Subfunctionalized expression drives evolutionary retention of ribosomal protein paralogs Rps27 and Rps27l in vertebrates
Abstract
The formation of paralogs through gene duplication is a core evolutionary process. For paralogs that encode components of protein complexes such as the ribosome, a central question is whether they encode functionally distinct proteins, or whether they exist to maintain appropriate total expression of equivalent proteins. Here, we systematically tested evolutionary models of paralog function using the ribosomal protein paralogs Rps27 (eS27) and Rps27l (eS27L) as a case study. Evolutionary analysis suggests that Rps27 and Rps27l likely arose during whole-genome duplication(s) in a common vertebrate ancestor. We show that Rps27 and Rps27l have inversely correlated mRNA abundance across mouse cell types, with the highest Rps27 in lymphocytes and the highest Rps27l in mammary alveolar cells and hepatocytes. By endogenously tagging the Rps27 and Rps27l proteins, we demonstrate that Rps27- and Rps27l-ribosomes associate preferentially with different transcripts. Furthermore, murine Rps27 and Rps27l loss-of-function alleles are homozygous lethal at different developmental stages. However, strikingly, expressing Rps27 protein from the endogenous Rps27l locus or vice versa completely rescues loss-of-function lethality and yields mice with no detectable deficits. Together, these findings suggest that Rps27 and Rps27l are evolutionarily retained because their subfunctionalized expression patterns render both genes necessary to achieve the requisite total expression of two equivalent proteins across cell types. Our work represents the most in-depth characterization of a mammalian ribosomal protein paralog to date and highlights the importance of considering both protein function and expression when investigating paralogs.
Data availability
Ribosome profiling sequencing data have been deposited in GEO under accession code GSE201845. Other data generated in this study are provided in the supplementary materials and Source Data files. Code used for data analysis is available at https://gitfront.io/r/adelefxu/f94QE89EJwyp/eS27-paralogs/.
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Subfunctionalized expression drives evolutionary retention of ribosomal protein paralogs in vertebratesNCBI Gene Expression Omnibus, GSE201845.
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Differentiation dynamics of mammary epithelial cells revealed by single-cell RNA-sequencingNCBI Gene Expression Omnibus, GSE106273.
Article and author information
Author details
Funding
National Institutes of Health (F30HD100123)
- Adele Francis Xu
Stanford Bio-X
- Adele Francis Xu
National Institutes of Health (5R01HG008140)
- Jonathan K Pritchard
New York Stem Cell Foundation (NYSCF-R-I36)
- Maria Barna
National Institutes of Health (R01HD086634)
- Maria Barna
Alfred P. Sloan Foundation
- Maria Barna
Pew Charitable Trusts
- Maria Barna
National Institutes of Health (R01HD098722)
- Lindsay Hinck
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Shigehiro Kuraku, National Institute of Genetics, Japan
Ethics
Animal experimentation: All animal work was reviewed and approved by the Stanford Administrative Panel on Laboratory Animal Care (APLAC, protocol #27463). The Stanford APLAC is accredited by the American Association for the Accreditation of Laboratory Animal Care. All mice used in the study were housed at Stanford University except where otherwise noted. CRISPR-edited mouse lines were generated at the Gladstone Institute Transgenic Gene Targeting Core (San Francisco, CA). All animal procedures were approved by the Institutional Animal Care and Use Committee at the University of California, San Francisco (protocol #AN180952-01B).
Version history
- Received: March 16, 2022
- Preprint posted: May 4, 2022 (view preprint)
- Accepted: June 9, 2023
- Accepted Manuscript published: June 12, 2023 (version 1)
- Version of Record published: June 30, 2023 (version 2)
Copyright
© 2023, Xu et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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