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Title: Gene expression of functionally-related genes coevolves across fungal species: detecting coevolution of gene expression using phylogenetic comparative methods

Abstract

Researchers often measure changes in gene expression across conditions to better understand the shared functional roles and regulatory mechanisms of different genes. Analogous to this is comparing gene expression across species, which can improve our understanding of the evolutionary processes shaping the evolution of both individual genes and functional pathways. One area of interest is determining genes showing signals of coevolution, which can also indicate potential functional similarity, analogous to co-expression analysis often performed across conditions for a single species. However, as with any trait, comparing gene expression across species can be confounded by the non-independence of species due to shared ancestry, making standard hypothesis testing inappropriate. We compared RNA-Seq data across 18 fungal species using a multivariate Brownian Motion phylogenetic comparative method (PCM), which allowed us to quantify coevolution between protein pairs while directly accounting for the shared ancestry of the species. Our work indicates proteins which physically-interact show stronger signals of coevolution than randomly-generated pairs. Interactions with stronger empirical and computational evidence also showing stronger signals of coevolution. We examined the effects of number of protein interactions and gene expression levels on coevolution, finding both factors are overall poor predictors of the strength of coevolution between a proteinmore » pair. Simulations further demonstrate the potential issues of analyzing gene expression coevolution without accounting for shared ancestry in a standard hypothesis testing framework. Furthermore, our simulations indicate the use of a randomly-generated null distribution as a means of determining statistical significance for detecting coevolving genes with phylogenetically-uncorrected correlations, as has previously been done, is less accurate than PCMs, although is a significant improvement over standard hypothesis testing. These methods are further improved by using a phylogenetically-corrected correlation metric. Our work highlights potential benefits of using PCMs to detect gene expression coevolution from high-throughput omics scale data. This framework can be built upon to investigate other evolutionary hypotheses, such as changes in transcription regulatory mechanisms across species.« less

Authors:
ORCiD logo [1];  [2];  [3]
  1. Univ. of Tennessee, Knoxville, TN (United States). Genome Science and Technology; Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Chemical Sciences Division
  2. Univ. of Tennessee, Knoxville, TN (United States). Dept. of Ecology and Evolutionary Biology; Univ. of Tennessee, Knoxville, TN (United States). Mathematical and Biological Synthesis
  3. Univ. of Tennessee, Knoxville, TN (United States). Genome Science and Technology; Univ. of Tennessee, Knoxville, TN (United States). Dept. of Ecology and Evolutionary Biology; Univ. of Tennessee, Knoxville, TN (United States). Mathematical and Biological Synthesis
Publication Date:
Research Org.:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
OSTI Identifier:
1815917
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
BMC Genomics
Additional Journal Information:
Journal Volume: 21; Journal Issue: 1; Journal ID: ISSN 1471-2164
Publisher:
Springer
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; Gene expression; Coevolution; Phylogenetic comparative methods

Citation Formats

Cope, Alexander L., O’Meara, Brian C., and Gilchrist, Michael A. Gene expression of functionally-related genes coevolves across fungal species: detecting coevolution of gene expression using phylogenetic comparative methods. United States: N. p., 2020. Web. doi:10.1186/s12864-020-6761-3.
Cope, Alexander L., O’Meara, Brian C., & Gilchrist, Michael A. Gene expression of functionally-related genes coevolves across fungal species: detecting coevolution of gene expression using phylogenetic comparative methods. United States. https://doi.org/10.1186/s12864-020-6761-3
Cope, Alexander L., O’Meara, Brian C., and Gilchrist, Michael A. Wed . "Gene expression of functionally-related genes coevolves across fungal species: detecting coevolution of gene expression using phylogenetic comparative methods". United States. https://doi.org/10.1186/s12864-020-6761-3. https://www.osti.gov/servlets/purl/1815917.
@article{osti_1815917,
title = {Gene expression of functionally-related genes coevolves across fungal species: detecting coevolution of gene expression using phylogenetic comparative methods},
author = {Cope, Alexander L. and O’Meara, Brian C. and Gilchrist, Michael A.},
abstractNote = {Researchers often measure changes in gene expression across conditions to better understand the shared functional roles and regulatory mechanisms of different genes. Analogous to this is comparing gene expression across species, which can improve our understanding of the evolutionary processes shaping the evolution of both individual genes and functional pathways. One area of interest is determining genes showing signals of coevolution, which can also indicate potential functional similarity, analogous to co-expression analysis often performed across conditions for a single species. However, as with any trait, comparing gene expression across species can be confounded by the non-independence of species due to shared ancestry, making standard hypothesis testing inappropriate. We compared RNA-Seq data across 18 fungal species using a multivariate Brownian Motion phylogenetic comparative method (PCM), which allowed us to quantify coevolution between protein pairs while directly accounting for the shared ancestry of the species. Our work indicates proteins which physically-interact show stronger signals of coevolution than randomly-generated pairs. Interactions with stronger empirical and computational evidence also showing stronger signals of coevolution. We examined the effects of number of protein interactions and gene expression levels on coevolution, finding both factors are overall poor predictors of the strength of coevolution between a protein pair. Simulations further demonstrate the potential issues of analyzing gene expression coevolution without accounting for shared ancestry in a standard hypothesis testing framework. Furthermore, our simulations indicate the use of a randomly-generated null distribution as a means of determining statistical significance for detecting coevolving genes with phylogenetically-uncorrected correlations, as has previously been done, is less accurate than PCMs, although is a significant improvement over standard hypothesis testing. These methods are further improved by using a phylogenetically-corrected correlation metric. Our work highlights potential benefits of using PCMs to detect gene expression coevolution from high-throughput omics scale data. This framework can be built upon to investigate other evolutionary hypotheses, such as changes in transcription regulatory mechanisms across species.},
doi = {10.1186/s12864-020-6761-3},
journal = {BMC Genomics},
number = 1,
volume = 21,
place = {United States},
year = {Wed May 20 00:00:00 EDT 2020},
month = {Wed May 20 00:00:00 EDT 2020}
}

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