Modelling the physiological status of yeast during wine fermentation enables the prediction of secondary metabolism

Saccharomyces non-cerevisiae yeasts are gaining momentum in wine fermentation due to their potential to reduce ethanol content and achieve attractive aroma profiles. However, the design of the fermentation process for new species requires intensive experimentation. The use of mechanistic models coul...

Descripción completa

Detalles Bibliográficos
Autores: Moimenta, Artai R, Henriques, David, Minebois, Romain, Querol, Amparo, Balsa-Canto, Eva
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/297087
Acceso en línea:http://hdl.handle.net/10261/297087
https://api.elsevier.com/content/abstract/scopus_id/85147349834
Access Level:acceso abierto
Palabra clave:Saccharomyces non-cerevisiae yeasts
Wine fermentation
Mechanistic models
Secondary metabolites
Wine quality
id ES_62e2fd620cfe074c1405dcfd1dafe35a
oai_identifier_str oai:digital.csic.es:10261/297087
network_acronym_str ES
network_name_str España
repository_id_str
spelling Modelling the physiological status of yeast during wine fermentation enables the prediction of secondary metabolismMoimenta, Artai RHenriques, DavidMinebois, RomainQuerol, AmparoBalsa-Canto, EvaSaccharomyces non-cerevisiae yeastsWine fermentationMechanistic modelsSecondary metabolitesWine qualitySaccharomyces non-cerevisiae yeasts are gaining momentum in wine fermentation due to their potential to reduce ethanol content and achieve attractive aroma profiles. However, the design of the fermentation process for new species requires intensive experimentation. The use of mechanistic models could automate process design, yet to date, most fermentation models have focused on primary metabolism. Therefore, these models do not provide insight into the production of secondary metabolites essential for wine quality, such as aromas. In this work, we formulate a continuous model that accounts for the physiological status of yeast, that is, exponential growth, growth under nitrogen starvation and transition to stationary or decay phases. To do so, we assumed that nitrogen starvation is associated with carbohydrate accumulation and the induction of a set of transcriptional changes associated with the stationary phase. The model accurately described the dynamics of time series data for biomass and primary and secondary metabolites obtained for various yeast species in single culture fermentations. We also used the proposed model to explore different process designs, showing how the addition of nitrogen could affect the aromatic profile of wine. This study underlines the potential of incorporating yeast physiology into batch fermentation modelling and provides a new means of automating process design.This work has received funding from MCIU/AEI/FEDER, UE grant references: RTI2018-093744-B-C31, RTI2018-093744-B-C33; MCIN/AEI/10.13039/501100011033 and NextGenerationEU/PRTR grant reference: PLEC2021-007827 and Xunta de Galicia (IN607B 2020/03). RM was supported by an FPI grant from the Ministerio de Economía y Competitividad, Spain (ref. BES-2016-078202).With funding from the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2021-001189-S).Peer reviewedJohn Wiley & SonsXunta de GaliciaAgencia Estatal de Investigación (España)European Commission0000-0002-5609-23170000-0002-9477-292X0000-0001-6959-15720000-0002-6478-68450000-0002-1978-2626Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202320232023info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/297087https://api.elsevier.com/content/abstract/scopus_id/85147349834reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI//CEX2021-001189-Sinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-093744-B-C31info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-093744-B-C33info:eu-repo/grantAgreement/AEI//PLEC2021-007827Microbial biotechnologyhttps://doi.org/10.1111/1751-7915.14211Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2970872026-05-22T06:33:51Z
dc.title.none.fl_str_mv Modelling the physiological status of yeast during wine fermentation enables the prediction of secondary metabolism
title Modelling the physiological status of yeast during wine fermentation enables the prediction of secondary metabolism
spellingShingle Modelling the physiological status of yeast during wine fermentation enables the prediction of secondary metabolism
Moimenta, Artai R
Saccharomyces non-cerevisiae yeasts
Wine fermentation
Mechanistic models
Secondary metabolites
Wine quality
title_short Modelling the physiological status of yeast during wine fermentation enables the prediction of secondary metabolism
title_full Modelling the physiological status of yeast during wine fermentation enables the prediction of secondary metabolism
title_fullStr Modelling the physiological status of yeast during wine fermentation enables the prediction of secondary metabolism
title_full_unstemmed Modelling the physiological status of yeast during wine fermentation enables the prediction of secondary metabolism
title_sort Modelling the physiological status of yeast during wine fermentation enables the prediction of secondary metabolism
dc.creator.none.fl_str_mv Moimenta, Artai R
Henriques, David
Minebois, Romain
Querol, Amparo
Balsa-Canto, Eva
author Moimenta, Artai R
author_facet Moimenta, Artai R
Henriques, David
Minebois, Romain
Querol, Amparo
Balsa-Canto, Eva
author_role author
author2 Henriques, David
Minebois, Romain
Querol, Amparo
Balsa-Canto, Eva
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Xunta de Galicia
Agencia Estatal de Investigación (España)
European Commission
0000-0002-5609-2317
0000-0002-9477-292X
0000-0001-6959-1572
0000-0002-6478-6845
0000-0002-1978-2626
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Saccharomyces non-cerevisiae yeasts
Wine fermentation
Mechanistic models
Secondary metabolites
Wine quality
topic Saccharomyces non-cerevisiae yeasts
Wine fermentation
Mechanistic models
Secondary metabolites
Wine quality
description Saccharomyces non-cerevisiae yeasts are gaining momentum in wine fermentation due to their potential to reduce ethanol content and achieve attractive aroma profiles. However, the design of the fermentation process for new species requires intensive experimentation. The use of mechanistic models could automate process design, yet to date, most fermentation models have focused on primary metabolism. Therefore, these models do not provide insight into the production of secondary metabolites essential for wine quality, such as aromas. In this work, we formulate a continuous model that accounts for the physiological status of yeast, that is, exponential growth, growth under nitrogen starvation and transition to stationary or decay phases. To do so, we assumed that nitrogen starvation is associated with carbohydrate accumulation and the induction of a set of transcriptional changes associated with the stationary phase. The model accurately described the dynamics of time series data for biomass and primary and secondary metabolites obtained for various yeast species in single culture fermentations. We also used the proposed model to explore different process designs, showing how the addition of nitrogen could affect the aromatic profile of wine. This study underlines the potential of incorporating yeast physiology into batch fermentation modelling and provides a new means of automating process design.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/297087
https://api.elsevier.com/content/abstract/scopus_id/85147349834
url http://hdl.handle.net/10261/297087
https://api.elsevier.com/content/abstract/scopus_id/85147349834
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/AEI//CEX2021-001189-S
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-093744-B-C31
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-093744-B-C33
info:eu-repo/grantAgreement/AEI//PLEC2021-007827
Microbial biotechnology
https://doi.org/10.1111/1751-7915.14211
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv John Wiley & Sons
publisher.none.fl_str_mv John Wiley & Sons
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869409531149156352
score 15,81155

Similares en LA Referencia