A Minimal Set of Glycolytic Genes Reveals Strong Redundancies in Saccharomyces cerevisiae Central Metabolism.
Daniel Solis-EscalanteNiels G A KuijpersNuria Barrajon-SimancasMarcel van den BroekJack T PronkJean-Marc G DaranPascale Daran-LapujadePublished in: Eukaryotic cell (2015)
As a result of ancestral whole-genome and small-scale duplication events, the genomes of Saccharomyces cerevisiae and many eukaryotes still contain a substantial fraction of duplicated genes. In all investigated organisms, metabolic pathways, and more particularly glycolysis, are specifically enriched for functionally redundant paralogs. In ancestors of the Saccharomyces lineage, the duplication of glycolytic genes is purported to have played an important role leading to S. cerevisiae's current lifestyle favoring fermentative metabolism even in the presence of oxygen and characterized by a high glycolytic capacity. In modern S. cerevisiae strains, the 12 glycolytic reactions leading to the biochemical conversion from glucose to ethanol are encoded by 27 paralogs. In order to experimentally explore the physiological role of this genetic redundancy, a yeast strain with a minimal set of 14 paralogs was constructed (the "minimal glycolysis" [MG] strain). Remarkably, a combination of a quantitative systems approach and semiquantitative analysis in a wide array of growth environments revealed the absence of a phenotypic response to the cumulative deletion of 13 glycolytic paralogs. This observation indicates that duplication of glycolytic genes is not a prerequisite for achieving the high glycolytic fluxes and fermentative capacities that are characteristic of S. cerevisiae and essential for many of its industrial applications and argues against gene dosage effects as a means of fixing minor glycolytic paralogs in the yeast genome. The MG strain was carefully designed and constructed to provide a robust prototrophic platform for quantitative studies and has been made available to the scientific community.
Keyphrases
- saccharomyces cerevisiae
- genome wide
- genome wide identification
- wastewater treatment
- dna methylation
- high resolution
- healthcare
- copy number
- metabolic syndrome
- escherichia coli
- cardiovascular disease
- gene expression
- type diabetes
- genome wide analysis
- bioinformatics analysis
- blood pressure
- heavy metals
- physical activity
- adipose tissue
- cell wall