Transcriptome profiling of 15 conditions for Escherichia coli selected from optimal experiment design, and targeted experimentation to increase functional coverage in omics dataset
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ABSTRACT: This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
Project description:To increase the GO coverage of Ecomics, we investigated which new conditions would lead to the most informative transcriptional profiles. We first transcriptionally profiled and included in the compendium 9 KO experiments that we had identified before as being highly informative for both GO coverage and model performance. We assumed that a GO term is represented in the compendium if the compendium has profiles where one or more genes that include that GO term have been perturbed. We identified the gene rank iteratively under the assumption that all genes of higher rank have been perturbed. Based on the resulting ranked list, we performed transcriptional profiling for the top 16 genetic perturbations (in triplicate; 48 profiles total), which led to an increase in coverage by 19.8% for KEGG, 24.3% for BP and 63.9% for MF. Genome-wide expression profiling of 25 single-gene knockouts for BW25113 and its wild-type grown with either LB or M9 media supplemented with glucose (total 87 samples).
Project description:For finding new conditions that show maximum entropy and highest prediction interval, we bound the condition space to explore by making a list of top 98 conditions for MG1655 without genetic perturbations that use 13 most populated stresses (acidic, Ax, Bm, butanol, Cfs, cold, ethanol, heat, Mcn, Nx, hypoxia, osmotic, oxidative) or no stress and 7 most-used carbon sources (Glu 0.4%, Gly 0.4%, Lac 0.4%, Galactose, Arabinose 0.4%, Glucose 0.2%, and Alanine) for M9 medium. Among them, 13 conditions were in Ecomics dataset. For the 85 unexplored conditions, we identify the top 15 conditions that show maximum entropy and highest prediction interval in an adaptive fashion. That is, for each iteration, we find a condition in the list that shows maximum entropy and highest prediction interval from the model that was built from the training data. Since the maximum entropy quantification and prediction interval value are not at the same scale, we bound the two measures between zero and one by min-max normalization for 85 conditions. Then we supplement the predicted expression levels for that candidate condition and repeat the next iteration of the procedure, until we identify all 15 conditions. The initial training data is 2610 profiles of 178 transcription factors. Transcriptome profiling of 45 samples (3 replicates for each condition) for E. coli selected from optimal experiment design for genome-scale model.
Project description:This entry refers to transcriptome analysis of five E. coli strains: E. coli K-12 MG1655, single deletions of rsd, ssrS, and rpoS, as well as a double deletion of rsd and ssrS, in five growth phases. 25 samples (5 strains in 5 growth phases) with 2 replicates each.
Project description:Sinorhizobium meliloti can live as a soil saprophyte, and can engage in a nitrogen fixing symbiosis with plant roots. To succeed in such diverse environments, the bacteria must continually adjust gene expression. Transcriptional plasticity in eubacteria is often mediated by alternative sigma factors interacting with core RNA polymerase. The S. meliloti genome encodes 14 of these alternative sigmas, including two putative RpoH (heat shock) sigmas. We used custom Affymetrix Symbiosis Chips to characterize the global transcriptional response of S. meliloti rpoH1, rpoH2 and rpoH1 rpoH2 mutants during heat shock and stationary phase growth. Under these conditions, expression of over 300 genes is dependent on rpoH1 and rpoH2. Gene expression profiling of Sinorhizobium meliloti Rm1021 and its isogenic rpoH1, rpoH2, and rpoH1rpoH2 mutants, subjected to heat shock or stationary phase growth, was performed using custom Affymetrix GeneChips
Project description:StpA is a paralogue of the nucleoid associated protein H-NS that is conserved in a range of enteric bacteria and had no known function in Salmonella enterica serovar Typhimurium. Here, we show that 5% of the Salmonella genome is regulated by StpA, which contrasts with the situation in Escherichia coli where deletion of stpA only had minor effects on gene expression. The StpA-dependent genes of S. Typhimurium are a specific subset of the H-NS regulon that are predominantly under the positive control of sigma38 (RpoS), CRP-cAMP and PhoP. The regulatory role of StpA varied at different growth phases; StpA only controlled sigma38 levels at mid-exponential phase when it prevented inappropriate activation of sigma38 during rapid bacterial growth. In contrast, StpA only activated the CRP-cAMP regulon during late exponential phase. The effect of stpA deletion on S. Typhimurium gene expression during growth in LB was analysed at 4 different time points (early-log, mid-log, late-log, and stationary phase) where the gene expression profile of the stpA-deletion strain was compared to that of the parental strain. Between two and three biological replicates were performed for each strain and time point. For this study, we used Salmonella genomic DNA as the comparator which also acted as the control for spot quality.
Project description:This SuperSeries is composed of the following subset Series: GSE18424: The effect of stpA deletion on S. Typhimurium gene expression during growth in rich medium GSE18428: StpA prevents RpoS-dependent transcription during mid-exponential growth in S. Typhimurium GSE18450: Identification of StpA-binding sites on the Salmonella genome Refer to individual Series
Project description:Transcriptional profiling of log and stationary phase S. Typhimurium, comparing untreated controls with Deoxynivalenol treated samples. Each array used labelled cDNA against a common genomic DNA reference. Triplicate arrays were carried out for each of the 4 conditions: untreated log phase, untreated stationary phase, DON treated log phase and DON treated stationary phase
Project description:In the present study, we employed Affymetrix Pseudomonas aeruginosa GeneChip arrays to investigate the dynamics of global gene expression profiles during the cellular response of Pseudomonas aeruginosa to Chlorhexidine diacetate, which involved initial growth inhibition and metabolism. Experiment Overall Design: We conducted three independent microarray experiments (biological replicates) in the absence (control) and the presence (experimental) of Chlorhexidine diacetate. We calculated fold change as the ratio between the signal averages of three untreated (control) and three chlorhexidine diacetate-treated (experimental) cultures for 0, 10 and 60 min exposures.
Project description:Background: Pseudomonas aeruginosa, a pathogen infecting those with cystic fibrosis, encounters toxicity from phagocyte-derived reactive oxidants including hydrogen peroxide during active infection. P. aeruginosa responds with adaptive and protective strategies against these toxic species to effectively infect humans. Despite advances in our understanding of the responses to oxidative stress in many specific cases, the connectivity between targeted protective genes and the rest of cell metabolism remains obscure. Results: Herein, we performed a genome-wide transcriptome analysis of the cellular responses to hydrogen peroxide in order to determine a more complete picture of how oxidative stress-induced genes are related and regulated. Our data reinforce the previous conclusion that DNA repair proteins and catalases may be among the most vital antioxidant defense systems of P. aeruginosa. Our results also suggest that sublethal oxidative damage reduces active and/or facilitated transport and that intracellular iron might be a key factor for a relationship between oxidative stress and iron regulation. Perhaps most intriguingly, we revealed that the transcription of all F-, R-, and S-type pyocins was upregulated by oxidative stress and at the same time, a cell immunity protein (pyocin S2 immunity protein) was downregulated, possibly leading to self-killing activity. Conclusions: This finding proposes that pyocin production might be another novel defensive scheme against oxidative attack by host cells. Experiment Overall Design: We conducted four and five independent microarray experiments with biological replicates in the absence (control) and the presence (experimental) of hydrogen peroxide, respectively.