Abstract
With detailed data on gene expression accessible from an increasingly broad array of species, we can test the extent to which our developmental genetic knowledge from model organisms predicts expression patterns and variation across species. But to know when differences in gene expression across species are significant, we first need to know how much evolutionary variation in gene expression we expect to observe. Here we provide an answer by analyzing RNAseq data across twelve species of Hawaiian Drosophilidae flies, focusing on gene expression differences between the ovary and other tissues. We show that over evolutionary time, there exists a cohort of ovary specific genes that is stable and that largely corresponds to described expression patterns from laboratory model Drosophila species. Our results also provide a demonstration of the prediction that, as phylogenetic distance increases, variation between species overwhelms variation between tissue types. Using ancestral state reconstruction of expression, we describe the distribution of evolutionary changes in tissue-biased expression, and use this to identify gains and losses of ovary-biased expression across these twelve species. We then use this distribution to calculate the evolutionary correlation in expression changes between genes, and demonstrate that genes with known interactions in D. melanogaster are significantly more correlated in their evolution than genes with no or unknown interactions. Finally, we use this correlation matrix to infer new networks of genes that share evolutionary trajectories, and we present these results as a dataset of new testable hypotheses about genetic roles and interactions in the function and evolution of the Drosophila ovary.
Funder
National Science Foundation
NIH
Publisher
Public Library of Science (PLoS)
Subject
Cancer Research,Genetics (clinical),Genetics,Molecular Biology,Ecology, Evolution, Behavior and Systematics
Reference70 articles.
1. RNA-Seq: a revolutionary tool for transcriptomics;Z Wang;Nature Reviews Genetics,2009
2. RNA sequencing: advances, challenges and opportunities;F Ozsolak;Nature Reviews Genetics,2011
3. Ten years of next-generation sequencing technology;EL Van Dijk;Trends in Genetics,2014
4. Interpretation of differential gene expression results of RNA-seq data: review and integration;A McDermaid;Briefings in Bioinformatics,2019
5. Pairwise comparisons across species are problematic when analyzing functional genomic data;CW Dunn;Proceedings of the National Academy of Sciences,2018
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