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Jonathan by Edited and Brazil; 05503-900, neovd(1 12). remains complexity (11, rel- trait unresolved most of is evolution species the understanding interacting for of evant nature (8–10). or number strategies the infection phylogenetically Whether divergent with for guilds reserved parasite alter- units distant or, defensive (4–7), to might modular diversity) response parasites species natively, (a or species many richness parasite community against to each overall for trajectory. defending responds defenses evolutionary trait host unique trait’s evolve a a the pressures example, on specific impacts For critical func- The in have 3). of complexity can forms (2, favor varied to traits interac- expected to species tional are rise and that give selection evolution early interactions natural trait an species between placing Diverse link (1), tions. the nature in on complexity focus of maintenance and D Fundac¸ 02138; MA Cambridge, University, S Butantan, h Instituto Imunopatologia, de Ju Universidad 32306; a Parkinson L. Christopher Moura-da-Silva M. Ana Viala Louis Vincent Hogan P. Michael aailCasta Gamaliel Holding L. pitvipers Matthew American North in complex venoms more favor diets diverse Phylogenetically PNAS eateto ilgclSine,CesnUiest,Cesn C29634; SC Clemson, University, Clemson Sciences, Biological of Department etro ois mueRsos n elSgaig S Signaling, Cell and Immune-Response Toxins, of Center n o aua eeto ol rmt h evolution the promote could hypothesiz- selection when natural riverbank entangled how an ing evoked arwin 01Vl 1 o 7e2015579118 17 No. 118 Vol. 2021 od eiiaToia otrHio iiaDuao aas600 Brazil; 69040, Manaus Dourado, Vieira Heitor Doutor Tropical Medicina de ao c ˜ eateto vlto,EooyadOgnsa ilg,TeOi tt nvriy oubs H43210; OH Columbus, University, State Ohio The Biology, Organismal and Ecology Evolution, of Department | predator rzdlEtd eDrno ..300G 35010 C.P. Durango, de Estado del arez ´ neda-Gayt ˜ | toxin b m eateto oetyadEvrnetlCnevto,CesnUiest,Cesn C29634 SC Clemson, University, Clemson Conservation, Environmental and Forestry of Department g,h unrS Nystrom S. Gunnar , a,b,1 | transcriptomics f,k akJ Margres J. Mark , a,m,1 an ao .Strickland L. Jason , ´ j eieG Grazziotin G. Felipe , ue Biol Museu d hitp .Gr I. Christoph , oPuo05390 Brazil; 05503-900, Paulo ao ˜ | gc,IsiuoBtna,S Butantan, Instituto ogico, ´ itbreadth diet b a,i,5 cye .Ellsworth A. Schyler , mzPlco g. Mexico; Dgo., Palacio, omez ´ oPuo05390 Brazil; 05503-900, Paulo ao ˜ a,2 rk Hingst-Zaher Erika , unwald ¨ l ht .Rautsaw M. Rhett , .LseGibbs Lisle H. , g Laborat e ao .Jones M. Jason , oPuo05390 Brazil; 05503-900, Paulo ao ˜ b eateto ilgclSine lrd tt nvriy alhse,FL Tallahassee, University, State Florida Science, Biological of Department rod oiooi piaa nttt uatn S Butantan, Instituto Aplicada, Toxinologia de orio ´ doi:10.1073/pnas.2015579118/-/DCSupplemental at online information supporting contains article This 5 4 3 2 ulse pi 9 2021. 19, April Published 1 the under Published Submission.y Direct PNAS a is article This H.L.G., interest.y F.G.G., competing A.M.M.-d.-S., no declare M.J.M., authors The A.J.M., F.G.G. paper. R.M.R., y the and wrote J.L.S., C.L.P. I.L.M.J.-d.-A., and M.L.H., D.R.R., M.J.M., L.A.F.-d.-S., and T.J.C., data; S.A.E., J.L.S., analyzed G.S.N., M.L.H., tools; A.J.M., and reagents/analytic E.P.H., D.R.R., new R.M.R., F.G.G., contributed I.L.M.J.-d.-A., D.R.R. E.H.-Z., research; M.B., T.J.C., performed M.J.M., V.L.V., C.L.P. S.A.E., L.A.F.-d.-S., G.S.N., M.P.H., J.M.J., C.L.P. A.J.M., C.I.G., and E.P.H., G.C.-G., G.C.-G., D.R.R., R.M.R., M.B., H.L.G., J.L.S., F.G.G., T.J.C., M.L.H., A.M.M.-d.-S., M.P.H., research; I.L.M.J.-d.-A., designed A.J.M., E.H.-Z., E.P.H., M.J.M., R.M.R., J.M.J., J.L.S., C.I.G., M.L.H., contributions: Author xml,Shannon’s example, rcsl unie ytenme n bnac funique of such metrics abundance diversity Shannon’s of as and use the diver- number allows be This the the can (16–19). components complexity to by their complexity quantified because trait precisely communities linking ecological are of for interactions sity models antagonistic the in as by involved them emerging often produce traits to and required Molecular 14) genome (15). (13, the in phenotype information functional of amount final the to ing rsn drs:Dprmn fItgaieBooy nvriyo ot lrd,Tampa, Florida, South of University Biology, Integrative 32611. y of FL Department Gainesville, address: Florida, Present of University Biology, of Department address: NC Present Wilmington, College, Community AL Fear Mobile, Cape Department, Alabama, Science South address: Present of University Biology, of Department address: Present owo orsodnemyb drse.Eal [email protected] rmatthe- or [email protected] Email: addressed. be may correspondence whom To L33620.y FL 28401. 36688. [email protected]. b pce omnt,rte hnterrcns ln,i an is traits. complex alone, of of richness evolution nature the their The in than feature diver- venom. important rather reflecting of community, targets likely species physiological a species, the of in phylogenetic community gence to a response in in indicate diversity evolves results complexity The venom diets. snake that high- of history diversity Using natural phylogenetic to complexity unclear. via in changes link remain we Amer- pitvipers, favor- North traits ican in pressures complexity in complex venom selection of question measurements or resolution specific major the simple a but ing is sciences, life evolves the complexity biological Why Significance ope risaedfie ymlil opnnscontribut- components multiple by defined are traits Complex c j e ioh .Colston J. Timothy , l i EPM .. il del Villa A.C., HERP.MX Laborat eateto raimcadEouinr ilg,Harvard Biology, Evolutionary and Organismic of Department y y ai .Rokyta R. Darin , In , a coL .Junqueira-de-Azevedo M. L. acio ´ rc .Hofmann P. Erich , e rod Colec¸ de orio ´ H uin .Freitas-de-Sousa A. Luciana , NSlicense.y PNAS ne 1,2)t umrz ri opeiy For complexity. trait summarize to 20) (16, Index k y nttt ePsus Cl Pesquisa de Instituto H https://doi.org/10.1073/pnas.2015579118 e Zool oes ˜ a sdt ikmjrhistocompatibility major link to used was b lae,Clm 87,Mexico; 28973, Colima Alvarez, ´ b,4 and , a,3 gcs nttt uatn S Butantan, Instituto ogicas, iulBorja Miguel , ´ d autdd inisBiol Ciencias de Facultad nrwJ Mason J. Andrew , . y https://www.pnas.org/lookup/suppl/ nc alsBorborema, Carlos ´ ınica oPuo05390 Brazil; 05503-900, Paulo ao ˜ f d g,h , , , f Laborat ogicas, ´ oPaulo ao ˜ a,c | , f10 of 1 orio ´

EVOLUTION complex diversity with the richness of local parasite species (21, tive framework. We collected venom and venom-gland samples 22), as well as phytochemical defenses in plant leaves to the from Agkistrodon, Crotalus, and Sistrurus across North America α-diversity of herbivorous insects (16, 17). to generate the largest dataset of proteomes and venom-gland Animal venoms function by disrupting homeostatic physiolog- transcriptomes for this group to date (68 lineages). We gen- ical processes to rapidly incapacitate prey (23) and provide the erated a phylogeny by harvesting 1,525 nonvenom genetic loci opportunity to understand the forces mediating trait complexity from transcriptomes and collated diet studies on these snakes. in predator–prey interactions. As protein mixtures, venom phe- Specifically, we tested three primary hypotheses for the evo- notype complexity can be quantified through chromatographic lution of venom complexity levels (shown in SI Appendix, Fig. separation. Additionally, the venom-gland transcriptome directly S1). First, if intense pairwise coevolution favored trait com- links the venom proteome to the genotype (24–26). Transcrip- plexity, we expected to observe a negative relationship between tomes, therefore, provide a second independent measure of venom complexity and prey species diversity, as the intensity venom complexity via expressed genomic sequence complex- of coevolutionary selection is expected to be highest when ity (14). Recent studies across several venomous lineages have only a few key prey species are consumed (45, 46). In con- shown that the transcript (24), protein (25), and enzymatic activ- trast, if diffuse coevolution with multiple species exerted the ities (26) of venom are more complex when more prey classes are strongest selection on trait complexity, we predicted venom com- consumed. However, these studies do not directly test alternative plexity to be positively associated with prey species diversity, hypotheses for how prey diversity may select for either reduc- as diffuse coevolution is expected to be strongest when sev- tions or increases in venom complexity. Disentangling the roles eral prey species are important (17, 47). If prey divergence, of species richness and phylogenetic diversity of diets in the evo- rather than the simple number of prey, exerted the strongest lution of venom complexity requires taxon-wide diet and venom selection on trait complexity, we expected venom complexity characterization in a system known to exhibit adaptive evolution to be correlated with phylogenetic diversity of prey consumed of prey-specific venoms. and for prey phylogenetic diversity to explain more varia- Functional studies of snake venom have revealed several tion in complexity than prey species diversity alone (48, 49). examples of variable complexity and evolved prey specificity. Finally, a lack of any relationship between dietary ecology and For example, feeding solely on fish eggs has relaxed selection venom complexity accompanied by strong phylogenetic signal in in the sea snake Aipysurus eydouxii, resulting in a simplified venom complexity would support neutral evolution of complexity venom arsenal and frequent null and deleterious mutations in levels. venom genes (27, 28). Additionally, adaptive specialization of snake venom function occurs toward congeneric prey species Results (29) and populations (30–32). Finally, highly expressed venom Phylogenetic Relationships. We inferred phylogenies using both gene paralogs from the same snake can display taxonomic speci- species-tree and concatenation approaches (Fig. 1 and SI ficity. Paralogous three-finger toxins in the Amazon puffing Appendix, Figs. S2–S4) using sequences from 169 individuals snake (Spilotes sulphureus) are alternatively lethal to mammals representing 46 species of Agkistrodon, Crotalus, and Sistrurus or lizards (33), while snake venom metalloproteinase (SVMP) (71.8% of species-level diversity) (38). To capture maximum paralogs in the Brazilian lancehead (Bothrops neuwiedi) differen- diversity within our focal clade, we expanded our sampling to tially enact procoagulant function in blood of either mammals or include phylogenetically discrete subspecies and lineages with birds (34). Despite deep conservation of the individual metabolic described phylogenetic diversity, resulting in a final dataset of and homeostatic targets of venom (35, 36), the sequence diver- 9 Agkistrodon lineages, 5 Sistrurus lineages, and 54 Crotalus lin- gence that has accumulated in these systems appears to influ- eages. See SI Appendix, Results for an abbreviated discussion of ence venom protein function and may exert selection on venom rattlesnake relationships based on our topologies. We used these complexity. trees for phylogenetic comparative analyses of the relationship Unlike other venomous groups (24, 25), dietary ecology in between diet diversity and venom complexity. viperid snakes has been researched extensively (Dataset S1 A and B), allowing detailed quantification of dietary variation. Most Complex Genotypes Underlie Complex Phenotypes. Venom tran- vipers feed mostly on small vertebrates, with the ancestral diet scriptomic and proteomic complexities were positively related 2 of the clade likely being mammals, lizards, and frogs (37). Rat- (R = 0.45, β = 0.0004, T2,62 = 7.1, P < 0.001; Fig. 1, Inset tlesnakes (Crotalus and Sistrurus), copperheads, cottonmouths, [scatterplot]), affirming a link between the expressed genotype and cantils (Agkistrodon) comprise the largest clade of the front- and the phenotype complexity in snake venoms. The lack of a fanged venomous snake species in North America, with between stronger relationship between these metrics likely underscores 45 and 64 recognized species (38). Species range widely in dietary the nature of our sequence data as a richer source of infor- ecology. For example, the Timber Rattlesnake (Crotalus hor- mation. In high-performance liquid chromatography (HPLC), ridus) is a mammal specialist (39), whereas the exceptionally similar proteins may elute together as a single peak in chro- diverse diet of the Florida Cottonmouth (Agkistrodon piscivorus matograms, potentially underrepresenting phenotype complexity conanti) (40) includes fish, frogs, mammals, snakes, lizards, birds, at the level of individual components. Our transcriptomic com- and even turtles. The venoms of these three genera consist of plexity measure will instead quantify such differences, while between 10 and 70 proteins from 15 to 25 distinct gene families the equal weighting of all unique sequences regardless of tran- (41, 42). Their venom cocktails underlie diverse functionality, script length provides a complimentary focus on total sequence with venoms enacting neurotoxic, coagulopathic, hemorrhagic, diversity, rather than organization into transcripts. There was and myotoxic effects in accordance with their composition (41). phylogenetic signal in both transcriptomic complexity (Pagel’s λ Functional variation also exists within gene families, where neo- = 0.79 ± 0.014, P = 0.002) and proteomic complexity (λ = 0.96 functionalization has generated phospholipase A2 (PLA2) par- ± 0.009, P < 0.0001; Fig. 1), as well as marked variation among alogs that produce both cytotoxic and neurotoxic functions (43, lineages. The transcriptomic complexity underlying venoms 44) and SVMP paralogs with hemorrhagic and coagulopathic varied by a factor of 10 among lineages, with the most com- functions. plex, the Desert Massasauga Rattlesnake (Sistrurus tergeminus To test alternative hypotheses for the link between ecologi- edwardsii), expressing 45,191 effective k-mers and the simplest, cal communities and complexity, we have combined analysis of the South American Rattlesnake (Crotalus durissus terrificus), snake diets and independent, standardized venom proteomic and expressing only 4,723 effective k-mers. Venom proteomic com- venom-gland transcriptomic complexity measures in a compara- plexity also varied considerably, with the most complex being the

2 of 10 | PNAS Holding et al. https://doi.org/10.1073/pnas.2015579118 Phylogenetically diverse diets favor more complex venoms in North American pitvipers Downloaded at CLEMSON UNIV ACQ/RECEIVING on April 19, 2021 Downloaded at CLEMSON UNIV ACQ/RECEIVING on April 19, 2021 vlto oadbt ipe n oecmlxvnm has venoms S5 complex Fig. Appendix, more (SI time and over simpler occurred both toward evolution of ancestor mon effective and (28,751 peaks) (22.3 complexity protein on transcriptomic intermediate indicate peaks (54) in Tiger 5 effective function version ace the ape 8 the using being reconstructions only simplest state Ancestral having the average. ), tigris and (Crotalus peaks protein Rattlesnake effective mean a 32 with of lepidus ) lepidus (Crotalus Rattlesnake Rock Mottled hlgntclydvredesfvrmr ope eosi ot mrcnpitvipers American North in venoms complex more favor diets diverse Phylogenetically al. et Holding species. across (Inset gene venom scatterplot venom the The to of available. aligning not complexity phylogeny were reads protein of measures of and tips complexity complexity the venom complexity to transcriptome which transcriptomic next for mean nuntius, between taxa Bars and viridus indicate relationship 70%. Crotalus gray) bars than positive of (dark less significant place species values the in support each Dashes shows posterior from gray). indicate (light samples nodes regions venom on coding of circles transcript open complexity and protein estimates, mean date indicate divergence for bars) (blue CIs 1. Fig. Protein Complexity (peaks) 10 15 20 25 30 18 ae hlgn of phylogeny Dated R p 00 00 00 40000 30000 20000 10000 Transcript Complexity(kmers) <0.001 2 =0.45 Agkistrodon and cerastes, cerastes Crotalus Neogene

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10 15 20 25 30 35 Venom Protein Complexity

Fig. 2. Phylogenetic diversity of all prey species used to calculate diet diversity. Topology was produced by joining published supertrees (50–53) using divergence dates from TimeTree.org (52). The pie chart (Top Left) shows the overall percentage of recorded snake stomach contents belonging to each prey taxa, and the pie chart colors correspond to colors on the prey phylogeny. Tracks outside the tree represent the diet composition of species with the lowest (blue), nearest to average (orange), and highest (red) phylogenetic diet diversity, as measured by standardized MPD. Gray dashed lines on track mark 50% of the diet. Insets show three representative HPLC chromatograms of a venom sample from these snake lineages with absorbance values standardized to the tallest peak in each sample and a density plot of all species’ mean HPLC complexity values for which diet data were also tabulated, with values for species featured in chromatograms marked with vertical lines and color-coded as for diet tracks.

relationship (change in sample-size corrected Akaike Informa- complexity and MPD (Fig. 3), model selection supports the phy- tion Criterion [∆AICc] > 2 for all MPD models compared to logenetic diversity hypothesis for the evolution of more complex complementary species diversity models; Table 1). Furthermore, venom traits across these predators. For brevity, we discuss pgls the MPD models were significantly better fits to the data com- analyses using our Astral-III species tree here, with replicate pared with intercept-only null phylogenetic generalized least analyses using other phylogenetic hypotheses reported in Table squares (pgls) models (∆AICc > 2), suggesting neutral evo- 1. Venom proteomic complexity and MPD were positively cor- 2 lution alone does not explain variation in venom complexity. related (β = 0.73, R = 0.17, T2,26 = 2.3, P = 0.028; Figs. 2 and Together with positive slopes for the relationship between venom 3), but we did not detect a significant association between species

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(45), (58, traits accel- predators of can evolution which 31), the processes (30, erate prey key resistant diversity Other with prey complexity. coevolution venom in include evolved trends in stable reflected long-term are if avail- address prey Future could in variation 31). ability temporal (30, and geographic adaptation estimating local work venom even complexity, intraspecific an venom and measure assuming MPD would prey sampling between local- relationship venom stronger matched and that diet possible between is ities It variation undersampled. population-level species animals, undoubtedly variation adult is whole ontogenetic sampling to for by venoms controlled locations in we single while from Similarly, scale ranges. geographic var- here in used studies ied Diet vary (68–70). can composition snakes venom of and populations diet among both caveat that One is complexity. approach venom our of in variation context. the of that portion on stantial based of of system, evolution this the all, in in complexity role not trait a play of but likely communities processes some, unmeasured where While represent (67), diversity factors contextual varying several on dent present the in shown results functional the paper. such of that much hypothesize explains We specialization (34). phyloge- prey in distinct targets physiological netically disrupt to facil- function complexity venom venom itates more in evolving where paralogs examples toxin concrete three-finger procoagulant of of ity specificity phylogenetic in from taxonomic venoms in paralogs The SVMP simple venoms 66). from complex (65, continuum generalists and a specialists is taxonomic phylogenetic result with proteins The venom for specificity. generalism selects phylogenetic preference toxic- suggest dietary in complexity in here trends venom shown These between (64). MPD/MCCD relationships prey and the of with set combined diverse highly ity is more that a venom to predicts and diet lethal 63) snakes’ relevant (62, a in prey functionally with breadth natural decreases taxonomic of from potency distance venom accrual phylogenetic snake increasing the targets, venom with in changes associated is gence venoms. some nico- of and for specificity relevance (60) taxonomic functional variation channels carries (61), ion sequence receptors as acetylcholine vivo, such tinic proteins, in conserved unexplored other largely in snake fac- is in coagulation homologs SVSPs divergent tor for to specificity extend SVSP may While with predators. that interaction (59) their proteins for heterologous variation prey coagulation of sequence species’ consequences of factor functional homologs coagulation snake well-documented on are a based There their that MCCD factors. 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(88) existence plant families the lar plant by of richness explained species be the and herbiv- of insects diversity the orous species example, explain dietary For between help diversity. relationship can biological negative in taxa, trends between global distance important with varia- physiological accumulates and/or tion for functional context where phylogenetic phylogenetic eco- generalism, The elevating dietary pressure. by selection specifically that maintained a as 17), suggest is generalism (16, quantified and complex- generalism have evolves logical trait complexity we molecular trait trends predicts molecular evolutionary that of The form species the ity. prey (in diversity of nature (species number the alone) the is than rather it diversity) interactions, phylogenetic species of diverse context to responses modular forming in species. expression roles 87) prey and relative 86, variation, the allelic (31, variation, framework, variation number comparative copy cru- a gene be in of will testing, paralogs for venom cial of characterization functional combined with data sequence The whole-genome generated. of scores availability increasing complexity paralogs, parse the of to to counts variation inability length, expression its sequence refer- and by coding a of limited of contributions is the free approach with complexity this abundance, sequence However, relative ence. quantifying their and of bases benefit 60 the selection of balancing strings unique as or as genes underexpression of the sets The to wanes. entire lead of may deletion mam- diets or genomic reptiles simplified as and such and scenario, mals, guilds, SVSP, simplest prey clustered SVMP, the proteins phylogenetically modular of Under of a module (83–85). composed in a families units varies multiple (82). functional venom chromosomes from separate that different with evidence on fashion, mounting occur is regions, families genomic There gene unlinked these multiple as across ecologically selection to response mediated variable a highlights tested families gene multigene of dynamics. example evolutionary an class-specific coevolv- such provide with plants We families parsnip protein (20). webworms in with classes ing toxin occurring unprecedented, phytochemical evolu- not evolutionary are Different among rapid (71). classes vWF toxin by the among on CTLs dynamics sites binding to the resistance of these tion coevolved and rattlesnakes, have including pitvipers, mammals on feed (Didelphidae) to in Opossums known function are their predators. by sim- coevolving influenced between be against given also complexity defense issue may sequence CTLs an The in not CTLs. variation and likely of are 81), levels variation (80, ilar extant evolution protein of of levels rates qua- but constrain as Forming can multiple 79). such of chains composed (78, receptors protein sites GP1b ligand-binding platelet and or to structures (vWF) ternary bind factor instead Willebrand but von families enzymes act gene and as heterodimers venom not multimeric other as only the function major They and Two standout: families. 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EVOLUTION community phylogenetic diversity will produce more complex and longer k-mer sizes (SI Appendix, Fig. S8), and simulated transcriptomes defense traits on average within a community. Indeed, taxa rang- showed they are sensitive to single mutations and small changes in expres- ing from invertebrates to plants tend to be better chemically sion and gene copy number (SI Appendix, Methods and Fig. S9). Variance in defended against multiple enemies in the tropics (89), where these transcriptome complexity scores was not clearly associated with sam- phylogenetic diversity also happens to be highest in many taxa ple size and showed significant variation among lineages (SI Appendix, Fig. S10). We therefore used the mean transcriptomic complexity value for each (90, 91). Overall, our results suggest the following general princi- lineage in downstream comparative analyses, with sample size ranging from ple governing trait evolution in the planet’s entangled bank: In a 1 to 17 individuals (mean = 2.8 individuals). given ecological community, species’ phylogenetic diversity plays a large role in shaping the evolutionary trajectory of complex Measuring Venom Phenotypic Complexity. To measure phenotypic complex- traits of community members. ity, we subjected 155 whole venom samples to HPLC on a Shimadzu Prominence HPLC System (Shimadzu Scientific Instruments). We measured Materials and Methods absorbance at 220 nm for 140 min (see SI Appendix, Methods for full analyt- Sample Collection and Transcriptome Analyses. Snakes were collected from ical run parameters) and used the Chromatopac feature in the Lab Solutions the field in numerous localities across the United States, Mexico, and Brazil version 5.92 software (Shimadzu) to call HPLC peaks between 10 and 125 (Dataset S2 A and B). Venom was collected from each snake for proteomic min, where peaks generally represent separate venom protein classes or iso- analysis and venom glands were dissected 4 d post-venom collection for RNA forms. We retained all called peaks contributing at least 0.2% of total peak sequencing. Transcriptomes were assembled following Holding et al. (92). area and then recalculated the proportional area of each peak on the final See SI Appendix, Methods for full details. peak set. Venom protein complexity was then calculated by using the area of each HPLC peak as input into the equation for Shannon’s H Index, where Phylotranscriptomic Analysis of Nontoxin Genes. We extracted nontoxin cod- pi was the proportional area of peak i, and converting H to effective num- ing regions from the Trinity assembly of a subsample of up to five individuals ber of protein peaks as exp(H). We used the mean protein complexity value of each known lineage within our sampled transcriptomes (169 individu- for each lineage in downstream analyses, with sample size ranging from 1 als from the 68 focal taxa and 6 individuals comprising 6 outgroup taxa) to 5 individuals (mean = 2.1). using BUSCO version 3.0.1 (93, 94) searches against the “tetrapoda odb9” set of 3,950 genes from OrthoDB (95). BUSCO identified 3,633 of these Measuring Snake Diet Diversity. We measured diet diversity by collating pub- genes as “complete and single copy” sequences in at least one individ- lished gut content data from Agkistrodon, Crotalus, and Sistrurus (Dataset ual. We produced alignments of each BUSCO locus using MAFFT version S1 A and B). We tabulated counts of prey items identified to the species 7.313 (96, 97) with the GINSI algorithm to prevent overalignment: “mafft level. If prey were identified only to the genus level or higher, the counts –adjustdirectionaccurately –allowshift –unalignlevel 0.8 –leavegappyregion of these prey items were proportionally distributed among the prey identi- –maxiterate 10.” Alignments were trimmed using trimAl version 1.4.15 fied to species. For example, if a diet study included 3 entries—3 Peromyscus (98) using the “automated1” setting, manually inspected alignments in sp., 10 Peromyscus leucopus, and 5 Peromyscus maniculatus—we would pro- Geneious version 10.2.4 (https://www.geneious.com), kept loci present in at portionally assign the 3 genus-level observations among the species-level least 50% of samples for phylogenetic analysis. This resulted in 1,525 loci observations, resulting in 12 P. leucopus and 6 P. maniculatus as input to totaling 1,997,137 bp. later analysis. Some sampled diet species were not present in available ver- We used RAxML version 8.2.10 (99) to infer a best tree estimate for each tebrate phylogenies, and counts of these prey species were assigned to the gene based on 100 independent searches and a GTRGAMMA model. Nodal nearest congener present in the phylogeny. To control for different prey support values for the best tree estimate were attained through 1,000 boot- sample size in diet studies, we generated 1,000 random subsamples for 15 strap replicates. The best tree for each gene was then used as input for prey items. These species and count data were used to calculate Shannon’s species-tree inference in Astral-III version 5.6.1 (100). Gene tree nodes with H Index of snake diet diversity, where pi was the proportion of all diet items less than 10% bootstrap support were collapsed to polytomies (101), and we composed of species i, and H was converted to effective number of prey incorporated multiple individuals per lineage in species-tree inference (102). species as exp(H). The mean effective number of species from the 1,000 To estimate divergence dates with credibility intervals, we dated our trees subsamples was used as the prey species diversity measure for each snake with the autocorrelated relaxed clock based on the penalized-likelihood lineage. We required at least 10 diet items to be reported in the literature approach as implemented in TreePL (103) (see SI Appendix, Methods for to consider a snake lineage for comparative analyses, resulting in N = 29 more details). lineages for hypotheses testing. We incorporated evolutionary distance as a second measure of diet diver- Measuring Toxin Transcriptomic Complexity. We quantified expressed venom sity by calculating abundance-weighted MPD of snake diet diversity, using gene transcriptomic complexity by developing a k-mer–based measure sim- the ses.mpd function from picante (110). To incorporate phylogeny we gen- ilar to those used in assessments of microbial community diversity (104), erated a combined phylogeny of all listed prey species using published, counting appearances of all unique k = 60 bp sequences in the subset of dated supertrees for mammals (50), squamate reptiles (53), and amphibians venom-gland RNA-seq reads mapping to venom gene transcripts. Venom (51), as well as median node-depth consensus phylogenies for the archosaur k-mer complexity is unaffected by known sources of bias in venom studies and invertebrate taxa produced using www.timetree.org (52). We united attempting to call paralogs versus alleles and filter chimeric assemblies in these separate trees into one at the median node depth for their most large, rapidly diverging multigene families (92). We first identified assem- recent common ancestors, again provided by www.timetree.org. bled contigs containing toxin sequences using Basic Local Alignment Search Tool (BLAST) (105) searches (blastn) (e = 10−8) against a curated set of 804 Statistical Analyses. All analyses were conducted in R version 3.6.1 (111). To viperid toxin coding sequences (CDSs) spanning the known viperid venom calculate phylogenetic signal for our venom complexity metrics, we used gene families. We also used this curated set of coding sequences to calculate our Astral-III species tree (Fig. 1 and the pmc function in the pmc R package) the mean sequence length of the SVMP, SVSP, PLA2, and CTL gene fami- (112) with 1,000 bootstraps for confidence intervals. To assess relationships lies. Portions of contigs with a BLAST hit to a toxin CDS were concatenated between each diet complexity measure and each venom complexity mea- into a master database of all 191 adult snakes sampled, creating a compre- sure, we used pgls analyses as implemented in the pgls function of the caper hensive database of sampled toxin sequence diversity across Agkistrodon, R package (113), with lambda set to “ML,” various phylogenetic trees named Crotalus, and Sistrurus. We then used the mem algorithm within bwa (106) in Table 1 to accommodate alternative phylogenetic hypotheses, and venom to align the cleaned reads from each sample to this master database, pro- transcriptomic or proteomic complexity as the dependent variable. The inde- viding us with the set of reads that align to toxins for each snake. We pendent variables were prey species diversity or MPD. We conducted our randomly sampled 4 million toxin reads per snake using seqtk (107) to pgls analyses both with and without the South American rattlesnake C. standardize sampled reads per individual and then used the count utility durissus terrificus in the dataset, as it presented a large standardized resid- in Jellyfish version 2.2.1 (108) to count all unique 60-mers in the sampled ual (−2.8) and high leverage in having both the simplest transcriptome (SD = toxin reads. Finally, we used the 60-mer counts to calculate expressed toxin −3.0), simplest proteome (SD = −2.4), and the third lowest MPD of any lin- Ps eage in our pgls dataset. We thus sought to ensure that our pgls results were sequence complexity as the Shannon Diversity Index [H = − i=1 piln(pi)] of the k-mer count table from Jellyfish, where i is the ith k-mer and pi is not driven by this datapoint. Regression results were robust to its removal the proportion of all k-mers counted comprising k-mer i. Shannon’s H was (SI Appendix, Fig. S11). We present the relationships between venom com- translated to effective number of k-mer as exp(H) for downstream analyses plexity and diet diversity in the Results section with this lineage removed. (109). These transcriptomic complexity measurements were robust to shorter These results are thus unbiased by potential altered relationships between

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B. diet: Li, and M. activities toxic 27. venom snake of Coevolution Arbuckle, K. Davies, L. E. 26. rc,L lna,L ihrs n he nnmu eiwr mrvdthe improved reviewers anonymous three and General S. manuscript. Richards, by of L. suggestions Alencar, Helpful Institute L. P20GM109094). an (Grant National Price, NIH by Clemson the the of supported the Sciences from is Medical Award from which Computing Development Facility, resources High-Performance Genomics Institutional computing and Palmetto with Bioinformatics Clemson University supplemented the and resources computing by Cluster American Parallel the and J.L.S.). provided through and A.J.M., (A.J.M. were Fund History J.L.S., Memorial Natural (to Roosevelt of Museum Theodore Grant Southwestern the Research the and McCarley (J.L.S.), M.B.), Naturalists Grant Grants-in-Aid- of Research Xi Sigma SnakeDays Association (J.L.S.), a Inc. (J.L.S.), Research Biotic of-Research Grant Prairie Tecnologia M.B.), y Ciencia (to de 247437 Nacional through Consejo University (C.L.P.), Clemson funds Professor), startup Visiting faculty provided Manaus a was as A.M.M.-d.-S. support (to Additional I.L.M.J.-d.-A.). Fundac¸by A.M.M.-d.-S., DEB and F.G.G., and M.L.H.); and (to (to 1145978 J.L.S. 5 1711141 DEB PRFB funded C.L.P.), H.L.G.), which (to (to Fundac¸ 1638872 and 1822417 DEB C.L.P. D.R.R.), DEB (to (to and 1638902 pro- 1161228 1638879 was DUE DEB support Grants A.J.M.), Financial NSF Coleccion Biologicas. 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