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author Ivan Rodrigo Wolf
Lucas Farinazzo Marques
Lauana Fogaça de Almeida
Lucas Cardoso Lázari
Leonardo Nazário de Moraes
Luiz Henrique Cardoso
Camila Cristina de Oliveira Alves
Rafael Takahiro Nakajima
Amanda Piveta Schnepper
Marjorie de Assis Golim
Thais Regiani Cataldi
Jeroen G. Nijland
Camila Moreira Pinto
Matheus Naia Fioretto
Rodrigo Oliveira Almeida
Arnold J. M. Driessen
Rafael Plana Simōes
Mônica Veneziano Labate
Rejane Maria Tommasini Grotto
Carlos Alberto Labate
Ary Fernandes Junior
Luis Antonio Justulin
Rafael Luiz Buogo Coan
Érica Ramos
Fabiana Barcelos Furtado
Cesar Martins
Guilherme Targino Valente
author_facet Ivan Rodrigo Wolf
Lucas Farinazzo Marques
Lauana Fogaça de Almeida
Lucas Cardoso Lázari
Leonardo Nazário de Moraes
Luiz Henrique Cardoso
Camila Cristina de Oliveira Alves
Rafael Takahiro Nakajima
Amanda Piveta Schnepper
Marjorie de Assis Golim
Thais Regiani Cataldi
Jeroen G. Nijland
Camila Moreira Pinto
Matheus Naia Fioretto
Rodrigo Oliveira Almeida
Arnold J. M. Driessen
Rafael Plana Simōes
Mônica Veneziano Labate
Rejane Maria Tommasini Grotto
Carlos Alberto Labate
Ary Fernandes Junior
Luis Antonio Justulin
Rafael Luiz Buogo Coan
Érica Ramos
Fabiana Barcelos Furtado
Cesar Martins
Guilherme Targino Valente
author_sort Ivan Rodrigo Wolf
collection DOAJ
description Ethanol (EtOH) alters many cellular processes in yeast. An integrated view of different EtOH-tolerant phenotypes and their long noncoding RNAs (lncRNAs) is not yet available. Here, large-scale data integration showed the core EtOH-responsive pathways, lncRNAs, and triggers of higher (HT) and lower (LT) EtOH-tolerant phenotypes. LncRNAs act in a strain-specific manner in the EtOH stress response. Network and omics analyses revealed that cells prepare for stress relief by favoring activation of life-essential systems. Therefore, longevity, peroxisomal, energy, lipid, and RNA/protein metabolisms are the core processes that drive EtOH tolerance. By integrating omics, network analysis, and several other experiments, we showed how the HT and LT phenotypes may arise: (1) the divergence occurs after cell signaling reaches the longevity and peroxisomal pathways, with CTA1 and ROS playing key roles; (2) signals reaching essential ribosomal and RNA pathways via SUI2 enhance the divergence; (3) specific lipid metabolism pathways also act on phenotype-specific profiles; (4) HTs take greater advantage of degradation and membraneless structures to cope with EtOH stress; and (5) our EtOH stress-buffering model suggests that diauxic shift drives EtOH buffering through an energy burst, mainly in HTs. Finally, critical genes, pathways, and the first models including lncRNAs to describe nuances of EtOH tolerance are reported here.
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spelling doaj.art-b3041eb4384446098a818c7e3845bb7f2023-11-17T11:36:58ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672023-03-01246564610.3390/ijms24065646Integrative Analysis of the Ethanol Tolerance of <i>Saccharomyces cerevisiae</i>Ivan Rodrigo Wolf0Lucas Farinazzo Marques1Lauana Fogaça de Almeida2Lucas Cardoso Lázari3Leonardo Nazário de Moraes4Luiz Henrique Cardoso5Camila Cristina de Oliveira Alves6Rafael Takahiro Nakajima7Amanda Piveta Schnepper8Marjorie de Assis Golim9Thais Regiani Cataldi10Jeroen G. Nijland11Camila Moreira Pinto12Matheus Naia Fioretto13Rodrigo Oliveira Almeida14Arnold J. M. Driessen15Rafael Plana Simōes16Mônica Veneziano Labate17Rejane Maria Tommasini Grotto18Carlos Alberto Labate19Ary Fernandes Junior20Luis Antonio Justulin21Rafael Luiz Buogo Coan22Érica Ramos23Fabiana Barcelos Furtado24Cesar Martins25Guilherme Targino Valente26Department of Bioprocess and Biotechnology, School of Agriculture, São Paulo State University (UNESP), Botucatu 18610-034, BrazilDepartment of Bioprocess and Biotechnology, School of Agriculture, São Paulo State University (UNESP), Botucatu 18610-034, BrazilDepartment of Bioprocess and Biotechnology, School of Agriculture, São Paulo State University (UNESP), Botucatu 18610-034, BrazilDepartment of Bioprocess and Biotechnology, School of Agriculture, São Paulo State University (UNESP), Botucatu 18610-034, BrazilDepartment of Bioprocess and Biotechnology, School of Agriculture, São Paulo State University (UNESP), Botucatu 18610-034, BrazilDepartment of Bioprocess and Biotechnology, School of Agriculture, São Paulo State University (UNESP), Botucatu 18610-034, BrazilDepartment of Bioprocess and Biotechnology, School of Agriculture, São Paulo State University (UNESP), Botucatu 18610-034, BrazilDepartment of Structural and Functional Biology, Institute of Biosciences, São Paulo State University (UNESP), Botucatu 18618-689, BrazilDepartment of Bioprocess and Biotechnology, School of Agriculture, São Paulo State University (UNESP), Botucatu 18610-034, BrazilLaboratory of Applied Biotechnology, Clinical Hospital of the Medical School, São Paulo State University (UNESP), Botucatu 18618-970, BrazilLaboratório Max Feffer de Genética de Plantas, Escola Superior de Agricultura Luiz de Queiroz, Universidade de São Paulo (USP), Piracicaba 13418-900, BrazilMolecular Microbiology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Nijenborgh 7, 9747 AG Groningen, The NetherlandsDepartment of Bioprocess and Biotechnology, School of Agriculture, São Paulo State University (UNESP), Botucatu 18610-034, BrazilDepartment of Structural and Functional Biology, Institute of Biosciences, São Paulo State University (UNESP), Botucatu 18618-689, BrazilInstituto Federal de Educação, Ciência e Tecnologia do Sudeste de Minas Gerais–Campus Muriaé, Muriaé 36884-036, BrazilMolecular Microbiology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Nijenborgh 7, 9747 AG Groningen, The NetherlandsDepartment of Bioprocess and Biotechnology, School of Agriculture, São Paulo State University (UNESP), Botucatu 18610-034, BrazilLaboratório Max Feffer de Genética de Plantas, Escola Superior de Agricultura Luiz de Queiroz, Universidade de São Paulo (USP), Piracicaba 13418-900, BrazilDepartment of Bioprocess and Biotechnology, School of Agriculture, São Paulo State University (UNESP), Botucatu 18610-034, BrazilLaboratório Max Feffer de Genética de Plantas, Escola Superior de Agricultura Luiz de Queiroz, Universidade de São Paulo (USP), Piracicaba 13418-900, BrazilLaboratory of Bacteriology, Department of Chemical and Biological Sciences, Institute of Biosciences, São Paulo State University (UNESP), Botucatu 18618-689, BrazilDepartment of Structural and Functional Biology, Institute of Biosciences, São Paulo State University (UNESP), Botucatu 18618-689, BrazilDepartment of Biophysics and Pharmacology, Institute of Biosciences, São Paulo State University (UNESP), Botucatu 18618-689, BrazilDepartment of Structural and Functional Biology, Institute of Biosciences, São Paulo State University (UNESP), Botucatu 18618-689, BrazilLaboratory of Applied Biotechnology, Clinical Hospital of the Medical School, São Paulo State University (UNESP), Botucatu 18618-970, BrazilDepartment of Structural and Functional Biology, Institute of Biosciences, São Paulo State University (UNESP), Botucatu 18618-689, BrazilMax Planck Institute for Heart and Lung Research, 61231 Bad Nauheim, GermanyEthanol (EtOH) alters many cellular processes in yeast. An integrated view of different EtOH-tolerant phenotypes and their long noncoding RNAs (lncRNAs) is not yet available. Here, large-scale data integration showed the core EtOH-responsive pathways, lncRNAs, and triggers of higher (HT) and lower (LT) EtOH-tolerant phenotypes. LncRNAs act in a strain-specific manner in the EtOH stress response. Network and omics analyses revealed that cells prepare for stress relief by favoring activation of life-essential systems. Therefore, longevity, peroxisomal, energy, lipid, and RNA/protein metabolisms are the core processes that drive EtOH tolerance. By integrating omics, network analysis, and several other experiments, we showed how the HT and LT phenotypes may arise: (1) the divergence occurs after cell signaling reaches the longevity and peroxisomal pathways, with CTA1 and ROS playing key roles; (2) signals reaching essential ribosomal and RNA pathways via SUI2 enhance the divergence; (3) specific lipid metabolism pathways also act on phenotype-specific profiles; (4) HTs take greater advantage of degradation and membraneless structures to cope with EtOH stress; and (5) our EtOH stress-buffering model suggests that diauxic shift drives EtOH buffering through an energy burst, mainly in HTs. Finally, critical genes, pathways, and the first models including lncRNAs to describe nuances of EtOH tolerance are reported here.https://www.mdpi.com/1422-0067/24/6/5646omicsdata integrationsystems biologylncRNAslncRNA–protein interactionsmembraneless organelles
spellingShingle Ivan Rodrigo Wolf
Lucas Farinazzo Marques
Lauana Fogaça de Almeida
Lucas Cardoso Lázari
Leonardo Nazário de Moraes
Luiz Henrique Cardoso
Camila Cristina de Oliveira Alves
Rafael Takahiro Nakajima
Amanda Piveta Schnepper
Marjorie de Assis Golim
Thais Regiani Cataldi
Jeroen G. Nijland
Camila Moreira Pinto
Matheus Naia Fioretto
Rodrigo Oliveira Almeida
Arnold J. M. Driessen
Rafael Plana Simōes
Mônica Veneziano Labate
Rejane Maria Tommasini Grotto
Carlos Alberto Labate
Ary Fernandes Junior
Luis Antonio Justulin
Rafael Luiz Buogo Coan
Érica Ramos
Fabiana Barcelos Furtado
Cesar Martins
Guilherme Targino Valente
Integrative Analysis of the Ethanol Tolerance of <i>Saccharomyces cerevisiae</i>
International Journal of Molecular Sciences
omics
data integration
systems biology
lncRNAs
lncRNA–protein interactions
membraneless organelles
title Integrative Analysis of the Ethanol Tolerance of <i>Saccharomyces cerevisiae</i>
title_full Integrative Analysis of the Ethanol Tolerance of <i>Saccharomyces cerevisiae</i>
title_fullStr Integrative Analysis of the Ethanol Tolerance of <i>Saccharomyces cerevisiae</i>
title_full_unstemmed Integrative Analysis of the Ethanol Tolerance of <i>Saccharomyces cerevisiae</i>
title_short Integrative Analysis of the Ethanol Tolerance of <i>Saccharomyces cerevisiae</i>
title_sort integrative analysis of the ethanol tolerance of i saccharomyces cerevisiae i
topic omics
data integration
systems biology
lncRNAs
lncRNA–protein interactions
membraneless organelles
url https://www.mdpi.com/1422-0067/24/6/5646
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