Project description:Deciphering gene regulatory mechanisms through the analysis of high-throughput expression data is a challenging computational problem. Previous computational studies have used large expression datasets in order to resolve fine patterns of coexpression, producing clusters or modules of potentially coregulated genes. These methods typically examine promoter sequence information, such as DNA motifs or transcription factor occupancy data, in a separate step after clustering. We needed an alternative and more integrative approach to study the oxygen regulatory network in Saccharomyces cerevisiae using a small dataset of perturbation experiments. Mechanisms of oxygen sensing and regulation underlie many physiological and pathological processes, and only a handful of oxygen regulators have been identified in previous studies. We used a new machine learning algorithm called MEDUSA to uncover detailed information about the oxygen regulatory network using genome-wide expression changes in response to perturbations in the levels of oxygen, heme, Hap1, and Co2+. MEDUSA integrates mRNA expression, promoter sequence, and ChIP-chip occupancy data to learn a model that accurately predicts the differential expression of target genes in held-out data. We used a novel margin-based score to extract significant condition-specific regulators and assemble a global map of the oxygen sensing and regulatory network. This network includes both known oxygen and heme regulators, such as Hap1, Mga2, Hap4, and Upc2, as well as many new candidate regulators. MEDUSA also identified many DNA motifs that are consistent with previous experimentally identified transcription factor binding sites. Because MEDUSA's regulatory program associates regulators to target genes through their promoter sequences, we directly tested the predicted regulators for OLE1, a gene specifically induced under hypoxia, by experimental analysis of the activity of its promoter. In each case, deletion of the candidate regulator resulted in the predicted effect on promoter activity, confirming that several novel regulators identified by MEDUSA are indeed involved in oxygen regulation. MEDUSA can reveal important information from a small dataset and generate testable hypotheses for further experimental analysis.

Project description:Yeast cell growth and treatment:; Yeast strains used were L51 (MATa, ura3-52, leu2-3, 112, his4-519, ade1-100, trp1::HisG, hap1::LEU2) and MHY100 (MATa, ura3-52, leu2-3, 112, his4-519, ade1-100, hem1-delta100). L51 was used for studies of oxygen regulation, and MHY100 was used for studies of heme regulation. To avoid variations from the differences accumulated after many generations of growth of strains, we transformed the L51 strain with the HAP1 gene deleted for studies of Hap1 function. Hap1 protein was expressed in L51 cells by transforming an ARS-CEN plasmid bearing the complete HAP1 genomic sequence. For comparison with cells without Hap1 expressed, an empty vector was transformed into L51 cells. The use of Hap1 expression plasmid generated much more reproducible results than the use of different strains. Yeast cells with or without Hap1 expressed grew at similar rates under both anaerobic and aerobic conditions. We chose to use a low oxygen level (~10 ppb) in this study, in order to identify all oxygen-regulated genes. Previous studies have shown that most, if not all, oxygen-regulated genes are affected a low concentrations, but some genes are not affected at higher oxygen levels (for example, > 1 ppm). Anaerobic (~10 ppb O2, measured by using an oxygen monitor and confirmed by CHEMetrics oxygen kits) growth condition was created by using an anaerobic chamber (Coy Laboratory, Inc.) and by filling the chamber with a mixture of 5% H2 and 95% N2 in the presence of palladium catalyst [61]. L51 cells bearing the Hap1 expression or empty vector were grown under normoxic or anaerobic conditions for 1.5 or 6 hours. The UAS1/CYC1-lacZ reporter plasmid was transformed into yeast cells to confirm the expression of Hap1 and the oxygen levels. Cells were grown in yeast synthetic complete media. Co2+-induced cells were grown in the presence of 400 microM cobalt chloride for 6 hours, as described previously. MHY100 cells were grown in medium containing 2.5 microg/ml (heme-deficient) or 250 microg/ml (heme-sufficient) 5-aminolevulinic acid. For RNA preparations, yeast cells were inoculated so that the optical density of yeast cells was in the range of 0.8-1.0 immediately before the collection of cells. RNA preparation and microarray gene expression profiling:; RNA was extracted from yeast cells exactly as previously described. RNA samples were prepared from 8 different experimental conditions: (1) L51 yeast cells bearing the Hap1 expression plasmid maintained under aerobic conditions, (2) L51 yeast cells bearing the empty expression plasmid maintained under aerobic conditions, (3) L51 yeast cells bearing the Hap1 expression plasmid maintained under anaerobic conditions for 1.5 hours, (4) L51 yeast cells bearing the Hap1 expression plasmid maintained under anaerobic conditions for 6 hours, (5) L51 yeast cells bearing the empty expression plasmid maintained under anaerobic conditions for 6 hours, (6) L51 yeast cells bearing the empty expression plasmid in the presence of 400 microM cobalt chloride for 6 hours, (7) MHY100 cells grown in medium containing 250 microg/ml (heme-sufficient) 5-aminolevulinic acid, and (8) MHY100 cells grown in medium containing 2.5 microg/ml (heme-deficient) 5-aminolevulinic acid. For each condition, three replicates were generated by preparing RNA samples from three batches of independently grown cells. Microarray expression analyses were performed by using three batches of replicate RNA samples. The quality of RNA was high as assessed by measuring absorbance at 260 and 280 nm, by gel electrophoresis, and by the quality of microarray data (see below). The synthesis of cDNA and biotin-labeled cRNA were carried out exactly as described in the Affymetrix GeneChip Expression Analysis Technical Manual (2000). The yeast Saccharomyces cerevisiae genome 2.0 arrays were purchased from Affymetrix, Inc. Probe hybridization and data collection were carried out by the Columbia University Affymetrix GeneChip processing center. Specifically, the Affymetrix GeneChip Hybridization Oven 640 and the next generation GeneChip Fluidics Station 450 were used for hybridization and chip processing. Chip scanning was performed by using the GeneChip scanner 3000. Initial data acquisition, analysis was performed by using the Affymetrix Microarray suite. By using GCOS1.2 with the advanced PLIER (probe logarithmic intensity error) algorithm, we calculated and examined the parameters reflecting the image quality of the arrays. Arrays with a high background level in any region were discarded and replaced. The average noise or background level was limited to less than 5%. The average intensity for those genes judged to be present was at least 10-fold higher than those judged to be absent. Also, arrays that deviated considerably in the percentage of present and absent genes from the majority of the arrays were replaced. Arrays with a ï¢-actin 3â/5â ratio greater than 2 were replaced. Normalization of microarray data:; For each microarray, we converted the .DAT image files into .CEL files using the Affymetrix GCOS software. These raw .CEL files were further processed into expression values using the RMA express software by Bolstad. This software uses the robust multiarray average method by Irrizary et al., which involves a background correction and a quantile-based normalization scheme. Experiment Overall Design: Yeast cell growth and treatment: Experiment Overall Design: Yeast strains used were L51 (MATa, ura3-52, leu2-3, 112, his4-519, ade1-100, trp1::HisG, hap1::LEU2) and MHY100 (MATa, ura3-52, leu2-3, 112, his4-519, ade1-100, hem1-delta100). L51 was used for studies of oxygen regulation, and MHY100 was used for studies of heme regulation. To avoid variations from the differences accumulated after many generations of growth of strains, we transformed the L51 strain with the HAP1 gene deleted for studies of Hap1 function. Hap1 protein was expressed in L51 cells by transforming an ARS-CEN plasmid bearing the complete HAP1 genomic sequence. For comparison with cells without Hap1 expressed, an empty vector was transformed into L51 cells. The use of Hap1 expression plasmid generated much more reproducible results than the use of different strains. Yeast cells with or without Hap1 expressed grew at similar rates under both anaerobic and aerobic conditions. Experiment Overall Design: We chose to use a low oxygen level (~10 ppb) in this study, in order to identify all oxygen-regulated genes. Previous studies have shown that most, if not all, oxygen-regulated genes are affected a low concentrations, but some genes are not affected at higher oxygen levels (for example, > 1 ppm). Anaerobic (~10 ppb O2, measured by using an oxygen monitor and confirmed by CHEMetrics oxygen kits) growth condition was created by using an anaerobic chamber (Coy Laboratory, Inc.) and by filling the chamber with a mixture of 5% H2 and 95% N2 in the presence of palladium catalyst [61]. L51 cells bearing the Hap1 expression or empty vector were grown under normoxic or anaerobic conditions for 1.5 or 6 hours. The UAS1/CYC1-lacZ reporter plasmid was transformed into yeast cells to confirm the expression of Hap1 and the oxygen levels. Cells were grown in yeast synthetic complete media. Co2+-induced cells were grown in the presence of 400 microM cobalt chloride for 6 hours, as described previously. MHY100 cells were grown in medium containing 2.5 microg/ml (heme-deficient) or 250 microg/ml (heme-sufficient) 5-aminolevulinic acid. For RNA preparations, yeast cells were inoculated so that the optical density of yeast cells was in the range of 0.8-1.0 immediately before the collection of cells. Experiment Overall Design: RNA preparation and microarray gene expression profiling: Experiment Overall Design: RNA was extracted from yeast cells exactly as previously described. RNA samples were prepared from 8 different experimental conditions: (1) L51 yeast cells bearing the Hap1 expression plasmid maintained under aerobic conditions, (2) L51 yeast cells bearing the empty expression plasmid maintained under aerobic conditions, (3) L51 yeast cells bearing the Hap1 expression plasmid maintained under anaerobic conditions for 1.5 hours, (4) L51 yeast cells bearing the Hap1 expression plasmid maintained under anaerobic conditions for 6 hours, (5) L51 yeast cells bearing the empty expression plasmid maintained under anaerobic conditions for 6 hours, (6) L51 yeast cells bearing the Hap1 expression plasmid in the presence of 400 microM cobalt chloride for 6 hours, (7) MHY100 cells grown in medium containing 250 microg/ml (heme-sufficient) 5-aminolevulinic acid, and (8) MHY100 cells grown in medium containing 2.5 microg/ml (heme-deficient) 5-aminolevulinic acid. For each condition, three replicates were generated by preparing RNA samples from three batches of independently grown cells. Microarray expression analyses were performed by using three batches of replicate RNA samples. The quality of RNA was high as assessed by measuring absorbance at 260 and 280 nm, by gel electrophoresis, and by the quality of microarray data (see below). Experiment Overall Design: The synthesis of cDNA and biotin-labeled cRNA were carried out exactly as described in the Affymetrix GeneChip Expression Analysis Technical Manual (2000). The yeast Saccharomyces cerevisiae genome 2.0 arrays were purchased from Affymetrix, Inc. Probe hybridization and data collection were carried out by the Columbia University Affymetrix GeneChip processing center. Specifically, the Affymetrix GeneChip Hybridization Oven 640 and the next generation GeneChip Fluidics Station 450 were used for hybridization and chip processing. Chip scanning was performed by using the GeneChip scanner 3000. Initial data acquisition, analysis was performed by using the Affymetrix Microarray suite. By using GCOS1.2 with the advanced PLIER (probe logarithmic intensity error) algorithm, we calculated and examined the parameters reflecting the image quality of the arrays. Arrays with a high background level in any region were discarded and replaced. The average noise or background level was limited to less than 5%. The average intensity for those genes judged to be present was at least 10-fold higher than those judged to be absent. Also, arrays that deviated considerably in the percentage of present and absent genes from the majority of the arrays were replaced. Arrays with a ï¢-actin 3â/5â ratio greater than 2 were replaced. Experiment Overall Design: Normalization of microarray data: Experiment Overall Design: For each microarray, we converted the .DAT image files into .CEL files using the Affymetrix GCOS software. These raw .CEL files were further processed into expression values using the RMA express software by Bolstad. This software uses the robust multiarray average method by Irrizary et al., which involves a background correction and a quantile-based normalization scheme.

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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Saccharomyces cerevisiae

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