Whole genome sequencing of Saccharomyces cerevisiae: from genotype to phenotype for improved metabolic engineering applications
Journal article, 2010

Background: The need for rapid and efficient microbial cell factory design and construction are possible through the enabling technology, metabolic engineering, which is now being facilitated by systems biology approaches. Metabolic engineering is often complimented by directed evolution, where selective pressure is applied to a partially genetically engineered strain to confer a desirable phenotype. The exact genetic modification or resulting genotype that leads to the improved phenotype is often not identified or understood to enable further metabolic engineering. Results: In this work we performed whole genome high-throughput sequencing and annotation can be used to identify single nucleotide polymorphisms (SNPs) between Saccharomyces cerevisiae strains S288c and CEN.PK113-7D. The yeast strain S288c was the first eukaryote sequenced, serving as the reference genome for the Saccharomyces Genome Database, while CEN.PK113-7D is a preferred laboratory strain for industrial biotechnology research. A total of 13,787 high-quality SNPs were detected between both strains (reference strain: S288c). Considering only metabolic genes (782 of 5,596 annotated genes), a total of 219 metabolism specific SNPs are distributed across 158 metabolic genes, with 85 of the SNPs being nonsynonymous (e. g., encoding amino acid modifications). Amongst metabolic SNPs detected, there was pathway enrichment in the galactose uptake pathway (GAL1, GAL10) and ergosterol biosynthetic pathway (ERG8, ERG9). Physiological characterization confirmed a strong deficiency in galactose uptake and metabolism in S288c compared to CEN.PK113-7D, and similarly, ergosterol content in CEN.PK113-7D was significantly higher in both glucose and galactose supplemented cultivations compared to S288c. Furthermore, DNA microarray profiling of S288c and CEN.PK113-7D in both glucose and galactose batch cultures did not provide a clear hypothesis for major phenotypes observed, suggesting that genotype to phenotype correlations are manifested post-transcriptionally or post-translationally either through protein concentration and/or function. Conclusions: With an intensifying need for microbial cell factories that produce a wide array of target compounds, whole genome high-throughput sequencing and annotation for SNP detection can aid in better reducing and defining the metabolic landscape. This work demonstrates direct correlations between genotype and phenotype that provides clear and high-probability of success metabolic engineering targets. The genome sequence, annotation, and a SNP viewer of CEN.PK113-7D are deposited at http://www.sysbio.se/cenpk.

Author

José Manuel Otero

Chalmers, Chemical and Biological Engineering, Life Sciences

Wanwipa Vongsangnak

Chalmers, Chemical and Biological Engineering, Life Sciences

M. A. Asadollahi

Chalmers, Chemical and Biological Engineering

Roberto Olivares Hernandez

Chalmers, Chemical and Biological Engineering, Life Sciences

J. Maury

Technical University of Denmark (DTU)

Fluxome Sciences A/S

Laurent Farinelli

Fasteris SA

Loïc Barlocher

Fasteris SA

Magne Österås

Fasteris SA

M. Schalk

Firmenich Inc.

A. Clark

Firmenich Inc.

Jens B Nielsen

Chalmers, Chemical and Biological Engineering, Life Sciences

BMC Genomics

14712164 (eISSN)

Vol. 11 1 723- 723

Subject Categories

Industrial Biotechnology

Areas of Advance

Life Science Engineering (2010-2018)

DOI

10.1186/1471-2164-11-723

More information

Latest update

9/6/2018 1