Pervasive Correlated Evolution in Gene Expression Shapes Cell and Tissue Type Transcriptomes
© The Author(s) 2018. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution..
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 nonindependence can arise for several reasons, such as common regulatory sequences for genes expressed in multiple tissues, that is, pleiotropic effects of mutations. We develop a model to estimate the level of correlated transcriptome evolution (LCE) and apply it to different data sets. 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. To identify genes that most strongly contribute to the correlated evolution signal, we performed a gene-wise estimation of LCE on a data set with ten species. 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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2018 |
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| Erschienen: |
2018 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:10 |
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| Enthalten in: |
Genome biology and evolution - 10(2018), 2 vom: 01. Feb., Seite 538-552 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Liang, Cong [VerfasserIn] |
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| Links: |
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| Themen: |
Cell type evolution |
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| Anmerkungen: |
Date Completed 15.10.2018 Date Revised 13.11.2018 published: Print Citation Status MEDLINE |
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| doi: |
10.1093/gbe/evy016 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM280340060 |
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © The Author(s) 2018. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. | ||
| 520 | |a 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 nonindependence can arise for several reasons, such as common regulatory sequences for genes expressed in multiple tissues, that is, pleiotropic effects of mutations. We develop a model to estimate the level of correlated transcriptome evolution (LCE) and apply it to different data sets. 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. To identify genes that most strongly contribute to the correlated evolution signal, we performed a gene-wise estimation of LCE on a data set with ten species. 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Research Support, U.S. Gov't, Non-P.H.S. | |
| 650 | 4 | |a cell type evolution | |
| 650 | 4 | |a comparative transcriptomincs | |
| 650 | 4 | |a correlated evolution | |
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| 700 | 1 | |a Musser, Jacob M |e verfasserin |4 aut | |
| 700 | 1 | |a Cloutier, Alison |e verfasserin |4 aut | |
| 700 | 1 | |a Prum, Richard O |e verfasserin |4 aut | |
| 700 | 1 | |a Wagner, Günter P |e verfasserin |4 aut | |
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