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Open Access Highly Accessed Research article

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

José Manuel Otero125, Wanwipa Vongsangnak128, Mohammad A Asadollahi126, Roberto Olivares-Hernandes12, Jérôme Maury27, Laurent Farinelli3, Loïc Barlocher3, Magne Østerås3, Michel Schalk4, Anthony Clark4 and Jens Nielsen12*

Author affiliations

1 Department of Chemical and Biological Engineering, Chalmers University of Technology, SE-41296 Gothenburg, Sweden

2 Center for Microbial Biotechnology, Department of Systems Biology, Technical University of Denmark DK-2800, Kgs. Lyngby, Denmark

3 Fasteris SA, Geneva, Switzerland

4 Firmenich SA, Corporate Research & Development Division, Geneva, Switzerland

5 Vaccine & Biologics Process Development, Vaccine Research & Development, Merck Research Labs, West Point, PA, USA

6 Biotechnology Group, Faculty of Advanced Sciences and Technologies, University of Isfahan, Isfahan 81746-73441, Iran

7 Fluxome Sciencies A/S, Research & Development, DK-3660 Stenlose, Denmark

8 Center for Systems Biology, Soochow University, Suzhou 215006, China

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Citation and License

BMC Genomics 2010, 11:723  doi:10.1186/1471-2164-11-723

Published: 22 December 2010

Abstract

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 webcite.