Genome ResearchGenomicsPeer-reviewed journal article
Rapid evolution of female-biased genes among four species of Anopheles malaria mosquitoes
Francesco Papa, Nikolai WindbichleriD, Robert Michael WaterhouseiD, Alessia CagnettiiD, Rocco D'Amato, Tania Persampieri, Mara LawniczakiD, Tony NolaniD, Philippos Aris PapathanosiD
- Citations
- 79
Abstract
Understanding how phenotypic differences between males and females arise from the sex-biased expression of nearly identical genomes can often reveal important insights into the biology and evolution of a species. Among Anopheles mosquito species, these phenotypic differences include vectorial capacity, as it is only females that blood feed and thus transmit human malaria. Here, we use RNA-seq data from multiple tissues of four vectors spanning the Anopheles phylogeny to explore the genomic and evolutionary properties of sex-biased genes. We find that in these mosquitoes, in contrast to what has been found in many other organisms, female-biased genes are more rapidly evolving in sequence, expression, and genic turnover, than male-biased genes. Our results suggests that this atypical pattern may be due to the combination of sex-specific life history challenges encountered by females, such as blood feeding. Furthermore, female propensity to only mate once in nature in male swarms likely diminishes sexual selection of post-reproductive traits related to sperm competition among males. We also develop a comparative framework to systematically explore tissue- and sex-specific splicing, to document its conservation throughout the genus and identify a set of candidate genes for future functional analyses of sex-specific isoform usage. Finally, our data reveals that the deficit of male-biased genes on the X chromosomes in Anopheles is a conserved feature in this genus and can be directly attributed to chromosome-wide transcriptional regulation that demasculinizes the X in male reproductive tissues.
- organism: multiple species
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- research focus: differential expression
- approach: statistical model
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