Design principles of gene evolution for niche adaptation through changes in protein–protein interaction networks

Gon Carmi, Somnath Tagore, Alessandro Gorohovski, Aviad Sivan, Dorith Raviv-Shay, Milana Frenkel-Morgenstern

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

In contrast to fossorial and above-ground organisms, subterranean species have adapted to the extreme stresses of living underground. We analyzed the predicted protein–protein interactions (PPIs) of all gene products, including those of stress-response genes, among nine subterranean, ten fossorial, and 13 aboveground species. We considered 10,314 unique orthologous protein families and constructed 5,879,879 PPIs in all organisms using ChiPPI. We found strong association between PPI network modulation and adaptation to specific habitats, noting that mutations in genes and changes in protein sequences were not linked directly with niche adaptation in the organisms sampled. Thus, orthologous hypoxia, heat-shock, and circadian clock proteins were found to cluster according to habitat, based on PPIs rather than on sequence similarities. Curiously, "ordered" domains were preserved in aboveground species, while "disordered" domains were conserved in subterranean organisms, and confirmed for proteins in DistProt database. Furthermore, proteins with disordered regions were found to adopt significantly less optimal codon usage in subterranean species than in fossorial and above-ground species. These findings reveal design principles of protein networks by means of alterations in protein domains, thus providing insight into deep mechanisms of evolutionary adaptation, generally, and particularly of species to underground living and other confined habitats.

Original languageEnglish
Article number15628
JournalScientific Reports
Volume10
Issue number1
DOIs
StatePublished - 24 Sep 2020

Bibliographical note

Publisher Copyright:
© 2020, The Author(s).

Funding

We thank Dr. Eviatar Nevo for his expertise and helpful comments on the manuscript. This work was supported by PBC (VATAT) fellowships for outstanding post-Docs from China and India (22351 and 20027) and Israel Cancer Association Grants (24562-01 and 24562-02). M.F.M. is a member of the Dangoor Center for Personalized Medicine and the Data Science Institute (DSI), Bar-Ilan University, Israel.

FundersFunder number
VATAT20027, 22351
Israel Cancer Association24562-02, 24562-01
Planning and Budgeting Committee of the Council for Higher Education of Israel

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