3.1. Male and Female Genetic Networks
In nearly all sexual taxa surveyed, reproductive traits and genes are consistently rank among the most rapidly evolving functional classes (see [2
]). Most reproductive genes are sex-specific and play a role in maintaining and promoting the divergence of sexually dimorphic traits over time. Sexual antagonism, or conflict, can provide an evolutionary and molecular mechanism to explain the rapid divergence of reproductive genes on a genome-wide scale. The goal of this present study was to identify interaction targets, using a genomics approach, that may potentially be in conflict with each other. To accomplish this goal, we first generated male, female, and sex unbiased networks in D. melanogaster
by combining sex-specific gene expression with available curated interaction data.
In total, 12,453 genes (12,628 from the permissive set), representing over three quarters of all known D. melanogaster genes, were used in this analysis and 237,954 (403,518 using the permissive criterion) interaction partners were identified. provides a summary of all interaction subnetworks. 1,327 male nodes interacted with another male node (the male-male subnetwork), representing over 10% of the total number of assessed genes. Similarly, 1,348 female nodes interacted with at least another female node (the female-female subnetwork). However, unlike the male network with 1,248 male nodes that included interactions not involving other male nodes, the female network only contained 319 non-female-interacting nodes. This may indicate that the female network contains a much larger fraction of shared subnetworks (e.g., female-unbiased, female-female, and female-male) that are more interconnected relative to the male network. Overall, the sex-unbiased network comprised of a much larger fraction of genes (); however, this high proportion is partially due to the use a very conservative sex-bias stringency.
Table 1 Characteristics of sex-biased and unbiased networks. Each of the sex-unbiased, male-, and female-biased networks are classified into subnetworks by the expression bias of the interacting nodes. The letter codes A, U, F, and M refer to the node types “All”, (more ...)
The bin counts on the number of edges per node of the subnetworks, shown in , display the often quoted power law behavior of genetic interaction networks, at least on the high degree tails (right tail). Nodes of lower edge/node degree deviate from this scale-free pattern, resulting in a complex network containing at least two distribution behaviors. This observation highlights that care must be taken when fitting degree distributions of genetic interaction networks to power laws. Here, we used a lower cutoff for the degree and only used nodes with number of edges greater than or equal to the cutoff when computing the power law fit. The specific cutoff values, which vary across the subnetwork types, were determined by visual inspection of the distributions, and tend to accord with the average edges per node values in . If the entire data set is fit to a power law without regard to whether a power law is appropriate over the entire range of node degrees, the resulting best fit power law exponents are problematic to interpret. For example, the female subnetwork shown in (red) displays a peak in the distribution around 5 edges per node. At higher degree values the distribution is approximately a power law with exponent, −3.01 (). If a cutoff was not used, the resulting power law exponent would be much smaller, −1.52 (data not shown). In some contrast, the male subnetwork, shown in (blue), has a monotonic distribution but tends to deviate from power law behavior at the smallest degree bin size (the distribution flattens out at lower degree values). Beyond the first bin, the best fit power law exponent for the male subnetwork is −2.75. If all the data were used in the power law fit, the resulting best fit exponent is −2.15 (data not shown), which is lower than the power law tail value, as in the female case. The impact of fitting the entire data set, even among very low degree nodes, in the power law fit has the greatest impact on the female subnetwork, and overstates the shallowness of female subnetworks compared to male subnetworks. In fact, both female and male subnetworks have similar power laws in their high degree tails, but differ mainly in the distribution on the low degree end.
Figure 1 Frequency distribution of the number of edges per node for various classes of interactions. A log-log plot of the edges per node bin counts is shown for four (sub)networks, as indicated in the legend. The bin size is 5, and the bin counts are unnormalized. (more ...)
Malone et al. [18
] recently generated male and female genetic networks in D. melanogaster
, based on coexpression correlations. In total, they identified 4,104 female-biased genes and 2,694 male-biased genes using a more quantitative approach (our very conservative approach identifies a smaller gene set among male and female networks). Almost 60% of male nodes from our study matched the Malone et al. male network (1,494 out of 2,575). Similarly, there was a nearly 70% concordance among our study's identified females nodes compared to Malone et al.'s female network (1,012 out of 1,446). These overlaps are surprising since each study used very different approaches to assign interaction. Both our studies, report that the female and male subnetworks display a different overall structure. However, while Malone et al. 2012 base their conclusions on different power law exponents for the female and male subnetworks (−1.06 and −1.35, resp.), we find that the difference is not in the power law behavior on the high degree end (which are qualitatively, if not quantitatively similar) but in the deviation from power law behavior at the low degree end. Female subnetworks have a large cluster of genes with: 5–10 interaction partners, while for genes in the male subnetwork, the most frequent number of interaction partners is unity.
This difference between male and female networks, in terms of the identity of their interaction partners, is most easily observed as the total number of interactions that both networks harbor. While the number of female-female interactions, or edges, is an order of magnitude higher than the number of female nodes, male-male nodes have a much smaller number of partners (). This pattern can also be seen in the frequency distribution of the number of edges (). The male degree distribution peaks at its lowest value (a single edge), while the female distribution peaks at 28 edges, more similar to the distribution of all genes without regard to their sex-bias. In other words, male-biased genes appear to be far less interconnected with each other than similar nodes from the female network.
The less interactive nature of male networks (male-any) and subnetworks (male-male) is supported by evolutionary analyses that characterize new gene formation. These genome-wide analyses find that de novo
genes are expressed often exclusively in males, and preferentially in the testis [19
]. It is possible that these genes generally become immediately coopted into male gametogenesis and fertility functions without embedding themselves into the existing male network. In contrast, female genes are rarely found among new genes and often have functions that overlap with embryogenesis and early development [21
]. Furthermore, male-driven sexual selection may provide higher selective pressure to maintain and fix these male genes in populations, relative to female genes [22
]. Thus, the interconnectivity of the female versus male networks may be the result of a combination of developmental system constraints and historical selective pressures.
3.2. Genome-Wide Sexual Conflict
By annotating the male and female networks using the unprecedented resources of Drosophila melanogaster, we are able, for the first time, to identify putative interacting nodes of conflict, across the genome. To understand the potential extent of genome-wide sexual conflict, we characterized sex-unbiased nodes (i.e., not already part of the reproductive network) that had connections to both highly male and highly female nodes (indirect conflict) as well as candidate nodes under direct conflict, that is, male-female edges (see ).
Figure 2 Identifying potential nodes of interlocus sexual conflict from male and female gene networks. Genes (nodes) are classified as male-biased (blue), female-biased (red), and sex-unbiased (open) according to their relative expression levels in each of the (more ...)
Our results supports the contention that sexual conflict has an enormous pool of indirect and direct targets to act extensively upon in the genome. We identified 598 sex-unbiased genes that can potentially act as indirect nodes of interlocus sexual antagonism in addition to 271 direct nodal pairs of potential conflict between male- and female-biased genes. (FlyBase accession lists for indirect and direct, permissive and strict, and male and female networks, are found in Supplementary Files, available on line at http://dx.doi.org/10.1155/2013/545392). A cursory GO analysis of these potentially conflicted sex-unbiased genes identified a variety of development and morphogenesis functional classes that were significantly overrepresented among the sex-unbiased, indirect candidate nodes (). Since developmental genes are generally more pleiotropic than other genes [23
], they may be indirectly involved in various male and female functions including testis and ovary development. It is also possible that different tissue types and developmental stages harbor different interactions. For future studies, it would be important to ensure that interaction datasets are derived from the same tissues and development timepoint as their sex-based expression experimental counterparts. Among the direct male-female conflict candidates, there was a lack of statistically significant gene ontology classes across male genes. However, female-biased genes involved in a direct interaction with a male node contained a range of GO terms with female gametogenesis and reproduction featured prominently ().
Table 2 Significant gene ontology (GO) categories for sex-unbiased nodes that interact with at least one male-biased gene and at least one female-biased gene (indirect nodes of sexual conflict). Interactions using the strict criterion were used and only significant (more ...)
Table 3 Significant gene ontology (GO) categories for female-biased genes that interact directly with male-biased genes (direct nodes of sexual conflict). Interactions using the strict criterion were used and only significant (Bonferroni corrected P values ≤0.05) (more ...)
Innocenti and Morrow [25
] used a different genome-wide approach to characterize potential nodes of conflict in flies by combining fitness levels of various lines with their gene expression levels, [25
]. Specifically, they sampled gene expression levels in males and females across a sample of hemiclonal lines with opposing fitnesses between sexes. Their results identified putative genes involved in sexual conflict (and not the particular gene-pair interactions, as in our work). Overall, their screen found that 8% of all genes may be involved in sexual conflict. We looked for any significant overlap between our putatively conflicted genes and those genes identified by Innocenti and Morrow [25
]. There was no significant overlap between our indirect conflict genes, for either the strict or permissive network, and those listed in Innocenti and Morrow [25
] (hypergeometric test, two-tailed P
value ≥0.5). On the contrary, there was a significant lack of overlap between our candidate genes for both the strict and permissive network, and those found in their survey (hypergeometric test, one-tailed P
value ≤0.01). This suggests that there are other classes of epistatic interactions that have the potential to harbor conflict dynamics.
From these two complementary studies, it appears that the genome provides a potentially large arena to precipitate an extensive evolutionary arms race between the sexes. However, while intuitively appealing, sexual conflict represents just one theoretical perspective to explain such sexual selection phenomena as rapidly evolving reproductive traits and genes, exaggerated sexual characters, and hybrid incompatibility [26
]. Catalyzed by large variances in reproductive success, sexual selection can also be explained by alternative coevolutionary processes. Civetta and Singh suggest that sexual traits (and by extension, genes) can evolve rapidly under a process of sexual coadaptation that would harbor a different evolutionary dynamic including greater intraspecific variation [27
]. Further work using population and interspecific variation may shed light on these alternative hypotheses.