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Extracting regulator activity profiles by integration of de novo motifs and expression data: characterizing key regulators of nutrient depletion responses in Streptomyces coelicolor.

ABSTRACT: Determining transcriptional regulator activities is a major focus of systems biology, providing key insight into regulatory mechanisms and co-regulators. For organisms such as Escherichia coli, transcriptional regulator binding site data can be integrated with expression data to infer transcriptional regulator activities. However, for most organisms there is only sparse data on their transcriptional regulators, while their associated binding motifs are largely unknown. Here, we address the challenge of inferring activities of unknown regulators by generating de novo (binding) motifs and integrating with expression data. We identify a number of key regulators active in the metabolic switch, including PhoP with its associated directed repeat PHO box, candidate motifs for two SARPs, a CRP family regulator, an iron response regulator and that for LexA. Experimental validation for some of our predictions was obtained using gel-shift assays. Our analysis is applicable to any organism for which there is a reasonable amount of complementary expression data and for which motifs (either over represented or evolutionary conserved) can be identified in the genome.

SUBMITTER: Iqbal M

PROVIDER: S-EPMC3384326 | biostudies-literature | 2012 Jul

REPOSITORIES: biostudies-literature

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Extracting regulator activity profiles by integration of de novo motifs and expression data: characterizing key regulators of nutrient depletion responses in Streptomyces coelicolor.

Iqbal Mudassar M Mast Yvonne Y Amin Rafat R Hodgson David A DA Wohlleben Wolfgang W Burroughs Nigel J NJ

Nucleic acids research 20120309 12

Determining transcriptional regulator activities is a major focus of systems biology, providing key insight into regulatory mechanisms and co-regulators. For organisms such as Escherichia coli, transcriptional regulator binding site data can be integrated with expression data to infer transcriptional regulator activities. However, for most organisms there is only sparse data on their transcriptional regulators, while their associated binding motifs are largely unknown. Here, we address the chall ...[more]

PMID: 22406834

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Project description:BackgroundStreptomyces coelicolor is a bacterium with a vast repertoire of metabolic functions and complex systems of cellular development. Its genome sequence is rich in genes that encode regulatory proteins to control these processes in response to its changing environment. We wished to apply a recently published bioinformatic method for identifying novel regulatory sequence signals to gain new insights into regulation in S. coelicolor.ResultsThe method involved production of position-specific weight matrices from alignments of over-represented words of DNA sequence. We generated 2497 weight matrices, each representing a candidate regulatory DNA sequence motif. We scanned the genome sequence of S. coelicolor against each of these matrices. A DNA sequence motif represented by one of the matrices was found preferentially in non-coding sequences immediately upstream of genes involved in polysaccharide degradation, including several that encode chitinases. This motif (TGGTCTAGACCA) was also found upstream of genes encoding components of the phosphoenolpyruvate phosphotransfer system (PTS). We hypothesise that this DNA sequence motif represents a regulatory element that is responsive to availability of carbon-sources. Other motifs of potential biological significance were found upstream of genes implicated in secondary metabolism (TTAGGTtAGgCTaACCTAA), sigma factors (TGACN19TGAC), DNA replication and repair (ttgtCAGTGN13TGGA), nucleotide conversions (CTACgcNCGTAG), and ArsR (TCAGN12TCAG). A motif found upstream of genes involved in chromosome replication (TGTCagtgcN7Tagg) was similar to a previously described motif found in UV-responsive promoters.ConclusionsWe successfully applied a recently published in silico method to identify conserved sequence motifs in S. coelicolor that may be biologically significant as regulatory elements. Our data are broadly consistent with and further extend data from previously published studies. We invite experimental testing of our hypotheses in vitro and in vivo.

Project description:Carbohydrates, or glycans, are one of the most abundant and structurally diverse biopolymers constitute the third major class of biomolecules, following DNA and proteins. However, the study of carbohydrate sugar chains has lagged behind compared to that of DNA and proteins, mainly due to their inherent structural complexity. However, their analysis is important because they serve various important roles in biological processes, including signaling transduction and cellular recognition. In order to glean some light into glycan function based on carbohydrate structure, kernel methods have been developed in the past, in particular to extract potential glycan biomarkers by classifying glycan structures found in different tissue samples. The recently developed weighted qgram method (LK-method) exhibits good performance on glycan structure classification while having limitations in feature selection. That is, it was unable to extract biologically meaningful features from the data. Therefore, we propose a biochemicallyweighted tree kernel (BioLK-method) which is based on a glycan similarity matrix and also incorporates biochemical information of individual q-grams in constructing the kernel matrix. We further applied our new method for the classification and recognition of motifs on publicly available glycan data. Our novel tree kernel (BioLK-method) using a Support Vector Machine (SVM) is capable of detecting biologically important motifs accurately while LK-method failed to do so. It was tested on three glycan data sets from the Consortium for Functional Glycomics (CFG) and Kyoto Encyclopedia of Genes and Genomes (KEGG) GLYCAN and showed that the results are consistent with the literature. The newly developed BioLK-method also maintains comparable classification performance with the LK-method. Our results obtained here indicate that the incorporation of biochemical information of q-grams further shows the flexibility and capability of the novel kernel in feature extraction, which may aid in the prediction of glycan biomarkers.

Project description:Nucleoid-associated proteins (NAPs) are small, highly abundant transcriptional regulators with low sequence specificity which are involved in multiple DNA-related processes including gene expression, DNA protection, recombination/repair and nucleoid structuring. Through these functions they are able to regulate important phenotypic properties including virulence, secondary metabolism and stress resistance. However the set of NAPs known within the Actinobacteria is small and incomplete. The missing proteins are likely to be key regulators of virulence in pathogens such as Mycobacterium tuberculosis and also of development and secondary metabolism in industrially-important species such as Streptomyces. Here, we use label-free LC-MS/MS to systematically search for novel NAPs in isolated nucleoids of the model actinomycete Streptomyces coelicolor. Based on the criteria of high abundance (emPAI score) and predicted DNA-binding ability (DNAbinder score) we identified a set of 24 proteins with a high predicted likelihood of being NAPs. The approach was deemed successful as the set included known major NAPs HupA, HupS, sIHF and Lsr2 as well as the global transcriptional regulators BldD and CRP and the pleiotropic response regulator AfsQ1. It also included a number of proteins whose functions are not yet known from recognisable classes of transcription factor (SCO2140, SCO4493, SCO1839, SCO1210, SCO5405, SCO4229, SCO3198) or from uncharacterised protein families (SCO5783, SCO5592, SCO3793, SCO6482) which comprise a valuable set of candidates for further study.In this paper we establish a robust protocol for preparing S. coelicolor nucleoids for mass spectrometric analysis and develop a workflow for identifying novel nucleoid-associated proteins (NAPs) by combining LC-MS/MS with a bioinformatical analysis. The nucleoid-associated proteins of many species are known to be key regulators of virulence, stress tolerance and global patterns of gene expression. Identifying the "missing" nucleoid proteins of S. coelicolor is likely to have important implications for manipulating the production of secondary metabolites such as antibiotics. Candidate NAPs were identified. Several of these are highly conserved in clinically important species such as Mycobacterium and in many commercially important species such as Salinispora and Micromonospora which represent a vital source of novel drugs such as antibiotics, antifungals and anticancer agents.

Dataset Information

Extracting regulator activity profiles by integration of de novo motifs and expression data: characterizing key regulators of nutrient depletion responses in Streptomyces coelicolor.

Publications

Extracting regulator activity profiles by integration of de novo motifs and expression data: characterizing key regulators of nutrient depletion responses in Streptomyces coelicolor.

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