Saccharomyces cerevisiae
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA257935 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR1542430.fastq.gz | Fastqsanger.gz | |||
| SRR1542440.fastq.gz | Fastqsanger.gz | |||
| SRR1542450.fastq.gz | Fastqsanger.gz | |||
| SRR1542460.fastq.gz | Fastqsanger.gz | |||
| SRR1542470.fastq.gz | Fastqsanger.gz |
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Project description:In wine fermentation, the blending of non-Saccharomyces yeast with Saccharomyces cerevisiae to improve the complexity of wine has become common practice, but data regarding the impact on yeast physiology and on genetic and metabolic regulation remain limited. Here we describe a transcriptomic analysis of single species and mixed species fermentations.
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Project description:Background Microorganisms adapt their transcriptome by integrating multiple chemical and physical signals from their environment. Shake-flask cultivation does not allow precise manipulation of individual culture parameters and therefore precludes a quantitative analysis of the (combinatorial) influence of these parameters on transcriptional regulation. Steady-state chemostat cultures, which do enable accurate control, measurement and manipulation of individual cultivation parameters (e.g. specific growth rate, temperature, identity of the growth-limiting nutrient) appear to provide a promising experimental platform for such a combinatorial analysis. Results A microarray compendium of 170 steady-state chemostat cultures of the yeast Saccharomyces cerevisiae is presented and analyzed. The 170 microarrays encompass 55 unique conditions, which can be characterized by the combined settings of 10 different cultivation parameters. By applying a regression model to assess the impact of (combinations of) cultivation parameters on the transcriptome, most S. cerevisiae genes were shown to be influenced by multiple cultivation parameters, and in many cases by combinatorial effects of cultivation parameters. The inclusion of these combinatorial effects in the regression model led to higher explained variance of the gene expression patterns and resulted in higher function enrichment in subsequent analysis. We further demonstrate the usefulness of the compendium and regression analysis for interpretation of shake-flask-based transcriptome studies and for guiding functional analysis of (uncharacterized) genes and pathways. Conclusions Modeling the combinatorial effects of environmental parameters on the transcriptome is crucial for understanding transcriptional regulation. Chemostat cultivation offers a powerful tool for such an approach. Keywords: chemostat steady state samples
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Project description:LPS was used as a stressor to stimulate the model organism Saccharomyces cerevisiae. To detect extracellular metabolic information of VOCs. To provide a molecular basis for cellular metabolism of VOCs by proteome.
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Project description:Transcriptional control of gene expression is a result of complex interactions between the cis-regulatory elements (CRE) at gene promoters. To understand the regulatory logic of a cell, we need to identify the CRE combinations that regulate gene expression. We developed a sensitive computational method to identify phylogenetically conserved CRE combinations for any species of interest. In contrast to previous methods, we do not need to align genomes to identify these combinations. We applied the method in 7 sensu stricto and sensu lato Saccharomyces species. 80% of the predictions displayed some evidence of combinatorial transcriptional behavior in several existing datasets including 1) ChIP-chip data for co-localization of transcription factors, 2) gene expression data for co-expression of predicted regulatory targets, and 3) gene ontology databases for common pathway membership of predicted regulatory targets. To establish definitive evidence that these CRE interactions influence TF occupancy, we performed ChIPseq experiments on transcription factors in a wild-type strain and strains in which a predicted cofactor was deleted. Our experiments showed that TF occupancy at the promoters of the CRE combination target genes depends on the predicted cofactor while occupancy of other promoters is independent of the predicted cofactor. ChIP-seq of 6 myc-tagged Transcription factors in wild-type and predicted co-factor knockout yeast strains in triplicate
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Project description:Saccharomyces cerevisiae is an excellent microorganism for industrial succinic acid production, but high succinic acid concentration will inhibit the growth of Saccharomyces cerevisiae then reduce the production of succinic acid. Through analysis the transcriptomic data of Saccharomyces cerevisiae with different genetic backgrounds under different succinic acid stress, we hope to find the response mechanism of Saccharomyces cerevisiae to succinic acid.
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Project description:Transcriptional control of gene expression is a result of complex interactions between the cis-regulatory elements (CRE) at gene promoters. To understand the regulatory logic of a cell, we need to identify the CRE combinations that regulate gene expression. We developed a sensitive computational method to identify phylogenetically conserved CRE combinations for any species of interest. In contrast to previous methods, we do not need to align genomes to identify these combinations. We applied the method in 7 sensu stricto and sensu lato Saccharomyces species. 80% of the predictions displayed some evidence of combinatorial transcriptional behavior in several existing datasets including 1) ChIP-chip data for co-localization of transcription factors, 2) gene expression data for co-expression of predicted regulatory targets, and 3) gene ontology databases for common pathway membership of predicted regulatory targets. To establish definitive evidence that these CRE interactions influence TF occupancy, we performed ChIPseq experiments on transcription factors in a wild-type strain and strains in which a predicted cofactor was deleted. Our experiments showed that TF occupancy at the promoters of the CRE combination target genes depends on the predicted cofactor while occupancy of other promoters is independent of the predicted cofactor.
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Project description:Background: Recent studies have demonstrated that antisense transcription is pervasive in budding yeasts and is conserved between Saccharomyces cerevisiae and S. paradoxus. While studies have examined antisense transcripts of S. cerevisiae for inverse transcription in stationary phase and stress conditions, there is a lack of comprehensive analysis of the conditional specific evolutionary characteristics of antisense transcription between yeasts. Here we attempt to decipher the evolutionary relationship of antisense transcription of S. cerevisiae and S. paradoxus cultured in mid log, early stationary phase, and heat shock conditions. Results: Massively parallel sequencing of sequence strand-specific cDNA library was performed from RNA isolated from S. cerevisiae and S. paradoxus cells at mid log, stationary phase and heat shock conditions. We performed this analysis using a stringent set of sense ORF transcripts and non-coding antisense transcripts that were expressed in all the three conditions, as well as in both species. We found the divergence of the condition specific anti-sense transcription levels is higher than that in condition specific sense transcription levels, suggesting that antisense transcription played a potential role in adapting to different conditions. Furthermore, 43% of sense-antisense pairs demonstrated inverse transcription in either stationary phase or heat shock conditions relative to the mid log conditions. In addition, a large part of sense-antisense pairs (67%), which demonstrated inverse transcription, were highly conserved between the two species. Our results were also concordant with known functional analyses from previous studies and with the evidence from mechanistic experiments of role of individual genes. Conclusions: This study provides a comprehensive picture of the role of antisense transcription mediating sense transcription in different conditions across yeast species. We can conclude from our findings that antisense regulation could act like an on-off switch on sense regulation in different conditions.
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