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Evolutionary rewiring of regulatory networks contributes to phenotypic differences between human and mouse orthologous genes
Mouse models have been engineered to reveal the biological mechanisms of human diseases based on an assumption. The assumption is that orthologous genes underlie conserved phenotypes across species. However, genetically modified mouse orthologs of human genes do not often recapitulate human disease...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8887464/ https://www.ncbi.nlm.nih.gov/pubmed/35137181 http://dx.doi.org/10.1093/nar/gkac050 |
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author | Ha, Doyeon Kim, Donghyo Kim, Inhae Oh, Youngchul Kong, JungHo Han, Seong Kyu Kim, Sanguk |
author_facet | Ha, Doyeon Kim, Donghyo Kim, Inhae Oh, Youngchul Kong, JungHo Han, Seong Kyu Kim, Sanguk |
author_sort | Ha, Doyeon |
collection | PubMed |
description | Mouse models have been engineered to reveal the biological mechanisms of human diseases based on an assumption. The assumption is that orthologous genes underlie conserved phenotypes across species. However, genetically modified mouse orthologs of human genes do not often recapitulate human disease phenotypes which might be due to the molecular evolution of phenotypic differences across species from the time of the last common ancestor. Here, we systematically investigated the evolutionary divergence of regulatory relationships between transcription factors (TFs) and target genes in functional modules, and found that the rewiring of gene regulatory networks (GRNs) contributes to the phenotypic discrepancies that occur between humans and mice. We confirmed that the rewired regulatory networks of orthologous genes contain a higher proportion of species-specific regulatory elements. Additionally, we verified that the divergence of target gene expression levels, which was triggered by network rewiring, could lead to phenotypic differences. Taken together, a careful consideration of evolutionary divergence in regulatory networks could be a novel strategy to understand the failure or success of mouse models to mimic human diseases. To help interpret mouse phenotypes in human disease studies, we provide quantitative comparisons of gene expression profiles on our website (http://sbi.postech.ac.kr/w/RN). |
format | Online Article Text |
id | pubmed-8887464 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-88874642022-03-02 Evolutionary rewiring of regulatory networks contributes to phenotypic differences between human and mouse orthologous genes Ha, Doyeon Kim, Donghyo Kim, Inhae Oh, Youngchul Kong, JungHo Han, Seong Kyu Kim, Sanguk Nucleic Acids Res Computational Biology Mouse models have been engineered to reveal the biological mechanisms of human diseases based on an assumption. The assumption is that orthologous genes underlie conserved phenotypes across species. However, genetically modified mouse orthologs of human genes do not often recapitulate human disease phenotypes which might be due to the molecular evolution of phenotypic differences across species from the time of the last common ancestor. Here, we systematically investigated the evolutionary divergence of regulatory relationships between transcription factors (TFs) and target genes in functional modules, and found that the rewiring of gene regulatory networks (GRNs) contributes to the phenotypic discrepancies that occur between humans and mice. We confirmed that the rewired regulatory networks of orthologous genes contain a higher proportion of species-specific regulatory elements. Additionally, we verified that the divergence of target gene expression levels, which was triggered by network rewiring, could lead to phenotypic differences. Taken together, a careful consideration of evolutionary divergence in regulatory networks could be a novel strategy to understand the failure or success of mouse models to mimic human diseases. To help interpret mouse phenotypes in human disease studies, we provide quantitative comparisons of gene expression profiles on our website (http://sbi.postech.ac.kr/w/RN). Oxford University Press 2022-02-07 /pmc/articles/PMC8887464/ /pubmed/35137181 http://dx.doi.org/10.1093/nar/gkac050 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Computational Biology Ha, Doyeon Kim, Donghyo Kim, Inhae Oh, Youngchul Kong, JungHo Han, Seong Kyu Kim, Sanguk Evolutionary rewiring of regulatory networks contributes to phenotypic differences between human and mouse orthologous genes |
title | Evolutionary rewiring of regulatory networks contributes to phenotypic differences between human and mouse orthologous genes |
title_full | Evolutionary rewiring of regulatory networks contributes to phenotypic differences between human and mouse orthologous genes |
title_fullStr | Evolutionary rewiring of regulatory networks contributes to phenotypic differences between human and mouse orthologous genes |
title_full_unstemmed | Evolutionary rewiring of regulatory networks contributes to phenotypic differences between human and mouse orthologous genes |
title_short | Evolutionary rewiring of regulatory networks contributes to phenotypic differences between human and mouse orthologous genes |
title_sort | evolutionary rewiring of regulatory networks contributes to phenotypic differences between human and mouse orthologous genes |
topic | Computational Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8887464/ https://www.ncbi.nlm.nih.gov/pubmed/35137181 http://dx.doi.org/10.1093/nar/gkac050 |
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