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

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:Lithium has been used for more than 50 years as the primary therapy for bipolar affective disorder (BPD) and is highly effective in its treatment. However, the molecular and cellular mechanisms underlying the lithiumâs therapeutic action still remains elusive. The goal of the proposed study is to identify genes involved in the lithium-responsive neurological pathway using the fruit fly as a model organism. Based on the fact that the fundamental molecular and cellular mechanisms are well conserved between the fruit fly and vertebrates, the outcomes of this project are expected to lead to the recognition of uncharacterized players or processes responsible for the lithium action in the vertebrates, which would open up the future possibility to develop novel and improved therapies for BPD. The proposed gene-profiling experiment is designed to identify candidate genes and genetic pathways involved in the lithium-responsive biological process. Specific aims of this project are; 1) identify genes whose expression levels are significantly different between the wild type and Shu, 2) identify genes whose expression levels are enhanced or suppressed after lithium treatment, and 3) identify genes whose expression is differentially regulated in the wild type and Shu after lithium treatment. We expect that the genes identified through the proposed microarray analysis will provide important clues for elucidating the molecular and cellular processes responsible for the lithium action. A Drosophila mutant Shudderer (Shu) exhibits various neurological phenotypes, including hyperactivity, uncoordinated movements, and sporadically occurring jerks. Interestingly, many of these phenotypes are greatly suppressed by lithium with the internal concentrations used for treatment of human BPD patients. In addition, Shu mutant is more resistant to the toxic effects of lithium than the wild type strain. These data have led us to the hypothesis that the biological process responsive to lithium is affected in the Shu mutant. We expect that we will be able to identify genes and genetic pathways involved in the lithium-responsive process by comparing the gene expression profiles between the wild type and the Shu mutant with or without lithium treatment. Newly eclosed Canton-S (wild type) and Shu mutant females will be placed in vials containing fly food with or without 50mM LiCl. They will be cultured at 25oC with 65% humidity for 5 days. Three groups of fifty flies (150 flies in total) will be collected for each experimental condition (i.e., genotype and lithium treatment) and frozen on dry ice. The head will be separated and total RNA will be extracted from each group using the TRIzol Reagent (Life Technologies) followed by an RNeasy (Qiagen) cleanup step and a DNase I digestion step. The RNA will be resuspended in DEPC-treated water and the absorbance will be checked at 260 and 280 nm for determination of sample purity and concentration. The GeneChip Drosophila Genome 2.0 Arrays will be used to quantify the levels of transcripts in each sample. The total number of samples will be 4 (2 genotypes and 2 treatments) X 3 (triplicate) = 12.

Dataset Information

Response to LiCl of galactose grown cells

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