Seminal fluid proteins (SFPs) are a group of reproductive proteins that are among the most evolutionarily divergent known. As SFPs can impact male and female fitness, these proteins have been proposed to evolve under postcopulatory sexual selection (PCSS). However, the fast change of the SFPs can also result from nonadaptive evolution, and the extent to which selective constraints prevent SFPs rapid evolution remains unknown. Using intra- and interspecific sequence information, along with genomics and functional data, we examine the molecular evolution of approximately 300 SFPs in Drosophila. We found that 50-57% of the SFP genes, depending on the population examined, are evolving under relaxed selection. Only 7-12% showed evidence of positive selection, with no evidence supporting other forms of PCSS, and 35-37% of the SFP genes were selectively constrained. Further, despite associations of positive selection with gene location on the X chromosome and protease activity, the analysis of additional genomic and functional features revealed their lack of influence on SFPs evolving under positive selection. Our results highlight a lack of sufficient evidence to claim that most SFPs are driven to evolve rapidly by PCSS while identifying genomic and functional attributes that influence different modes of SFPs evolution.

MicroRNAs (miRNAs) are endogenous RNA molecules that regulate gene expression posttranscriptionally. To date, the emergence of miRNAs and their patterns of sequence evolution have been analyzed in great detail. However, the extent to which miRNA expression levels have evolved over time, the role different evolutionary forces play in shaping these changes, and whether this variation in miRNA expression can reveal the interplay between miRNAs and mRNAs remain poorly understood. This is especially true for miRNA expressed during key developmental transitions. Here, we assayed miRNA expression levels immediately before (≥18BPF [18 h before puparium formation]) and after (PF) the increase in the hormone ecdysone responsible for triggering metamorphosis. We did so in four strains of Drosophila melanogaster and two closely related species. In contrast to their sequence conservation, approximately 25% of miRNAs analyzed showed significant within-species variation in male expression levels at ≥18BPF and/or PF. Additionally, approximately 33% showed modifications in their pattern of expression bias between developmental timepoints. A separate analysis of the ≥18BPF and PF stages revealed that changes in miRNA abundance accumulate linearly over evolutionary time at PF but not at ≥18BPF. Importantly, ≥18BPF-enriched miRNAs showed the greatest variation in expression levels both within and between species, so are the less likely to evolve under stabilizing selection. Functional attributes, such as expression ubiquity, appeared more tightly associated with lower levels of miRNA expression polymorphism at PF than at ≥18BPF. Furthermore, ≥18BPF- and PF-enriched miRNAs showed opposite patterns of covariation in expression with mRNAs, which denoted the type of regulatory relationship between miRNAs and mRNAs. Collectively, our results show contrasting patterns of functional divergence associated with miRNA expression levels during Drosophila ontogeny.

The magnitude and functional patterns of intraspecific transcriptional variation in the anophelines, including those of sex-biased genes underlying sex-specific traits relevant for malaria transmission, remain understudied. As a result, how changes in expression levels drive adaptation in these species is poorly understood. We sequenced the female, male, and larval transcriptomes of three populations of Anopheles arabiensis from Burkina Faso. One-third of the genes were differentially expressed between populations, often involving insecticide resistance-related genes in a sample type-specific manner, and with the females showing the largest number of differentially expressed genes. At the genomic level, the X chromosome appears depleted of differentially expressed genes compared with the autosomes, chromosomes harboring inversions do not exhibit evidence for enrichment of such genes, and genes that are top contributors to functional enrichment patterns of population differentiation tend to be clustered in the genome. Further, the magnitude of variation for the sex expression ratio across populations did not substantially differ between male- and female-biased genes, except for some populations in which male-limited expressed genes showed more variation than their female counterparts. In fact, female-biased genes exhibited a larger level of interpopulation variation than male-biased genes, both when assayed in males and females. Beyond uncovering the extensive adaptive potential of transcriptional variation in An. Arabiensis, our findings suggest that the evolutionary rate of changes in expression levels on the X chromosome exceeds that on the autosomes, while pointing to female-biased genes as the most variable component of the An. Arabiensis transcriptome.

Cancer is driven by genomic alterations, but the processes causing this disease are largely performed by proteins. However, proteins are harder and more expensive to measure than genes and transcripts. To catalyze developments of methods to infer protein levels from other omics measurements, we leveraged crowdsourcing via the NCI-CPTAC DREAM proteogenomic challenge. We asked for methods to predict protein and phosphorylation levels from genomic and transcriptomic data in cancer patients. The best performance was achieved by an ensemble of models, including as predictors transcript level of the corresponding genes, interaction between genes, conservation across tumor types, and phosphosite proximity for phosphorylation prediction. Proteins from metabolic pathways and complexes were the best and worst predicted, respectively. The performance of even the best-performing model was modest, suggesting that many proteins are strongly regulated through translational control and degradation. Our results set a reference for the limitations of computational inference in proteogenomics. A record of this paper's transparent peer review process is included in the Supplemental Information.