Male manipulation impinges on social-dependent tumor suppression in Drosophila melanogaster females.

Physiological status can influence social behavior, which in turn can affect physiology and health. Previously, we reported that tumor growth in Drosophila virgin females depends on the social context, but did not investigate the underlying physiological mechanisms. Here, we sought to characterize the signal perceived between tumorous flies, ultimately discovering that the tumor suppressive effect varies depending on reproductive status. Firstly, we show that the tumor suppressive effect is neither dependent on remnant pheromone-like products nor on the microbiota. Transcriptome analysis of the heads of these tumorous flies reveals social-dependent gene-expression changes related to nervous-system activity, suggesting that a cognitive-like relay might mediate the tumor suppressive effect. The transcriptome also reveals changes in the expression of genes related to mating behavior. Surprisingly, we observed that this social-dependent tumor-suppressive effect is lost in fertilized females. After mating, Drosophila females change their behavior-favoring offspring survival-in response to peptides transferred via the male ejaculate, a phenomenon called "male manipulation". Remarkably, the social-dependent tumor suppressive effect is restored in females mated by sex-peptide deficient males. Since male manipulation has likely been selected to favor male gene transmission, our findings indicate that this evolutionary trait impedes social-dependent tumor growth slowdown.

Correlated stabilizing selection shapes the topology of gene regulatory networks.

The evolution of gene expression is constrained by the topology of gene regulatory networks, as co-expressed genes are likely to have their expressions affected together by mutations. Conversely, co-expression can also be an advantage when genes are under joint selection. Here, we assessed theoretically whether correlated selection (selection for a combination of traits) was able to affect the pattern of correlated gene expressions and the underlying gene regulatory networks. We ran individual-based simulations, applying a stabilizing correlated fitness function to three genetic architectures: a quantitative genetics (multilinear) model featuring epistasis and pleiotropy, a quantitative genetics model where each genes has an independent mutational structure, and a gene regulatory network model, mimicking the mechanisms of gene expression regulation. Simulations showed that correlated mutational effects evolved in the three genetic architectures as a response to correlated selection, but the response in gene networks was specific. The intensity of gene co-expression was mostly explained by the regulatory distance between genes (largest correlations being associated to genes directly interacting with each other), and the sign of co-expression was associated with the nature of the regulation (transcription activation or inhibition). These results concur to the idea that gene network topologies could partly reflect past selection patterns on gene expression.