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Dembech E, Malatesta M, De Rito C, Mori G, Cavazzini D, Secchi A, Morandin F, Percudani R. Identification of hidden associations among eukaryotic genes through statistical analysis of coevolutionary transitions. Proc Natl Acad Sci U S A 2023; 120:e2218329120. [PMID: 37043529 PMCID: PMC10120013 DOI: 10.1073/pnas.2218329120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 03/10/2023] [Indexed: 04/13/2023] Open
Abstract
Coevolution at the gene level, as reflected by correlated events of gene loss or gain, can be revealed by phylogenetic profile analysis. The optimal method and metric for comparing phylogenetic profiles, especially in eukaryotic genomes, are not yet established. Here, we describe a procedure suitable for large-scale analysis, which can reveal coevolution based on the assessment of the statistical significance of correlated presence/absence transitions between gene pairs. This metric can identify coevolution in profiles with low overall similarities and is not affected by similarities lacking coevolutionary information. We applied the procedure to a large collection of 60,912 orthologous gene groups (orthogroups) in 1,264 eukaryotic genomes extracted from OrthoDB. We found significant cotransition scores for 7,825 orthogroups associated in 2,401 coevolving modules linking known and unknown genes in protein complexes and biological pathways. To demonstrate the ability of the method to predict hidden gene associations, we validated through experiments the involvement of vertebrate malate synthase-like genes in the conversion of (S)-ureidoglycolate into glyoxylate and urea, the last step of purine catabolism. This identification explains the presence of glyoxylate cycle genes in metazoa and suggests an anaplerotic role of purine degradation in early eukaryotes.
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Affiliation(s)
- Elena Dembech
- Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, Parma43124, Italy
| | - Marco Malatesta
- Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, Parma43124, Italy
| | - Carlo De Rito
- Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, Parma43124, Italy
| | - Giulia Mori
- Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, Parma43124, Italy
| | - Davide Cavazzini
- Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, Parma43124, Italy
| | - Andrea Secchi
- Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, Parma43124, Italy
| | - Francesco Morandin
- Department of Mathematical, Physical and Computer Sciences, University of Parma, Parma43124, Italy
| | - Riccardo Percudani
- Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, Parma43124, Italy
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Ji F, Bonilla G, Krykbaev R, Ruvkun G, Tabach Y, Sadreyev RI. DEPCOD: a tool to detect and visualize co-evolution of protein domains. Nucleic Acids Res 2022; 50:W246-W253. [PMID: 35536332 PMCID: PMC9252791 DOI: 10.1093/nar/gkac349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 04/13/2022] [Accepted: 04/26/2022] [Indexed: 11/14/2022] Open
Abstract
Proteins with similar phylogenetic patterns of conservation or loss across evolutionary taxa are strong candidates to work in the same cellular pathways or engage in physical or functional interactions. Our previously published tools implemented our method of normalized phylogenetic sequence profiling to detect functional associations between non-homologous proteins. However, many proteins consist of multiple protein domains subjected to different selective pressures, so using protein domain as the unit of analysis improves the detection of similar phylogenetic patterns. Here we analyze sequence conservation patterns across the whole tree of life for every protein domain from a set of widely studied organisms. The resulting new interactive webserver, DEPCOD (DEtection of Phylogenetically COrrelated Domains), performs searches with either a selected pre-defined protein domain or a user-supplied sequence as a query to detect other domains from the same organism that have similar conservation patterns. Top similarities on two evolutionary scales (the whole tree of life or eukaryotic genomes) are displayed along with known protein interactions and shared complexes, pathway enrichment among the hits, and detailed visualization of sources of detected similarities. DEPCOD reveals functional relationships between often non-homologous domains that could not be detected using whole-protein sequences. The web server is accessible at http://genetics.mgh.harvard.edu/DEPCOD.
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Affiliation(s)
- Fei Ji
- Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA.,Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Gracia Bonilla
- Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA.,Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Rustem Krykbaev
- Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA
| | - Gary Ruvkun
- Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA.,Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Yuval Tabach
- Department of Developmental Biology and Cancer Research, Faculty of Medicine, The Hebrew University of Jerusalem, Ein Kerem 9112102, Israel
| | - Ruslan I Sadreyev
- Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA.,Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
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