bioRxivEvolutionary Biology
Fast evolutionary turnover and overlapping variances of sex-biased gene expression patterns defy a simple binary sex-classification of somatic tissues
Abstract
The phenotypic differences between the sexes are generated by genes with sex-biased expression. These range from a few major regulators to large numbers of organ-specific effector genes in sexually mature individuals. We explore the variation and micro-evolutionary patterns of these genes in a large dataset from natural populations of sub-species and species of mice, with a particular focus on somatic organs. Within these short phylogenetic distances, we find a faster evolutionary turnover of sex-biased gene expression in somatic tissues, but not in the gonads, when compared to the turnover of non-sex-biased genes. We show that somatically expressed sex-biased genes occur mostly only in a subset of the co-expression modules of each organ and the turnover of genes between the taxa occurs often within the main modules. Given the fast evolution of somatic sex-biased expression patterns, we were interested to study the within-group variances and their evolutionary turnover. To visualize the individual variances, we have developed a sex-biased gene expression index (SBI) that represents the cumulative expression of all sex-biased genes for each individual in each organ. We find that SBI distributions range from binary patterns in the gonads to overlapping patterns in the somatic organs. They do not correlate between organs of the same individuals, thus supporting a mosaic model of somatic sex-determination of individuals. Comparison with data from humans shows fewer sex-biased genes compared to mice and strongly overlapping SBI distributions between the somatic organs of the sexes. We conclude that adult individuals are composed of a mosaic spectrum of sex characteristics in their somatic tissues that should not be cumulated into a simple binary classification.
- organism: mouse
- organism: human
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: clustering
- approach: statistical model
- Citations
- 5
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