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Zavala B, Dineen L, Fisher KJ, Opulente DA, Harrison MC, Wolters JF, Shen XX, Zhou X, Groenewald M, Hittinger CT, Rokas A, LaBella AL. Genomic factors shaping codon usage across the Saccharomycotina subphylum. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.23.595506. [PMID: 38826271 PMCID: PMC11142207 DOI: 10.1101/2024.05.23.595506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Codon usage bias, or the unequal use of synonymous codons, is observed across genes, genomes, and between species. The biased use of synonymous codons has been implicated in many cellular functions, such as translation dynamics and transcript stability, but can also be shaped by neutral forces. The Saccharomycotina, the fungal subphylum containing the yeasts Saccharomyces cerevisiae and Candida albicans , has been a model system for studying codon usage. We characterized codon usage across 1,154 strains from 1,051 species to gain insight into the biases, molecular mechanisms, evolution, and genomic features contributing to codon usage patterns across the subphylum. We found evidence of a general preference for A/T-ending codons and correlations between codon usage bias, GC content, and tRNA-ome size. Codon usage bias is also distinct between the 12 orders within the subphylum to such a degree that yeasts can be classified into orders with an accuracy greater than 90% using a machine learning algorithm trained on codon usage. We also characterized the degree to which codon usage bias is impacted by translational selection. Interestingly, the degree of translational selection was influenced by a combination of genome features and assembly metrics that included the number of coding sequences, BUSCO count, and genome length. Our analysis also revealed an extreme bias in codon usage in the Saccharomycodales associated with a lack of predicted arginine tRNAs. The order contains 24 species, and 23 are computationally predicted to lack tRNAs that decode CGN codons, leaving only the AGN codons to encode arginine. Analysis of Saccharomycodales gene expression, tRNA sequences, and codon evolution suggests that extreme avoidance of the CGN codons is associated with a decline in arginine tRNA function. Codon usage bias within the Saccharomycotina is generally consistent with previous investigations in fungi, which show a role for both genomic features and GC bias in shaping codon usage. However, we find cases of extreme codon usage preference and avoidance along yeast lineages, suggesting additional forces may be shaping the evolution of specific codons.
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Gao W, Chen X, He J, Sha A, Luo Y, Xiao W, Xiong Z, Li Q. Intraspecific and interspecific variations in the synonymous codon usage in mitochondrial genomes of 8 pleurotus strains. BMC Genomics 2024; 25:456. [PMID: 38730418 PMCID: PMC11084086 DOI: 10.1186/s12864-024-10374-3] [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: 06/12/2023] [Accepted: 05/03/2024] [Indexed: 05/12/2024] Open
Abstract
In this study, we investigated the codon bias of twelve mitochondrial core protein coding genes (PCGs) in eight Pleurotus strains, two of which are from the same species. The results revealed that the codons of all Pleurotus strains had a preference for ending in A/T. Furthermore, the correlation between codon base compositions and codon adaptation index (CAI), codon bias index (CBI) and frequency of optimal codons (FOP) indices was also detected, implying the influence of base composition on codon bias. The two P. ostreatus species were found to have differences in various base bias indicators. The average effective number of codons (ENC) of mitochondrial core PCGs of Pleurotus was found to be less than 35, indicating strong codon preference of mitochondrial core PCGs of Pleurotus. The neutrality plot analysis and PR2-Bias plot analysis further suggested that natural selection plays an important role in Pleurotus codon bias. Additionally, six to ten optimal codons (ΔRSCU > 0.08 and RSCU > 1) were identified in eight Pleurotus strains, with UGU and ACU being the most widely used optimal codons in Pleurotus. Finally, based on the combined mitochondrial sequence and RSCU value, the genetic relationship between different Pleurotus strains was deduced, showing large variations between them. This research has improved our understanding of synonymous codon usage characteristics and evolution of this important fungal group.
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Affiliation(s)
- Wei Gao
- Clinical Medical College & Affiliated Hospital of Chengdu University, Chengdu University, Chengdu, Sichuan, China
| | - Xiaodie Chen
- Key Laboratory of Coarse Cereal Processing, Ministry of Agriculture and Rural Affairs, School of Food and Biological Engineering, Chengdu University, Chengdu, Sichuan, China
| | - Jing He
- Key Laboratory of Coarse Cereal Processing, Ministry of Agriculture and Rural Affairs, School of Food and Biological Engineering, Chengdu University, Chengdu, Sichuan, China
| | - Ajia Sha
- Key Laboratory of Coarse Cereal Processing, Ministry of Agriculture and Rural Affairs, School of Food and Biological Engineering, Chengdu University, Chengdu, Sichuan, China
| | - Yingyong Luo
- Key Laboratory of Coarse Cereal Processing, Ministry of Agriculture and Rural Affairs, School of Food and Biological Engineering, Chengdu University, Chengdu, Sichuan, China
| | - Wenqi Xiao
- Key Laboratory of Coarse Cereal Processing, Ministry of Agriculture and Rural Affairs, School of Food and Biological Engineering, Chengdu University, Chengdu, Sichuan, China
| | - Zhuang Xiong
- Key Laboratory of Coarse Cereal Processing, Ministry of Agriculture and Rural Affairs, School of Food and Biological Engineering, Chengdu University, Chengdu, Sichuan, China
| | - Qiang Li
- Key Laboratory of Coarse Cereal Processing, Ministry of Agriculture and Rural Affairs, School of Food and Biological Engineering, Chengdu University, Chengdu, Sichuan, China.
- School of Food and Biological Engineering, Chengdu University, 2025 # Chengluo Avenue, Longquanyi District, Chengdu, Sichuan, 610106, China.
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3
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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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4
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Wang JJT, Steenwyk JL, Brem RB. Natural trait variation across Saccharomycotina species. FEMS Yeast Res 2024; 24:foae002. [PMID: 38218591 PMCID: PMC10833146 DOI: 10.1093/femsyr/foae002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 10/13/2023] [Accepted: 01/12/2024] [Indexed: 01/15/2024] Open
Abstract
Among molecular biologists, the group of fungi called Saccharomycotina is famous for its yeasts. These yeasts in turn are famous for what they have in common-genetic, biochemical, and cell-biological characteristics that serve as models for plants and animals. But behind the apparent homogeneity of Saccharomycotina species lie a wealth of differences. In this review, we discuss traits that vary across the Saccharomycotina subphylum. We describe cases of bright pigmentation; a zoo of cell shapes; metabolic specialties; and species with unique rules of gene regulation. We discuss the genetics of this diversity and why it matters, including insights into basic evolutionary principles with relevance across Eukarya.
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Affiliation(s)
- Johnson J -T Wang
- Department of Plant and Microbial Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Jacob L Steenwyk
- Howard Hughes Medical Institute and Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Rachel B Brem
- Department of Plant and Microbial Biology, University of California, Berkeley, Berkeley, CA 94720, USA
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5
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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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6
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Nalabothu RL, Fisher KJ, LaBella AL, Meyer TA, Opulente DA, Wolters JF, Rokas A, Hittinger CT. Codon Optimization Improves the Prediction of Xylose Metabolism from Gene Content in Budding Yeasts. Mol Biol Evol 2023; 40:msad111. [PMID: 37154525 PMCID: PMC10263009 DOI: 10.1093/molbev/msad111] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 02/28/2023] [Accepted: 05/04/2023] [Indexed: 05/10/2023] Open
Abstract
Xylose is the second most abundant monomeric sugar in plant biomass. Consequently, xylose catabolism is an ecologically important trait for saprotrophic organisms, as well as a fundamentally important trait for industries that hope to convert plant mass to renewable fuels and other bioproducts using microbial metabolism. Although common across fungi, xylose catabolism is rare within Saccharomycotina, the subphylum that contains most industrially relevant fermentative yeast species. The genomes of several yeasts unable to consume xylose have been previously reported to contain the full set of genes in the XYL pathway, suggesting the absence of a gene-trait correlation for xylose metabolism. Here, we measured growth on xylose and systematically identified XYL pathway orthologs across the genomes of 332 budding yeast species. Although the XYL pathway coevolved with xylose metabolism, we found that pathway presence only predicted xylose catabolism about half of the time, demonstrating that a complete XYL pathway is necessary, but not sufficient, for xylose catabolism. We also found that XYL1 copy number was positively correlated, after phylogenetic correction, with xylose utilization. We then quantified codon usage bias of XYL genes and found that XYL3 codon optimization was significantly higher, after phylogenetic correction, in species able to consume xylose. Finally, we showed that codon optimization of XYL2 was positively correlated, after phylogenetic correction, with growth rates in xylose medium. We conclude that gene content alone is a weak predictor of xylose metabolism and that using codon optimization enhances the prediction of xylose metabolism from yeast genome sequence data.
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Affiliation(s)
- Rishitha L Nalabothu
- Laboratory of Genetics, J. F. Crow Institute for the Study of Evolution, Wisconsin Energy Institute, Center for Genomic Science Innovation, University of Wisconsin-Madison, Madison, WI
- DOE Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI
| | - Kaitlin J Fisher
- Laboratory of Genetics, J. F. Crow Institute for the Study of Evolution, Wisconsin Energy Institute, Center for Genomic Science Innovation, University of Wisconsin-Madison, Madison, WI
- Department of Biological Sciences, State University of New York at Oswego, Oswego, NY
| | - Abigail Leavitt LaBella
- Department of Biological Sciences, Vanderbilt University, Nashville, TN
- Evolutionary Studies Initiative, Vanderbilt University, Nashville, TN
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC
| | - Taylor A Meyer
- Laboratory of Genetics, J. F. Crow Institute for the Study of Evolution, Wisconsin Energy Institute, Center for Genomic Science Innovation, University of Wisconsin-Madison, Madison, WI
- DOE Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI
| | - Dana A Opulente
- Laboratory of Genetics, J. F. Crow Institute for the Study of Evolution, Wisconsin Energy Institute, Center for Genomic Science Innovation, University of Wisconsin-Madison, Madison, WI
- DOE Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI
- Department of Biology, Villanova University, Villanova, PA
| | - John F Wolters
- Laboratory of Genetics, J. F. Crow Institute for the Study of Evolution, Wisconsin Energy Institute, Center for Genomic Science Innovation, University of Wisconsin-Madison, Madison, WI
- DOE Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI
| | - Antonis Rokas
- Department of Biological Sciences, Vanderbilt University, Nashville, TN
- Evolutionary Studies Initiative, Vanderbilt University, Nashville, TN
| | - Chris Todd Hittinger
- Laboratory of Genetics, J. F. Crow Institute for the Study of Evolution, Wisconsin Energy Institute, Center for Genomic Science Innovation, University of Wisconsin-Madison, Madison, WI
- DOE Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI
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7
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Sun X, Yu J, Zhu C, Mo X, Sun Q, Yang D, Su C, Lu Y. Recognition of galactose by a scaffold protein recruits a transcriptional activator for the GAL regulon induction in Candida albicans. eLife 2023; 12:84155. [PMID: 36723430 PMCID: PMC9925049 DOI: 10.7554/elife.84155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 01/31/2023] [Indexed: 02/02/2023] Open
Abstract
The GAL pathway of yeasts has long served as a model system for understanding of how regulatory mode of eukaryotic metabolic pathways evolves. While Gal4 mode has been well-characterized in Saccharomycetaceae clade, little is known about the regulation of the GAL pathway in other yeasts. Here, we find that Rep1, a Ndt80-like family transcription factor, serves as a galactose sensor in the commensal-pathogenic fungus Candida albicans. It is presented at the GAL gene promoters independent of the presence of galactose. Rep1 recognizes galactose via a direct physical interaction. The net result of this interaction is the recruitment of a transcriptional activator Cga1 (Candida galactose gene activator, orf19.4959) and transcription of the GAL genes proceeds. Rep1 and Cga1 are conserved across the CTG species. Rep1 itself does not possess transcriptional activity. Instead, it provides a scaffold to recruit different factors for transcriptional regulation. Rep1-Cga1 mode of regulation represents a new example of network rewiring in fungi, which provides insight into how C. albicans evolves transcriptional programs to colonize diverse host niches.
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Affiliation(s)
- Xun Sun
- College of Life Sciences, TaiKang Center for Life and Medical Sciences, Wuhan UniversityWuhanChina
| | - Jing Yu
- Hubei Key Laboratory of Cell Homeostasis, Wuhan UniversityWuhanChina
| | - Cheng Zhu
- Tianjin Key Laboratory of Function and Application of Biological Macromolecular Structures, Tianjin UniversityTianjinChina
| | - Xinreng Mo
- Hubei Key Laboratory of Cell Homeostasis, Wuhan UniversityWuhanChina
| | - Qiangqiang Sun
- Hubei Key Laboratory of Cell Homeostasis, Wuhan UniversityWuhanChina
| | - Dandan Yang
- College of Life Sciences, TaiKang Center for Life and Medical Sciences, Wuhan UniversityWuhanChina
| | - Chang Su
- Hubei Key Laboratory of Cell Homeostasis, Wuhan UniversityWuhanChina
| | - Yang Lu
- College of Life Sciences, TaiKang Center for Life and Medical Sciences, Wuhan UniversityWuhanChina
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8
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Selection for Translational Efficiency in Genes Associated with Alphaproteobacterial Gene Transfer Agents. mSystems 2022; 7:e0089222. [PMID: 36374047 PMCID: PMC9765227 DOI: 10.1128/msystems.00892-22] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Gene transfer agents (GTAs) are virus-like elements that are encoded by some bacterial and archaeal genomes. The production of GTAs can be induced by carbon depletion and results in host lysis and the release of virus-like particles that contain mostly random fragments of the host DNA. The remaining members of a GTA-producing population act as GTA recipients by producing proteins needed for GTA-mediated DNA acquisition. Here, we detected a codon usage bias toward codons with more readily available tRNAs in the RcGTA-like GTA genes of alphaproteobacterial genomes. Such bias likely improves the translational efficacy during GTA gene expression. While the strength of codon usage bias fluctuates substantially among individual GTA genes and across taxonomic groups, it is especially pronounced in Sphingomonadales, whose members are known to inhabit nutrient-depleted environments. By screening genomes for gene families with trends in codon usage biases similar to those in GTA genes, we found a gene that likely encodes head completion protein in some GTAs where it appeared missing, and 13 genes previously not implicated in the GTA life cycle. The latter genes are involved in various molecular processes, including the homologous recombination and transport of scarce organic matter. Our findings provide insights into the role of selection for translational efficiency in the evolution of GTA genes and outline genes that are potentially involved in the previously hypothesized integration of GTA-delivered DNA into the host genome. IMPORTANCE Horizontal gene transfer (HGT) is a fundamental process that drives evolution of microorganisms. HGT can result in a rapid dissemination of beneficial genes within and among microbial communities and can be achieved via multiple mechanisms. One peculiar HGT mechanism involves viruses "domesticated" by some bacteria and archaea (their hosts). These so-called gene transfer agents (GTAs) are encoded in hosts' genomes, produced under starvation conditions, and cannot propagate themselves as viruses. We show that GTA genes are under selection to improve the efficiency of their translation when the host activates GTA production. The selection is especially pronounced in bacteria that occupy nutrient-depleted environments. Intriguingly, several genes involved in incorporation of DNA into a genome are under similar selection pressure, suggesting that they may facilitate the integration of GTA-delivered DNA into the host genome. Our findings underscore the potential importance of GTAs as a mechanism of HGT under nutrient-limited conditions, which are widespread in microbial habitats.
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9
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Li Y, Liu H, Steenwyk JL, LaBella AL, Harrison MC, Groenewald M, Zhou X, Shen XX, Zhao T, Hittinger CT, Rokas A. Contrasting modes of macro and microsynteny evolution in a eukaryotic subphylum. Curr Biol 2022; 32:5335-5343.e4. [PMID: 36334587 PMCID: PMC10615371 DOI: 10.1016/j.cub.2022.10.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 08/24/2022] [Accepted: 10/13/2022] [Indexed: 11/06/2022]
Abstract
Examination of the changes in order and arrangement of homologous genes is key for understanding the mechanisms of genome evolution in eukaryotes. Previous comparisons between eukaryotic genomes have revealed considerable conservation across species that diverged hundreds of millions of years ago (e.g., vertebrates,1,2,3 bilaterian animals,4,5 and filamentous fungi6). However, understanding how genome organization evolves within and between eukaryotic major lineages remains underexplored. We analyzed high-quality genomes of 120 representative budding yeast species (subphylum Saccharomycotina) spanning ∼400 million years of eukaryotic evolution to examine how their genome organization evolved and to compare it with the evolution of animal and plant genome organization.7 We found that the decay of both macrosynteny (the conservation of homologous chromosomes) and microsynteny (the conservation of local gene content and order) was strongly associated with evolutionary divergence across budding yeast major clades. However, although macrosynteny decayed very fast, within ∼100 million years, the microsynteny of many genes-especially genes in metabolic clusters (e.g., in the GAL gene cluster8)-was much more deeply conserved both within major clades and across the subphylum. We further found that when genomes with similar evolutionary divergence times were compared, budding yeasts had lower macrosynteny conservation than animals and filamentous fungi but higher conservation than angiosperms. In contrast, budding yeasts had levels of microsynteny conservation on par with mammals, whereas angiosperms exhibited very low conservation. Our results provide new insight into the tempo and mode of the evolution of gene and genome organization across an entire eukaryotic subphylum.
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Affiliation(s)
- Yuanning Li
- Institute of Marine Science and Technology, Shandong University, 72 Binhai Road, Qingdao 266237, China.
| | - Hongyue Liu
- Institute of Marine Science and Technology, Shandong University, 72 Binhai Road, Qingdao 266237, China
| | - Jacob L Steenwyk
- Department of Biological Sciences, Vanderbilt University, VU Station B#35-1634, Nashville, TN 37235, USA; Vanderbilt Evolutionary Studies Initiative, Vanderbilt University, VU Station B#35-1634, Nashville, TN 37235, USA
| | - Abigail L LaBella
- Department of Biological Sciences, Vanderbilt University, VU Station B#35-1634, Nashville, TN 37235, USA; Vanderbilt Evolutionary Studies Initiative, Vanderbilt University, VU Station B#35-1634, Nashville, TN 37235, USA
| | - Marie-Claire Harrison
- Department of Biological Sciences, Vanderbilt University, VU Station B#35-1634, Nashville, TN 37235, USA; Vanderbilt Evolutionary Studies Initiative, Vanderbilt University, VU Station B#35-1634, Nashville, TN 37235, USA
| | - Marizeth Groenewald
- Westerdijk Fungal Biodiversity Institute, Uppsalalaan 8, 3584 CT Utrecht, the Netherlands
| | - Xiaofan Zhou
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangdong Province Key Laboratory of Microbial Signals and Disease Control, Integrative Microbiology Research Centre, South China Agricultural University, 483 Wushan Road, Guangzhou 520643, China
| | - Xing-Xing Shen
- Key Laboratory of Biology of Crop Pathogens and Insects of Zhejiang Province, Institute of Insect Sciences, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Tao Zhao
- State Key Laboratory of Crop Stress Biology for Arid Areas, Shaanxi Key Laboratory of Apple, College of Horticulture, Northwest A&F University, Taicheng Road 3, Yangling 712100, China
| | - 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, 1552 University Avenue, University of Wisconsin-Madison, Madison, WI 53726-4084, USA
| | - Antonis Rokas
- Department of Biological Sciences, Vanderbilt University, VU Station B#35-1634, Nashville, TN 37235, USA; Vanderbilt Evolutionary Studies Initiative, Vanderbilt University, VU Station B#35-1634, Nashville, TN 37235, USA; Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, 69118 Heidelberg, Germany.
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10
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Hugaboom M, Hatmaker EA, LaBella AL, Rokas A. Evolution and codon usage bias of mitochondrial and nuclear genomes in Aspergillus section Flavi. G3 (BETHESDA, MD.) 2022; 13:6777267. [PMID: 36305682 PMCID: PMC9836360 DOI: 10.1093/g3journal/jkac285] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 10/24/2022] [Indexed: 11/06/2022]
Abstract
The fungal genus Aspergillus contains a diversity of species divided into taxonomic sections of closely related species. Section Flavi contains 33 species, many of industrial, agricultural, or medical relevance. Here, we analyze the mitochondrial genomes (mitogenomes) of 20 Flavi species-including 18 newly assembled mitogenomes-and compare their evolutionary history and codon usage bias patterns to their nuclear counterparts. Codon usage bias refers to variable frequencies of synonymous codons in coding DNA and is shaped by a balance of neutral processes and natural selection. All mitogenomes were circular DNA molecules with highly conserved gene content and order. As expected, genomic content, including GC content, and genome size differed greatly between mitochondrial and nuclear genomes. Phylogenetic analysis based on 14 concatenated mitochondrial genes predicted evolutionary relationships largely consistent with those predicted by a phylogeny constructed from 2,422 nuclear genes. Comparing similarities in interspecies patterns of codon usage bias between mitochondrial and nuclear genomes showed that species grouped differently by patterns of codon usage bias depending on whether analyses were performed using mitochondrial or nuclear relative synonymous usage values. We found that patterns of codon usage bias at gene level are more similar between mitogenomes of different species than the mitogenome and nuclear genome of the same species. Finally, we inferred that, although most genes-both nuclear and mitochondrial-deviated from the neutral expectation for codon usage, mitogenomes were not under translational selection while nuclear genomes were under moderate translational selection. These results contribute to the study of mitochondrial genome evolution in filamentous fungi.
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Affiliation(s)
- Miya Hugaboom
- Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA
| | - Elizabeth Anne Hatmaker
- Corresponding author: Department of Biological Sciences, Vanderbilt University, VU Station B 35-1364, Nashville, TN 37235, USA. (AH)
| | - Abigail L LaBella
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Antonis Rokas
- Corresponding author: Department of Biological Sciences, Vanderbilt University, VU Station B 35-1364, Nashville, TN 37235, USA. (AR)
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11
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Gonçalves P, Gonçalves C. Horizontal gene transfer in yeasts. Curr Opin Genet Dev 2022; 76:101950. [PMID: 35841879 DOI: 10.1016/j.gde.2022.101950] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 06/01/2022] [Accepted: 06/12/2022] [Indexed: 11/29/2022]
Abstract
Horizontal gene transfer (HGT), defined as the exchange of genetic material other than from parent to progeny, is very common in bacteria and appears to constitute the most important mechanism contributing to enlarge a species gene pool. However, in eukaryotes, HGT is certainly much less common and some early insufficiently consubstantiated cases involving bacterial donors led some to consider that it was unlikely to occur in eukaryotes outside the host/endosymbiont relationship. More recently, plenty of reports of interdomain HGT have seen the light based on the strictest criteria, many concerning filamentous fungi and yeasts. Here, we attempt to summarize the most prominent instances of HGT reported in yeasts as well as what we have been able to learn so far concerning frequency and distribution, mechanisms, barriers, function of horizontally acquired genes, and the role of HGT in domestication.
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Affiliation(s)
- Paula Gonçalves
- Associate Laboratory i4HB - Institute for Health and Bioeconomy, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal; UCIBIO - Applied Molecular Biosciences Unit, Department of Life Sciences, NOVA School of Science and Technology, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal.
| | - Carla Gonçalves
- Vanderbilt University, Department of Biological Sciences, VU Station B #35-1634, Nashville, TN 37235, United States of America; Evolutionary Studies Initiative, Vanderbilt University, VU Station B #35-1634, Nashville, TN 37235, United States of America. https://twitter.com/@ciggoncalves
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12
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Steenwyk JL, Buida Iii TJ, Gonçalves C, Goltz DC, Morales G, Mead ME, LaBella AL, Chavez CM, Schmitz JE, Hadjifrangiskou M, Li Y, Rokas A. BioKIT: a versatile toolkit for processing and analyzing diverse types of sequence data. Genetics 2022; 221:6583183. [PMID: 35536198 PMCID: PMC9252278 DOI: 10.1093/genetics/iyac079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 05/03/2022] [Indexed: 11/14/2022] Open
Abstract
Bioinformatic analysis-such as genome assembly quality assessment, alignment summary statistics, relative synonymous codon usage, file format conversion, and processing and analysis-is integrated into diverse disciplines in the biological sciences. Several command-line pieces of software have been developed to conduct some of these individual analyses, but unified toolkits that conduct all these analyses are lacking. To address this gap, we introduce BioKIT, a versatile command line toolkit that has, upon publication, 42 functions, several of which were community-sourced, that conduct routine and novel processing and analysis of genome assemblies, multiple sequence alignments, coding sequences, sequencing data, and more. To demonstrate the utility of BioKIT, we conducted a comprehensive examination of relative synonymous codon usage across 171 fungal genomes that use alternative genetic codes, showed that the novel metric of gene-wise relative synonymous codon usage can accurately estimate gene-wise codon optimization, evaluated the quality and characteristics of 901 eukaryotic genome assemblies, and calculated alignment summary statistics for 10 phylogenomic data matrices. BioKIT will be helpful in facilitating and streamlining sequence analysis workflows. BioKIT is freely available under the MIT license from GitHub (https://github.com/JLSteenwyk/BioKIT), PyPi (https://pypi.org/project/jlsteenwyk-biokit/), and the Anaconda Cloud (https://anaconda.org/jlsteenwyk/jlsteenwyk-biokit). Documentation, user tutorials, and instructions for requesting new features are available online (https://jlsteenwyk.com/BioKIT).
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Affiliation(s)
- Jacob L Steenwyk
- Department of Biological Sciences, Vanderbilt University, VU Station B #35-1634, Nashville, TN 37235, USA.,Evolutionary Studies Initiative, Vanderbilt University, Nashville, TN 37235, USA
| | | | - Carla Gonçalves
- Department of Biological Sciences, Vanderbilt University, VU Station B #35-1634, Nashville, TN 37235, USA.,Evolutionary Studies Initiative, Vanderbilt University, Nashville, TN 37235, USA.,Associate Laboratory i4HB-Institute for Health and Bioeconomy, NOVA School of Science and Technology, NOVA University Lisbon, 2819-516 Caparica, Portugal.,UCIBIO-Applied Molecular Biosciences Unit, Department of Life Sciences, NOVA School of Science and Technology, NOVA University Lisbon, 2819-516 Caparica, Portugal
| | | | - Grace Morales
- Department of Pathology, Microbiology & Immunology, Center for Personalized Microbiology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Matthew E Mead
- Department of Biological Sciences, Vanderbilt University, VU Station B #35-1634, Nashville, TN 37235, USA.,Evolutionary Studies Initiative, Vanderbilt University, Nashville, TN 37235, USA
| | - Abigail L LaBella
- Department of Biological Sciences, Vanderbilt University, VU Station B #35-1634, Nashville, TN 37235, USA.,Evolutionary Studies Initiative, Vanderbilt University, Nashville, TN 37235, USA
| | - Christina M Chavez
- Department of Biological Sciences, Vanderbilt University, VU Station B #35-1634, Nashville, TN 37235, USA.,Evolutionary Studies Initiative, Vanderbilt University, Nashville, TN 37235, USA
| | - Jonathan E Schmitz
- Department of Pathology, Microbiology & Immunology, Center for Personalized Microbiology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Maria Hadjifrangiskou
- Evolutionary Studies Initiative, Vanderbilt University, Nashville, TN 37235, USA.,Department of Pathology, Microbiology & Immunology, Center for Personalized Microbiology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Yuanning Li
- Department of Biological Sciences, Vanderbilt University, VU Station B #35-1634, Nashville, TN 37235, USA
| | - Antonis Rokas
- Department of Biological Sciences, Vanderbilt University, VU Station B #35-1634, Nashville, TN 37235, USA.,Evolutionary Studies Initiative, Vanderbilt University, Nashville, TN 37235, USA
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13
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Hill R, Buggs RJA, Vu DT, Gaya E. Lifestyle Transitions in Fusarioid Fungi are Frequent and Lack Clear Genomic Signatures. Mol Biol Evol 2022; 39:6575681. [PMID: 35484861 PMCID: PMC9051438 DOI: 10.1093/molbev/msac085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
The fungal genus Fusarium (Ascomycota) includes well-known plant pathogens that are implicated in diseases worldwide, and many of which have been genome sequenced. The genus also encompasses other diverse lifestyles, including species found ubiquitously as asymptomatic-plant inhabitants (endophytes). Here, we produced structurally annotated genome assemblies for five endophytic Fusarium strains, including the first whole-genome data for Fusarium chuoi. Phylogenomic reconstruction of Fusarium and closely related genera revealed multiple and frequent lifestyle transitions, the major exception being a monophyletic clade of mutualist insect symbionts. Differential codon usage bias and increased codon optimisation separated Fusarium sensu stricto from allied genera. We performed computational prediction of candidate secreted effector proteins (CSEPs) and carbohydrate-active enzymes (CAZymes)—both likely to be involved in the host–fungal interaction—and sought evidence that their frequencies could predict lifestyle. However, phylogenetic distance described gene variance better than lifestyle did. There was no significant difference in CSEP, CAZyme, or gene repertoires between phytopathogenic and endophytic strains, although we did find some evidence that gene copy number variation may be contributing to pathogenicity. Large numbers of accessory CSEPs (i.e., present in more than one taxon but not all) and a comparatively low number of strain-specific CSEPs suggested there is a limited specialisation among plant associated Fusarium species. We also found half of the core genes to be under positive selection and identified specific CSEPs and CAZymes predicted to be positively selected on certain lineages. Our results depict fusarioid fungi as prolific generalists and highlight the difficulty in predicting pathogenic potential in the group.
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Affiliation(s)
- Rowena Hill
- Comparative Fungal Biology, Royal Botanic Gardens Kew, Jodrell Laboratory, Richmond, United Kingdom.,School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK
| | - Richard J A Buggs
- Comparative Fungal Biology, Royal Botanic Gardens Kew, Jodrell Laboratory, Richmond, United Kingdom.,School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK
| | - Dang Toan Vu
- Research Planning and International Cooperation Department, Plant Resources Center, Hanoi, Vietnam
| | - Ester Gaya
- Comparative Fungal Biology, Royal Botanic Gardens Kew, Jodrell Laboratory, Richmond, United Kingdom
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14
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The evolution of the GALactose utilization pathway in budding yeasts. Trends Genet 2022; 38:97-106. [PMID: 34538504 PMCID: PMC8678326 DOI: 10.1016/j.tig.2021.08.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 08/20/2021] [Accepted: 08/24/2021] [Indexed: 01/03/2023]
Abstract
The Leloir galactose utilization or GAL pathway of budding yeasts, including that of the baker's yeast Saccharomyces cerevisiae and the opportunistic human pathogen Candida albicans, breaks down the sugar galactose for energy and biomass production. The GAL pathway has long served as a model system for understanding how eukaryotic metabolic pathways, including their modes of regulation, evolve. More recently, the physical linkage of the structural genes GAL1, GAL7, and GAL10 in diverse budding yeast genomes has been used as a model for understanding the evolution of gene clustering. In this review, we summarize exciting recent work on three different aspects of this iconic pathway's evolution: gene cluster organization, GAL gene regulation, and the population genetics of the GAL pathway.
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15
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Jiang S, Du Q, Feng C, Ma L, Zhang Z. CompoDynamics: a comprehensive database for characterizing sequence composition dynamics. Nucleic Acids Res 2021; 50:D962-D969. [PMID: 34718745 PMCID: PMC8728180 DOI: 10.1093/nar/gkab979] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 10/02/2021] [Accepted: 10/06/2021] [Indexed: 11/15/2022] Open
Abstract
Sequence compositions of nucleic acids and proteins have significant impact on gene expression, RNA stability, translation efficiency, RNA/protein structure and molecular function, and are associated with genome evolution and adaptation across all kingdoms of life. Therefore, a devoted resource of sequence compositions and associated features is fundamentally crucial for a wide range of biological research. Here, we present CompoDynamics (https://ngdc.cncb.ac.cn/compodynamics/), a comprehensive database of sequence compositions of coding sequences (CDSs) and genomes for all kinds of species. Taking advantage of the exponential growth of RefSeq data, CompoDynamics presents a wealth of sequence compositions (nucleotide content, codon usage, amino acid usage) and derived features (coding potential, physicochemical property and phase separation) for 118 689 747 high-quality CDSs and 34 562 genomes across 24 995 species. Additionally, interactive analytical tools are provided to enable comparative analyses of sequence compositions and molecular features across different species and gene groups. Collectively, CompoDynamics bears the great potential to better understand the underlying roles of sequence composition dynamics across genes and genomes, providing a fundamental resource in support of a broad spectrum of biological studies.
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Affiliation(s)
- Shuai Jiang
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,China National Center for Bioinformation, Beijing 100101, China
| | - Qiang Du
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,China National Center for Bioinformation, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Changrui Feng
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,China National Center for Bioinformation, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lina Ma
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,China National Center for Bioinformation, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhang Zhang
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,China National Center for Bioinformation, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China
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