Molecular Ecology (2015) 24, 4286–4295

doi: 10.1111/mec.13222

Major histocompatibility complex similarity and sexual selection: different does not always mean attractive C L E L I A G A S P A R I N I , * † L E O N A R D O C O N G I U * and A N D R E A P I L A S T R O * *Department of Biology, University of Padova, Padova 35100, Italy, †Centre for Evolutionary Biology, School of Animal Biology, University of Western Australia, Crawley, WA 6009, Australia

Abstract Females that mate multiply have the possibility to exert postcopulatory choice and select more compatible sperm to fertilize eggs. Prior work suggests that dissimilarity in major histocompatibility complex (MHC) plays an important role in determining genetic compatibility between partners. Favouring a partner with dissimilar MHC alleles would result in offspring with high MHC diversity and therefore with enhanced survival thanks to increased resistance to pathogens and parasites. The high variability of MHC genes may further allow discrimination against the sperm from related males, reducing offspring homozygosity and inbreeding risk. Despite the large body of work conducted at precopulatory level, the role of MHC similarity between partners at postcopulatory level has been rarely investigated. We used an internal fertilizing fish with high level of multiple matings (Poecilia reticulata) to study whether MHC similarity plays a role in determining the outcome of fertilization when sperm from two males compete for the same set of eggs. We also controlled for genomewide similarity by determining similarity at 10 microsatellite loci. Contrary to prediction, we found that the more MHC-similar male sired more offspring while similarity at the microsatellite loci did not predict the outcome of sperm competition. Our results suggest that MHC discrimination may be involved in avoidance of hybridization or outbreeding rather than inbreeding avoidance. This, coupled with similar findings in salmon, suggests that the preference for MHC-dissimilar mates is far from being unanimous and that pre- and postcopulatory episodes of sexual selection can indeed act in opposite directions. Keywords: major histocompatibility complex, Poecilia reticulata, postcopulatory, sexual selection, sperm competition Received 3 February 2015; revision received 3 April 2015; accepted 8 April 2015

Introduction The adaptive function of polyandrous mating behaviour (multiple mating with more than one male) by females in the absence of direct benefits is still one of the most debated topics in evolutionary biology (e.g. Zeh & Zeh 1997; Jennions & Petrie 2000; Simmons 2001; Slatyer et al. 2012). In those species in which males contribute to reproduction only with their sperm, and females are

Correspondence: Clelia Gasparini, Fax: +61 (8) 64881029; E-mail: [email protected] Clelia Gasparini and Leonardo Congiu equally contributed to this work.

not sperm limited, a number of hypotheses involving different indirect (genetic) benefits have been proposed to explain the evolution of polyandry. Among these hypotheses, the ‘genetic compatibility’ hypothesis proposes that females obtain nonadditive benefits arising from the optimal combination of maternal and paternal haplotypes (Zeh & Zeh 1996, 1997; Jennions 1997), with inbreeding avoidance being a special case of incompatibility avoidance (Tregenza & Wedell 2000). When precopulatory mate choice is constrained or not efficient (e.g. because cues to assess relatedness are lacking or forced copulations occur), mating with multiple males creates the opportunity for females to use postcopulatory mechanisms to bias the outcome of sperm © 2015 John Wiley & Sons Ltd

M H C A N D P O S T C O P U L A T O R Y S E X U A L S E L E C T I O N 4287 competition (when sperm from more than one male compete to fertilize the eggs) in favour of the most compatible (or less related) mate. The major histocompatibility complex (MHC), a group of highly polymorphic genes responsible for disease resistance and immune function in vertebrates (Bernatchez & Landry 2003), is likely to be involved in determining compatibility between mates (Tregenza & Wedell 2000; Ejsmond et al. 2014). In a sexual selection context, MHC-driven female choice can evolve as a means of increasing MHC heterozygosity in the offspring or avoiding inbreeding, with the relative importance of these mechanisms depending on the relative risk and costs of inbreeding and pathogen-related diseases in the population (Reusch et al. 2001). If the risk of inbreeding is high, females should prefer MHC-dissimilar males as a means to avoid relatives, but when parasites are prevalent, females should seek not just partners with dissimilar MHC alleles but with the ideal MHC pattern to complement with their own, in order to produce offspring with enhanced survival thanks to increased resistance to pathogens and parasites (Milinski 2006). The role of MHC-based female choice at precopulatory level has been investigated in a range of different species (for reviews see for example Milinski 2006; Kamiya et al. 2014). Studies on postcopulatory preference at MHC level are, in contrast, very scarce, also due to the technical challenges of disentangling precopulatory from postcopulatory mechanisms. MHC loci are among the most variable regions in the genome in several species (Spurgin & Richardson 2010) and provide a genetic cue that can potentially mediate the recognition of incompatible partners through sperm choice. In particular, it has been hypothesized that polymorphic ligands on egg surface or within the female genital tract, such as soluble MHC molecules or MHCbound peptides, may bind sperm cells if their ligands appear as nonself (Ziegler et al. 2005). Indeed, a MHClike gene is involved in self-recognition and sperm–egg incompatibility in the protochordate Botryllus schlosseri (Scofield et al. 1982). Furthermore, the expression of MHC-linked odorant receptors on sperm can potentially enable females to directly affect sperm motility or create chemotactic cues (e.g. Fukuda et al. 2004; Spehr et al. 2006; Pitts et al. 2014). Therefore, there is the intriguing possibility that MHC genes are involved in mediating sperm choice. The few studies investigating the role of MHC at the postcopulatory level failed to support the prediction that MHC-dissimilar males are favoured at the postcopulatory level. In particular, MHC did not significantly influence female sperm choice in four different studies (Wedekind et al. 1996, 2004; Skarstein et al. 2005; Løvlie et al. 2013), and, contrary to predictions, MHC-similar © 2015 John Wiley & Sons Ltd

males fertilize a greater proportion of eggs than MHCdissimilar males in the Atlantic salmon (Salmo salar) (Yeates et al. 2009, 2013). One particular case of incompatibility avoidance is inbreeding avoidance, and polyandry can allow females to select sperm from unrelated males when female precopulatory choice is constrained. Indeed, kin discrimination at the postcopulatory level has been found in the guppy (Poecilia reticulata) (Gasparini & Pilastro 2011; Fitzpatrick & Evans 2014). Both studies found that females that were artificially inseminated with equal number of sperm from one related and one unrelated male have a higher proportion of offspring sired by the nonsibling male than by full-sibling male. Moreover, Gasparini & Pilastro (2011) found that the process is likely to be mediated by the differential effect of the ovarian fluid (the fluid obtained from female reproductive tract) on sperm velocity, an important predictor of sperm competition success in this species (Boschetto et al. 2011). This latter finding suggests that the outcome of competitive fertilization is determined by the interaction between sperm and the female reproductive tract, suggesting MHC as an excellent candidate for the underlying sperm recognition mechanism. Therefore, this species is ideal to investigate whether MHC plays a role in postcopulatory processes and in particular in determining the outcome of competitive fertilization. In this study, we investigate the role of MHC in postcopulatory sexual selection using the guppy. The guppy is a livebearing fish, with internal fertilization and an elevated level of polyandry (Neff et al. 2008b). Guppies show high genetic variability at these loci (Fraser & Neff 2009; Fraser et al. 2010a,b; Herdegen et al. 2014; Lighten et al. 2014), which is compatible with involvement of this genetic marker in postcopulatory mechanisms of kin recognition at the gamete level. We predict that under competitive conditions, fertilization success will be biased towards males with a lower allele similarity with the female (i.e. the more MHC-dissimilar male). To test our idea, we used a paired experimental design in which equal numbers of sperm from two randomly chosen males were used to artificially inseminate two virgin, unrelated females. The use of a paired design, in which ejaculate from the same male is split and used with two different females, allowed us to control for intrinsic differences in sperm competitiveness among competitors (Garcıa-Gonz alez 2008), whereas the use of artificial insemination permitted to control for precopulatory effects (e.g. MHC-based female preference for dissimilar mate) that may influence the outcome of fertilization success (see Pilastro et al. 2004, 2007 for an effect of female precopulatory preference on insemination success; and Gasparini & Pilastro 2011 for a similar experimental design). We assessed the

4288 C . G A S P A R I N I , L . C O N G I U and A . P I L A S T R O paternity of the resulting broods and related this to the difference among mates in their genetic similarity at the MHC class II locus. We also controlled for relatedness and genomewide effects by further genotyping males and females at 13 microsatellite loci.

Materials and methods Study population The fish used were descendants of wild-caught fish from the lower part of Tacarigua River in Trinidad (national grid reference PS 787 804) in 2002. Experimental fish descend from about 400 adult founders, which included about 200 large, pregnant females and, after foundation, were maintained at a population size constantly >1000. Males were reared in large stock tanks (around 100 individuals) with an approximately 1:1 sex ratio, whereas virgin females were kept in single-sex tanks. Using virgin females allowed us to exclude any influence of stored sperm on fertilization. All fish used in the experiment were maintained under the same conditions of temperature (26  1 °C) and light (12:12-h light/dark cycle) and fed a mixed diet of brine shrimp nauplii (Artemia salina) and commercial food.

Artificial inseminations experiment and paternity analysis We investigated whether genetic similarity at MHC and neutral loci affects the outcome of fertilization under sperm competition using artificial insemination technique (as in Gasparini & Pilastro 2011; Fitzpatrick & Evans 2014) to simultaneously control for precopulatory male–female interactions and standardize the number of sperm from the two males. Ejaculates were collected from two males from stock populations, and equal numbers of sperm were used to artificially inseminate two randomly chosen virgin females (following an established procedure, Evans et al. 2003). All fish were randomly selected and therefore likely to be unrelated among each other. The use of a paired mating design (same pair of males with two different females) allowed us to control for intrinsic differences among males in sperm competitiveness (Evans & Rutstein 2008; this study, see below) and for the stochastic effects due to the random assignment of competitors (Garcıa-Gonz alez 2008). This procedure was replicated for 18 fish quartets. Four females (from four different quartets) did not produce offspring, and their quartets were therefore excluded from subsequent genetic analyses. Our final sample size, therefore, included two broods (one for each female in the quartet) in 14 quartets (total of 28 broods, 28 males and 28 females). No post-partum mor-

tality was recorded in the offspring, and newborns were killed with an excess of anaesthetic (MS222) within a few hours after birth. Tissues for DNA analyses (whole body of newborn offspring and fin clip collected from parents) were preserved in absolute ethanol until required. Paternity was unequivocally assigned to putative sires for all offspring (N = 351) according to allele sharing at three microsatellite loci (see later). Paternity assignment using CERVUS v. 3.0 (http://www.fieldgenetics.com) yielded identical results and all offspring were assigned with >99% probability.

Microsatellites and MHC analysis DNA was extracted from offspring tissues using a Chelex protocol (Walsh et al. 1991) and from adult fin clips using a standard salting out protocol (Patwary et al. 1994). To assess genetic similarity at neutral loci between partners, samples from adults were genotyped at 13 microsatellite loci (Table S1, Supporting information). PCR amplifications consisted of an initial denaturation step of 1 min at 95 °C followed by 27 cycles at 95 °C for 10 s, a primer-specific annealing temperature for 30 s, 72 °C for 30 s and with a final extension 72 °C for 5 min. PCR products were genotyped using an ABI 3100 sequencer and analysed using PEAK SCANNER software v 1.0 (Applied Biosystems). Variation at MHC loci was assessed by sequencing the exon 2 of MHC class IIB, containing 12 putative antigen recognition sites (van Oosterhout et al. 2006a). Guppy-specific primers Tu 1292 and Tu 1293 (Sato et al. 2000) were used for all amplifications. Amplifications were performed in a total volume of 20 lL with 1X PCR Buffer (GE Healthcare), 1.5 mM MgCl2, 0.75 lM of each primer, 0.2 mM of each dNTP and 10–50 ng of extracted DNA template. An Applied Biosystems GeneAmp PCR System2700 was set up with the following temperature profile: 94 °C for 3 min, followed by 35 cycles of 1 min at 94 °C, 30 s at 52 °C, 1 min at 72 °C and a final extension for 7 min at 72 °C. PCR products were checked by 1.8% agarose gel electrophoresis and purified using ExoSAP-IT (USB). Purified PCR products were directly sequenced using an ABI 3100 automated sequencer. Amplification products were also cloned into JM109 competent cells using the P-GEM-T Easy vectors (Promega) following the manufacturer’s recommendations. A minimum of 10 clones per individual were sequenced for allele identification, and this number was increased until all the polymorphisms observed as multiple peaks in the genomic sequence profiles were detected. This procedure reduces the possibility of missing some alleles but does not completely exclude it, especially in the case of rare alleles which are likely to be present in a single copy within individual. However, © 2015 John Wiley & Sons Ltd

M H C A N D P O S T C O P U L A T O R Y S E X U A L S E L E C T I O N 4289 possible unsampled, rare alleles are unlikely to introduce a significant bias in our analyses, as we do not expect any kind of preferential sharing of these alleles between the individuals analysed. Consequently, such omissions are likely to have little effect on our similarity estimates. Sequences were edited and aligned using CHROMAS 1.41 and CLUSTALW (http://www.ebi.ac.uk/ Tools/clustalw/index.html, Thompson et al. 1994). As the cloning of PCR product can generate artefacts, we followed conservative criteria for allele identification: sequences were considered to be true alleles only when occurring in more than one clone, from the same or from different individuals, and confirmed by two independent PCRs. Sequences observed in the same individual and differing by a single nucleotide were considered as sequencing errors and were accordingly collapsed if not observed in other animals. Moreover, some individuals with putative rare alleles were also recloned from a second, independent PCR (Barbisan et al. 2009). Nucleotide sequences were translated to amino acid sequences using the software MEGA 5.04 (Tamura et al. 2011) for the identification of indels or stop codon that would be indicative of pseudogenes. The same programme was also used to compute overall mean distances estimated as number of differences at both nucleotide and amino acid level. All nucleotide and amino acid sequences were compared with GenBank database to check the identity with previously published alleles.

Statistical analysis Paternity analysis. For each pair of competing males, one putative sire was randomly labelled as male B. Sperm competition success of male B was determined from his paternity share (PB) with each of the two females. The observed PB distribution (observed SD in PB) was compared with the expected null distribution assuming equal sperm competition success (Evans et al. 2003), by generating 10 000 simulated distributions of PB with expected sperm competition success = 0.5 and the observed brood sizes, using POPTOOLS v3.2.5 (available at http://www.poptools.org). One-way ANOVA of arcsine-transformed PB values was used for assessing whether males differ in their relative fertilization success (Evans & Rutstein 2008). Intraclass correlation coefficient was used to estimate repeatability of fertilization success (Lessells & Boag 1987). Genetic similarity. The presence of null alleles and consequent deviation from Hardy–Weinberg equilibrium was detected by Micro-checker (Van Oosterhout et al. 2004) at three loci (pr80, pr171 and pr172). These microsatellites were therefore excluded from analyses, and © 2015 John Wiley & Sons Ltd

the overall genetic similarity between partners was inferred at the 10 remaining loci using the estimator of Lynch & Ritland (1999) and Queller & Goodnight (1989), as implemented in the software GENALEX version 6.1 (Peakall & Smouse 2006). However, results did not change when all 13 microsatellite loci were used (data not shown). Genetic similarity at the MHC locus was expressed as allele sharing and calculated as the twice the number of alleles shared between the two individuals over the total number of alleles present in the two individuals (Wetton et al. 1987). Genetic similarity was first calculated for each female with each of the two males (A and B) (i.e. SimA-F1 and SimB-F1, for female F1; SimA-F2 and SimB-F2, for female F2). A similarity index of 0 for a given pair means that male and female did not share any alleles, while an index of 1 means that they share all alleles. Then, for each quartet, we calculated the difference in genetic similarity (hereafter DSim) between each female and the two males (DSimBASimA-F1, and DSimBA-F2 = SimB-F2 F1 = SimB-F1 SimA-F2, for F1 and F2, respectively). A positive value of, for example, DSimBA-F1 means that male B is more similar to female 1 than male A, while a negative value means that male A is more similar. Effect of MHC on fertilization success. To analyse the effect of MHC on the fertilization outcome, we used a generalized linear mixed model (GLMM), as implemented in GENSTAT v17 (Payne & Arnold 2003). GLMM is a quasi-likelihood method for fitting marginal models to repeated measurements that can be used when the response has a binomial distribution. Quartet identity was entered as repeated subject, the total number of offspring sired by male B with female 1 and female 2 were the dependent variables (with brood sizes of the two females as binomial totals), and the difference in genetic similarity with the two females (DSimAB-F1 and DSimABF2) at MHC and microsatellite loci were the covariates. This model allowed us to control for statistical nonindependence of the relative sperm competition success of the same male across the two females within each quartet (see results section for the repeatability of fertilization success across females). Significance of each term was assessed using Wald chi-square statistic. The Wald chi-square test statistic is the squared ratio of the parameter estimate to its standard error for the respective predictor, and it approximates a chi-square distribution with one degree of freedom. The test statistic and its associated P-value test for the null hypothesis that an individual predictor’s regression coefficient is zero given the rest of the predictors, which are in the model. Similar results were obtained using an alternative likelihood method, the generalized estimating equations model (GEE, data not shown), which allows for

4290 C . G A S P A R I N I , L . C O N G I U and A . P I L A S T R O

Results Genetic variation at MHC A total of 28 different MHC class IIB exon 2 nucleotide sequences were observed in the 56 individuals analysed. Number of alleles per individual ranged from one to five confirming the triplication of this locus in guppy (Lighten et al. 2014). The exact number of allele copies occurring in each individual is unknown, precluding the possibility to calculate reliable allele frequencies. The relative frequency of each allele across individuals, along with the corresponding Accession nos, is reported in Table S2 (Supporting information). No indels or stop codons were found in our sequences, suggesting that it is unlikely that any of them represent pseudogenes. Of the 230 nucleotides in the coding region examined, 105 (46%) were polymorphic and the overall mean pairwise nucleotide difference was 33.4. The 28 alleles resulted into 23 different amino acid sequences considered suitable for the estimates of genetic similarity between mating partners. Forty-six (60%) of the 76 codons identified in the open reading frame were variable, and the overall mean pairwise amino acid difference was 17. BLAST analyses of both nucleotide and amino acid sequences never yielded complete identity with previously published alleles, and all sequences were submitted to GenBank (see Table S2, Supporting information).

Effect of genetic similarity on fertilization success We obtained 28 broods for a total of 351 offspring (mean brood size = 12.54  1.52 SE). As expected, mean PB was not different from 0.5 (mean = 0.500  0.228 SD, range 0.14–1.00, one-sample t test, t = 0.001, P = 0.99), considering that label B was assigned randomly to males. However, the observed variance in PB was significantly larger than that expected simply because of the binomial error associated with small brood sizes (mean simulated SD = 0.177, 95% C.I. = 0.127–0.227, P = 0.024, Monte Carlo simulation with 10 000 iterations). This indicates that the variation in fertilization success was influenced also by other factors such as male variation in ejaculate quality and/or male–female interactions. One-way analysis of variance revealed a significant difference in relative fertilization success among males (F13,14 = 3.71, P = 0.018) and significant repeatability of sperm competition success of

the same male across the two females (intraclass correlation repeatability coefficient, ri = 0.53  0.20 SE), indicating that the deviance from the expected distribution is due to intrinsic differences in ejaculate competitiveness across males. The proportion of MHC alleles shared between females and males ranged between 0 to 1, with an average proportion of shared alleles of 0.428 (0.038 SE). The proportion of offspring sired by male B (PB) was significantly correlated with the difference in MHC similarity between male A and B and the two females in the quartet. However, this association was in the opposite direction as predicted, as paternity share increased as MHC similarity between mates increased (difference in MHC similarity: b = 0.97  0.43 SE; F1,25.7 = 5.09, P = 0.033, dispersion parameter = 1.031, Fig. 1). In contrast, overall genetic similarity, as estimated from 10 microsatellite loci, was not significantly correlated with fertilization success (L&R (covariate): b = 2.73  3.47 SE; F1,23.6 = 0.62, P = 0.44; dispersion parameter = 1.216; Q&G (covariate): b = 0.08  0.93 SE; F1,24.7 = 0.01, P = 0.93; dispersion parameter = 1.159). Results remained substantially unchanged when both MHC and neutral genetic similarity were simultaneously entered into the model (Table 1). The difference in similarity between the two males and the female at MHC did not strongly correlate with the difference at neutral loci (L&R: r = 0.259, N = 28, P = 0.19; Q&G: r = 0.051, N = 28 P = 0.80). At level of individuals, number of MHC alleles and microsatellite heterozygosity were not correlated (r = 0.042, N = 56, P = 0.13).

1.0

Male B fertilization success (PB)

overdispersion and does not require distribution assumptions (Liang & Zeger 1986), indicating that our parameter estimation is robust, independently of the specific statistical model used.

0.8

0.6

0.4

0.2

0.0 –1.0

–0.5

0.0

0.5

1.0

Relative MHC similarity of male B

Fig. 1 MHC similarity and fertilization success of male B. MHC similarity is reported as relative between the two competing males and the female, so that positive values indicate male B is more similar to the female than male A (see main text for further details). © 2015 John Wiley & Sons Ltd

M H C A N D P O S T C O P U L A T O R Y S E X U A L S E L E C T I O N 4291 Table 1 Results of the generalized linear mixed model analysis. Proportion of offspring sired by male B was the dependent variable across the two females, and the relative genetic similarity between male B and females 1 and 2 was the predictors (covariate). Positive parameter estimate indicates that paternity share increases as the relative genetic similarity across females increases b

Predictors Lynch & Ritland* Relative genetic similarity Relative genetic similarity Queller & Goodnight† Relative genetic similarity Relative genetic similarity

SE

F

d.f.

P

at 10 microsatellites loci at MHC

0.340 0.998

0.903 0.448

0.14 4.85

124.8 124.4

0.71 0.037

at 10 microsatellites loci at MHC

0.286 0.954

3.503 0.446

0.01 4.93

124.8 124.5

0.94 0.037

*Lynch & Ritland (1999). † Queller & Goodnight (1989).

Discussion The findings of this study suggest that MHC-similar males sire a greater proportion of offspring under competitive conditions. This result was maintained after controlling for the overall genetic relatedness estimated at neutral microsatellite loci. These results were unexpected, as preference for MHC-dissimilar males was predicted according to the hypothesis that MHC dissimilarity will maximize heterozygosity or rare alleles at MHC loci in the offspring, which in turn is expected to increase parasite resistance (Penn et al. 2002; McClelland et al. 2003; and van Oosterhout et al. 2006b for a specific study on guppies). The approach used in the present study for the characterization of MHC genotypes revealed an average of 2.29 alleles per individuals. The number of alleles we found is slightly smaller than that observed, using another methodological approach based on Illumina sequencing, in wild guppy populations (mean of 3.15 alleles per fish, Lighten et al. 2014). Despite the large number of founders of our stock population (see methods for details on fish maintenance), this population has been bred in captivity for several generations (approximately 10), and a loss of genetic variability may have therefore occurred. Alternatively, given the finite number of clones we screened (up to 20 clones per individual), our method may have a lower intrinsic power to detect the genetic variability at MHC as compared to methods based on high-throughput sequencing (Lighten et al. 2014). When 20 independent clones are sequenced from the same individual, the probability to miss a single-copy allele by chance is 0.071. Considering that we have genotyped 56 individuals, the mean number of alleles per individual we observed is probably only slightly lower than estimated (expected number should be approximately 2.36). We do not exclude, however, that using NGS sequence may reveal more alleles in our experimental fish than we detected. Selection for MHC-similar males can be adaptive if offspring produced with MHC-different males disrupts © 2015 John Wiley & Sons Ltd

local adaptations and coadapted gene complexes (Hendry et al. 2000), likely to occur in this species as wild guppy populations are well known to be geographically structured, or because increase the risk of autoimmune disease (Wegner et al. 2003). A study using ten natural populations of guppies in Trinidad (including the one we used in this study) found no directional selection for increased heterozygosity at MHC (Fraser et al. 2010a), suggesting that the evolution of strategies to avoid very dissimilar males or males carrying rare alleles cannot be ruled out. Indeed, a study in which offspring performance was related to MHC genotype revealed a complex relationship between fitness and MHC genotype in guppies: while MHC heterozygosity had a beneficial effect on offspring fitness, genotypes combining common alleles with rare MHC alleles produced both positive and negative nonadditive effects on growth rate and parasite resistance of the offspring (Fraser & Neff 2009). Yet other studies have suggested that in fish females could benefit from avoiding males too different from themselves. In brown trout (Salmo trutta), females prefer males with an intermediate MHC similarity (Forsberg et al. 2007), and female three-spined sticklebacks (Gasterosteus aculeatus) chose males to produce offspring with an optimal intermediate allelic diversity (Reusch et al. 2001). An intermediate number of alleles may confer a better pathogen resistance (Kurtz et al. 2004). Yeates et al. (2009), using a paired design, found a similar pattern in Atlantic salmon, and they advanced the hypothesis that selection for MHC-similar males may be adaptive in the light of avoiding hybridization. In guppies, heterospecific matings can occur because, although females avoid heterospecific males, they can nevertheless receive heterospecific sperm through forced copulation (Russell et al. 2006), with obvious costs for the offspring (Rosenthal & Garcıa de Le on 2011). Also ‘hybridization’ among different natural populations can impair offspring fitness (Russell & Magurran 2006), and the results we found in the present study (although our study involved only one

4292 C . G A S P A R I N I , L . C O N G I U and A . P I L A S T R O population) are in agreement with the findings that fertilization success is biased towards males from the female’s own population (Ludlow & Magurran 2006), likely more genetically similar to the female. If females cryptically select against sperm from males of foreign populations, preference of MHC similarity could be the underlying mechanism. However, specific studies are needed to corroborate this idea and ultimately shed light on whether this fertilization pattern is adaptive in the light of hybridization avoidance at both species and population level. A similar pattern of positive correlation between genetic similarity and competitive fertilization success (under standard conditions) was also found in the Peron’s tree frog (Litoria peronii), although MHC diversity was not measured (Sherman et al. 2008). In a previous study, Gasparini & Pilastro (2011) argued that MHC-mediated cryptic female choice may explain postcopulatory inbreeding avoidance found in the guppy. That study showed that unrelated males sire more offspring when competing with a male that is related to the female (Gasparini & Pilastro 2011; recently corroborated by Fitzpatrick & Evans 2014). Close kin should share more MHC alleles than unrelated individuals (Brown & Eklund 1994), and therefore, MHC-related preference may serve to avoid inbreeding. In this scenario, females should prefer mating with MHC-dissimilar males as a means to avoid the costs of producing inbred offspring. However, in this study, we did not find a correlation between MHC and relatedness at microsatellites (differently from what reported for example in Herdegen et al. 2014), and therefore, selecting for MHCdiverse males would have not translated into an inbreeding avoidance mechanism. In addition, recent findings (Fitzpatrick & Evans 2014) suggest that postcopulatory inbreeding avoidance occurs only for very closely related pairs of individuals (brothers but not half-siblings). Therefore, with postcopulatory inbreeding avoidance limited to full-sibling pairings, our design (that did not include full-sibling pairs) is likely to have had limited power to expose inbreeding avoidance at postcopulatory level based on MHC similarity. Also, it is not surprising that we failed to find an effect of overall genetic similarity between male and female on fertilization success, as the individuals used in this experiment were randomly chosen from large stocks and the probability that two full-sibs were involved in any of the matings is probably exceedingly low. Studies that investigate the role of MHC at the postcopulatory level, while controlling for precopulatory influences, using artificial inseminations or in vitro tests are extremely scarce compared to the large body of work done on precopulatory MHC-based preference. While precopulatory preference for MHC-dissimilar mates is generally supported (Kamiya et al. 2014), post-

copulatory evidence is, at best, weak. Studies on mice (Wedekind et al. 1996), whitefish (Wedekind et al. 2004) and junglefowls (Løvlie et al. 2013) failed to provide evidence of a postcopulatory role of MHC. In the latter study, sperm from MHC-dissimilar males are more likely to reach the eggs following natural matings, but this pattern is lost after artificial insemination (Løvlie et al. 2013). In the Arctic charr (Salvelinus alpinus), eggs were more likely to be fertilized by heterozygous males, but MHC similarity had no effect on the fertilization success (Skarstein et al. 2005). However, it is important to note that in these above-mentioned studies, fertilization success was tested in the absence of sperm competition, a condition that could have masked the cryptic female choice potential. Besides the current study, the only other work in which sperm competition was included in the experimental design was conducted on the Atlantic salmon (Yeates et al. 2009), and, similarly to what we found in guppies, evidenced a higher competitive fertilization success of MHC-similar males. This finding contrasts with indirect evidence that preference for MHC-dissimilar males occurs at precopulatory level in salmon (e.g. Landry et al. 2001; Neff et al. 2008a). Similarly, in the junglefowl, males show a preference for more MHC-dissimilar females by allocating less sperm when mating with MHC-similar females (Gillingham et al. 2009). These contrasts between episodes of pre- and postcopulatory sexual selection could indicate that cryptic (i.e. postejaculatory, sensu Parker 2014) preference is less likely to evolve than preference mediated by precopulatory cues or that MHC selection at the gamete level is more efficient in species/population recognition (i.e. in the choice of the right species rather than of the more compatible partner within a population). In guppies, MHC-based mating or social preference has never been directly investigated, and all studies conducted so far failed to demonstrate that guppies discriminate kin at precopulatory level (Viken et al. 2006; Pitcher et al. 2008; Guevara-Fiore et al. 2010). Olfactory capabilities of guppies are probably limited (Bettini et al. 2009) suggesting that guppies may not assess MHC similarity at the precopulatory level. With guppies having internal gestation, we scored paternity on newborns. This makes difficult to disentangle the effect of fertilization success per se from MHCrelated differential embryo mortality. Although we cannot therefore exclude the possibility that eggs sired by the most MHC-dissimilar male died before parturition, it has to be noted that embryo mortality is thought to be rare in the guppy (Gasparini & Pilastro 2011), and hence, it is unlikely to explain the fertilization success of MHCsimilar males. The step at which postcopulatory processes bias paternity towards more MHC-similar males is still unknown. It could occur at the level of ovarian © 2015 John Wiley & Sons Ltd

M H C A N D P O S T C O P U L A T O R Y S E X U A L S E L E C T I O N 4293 fluid–sperm interaction, as it has been shown the ovarian fluid affects sperm performance (Gasparini & Pilastro 2011; Gasparini et al. 2012; Evans et al. 2013; Gasparini & Evans 2013) or at the level of sperm–egg interaction (Scofield et al. 1982; Wedekind et al. 1996). Alternatively, it could be possible that the diploid imprinting from MHCsimilar males during the phase in which the ejaculate is transferred into female gonoduct favours the process of sperm storage until the fertilization event, at which point sperm are selected based on their specific haplotype (and not necessary the more similar) favouring the optimal combinations of alleles in the resulting zygotes. Further investigation will be necessary to identify which combinations of MHC alleles occur in the offspring and whether cryptic female choice will eventually select for increased homozygosity at the MHC class II locus.

Conclusions Our results provide evidence that a purely postcopulatory process favours sperm from MHC-similar males during competitive fertilization. Those findings are in agreement with the only other study at present that investigated the role of MHC in determining competitive fertilization success while controlling for precopulatory influences and using a powerful paired-design approach (Yeates et al. 2009). Our results also indirectly suggest that exon 2 of MHC class IIB is not involved in the postcopulatory kin avoidance that has been demonstrated to occur in guppies (Gasparini & Pilastro 2011; Fitzpatrick & Evans 2014). The postcopulatory preference for more MHC-similar males is in contradiction with the expected advantage in parasite resistance associated with MHC heterozygosity and suggests that specific genotype composition, rather than dissimilarity per se, drives sperm selection or that MHC may be involved in avoidance of hybridization or outbreeding, possibly representing a postcopulatory mechanism for reproductive isolation among natural populations. More generally, it concurs with other studies highlighting the importance of the sexual selection processes occurring postejaculation in this fish family (Evans & Pilastro 2011; Pollux et al. 2014).

Acknowledgements We thank Chiara Boschetto for her valuable help in collecting MHC data and Catherine Grueber for her useful comments on the manuscript. We also thank Matt Dean and another anonymous referee for constructive comments on an earlier draft of the manuscript. This work was supported by the Fondazione CARIPARO (Progetto di Eccellenza 2007) and the Ministero Istruzione Universita e Ricerca (PRIN 2008 no. 2008Z8ACTN) to AP. While writing this manuscript, CG was supported by a Marie Curie International Outgoing fellowship within the 7th European Community Framework Programme.

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A.P. conceived the study; C.G. and L.C. conducted the experiment and performed the genetic analyses; A.P. performed the statistical analyses; C.G. and A.P. wrote the article with contribution of L.C. All authors read and approved the final manuscript.

Data accessibility Data set used for GLMM is available in Dryad, along with the aligned MHC sequences, raw data and similarity data (doi: 10.5061/dryad.2v7kr). Microsatellite characteristics and GenBank Accession nos are reported in Table S1 (Supporting information). MHC sequences are available in GenBank: KP828018-KP828045 (for details see Table S2, Supporting information).

Supporting information Additional supporting information may be found in the online version of this article. Table S1 Characteristics of microsatellites used for estimating overall genetic similarity between partners. Table S2 Different MHC sequences observed at nucleotide level (NU), corresponding amino acid allele (AA), number of individuals in which each allele was observed (N), and GenBank accession numbers.