Cristin-prosjekt-ID: 636775
Sist endret: 18. januar 2019, 14:54

Cristin-prosjekt-ID: 636775
Sist endret: 18. januar 2019, 14:54
Prosjekt

Model-guided evolution for balanced attenuation of wine ethanol content by developing non-GMO yeast strains and communities (CoolWine)

prosjektleder

Eivind Almaas
ved Institutt for bioteknologi og matvitenskap ved Norges teknisk-naturvitenskapelige universitet

prosjekteier / koordinerende forskningsansvarlig enhet

  • Institutt for bioteknologi og matvitenskap ved Norges teknisk-naturvitenskapelige universitet

Finansiering

  • TotalbudsjettNOK 5.701.000
  • Norges forskningsråd
    Prosjektkode: 283862

Klassifisering

Vitenskapsdisipliner

Bioteknologi

Kategorier

Prosjektkategori

  • Grunnforskning

Kontaktinformasjon

Tidsramme

Avsluttet
Start: 1. mai 2018 Slutt: 31. desember 2021

Beskrivelse Beskrivelse

Tittel

Model-guided evolution for balanced attenuation of wine ethanol content by developing non-GMO yeast strains and communities (CoolWine)

Populærvitenskapelig sammendrag

Increasing temperature in the European wine producing regions is having a negative impact on this key sector. Climate change results in a lack of balance between technological and phenolic ripening of wine grapes and, as a consequence, alcohol increase in wines. This trend is of great concern for the European wine industry because it has a negative impact on wine quality, becomes a hurdle for international trade, and jeopardizes compatibility of moderate wine consumption with a healthy lifestyle. In this project, we will use model-guided computational analysis in combination with adaptive laboratory evolution to develop GMO-free yeasts that will produce wine with reduced alcohol content.

Vitenskapelig sammendrag

Biotechnological Problem:
Increasing temperature in the European wine producing regions is having a negative impact on this key sector. Climate change results in a lack of balance between technological and phenolic ripening of wine grapes and, as a consequence, alcohol increase in wines. This trend is of great concern for the European wine industry because it has a negative impact on wine quality, becomes a hurdle for international trade, and jeopardizes compatibility of moderate wine consumption with a healthy lifestyle.

CoolWine Strategy:
We propose a two-track strategy to reduce ethanol yield during wine fermentation Track 1: model-guided adaptive laboratory evolution of wine yeasts. Track 2: model-guided assembly of improved communities including S. cerevisiae as well as alternative yeast species.

Scientific Approach:
We have previously successfully used microbial consortia and oxygenated fermentations to reduce ethanol content of wines. Currently, several companies and research groups are also trying to follow this path, thus endorsing the technological and commercial validity of the approach. Although the current results are encouraging, we have also identified some bottlenecks (e.g. increased acetate production that is harmful for wine quality). In order to overcome these hindrances, we will tackle both applied and basic scientific challenges. For developing improved wine yeasts through model-guided adaptive laboratory evolution (ALE), currently available metabolic models and computational tools will be improved in order to account for data concerning respiro-fementative balance and acetic acid production. Computational models will be further informed by experimental data from different yeast mutants. In terms of the community models, we will identify metabolic pathways to be positively or negatively selected for in each species during ALE. This will allow us to develop microbial consortia suitable for alcohol level reduction.

prosjektdeltakere

prosjektleder

Eivind Almaas

  • Tilknyttet:
    Prosjektleder
    ved Institutt for bioteknologi og matvitenskap ved Norges teknisk-naturvitenskapelige universitet
Aktiv cristin-person

Lars Øystein Ursin

  • Tilknyttet:
    Prosjektdeltaker
    ved Institutt for samfunnsmedisin og sykepleie ved Norges teknisk-naturvitenskapelige universitet

Rune Nydal

  • Tilknyttet:
    Prosjektdeltaker
    ved Institutt for filosofi og religionsvitenskap ved Norges teknisk-naturvitenskapelige universitet

Ramon Gonzalez

  • Tilknyttet:
    Prosjektdeltaker
    ved Consejo Superior de Investigaciones Cientificas

Albert Mas

  • Tilknyttet:
    Prosjektdeltaker
    ved Universitat Rovira i Virgili
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Resultater Resultater

Computational analysis & prediction of phenotypes.. and some experiments.

Almaas, Eivind. 2019, Guest Lecture. NTNUVitenskapelig foredrag
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