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. 2011 Sep 22;29(1):229–238. doi: 10.1093/molbev/msr197

Temporally Variable Selection on Proteolysis-Related Reproductive Tract Proteins in Drosophila

Alex Wong 1,*,†, Michael Turchin 1, Mariana F Wolfner 1, Charles F Aquadro 1
PMCID: PMC3283112  PMID: 21940639

Abstract

In order to gain further insight into the processes underlying rapid reproductive protein evolution, we have conducted a population genetic survey of 44 reproductive tract–expressed proteases, protease inhibitors, and targets of proteolysis in Drosophila melanogaster and Drosophila simulans. Our findings suggest that positive selection on this group of genes is temporally heterogeneous, with different patterns of selection inferred using tests sensitive at different time scales. Such variation in the strength and targets of selection through time may be expected under models of sexual conflict and/or host–pathogen interaction. Moreover, available functional information concerning the genes that show evidence of selection suggests that both sexual selection and immune processes have been important in the evolutionary history of this group of molecules.

Keywords: seminal proteins, sexual conflict, sperm competition, proteolysis, coevolution, immunity

Introduction

Comparative and population genetic studies have documented positive selection on reproductive tract proteins in a broad range of organisms, including invertebrates, vertebrates, and plants (Clark et al. 2006; Panhuis et al. 2006). Several hypotheses have been put forward to explain the phenomenon of rapid reproductive tract protein evolution in different taxa. These hypotheses include sperm competition, sexual conflict, host–pathogen interactions, avoidance of selfing, or avoidance of interspecific fertilization (reviewed in Nasrallah 2002; Swanson and Vacquier 2002; Takebayashi et al. 2003; Clark et al. 2006; Panhuis et al. 2006; Turner and Hoekstra 2008). Determining the contributions of these different mechanisms toward rapid reproductive protein evolution will require a combination of functional, comparative, and population genetic approaches.

In this study, we investigated patterns of reproductive protein evolution in Drosophila, focusing particularly on proteolysis regulators and targets of proteolysis. Proteolysis regulators include proteases as well as proteins that modulate protease activity, for example, protease inhibitors (PIs) and protease homologs (proteins that resemble proteases in sequence and structure but are thought to have regulatory noncatalytic functions; Ross et al. 2003).

Proteolysis regulators and their targets are of interest for several reasons. First, proteolysis is thought to play an important role in regulating the activity of other reproductive proteins, such as the male accessory gland proteins of Drosophila (Acps; Peng et al. 2005; Ravi Ram et al. 2006), as well as a number of mammalian seminal proteins (Szecsi and Lilja 1993; Malm et al. 2000; de Lamirande 2007). For example, proteolytic cleavage of Drosophila sex peptide (SP), a well-studied Acp, releases its bioactive C-terminal peptide from sperm, allowing this peptide to reach its targets and mediate several postmating responses over the long term (Peng et al. 2005). In addition, several putative cleavage products of the egg-laying prohormone ovulin are capable of inducing ovulation, suggesting that proteolysis of ovulin releases active peptide hormones (Heifetz et al. 2005). At least one protease produced in the accessory gland is necessary for ovulin cleavage (Ravi Ram et al. 2006), and it is thought that female factors are also required (Park and Wolfner 1995).

A second reason for our focus on proteolysis regulators is that expression and proteomic screens have identified transcripts or peptides of many of these genes in both the male and the female reproductive tracts in Drosophila (Swanson et al. 2001, 2004; Lawniczak and Begun 2004; Mueller et al. 2004; Mack et al. 2006; Kelleher et al. 2007; Allen and Spradling 2008; Findlay et al. 2008, 2009; Kapelnikov et al. 2008; Prokupek et al. 2008; Almeida and Desalle 2009). The diversity of these molecules in reproductive tracts suggests that they play important functional roles. Moreover, such proteins represent a promising set of molecules for the study of male–female coevolution due to the presence of both male- and female-derived proteolysis regulators and targets in the female reproductive tract following mating.

Finally, evolutionary considerations point at proteolysis regulators and their targets as interesting objects of study. Several previous studies have documented positive selection and/or rapid rates of duplication for reproductive tract proteolysis regulators (Panhuis et al. 2003; Swanson et al. 2004; Kelleher et al. 2007; Lawniczak and Begun 2007; Almeida and Desalle 2008; Findlay et al. 2008; Wong, Turchin, et al. 2008; Findlay et al. 2009; Kelleher and Markow 2009; Kelleher and Pennington 2009; Kelleher et al. 2011), suggesting that they may be involved in processes, such as sperm competition, sexual conflict, and host–pathogen interactions. Interestingly, although there is evidence for rapid evolution of individual proteolytic proteins, proteolysis regulators are components of seminal fluid in a broad range of taxa, including not only Drosophila but also other insects (Andrés et al. 2006; Braswell et al. 2006; Sirot et al. 2008) and mammals (Szecsi and Lilja 1993; Malm et al. 2000; Veveris-Lowe et al. 2007). This conservation of protein classes suggests an important reproductive function for proteolysis throughout animals.

Here, we investigate patterns of reproductive protein evolution using polymorphism data from 41 proteolysis regulators and three targets of proteolysis in Drosophila melanogaster and Drosophila simulans. We find evidence that selection on this group of genes is temporally heterogeneous, suggesting that the processes underlying selection are not constant through time. Such temporal variation can be generated in evolutionary arms races, such as are thought to occur in host–pathogen interactions and in sexual conflict. Interestingly, several genes subject to positive selection have documented or suspected roles in immunity or in sperm storage, further suggesting an important role for host–pathogen interactions and sperm competition in reproductive tract protein evolution.

Materials and Methods

Loci

We surveyed polymorphism at 3 loci encoding known targets of proteolysis, and 41 loci encoding proteolysis regulators—predicted proteases, PIs, or protease homologs. Protease homologs resemble proteases in primary sequence and tertiary structure but carry one or more catalytic site mutations such that they probably lack normal catalytic activity. Nonetheless, protease homologs have been reported to modulate protease activity either as agonists or as antagonists (e.g., Kwon et al. 2000; Lee et al. 2002; Asgari et al. 2003; Zhang et al. 2004; Gupta et al. 2005). Of the 44 loci that we studied, 17 (7 PIs and 10 protease/protease homologs) are known from an expressed-sequence tag screen by Swanson et al. (2004) to be expressed in the somatic portion of the female reproductive tract. Twenty-nine genes (9 PIs, 17 protease/protease homologs, and 3 targets) have strongly male accessory gland-biased expression and were initially identified as accessory gland specific by Swanson et al. (2001). Two genes (CG10363 and CG9456) were identified in both male and female reproductive tract screens. It should be noted that the degree of tissue specificity differs substantially between the male and female samples: The male accessory gland genes were selected on the basis of strong expression bias in the accessory glands (Swanson et al. 2001), and microarray studies examining 14 adult tissues support their high specificity (Chintapalli et al. 2007; FlyAtlas.org). The female reproductive tract genes, by contrast, were selected for expression in the uterus, oviducts and/or sperm storage organs but not for strongly biased expression and thus have varying degrees of tissue specificity. As such, we calculated the tissue specificity measure τ (Yanai et al. 2005) for each gene examined in this study using publicly available microarray data for 14 adult tissues (FlyAtlas.org). τ ranges between 0 and 1, with higher values indicating a higher degree of tissue specificity.

Previous studies have identified six Acps that undergo proteolysis following transfer to the female: SP, ovulin, the sperm storage protein Acp36DE, the protease CG11864, the protease homolog CG9997, and the PI CG9334. Here, we use the term “targets” to refer only to the first three of the six known targets of proteolysis, with the latter three considered under proteases or PIs, respectively. Table 1 lists all 44 loci, with predicted molecular functions, known biological roles, tissue specificity, sample sizes, and the sex in which reproductive tract expression was initially identified.

Table 1.

Genes Surveyed in this Study.

Gene Type Function/Effects Max. Tissue τ Sex Sample Size D. melanogaster Sample Size D. simulans
CG8982 (Acp26Aa, ovulin) Target Ovulation Male acc. gland 1 M 13 14
CG7157 (Acp36DE) Target Sperm storage Male acc. gland 1 M 11 10
CG17673 (Acp70A, sexpeptide) Target Remating, eggproduction and laying, feeding Male acc. gland 1 M 14 12
CG1262 (Acp62F) PI Sperm competition, toxic Male acc. gland 1 M 16 13
CG1342 PI Unknown Male acc. gland NA M 14 11
CG8137 PI Toxic Male acc. gland 1 M 11 14
CG10956 PI Unknown Male acc. gland 1 M 14 8
CG31902 PI Unknown Male acc. gland 1 M 12 11
CG32203 PI Unknown Male acc. gland 1 M 14 13
CG33121 PI Unknown Male acc. gland 1 M 14 14
CG9997 Prot. hom. Remating, eggproduction and laying, sperm release Male acc. gland 1 M 13 12
CG11864 Prot. Cleavage of ovulin Male acc. gland 1 M 12 15
CG6168 Prot. Immunity Male acc. gland 1 M 18 12
CG32382 Prot. hom. Immunity Male acc. gland 1 M 14 11
CG32383 Prot. hom. Immunity Male acc. gland 1 M 14 8
CG1895 Prot. Unknown Male acc. gland 1 M 15 14
CG6069 Prot. hom. Unknown Male acc. gland 1 M 20 12
CG9806 Prot. Unknown Male acc. gland 0.99 M 14 11
CG10586 Prot. Unknown Male acc. gland 1 M 14 15
CG10587 Prot. Unknown Male acc. gland 1 M 16 13
CG11037 Prot. Unknown Male acc. gland 1 M 16 15
CG11664 Prot. hom. Unknown Male acc. gland 1 M 14 14
C13518 Prot. Unknown Male acc. gland NA M 14 13
CG17242 Prot. Unknown Male acc. gland 1 M 13 12
CG18557 Prot. Unknown Male acc. gland 0.85 M 14 13
CG4847 Prot. Unknown Male acc. gland 0.96 M 10 9
CG32833 Prot. Unknown Male acc. gland 1 M 14 12
CG9456 PI Unknown Male acc. gland 0.96 F + M 14 14
CG10363 (TepIV) PI Immunity Hindgut 0.77 F + M 13 15
CG1857 (necrotic) PI Immunity Fat body 0.8 F 12 12
CG11331 PI Immunity Crop 0.86 F 15 14
CG1865 PI Unknown Spermathecae 0.7 F 12 13
CG3604 PI Unknown Hindgut 0.99 F 16 15
CG18525 PI Unknown Spermathecae 0.8 F 15 14
CG3066 Prot. Immunity Crop 0.68 F 14 12
CG3074 Prot. Eggshell matrix Crop 0.8 F 14 12
CG3097 Prot. Unknown Crop 0.91 F 15 13
CG9849 Prot. Unknown Salivary gland 0.5 F 13 12
CG9897 Prot. hom. Unknown Spermathecae 1 F 7 13
CG13318 Prot. hom. Unknown Spermathecae 0.95 F 15 12
CG14642 Prot. Unknown Spermathecae 0.86 F 11 12
CG18125 Prot. Unknown Spermathecae 1 F 12 12
CG31199 Prot. Unknown Eye 0.98 F 13 12
CG31681 Prot. Unknown Spermathecae 1 F 14 14
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Note. —Targets are proteins known to undergo proteolysis following mating. PI: Predicted protease inhibitors. Prot.: Predicted catalytic proteases. Prot. hom.: Predicted protease homologs. “Max. tissue” indicates tissue of highest expression, and τ is the tissue specificity measure of Yanai et al. (2005), with τ = 1 indicating absolute specificity and τ = 0 indicating equal expression in all tissues. “NA” in the τ column indicates that τ cannot be calculated because of low expression levels. “Sex” indicates whether a gene was included because of its identification in screens of the male (M) or female (F) reproductive tracts (Swanson et al. 2001; Swanson et al. 2004).

Drosophila Strains and DNA Sequencing

For polymorphism analyses in D. melanogaster, we used chromosome extraction lines for the X, second, and third chromosomes, isolated from isofemale lines derived from an Ugandan population (population samples are described in Pool and Aquadro 2006. Drosophila simulans sequences were collected from isofemale lines derived from a Madagascar population. Populations were chosen to reflect ancestral variation in D. melanogaster (Uganda: Pool and Aquadro 2006 and D. simulans (Madagascar: e.g., Kopp et al. 2006) in order to minimize the confounding effects of population bottlenecks associated with recent colonization events (e.g., Haddrill et al. 2005). For heterozygous sites in sequences from the D. simulans isofemale lines, one of the two bases was randomly selected. Phasing of multiple heterozygous bases in a single gene was not required since no gene harbored more than one heterozygous base.

DNA was extracted using the Puregene DNA purification kit (Gentra Systems, Minneapolis, MN). Loci were amplified by polymerase chain reaction (PCR), and PCR products were sequenced using BigDye chemistry (Applied Biosystems, Foster City, CA) on an ABI 3730 automated sequencer at the Cornell University Life Sciences Core Laboratories Center. PCR and sequencing primer sequences are given in the supplementary data, Supplementary Material online. Sequence alignments were performed using the ClustalW algorithm as implemented in CodonCode Aligner (CodonCode Corp., Dedham, MA).

Molecular Population Genetics

Summary statistics (π, θ) were calculated using the Analysis software package, which is based on the libsequence C++ libraries (Thornton 2003). For inferences of selection at loci encoding putative proteolysis regulators and targets of proteolysis, we used two classes of method: those that infer selection on a recent timescale (∼0.1 Ne—Przeworski 2003) from the site–frequency spectrum (SFS) and those that infer historical selection using both polymorphism and divergence data. For inference of recent selection, we used Tajima’s D (Tajima 1989) and Fay and Wu’s H (Fay and Wu 2000), both of which test predictions concerning specific subsets of the SFS. We also used the clsw method of Kim and Stephan (2002), which uses several features of the SFS to increase power and reduce the chance of false positives. We also applied to our data a multilocus version of the Hudson–Kreitman–Aguadé (HKA) test (Hudson et al. 1987; Wright and Charlesworth 2004), which can detect reductions in sequence variability following selective sweeps. As presumably neutral reference loci, we used four noncoding intergenic regions sequenced in the Uganda population of D. melanogaster by Pool and Aquadro (2006) (see also Wong, Turchin, et al. 2008) or five noncoding regions sequenced in the Madagascar population of D. simulans by Nolte and Schlötterer (2008).

We used the McDonald–Kreitman (MK) test (McDonald and Kreitman 1991) to make inferences about historical selection using a combination of polymorphism and divergence data. We also used our MK data to estimate the rate of adaptive amino acid substitution using the method of Bierne and Eyre-Walker (2004).

Interlocus linkage disequilibrium (LD) parameters were estimated using a custom Perl script (available upon request). For every pair of loci, the correlation coefficient r2 was calculated for each pair of polymorphic amino acid sites (each with the minor allele represented at least twice in the sample). For each pair of loci, ZnS, the average of all pairwise values of r2, was used as a summary measure of interlocus LD (Kelly 1997). We did not analyze pairs of genes sequenced in fewer than five strains. The significance of each interlocus ZnS value was assessed via permutation tests, whereby 10,000 permutations were generated by swapping labels (strain names) on haplotypes. In this way, intralocus haplotype structure was maintained for the permutations, but interlocus LD was randomized.

Results and Discussion

A variety of hypotheses have been proposed to explain the observation that an unusual proportion of reproductive tract proteins in Drosophila and other organisms evolve rapidly and adaptively. Male–female coevolution, sperm competition, and host–pathogen interactions are among the leading proposals (Civetta 2003; Clark et al. 2006; Panhuis et al. 2006; Chapman 2008; Turner and Hoekstra 2008), but it has proven difficult to distinguish between these potential mechanisms. Here, we present results from a molecular population genetic survey of reproductive tract proteolysis regulators and targets of proteolysis in Drosophila. Our findings have implications for the broad understanding of the molecular evolution of reproductive tract proteins.

Patterns of Diversity

We sequenced 44 loci encoding known targets of proteolysis and putative proteolysis regulators in population samples of D. melanogaster and D. simulans. An average of 13.7 and 12.5 alleles were sequenced for each locus in the D. melanogaster and D. simulans samples, respectively. Over all loci, average diversity in D. melanogaster (π) was 0.007 (standard deviation [SD] = 0.005). Diversity was substantially higher in D. simulans, where mean π = 0.015 (SD = 0.006). Both estimates are similar to those previously documented in the literature (e.g., Andolfatto 2005; Begun et al. 2007). The difference in diversity between the two species was highly significant (paired t-test P = 3.3 × 10−8), consistent with a larger effective population size in D. simulans. Systematic differences were also observed in the SFS between the two species, with a significantly lower Tajima’s D in D. simulans indicating a relative excess of rare alleles in that species (mean D = −0.36 in D. melanogaster, −0.87 in D. simulans; paired t-test P = 0.00013).

Inferences of Recent Selection

We used several methods to infer the action of recent directional selection in D. melanogaster and D. simulans. Tajima’s D and Fay and Wu’s H detect departures from the neutral equilibrium model using specific portions of the SFS (an excess of rare alleles and high frequency–derived alleles, respectively), and the more recent compositive likelihood method of Kim and Stephan (2002) (clsw) uses a spatially explicit model of selection to test several features of the standard hitchhiking model. Additionally, the HKA test detects local reductions in polymorphism; in the multilocus version implemented here (Wright and Charlesworth 2004), variation at a gene of interest is compared with variation at multiple presumably neutral noncoding loci.

Tests of the SFS find virtually no evidence of recent selection on any of the 44 loci tested in either D. melanogaster or D. simulans (fig. 1). At a false discovery rate (FDR; Storey and Tibshirani 2003) of 0.1, no locus in either species shows significant deviations from neutrality using Tajima’s D test, Fay and Wu’s H test, or Kim and Stephan’s clsw test. These data suggest that selection has not acted on the sampled loci on a time scale that leaves a signature in the SFS.

FIG. 1.

FIG. 1.

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Temporally variable selection on reproductive tract proteolysis regulators and targets. P values are given for all 44 genes surveyed in each species for a variety of neutrality tests (Tajima’s D, Fay and Wu’s H, Kim and Stephan’s clsw, the HKA test, and the McDonald–Kreitman test). The dotted line represents P = 0.05.

In D. simulans, HKA tests similarly suggest a paucity of recent selection on the sampled genes with no locus rejecting neutrality at an FDR of 0.1. By contrast, we find that 7 loci of 44 show evidence for a local reduction in variation in D. melanogaster using the HKA test, consistent with the action of recent selection (fig. 1, table 2). Our inference of selection using the HKA test but not with methods that detect skews in the SFS toward rare alleles (D, clsw) is consistent with very recent selection such that there are insufficient rare variants present in the sequenced region to have power to detect selection.

Table 2.

P Values for Statistical Tests of Selective Neutrality on Genes Evidence for Positive Selection Along the D. melanogaster Lineage.

Gene Sex Ontology Tajima’s D Fay and Wu’s H clsw HKA MK
CG10587 M Prot. 0.44 0.93 0.98 0.0049 0.56
CG11037 M Prot. 0.11 0.62 0.94 0.0023 0.51
CG14642 M Prot. 0.09 0.2 0.57 0.00026 1
CG17242 M Prot. 0.58 0.13 0.95 0.93 0.00087
CG32382 M Prot. hom. 0.51 0.097 0.85 0.97 0.0033
CG8137 M PI 0.21 0.18 0.97 0.88 0.00065
CG8982 (ovulin) M Target 0.14 0.031 0.63 0.43 0.0026
CG9456 F + M PI 0.41 0.75 0.89 0.00093 0.083
CG18125 F Prot. 0.15 0.12 0.91 0.0056 0.42
CG1865 F PI 0.11 0.098 0.38 0.016 0.067
CG3066 F Prot. 0.52 0.16 0.96 0.0022 1
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Note. —P values in bold are significant at a FDR of 0.1.

Inferences of Ancestral Selection

We used MK tests to infer historical selection on protein sequences in each species individually. This test is capable of detecting an excess of amino acid substitutions between two species, suggesting a history of repeated positive selection on protein sequence. Using this approach, we find evidence for selection at multiple loci in each species: At an FDR of 0.1, four loci reject neutrality in D. melanogaster (table 2) and nine loci reject neutrality in D. simulans (table 3). Thus, although patterns of very recent selection appear to differ for the two species, reproductive tract proteolysis regulators and targets have been subject to selection on a deeper time scale in both species. Notably, the sets of loci showing evidence for selection using the MK test on the one hand and the HKA test on the other are mutually exclusive—that is, no locus rejects neutrality using both tests (table 2).

Table 3.

P Values for Statistical Tests of Selective Neutrality on Genes Evidence for Positive Selection Along the D. simulans Lineage.

Gene Sex Ontology Tajima's D Fay and Wu’s H clsw HKA MK
CG10363 M/F PI 0.14 0.47 1 0.00034
CG17242 M Prot. 0.47 0.38 0.99 0.56 0.022
CG32203 M PI 0.039 0.93 1 0.39 3.1x106
CG32833 M Prot. 0.10 0.27 0.97 0.70 0.00031
CG4847 M Prot. 0.21 0.21 0.81 0.47 0.00015
CG6069 M Prot. hom. 0.18 0.26 0.91 1 0.022
CG8137 M PI 0.40 0.33 0.98 1 0.0051
CG8982 M Target 0.18 0.20 0.88 0.47 0.0065
CG9997 M Prot. hom. 0.26 0.31 0.91 0.67 0.010
CG3066 M Prot. 0.30 0.25 0.67 0.74 0.0033
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Note. —P values in bold are significant at a FDR of 0.1.

In order to make a quantitative comparison of the rate of adaptive substitution between species and between sexes, we used the method of Bierne and Eyre-Walker (2004) (fig. 2). Combining data from multiple genes, this method estimates α, the proportion of amino acid substitutions that have been adaptive, under the assumption that synonymous polymorphisms and substitutions are neutral. In our data set, estimates of α show differences both between species and between sexes. Averaging over all loci (i.e., for genes expressed in either sex), α is significantly higher in D. simulans than in D. melanogaster (simulans: α = 0.52, 95% confidence interval [CI]: 0.44, 0.59; melanogaster: α = 0.27, 95% CI: 0.12, 0.40), reflecting the finding that more than twice as many single loci reject neutrality in single gene MK tests. Within D. melanogaster, α is higher for male-specific genes than for female reproductive tract genes, although not significantly so (males: α = 0.34, 95% CI: 0.18, 0.47; females: α = −0.04, 95% CI: −0.47, 0.27). No difference is apparent in D. simulans (males: α = 0.52, 95% CI: 0.43, 0.60; females: α = 0.54, 95% CI: 0.44, 0.59). Thus, these estimates also suggest variation in the rate of adaptive substitution, particularly for genes expressed in the female reproductive tract.

FIG. 2.

FIG. 2.

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Estimates of the rate of adaptive amino acid substitution (α) for 44 reproductive tract genes in D. melanogaster and D. simulans using the method of Bierne and Eyre-Walker (2004). Genes are separated according to their expression in the male or female reproductive tracts, with “all” representing all genes regardless of site of expression.

Temporally Variable Selection

By using statistical tests that are sensitive to selection at different timescales, we can begin to make inferences concerning the consistency—or lack thereof—of selection over time (e.g., Jensen and Bachtrog 2010). Our population genetic data suggest that selection on reproductive tract proteolysis regulators and targets is temporally heterogeneous. On a recent timescale, we find evidence for recent selection on three accessory gland proteins, three female reproductive tract proteins, and one protein present in the reproductive tracts of both sexes in D. melanogaster as indicated by a reduction in polymorphism in HKA tests (table 2). By contrast, our D. simulans population sample shows no evidence for recent selection at any locus by either the HKA test or the neutrality tests based on the SFS. On a deeper timescale, estimates of the rate of adaptation suggest similar patterns of adaptive evolution for male and female reproductive tract genes in D. simulans but a slower rate for female reproductive tract genes in D. melanogaster.

Models of sexual selection and sexual conflict generate a variety of predictions with respect to the frequency, direction, and extent of trait evolution (e.g., Iwasa and Pomiankowski 1995; Gavrilets 2000; Gavrilets and Hayashi 2006). Predictions of a long-term coevolutionary chase are frequently derived, and indeed, such an evolutionary regime could generate some of the patterns described in this study (e.g., consistently strong selection on male reproductive tract proteins). Gavrilets and Hayashi (2006) emphasized that initial values of key parameters, such as genetic variance in male and female traits and the strength of selection on males and females, can have important consequences for the outcome of sexual conflict. For example, differences in these parameters can determine whether a population undergoes a long-term arms race or if it ultimately evolves toward the male optimum. We therefore suggest that differences in such parameters may underlie our inference of temporally variable selection on reproductive tract proteins.

Functional Characteristics of Positively Selected Loci

The functional characteristics of positively selected genes suggest roles for immunity and sexual selection/sexual conflict in driving reproductive tract protein evolution. At least three genes showing evidence for positive selection in this study have documented or suspected roles in immunity (CG32382—Kambris et al. 2006; CG3066—Castillejo-López and Häcker 2005; CG10363—De Gregorio et al. 2001). As such, host–pathogen interactions may underlie their rapid evolution. Naturally occurring sexually transmitted diseases have not to our knowledge been documented in Drosophila, but the risk of pathogen introduction during mating has been demonstrated (Miest and Bloch-Qazi 2008). Several Acps appear to have antibacterial activity (Lung et al. 2001; Mueller et al. 2007), and genes with known roles in immunity are expressed in the reproductive tracts of both males and females (table 1). Mating alters the expression levels of several antimicrobial peptides in females (Lawniczak and Begun 2004; McGraw et al. 2004; Peng, Zipperlen, et al. 2005; Mack et al. 2006; Domanitskaya et al. 2007; Kapelnikov et al. 2008; Winterhalter and Fedorka 2009), although the physiological consequences of these gene expression changes are not clear (Fedorka et al. 2007; Wigby et al. 2008; ). Together, these observations raise the possibility that host–pathogen interactions in the female reproductive tract could also contribute to rapid Acp evolution (see also Lawniczak et al. 2007).

Furthermore, three additional positively selected genes may have roles in sperm storage or sperm competition: The Acp PI CG8137 localizes to the sperm storage organs (SSO) following mating (Ravi Ram et al. 2005), and the predicted PI CG1865 and the predicted protease CG18125 have biased expression in the female sperm storage organs (table 1; FlyAtlas.org). Previous studies have found roles in sperm storage for several Acps that localize to the SSO (Bertram et al. 1996; Ravi Ram and Wolfner 2007; Wong, Albright, et al. 2008). Thus, effects of these genes on sperm competition and/or sperm preference may underlie selection.

Finally, we also found evidence for positive selection on two genes with known effects on female egg laying, CG8982 (which encodes the ovulation hormone Ovulin) and the predicted protease homolog CG9997 (table 3). CG9997 is necessary for the maintenance of SP (Ravi Ram and Wolfner 2009), a small peptide hormone that induces egg production and egg laying (Chen et al. 1988; Aigaki et al. 1991; Chapman et al. 2003; Liu and Kubli 2003), increases female feeding postmating (Carvalho et al. 2006) and decreases female sleep and lifespan (Wigby and Chapman 2005; Isaac et al. 2010). Given its effects on the mated female, SP is an excellent candidate as a molecular agent of sexual conflict. Nonetheless, SP shows little evidence for positive selection; it may be that interactions with multiple receptors impose substantial constraints on its evolution (Yapici et al. 2008; Ja et al. 2009). We suggest that selection may instead act on molecules that modulate SP activity, such as CG9997. In this regard, it will be interesting to investigate the molecular evolution of other proteins that interact with SP and CG9997 (Ravi Ram and Wolfner 2009).

Linkage Disequilibrium

Models of mate choice predict LD between trait and preference loci (e.g., Kirkpatrick 1982). Importantly, this LD arises solely as a consequence of biased mating such that physical linkage is not a prerequisite. If females bearing a preference allele P mate preferentially with males bearing trait allele T, then we should expect to see an excess of offspring carrying both the P and the T alleles. By analogy, we predicted that loci involved in postcopulatory sexual selection might show elevated LD as has been shown to be the case in abalone (Clark et al. 2009). Thus, in an attempt to identify coevolving genes in our sample, we calculated the LD summary statistic ZnS (Kelly 1997) for pairs of genes. We considered only LD between amino acid polymorphisms since these are most likely to reflect protein coevolution. Furthermore, we limited our analysis to pairs of genes sequenced in five or more strains. Significance of individual ZnS values was assessed using a permutation test (10,000 permutations).

No pair of loci showed significant LD at an FDR of 0.1. We note, however, that our data set is not ideal for this analysis because of our relatively low sample size (between 5 and 20 chromosomes sampled per locus) and a large number of tests. Several pairs of loci do show elevated ZnS when using a less stringent cut-off of P < 0.01 (table 4) and may represent promising candidates for future biochemical, genetic, and population genetic studies. Of particular interest are three gene pairs with high levels of LD between targets of proteolysis and proteolysis regulators: The proteolysis target Acp36DE (CG7157) shows relatively high LD with the predicted protease CG9806 in D. simulans, and ovulin (Acp26Aa/CG8982) is in high LD with the predicted PIs CG33121 and CG18525 (also in D. simulans).

Table 4.

Pairs of Loci with High Levels of LD.

Locus 1 Locus 2 Sex—Locus 1 Sex—Locus 2 Ontology—Locus 1 Ontology—Locus 2 Species ZnS Pa
CG32203 CG13318 M F PI Prot. hom. D. melanogaster 0.47 0.0033
CG9806 CG7157 (Acp36DE) M M Prot. Target D. simulans 0.17 0.0016
CG8982 (ovulin) CG33121 M M Target PI D. simulans 0.18 0.0055
CG8982 CG18525 M F Target PI D. simulans 0.21 0.0088
CG1342 CG1857 M F PI PI D. simulans 0.27 0.0091
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a

P values were calculated from 10,000 permutations.

Segregating and Fixed Putative Loss-of-function Alleles

Previous studies have suggested that Acps tend to turn over rapidly between species. Orthologs to many D. melanogaster Acps are not detected in distantly related species (Mueller et al. 2005; Wagstaff and Begun 2005a; Haerty et al. 2007) (although in some cases, high levels of sequence divergence may preclude detection using reciprocal blast), and many Acps from other species of Drosophila are similarly lineage specific (Holloway and Begun 2004; Wagstaff and Begun 2005b; Begun et al. 2006; Kelleher et al. 2007; Findlay et al. 2008, 2009). The population samples that we sequenced in this study harbored a number of putative loss-of-function alleles at multiple loci (table 5) that may represent loci becoming pseudogenized. In two cases (CG31681 and CG32383), a single allele was sequenced with a premature stop codon. The low frequencies of these alleles may be consistent with mutation–selection balance. However, in the case of CG14642, a female-expressed protease, 6 of 16 D. melanogaster alleles carried single base pair frameshifts due to at least three independent mutational events (fig. 3). It is tempting to posit that CG14642 is in transit toward becoming a pseudogene.

Table 5.

Putative LOF Aalleles Observed in D. melanogaster at 3 of the 41 Proteolysis Regulators and Three Targets of Proteolysis.

Gene # Uunique LOF Alleles Type Frequency
CG31681 1 Premature stop 1/14
CG32383 1 Premature stop 1/14
CG14642 3 Frameshift, premature stop 4/16, 1/16,1/16
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Note. —Frequency indicates the number of LOF alleles observed at each gene relative to the number of chromosomes sampled from the population. LOF = loss of function.

FIG. 3.

FIG. 3.

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Putative loss-of-function alleles of D. melanogaster CG14642. In this schematic of the protein, red represents the predicted proteolytic domain of this protein, with the catalytic residues H189, D239, and S337 indicated as black bars. Two frame shift mutations (fs 82 and fs 303) as well as an allele with a premature stop codon (G943→T) were detected in our population sample of 16, with frequencies indicated in parentheses.

Summary

Our population genetic survey of 44 reproductive tract proteolysis regulators and targets of proteolysis revealed evidence that positive selection on these genes is not only frequent but is also variable through time and between species. For this group of genes, very recent selection appears to be more common in D. melanogaster than in D. simulans (tables 2 and 3), whereas on a deeper time scale, the rate of adaptive substitution is higher in D. simulans (fig. 2). These findings suggest that the strength and targets of selection change over time, consistent with an ongoing arms race between sexes and/or between host and pathogen. Moreover, available functional data on genes subject to positive selection suggest roles for both sexual selection and immunity in driving their evolution.

Supplementary Material

Supplementary data are available at Molecular Biology and Evolution online (www.mbe.oxfordjournals.org/).

Supplementary Data
supp_29_1_229__index.html (762B, html)

Acknowledgments

We thank Andrew Clark, Rick Harrison, and an anonymous reviewer for helpful comments on this manuscript. We acknowledge funding support from National Institute of Health grants to M.F.W. (R01-HD038921) and CFA (R01-GM036431) and from an National Science Foundation Dissertation Improvement Grant to A.W. (DEB 0508152). A.W. was a Howard Hughes Medical Institute predoctoral fellow during this work. M.C.T. received support from the Cornell-HHMI Scholar Summer Fellowship Program.

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