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Understanding the joint roles of amino acid sequences variation of proteins and differential expression during adaptive evolution is a fundamental, yet largely unrealized, goal of evolutionary biology. Here, we use phylogenetic path analysis to analyze a comprehensive venom gland transcriptome dataset spanning three genera of pitvipers to identify the functional genetic basis of a key adaptation (venom complexity) linked to diet breadth. Analysis of gene family-specific patterns reveal that, for genes encoding two of the most important venom proteins (SVMPs and SVSPs), there are direct, positive relationships between sequence diversity, evenness of expression, and increased diet breadth. Further analysis of gene family diversification for these proteins showed no constraint on how individual lineages achieved toxin gene sequence diversity in terms of patterns of paralog diversification. In contrast, another major venom protein family (PLA2s) showed no relationship between venom molecular diversity and diet breadth. Additional analyses suggest that other molecular mechanisms-such as higher absolute levels of expression-are responsible for diet adaptation involving these venom proteins. Broadly, our findings argue that functional diversity generated through sequence and expression variation determine adaptation in key components of pitviper venoms, which mediate complex molecular interactions between the snakes and their prey.
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Venom Gene Sequence Diversity and Expression Jointly
Shape Diet Adaptation in Pitvipers
Andrew J. Mason ,*
,1
Matthew L. Holding ,
2
Rhett M. Rautsaw ,
3
Darin R. Rokyta ,
4
Christopher L. Parkinson ,
3,5
and H. Lisle Gibbs *
,1
1
Department of Evolution, Ecology, and Organismal Biology, The Ohio State University, Columbus, OH, USA
2
Life Sciences Institute, University of Michigan, Ann Arbor, MI, USA
3
Department of Biological Sciences, Clemson University, Clemson, SC, USA
4
Department of Biological Science, Florida State University, Tallahassee, FL, USA
5
Department of Forestry and Environmental Conservation, Clemson University, Clemson, SC, USA
*Corresponding authors: E-mails: mason.501@osu.edu; gibbs.128@osu.edu.
Associate editor: Anne Yoder
Abstract
Understanding the joint roles of protein sequence variation and differential expression during adaptive evolution is a
fundamental, yet largely unrealized goal of evolutionary biology. Here, we use phylogenetic path analysis to analyze a
comprehensive venom-gland transcriptome dataset spanning three genera of pitvipers to identify the functional gen-
etic basis of a key adaptation (venom complexity) linked to diet breadth (DB). The analysis of gene-family-specic
patterns reveals that, for genes encoding two of the most important venom proteins (snake venom metalloproteases
and snake venom serine proteases), there are direct, positive relationships between sequence diversity (SD), expres-
sion diversity (ED), and increased DB. Further analysis of gene-family diversication for these proteins showed no
constraint on how individual lineages achieved toxin gene SD in terms of the patterns of paralog diversication.
In contrast, another major venom protein family (PLA
2
s) showed no relationship between venom molecular diversity
and DB. Additional analyses suggest that other molecular mechanismssuch as higher absolute levels of expression
are responsible for diet adaptation involving these venom proteins. Broadly, our ndings argue that functional
diversity generated through sequence and expression variations jointly determine adaptation in the key components
of pitviper venoms, which mediate complex molecular interactions between the snakes and their prey.
Key words: genotypephenotype, venom, diversity, adaptation, diet breadth.
Introduction
Adaptation at the molecular level can occur through
changes in protein-coding sequence or the patterns of
gene expression, and identifying the relative roles of these
mechanisms is central to understanding trait evolution
(Barrett and Hoekstra 2011;Rockman 2012;Rausher and
Delph 2015;Smith et al. 2020). Although both mechanisms
play important roles in evolution (Carroll 2005,2008;
Hoekstra and Coyne 2007), there are differing expectations
for their relative contributions to complex traits.
Protein-coding mutations can produce novel functions, es-
pecially when coupled with gene duplications that reduce
selective constraints (Ohno 1970;Hoekstra and Coyne
2007). Regulatory changes serve critical roles in morpho-
logical evolution, and the time and tissue-specic nature
of gene expression is expected to reduce the pleiotropic ef-
fects of regulatory variation, facilitating the evolution of
novel adaptations (Carroll 2008;Stern and Orgogozo
2008). Moreover, because there are more pathways for
altering the expression of a gene compared with altering
its sequence, regulatory mechanisms present larger
mutational targets, which lead to differences in their evo-
lutionary rates and lability compared with protein-coding
regions (Rokyta, Wray et al. 2015;Besnard et al. 2020).
Understanding how protein-coding and/or regulatory
changes mediate realized adaptive function has signicant
implications for identifying general evolutionary processes
linking genomic variation to adaptive phenotypes (Smith
et al. 2020). This requires the development and use of de-
tailed genotypephenotype maps that are linked to rea-
lized ecological variation from diverse species groups.
Traditionally, genotype-to-phenotype maps for adap-
tive traits have been constructed using a forward genet-
icsapproach which focuses on experimental analyses of
segregating genetic variation in model species (Barrett
and Hoekstra 2011). Forward genetics has proved highly
successful for identifying the molecular basis of many
adaptations, but is limited by the need to work with model
species amenable to either experimental manipulation or
observational studies that link segregating genetic variants
to phenotypes with statistical association methods
(Tanksley 1993;Marigorta et al. 2018). These methods
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Mol. Biol. Evol. 39(4):msac082 https://doi.org/10.1093/molbev/msac082 Advance Access publication April 13, 2022 1
are incompatible with many adaptive phenotypes of inter-
est to evolutionary biologists because such traits may oc-
cur in species that cannot be interbred or where the
phenotypic variation of interest may only occur between
species (Smith et al. 2020). Studies to date are limited to
a small number of species in which the forward genetics
paradigm can be applied, which raises questions about the
generality of their results, especially at macroevolutionary
scales.
A recently proposed approach to overcome these issues
is to use comparative phylogenetic methods to analyze
clade-wide genomic datasets to link phenotypic variation
to its genetic underpinnings (Nagy et al. 2020;Smith et al.
2020). This approach builds on the increasing availability
of genomic datasets and uses the long-standing compara-
tive phylogenetic methods to identify associations between
functionally relevant genetic and phenotypic
variation while accounting for a shared ancestry (Smith
et al. 2020). Although lacking the experimental certainty
of forward genetic approaches, comparative phylogenetics
methods broaden the scope of studies of adaptive pheno-
types and can yield new insights into how evolutionary me-
chanisms mold the genetic basis of phenotypic variation
(Pease et al. 2016;Hu et al. 2019;Sackton et al. 2019).
Comparative methods like phylogenetic path analysis that
test for a causal structure among a suite of compared vari-
ables have recently been used to understand genomeen-
vironment interactions in multiple groups (von
Hardenberg and Gonzalez-Voyer 2013;Voyer and
Garamszegi 2014;Guignard et al. 2019;Chak et al. 2021).
Phylogenetic path analysis, therefore, provides a useful
method to apply to genome scale data for analyzing func-
tional genetic variation from multiple species, especially
when the genetic and phenotypic variations are closely
tied to ecological functions.
Animal venoms are a model system for investigating the
molecular mechanisms that underlie adaptive traits be-
cause of the unusually direct connection between venom
genes, phenotypes, and adaptive function that allows com-
prehensive investigation across multiple levels of biological
organization (Gibbs and Rossiter 2008;Casewell et al. 2012,
2014;Rokyta, Margres, et al. 2015). Whole venoms are
complex adaptive phenotypes that can be broken down
into distinct componentsindividual proteins making
up the venomand linked to known molecular underpin-
nings, and their functional impacts (Casewell et al. 2013;
Zancolli and Casewell 2020). Several of the major gene
families that contribute to venom occur as tandemly
arrayed gene islands in distinct genomic locations
(Sanggaard et al. 2014;Gendreau et al. 2017;Casewell
et al. 2019;Schield et al. 2019;Margres et al. 2021). This
genomic architecture means the evolution of venom genes
and the pathway from genotype to a complex phenotype
can be investigated in multiple gene families across a set of
venomous species. These features make venom an excep-
tional system for examining how complex adaptive pheno-
types are assembled and evolve, and for understanding the
impact of phenotypic complexity on ecological function
(Holding, Drabeck, et al. 2016;Sunagar et al. 2016;
Arbuckle 2020;Giorgianni et al. 2020;Zancolli and
Casewell 2020;Holding et al. 2021).
Studies of venomous species have yielded numerous im-
portant insights into how molecular adaptations arise. For
example, molecular and ecological studies in cone snails
have provided evidence for the dynamic expansion of tox-
in gene families, evidence of pervasive positive selection,
and correlations between venom compositions and diet
(Duda and Palumbi 1999,2004;Duda and Remigio 2008;
Remigio and Duda 2008;Chang and Duda 2012,2014;
Phuong et al. 2016;Li et al. 2017). In spiders, venom com-
plexity has been shown to vary based on feeding ecologies
(Pekár et al. 2018). Several studies on individual snake spe-
cies have also evaluated the roles of sequence and expres-
sion evolution in venom toxins and indicate that both
mechanisms facilitate phenotypic evolution, possibly in
different evolutionary or ecological contexts (Margres
et al. 2016;Margres, Bigelow et al. 2017;Margres, Wray
et al. 2017;Hofmann et al. 2018;Rautsaw et al. 2019;
Zancolli et al. 2019).
At the macroevolutionary scale, a recent study by
Holding et al. (2021) used k-mer based metrics from
venom-gland transcriptomes and whole venom RP-HPLC
data from 68 primarily North American pitvipers (rattle-
snakes and moccasins) to show a strong positive relation-
ship between the molecular complexity of venom and
phylogenetic diversity in diet. This study identied the mo-
lecular complexity of venom as an adaptive phenotype
that is correlated with a key ecological trait (diet breadth
[DB]) in these snakes, although their reliance on k-mers
prevented the specic genetic mechanisms from being
identied. Nonetheless, the availability of a comprehensive
molecular dataset on venom variation for a phylogenetic-
ally diverse snake clade opens the door to using a com-
parative phylogenetics approach to identify the specic
genetic mechanisms underlying this adaptive trait.
Here, we analyze fully assembled venom-gland tran-
scriptomes for the 68 lineages represented in Holding
et al. (2021) using the phylogenetic path analysis (von
Hardenberg and Gonzalez-Voyer 2013;Voyer and
Garamszegi 2014) to dissect the relative roles of gene com-
position, protein sequence diversity (SD), and expression
diversity (ED) as they relate to DB in these snakes. In
addition, we capitalized on the nature of venom as a mix-
ture of proteins from distinct multi-gene families to deter-
mine if separate or concerted evolutionary processes
contribute to venom diversity from separate regions of
the genome. Finally, for two families where toxin SD
showed signicant associations with dietary breadth, we
tested whether lineages show evidence for similar or di-
vergent evolutionary pathways for generating protein
SD. Our results show that both SD and expression vari-
ation mediate adaptation in pitviper venoms, but the
roles of SD and expression vary for different components
of this complex phenotype. These results highlight how
complex molecular traits can evolve via alternative routes
to adaptation.
Mason et al. · https://doi.org/10.1093/molbev/msac082 MBE
2
Results
Venom-Gland Transcriptomes
We assembled and annotated venom-gland transcrip-
tomes for the 214 individuals comprising 68 rattlesnake
and moccasin lineages used in Holding et al. (2021),
with specimen representation for each lineage varying
from 1 to 10 individuals (supplementary tables S1 and
S2, Supplementary Material online). Individual snakes
expressed on average 78.4 transcripts encoding toxin pro-
teins (range =32128). Using the annotated transcrip-
tomes, we calculated gene content (GC) as the total
number of toxins, toxin SD as the effective number of ami-
no acid 20-mers (the number of unique k-mers that would
represent equivalent diversity with uniform occurrence,
see Materials and Methods), and toxin ED as the effective
number of expressed toxin transcripts (the number of ex-
pressed toxins that would represent equivalent diversity
with uniform expression, see Materials and Methods).
Lineage-specic estimates of these measures were ob-
tained by averaging across samples, though variation in
these metrics was apparent within several lineages
(supplementary gs. S1S3, Supplementary Material
online).
To verify that technical variation in sample treatment
(e.g., differences in sequencing the depth and numbers
of assembled transcripts) did not bias statistical inference,
we tested for a relationship between these variables and
the number of recovered toxins. Although we found
some evidence of a marginally signicant correlation be-
tween the number of recovered toxins and the number
of merged reads among samples (P=0.063,
supplementary g. S4, Supplementary Material online),
this relationship explained a relatively small amount of
variation (R
2
=0.016). Similarly, we found no signicant re-
lationship between the number of expressed transcripts
and recovered toxins (P=0.664, R
2
,0.001,
supplementary g. S5, Supplementary Material online).
Importantly, we found no evidence of an interaction be-
tween the number of merged reads (P=0.369,
supplementary table S3, Supplementary Material online)
or expressed transcripts with lineage assignment (P=
0.618, supplementary table S4, Supplementary Material
online), indicating that inferences made among lineages
are unbiased by technical variation.
We tested for evidence of phylogenetic signal among
GC, SD, and ED metrics with BlombergsKand lambda.
GC, SD, and toxin ED all showed evidence of signicant
phylogenetic signal based on estimates of BlombergsK
(GC =0.47, ED =0.38, SD =0.46), and both GC and SD
showed evidence of signicant phylogenetic signal based
on lambda (supplementary table S5 and g. S6,
Supplementary Material online). Evidence of phylogenetic
signal in these metrics indicates a moderate degree of pre-
dictability in the venom genotype-to-phenotype map
based on the degrees of evolutionary divergence among re-
lated snake lineages.
Path Analysis
To examine how expression and protein-coding sequence
evolution affect the dynamics of venom and diet diversity,
we tested 10 path models dening hypothesized relation-
ships among GC, SD, ED, and DB (supplementary g. S7,
Supplementary Material online) for 30 snake lineages, for
which we had reliable diet data. Here, DB corresponded
to the mean phylogenetic distance (MPD) measure of
diet used in Holding et al. (2021), who showed that snake
DB as a function of its phylogenetic diversity of prey spe-
cies was a better predictor of venom complexity than
prey species richness alone. Phylogenetic path models re-
presented varying roles of SD and ED as having direct or
indirect effects on DB, independently or in combination,
whereas GC was modeled as acting indirectly through
these variables.
We found the highest support for Model 3 in which SD
had a moderate, positive correlation with DB, and surpris-
ingly, ED had a moderate negative correlation with DB (g.
1,supplementary g. S8 and table S6, Supplementary
Material online). Hence, snakes with more diverse, but
less evenly expressed sequences had broader diets. As
expected, GC was positively correlated with SD and ED
in this model, showing a strong indirect association with
DB mediated through SD and expression. However, sup-
port for Model 3 was not absolute. Model 1 was
within the 2 C statistic Information Criterion (CICc) of
Model 3, indicating similar statistical support (g. 1,
supplementary g. S3, Supplementary Material online).
Unlike Model 3, Model 1 did not include a connection be-
tween ED and DB, and showed a weaker relative relation-
ship between SD and diet (supplementary g. S8,
Supplementary Material online). Because of the overall
similarity of Model 3 and Model 1, the weighted average
model we recovered was similar to Model 3 (g. 1).
In both top-performing models, SD and ED predicted
changes in diet. Importantly, although our path models
modeled venom SD and ED as predictors of DB, these rela-
tionships do not imply directional causality. Rather, the dir-
ect positive correlation between SD and DB indicates that
increased sequence variation is associated with more di-
verse diets. Sequence variation, in turn, is heavily inuenced
by the underlying GC. In contrast, a more even, and hence,
diverse toxin expression is associated with a narrower diet.
Next, we sought to explore this initially counterintuitive re-
sult for ED in more detail.
We suspected that the analysis of pooled data may ob-
scure more subtle relationships between expression and
DB for individual toxin gene families which, because they
are found at distinct genomic locations in these snakes
(Schield et al. 2019), represent semi-independent repli-
cates of how venom complexity evolves. To examine
whether the patterns of complexity detected for the whole
venom phenotype are representative of the patterns found
in individual toxin families, we tested the possible path
models in four tandemly arrayed toxin families: C-type lec-
tins (CTLs), phospholipase A
2
s (PLA
2
s), snake venom
Venom Gene Sequence Diversity and Expression · https://doi.org/10.1093/molbev/msac082 MBE
3
metalloproteases (SVMPs), and snake venom serine pro-
teases (SVSPs). These toxin families have previously shown
heterogeneous relationships between expressed transcript
sequence complexity (measured in k-mers) and DB, with
three of the families having positive relationships, whereas
CTLs displayed no relationship (Holding et al. 2021).
Here, we report substantial differences in the optimal
models for family-specic path analyses. In particular, the
FIG.1.Path analysis for models of venom evolution and DB for an overall venom model (a) and the CTL (b), PLA
2
(c), SVMP (d), and SVSP (e)
toxin families. Path models test for varying effects among GC, SD, ED, and DB and are dened in supplementary gure S7, Supplementary
Material online. The barplots show model weights for CICc comparisons. Numbers adjacent to the bars represent p-values for the test of
the null hypothesis that the model ts the data structure. Models with P,0.05 are statistically untenable. The best performing and averaged
models are shown at the right with path coefcients (partial regression coefcients standardized to the other independent variables) indicated
by numbers adjacent to arrows. Dashed lines in the graphical models indicate negative relationships. Averaged models were calculated based on
a model weight of all top models within two CICc.
Mason et al. · https://doi.org/10.1093/molbev/msac082 MBE
4
analyses of SVMP and SVSP families separately showed sup-
port for models where both SD and ED had direct positive
correlations with DB (g. 1dand e). Thus, in contrast with
the overall analyses, within each of these toxin families,
more diverse patterns of expression were associated with
increased DB. All competitive models for the SVSP family
also supported a direct relationship between SD and ED.
Models with opposing directions of the relationship be-
tween SD and expression showed equivalent support, as ex-
pected, but varied in effect estimates (g. 1e). This nding
indicates an interacting effect of the sequence and expres-
sion evolution in SVSPs where increased SD and more even
toxin expression are linked.
In contrast, for analyses of the CTL and PLA
2
gene fam-
ilies, the top ranked model set included the null model,
which did not include any direct connection between se-
quence and ED and DB (g. 1band c). This result suggests
that functional diversityin CTLs and PLA
2
s does not inu-
ence the ability of these snakes to consume phylogenetic-
ally diverse prey but that other characteristics, such as
the total expression or the presence of paralogs with specif-
ic functions, may play more important roles for these toxin
family.
Variation in Expression
To explore how other aspects of venom composition are
associated with DB, we compared how absolute expression
patterns (rather than complexity in expression) varied
among and within major families, and tested for correla-
tions with DB. As expected, the number and mean
expression of toxins varied signicantly among families
with PLA
2
s exhibiting the lowest number of toxins per
lineage (P,0.001, supplementary g. S9 and table S2,
Supplementary Material online), but the highest mean
expression levels (P,0.001, supplementary g. S9,
Supplementary Material online). PLA
2
s also exhibited a
positive correlation between mean expression and DB (P
=0.03, R
2
=0.38) (g. 2). This relationship becomes even
stronger when a single, high leverage outlier (the South
American Rattlesnake, Crotalus durissus) is excluded
from the analysis (P,0.001, R
2
=0.68; g. 2).
These relationships explain why the global path analysis
shows a negative relationship between ED and DB. The in-
dices used for path analyses measure diversity as a function
of richness and relative abundance. ED specically is de-
rived from the number of expressed transcripts and their
relative expression (evenness), where we consider more
even expressions to be more complex. Because PLA
2
s con-
sist of only a few, often highly expressed transcripts, they
exert a disproportionate effect on expression evenness.
Thus, lineages with more complex diets with more highly
expressed PLA
2
s can show less diverse expression patterns
overall. In sum, the strong positive relationship between
the mean PLA
2
expression and DB suggests that abun-
dance rather than compositional diversity of PLA
2
s facili-
tates eating a broader range of prey.
Mechanisms of Gene-Family Diversication
Our analysis showed that the SVMP and SVSP venom gene
families both showed evidence of positive relationships
p=0.03
p=0.38
p=0.37
p=0.49
Mean Expression (clrTPM)
Mean Expression (clrTPM)
Mean Expression (clrTPM)
Mean Expression (clrTPM)
Diet Breadth
Diet BreadthDiet Breadth
Diet Breadth
CTL
PLA2
SVMP
SVSP
R2=0.26
R2=0.38
R2=0.28
R2=0.28
p<0.001
R2=0.68
FIG.2.Comparison of DB and mean expression for CTLs, PLA
2
s,
SVMPs, and SVSPs. Mean expression is measured as center log-ratio
transformed TPM. Black dashed lines indicate the lines of best t in-
ferred with phylogenetic linear models. The red dotted and dashed
line and lower R
2
and P-value in PLA
2
s displays the line of best tif
the outlying datapoint for C. durissus is excluded.
Venom Gene Sequence Diversity and Expression · https://doi.org/10.1093/molbev/msac082 MBE
5
between amino acid SD and DB. In large gene families, gene
SD is inextricably linked to gene duplications and diver-
gence which collectively produce diverse paralogs. Most
pitviper lineages express multiple SVMP and SVSP toxin
paralogs and the diversity of these toxin assemblages can
lend insight into the patterns of gene diversication.
Ancient duplications may be observed as highly divergent
paralogs in modern taxa, but recent duplications also oc-
cur in many venom gene families (Wong and Belov 2012;
Giorgianni et al. 2020). The assemblage of toxin paralogs
in the venom of a given lineage may consist primarily of
conserved ancient paralogs, less divergent recent paralogs,
or a combination (g. 3a). Each of these scenarios can gen-
erate sequence variation, but whether either is overrepre-
sented as an evolutionary pathway in venoms is not clear.
To assess what patterns of paralog diversication char-
acterized venom gene diversity, we used a similar method
to that of Chang and Duda (2014) to compare the within-
family toxin diversity of each individual against the within-
family toxin diversity across Agkistrodon,Crotalus, and
Sistrurus. Specically, we calculated phylogenetically
weighted, standardized mean genetic distance (MGD)
for two toxin families where we expected paralog diversi-
cation could have an ecological impact acting through
SD: SVMPs and SVSPs. The standardized values of MGD re-
present the diversity of toxins in a toxin family (i.e., SVMPs
or SVSPs) expressed by an individual compared with the
total diversity of the toxin family. In the context of a
gene family, low estimates of assemblage MGD would oc-
cur through the assemblages of highly similar
(phylogenetically clustered) paralogs, whereas high esti-
mates of MGD would result from assemblages that were
very diverse (phylogenetically dispersed) (g. 3b). This ap-
proach, therefore, allowed us to infer whether diversity in
these families arose primarily through expression/reliance
on highly divergent genes such as ancient or highly derived
paralogs versus clusters of more recently duplicated, less
differentiated paralogs (g. 3b).
We observed a range of negative and positive standar-
dized MGD values for SVMPs and SVSPs, with slightly posi-
tive means for the overall distribution for both families
(mean SVMP =0.29, median SVMP =0.39, mean SVSP =
0.21, median SVSP =−0.03, supplementary gs. S10 and
S11, Supplementary Material online). These results indi-
cate that on average, expressed genes tend to be more di-
vergent than would be expected by chance alone.
However, both the SVMP and SVSP distributions appeared
multimodal (g. 4) and Wilcoxon signed rank tests found
the distribution of SVMP standardized MGD values to be
different than 0 (P=0.005), although SVSPs were not (P=
0.247). In the case of SVMPs, two clear peaks were visibly
centered at approximately 2 and 0.5, with some indica-
tion that the larger peak could be considered multimodal
with peaks occurring at 0, and slightly ,1(
g. 4).
Interestingly, the lower peak (centered at approximately
2) in the SVMP distribution was composed exclusively
of Agkistrodon contortrix and A. piscivorus lineages, sug-
gesting that reliance on a particular subset of SVMP para-
logs may be characteristic of the A. contortrix +A.
piscivorus lineage. In SVSPs, the two apparent modes of
Species 1
moderate
gene loss/
duplication
high
duplication
gene loss/
Ancestor
high
divergence/
limited
duplication
Species 2 Species 3
Species3-Gene 1
Species2-Gene 1
Species3-Gene 2
Species2-Gene 2a
Species2-Gene 2b
Species1-Gene 3a
Species1-Gene 3b
Species1-Gene 3c
Species3-Gene 3
0
Standardized MGD
Species 2
Species 3
Species 1
(a)
(b)
FIG.3.Graphical representation
of how MGD informs the un-
derstanding of the patterns of
gene-family diversication. (a)
Three hypothetical lineages
descending from a common
ancestor with differing pat-
terns of gene diversication.
Individual genes are shown as
colored circles on gray lines.
(b) Hypothetical gene-family
phylogeny derived from the
three lineages in (a) and a re-
presentation of hypothetical
MGD metrics based on the
phylogeny.
Mason et al. · https://doi.org/10.1093/molbev/msac082 MBE
6
the distribution appeared centered at approximately 0.5
and slightly ,1(g. 4), though there was no apparent
taxonomic pattern associated with either mode.
Under scenarios where SVMP and SVSP assemblages are
evolutionarily constrained to emphasize either ancient
orthologs or recent paralogs, we would expect distributions
centered above or below zero, respectively. In contrast, the
observed patterns suggest that the SVMP and SVSP evolu-
tion occurs through a combination of gene duplication, di-
vergence, and loss rather than either extreme mechanisms
of high duplication or high divergence (g. 4). Moreover,
the multimodal patterns of each distribution indicate
that, whereas there is substantial variation in the diversity
of assemblages, subsets of taxa exhibit especially similar
or especially diverse SVMP and SVSP assemblages.
Expression-weighted MGD was highly correlated with stan-
dardized MGD for both metrics (g. 4), demonstrating that
lineages did not emphasize the expression of more or less
diverse paralogs in their total toxin assemblage.
Although we found no evidence of constraint on the
genetic mechanisms for generating SD, it is possible that
different mechanisms of generating diversity could facili-
tate broader diets. For example, more genetically diverse
toxin assemblages might affect a wider phylogenetic diver-
sity of prey, increasing DB. To test this possibility, we com-
pared the MGD estimates (which represented more and
less diverse toxin assemblages) to DB estimates for each
lineage. However, we found no evidence for a relationship
between DB and MGD (supplementary g. S12,
Supplementary Material online), indicating that the genet-
ic diversity of toxin assemblages (i.e., emphasis on highly
diverged vs. recently diverged paralogs) did not constrain
the ecological function of venoms.
Discussion
Our results demonstrate that both SD and expression vari-
ation in toxin genes jointly shape variation in venom, a cru-
cial adaptive trait related to DB in pitvipers. Previous
studies have provided evidence for positive selection act-
ing on toxin genes implicating the proteins they encode
in trophic adaptions (Duda and Palumbi 1999;Li et al.
2005;Gibbs and Rossiter 2008;Sunagar and Moran 2015;
Haney et al. 2016). Similarly, there is substantial indirect
evidence for the role of expression variation in single toxins
mediating trophic adaptations (Gibbs and Chiucchi 2011;
Aird et al. 2015;Margres et al. 2016;Margres, Wray et al.
2017;Barua and Mikheyev 2019;Barua and Mikheyev
2020). Our study represents an advance by using compara-
tive methods to simultaneously link the contribution of
each molecular mechanism to phenotypic variation direct-
ly related to diet across diverse lineages. For certain key ve-
nom proteins, SD and expression appear to act in a
hierarchical manner to generate the realized adaptive
phenotype (whole venom composition). Diversity in pro-
tein sequence denes the fundamental functional se-
quence space for toxin proteins and expression variation
brings about the realized toxin phenotype as a rened sub-
set of sequence space. Such a model has been proposed to
explain diversity in other venomous systems and variation
in expression more broadly (Raser and OShea 2005;
Lluisma et al. 2012). We suspect that a similar relationship
will hold for other adaptive phenotypes whose function is
driven by additive effects among component proteins.
The positive relationship between toxin SD and DB re-
inforces the idea that target-mediated interactions at the
protein sequence level are a fundamental mechanism me-
diating predatorprey interactions through molecular
phenotypes (Gibbs et al. 2020;Holding et al. 2021).
Holding et al. (2021) demonstrated a correlation between
overall toxin diversity and divergence in homologous ve-
nom targets involved in interactions with a single venom
toxin (SVSPs). Our results build on this nding by demon-
strating that both increased sequence and ED jointly
underlie more diverse toxin compositions. A higher diver-
sity of toxins may increase the number and type of physio-
logical targets, and by extension, the number of
standardized MGD SVMPs
standardized MGD SVSPs
Expression weighted
standardized MGD
Expression weighted
standardized MGD
slope=0.82
p < 0.001
R
2
=0.75
standardized MGD
(a)
(b)
standardized MGD
slope=1.33
p<0.001
R
2
=0.76
FIG.4.Density distributions of standardized MGD for the SVMP (a)
and SVSP (b) gene families. Correlations between expression-
weighted and unweighted standardized MGD are shown as insets
with P-values and R
2
values inferred by linear regression. Dashed
red lines show the tted slopes and solid black lines show the
one-to-one line.
Venom Gene Sequence Diversity and Expression · https://doi.org/10.1093/molbev/msac082 MBE
7
physiologically distinct prey taxa that venom can affect
(Davies and Arbuckle 2019). We suggest that these same
mechanisms underlie positive correlations between ve-
nom and diet diversity that have been documented in
other venomous animals such as snails and spiders
(Phuong et al. 2016;Pekár et al. 2018).
We have modeled the relationship between DB, venom,
and its genetic underpinning as a unidirectional genotype
phenotype relationship. This approach was effective for
identifying how particular genetic mechanisms shape ve-
nom evolution but has limitations. In particular, path ana-
lyses cannot model bidirectional relationships as might be
most appropriate in a feedback or coevolutionary system.
This is potentially important because venoms that func-
tion primarily for prey capture likely evolve in complex, co-
evolutionary arms races with their prey in a variety of
ecological scenarios (Barlow et al. 2009;Holding, Biardi,
et al. 2016;Davies and Arbuckle 2019;Gibbs et al. 2020).
Deciphering if and how prey characteristics like molecular
resistance to venoms (Holding et al. 2018;Gibbs et al.
2020) shape snake venoms through coevolutionary inter-
actions would be a valuable direction for future studies.
Our analysis of gene-family evolution in SVMP and SVSP
paralogs shows no dominant mode of paralog duplication
in achieving SD in toxin coding sequences. Instead, diverse
toxin repertoires have emerged through the retention of
deeply divergent paralogs, duplication, and comparatively
minor divergence of paralogs, or a combination of these pro-
cesses with equal likelihood. These ndings are consistent
with a previous study assessing expressed toxin assemblages
in cone snails. Of the four species compared in cone snails
(Chang and Duda 2014), two species expressed mostly simi-
lar paralogs (genetic underdispersion), one species expressed
mostly divergent paralogs (genetic overdispersion), and one
species fell between these extremes. Thus, in both snakes
and cone snails, there is little constraint on the evolutionary
pathway to achieving high SD in toxin genesrather all
pathways seem equally likely. Moreover, we found no asso-
ciation between the genetic diversity of toxin assemblages
(MGD) and DB, indicating that having few, highly divergent
paralogs versus many, less divergent paralogs did not have
functional consequences for prey acquisition.
Given that venom targets basal physiological processes
such as the coagulation cascade (Serrano 2013) and neuro-
transmission sites (Fry et al. 2009), it may be that relatively
few amino acid substitutions can rene venom targeting
for divergent prey tissues. The further divergence in
more ancient paralogs may reect the combined effects
of neutral evolution (Aird et al. 2017) and renements
to protein function not tied to prey specicity, such as
structural stability of the protein (Sunagar et al. 2014),
neofunctionalization for novel physiological targets
(Whittington et al. 2018), and modications during pair-
wise coevolution to avoid inhibitor molecules of resistant
prey (Holding, Biardi, et al. 2016;Margres, Bigelow, et al.
2017). Broadly, diet expansion appears possible through se-
quence variation derived from multiple possible pathways
rather than any specic type of variation.
Importantly, the variation in modes of adaptions that
we observed among different toxin families and the differ-
ences in their contribution to a complex phenotype dem-
onstrate genomic heterogeneity in response to selective
pressures associated with prey acquisition. In our study,
the SVMP and SVSP toxins appear to inuence DB through
the maximization of toxin SD and ED. However, we did
nd some evidence of nonindependence of these mechan-
isms in SVSPs, where phylogenetic path analyses suggested
direct interactions between SD and ED. Such a case may
reect scenarios, where differentially expressed toxins are
experiencing differential rates of sequence evolution or
cases where selection to increase expression leads to in-
creased gene duplication and differentiation
(Kondrashov and Kondrashov 2006;Kondrashov 2012;
Aird et al. 2015;Margres, Bigelow, et al. 2017).
In contrast, the path analysis of PLA
2
sshowednosupport
for a SD mediated relationship with diet. Rather, PLA
2
s
showed a strong positive relationship between mean ex-
pression and DB, suggesting that an investment in PLA
2
ex-
pression is associated with increased prey diversity. Why
PLA
2
s exhibit this distinct relationship between diet and ex-
pression is not clear, but one possibility is that it reects a
broad functional efcacy of the same proteinsacross diverse
taxa. PLA
2
s exhibit a wide range of functional effects includ-
ing muscular and nervous system targeted neurotoxicity
and myotoxicity (Gutiérrez and Lomonte 2013), which
may be less specialized, but similarly effective among phylo-
genetically distinct prey groups. Thus, the role of PLA
2
sin
shaping diet diversity might be better described by a mech-
anism whereby a given toxin or toxin family is broadly ef-
fective in a variety of scenarios at the cost of being less
effective at targeting specicdietitems.Alternatively,
PLA
2
s may be especially effective against taxonomic groups
that tend to be or are exclusively associated with broader
diets, although evidence for this hypothesis is mixed and
in need of further investigation (Lomonte et al. 2009).
The functions and effects of CTL diversity on diets remain
unclear, as we found no evidence of an association between
genetic variation and DB in this toxin family. The deviation
of CTLs from other snake venom families is consistent with
earlier tests comparing the relationship between DB and
mRNA k-mer diversity among toxin families (Holding
et al. 2021). Notably, CTLs are unique among snake venom
toxins for functioning as multimeric heterodimers, which
could impose unique restrictions on their evolvability or de-
couple a direct relationship between genetic and functional
variation (Arlinghaus and Eble 2012;Eble 2019).
In conclusion, our study demonstrates the power of
combining high-resolution transcriptomic datasets with
comparative approaches to identify the molecular under-
pinnings of key adaptations in phylogenetically diverse
nonmodel and emerging-model organisms. Our ndings
suggest both SD in protein-coding genes and how this di-
versity is regulated and ultimately expressed play key roles
in mediating functional variation in the components of ve-
nom, but that the role of these mechanisms is not ubiqui-
tous for all components. Molecular traits such as animal
Mason et al. · https://doi.org/10.1093/molbev/msac082 MBE
8
venoms, phytochemicals, and immune gene products are
at the interface of antagonistic interactions among much
of the planets biodiversity. Our study demonstrates that
the genomic pathways to adaptive variation in these sys-
tems are as multifaceted and complex as the phenotypes
themselves.
Materials and Methods
Bioinformatic Processing of Transcriptomes
We assembled and annotated venom-gland transcrip-
tomes for 214 individuals from 68 rattlesnake and mocca-
sin lineages used in Holding et al. (2021). All data
processing was conducted using the Owens computing
cluster at the Ohio Supercomputing Center (Center
1987). Briey, raw sequence data were trimmed using
TrimGalore! v.0.6.4 (Krueger 2015) and merged using
PEAR v0.9.6 (Zhang et al. 2014). Merged reads were used
to generate three transcriptome assemblies for each indi-
vidual following the recommendations of (Holding et al.
2018). We used Trinity v.2.9.1 (Grabherr et al. 2011) and
Seqman NGen 14 with default settings, and Extender
v1.03 (Rokyta et al. 2012) with an overlap value of 120, a
minimum seed quality of 30, replicates value of 20, and a
minimum of 20 passes. These three assemblies were com-
bined into a single master assembly and annotated with
ToxCodAn (Nachtigall et al. 2021).
Annotated transcriptomes were subjected to several l-
ters to reduce the inclusion of erroneously recovered tran-
scripts. First, a custom python script, ChimeraKiller v.0.7.3
(https://github.com/masonaj157/ChimeraKiller) was used
to lter out likely chimeric sequences based on the distri-
bution of reads across each site in the coding region.
Second, transcripts were ltered for incomplete coding re-
gions and putatively premature stop codons. Third, we l-
tered out sequences with unreliable read coverage. These
were dened as sequences with ,10×coverage for
.10% of the sequence. Finally, we removed transcripts
from the four largest snake toxin families (CTLs, PLA
2
s,
SVMPs, and SVSPs) with transcript per million (TPM) esti-
mates ,300, which may have been assembled due to bar-
code misassignment during sequencing. All python scripts
used in transcriptome ltering steps are available on
GitHub at https://github.com/masonaj157/Statistical_
Analyses_For_Phylogenetic_Comparisons_of_North_
American_Pitviper_Transcriptomes.
After ltering, transcripts were clustered at a 98% simi-
larity using cd-hit-est v.4.8.1 (Fu et al. 2012) to cluster al-
leles or very recent paralogs (Hofmann et al. 2018;
Strickland et al. 2018). This represented the nal transcrip-
tome assembly for each sample. To estimate transcript ex-
pression, merged reads for each individual were mapped to
their nal transcriptome using Bowtie2 (Langmead and
Salzberg 2012) as implemented in RSEM v.1.3.3 (Li and
Dewey 2011). At this stage, we excluded one sample, C.
durissus SB0275, from downstream analysis because it
had an unusually low number of raw reads which resulted
in a low-quality transcriptome assembly.
Using the nal transcriptome and estimated expression,
we calculated three metrics characterizing genetic sources
of complexity in venom toxins: (1) GC, (2) toxin amino
acid SD, and (3) ED. We calculated GC of the transcriptome
as the total number of unique toxin transcripts recovered in
the nal transcriptomes. We use GC as an estimate of the
number of distinct loci present in a given sample. Because
the venom phenotypes interaction with prey is a function
of protein composition, we characterized toxin SD through
amino acid 20-mer content. For each individual, we trans-
lated toxins, counted all unique 20-mers (script available
on the project GitHub), and summarized amino acid diver-
sity with Shannons diversity index (H) converted to effect-
ive numbersof k-mers. We assume this measure captures the
overall functional diversity in protein-coding sequences pre-
sent in a transcriptome. Finally, to estimate ED, we calculated
ShannonsHper specimen treating toxins as individuals
and TPM as counts,which were converted to effective
numbers of transcripts. For this measure of ED, higher values
represent more even expression across transcripts, and
therefore, greater functional diversity. These metrics were
then averaged among specimens belonging to the same lin-
eage to attain lineage-level estimates that were used in sub-
sequent analyses. Further details on the calculation of each
index are provided in the supplementary Material,
Supplementary Material online.
We assessed the possible inuence of technical varia-
tions, such as variation in sequencing effort and transcrip-
tome completeness, on toxin transcript recovery by testing
for correlations between GC versus the number of reads
and the total numbers of expressed transcripts with linear
models implemented with the lm function in R. To further
ensure that these technical sources of variation did not af-
fect downstream analyses through phylogenetic biases, we
also tested for an interaction between lineage and either
the read numbers or total numbers of expressed tran-
scripts on toxin GC with two linear models implemented
in R and summarized with the Anovafunction of the
car v.3.0-10 package (Fox and Weisberg 2019).
We tested whether our calculated variables for venom
diversity exhibited evidence of phylogenetic signal as was
found for the whole venom phenotype by testing for the
signicance of BlombergsKand lambda, two common
metrics of phylogenetic signal. BlombergsKassesses the
variance among species compared with the expected vari-
ance under Brownian motion, whereas lambda is a tree
scaling parameter with an expected value of 0 if there is
no correlation among species and 1 if correlation matches
Brownian motion. For each variable, we assessed the
phylogenetic signal and tested for a signicant phylogenet-
ic signal using the phylosigfunction of phytools (Revell
2012) specifying either method =Kor method =lamb-
daand test =TRUE.
Phylogenetic Path Analysis
To test for possible causal relationships between DB and
molecular sources of venom variation, we evaluated a
Venom Gene Sequence Diversity and Expression · https://doi.org/10.1093/molbev/msac082 MBE
9
range of phylogenetic path models for the 30 pitvipers
with reliable diet information (Holding et al. 2021) using
the R package phylopath (van der Bijl 2018). We tested
10 models representing different hypotheses regarding
the direct and indirect inuences of GC, SD, ED (dened
as above), and DB (as measured by the standardized
MPD of preysee Holding et al. 2021)(supplementary
g. S7, Supplementary Material online). We used MPD of
prey as our measure of DB because Holding et al. (2021)
found that this estimate of diet showed the strongest posi-
tive relationship to different measures of venom complex-
ity likely because it incorporates information on functional
diversity of venom targets in prey. Values for this index
have a positive relationship with DB with higher values in-
dicating broader diets. Generally, these models incorpor-
ate varying roles of SD and ED as directly or indirectly
predicting DB, independently or in combination, whereas
GC acted indirectly through these variables. This frame-
work, where venom variables predict diet breath is consist-
ent with a hierarchical genotype phenotype
ecological-outcomeframework (Barrett and Hoekstra
2011), which models how species adapt to their environ-
ments. Importantly, this model allows the cumulative vari-
ation of GC, SD, and ED cumulatively to predict DB, but
should not be taken to imply directionality in the ve-
nomdiet association (supplementary methods,
Supplementary Material online).
Because the cumulative sequence and expression diver-
sity are partially a function of what genes are expressed,
they covary with one another. To account for this covari-
ance, we included the direct effects of GC on SD and ED in
all tested models. A model which only included the effects
of GC on SD and ED, but no relationship between the SD
and ED on diet diversity was used as the null model to ac-
count for any consistent correlation that is otherwise un-
related to diet (supplementary g. S7, Supplementary
Material online). Likewise, because the effect of differential
GC can only be realized in the venom phenotype through
changes in toxin SD and/or expression, no models included
a direct relationship between the GC and DB.
All path models were estimated under a lambda model
of evolution and compared using CICc. The framework for
CIC was proposed by Cardon et al. (2011) and has recently
been established for use in the phylogenetic path analysis
(von Hardenberg and Gonzalez-Voyer 2013;Voyer and
Garamszegi 2014). Briey, CICc is calculated using a mod-
elsCstatistic, a number of parameters, and a correction
for small sample size (Voyer and Garamszegi 2014).
Under this framework, models with the same numbers
of variable relationships but different directionalities are
expected to show similar statistical support, but their dif-
fering effect estimates may still be informative. Because a
single model was not statistically preferred over all other
models, we also estimated a weighted average model with
weights determined from model likelihoods. All paths with-
in comparably performing models (i.e., those within two
CICc) were averaged. We also obtained condence intervals
for path coefcient estimates (partial regression coefcients
standardized to the other independent variables) with 500
bootstraps. The parameters provided to the phylo_path
function were the predened model set, the data frame
of venom and DB variables, the calibrated phylogeny, and
the model specication model =lambda.All other para-
meters were left as defaults.
In addition to performing the phylogenetic path ana-
lysis for the overall venom dataset (all toxin classes com-
bined), we also examined variation among the patterns
of evolution within four major toxin families: CTLs,
PLA
2
s, SVMPs, and SVSPs which represent major compo-
nents of venom in these snakes (Holding et al. 2021). For
each family, we restricted the dataset to toxins assigned
to that family based on ToxCodAn annotation and esti-
mated GC, SD, and ED. Each family was subsequently
tested with the phylogenetic path analysis using the
same methods that had been applied to the whole dataset.
Variation in Expression
Phylogenetic path analyses found counterintuitive and
conicting results for the role of ED at the whole venom
level compared with what was recovered for the SVMP
and SVSP families. Because ED can be decomposed into
the roles of richness (number of transcripts) and relative
expression of each transcript, we hypothesized that differ-
ences in the number and expression of toxins in highly ex-
pressed toxin families would explain the trends observed in
the path analyses. To assess how transcript numbers and ex-
pression varied among large, highly expressed toxin families,
we compared transcript numbers and mean toxin expres-
sion in CTLs, PLA
2
s, SVMPs, and SVSPs. We then tested
for a correlation between expression and DB in these fam-
ilies to identify the disproportionate drivers of ED.
First, to account for the compositional constraints of ex-
pression estimates, we performed a centered log-ratio
(CLR) transformation of TPM data for each individual.
The CLR transformed TPM values were then used in all
subsequent comparisons of expression. We then calcu-
lated the mean expression of transcripts in the CTL,
PLA
2
, SVMP, and SVSP families. For a few samples, no tox-
ins were recovered for a particular gene family (i.e., CTLs,
PLA
2
s, SVMPs, or SVSPs) and their toxin numbers and ex-
pression values were encoded as NA. As a failure to recover
a toxin could occur because of stochastic variation in tran-
scriptome assembly or our conservative approach to toxin
ltering, such samples were excluded from the analysis of
that gene family. To attain lineage-specic estimates, we
averaged the number of expressed transcripts and mean
expression of individuals in a phylogenetic lineage. We
tested for the overall differences in the numbers of ex-
pressed toxins and mean toxin expression among toxin
families with an ANOVA in R treating toxin family as the
independent variable and lineage as a block variable.
Differences among treatments were tested with
Bonferroni corrected post hoc t-tests. Finally, to determine
if any variation in expression was associated with DB, we
tested for relationships between DB and mean toxin
Mason et al. · https://doi.org/10.1093/molbev/msac082 MBE
10
expression within each toxin family with a phylogenetic
linear regression implemented with phylolm v.2.6 (Ho
and Ane 2014).
Evolution of Genetic Diversity of SVMP and SVSP
Paralogs
Our path analyses showed a direct relationship between
toxin SD and DB. To explore how SD was generated at
the gene level for these toxins, we used an approach pro-
posed by Chang and Duda (2014), which uses community
phylogenetics indices to characterize the diversity of a tox-
in assemblage against the total diversity of a gene family
in this case, the total diversity of SVMP or SVSP paralogs
observed in Agkistrodon,Crotalus, and Sistrurus. As individ-
ual snakes normally express several SVMP and SVSP para-
logs, metrics such as standardized MGD can be calculated
for each gene family in each individual. These indices iden-
tify where on a continuum that ranges from a high diver-
gence between distinct paralogs to a limited divergence
between related paralogs, a given set of expressed tran-
scripts falls. This permits an indirect but quantitative infer-
ence of the evolutionary processes in terms of gene family
and sequence evolution.
To conduct these analyses on our data, we rst com-
piled translated mRNA sequences for all recovered toxins
in each family and generated a gene-family alignment
using MUSCLE v3.8.1551 (Edgar 2004). We then generated
separate maximum-likelihood gene-family phylogenies for
the SVMP and SVSP gene families using iqtree (Nguyen
et al. 2015). Evolutionary models were selected for each
family using iqtrees ModelFinder feature and we recov-
ered branch support estimates with 1000 ultrafast boot-
straps. These full gene-family phylogenies represented
the full diversity of SVMPs and SVSPs observed among
all Agkistrodon,Crotalus, and Sistrurus. Using these two
trees, we calculated standardized MGD for the SVMP
and SVSP gene families for each individual using the
ses.mpd function in the picantepackage in R (Kembel
et al. 2010). The resultant standardized MGD values repre-
sented the relative diversity of SVMP or SVSP paralogs ex-
pressed by a given individual compared with the total
diversity of SVMP or SVSP paralogs in Agkistrodon,
Crotalus, and Sistrurus. To account for the possible role
of expression variation in altering realized the diversity of
toxin assemblages, we also calculated expression-weighted
standardized MGD using the TPM values of each toxin as
abundance estimates. Standardized and expression-
weighted MGD values were then averaged across indivi-
duals for lineages with multiple representatives for lineage-
level estimates of standardized MGD. Additional details on
the calculation of MGD and weighted MGD are provided
in the supplementary material, Supplementary Material
online.
Using the standardized MGD values, we estimated
whether expression weighting had a strong effect on altering
diversity and we tested for a relationship between standar-
dized MGD, SD, and DB. We tested for differences between
the standardized and expression-weighted MGD with a
standard linear regression and R
2
estimate using the lm
function in R. Because distributions appeared multimodal,
we also tested whether each distribution was signicantly
different than 0 with a one-sided Wilcoxon signed rank
test with the wilcox.textfunction in R. To determine if
the genetic diversity of toxin assemblages was associated
with venom evolution, we then tested for relationships be-
tween standardized MGD and SD with phylogenetic linear
regression using the phylolmpackage in R.
Data Availability
The data underlying this article are available in the article
or on the GenBank SRR and SRA databases under
the accession numbers provided in supplemental tables
S1 and S2, Supplementary Material online. The data on
the metrics of phylogenetic diet complexity were collected
from and are available in Holding et al. (2021). Copies of
the input data les and R script used for data analysis
are available on GitHub at: https://github.com/
masonaj157/Statistical_Analyses_For_Phylogenetic_
Comparisons_of_North_American_Pitviper_
Transcriptomes.
Supplementary Material
Supplementary data are available at Molecular Biology and
Evolution online.
Acknowledgments
This study was funded by the National Science Foundation
(DEB 1638872 to H.L.G., DEB 1638879 and DEB 1822417 to
C.L.P., and DEB 1638902 to D.R.R.). We thank Matthew
Hahn and Samarth Mathur for their comments on the manu-
script. We gratefully acknowledge the Ohio Supercomputing
Center which provided the high-performance computing re-
sourcesusedinthisstudy.Animaliconsusedingures were
retreived from PhyloPic and were originally provided by Bill
Bouton, T. Michael Keesey, Steven Traver, Beth Reinke,
Natasha Vitek, and Blair Perry. Bill Bouton and T. Michael
Keesey graciously granted permission to use the snake icon
presented in g. 1.
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