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Published June 2024 | Published
Journal Article Open

A novel approach to comparative RNA-Seq does not support a conserved set of orthologs underlying animal regeneration

Creators
Sierra, Noemie ORCID icon
Olsman, Noah ORCID icon
Yi, Lynn ORCID icon
Pachter, Lior1 ORCID icon
Goentoro, Lea1 ORCID icon
Gold, David A. ORCID icon
  • 1. ROR icon California Institute of Technology
Editor:
Pisani, Davide

Abstract

Molecular studies of animal regeneration typically focus on conserved genes and signaling pathways that underlie morphogenesis. To date, a holistic analysis of gene expression across animals has not been attempted, as it presents a suite of problems related to differences in experimental design and gene homology. By combining orthology analyses with a novel statistical method for testing gene enrichment across large datasets, we are able to test whether tissue regeneration across animals share transcriptional regulation. We applied this method to a meta-analysis of 6 publicly available RNA-Seq datasets from diverse examples of animal regeneration. We recovered 160 conserved orthologous gene clusters, which are enriched in structural genes as opposed to those regulating morphogenesis. A breakdown of gene presence/absence provides limited support for the conservation of pathways typically implicated in regeneration, such as Wnt signaling and cell pluripotency pathways. Such pathways are only conserved if we permit large amounts of paralog switching through evolution. Overall, our analysis does not support the hypothesis that a shared set of ancestral genes underlie regeneration mechanisms in animals. After applying the same method to heat shock studies and getting similar results, we raise broader questions about the ability of comparative RNA-Seq to reveal conserved gene pathways across deep evolutionary relationships.

Copyright and License

© The Author(s) 2024. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

Acknowledgement

We thank Blythe Durbin-Johnson at the UC Davis Bioinformatics Core for help with some of the statistical tests performed in this manuscript. We thank Todd Oakley, Rachael Bay, and Celina Juliano for their helpful contributions to this paper. The work was supported by the James E. and Charlotte Fedde Cordes Postdoctoral Fellowship in Biology at Caltech (D.A.G.), the National Science Foundation Graduate Research Fellowship (N.C.S.) and National Science Foundation grant 2044871 (D.A.G.).

Data Availability

Additional File 1, along with the data and code used in this 39 study are available on GitHub at https://github.com/DavidGoldLab/2023_Comp_Regen.

evae120_Supplementary_Data - zip file
 

Conflict of Interest

The authors declare no conflicts of interest.

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Additional details

Identifiers Funding Caltech Custom Metadata
DOI
10.1093/gbe/evae120
PMCID
PMC11214158
California Institute of Technology
James E. and Charlotte Fedde Cordes Postdoctoral Fellowship in Biology Division of Biology and Biological Engineering
National Science Foundation
NSF Graduate Research Fellowship
National Science Foundation
EAR-2044871
Caltech groups
Division of Biology and Biological Engineering (BBE), Tianqiao and Chrissy Chen Institute for Neuroscience
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Resource type
Journal Article
Publisher
Oxford University Press
Published in
Genome Biology and Evolution, 16(6), evae120, ISSN: 1759-6653.
Languages
English

Rights

  • Creative Commons Attribution 4.0 International
    No further description. Read more

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Created:
July 2, 2024
Modified:
July 2, 2024
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