RegulonDB version 9.0: high-level integration of gene regulation, coexpression, motif clustering and beyond

Abstract

RegulonDB (http://regulondb.ccg.unam.mx) is one of the most useful and important resources on bacterial gene regulation,as it integrates the scattered scientific knowledge of the best-characterized organism, Escherichia coli K-12, in a database that organizes large amounts of data. Its electronic format enables researchers to compare their results with the legacy of previous knowledge and supports bioinformatics tools and model building. Here, we summarize our progress with RegulonDB since our last Nucleic Acids Research publication describing RegulonDB, in 2013. In addition to maintaining curation up-to-date, we report a collection of 232 interactions with small RNAs affecting 192 genes, and the complete repertoire of 189 Elementary Genetic Sensory-Response units (GENSOR units), integrating the signal, regulatory interactions, and metabolic pathways they govern. These additions represent major progress to a higher level of understanding of regulated processes. We have updated the computationally predicted transcription factors, which total 304 (184 with experimental evidence and 120 from computational predictions); we updated our position-weight matrices and have included tools for clustering them in evolutionary families. We describe our semiautomatic strategy to accelerate curation, including datasets from high-throughput experiments, a novel coexpression distance to search for ‘neighborhood’ genes to known operons and regulons, and computational developments.

Internal RNAs overlapping coding sequences can drive the production of alternative proteins in archaea

“…In bacteria, from which archaea probably acquired dnaK , we were able to see a similar phenomenon searching on RegulonDB [59] for iTSS and SWISS-2DPAGE [61] for 2Dgel/MS data. In E. coli there is an iTSS (TSS_31 cluster) inside dnaK CDS ( locus b0014) which defines an intraORF that has no 5ʹ UTR, starts from an ATG codon and would translate a 37.0 kDa MW isoform with pI of 4.84.…”

“…For that, we took advantage of the well established RegulonDB [59] since this database includes TSS and promoters datasets. The database registers a putative intraRNA (TSS_3889 cluster) that would translate a 30.1 kDa product starting at an ATG codon 6 nt downstream of the iTSS, with predicted pI of 5.79 and acidity-correct apparent MW of 32.7 kDa.…”

The essential activities of the bacterial sigma factor

“…(a) Subunit architecture of holoenzyme (core subunits presented in various shades of grey); represented as 4 domains colour-coded according to (b) regions and subregions of contiguous amino acid residues in , and indicating typical interactions with (c) a representative 70 promoter with key elements indicated (EXT, extended -10 element; DISC, discriminator sequence). (d) Sequence logo (Schneider and Stephens 1990) for the Escherichia coli primary factor -10 element generated from 950 sequences (Gama-Castro et al 2015). (e) Sequence logo for Bacillus subtilis primary factor -10 element generated from 656 sequences (Sierro et al 2008).…”

Mathematical modelling of microbes: metabolism, gene expression and growth

“…The large and complex networks of biochemical reactions enabling microbial growth have been mapped in great detail over the past decades and, for some model organisms, much of this information is available in structured and curated databases [18,19]. While a huge amount of knowledge has thus accumulated, a clear understanding of the precise role played by individual constituents and mechanisms in the functioning of the system as a whole has remained elusive.…”