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

Abstract

BackgroundLittle 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 ~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.ResultsWe 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.ConclusionThe 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.

Comparative analysis of the complete genome of KPC-2-producing Klebsiella pneumoniae Kp13 reveals remarkable genome plasticity and a wide repertoire of virulence and resistance mechanisms

“…Ω, based in the literature found for each system. Substrate nomenclature adapted from [ 54 ]: AC, acriflavine; AG, aminoglycosides; BI, bicyclomycin; BL, beta-lactams; BS, bile salts; BENZ, benzalkonium chloride; CCC, carbonyl cyanide chlorophenylhydrazone; CM, chloramphenicol; CV, crystal violet; DC, deoxycholate; EB, ethidium bromide; EM, erythromycin; ENX, enoxacin; FA, fatty acids; FQ, fluoroquinolones; FOM, fosfomycin; FM, fosmidomycin; FU, fusidic acid; ML, macrolides; NA, nalidixic acid; NFX, norfloxacin; NO, novobiocin; OS, organic solvents; RD, rhodamine; RF, rifampicin; SDS, sodium dodecyl sulfate; STZ, sulfathiazole; TC, tetracycline; TCS, tetrachlorosalicylanilide; TLM, thiolactomycin; TPP, tetraphenylphosphonium chloride; TX, Triton X-100; ?, regarded as related to multidrug-resistance, but with an unknown resistance spectrum. *, evaluated by BLASTP against the target genome.…”

Exposure of Salmonella enterica Serovar Typhimurium to High Level Biocide Challenge Can Select Multidrug Resistant Mutants in a Single Step

“…The fact that both the mutants recovered from two different biocides had this phenotype suggests a common mechanism of cell survival to biocides with de-repression of AcrEF (mediated by up-regulation of MarA) favoured over de-repression of AcrAB. The MarA regulon has not been shown to include acrEF in Salmonella; however in E. coli a putative MarA binding site in the acrEF promoter has been described [22] . Study of the S .…”

Network inference and network response identification: moving genome-scale data to the next level of biological discovery

“…81 For instance, a large number of feed-forward (FF) motifs and bi-fan (BF) motifs are present in the E. coli TRN. Veiga et al 82 designed FF and BF predictors, using artificial neural networks that classify whether a group of genes are likely to form a motif. These motif predictors were applied to identify new TFs regulating transport proteins acting in efflux pumps linked to multidrug resistance in E. coli.…”