Gunter Wagner | Search bioRxiv

bioRxiv2016

Pervasive concerted evolution in gene expression shapes cell type transcriptomes

The evolution and diversification of cell types is a key means by which animal complexity evolves. Recently, hierarchical clustering and phylogenetic methods have been applied to RNA-seq data to infer cell type evolutionary history and homology. A major challenge for interpreting this data is that cell type transcriptomes may not evolve independently due to correlated changes in gene expression. This non-independence can arise for several reasons, such as when different tissues share common regulatory sequences for regulating genes expressed in multiple tissues, i.e. pleiotropic effects of mutations. We develop a model to estimate the level of correlated transcriptome evolution (LCE) and apply it to different datasets. The results reveal pervasive correlated transcriptome evolution among different cell and tissue types. In general, tissues related by morphology or developmental lineage exhibit higher LCE than more distantly related tissues. Analyzing new data collected from bird skin appendages suggests that LCE decreases with the phylogenetic age of tissues compared, with recently evolved tissues exhibiting the highest LCE. Furthermore, we show correlated evolution can alter patterns of hierarchical clustering, causing different tissue types from the same species to cluster together. Using a dataset with sufficient taxon sampling, we performed a gene-wise estimation of LCE, identifying genes that most strongly contribute to the correlated evolution signal. Removing genes with high LCE allows for accurate reconstruction of evolutionary relationships among tissue types. Our study provides a statistical method to measure and account for correlated gene expression evolution when interpreting comparative transcriptome data.

Bioinformatics↗

bioRxiv2015

Character trees from transcriptome data: origin and individuation of morphological characters and the so-called “species signal”

We elaborate a framework for investigating the evolutionary history of morphological characters. We argue that morphological character trees generated by phylogenetic analysis of transcriptomes provide a useful tool for identifying causal gene expression differences underlying the development and evolution of morphological characters. They also enable rigorous testing of different models of morphological character evolution and origination, including the hypothesis that characters originate via divergence of repeated ancestral characters. Finally, morphological character trees provide evidence that character transcriptomes undergo concerted evolution. We argue that concerted evolution of transcriptomes can explain the so-called \"species-specific clustering\" found in several recent comparative transcriptome studies. The species signal is the phenomenon that transcriptomes cluster by species rather than character type, even though the characters are older than the respective species. We suggest that concerted gene expression evolution results from mutations that alter gene regulatory network interactions shared by the characters under comparison. Thus, character trees generated from transcriptomes allow us to investigate the variational independence, or individuation, of morphological characters at the level of genetic programs.

Genomics↗