QTL mapping of modelled metabolic fluxes reveals gene variants impacting yeast central carbon metabolism

Eder, Matthias; Nidelet, Thibault; Sanchez, Isabelle; Camarasa, Carole; Legras, Jean Luc; Dequin, Sylvie

doi:10.1038/s41598-020-57857-3

Cited by 7 publications

(4 citation statements)

References 80 publications

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“…Most previous yeast QTL analyses have been conducted in S . cerevisiae [ 19 ], and they led to the identification of genes responsible for several industrially relevant polygenic phenotypes in that species [ 20 – 24 ]. QTL mapping has been applied to only a few other yeast species, including Schizosaccharomyces [ 25 ], Lachancea [ 26 , 27 ], and Cryptococcus [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

Identification of genetic variants of the industrial yeast Komagataella phaffii (Pichia pastoris) that contribute to increased yields of secreted heterologous proteins

et al. 2022

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The yeast Komagataella phaffii (formerly called Pichia pastoris) is used widely as a host for secretion of heterologous proteins, but only a few isolates of this species exist and all the commonly used expression systems are derived from a single genetic background, CBS7435 (NRRL Y-11430). We hypothesized that other genetic backgrounds could harbor variants that affect yields of secreted proteins. We crossed CBS7435 with 2 other K. phaffii isolates and mapped quantitative trait loci (QTLs) for secretion of a heterologous protein, β-glucosidase, by sequencing individual segregant genomes. A major QTL mapped to a frameshift mutation in the mannosyltransferase gene HOC1, which gives CBS7435 a weaker cell wall and higher protein secretion than the other isolates. Inactivation of HOC1 in the other isolates doubled β-glucosidase secretion. A second QTL mapped to an amino acid substitution in IRA1 that tripled β-glucosidase secretion in 1-week batch cultures but reduced cell viability, and its effects are specific to this heterologous protein. Our results demonstrate that QTL analysis is a powerful method for dissecting the basis of biotechnological traits in nonconventional yeasts, and a route to improving their industrial performance.

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Section: Introductionmentioning

confidence: 99%

Identification of genetic variants of the industrial yeast Komagataella phaffii (Pichia pastoris) that contribute to increased yields of secreted heterologous proteins

et al. 2022

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“…The last group (in pink), composed of strains S4, S6, and S8, was characterized by oxidation terms as "quince paste", "honey", "butter". Our results showed quite distinctive aroma profiles among the strains studied, demonstrating intraspecific diversity at the sensory level [52,53]. It was even possible to discriminate finished wines fermented by certain related strains.…”

Section: Sensory Impact Of Intraspecific Diversitymentioning

confidence: 53%

Different Wines from Different Yeasts? “Saccharomyces cerevisiae Intraspecies Differentiation by Metabolomic Signature and Sensory Patterns in Wine”

et al. 2021

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Alcoholic fermentation is known to be a key stage in the winemaking process that directly impacts the composition and quality of the final product. Twelve wines were obtained from fermentations of Chardonnay must made with twelve different commercial wine yeast strains of Saccharomyces cerevisiae. In our study, FT-ICR-MS, GC-MS, and sensory analysis were combined with multivariate analysis. Ultra-high-resolution mass spectrometry (uHRMS) was able to highlight hundreds of metabolites specific to each strain from the same species, although they are characterized by the same technological performances. Furthermore, the significant involvement of nitrogen metabolism in this differentiation was considered. The modulation of primary metabolism was also noted at the volatilome and sensory levels. Sensory analysis allowed us to classify wines into three groups based on descriptors associated with white wine. Thirty-five of the volatile compounds analyzed, including esters, medium-chain fatty acids, superior alcohols, and terpenes discriminate and give details about differences between wines. Therefore, phenotypic differences within the same species revealed metabolic differences that resulted in the diversity of the volatile fraction that participates in the palette of the sensory pattern. This original combination of metabolomics with the volatilome and sensory approaches provides an integrative vision of the characteristics of a given strain. Metabolomics shine the new light on intraspecific discrimination in the Saccharomyces cerevisiae species.

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“…To this end, all the statistical approaches for genetic mapping mentioned above can be readily used to determine the genetic architecture of of different enzymes as well as reaction fluxes, if these are measured in an investigated population. For instance, the only study to date that has performed QTL mapping of reaction fluxes uses flux estimations from a small model of Saccharomyces cerevisiae (yeast) central carbon metabolism [ 92 ] based on bounds of measured extracellular fluxes and profiling of dry weight in 125 F 2 -segregants (genotyped by 3727 SNPs) from a cross of two yeast strains [ 93 ]. These approaches identified four flux QTL and two gene variants that contribute to the explanation of the variations in the flux distributions in the population.…”

Section: Application Of Genetic Mapping Approaches For Maximal Enzyme Activity In Plantsmentioning

confidence: 99%

Characterization of effects of genetic variants via genome-scale metabolic modelling

Tong

Küken

Razaghi-Moghadam

et al. 2021

Cell. Mol. Life Sci.

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Genome-scale metabolic networks for model plants and crops in combination with approaches from the constraint-based modelling framework have been used to predict metabolic traits and design metabolic engineering strategies for their manipulation. With the advances in technologies to generate large-scale genotyping data from natural diversity panels and other populations, genome-wide association and genomic selection have emerged as statistical approaches to determine genetic variants associated with and predictive of traits. Here, we review recent advances in constraint-based approaches that integrate genetic variants in genome-scale metabolic models to characterize their effects on reaction fluxes. Since some of these approaches have been applied in organisms other than plants, we provide a critical assessment of their applicability particularly in crops. In addition, we further dissect the inferred effects of genetic variants with respect to reaction rate constants, abundances of enzymes, and concentrations of metabolites, as main determinants of reaction fluxes and relate them with their combined effects on complex traits, like growth. Through this systematic review, we also provide a roadmap for future research to increase the predictive power of statistical approaches by coupling them with mechanistic models of metabolism.

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QTL mapping of modelled metabolic fluxes reveals gene variants impacting yeast central carbon metabolism

Cited by 7 publications

References 80 publications

Identification of genetic variants of the industrial yeast Komagataella phaffii (Pichia pastoris) that contribute to increased yields of secreted heterologous proteins

Identification of genetic variants of the industrial yeast Komagataella phaffii (Pichia pastoris) that contribute to increased yields of secreted heterologous proteins

Different Wines from Different Yeasts? “Saccharomyces cerevisiae Intraspecies Differentiation by Metabolomic Signature and Sensory Patterns in Wine”

Characterization of effects of genetic variants via genome-scale metabolic modelling

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