparatus. This supports the hypothesis that

venom can lead to prey specificity (Chapter 4).

5. This dissertation was the first to identify the bioactivity and potential molecular targets of

two novel teretoxins, Tsu1.1, from Terebra subulata, and Tv1, from Terebra variegate. A

high-throughput bioassay using Drosophila determined that Tsu1.1 has an orexigenic

effect in Drosophila and may have a molecular pathway similar to cannabidiol, which

inhibits GABA receptors and induces hunger. Alternatively, Tv1 has an analgesic effect

and may have a possible mechanism of action that modulates TRP channels, which is a

critical mediator of nociception in fruit flies, as well as in mammals (Chapter 5).

It is important to note the results obtained in this dissertation would not be possible without the innovative advances made in molecular technological capabilities. Due to what we refer to as the “rise of the –omics” (genomics, transcriptomics, and proteomics) studying an underrepresented group of marine snails that are small in size is now feasible (Gorson & Holford 2016). Several of our findings are reported for the first time, for example, we are the first to molecularly identify the prey of terebrids, and these firsts are in large part due to the use of Next Generation Sequencing, bioinformatics, and high-powered computational clusters. While transcriptomics, was predominantly applied to obtain our results, a concerted effort was made to validate our conclusions using genomics and proteomics. Unfortunately, the small size of terebrid venom ducts, coupled with the solvent in which our venom gland samples were stored (RNAlater) impeded our efforts to obtain sufficient amounts of crude venom for proteomic examination. Proteomic analysis would validate the putative teretoxins identified through transcriptomics and provide data not

- 108 - possible by transcriptomics, such as identifying post-translational modifications and elucidating low expressed or unique venom peptides that were undiscoverable through homology searching.

The negative data from multiple attempts for proteomic analyses were not included in this dissertation. Similarly, multiple attempts were made to solve the genome of several species of terebrids representative of those with and without a venom gland. A genomic analysis would provide details about the structure, organization and expression of venom peptide genes, which could provide insights into the molecular mechanisms that are driving the evolution of venom genes. However, the genomes of venomous animals are very complex and include a large number of repetitive genomic elements that make it difficult to assemble (Vonk et al. 2013; Elsik et al.

2014). As with the proteomic data, we did not include the negative results obtained from our genomic attempts in this document.

Explaining the enormous biodiversity on our planet remains an ongoing quest for scientists.

This dissertation is a significant advancement in determining how predatory marine snails of the

Terebridae evolved and in a broader sense addresses the convergent evolution of venom. Terebrids are a non-model organism and studying them have led to findings that question what are considered norms of venom evolution, e.g. venom variation is an innovation that leads to speciation and diversification of venomous animals. While this appears to be the trend for snakes, our findings, while limited, do not directly support this hypothesis. This dissertation strongly suggests that neglected predatory organisms such as venomous sea snails of the family Terebridae, offer a unique perspective for investigating the evolution of venomous animals.

- 109 -

APPENDIX

Figure S2.1. Terebrid tree, with rates of shell length evolution inferred using BAMM. Red circles represent the lineages that likely underwent evolutionary rate shifts. The red line represents the single species Myurella pertusa, while the light blue lines represent clades B and C.

Figure S2.2. BAMM results for size rate over time. Rate vs. time plot from the size trait analysis, where “trait rate” is given as change per million years, and “time before present” is in millions of years.

- 110 -

Figure S3.1. Assembly completeness statistics based on BUSCO percentages. Percentages of BUSCO metazoan genes represented in the transcriptome were calculated. Light blue represents complete and single copies of genes, dark blue represents complete and duplicated copies of genes, yellow represents fragmented genes and red represents missing genes.

- 111 -

Table S3.1. Terebrid Trinity de novo assembly statistics.

- 112 -

Figure S4.1. Correlation of terebrid diet and phylogenetic clade (A) and shell length (B). There is no statistical correlation between diet breadth and phylogenetic clade, but we do see that clades C, D, and E2 have the lowest gut diversity, while clade B has the highest gut diversity. Similarly, we do not see a statistical correlation between shell length and genera of prey consumed, but our data is biased because the majority of samples being analyzed are < 90 mm.

- 113 -

Figure S5.1. Mortality effects of Tv1 and Tsu1.1. The venom peptides Tv1 and Tsu1.1 have little if any effect on 72-h mortality. The percent mortality is corrected for the mortality of vehicle-treated controls.

Figure S5.2. Solid-phase synthesis of Tsu1.1 peptide. RP-UHPLC chromatogram of Tsu1.1 linear peptide at 214nm. Linear peptide show a single peak at 2.3 min on a gradient of 0% to 35% B (80% acetonitrile in water).

- 114 -

Figure S5.3. Mass Spectrometry analyses of linear Tsu1.1. LC-MS showing the monoisotopic mass of the linear peptide 2194.92Da (M+1) with the expected mass of 2194.88 (M+1). Spectrum shows m/z of +2 as 1098.46 and +3 as 732.65Da.

Figure S5.4. Oxidative folding of Tsu1.1. RP-UHPLC chromatogram of Tsu1.1 fully oxidized peptide at 214nm. Folded peptide show a single peak at 1.52 min on a gradient of 0% to 35% B (80% acetonitrile in water).

- 115 -

Figure S5.5. Mass Spectrometry analyses of linear Tsu1.1. LC-MS showing the monoisotopic mass of the linear peptide 2194.92Da (M+1) with the expected mass of 2194.88 (M+1). Spectrum shows m/z of +2 as 1098.46 , +3 as 732.65Da and +4 as 548.72Da.

- 116 -

BIBLIOGRAPHY

Abdel-Rahman MA, Abdel-Nabi IM, El-Naggar MS, Abbas OA, Strong PN. 2011. Intraspecific variation in the venom of the vermivorous cone snail Conus vexillum. Comp. Biochem. Physiol. - C Toxicol. Pharmacol. 154:318–325. doi: 10.1016/j.cbpc.2011.06.019.

Ackerly DD, Schwilk DW, Webb CO. 2006. Niche evolution and adaptive radiation: Testing the order of trait divergence. Ecology. 87. doi: 10.1890/0012-9658(2006)87[50:NEAART]2.0.CO;2.

Adams DJ, Smith AB, Schroeder CI, Yasuda T, Lewis RJ. 2003. alpha-conotoxin CVID inhibits a pharmacologically distinct voltage-sensitive calcium channel associated with transmitter release from preganglionic nerve terminals. J. Biol. Chem. 278:4057–4062. doi: 10.1074/jbc.M209969200.

Aguilar MB, de la Rosa R a C, Falcón A, Olivera BM, Heimer de la Cotera EP. 2009. Peptide pal9a from the venom of the turrid snail Polystira albida from the Gulf of Mexico: purification, characterization, and comparison with P-conotoxin-like (framework IX) conoidean peptides. Peptides. 30:467–76. doi: 10.1016/j.peptides.2008.09.016.

Akondi KB et al. 2014. Discovery, synthesis, and structure-activity relationships of conotoxins. Chem. Rev. 114:5815–47. doi: 10.1021/cr400401e.

Alfaro ME, Brock CD, Banbury BL, Wainwright PC. 2009. Does evolutionary innovation in pharyngeal jaws lead to rapid lineage diversification in labrid fishes? BMC Evol. Biol. 9:1–14. doi: 10.1186/1471-2148-9-255.

Allott EH, Hursting SD. 2015. Obesity and cancer: Mechanistic insights from transdisciplinary studies. Endocr. Relat. Cancer. 22:R365–R386. doi: 10.1530/ERC-15-0400.

Altman D, Machin D, Bryant T, Gardner S. 2000. Statistics with confidence: confidence interval and statistical guidelines. Bristol BMJ Books.

Altschul S. 1997. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25:3389–3402. doi: 10.1093/nar/25.17.3389.

Altschup SF et al. 1990. Basic local alignment search tool. J Mol Biol. 215:403–410. doi: 10.1016/S0022-2836(05)80360-2.

Anand P et al. 2014. Sample limited characterization of a novel disulfide-rich venom peptide toxin from terebrid marine snail Terebra variegata. PLoS One. 9:e94122. doi: 10.1371/journal.pone.0094122.

Andrews S. 2010. FastQC: A quality control tool for high throughput sequence data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/. http://www.bioinformatics.babraham.ac.uk/projects/. doi: citeulike-article-id:11583827.

- 117 -

Armishaw CJ, Alewood PF. 2005. Conotoxins as Research Tools and Drug Leads. Curr. Protein Pept. Sci. 6:221–240. doi: 10.2174/1389203054065437.

Azam L et al. 2005. Alpha-conotoxin BuIA, a novel peptide from Conus bullatus, distinguishes among neuronal nicotinic acetylcholine receptors. J. Biol. Chem. 280:80–7. doi: 10.1074/jbc.M406281200.

Bandstein M. 2016. The role of genetics in regulation of weight loss and food intake. Uppsala University, Functional Pharmacology.

Barghi N, Concepcion GP, Olivera BM, Lluisma AO. 2015. Comparison of the Venom Peptides and Their Expression in Closely Related Conus Species: Insights into Adaptive Post-speciation Evolution of Conus Exogenomes. Genome Biol. Evol. . 7:1797–1814. doi: 10.1093/gbe/evv109.

Barlow A, Pook CE, Harrison RA, Wuster W. 2009. Coevolution of diet and prey-specific venom activity supports the role of selection in snake venom evolution. Proc. R. Soc. B Biol. Sci. 276:2443–2449. doi: 10.1098/rspb.2009.0048.

Bartholomew NR, Burdett JM, Vandenbrooks JM, Quinlan MC, Call GB. 2015. Impaired climbing and flight behaviour in Drosophila melanogaster following carbon dioxide anaesthesia. Sci. Rep. 5. doi: 10.1038/srep15298.

Beaulieu JM, Jhwueng DC, Boettiger C, O’Meara BC. 2012. Modeling stabilizing selection: Expanding the Ornstein-Uhlenbeck model of adaptive evolution. Evolution (N. Y). 66:2369– 2383. doi: 10.1111/j.1558-5646.2012.01619.x.

Beaulieu MJ. 2016. Package ‘ OUwie ’. R Packag. version 1.5. 1–19.

Becker S, Terlau H. 2008. Toxins from cone snails: properties, applications and biotechnological production. Appl. Microbiol. Biotechnol. 79:1–9. doi: 10.1007/s00253-008-1385-6.

Belgardt BF, Brüning JC. 2010. CNS leptin and insulin action in the control of energy homeostasis. Ann. N. Y. Acad. Sci. 1212:97–113. doi: 10.1111/j.1749-6632.2010.05799.x.

Bhatia S et al. 2012. Constrained de novo sequencing of conotoxins. J. Proteome Res. 11:4191– 200. doi: 10.1021/pr300312h.

Biass D et al. 2015. Uncovering intense protein diversification in a cone snail venom gland using an integrative venomics approach. J. Proteome Res. 14:628–638. doi: 10.1021/pr500583u.

Biggs JS, Olivera BM, Kantor YI. 2008. α-Conopeptides specifically expressed in the salivary gland of Conus pulicarius. Toxicon. 52:101–105. doi: 10.1016/j.toxicon.2008.05.004.

Bolger AM, Lohse M, Usadel B. 2014. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics. 30:2114–2120. doi: 10.1093/bioinformatics/btu170.

Bose U et al. 2017. Multiomics analysis of the giant triton snail salivary gland, a crown-of-thorns starfish predator. Sci. Rep. 7. doi: 10.1038/s41598-017-05974-x.

- 118 -

Bouchet P, Kantor YI, Sysoev A, Puillandre N. 2011. A new operational classification of the Conoidea (Gastropoda). J. Molluscan Stud. 77:273–308. doi: 10.1093/mollus/eyr017.

Bradbury IR, Laurel B, Snelgrove PV, Bentzen P, Campana SE. 2008. Global patterns in marine dispersal estimates: the influence of geography, taxonomic category and life history. Proc. R. Soc. B Biol. Sci. 275:1803–1809. doi: 10.1098/rspb.2008.0216.

Bratcher T, Cernohorsky W. 1987. Living Terebras of the World: A Monograph of the Recent Terebridae of the World. American Malacologists.

Breuker K, Jin M, Han X, Jiang H, McLafferty FW. 2008. Top-Down Identification and Characterization of Biomolecules by Mass Spectrometry. J. Am. Soc. Mass Spectrom. 19:1045– 1053.

Brinkman DL et al. 2015. Transcriptome and venom proteome of the box jellyfish Chironex fleckeri. BMC Genomics. 16:407. doi: 10.1186/s12864-015-1568-3.

Buczek O, Bulaj G, Olivera BM. 2005. Conotoxins and the posttranslational modification of secreted gene products. Cell. Mol. Life Sci. 62:3067–79. doi: 10.1007/s00018-005-5283-0.

Burnham KP, Anderson D. 2002. Model selection and multimodel inference: a practical information-theoretic approach. 488. doi: 10.1007/b97636.

Calvete JJ. 2014. Next-generation snake venomics: protein-locus resolution through venom proteome decomplexation. Expert Rev. Proteomics. 11:315–329. doi: 10.1586/14789450.2014.900447.

Calvete JJ, Juárez P, Sanz L. 2007. Snake venomics. Strategy and applications. In: Journal of Mass Spectrometry.Vol. 42 pp. 1405–1414. doi: 10.1002/jms.1242.

Calvete JJ, Sanz L, Angulo Y, Lomonte B, Gutiérrez JM. 2009. Venoms, venomics, antivenomics. FEBS Lett. 583:1736–43. doi: 10.1016/j.febslet.2009.03.029.

Cao Z et al. 2013. The genome of Mesobuthus martensii reveals a unique adaptation model of arthropods. Nat. Commun. 4:2602. doi: 10.1038/ncomms3602.

Carstens BC, Knowles LL. 2007. Shifting distributions and speciation: Species divergence during rapid climate change. Mol. Ecol. 16:619–627. doi: 10.1111/j.1365-294X.2006.03167.x.

Casewell NR, Wüster W, Vonk FJ, Harrison RA, Fry BG. 2013a. Complex cocktails: The evolutionary novelty of venoms. 2012/12/12. doi: 10.1016/j.tree.2012.10.020.

Casewell NR, Wüster W, Vonk FJ, Harrison RA, Fry BG. 2013b. Complex cocktails: The evolutionary novelty of venoms. Trends Ecol. Evol. 28:219–229. doi: 10.1016/j.tree.2012.10.020.

Castelin M et al. 2012. Macroevolution of venom apparatus innovations in auger snails (Gastropoda; Conoidea; Terebridae). Mol. Phylogenet. Evol. 64:21–44. doi:

- 119 -

10.1016/j.ympev.2012.03.001.

Chakona A, Swartz ER, Gouws G. 2013. Evolutionary Drivers of Diversification and Distribution of a Southern Temperate Stream Fish Assemblage: Testing the Role of Historical Isolation and Spatial Range Expansion. PLoS One. 8. doi: 10.1371/journal.pone.0070953.

Chang D, Duda TF, Jr. 2016. Age-related association of venom gene expression and diet of predatory gastropods. BMC Evol. Biol. 16:27. doi: 10.1186/s12862-016-0592-5.

Cipriani V et al. 2017. Correlation between ontogenetic dietary shifts and venom variation in Australian brown snakes ( Pseudonaja ). Comp. Biochem. Physiol. Part C Toxicol. Pharmacol. 197:53–60. doi: 10.1016/j.cbpc.2017.04.007.

Cirelli C, Bushey D. 2008. Sleep and wakefulness in Drosophila melanogaster. In: Annals of the New York Academy of Sciences.Vol. 1129 pp. 323–329. doi: 10.1196/annals.1417.017.

Claramunt S, Derryberry EP, Brumfield RT, Remsen J V. 2012. Ecological Opportunity and Diversification in a Continental Radiation of Birds: Climbing Adaptations and Cladogenesis in the Furnariidae. Am. Nat. 179:649–666. doi: 10.1086/664998.

Claridge-Chang A, Assam PN. 2016. Estimation statistics should replace significance testing. Nat. Methods. 13:108–109. doi: 10.1038/nmeth.3729.

Codron D et al. 2007. Significance of diet type and diet quality for ecological diversity of African ungulates. J. Anim. Ecol. 76:526–537. doi: 10.1111/j.1365-2656.2007.01222.x.

Colinet D et al. 2013. Extensive inter- and intraspecific venom variation in closely related parasites targeting the same host: The case of Leptopilina parasitoids of Drosophila. Insect Biochem Mol Biol. 43. doi: 10.1016/j.ibmb.2013.03.010.

Collin R, Chaparro OR, Winkler F, Véliz D. 2007. Molecular phylogenetic and embryological evidence that feeding larvae have been reacquired in a marine gastropod. Biol. Bull. 212:83–92. doi: 10.2307/25066586.

Craig AG, Bandyopadhyay P, Olivera BM. 1999. Post-translationally modified neuropeptides from Conus venoms. Eur. J. Biochem. 264:271–5. http://www.ncbi.nlm.nih.gov/pubmed/10491070 (Accessed May 22, 2014).

Craik DJ, Fairlie DP, Liras S, Price D. 2013. The Future of Peptide-based Drugs. Chem. Biol. Drug Des. 81:136–147. doi: 10.1111/cbdd.12055.

Cruz LJ et al. 1987. Invertebrate vasopressin/oxytocin homologs. Characterization of peptides from Conus geographus and Conus straitus venoms. J. Biol. Chem. 262:15821–4. http://www.ncbi.nlm.nih.gov/pubmed/3680228 (Accessed August 3, 2014).

Cumming G. 2012. Understanding The New Statistics: Effect Sizes, Confidence Intervals, and Meta-Analysis. doi: 10.1037/a0028079.

- 120 -

Cumming G, Calin-Jageman R. 2016. Introduction to the New Statistics: Estimation, Open Science, and Beyond. Routledge.

Cushman DW, Ondetti MA. 1991. History of the design of captopril and related inhibitors of angiotensin converting enzyme. Hypertension. 17:589–592. doi: 10.1161/01.HYP.17.4.589.

Daltry JC, Wüster W, Thorpe RS. 1996. Diet and snake venom evolution. Nature. 379:537–540. doi: 10.1038/379537a0.

Darriba D, Taboada GL, Doallo R, Posada D. 2011. ProtTest 3: fast selection of best-fit models of protein evolution. Bioinformatics. 27:1164–1165. doi: 10.1093/bioinformatics/btr088.

Dave K, Lahiry A. 2012. Conotoxins: review and docking studies to determine potentials of conotoxin as an anticancer drug molecule. Curr. Top. Med. Chem. 12:845–51. http://www.ncbi.nlm.nih.gov/pubmed/22352912.

Davis J, Jones A, Lewis RJ. 2009. Remarkable inter- and intra-species complexity of conotoxins revealed by LC/MS. Peptides. 30:1222–1227. doi: 10.1016/j.peptides.2009.03.019.

Dawkins R, Krebs JR. 1979. Arms races between and within species. Proc. R. Soc. Lond. B. Biol. Sci. 205:489–511. doi: 10.1098/rspb.1979.0081.

Delicado D, Hauffe T, Wilke T. 2018. Ecological opportunity may facilitate diversification in Palearctic freshwater organisms: a case study on hydrobiid gastropods. BMC Evol. Biol. 18:55. doi: 10.1186/s12862-018-1169-2.

Diochot S et al. 2012. Black mamba venom peptides target acid-sensing ion channels to abolish pain. Nature. 490:552–555.

Dornburg A et al. 2011. The influence of an innovative locomotor strategy on the phenotypic diversification of triggerfish (family: Balistidae). Evolution (N. Y). 65:1912–1926. doi: 10.1111/j.1558-5646.2011.01275.x.

Drummond AJ, Rambaut A. 2007. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol Biol. 7:214. doi: Artn 214Doi 10.1186/1471-2148-7-214.

Drummond CS, Eastwood RJ, Miotto STS, Hughes CE. 2012. Multiple continental radiations and correlates of diversification in lupinus (leguminosae): Testing for key innovation with incomplete taxon sampling. Syst. Biol. 61:443–460. doi: 10.1093/sysbio/syr126.

Duda TF, Palumbi SR. 1999. Molecular genetics of ecological diversification: duplication and rapid evolution of toxin genes of the venomous gastropod Conus. Proc. Natl. Acad. Sci. USA. 96:6820–6823. doi: 10.1073/pnas.96.12.6820.

Durban J et al. 2013. Integrated ‘omics’ profiling indicates that miRNAs are modulators of the ontogenetic venom composition shift in the Central American rattlesnake, Crotalus simus simus. BMC Genomics. 14:234. doi: 10.1186/1471-2164-14-234.

- 121 -

Durban J et al. 2011. Profiling the venom gland transcriptomes of Costa Rican snakes by 454 pyrosequencing. BMC Genomics. 12:259. doi: 1471-2164-12-259 [pii]\r10.1186/1471-2164-12- 259.

Dutertre S et al. 2014. Evolution of separate predation- and defence-evoked venoms in carnivorous cone snails. Nat. Commun. 5:3521. doi: 10.1038/ncomms4521.

Edgar RC. 2004. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32:1792–1797. doi: 10.1093/nar/gkh340.

Eichberg S, Sanz L, Calvete JJ, Pla D. 2015. Constructing comprehensive venom proteome reference maps for integrative venomics. Expert Rev. Proteomics. 12:557–573. doi: 10.1586/14789450.2015.1073590.

Eisapoor SS, Jamili S, Shahbazzadeh D, Ghavam Mostafavi P, Pooshang Bagheri K. 2016. A New, High Yield, Rapid, and Cost-Effective Protocol to Deprotection of Cysteine-Rich Conopeptide, Omega-Conotoxin MVIIA. Chem. Biol. Drug Des. 87:687–693. doi: 10.1111/cbdd.12702.

Eldredge N et al. 2005. The dynamics of evolutionary stasis. 31:133–145.

Elmer KR et al. 2010. Rapid evolution and selection inferred from the transcriptomes of sympatric crater lake cichlid fishes. Mol Ecol. 19 Suppl 1:197–211. doi: 10.1111/j.1365- 294X.2009.04488.x.

Elsik CG et al. 2014. Finding the missing honey bee genes: lessons learned from a genome upgrade. BMC Genomics. 15:1–29. doi: 10.1186/1471-2164-15-86.

Emery NC, Ackerly DD. 2014. Ecological release exposes genetically based niche variation. Ecol. Lett. 17:1149–1157. doi: 10.1111/ele.12321.

Erak M, Bellmann-Sickert K, Els-Heindl S, Beck-Sickinger AG. 2018. Peptide chemistry toolbox - Transforming natural peptides into peptide therapeutics. Bioorganic and Medicinal Chemistry doi: 10.1016/j.bmc.2018.01.012.

Eriksson A et al. 2017. Neuromodulatory circuit effects on Drosophila feeding behaviour and metabolism. Sci. Rep. 7:8839. doi: 10.1038/s41598-017-08466-0.

Escoubas P, King GF. 2009. Venomics as a drug discovery platform. Expert Rev. Proteomics. 6:221–4. doi: 10.1586/epr.09.45.

Escoubas P, Quinton L, Nicholson GM. 2008. Venomics: unravelling the complexity of animal venoms with mass spectrometry. J. Mass Spectrom. 43:279–295. doi: 10.1002/jms.

Espiritu DJD et al. 2001. Venomous cone snails: Molecular phylogeny and the generation of toxin diversity. Toxicon. 39:1899–1916. doi: 10.1016/S0041-0101(01)00175-1.

Essack M, Bajic VB, Archer J a C. 2012. Conotoxins that confer therapeutic possibilities. Mar.

- 122 -

Drugs. 10:1244–65. doi: 10.3390/md10061244.

Etienne RS, Haegeman B. 2012. A Conceptual and Statistical Framework for Adaptive Radiations with a Key Role for Diversity Dependence. Am. Nat. 180:E75–E89. doi: 10.1086/667574.

Faith DP. 1992. Conservation evaluation and phylogentic diversity. Biol. Conserv. 61:1–10. doi: 10.1890/0012-9658(2006)87[1465:ATTFHF]2.0.CO;2.

Faith M, Kral T. 2006. Social environmental and genetic influences on obesity and obesity- promoting behaviors: Fostering research integration. In: Genes, behavior, and the social environment: Moving beyond the nature/nurture debate. pp. 236–280. doi: 0-309-10196-4.

Favreau P, Stöcklin R. 2009. Marine snail venoms: use and trends in receptor and channel neuropharmacology. Curr. Opin. Pharmacol. 9:594–601. doi: 10.1016/j.coph.2009.05.006.

Felsenstein J. 1985. Confidence limits on phylogenies: an approach using the bootstrap. Evolution (N. Y). 783–791. doi: 10.2307/2408678.

Felsenstein J. 2001. Taking variation of evolutionary rates between sites into account in inferring phylogenies. J. Mol. Evol. 53:447–55. doi: 10.1007/s002390010234.

Fenn JB. 2003. Electrospray wings for molecular elephants (Nobel lecture). In: Angewandte Chemie - International Edition.Vol. 42 pp. 3871–3894. doi: 10.1002/anie.200300605.

Fernández J, Gutiérrez JM, Calvete JJ, Sanz L, Lomonte B. 2016. Characterization of a novel snake venom component: Kazal-type inhibitor-like protein from the arboreal pitviper Bothriechis schlegelii. Biochimie. 125:83–90. doi: 10.1016/j.biochi.2016.03.004.

Fosgerau K, Hoffmann T. 2015. Peptide therapeutics: Current status and future directions. Drug Discov. Today. 20:122–128. doi: 10.1016/j.drudis.2014.10.003.

Francischetti IMB et al. 2013. The ‘Vampirome’: Transcriptome and proteome analysis of the principal and accessory submaxillary glands of the vampire bat Desmodus rotundus, a vector of human rabies. J. Proteomics. 82:288–319. doi: 10.1016/j.jprot.2013.01.009.

Fry BG et al. 2006. Early evolution of the venom system in lizards and snakes. Nature. 439:584– 588. doi: 10.1038/nature04328.

Fry BG et al. 2008. Evolution of an Arsenal Structural and Functional Diversification of the Venom System in the Advanced Snakes (Caenophidia). Mol. Cell. Proteomics. 7:215–246. doi: 10.1074/mcp.M700094-MCP200.

Fry BG et al. 2003. Isolation of a Neurotoxin (α-colubritoxin) from a Nonvenomous Colubrid: Evidence for Early Origin of Venom in Snakes. J. Mol. Evol. 57:446–452. doi: 10.1007/s00239- 003-2497-3.

Fry BG et al. 2013. ‘Seeing the woods for the trees: understanding venom evolution as a guide

- 123 - for biodiscovery’ Venoms to drugs: venom as a source for the development of human therapeutics. Royal Society of Chemistry : London, UK.

Fry BG et al. 2012. The structural and functional diversification of the Toxicofera reptile venom system. Toxicon. 60:434–448. doi: 10.1016/j.toxicon.2012.02.013.

Fry BG et al. 2009. The toxicogenomic multiverse: convergent recruitment of proteins into animal venoms. Annu. Rev. Genomics Hum. Genet. 10:483–511. doi: 10.1146/annurev.genom.9.081307.164356.

Gale SM, Castracane VD, Mantzoros CS. 2004. Energy homeostasis, obesity and eating disorders: recent advances in endocrinology. J. Nutr. 134:295–298.

Galindo LA, Puillandre N, Strong EE, Bouchet P. 2014. Using microwaves to prepare gastropods for DNA barcoding. Mol. Ecol. Resour. 14:700–705. doi: 10.1111/1755-0998.12231.

Gallagher EJ, LeRoith D. 2015. Obesity and Diabetes: The Increased Risk of Cancer and Cancer-Related Mortality. Physiol. Rev. 95:727–748. doi: 10.1152/physrev.00030.2014.

García-Ortega L et al. 2011. The behavior of sea anemone actinoporins at the water-membrane interface. Biochim. Biophys. Acta - Biomembr. 1808:2275–2288. doi: 10.1016/j.bbamem.2011.05.012.

Gibbs HL, Sanz L, Sovic MG, Calvete JJ. 2013. Phylogeny-Based Comparative Analysis of Venom Proteome Variation in a Clade of Rattlesnakes (Sistrurus sp.). PLoS One. 8. doi: 10.1371/journal.pone.0067220.

Gkika D, Prevarskaya N. 2011. TRP channels in prostate cancer: the good, the bad and the ugly? Asian J Androl. 13:673–676. doi: 10.1038/aja.2011.18.

Gonzales DTT, Saloma CP. 2014. A bioinformatics survey for conotoxin-like sequences in three turrid snail venom duct transcriptomes. Toxicon. 92:66–74. doi: 10.1016/j.toxicon.2014.10.003.

Gonzales DTT, Saloma CP. 2014. A bioinformatics survey for conotoxin-like sequences in three turrid snail venom duct transcriptomes. Toxicon. 92:66–74. doi: 10.1016/j.toxicon.2014.10.003.

Gorson J, Ramrattan G, Verdes A, Wright ME, et al. 2015. Molecular Diversity and Gene Evolution of the Venom Arsenal of Terebridae Predatory Marine Snails. Genome Biol. Evol. 7:1761–78. doi: 10.1093/gbe/evv104.

Gorson J, Holford M. 2016. Small Packages, Big Returns: Uncovering the Venom Diversity of Small Invertebrate Conoidean Snails. In: Integrative and Comparative Biology.Vol. 56 pp. 962– 972. doi: 10.1093/icb/icw063.

Gotelli NJ, Colwell RK. 2001. Quantifyinf Biodiversity: Procedures and Pitfalls in the Measurement and Comparison of Species Richness. Ecol. Lett. 4:379–391. doi: 10.1046/j.1461- 0248.2001.00230.x.

- 124 -

Gould SJ, Eldredge N. 1986. Punctuated equilibrium at the third stage. Syst. Biol. 35:143–148. doi: 10.1093/sysbio/35.1.143.

Grabherr MG et al. 2011a. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat. Biotechnol. 29:644–52. doi: 10.1038/nbt.1883.

Grabherr MG et al. 2011b. Trinity: recontructing a full-length transcriptome assembly without a genome from RNA-Seq data. Nat. Biotechnol. 29:644–52. doi: 10.1038/nbt.1883.

Gray WR, Olivera BM, Cruz LJ. 1988. Peptide toxins from venomous Conus snails. Annu. Rev. Biochem. 57:665–700.

Haas BJ et al. 2013. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat. Protoc. 8:1494–512. doi: 10.1038/nprot.2013.084.

Halai R, Craik DJ. 2009. Conotoxins: natural product drug leads. Nat. Prod. Rep. 26:526–536. doi: 10.1039/b819311h.

Hall T. 1999. BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symp. Ser. 41:95–98. doi: citeulike-article-id:691774.

Hamada FN et al. 2008. An internal thermal sensor controlling temperature preference in Drosophila. Nature. 454:217–220. doi: 10.1038/nature07001.

Han TS, Teichert RW, Olivera BM, Bulaj G. 2008. Conus venoms- A Rich Source of Peptide- Based Therapeutics. Curr. Pharmacuetical Des. 14:2462–79. http://www.ncbi.nlm.nih.gov/pubmed/18781995 (Accessed December 12, 2013).

HannonLab. 2014. FASTX toolkit. Cold Spring Harb. Lab. Cold Spring Harb. NY.

Hansen TA. 1978. Larval Dispersal and Species Longevity in Lower Tertiary Gastropods. Science. 11–13. doi: 10.1126/science.199.4331.885.

Hansen TF. 1997. Stabilizing selection and the comparative analysis of adaptation. Evolution (N. Y). 51:1341–1351. doi: 10.2307/2411186.

Harvey AL. 2014. Toxins and drug discovery. Toxicon. 92:193–200. doi: 10.1016/j.toxicon.2014.10.020.

Haszprunar G. 1988. On the origin and evolution of major gastropods group, with special reference to the streptoneura. J. Molluscan Stud. 54:367–441. doi: 10.1093/mollus/54.4.367.

Hebebrand J, Hinney A. 2009. Environmental and Genetic Risk Factors in Obesity. Child Adolesc. Psychiatr. Clin. N. Am. 18:83–94. doi: 10.1016/j.chc.2008.07.006.

Helmus MR, Bland TJ, Williams CK, Ives AR. 2007. Phylogenetic Measures of Biodiversity. Am. Nat. 169:E68–E83. doi: 10.1086/511334.

- 125 -

Heralde FM et al. 2008. A rapidly diverging superfamily of peptide toxins in venomous Gemmula species. Toxicon. 51:890–7. doi: 10.1016/j.toxicon.2007.12.022.

Holding ML, Biardi JE, Gibbs HL. 2016. Coevolution of venom function and venom resistance in a rattlesnake predator and its squirrel prey. Proc. R. Soc. London B Biol. Sci. 283. doi: 10.1098/rspb.2015.2841.

Holford M et al. 2009. Correlating Molecular Phylogeny with Venom Apparatus Occurrence in Panamic Auger Snails (Terebridae). PLoS One. 4:e7667. doi: 10.1371/journal.pone.0007667.

Holford Puillandre, N, Terryn, Y, Cruaud, C, Olivera, BM, Bouchet, P M. 2009. Evolution of the Toxoglossa Venom Apparatus as Inferred by Molecualr Phylogeny of the Terebridae. Mol. Biol. Evol. 26:15–25. doi: doi:10.1093/molbev/msn211.

Huang C et al. 2016. The transcriptome of the zoanthid Protopalythoa variabilis (Cnidaria, Anthozoa) predicts a basal repertoire of toxin-like and venom-auxiliary polypeptides. Genome Biol. Evol. 8:3045–3064. doi: 10.1093/gbe/evw204.

Hurvich CM, Tsai C-L. 1989. Regression and Time Series Model Selection in Small Samples. Biometrika. 76:297. doi: 10.2307/2336663.

Illumina. 2014. Nextera ® XT DNA Library Preparation Kit. Reporter. 8–11.

Imperial JS et al. 2007. Venomous auger snail Hastula (Impages) hectica (Linnaeus, 1758): Molecular phylogeny, foregut anatomy and comparative toxinology. J. Exp. Zool. Part B Mol. Dev. Evol. 308:744–756. doi: 10.1002/jez.b.21195.

Inagaki HK et al. 2012. Visualizing neuromodulation in vivo: TANGO-mapping of dopamine signaling reveals appetite control of sugar sensing. Cell. 148:583–595. doi: 10.1016/j.cell.2011.12.022.

Ison JC, Rice PM, Bleasby AJ. 2011. EMBOSS Developer’s Guide: Bioinformatics Programming. Cambridge University Press http://www.amazon.com/EMBOSS-Developers- Guide-Bioinformatics- Programming/dp/0521607248?SubscriptionId=1V7VTJ4HA4MFT9XBJ1R2&tag=mekentosjco m-20&linkCode=xm2&camp=2025&creative=165953&creativeASIN=0521607248.

Ja WW et al. 2007. Prandiology of Drosophila and the CAFE assay. Proc. Natl. Acad. Sci. 104:8253–8256. doi: 10.1073/pnas.0702726104.

Jablonski D. 1986. Larval ecology and macroevolution in marine invertebrates. Bull. Mar. Sci. 39:565–587.

Jablonski D, Lutz R a. 1983. Larval Ecology of Marine Benthic Invertebrates: Paleobiological Implications. Biol. Rev. 58:21–89. doi: 10.1111/j.1469-185X.1983.tb00380.x.

Jacob RB, McDougal OM. 2010. The M-superfamily of conotoxins: a review. Cell. Mol. Life Sci. 67:17–27. doi: 10.1007/s00018-009-0125-0.

- 126 -

Jakubowski JA, Kelley WP, Sweedler J V, Gilly WF, Schulz JR. 2005. Intraspecific variation of venom injected by fish-hunting Conus snails. J. Exp. Biol. 208:2873–2883. doi: 10.1242/jeb.01713.

Johnson M et al. 2008. NCBI BLAST: a better web interface. Nucleic Acids Res. 36:W5-9. doi: 10.1093/nar/gkn201.

Jones P et al. 2014. InterProScan 5: Genome-scale protein function classification. Bioinformatics. 30:1236–1240. doi: 10.1093/bioinformatics/btu031.

Jouiaei M et al. 2015. Evolution of an ancient venom: Recognition of a novel family of cnidarian toxins and the common evolutionary origin of sodium and potassium neurotoxins in sea anemone. Mol. Biol. Evol. 32:1598–1610. doi: 10.1093/molbev/msv050.

Jungo F, Bairoch A. 2005. Tox-Prot, the toxin protein annotation program of the Swiss-Prot protein knowledgebase. Toxicon. 45:293–301. doi: 10.1016/j.toxicon.2004.10.018.

Jungo F, Bougueleret L, Xenarios I, Poux S. 2012. The UniProtKB/Swiss-Prot Tox-Prot program: A central hub of integrated venom protein data. Toxicon. 60:551–557. doi: 10.1016/j.toxicon.2012.03.010.

Kaas Q, Westermann JC, Craik DJ. 2010. Conopeptide characterization and classifications: an analysis using ConoServer. Toxicon. 55:1491–509. doi: 10.1016/j.toxicon.2010.03.002.

Kaas Q, Westermann JC, Halai R, Wang CKL, Craik DJ. 2008. ConoServer, a database for conopeptide sequences and structures. Bioinformatics. 24:445–446. doi: 10.1093/bioinformatics/btm596.

Kaas Q, Yu R, Jin AH., Dutertre S, Craik DJ. 2012. ConoServer: updated content, knowledge, and discovery tools in the conopeptide database. Nucleic Acids Res. 40:D325-30. doi: 10.1093/nar/gkr886.

Kalso E. 2005. Sodium channel blockers in neuropathic pain. Curr. Pharm. Des. 11:3005–11. doi: 10.2174/1381612054865028.

Kantor YI, Puillandre N. 2012. Evolution of the Radular Apparatus in Conoidea (Gastropoda: ) as Inferred from a Molecular Phylogeny. Malacologia. 55:55–90. doi: 10.4002/040.055.0105.

Kantor YI, Taylor JD. 2000. Formation of marginal radular teeth in Conoidea (Neogastropoda) and the evolution of the hypodermic envenomation mechanism. J. Zool. 252:251–262.

Karas M, Hillenkamp F. 1988. Laser desorption ionization of proteins with molecular masses exceeding 10,000 daltons. Anal. Chem. 60:2299–2301. doi: 10.1021/ac00171a028.

Katoh K, Misawa K, Kuma K, Miyata T. 2002. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30:3059–3066. doi: 10.1093/nar/gkf436.

- 127 -

Katoh K, Standley DM. 2013. MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability. Mol. Biol. Evol. 30:772–780. doi: DOI 10.1093/molbev/mst010.

Kembel ASW et al. 2013. Package ‘ picante ’.

Kendel Y et al. 2013. Venomous secretions from marine snails of the Terebridae family target acetylcholine receptors. Toxins (Basel). 5:1043–1050.

Kim BY et al. 2013. Antimicrobial activity of a honeybee (Apis cerana) venom Kazal-type serine protease inhibitor. Toxicon. 76:110–117. doi: 10.1016/j.toxicon.2013.09.017.

King GF. 2011. Venoms as a platform for human drugs: translating toxins into therapeutics. Expert Opin. Biol. Ther. 11:1469–1484. doi: 10.1517/14712598.2011.621940.

King GF. 2015. Venoms to Drugs: Venom as a Source for the Development of Human Therapeutics. The Royal Society of Chemistry doi: 10.1039/9781849737876.

Knudsen R, Klemetsen A, Amundsen P-A, Hermansen B. 2006. Incipient speciation through niche expansion: an example from the Arctic charr in a subarctic lake. Proc. R. Soc. B Biol. Sci. 273:2291–2298. doi: 10.1098/rspb.2006.3582.

Koch ED, Olivera BM, Terlau H, Conti F. 2004. The binding of kappa-Conotoxin PVIIA and fast C-type inactivation of Shaker K+ channels are mutually exclusive. Biophys. J. 86:191–209. doi: 10.1016/S0006-3495(04)74096-5.

Koh CY, Kini RM. 2012. From snake venom toxins to therapeutics--cardiovascular examples. Toxicon. 59:497–506. doi: 10.1016/j.toxicon.2011.03.017.

Kordis D, Gubensek F, Kordiš D, Gubenšek F. 2000. Adaptive evolution of animal toxin multigene families. Gene. 261:43–52. doi: 10.1016/S0378-1119(00)00490-X.

Kvist S, Brugler MR, Goh TG, Giribet G, Siddall ME. 2014. Pyrosequencing the salivary transcriptome of Haemadipsa interrupta (Annelida: Clitellata: Haemadipsidae): Anticoagulant diversity and insight into the evolution of anticoagulation capabilities in leeches. Invertebr. Biol. 133:74–98. doi: 10.1111/ivb.12039.

Lang F, Stournaras C, B PTRS. 2014. Ion channels in cancer : future perspectives and clinical potential Ion channels in cancer : future perspectives and clinical potential. Phil. Trans. Roy. Soc. B. 369:20130108.

Lavergne V et al. 2015. Optimized deep-targeted proteotranscriptomic profiling reveals unexplored Conus toxin diversity and novel cysteine frameworks. Proc Natl Acad Sci. 112:E3782-91. doi: 10.1073/pnas.1501334112.

Layer RT, Mcintosh JM. 2006. Conotoxins: Therapeutic Potential and Application. Mar. Drugs. 4:119–142. doi: 10.3390/md403119.

- 128 -

Lee Y et al. 2005. Pyrexia is a new thermal transient receptor potential channel endowing tolerance to high temperatures in Drosophila melanogaster. Nat Genet. 37:305–310. doi: 10.1038/ng1513.

Lewis RJ, Dutertre S, Vetter I, Christie MJ. 2012. Conus Venom Peptide Pharmacology. Pharmacol. Rev. 64:259–298. doi: 10.1124/pr.111.005322.

Lewis RJ, Garcia ML. 2003. Therapeutic potential of venom peptides. Nat. Rev. Drug Discov. 2:790–802. doi: 10.1038/nrd1197.

Li M, Fry BG, Kini RM. 2005. Eggs-only diet: its implications for the toxin profile changes and ecology of the marbled sea snake (Aipysurus eydouxii). J Mol Evol. 60. doi: 10.1007/s00239- 004-0138-0.

Li W, Godzik A. 2006. Cd-hit: A fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics. 22:1658–1659. doi: 10.1093/bioinformatics/btl158.

Lima GM, Lutz RA. 1990. The relationship of larval shell morphology to mode of development in marine prosobranch gastropods. J. Mar. Biol. Assoc. United Kingdom. 70:611–637. doi: 10.1017/S0025315400036626.

Liu G et al. 2015. Global transcriptome analysis of the tentacle of the Jellyfish Cyanea capillata using deep sequencing and expressed sequence tags: Insight into the toxin-and degenerative disease-related transcripts Silman, I, editor. PLoS One. 10:e0142680. doi: 10.1371/journal.pone.0142680.

Lobato FL et al. 2014. Diet and diversification in the evolution of coral reef fishes. PLoS One. 9. doi: 10.1371/journal.pone.0102094.

Low DHW et al. 2013. Dracula’s children: Molecular evolution of vampire bat venom. J. Proteomics. 89:95–111. doi: 10.1016/j.jprot.2013.05.034.

Lutz RA, Bouchet P, Jablonski D, Turner RD, Waren A. 1986. Larval ecology of mollusks at deep-sea hydrothermal vents. Am. Malacol. Bull. [AM. MALACOL. BULL.]. 4:49–54.

Magoč T, Salzberg SL. 2011. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics. 27. doi: 10.1093/bioinformatics/btr507.

Malmberg AB, Gilbert H, McCabe RT, Basbaum AI. 2003. Powerful antinociceptive effects of the cone snail venom-derived subtype-selective NMDA receptor antagonists conantokins G and T. Pain. 101:109–116. doi: 10.1016/S0304-3959(02)00303-2.

Mans BJ, Louw AI, Neitz AWH. 2003. The major tick salivary gland proteins and toxins from the soft tick, Ornithodoros savignyi, are part of the tick lipocalin family: Implications for the origins of tick toxicoses. Mol. Biol. Evol. 20:1158–1167. doi: 10.1093/molbev/msg126.

Marazzi B, Sanderson MJ. 2010. Large-scale patterns of diversification in the widespread legume genus senna and the evolutionary role of extrafloral nectaries. Evolution (N. Y).

- 129 -

64:3570–3592. doi: 10.1111/j.1558-5646.2010.01086.x.

Marella S, Mann K, Scott K. 2012. Dopaminergic Modulation of Sucrose Acceptance Behavior in Drosophila. Neuron. 73:941–950. doi: 10.1016/j.neuron.2011.12.032.

Marin J, Hedges SB. 2018. Undersampling Genomes has Biased Time and Rate Estimates Throughout the Tree of Life. Molecular BIology and Evolution. 35:2077-2084.

McGivern JG. 2007. Ziconotide: a review of its pharmacology and use in the treatment of pain. Neuropsychiatr. Dis. Treat. 3:69–85).

McIntosh JM, Absalom N, Chebib M, Elgoyhen AB, Vincler M. 2009. Alpha9 nicotinic acetylcholine receptors and the treatment of pain. Biochem. Pharmacol. 78:693–702. doi: 10.1016/j.bcp.2009.05.020.

Miljanich G. 2004. Ziconotide: Neuronal Calcium Channel Blocker for Treating Severe Chronic Pain. Curr. Med. Chem. 11:3029–3040. doi: 10.2174/0929867043363884.

Miljanich GP, and Ramachandran J. 1995. Antagonists of Neuronal Calcium Channels: Structure, Function, and Therapeutic Implications. Annu. Rev. Pharmacol. Toxicol. 35:707–734. doi: 10.1146/annurev.pa.35.040195.003423.

Miller B. 1970. Feeding mechanisms of the family Terebridae. 72–74.

Miller B. 1971. Feeding mechanisms of the family Terebridae. Ann. Rep. Am. Mal. Union. 1970:72–74.

Miller B. 1970. Studies on the biology of Indo-Pacific Terebra (Ph.D. dissertation). University of New Hampshire: Durham, NH.

Miller MA, Pfeiffer W, Schwartz T. 2010. Creating the CIPRES Science Gateway for inference of large phylogenetic trees. In: 2010 Gateway Computing Environments Workshop, GCE 2010. doi: 10.1109/GCE.2010.5676129.

Mills P. 1979. Radular tooth structure of three species of Terebridae (Mollusca: Toxoglossa). Veliger. 19:259–265.

Min G-S, Sarkar IN, Siddall ME. 2010. Salivary transcriptome of the North American medicinal leech, Macrobdella decora. J. Parasitol. 96:1211–21. doi: 10.1645/GE-2496.1.

Modica MV, Holford M. 2010. The Neogastropoda: Evolutionary Innovations of Predatory Marine Snails with Remarkable Pharmacological Potential. 249–270. doi: 10.1007/978-3-642- 12340-5_15.

Modica MV, Puillandre N, Castelin M, Zhang Y, Holford M. 2014. A good compromise: Rapid and robust species proxies for inventorying biodiversity hotspots using the Terebridae (Gastropoda: Conoidea). PLoS One. 9. doi: 10.1371/journal.pone.0102160.

- 130 -

Modica MV, Holford, M. 2010. The Neogastropoda: Evolutionary Innovations of Predatory Marine Snails with Remarkable Pharmacological Potential. Evolutionary Biology – Concepts, Molecular and Morphological Evolution. Springer-Verlag: Berlin Heidelberg. doi: 10.1007/978- 3-642-12340-5_15.

Moon J et al. 2016. Characterization and recombinant expression of terebrid venom peptide from Terebra guttata. Toxins (Basel). 8. doi: 10.3390/toxins8030063.

Moore BR, Donoghue MJ. 2007. Correlates of Diversification in the Plant Clade Dipsacales: Geographic Movement and Evolutionary Innovations. Am. Nat. 170:S28–S55. doi: 10.1086/519460.

Moran Y et al. 2008. Concerted evolution of sea anemone neurotoxin genes is revealed through analysis of the Nematostella vectensis genome. Mol. Biol. Evol. 25:737–747. doi: 10.1093/molbev/msn021.

Morlon H et al. 2016. RPANDA: An R package for macroevolutionary analyses on phylogenetic trees. Methods Ecol. Evol. 7:589–597. doi: 10.1111/2041-210X.12526.

Morlon H, Parsons TL, Plotkin JB. 2011. Reconciling molecular phylogenies with the record. Proc Natl Acad Sci U S A. 108:16327–16332. doi: 10.1073/pnas.1102543108.

Nagy ZT, Sonet G, Glaw F, Vences M. 2012. First large-scale DNA barcoding assessment of reptiles in the biodiversity hotspot of madagascar, based on newly designed COI primers. PLoS One. doi: 10.1371/journal.pone.0034506.

Neely GG et al. 2010. A Genome-wide Drosophila screen for heat nociception identifies α2δ3 as an evolutionarily conserved pain gene. Cell. 143:628–638. doi: 10.1016/j.cell.2010.09.047.

Neely GG et al. 2011. TrpA1 regulates thermal nociception in Drosophila. PLoS One. 6. doi: 10.1371/journal.pone.0024343.

Negulescu H et al. 2015. A Kazal-Type Serine Protease Inhibitor from the Defense Gland Secretion of the Subterranean Termite Coptotermes formosanus Shiraki Mans, BJ, editor. PLoS One. 10:e0125376. doi: 10.1371/journal.pone.0125376.

Nentwig W. 2013. Spider ecophysiology. doi: 10.1007/978-3-642-33989-9.

Neves JLB et al. 2015. Small Molecules in the Cone Snail Arsenal. Org. Lett. 17:4933–4935. doi: 10.1021/acs.orglett.5b02389.

Nielsen C. 2009. How did indirect development with planktotrophic larvae evolve? Biol. Bull. 216:203–15.

Nisani Z, Dunbar SG, Hayes WK. 2007. Cost of venom regeneration in Parabuthus transvaalicus (Arachnida: Buthidae). Comp. Biochem. Physiol. - A Mol. Integr. Physiol. 147:509–513. doi: 10.1016/j.cbpa.2007.01.027.

- 131 -

Norton RS, Olivera BM. 2006. Conotoxins down under. Toxicon. 48:780–798. doi: 10.1016/j.toxicon.2006.07.022.

Ogawa LM, Pulgarin PC, Vance DA, Fjeldså J, van Tuinen M. 2015. Opposing demographic histories reveal rapid evolution in grebes (Aves: Podicipedidae). Auk. 132:771–786. doi: 10.1642/AUK-14-259.1.

Olivera BM. 2006. Conus Peptides: Biodiversity-based Discovery and Exogenomics. J. Biol. Chem. 281:31173–7. doi: 10.1074/jbc.R600020200.

Olivera BM. 2002. Conus Venom Peptides: Reflections from the Biology of Clades and Species. Annu. Rev. Ecol. Syst. 33:25–47. doi: 10.1146/annurev.ecolsys.33.010802.150424.

Olivera BM et al. 1987. Neuronal Calcium Channel Antagonists. Discrimination between Calcium Channel Subtypes Using co-Conotoxin from Conus magus Venom. Biochemistry. 26:2086–2090. doi: 10.1021/bi00382a004.

Olivera BM et al. 1985. Peptide neurotoxins from fish-hunting cone snails. Science. 230:1338– 43. doi: 10.1126/science.4071055.

Olivera BM et al. 1999. Speciation of cone snails and interspecific hyperdivergence of their venom peptides. Potential evolutionary significance of introns. In: Annals of the New York Academy of Sciences.Vol. 870 pp. 223–237. doi: 10.1111/j.1749-6632.1999.tb08883.x.

Olivera BM. 2000. ω-Conotoxin MVIIA: from marine snail venom to analgesic drug. In: Drugs from the Sea. Fusetani, N, editor. Karger pp. 75–85.

Olivera BM, Showers Corneli P, Watkins M, Fedosov A. 2014. Biodiversity of Cone Snails and Other Venomous Marine Gastropods: Evolutionary Success Through Neuropharmacology. Annu. Rev. Anim. Biosci. 2:487–513. doi: 10.1146/annurev-animal-022513-114124.

Onofrei M, Hunt J, Siemienczuk J, Touchette DR, Middleton B. 2004. A first step towards translating evidence into practice: heart failure in a community practice-based research network. Inform. Prim. Care. 12:139–45. doi: Article.

Ortiz E, Gurrola GB, Schwartz EF, Possani LD. 2015. Scorpion venom components as potential candidates for drug development. Toxicon. 93:125–135. doi: 10.1016/j.toxicon.2014.11.233.

Orzechowski EA et al. 2015. Marine extinction risk shaped by trait-environment interactions over 500 million years. Glob. Chang. Biol. doi: 10.1111/gcb.12963.

Otvos RA et al. 2013. Analytical workflow for rapid screening and purification of bioactives from venom proteomes. Toxicon. 76:270–281. doi: 10.1016/j.toxicon.2013.10.013.

Oury F, Karsenty G. 2011. Towards a serotonin-dependent leptin roadmap in the brain. Trends Endocrinol. Metab. 22:382–387. doi: 10.1016/j.tem.2011.04.006.

Page MJ, Di Cera E. 2008. Serine peptidases: Classification, structure and function. Cell. Mol.

- 132 -

Life Sci. 65:1220–1236. doi: 10.1007/s00018-008-7565-9.

Pahari S, Bickford D, Fry BG, Kini RM. 2007. Expression pattern of three-finger toxin and phospholipase A2 genes in the venom glands of two sea snakes, Lapemis curtus and Acalyptophis peronii: comparison of evolution of these toxins in land snakes, sea kraits and sea snakes. BMC Evol. Biol. 7:175. doi: 10.1186/1471-2148-7-175.

Park J, Morley TS, Kim M, Clegg DJ, Scherer PE. 2014. Obesity and cancer—mechanisms underlying tumour progression and recurrence. Nat. Rev. Endocrinol. 10:455–465. doi: 10.1038/nrendo.2014.94.

Pawlak J et al. 2006. Denmotoxin, a three-finger toxin from the colubrid snake Boiga dendrophila (mangrove catsnake) with bird-specific activity. J. Biol. Chem. 281:29030–29041. doi: 10.1074/jbc.M605850200.

Pawlak J et al. 2009. Irditoxin, a novel covalently linked heterodimeric three-finger toxin with high taxon-specific neurotoxicity. FASEB J. 23:534–545. doi: fj.08-113555 [pii]\r10.1096/fj.08- 113555.

Pechenik JA. 1999. On the advantages and disadvantages of larval stages in benthic marine invertebrate life cycles. Mar. Ecol. Prog. Ser. 177:269–297. doi: 10.3354/meps177269.

Perkins DN, Pappin DJ, Creasy DM, Cottrell JS. 1999. Probability-based protein identification by searching sequence databases using mass spectrometry data - Mascot-Electrophoresis- 1999.pdf. Electrophoresis. 20:3551–3567. http://www- bac.esi.umontreal.ca/~dbin3002/documents/contenuDB/Mascot-Electrophoresis- 1999.pdf%5Cnhttp://www.ncbi.nlm.nih.gov/pubmed/10612281.

Perkins JR, Smith B, et al. 1993. Application of electrospray mass spectrometry and matrix- assisted laser desorption ionization time-of-flight mass spectrometry for molecular weight assignment of peptides in complex mixtures. J. Am. Soc. Mass Spectrom. 4:670–84. doi: 10.1016/1044-0305(93)85032-S.

Perkins JR, Parker CE, Tomer KB. 1993. The characterization of snake venoms using capillary electrophoresis in conjunction with electrospray mass spectrometry: Black Mambas. Electrophoresis. 14:458–468. doi: 10.1002/elps.1150140171.

Petersen TN, Brunak S, von Heijne G, Nielsen H. 2011. SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat Methods. 8:785–786. doi: 10.1038/nmeth.1701.

Peterson A et al. 2009. Hybridization drives speciation in Gagea (Liliaceae). Plant Syst. Evol. 278:133–148. doi: 10.1007/s00606-008-0102-3.

Petras D, Heiss P, Süssmuth RD, Calvete JJ. 2015. Venom Proteomics of Indonesian King Cobra, Ophiophagus hannah: Integrating Top-Down and Bottom-Up Approaches. J. Proteome Res. 14:2539–56. doi: 10.1021/acs.jproteome.5b00305.

Pfennig DW et al. 2010. Phenotypic plasticity’s impacts on diversification and speciation.

- 133 -

Trends Ecol. Evol. 25:459–467. doi: 10.1016/j.tree.2010.05.006.

Philippe H, Telford MJ. 2006. Large-scale sequencing and the new animal phylogeny. Trends Ecol. Evol. 21:614–620. doi: 10.1016/j.tree.2006.08.004.

Phuong MA, Mahardika GN, Alfaro ME. 2016. Dietary breadth is positively correlated with venom complexity in cone snails. BMC Genomics. 17:401. doi: 10.1186/s12864-016-2755-6.

Pool AH et al. 2014. Four GABAergic interneurons impose feeding restraint in Drosophila. Neuron. 83:164–177. doi: 10.1016/j.neuron.2014.05.006.

Pope JE, Deer TR. 2013. Ziconotide: a clinical update and pharmacologic review. Expert Opin. Pharmacother. doi: 10.1517/14656566.2013.784269.

Posada D. 2008. jModelTest: Phylogenetic model averaging. Mol. Biol. Evol. 25:1253–1256. doi: 10.1093/molbev/msn083.

Powell A. 1966. The molluscan families Speightiidae and Turridae. An evaluation of the valid taxa, both recent and fossil, with lists of characteristics species. Bull. Auckl. Inst. Museum. 5:5– 184.

Prashanth JR, Hasaballah N, Vetter I. 2017. Pharmacological screening technologies for venom peptide discovery. Neuropharmacology. 127:4–19. doi: 10.1016/j.neuropharm.2017.03.038.

Prashanth JR, Lewis RJ, Dutertre S. 2012. Towards an integrated venomics approach for accelerated conopeptide discovery. Toxicon. 60:470–7. doi: 10.1016/j.toxicon.2012.04.340.

Puillandre N et al. 2014. Molecular phylogeny and evolution of the cone snails (Gastropoda, Conoidea). Mol. Phylogenet. Evol. 78:290–303. doi: 10.1016/j.ympev.2014.05.023.

Puillandre N et al. 2011. The Dragon Tamed? A Molecular Phylogeny of the Conoidea (Gastropoda). J. Molluscan Stud. 77:259–272. doi: Doi 10.1093/Mollus/Eyr015.

Puillandre N, Duda TF, Meyer C, Olivera BM, Bouchet P. 2015. One, four or 100 genera? A new classification of the cone snails. J. Molluscan Stud. 81:1–23. doi: 10.1093/mollus/eyu055.

Puillandre N, Holford M. 2010. The Terebridae and teretoxins: Combining phylogeny and anatomy for concerted discovery of bioactive compounds. BMC Chem. Biol. 10:7. doi: 10.1186/1472-6769-10-7.

Puillandre N, Koua D, Favreau P, Olivera BM, Stocklin R. 2012. Molecular phylogeny, classification and evolution of conopeptides. J. Mol. Evol. 74:297–309. doi: 10.1007/s00239- 012-9507-2.

Puillandre N, Lambert A, Brouillet S, Achaz G. 2012. ABGD, Automatic Barcode Gap Discovery for primary species delimitation. Mol. Ecol. 21:1864–1877. doi: 10.1111/j.1365- 294X.2011.05239.x.

- 134 -

Putnam NH et al. 2007. Sea Anemone Genome Reveals Ancestral Eumetazoan Gene Repertoire and Genomic Organization. Science (80-. ). 317:86–94. doi: 10.1126/science.1139158.

Pyron RA, Wiens JJ. 2011. A large-scale phylogeny of Amphibia including over 2800 species, and a revised classification of extant frogs, salamanders, and caecilians. Mol. Phylogenet. Evol. doi: 10.1016/j.ympev.2011.06.012.

Qi L, Cho YA. 2008. Gene-environment interaction and obesity. Nutr. Rev. 66:684–694. doi: 10.1111/j.1753-4887.2008.00128.x.

Qian C, Fang Q, Wang L, Ye G-Y. 2015. Molecular Cloning and Functional Studies of Two Kazal-Type Serine Protease Inhibitors Specifically Expressed by Nasonia vitripennis Venom Apparatus. Toxins (Basel). 7:2888–905. doi: 10.3390/toxins7082888.

Rabosky DL. 2014. Automatic detection of key innovations, rate shifts, and diversity- dependence on phylogenetic trees. PLoS One. 9. doi: 10.1371/journal.pone.0089543.

Rabosky DL et al. 2014. BAMMtools: An R package for the analysis of evolutionary dynamics on phylogenetic trees. Methods Ecol. Evol. 5:701–707. doi: 10.1111/2041-210X.12199.

Rabosky DL. 2012. Positive correlation between diversification rates and phenotypic evolvability can mimic punctuated equilibrium on molecular phylogenies. Evolution (N. Y). 66:2622–2627. doi: 10.1111/j.1558-5646.2012.01631.x.

Rabosky DL et al. 2013. Rates of speciation and morphological evolution are correlated across the largest vertebrate radiation. Nat. Commun. 4:1958. doi: 10.1038/ncomms2958.

Regier JC et al. 2013. A Large-Scale, Higher-Level, Molecular Phylogenetic Study of the Insect Order Lepidoptera (Moths and Butterflies). PLoS One. 8. doi: 10.1371/journal.pone.0058568. von Reumont B, Campbell L, Jenner R. 2014. Quo Vadis Venomics? A Roadmap to Neglected Venomous Invertebrates. Toxins (Basel). 6:3488–3551. doi: 10.3390/toxins6123488. von Reumont BM et al. 2014. A Polychaete’s Powerful Punch: Venom Gland Transcriptomics of Glycera Reveals a Complex Cocktail of Toxin Homologs. Genome Biol. Evol. . 6:2406–2423. doi: 10.1093/gbe/evu190. von Reumont BM et al. 2014. The first venomous crustacean revealed by transcriptomics and functional morphology: remipede venom glands express a unique toxin cocktail dominated by enzymes and a neurotoxin. Mol Biol Evol. 31:48–58. doi: 10.1093/molbev/mst199.

Ribeiro JMC et al. 2007. An annotated catalogue of salivary gland transcripts in the adult female mosquito, Aedes aegypti. BMC Genomics. 8:6. doi: 10.1186/1471-2164-8-6.

Rice P, Longden I, Bleasby A. 2000. EMBOSS: the European Molecular Biology Open Software Suite. Trends Genet. 16:276–277. doi: S0168-9525(00)02024-2 [pii].

Richards DP, Barlow A, Wüster W. 2012. Venom lethality and diet: Differential responses of

- 135 - natural prey and model organisms to the venom of the saw-scaled vipers (Echis). Toxicon. 59:110–116. doi: 10.1016/j.toxicon.2011.10.015.

Robinson SD et al. 2014. Diversity of conotoxin gene superfamilies in the venomous snail, Conus victoriae. PLoS One. 9:e87648. doi: 10.1371/journal.pone.0087648.

Robinson SD, Norton RS. 2014. Conotoxin gene superfamilies. Mar. Drugs. 12:6058–6101. doi: 10.3390/md12126058.

Romeo C et al. 2008. Conus ventricosus venom peptides profiling by HPLC-MS: a new insight in the intraspecific variation. J. Sep. Sci. 31:488–98. doi: 10.1002/jssc.200700448.

Rosenzweig M et al. 2005. The Drosophila ortholog of vertebrate TRPA1 regulates thermotaxis. Genes Dev. 19:419–424. doi: 10.1101/gad.1278205.

Rouse GW. 2000. The epitome of hand waving? Larval feeding and hypotheses of metazoan phylogeny. Evol. Dev. 2:222–233. doi: 10.1046/j.1525-142X.2000.00063.x.

Roy A, Kucukural A, Zhang Y. 2010. I-TASSER: A unified platform for automated protein structure and function prediction. Nat. Protoc. 5:725–738. doi: 10.1038/nprot.2010.5.

Rudd RA, Seth P, David F, Scholl L. 2016. Increases in Drug and Opioid-Involved Overdose Deaths — United States, 2010–2015. MMWR. Morb. Mortal. Wkly. Rep. 65:1445–1452. doi: 10.15585/mmwr.mm655051e1.

Safavi-Hemami H et al. 2014. Specialized insulin is used for chemical warfare by fish-hunting cone snails. Proc. Natl. Acad. Sci. U. S. A. 1–6. doi: 10.1073/pnas.1423857112.

Safavi-Hemami H, Bulaj G, Olivera BM, Williamson NA, Purcell AW. 2010. Identification of Conus peptidylprolyl cis-trans isomerases (PPIases) and assessment of their role in the oxidative folding of conotoxins. J. Biol. Chem. 285:12735–46. doi: 10.1074/jbc.M109.078691.

Sanford M. 2013. Intrathecal ziconotide: A review of its use in patients with chronic pain refractory to other systemic or intrathecal analgesics. CNS Drugs. doi: 10.1007/s40263-013- 0107-5.

Sanggaard KW et al. 2014. Spider genomes provide insight into composition and evolution of venom and silk. Nat. Commun. 5:3765. doi: 10.1038/ncomms4765.

Scarborough RM, Kleiman NS, Phillips DR. 1999. Platelet Glycoprotein IIb/IIIa Antagonists : What Are the Relevant Issues Concerning Their Pharmacology and Clinical Use? Circulation. 100:437–444. doi: 10.1161/01.CIR.100.4.437.

Schmidtko A, Lötsch J, Freynhagen R, Geisslinger G. 2010. Ziconotide for treatment of severe chronic pain. Lancet. 375:1569–1577. doi: 10.1016/S0140-6736(10)60354-6.

Schoofs A et al. 2014. Selection of Motor Programs for Suppressing Food Intake and Inducing Locomotion in the Drosophila Brain. PLoS Biol. 12. doi: 10.1371/journal.pbio.1001893.

- 136 -

Selkoe KA, Toonen RJ. 2011. Marine connectivity: A new look at pelagic larval duration and genetic metrics of dispersal. Mar. Ecol. Prog. Ser. 436:291–305. doi: 10.3354/meps09238.

Shammas NW. 2005. Bivalirudin: pharmacology and clinical applications. Cardiovasc. Drug Rev. 23:345–360.

Shiomi K, Kawashima Y, Mizukami M, Nagashima Y. 2002. Properties of proteinaceous toxins in the salivary gland of the marine gastropod (Monoplex echo). Toxicon. 40:563–571. doi: 10.1016/S0041-0101(01)00256-2.

Shuto T. 1974. Larval ecology of prosobranch gastropods and its bearing on biogeography and paleontology. Lethaia. 7:239–256. doi: 10.1111/j.1502-3931.1974.tb00899.x.

Sigle LT, Ramalho-Ortigão M. 2013. Kazal-type serine proteinase inhibitors in the midgut of Phlebotomus papatasi. Mem. Inst. Oswaldo Cruz. 108:671–8. doi: 10.1590/0074- 0276108062013001.

Silva NJ, Aird SD. 2001. Prey specificity, comparative lethality and compositional differences of coral snake venoms. Comp Biochem Physiol Part C Toxicol Pharmacol. 128. doi: 10.1016/S1532-0456(00)00215-5.

Sokabe T, Tsujiuchi S, Kadowaki T, Tominaga M. 2008. Drosophila Painless Is a Ca2+- Requiring Channel Activated by Noxious Heat. J. Neurosci. 28:9929–9938. doi: 10.1523/JNEUROSCI.2757-08.2008.

Stafford-Banks CA, Rotenberg D, Johnson BR, Whitfield AE, Ullman DE. 2014. Analysis of the Salivary Gland Transcriptome of Frankliniella occidentalis. PLoS One. 9:e94447. doi: 10.1371/journal.pone.0094447.

Stamatakis A. 2006a. RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics. 22:2688–2690. doi: 10.1093/bioinformatics/btl446.

Stamatakis A. 2006b. RAxML 7.0.4 Manual. Bioinformatics. 22(21):2688–2690.

Stamatakis A. 2014. RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 30:1312–1313. doi: 10.1093/bioinformatics/btu033.

Stauffer JR, van Snik Gray E. 2004. Phenotypic plasticity: Its role in trophic radiation and explosive speciation in cichlids (Teleostei: Cichlidae). Anim. Biol. 54:137–158. doi: 10.1163/1570756041445191.

Strathmann RR. 1977. Egg Size, Larval Development, and Juvenile Size in Benthic Marine Invertebrates. Am. Nat. 111:373–376.

Strathmann RR. 1978. The evolution and loss of feeding larval stages of marine invertebrates. Evolution (N. Y). 32:894–906. doi: 10.2307/2407502.

- 137 -

Sturm R, Hattori A. 2013. Morbid obesity rates continue to rise rapidly in the United States. Int. J. Obes. 37:889–891. doi: 10.1038/ijo.2012.159.

Sunagar K et al. 2014. Intraspecific venom variation in the medically significant Southern Pacific Rattlesnake (Crotalus oreganus helleri): Biodiscovery, clinical and evolutionary implications. J. Proteomics. 99:68–83. doi: 10.1016/j.jprot.2014.01.013.

Sunagar K, Morgenstern D, Reitzel AM, Moran Y. 2015. Ecological venomics: How genomics, transcriptomics and proteomics can shed new light on the ecology and evolution of venom. J. Proteomics. 135:62–72. doi: 10.1016/j.jprot.2015.09.015.

Takác P et al. 2006. Vasotab, a vasoactive peptide from horse fly Hybomitra bimaculata (Diptera, Tabanidae) salivary glands. J. Exp. Biol. 209:343–352. doi: 10.1242/jeb.02003.

Tamura K, Dudley J, Nei M, Kumar S. 2007. MEGA4: Molecular Evolutionary Genetics Analysis (MEGA) software version 4.0. Mol. Biol. Evol. 24:1596–1599. doi: 10.1093/molbev/msm092.

Tanaka K. 2003. The origin of macromolecule ionization by laser irradiation (Nobel lecture). Angew. Chem. Int. Ed. Engl. 3860–3870. doi: 10.1002/anie.200300585.

Taylor JD. 1977. Food and habitats of predatory gastropods on coral reefs. Prog. Underw. Sci. Rep Underw. Assoc. (New Ser. 2:17–34.

Taylor JD. 1990. The anatomy of the foregut and relationships in the Terebridae. Malacologia. 32:19–34.

Terlau H, Olivera BM. 2004. Conus Venoms: a rich source of novel ion channel-targeted peptides. Physiol. Rev. 84:41–68. doi: 10.3109/15569548509014416.

Terryn Y. 2007. A Collectors Guide to Recent Terebridae (Mollusca: Neogastropoda). Conchbooks & NaturalArt: Hackenheim, Germany.

Terryn Y, Holford M. 2008. The Terebridae of the Vanuatu Archipelago with a Revision of the Genus Granuliterebra Oyama 1961, the Description of a New Genus and a Three New Species. Visaya. 3.

Thompson JD, Higgins DG, Gibson TJ. 1994. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22:4673–4680.

Thorson G. 1950. Reproductive and larval ecology of marine bottom invertebrates. Biol. Rev. 25:1–45. doi: 10.1111/j.1469-185X.1950.tb00585.x.

Todd CD, Doyle RW. 1981. Reproductive Strategies of Marine Benthic Invertebrates: A Settlement-Timing Hypothesis. Mar. Ecol. Prog. Ser. 4:75–83. doi: 10.3354/meps004075.

Tran LAP. 2014. The role of ecological opportunity in shaping disparate diversification

- 138 - trajectories in a bicontinental primate radiation. Proc. R. Soc. B Biol. Sci. 281:20131979– 20131979. doi: 10.1098/rspb.2013.1979.

Tsirigos KD, Peters C, Shu N, Käll L, Elofsson A. 2015. The TOPCONS web server for consensus prediction of membrane protein topology and signal peptides. Nucleic Acids Res. 43:W401–W407. doi: 10.1093/nar/gkv485.

Tucker JK, Tenorio MJ. 2009. Systematic classification of recent and fossil conoidean gastropods : with keys to the genera of cone shells. ConchBooks: Hackenheim.

Twede VD, Miljanich G, Olivera BM, Bulaj G. 2009. Neuroprotective and cardioprotective conopeptides: an emerging class of drug leads. Curr Opin Drug Discov Devel. 12:231–239. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2878759&tool=pmcentrez&renderty pe=abstract (Accessed July 31, 2014).

Ueberheide BM, Fenyö D, Alewood PF, Chait BT. 2009. Rapid sensitive analysis of cysteine rich peptide venom components. Proc. Natl. Acad. Sci. U. S. A. 106:6910–5. doi: 10.1073/pnas.0900745106.

Uhlig T et al. 2014. The emergence of peptides in the pharmaceutical business: From exploration to exploitation. EuPA Open Proteomics. 4:58–69. doi: 10.1016/j.euprot.2014.05.003.

Van Valen L. 1973. A new evolutionary theory. Evol. Theory. 1:1–30. doi: Evolutinary Theory.

Van Valen L. 1965. Morphological Variation and Width of Ecological Niche. Am. Nat. 99:377– 390. doi: 10.1086/282379.

Vance R. 1973. More on reproductive strategies in marine benthic invertebrates. Am. Nat. 107:353–361.

Veiga ABG, Ribeiro JMC, Guimarães JA, Francischetti IMB. 2005. A catalog for the transcripts from the venomous structures of the caterpillar Lonomia obliqua: Identification of the proteins potentially involved in the coagulation disorder and hemorrhagic syndrome. Gene. 355:11–27. doi: 10.1016/j.gene.2005.05.002.

Veiseh M et al. 2007. Tumor paint: a chlorotoxin:Cy5.5 bioconjugate for intraoperative visualization of cancer foci. Cancer Res. 67:6882–8. doi: 10.1158/0008-5472.CAN-06-3948.

Venkatachalam K, Montell C. 2007. TRP channels. Annu. Rev. Biochem. 76:387–417. doi: 10.1146/annurev.biochem.75.103004.142819.

Verdes A et al. 2016. From mollusks to medicine: A venomics approach for the discovery and characterization of therapeutics from terebridae peptide Toxins. Toxins (Basel). 8. doi: 10.3390/toxins8040117.

Vetter I, Lewis RJ. 2012. Therapeutic potential of cone snail venom peptides. Curr. Top. Med. Chem. 12:1546–52. http://www.ncbi.nlm.nih.gov/pubmed/22827523 (Accessed December 18, 2013).

- 139 -

Vidal N, Hedges SB. 2005. The phylogeny of squamate reptiles (lizards, snakes, and amphisbaenians) inferred from nine nuclear protein-coding genes. Comptes Rendus - Biol. 328:1000–1008. doi: 10.1016/j.crvi.2005.10.001.

Vincler M et al. 2006. Molecular mechanism for analgesia involving specific antagonism of alpha9alpha10 nicotinic acetylcholine receptors. Proc. Natl. Acad. Sci. U. S. A. 103:17880– 17884. doi: 10.1073/pnas.0608715103.

Vonk FJ et al. 2011. Snake venom: from fieldwork to the clinic. Bioessays. 33. doi: 10.1002/bies.201000117.

Vonk FJ et al. 2013. The king cobra genome reveals dynamic gene evolution and adaptation in the snake venom system. Proc. Natl. Acad. Sci. U. S. A. 110:20651–6. doi: 10.1073/pnas.1314702110.

Wachholtz A, Gonzalez G. 2014. Co-morbid pain and opioid addiction: Long term effect of opioid maintenance on acute pain. Drug Alcohol Depend. 145:143–149. doi: 10.1016/j.drugalcdep.2014.10.010.

Wallace MS. 2006. Ziconotide: A new nonopioid intrathecal analgesic for the treatment of chronic pain. Expert Rev. Neurother. 6:1423–1428. doi: 10.1586/14737175.6.10.1423.

Warren WC et. al. WRK et al. 2008. Genome analysis of the platypus reveals unique signatures of evolution. Nature. 453:175–183. doi: 10.1038/nature07253.

Watkins M, Hillyard DR, Olivera BM. 2006. Genes Expressed in a Turrid Venom Duct: Divergence and Similarity to Conotoxins. J. Mol. Evol. 62:247–256. doi: 10.1007/s00239-005- 0010-x.

Webb CO, Ackerly DD, McPeek MA, Donoghue MJ. 2002. Phylogenies and Community Ecology. Annu. Rev. Ecol. Syst. 33:475–505. doi: 10.1146/annurev.ecolsys.33.010802.150448.

Weinstock GM et al. 2006. Insights into social insects from the genome of the honeybee Apis mellifera. Nature. 443:931–949. doi: 10.1038/nature05260.

Williams MJ et al. 2014. Obesity-Linked Homologues TfAP-2 and Twz Establish Meal Frequency in Drosophila melanogaster. PLoS Genet. 10. doi: 10.1371/journal.pgen.1004499.

Wood JN, Boorman JP, Okuse K, Bake MD. 2004. Voltage-gated sodium channels and pain pathways. J. Neurobiol. 61:55–71.

Wray GA. 1995. Evolution of Larvae and Developmental Modes. In: Ecology of marine invertebrate larvae. pp. 414–447.

Wurm Y et al. 2011. The genome of the fire ant Solenopsis invicta. Proc. Natl. Acad. Sci. U. S. A. 108:5679–5684. doi: 10.1073/pnas.1009690108.

Xu J, Zhang Y. 2010. How significant is a protein structure similarity with TM-score = 0.5?

- 140 -

Bioinformatics. 26:889–895. doi: 10.1093/bioinformatics/btq066.

Xu X et al. 2014. Proteomic analysis of the venom from the scorpion Mesobuthus martensii. J. Proteomics. 106:162–180. doi: 10.1016/j.jprot.2014.04.032.

Yang J, Zhang Y. 2015. Protein Structure and Function Prediction Using I-TASSER. Curr. Protoc. Bioinforma. 52:5.8.1-5.8.15. doi: 10.1002/0471250953.bi0508s52.

Yao S et al. 2008. Structure, dynamics, and selectivity of the sodium channel blocker mu- conotoxin SIIIA. Biochemistry. 47:10940–10949. doi: 10.1021/bi801010u.

Zdobnov EM, Apweiler R. 2001. InterProScan–an integration platform for the signature- recognition methods in InterPro. Bioinformatics. 17:847–848.

Zelditch ML, Li J, Tran LAP, Swiderski DL. 2015. Relationships of diversity, disparity, and their evolutionary rates in squirrels (Sciuridae). Evolution (N. Y). 69:1284–1300. doi: 10.1111/evo.12642.

Zhang S, Gao B, Zhu S. 2015. Target-Driven Evolution of Scorpion Toxins. Sci. Rep. 5:14973. doi: 10.1038/srep14973.

Zhang Y. 2007. Template-based modeling and free modeling by I-TASSER in CASP7. In: Proteins: Structure, Function and Genetics.Vol. 69 pp. 108–117. doi: 10.1002/prot.21702.

Zhang Y, Skolnick J. 2004. Scoring function for automated assessment of protein structure template quality. Proteins Struct. Funct. Genet. 57:702–710. doi: 10.1002/prot.20264.

- 141 -