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Predicting transcriptional regulatory interactions with artificial neural networks applied to E. coli multidrug resistance efflux pumps.

ABSTRACT: BACKGROUND: Little is known about bacterial transcriptional regulatory networks (TRNs). In Escherichia coli, which is the organism with the largest wet-lab validated TRN, its set of interactions involves only approximately 50% of the repertoire of transcription factors currently known, and ~25% of its genes. Of those, only a small proportion describes the regulation of processes that are clinically relevant, such as drug resistance mechanisms. RESULTS: We designed feed-forward (FF) and bi-fan (BF) motif predictors for E. coli using multi-layer perceptron artificial neural networks (ANNs). The motif predictors were trained using a large dataset of gene expression data; the collection of motifs was extracted from the E. coli TRN. Each network motif was mapped to a vector of correlations which were computed using the gene expression profile of the elements in the motif. Thus, by combining network structural information with transcriptome data, FF and BF predictors were able to classify with a high precision of 83% and 96%, respectively, and with a high recall of 86% and 97%, respectively. These results were found when motifs were represented using different types of correlations together, i.e., Pearson, Spearman, Kendall, and partial correlation. We then applied the best predictors to hypothesize new regulations for 16 operons involved with multidrug resistance (MDR) efflux pumps, which are considered as a major bacterial mechanism to fight antimicrobial agents. As a result, the motif predictors assigned new transcription factors for these MDR proteins, turning them into high-quality candidates to be experimentally tested. CONCLUSION: The motif predictors presented herein can be used to identify novel regulatory interactions by using microarray data. The presentation of an example motif to predictors will make them categorize whether or not the example motif is a BF, or whether or not it is an FF. This approach is useful to find new "pieces" of the TRN, when inspecting the regulation of a small set of operons. Furthermore, it shows that correlations of expression data can be used to discriminate between elements that are arranged in structural motifs and those in random sets of transcripts.

SUBMITTER: Veiga DF

PROVIDER: S-EPMC2453137 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature

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Predicting transcriptional regulatory interactions with artificial neural networks applied to E. coli multidrug resistance efflux pumps.

Veiga Diogo F T DF Vicente Fábio F R FF Nicolás Marisa F MF Vasconcelos Ana Tereza R AT

BMC microbiology 20080619

<h4>Background</h4>Little is known about bacterial transcriptional regulatory networks (TRNs). In Escherichia coli, which is the organism with the largest wet-lab validated TRN, its set of interactions involves only approximately 50% of the repertoire of transcription factors currently known, and ~25% of its genes. Of those, only a small proportion describes the regulation of processes that are clinically relevant, such as drug resistance mechanisms.<h4>Results</h4>We designed feed-forward (FF) ...[more]

PMID: 18565227

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Project description:RND (Resistance-Nodulation-Division) family transporters are widespread especially among Gram-negative bacteria, and catalyze the active efflux of many antibiotics and chemotherapeutic agents. They have very large periplasmic domains, and form tripartite complexes with outer membrane channels and periplasmic adaptor proteins. AcrAB-TolC complex of Escherichia coli, which pumps out a very wide range of drugs, has been studied most intensively. Early studies showed that the transporter captures even those substrates that cannot permeate across the cytoplasmic membrane, such as dianionic beta-lactams, suggesting that the capture can occur from the periplasm. It was also suggested that the capture occurs from the cytoplasmic membrane/periplasm interface, because most substrates contain a sizable hydrophobic domain; however, this may simply be a reflection of the nature of the binding site within AcrB. Genetic studies of chimeric transporters showed that much of the substrate specificity is determined by their periplasmic domains. Biochemical studies with intact cells recently led to the determination of the kinetic constants of AcrB for some beta-lactams, and the result confirms the old prediction that AcrB is a rather slow pump. Reconstitution of purified AcrB and its relatives showed that the pump is a drug/proton antiporter, that AcrA strongly stimulates the activity of the pump, and that AcrB seems to have a highest affinity for conjugated bile salts. Structural study with mutants of the network of charged residues in the transmembrane domain showed that protonation here produced a far-reaching conformational change, which was found to be present in one of the protomers in the asymmetric crystal structure of the wild-type AcrB. The functional rotatory hypothesis then predicts that the drug bound in the periplasmic domain is extruded through this conformational change initiated by the protonation of one of the residues in the aforementioned network, an idea that was recently supported by disulfide cross-linking as well as by the behavior of linked AcrB protomers.

Project description:Benzoic acid, a partial uncoupler of the proton motive force (PMF), selects for sensitivity to chloramphenicol and tetracycline during the experimental evolution of Escherichia coli K-12. Transcriptomes of E. coli isolates evolved with benzoate showed the reversal of benzoate-dependent regulation, including the downregulation of multidrug efflux pump genes, the gene for the Gad acid resistance regulon, the nitrate reductase genes narHJ, and the gene for the acid-consuming hydrogenase Hyd-3. However, the benzoate-evolved strains had increased expression of OmpF and other large-hole porins that admit fermentable substrates and antibiotics. Candidate genes identified from benzoate-evolved strains were tested for their roles in benzoate tolerance and in chloramphenicol sensitivity. Benzoate or salicylate tolerance was increased by deletion of the Gad activator ariR or of the acid fitness island from slp to the end of the gadX gene encoding Gad regulators and the multidrug pump genes mdtEF Benzoate tolerance was also increased by deletion of multidrug component gene emrA, RpoS posttranscriptional regulator gene cspC, adenosine deaminase gene add, hydrogenase gene hyc (Hyd-3), and the RNA chaperone/DNA-binding regulator gene hfq Chloramphenicol resistance was decreased by mutations in genes for global regulators, such as RNA polymerase alpha subunit gene rpoA, the Mar activator gene rob, and hfq Deletion of lipopolysaccharide biosynthetic kinase gene rfaY decreased the rate of growth in chloramphenicol. Isolates from experimental evolution with benzoate had many mutations affecting aromatic biosynthesis and catabolism, such as aroF (encoding tyrosine biosynthesis) and apt (encoding adenine phosphoribosyltransferase). Overall, benzoate or salicylate exposure selects for the loss of multidrug efflux pumps and of hydrogenases that generate a futile cycle of PMF and upregulates porins that admit fermentable nutrients and antibiotics.IMPORTANCE Benzoic acid is a common food preservative, and salicylic acid (2-hydroxybenzoic acid) is the active form of aspirin. At high concentrations, benzoic acid conducts a proton across the membrane, depleting the proton motive force. In the absence of antibiotics, benzoate exposure selects against proton-driven multidrug efflux pumps and upregulates porins that admit fermentable substrates but that also allow the entry of antibiotics. Thus, evolution with benzoate and related molecules, such as salicylates, requires a trade-off for antibiotic sensitivity, a trade-off that could help define a stable gut microbiome. Benzoate and salicylate are naturally occurring plant signal molecules that may modulate the microbiomes of plants and animal digestive tracts so as to favor fermenters and exclude drug-resistant pathogens.

Dataset Information

Predicting transcriptional regulatory interactions with artificial neural networks applied to E. coli multidrug resistance efflux pumps.

Publications

Predicting transcriptional regulatory interactions with artificial neural networks applied to E. coli multidrug resistance efflux pumps.

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