1
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Muenzner J, Trébulle P, Agostini F, Zauber H, Messner CB, Steger M, Kilian C, Lau K, Barthel N, Lehmann A, Textoris-Taube K, Caudal E, Egger AS, Amari F, De Chiara M, Demichev V, Gossmann TI, Mülleder M, Liti G, Schacherer J, Selbach M, Berman J, Ralser M. Natural proteome diversity links aneuploidy tolerance to protein turnover. Nature 2024; 630:149-157. [PMID: 38778096 PMCID: PMC11153158 DOI: 10.1038/s41586-024-07442-9] [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: 04/07/2022] [Accepted: 04/19/2024] [Indexed: 05/25/2024]
Abstract
Accessing the natural genetic diversity of species unveils hidden genetic traits, clarifies gene functions and allows the generalizability of laboratory findings to be assessed. One notable discovery made in natural isolates of Saccharomyces cerevisiae is that aneuploidy-an imbalance in chromosome copy numbers-is frequent1,2 (around 20%), which seems to contradict the substantial fitness costs and transient nature of aneuploidy when it is engineered in the laboratory3-5. Here we generate a proteomic resource and merge it with genomic1 and transcriptomic6 data for 796 euploid and aneuploid natural isolates. We find that natural and lab-generated aneuploids differ specifically at the proteome. In lab-generated aneuploids, some proteins-especially subunits of protein complexes-show reduced expression, but the overall protein levels correspond to the aneuploid gene dosage. By contrast, in natural isolates, more than 70% of proteins encoded on aneuploid chromosomes are dosage compensated, and average protein levels are shifted towards the euploid state chromosome-wide. At the molecular level, we detect an induction of structural components of the proteasome, increased levels of ubiquitination, and reveal an interdependency of protein turnover rates and attenuation. Our study thus highlights the role of protein turnover in mediating aneuploidy tolerance, and shows the utility of exploiting the natural diversity of species to attain generalizable molecular insights into complex biological processes.
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Affiliation(s)
- Julia Muenzner
- Department of Biochemistry, Charité Universitätsmedizin, Berlin, Germany
| | - Pauline Trébulle
- Molecular Biology of Metabolism Laboratory, Francis Crick Institute, London, UK
- Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Federica Agostini
- Department of Biochemistry, Charité Universitätsmedizin, Berlin, Germany
| | - Henrik Zauber
- Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Christoph B Messner
- Molecular Biology of Metabolism Laboratory, Francis Crick Institute, London, UK
- Precision Proteomics Center, Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland
| | - Martin Steger
- Evotec (München), Martinsried, Germany
- NEOsphere Biotechnologies, Martinsried, Germany
| | - Christiane Kilian
- Department of Biochemistry, Charité Universitätsmedizin, Berlin, Germany
| | - Kate Lau
- Department of Biochemistry, Charité Universitätsmedizin, Berlin, Germany
| | - Natalie Barthel
- Department of Biochemistry, Charité Universitätsmedizin, Berlin, Germany
| | - Andrea Lehmann
- Department of Biochemistry, Charité Universitätsmedizin, Berlin, Germany
| | - Kathrin Textoris-Taube
- Department of Biochemistry, Charité Universitätsmedizin, Berlin, Germany
- Core Facility High-Throughput Mass Spectrometry, Charité Universitätsmedizin, Berlin, Germany
| | - Elodie Caudal
- Université de Strasbourg, CNRS GMGM UMR 7156, Strasbourg, France
| | - Anna-Sophia Egger
- Molecular Biology of Metabolism Laboratory, Francis Crick Institute, London, UK
| | - Fatma Amari
- Department of Biochemistry, Charité Universitätsmedizin, Berlin, Germany
- Core Facility High-Throughput Mass Spectrometry, Charité Universitätsmedizin, Berlin, Germany
| | | | - Vadim Demichev
- Department of Biochemistry, Charité Universitätsmedizin, Berlin, Germany
- Molecular Biology of Metabolism Laboratory, Francis Crick Institute, London, UK
| | - Toni I Gossmann
- Computational Systems Biology, Faculty of Biochemical and Chemical Engineering, TU Dortmund University, Dortmund, Germany
| | - Michael Mülleder
- Core Facility High-Throughput Mass Spectrometry, Charité Universitätsmedizin, Berlin, Germany
| | - Gianni Liti
- Université Côte d'Azur, CNRS, INSERM, IRCAN, Nice, France
| | - Joseph Schacherer
- Université de Strasbourg, CNRS GMGM UMR 7156, Strasbourg, France
- Institut Universitaire de France (IUF), Paris, France
| | | | - Judith Berman
- Shmunis School of Biomedical and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Ramat Aviv, Israel.
| | - Markus Ralser
- Department of Biochemistry, Charité Universitätsmedizin, Berlin, Germany.
- Molecular Biology of Metabolism Laboratory, Francis Crick Institute, London, UK.
- Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- Max Planck Institute for Molecular Genetics, Berlin, Germany.
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2
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Vaughn JN, Korani W, Clevenger J, Ozias-Akins P. Agile Genetics: Single gene resolution without the fuss. Bioessays 2024:e2300206. [PMID: 38769697 DOI: 10.1002/bies.202300206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 03/06/2024] [Accepted: 05/08/2024] [Indexed: 05/22/2024]
Abstract
Gene discovery reveals new biology, expands the utility of marker-assisted selection, and enables targeted mutagenesis. Still, such discoveries can take over a decade. We present a general strategy, "Agile Genetics," that uses nested, structured populations to overcome common limits on gene resolution. Extensive simulation work on realistic genetic architectures shows that, at population sizes of >5000 samples, single gene-resolution can be achieved using bulk segregant pools. At this scale, read depth and technical replication become major drivers of resolution. Emerging enrichment methods to address coverage are on the horizon; we describe one possibility - iterative depth sequencing (ID-seq). In addition, graph-based pangenomics in experimental populations will continue to maximize accuracy and improve interpretation. Based on this merger of agronomic scale with molecular and bioinformatic innovation, we predict a new age of rapid gene discovery.
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Affiliation(s)
| | - Walid Korani
- Hudson-Alpha Institute of Biotechnology, Huntsville, Alabama, USA
| | - Josh Clevenger
- Hudson-Alpha Institute of Biotechnology, Huntsville, Alabama, USA
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3
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Siraj L, Castro RI, Dewey H, Kales S, Nguyen TTL, Kanai M, Berenzy D, Mouri K, Wang QS, McCaw ZR, Gosai SJ, Aguet F, Cui R, Vockley CM, Lareau CA, Okada Y, Gusev A, Jones TR, Lander ES, Sabeti PC, Finucane HK, Reilly SK, Ulirsch JC, Tewhey R. Functional dissection of complex and molecular trait variants at single nucleotide resolution. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.05.592437. [PMID: 38766054 PMCID: PMC11100724 DOI: 10.1101/2024.05.05.592437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Identifying the causal variants and mechanisms that drive complex traits and diseases remains a core problem in human genetics. The majority of these variants have individually weak effects and lie in non-coding gene-regulatory elements where we lack a complete understanding of how single nucleotide alterations modulate transcriptional processes to affect human phenotypes. To address this, we measured the activity of 221,412 trait-associated variants that had been statistically fine-mapped using a Massively Parallel Reporter Assay (MPRA) in 5 diverse cell-types. We show that MPRA is able to discriminate between likely causal variants and controls, identifying 12,025 regulatory variants with high precision. Although the effects of these variants largely agree with orthogonal measures of function, only 69% can plausibly be explained by the disruption of a known transcription factor (TF) binding motif. We dissect the mechanisms of 136 variants using saturation mutagenesis and assign impacted TFs for 91% of variants without a clear canonical mechanism. Finally, we provide evidence that epistasis is prevalent for variants in close proximity and identify multiple functional variants on the same haplotype at a small, but important, subset of trait-associated loci. Overall, our study provides a systematic functional characterization of likely causal common variants underlying complex and molecular human traits, enabling new insights into the regulatory grammar underlying disease risk.
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Affiliation(s)
- Layla Siraj
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Program in Biophysics, Harvard Graduate School of Arts and Sciences, Boston, MA, USA
- Harvard-Massachusetts Institute of Technology MD/PhD Program, Harvard Medical School, Boston, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | | | | | | | | | - Masahiro Kanai
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA USA
- Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA, USA
| | | | | | - Qingbo S. Wang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA USA
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan
| | | | - Sager J. Gosai
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - François Aguet
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Illumina Artificial Intelligence Laboratory, Illumina, San Diego, CA, USA
| | - Ran Cui
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Caleb A. Lareau
- Program in Computational and Systems Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Alexander Gusev
- Harvard Medical School and Dana-Farber Cancer Institute, Boston, MA, USA
| | - Thouis R. Jones
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Eric S. Lander
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Biology, MIT, Cambridge, MA, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Pardis C. Sabeti
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Hilary K. Finucane
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA USA
| | - Steven K. Reilly
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
| | - Jacob C. Ulirsch
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA USA
- Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA
- Illumina Artificial Intelligence Laboratory, Illumina, San Diego, CA, USA
| | - Ryan Tewhey
- The Jackson Laboratory, Bar Harbor, ME, USA
- Graduate School of Biomedical Sciences and Engineering, University of Maine, Orono, ME, USA
- Graduate School of Biomedical Sciences, Tufts University School of Medicine, Boston, MA, USA
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4
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Opulente DA, LaBella AL, Harrison MC, Wolters JF, Liu C, Li Y, Kominek J, Steenwyk JL, Stoneman HR, VanDenAvond J, Miller CR, Langdon QK, Silva M, Gonçalves C, Ubbelohde EJ, Li Y, Buh KV, Jarzyna M, Haase MAB, Rosa CA, ČCadež N, Libkind D, DeVirgilio JH, Hulfachor AB, Kurtzman CP, Sampaio JP, Gonçalves P, Zhou X, Shen XX, Groenewald M, Rokas A, Hittinger CT. Genomic factors shape carbon and nitrogen metabolic niche breadth across Saccharomycotina yeasts. Science 2024; 384:eadj4503. [PMID: 38662846 DOI: 10.1126/science.adj4503] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 03/22/2024] [Indexed: 05/03/2024]
Abstract
Organisms exhibit extensive variation in ecological niche breadth, from very narrow (specialists) to very broad (generalists). Two general paradigms have been proposed to explain this variation: (i) trade-offs between performance efficiency and breadth and (ii) the joint influence of extrinsic (environmental) and intrinsic (genomic) factors. We assembled genomic, metabolic, and ecological data from nearly all known species of the ancient fungal subphylum Saccharomycotina (1154 yeast strains from 1051 species), grown in 24 different environmental conditions, to examine niche breadth evolution. We found that large differences in the breadth of carbon utilization traits between yeasts stem from intrinsic differences in genes encoding specific metabolic pathways, but we found limited evidence for trade-offs. These comprehensive data argue that intrinsic factors shape niche breadth variation in microbes.
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Affiliation(s)
- Dana A Opulente
- Laboratory of Genetics, Wisconsin Energy Institute, Center for Genomic Science Innovation, J. F. Crow Institute for the Study of Evolution, University of Wisconsin-Madison, Madison, WI 53726, USA
- DOE Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI 53726, USA
- Biology Department, Villanova University, Villanova, PA 19085, USA
| | - Abigail Leavitt LaBella
- Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA
- Evolutionary Studies Initiative, Vanderbilt University, Nashville, TN 37235, USA
- North Carolina Research Center (NCRC), Department of Bioinformatics and Genomics, The University of North Carolina at Charlotte, Kannapolis, NC 28081, USA
| | - Marie-Claire Harrison
- Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA
- Evolutionary Studies Initiative, Vanderbilt University, Nashville, TN 37235, USA
| | - John F Wolters
- Laboratory of Genetics, Wisconsin Energy Institute, Center for Genomic Science Innovation, J. F. Crow Institute for the Study of Evolution, University of Wisconsin-Madison, Madison, WI 53726, USA
- DOE Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI 53726, USA
| | - Chao Liu
- College of Agriculture and Biotechnology and Centre for Evolutionary and Organismal Biology, Zhejiang University, Hangzhou 310058, China
| | - Yonglin Li
- Guangdong Province Key Laboratory of Microbial Signals and Disease Control, Integrative Microbiology Research Center, South China Agricultural University, Guangzhou 510642, China
| | - Jacek Kominek
- Laboratory of Genetics, Wisconsin Energy Institute, Center for Genomic Science Innovation, J. F. Crow Institute for the Study of Evolution, University of Wisconsin-Madison, Madison, WI 53726, USA
- DOE Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI 53726, USA
- LifeMine Therapeutics, Inc., Cambridge, MA 02140, USA
| | - Jacob L Steenwyk
- Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA
- Evolutionary Studies Initiative, Vanderbilt University, Nashville, TN 37235, USA
- Howard Hughes Medical Institute and the Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Hayley R Stoneman
- Laboratory of Genetics, Wisconsin Energy Institute, Center for Genomic Science Innovation, J. F. Crow Institute for the Study of Evolution, University of Wisconsin-Madison, Madison, WI 53726, USA
- DOE Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI 53726, USA
- University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Jenna VanDenAvond
- Laboratory of Genetics, Wisconsin Energy Institute, Center for Genomic Science Innovation, J. F. Crow Institute for the Study of Evolution, University of Wisconsin-Madison, Madison, WI 53726, USA
- DOE Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI 53726, USA
| | - Caroline R Miller
- Laboratory of Genetics, Wisconsin Energy Institute, Center for Genomic Science Innovation, J. F. Crow Institute for the Study of Evolution, University of Wisconsin-Madison, Madison, WI 53726, USA
- DOE Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI 53726, USA
| | - Quinn K Langdon
- Laboratory of Genetics, Wisconsin Energy Institute, Center for Genomic Science Innovation, J. F. Crow Institute for the Study of Evolution, University of Wisconsin-Madison, Madison, WI 53726, USA
| | - Margarida Silva
- UCIBIO, Department of Life Sciences, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Caparica, Portugal
- Associate Laboratory i4HB, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Caparica, Portugal
| | - Carla Gonçalves
- Laboratory of Genetics, Wisconsin Energy Institute, Center for Genomic Science Innovation, J. F. Crow Institute for the Study of Evolution, University of Wisconsin-Madison, Madison, WI 53726, USA
- Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA
- Evolutionary Studies Initiative, Vanderbilt University, Nashville, TN 37235, USA
- UCIBIO, Department of Life Sciences, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Caparica, Portugal
- Associate Laboratory i4HB, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Caparica, Portugal
| | - Emily J Ubbelohde
- Laboratory of Genetics, Wisconsin Energy Institute, Center for Genomic Science Innovation, J. F. Crow Institute for the Study of Evolution, University of Wisconsin-Madison, Madison, WI 53726, USA
- DOE Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI 53726, USA
| | - Yuanning Li
- Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA
- Institute of Marine Science and Technology, Shandong University, Qingdao 266237, China
- Laboratory for Marine Biology and Biotechnology, Qingdao Marine Science and Technology Center, Qingdao 266237, China
| | - Kelly V Buh
- Laboratory of Genetics, Wisconsin Energy Institute, Center for Genomic Science Innovation, J. F. Crow Institute for the Study of Evolution, University of Wisconsin-Madison, Madison, WI 53726, USA
| | - Martin Jarzyna
- Laboratory of Genetics, Wisconsin Energy Institute, Center for Genomic Science Innovation, J. F. Crow Institute for the Study of Evolution, University of Wisconsin-Madison, Madison, WI 53726, USA
- Graduate Program in Neuroscience and Department of Biology, Washington University School of Medicine, St. Louis, MO 63130, USA
| | - Max A B Haase
- Laboratory of Genetics, Wisconsin Energy Institute, Center for Genomic Science Innovation, J. F. Crow Institute for the Study of Evolution, University of Wisconsin-Madison, Madison, WI 53726, USA
- DOE Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI 53726, USA
- Vilcek Institute of Graduate Biomedical Sciences and Institute for Systems Genetics, NYU Langone Health, New York, NY 10016, USA
- Department of Mechanistic Cell Biology, Max Planck Institute of Molecular Physiology, 44227 Dortmund, Germany
| | - Carlos A Rosa
- Departamento de Microbiologia, ICB, C.P. 486, Universidade Federal de Minas Gerais, Belo Horizonte, MG, 31270-901, Brazil
| | - Neža ČCadež
- Food Science and Technology Department, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Diego Libkind
- Centro de Referencia en Levaduras y Tecnología Cervecera (CRELTEC), Instituto Andino Patagónico de Tecnologías Biológicas y Geoambientales (IPATEC), Universidad Nacional del Comahue, CONICET, CRUB, Quintral 1250, San Carlos de Bariloche, 8400, Río Negro, Argentina
| | - Jeremy H DeVirgilio
- Mycotoxin Prevention and Applied Microbiology Research Unit, National Center for Agricultural Utilization Research, Agricultural Research Service, US Department of Agriculture, Peoria, IL 61604, USA
| | - Amanda Beth Hulfachor
- Laboratory of Genetics, Wisconsin Energy Institute, Center for Genomic Science Innovation, J. F. Crow Institute for the Study of Evolution, University of Wisconsin-Madison, Madison, WI 53726, USA
- DOE Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI 53726, USA
| | - Cletus P Kurtzman
- Mycotoxin Prevention and Applied Microbiology Research Unit, National Center for Agricultural Utilization Research, Agricultural Research Service, US Department of Agriculture, Peoria, IL 61604, USA
| | - José Paulo Sampaio
- UCIBIO, Department of Life Sciences, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Caparica, Portugal
- Associate Laboratory i4HB, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Caparica, Portugal
| | - Paula Gonçalves
- UCIBIO, Department of Life Sciences, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Caparica, Portugal
- Associate Laboratory i4HB, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Caparica, Portugal
| | - Xiaofan Zhou
- Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA
- Guangdong Province Key Laboratory of Microbial Signals and Disease Control, Integrative Microbiology Research Center, South China Agricultural University, Guangzhou 510642, China
| | - Xing-Xing Shen
- Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA
- College of Agriculture and Biotechnology and Centre for Evolutionary and Organismal Biology, Zhejiang University, Hangzhou 310058, China
| | | | - Antonis Rokas
- Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA
- Evolutionary Studies Initiative, Vanderbilt University, Nashville, TN 37235, USA
| | - Chris Todd Hittinger
- Laboratory of Genetics, Wisconsin Energy Institute, Center for Genomic Science Innovation, J. F. Crow Institute for the Study of Evolution, University of Wisconsin-Madison, Madison, WI 53726, USA
- DOE Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI 53726, USA
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5
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Jiang D, Zhang J. Ascertainment Bias in the Genomic Test of Positive Selection on Regulatory Sequences. Mol Biol Evol 2024; 41:msad284. [PMID: 38149460 PMCID: PMC10766478 DOI: 10.1093/molbev/msad284] [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: 08/06/2023] [Revised: 11/12/2023] [Accepted: 12/22/2023] [Indexed: 12/28/2023] Open
Abstract
Evolution of gene expression mediated by cis-regulatory changes is thought to be an important contributor to organismal adaptation, but identifying adaptive cis-regulatory changes is challenging due to the difficulty in knowing the expectation under no positive selection. A new approach for detecting positive selection on transcription factor binding sites (TFBSs) was recently developed, thanks to the application of machine learning in predicting transcription factor (TF) binding affinities of DNA sequences. Given a TFBS sequence from a focal species and the corresponding inferred ancestral sequence that differs from the former at n sites, one can predict the TF-binding affinities of many n-step mutational neighbors of the ancestral sequence and obtain a null distribution of the derived binding affinity, which allows testing whether the binding affinity of the real derived sequence deviates significantly from the null distribution. Applying this test genomically to all experimentally identified binding sites of 3 TFs in humans, a recent study reported positive selection for elevated binding affinities of TFBSs. Here, we show that this genomic test suffers from an ascertainment bias because, even in the absence of positive selection for strengthened binding, the binding affinities of known human TFBSs are more likely to have increased than decreased in evolution. We demonstrate by computer simulation that this bias inflates the false positive rate of the selection test. We propose several methods to mitigate the ascertainment bias and show that almost all previously reported positive selection signals disappear when these methods are applied.
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Affiliation(s)
- Daohan Jiang
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA
- Present address: Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Jianzhi Zhang
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA
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6
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Zhang MJ, Durvasula A, Chiang C, Koch EM, Strober BJ, Shi H, Barton AR, Kim SS, Weissbrod O, Loh PR, Gazal S, Sunyaev S, Price AL. Pervasive correlations between causal disease effects of proximal SNPs vary with functional annotations and implicate stabilizing selection. RESEARCH SQUARE 2023:rs.3.rs-3707248. [PMID: 38168385 PMCID: PMC10760228 DOI: 10.21203/rs.3.rs-3707248/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
The genetic architecture of human diseases and complex traits has been extensively studied, but little is known about the relationship of causal disease effect sizes between proximal SNPs, which have largely been assumed to be independent. We introduce a new method, LD SNP-pair effect correlation regression (LDSPEC), to estimate the correlation of causal disease effect sizes of derived alleles between proximal SNPs, depending on their allele frequencies, LD, and functional annotations; LDSPEC produced robust estimates in simulations across various genetic architectures. We applied LDSPEC to 70 diseases and complex traits from the UK Biobank (average N=306K), meta-analyzing results across diseases/traits. We detected significantly nonzero effect correlations for proximal SNP pairs (e.g., -0.37±0.09 for low-frequency positive-LD 0-100bp SNP pairs) that decayed with distance (e.g., -0.07±0.01 for low-frequency positive-LD 1-10kb), varied with allele frequency (e.g., -0.15±0.04 for common positive-LD 0-100bp), and varied with LD between SNPs (e.g., +0.12±0.05 for common negative-LD 0-100bp) (because we consider derived alleles, positive-LD and negative-LD SNP pairs may yield very different results). We further determined that SNP pairs with shared functions had stronger effect correlations that spanned longer genomic distances, e.g., -0.37±0.08 for low-frequency positive-LD same-gene promoter SNP pairs (average genomic distance of 47kb (due to alternative splicing)) and -0.32±0.04 for low-frequency positive-LD H3K27ac 0-1kb SNP pairs. Consequently, SNP-heritability estimates were substantially smaller than estimates of the sum of causal effect size variances across all SNPs (ratio of 0.87±0.02 across diseases/traits), particularly for certain functional annotations (e.g., 0.78±0.01 for common Super enhancer SNPs)-even though these quantities are widely assumed to be equal. We recapitulated our findings via forward simulations with an evolutionary model involving stabilizing selection, implicating the action of linkage masking, whereby haplotypes containing linked SNPs with opposite effects on disease have reduced effects on fitness and escape negative selection.
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Affiliation(s)
- Martin Jinye Zhang
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Arun Durvasula
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
| | - Colby Chiang
- Department of Pediatrics, Division of Genetics and Genomics, Boston Children’s Hospital, Boston, MA
| | - Evan M. Koch
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Benjamin J. Strober
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Huwenbo Shi
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Alison R. Barton
- Department of Human Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America
| | - Samuel S. Kim
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Omer Weissbrod
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Po-Ru Loh
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Steven Gazal
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
- Department of Quantitative and Computational Biology, University of Southern California
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California
| | - Shamil Sunyaev
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Alkes L. Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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7
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Zhang MJ, Durvasula A, Chiang C, Koch EM, Strober BJ, Shi H, Barton AR, Kim SS, Weissbrod O, Loh PR, Gazal S, Sunyaev S, Price AL. Pervasive correlations between causal disease effects of proximal SNPs vary with functional annotations and implicate stabilizing selection. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.04.23299391. [PMID: 38106023 PMCID: PMC10723494 DOI: 10.1101/2023.12.04.23299391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
The genetic architecture of human diseases and complex traits has been extensively studied, but little is known about the relationship of causal disease effect sizes between proximal SNPs, which have largely been assumed to be independent. We introduce a new method, LD SNP-pair effect correlation regression (LDSPEC), to estimate the correlation of causal disease effect sizes of derived alleles between proximal SNPs, depending on their allele frequencies, LD, and functional annotations; LDSPEC produced robust estimates in simulations across various genetic architectures. We applied LDSPEC to 70 diseases and complex traits from the UK Biobank (average N=306K), meta-analyzing results across diseases/traits. We detected significantly nonzero effect correlations for proximal SNP pairs (e.g., -0.37±0.09 for low-frequency positive-LD 0-100bp SNP pairs) that decayed with distance (e.g., -0.07±0.01 for low-frequency positive-LD 1-10kb), varied with allele frequency (e.g., -0.15±0.04 for common positive-LD 0-100bp), and varied with LD between SNPs (e.g., +0.12±0.05 for common negative-LD 0-100bp) (because we consider derived alleles, positive-LD and negative-LD SNP pairs may yield very different results). We further determined that SNP pairs with shared functions had stronger effect correlations that spanned longer genomic distances, e.g., -0.37±0.08 for low-frequency positive-LD same-gene promoter SNP pairs (average genomic distance of 47kb (due to alternative splicing)) and -0.32±0.04 for low-frequency positive-LD H3K27ac 0-1kb SNP pairs. Consequently, SNP-heritability estimates were substantially smaller than estimates of the sum of causal effect size variances across all SNPs (ratio of 0.87±0.02 across diseases/traits), particularly for certain functional annotations (e.g., 0.78±0.01 for common Super enhancer SNPs)-even though these quantities are widely assumed to be equal. We recapitulated our findings via forward simulations with an evolutionary model involving stabilizing selection, implicating the action of linkage masking, whereby haplotypes containing linked SNPs with opposite effects on disease have reduced effects on fitness and escape negative selection.
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Affiliation(s)
- Martin Jinye Zhang
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Arun Durvasula
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
| | - Colby Chiang
- Department of Pediatrics, Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA
| | - Evan M Koch
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Benjamin J Strober
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Huwenbo Shi
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Alison R Barton
- Department of Human Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America
| | - Samuel S Kim
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Omer Weissbrod
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Po-Ru Loh
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Steven Gazal
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
- Department of Quantitative and Computational Biology, University of Southern California
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California
| | - Shamil Sunyaev
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Alkes L Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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8
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Kyriazis CC, Robinson JA, Lohmueller KE. Using Computational Simulations to Model Deleterious Variation and Genetic Load in Natural Populations. Am Nat 2023; 202:737-752. [PMID: 38033186 PMCID: PMC10897732 DOI: 10.1086/726736] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2023]
Abstract
AbstractDeleterious genetic variation is abundant in wild populations, and understanding the ecological and conservation implications of such variation is an area of active research. Genomic methods are increasingly used to quantify the impacts of deleterious variation in natural populations; however, these approaches remain limited by an inability to accurately predict the selective and dominance effects of mutations. Computational simulations of deleterious variation offer a complementary tool that can help overcome these limitations, although such approaches have yet to be widely employed. In this perspective article, we aim to encourage ecological and conservation genomics researchers to adopt greater use of computational simulations to aid in deepening our understanding of deleterious variation in natural populations. We first provide an overview of the components of a simulation of deleterious variation, describing the key parameters involved in such models. Next, we discuss several approaches for validating simulation models. Finally, we compare and validate several recently proposed deleterious mutation models, demonstrating that models based on estimates of selection parameters from experimental systems are biased toward highly deleterious mutations. We describe a new model that is supported by multiple orthogonal lines of evidence and provide example scripts for implementing this model (https://github.com/ckyriazis/simulations_review).
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9
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Jakobson CM, Aguilar-Rodríguez J, Jarosz DF. Hsp90 shapes adaptation by controlling the fitness consequences of regulatory variation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.30.564848. [PMID: 37961536 PMCID: PMC10634948 DOI: 10.1101/2023.10.30.564848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
The essential stress-responsive chaperone Hsp90 impacts development and adaptation from microbes to humans. Yet despite evidence of its role in evolution, pathogenesis, and oncogenic transformation, the molecular mechanisms by which Hsp90 alters the consequences of mutations remain vigorously debated. Here we exploit the power of nucleotide-resolution genetic mapping in Saccharomyces cerevisiae to uncover more than 1,000 natural variant-to-phenotype associations governed by this molecular chaperone. Strikingly, Hsp90 more frequently modified the phenotypic effects of cis-regulatory variation than variants that altered protein sequence. Moreover, these interactions made the largest contribution to Hsp90-dependent heredity. Nearly all interacting variants-both regulatory and protein-coding-fell within clients of Hsp90 or targets of its direct binding partners. Hsp90 activity affected mutations in evolutionarily young genes, segmental deletions, and heterozygotes, highlighting its influence on variation central to evolutionary novelty. Reconciling the diverse epistatic effects of this chaperone, synthetic transcriptional regulation and reconstructions of natural alleles by genome editing revealed a central role for Hsp90 in regulating the fundamental relationship between activity and phenotype. Our findings establish that non-coding variation is a core driver of Hsp90's influence on heredity, offering a mechanistic explanation for the chaperone's strong effects on evolution and development across species.
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Affiliation(s)
- Christopher M. Jakobson
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
- These authors contributed equally
| | - José Aguilar-Rodríguez
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biology, Stanford University, Stanford, CA, USA
- These authors contributed equally
| | - Daniel F. Jarosz
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA, USA
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10
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Opulente DA, Leavitt LaBella A, Harrison MC, Wolters JF, Liu C, Li Y, Kominek J, Steenwyk JL, Stoneman HR, VanDenAvond J, Miller CR, Langdon QK, Silva M, Gonçalves C, Ubbelohde EJ, Li Y, Buh KV, Jarzyna M, Haase MAB, Rosa CA, Čadež N, Libkind D, DeVirgilio JH, Beth Hulfachor A, Kurtzman CP, Sampaio JP, Gonçalves P, Zhou X, Shen XX, Groenewald M, Rokas A, Hittinger CT. Genomic and ecological factors shaping specialism and generalism across an entire subphylum. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.19.545611. [PMID: 37425695 PMCID: PMC10327049 DOI: 10.1101/2023.06.19.545611] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Organisms exhibit extensive variation in ecological niche breadth, from very narrow (specialists) to very broad (generalists). Paradigms proposed to explain this variation either invoke trade-offs between performance efficiency and breadth or underlying intrinsic or extrinsic factors. We assembled genomic (1,154 yeast strains from 1,049 species), metabolic (quantitative measures of growth of 843 species in 24 conditions), and ecological (environmental ontology of 1,088 species) data from nearly all known species of the ancient fungal subphylum Saccharomycotina to examine niche breadth evolution. We found large interspecific differences in carbon breadth stem from intrinsic differences in genes encoding specific metabolic pathways but no evidence of trade-offs and a limited role of extrinsic ecological factors. These comprehensive data argue that intrinsic factors driving microbial niche breadth variation.
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Affiliation(s)
- Dana A. Opulente
- Laboratory of Genetics, DOE Great Lakes Bioenergy Research Center, Center for Genomic Science Innovation, J. F. Crow Institute for the Study of Evolution, Wisconsin Energy Institute, University of Wisconsin-Madison, Madison, WI 53726, USA; Biology Department Villanova University, Villanova, PA 19085, USA
| | - Abigail Leavitt LaBella
- Department of Biological Sciences, Vanderbilt University, Nashville, TN 37232 USA; Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte NC 28223
| | - Marie-Claire Harrison
- Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA; Evolutionary Studies Initiative, Vanderbilt University, Nashville, TN 37235, USA
| | - John F. Wolters
- Laboratory of Genetics, DOE Great Lakes Bioenergy Research Center, Center for Genomic Science Innovation, J. F. Crow Institute for the Study of Evolution, Wisconsin Energy Institute, University of Wisconsin-Madison, Madison, WI 53726, USA
| | - Chao Liu
- College of Agriculture and Biotechnology and Centre for Evolutionary & Organismal Biology, Zhejiang University, Hangzhou 310058, China
| | - Yonglin Li
- Guangdong Province Key Laboratory of Microbial Signals and Disease Control, Integrative Microbiology Research Center, South China Agricultural University, Guangzhou 510642, China
| | - Jacek Kominek
- Laboratory of Genetics, DOE Great Lakes Bioenergy Research Center, Center for Genomic Science Innovation, J. F. Crow Institute for the Study of Evolution, Wisconsin Energy Institute, University of Wisconsin-Madison, Madison, WI 53726, USA; LifeMine Therapeutics, Inc., Cambridge, MA 02140, USA
| | - Jacob L. Steenwyk
- Howards Hughes Medical Institute and the Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA; Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA; Evolutionary Studies Initiative, Vanderbilt University, Nashville, TN 37235, USA
| | - Hayley R. Stoneman
- Laboratory of Genetics, DOE Great Lakes Bioenergy Research Center, Center for Genomic Science Innovation, J. F. Crow Institute for the Study of Evolution, Wisconsin Energy Institute, University of Wisconsin-Madison, Madison, WI 53726, USA
| | - Jenna VanDenAvond
- Laboratory of Genetics, DOE Great Lakes Bioenergy Research Center, Center for Genomic Science Innovation, J. F. Crow Institute for the Study of Evolution, Wisconsin Energy Institute, University of Wisconsin-Madison, Madison, WI 53726, USA
| | - Caroline R. Miller
- Laboratory of Genetics, DOE Great Lakes Bioenergy Research Center, Center for Genomic Science Innovation, J. F. Crow Institute for the Study of Evolution, Wisconsin Energy Institute, University of Wisconsin-Madison, Madison, WI 53726, USA
| | - Quinn K. Langdon
- Laboratory of Genetics, Center for Genomic Science Innovation, J. F. Crow Institute for the Study of Evolution, Wisconsin Energy Institute, University of Wisconsin-Madison, Madison, WI 53726, USA
| | - Margarida Silva
- UCIBIO, Department of Life Sciences, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Caparica, Portugal; Associate Laboratory i4HB, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Caparica, Portugal
| | - Carla Gonçalves
- UCIBIO, Department of Life Sciences, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Caparica, Portugal; Associate Laboratory i4HB, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Caparica, Portugal; Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA; Evolutionary Studies Initiative, Vanderbilt University, Nashville, TN 37235, USA; Laboratory of Genetics, Center for Genomic Science Innovation, J. F. Crow Institute for the Study of Evolution, Wisconsin Energy Institute, University of WisconsinMadison, Madison, WI 53726, USA
| | - Emily J. Ubbelohde
- Laboratory of Genetics, DOE Great Lakes Bioenergy Research Center, Center for Genomic Science Innovation, J. F. Crow Institute for the Study of Evolution, Wisconsin Energy Institute, University of Wisconsin-Madison, Madison, WI 53726, USA
| | - Yuanning Li
- Institute of Marine Science and Technology, Shandong University, Qingdao 266237, China
| | - Kelly V. Buh
- Laboratory of Genetics, DOE Great Lakes Bioenergy Research Center, Center for Genomic Science Innovation, J. F. Crow Institute for the Study of Evolution, Wisconsin Energy Institute, University of Wisconsin-Madison, Madison, WI 53726, USA
| | - Martin Jarzyna
- Laboratory of Genetics, DOE Great Lakes Bioenergy Research Center, Center for Genomic Science Innovation, J. F. Crow Institute for the Study of Evolution, Wisconsin Energy Institute, University of Wisconsin-Madison, Madison, WI 53726, USA; Graduate Program in Neuroscience and Department of Biology, Washington University School of Medicine, St. Louis, MO 63130, USA
| | - Max A. B. Haase
- Laboratory of Genetics, DOE Great Lakes Bioenergy Research Center, Center for Genomic Science Innovation, J. F. Crow Institute for the Study of Evolution, Wisconsin Energy Institute, University of Wisconsin-Madison, Madison, WI 53726, USA; Vilcek Institute of Graduate Biomedical Sciences and Institute for Systems Genetics, NYU Langone Health, New York, NY 10016, USA
| | - Carlos A. Rosa
- Departamento de Microbiologia, ICB, C.P. 486, Universidade Federal de Minas Gerais, Belo Horizonte, MG, 31270-901, Brazil
| | - Neža Čadež
- Food Science and Technology Department, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Diego Libkind
- Centro de Referencia en Levaduras y Tecnología Cervecera (CRELTEC), Instituto Andino Patagónico de Tecnologías Biológicas y Geoambientales (IPATEC), Universidad Nacional del Comahue, CONICET, CRUB, Quintral 1250, San Carlos de Bariloche, 8400, Río Negro, Argentina
| | - Jeremy H. DeVirgilio
- Mycotoxin Prevention and Applied Microbiology Research Unit, National Center for Agricultural Utilization Research, Agricultural Research Service, U.S. Department of Agriculture, Peoria, IL 61604, USA
| | - Amanda Beth Hulfachor
- Laboratory of Genetics, DOE Great Lakes Bioenergy Research Center, Center for Genomic Science Innovation, J. F. Crow Institute for the Study of Evolution, Wisconsin Energy Institute, University of Wisconsin-Madison, Madison, WI 53726, USA
| | - Cletus P. Kurtzman
- Mycotoxin Prevention and Applied Microbiology Research Unit, National Center for Agricultural Utilization Research, Agricultural Research Service, U.S. Department of Agriculture, Peoria, IL 61604, USA
| | - José Paulo Sampaio
- UCIBIO, Department of Life Sciences, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Caparica, Portugal; Associate Laboratory i4HB, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Caparica, Portugal
| | - Paula Gonçalves
- UCIBIO, Department of Life Sciences, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Caparica, Portugal; Associate Laboratory i4HB, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Caparica, Portugal
| | - Xiaofan Zhou
- Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA; Guangdong Province Key Laboratory of Microbial Signals and Disease Control, Integrative Microbiology Research Center, South China Agricultural University, Guangzhou 510642, China
| | - Xing-Xing Shen
- Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA; College of Agriculture and Biotechnology and Centre for Evolutionary & Organismal Biology, Zhejiang University, Hangzhou 310058, China
| | | | - Antonis Rokas
- Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA; Evolutionary Studies Initiative, Vanderbilt University, Nashville, TN 37235, USA
| | - Chris Todd Hittinger
- Laboratory of Genetics, DOE Great Lakes Bioenergy Research Center, Center for Genomic Science Innovation, J. F. Crow Institute for the Study of Evolution, Wisconsin Energy Institute, University of Wisconsin-Madison, Madison, WI 53726, USA
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11
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Jiang D, Zhang J. Ascertainment bias in the genomic test of positive selection on regulatory sequences. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.20.554030. [PMID: 37662307 PMCID: PMC10473660 DOI: 10.1101/2023.08.20.554030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Evolution of gene expression mediated by cis-regulatory changes is thought to be an important contributor to organismal adaptation, but identifying adaptive cis-regulatory changes is challenging due to the difficulty in knowing the expectation under no positive selection. A new approach for detecting positive selection on transcription factor binding sites (TFBSs) was recently developed, thanks to the application of machine learning in predicting transcription factor (TF) binding affinities of DNA sequences. Given a TFBS sequence from a focal species and the corresponding inferred ancestral sequence that differs from the former at n sites, one can predict the TF binding affinities of many n-step mutational neighbors of the ancestral sequence and obtain a null distribution of the derived binding affinity, which allows testing whether the binding affinity of the real derived sequence deviates significantly from the null distribution. Applying this test genomically to all experimentally identified binding sites of three TFs in humans, a recent study reported positive selection for elevated binding affinities of TFBSs. Here we show that this genomic test suffers from an ascertainment bias because, even in the absence of positive selection for strengthened binding, the binding affinities of known human TFBSs are more likely to have increased than decreased in evolution. We demonstrate by computer simulation that this bias inflates the false positive rate of the selection test. We propose several methods to mitigate the ascertainment bias and show that almost all previously reported positive selection signals disappear when these methods are applied.
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Affiliation(s)
- Daohan Jiang
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Jianzhi Zhang
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan 48109, USA
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12
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Kinsler G, Schmidlin K, Newell D, Eder R, Apodaca S, Lam G, Petrov D, Geiler-Samerotte K. Extreme Sensitivity of Fitness to Environmental Conditions: Lessons from #1BigBatch. J Mol Evol 2023; 91:293-310. [PMID: 37237236 PMCID: PMC10276131 DOI: 10.1007/s00239-023-10114-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 04/30/2023] [Indexed: 05/28/2023]
Abstract
The phrase "survival of the fittest" has become an iconic descriptor of how natural selection works. And yet, precisely measuring fitness, even for single-celled microbial populations growing in controlled laboratory conditions, remains a challenge. While numerous methods exist to perform these measurements, including recently developed methods utilizing DNA barcodes, all methods are limited in their precision to differentiate strains with small fitness differences. In this study, we rule out some major sources of imprecision, but still find that fitness measurements vary substantially from replicate to replicate. Our data suggest that very subtle and difficult to avoid environmental differences between replicates create systematic variation across fitness measurements. We conclude by discussing how fitness measurements should be interpreted given their extreme environment dependence. This work was inspired by the scientific community who followed us and gave us tips as we live tweeted a high-replicate fitness measurement experiment at #1BigBatch.
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Affiliation(s)
| | - Kara Schmidlin
- Center for Mechanisms of Evolution, Arizona State University, Tempe, USA
| | - Daphne Newell
- Center for Mechanisms of Evolution, Arizona State University, Tempe, USA
- School of Life Sciences, Arizona State University, Tempe, USA
| | - Rachel Eder
- Center for Mechanisms of Evolution, Arizona State University, Tempe, USA
- School of Life Sciences, Arizona State University, Tempe, USA
| | - Sam Apodaca
- Center for Mechanisms of Evolution, Arizona State University, Tempe, USA
- School of Life Sciences, Arizona State University, Tempe, USA
| | | | | | - Kerry Geiler-Samerotte
- Center for Mechanisms of Evolution, Arizona State University, Tempe, USA.
- School of Life Sciences, Arizona State University, Tempe, USA.
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13
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Chen SAA, Kern AF, Ang RML, Xie Y, Fraser HB. Gene-by-environment interactions are pervasive among natural genetic variants. CELL GENOMICS 2023; 3:100273. [PMID: 37082145 PMCID: PMC10112290 DOI: 10.1016/j.xgen.2023.100273] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 10/09/2022] [Accepted: 01/31/2023] [Indexed: 04/22/2023]
Abstract
Gene-by-environment (GxE) interactions, in which a genetic variant's phenotypic effect is condition specific, are fundamental for understanding fitness landscapes and evolution but have been difficult to identify at the single-nucleotide level. Although many condition-specific quantitative trait loci (QTLs) have been mapped, these typically contain numerous inconsequential variants in linkage, precluding understanding of the causal GxE variants. Here, we introduce BARcoded Cas9 retron precise parallel editing via homology (CRISPEY-BAR), a high-throughput precision genome editing strategy, and use it to map GxE interactions of naturally occurring genetic polymorphisms impacting yeast growth. We identified hundreds of GxE variants within condition-specific QTLs, revealing unexpected genetic complexity. Moreover, we found that 93.7% of non-neutral natural variants within ergosterol biosynthesis pathway genes showed GxE interactions, including many impacting antifungal drug resistance through diverse molecular mechanisms. In sum, our results suggest an extremely complex, context-dependent fitness landscape characterized by pervasive GxE interactions while also demonstrating massively parallel genome editing as an effective means for investigating this complexity.
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Affiliation(s)
- Shi-An A. Chen
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Alexander F. Kern
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Roy Moh Lik Ang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Yihua Xie
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Hunter B. Fraser
- Department of Biology, Stanford University, Stanford, CA 94305, USA
- Corresponding author
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14
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Densi A, Iyer RS, Bhat PJ. Synonymous and Nonsynonymous Substitutions in Dictyostelium discoideum Ammonium Transporter amtA Are Necessary for Functional Complementation in Saccharomyces cerevisiae. Microbiol Spectr 2023; 11:e0384722. [PMID: 36840598 PMCID: PMC10100761 DOI: 10.1128/spectrum.03847-22] [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: 09/21/2022] [Accepted: 01/24/2023] [Indexed: 02/24/2023] Open
Abstract
Ammonium transporters are present in all three domains of life. They have undergone extensive horizontal gene transfer (HGT), gene duplication, and functional diversification and therefore offer an excellent paradigm to study protein evolution. We attempted to complement a mep1Δmep2Δmep3Δ strain of Saccharomyces cerevisiae (triple-deletion strain), which otherwise cannot grow on ammonium as a sole nitrogen source at concentrations of <3 mM, with amtA of Dictyostelium discoideum, an orthologue of S. cerevisiae MEP2. We observed that amtA did not complement the triple-deletion strain of S. cerevisiae for growth on low-ammonium medium. We isolated two mutant derivatives of amtA (amtA M1 and amtA M2) from a PCR-generated mutant plasmid library that complemented the triple-deletion strain of S. cerevisiae. amtA M1 bears three nonsynonymous and two synonymous substitutions, which are necessary for its functionality. amtA M2 bears two nonsynonymous substitutions and one synonymous substitution, all of which are necessary for functionality. Interestingly, AmtA M1 transports ammonium but does not confer methylamine toxicity, while AmtA M2 transports ammonium and confers methylamine toxicity, demonstrating functional diversification. Preliminary biochemical analyses indicated that the mutants differ in their conformations as well as their mechanisms of ammonium transport. These intriguing results clearly point out that protein evolution cannot be fathomed by studying nonsynonymous and synonymous substitutions in isolation. The above-described observations have significant implications for various facets of biological processes and are discussed in detail. IMPORTANCE Functional diversification following gene duplication is one of the major driving forces of protein evolution. While the role of nonsynonymous substitutions in the functional diversification of proteins is well recognized, knowledge of the role of synonymous substitutions in protein evolution is in its infancy. Using functional complementation, we isolated two functional alleles of the D. discoideum ammonium transporter gene (amtA), which otherwise does not function in S. cerevisiae as an ammonium transporters. One of them is an ammonium transporter, while the other is an ammonium transporter that also confers methylammonium (ammonium analogue) toxicity, suggesting functional diversification. Surprisingly, both alleles require a combination of synonymous and nonsynonymous substitutions for their functionality. These results bring out a hitherto-unknown pathway of protein evolution and pave the way for not only understanding protein evolution but also interpreting single nucleotide polymorphisms (SNPs).
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Affiliation(s)
- Asha Densi
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Revathi S. Iyer
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Paike Jayadeva Bhat
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, India
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15
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Valette T, Leitwein M, Lascaux JM, Desmarais E, Berrebi P, Guinand B. Redundancy analysis, genome-wide association studies and the pigmentation of brown trout (Salmo trutta L.). JOURNAL OF FISH BIOLOGY 2023; 102:96-118. [PMID: 36218076 DOI: 10.1111/jfb.15243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Accepted: 10/04/2022] [Indexed: 06/16/2023]
Abstract
The association of molecular variants with phenotypic variation is a main issue in biology, often tackled with genome-wide association studies (GWAS). GWAS are challenging, with increasing, but still limited, use in evolutionary biology. We used redundancy analysis (RDA) as a complimentary ordination approach to single- and multitrait GWAS to explore the molecular basis of pigmentation variation in brown trout (Salmo trutta) belonging to wild populations impacted by hatchery fish. Based on 75,684 single nucleotide polymorphic (SNP) markers, RDA, single- and multitrait GWAS allowed the extraction of 337 independent colour patterning loci (CPLs) associated with trout pigmentation traits, such as the number of red and black spots on flanks. Collectively, these CPLs (i) mapped onto 35 out of 40 brown trout linkage groups indicating a polygenic genomic architecture of pigmentation, (ii) were found to be associated with 218 candidate genes, including 197 genes formerly mentioned in the literature associated to skin pigmentation, skin patterning, differentiation or structure notably in a close relative, the rainbow trout (Onchorhynchus mykiss), and (iii) related to functions relevant to pigmentation variation (e.g., calcium- and ion-binding, cell adhesion). Annotated CPLs include genes with well-known pigmentation effects (e.g., PMEL, SLC45A2, SOX10), but also markers associated with genes formerly found expressed in rainbow or brown trout skins. RDA was also shown to be useful to investigate management issues, especially the dynamics of trout pigmentation submitted to several generations of hatchery introgression.
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16
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High-throughput approaches to functional characterization of genetic variation in yeast. Curr Opin Genet Dev 2022; 76:101979. [PMID: 36075138 DOI: 10.1016/j.gde.2022.101979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 07/29/2022] [Accepted: 08/02/2022] [Indexed: 11/20/2022]
Abstract
Expansion of sequencing efforts to include thousands of genomes is providing a fundamental resource for determining the genetic diversity that exists in a population. Now, high-throughput approaches are necessary to begin to understand the role these genotypic changes play in affecting phenotypic variation. Saccharomyces cerevisiae maintains its position as an excellent model system to determine the function of unknown variants with its exceptional genetic diversity, phenotypic diversity, and reliable genetic manipulation tools. Here, we review strategies and techniques developed in yeast that scale classic approaches of assessing variant function. These approaches improve our ability to better map quantitative trait loci at a higher resolution, even for rare variants, and are already providing greater insight into the role that different types of mutations play in phenotypic variation and evolution not just in yeast but across taxa.
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17
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Synonymous mutations in representative yeast genes are mostly strongly non-neutral. Nature 2022; 606:725-731. [PMID: 35676473 PMCID: PMC9650438 DOI: 10.1038/s41586-022-04823-w] [Citation(s) in RCA: 122] [Impact Index Per Article: 61.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 04/28/2022] [Indexed: 01/12/2023]
Abstract
Synonymous mutations in protein-coding genes do not alter protein sequences and are thus generally presumed to be neutral or nearly neutral1-5. Here, to experimentally verify this presumption, we constructed 8,341 yeast mutants each carrying a synonymous, nonsynonymous or nonsense mutation in one of 21 endogenous genes with diverse functions and expression levels and measured their fitness relative to the wild type in a rich medium. Three-quarters of synonymous mutations resulted in a significant reduction in fitness, and the distribution of fitness effects was overall similar-albeit nonidentical-between synonymous and nonsynonymous mutations. Both synonymous and nonsynonymous mutations frequently disturbed the level of mRNA expression of the mutated gene, and the extent of the disturbance partially predicted the fitness effect. Investigations in additional environments revealed greater across-environment fitness variations for nonsynonymous mutants than for synonymous mutants despite their similar fitness distributions in each environment, suggesting that a smaller proportion of nonsynonymous mutants than synonymous mutants are always non-deleterious in a changing environment to permit fixation, potentially explaining the common observation of substantially lower nonsynonymous than synonymous substitution rates. The strong non-neutrality of most synonymous mutations, if it holds true for other genes and in other organisms, would require re-examination of numerous biological conclusions about mutation, selection, effective population size, divergence time and disease mechanisms that rely on the assumption that synoymous mutations are neutral.
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18
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Genetic bases for the metabolism of the DMS precursor S-methylmethionine by Saccharomyces cerevisiae. Food Microbiol 2022; 106:104041. [DOI: 10.1016/j.fm.2022.104041] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 04/15/2022] [Accepted: 04/19/2022] [Indexed: 01/27/2023]
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19
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Abell NS, DeGorter MK, Gloudemans MJ, Greenwald E, Smith KS, He Z, Montgomery SB. Multiple causal variants underlie genetic associations in humans. Science 2022; 375:1247-1254. [PMID: 35298243 DOI: 10.1126/science.abj5117] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Associations between genetic variation and traits are often in noncoding regions with strong linkage disequilibrium (LD), where a single causal variant is assumed to underlie the association. We applied a massively parallel reporter assay (MPRA) to functionally evaluate genetic variants in high, local LD for independent cis-expression quantitative trait loci (eQTL). We found that 17.7% of eQTLs exhibit more than one major allelic effect in tight LD. The detected regulatory variants were highly and specifically enriched for activating chromatin structures and allelic transcription factor binding. Integration of MPRA profiles with eQTL/complex trait colocalizations across 114 human traits and diseases identified causal variant sets demonstrating how genetic association signals can manifest through multiple, tightly linked causal variants.
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Affiliation(s)
- Nathan S Abell
- Department of Genetics, School of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Marianne K DeGorter
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA 94305, USA
| | | | - Emily Greenwald
- Department of Genetics, School of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Kevin S Smith
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Zihuai He
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA.,Quantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Stephen B Montgomery
- Department of Genetics, School of Medicine, Stanford University, Stanford, CA 94305, USA.,Department of Pathology, School of Medicine, Stanford University, Stanford, CA 94305, USA
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20
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Giordani W, Gama HC, Chiorato AF, Marques JPR, Huo H, Benchimol-Reis LL, Camargo LEA, Garcia AAF, Vieira MLC. Genetic mapping reveals complex architecture and candidate genes involved in common bean response to Meloidogyne incognita infection. THE PLANT GENOME 2022; 15:e20161. [PMID: 34806826 DOI: 10.1002/tpg2.20161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 09/05/2021] [Indexed: 06/13/2023]
Abstract
Root-knot nematodes (RKNs), particularly Meloidogyne incognita, are among the most damaging and prevalent agricultural pathogens due to their ability to infect roots of almost all crops. The best strategy for their control is through the use of resistant cultivars. However, laborious phenotyping procedures make it difficult to assess nematode resistance in breeding programs. For common bean, this task is especially challenging because little has been done to discover resistance genes or markers to assist selection. We performed genome-wide association studies and quantitative trait loci mapping to explore the genetic architecture and genomic regions underlying the resistance to M. incognita and to identify candidate resistance genes. Phenotypic data were collected by a high-throughput assay, and the number of egg masses and the root-galling index were evaluated. Complex genetic architecture and independent genomic regions were associated with each trait. Single nucleotide polymorphisms on chromosomes Pv06, Pv07, Pv08, and Pv11 were associated with the number of egg masses, and SNPs on Pv01, Pv02, Pv05, and Pv10 were associated with root-galling. A total of 216 candidate genes were identified, including 14 resistance gene analogs and five differentially expressed in a previous RNA sequencing analysis. Histochemical analysis indicated that reactive oxygen species might play a role in the resistance response. Our findings open new perspectives to improve selection efficiency for RKN resistance, and the candidate genes are valuable targets for functional investigation and gene editing approaches.
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Affiliation(s)
- Willian Giordani
- "Luiz de Queiroz" College of Agriculture, Univ. of São Paulo, Piracicaba, São Paulo, 13418-900, Brazil
| | - Henrique Castro Gama
- "Luiz de Queiroz" College of Agriculture, Univ. of São Paulo, Piracicaba, São Paulo, 13418-900, Brazil
| | | | | | - Heqiang Huo
- Mid-Florida Research and Education Center, Univ. of Florida, Apopka, FL, 32703, USA
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21
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Nguyen Ba AN, Lawrence KR, Rego-Costa A, Gopalakrishnan S, Temko D, Michor F, Desai MM. Barcoded Bulk QTL mapping reveals highly polygenic and epistatic architecture of complex traits in yeast. eLife 2022; 11:73983. [PMID: 35147078 PMCID: PMC8979589 DOI: 10.7554/elife.73983] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 02/11/2022] [Indexed: 11/25/2022] Open
Abstract
Mapping the genetic basis of complex traits is critical to uncovering the biological mechanisms that underlie disease and other phenotypes. Genome-wide association studies (GWAS) in humans and quantitative trait locus (QTL) mapping in model organisms can now explain much of the observed heritability in many traits, allowing us to predict phenotype from genotype. However, constraints on power due to statistical confounders in large GWAS and smaller sample sizes in QTL studies still limit our ability to resolve numerous small-effect variants, map them to causal genes, identify pleiotropic effects across multiple traits, and infer non-additive interactions between loci (epistasis). Here, we introduce barcoded bulk quantitative trait locus (BB-QTL) mapping, which allows us to construct, genotype, and phenotype 100,000 offspring of a budding yeast cross, two orders of magnitude larger than the previous state of the art. We use this panel to map the genetic basis of eighteen complex traits, finding that the genetic architecture of these traits involves hundreds of small-effect loci densely spaced throughout the genome, many with widespread pleiotropic effects across multiple traits. Epistasis plays a central role, with thousands of interactions that provide insight into genetic networks. By dramatically increasing sample size, BB-QTL mapping demonstrates the potential of natural variants in high-powered QTL studies to reveal the highly polygenic, pleiotropic, and epistatic architecture of complex traits.
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Affiliation(s)
- Alex N Nguyen Ba
- Department of Organismic and Evolutionary Biology, Harvard University
| | | | - Artur Rego-Costa
- Department of Organismic and Evolutionary Biology, Harvard University
| | | | | | | | - Michael M Desai
- Department of Organismic and Evolutionary Biology, Harvard University
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22
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Lutz S, Van Dyke K, Feraru MA, Albert FW. Multiple epistatic DNA variants in a single gene affect gene expression in trans. Genetics 2022; 220:iyab208. [PMID: 34791209 PMCID: PMC8733636 DOI: 10.1093/genetics/iyab208] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 11/09/2021] [Indexed: 01/08/2023] Open
Abstract
DNA variants that alter gene expression in trans are important sources of phenotypic variation. Nevertheless, the identity of trans-acting variants remains poorly understood. Single causal variants in several genes have been reported to affect the expression of numerous distant genes in trans. Whether these simple molecular architectures are representative of trans-acting variation is unknown. Here, we studied the large RAS signaling regulator gene IRA2, which contains variants with extensive trans-acting effects on gene expression in the yeast Saccharomyces cerevisiae. We used systematic CRISPR-based genome engineering and a sensitive phenotyping strategy to dissect causal variants to the nucleotide level. In contrast to the simple molecular architectures known so far, IRA2 contained at least seven causal nonsynonymous variants. The effects of these variants were modulated by nonadditive, epistatic interactions. Two variants at the 5'-end affected gene expression and growth only when combined with a third variant that also had no effect in isolation. Our findings indicate that the molecular basis of trans-acting genetic variation may be considerably more complex than previously appreciated.
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Affiliation(s)
- Sheila Lutz
- Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, MN 55455, USA
| | - Krisna Van Dyke
- Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, MN 55455, USA
| | - Matthew A Feraru
- Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, MN 55455, USA
| | - Frank W Albert
- Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, MN 55455, USA
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23
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Ho PW, Piampongsant S, Gallone B, Del Cortona A, Peeters PJ, Reijbroek F, Verbaet J, Herrera B, Cortebeeck J, Nolmans R, Saels V, Steensels J, Jarosz DF, Verstrepen KJ. Massive QTL analysis identifies pleiotropic genetic determinants for stress resistance, aroma formation, and ethanol, glycerol and isobutanol production in Saccharomyces cerevisiae. BIOTECHNOLOGY FOR BIOFUELS 2021; 14:211. [PMID: 34727964 PMCID: PMC8564995 DOI: 10.1186/s13068-021-02059-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 10/16/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND The brewer's yeast Saccharomyces cerevisiae is exploited in several industrial processes, ranging from food and beverage fermentation to the production of biofuels, pharmaceuticals and complex chemicals. The large genetic and phenotypic diversity within this species offers a formidable natural resource to obtain superior strains, hybrids, and variants. However, most industrially relevant traits in S. cerevisiae strains are controlled by multiple genetic loci. Over the past years, several studies have identified some of these QTLs. However, because these studies only focus on a limited set of traits and often use different techniques and starting strains, a global view of industrially relevant QTLs is still missing. RESULTS Here, we combined the power of 1125 fully sequenced inbred segregants with high-throughput phenotyping methods to identify as many as 678 QTLs across 18 different traits relevant to industrial fermentation processes, including production of ethanol, glycerol, isobutanol, acetic acid, sulfur dioxide, flavor-active esters, as well as resistance to ethanol, acetic acid, sulfite and high osmolarity. We identified and confirmed several variants that are associated with multiple different traits, indicating that many QTLs are pleiotropic. Moreover, we show that both rare and common variants, as well as variants located in coding and non-coding regions all contribute to the phenotypic variation. CONCLUSIONS Our findings represent an important step in our understanding of the genetic underpinnings of industrially relevant yeast traits and open new routes to study complex genetics and genetic interactions as well as to engineer novel, superior industrial yeasts. Moreover, the major role of rare variants suggests that there is a plethora of different combinations of mutations that can be explored in genome editing.
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Affiliation(s)
- Ping-Wei Ho
- VIB–KU Leuven Center for Microbiology, Leuven, Belgium
- CMPG Laboratory of Genetics and Genomics, Department M2S, KU Leuven, Leuven, Belgium
- Leuven Institute for Beer Research, Leuven, Belgium
| | - Supinya Piampongsant
- VIB–KU Leuven Center for Microbiology, Leuven, Belgium
- CMPG Laboratory of Genetics and Genomics, Department M2S, KU Leuven, Leuven, Belgium
- Leuven Institute for Beer Research, Leuven, Belgium
| | - Brigida Gallone
- VIB–KU Leuven Center for Microbiology, Leuven, Belgium
- CMPG Laboratory of Genetics and Genomics, Department M2S, KU Leuven, Leuven, Belgium
- Leuven Institute for Beer Research, Leuven, Belgium
| | - Andrea Del Cortona
- VIB–KU Leuven Center for Microbiology, Leuven, Belgium
- CMPG Laboratory of Genetics and Genomics, Department M2S, KU Leuven, Leuven, Belgium
- Leuven Institute for Beer Research, Leuven, Belgium
| | - Pieter-Jan Peeters
- VIB–KU Leuven Center for Microbiology, Leuven, Belgium
- CMPG Laboratory of Genetics and Genomics, Department M2S, KU Leuven, Leuven, Belgium
- Leuven Institute for Beer Research, Leuven, Belgium
| | - Frank Reijbroek
- VIB–KU Leuven Center for Microbiology, Leuven, Belgium
- CMPG Laboratory of Genetics and Genomics, Department M2S, KU Leuven, Leuven, Belgium
- Leuven Institute for Beer Research, Leuven, Belgium
| | - Jules Verbaet
- VIB–KU Leuven Center for Microbiology, Leuven, Belgium
- CMPG Laboratory of Genetics and Genomics, Department M2S, KU Leuven, Leuven, Belgium
- Leuven Institute for Beer Research, Leuven, Belgium
| | - Beatriz Herrera
- VIB–KU Leuven Center for Microbiology, Leuven, Belgium
- CMPG Laboratory of Genetics and Genomics, Department M2S, KU Leuven, Leuven, Belgium
- Leuven Institute for Beer Research, Leuven, Belgium
| | - Jeroen Cortebeeck
- VIB–KU Leuven Center for Microbiology, Leuven, Belgium
- CMPG Laboratory of Genetics and Genomics, Department M2S, KU Leuven, Leuven, Belgium
- Leuven Institute for Beer Research, Leuven, Belgium
| | - Robbe Nolmans
- VIB–KU Leuven Center for Microbiology, Leuven, Belgium
- CMPG Laboratory of Genetics and Genomics, Department M2S, KU Leuven, Leuven, Belgium
- Leuven Institute for Beer Research, Leuven, Belgium
| | - Veerle Saels
- VIB–KU Leuven Center for Microbiology, Leuven, Belgium
- CMPG Laboratory of Genetics and Genomics, Department M2S, KU Leuven, Leuven, Belgium
- Leuven Institute for Beer Research, Leuven, Belgium
| | - Jan Steensels
- VIB–KU Leuven Center for Microbiology, Leuven, Belgium
- CMPG Laboratory of Genetics and Genomics, Department M2S, KU Leuven, Leuven, Belgium
- Leuven Institute for Beer Research, Leuven, Belgium
| | - Daniel F. Jarosz
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA 94305 USA
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA 94305 USA
| | - Kevin J. Verstrepen
- VIB–KU Leuven Center for Microbiology, Leuven, Belgium
- CMPG Laboratory of Genetics and Genomics, Department M2S, KU Leuven, Leuven, Belgium
- Leuven Institute for Beer Research, Leuven, Belgium
- Labo VIB-CMPG, Bio-Incubator, Gaston Geenslaan 1, 3001 Leuven, Heverlee Belgium
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24
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Feng L, Ma A, Song B, Yu S, Qi X. Mapping causal genes and genetic interactions for agronomic traits using a large F2 population in rice. G3 (BETHESDA, MD.) 2021; 11:6369515. [PMID: 34515770 PMCID: PMC8527483 DOI: 10.1093/g3journal/jkab318] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 08/26/2021] [Indexed: 11/13/2022]
Abstract
Dissecting the genetic mechanisms underlying agronomic traits is of great importance for crop breeding. Agronomic traits are usually controlled by multiple quantitative trait loci (QTLs) and genetic interactions, and mapping the underlying causal genes is still labor-intensive and time-consuming. Here, we present a genetic tool for directly targeting the specific causal genes by using a single-gene resolution linkage map that was constructed from 3756 F2 rice plants via targeted sequencing technology and Tukey-Kramer multiple comparisons test. Three large- and moderate-effect QTLs, qHD6-2, qGL3-1, and qGW5-2, were successfully mapped to their specific causal genes, Hd1, GS3, and GW5, respectively. A complex genetic interaction network containing 30 QTL-QTL interactions was constructed, revealing that the alternative allele of hub QTL, qHD6-2, can hide or release the genetic contributions of the alleles at interacting loci. Moreover, arranging genetic interactions in the models lead to more accurate phenotypic predictions. These results provide a community resource and new feasible strategy for deciphering the genetic mechanisms of complex agronomic traits and accelerating crop breeding.
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Affiliation(s)
- Laibao Feng
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing 100049, China
| | - Aimin Ma
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China.,Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing 100049, China
| | - Bo Song
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China.,Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing 100049, China
| | - Sibin Yu
- National Key Laboratory of Crop Genetic Improvement, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, Hubei, China
| | - Xiaoquan Qi
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China.,Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing 100049, China
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25
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Garcia DM, Campbell EA, Jakobson CM, Tsuchiya M, Shaw EA, DiNardo AL, Kaeberlein M, Jarosz DF. A prion accelerates proliferation at the expense of lifespan. eLife 2021; 10:e60917. [PMID: 34545808 PMCID: PMC8455135 DOI: 10.7554/elife.60917] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 08/12/2021] [Indexed: 12/23/2022] Open
Abstract
In fluctuating environments, switching between different growth strategies, such as those affecting cell size and proliferation, can be advantageous to an organism. Trade-offs arise, however. Mechanisms that aberrantly increase cell size or proliferation-such as mutations or chemicals that interfere with growth regulatory pathways-can also shorten lifespan. Here we report a natural example of how the interplay between growth and lifespan can be epigenetically controlled. We find that a highly conserved RNA-modifying enzyme, the pseudouridine synthase Pus4/TruB, can act as a prion, endowing yeast with greater proliferation rates at the cost of a shortened lifespan. Cells harboring the prion grow larger and exhibit altered protein synthesis. This epigenetic state, [BIG+] (better in growth), allows cells to heritably yet reversibly alter their translational program, leading to the differential synthesis of dozens of proteins, including many that regulate proliferation and aging. Our data reveal a new role for prion-based control of an RNA-modifying enzyme in driving heritable epigenetic states that transform cell growth and survival.
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Affiliation(s)
- David M Garcia
- Department of Chemical & Systems Biology, Stanford University School of Medicine, Stanford, United States
- Institute of Molecular Biology, Department of Biology, University of Oregon, Eugene, United States
| | - Edgar A Campbell
- Department of Chemical & Systems Biology, Stanford University School of Medicine, Stanford, United States
| | - Christopher M Jakobson
- Department of Chemical & Systems Biology, Stanford University School of Medicine, Stanford, United States
| | - Mitsuhiro Tsuchiya
- Department of Pathology, University of Washington, Seattle, United States
| | - Ethan A Shaw
- Institute of Molecular Biology, Department of Biology, University of Oregon, Eugene, United States
| | - Acadia L DiNardo
- Institute of Molecular Biology, Department of Biology, University of Oregon, Eugene, United States
| | - Matt Kaeberlein
- Department of Pathology, University of Washington, Seattle, United States
| | - Daniel F Jarosz
- Department of Chemical & Systems Biology, Stanford University School of Medicine, Stanford, United States
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, United States
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26
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Liu Y, Lin Y, Guo Y, Wu F, Zhang Y, Qi X, Wang Z, Wang Q. Stress tolerance enhancement via SPT15 base editing in Saccharomyces cerevisiae. BIOTECHNOLOGY FOR BIOFUELS 2021; 14:155. [PMID: 34229745 PMCID: PMC8259078 DOI: 10.1186/s13068-021-02005-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 06/26/2021] [Indexed: 05/12/2023]
Abstract
BACKGROUND Saccharomyces cerevisiae is widely used in traditional brewing and modern fermentation industries to produce biofuels, chemicals and other bioproducts, but challenged by various harsh industrial conditions, such as hyperosmotic, thermal and ethanol stresses. Thus, its stress tolerance enhancement has been attracting broad interests. Recently, CRISPR/Cas-based genome editing technology offers unprecedented tools to explore genetic modifications and performance improvement of S. cerevisiae. RESULTS Here, we presented that the Target-AID (activation-induced cytidine deaminase) base editor of enabling C-to-T substitutions could be harnessed to generate in situ nucleotide changes on the S. cerevisiae genome, thereby introducing protein point mutations in cells. The general transcription factor gene SPT15 was targeted, and total 36 mutants with diversified stress tolerances were obtained. Among them, the 18 tolerant mutants against hyperosmotic, thermal and ethanol stresses showed more than 1.5-fold increases of fermentation capacities. These mutations were mainly enriched at the N-terminal region and the convex surface of the saddle-shaped structure of Spt15. Comparative transcriptome analysis of three most stress-tolerant (A140G, P169A and R238K) and two most stress-sensitive (S118L and L214V) mutants revealed common and distinctive impacted global transcription reprogramming and transcriptional regulatory hubs in response to stresses, and these five amino acid changes had different effects on the interactions of Spt15 with DNA and other proteins in the RNA Polymerase II transcription machinery according to protein structure alignment analysis. CONCLUSIONS Taken together, our results demonstrated that the Target-AID base editor provided a powerful tool for targeted in situ mutagenesis in S. cerevisiae and more potential targets of Spt15 residues for enhancing yeast stress tolerance.
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Affiliation(s)
- Yanfang Liu
- CAS Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yuping Lin
- CAS Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yufeng Guo
- CAS Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
| | - Fengli Wu
- CAS Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
| | - Yuanyuan Zhang
- CAS Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
| | - Xianni Qi
- CAS Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
| | - Zhen Wang
- CAS Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Qinhong Wang
- CAS Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
- University of Chinese Academy of Sciences, Beijing, China
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27
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Pačnik K, Ogrizović M, Diepold M, Eisenberg T, Žganjar M, Žun G, Kužnik B, Gostinčar C, Curk T, Petrovič U, Natter K. Identification of novel genes involved in neutral lipid storage by quantitative trait loci analysis of Saccharomyces cerevisiae. BMC Genomics 2021; 22:110. [PMID: 33563210 PMCID: PMC7871550 DOI: 10.1186/s12864-021-07417-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Accepted: 01/31/2021] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND The accumulation of intracellular fat depots is a polygenic trait. Therefore, the extent of lipid storage in the individuals of a species covers a broad range and is determined by many genetic factors. Quantitative trait loci analysis can be used to identify those genetic differences between two strains of the same species that are responsible for the differences in a given phenotype. We used this method and complementary approaches to identify genes in the yeast Saccharomyces cerevisiae that are involved in neutral lipid storage. RESULTS We selected two yeast strains, the laboratory strain BY4741 and the wine yeast AWRI1631, with a more than two-fold difference in neutral lipid content. After crossing, sporulation and germination, we used fluorescence activated cell sorting to isolate a subpopulation of cells with the highest neutral lipid content from the pool of segregants. Whole genome sequencing of this subpopulation and of the unsorted pool of segregants implicated several loci that are involved in lipid accumulation. Three of the identified genes, PIG1, PHO23 and RML2, were investigated in more detail. Deletions of these genes and the exchange of the alleles between the two parental strains confirmed that the encoded proteins contribute to neutral lipid storage in S. cerevisiae and that PIG1, PHO23 and RML2 are the major causative genes. Backcrossing of one of the segregants with the parental strains for seven generations revealed additional regions in the genomes of both strains with potential causative genes for the high lipid accumulation phenotype. CONCLUSIONS We identified several genes that contribute to the phenotype of lipid accumulation in an allele-specific manner. Surprisingly, no allelic variations of genes with known functions in lipid metabolism were found, indicating that the level of storage lipid accumulation is determined by many cellular processes that are not directly related to lipid metabolism.
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Affiliation(s)
- Klavdija Pačnik
- Institute of Molecular Biosciences, NAWI Graz, University of Graz, Graz, Austria
| | - Mojca Ogrizović
- Department of Molecular and Biomedical Sciences, Jožef Stefan Institute, Ljubljana, Slovenia
| | - Matthias Diepold
- Institute of Molecular Biosciences, NAWI Graz, University of Graz, Graz, Austria
| | - Tobias Eisenberg
- Institute of Molecular Biosciences, NAWI Graz, University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
- Field of Excellence BioHealth - University of Graz, Graz, Austria
| | - Mia Žganjar
- Institute of Molecular Biosciences, NAWI Graz, University of Graz, Graz, Austria
- Faculty of Chemistry and Chemical Technology, University of Ljubljana, Ljubljana, Slovenia
| | - Gašper Žun
- Department of Molecular and Biomedical Sciences, Jožef Stefan Institute, Ljubljana, Slovenia
- Faculty of Chemistry and Chemical Technology, University of Ljubljana, Ljubljana, Slovenia
| | - Beti Kužnik
- Department of Molecular and Biomedical Sciences, Jožef Stefan Institute, Ljubljana, Slovenia
| | - Cene Gostinčar
- Department of Molecular and Biomedical Sciences, Jožef Stefan Institute, Ljubljana, Slovenia
- Department of Biology, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Tomaž Curk
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Uroš Petrovič
- Department of Molecular and Biomedical Sciences, Jožef Stefan Institute, Ljubljana, Slovenia.
- Department of Biology, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia.
| | - Klaus Natter
- Institute of Molecular Biosciences, NAWI Graz, University of Graz, Graz, Austria.
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Goldstein I, Ehrenreich IM. The complex role of genetic background in shaping the effects of spontaneous and induced mutations. Yeast 2020; 38:187-196. [PMID: 33125810 PMCID: PMC7984271 DOI: 10.1002/yea.3530] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 10/09/2020] [Accepted: 10/24/2020] [Indexed: 12/27/2022] Open
Abstract
Spontaneous and induced mutations frequently show different phenotypic effects across genetically distinct individuals. It is generally appreciated that these background effects mainly result from genetic interactions between the mutations and segregating loci. However, the architectures and molecular bases of these genetic interactions are not well understood. Recent work in a number of model organisms has tried to advance knowledge of background effects both by using large‐scale screens to find mutations that exhibit this phenomenon and by identifying the specific loci that are involved. Here, we review this body of research, emphasizing in particular the insights it provides into both the prevalence of background effects across different mutations and the mechanisms that cause these background effects. A large fraction of mutations show different effects in distinct individuals. These background effects are mainly caused by epistasis with segregating loci. Mapping studies show a diversity of genetic architectures can be involved. Genetically complex changes in gene expression are often, but not always, causative.
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Affiliation(s)
- Ilan Goldstein
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, California, 90089-2910, USA
| | - Ian M Ehrenreich
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, California, 90089-2910, USA
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29
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Jakobson CM, Jarosz DF. What Has a Century of Quantitative Genetics Taught Us About Nature's Genetic Tool Kit? Annu Rev Genet 2020; 54:439-464. [PMID: 32897739 DOI: 10.1146/annurev-genet-021920-102037] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The complexity of heredity has been appreciated for decades: Many traits are controlled not by a single genetic locus but instead by polymorphisms throughout the genome. The importance of complex traits in biology and medicine has motivated diverse approaches to understanding their detailed genetic bases. Here, we focus on recent systematic studies, many in budding yeast, which have revealed that large numbers of all kinds of molecular variation, from noncoding to synonymous variants, can make significant contributions to phenotype. Variants can affect different traits in opposing directions, and their contributions can be modified by both the environment and the epigenetic state of the cell. The integration of prospective (synthesizing and analyzing variants) and retrospective (examining standing variation) approaches promises to reveal how natural selection shapes quantitative traits. Only by comprehensively understanding nature's genetic tool kit can we predict how phenotypes arise from the complex ensembles of genetic variants in living organisms.
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Affiliation(s)
- Christopher M Jakobson
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, California 94305, USA;
| | - Daniel F Jarosz
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, California 94305, USA; .,Department of Developmental Biology, Stanford University School of Medicine, Stanford, California 94305, USA
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30
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Mariotti R, Belaj A, De La Rosa R, Leòn L, Brizioli F, Baldoni L, Mousavi S. EST-SNP Study of Olea europaea L. Uncovers Functional Polymorphisms between Cultivated and Wild Olives. Genes (Basel) 2020; 11:E916. [PMID: 32785094 PMCID: PMC7465833 DOI: 10.3390/genes11080916] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 08/04/2020] [Accepted: 08/07/2020] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND The species Olea europaea includes cultivated varieties (subsp. europaea var. europaea), wild plants (subsp. europaea var. sylvestris), and five other subspecies spread over almost all continents. Single nucleotide polymorphisms in the expressed sequence tag able to underline intra-species differentiation are not yet identified, beyond a few plastidial markers. METHODS In the present work, more than 1000 transcript-specific SNP markers obtained by the genotyping of 260 individuals were studied. These genotypes included cultivated, oleasters, and samples of subspecies guanchica, and were analyzed in silico, in order to identify polymorphisms on key genes distinguishing different Olea europaea forms. RESULTS Phylogeny inference and principal coordinate analysis allowed to detect two distinct clusters, clearly separating wilds and guanchica samples from cultivated olives, meanwhile the structure analysis made possible to differentiate these three groups. Sequences carrying the polymorphisms that distinguished wild and cultivated olives were analyzed and annotated, allowing to identify 124 candidate genes that have a functional role in flower development, stress response, or involvement in important metabolic pathways. Signatures of selection that occurred during olive domestication, were detected and reported. CONCLUSION This deep EST-SNP analysis provided important information on the genetic and genomic diversity of the olive complex, opening new opportunities to detect gene polymorphisms with potential functional and evolutionary roles, and to apply them in genomics-assisted breeding, highlighting the importance of olive germplasm conservation.
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Affiliation(s)
- Roberto Mariotti
- CNR—Institute of Biosciences and Bioresources, Via Madonna Alta 130, 06128 Perugia, Italy; (R.M.); (F.B.); (S.M.)
| | - Angjelina Belaj
- IFAPA—Centro Alameda del Obispo, Avda Menendez Pidal, s/n, E-14004 Cordoba, Spain; (A.B.); (R.D.L.R.); (L.L.)
| | - Raul De La Rosa
- IFAPA—Centro Alameda del Obispo, Avda Menendez Pidal, s/n, E-14004 Cordoba, Spain; (A.B.); (R.D.L.R.); (L.L.)
| | - Lorenzo Leòn
- IFAPA—Centro Alameda del Obispo, Avda Menendez Pidal, s/n, E-14004 Cordoba, Spain; (A.B.); (R.D.L.R.); (L.L.)
| | - Federico Brizioli
- CNR—Institute of Biosciences and Bioresources, Via Madonna Alta 130, 06128 Perugia, Italy; (R.M.); (F.B.); (S.M.)
| | - Luciana Baldoni
- CNR—Institute of Biosciences and Bioresources, Via Madonna Alta 130, 06128 Perugia, Italy; (R.M.); (F.B.); (S.M.)
| | - Soraya Mousavi
- CNR—Institute of Biosciences and Bioresources, Via Madonna Alta 130, 06128 Perugia, Italy; (R.M.); (F.B.); (S.M.)
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Stojiljkovic M, Foulquié-Moreno MR, Thevelein JM. Polygenic analysis of very high acetic acid tolerance in the yeast Saccharomyces cerevisiae reveals a complex genetic background and several new causative alleles. BIOTECHNOLOGY FOR BIOFUELS 2020; 13:126. [PMID: 32695222 PMCID: PMC7364526 DOI: 10.1186/s13068-020-01761-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Accepted: 07/04/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND High acetic acid tolerance is of major importance in industrial yeast strains used for second-generation bioethanol production, because of the high acetic acid content of lignocellulose hydrolysates. It is also important in first-generation starch hydrolysates and in sourdoughs containing significant acetic acid levels. We have previously identified snf4 E269* as a causative allele in strain MS164 obtained after whole-genome (WG) transformation and selection for improved acetic acid tolerance. RESULTS We have now performed polygenic analysis with the same WG transformant MS164 to identify novel causative alleles interacting with snf4 E269* to further enhance acetic acid tolerance, from a range of 0.8-1.2% acetic acid at pH 4.7, to previously unmatched levels for Saccharomyces cerevisiae. For that purpose, we crossed the WG transformant with strain 16D, a previously identified strain displaying very high acetic acid tolerance. Quantitative trait locus (QTL) mapping with pooled-segregant whole-genome sequence analysis identified four major and two minor QTLs. In addition to confirmation of snf4 E269* in QTL1, we identified six other genes linked to very high acetic acid tolerance, TRT2, MET4, IRA2 and RTG1 and a combination of MSH2 and HAL9, some of which have never been connected previously to acetic acid tolerance. Several of these genes appear to be wild-type alleles that complement defective alleles present in the other parent strain. CONCLUSIONS The presence of several novel causative genes highlights the distinct genetic basis and the strong genetic background dependency of very high acetic acid tolerance. Our results suggest that elimination of inferior mutant alleles might be equally important for reaching very high acetic acid tolerance as introduction of rare superior alleles. The superior alleles of MET4 and RTG1 might be useful for further improvement of acetic acid tolerance in specific industrial yeast strains.
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Affiliation(s)
- Marija Stojiljkovic
- Laboratory of Molecular Cell Biology, Institute of Botany and Microbiology, Department of Biology, KU Leuven, Kasteelpark Arenberg 31, 3001 Leuven-Heverlee, Flanders Belgium
- Center for Microbiology, VIB, Kasteelpark Arenberg 31, 3001 Leuven-Heverlee, Flanders Belgium
| | - María R. Foulquié-Moreno
- Laboratory of Molecular Cell Biology, Institute of Botany and Microbiology, Department of Biology, KU Leuven, Kasteelpark Arenberg 31, 3001 Leuven-Heverlee, Flanders Belgium
- Center for Microbiology, VIB, Kasteelpark Arenberg 31, 3001 Leuven-Heverlee, Flanders Belgium
| | - Johan M. Thevelein
- Laboratory of Molecular Cell Biology, Institute of Botany and Microbiology, Department of Biology, KU Leuven, Kasteelpark Arenberg 31, 3001 Leuven-Heverlee, Flanders Belgium
- Center for Microbiology, VIB, Kasteelpark Arenberg 31, 3001 Leuven-Heverlee, Flanders Belgium
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32
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Tattini L, Tellini N, Mozzachiodi S, D'Angiolo M, Loeillet S, Nicolas A, Liti G. Accurate Tracking of the Mutational Landscape of Diploid Hybrid Genomes. Mol Biol Evol 2020; 36:2861-2877. [PMID: 31397846 PMCID: PMC6878955 DOI: 10.1093/molbev/msz177] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Mutations, recombinations, and genome duplications may promote genetic diversity and trigger evolutionary processes. However, quantifying these events in diploid hybrid genomes is challenging. Here, we present an integrated experimental and computational workflow to accurately track the mutational landscape of yeast diploid hybrids (MuLoYDH) in terms of single-nucleotide variants, small insertions/deletions, copy-number variants, aneuploidies, and loss-of-heterozygosity. Pairs of haploid Saccharomyces parents were combined to generate ancestor hybrids with phased genomes and varying levels of heterozygosity. These diploids were evolved under different laboratory protocols, in particular mutation accumulation experiments. Variant simulations enabled the efficient integration of competitive and standard mapping of short reads, depending on local levels of heterozygosity. Experimental validations proved the high accuracy and resolution of our computational approach. Finally, applying MuLoYDH to four different diploids revealed striking genetic background effects. Homozygous Saccharomyces cerevisiae showed a ∼4-fold higher mutation rate compared with its closely related species S. paradoxus. Intraspecies hybrids unveiled that a substantial fraction of the genome (∼250 bp per generation) was shaped by loss-of-heterozygosity, a process strongly inhibited in interspecies hybrids by high levels of sequence divergence between homologous chromosomes. In contrast, interspecies hybrids exhibited higher single-nucleotide mutation rates compared with intraspecies hybrids. MuLoYDH provided an unprecedented quantitative insight into the evolutionary processes that mold diploid yeast genomes and can be generalized to other genetic systems.
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Affiliation(s)
- Lorenzo Tattini
- CNRS UMR7284, INSERM, IRCAN, Université Côte d'Azur, Nice, France
| | - Nicolò Tellini
- CNRS UMR7284, INSERM, IRCAN, Université Côte d'Azur, Nice, France
| | | | | | - Sophie Loeillet
- CNRS UMR3244, Institut Curie, PSL Research University, Paris, France
| | - Alain Nicolas
- CNRS UMR3244, Institut Curie, PSL Research University, Paris, France
| | - Gianni Liti
- CNRS UMR7284, INSERM, IRCAN, Université Côte d'Azur, Nice, France
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33
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Machado HE, Lawrie DS, Petrov DA. Pervasive Strong Selection at the Level of Codon Usage Bias in Drosophila melanogaster. Genetics 2020; 214:511-528. [PMID: 31871131 PMCID: PMC7017021 DOI: 10.1534/genetics.119.302542] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Accepted: 12/12/2019] [Indexed: 11/18/2022] Open
Abstract
Codon usage bias (CUB), where certain codons are used more frequently than expected by chance, is a ubiquitous phenomenon and occurs across the tree of life. The dominant paradigm is that the proportion of preferred codons is set by weak selection. While experimental changes in codon usage have at times shown large phenotypic effects in contrast to this paradigm, genome-wide population genetic estimates have supported the weak selection model. Here we use deep genomic population sequencing of two Drosophila melanogaster populations to measure selection on synonymous sites in a way that allowed us to estimate the prevalence of both weak and strong purifying selection. We find that selection in favor of preferred codons ranges from weak (|Nes| ∼ 1) to strong (|Nes| > 10), with strong selection acting on 10-20% of synonymous sites in preferred codons. While previous studies indicated that selection at synonymous sites could be strong, this is the first study to detect and quantify strong selection specifically at the level of CUB. Further, we find that CUB-associated polymorphism accounts for the majority of strong selection on synonymous sites, with secondary contributions of splicing (selection on alternatively spliced genes, splice junctions, and spliceosome-bound sites) and transcription factor binding. Our findings support a new model of CUB and indicate that the functional importance of CUB, as well as synonymous sites in general, have been underestimated.
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Affiliation(s)
- Heather E Machado
- Cancer, Ageing, and Somatic Mutation, Wellcome Sanger Institute, Hinxton CB10 1SA, UK
| | - David S Lawrie
- Department of Ecology and Evolutionary Biology, University of California, Irvine, California 92697-3958
| | - Dmitri A Petrov
- Department of Biology, Stanford University, California 94305-5020
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34
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Gauthier L, Stynen B, Serohijos AWR, Michnick SW. Genetics' Piece of the PI: Inferring the Origin of Complex Traits and Diseases from Proteome-Wide Protein-Protein Interaction Dynamics. Bioessays 2019; 42:e1900169. [PMID: 31854021 DOI: 10.1002/bies.201900169] [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: 09/16/2019] [Revised: 11/15/2019] [Indexed: 11/07/2022]
Abstract
How do common and rare genetic polymorphisms contribute to quantitative traits or disease risk and progression? Multiple human traits have been extensively characterized at the genomic level, revealing their complex genetic architecture. However, it is difficult to resolve the mechanisms by which specific variants contribute to a phenotype. Recently, analyses of variant effects on molecular traits have uncovered intermediate mechanisms that link sequence variation to phenotypic changes. Yet, these methods only capture a fraction of genetic contributions to phenotype. Here, in reviewing the field, it is proposed that complex traits can be understood by characterizing the dynamics of biochemical networks within living cells, and that the effects of genetic variation can be captured on these networks by using protein-protein interaction (PPI) methodologies. This synergy between PPI methodologies and the genetics of complex traits opens new avenues to investigate the molecular etiology of human diseases and to facilitate their prevention or treatment.
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Affiliation(s)
- Louis Gauthier
- Departement de Biochimie, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada.,Centre Robert-Cedergren en Bioinformatique et Génomique, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada
| | - Bram Stynen
- Departement de Biochimie, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada.,Centre Robert-Cedergren en Bioinformatique et Génomique, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada
| | - Adrian W R Serohijos
- Departement de Biochimie, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada.,Centre Robert-Cedergren en Bioinformatique et Génomique, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada
| | - Stephen W Michnick
- Departement de Biochimie, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada.,Centre Robert-Cedergren en Bioinformatique et Génomique, Université de Montréal, 2900 Édouard-Montpetit, Montréal, Quebec, H3T 1J4, Canada
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35
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LaBella AL, Opulente DA, Steenwyk JL, Hittinger CT, Rokas A. Variation and selection on codon usage bias across an entire subphylum. PLoS Genet 2019; 15:e1008304. [PMID: 31365533 PMCID: PMC6701816 DOI: 10.1371/journal.pgen.1008304] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Revised: 08/20/2019] [Accepted: 07/11/2019] [Indexed: 01/04/2023] Open
Abstract
Variation in synonymous codon usage is abundant across multiple levels of organization: between codons of an amino acid, between genes in a genome, and between genomes of different species. It is now well understood that variation in synonymous codon usage is influenced by mutational bias coupled with both natural selection for translational efficiency and genetic drift, but how these processes shape patterns of codon usage bias across entire lineages remains unexplored. To address this question, we used a rich genomic data set of 327 species that covers nearly one third of the known biodiversity of the budding yeast subphylum Saccharomycotina. We found that, while genome-wide relative synonymous codon usage (RSCU) for all codons was highly correlated with the GC content of the third codon position (GC3), the usage of codons for the amino acids proline, arginine, and glycine was inconsistent with the neutral expectation where mutational bias coupled with genetic drift drive codon usage. Examination between genes’ effective numbers of codons and their GC3 contents in individual genomes revealed that nearly a quarter of genes (381,174/1,683,203; 23%), as well as most genomes (308/327; 94%), significantly deviate from the neutral expectation. Finally, by evaluating the imprint of translational selection on codon usage, measured as the degree to which genes’ adaptiveness to the tRNA pool were correlated with selective pressure, we show that translational selection is widespread in budding yeast genomes (264/327; 81%). These results suggest that the contribution of translational selection and drift to patterns of synonymous codon usage across budding yeasts varies across codons, genes, and genomes; whereas drift is the primary driver of global codon usage across the subphylum, the codon bias of large numbers of genes in the majority of genomes is influenced by translational selection. Synonymous mutations in genes have no effect on the encoded proteins and were once thought to be evolutionarily neutral. By examining codon usage bias across codons, genes, and genomes of 327 species in the budding yeast subphylum, we show that synonymous codon usage is shaped by both neutral processes and selection for translational efficiency. Specifically, whereas codon usage bias for most codons appears to be strongly associated with mutational bias and largely driven by genetic drift across the entire subphylum, patterns of codon usage bias in a few codons, as well as in many genes in nearly all genomes of budding yeasts, deviate from neutral expectations. Rather, the synonymous codons used within genes in most budding yeast genomes are adapted to the tRNAs present within each genome, a result most likely due to translational selection that optimizes codons to match the tRNAs. Our results suggest that patterns of codon usage bias in budding yeasts, and perhaps more broadly in fungi and other microbial eukaryotes, are shaped by both neutral and selective processes.
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Affiliation(s)
- Abigail L. LaBella
- Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Dana A. Opulente
- Laboratory of Genetics, Genome Center of Wisconsin, DOE Great Lakes Bioenergy Research Center, Wisconsin Energy Institute, J. F. Crow Institute for the Study of Evolution, University of Wisconsin–Madison, Wisconsin, United States of America
| | - Jacob L. Steenwyk
- Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Chris Todd Hittinger
- Laboratory of Genetics, Genome Center of Wisconsin, DOE Great Lakes Bioenergy Research Center, Wisconsin Energy Institute, J. F. Crow Institute for the Study of Evolution, University of Wisconsin–Madison, Wisconsin, United States of America
| | - Antonis Rokas
- Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee, United States of America
- * E-mail:
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36
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Placek K, Schultze JL, Aschenbrenner AC. Epigenetic reprogramming of immune cells in injury, repair, and resolution. J Clin Invest 2019; 129:2994-3005. [PMID: 31329166 DOI: 10.1172/jci124619] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Immune cells are pivotal in the reaction to injury, whereupon, under ideal conditions, repair and resolution phases restore homeostasis following initial acute inflammation. Immune cell activation and reprogramming require transcriptional changes that can only be initiated if epigenetic alterations occur. Recently, accelerated deciphering of epigenetic mechanisms has extended knowledge of epigenetic regulation, including long-distance chromatin remodeling, DNA methylation, posttranslational histone modifications, and involvement of small and long noncoding RNAs. Epigenetic changes have been linked to aspects of immune cell development, activation, and differentiation. Furthermore, genome-wide epigenetic landscapes have been established for some immune cells, including tissue-resident macrophages, and blood-derived cells including T cells. The epigenetic mechanisms underlying developmental steps from hematopoietic stem cells to fully differentiated immune cells led to development of epigenetic technologies and insights into general rules of epigenetic regulation. Compared with more advanced research areas, epigenetic reprogramming of immune cells in injury remains in its infancy. While the early epigenetic mechanisms supporting activation of the immune response to injury have been studied, less is known about resolution and repair phases and cell type-specific changes. We review prominent recent findings concerning injury-mediated epigenetic reprogramming, particularly in stroke and myocardial infarction. Lastly, we illustrate how single-cell technologies will be crucial to understanding epigenetic reprogramming in the complex sequential processes following injury.
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Affiliation(s)
- Katarzyna Placek
- Immunology and Metabolism, LIMES Institute, University of Bonn, Bonn, Germany
| | - Joachim L Schultze
- Platform for Single Cell Genomics and Epigenomics at the German Center for Neurodegenerative Diseases and the University of Bonn, Bonn, Germany.,Genomics and Immunoregulation, LIMES Institute, University of Bonn, Bonn, Germany
| | - Anna C Aschenbrenner
- Genomics and Immunoregulation, LIMES Institute, University of Bonn, Bonn, Germany
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37
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Lebeuf-Taylor E, McCloskey N, Bailey SF, Hinz A, Kassen R. The distribution of fitness effects among synonymous mutations in a gene under directional selection. eLife 2019; 8:45952. [PMID: 31322500 PMCID: PMC6692132 DOI: 10.7554/elife.45952] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2019] [Accepted: 07/18/2019] [Indexed: 12/21/2022] Open
Abstract
The fitness effects of synonymous mutations, nucleotide changes that do not alter the encoded amino acid, have often been assumed to be neutral, but a growing body of evidence suggests otherwise. We used site-directed mutagenesis coupled with direct measures of competitive fitness to estimate the distribution of fitness effects among synonymous mutations for a gene under directional selection and capable of adapting via synonymous nucleotide changes. Synonymous mutations had highly variable fitness effects, both deleterious and beneficial, resembling those of nonsynonymous mutations in the same gene. This variation in fitness was underlain by changes in transcription linked to the creation of internal promoter sites. A positive correlation between fitness and the presence of synonymous substitutions across a phylogeny of related Pseudomonads suggests these mutations may be common in nature. Taken together, our results provide the most compelling evidence to date that synonymous mutations with non-neutral fitness effects may in fact be commonplace.
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Affiliation(s)
| | - Nick McCloskey
- Department of Biology, University of Ottawa, Ottawa, Canada
| | - Susan F Bailey
- Department of Biology, Clarkson University, Potsdam, United States
| | - Aaron Hinz
- Department of Biology, University of Ottawa, Ottawa, Canada
| | - Rees Kassen
- Department of Biology, University of Ottawa, Ottawa, Canada
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Jakobson CM, Jarosz DF. Molecular Origins of Complex Heritability in Natural Genotype-to-Phenotype Relationships. Cell Syst 2019; 8:363-379.e3. [PMID: 31054809 PMCID: PMC6560647 DOI: 10.1016/j.cels.2019.04.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Revised: 03/25/2019] [Accepted: 04/05/2019] [Indexed: 01/09/2023]
Abstract
The statistical complexity of heredity has long been evident, but its molecular origins remain elusive. To investigate, we charted 90 comprehensive genotype-to-phenotype maps in a large population of wild diploid yeast. In contrast to long-standing assumptions, all types of genetic variation contributed similarly to phenotype. Causal synonymous and regulatory variants exhibited distinct molecular signatures, as did nonlinearities in heterozygote fitness that likely contribute to hybrid vigor. Highly pleiotropic variants altered disordered sequences within signaling hubs, and their effects correlated across environments-even when antagonistic-suggesting that large fitness gains bring concomitant costs. Natural genetic networks defined by the causal loci differed from those determined by precise gene deletions or protein-protein interactions. Finally, we found that traits that would appear omnigenic in less powered studies do in fact have finite genetic determinants. Integrating these molecular principles will be crucial as genome reading and writing become routine in research, industry, and medicine.
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Affiliation(s)
- Christopher M Jakobson
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Daniel F Jarosz
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA 94305, USA.
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Variation in Filamentous Growth and Response to Quorum-Sensing Compounds in Environmental Isolates of Saccharomyces cerevisiae. G3-GENES GENOMES GENETICS 2019; 9:1533-1544. [PMID: 30862622 PMCID: PMC6505140 DOI: 10.1534/g3.119.400080] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In fungi, filamentous growth is a major developmental transition that occurs in response to environmental cues. In diploid Saccharomyces cerevisiae, it is known as pseudohyphal growth and presumed to be a foraging mechanism. Rather than unicellular growth, multicellular filaments composed of elongated, attached cells spread over and into surfaces. This morphogenetic switch can be induced through quorum sensing with the aromatic alcohols phenylethanol and tryptophol. Most research investigating pseudohyphal growth has been conducted in a single lab background, Σ1278b. To investigate the natural variation in this phenotype and its induction, we assayed the diverse 100-genomes collection of environmental isolates. Using computational image analysis, we quantified the production of pseudohyphae and observed a large amount of variation. Population origin was significantly associated with pseudohyphal growth, with the West African population having the most. Surprisingly, most strains showed little or no response to exogenous phenylethanol or tryptophol. We also investigated the amount of natural genetic variation in pseudohyphal growth using a mapping population derived from a highly-heterozygous clinical isolate that contained as much phenotypic variation as the environmental panel. A bulk-segregant analysis uncovered five major peaks with candidate loci that have been implicated in the Σ1278b background. Our results indicate that the filamentous growth response is a generalized, highly variable phenotype in natural populations, while response to quorum sensing molecules is surprisingly rare. These findings highlight the importance of coupling studies in tractable lab strains with natural isolates in order to understand the relevance and distribution of well-studied traits.
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Marchadier E, Hanemian M, Tisné S, Bach L, Bazakos C, Gilbault E, Haddadi P, Virlouvet L, Loudet O. The complex genetic architecture of shoot growth natural variation in Arabidopsis thaliana. PLoS Genet 2019; 15:e1007954. [PMID: 31009456 PMCID: PMC6476473 DOI: 10.1371/journal.pgen.1007954] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2018] [Accepted: 01/11/2019] [Indexed: 12/16/2022] Open
Abstract
One of the main outcomes of quantitative genetics approaches to natural variation is to reveal the genetic architecture underlying the phenotypic space. Complex genetic architectures are described as including numerous loci (or alleles) with small-effect and/or low-frequency in the populations, interactions with the genetic background, environment or age. Linkage or association mapping strategies will be more or less sensitive to this complexity, so that we still have an unclear picture of its extent. By combining high-throughput phenotyping under two environmental conditions with classical QTL mapping approaches in multiple Arabidopsis thaliana segregating populations as well as advanced near isogenic lines construction and survey, we have attempted to improve our understanding of quantitative phenotypic variation. Integrative traits such as those related to vegetative growth used in this work (highlighting either cumulative growth, growth rate or morphology) all showed complex and dynamic genetic architecture with respect to the segregating population and condition. The more resolutive our mapping approach, the more complexity we uncover, with several instances of QTLs visible in near isogenic lines but not detected with the initial QTL mapping, indicating that our phenotyping accuracy was less limiting than the mapping resolution with respect to the underlying genetic architecture. In an ultimate approach to resolve this complexity, we intensified our phenotyping effort to target specifically a 3Mb-region known to segregate for a major quantitative trait gene, using a series of selected lines recombined every 100kb. We discovered that at least 3 other independent QTLs had remained hidden in this region, some with trait- or condition-specific effects, or opposite allelic effects. If we were to extrapolate the figures obtained on this specific region in this particular cross to the genome- and species-scale, we would predict hundreds of causative loci of detectable phenotypic effect controlling these growth-related phenotypes.
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Affiliation(s)
- Elodie Marchadier
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, 78000, Versailles, France
| | - Mathieu Hanemian
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, 78000, Versailles, France
| | - Sébastien Tisné
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, 78000, Versailles, France
| | - Liên Bach
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, 78000, Versailles, France
| | - Christos Bazakos
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, 78000, Versailles, France
| | - Elodie Gilbault
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, 78000, Versailles, France
| | - Parham Haddadi
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, 78000, Versailles, France
| | - Laetitia Virlouvet
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, 78000, Versailles, France
| | - Olivier Loudet
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, 78000, Versailles, France
- * E-mail:
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Jakobson CM, She R, Jarosz DF. Pervasive function and evidence for selection across standing genetic variation in S. cerevisiae. Nat Commun 2019; 10:1222. [PMID: 30874558 PMCID: PMC6420628 DOI: 10.1038/s41467-019-09166-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2018] [Accepted: 02/21/2019] [Indexed: 02/06/2023] Open
Abstract
Quantitative genetics aims to map genotype to phenotype, often with the goal of understanding how organisms evolved. However, it remains unclear whether the genetic variants identified are exemplary of evolution. Here we analyzed progeny of two wild Saccharomyces cerevisiae isolates to identify 195 loci underlying complex metabolic traits, resolving 107 to single polymorphisms with diverse molecular mechanisms. More than 20% of causal variants exhibited patterns of emergence inconsistent with neutrality. Moreover, contrary to drift-centric expectation, variation in diverse wild yeast isolates broadly exhibited this property: over 30% of shared natural variants exhibited phylogenetic signatures suggesting that they are not neutral. This pattern is likely attributable to both homoplasy and balancing selection on ancestral polymorphism. Variants that emerged repeatedly were more likely to have done so in isolates from the same ecological niche. Our results underscore the power of super-resolution mapping of ecologically relevant traits in understanding adaptation and evolution.
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Affiliation(s)
- Christopher M Jakobson
- Department of Chemical & Systems Biology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Richard She
- Department of Chemical & Systems Biology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Daniel F Jarosz
- Department of Chemical & Systems Biology, Stanford University School of Medicine, Stanford, CA, 94305, USA.
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA, 94305, USA.
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Regulation of MFGE8 by the intergenic coronary artery disease locus on 15q26.1. Atherosclerosis 2019; 284:11-17. [PMID: 30861420 DOI: 10.1016/j.atherosclerosis.2019.02.012] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Revised: 01/24/2019] [Accepted: 02/08/2019] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND AIMS A recently identified locus for coronary artery disease (CAD) tagged by rs8042271 is in a region of tight linkage disequilibrium (LD) between 2 genes (MFGE8, ABHD2) previously linked to atherosclerosis. Here we have explored the regulatory framework of this region to identify its functional relationship to CAD. METHODS The CAD Associated Region between MFGE8 and ABHD2 (CARMA) was investigated by bioinformatic approaches and transcriptional reporter assays to prioritize target genes and identify putative causal variants. Findings were integrated with publicly available gene expression datasets. MFGE8 silencing was performed in cell models relevant to CAD. RESULTS The regulatory potential of CARMA is disseminated sparsely over the entire region. CARMA contains multiple eQTL that regulate MFGE8 in coronary artery and coronary artery smooth muscle cell (CoSMC). SNPs that predict the expression of MFGE8 in artery are concordantly associated with higher risk of CAD (pval = 0.0014). Targeting CARMA by CRISPR/Cas9 in a cellular model increased MFGE8 expression. MFGE8 silencing was found to reduce CoSMC and monocyte (THP-1) but not endothelial cell proliferation. CONCLUSIONS These findings support a mechanistic link between a GWAS identified CAD risk locus and atherosclerosis. The intergenic locus CARMA regulates MFGE8 in a haplotype dependent manner. Individuals genetically susceptible to increased MFGE8 expression exhibit greater CAD risk. Suppressing MFGE8 expression reduced SMC and THP-1 proliferation. These data support an atherogenic contribution of CARMA/MFGE8 that may be linked to cell proliferation and/or improved survival of CAD relevant cell types.
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Abstract
The first and only published version of the rat reference genome sequence was RGSC3.1, accomplished by the Rat Genome Sequencing Project Consortium. Here we present the history of the community effort in the correction of sequence errors and filling missing gaps in the process of refining and providing researchers with a high-quality rat reference sequence. The genome assembly improvements, addition of different evidence resources over time, such as RNA-Seq data, and software development methodologies had a positive impact on the gene model annotations. Over the years we observed a great increase in the numbers of genes, protein coding sequences, predicted transcripts and transcript features. Before the sequencing of the rat genome was possible, first biochemical and next genomic markers like RAPD, AFLP, RFLP, and SSLP were fundamental in research studies involving cross-breeding between different rat strains, in finding the level of polymorphism, linkage mapping, and phylogeny. Linkage maps provide information on recombination rates, give insight into intra- and interspecies gene rearrangements, and help to identify Mendelian loci and Quantitative Trait Loci (QTL). In the 1990s many reports were published on the construction of rat linkage maps that incorporated increasing numbers of markers and facilitated the localization of disease loci. Current genetic monitoring and linkage mapping relies on single nucleotide polymorphisms (SNPs). The Rat Genome Database collects information on genetic variation from the worldwide community of rat researchers and provides tools for searching and retrieving these data. As of today we show details about almost 605 million variants coming from many studies in our Variant Visualizer tool.
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Slomka S, Pilpel Y. Meiotic Recombination: Genetics' Good Old Scalpel. Cell 2018; 172:391-392. [PMID: 29373827 DOI: 10.1016/j.cell.2018.01.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In the era of genome engineering, a new study returns to classical genetics to decipher genotype-phenotype relationships in unprecedented throughput and with unprecedented accuracy. Capitalizing on natural variation in yeast strains and frequent meiotic recombination, She and Jarosz (2018) dissect and map to nucleotide resolution, simple and complex determinants of diverse phenotypic traits.
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Affiliation(s)
- Shai Slomka
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, 76100, Israel
| | - Yitzhak Pilpel
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, 76100, Israel.
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Abstract
Genome-wide association studies (GWASs) have identified thousands of loci associated with hundreds of complex diseases and traits, and progress is being made toward elucidating the causal variants and genes underlying these associations. Functional characterization of mechanisms at GWAS loci is a multi-faceted challenge. Challenges include linkage disequilibrium and allelic heterogeneity at each locus, the noncoding nature of most loci, and the time and cost needed for experimentally evaluating the potential mechanistic contributions of genes and variants. As GWAS sample sizes increase, more loci are identified, and the complexities of individual loci emerge. Loci can consist of multiple association signals, each of which can reflect the influence of multiple variants, inseparable by association analyses. Each signal within a locus can influence the same or different target genes. Experimental studies of genes and variants can differ on the basis of cell type, cellular environment, or other context-specific variables. In this review, we describe the complexity of mechanisms at GWAS loci-including multiple signals, multiple variants, and/or multiple genes-and the implications these complexities hold for experimental study design and interpretation of GWAS mechanisms.
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Affiliation(s)
- Maren E Cannon
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA.
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Sharon E, Chen SAA, Khosla NM, Smith JD, Pritchard JK, Fraser HB. Functional Genetic Variants Revealed by Massively Parallel Precise Genome Editing. Cell 2018; 175:544-557.e16. [PMID: 30245013 DOI: 10.1016/j.cell.2018.08.057] [Citation(s) in RCA: 130] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2018] [Revised: 06/21/2018] [Accepted: 08/22/2018] [Indexed: 12/26/2022]
Abstract
A major challenge in genetics is to identify genetic variants driving natural phenotypic variation. However, current methods of genetic mapping have limited resolution. To address this challenge, we developed a CRISPR-Cas9-based high-throughput genome editing approach that can introduce thousands of specific genetic variants in a single experiment. This enabled us to study the fitness consequences of 16,006 natural genetic variants in yeast. We identified 572 variants with significant fitness differences in glucose media; these are highly enriched in promoters, particularly in transcription factor binding sites, while only 19.2% affect amino acid sequences. Strikingly, nearby variants nearly always favor the same parent's alleles, suggesting that lineage-specific selection is often driven by multiple clustered variants. In sum, our genome editing approach reveals the genetic architecture of fitness variation at single-base resolution and could be adapted to measure the effects of genome-wide genetic variation in any screen for cell survival or cell-sortable markers.
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Affiliation(s)
- Eilon Sharon
- Department of Biology, Stanford University, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA.
| | - Shi-An A Chen
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Neil M Khosla
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Justin D Smith
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Jonathan K Pritchard
- Department of Biology, Stanford University, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
| | - Hunter B Fraser
- Department of Biology, Stanford University, Stanford, CA 94305, USA.
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Zabinsky RA, Mason GA, Queitsch C, Jarosz DF. It's not magic - Hsp90 and its effects on genetic and epigenetic variation. Semin Cell Dev Biol 2018; 88:21-35. [PMID: 29807130 DOI: 10.1016/j.semcdb.2018.05.015] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Revised: 04/15/2018] [Accepted: 05/15/2018] [Indexed: 10/14/2022]
Abstract
Canalization, or phenotypic robustness in the face of environmental and genetic perturbation, is an emergent property of living systems. Although this phenomenon has long been recognized, its molecular underpinnings have remained enigmatic until recently. Here, we review the contributions of the molecular chaperone Hsp90, a protein that facilitates the folding of many key regulators of growth and development, to canalization of phenotype - and de-canalization in times of stress - drawing on studies in eukaryotes as diverse as baker's yeast, mouse ear cress, and blind Mexican cavefish. Hsp90 is a hub of hubs that interacts with many so-called 'client proteins,' which affect virtually every aspect of cell signaling and physiology. As Hsp90 facilitates client folding and stability, it can epistatically suppress or enable the expression of genetic variants in its clients and other proteins that acquire client status through mutation. Hsp90's vast interaction network explains the breadth of its phenotypic reach, including Hsp90-dependent de novo mutations and epigenetic effects on gene regulation. Intrinsic links between environmental stress and Hsp90 function thus endow living systems with phenotypic plasticity in fluctuating environments. As environmental perturbations alter Hsp90 function, they also alter Hsp90's interaction with its client proteins, thereby re-wiring networks that determine the genotype-to-phenotype map. Ensuing de-canalization of phenotype creates phenotypic diversity that is not simply stochastic, but often has an underlying genetic basis. Thus, extreme phenotypes can be selected, and assimilated so that they no longer require environmental stress to manifest. In addition to acting on standing genetic variation, Hsp90 perturbation has also been linked to increased frequency of de novo variation and several epigenetic phenomena, all with the potential to generate heritable phenotypic change. Here, we aim to clarify and discuss the multiple means by which Hsp90 can affect phenotype and possibly evolutionary change, and identify their underlying common feature: at its core, Hsp90 interacts epistatically through its chaperone function with many other genes and their gene products. Its influence on phenotypic diversification is thus not magic but rather a fundamental property of genetics.
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Affiliation(s)
- Rebecca A Zabinsky
- Department of Chemical and Systems Biology, Stanford School of Medicine, Stanford, CA, United States
| | | | - Christine Queitsch
- Department of Genome Sciences, University of Washington, Seattle, WA, United States.
| | - Daniel F Jarosz
- Department of Chemical and Systems Biology, Stanford School of Medicine, Stanford, CA, United States; Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA, United States.
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48
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Perdigoto C. Genetic variation: Putting causal variants on the map. Nat Rev Genet 2018; 19:188-189. [PMID: 29456248 DOI: 10.1038/nrg.2018.11] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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