1
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Wooller SK, Pearl LH, Pearl FMG. Identifying actionable synthetically lethal cancer gene pairs using mutual exclusivity. FEBS Lett 2024; 598:2028-2039. [PMID: 38977941 DOI: 10.1002/1873-3468.14950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 04/25/2024] [Accepted: 05/09/2024] [Indexed: 07/10/2024]
Abstract
Mutually exclusive loss-of-function alterations in gene pairs are those that occur together less frequently than may be expected and may denote a synthetically lethal relationship (SSL) between the genes. SSLs can be exploited therapeutically to selectively kill cancer cells. Here, we analysed mutation, copy number variation, and methylation levels in samples from The Cancer Genome Atlas, using the hypergeometric and the Poisson binomial tests to identify mutually exclusive inactivated genes. We focused on gene pairs where one is an inactivated tumour suppressor and the other a gene whose protein product can be inhibited by known drugs. This provided an abundance of potential targeted therapeutics and repositioning opportunities for several cancers. These data are available on the MexDrugs website, https://bioinformaticslab.sussex.ac.uk/mexdrugs.
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Affiliation(s)
- Sarah K Wooller
- Bioinformatics Lab, School of Life Sciences, University of Sussex, Brighton, UK
| | - Laurence H Pearl
- Genome Damage Stability Centre, School of Life Sciences, University of Sussex, Brighton, UK
| | - Frances M G Pearl
- Bioinformatics Lab, School of Life Sciences, University of Sussex, Brighton, UK
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2
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Dibyachintan S, Dube AK, Bradley D, Lemieux P, Dionne U, Landry CR. Cryptic genetic variation shapes the fate of gene duplicates in a protein interaction network. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.23.581840. [PMID: 38464075 PMCID: PMC10925128 DOI: 10.1101/2024.02.23.581840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Paralogous genes are often redundant for long periods of time before they diverge in function. While their functions are preserved, paralogous proteins can accumulate mutations that, through epistasis, could impact their fate in the future. By quantifying the impact of all single-amino acid substitutions on the binding of two myosin proteins to their interaction partners, we find that the future evolution of these proteins is highly contingent on their regulatory divergence and the mutations that have silently accumulated in their protein binding domains. Differences in the promoter strength of the two paralogs amplify the impact of mutations on binding in the lowly expressed one. While some mutations would be sufficient to non-functionalize one paralog, they would have minimal impact on the other. Our results reveal how functionally equivalent protein domains could be destined to specific fates by regulatory and cryptic coding sequence changes that currently have little to no functional impact.
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Affiliation(s)
- Soham Dibyachintan
- PROTEO-Regroupement Québécois de Recherche sur la Fonction, l'Ingénierie et les Applications des Protéines, Québec, QC, Canada
- Centre de Recherche en Données Massives de l'Université Laval, Université Laval, Québec, QC, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, QC, Canada
- Département de Biochimie, de Microbiologie et de Bio-Informatique, Université Laval, Québec, QC, Canada
| | - Alexandre K Dube
- PROTEO-Regroupement Québécois de Recherche sur la Fonction, l'Ingénierie et les Applications des Protéines, Québec, QC, Canada
- Centre de Recherche en Données Massives de l'Université Laval, Université Laval, Québec, QC, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, QC, Canada
- Département de Biochimie, de Microbiologie et de Bio-Informatique, Université Laval, Québec, QC, Canada
- Département de Biologie, Université Laval, Québec, QC, Canada
| | - David Bradley
- PROTEO-Regroupement Québécois de Recherche sur la Fonction, l'Ingénierie et les Applications des Protéines, Québec, QC, Canada
- Centre de Recherche en Données Massives de l'Université Laval, Université Laval, Québec, QC, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, QC, Canada
- Département de Biochimie, de Microbiologie et de Bio-Informatique, Université Laval, Québec, QC, Canada
- Département de Biologie, Université Laval, Québec, QC, Canada
| | - Pascale Lemieux
- PROTEO-Regroupement Québécois de Recherche sur la Fonction, l'Ingénierie et les Applications des Protéines, Québec, QC, Canada
- Centre de Recherche en Données Massives de l'Université Laval, Université Laval, Québec, QC, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, QC, Canada
- Département de Biochimie, de Microbiologie et de Bio-Informatique, Université Laval, Québec, QC, Canada
| | - Ugo Dionne
- PROTEO-Regroupement Québécois de Recherche sur la Fonction, l'Ingénierie et les Applications des Protéines, Québec, QC, Canada
- Centre de Recherche en Données Massives de l'Université Laval, Université Laval, Québec, QC, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, QC, Canada
- Current affiliation: Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada
| | - Christian R Landry
- PROTEO-Regroupement Québécois de Recherche sur la Fonction, l'Ingénierie et les Applications des Protéines, Québec, QC, Canada
- Centre de Recherche en Données Massives de l'Université Laval, Université Laval, Québec, QC, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, QC, Canada
- Département de Biochimie, de Microbiologie et de Bio-Informatique, Université Laval, Québec, QC, Canada
- Département de Biologie, Université Laval, Québec, QC, Canada
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3
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Hebert JD, Tang YJ, Andrejka L, Lopez SS, Petrov DA, Boross G, Winslow MM. Combinatorial in vivo genome editing identifies widespread epistasis during lung tumorigenesis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.07.583981. [PMID: 38496564 PMCID: PMC10942407 DOI: 10.1101/2024.03.07.583981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Lung adenocarcinoma, the most common subtype of lung cancer, is genomically complex, with tumors containing tens to hundreds of non-synonymous mutations. However, little is understood about how genes interact with each other to enable tumorigenesis in vivo , largely due to a lack of methods for investigating genetic interactions in a high-throughput and multiplexed manner. Here, we employed a novel platform to generate tumors with all pairwise inactivation of ten tumor suppressor genes within an autochthonous mouse model of oncogenic KRAS-driven lung cancer. By quantifying the fitness of tumors with every single and double mutant genotype, we show that most tumor suppressor genetic interactions exhibited negative epistasis, with diminishing returns on tumor fitness. In contrast, Apc inactivation showed positive epistasis with the inactivation of several other genes, including dramatically synergistic effects on tumor fitness in combination with Lkb1 or Nf1 inactivation. This approach has the potential to expand the scope of genetic interactions that may be functionally characterized in vivo , which could lead to a better understanding of how complex tumor genotypes impact each step of carcinogenesis.
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4
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De Kegel B, Ryan CJ. Paralog dispensability shapes homozygous deletion patterns in tumor genomes. Mol Syst Biol 2023; 19:e11987. [PMID: 37963083 DOI: 10.15252/msb.202311987] [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: 09/05/2023] [Revised: 10/24/2023] [Accepted: 10/26/2023] [Indexed: 11/16/2023] Open
Abstract
Genomic instability is a hallmark of cancer, resulting in tumor genomes having large numbers of genetic aberrations, including homozygous deletions of protein coding genes. That tumor cells remain viable in the presence of such gene loss suggests high robustness to genetic perturbation. In model organisms and cancer cell lines, paralogs have been shown to contribute substantially to genetic robustness-they are generally more dispensable for growth than singletons. Here, by analyzing copy number profiles of > 10,000 tumors, we test the hypothesis that the increased dispensability of paralogs shapes tumor genome evolution. We find that genes with paralogs are more likely to be homozygously deleted and that this cannot be explained by other factors known to influence copy number variation. Furthermore, features that influence paralog dispensability in cancer cell lines correlate with paralog deletion frequency in tumors. Finally, paralogs that are broadly essential in cancer cell lines are less frequently deleted in tumors than non-essential paralogs. Overall, our results suggest that homozygous deletions of paralogs are more frequently observed in tumor genomes because paralogs are more dispensable.
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Affiliation(s)
- Barbara De Kegel
- School of Computer Science and Systems Biology Ireland, University College Dublin, Dublin, Ireland
| | - Colm J Ryan
- School of Computer Science and Systems Biology Ireland, University College Dublin, Dublin, Ireland
- Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland
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5
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Vande Zande P, Siddiq MA, Hodgins-Davis A, Kim L, Wittkopp PJ. Active compensation for changes in TDH3 expression mediated by direct regulators of TDH3 in Saccharomyces cerevisiae. PLoS Genet 2023; 19:e1011078. [PMID: 38091349 PMCID: PMC10752532 DOI: 10.1371/journal.pgen.1011078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 12/27/2023] [Accepted: 11/22/2023] [Indexed: 12/26/2023] Open
Abstract
Genetic networks are surprisingly robust to perturbations caused by new mutations. This robustness is conferred in part by compensation for loss of a gene's activity by genes with overlapping functions, such as paralogs. Compensation occurs passively when the normal activity of one paralog can compensate for the loss of the other, or actively when a change in one paralog's expression, localization, or activity is required to compensate for loss of the other. The mechanisms of active compensation remain poorly understood in most cases. Here we investigate active compensation for the loss or reduction in expression of the Saccharomyces cerevisiae gene TDH3 by its paralog TDH2. TDH2 is upregulated in a dose-dependent manner in response to reductions in TDH3 by a mechanism requiring the shared transcriptional regulators Gcr1p and Rap1p. TDH1, a second and more distantly related paralog of TDH3, has diverged in its regulation and is upregulated by another mechanism. Other glycolytic genes regulated by Rap1p and Gcr1p show changes in expression similar to TDH2, suggesting that the active compensation by TDH3 paralogs is part of a broader homeostatic response mediated by shared transcriptional regulators.
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Affiliation(s)
- Pétra Vande Zande
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Mohammad A. Siddiq
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Andrea Hodgins-Davis
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Lisa Kim
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Patricia J. Wittkopp
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, United States of America
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6
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Dandage R, Papkov M, Greco BM, Fishman D, Friesen H, Wang K, Styles E, Kraus O, Grys B, Boone C, Andrews B, Parts L, Kuzmin E. Single-cell imaging of protein dynamics of paralogs reveals mechanisms of gene retention. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.23.568466. [PMID: 38045359 PMCID: PMC10690282 DOI: 10.1101/2023.11.23.568466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Gene duplication is common across the tree of life, including yeast and humans, and contributes to genomic robustness. In this study, we examined changes in the subcellular localization and abundance of proteins in response to the deletion of their paralogs originating from the whole-genome duplication event, which is a largely unexplored mechanism of functional divergence. We performed a systematic single-cell imaging analysis of protein dynamics and screened subcellular redistribution of proteins, capturing their localization and abundance changes, providing insight into forces determining paralog retention. Paralogs showed dependency, whereby proteins required their paralog to maintain their native abundance or localization, more often than compensation. Network feature analysis suggested the importance of functional redundancy and rewiring of protein and genetic interactions underlying redistribution response of paralogs. Translation of non-canonical protein isoform emerged as a novel compensatory mechanism. This study provides new insights into paralog retention and evolutionary forces that shape genomes.
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7
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Dani R, Pawloski W, Chaurasiya DK, Srilatha NS, Agarwal S, Fushman D, Naganathan AN. Conformational Tuning Shapes the Balance between Functional Promiscuity and Specialization in Paralogous Plasmodium Acyl-CoA Binding Proteins. Biochemistry 2023; 62:2982-2996. [PMID: 37788430 PMCID: PMC10774088 DOI: 10.1021/acs.biochem.3c00449] [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] [Indexed: 10/05/2023]
Abstract
Paralogous proteins confer enhanced fitness to organisms via complex sequence-conformation codes that shape functional divergence, specialization, or promiscuity. Here, we dissect the underlying mechanism of promiscuous binding versus partial subfunctionalization in paralogues by studying structurally identical acyl-CoA binding proteins (ACBPs) from Plasmodium falciparum that serve as promising drug targets due to their high expression during the protozoan proliferative phase. Combining spectroscopic measurements, solution NMR, SPR, and simulations on two of the paralogues, A16 and A749, we show that minor sequence differences shape nearly every local and global conformational feature. A749 displays a broader and heterogeneous native ensemble, weaker thermodynamic coupling and cooperativity, enhanced fluctuations, and a larger binding pocket volume compared to A16. Site-specific tryptophan probes signal a graded reduction in the sampling of substates in the holo form, which is particularly apparent in A749. The paralogues exhibit a spectrum of binding affinities to different acyl-CoAs with A749, the more promiscuous and hence the likely ancestor, binding 1000-fold stronger to lauroyl-CoA under physiological conditions. We thus demonstrate how minor sequence changes modulate the extent of long-range interactions and dynamics, effectively contributing to the molecular evolution of contrasting functional repertoires in paralogues.
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Affiliation(s)
- Rahul Dani
- Department of Biotechnology, Bhupat & Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
| | - Westley Pawloski
- Center for Biomolecular Structure & Organization, Department of Chemistry & Biochemistry, University of Maryland, College Park, Maryland 20742, United States
| | - Dhruv Kumar Chaurasiya
- Department of Biotechnology, Bhupat & Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
| | | | - Sonal Agarwal
- Department of Biotechnology, Bhupat & Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
| | - David Fushman
- Center for Biomolecular Structure & Organization, Department of Chemistry & Biochemistry, University of Maryland, College Park, Maryland 20742, United States
| | - Athi N Naganathan
- Department of Biotechnology, Bhupat & Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
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8
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Zhu SB, Jiang QH, Chen ZG, Zhou X, Jin YT, Deng Z, Guo FB. Mslar: Microbial synthetic lethal and rescue database. PLoS Comput Biol 2023; 19:e1011218. [PMID: 37289843 DOI: 10.1371/journal.pcbi.1011218] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 05/26/2023] [Indexed: 06/10/2023] Open
Abstract
Synthetic lethality (SL) occurs when mutations in two genes together lead to cell or organism death, while a single mutation in either gene does not have a significant impact. This concept can also be extended to three or more genes for SL. Computational and experimental methods have been developed to predict and verify SL gene pairs, especially for yeast and Escherichia coli. However, there is currently a lack of a specialized platform to collect microbial SL gene pairs. Therefore, we designed a synthetic interaction database for microbial genetics that collects 13,313 SL and 2,994 Synthetic Rescue (SR) gene pairs that are reported in the literature, as well as 86,981 putative SL pairs got through homologous transfer method in 281 bacterial genomes. Our database website provides multiple functions such as search, browse, visualization, and Blast. Based on the SL interaction data in the S. cerevisiae, we review the issue of duplications' essentiality and observed that the duplicated genes and singletons have a similar ratio of being essential when we consider both individual and SL. The Microbial Synthetic Lethal and Rescue Database (Mslar) is expected to be a useful reference resource for researchers interested in the SL and SR genes of microorganisms. Mslar is open freely to everyone and available on the web at http://guolab.whu.edu.cn/Mslar/.
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Affiliation(s)
- Sen-Bin Zhu
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Ministry of Education, and School of Pharmaceutical Sciences, Wuhan University, Wuhan, China
| | - Qian-Hu Jiang
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhi-Guo Chen
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiang Zhou
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yan-Ting Jin
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zixin Deng
- Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Ministry of Education, and School of Pharmaceutical Sciences, Wuhan University, Wuhan, China
| | - Feng-Biao Guo
- Department of Respiratory and Critical Care Medicine, Zhongnan Hospital of Wuhan University, Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Ministry of Education and School of Pharmaceutical Sciences, Wuhan University, Wuhan, China
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9
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Ryan CJ, Mehta I, Kebabci N, Adams DJ. Targeting synthetic lethal paralogs in cancer. Trends Cancer 2023; 9:397-409. [PMID: 36890003 DOI: 10.1016/j.trecan.2023.02.002] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Revised: 02/02/2023] [Accepted: 02/07/2023] [Indexed: 03/08/2023]
Abstract
Synthetic lethal interactions, where mutation of one gene renders cells sensitive to inhibition of another gene, can be exploited for the development of targeted therapeutics in cancer. Pairs of duplicate genes (paralogs) often share common functionality and hence are a potentially rich source of synthetic lethal interactions. Because the majority of human genes have paralogs, exploiting such interactions could be a widely applicable approach for targeting gene loss in cancer. Moreover, existing small-molecule drugs may exploit synthetic lethal interactions by inhibiting multiple paralogs simultaneously. Consequently, the identification of synthetic lethal interactions between paralogs could be extremely informative for drug development. Here we review approaches to identify such interactions and discuss some of the challenges of exploiting them.
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Affiliation(s)
- Colm J Ryan
- Conway Institute and School of Computer Science, University College Dublin, Dublin, Ireland; Systems Biology Ireland, University College Dublin, Dublin, Ireland.
| | - Ishan Mehta
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Narod Kebabci
- Conway Institute and School of Computer Science, University College Dublin, Dublin, Ireland; Science Foundation Ireland (SFI) Centre for Research Training in Genomics Data Science, University College Dublin, Dublin, Ireland
| | - David J Adams
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
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10
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Evolutionary conservation of sequence motifs at sites of protein modification. J Biol Chem 2023; 299:104617. [PMID: 36933807 PMCID: PMC10139944 DOI: 10.1016/j.jbc.2023.104617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 02/20/2023] [Accepted: 03/11/2023] [Indexed: 03/18/2023] Open
Abstract
Gene duplications are common in biology and are likely to be an important source of functional diversification and specialization. The yeast Saccharomyces cerevisiae underwent a whole genome duplication event early in evolution, and a substantial number of duplicated genes have been retained. We identified more than 3,500 instances where only one of two paralogous proteins undergoes post-translational modification despite having retained the same amino acid residue in both. We also developed a web-based search algorithm (CoSMoS.c.) that scores conservation of amino acid sequences based on 1011 wild and domesticated yeast isolates and used it to compare differentially-modified pairs of paralogous proteins. We found that the most common modifications - phosphorylation, ubiquitylation and acylation but not N-glycosylation - occur in regions of high sequence conservation. Such conservation is evident even for ubiquitylation and succinylation, where there is no established 'consensus site' for modification. Differences in phosphorylation were not associated with predicted secondary structure or solvent accessibility, but did mirror known differences in kinase-substrate interactions. Thus, differences in post-translational modification likely result from differences in adjoining amino acids and their interactions with modifying enzymes. By integrating data from large scale proteomics and genomics analysis, in a system with such substantial genetic diversity, we obtained a more comprehensive understanding of the functional basis for genetic redundancies that have persisted for 100 million years.
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11
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Zande PV, Wittkopp PJ. Active compensation for changes in TDH3 expression mediated by direct regulators of TDH3 in Saccharomyces cerevisiae. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.13.523977. [PMID: 36711763 PMCID: PMC9882118 DOI: 10.1101/2023.01.13.523977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Genetic networks are surprisingly robust to perturbations caused by new mutations. This robustness is conferred in part by compensation for loss of a gene's activity by genes with overlapping functions, such as paralogs. Compensation occurs passively when the normal activity of one paralog can compensate for the loss of the other, or actively when a change in one paralog's expression, localization, or activity is required to compensate for loss of the other. The mechanisms of active compensation remain poorly understood in most cases. Here we investigate active compensation for the loss or reduction in expression of the Saccharomyces cerevisiae gene TDH3 by its paralogs TDH1 and TDH2. TDH1 and TDH2 are upregulated in a dose-dependent manner in response to reductions in TDH3 by a mechanism requiring the shared transcriptional regulators Gcr1p and Rap1p. Other glycolytic genes regulated by Rap1p and Gcr1p show changes in expression similar to TDH2, suggesting that the active compensation by TDH3 paralogs is part of a broader homeostatic response mediated by shared transcriptional regulators.
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Affiliation(s)
- Pétra Vande Zande
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI, USA
- Current address: Department of Microbiology and Immunology, University of Minnesota, Minneapolis, MN, USA
| | - Patricia J Wittkopp
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI, USA
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA
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12
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Mallik S, Tawfik DS, Levy ED. How gene duplication diversifies the landscape of protein oligomeric state and function. Curr Opin Genet Dev 2022; 76:101966. [PMID: 36007298 PMCID: PMC9548406 DOI: 10.1016/j.gde.2022.101966] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 07/01/2022] [Accepted: 07/08/2022] [Indexed: 11/29/2022]
Abstract
Oligomeric proteins are central to cellular life and the duplication and divergence of their genes is a key driver of evolutionary innovations. The duplication of a gene coding for an oligomeric protein has numerous possible outcomes, which motivates questions on the relationship between structural and functional divergence. How do protein oligomeric states diversify after gene duplication? In the simple case of duplication of a homo-oligomeric protein gene, what properties can influence the fate of descendant paralogs toward forming independent homomers or maintaining their interaction as a complex? Furthermore, how are functional innovations associated with the diversification of oligomeric states? Here, we review recent literature and present specific examples in an attempt to illustrate and answer these questions.
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Affiliation(s)
- Saurav Mallik
- Department of Chemical and Structural Biology, The Weizmann Institute of Science, Rehovot 7610001, Israel; Department of Biomolecular Sciences, The Weizmann Institute of Science, Rehovot 7610001, Israel.
| | - Dan S Tawfik
- Department of Biomolecular Sciences, The Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Emmanuel D Levy
- Department of Chemical and Structural Biology, The Weizmann Institute of Science, Rehovot 7610001, Israel.
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13
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Gu X. A Simple Evolutionary Model of Genetic Robustness After Gene Duplication. J Mol Evol 2022; 90:352-361. [PMID: 35913597 DOI: 10.1007/s00239-022-10065-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 06/23/2022] [Indexed: 10/16/2022]
Abstract
When a dispensable gene is duplicated (referred to the ancestral dispensability denoted by O+), genetic buffering and duplicate compensation together maintain the duplicate redundancy, whereas duplicate compensation is the only mechanism when an essential gene is duplicated (referred to the ancestral essentiality denoted by O-). To investigate these evolutionary scenarios of genetic robustness, I formulated a simple mixture model for analyzing duplicate pairs with one of the following states: double dispensable (DD), semi-dispensable (one dispensable one essential, DE), or double essential (EE). This model was applied to the yeast duplicate pairs from a whole-genome duplication (WGD) occurred about 100 million years ago (mya), and the mouse duplicate pairs from a WGD occurred about more than 500 mya. Both case studies revealed that the proportion of essentiality for those duplicates with ancestral essentiality [PE(O-)] was much higher than that for those with ancestral dispensability [PE(O+)]. While it was negligible in the yeast duplicate pairs, PE(O+) (about 20%) was shown statistically significant in the mouse duplicate pairs. These findings, together, support the hypothesis that both sub-functionalization and neo-functionalization may play some roles after gene duplication, though the former may be much faster than the later.
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Affiliation(s)
- Xun Gu
- The Laurence H. Baker Center in Bioinformatics on Biological Statistics, Department of Genetics, Development and Cell Biology, Program of Ecological and Evolutionary Biology, Iowa State University, Ames, IA, 50011, USA.
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14
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Gera T, Jonas F, More R, Barkai N. Evolution of binding preferences among whole-genome duplicated transcription factors. eLife 2022; 11:73225. [PMID: 35404235 PMCID: PMC9000951 DOI: 10.7554/elife.73225] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Accepted: 01/20/2022] [Indexed: 01/10/2023] Open
Abstract
Throughout evolution, new transcription factors (TFs) emerge by gene duplication, promoting growth and rewiring of transcriptional networks. How TF duplicates diverge was studied in a few cases only. To provide a genome-scale view, we considered the set of budding yeast TFs classified as whole-genome duplication (WGD)-retained paralogs (~35% of all specific TFs). Using high-resolution profiling, we find that ~60% of paralogs evolved differential binding preferences. We show that this divergence results primarily from variations outside the DNA-binding domains (DBDs), while DBD preferences remain largely conserved. Analysis of non-WGD orthologs revealed uneven splitting of ancestral preferences between duplicates, and the preferential acquiring of new targets by the least conserved paralog (biased neo/sub-functionalization). Interactions between paralogs were rare, and, when present, occurred through weak competition for DNA-binding or dependency between dimer-forming paralogs. We discuss the implications of our findings for the evolutionary design of transcriptional networks.
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Affiliation(s)
- Tamar Gera
- Department of Molecular Genetics, Weizmann Institute of Science
| | - Felix Jonas
- Department of Molecular Genetics, Weizmann Institute of Science
| | - Roye More
- Department of Molecular Genetics, Weizmann Institute of Science
| | - Naama Barkai
- Department of Molecular Genetics, Weizmann Institute of Science
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15
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Multi-Omics Analysis of Multiple Glucose-Sensing Receptor Systems in Yeast. Biomolecules 2022; 12:biom12020175. [PMID: 35204676 PMCID: PMC8961648 DOI: 10.3390/biom12020175] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 01/14/2022] [Accepted: 01/18/2022] [Indexed: 12/13/2022] Open
Abstract
The yeast Saccharomyces cerevisiae has long been used to produce alcohol from glucose and other sugars. While much is known about glucose metabolism, relatively little is known about the receptors and signaling pathways that indicate glucose availability. Here, we compare the two glucose receptor systems in S. cerevisiae. The first is a heterodimer of transporter-like proteins (transceptors), while the second is a seven-transmembrane receptor coupled to a large G protein (Gpa2) that acts in coordination with two small G proteins (Ras1 and Ras2). Through comprehensive measurements of glucose-dependent transcription and metabolism, we demonstrate that the two receptor systems have distinct roles in glucose signaling: the G-protein-coupled receptor directs carbohydrate and energy metabolism, while the transceptors regulate ancillary processes such as ribosome, amino acids, cofactor and vitamin metabolism. The large G-protein transmits the signal from its cognate receptor, while the small G-protein Ras2 (but not Ras1) integrates responses from both receptor pathways. Collectively, our analysis reveals the molecular basis for glucose detection and the earliest events of glucose-dependent signal transduction in yeast.
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16
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Kuzmin E, Taylor JS, Boone C. Retention of duplicated genes in evolution. Trends Genet 2022; 38:59-72. [PMID: 34294428 PMCID: PMC8678172 DOI: 10.1016/j.tig.2021.06.016] [Citation(s) in RCA: 67] [Impact Index Per Article: 33.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 06/22/2021] [Accepted: 06/24/2021] [Indexed: 01/03/2023]
Abstract
Gene duplication is a prevalent phenomenon across the tree of life. The processes that lead to the retention of duplicated genes are not well understood. Functional genomics approaches in model organisms, such as yeast, provide useful tools to test the mechanisms underlying retention with functional redundancy and divergence of duplicated genes, including fates associated with neofunctionalization, subfunctionalization, back-up compensation, and dosage amplification. Duplicated genes may also be retained as a consequence of structural and functional entanglement. Advances in human gene editing have enabled the interrogation of duplicated genes in the human genome, providing new tools to evaluate the relative contributions of each of these factors to duplicate gene retention and the evolution of genome structure.
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Affiliation(s)
- Elena Kuzmin
- Department of Biochemistry, Rosalind and Morris Goodman Cancer Research Centre, McGill University, 1160 Ave des Pins Ouest, Montreal, QC, Canada H3A 1A3.
| | - John S Taylor
- Department of Biology, University of Victoria, PO Box 1700, Station CSC, Victoria, BC, Canada V8W 2Y2
| | - Charles Boone
- Department of Molecular Genetics, Donnelly Centre, University of Toronto, 160 College Street, Toronto, ON, Canada M5S 3E1; RIKEN Center for Sustainable Resource Science, 2-1 Hirosawa, Wako, Saitama, Japan, 351-0198
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17
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Comprehensive prediction of robust synthetic lethality between paralog pairs in cancer cell lines. Cell Syst 2021; 12:1144-1159.e6. [PMID: 34529928 DOI: 10.1016/j.cels.2021.08.006] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 07/08/2021] [Accepted: 08/18/2021] [Indexed: 12/15/2022]
Abstract
Pairs of paralogs may share common functionality and, hence, display synthetic lethal interactions. As the majority of human genes have an identifiable paralog, exploiting synthetic lethality between paralogs may be a broadly applicable approach for targeting gene loss in cancer. However, only a biased subset of human paralog pairs has been tested for synthetic lethality to date. Here, by analyzing genome-wide CRISPR screens and molecular profiles of over 700 cancer cell lines, we identify features predictive of synthetic lethality between paralogs, including shared protein-protein interactions and evolutionary conservation. We develop a machine-learning classifier based on these features to predict which paralog pairs are most likely to be synthetic lethal and to explain why. We show that our classifier accurately predicts the results of combinatorial CRISPR screens in cancer cell lines and furthermore can distinguish pairs that are synthetic lethal in multiple cell lines from those that are cell-line specific. A record of this paper's transparent peer review process is included in the supplemental information.
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18
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Johnstun JA, Shankar V, Mokashi SS, Sunkara LT, Ihearahu UE, Lyman RL, Mackay TFC, Anholt RRH. Functional Diversification, Redundancy, and Epistasis among Paralogs of the Drosophila melanogaster Obp50a-d Gene Cluster. Mol Biol Evol 2021; 38:2030-2044. [PMID: 33560417 PMCID: PMC8097280 DOI: 10.1093/molbev/msab004] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Large multigene families, such as the insect odorant-binding proteins (OBPs), are thought to arise through functional diversification after repeated gene duplications. Whereas many OBPs function in chemoreception, members of this family are also expressed in tissues outside chemosensory organs. Paralogs of the Obp50 gene cluster are expressed in metabolic and male reproductive tissues, but their functions and interrelationships remain unknown. Here, we report the genetic dissection of four members of the Obp50 cluster, which are in close physical proximity without intervening genes. We used CRISPR technology to excise the entire cluster while introducing a PhiC31 reintegration site to reinsert constructs in which different combinations of the constituent Obp genes were either intact or rendered inactive. We performed whole transcriptome sequencing and assessed sexually dimorphic changes in transcript abundances (transcriptional niches) associated with each gene-edited genotype. Using this approach, we were able to estimate redundancy, additivity, diversification, and epistasis among Obp50 paralogs. We analyzed the effects of gene editing of this cluster on organismal phenotypes and found a significant skewing of sex ratios attributable to Obp50a, and sex-specific effects on starvation stress resistance attributable to Obp50d. Thus, there is functional diversification within the Obp50 cluster with Obp50a contributing to development and Obp50d to stress resistance. The deletion-reinsertion approach we applied to the Obp50 cluster provides a general paradigm for the genetic dissection of paralogs of multigene families.
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Affiliation(s)
- Joel A Johnstun
- Department of Biological Sciences, Program in Genetics and W.M. Keck Center for Behavioral Biology, North Carolina State University, Raleigh, NC, USA
| | - Vijay Shankar
- Department of Genetics and Biochemistry and Center for Human Genetics, Clemson University, Greenwood, SC, USA
| | - Sneha S Mokashi
- Department of Biological Sciences, Program in Genetics and W.M. Keck Center for Behavioral Biology, North Carolina State University, Raleigh, NC, USA
- Department of Genetics and Biochemistry and Center for Human Genetics, Clemson University, Greenwood, SC, USA
| | - Lakshmi T Sunkara
- Department of Genetics and Biochemistry and Center for Human Genetics, Clemson University, Greenwood, SC, USA
| | - Ugonna E Ihearahu
- Department of Genetics and Biochemistry and Center for Human Genetics, Clemson University, Greenwood, SC, USA
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
| | - Roberta L Lyman
- Department of Genetics and Biochemistry and Center for Human Genetics, Clemson University, Greenwood, SC, USA
| | - Trudy F C Mackay
- Department of Biological Sciences, Program in Genetics and W.M. Keck Center for Behavioral Biology, North Carolina State University, Raleigh, NC, USA
- Department of Genetics and Biochemistry and Center for Human Genetics, Clemson University, Greenwood, SC, USA
| | - Robert R H Anholt
- Department of Biological Sciences, Program in Genetics and W.M. Keck Center for Behavioral Biology, North Carolina State University, Raleigh, NC, USA
- Department of Genetics and Biochemistry and Center for Human Genetics, Clemson University, Greenwood, SC, USA
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19
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Affiliation(s)
- Ian M Ehrenreich
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA.
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20
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Kuzmin E, VanderSluis B, Nguyen Ba AN, Wang W, Koch EN, Usaj M, Khmelinskii A, Usaj MM, van Leeuwen J, Kraus O, Tresenrider A, Pryszlak M, Hu MC, Varriano B, Costanzo M, Knop M, Moses A, Myers CL, Andrews BJ, Boone C. Exploring whole-genome duplicate gene retention with complex genetic interaction analysis. Science 2020; 368:eaaz5667. [PMID: 32586993 PMCID: PMC7539174 DOI: 10.1126/science.aaz5667] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Accepted: 05/06/2020] [Indexed: 12/25/2022]
Abstract
Whole-genome duplication has played a central role in the genome evolution of many organisms, including the human genome. Most duplicated genes are eliminated, and factors that influence the retention of persisting duplicates remain poorly understood. We describe a systematic complex genetic interaction analysis with yeast paralogs derived from the whole-genome duplication event. Mapping of digenic interactions for a deletion mutant of each paralog, and of trigenic interactions for the double mutant, provides insight into their roles and a quantitative measure of their functional redundancy. Trigenic interaction analysis distinguishes two classes of paralogs: a more functionally divergent subset and another that retained more functional overlap. Gene feature analysis and modeling suggest that evolutionary trajectories of duplicated genes are dictated by combined functional and structural entanglement factors.
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Affiliation(s)
- Elena Kuzmin
- Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Benjamin VanderSluis
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Alex N Nguyen Ba
- Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada
- Center for Analysis of Evolution and Function, University of Toronto, Toronto, Ontario, Canada
| | - Wen Wang
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Elizabeth N Koch
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Matej Usaj
- Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Anton Khmelinskii
- Zentrum für Molekulare Biologie der Universität Heidelberg (ZMBH), DKFZ-ZMBH Alliance, 69120 Heidelberg, Germany
| | | | | | - Oren Kraus
- Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Amy Tresenrider
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA
| | - Michael Pryszlak
- Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Ming-Che Hu
- Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Brenda Varriano
- Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Michael Costanzo
- Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Michael Knop
- Zentrum für Molekulare Biologie der Universität Heidelberg (ZMBH), DKFZ-ZMBH Alliance, 69120 Heidelberg, Germany
- Cell Morphogenesis and Signal Transduction, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Alan Moses
- Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada
- Center for Analysis of Evolution and Function, University of Toronto, Toronto, Ontario, Canada
- Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA.
| | - Brenda J Andrews
- Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada.
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Charles Boone
- Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada.
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada
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21
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Aregger M, Lawson KA, Billmann M, Costanzo M, Tong AHY, Chan K, Rahman M, Brown KR, Ross C, Usaj M, Nedyalkova L, Sizova O, Habsid A, Pawling J, Lin ZY, Abdouni H, Wong CJ, Weiss A, Mero P, Dennis JW, Gingras AC, Myers CL, Andrews BJ, Boone C, Moffat J. Systematic mapping of genetic interactions for de novo fatty acid synthesis identifies C12orf49 as a regulator of lipid metabolism. Nat Metab 2020; 2:499-513. [PMID: 32694731 PMCID: PMC7566881 DOI: 10.1038/s42255-020-0211-z] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Accepted: 04/23/2020] [Indexed: 02/06/2023]
Abstract
The de novo synthesis of fatty acids has emerged as a therapeutic target for various diseases, including cancer. Because cancer cells are intrinsically buffered to combat metabolic stress, it is important to understand how cells may adapt to the loss of de novo fatty acid biosynthesis. Here, we use pooled genome-wide CRISPR screens to systematically map genetic interactions (GIs) in human HAP1 cells carrying a loss-of-function mutation in fatty acid synthase (FASN), whose product catalyses the formation of long-chain fatty acids. FASN-mutant cells show a strong dependence on lipid uptake that is reflected in negative GIs with genes involved in the LDL receptor pathway, vesicle trafficking and protein glycosylation. Further support for these functional relationships is derived from additional GI screens in query cell lines deficient in other genes involved in lipid metabolism, including LDLR, SREBF1, SREBF2 and ACACA. Our GI profiles also identify a potential role for the previously uncharacterized gene C12orf49 (which we call LUR1) in regulation of exogenous lipid uptake through modulation of SREBF2 signalling in response to lipid starvation. Overall, our data highlight the genetic determinants underlying the cellular adaptation associated with loss of de novo fatty acid synthesis and demonstrate the power of systematic GI mapping for uncovering metabolic buffering mechanisms in human cells.
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Affiliation(s)
- Michael Aregger
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Corresponding authors: , , ,
| | - Keith A. Lawson
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Division of Urology, Department of Surgery, University of Toronto
- Corresponding authors: , , ,
| | - Maximillian Billmann
- Department of Computer Science and Engineering, University of Minnesota – Twin Cities, Minneapolis, Minnestota, USA
- Corresponding authors: , , ,
| | - Michael Costanzo
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Amy H. Y. Tong
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Katherine Chan
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Mahfuzur Rahman
- Department of Computer Science and Engineering, University of Minnesota – Twin Cities, Minneapolis, Minnestota, USA
| | - Kevin R. Brown
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Catherine Ross
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Matej Usaj
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Lucy Nedyalkova
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Olga Sizova
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Andrea Habsid
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Judy Pawling
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Zhen-Yuan Lin
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Hala Abdouni
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Cassandra J. Wong
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Alexander Weiss
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Patricia Mero
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - James W. Dennis
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Anne-Claude Gingras
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Chad L. Myers
- Department of Computer Science and Engineering, University of Minnesota – Twin Cities, Minneapolis, Minnestota, USA
- Bioinformatics and Computational Biology Graduate Program, University of Minnesota – Twin Cities, Minneapolis, Minnestota, USA
- Corresponding authors: , , ,
| | - Brenda J. Andrews
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Corresponding authors: , , ,
| | - Charles Boone
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Corresponding authors: , , ,
| | - Jason Moffat
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Institute for Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
- Corresponding authors: , , ,
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22
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De Kegel B, Ryan CJ. Paralog buffering contributes to the variable essentiality of genes in cancer cell lines. PLoS Genet 2019; 15:e1008466. [PMID: 31652272 PMCID: PMC6834290 DOI: 10.1371/journal.pgen.1008466] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Revised: 11/06/2019] [Accepted: 10/08/2019] [Indexed: 12/26/2022] Open
Abstract
What makes a gene essential for cellular survival? In model organisms, such as budding yeast, systematic gene deletion studies have revealed that paralog genes are less likely to be essential than singleton genes and that this can partially be attributed to the ability of paralogs to buffer each other's loss. However, the essentiality of a gene is not a fixed property and can vary significantly across different genetic backgrounds. It is unclear to what extent paralogs contribute to this variation, as most studies have analyzed genes identified as essential in a single genetic background. Here, using gene essentiality profiles of 558 genetically heterogeneous tumor cell lines, we analyze the contribution of paralogy to variable essentiality. We find that, compared to singleton genes, paralogs are less frequently essential and that this is more evident when considering genes with multiple paralogs or with highly sequence-similar paralogs. In addition, we find that paralogs derived from whole genome duplication exhibit more variable essentiality than those derived from small-scale duplications. We provide evidence that in 13–17% of cases the variable essentiality of paralogs can be attributed to buffering relationships between paralog pairs, as evidenced by synthetic lethality. Paralog pairs derived from whole genome duplication and pairs that function in protein complexes are significantly more likely to display such synthetic lethal relationships. Overall we find that many of the observations made using a single strain of budding yeast can be extended to understand patterns of essentiality in genetically heterogeneous cancer cell lines. Somewhat surprisingly, the majority of human genes can be mutated or deleted in individual cell lines without killing the cells. This observation raises a number of questions—which genes can be lost and why? Here we address these questions by analyzing data on which genes are essential for the growth of over 500 cancer cell lines. In general we find that paralog genes are essential in fewer cell lines than genes that are not paralogs. Paralogs are genes that have been duplicated at some point in evolutionary history, resulting in our genome having two copies of the same gene—a paralog pair. These paralog pairs are a potential source of redundancy, similar to a car having a spare tire. If this is the case, one might expect that losing one gene from a paralog pair could be tolerated by cells, due to the existence of a 'backup gene', but losing both members would cause cells to die. By analyzing the cancer cell lines we estimate this to be the case for 13–17% of paralog pairs, and that this provides an explanation for why some genes are essential in some cell lines but not others.
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Affiliation(s)
- Barbara De Kegel
- School of Computer Science and Systems Biology Ireland, University College Dublin, Belfield, Dublin, Ireland
| | - Colm J. Ryan
- School of Computer Science and Systems Biology Ireland, University College Dublin, Belfield, Dublin, Ireland
- * E-mail:
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23
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Yang YF, Cao W, Wu S, Qian W. Genetic Interaction Network as an Important Determinant of Gene Order in Genome Evolution. Mol Biol Evol 2018; 34:3254-3266. [PMID: 29029158 PMCID: PMC5850728 DOI: 10.1093/molbev/msx264] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Although it is generally accepted that eukaryotic gene order is not random, the basic principles of gene arrangement on a chromosome remain poorly understood. Here, we extended existing population genetics theories that were based on two-locus models and proposed a hypothesis that genetic interaction networks drive the evolution of eukaryotic gene order. We predicted that genes with positive epistasis would move toward each other in evolution, during which a negative correlation between epistasis and gene distance formed. We tested and confirmed our prediction with computational simulations and empirical data analyses. Importantly, we demonstrated that gene order in the budding yeast could be successfully predicted from the genetic interaction network. Taken together, our study reveals the role of the genetic interaction network in the evolution of gene order, extends our understanding of the encoding principles in genomes, and potentially offers new strategies to improve synthetic biology.
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Affiliation(s)
- Yu-Fei Yang
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China.,Key Laboratory of Genetic Network Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Wenqing Cao
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China.,Key Laboratory of Genetic Network Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Shaohuan Wu
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China.,Key Laboratory of Genetic Network Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Wenfeng Qian
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China.,Key Laboratory of Genetic Network Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
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24
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Kuzmin E, VanderSluis B, Wang W, Tan G, Deshpande R, Chen Y, Usaj M, Balint A, Mattiazzi Usaj M, van Leeuwen J, Koch EN, Pons C, Dagilis AJ, Pryszlak M, Wang JZY, Hanchard J, Riggi M, Xu K, Heydari H, San Luis BJ, Shuteriqi E, Zhu H, Van Dyk N, Sharifpoor S, Costanzo M, Loewith R, Caudy A, Bolnick D, Brown GW, Andrews BJ, Boone C, Myers CL. Systematic analysis of complex genetic interactions. Science 2018; 360:eaao1729. [PMID: 29674565 PMCID: PMC6215713 DOI: 10.1126/science.aao1729] [Citation(s) in RCA: 159] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2017] [Accepted: 02/23/2018] [Indexed: 12/11/2022]
Abstract
To systematically explore complex genetic interactions, we constructed ~200,000 yeast triple mutants and scored negative trigenic interactions. We selected double-mutant query genes across a broad spectrum of biological processes, spanning a range of quantitative features of the global digenic interaction network and tested for a genetic interaction with a third mutation. Trigenic interactions often occurred among functionally related genes, and essential genes were hubs on the trigenic network. Despite their functional enrichment, trigenic interactions tended to link genes in distant bioprocesses and displayed a weaker magnitude than digenic interactions. We estimate that the global trigenic interaction network is ~100 times as large as the global digenic network, highlighting the potential for complex genetic interactions to affect the biology of inheritance, including the genotype-to-phenotype relationship.
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Affiliation(s)
- Elena Kuzmin
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Benjamin VanderSluis
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Wen Wang
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Guihong Tan
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Raamesh Deshpande
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Yiqun Chen
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Matej Usaj
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Attila Balint
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
- Department of Biochemistry, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Mojca Mattiazzi Usaj
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Jolanda van Leeuwen
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Elizabeth N Koch
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Carles Pons
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Andrius J Dagilis
- Department of Integrative Biology, 1 University Station C0990, University of Texas at Austin, Austin, TX 78712, USA
| | - Michael Pryszlak
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Jason Zi Yang Wang
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Julia Hanchard
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Margot Riggi
- Department of Molecular Biology, University of Geneva, Geneva 1211, Switzerland
- Department of Biochemistry, University of Geneva, 1211 Geneva, Switzerland
- iGE3 (Institute of Genetics and Genomics of Geneva), 1211 Geneva, Switzerland
- Swiss National Centre for Competence in Research Programme Chemical Biology, 1211 Geneva, Switzerland
| | - Kaicong Xu
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Hamed Heydari
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Bryan-Joseph San Luis
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Ermira Shuteriqi
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Hongwei Zhu
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Nydia Van Dyk
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Sara Sharifpoor
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Michael Costanzo
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Robbie Loewith
- Department of Molecular Biology, University of Geneva, Geneva 1211, Switzerland
- iGE3 (Institute of Genetics and Genomics of Geneva), 1211 Geneva, Switzerland
- Swiss National Centre for Competence in Research Programme Chemical Biology, 1211 Geneva, Switzerland
| | - Amy Caudy
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Daniel Bolnick
- Department of Integrative Biology, 1 University Station C0990, University of Texas at Austin, Austin, TX 78712, USA
| | - Grant W Brown
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
- Department of Biochemistry, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Brenda J Andrews
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada.
- Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
| | - Charles Boone
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada.
- Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
- RIKEN Center for Sustainable Resource Science, Wako, Saitama, Japan
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA.
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Ignatius Pang CN, Goel A, Wilkins MR. Investigating the Network Basis of Negative Genetic Interactions in Saccharomyces cerevisiae with Integrated Biological Networks and Triplet Motif Analysis. J Proteome Res 2018; 17:1014-1030. [DOI: 10.1021/acs.jproteome.7b00649] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Affiliation(s)
- Chi Nam Ignatius Pang
- Systems
Biology Initiative, School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, New South Wales 2052, Australia
| | - Apurv Goel
- Systems
Biology Initiative, School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, New South Wales 2052, Australia
| | - Marc R. Wilkins
- Systems
Biology Initiative, School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, New South Wales 2052, Australia
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26
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Benstead-Hume G, Wooller SK, Pearl FM. Computational Approaches to Identify Genetic Interactions for Cancer Therapeutics. J Integr Bioinform 2017; 14:/j/jib.2017.14.issue-3/jib-2017-0027/jib-2017-0027.xml. [PMID: 28941356 PMCID: PMC6042820 DOI: 10.1515/jib-2017-0027] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2017] [Revised: 07/28/2017] [Accepted: 08/10/2017] [Indexed: 12/17/2022] Open
Abstract
The development of improved cancer therapies is frequently cited as an urgent unmet medical need. Here we describe how genetic interactions are being therapeutically exploited to identify novel targeted treatments for cancer. We discuss the current methodologies that use 'omics data to identify genetic interactions, in particular focusing on synthetic sickness lethality (SSL) and synthetic dosage lethality (SDL). We describe the experimental and computational approaches undertaken both in humans and model organisms to identify these interactions. Finally we discuss some of the identified targets with licensed drugs, inhibitors in clinical trials or with compounds under development.
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27
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Costanzo M, VanderSluis B, Koch EN, Baryshnikova A, Pons C, Tan G, Wang W, Usaj M, Hanchard J, Lee SD, Pelechano V, Styles EB, Billmann M, van Leeuwen J, van Dyk N, Lin ZY, Kuzmin E, Nelson J, Piotrowski JS, Srikumar T, Bahr S, Chen Y, Deshpande R, Kurat CF, Li SC, Li Z, Usaj MM, Okada H, Pascoe N, San Luis BJ, Sharifpoor S, Shuteriqi E, Simpkins SW, Snider J, Suresh HG, Tan Y, Zhu H, Malod-Dognin N, Janjic V, Przulj N, Troyanskaya OG, Stagljar I, Xia T, Ohya Y, Gingras AC, Raught B, Boutros M, Steinmetz LM, Moore CL, Rosebrock AP, Caudy AA, Myers CL, Andrews B, Boone C. A global genetic interaction network maps a wiring diagram of cellular function. Science 2017; 353:353/6306/aaf1420. [PMID: 27708008 DOI: 10.1126/science.aaf1420] [Citation(s) in RCA: 775] [Impact Index Per Article: 110.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
We generated a global genetic interaction network for Saccharomyces cerevisiae, constructing more than 23 million double mutants, identifying about 550,000 negative and about 350,000 positive genetic interactions. This comprehensive network maps genetic interactions for essential gene pairs, highlighting essential genes as densely connected hubs. Genetic interaction profiles enabled assembly of a hierarchical model of cell function, including modules corresponding to protein complexes and pathways, biological processes, and cellular compartments. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections among gene pairs, rather than shared functionality. The global network illustrates how coherent sets of genetic interactions connect protein complex and pathway modules to map a functional wiring diagram of the cell.
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Affiliation(s)
- Michael Costanzo
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1
| | - Benjamin VanderSluis
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA. Simons Center for Data Analysis, Simons Foundation, 160 Fifth Avenue, New York, NY 10010, USA
| | - Elizabeth N Koch
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Anastasia Baryshnikova
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
| | - Carles Pons
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Guihong Tan
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1
| | - Wen Wang
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Matej Usaj
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1
| | - Julia Hanchard
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1. Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1
| | - Susan D Lee
- Department of Developmental, Molecular and Chemical Biology, Tufts University School of Medicine, Boston, MA 02111, USA
| | - Vicent Pelechano
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69117 Heidelberg, Germany
| | - Erin B Styles
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1. Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1
| | - Maximilian Billmann
- Division of Signaling and Functional Genomics, German Cancer Research Center (DKFZ) and Heidelberg University, Heidelberg, Germany
| | - Jolanda van Leeuwen
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1
| | - Nydia van Dyk
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1
| | - Zhen-Yuan Lin
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto ON, Canada
| | - Elena Kuzmin
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1. Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1
| | - Justin Nelson
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA. Program in Biomedical Informatics and Computational Biology, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Jeff S Piotrowski
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1. Chemical Genomics Research Group, RIKEN Center for Sustainable Resource Sciences (CSRS), Saitama, Japan
| | - Tharan Srikumar
- Princess Margaret Cancer Centre, University Health Network and Department of Medical Biophysics, University of Toronto, Toronto ON, Canada
| | - Sondra Bahr
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1
| | - Yiqun Chen
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1
| | - Raamesh Deshpande
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Christoph F Kurat
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1
| | - Sheena C Li
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1. Chemical Genomics Research Group, RIKEN Center for Sustainable Resource Sciences (CSRS), Saitama, Japan
| | - Zhijian Li
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1
| | - Mojca Mattiazzi Usaj
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1
| | - Hiroki Okada
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, University of Tokyo, Kashiwa, Chiba, Japan 277-8561
| | - Natasha Pascoe
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1. Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1
| | - Bryan-Joseph San Luis
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1
| | - Sara Sharifpoor
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1
| | - Emira Shuteriqi
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1
| | - Scott W Simpkins
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA. Program in Biomedical Informatics and Computational Biology, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Jamie Snider
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1
| | - Harsha Garadi Suresh
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1
| | - Yizhao Tan
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1
| | - Hongwei Zhu
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1
| | - Noel Malod-Dognin
- Computer Science Deptartment, University College London, London WC1E 6BT, UK
| | - Vuk Janjic
- Department of Computing, Imperial College London, UK
| | - Natasa Przulj
- Computer Science Deptartment, University College London, London WC1E 6BT, UK. School of Computing (RAF), Union University, Belgrade, Serbia
| | - Olga G Troyanskaya
- Simons Center for Data Analysis, Simons Foundation, 160 Fifth Avenue, New York, NY 10010, USA. Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
| | - Igor Stagljar
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1. Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1. Department of Biochemistry, University of Toronto, Toronto, ON, Canada
| | - Tian Xia
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA. School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China, 430074
| | - Yoshikazu Ohya
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, University of Tokyo, Kashiwa, Chiba, Japan 277-8561
| | - Anne-Claude Gingras
- Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1. Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto ON, Canada
| | - Brian Raught
- Princess Margaret Cancer Centre, University Health Network and Department of Medical Biophysics, University of Toronto, Toronto ON, Canada
| | - Michael Boutros
- Division of Signaling and Functional Genomics, German Cancer Research Center (DKFZ) and Heidelberg University, Heidelberg, Germany
| | - Lars M Steinmetz
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69117 Heidelberg, Germany. Department of Genetics, School of Medicine and Stanford Genome Technology Center Stanford University, Palo Alto, CA 94304, USA
| | - Claire L Moore
- Department of Developmental, Molecular and Chemical Biology, Tufts University School of Medicine, Boston, MA 02111, USA
| | - Adam P Rosebrock
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1. Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1
| | - Amy A Caudy
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1. Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA. Program in Biomedical Informatics and Computational Biology, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA.
| | - Brenda Andrews
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1. Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1.
| | - Charles Boone
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1. Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto ON, Canada M5S 3E1. Chemical Genomics Research Group, RIKEN Center for Sustainable Resource Sciences (CSRS), Saitama, Japan.
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Differential paralog divergence modulates genome evolution across yeast species. PLoS Genet 2017; 13:e1006585. [PMID: 28196070 PMCID: PMC5308817 DOI: 10.1371/journal.pgen.1006585] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2016] [Accepted: 01/13/2017] [Indexed: 11/24/2022] Open
Abstract
Evolutionary outcomes depend not only on the selective forces acting upon a species, but also on the genetic background. However, large timescales and uncertain historical selection pressures can make it difficult to discern such important background differences between species. Experimental evolution is one tool to compare evolutionary potential of known genotypes in a controlled environment. Here we utilized a highly reproducible evolutionary adaptation in Saccharomyces cerevisiae to investigate whether experimental evolution of other yeast species would select for similar adaptive mutations. We evolved populations of S. cerevisiae, S. paradoxus, S. mikatae, S. uvarum, and interspecific hybrids between S. uvarum and S. cerevisiae for ~200–500 generations in sulfate-limited continuous culture. Wild-type S. cerevisiae cultures invariably amplify the high affinity sulfate transporter gene, SUL1. However, while amplification of the SUL1 locus was detected in S. paradoxus and S. mikatae populations, S. uvarum cultures instead selected for amplification of the paralog, SUL2. We measured the relative fitness of strains bearing deletions and amplifications of both SUL genes from different species, confirming that, converse to S. cerevisiae, S. uvarum SUL2 contributes more to fitness in sulfate limitation than S. uvarum SUL1. By measuring the fitness and gene expression of chimeric promoter-ORF constructs, we were able to delineate the cause of this differential fitness effect primarily to the promoter of S. uvarum SUL1. Our data show evidence of differential sub-functionalization among the sulfate transporters across Saccharomyces species through recent changes in noncoding sequence. Furthermore, these results show a clear example of how such background differences due to paralog divergence can drive changes in genome evolution. Both comparative genomics and experimental evolution are powerful tools that can be used to make inferences about evolutionary processes. Together, these approaches provide the opportunity to observe evolutionary adaptation over millions of years where selective history is largely unknown, and over short timescales under controlled selective pressures in the laboratory. We have used comparative experimental evolution to observe the evolutionary fate of an adaptive mutation, and determined to what degree the outcome is conditional on the genetic background. We evolved several populations of different yeast species for over 200 generations in sulfate-limited conditions to determine how the differences in genomic context can alter evolutionary routes when challenged with a nutrient limitation selection pressure. We find that the gene encoding a high affinity sulfur transporter becomes amplified in most species of Saccharomyces, except in S. uvarum, in which the amplification of the paralogous sulfate transporter gene SUL2 is recovered. We attribute this change in amplification preference to mutations in the non-coding region of SUL1, likely due to reduced expression of this gene in S. uvarum. We conclude that the adaptive mutations selected for in each organism depend on the genomic context, even when faced with the same environmental condition.
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The Phenotypic Plasticity of Duplicated Genes in Saccharomyces cerevisiae and the Origin of Adaptations. G3-GENES GENOMES GENETICS 2017; 7:63-75. [PMID: 27799339 PMCID: PMC5217124 DOI: 10.1534/g3.116.035329] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Gene and genome duplication are the major sources of biological innovations in plants and animals. Functional and transcriptional divergence between the copies after gene duplication has been considered the main driver of innovations . However, here we show that increased phenotypic plasticity after duplication plays a more major role than thought before in the origin of adaptations. We perform an exhaustive analysis of the transcriptional alterations of duplicated genes in the unicellular eukaryote Saccharomyces cerevisiae when challenged with five different environmental stresses. Analysis of the transcriptomes of yeast shows that gene duplication increases the transcriptional response to environmental changes, with duplicated genes exhibiting signatures of adaptive transcriptional patterns in response to stress. The mechanism of duplication matters, with whole-genome duplicates being more transcriptionally altered than small-scale duplicates. The predominant transcriptional pattern follows the classic theory of evolution by gene duplication; with one gene copy remaining unaltered under stress, while its sister copy presents large transcriptional plasticity and a prominent role in adaptation. Moreover, we find additional transcriptional profiles that are suggestive of neo- and subfunctionalization of duplicate gene copies. These patterns are strongly correlated with the functional dependencies and sequence divergence profiles of gene copies. We show that, unlike singletons, duplicates respond more specifically to stress, supporting the role of natural selection in the transcriptional plasticity of duplicates. Our results reveal the underlying transcriptional complexity of duplicated genes and its role in the origin of adaptations.
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30
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Coulombe-Huntington J, Xia Y. Network Centrality Analysis in Fungi Reveals Complex Regulation of Lost and Gained Genes. PLoS One 2017; 12:e0169459. [PMID: 28046110 PMCID: PMC5207763 DOI: 10.1371/journal.pone.0169459] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2016] [Accepted: 12/16/2016] [Indexed: 01/26/2023] Open
Abstract
Gene gain and loss shape both proteomes and the networks they form. The increasing availability of closely related sequenced genomes and of genome-wide network data should enable a better understanding of the evolutionary forces driving gene gain, gene loss and evolutionary network rewiring. Using orthology mappings across 23 ascomycete fungi genomes, we identified proteins that were lost, gained or universally conserved across the tree, enabling us to compare genes across all stages of their life-cycle. Based on a collection of genome-wide network and gene expression datasets from baker's yeast, as well as a few from fission yeast, we found that gene loss is more strongly associated with network and expression features of closely related species than that of distant species, consistent with the evolutionary modulation of gene loss propensity through network rewiring. We also discovered that lost and gained genes, as compared to universally conserved "core" genes, have more regulators, more complex expression patterns and are much more likely to encode for transcription factors. Finally, we found that the relative rate of network integration of new genes into the different types of networks agrees with experimentally measured rates of network rewiring. This systems-level view of the life-cycle of eukaryotic genes suggests that the gain and loss of genes is tightly coupled to the gain and loss of network interactions, that lineage-specific adaptations drive regulatory complexity and that the relative rates of integration of new genes are consistent with network rewiring rates.
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Affiliation(s)
| | - Yu Xia
- Department of Bioengineering, Faculty of Engineering, McGill University, Montreal, Quebec, Canada
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31
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Abstract
NMD is a highly conserved pathway that degrades specific subsets of RNAs. There is increasing evidence for roles of NMD in development. In this commentary, we focus on spermatogenesis, a process dramatically impeded upon loss or disruption of NMD. NMD requires strict regulation for normal spermatogenesis, as loss of a newly discovered NMD repressor, UPF3A, also causes spermatogenic defects, most prominently during meiosis. We discuss the unusual evolution of UPF3A, whose paralog, UPF3B, has the opposite biochemical function and acts in brain development. We also discuss the regulation of NMD during germ cell development, including in chromatoid bodies, which are specifically found in haploid germ cells. The ability of NMD to coordinately degrade batteries of RNAs in a regulated fashion during development is akin to the action of transcriptional pathways, yet has the advantage of driving rapid changes in gene expression.
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Affiliation(s)
- Samantha H Jones
- a Department of Reproductive Medicine , School of Medicine, University of California, San Diego , La Jolla , CA , USA
| | - Miles Wilkinson
- a Department of Reproductive Medicine , School of Medicine, University of California, San Diego , La Jolla , CA , USA.,b Institute of Genomic Medicine, University of California , San Diego, La Jolla , CA , USA
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32
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Poelwijk FJ, Krishna V, Ranganathan R. The Context-Dependence of Mutations: A Linkage of Formalisms. PLoS Comput Biol 2016; 12:e1004771. [PMID: 27337695 PMCID: PMC4919011 DOI: 10.1371/journal.pcbi.1004771] [Citation(s) in RCA: 64] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Affiliation(s)
- Frank J. Poelwijk
- Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
- * E-mail: (FJP); (RR)
| | - Vinod Krishna
- Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
| | - Rama Ranganathan
- Green Center for Systems Biology and Departments of Biophysics and Pharmacology, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
- * E-mail: (FJP); (RR)
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33
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Toll-Riera M, San Millan A, Wagner A, MacLean RC. The Genomic Basis of Evolutionary Innovation in Pseudomonas aeruginosa. PLoS Genet 2016; 12:e1006005. [PMID: 27149698 PMCID: PMC4858143 DOI: 10.1371/journal.pgen.1006005] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2015] [Accepted: 04/04/2016] [Indexed: 11/29/2022] Open
Abstract
Novel traits play a key role in evolution, but their origins remain poorly understood. Here we address this problem by using experimental evolution to study bacterial innovation in real time. We allowed 380 populations of Pseudomonas aeruginosa to adapt to 95 different carbon sources that challenged bacteria with either evolving novel metabolic traits or optimizing existing traits. Whole genome sequencing of more than 80 clones revealed profound differences in the genetic basis of innovation and optimization. Innovation was associated with the rapid acquisition of mutations in genes involved in transcription and metabolism. Mutations in pre-existing duplicate genes in the P. aeruginosa genome were common during innovation, but not optimization. These duplicate genes may have been acquired by P. aeruginosa due to either spontaneous gene amplification or horizontal gene transfer. High throughput phenotype assays revealed that novelty was associated with increased pleiotropic costs that are likely to constrain innovation. However, mutations in duplicate genes with close homologs in the P. aeruginosa genome were associated with low pleiotropic costs compared to mutations in duplicate genes with distant homologs in the P. aeruginosa genome, suggesting that functional redundancy between duplicates facilitates innovation by buffering pleiotropic costs.
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Affiliation(s)
- Macarena Toll-Riera
- Department of Zoology, University of Oxford, Oxford, United Kingdom
- Institute of Evolutionary Biology and Environmental Studies, University of Zurich, Zürich, Switzerland
- The Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | | | - Andreas Wagner
- Institute of Evolutionary Biology and Environmental Studies, University of Zurich, Zürich, Switzerland
- The Swiss Institute of Bioinformatics, Lausanne, Switzerland
- The Santa Fe Institute, Santa Fe, New Mexico, United States of America
| | - R. Craig MacLean
- Department of Zoology, University of Oxford, Oxford, United Kingdom
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34
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Kumar A, Beloglazova N, Bundalovic-Torma C, Phanse S, Deineko V, Gagarinova A, Musso G, Vlasblom J, Lemak S, Hooshyar M, Minic Z, Wagih O, Mosca R, Aloy P, Golshani A, Parkinson J, Emili A, Yakunin AF, Babu M. Conditional Epistatic Interaction Maps Reveal Global Functional Rewiring of Genome Integrity Pathways in Escherichia coli. Cell Rep 2016; 14:648-661. [PMID: 26774489 DOI: 10.1016/j.celrep.2015.12.060] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2015] [Revised: 11/08/2015] [Accepted: 12/10/2015] [Indexed: 11/27/2022] Open
Abstract
As antibiotic resistance is increasingly becoming a public health concern, an improved understanding of the bacterial DNA damage response (DDR), which is commonly targeted by antibiotics, could be of tremendous therapeutic value. Although the genetic components of the bacterial DDR have been studied extensively in isolation, how the underlying biological pathways interact functionally remains unclear. Here, we address this by performing systematic, unbiased, quantitative synthetic genetic interaction (GI) screens and uncover widespread changes in the GI network of the entire genomic integrity apparatus of Escherichia coli under standard and DNA-damaging growth conditions. The GI patterns of untreated cultures implicated two previously uncharacterized proteins (YhbQ and YqgF) as nucleases, whereas reorganization of the GI network after DNA damage revealed DDR roles for both annotated and uncharacterized genes. Analyses of pan-bacterial conservation patterns suggest that DDR mechanisms and functional relationships are near universal, highlighting a modular and highly adaptive genomic stress response.
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Affiliation(s)
- Ashwani Kumar
- Department of Computer Science, University of Regina, Regina, SK S4S 0A2, Canada
| | - Natalia Beloglazova
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON M5S 3E5, Canada
| | - Cedoljub Bundalovic-Torma
- Hospital for Sick Children, 686 Bay Street, Toronto, ON M5G OX4, Canada; Department of Biochemistry, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Sadhna Phanse
- Terrence Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Biochemistry, University of Regina, Regina, SK S4S 0A2, Canada
| | - Viktor Deineko
- Department of Biochemistry, University of Regina, Regina, SK S4S 0A2, Canada
| | - Alla Gagarinova
- Terrence Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Biochemistry, University of Saskatchewan, Saskatoon, SK S7N 5E5, Canada
| | - Gabriel Musso
- Department of Medicine, Harvard Medical School and Cardiovascular Division, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - James Vlasblom
- Department of Biochemistry, University of Regina, Regina, SK S4S 0A2, Canada
| | - Sofia Lemak
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON M5S 3E5, Canada
| | - Mohsen Hooshyar
- Department of Biology, Carleton University, Ottawa, ON K1S 5B6, Canada
| | - Zoran Minic
- Department of Biochemistry, University of Regina, Regina, SK S4S 0A2, Canada
| | - Omar Wagih
- Terrence Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Roberto Mosca
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, c/Baldiri i Reixac 10-12, Barcelona, 08028, Catalonia, Spain
| | - Patrick Aloy
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, c/Baldiri i Reixac 10-12, Barcelona, 08028, Catalonia, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), Pg. Lluís Companys 23, Barcelona, 08010, Catalonia, Spain
| | - Ashkan Golshani
- Department of Biology, Carleton University, Ottawa, ON K1S 5B6, Canada
| | - John Parkinson
- Hospital for Sick Children, 686 Bay Street, Toronto, ON M5G OX4, Canada; Department of Biochemistry, University of Toronto, Toronto, ON M5S 1A8, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Andrew Emili
- Terrence Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Alexander F Yakunin
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON M5S 3E5, Canada
| | - Mohan Babu
- Department of Biochemistry, University of Regina, Regina, SK S4S 0A2, Canada.
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Sameith K, Amini S, Groot Koerkamp MJA, van Leenen D, Brok M, Brabers N, Lijnzaad P, van Hooff SR, Benschop JJ, Lenstra TL, Apweiler E, van Wageningen S, Snel B, Holstege FCP, Kemmeren P. A high-resolution gene expression atlas of epistasis between gene-specific transcription factors exposes potential mechanisms for genetic interactions. BMC Biol 2015; 13:112. [PMID: 26700642 PMCID: PMC4690272 DOI: 10.1186/s12915-015-0222-5] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2015] [Accepted: 12/14/2015] [Indexed: 01/14/2023] Open
Abstract
BACKGROUND Genetic interactions, or non-additive effects between genes, play a crucial role in many cellular processes and disease. Which mechanisms underlie these genetic interactions has hardly been characterized. Understanding the molecular basis of genetic interactions is crucial in deciphering pathway organization and understanding the relationship between genotype, phenotype and disease. RESULTS To investigate the nature of genetic interactions between gene-specific transcription factors (GSTFs) in Saccharomyces cerevisiae, we systematically analyzed 72 GSTF pairs by gene expression profiling double and single deletion mutants. These pairs were selected through previously published growth-based genetic interactions as well as through similarity in DNA binding properties. The result is a high-resolution atlas of gene expression-based genetic interactions that provides systems-level insight into GSTF epistasis. The atlas confirms known genetic interactions and exposes new ones. Importantly, the data can be used to investigate mechanisms that underlie individual genetic interactions. Two molecular mechanisms are proposed, "buffering by induced dependency" and "alleviation by derepression". CONCLUSIONS These mechanisms indicate how negative genetic interactions can occur between seemingly unrelated parallel pathways and how positive genetic interactions can indirectly expose parallel rather than same-pathway relationships. The focus on GSTFs is important for understanding the transcription regulatory network of yeast as it uncovers details behind many redundancy relationships, some of which are completely new. In addition, the study provides general insight into the complex nature of epistasis and proposes mechanistic models for genetic interactions, the majority of which do not fall into easily recognizable within- or between-pathway relationships.
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Affiliation(s)
- Katrin Sameith
- Molecular Cancer Research, University Medical Centre Utrecht, Universiteitsweg 100, Utrecht, The Netherlands
| | - Saman Amini
- Molecular Cancer Research, University Medical Centre Utrecht, Universiteitsweg 100, Utrecht, The Netherlands
| | - Marian J A Groot Koerkamp
- Molecular Cancer Research, University Medical Centre Utrecht, Universiteitsweg 100, Utrecht, The Netherlands
| | - Dik van Leenen
- Molecular Cancer Research, University Medical Centre Utrecht, Universiteitsweg 100, Utrecht, The Netherlands
| | - Mariel Brok
- Molecular Cancer Research, University Medical Centre Utrecht, Universiteitsweg 100, Utrecht, The Netherlands
| | - Nathalie Brabers
- Molecular Cancer Research, University Medical Centre Utrecht, Universiteitsweg 100, Utrecht, The Netherlands
| | - Philip Lijnzaad
- Molecular Cancer Research, University Medical Centre Utrecht, Universiteitsweg 100, Utrecht, The Netherlands
| | - Sander R van Hooff
- Molecular Cancer Research, University Medical Centre Utrecht, Universiteitsweg 100, Utrecht, The Netherlands
| | - Joris J Benschop
- Molecular Cancer Research, University Medical Centre Utrecht, Universiteitsweg 100, Utrecht, The Netherlands
| | - Tineke L Lenstra
- Molecular Cancer Research, University Medical Centre Utrecht, Universiteitsweg 100, Utrecht, The Netherlands
| | - Eva Apweiler
- Molecular Cancer Research, University Medical Centre Utrecht, Universiteitsweg 100, Utrecht, The Netherlands
| | - Sake van Wageningen
- Molecular Cancer Research, University Medical Centre Utrecht, Universiteitsweg 100, Utrecht, The Netherlands
| | - Berend Snel
- Theoretical Biology and Bioinformatics, Department of Biology, Utrecht University, Padualaan 8, Utrecht, The Netherlands
| | - Frank C P Holstege
- Molecular Cancer Research, University Medical Centre Utrecht, Universiteitsweg 100, Utrecht, The Netherlands
| | - Patrick Kemmeren
- Molecular Cancer Research, University Medical Centre Utrecht, Universiteitsweg 100, Utrecht, The Netherlands.
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Madhukar NS, Elemento O, Pandey G. Prediction of Genetic Interactions Using Machine Learning and Network Properties. Front Bioeng Biotechnol 2015; 3:172. [PMID: 26579514 PMCID: PMC4620407 DOI: 10.3389/fbioe.2015.00172] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2015] [Accepted: 10/12/2015] [Indexed: 12/04/2022] Open
Abstract
A genetic interaction (GI) is a type of interaction where the effect of one gene is modified by the effect of one or several other genes. These interactions are important for delineating functional relationships among genes and their corresponding proteins, as well as elucidating complex biological processes and diseases. An important type of GI - synthetic sickness or synthetic lethality - involves two or more genes, where the loss of either gene alone has little impact on cell viability, but the combined loss of all genes leads to a severe decrease in fitness (sickness) or cell death (lethality). The identification of GIs is an important problem for it can help delineate pathways, protein complexes, and regulatory dependencies. Synthetic lethal interactions have important clinical and biological significance, such as providing therapeutically exploitable weaknesses in tumors. While near systematic high-content screening for GIs is possible in single cell organisms such as yeast, the systematic discovery of GIs is extremely difficult in mammalian cells. Therefore, there is a great need for computational approaches to reliably predict GIs, including synthetic lethal interactions, in these organisms. Here, we review the state-of-the-art approaches, strategies, and rigorous evaluation methods for learning and predicting GIs, both under general (healthy/standard laboratory) conditions and under specific contexts, such as diseases.
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Affiliation(s)
- Neel S Madhukar
- Department of Physiology and Biophysics, Meyer Cancer Center, Institute for Precision Medicine and Institute for Computational Biomedicine, Weill Cornell Medical College , New York, NY , USA ; Tri-Institutional Training Program in Computational Biology and Medicine , New York, NY , USA
| | - Olivier Elemento
- Department of Physiology and Biophysics, Meyer Cancer Center, Institute for Precision Medicine and Institute for Computational Biomedicine, Weill Cornell Medical College , New York, NY , USA ; Tri-Institutional Training Program in Computational Biology and Medicine , New York, NY , USA
| | - Gaurav Pandey
- Department of Genetics and Genomic Sciences and Graduate School of Biomedical Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai , New York, NY , USA
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Srihari S, Singla J, Wong L, Ragan MA. Inferring synthetic lethal interactions from mutual exclusivity of genetic events in cancer. Biol Direct 2015; 10:57. [PMID: 26427375 PMCID: PMC4590705 DOI: 10.1186/s13062-015-0086-1] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2015] [Accepted: 09/23/2015] [Indexed: 12/21/2022] Open
Abstract
Background Synthetic lethality (SL) refers to the genetic interaction between two or more genes where only their co-alteration (e.g. by mutations, amplifications or deletions) results in cell death. In recent years, SL has emerged as an attractive therapeutic strategy against cancer: by targeting the SL partners of altered genes in cancer cells, these cells can be selectively killed while sparing the normal cells. Consequently, a number of studies have attempted prediction of SL interactions in human, a majority by extrapolating SL interactions inferred through large-scale screens in model organisms. However, these predicted SL interactions either do not hold in human cells or do not include genes that are (frequently) altered in human cancers, and are therefore not attractive in the context of cancer therapy. Results Here, we develop a computational approach to infer SL interactions directly from frequently altered genes in human cancers. It is based on the observation that pairs of genes that are altered in a (significantly) mutually exclusive manner in cancers are likely to constitute lethal combinations. Using genomic copy-number and gene-expression data from four cancers, breast, prostate, ovarian and uterine (total 3980 samples) from The Cancer Genome Atlas, we identify 718 genes that are frequently amplified or upregulated, and are likely to be synthetic lethal with six key DNA-damage response (DDR) genes in these cancers. By comparing with published data on gene essentiality (~16000 genes) from ten DDR-deficient cancer cell lines, we show that our identified genes are enriched among the top quartile of essential genes in these cell lines, implying that our inferred genes are highly likely to be (synthetic) lethal upon knockdown in these cell lines. Among the inferred targets are tousled-like kinase 2 (TLK2) and the deubiquitinating enzyme ubiquitin-specific-processing protease 7 (USP7) whose overexpression correlates with poor survival in cancers. Conclusion Mutual exclusivity between frequently occurring genetic events identifies synthetic lethal combinations in cancers. These identified genes are essential in cell lines, and are potential candidates for targeted cancer therapy. Availability: http://bioinformatics.org.au/tools-data/underMutExSL Reviewers This article was reviewed by Dr Michael Galperin, Dr Sebastian Maurer-Stroh and Professor Sanghyuk Lee. Electronic supplementary material The online version of this article (doi:10.1186/s13062-015-0086-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Sriganesh Srihari
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Queensland, 4072, Australia
| | - Jitin Singla
- Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Uttarakhand, 247667, India
| | - Limsoon Wong
- Department of Computer Science, National University of Singapore, Singapore, 117417, Singapore.
| | - Mark A Ragan
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Queensland, 4072, Australia.
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Espinosa-Cantú A, Ascencio D, Barona-Gómez F, DeLuna A. Gene duplication and the evolution of moonlighting proteins. Front Genet 2015. [PMID: 26217376 PMCID: PMC4493404 DOI: 10.3389/fgene.2015.00227] [Citation(s) in RCA: 58] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Gene duplication is a recurring phenomenon in genome evolution and a major driving force in the gain of biological functions. Here, we examine the role of gene duplication in the origin and maintenance of moonlighting proteins, with special focus on functional redundancy and innovation, molecular tradeoffs, and genetic robustness. An overview of specific examples-mainly from yeast-suggests a widespread conservation of moonlighting behavior in duplicate genes after long evolutionary times. Dosage amplification and incomplete subfunctionalization appear to be prevalent in the maintenance of multifunctionality. We discuss the role of gene-expression divergence and paralog responsiveness in moonlighting proteins with overlapping biochemical properties. Future studies analyzing multifunctional genes in a more systematic and comprehensive manner will not only enable a better understanding of how this emerging class of protein behavior originates and is maintained, but also provide new insights on the mechanisms of evolution by gene duplication.
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Affiliation(s)
- Adriana Espinosa-Cantú
- Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV), Irapuato, Mexico
| | - Diana Ascencio
- Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV), Irapuato, Mexico
| | - Francisco Barona-Gómez
- Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV), Irapuato, Mexico
| | - Alexander DeLuna
- Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV), Irapuato, Mexico
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Fares MA. The origins of mutational robustness. Trends Genet 2015; 31:373-81. [PMID: 26013677 DOI: 10.1016/j.tig.2015.04.008] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2015] [Revised: 04/27/2015] [Accepted: 04/28/2015] [Indexed: 11/17/2022]
Abstract
Biological systems are resistant to genetic changes; a property known as mutational robustness, the origin of which remains an open question. In recent years, researchers have explored emergent properties of biological systems and mechanisms of genetic redundancy to reveal how mutational robustness emerges and persists. Several mechanisms have been proposed to explain the origin of mutational robustness, including molecular chaperones and gene duplication. The latter has received much attention, but its role in robustness remains controversial. Here, I examine recent findings linking genetic redundancy through gene duplication and mutational robustness. Experimental evolution and genome resequencing have made it possible to test the role of gene duplication in tolerating mutations at both the coding and regulatory levels. This evidence as well as previous findings on regulatory reprogramming of duplicates support the role of gene duplication in the origin of robustness.
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Affiliation(s)
- Mario A Fares
- Instituto de Biología Molecular y Celular de Plantas (CSIC-UPV), Valencia, Spain; Department of Genetics, Smurfit Institute of Genetics, University of Dublin, Trinity College Dublin, Dublin, Ireland.
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40
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Zhang S, Zhang JS, Zhao J, He C. Distinct subfunctionalization and neofunctionalization of the B-class MADS-box genes in Physalis floridana. PLANTA 2015; 241:387-402. [PMID: 25326772 DOI: 10.1007/s00425-014-2190-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2014] [Accepted: 10/02/2014] [Indexed: 05/24/2023]
Abstract
This work suggested that in Physalis PFGLO1-PFDEF primarily determined corolla and androecium identity, and acquired a novel role in gynoecia functionality, while PFGLO2-PFTM6 functioned in pollen maturation only. The B-class MADS-box genes play a crucial role in determining the organ identity of the corolla and androecium. Two GLOBOSA-like (GLO-like) PFGLO1 and PFGLO2 and two DEFICIENS-like (DEF-like) PFDEF and PFTM6 genes were present in Physalis floridana. However, the double-layered-lantern1 (doll1) mutant is the result of a single recessive mutation in PFGLO1, hinting a distinct divergent pattern of B-class genes. In this work, we utilized the tobacco rattle virus (TRV)-mediated gene silencing approach to further verify this assumption in P. floridana. Silencing of PFGLO1 or/and PFDEF demonstrated their primary role in determining corolla and androecium identity. However, specific PFGLO2 or/and PFTM6 silencing did not affect any organ identity but showed a reduction in mature pollen. These results suggested that both PFGLO2 and PFTM6 had lost their role in organ identity determination but functioned in pollen maturation. Evaluation of fruit setting in reciprocal crosses suggested that both PFGLO1 and PFDEF might have acquired an essential and novel role in the functionality of gynoecia. Such a divergence of the duplicated GLO-DEF heterodimer genes in floral development is different from the existing observations within Solanaceae. Therefore, our research sheds new light on the functional evolution of the duplicated B-class MADS-box genes in angiosperms.
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Affiliation(s)
- Shaohua Zhang
- State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, Nanxincun 20, Xiangshan, Beijing, 100093, China
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41
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Survival and innovation: The role of mutational robustness in evolution. Biochimie 2014; 119:254-61. [PMID: 25447135 DOI: 10.1016/j.biochi.2014.10.019] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2014] [Accepted: 10/15/2014] [Indexed: 11/23/2022]
Abstract
Biological systems are resistant to perturbations caused by the environment and by the intrinsic noise of the system. Robustness to mutations is a particular aspect of robustness in which the phenotype is resistant to genotypic variation. Mutational robustness has been linked to the ability of the system to generate heritable genetic variation (a property known as evolvability). It is known that greater robustness leads to increased evolvability. Therefore, mechanisms that increase mutational robustness fuel evolvability. Two such mechanisms, molecular chaperones and gene duplication, have been credited with enormous importance in generating functional diversity through the increase of system's robustness to mutational insults. However, the way in which such mechanisms regulate robustness remains largely uncharacterized. In this review, I provide evidence in support of the role of molecular chaperones and gene duplication in innovation. Specifically, I present evidence that these mechanisms regulate robustness allowing unstable systems to survive long periods of time, and thus they provide opportunity for other mutations to compensate the destabilizing effects of functionally innovative mutations. The findings reported in this study set new questions with regards to the synergy between robustness mechanisms and how this synergy can alter the adaptive landscape of proteins. The ideas proposed in this article set the ground for future research in the understanding of the role of robustness in evolution.
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42
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Gopinath RK, You ST, Chien KY, Swamy KBS, Yu JS, Schuyler SC, Leu JY. The Hsp90-dependent proteome is conserved and enriched for hub proteins with high levels of protein-protein connectivity. Genome Biol Evol 2014; 6:2851-65. [PMID: 25316598 PMCID: PMC4224352 DOI: 10.1093/gbe/evu226] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Hsp90 is one of the most abundant and conserved proteins in the cell. Reduced levels or activity of Hsp90 causes defects in many cellular processes and also reveals genetic and nongenetic variation within a population. Despite information about Hsp90 protein–protein interactions, a global view of the Hsp90-regulated proteome in yeast is unavailable. To investigate the degree of dependency of individual yeast proteins on Hsp90, we used the “stable isotope labeling by amino acids in cell culture” method coupled with mass spectrometry to quantify around 4,000 proteins in low-Hsp90 cells. We observed that 904 proteins changed in their abundance by more than 1.5-fold. When compared with the transcriptome of the same population of cells, two-thirds of the misregulated proteins were observed to be affected posttranscriptionally, of which the majority were downregulated. Further analyses indicated that the downregulated proteins are highly conserved and assume central roles in cellular networks with a high number of protein interacting partners, suggesting that Hsp90 buffers genetic and nongenetic variation through regulating protein network hubs. The downregulated proteins were enriched for essential proteins previously not known to be Hsp90-dependent. Finally, we observed that downregulation of transcription factors and mating pathway components by attenuating Hsp90 function led to decreased target gene expression and pheromone response, respectively, providing a direct link between observed proteome regulation and cellular phenotypes.
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Affiliation(s)
- Rajaneesh Karimpurath Gopinath
- Molecular and Cell Biology, Taiwan International Graduate Program, Graduate Institute of Life Sciences, National Defense Medical Center and Academia Sinica Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan
| | - Shu-Ting You
- Molecular and Cell Biology, Taiwan International Graduate Program, Graduate Institute of Life Sciences, National Defense Medical Center and Academia Sinica Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan
| | - Kun-Yi Chien
- Molecular Medicine Research Center, Department of Biochemistry and Molecular Biology, College of Medicine, Chang Gung University, Tao-Yuan, Taiwan
| | | | - Jau-Song Yu
- Department of Cell and Molecular Biology, College of Medicine, Chang Gung University, Tao-Yuan, Taiwan
| | - Scott C Schuyler
- Department of Biomedical Sciences, College of Medicine, Chang Gung University, Tao-Yuan, Taiwan
| | - Jun-Yi Leu
- Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan
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43
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Gossani C, Bellieny-Rabelo D, Venancio TM. Evolutionary analysis of multidrug resistance genes in fungi - impact of gene duplication and family conservation. FEBS J 2014; 281:4967-77. [PMID: 25220072 DOI: 10.1111/febs.13046] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2014] [Revised: 08/18/2014] [Accepted: 09/09/2014] [Indexed: 11/30/2022]
Abstract
Although the emergence of bacterial drug resistance is of great concern to the scientific community, few studies have evaluated this phenomenon systematically in fungi by using genome-wide datasets. In the present study, we assembled a large compendium of Saccharomyces cerevisiae chemical genetic data to study the evolution of multidrug resistance genes (MDRs) in the fungal lineage. We found that MDRs typically emerge in widely conserved families, most of which containing homologs from pathogenic fungi, such as Candida albicans and Coccidioides immitis, which could favor the evolution of drug resistance in those species. By integrating data from chemical genetics with protein family conservation, genetic and protein interactions, we found that gene families rarely have more than one MDR, indicating that paralogs evolve asymmetrically with regard to multidrug resistance roles. Furthermore, MDRs have more genetic and protein interaction partners than non-MDRs, supporting their participation in complex biochemical systems underlying the tolerance to multiple bioactive molecules. MDRs share more chemical genetic interactions with other MDRs than with non-MDRs, regardless of their evolutionary affinity. These results suggest the existence of an intricate system involved in the global drug tolerance phenotypes. Finally, MDRs are more likely to be hit repeatedly by mutations in laboratory evolution experiments, indicating that they have great adaptive potential. The results presented here not only reveal the main genomic features underlying the evolution of MDRs, but also shed light on the gene families from which drug resistance is more likely to emerge in fungi.
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Affiliation(s)
- Cristiani Gossani
- Laboratório de Química e Função de Proteínas e Peptídeos, Centro de Biociências e Biotecnologia, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Campos dos Goytacazes, Brazil
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44
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Keane OM, Toft C, Carretero-Paulet L, Jones GW, Fares MA. Preservation of genetic and regulatory robustness in ancient gene duplicates of Saccharomyces cerevisiae. Genome Res 2014; 24:1830-41. [PMID: 25149527 PMCID: PMC4216924 DOI: 10.1101/gr.176792.114] [Citation(s) in RCA: 56] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Biological systems remain robust against certain genetic and environmental challenges. Robustness allows the exploration of ecological adaptations. It is unclear what factors contribute to increasing robustness. Gene duplication has been considered to increase genetic robustness through functional redundancy, accelerating the evolution of novel functions. However, recent findings have questioned the link between duplication and robustness. In particular, it remains elusive whether ancient duplicates still bear potential for innovation through preserved redundancy and robustness. Here we have investigated this question by evolving the yeast Saccharomyces cerevisiae for 2200 generations under conditions allowing the accumulation of deleterious mutations, and we put mechanisms of mutational robustness to a test. S. cerevisiae declined in fitness along the evolution experiment, but this decline decelerated in later passages, suggesting functional compensation of mutated genes. We resequenced 28 genomes from experimentally evolved S. cerevisiae lines and found more mutations in duplicates—mainly small-scale duplicates—than in singletons. Genetically interacting duplicates evolved similarly and fixed more amino acid–replacing mutations than expected. Regulatory robustness of the duplicates was supported by a larger enrichment for mutations at the promoters of duplicates than at those of singletons. Analyses of yeast gene expression conditions showed a larger variation in the duplicates’ expression than that of singletons under a range of stress conditions, sparking the idea that regulatory robustness allowed a wider range of phenotypic responses to environmental stresses, hence faster adaptations. Our data support the persistence of genetic and regulatory robustness in ancient duplicates and its role in adaptations to stresses.
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Affiliation(s)
- Orla M Keane
- Department of Genetics, Smurfit Institute of Genetics, School of Genetics and Microbiology, University of Dublin, Trinity College Dublin, Dublin 2, Ireland; Animal and Bioscience Department, Teagasc, Dunsany, County Meath, Ireland
| | - Christina Toft
- Department of Genetics, University of Valencia, 46100 Valencia, Spain; Departamento de Biotecnología, Instituto de Agroquímica y Tecnología de los Alimentos (CSIC), 46100 Valencia, Spain
| | | | - Gary W Jones
- Department of Biology, National University of Ireland, Maynooth, County Kildare, Ireland
| | - Mario A Fares
- Department of Genetics, Smurfit Institute of Genetics, School of Genetics and Microbiology, University of Dublin, Trinity College Dublin, Dublin 2, Ireland; Integrative and Systems Biology Group, Department of Abiotic Stress, Instituto de Biología Molecular y Celular de Plantas (CSIC-UPV), 46022 Valencia, Spain
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45
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VanderSluis B, Hess DC, Pesyna C, Krumholz EW, Syed T, Szappanos B, Nislow C, Papp B, Troyanskaya OG, Myers CL, Caudy AA. Broad metabolic sensitivity profiling of a prototrophic yeast deletion collection. Genome Biol 2014; 15:R64. [PMID: 24721214 PMCID: PMC4053978 DOI: 10.1186/gb-2014-15-4-r64] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2013] [Accepted: 04/10/2014] [Indexed: 01/15/2023] Open
Abstract
Background Genome-wide sensitivity screens in yeast have been immensely popular following the construction of a collection of deletion mutants of non-essential genes. However, the auxotrophic markers in this collection preclude experiments on minimal growth medium, one of the most informative metabolic environments. Here we present quantitative growth analysis for mutants in all 4,772 non-essential genes from our prototrophic deletion collection across a large set of metabolic conditions. Results The complete collection was grown in environments consisting of one of four possible carbon sources paired with one of seven nitrogen sources, for a total of 28 different well-defined metabolic environments. The relative contributions to mutants' fitness of each carbon and nitrogen source were determined using multivariate statistical methods. The mutant profiling recovered known and novel genes specific to the processing of nutrients and accurately predicted functional relationships, especially for metabolic functions. A benchmark of genome-scale metabolic network modeling is also given to demonstrate the level of agreement between current in silico predictions and hitherto unavailable experimental data. Conclusions These data address a fundamental deficiency in our understanding of the model eukaryote Saccharomyces cerevisiae and its response to the most basic of environments. While choice of carbon source has the greatest impact on cell growth, specific effects due to nitrogen source and interactions between the nutrients are frequent. We demonstrate utility in characterizing genes of unknown function and illustrate how these data can be integrated with other whole-genome screens to interpret similarities between seemingly diverse perturbation types.
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Zhang JS, Li Z, Zhao J, Zhang S, Quan H, Zhao M, He C. Deciphering the Physalis floridana double-layered-lantern1 mutant provides insights into functional divergence of the GLOBOSA duplicates within the Solanaceae. PLANT PHYSIOLOGY 2014; 164:748-64. [PMID: 24390390 PMCID: PMC3912103 DOI: 10.1104/pp.113.233072] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2013] [Accepted: 01/02/2014] [Indexed: 05/25/2023]
Abstract
Physalis spp. develop the "Chinese lantern" trait, also known as inflated calyx syndrome, that is a morphological novelty. Here, we identified the double-layered-lantern1 (doll1) mutant, a recessive and monofactorial mutation, in Physalis floridana; its corolla and androecium were transformed into the calyx and gynoecium, respectively. Two GLOBOSA-like MADS-box paralogous genes PFGLO1 and PFGLO2 were found in Physalis floridana, while the mutated phenotype was cosegregated with a large deletion harboring PFGLO1 and was complemented by the PFGLO1 genomic locus in transgenic plants, and severe PFGLO1 knockdowns phenocopied doll1. Thus, DOLL1 encodes the PFGLO1 protein and plays a primary role in determining corolla and androecium identity. However, specific PFGLO2 silencing showed no homeotic variation but rather affected pollen maturation. The two genes featured identical floral expression domains, but the encoding proteins shared 67% identity in sequences. PFGLO1 was localized in the nucleus when expressed in combination with a DEFICIENS homolog from Physalis floridana, whereas PFGLO2 was imported to the nucleus on its own. The two proteins were further found to have evolved different interacting partners and regulatory patterns, supporting the hypothesis that PFGLO2 is functionally separated from organ identity. Such a divergent pattern of duplicated GLO genes is unusual within the Solanaceae. Moreover, the phenotypes of the PFGLO1PFGLO2 double silencing mutants suggested that PFGLO2, through genetically interacting with PFGLO1, also exerts a role in the control of organ number and tip development of the second floral whorl. Our results, therefore, shed new light on the functional evolution of the duplicated GLO genes.
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Ryan CJ, Krogan NJ, Cunningham P, Cagney G. All or nothing: protein complexes flip essentiality between distantly related eukaryotes. Genome Biol Evol 2013; 5:1049-59. [PMID: 23661563 PMCID: PMC3698920 DOI: 10.1093/gbe/evt074] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
In the budding yeast Saccharomyces cerevisiae, the subunits of any given protein complex are either mostly essential or mostly nonessential, suggesting that essentiality is a property of molecular machines rather than individual components. There are exceptions to this rule, however, that is, nonessential genes in largely essential complexes and essential genes in largely nonessential complexes. Here, we provide explanations for these exceptions, showing that redundancy within complexes, as revealed by genetic interactions, can explain many of the former cases, whereas “moonlighting,” as revealed by membership of multiple complexes, can explain the latter. Surprisingly, we find that redundancy within complexes cannot usually be explained by gene duplication, suggesting alternate buffering mechanisms. In the distantly related Schizosaccharomyces pombe, we observe the same phenomenon of modular essentiality, suggesting that it may be a general feature of eukaryotes. Furthermore, we show that complexes flip essentiality in a cohesive fashion between the two species, that is, they tend to change from mostly essential to mostly nonessential, or vice versa, but not to mixed patterns. We show that these flips in essentiality can be explained by differing lifestyles of the two yeasts. Collectively, our results support a previously proposed model where proteins are essential because of their involvement in essential functional modules rather than because of specific topological features such as degree or centrality.
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Affiliation(s)
- Colm J Ryan
- School of Computer Science and Informatics, University College Dublin, Ireland.
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D'Antonio M, Guerra RF, Cereda M, Marchesi S, Montani F, Nicassio F, Di Fiore PP, Ciccarelli FD. Recessive cancer genes engage in negative genetic interactions with their functional paralogs. Cell Rep 2013; 5:1519-26. [PMID: 24360954 DOI: 10.1016/j.celrep.2013.11.033] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2013] [Revised: 09/25/2013] [Accepted: 11/18/2013] [Indexed: 01/01/2023] Open
Abstract
Cancer genetic heterogeneity offers a wide repertoire of molecular determinants to be screened as therapeutic targets. Here, we identify potential anticancer targets by exploiting negative genetic interactions between genes with driver loss-of-function mutations (recessive cancer genes) and their functionally redundant paralogs. We identify recessive genes with additional copies and experimentally test our predictions on three paralogous pairs. We confirm digenic negative interactions between two cancer genes (SMARCA4 and CDH1) and their corresponding paralogs (SMARCA2 and CDH3). Furthermore, we identify a trigenic negative interaction between the cancer gene DNMT3A, its functional paralog DNMT3B, and a third gene, DNMT1, which encodes the only other human DNA-methylase domain. Although our study does not exclude other causes of synthetic lethality, it suggests that functionally redundant paralogs of cancer genes could be targets in anticancer therapy.
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Affiliation(s)
- Matteo D'Antonio
- Department of Experimental Oncology, European Institute of Oncology, IFOM-IEO Campus, Via Adamello 16, 20139 Milan, Italy
| | - Rosalinda F Guerra
- Department of Experimental Oncology, European Institute of Oncology, IFOM-IEO Campus, Via Adamello 16, 20139 Milan, Italy
| | - Matteo Cereda
- Department of Experimental Oncology, European Institute of Oncology, IFOM-IEO Campus, Via Adamello 16, 20139 Milan, Italy
| | - Stefano Marchesi
- Department of Experimental Oncology, European Institute of Oncology, IFOM-IEO Campus, Via Adamello 16, 20139 Milan, Italy
| | - Francesca Montani
- Department of Experimental Oncology, European Institute of Oncology, IFOM-IEO Campus, Via Adamello 16, 20139 Milan, Italy
| | - Francesco Nicassio
- Department of Experimental Oncology, European Institute of Oncology, IFOM-IEO Campus, Via Adamello 16, 20139 Milan, Italy; Center for Genomic Science of IIT@SEMM, Istituto Italiano di Tecnologia, 20139 Milan, Italy
| | - Pier Paolo Di Fiore
- Department of Experimental Oncology, European Institute of Oncology, IFOM-IEO Campus, Via Adamello 16, 20139 Milan, Italy; IFOM, Fondazione Istituto FIRC di Oncologia Molecolare, Via Adamello 16, 20139 Milan, Italy; Dipartimento di Scienze della Salute, Università degli Studi di Milano, Via di Rudinì 8, 20122 Milan, Italy
| | - Francesca D Ciccarelli
- Department of Experimental Oncology, European Institute of Oncology, IFOM-IEO Campus, Via Adamello 16, 20139 Milan, Italy; Division of Cancer Studies, King's College London, London SE1 1UL, UK.
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Boucher B, Jenna S. Genetic interaction networks: better understand to better predict. Front Genet 2013; 4:290. [PMID: 24381582 PMCID: PMC3865423 DOI: 10.3389/fgene.2013.00290] [Citation(s) in RCA: 55] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2013] [Accepted: 11/28/2013] [Indexed: 12/21/2022] Open
Abstract
A genetic interaction (GI) between two genes generally indicates that the phenotype of a double mutant differs from what is expected from each individual mutant. In the last decade, genome scale studies of quantitative GIs were completed using mainly synthetic genetic array technology and RNA interference in yeast and Caenorhabditis elegans. These studies raised questions regarding the functional interpretation of GIs, the relationship of genetic and molecular interaction networks, the usefulness of GI networks to infer gene function and co-functionality, the evolutionary conservation of GI, etc. While GIs have been used for decades to dissect signaling pathways in genetic models, their functional interpretations are still not trivial. The existence of a GI between two genes does not necessarily imply that these two genes code for interacting proteins or that the two genes are even expressed in the same cell. In fact, a GI only implies that the two genes share a functional relationship. These two genes may be involved in the same biological process or pathway; or they may also be involved in compensatory pathways with unrelated apparent function. Considering the powerful opportunity to better understand gene function, genetic relationship, robustness and evolution, provided by a genome-wide mapping of GIs, several in silico approaches have been employed to predict GIs in unicellular and multicellular organisms. Most of these methods used weighted data integration. In this article, we will review the later knowledge acquired on GI networks in metazoans by looking more closely into their relationship with pathways, biological processes and molecular complexes but also into their modularity and organization. We will also review the different in silico methods developed to predict GIs and will discuss how the knowledge acquired on GI networks can be used to design predictive tools with higher performances.
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Affiliation(s)
- Benjamin Boucher
- Laboratory of Integrative Genomics and Cell Signalling, Pharmaqam, Biomed, Department of Chemistry, Université du Québec à Montréal Montréal, QC, Canada
| | - Sarah Jenna
- Laboratory of Integrative Genomics and Cell Signalling, Pharmaqam, Biomed, Department of Chemistry, Université du Québec à Montréal Montréal, QC, Canada
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