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

Abstract

Background: Our 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.

Results: In 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.

Conclusions: This 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.

Keywords: ChIP-seq; Integrative analyses; Systems biology; Transcriptional regulation; Transcriptomics; gSELEX.

Fig. 1

Number of publications studying transcriptional regulation in E. coli K-12, using either classic molecular biology or HT technologies through the years

Fig. 2

Diagram of the annotation process. We collect publications in PubMed involving the HT methodologies. Searches were made for the term “coli” in the title or in the abstract and the name of the method or different synonyms or keywords related to the method in all fields of publications. The results were filtered to get unique results. We read the abstracts and eliminate all those papers not reporting experiments performed in E. coli. Frequently, the papers include additional experimental characterization for a subset of the sites based on classic methods. Metadata are extracted from each publication. For more detail see main text. The growth condition and strain are described using the controlled vocabulary defined by Tierrafría et al. [7]. The products of curation are added to RegulonDB either together with the classic curation or as a separate dataset. Image from RegulonDB [8]

Fig. 3

Elementary pieces of curation. As new methods emerge, we need to separately curate evidence and references for each elementary piece of knowledge that, when combined, supports our understanding. Here, we have separated evidence for binding of TFs and evidence for an effect on transcription either of a known promoter or on a target gene or TU for which the promoter is not known