Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Systematic identification and characterization of uncharacterized transcription factors in Escherichia coli K-12 MG1655

ABSTRACT: Bacteria regulate gene expression to adapt to changing environments through transcriptional regulatory networks (TRNs). Although extensively studied, TRNs are not fully characterized since we do not know the identity of all the transcriptional regulators that comprise a TRN. Here, we experimentally evaluate 40 uncharacterized proteins in the model organisms Escherichia coli K-12 MG1655, which were previously predicted to be transcription factors (TFs). First, we used myc-tagging and a multiplexed ChIP-exo assay to characterize genome-wide binding sites for the 40 candidate TFs. Then, we compared those binding sites with those of RNA polymerase and sigma factors to increase the confidence in the measured DNA-binding events. Finally, we used these data to infer potential functional annotation for 13 candidate TFs. As a result, we characterized 570 new binding sites for 34 of 40 candidate TFs. Taken together, the study; 1) expands the number of confirmed TFs from 249 to 278, that is close to the estimated total of about 280 TFs in E. coli K-12 MG1655, 2) it provides a roadmap for functional characterization of the 34 discovered TFs, and 3) it enables the elucidation of the first comprehensive TRN.

ORGANISM(S): Escherichia coli K-12

PROVIDER: GSE159658 | GEO | 2021/08/26

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

ChIP-exo analysis reveals novel uncharacterized transcription factors involved in the diverse regulatory functions of Escherichia coli

Project description:Bacteria regulate gene expression to adapt to changing environments through transcriptional regulatory networks (TRNs). Although extensively studied, TRNs are not fully characterized since the identity of all the transcriptional regulators that comprise a TRN are not known. Here, we experimentally evaluate 40 uncharacterized proteins in the model organism Escherichia coli K-12 MG1655, which were previously predicted to be transcription factors (TFs). First, we used myc-tagging and a multiplexed ChIP-exo assay to characterize genome-wide binding sites for these candidate TFs. We then compared those binding sites with those of RNA polymerase and sigma factors to increase the confidence in the measured DNA-binding events. Finally, we used these data to infer potential functional annotations for 13 candidate TFs. Our results revealed that these candidate TFs have a varied number of regulatory genes and are involved in E. coli’s primary cellular processes. Taken together, thes results: (1) expand the number of confirmed TFs from 249 to 276, close to the estimated total of 280 TFs in E. coli K-12 MG1655; (2) provide a roadmap for the functional characterization of the newly discovered TFs; and (3) enable the elucidation of the first comprehensive TRN in this model organism.

2021-08-17 | GSE159777 | GEO

A chromatin immunoprecipitation database for prokarytic organisms [RpoD]

Project description:Bacterial transcription factors (TFs) regulate gene expression to adapt to changing environments; when combined, the TF’s regulatory actions comprise transcriptional regulatory networks (TRNs). The chromatin immunoprecipitation (ChIP) assay is the major contemporary method for mapping in vivo protein-DNA interactions in the genome. It enables the genome-wide study of transcription factor binding sites (TFBSs) and gene regulation. Although rapidly accumulating publicly-available ChIP data are a valuable resource for the study of gene regulation, there are no full datasets of key regulators in E. coli K-12 MG1655. Here, we present the genome-wide binding for major TFs in the E. coli K-12 MG1655.

2021-08-18 | GSE182128 | GEO

A chromatin immunoprecipitation database for prokarytic organisms [E. coli K-12 MG1655 mixTF]

2021-08-17 | GSE182078 | GEO

A chromatin immunoprecipitation database for prokarytic organisms [E. coli K-12 MG1655 ArcA]

2021-08-17 | GSE182077 | GEO

A chromatin immunoprecipitation database for prokarytic organisms [multi_tf]

2021-08-15 | GSE181772 | GEO

A chromatin immunoprecipitation database for prokarytic organisms [Escherichia coli]

2021-08-15 | GSE181993 | GEO

A chromatin immunoprecipitation database for prokarytic organisms [NAP]

Project description:Bacterial transcription factors (TFs) regulate gene expression to adapt to changing environments; when combined, the TF’s regulatory actions comprise transcriptional regulatory networks (TRNs). The chromatin immunoprecipitation (ChIP) assay is the major contemporary method for mapping in vivo protein-DNA interactions in the genome. It enables the genome-wide study of transcription factor binding sites (TFBSs) and gene regulation. Although rapidly accumulating publicly-available ChIP data are a valuable resource for the study of gene regulation, there are no full datasets of key regulators and nucleoid associated proteins (NAPs) in E. coli K-12 MG1655. Here, we present the genome-wide binding for dozens of major TFs and NAPs in the E. coli K-12 MG1655.

2021-08-15 | GSE181767 | GEO

Validated transcriptional regulatory roles for five two-component systems in E. coli (RNA-seq dataset)

Project description:Escherichia coli use two-component systems (TCSs) to respond to environmental signals. TCSs affect gene expression and are parts of E. coli’s global transcriptional regulatory network (TRN). Here, we identified the regulons of five TCSs in E. coli MG1655: BaeSR and CpxAR, which were stimulated by ethanol stress, and KdpDE, PhoRB, and ZraSR, which were stimulated by potassium, phosphate, and zinc, respectively. We analyzed RNA-seq data using independent component analysis (ICA). ChIP-exo data was used to validate condition-specific target gene binding sites. Based on this data we (1) identify the target genes for each TCS; (2) show how the target genes are transcribed in response to stimulus; and (3) reveal novel relationships between TCSs, which indicate non-cognate inducers for various response regulators, such as BaeR to iron starvation, CpxR to phosphate limitation, and PhoB and ZraR to cell envelope stress. Our understanding of the TRN in E. coli is thus notably expanded.

2020-10-21 | GSE143855 | GEO

Validated transcriptional regulatory roles for five two-component systems in E. coli (ChIP-exo dataset)

2020-10-21 | GSE143851 | GEO

Leveraging chromatin accessibility for transcriptional regulatory network inference in T Helper 17 Cells [RNA-seq]

Project description:Transcriptional regulatory networks (TRNs) provide insight into cellular behavior by describing interactions between transcription factors (TFs) and their gene targets. The Assay for Transposase Accessible Chromatin (ATAC)-seq, coupled with transcription-factor motif analysis, provides indirect evidence of chromatin binding for hundreds of TFs genome-wide. Here, we propose methods for TRN inference in a mammalian setting, using ATAC-seq data to influence gene expression modeling. We rigorously test our methods in the context of T Helper Cell Type 17 (Th17) differentiation, generating new ATAC-seq data to complement existing Th17 genomic resources (plentiful gene expression data, TF knock-outs and ChIP-seq experiments). In this resource-rich mammalian setting our extensive benchmarking provides quantitative, genome-scale evaluation of TRN inference combining ATAC-seq and RNA-seq data. We refine and extend our previous Th17 TRN, using our new TRN inference methods to integrate all Th17 data (gene expression, ATAC-seq, TF KO, ChIP-seq). We highlight new roles for individual TFs and groups of TFs (“TF-TF modules”) in Th17 gene regulation. Given the popularity of ATAC-seq (a widely adapted protocol with high resolution and low sample input requirements), we anticipate that application of our methods will improve TRN inference in new mammalian systems and be of particular use for rare, uncharacterized cell types.

2018-12-16 | GSE113720 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data