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Discovery of mutations in Saccharomyces cerevisiae by pooled linkage analysis and whole-genome sequencing.

ABSTRACT: Many novel and important mutations arise in model organisms and human patients that can be difficult or impossible to identify using standard genetic approaches, especially for complex traits. Working with a previously uncharacterized dominant Saccharomyces cerevisiae mutant with impaired vacuole inheritance, we developed a pooled linkage strategy based on next-generation DNA sequencing to specifically identify functional mutations from among a large excess of polymorphisms, incidental mutations, and sequencing errors. The VAC6-1 mutation was verified to correspond to PHO81-R701S, the highest priority candidate reported by VAMP, the new software platform developed for these studies. Sequence data further revealed the large extent of strain background polymorphisms and structural alterations present in the host strain, which occurred by several mechanisms including a novel Ty insertion. The results provide a snapshot of the ongoing genomic changes that ultimately result in strain divergence and evolution, as well as a general model for the discovery of functional mutations in many organisms.

SUBMITTER: Birkeland SR

PROVIDER: S-EPMC2998298 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature

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Discovery of mutations in Saccharomyces cerevisiae by pooled linkage analysis and whole-genome sequencing.

Birkeland Shanda R SR Jin Natsuko N Ozdemir Alev Cagla AC Lyons Robert H RH Weisman Lois S LS Wilson Thomas E TE

Genetics 20101005 4

Many novel and important mutations arise in model organisms and human patients that can be difficult or impossible to identify using standard genetic approaches, especially for complex traits. Working with a previously uncharacterized dominant Saccharomyces cerevisiae mutant with impaired vacuole inheritance, we developed a pooled linkage strategy based on next-generation DNA sequencing to specifically identify functional mutations from among a large excess of polymorphisms, incidental mutations ...[more]

PMID: 20923977

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Project description: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.

Project description:BACKGROUND:Although the Illumina 1 G Genome Analyzer generates billions of base pairs of sequence data, challenges arise in sequence selection due to the varying sequence quality. Therefore, in the framework of the International Porcine SNP Chip Consortium, this pilot study aimed to evaluate the impact of the quality level of the sequenced bases on mapping quality and identification of true SNPs on a large scale. RESULTS:DNA pooled from five animals from a commercial boar line was digested with DraI; 150-250-bp fragments were isolated and end-sequenced using the Illumina 1 G Genome Analyzer, yielding 70,348,064 sequences 36-bp long. Rules were developed to select sequences, which were then aligned to unique positions in a reference genome. Sequences were selected based on quality, and three thresholds of sequence quality (SQ) were compared. The highest threshold of SQ allowed identification of a larger number of SNPs (17,489), distributed widely across the pig genome. In total, 3,142 SNPs were validated with a success rate of 96%. The correlation between estimated minor allele frequency (MAF) and genotyped MAF was moderate, and SNPs were highly polymorphic in other pig breeds. Lowering the SQ threshold and maintaining the same criteria for SNP identification resulted in the discovery of fewer SNPs (16,768), of which 259 were not identified using higher SQ levels. Validation of SNPs found exclusively in the lower SQ threshold had a success rate of 94% and a low correlation between estimated MAF and genotyped MAF. Base change analysis suggested that the rate of transitions in the pig genome is likely to be similar to that observed in humans. Chromosome X showed reduced nucleotide diversity relative to autosomes, as observed for other species. CONCLUSION:Large numbers of SNPs can be identified reliably by creating strict rules for sequence selection, which simultaneously decreases sequence ambiguity. Selection of sequences using a higher SQ threshold leads to more reliable identification of SNPs. Lower SQ thresholds can be used to guarantee sufficient sequence coverage, resulting in high success rate but less reliable MAF estimation. Nucleotide diversity varies between porcine chromosomes, with the X chromosome showing less variation as observed in other species.

Dataset Information

Discovery of mutations in Saccharomyces cerevisiae by pooled linkage analysis and whole-genome sequencing.

Publications

Discovery of mutations in Saccharomyces cerevisiae by pooled linkage analysis and whole-genome sequencing.

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