HIGHLIGHTED ARTICLE| INVESTIGATION

Survey of Global Genetic Diversity Within theDrosophila Immune System

Angela M. Early,*,†,1 J. Roman Arguello,†,2 Margarida Cardoso-Moreira,†,3 Srikanth Gottipati,†,4

Jennifer K. Grenier,† and Andrew G. Clark*,†

*Department of Ecology and Evolutionary Biology, and †Department of Molecular Biology and Genetics, Cornell University, Ithaca,New York 14853

ABSTRACT Numerous studies across a wide range of taxa have demonstrated that immune genes are routinely among the mostrapidly evolving genes in the genome. This observation, however, does not address what proportion of immune genes undergo strongselection during adaptation to novel environments. Here, we determine the extent of very recent divergence in genes with immunefunction across five populations of Drosophila melanogaster and find that immune genes do not show an overall trend of recent rapidadaptation. Our population-based approach uses a set of carefully matched control genes to account for the effects of demographyand local recombination rate, allowing us to identify whether specific immune functions are putative targets of strong selection. Wefind evidence that viral-defense genes are rapidly evolving in Drosophila at multiple timescales. Local adaptation to bacteria and fungi isless extreme and primarily occurs through changes in recognition and effector genes rather than large-scale changes to the regulationof the immune response. Surprisingly, genes in the Toll pathway, which show a high rate of adaptive substitution between theD. melanogaster and D. simulans lineages, show little population differentiation. Quantifying the flies for resistance to a generalistGram-positive bacterial pathogen, we found that this genetic pattern of low population differentiation was recapitulated at thephenotypic level. In sum, our results highlight the complexity of immune evolution and suggest that Drosophila immune genes donot follow a uniform trajectory of strong directional selection as flies encounter new environments.

KEYWORDS evolution; local adaptation; population genetics; immunity; Drosophila melanogaster

A current challenge in evolutionary biology is to under-stand the genetic changes that drive local phenotypic

adaptation (Hereford 2009; Stapley et al. 2010; Stephan2016). In recent years, numerous studies have addressed thischallenge by examining genome-wide changes in allele fre-quencies across clines or between populations (Turner et al.2008; Stapley et al. 2010; Lamichhaney et al. 2012; Pespeniet al. 2012; Hubner et al. 2013). In limited instances, suchgenome-wide patterns could even be connected back to spe-

cific phenotypic traits or environmental variables, greatlyincreasing our understanding of the genetic processes under-lying phenotypic evolution (Turner et al. 2010; Jones et al.2012; Jeong and Di Rienzo 2014). While analytic approachesdiffer, these studies often rely on the detection of outlier locithat display patterns of high population differentiation orother extreme signatures of local selection. Such approachesare particularly adept at detecting single loci that have expe-rienced strong selective sweeps.

Immune genes are often evolutionary outliers in suchstudies, displaying fast rates of evolution and high popula-tion differentiation across multiple taxa including humans(Fumagalli et al. 2011; Daub et al. 2013; Quintana-Murci andClark 2013), Daphnia (McTaggart et al. 2012), mosquitoes(Waterhouse et al. 2007; Crawford et al. 2010), and bees(Chavez-Galarza et al. 2013; Erler et al. 2014). Across Dro-sophila species, immune genes are known to evolve fasterthan the genome average (Sackton et al. 2007), and onshorter timescales, both genome-wide studies and studiesof individual genes have highlighted examples of immune

Copyright © 2017 by the Genetics Society of Americadoi: 10.1534/genetics.116.195016Manuscript received August 29, 2016; accepted for publication October 28, 2016;published Early Online November 4, 2016.Supplemental material is available online at www.genetics.org/lookup/suppl/doi:10.1534/genetics.116.195016/-/DC1.1Corresponding author: Genomic Center for Infectious Diseases, Broad Institute of MITand Harvard, 415 Main St., Cambridge, MA 02142. E-mail: ame54@cornell.edu

2Present address: Center for Integrative Genomics, University of Lausanne, 1015Lausanne Switzerland.

3Present address: Zentrum für Molekulare Biologie der Universität Heidelberg,69120 Heidelberg, Germany.

4Present address: Translational Medicine and Think Team, Otsuka PharmaceuticalDevelopment and Commercialization, Inc., Princeton, NJ 08540.

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genes displaying unusually high population differentiationacross Drosophila melanogaster populations (Juneja andLazzaro 2010; Fabian et al. 2012; Hubner et al. 2013). Sim-ilarly, clinical studies have identified numerous immunegenes displaying high levels of population differentiation(Kolaczkowski et al. 2011b; Fabian et al. 2012).

These studies highlight the major role pathogens play inshaping the evolution of their hosts. But, while such obser-vations suggest that certainDrosophila immune genesmay besubject to unusually strong, spatially variable selection, theyprovide only a partial picture of how immunity, as an inte-grated complex phenotypic trait, is evolving. “Immune com-petence” is in reality a suite of phenotypes that are affectednot only by pathogen pressures but also by environmentalfactors and genotype-by-environment interactions (Lazzaroet al. 2008; McKean et al. 2008; Lazzaro and Little 2009;Howick and Lazzaro 2014). While their resistance to similartypes of pathogens may be weakly correlated (Lazzaro et al.2006), flies show no evidence of cross-resistance to distinctpathogen classes (Kraaijeveld et al. 2012). Similarly, resis-tance is often decoupled from tolerance, highlighting thenumerous physiological processes that influence host fitness(Ayres et al. 2008; Ayres and Schneider 2009). Further com-plicating the process of adaptation, trade-offs involvingimmune defense and other key life history traits are docu-mented (Kraaijeveld et al. 2001; McKean et al. 2008; Ye et al.2009), as are behavioral traits that lie outside the canonicalimmune system (Kacsoh et al. 2013; Babin et al. 2014). Suchfactors may lead to a scenario where populations experiencebroad shifts in immune regulation as they confront differentoptimal immune strategies in different environments. Forsuch a complex trait, local adaptation may occur through aprocess of polygenic selection, where multiple variants expe-rience weak selection leading to small allele frequencychanges (Pritchard and Di Rienzo 2010; Pritchard et al.2010; Turchin et al. 2012). Still, the literature contains nu-merous examples of single alleles that carry strong pheno-typic effects against specific pathogens (Magwire et al. 2011;Sironi et al. 2015).

Here, we seek to determine the extent and routes of recentD. melanogaster immune adaptation. As populations adapt tonovel environments do they experience large-scale changesacross immune genes, or alternatively, is immune adaptationdriven by a subset of rapidly evolving genes? Do we findchanges across all immune processes or does a limited num-ber of pathogen types drive adaptation? To answer thesequestions, we undertake a comprehensive analysis of im-mune gene diversity and divergence across five populationsof D. melanogaster. We compare known immune genes withgenomic controls matched for size, genome location, andlocal recombination rate—factors known to affect patternsof polymorphism and the efficacy of selection (Larracuenteet al. 2008; Comeron et al. 2012; Castellano et al. 2016).Withthis approach, we identify not only putative single-genetargets of local selection but also examine whether entirepathways and gene classes show evidence of heightened

selection. In addition, we compare patterns of interspecificdivergence in Drosophila immunity genes with patterns ofpopulation-level differentiation and find both similaritiesand differences between the results of these two differentanalyses.

Materials and Methods

Gene and fly line selection

Through literature searches, we assembled a list of 361 geneswith well-supported immune function, many of which haveappeared in previous large-scale studies (Sackton et al. 2007;Obbard et al. 2009).When possible, we assigned each gene tothe immune pathway(s) or process(es) in which it functions(Figure 1 and Table 1). In the humoral response—regulatedby the Toll and immune-deficiency (IMD) pathways—pat-tern recognition receptors bind common bacterial and fungalmembrane components, triggering the downstream produc-tion of antimicrobial peptides (AMPs) and other microbicidalcompounds. The cellular response coordinates the activitiesof specialized hemocytes such as phagocytes, which engulfforeign particles or necrotic cells, as well as lamellocytes andcrystal cells, which participate in defense against parasiticwasps through the encapsulation and melanization of thedeposited eggs. Finally, antiviral defense largely operatesthrough RNA interference (RNAi) but also involves theJAK-STAT and Toll pathways. Members of the JNK pathway,together with other genes, contribute to various aspects ofimmune tolerance and resistance by mediating tissue repairand wound closure. Categories were not mutually exclusiveand some were nested within larger umbrella categories (forinstance, all Toll and IMD genes were also included in thehumoral class). We also assigned a functional class to eachgene. These included the broad categories of recognition re-ceptor (identification of pathogens and parasites), signalingmolecule (signal transduction), and effector protein (patho-gen destruction). When applicable, we used more specificfunctional assignments (e.g., phagocytosis receptor, AMP).The full list of genes, as well as their pathway and functionalclass assignments, can be found in Supplemental Material,Table S1.

For each immune gene, we identified four control, protein-coding genes that were matched for size, genome location,and local recombination rate. For size and position, we re-quired that control genes had a total length (including in-trons) within either 1500 bp of, or 0.5–2 times, the totalimmune gene length, and we preferentially chose genes lo-cated within 50 kb of the immune gene. Using the Drosophilamelanogaster Recombination Rate Calculator version 2.3(Fiston-Lavier et al. 2010), we obtained the estimated localrecombination rate of each immune gene and required thatcontrol gene recombination rates be within 1 cM/Mb of thisvalue. The overall correlation between immune gene recom-bination rate and the mean control genes’ recombination rateswas high, even at low levels of recombination (R2 = 0.9997,

354 A. M. Early et al.

Figure S1). We additionally checked the genes using themore fine-scale recombination estimates of Comeron et al.(2012). As their estimates cover a wider range than the Re-combination Rate Calculator, the correlation between im-mune and control genes was weaker (R2 = 0.9443, FigureS2), but we ensured that all control gene recombination rateswere still within 2.5 cM/Mb of the immune gene rate. Ifimmune genes were found near the boundaries of knownsegregating inversions, we ensured that thematched controlswere similarly within or outside of the inversion. In the eventthat more than four control genes fulfilled these require-ments for a particular immune gene, we randomly chose four.Because of the restrictions, 15 immune genes had fewer thanfour controls in the final data set.

Wehadnowayof guaranteeing that the control genes haveno immune function. In particular, location matching carriesthis risk as genes of similar function—including immunefunction—often cluster in the genome. To minimize this risk,we excluded from the initial control pool 595 genes that hadweak evidence of immune involvement (for example, mod-erate expression changes in large-scale immunity screens orhomology with known immune gene families). We performedno further filtering on the control set. For example, we did notexclude genes that are known to be rapidly evolving.

We obtained information on nucleotide polymorphismwithin these genes for each member of the D. melanogasterGlobal Diversity Lines. These are a set of 84 inbred lines fromfive populations (15 from Beijing, China; 19 from Ithaca, NY;19 from the Netherlands; 18 from Tasmania; and 13 fromZimbabwe) that have been sequenced to an average depth of123 (Grenier et al. 2015). We masked genomic regions withpoor “callability” (as defined in Grenier et al. 2015) to ensurethat our analyses excluded genomic regions with largeamounts ofmissing data. Using the Ensembl database VariantEffect Predictor script (Berkeley Drosophila Genome Project5.25 assembly, release 64), we annotated the putative effectof each SNP within our genes of interest (nonsynonymouscoding, synonymous coding, intronic, splice site, 39-UTR,and 59-UTR).

Analysis of D. melanogaster-D. simulans divergence

Using FlyBase (version FB2013_06) we determined which ofour genes had knownone-to-one orthologs inD. simulans.Wethen downloaded all transcripts for each gene from FlyBaseand performed codon-based alignments for all D. mela-nogaster-D. simulans pairs using PRANK (Loytynoja andGoldman 2005). For each transcript pair, we used customPerl scripts to count nonsynonymous and fourfold degener-ate substitutions using the Nei–Gojobori method (Nei andGojobori 1986) and to calculate the length of the alignedregions after removing all gaps. For each gene, we then iden-tified the transcript pair with the longest aligned region andthe fewest number of nonsynonymous substitutions. We in-troduced this second, more conservative, criterion to reducethe likelihood of spuriously aligning paralogous transcripts forimmune genes that show a high level of alternative splicing.The values and the dN/dS values calculated using the longesttranscripts were highly correlated (immune genes, R2 = 0.93;control genes, R2 = 0.97), and our approach did exclude a fewincorrect immune gene alignments whose errors were easilydetected on visual inspection (Figure S3). We used these tran-scripts to represent the gene in all downstream analyses.

Incorporating polymorphism data from the ancestralZimbabwe population, we used DFE-alpha version 2.13(Eyre-Walker and Keightley 2009) to determine the propor-tion of adaptive substitutions (a) and the relative rate ofadaptive substitutions (va) within each pathway or functionalclass. Both statistics were calculated relative to substitutionsat fourfold degenerate sites. In brief, this method uses a max-imum likelihood method to infer the distribution of fitnesseffects of new mutations from the folded site frequency spec-trum. We ran DFE-alpha using a two-epoch model, allowingvariable mutation-effect sizes and variable shape parametersfor the gamma distribution. Gene classes with ,10 geneswere excluded from the analysis.

Population genetic analyses

Using custom Perl scripts, we calculated derived allele fre-quency, pairwise nucleotide diversity (p), pairwise FST, and

Figure 1 Main pathways in the Drosophila immunesystem.

Drosophila Immune Gene Diversity 355

global FST for each SNP. These same statistics, as well asWatterson’s u, Tajima’s D, KST, Hudson’s nearest neighbor (Snn),and normalized Fay and Wu’s H (Zeng et al. 2006), were cal-culated at the gene level. Although inbred, all fly lines retainedat least some residual heterozygosity. When a fly line was het-erozygous for a given site, we randomly sampled a single alleleto use in all analyses. In all calculations, we used only biallelicSNPs. For all calculations, we required that therewas a base callfor at least eight lines in any given population and accounted formissing data by adjusting the sample size at each SNP. Wecalculated FST at each SNP according to Weir and Cockerham(1984). The numerator and denominator were averaged sepa-rately across regions to calculate FST for transcripts. Negative FSTestimates were declared to be zero.

Analyzing gene classes using genomic controls

Evolutionary rate at a given gene can be influenced by factorsin the local genetic environment, such as recombination rateor selection on nearby alleles. To control for these factors, weleveraged genetic data from our set of matched control genesto more conservatively assess the probability of selectionwithin specific gene classes. We determined whether thegenes in a given pathway or functional class deviated, as aset, fromcontrol expectations by conducting a pairedWilcoxonsigned-rank test that compared the statistic for each immunegene within the class to the mean of its four control genes’statistics. This test therefore relied only on the relative, notabsolute, values of the immune and control statistics anddetermined whether there was a directional trend in the im-mune genes relative to the controls. Overall, this made thetest less sensitive to genome-wide outliers andmore sensitiveto local variation. The test was conducted for our major test

statistics: p, Fay and Wu’s H, Tajima’s D, and FST. Due to oursmall sample sizes, we determined significance through100,000 permutations. For each permutation round, we ran-domly chose one of the four control genes to serve as the“test” gene and averaged the statistics from the remainingthree controls and the immune gene. When genes had nopolymorphism in a given population, theywere dropped fromthe permutation round. As a result, the maximum V-statisticvaried among permutations and so we used the resultingP-values to construct our null distribution.

Weaccounted formultiple testingat the levelof the statistic(p, Fay and Wu’s H, Tajima’s D, and FST), correcting for mul-tiple testing across multiple gene process groups (n = 13,with RNAi) or gene functional groups (n = 8). To do this,we set a P-value threshold at the 0.19% tails. As many of ourgroups are nonindependent and their significance values areexpected to correlate, we did not attempt to identify P-valuesthat reached study-wide significance. All statistical analyseswere carried out in R (R Development Core Team 2011).

Systemic bacterial infections

Using a septic pinprick through the thoracic cuticle, we in-fected male flies (3–5 days old) with the bacterial pathogenEnterococcus faecalis (Lazzaro et al. 2004). Bacterial cultureshad been grown at 37� overnight to an OD600 of 1.0 (60.06).Flies were reared and maintained on standard glucose-yeastmedia at 25�with 12 hr light-dark cycles. Infections occurredin the 1–4 hr after the flies’ “dawn.” After 28 (61) hr, wehomogenized pools of three flies and plated the homogenateonto LB agar plates using a Spiral Biotech Autoplate 4000.We counted the number of colonies that grew on each platewith a QCount Colony Counter to infer the number of colony

Table 1 Immune gene groupings based on immune process and functional class

Immune process Genes Basic role(s)

Cellular 131 Hemocyte-mediated responses to all classes of parasites and pathogensEncapsulation 36 Initial recognition and coating of parasitoid wasp eggsPhagocytosis 45 Cellular uptake of bacteria, fungi, and necrotic cells

Epithelial 30 Regulation of bacteria and fungi on epithelial surfaces, including the gut, trachea, and reproductive tractHumoral 141 Recognition and elimination of bacteria and fungi in the hemolymph through the production of antimicrobial

compoundsIMD 57 Humoral pathway that targets mainly Gram-negative bacteriaToll 61 Humoral pathway that targets mainly Gram-positive bacteria and fungi

JAK-STAT 26 Signaling pathway implicated in viral response, control of hemocyte differentiation, and regulation of humoral responseJNK pathway and

wound repair44 Epithelial repair and cell growth

Melanization andPO production

32 Cell-mediated response that responds to wounding and the detection of parasitoid wasp eggs and microbes

ROS production 14 Production of reactive oxygen species; especially key in epithelial immune regulationViral defense 29 Destruction of viruses through RNAi; elimination of virus-infected cells

Functional class Genes Basic role(s)

Recognition 58 Initial detection of invading parasites and pathogens. Includes cell-surface (e.g., phagocytosis) receptors as well ashumoral-response pattern recognition proteins (e.g., GNBPs and PGRPs)

Signaling 198 Signal transduction following pathogen recognition; negative regulation of immune response; cross talk betweenpathways

Effector 75 Pathogen destruction or sequestration. Includes AMPs, lysozymes, and ROS

ROS, reactive oxygen species; GNBPs, Gram-negative bacteria binding proteins; PGRPs, peptidoglycan recognition proteins.

356 A. M. Early et al.

forming units per infected fly. Infections were carried out in ablock structure, with each fly line infected on four differentdays. To assess whether population of origin had a significanteffect on bacterial load, we constructed a linear model withinfector, experimental day, and experimental block as ran-dom effects. The residuals were used to calculate the meanand median for each fly line. We then conducted an ANOVAwith both the line means and medians to determine whetherthere was a population of origin effect. All statistical analysiswere carried out in R (R Development Core Team 2011).

Data availability

Table S1 and Table S2 list the names and coordinates of theimmune and control genes and transcripts used in the anal-ysis. Sequence data from the Global Diversity Lines are avail-able on the National Center for Biotechnology InformationSequence Read Archive as BioProject PRJNA268111. CustomPerl scripts used for population genetic analysis are availableupon request.

Results

Construction and annotation of gene sets

The D. melanogaster immune system encompasses a networkof interacting pathways that regulate diverse immune func-tions (reviewed in Ferrandon et al. 2007). By searching da-tabases and the D. melanogaster literature, we assembled alist of 361 genes with well-supported immune roles and cat-egorized each gene according to 12 immune pathways orprocesses and three functional classes, as outlined in Table1. Categories were not mutually exclusive and some geneswere assigned to multiple categories. While our list is ex-panded and updated, it maintains substantial overlap with

previous large-scale studies of Drosophila immune genes(Sackton et al. 2007; Obbard et al. 2009).

We acquired SNP calls for each gene from 84 inbredD. melanogaster lines from five populations (Zimbabwe; theNetherlands; Beijing, China; Ithaca, NY; Tasmania, Australia;Grenier et al. 2015). We discarded three genes thathad ,25% sequence coverage within these lines, leaving uswith a final study set of 358 immune genes. For each gene, wechose four control genes that were matched for size, location,and recombination rate. In the case of 15 immune genes, wewere unable to identify four adequate control genes, leavingus with a final set of 1402 control genes. Sequence data foreach control gene was similarly acquired for each of the 84 flylines. In total, we identified 129,083 SNPs, 21,519 of whichwere in coding regions (6193 were nonsynonymous while15,326 were synonymous; Figure S4).

Patterns of D. melanogaster-D. simulans divergencediffer among gene classes

Withinour full set of genes, therewere1208control genes and280 immune genes with one-to-one orthologs in D. simulans.For each of these genes, we calculated nucleotide divergenceat nonsynonymous and fourfold degenerate sites. Combiningthese values with polymorphism data from the ancestralZimbabwe population, we then estimated the proportionof adaptive substitutions (a) and the relative rate of adap-tive substitutions (va) for each gene class (Eyre-Walker andKeightley 2009). The absolute values we obtained for a andva are given in Table S3. We acknowledge that the valueswe obtained for these statistics are relatively high comparedto some other studies (but see Schneider et al. 2011). Ouraim, however, is not to present an absolute measure of ad-aptation, but rather compare the relative a and va values ofour various groups.

Figure 2 Adaptive divergence of Drosophila immune gene groups. (A) Proportion of adaptive substitutions (a) and (B) relative rate of adaptive substitutions(va) calculated from D. melanogaster-D. simulans alignments using the DFE-alpha method (Eyre-Walker and Keightley 2009). Shown are the meanestimates with 95% C.I. calculated with jackknife resampling at the gene level. The vertical gray lines denote the 95% C.I. as calculated with a set of1208 control genes. Values are presented as deviations from the control gene estimates. Rec, recognition; ROS, reactive oxygen species.

Drosophila Immune Gene Diversity 357

Consistent with previous cross-species comparisons(Sackton et al. 2007; Obbard et al. 2009), we found that thereis apparent heterogeneity in patterns of adaptive substitutionacross immune gene classes (Figure 2). The Toll pathway,cellular defense genes, encapsulation genes, wound-repairgenes, and genes involved in melanization and phenoloxi-dase (PO) production showed a significantly elevated a rel-ative to the control estimate; consistent with a role of naturalselection driving a high proportion of adaptive, amino acid-changing substitutions. Contrary to this, genes involved inhumoral defense and phagocytosis had low proportions ofadaptive substitutions relative to the control set (Figure2A). The estimated a for viral response genes was not ele-vated, a result that may appear surprising given that viralresponse genes, and in particular genes involved in RNAi,are among the fastest evolving genes across the Drosophilaphylogeny (Obbard et al. 2006, 2009; Kolaczkowski et al.2011a). These genes, however, did have a markedly higherrate of adaptive substitution (va) relative to control estimates(Figure 2B). When considered together, these values of a andva suggest that viral response genes have fixed adaptive mu-tations at a high rate (high va), but during this process havealso accrued a substantial number of substitutions that do notcarry a fitness advantage (nonsignificant a). In addition, va

was significantly elevated in IMD and Toll genes. Consistentwith previous observations (Obbard et al. 2009), the effectorclass of AMPs showed little evidence of adaptive evolution atthe nucleotide level (Table S3).

General patterns of polymorphism and FST arecomparable for immune and control genes

Using the polymorphism data from our 84 inbred D. mela-nogaster lines, we measured nucleotide diversity (p and u),

Tajima’s D, Fay and Wu’s H (Zeng et al. 2006), and Weir andCockerham’s unbiased estimator of FST (Weir and Cockerham1984) for each gene. The general population genetic patternsof both control and immune genes conformed to genome-wide observations made within these lines (Grenier et al.2015). For both sets of genes, nucleotide diversity and FSTbetween population pairs reflected known demographic pat-terns in D. melanogaster, which arose in sub-Saharan Africa,spread into Europe and Asia 10,000 years ago, and finallyreached Australia and North America from Europe in the last200 years (Figure 3; David and Capy 1988; Thornton andAndolfatto 2006; Keller 2007; Laurent et al. 2011). In thenon-African populations, genes on the X chromosome har-bored less nucleotide diversity at coding sites than genes onthe four major autosomes (both p and uw, Mann–WhitneyU-test, P , 0.001 for all control and immune comparisons).This is the expected pattern of polymorphism recovery fol-lowing a founding event. For the Zimbabwe flies, there wasno significant difference between nucleotide diversity on theX and autosomes.

We next compared large-scale polymorphism patternswithin immune and control genes. Taken as awhole, immunegenes did not display elevated population differentiation inany of the pairwise or global FST comparisons at nonsynon-ymous sites (Figure 3, Wilcoxon signed-rank test, P . 0.05).When comparing chromosomes individually, we did find differ-ences between the groups, but there was no consistent trendand none of the P-values survived multiple-test correction.

Only two immune processes show evidence ofaugmented population differentiation

Since general patterns of evolution are comparable across allimmune and control genes, we next investigated whether

Figure 3 Patterns of pairwise FST for control (blue) and immune (red) genes. Levels of FST reflect D. melanogaster’s spread out of its ancestral range insub-Saharan Africa (represented by the Zimbabwe population). Flies likely spread to Europe and Asia 10,000 years ago, only reaching Tasmania andNorth America through colonization by European populations within the last 200 years. Population samples are from Zimbabwe (Z); Beijing (B); theNetherlands (N); Ithaca, New York (I); and Tasmania (T).

358 A. M. Early et al.

adaptation was elevated in certain immune processes orfunctions. Todo so,wedividedgenes into two types of groups:(1) immune process or pathway, and (2) functional class(Table 1). Classifying genes by immune process allowed usto determine whether certain pathogen classes exert unusu-ally strong, spatially variable selection on immune genes inflies. Since the innate immune response is somewhat com-partmentalized, this would lead to signatures of selectionbeing enriched within specific gene classes (Figure 1). Forinstance, observing high signatures of selection within theIMD pathway would suggest that Gram-negative bacteriadisplay global variation in abundance or virulence, leadingto differential selection pressures across populations. In ad-dition, we analyzed genes grouped by functional class, a de-scription of the gene’s position or function within a pathway.Past work has found that species-level rates of evolution dif-fer among Drosophila genes based on their functional classi-fication as recognition receptors, signaling molecules, oreffector proteins (Sackton et al. 2007). This method of group-ing allowed us to determine whether genetic adaptation wasconcentrated in particular locations throughout immunepathways.

We compared each immune gene class to its set of genomiccontrol genes that were carefully matched for size, position,and local recombination rate. This approach partially con-trolled for potential evolutionary confounders that can influ-ence the signatures of evolution observed across genomes.Wecompared each immune group to the mean of its relevantcontrol group with a paired Wilcoxon signed-rank test anddetermined the significance of these comparisons throughpermutations. All statistics that fell within the 2.5 and0.19% (equivalent to a significance level of 0.05 after Bon-ferroni correction) tails of the permuted null distributions arelisted in Table 2 and Table S6.

Viral defense and phagocytosis are the only immuneprocesses showing signs of rapid population differentia-tion: Consistent with past studies, we found evidence ofdirectional selection and high population differentiationwithin viral defense genes—and in particular RNAi genes.At a significance level corrected for multiple testing,two population pairs showed high FST for viral defense(Zimbabwe-Ithaca and Zimbabwe-Tasmania) and RNAi (theNetherlands-Tasmania and Zimbabwe-Ithaca). For both genegroups, the tests for heightened FST between Zimbabweand all derived populations fell in the 2.5% tail. In addition,values of Fay and Wu’s H were significantly lower than thebackground for RNAi genes in Beijing and Zimbabwe, sug-gestive of higher levels of directional selection on derivedalleles in these genes.

Genes involved in phagocytosis also showed evidence ofunusually high population differentiation. In fact, the signalwe observed for phagocytosis genes was even stronger thanthat seen for viral defense: global FST (Figure 4 and Figure S7)as well as three pairwise FST comparisons were significantlyelevated compared to controls. As discussed below, however,

this pattern was driven by recognition receptor genes. Whenwe limited the analysis to phagocytosis genes without recep-tor function, we detected no deviations from backgroundpatterns. The larger class of cellular response genes (of whichphagocytosis genes are a subset) also showed significantlyhigher FST between a single population pair: the Netherlandsand Ithaca.

Rapid population differentiation is not a universal markof immune gene classes: The only additional statisticallysignificant FST comparison was lower—not higher—thanthe control expectation. This was the pairwise FST betweenthe Netherlands and Zimbabwe for Toll genes. While this

Table 2 Statistics showing different patterns in immune vs. controlgene classes

Gene class Population(s) StatisticDirection of immunevs. control statistica

Cellular Neth-Ith FST GreaterPhagocytosis Global FST Greater

Ith-Tas FST GreaterNeth-Ith FST GreaterNeth-Tas FST Greater

Epithelial Netherlands H LessIMD Zimbabwe p GreaterToll Zim-Neth FST LessJAK-STAT Tasmania D LessViral defense Zim-Ith FST Greater

Zim-Tas FST GreaterRNAi Beijing H Less

Zimbabwe H LessNeth-Tas FST GreaterZim-Ith FST Greater

Recognition Beijing p GreaterIthaca p GreaterNetherlands p GreaterTasmania p GreaterZimbabwe p GreaterIth-Tas FST GreaterNeth-Ith FST GreaterNeth-Tas FST Greater

Phagocytosisrecognitionreceptors

Beijing p GreaterIthaca p GreaterIth-Tas FST GreaterNeth-Ith FST GreaterNeth-Tas FST GreaterZim-Beij FST GreaterZim-Tas FST Greater

Humoralrecognitionreceptors

Beijing p GreaterIthaca p GreaterNetherlands p GreaterTasmania p GreaterZimbabwe p GreaterNetherlands D GreaterBeijing H LessIthaca H LessTasmania H Less

Effectors Tasmania p GreaterAMPs Tasmania p Greater

Neth, the Netherlands; Ith, Ithaca, NY; Tas, Tasmania; Zim, Zimbabwe; Beij, Beijing.a All statistics were corrected for multiple testing and were significant at an a of0.05, as determined by permutation.

Drosophila Immune Gene Diversity 359

population pair was the only one to survive multiple-testingcorrection, it was not an anomaly among Toll gene comparisons.Tests for low FST fell within the 2.5% tail for four addi-tional population pairs (Beijing-Tasmania, the Netherlands-Tasmania, Zimbabwe-Ithaca, and Zimbabwe-Tasmania).

Signatures of adaptation differ across functional classes:Analyzing genes based on their functional class showed thatrecognition receptors are highly diverse and differentiatedacross populations. Recognition molecules had elevated nu-cleotide diversity relative to controls in all five populations,and higher than background FST in three pairwise compari-sons.When tested individually, both subclasses of recognitionreceptors (phagocytosis and humoral) also showed strong

evidence of high polymorphism and population divergence.Effector proteins only showed significantly elevated polymor-phism in a single population (Tasmania) despite having amean level of polymorphism that is higher than that of rec-ognition genes. This somewhat counterintuitive observationis a result of our genomic control approach, which accountedfor recombination rate, gene location, and gene size. In otherwords, effector molecules display patterns of polymorphismand divergence that are largely consistent with other smallgenes (mean transcript length = 792 nt), whereas recogni-tion genes are unusual among the transcripts within theirlonger length class (mean transcript length = 1465 nt). Incontrast to recognition and effector molecules, which func-tion at the ends of their pathways, internal signaling

Figure 4 Levels of global FST within gene classes at nonsynonymous sites. Immune genes (red) were categorized according to (A) immune process or (B)functional class, and then matched with four control genes (blue) based on chromosomal location, gene length, and local recombination rate. Whencompared with a Wilcoxon signed-rank test, only phagocytosis genes displayed a significant difference between the two groups after multiple-testingcorrection (a # 0.05). ROS, reactive oxygen species.

360 A. M. Early et al.

molecules trended toward lower nucleotide diversity in allpopulations, although none of these tests were significantafter multiple-testing correction.

It is worth noting that other genetic processes may con-tribute to the adaptationof effectormolecules and recognitionreceptors. Both classes encompass large gene family groups,and effector gene families, in particular, are known to expandand contract at an unusually rapid pace (Sackton et al. 2007).Our focus on single nucleotide variants does not reflect thissource of genetic novelty, and we note that these fly lines docontain two segregating duplications in the drosomycin fam-ily, a group of antifungal AMPs (Cardoso-Moreira et al. 2016).

Populations display similar levels of resistance to ageneralist bacterial pathogen

In our gene class analysis, we found a trend toward lower-than-expected population differentiation in the canonicalgenes of the Toll pathway, which is the primary responsepathway to fungal andGram-negative bacterial infections.Wewere next interested in testing how the flies compared phe-notypically. To do so, we infected the 84 lines with thegeneralist Gram-positive bacterial pathogen, E. faecalis. Wechose to use a generalist pathogen to detect general, large-scale changes in immune response and regulation. This typeof phenotypic difference might result from geographic varia-tion in environmental factors like temperature (Lazzaro et al.2008), rather than adaptations to unique population-specificpathogens. Questions of how populations are adapting toDrosophila-specific and geographically variable pathogenswill need to be addressed once we gain a better understand-

ing of the identity and geographic distribution of Drosophilapathogens.

Individually, the 84 lines showed significant variation inbacterial load 28 hr after infection (Figure 5; ANOVA, d.f. =83, F = 1.7, P = 6.9 3 1025). This result shows that fliesharbor extensive variation in immune resistance. On a pop-ulation level, however, we detected no significant differencein bacterial load (ANOVA, d.f. = 4, F= 1.11, P= 0.36). Thisrelative phenotypic homogeneity across populations con-trasts with the population-level variation observed in a num-ber of other phenotypes assayed in these same lines,including lipid content (Scheitz et al. 2013), metabolic regu-lation (Greenberg et al. 2011), and transposable elementdefense (Shiqi Luo, Andrew G. Clark and Jian Lu, unpublisheddata). Bacterial load is, of course, only one metric of immunecompetence (Ayres and Schneider 2009; Howick and Lazzaro2014), and we only measured responses under a single envi-ronmental condition (Lazzaro et al. 2008). Nevertheless—andespecially when coupled with our genetic analysis—these re-sults suggest that global fly populations have not undergonewidespread changes in their magnitude of response to general-ist bacterial infection.

Immune genes with high population differentiation

Both our genetic and phenotypic analyses suggest that D.melanogaster populations do not vary widely in basic immuneprocesses outside of viral defense and phagocytosis. Still, thepopulations may have experienced more limited—perhapspathogen-specific—adaptations in a small number of genes.

Figure 5 Relative bacterial load in flies 28 hr after E. faecalis systemic infection. Flies were infected with the bacterial pathogen via septic pinprick andtotal bacterial load was measured after 28 hr. The values plotted are the mean residuals (6 SE) from a model that accounted for sources of experimentalerror. Higher values correspond to a higher bacterial load and lower immune resistance. The color represents population of origin: Zimbabwe (orange);Beijing, China (red); the Netherlands (green); Ithaca, New York (blue); Tasmania (purple).

Drosophila Immune Gene Diversity 361

We therefore identified the individual genes showing signa-tures most consistent with local adaptation. Because we wereprimarily interested in identifying changes that are mostlikely to carry phenotypic effects, we continued to focus onstatistics calculated at nonsynonymous sites (Table S5). It isimportant to note, therefore, that a number of genes’ statis-tics were calculated with data from a very small number ofSNPs. For comparison, statistics calculated across the entirecoding region are available in Table S4.

Table 3 lists the 20 immune genes that showed the highestdegree of global population differentiation at nonsynony-mous sites as measured by FST. Demonstrating that thesepatterns of population differentiation are robust to differentsampling schemes, population structure within several ofthese genes has been previously discussed in the literature.These include Tak1 (Yukilevich et al. 2010), lectin-24A(Keebaugh and Schlenke 2012), and CHKov1 (Aminetzachet al. 2005; Magwire et al. 2011). Naturally segregating hap-lotypes of CHKov1 (Magwire et al. 2011) and DptA (Uncklesset al. 2016) are even known to possess differential efficacyagainst certain viruses and bacterial strains, respectively. Toour knowledge, natural variation in the remaining genes inTable 3 has not been discussed in the literature, and so thesegenes may represent novel candidate loci of local adaptation.

Previously, Maruki et al. (2012) demonstrated that lociexperiencing higher purifying selection show lower levelsof population differentiation. This can be a confounding fac-tor in outlier approaches such as ours, biasing results towardgenes experiencing relaxed selective constraint. We, too, finda positive correlation within our immune genes betweenglobal FST at nonsynonymous sites and nonsynonymous diver-gence between D. melanogaster and D. simulans (Spearman’sr= 0.236, P= 0.0001; Figure 6). The pattern in two genes—Eph and lectin-24A—is consistent with a model of relaxedconstraint as they are outliers for both global nonsynonymousFST and Dn. Importantly, however, these are the only two highFST genes that are outliers for both statistics, suggesting thatalleles within the other genes may not experience relaxedconstraint.

For 10 of the outlier genes in Table 3, the high global FST islargely driven by differences between the ancestral Zim-babwe population and the other four derived populations.This pattern is consistent with D. melanogaster’s known de-mographic history, and so might be attributed to neutralforces. To further assess these genes’ validity as selectioncandidates, we therefore included other statistics like nucleo-tide diversity and Fay and Wu’s H, which specifically con-sider changes in the derived allele frequency (Table 3).Interestingly, two of the outlier genes (lectin-24A andAdgf-A) have low values of H that fall within the bottom5% of African haplotypes. This suggests that the differencesin population allele frequencies are partially attributed torecent changes in Africa and not solely the result of an out-of-Africa bottleneck.

There are also instances where a single derived pop-ulation largely drives the global signature of population Ta

ble

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2L24

4.90

42.64

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12.36

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124.87

521.29

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20.75

00.69

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80.09

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320.98

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90.41

30.41

80.47

90.43

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80.00

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50.81

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8NA

0.48

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322.64

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60.42

90.52

50.12

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90.16

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60.33

7he

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190.73

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70.69

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362 A. M. Early et al.

differentiation. For instance, the patterns of pairwise FST, re-duced p, and low H suggest that LysP and Vps33B may haveexperienced directional selection in the Netherlands beforethe recent colonization of North America and Australia.

Discussion

The results in this studydemonstrate that rapid adaptation is theexception,not therule,among immunegenes inD.melanogaster.As has been previously demonstrated (Obbard et al. 2006,2009), genes involved in viral defense—and in particular theRNAi portion of this process—display signs of elevated selec-tion. Our analysis of other pathways, however, shows that theextent of this adaptive signature is unusual, not only in agenome-wide context but also in comparison to other im-mune processes. Indeed, several immune processes evenshowed unexpectedly low levels of population differentiationthat were nominally significant when compared to the ge-netic background. In addition, instead of occurring throughsmall changes in numerous genes throughout a pathway—asin the polygenic selection model—most local immune adap-tation appears to progress through allele frequency changesin a few genes at the periphery that are likely to interactdirectly with pathogens. This suggests that abiotic factors inthe flies’ environment may not be major drivers of local im-mune adaptation at the amino acid level.

Across all populations, recognition genes showed highamino acid diversity and signs of heightened populationdifferentiation. While we have not shown that this variationdrives changes in pathogen recognition, previous lines ofevidence suggest that the variation in recognition receptorsdoes carry phenotypic effects. First, this observation is in linewith theoretical predictions that recognition molecules serve

as the main locus of host-pathogen coevolution (Nuismer andDybdahl 2016) and with observations in mice that upstreaminitiation genes are more likely to undergo positive selection(Casals et al. 2011; Webb et al. 2015). Second, past quanti-tative genetic studies in Drosophila have suggested that nat-ural variation in immune resistance is largely driven bypolymorphisms in recognition and, to a lesser extent, signal-ing genes (Lazzaro et al. 2006; Sackton et al. 2010). Thisimplies that D. melanogaster recognition genes carry a sub-stantial level of functional standing variation. Selection couldact on this standing variation in a population-specific manner,leading to differentiation across populations. Alternatively,recognition genes, like effector genes (Unckless and Lazzaro2016), may experience balancing selection, and the resultingasynchronous fluctuations across populations could heightenthe population differentiation measured in our samples.

Balancing selection and temporal fluctuations in allele fre-quencies might also play a role in one of the more intriguingresults from this analysis: the discordance between the species-level andpopulation-level analysesofadaptation. Phagocytosisgenes and recognition receptors showed no evidence of ele-vated rates of adaptation in the divergence data, but showedheightened levels of population differentiation. These geneclasses maintain a large number of polymorphic sites (FigureS5 and Figure S6), a common hallmark of balancing selection,which would drive down values of a and va. Conversely, wefound that both the Toll and IMD pathways showed elevatedrates of adaptive substitution, but Toll genes trended towardlower levels of population differentiation. The full reason forthis incongruity across different timescales remains an openquestion (Messer et al. 2016), but one likely factor is thatour analyses are static snapshots of dynamic processes. Ourpopulation samples captured a single time point that provides

Figure 6 Gene divergence and global population differ-entiation at nonsynonymous sites. Across 264 immunegenes (black), D. melanogaster-D. simulans nucleotide di-vergence and global FST correlate (Spearman’s r = 0.236,P = 0.0001). The solid, dashed, and dotted lines mark themedian, the lower and upper quartiles, and the quartiles61.5 interquartile range, respectively, as calculated withthe control genes (gray). The genes identified in Table 3are marked in red.

Drosophila Immune Gene Diversity 363

no information on the known temporal fluctuations that occurin wild populations (Bergland et al. 2014). Similarly, speciesdivergence data focus on fixed differences, an endpoint thatis largely blind to the trajectory alleles took on their way tofixation. Antiviral genes are perhaps the only class wheredirectional selection is sufficiently strong to create robustsignatures of positive selection in the genome on both time-scales. Further research on the interactions between theDrosophila host and its pathogens, the variability in patho-gen distributions, and the temporal dynamics of immuneprocesses will greatly extend our understanding of theseevolutionary patterns.

Lookingtostudiesperformedinotherorganisms,weseethatour results are not without precedent. In a laboratory-basedstudy with the insect Tribolium castaneum, Berenos et al.(2011) demonstrated that evolution in the presence of para-sites counteracted the effects of drift, leading to higher levelsof allelic diversity within populations and lower levels of dif-ferentiation between populations. Although more divergedand in possession of an adaptive immune response, humansalso show patterns in their innate immune genes that are sim-ilar to what we find here: while numerous single genes showsigns of directional selection, innate immune genes have glob-ally experienced stronger purifying selection than nonimmunegenes (Mukherjee et al. 2009; Deschamps et al. 2016). To-gether, these results highlight that immune evolution is morecomplex than the outlier scenarios that garner the greatestattention in the literature. To fully understand immune adap-tation, it will therefore be key to not only search out the pointsof rapid divergence, but also consider what drives—or allowsfor—the maintenance of low genetic variability across diverseenvironments. This more inclusive view of immune evolutionwill no doubt yield increased insight into the process of adap-tation in this key ecological trait.

Acknowledgments

We thank Brian Lazzaro, Tim Connallon, Charles Aquadro,Todd Schlenke, Richard Harrison, and members of the Clarkand Aquadro laboratory groups for helpful discussions,insights, and comments on this work and on an earlierversion of this manuscript. This work was supported by theNational Institutes of Health (grant number R01 AI-064950to A.G.C. and Brian Lazzaro) and by a National ScienceFoundation graduate research fellowship to A.M.E.

Literature Cited

Aminetzach, Y. T., J. M. Macpherson, and D. A. Petrov,2005 Pesticide resistance via transposition-mediated adaptivegene truncation in Drosophila. Science 309: 764–767.

Ayres, J. S., and D. S. Schneider, 2009 The role of anorexia inresistance and tolerance to infections in Drosophila. PLoS Biol.7: e1000150.

Ayres, J. S., N. Freitag, and D. S. Schneider, 2008 Identification ofDrosophila mutants altering defense of and endurance to Liste-ria monocytogenes infection. Genetics 178: 1807–1815.

Babin, A., S. Kolly, F. Schneider, V. Dolivo, M. Zini et al.,2014 Fruit flies learn to avoid odours associated with virulentinfection. Biol. Lett. 10: 20140048.

Berenos, C., K. M. Wegner, and P. Schmid-Hempel,2011 Antagonistic coevolution with parasites maintains host ge-netic diversity: an experimental test. Proc. Biol. Sci. 278: 218–224.

Bergland, A. O., E. L. Behrman, K. R. O’Brien, P. S. Schmidt, and D.A. Petrov, 2014 Genomic evidence of rapid and stable adaptiveoscillations over seasonal time scales in Drosophila. PLoS Genet.10: e1004775.

Cardoso-Moreira, M., J. R. Arguello, S. Gottipati, L. G. Harshman,J. K. Grenier et al., 2016 Evidence for the fixation of geneduplications by positive selection in Drosophila. Genome Re-search 26: 787–798.

Casals, F., M. Sikora, H. Laayouni, L. Montanucci, A. Muntasellet al., 2011 Genetic adaptation of the antibacterial human in-nate immunity network. BMC Evol. Biol. 11: 202.

Castellano, D., M. Coronado-Zamora, J. L. Campos, A. Barbadilla,and A. Eyre-Walker, 2016 Adaptive evolution is substantiallyimpeded by Hill-Robertson interference in Drosophila. Mol. Biol.Evol. 33: 442–455.

Chavez-Galarza, J., D. Henriques, J. S. Johnston, J. C. Azevedo,J. C. Patton et al., 2013 Signatures of selection in the Iberianhoney bee (Apis mellifera iberiensis) revealed by a genome scananalysis of single nucleotide polymorphisms. Mol. Ecol. 22:5890–5907.

Comeron, J. M., R. Ratnappan, and S. Bailin, 2012 The manylandscapes of recombination in Drosophila melanogaster. PLoSGenet. 8: e1002905.

Crawford, J. E., W. M. Guelbeogo, A. Sanou, A. Traore, K. D. Vernicket al., 2010 De novo transcriptome sequencing in Anopheles fu-nestus using Illumina RNA-seq technology. PLoS One 5: e14202.

Daub, J. T., T. Hofer, E. Cutivet, I. Dupanloup, L. Quintana-Murciet al., 2013 Evidence for polygenic adaptation to pathogens inthe human genome. Mol. Biol. Evol. 30: 1544–1558.

David, J. R., and P. Capy, 1988 Genetic-variation of Drosophilamelanogaster natural-populations. Trends Genet. 4: 106–111.

Deschamps, M., G. Laval, M. Fagny, Y. Itan, L. Abel et al.,2016 Genomic signatures of selective pressures and introgres-sion from archaic hominins at human innate immunity genes.Am. J. Hum. Genet. 98: 5–21.

Erler, S., P. Lhomme, P. Rasmont, and H. M. Lattorff, 2014 Rapidevolution of antimicrobial peptide genes in an insect host-socialparasite system. Infect. Genet. Evol. 23: 129–137.

Eyre-Walker, A., and P. D. Keightley, 2009 Estimating the rate ofadaptive molecular evolution in the presence of slightly delete-rious mutations and population size change. Mol. Biol. Evol. 26:2097–2108.

Fabian, D. K., M. Kapun, V. Nolte, R. Kofler, P. S. Schmidt et al.,2012 Genome-wide patterns of latitudinal differentiationamong populations of Drosophila melanogaster from NorthAmerica. Mol. Ecol. 21: 4748–4769.

Ferrandon, D., J. L. Imler, C. Hetru, and J. A. Hoffmann, 2007 TheDrosophila systemic immune response: sensing and signallingduring bacterial and fungal infections. Nat. Rev. Immunol. 7:862–874.

Fiston-Lavier, A. S., N. D. Singh, M. Lipatov, and D. A. Petrov,2010 Drosophila melanogaster recombination rate calculator.Gene 463: 18–20.

Fumagalli, M., M. Sironi, U. Pozzoli, A. Ferrer-Admetlla, L. Pattiniet al., 2011 Signatures of environmental genetic adaptationpinpoint pathogens as the main selective pressure through hu-man evolution. PLoS Genet. 7: e1002355.

Greenberg, A. J., S. R. Hackett, L. G. Harshman, and A. G. Clark,2011 Environmental and genetic perturbations reveal dif-ferent networks of metabolic regulation. Mol. Syst. Biol. 7:563.

364 A. M. Early et al.

Grenier, J. K., J. R. Arguello, M. C. Moreira, S. Gottipati, J.Mohammed et al., 2015 Global diversity lines—a five-continentreference panel of sequenced Drosophila melanogaster strains. G3(Bethesda) 5: 593–603.

Hereford, J., 2009 A quantitative survey of local adaptation andfitness trade-offs. Am. Nat. 173: 579–588.

Howick, V. M., and B. P. Lazzaro, 2014 Genotype and diet shaperesistance and tolerance across distinct phases of bacterial in-fection. BMC Evol. Biol. 14: 56.

Hubner, S., E. Rashkovetsky, Y. B. Kim, J. H. Oh, K. Michalak et al.,2013 Genome differentiation of Drosophila melanogaster froma microclimate contrast in Evolution Canyon, Israel. Proc. Natl.Acad. Sci. USA 110: 21059–21064.

Jeong, C., and A. Di Rienzo, 2014 Adaptations to local environmentsin modern human populations. Curr. Opin. Genet. Dev. 29: 1–8.

Jones, F. C., M. G. Grabherr, Y. F. Chan, P. Russell, E. Mauceli et al.,2012 The genomic basis of adaptive evolution in threespinesticklebacks. Nature 484: 55–61.

Juneja, P., and B. P. Lazzaro, 2010 Haplotype structure and ex-pression divergence at the Drosophila cellular immune geneeater. Mol. Biol. Evol. 27: 2284–2299.

Kacsoh, B. Z., Z. R. Lynch, N. T. Mortimer, and T. A. Schlenke,2013 Fruit flies medicate offspring after seeing parasites. Sci-ence 339: 947–950.

Keebaugh, E. S., and T. A. Schlenke, 2012 Adaptive evolution of anovel Drosophila lectin induced by parasitic wasp attack. Mol.Biol. Evol. 29: 565–577.

Keller, A., 2007 Drosophila melanogaster’s history as a humancommensal. Curr. Biol. 17: R77–R81.

Kolaczkowski, B., D. N. Hupalo, and A. D. Kern, 2011a Recurrentadaptation in RNA interference genes across the Drosophila phy-logeny. Mol. Biol. Evol. 28: 1033–1042.

Kolaczkowski, B., A. D. Kern, A. K. Holloway, and D. J. Begun,2011b Genomic differentiation between temperate and tropi-cal Australian populations of Drosophila melanogaster. Genetics187: 245–260.

Kraaijeveld, A. R., E. C. Limentani, and H. C. Godfray, 2001 Basisof the trade-off between parasitoid resistance and larval com-petitive ability in Drosophila melanogaster. Proc. Biol. Sci. 268:259–261.

Kraaijeveld, A. R., S. J. Layen, P. H. Futerman, and H. C. Godfray,2012 Lack of phenotypic and evolutionary cross-resistanceagainst parasitoids and pathogens in Drosophila melanogaster.PLoS One 7: e53002.

Lamichhaney, S., A. Martinez Barrio, N. Rafati, G. Sundstrom, C. J.Rubin et al., 2012 Population-scale sequencing reveals geneticdifferentiation due to local adaptation in Atlantic herring. Proc.Natl. Acad. Sci. USA 109: 19345–19350.

Larracuente, A. M., T. B. Sackton, A. J. Greenberg, A. Wong, N. D.Singh et al., 2008 Evolution of protein-coding genes in Dro-sophila. Trends Genet. 24: 114–123.

Laurent, S. J., A. Werzner, L. Excoffier, and W. Stephan,2011 Approximate Bayesian analysis of Drosophila mela-nogaster polymorphism data reveals a recent colonization ofSoutheast Asia. Mol. Biol. Evol. 28: 2041–2051.

Lazzaro, B. P., and T. J. Little, 2009 Immunity in a variable world.Philos. Trans. R. Soc. Lond. B Biol. Sci. 364: 15–26.

Lazzaro, B. P., B. K. Sceurman, and A. G. Clark, 2004 Geneticbasis of natural variation in D. melanogaster antibacterial immu-nity. Science 303: 1873–1876.

Lazzaro, B. P., T. B. Sackton, and A. G. Clark, 2006 Genetic var-iation in Drosophila melanogaster resistance to infection: a com-parison across bacteria. Genetics 174: 1539–1554.

Lazzaro, B. P., H. A. Flores, J. G. Lorigan, and C. P. Yourth,2008 Genotype-by-environment interactions and adaptationto local temperature affect immunity and fecundity in Drosoph-ila melanogaster. PLoS Pathog. 4: e1000025.

Loytynoja, A., and N. Goldman, 2005 An algorithm for progres-sive multiple alignment of sequences with insertions. Proc. Natl.Acad. Sci. USA 102: 10557–10562.

Magwire, M. M., F. Bayer, C. L. Webster, C. Cao, and F. M. Jiggins,2011 Successive increases in the resistance of Drosophila toviral infection through a transposon insertion followed by aduplication. PLoS Genet. 7: e1002337.

Maruki, T., S. Kumar, and Y. Kim, 2012 Purifying selection mod-ulates the estimates of population differentiation and confoundsgenome-wide comparisons across single-nucleotide polymor-phisms. Mol. Biol. Evol. 29: 3617–3623.

McKean, K. A., C. P. Yourth, B. P. Lazzaro, and A. G. Clark,2008 The evolutionary costs of immunological maintenanceand deployment. BMC Evol. Biol. 8: 76.

McTaggart, S. J., D. J. Obbard, C. Conlon, and T. J. Little,2012 Immune genes undergo more adaptive evolution thannon-immune system genes in Daphnia pulex. BMC Evol. Biol.12: 63.

Messer, P. W., S. P. Ellner, and N. G. Hairston, Jr., 2016 Canpopulation genetics adapt to rapid evolution? Trends Genet.32: 408–418.

Mukherjee, S., N. Sarkar-Roy, D. K. Wagener, and P. P. Majumder,2009 Signatures of natural selection are not uniform acrossgenes of innate immune system, but purifying selection is thedominant signature. Proc. Natl. Acad. Sci. USA 106: 7073–7078.

Nei, M., and T. Gojobori, 1986 Simple methods for estimating thenumbers of synonymous and nonsynonymous nucleotide substi-tutions. Mol. Biol. Evol. 3: 418–426.

Nuismer, S. L., and M. F. Dybdahl, 2016 Quantifying the coevo-lutionary potential of multistep immune defenses. Evolution 70:282–295.

Obbard, D. J., F. M. Jiggins, D. L. Halligan, and T. J. Little,2006 Natural selection drives extremely rapid evolution in an-tiviral RNAi genes. Curr. Biol. 16: 580–585.

Obbard, D. J., J. J. Welch, K. W. Kim, and F. M. Jiggins,2009 Quantifying adaptive evolution in the Drosophila im-mune system. PLoS Genet. 5: e1000698.

Pespeni, M. H., D. A. Garfield, M. K. Manier, and S. R. Palumbi,2012 Genome-wide polymorphisms show unexpected targetsof natural selection. Proc. Biol. Sci. 279: 1412–1420.

Pritchard, J. K., and A. Di Rienzo, 2010 Adaptation - not bysweeps alone. Nat. Rev. Genet. 11: 665–667.

Pritchard, J. K., J. K. Pickrell, and G. Coop, 2010 The genetics ofhuman adaptation: hard sweeps, soft sweeps, and polygenicadaptation. Curr. Biol. 20: R208–R215.

Quintana-Murci, L., and A. G. Clark, 2013 Population genetictools for dissecting innate immunity in humans. Nat. Rev. Im-munol. 13: 280–293.

R Development Core Team, 2011 R: A Language and Environmentfor Statistical Computing. R Foundation for Statistical Comput-ing, Vienna, Austria.

Sackton, T. B., B. P. Lazzaro, T. A. Schlenke, J. D. Evans, D. Hultmarket al., 2007 Dynamic evolution of the innate immune system inDrosophila. Nat. Genet. 39: 1461–1468.

Sackton, T. B., B. P. Lazzaro, and A. G. Clark, 2010 Genotype andgene expression associations with immune function in Drosoph-ila. PLoS Genet. 6: e1000797.

Scheitz, C. J., Y. Guo, A. M. Early, L. G. Harshman, and A. G. Clark,2013 Heritability and inter-population differences in lipid pro-files of Drosophila melanogaster. PLoS One 8: e72726.

Schneider, A., B. Charlesworth, A. Eyre-Walker, and P. D. Keightley,2011 A method for inferring the rate of occurrence and fit-ness effects of advantageous mutations. Genetics 189: 1427–1437.

Sironi, M., R. Cagliani, D. Forni, and M. Clerici, 2015 Evolutionaryinsights into host-pathogen interactions frommammalian sequencedata. Nat. Rev. Genet. 16: 224–236.

Drosophila Immune Gene Diversity 365

Stapley, J., J. Reger, P. G. Feulner, C. Smadja, J. Galindo et al.,2010 Adaptation genomics: the next generation. Trends Ecol.Evol. 25: 705–712.

Stephan, W., 2016 Signatures of positive selection: from selectivesweeps at individual loci to subtle allele frequency changes inpolygenic adaptation. Mol. Ecol. 25: 79–88.

Thornton, K., and P. Andolfatto, 2006 Approximate Bayesian in-ference reveals evidence for a recent, severe bottleneck in aNetherlands population of Drosophila melanogaster. Genetics172: 1607–1619.

Turchin, M. C., C. W. Chiang, C. D. Palmer, S. Sankararaman, D.Reich et al., 2012 Evidence of widespread selection on stand-ing variation in Europe at height-associated SNPs. Nat. Genet.44: 1015–1019.

Turner, T. L., M. T. Levine, M. L. Eckert, and D. J. Begun,2008 Genomic analysis of adaptive differentiation in Drosoph-ila melanogaster. Genetics 179: 455–473.

Turner, T. L., E. C. Bourne, E. J. Von Wettberg, T. T. Hu, and S. V.Nuzhdin, 2010 Population resequencing reveals local adapta-tion of Arabidopsis lyrata to serpentine soils. Nat. Genet. 42:260–263.

Unckless, R. L., and B. P. Lazzaro, 2016 The potential for adaptivemaintenance of diversity in insect antimicrobial peptides. Philos.Trans. R. Soc. Lond. B Biol. Sci. 371: 20150291.

Unckless, R. L., V. M. Howick, and B. P. Lazzaro, 2016 Convergentbalancing selection on an antimicrobial peptide in Drosophila.Curr. Biol. 26: 257–262.

Waterhouse, R. M., E. V. Kriventseva, S. Meister, Z. Xi, K. S. Alvarezet al., 2007 Evolutionary dynamics of immune-related genes andpathways in disease-vector mosquitoes. Science 316: 1738–1743.

Webb, A. E., Z. N. Gerek, C. C. Morgan, T. A. Walsh, C. E. Loscheret al., 2015 Adaptive evolution as a predictor of species-specificinnate immune response. Mol. Biol. Evol. 32: 1717–1729.

Weir, B. S., and C. C. Cockerham, 1984 Estimating F-statistics forthe analysis of population structure. Evolution 38: 1358–1370.

Ye, K., M. H. Schulz, Q. Long, R. Apweiler, and Z. Ning,2009 Pindel: a pattern growth approach to detect break pointsof large deletions and medium sized insertions from paired-endshort reads. Bioinformatics 25: 2865–2871.

Yukilevich, R., T. L. Turner, F. Aoki, S. V. Nuzhdin, and J. R. True,2010 Patterns and processes of genome-wide divergence be-tween North American and African Drosophila melanogaster. Ge-netics 186: 219–239.

Zeng, K., Y. X. Fu, S. Shi, and C. I. Wu, 2006 Statistical tests fordetecting positive selection by utilizing high-frequency variants.Genetics 174: 1431–1439.

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366 A. M. Early et al.

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0 1 2 3 4Immune gene recombination rate at gene center (RRC)

Mea

n co

ntro

l gen

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at g

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er (R

RC

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Figure S1. Plot of each immune gene’s center recombination rate versus the mean of the control genes’ center recombination rates as calculated with the Recombination Rate Calcula-tor v 2.3 (Fiston-Lavier et al. 2010).

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Figure S2. Plot of each immune gene’s center recombination rate versus the mean control genes’ center recombination rates as calculated by Comeron et al. (2012).

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nscr

ipt w

ith s

mal

lest

Dn

Figure S3. Plot of transcript-level D. melanogaster-D. simulans divergence (Dn/Ds) as calculated with the longest transcript pair versus our method which additionally chose the pair with the fewest non-synonymous changes.

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Global SFS

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Zimbabwe SFS

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Beijing SFS

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Ithaca, NY SFS

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0.0 0.4 0.8Derived allele frequency

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FUNCTIONGenic

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SYN

Tasmania SFS

Figure S4. Site frequency spectra for SNPs found within immune genes.

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IMD

Toll

Viral

RNAi

Recognition

Phagocytosis.Receptors

AMPs

Other

Figure S5. Plot of non-synonymous polymorphism (Pn) versus non-synonymous D. melanogaster-D. simu-lans divergence (Dn) by immune gene class.

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LabelPhagocytosis

IMD

Toll

Viral

RNAi

Recognition

Phagocytosis.Receptors

AMPs

Other

Figure S6. Plot of non-synonymous to four-fold degenerate site polymorphism (Pn/P�d) versus non-synonymous to four-fold degenerate site divergence (Dn/D�d) by immune gene class.

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e gl

obal

FS

T at

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nym

ous

site

s

ClassOther

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Anti-viral

RNAi

Figure S7. Plot of each immune gene’s global FST versus the mean global FST of its four control genes.

Table S1. List of immune genes used in analysis. (.xlsx, 42 KB)

www.genetics.org/lookup/suppl/doi:10.1534/genetics.116.195016/-/DC1/TableS1.xlsx

Table S2. List of control genes used in analysis. (.xlsx, 136 KB)

www.genetics.org/lookup/suppl/doi:10.1534/genetics.116.195016/-/DC1/TableS2.xlsx

Table S3. The proportion of adaptive substitutions (α) and the relative rate of adaptive substitutions (ωa)

for each gene class as calculated with DFE-alpha (Eyre-Walker and Keightley 2009). (.xlsx, 52 KB)

www.genetics.org/lookup/suppl/doi:10.1534/genetics.116.195016/-/DC1/TableS3.xlsx

Table S4. Population genetic parameters calculated across all coding sites of immune genes. (.xlsx, 279

KB)

www.genetics.org/lookup/suppl/doi:10.1534/genetics.116.195016/-/DC1/TableS4.xlsx

Table S5. Population genetic parameters calculated across nonsynonymous coding sites of immune

genes. (.xlsx, 26 KB)

www.genetics.org/lookup/suppl/doi:10.1534/genetics.116.195016/-/DC1/TableS5.xlsx

Table S6. Summary of results from the Wilcoxon sum-rank tests between immune genes and their

genomic controls at nonsynonymous sites. (.xlsx, 13 KB)

www.genetics.org/lookup/suppl/doi:10.1534/genetics.116.195016/-/DC1/TableS6.xlsx