scholarly journals Systematic discovery of uncharacterized transcription factors in Escherichia coli K-12 MG1655

2018 ◽  
Author(s):  
Ye Gao ◽  
James T. Yurkovich ◽  
Sang Woo Seo ◽  
Ilyas Kabimoldayev ◽  
Andreas Dräger ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Transcription Factors ◽  
Iron Homeostasis ◽  
Environmental Changes ◽  
Transcriptional Regulatory Network ◽  
Computational Prediction ◽  
Acetate Metabolism ◽  
Transcriptional Regulatory ◽  
K 12 ◽  
Regulatory Functions

ABSTRACTTranscriptional regulation enables cells to respond to environmental changes. Yet, among the estimated 304 candidate transcription factors (TFs) in Escherichia coli K-12 MG1655, 185 have been experimentally identified and only a few tens of them have been fully characterized by ChIP methods. Understanding the remaining TFs is key to improving our knowledge of the E. coli transcriptional regulatory network (TRN). Here, we developed an integrated workflow for the computational prediction and comprehensive experimental validation of TFs using a suite of genome-wide experiments. We applied this workflow to: 1) identify 16 candidate TFs from over a hundred candidate uncharacterized genes; 2) capture a total of 255 DNA binding peaks for 10 candidate TFs resulting in six high-confidence binding motifs; 3) reconstruct the regulons of these 10 TFs by determining gene expression changes upon deletion of each TF; and 4) determine the regulatory roles of three TFs (YiaJ, YdcI, and YeiE) as regulators of L-ascorbate utilization, proton transfer and acetate metabolism, and iron homeostasis under iron limited condition, respectively. Together, these results demonstrate how this workflow can be used to discover, characterize, and elucidate regulatory functions of uncharacterized TFs in parallel.

scholarly journals PRECISE 2.0: an expanded high-quality RNA-seq compendium for Escherichia coli K-12 reveals high-resolution transcriptional regulatory structure

2021 ◽  
Author(s):  
Cameron R. Lamoureux ◽  
Katherine T. Decker ◽  
Anand V. Sastry ◽  
John Luke McConn ◽  
Ye Gao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Escherichia Coli ◽  
Multiple Scales ◽  
Transcriptional Regulatory Network ◽  
Differential Expression Analysis ◽  
Rna Seq ◽  
High Quality ◽  
Standardized Protocol ◽  
Transcriptional Regulatory ◽  
K 12

Uncovering the structure of the transcriptional regulatory network (TRN) that modulates gene expression in prokaryotes remains an important challenge. Transcriptomics data is plentiful, necessitating the development of scalable methods for converting this data into useful knowledge about the TRN. Previously, we published the PRECISE dataset for Escherichia coli K-12 MG1655, containing 278 RNA-seq datasets created using a standardized protocol. Here, we present PRECISE 2.0, which is nearly three times the size of the original PRECISE dataset and also created using a standardized protocol. We analyze PRECISE 2.0 at multiple scales, demonstrating multiple analytical strategies for extracting knowledge from this dataset. Specifically, we: (1) highlight patterns in gene expression across the dataset; (2) utilize independent component analysis to extract 218 independently modulated groups of genes (iModulons) that describe the TRN at the systems level; (3) demonstrate the utility of iModulons over traditional differential expression analysis; and (4) uncover 6 new potential regulons. Thus, PRECISE 2.0 is a large-scale, high-quality transcriptomics dataset which may be analyzed at multiple scales to yield important biological insights.

scholarly journals Unraveling the functions of uncharacterized transcription factors in Escherichia coli using ChIP-exo

2021 ◽  
Author(s):  
Ye Gao ◽  
Hyun Gyu Lim ◽  
Hans Verkler ◽  
Richard Szubin ◽  
Daniel Quach ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Transcription Factors ◽  
Dna Binding ◽  
Binding Sites ◽  
Regulatory Networks ◽  
Transcriptional Regulatory Networks ◽  
Cellular Processes ◽  
Transcriptional Regulatory ◽  
Mutant Phenotypes ◽  
K 12

Bacteria regulate gene expression to adapt to changing environments through transcriptional regulatory networks (TRNs). Although extensively studied, no TRN is fully characterized since the identity and activity of all the transcriptional regulators that comprise a TRN are not known. Here, we experimentally evaluate 40 uncharacterized proteins in Escherichia coli K-12 MG1655, which were computationally predicted to be transcription factors (TFs). First, we used a multiplexed ChIP-exo assay to characterize genome-wide binding sites for these candidate TFs; 34 of them were found to be DNA-binding protein. We then compared the relative location between binding sites and RNA polymerase (RNAP). We found 48% (283/588) overlap between the TFs and RNAP. Finally, we used these data to infer potential functions for 10 of the 34 TFs with validated DNA binding sites and consensus binding motifs. These TFs were found to have various roles in regulating primary cellular processes in E. coli. Taken together, this study: (1) significantly expands the number of confirmed TFs, close to the estimated total of about 280 TFs; (2) predicts the putative functions of the newly discovered TFs, and (3) confirms the functions of representative TFs through mutant phenotypes.

The functional landscape bound to the transcription factors of Escherichia coli K-12

2015 ◽  
Vol 58 ◽  
pp. 93-103 ◽  
Author(s):  
Ernesto Pérez-Rueda ◽  
Silvia Tenorio-Salgado ◽  
Alejandro Huerta-Saquero ◽  
Yalbi I. Balderas-Martínez ◽  
Gabriel Moreno-Hagelsieb
Keyword(s):  
Escherichia Coli ◽  
Transcription Factors ◽  
K 12

scholarly journals Screening Driving Transcription Factors in the Processing of Gastric Cancer

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Guangzhong Xu ◽  
Kai Li ◽  
Nengwei Zhang ◽  
Bin Zhu ◽  
Guosheng Feng
Keyword(s):  
Gastric Cancer ◽  
Transcription Factors ◽  
Regulatory Network ◽  
Target Genes ◽  
Transcriptional Regulatory Network ◽  
Expression Profiles ◽  
Functional Enrichment ◽  
Regulatory Mechanisms ◽  
Integrated Analysis ◽  
Transcriptional Regulatory

Background. Construction of the transcriptional regulatory network can provide additional clues on the regulatory mechanisms and therapeutic applications in gastric cancer.Methods. Gene expression profiles of gastric cancer were downloaded from GEO database for integrated analysis. All of DEGs were analyzed by GO enrichment and KEGG pathway enrichment. Transcription factors were further identified and then a global transcriptional regulatory network was constructed.Results. By integrated analysis of the six eligible datasets (340 cases and 43 controls), a bunch of 2327 DEGs were identified, including 2100 upregulated and 227 downregulated DEGs. Functional enrichment analysis of DEGs showed that digestion was a significantly enriched GO term for biological process. Moreover, there were two important enriched KEGG pathways: cell cycle and homologous recombination. Furthermore, a total of 70 differentially expressed TFs were identified and the transcriptional regulatory network was constructed, which consisted of 566 TF-target interactions. The top ten TFs regulating most downstream target genes were BRCA1, ARID3A, EHF, SOX10, ZNF263, FOXL1, FEV, GATA3, FOXC1, and FOXD1. Most of them were involved in the carcinogenesis of gastric cancer.Conclusion. The transcriptional regulatory network can help researchers to further clarify the underlying regulatory mechanisms of gastric cancer tumorigenesis.

scholarly journals The Escherichia coli transcriptome mostly consists of independently regulated modules

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Anand V. Sastry ◽  
Ye Gao ◽  
Richard Szubin ◽  
Ying Hefner ◽  
Sibei Xu ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Transcriptional Regulatory Network ◽  
Transcriptional Regulators ◽  
Specific Gene ◽  
E Coli ◽  
Gene Sets ◽  
Transcriptional Regulatory ◽  
Genetic Perturbations ◽  
Function Discovery ◽  
Multiple Strains

AbstractUnderlying cellular responses is a transcriptional regulatory network (TRN) that modulates gene expression. A useful description of the TRN would decompose the transcriptome into targeted effects of individual transcriptional regulators. Here, we apply unsupervised machine learning to a diverse compendium of over 250 high-quality Escherichia coli RNA-seq datasets to identify 92 statistically independent signals that modulate the expression of specific gene sets. We show that 61 of these transcriptomic signals represent the effects of currently characterized transcriptional regulators. Condition-specific activation of signals is validated by exposure of E. coli to new environmental conditions. The resulting decomposition of the transcriptome provides: a mechanistic, systems-level, network-based explanation of responses to environmental and genetic perturbations; a guide to gene and regulator function discovery; and a basis for characterizing transcriptomic differences in multiple strains. Taken together, our results show that signal summation describes the composition of a model prokaryotic transcriptome.

scholarly journals Identification of a GrgA-Euo-HrcA Transcriptional Regulatory Network in Chlamydia

mSystems ◽  
2021 ◽  
Author(s):  
Wurihan Wurihan ◽  
Yi Zou ◽  
Alec M. Weber ◽  
Korri Weldon ◽  
Yehong Huang ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Regulatory Network ◽  
Transcriptional Regulatory Network ◽  
Bacterial Pathogen ◽  
Developed Countries ◽  
Developmental Cycle ◽  
Content Type ◽  
Sexually Transmitted ◽  
Transcriptional Regulatory ◽  
Preventable Blindness

Chlamydia trachomatis is the most prevalent sexually transmitted bacterial pathogen worldwide and is a leading cause of preventable blindness in underdeveloped areas as well as some developed countries. Chlamydia carries genes that encode a limited number of known transcription factors. While Euo is thought to be critical for early chlamydial development, the functions of GrgA and HrcA in the developmental cycle are unclear.

scholarly journals Identification of pathogenic genes and transcription factors in respiratory syncytial virus

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lei Li ◽  
Yong An Ni ◽  
Zhenfeng Song ◽  
Zhi Yi ◽  
Fang Wang
Keyword(s):  
Transcription Factors ◽  
Respiratory Syncytial Virus ◽  
Respiratory Infections ◽  
Transcriptional Regulatory Network ◽  
Enrichment Analysis ◽  
Microarray Dataset ◽  
Integrated Analysis ◽  
Pathogenic Genes ◽  
Transcriptional Regulatory ◽  
Syncytial Virus

Abstract Background Respiratory syncytial virus (RSV) is a major cause of acute lower respiratory infections in children, especially bronchiolitis. Our study aimed to identify the key genes and upstream transcription factors in RSV. Methods To screen for RSV pathogenic genes, an integrated analysis was performed using the RSV microarray dataset in GEO. Functional annotation and potential pathways for differentially expressed genes (DEGs) were further explored by GO and KEGG enrichment analysis. We constructed the RSV-specific transcriptional regulatory network to identify key transcription factors for DEGs in RSV. Results From three GEO datasets, we identified 1059 DEGs (493 up-regulated and 566 down-regulated genes, FDR < 0.05 and |Combined.ES| > 0.8) between RSV patients and normal controls. GO and KEGG analysis revealed that ‘response to virus’ (FDR = 7.13E-15), ‘mitochondrion’ (FDR = 1.39E-14) and ‘Asthma’ (FDR = 1.28E-06) were significantly enriched pathways for DEGs. The expression of IFI27, IFI44, IFITM3, FCER1A, and ISG15 were shown to be involved in the pathogenesis of RSV. Conclusions We concluded that IFI27, IFI44, IFITM3, FCER1A, and ISG15 may play a role in RSV. Our finding may contribute to the development of new potential biomarkers, reveal the underlying pathogenesis and also identify novel therapeutic targets for RSV.

scholarly journals Single-target regulators form a minor group of transcription factors in Escherichia coli K-12

2018 ◽  
Vol 46 (8) ◽  
pp. 3921-3936 ◽  
Author(s):  
Tomohiro Shimada ◽  
Hiroshi Ogasawara ◽  
Akira Ishihama
Keyword(s):  
Escherichia Coli ◽  
Transcription Factors ◽  
Single Target ◽  
K 12 ◽  
A Minor
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