ArticleScientific reports2020
QTL mapping of modelled metabolic fluxes reveals gene variants impacting yeast central carbon metabolism.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 7 citations in OpenAlex.
- QTL mapping reveals novel genes and mechanisms underlying variations in H2S production during alcoholic fermentation in Saccharomyces cerevisiae.FEMS yeast research · 2024Article
- Identification of genetic variants of the industrial yeast Komagataella phaffii (Pichia pastoris) that contribute to increased yields of secreted heterologous proteins.PLoS biology · 2022Article
- Different Wines from Different Yeasts? "Microorganisms · 2021Article
- Characterization of effects of genetic variants via genome-scale metabolic modelling.Cellular and molecular life sciences : CMLS · 2021Review
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6 authors at 1 institution in 1 country.
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Abstract
The yeast Saccharomyces cerevisiae is an attractive industrial microorganism for the production of foods and beverages as well as for various bulk and fine chemicals, such as biofuels or fragrances. Building blocks for these biosyntheses are intermediates of yeast central carbon metabolism (CCM), whose intracellular availability depends on balanced single reactions that form metabolic fluxes. Therefore, efficient product biosynthesis is influenced by the distribution of these fluxes. We recently demonstrated great variations in CCM fluxes between yeast strains of different origins. However, we have limited understanding of flux modulation and the genetic basis of flux variations. In this study, we investigated the potential of quantitative trait locus (QTL) mapping to elucidate genetic variations responsible for differences in metabolic flux distributions (fQTL). Intracellular metabolic fluxes were estimated by constraint-based modelling and used as quantitative phenotypes, and differences in fluxes were linked to genomic variations. Using this approach, we detected four fQTLs that influence metabolic pathways. The molecular dissection of these QTLs revealed two allelic gene variants, PDB1 and VID30, contributing to flux distribution. The elucidation of genetic determinants influencing metabolic fluxes, as reported here for the first time, creates new opportunities for the development of strains with optimized metabolite profiles for various applications.
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