A unified resource for transcriptional regulation in Escherichia coli K-12 incorporating high-throughput-generated binding data into RegulonDB version 10.0

Abstract

BackgroundOur understanding of the regulation of gene expression has benefited from the availability of high-throughput technologies that interrogate the whole genome for the binding of specific transcription factors and gene expression profiles. In the case of widely used model organisms, such as Escherichia coli K-12, the new knowledge gained from these approaches needs to be integrated with the legacy of accumulated knowledge from genetic and molecular biology experiments conducted in the pre-genomic era in order to attain the deepest level of understanding possible based on the available data.ResultsIn this paper, we describe an expansion of RegulonDB, the database containing the rich legacy of decades of classic molecular biology experiments supporting what we know about gene regulation and operon organization in E. coli K-12, to include the genome-wide dataset collections from 32 ChIP and 19 gSELEX publications, in addition to around 60 genome-wide expression profiles relevant to the functional significance of these datasets and used in their curation. Three essential features for the integration of this information coming from different methodological approaches are: first, a controlled vocabulary within an ontology for precisely defining growth conditions; second, the criteria to separate elements with enough evidence to consider them involved in gene regulation from isolated transcription factor binding sites without such support; and third, an expanded computational model supporting this knowledge. Altogether, this constitutes the basis for adequately gathering and enabling the comparisons and integration needed to manage and access such wealth of knowledge.ConclusionsThis version 10.0 of RegulonDB is a first step toward what should become the unifying access point for current and future knowledge on gene regulation in E. coli K-12. Furthermore, this model platform and associated methodologies and criteria can be emulated for gathering knowledge on other microbial organisms.

Massively Parallel Assays and Quantitative Sequence–Function Relationships

“…Each nucleotide contributes additively to the overall weight matrix score via a parameter called a weight. Figure 3e shows a weight matrix for CRP computed using an alignment of the 358 annotated CRP binding sites in the E. coli genome (135). We refer readers to other reviews (77,158) for a description of how weight matrices are constructed from alignments of binding sites such as this.…”

The Escherichia coli Transcriptome Mostly Consists of Independently Regulated Modules

“…Reconstruction of a genome-scale TRN requires a substantial number of experiments to integrate the binding sites for each regulator and characterize their activities 4,5 . Unlike eukaryotic TRNs, which contain highly-connected co-associations 6 , prokaryotic TRNs exhibit a simpler structure; over 75% of genes in the model bacteria Escherichia coli are known targets of two or fewer TFs 7 (Fig.…”

“…We hypothesized that each i-modulon was controlled by a particular transcriptional regulator, and that the i-modulon activity represented the condition-dependent activation state of the corresponding transcriptional regulator. To test this hypothesis, we examined the consistency between i-modulons and reported regulons, defined as the set of genes targeted by a common regulator, using a database of over 7,000 experimentally-derived regulatory interactions for E. coli 4 (Fig. S1f).…”

Xenobiotic Effects of Chlorine Dioxide to Escherichia coli O157:H7 on Non-host Tomato Environment Revealed by Transcriptional Network Modeling: Implications to Adaptation and Selection

“…In this study, we conducted transcriptome studies using RNA-Seq technology to reveal the genetic mechanism of E. coli O157:H7 in response to xenobiotic effects of different doses of ClO 2 on its non-host tomato across a range of exposure time. By dissecting the global dynamics of transcriptome changes (Bridges et al, 2018;Santos-Zavaleta et al, 2018), we reconstructed the regulatory networks associated with the responses of E. coli O157:H7 to 1 µg, 5 µg, and 10 µg of ClO 2 per gram of tomato fruits. We uncovered the potential network hubs that are likely to be critical in executing defense responses and possible adaptation.…”