2020
DOI: 10.1073/pnas.1918216117
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Absolute yeast mitochondrial proteome quantification reveals trade-off between biosynthesis and energy generation during diauxic shift

Abstract: Saccharomyces cerevisiae constitutes a popular eukaryal model for research on mitochondrial physiology. Being Crabtree-positive, this yeast has evolved the ability to ferment glucose to ethanol and respire ethanol once glucose is consumed. Its transition phase from fermentative to respiratory metabolism, known as the diauxic shift, is reflected by dramatic rearrangements of mitochondrial function and structure. To date, the metabolic adaptations that occur during the diauxic shift have not been fully character… Show more

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Cited by 114 publications
(183 citation statements)
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“…Glucose depletion also de-represses the use of other carbon sources and triggers cellular transition from aerobic fermentation to respiration. Mitochondria play a central role in respiratory growth and they expand and change substantially during the diauxic shift from glucose fermentation to ethanol utilization [35]. Our data show that MRN1 affects the expression of dozens of genes with mitochondrial functions, and the repressive effect of Mrn1 is weaker during respiratory growth.…”
Section: Mrn1 Represses Expression Of Mitochondrial Mrnas During Fermentative Growthmentioning
confidence: 79%
“…Glucose depletion also de-represses the use of other carbon sources and triggers cellular transition from aerobic fermentation to respiration. Mitochondria play a central role in respiratory growth and they expand and change substantially during the diauxic shift from glucose fermentation to ethanol utilization [35]. Our data show that MRN1 affects the expression of dozens of genes with mitochondrial functions, and the repressive effect of Mrn1 is weaker during respiratory growth.…”
Section: Mrn1 Represses Expression Of Mitochondrial Mrnas During Fermentative Growthmentioning
confidence: 79%
“…Mitochondria are optimized to fulfill the specific metabolic demands of different cell types. While it is becoming evident that mitochondrial gene expression is able to adapt to cell and tissue demands (Williams et al, 2018; Mootha et al, 2003; Di Bartolomeo et al, 2020), it is still unclear how mitochondrial gene expression is spatially coordinated to respond to differential demands within separate areas of single cells. To approach this question, we analyzed mitochondrial gene expression in three different cell types: human fibroblasts, human iPSCs-derived cardiomyocytes, and rat hippocampal neurons.…”
Section: Resultsmentioning
confidence: 99%
“…The previously mentioned module for integration of proteomics data generates a condition-dependent ecModel with proteomics constraints for each condition/replicate in a provided dataset of absolute protein abundances [mmol/gDw]. Even though absolute quantification of proteins is becoming more accessible and integrated into systems biology studies [58][59][60][61][62] , a major caveat of using proteomics data as constraints for quantitative models is their intrinsic high biological and technical variability 63 , therefore some of the incorporated data constraints need to be loosened in order to obtain functional ecModels. When needed, additional condition-dependent exchange fluxes of byproducts can also be used as constraints in order to limit the feasible solution space.…”
Section: Proteomics Constraints Refine Phenotype Predictions For Multiple Organisms and Conditionsmentioning
confidence: 99%