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Berger CM, Landry CR. Yeast proteins do not practice social distancing as species hybridize. Curr Genet 2021; 67:755-759. [PMID: 33948708 PMCID: PMC8096128 DOI: 10.1007/s00294-021-01188-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 04/12/2021] [Indexed: 11/26/2022]
Abstract
With the current COVID-19 pandemic, we all realized how important interactions are. Interactions are everywhere. At the cellular level, protein interactions play a key role and their ensemble, also called interactome, is often referred as the basic building blocks of life. Given its importance, the maintenance of the integrity of the interactome is a real challenge in the cell. Many events during evolution can disrupt interactomes and potentially result in different characteristics for the organisms. However, the molecular underpinnings of changes in interactions at the cellular level are still largely unexplored. Among the perturbations, hybridization puts in contact two different interactomes, which may lead to many changes in the protein interaction network of the hybrid, including gains and losses of interactions. We recently investigated the fate of the interactomes after hybridization between yeast species using a comparative proteomics approach. A large-scale conservation of the interactions was observed in hybrids, but we also noticed the presence of proteostasis-related changes. This suggests that, despite a general robustness, small differences may accumulate in hybrids and perturb their protein physiology. Here, we summarize our work with a broader perspective on the importance of interactions.
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Affiliation(s)
- Caroline M Berger
- Département de Biologie, Faculté des sciences et de génie, Université Laval, Quebec, QC, G1V0A6, Canada.
- Département de Biochimie, Microbiologie et Bio-informatique, Faculté des sciences et de génie, Université Laval, Quebec, QC, G1V0A6, Canada.
- Le réseau québécois de recherche sur la fonction, la structure et l'ingénierie de protéines, PROTEO, Université Laval, Quebec, QC, G1V0A6, Canada.
- Centre de Recherche en Données Massives (CRDM), Université Laval, Quebec, QC, G1V0A6, Canada.
| | - Christian R Landry
- Département de Biologie, Faculté des sciences et de génie, Université Laval, Quebec, QC, G1V0A6, Canada
- Département de Biochimie, Microbiologie et Bio-informatique, Faculté des sciences et de génie, Université Laval, Quebec, QC, G1V0A6, Canada
- Le réseau québécois de recherche sur la fonction, la structure et l'ingénierie de protéines, PROTEO, Université Laval, Quebec, QC, G1V0A6, Canada
- Centre de Recherche en Données Massives (CRDM), Université Laval, Quebec, QC, G1V0A6, Canada
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Diss G, Lehner B. The genetic landscape of a physical interaction. eLife 2018; 7:32472. [PMID: 29638215 PMCID: PMC5896888 DOI: 10.7554/elife.32472] [Citation(s) in RCA: 80] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Accepted: 03/02/2018] [Indexed: 12/26/2022] Open
Abstract
A key question in human genetics and evolutionary biology is how mutations in different genes combine to alter phenotypes. Efforts to systematically map genetic interactions have mostly made use of gene deletions. However, most genetic variation consists of point mutations of diverse and difficult to predict effects. Here, by developing a new sequencing-based protein interaction assay – deepPCA – we quantified the effects of >120,000 pairs of point mutations on the formation of the AP-1 transcription factor complex between the products of the FOS and JUN proto-oncogenes. Genetic interactions are abundant both in cis (within one protein) and trans (between the two molecules) and consist of two classes – interactions driven by thermodynamics that can be predicted using a three-parameter global model, and structural interactions between proximally located residues. These results reveal how physical interactions generate quantitatively predictable genetic interactions. Proteins, the molecular workhorses of the cell, are made of small units called amino acids attached together like the links of a chain. Each protein is composed of a unique combination of amino acids, which is determined by a specific sequence of DNA called a gene. A change in a gene – a mutation – can create a variation in the protein it codes for, for instance by swapping a type of amino acid for another. Different mutations in the same gene can alter a protein in different ways. Some of these changes are harmless, but other can hinder how the protein performs its role. For example, a small change in the structure of a protein could affect how it will bind to other molecules. It is possible for people to have identical mutations in the same genes, but experience different consequences. For instance, two persons could carry the same disease-inducing mutation, but one has a severe version of the condition and the other only mild symptoms. One reason is that changes in other genes cancel out or enhance the effect of a mutation. This phenomenon is known as a genetic interaction and it remains poorly understood, especially at the molecular level. Here, Diss and Lehner developed a method, called deepPCA, to study the consequences of mutations in proteins in the laboratory. The experiments focused on two human genes which code for two proteins that normally attach to each other. Two mutations were artificially created, either one in each gene, or two in one of them. Diss and Lehner then examined how strongly the two mutated proteins could still attach to each other. By repeating this process with over 120,000 different pairs of mutations, it became possible to study how one mutation can have different effects depending on the presence of other mutations in the same protein or in the binding partner. Overall, Diss and Lehner found that genetic interactions are the result of two mechanisms. In the first one, the two mutations together cause specific structural changes that modify how proteins bind to each other. In the second one, the changes solely depend on the magnitude of the initial, thermodynamic effects of individual mutations, but not on their specific physical and chemical properties. To predict the consequences of this second type of genetic interactions, knowing the identity or the exact effects of the two mutations is not necessary. Understanding and predicting genetic interactions is important to develop personalized medicine, where treatments are tailored based on the genetic make up of an individual. This knowledge will also help to study how genes have evolved together.
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Affiliation(s)
- Guillaume Diss
- Systems Biology Program, Centre for Genomic Regulation, The Barcelona Institute for Science and Technology, Barcelona, Spain.,Universitat Pompeu Fabra, Barcelona, Spain
| | - Ben Lehner
- Systems Biology Program, Centre for Genomic Regulation, The Barcelona Institute for Science and Technology, Barcelona, Spain.,Universitat Pompeu Fabra, Barcelona, Spain.,Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain
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Zhong Q, Pevzner SJ, Hao T, Wang Y, Mosca R, Menche J, Taipale M, Taşan M, Fan C, Yang X, Haley P, Murray RR, Mer F, Gebreab F, Tam S, MacWilliams A, Dricot A, Reichert P, Santhanam B, Ghamsari L, Calderwood MA, Rolland T, Charloteaux B, Lindquist S, Barabási AL, Hill DE, Aloy P, Cusick ME, Xia Y, Roth FP, Vidal M. An inter-species protein-protein interaction network across vast evolutionary distance. Mol Syst Biol 2016; 12:865. [PMID: 27107014 PMCID: PMC4848758 DOI: 10.15252/msb.20156484] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2015] [Revised: 02/22/2016] [Accepted: 03/04/2016] [Indexed: 12/20/2022] Open
Abstract
In cellular systems, biophysical interactions between macromolecules underlie a complex web of functional interactions. How biophysical and functional networks are coordinated, whether all biophysical interactions correspond to functional interactions, and how such biophysical-versus-functional network coordination is shaped by evolutionary forces are all largely unanswered questions. Here, we investigate these questions using an "inter-interactome" approach. We systematically probed the yeast and human proteomes for interactions between proteins from these two species and functionally characterized the resulting inter-interactome network. After a billion years of evolutionary divergence, the yeast and human proteomes are still capable of forming a biophysical network with properties that resemble those of intra-species networks. Although substantially reduced relative to intra-species networks, the levels of functional overlap in the yeast-human inter-interactome network uncover significant remnants of co-functionality widely preserved in the two proteomes beyond human-yeast homologs. Our data support evolutionary selection against biophysical interactions between proteins with little or no co-functionality. Such non-functional interactions, however, represent a reservoir from which nascent functional interactions may arise.
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Affiliation(s)
- Quan Zhong
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA Department of Genetics, Harvard Medical School, Boston, MA, USA Department of Biological Sciences, Wright State University, Dayton, OH, USA
| | - Samuel J Pevzner
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA Department of Genetics, Harvard Medical School, Boston, MA, USA Department of Biomedical Engineering, Boston University, Boston, MA, USA Boston University School of Medicine, Boston, MA, USA
| | - Tong Hao
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Yang Wang
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Roberto Mosca
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona Catalonia, Spain
| | - Jörg Menche
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA Center for Complex Network Research (CCNR) and Department of Physics, Northeastern University, Boston, MA, USA
| | - Mikko Taipale
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
| | - Murat Taşan
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA Departments of Molecular Genetics and Computer Science, University of Toronto, Toronto, ON, Canada Donnelly Centre, University of Toronto, Toronto, ON, Canada Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON, Canada
| | - Changyu Fan
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Xinping Yang
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Patrick Haley
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Ryan R Murray
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Flora Mer
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Fana Gebreab
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Stanley Tam
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Andrew MacWilliams
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Amélie Dricot
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Patrick Reichert
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Balaji Santhanam
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Lila Ghamsari
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Michael A Calderwood
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Thomas Rolland
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Benoit Charloteaux
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Susan Lindquist
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Albert-László Barabási
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA Center for Complex Network Research (CCNR) and Department of Physics, Northeastern University, Boston, MA, USA Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - David E Hill
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Patrick Aloy
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona Catalonia, Spain Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Michael E Cusick
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Yu Xia
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA Department of Bioengineering, McGill University, Montreal, QC, Canada
| | - Frederick P Roth
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA Departments of Molecular Genetics and Computer Science, University of Toronto, Toronto, ON, Canada Donnelly Centre, University of Toronto, Toronto, ON, Canada Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON, Canada Canadian Institute for Advanced Research, Toronto, ON, Canada
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA Department of Genetics, Harvard Medical School, Boston, MA, USA
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