RegTransBase--a database of regulatory sequences and interactions in a wide range of prokaryotic genomes

Kazakov, Alexei E.; Cipriano, Michael J.; Novichkov, Pavel S.; Minovitsky, Simon; Vinogradov, D. V.; Arkin, Adam P.; Mironov, Andrey A.; Gelfand, Mikhail S.; Dubchak, Inna

doi:10.1093/nar/gkl865

Cited by 87 publications

(89 citation statements)

References 21 publications

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“…Functional annotations of the predicted regulon members and the associated metabolic subsystems were collected from the SEED database using recently developed Web services (9,34). Initial data about target genes and TF binding sites for several regulons previously described for S. aureus (AgrA, CzrA, GlnR, LacR, MntR, Fur, Zur, and PerR) were extracted from the RegTransBase database (http://regtransbase.lbl.gov/), a database of regulatory interactions based on data in the literature (20). The reconstructed regulons, including TFs, their target genes and operons, and associated TFBSs, were uploaded to the RegPrecise database (http://regprecise.lbl .gov/) (29).…”

Section: Methodsmentioning

confidence: 99%

Inference of the Transcriptional Regulatory Network in Staphylococcus aureus by Integration of Experimental and Genomics-Based Evidence

et al. 2011

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Transcriptional regulatory networks are fine-tuned systems that help microorganisms respond to changes in the environment and cell physiological state. We applied the comparative genomics approach implemented in the RegPredict Web server combined with SEED subsystem analysis and available information on known regulatory interactions for regulatory network reconstruction for the human pathogen Staphylococcus aureus and six related species from the family Staphylococcaceae. The resulting reference set of 46 transcription factor regulons contains more than 1,900 binding sites and 2,800 target genes involved in the central metabolism of carbohydrates, amino acids, and fatty acids; respiration; the stress response; metal homeostasis; drug and metal resistance; and virulence. The inferred regulatory network in S. aureus includes ϳ320 regulatory interactions between 46 transcription factors and ϳ550 candidate target genes comprising 20% of its genome. We predicted ϳ170 novel interactions and 24 novel regulons for the control of the central metabolic pathways in S. aureus. The reconstructed regulons are largely variable in the Staphylococcaceae: only 20% of S. aureus regulatory interactions are conserved across all studied genomes. We used a large-scale gene expression data set for S. aureus to assess relationships between the inferred regulons and gene expression patterns. The predicted reference set of regulons is captured within the Staphylococcus collection in the RegPrecise database (http://regprecise.lbl.gov).

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Section: Methodsmentioning

confidence: 99%

Inference of the Transcriptional Regulatory Network in Staphylococcus aureus by Integration of Experimental and Genomics-Based Evidence

et al. 2011

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“…The ramp-up in microbial genome sequencing focused attention on the difficulty of accurate annotation and the need to take advantage of the information garnered for genes and proteins through painstaking biochemical and genetic research prior to the "genomics age". Therefore, an effort led by the Dubchak group (Cipriano et al, 2013;Kazakov et al, 2007) has attempted to capture the published information about gene regulation, regulon inference, transcription factors and transcription factor binding sites in a searchable format. The platform devised is RegTransBase (http://regtransbase.lbl.…”

Section: Community Resources For Comparative Genome Analysesmentioning

confidence: 99%

“…gov/cgi-bin/regtransbase?page¼main) that has two modules, a database of regulons deduced from published experimental data and a manually curated database of transcription factor-binding sites that provides the reference for determining new or conserved binding sites across species. As of November 2012, about 7200 articles back to 1977 have been curated that examined 666 bacteria (Cipriano et al, 2013;Kazakov et al, 2007).…”

Section: Community Resources For Comparative Genome Analysesmentioning

confidence: 99%

A Post-Genomic View of the Ecophysiology, Catabolism and Biotechnological Relevance of Sulphate-Reducing Prokaryotes

Rabus

Venceslau

Wöhlbrand

et al. 2015

Advances in Microbial Physiology

253

286

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“…11). To confirm that this motif matched the AraC consensus binding sequence, we used the TOMTOM program (17) to search RegTransBase (22), a curated database of bacterial transcription factor binding sites, and found that the top hit indeed matched the AraC consensus sequence (P Ͻ 10 Ϫ16 ). We also scanned all upstream sequences in the E. coli genome using FIMO (2) and found that the only significant hits (P Ͻ 10 Ϫ10 ; q Ͻ 0.01 [41]) were the arabinose and xylose promoters.…”

Section: Vol 76 2010 Regulation Of Arabinose and Xylose Metabolism mentioning

confidence: 99%

Regulation of Arabinose and Xylose Metabolism inEscherichia coli

Desai

Rao

2010

Appl Environ Microbiol

137

105

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Bacteria such as Escherichia coli will often consume one sugar at a time when fed multiple sugars, in a process known as carbon catabolite repression. The classic example involves glucose and lactose, where E. coli will first consume glucose, and only when it has consumed all of the glucose will it begin to consume lactose. In addition to that of lactose, glucose also represses the consumption of many other sugars, including arabinose and xylose. In this work, we characterized a second hierarchy in E. coli, that between arabinose and xylose. We show that, when grown in a mixture of the two pentoses, E. coli will consume arabinose before it consumes xylose. Consistent with a mechanism involving catabolite repression, the expression of the xylose metabolic genes is repressed in the presence of arabinose. We found that this repression is AraC dependent and involves a mechanism where arabinose-bound AraC binds to the xylose promoters and represses gene expression. Collectively, these results demonstrate that sugar utilization in E. coli involves multiple layers of regulation, where cells will consume first glucose, then arabinose, and finally xylose. These results may be pertinent in the metabolic engineering of E. coli strains capable of producing chemical and biofuels from mixtures of hexose and pentose sugars derived from plant biomass.The transporters and enzymes in many sugar metabolic pathways are conditionally expressed in response to their cognate sugar or a downstream pathway intermediate. While the induction of these pathways in response to a single sugar has been studied extensively (28), far less is known about how these pathways are induced in response to multiple sugars. One notable exception is the phenomenon observed when bacteria are grown in the presence of glucose and another sugar (10, 15). In such mixtures, the bacteria will often consume glucose first before consuming the other sugar, a process known as carbon catabolite repression (27). The classic example of carbon catabolite repression is the diauxic shift seen in the growth of Escherichia coli on mixtures of glucose and lactose, where the cells first consume glucose before consuming lactose. When the cells are consuming glucose, the genes in the lactose metabolic pathway are not induced, thus preventing the sugar from being consumed. A number of molecules participate in this regulation, including the cyclic AMP receptor protein (CRP), adenylate cyclase, cyclic AMP (cAMP), and EIIA from the phosphoenolpyruvate:glucose phosphotransferase system (PTS) (33). In addition to lactose, the metabolic genes for many other sugars are subject to catabolite repression by glucose in E. coli (27). While the preferential utilization of glucose is well known, it is an open question whether additional hierarchies exist among other sugars.Recently, substantial effort has been directed toward developing microorganisms capable of producing chemicals and biofuels from plant biomass (1, 34, 42). After glucose, L-arabinose and D-xylose are the next most abundant s...

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RegTransBase--a database of regulatory sequences and interactions in a wide range of prokaryotic genomes

Cited by 87 publications

References 21 publications

Inference of the Transcriptional Regulatory Network in Staphylococcus aureus by Integration of Experimental and Genomics-Based Evidence

Inference of the Transcriptional Regulatory Network in Staphylococcus aureus by Integration of Experimental and Genomics-Based Evidence

A Post-Genomic View of the Ecophysiology, Catabolism and Biotechnological Relevance of Sulphate-Reducing Prokaryotes

Regulation of Arabinose and Xylose Metabolism inEscherichia coli

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