Project description:Listeria monocytogenes is a gram-positive, food-borne pathogen responsible for invasive infections with high overall mortality. Early host defenses encountered by L. monocytogenes following ingestion include low pH of the stomach and bile present in the small intestinal lumen. We hypothesized that “epidemic” strains are better able to withstand exposure to low pH and bile encountered in the gastrointestinal tract as compared to most “environmental” strains. Furthermore, we hypothesized that epidemic and environmental strains would have distinct transcriptional programs upon exposure to these conditions. Our treatments included 1 hr exposure to acid (pH 5.5 and 3.5) and bile (0.3%) stress. Strains were pre-exposed to pH 5.5 (1 hr) before being treated with pH 3.5. We used a collection of 12 previously characterized epidemic and environmental strains and each strainXtreatment combination included 3 biological replicates for each microarray experiment. All microarray experiments were two color competitive hybridizations that paired experimental conditions with the same strain at neutral pH for acid stress and pH 5.5 for bile stress. Transcriptomes of environmental strains exposed to acid and bile stress showed remarkably greater number of genes with differences of ≥2-fold expression levels as compared to epidemic strains (5 and 7, respectively). Environmental strains were characterized by up-regulation of several stress related genes and down-regulation of several cell envelope biosynthesis and virulence related genes, suggesting that complex regulatory networks orchestrate the cellular changes in the environmental strains to overcome stressful environments. The transcriptome of epidemic strains, in contrast, showed muted responses to these stress conditions implying their pre-adaptability to acid and bile stress encountered during natural infection that may enable epidemic strains to survive and become “primed” for subsequent colonization and infection in the lower gastrointestinal tract. Keywords: stress response, comparative transcriptomics, acid-adaptation, differential virulence, acid-stress response, bile-stress response

Project description:The needs 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. In this work we establish proof-of-concept that 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 5,873 annotated genes), a total of 219 metabolism specific SNPs are distributed across 158 metabolic genes, with 85 of the SNPs being non-silent (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. 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 www.sysbio.se/cenpk. Keywords: Two strains and two different carbon sources Two conditions (glucose and galactose) with two biological replicates for S. cerevisiae strains S288c and CEN.PK113-7D

Project description:The needs 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. In this work we establish proof-of-concept that 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 5,873 annotated genes), a total of 219 metabolism specific SNPs are distributed across 158 metabolic genes, with 85 of the SNPs being non-silent (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. 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 www.sysbio.se/cenpk. Keywords: Two strains and two different carbon sources

Dataset Information

Approaching absolute quantification from chemostats cultures of Saccharomyces cerevisiae CEN.PK113-7D, Kluyveromyces marxianus CBS 6556 and Yarrowia lipolytica W29

Publications

Comparison of Different Label-Free Techniques for the Semi-Absolute Quantification of Protein Abundance.

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