bioRxivEvolutionary Biology
Extensive transgressive gene expression in testis but not ovary in the homoploid hybrid Italian sparrow
H. Papoli Yazdi, M. Ravinet, M. Rowe, G.-P. Saetre, C. Oien Guldvog, F. Eroukhmanoff, A. Marzal, S. Magallanes, A. Runemark
Abstract
Hybridization can result in novel allelic combinations which can impact the hybrid phenotype through changes in gene expression. While mis-expression in F1 hybrids is well documented, how gene expression evolves in stabilized hybrid taxa remains an open question. As gene expression evolves in a stabilizing manner, break-up of co-evolved cis- and trans-regulatory elements could lead to transgressive patterns of gene expression in hybrids. Here, we address to what extent gonad gene expression has evolved in an established and stable homoploid hybrid, the Italian sparrow (Passer italiae). Through comparison of gene expression in gonads from individuals of the two parental species (i.e., house and Spanish sparrow) to that of Italian sparrows, we find evidence for strongly transgressive expression in male Italian sparrows - 22% of testis genes exhibit expression patterns outside the range of both parent species. In contrast, Italian sparrow ovary expression was similar to that of one of the parent species, the house sparrow (Passer domesticus). Moreover, the Italian sparrow testis transcriptome is 26 times as diverged from those of the parent species as the parental transcriptomes are from each other, despite being genetically intermediate. This highlights the potential for regulation of gene expression to produce novel variation following hybridization. Genes involved in mitochondrial respiratory chain complexes and protein synthesis are enriched in the subset that is over-dominantly expressed in Italian sparrow testis, suggesting that selection on key functions have molded the hybrid Italian sparrow transcriptome.
- organism: multiple species
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
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