Modeling gene expression evolution with EvoGeneX uncovers differences in evolution of species, organs and sexes

Abstract

While DNA sequence evolution has been well studied, the expression of genes is also subject to evolution. Yet the evolution of gene expression is currently not well understood. In recent years, new tissue/organ specific gene expression datasets spanning several organisms across the tree of life, have become available providing the opportunity to study gene expression evolution in more detail. However, while a theoretical model to study evolution of continuous traits exist, in practice computational methods often cannot distinguish, with confidence, between alternative evolutionary scenarios. This lack of power has been attributed to the modest number of species with available expression data.To solve this challenge, we introduce EvoGeneX, a computationally efficient method to uncover the mode of gene expression evolution based on the Ornstein-Uhlenbeck process. Importantly, EvoGeneX in addition to modelling expression variations between species, models within species variation. To estimate the within species variation, EvoGeneX formally incorporates the data from biological replicates as a part of the mathematical model. We show that by modelling the within species diversity EvoGeneX significantly outperforms the currently available computational method. In addition, to facilitate comparative analysis of gene expression evolution, we introduce a new approach to measure the dynamics of evolutionary divergence of a group of genes.We used EvoGeneX to analyse the evolution of expression across different organs, species and sexes of the Drosophila genus. Our analysis revealed differences in the evolutionary dynamics of male and female gonads, and uncovered examples of adaptive evolution of genes expressed in the head and in the thorax.

Evolution and genetics of accessory gland transcriptome divergence betweenDrosophila melanogasterand D.simulans

“…RNAi knockdowns demonstrate that PMR phenotypes are sensitive to expression level of many Sfps (Ravi Ram and Wolfner 2007;Patlar and Civetta 2022), suggesting that Sfp expression is a plausible target of selection. Consistent with the observation that male-biased genes tend to have higher levels of interspecific expression divergence (Meiklejohn et al 2003;Parisi et al 2004;Ellegren and Parsch 2007;Brawand et al 2011;Graveley et al 2011;Assis, Zhou, and Bachtrog 2012;Whittle and Extavour 2019;Pal, Oliver, and Przytycka 2021), we recently reported rapid expression divergence as well as the evolution of novel genes and expression phenotype in the accessory gland (Cridland et al 2020). However, Cridland et al did not focus on the general properties of accessory gland transcriptome divergence, did not compare Sfp expression to expression divergence of other gene classes expressed in the accessory gland, and did not address the genetics of accessory gland expression divergence between species.…”

“…Indeed, empirical evidence supports the view that tissues and cell types exhibit varying rates of expression divergence (Gu and Su 2007;Brawand et al 2011;Romero, Ruvinsky, and Gilad 2012;Kryuchkova-Mostacci and Robinson-Rechavi 2015;Liang et al 2018;J. Chen et al 2019;Pal, Oliver, and Przytycka 2021). Intraspecific studies of mice (Babak et al 2015;Andergassen et al 2017;St Pierre et al 2022), humans (Babak et al 2015;Leung et al 2015;Castel et al 2020), birds (Wang, Uebbing, and Ellegren 2017), and Drosophila (Combs et al 2018), have revealed tissue-specific variance in cis-effects; genes may exhibit ASE in some tissues but not others, and the total number and magnitude of ciseffects also varies across tissues.…”

Gene expression phylogenies and ancestral transcriptome reconstruction resolves major transitions in the origins of pregnancy

“…While transforming gene expression count data into binary categories likely loses information about evolutionarily relevant variation in expression levels, it may not be possible to infer meaningful gene expression levels from bulk RNA-Seq. For example rather than evolutionary differences between individuals or species, variation in transcript abundance between samples can result from various sources of experimental noise such as technical variation in library preparation, sequencing, or batch effects ( Gilad and Mizrahi-Man, 2015 ; Tung et al, 2017 ), variation in cell-type composition of a tissue ( Price et al, 2022 ), sampling different timepoints in development or only a few individuals that do not capture the variance properties of gene expression levels within a population or species ( Pal et al, 2020 ; Thompson et al, 2020 ). Thus, by transforming gene expression data into not/expressed states we may reduce the potential for these and other biases to influence our ancestral transcriptome reconstructions, revealing biological signal.…”

Detecting signatures of selection on gene expression

“…subject to a high degree of measurement error), particularly when environmental and developmental variance is not strictly controlled for. The OU framework has been adapted to specifically include within-species expression variability as an error term 13,53,57 , and whilst it has been shown to reduce false inferences of stabilizing selection, this approach has only been employed by a handful of studies 18,58 .…”

“…Second, it is clear that using a single mean expression value for each species can lead to spurious inferences of selection 13 , making multiple replicates essential. Importantly, the OU framework has been extended to parameterise withinspecies variance as an error term 13,53,57 and appears to be a promising approach.…”