Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Expression analysis of Escherichia coli MG1655 K-12 WT and M-bM-^HM-^FarcA mutant

ABSTRACT: Investigation of whole genome gene expression level changes in a Escherichia coli MG1655 K-12 M-bM-^HM-^FarcA mutant, compared to the wild-type strain. The mutations engineered into this strain produce a strain lacking the ArcA protein. The results are further described in the manuscript The response regulator ArcA uses a diverse binding site architechture to globally regulate carbon oxidation in E. coli A six chip study using total RNA recovered from three separate cultures of Escherichia coli MG1655 K-12 WT and three separate cultures of the M-bM-^HM-^FarcA mutant strain. Each chip measures the expression level of 4,661 genes from Escherichia coli MG1655 K-12 with eight 60-mer probes per gene, with each probe represented twice on the array.

ORGANISM(S): Escherichia coli str. K-12 substr. MG1655

SUBMITTER: Dan Park

PROVIDER: E-GEOD-46412 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Shared Molecules

Only show the datasets with similarity scores above: 0.5

Threshold

0.5

Similar Datasets

Project description:Determining how facultative anaerobic organisms sense and direct cellular responses to electron acceptor availability has been a subject of intense study. However, even in the model organism Escherichia coli, established mechanisms only explain a small fraction of the hundreds of genes that are regulated during shifts in electron acceptor availability. Here we propose a qualitative model that accounts for the full breadth of regulated genes by detailing how two global transcription factors (TFs), ArcA and Fnr of E. coli, sense key metabolic redox ratios and act on a genome-wide basis to regulate anabolic, catabolic, and energy generation pathways. We first fill gaps in our knowledge of this transcriptional regulatory network by carrying out ChIP-chip and gene expression experiments to identify 463 regulatory events. We then interfaced this reconstructed regulatory network with a highly curated genome-scale metabolic model to show that ArcA and Fnr regulate > 80% of total metabolic flux and 96% of differential gene expression across fermentative and nitrate respiratory conditions. Finally, based on the data we propose a feedforward with feedback trim regulatory scheme by showing extensive repression of catabolic genes by ArcA and extensive activation of chemiosmotic genes by Fnr. We further corroborated this regulatory scheme by showing a 0.71 r2 (p < 1e-6) correlation between changes in metabolic flux and changes in regulatory activity across fermentative and nitrate respiratory conditions. We also are able to relate the proposed model to a wealth of previously generated data by contextualizing the existing transcriptional regulatory network. We integrated transcription factor binding regions and mRNA transcript abundance to elucidate the ArcA and Fnr regulons experimentally. To measure transcription factor binding at a genome scale, we employed a ChIP-chip method to derivative strains of E. coli K-12 MG1655 harboring ArcA-8myc or Fnr-8myc under various conditions. The E. coli strains harboring Fnr-8myc and ArcA-8myc were generated as described previously [PMID 16454042]. A 12 chip study with two different strains under two different culture conditions.