Dataset Information

Genome-Wide De Novo Prediction of Cis-Regulatory Binding Sites in Mycobacterium tuberculosis H37Rv.

ABSTRACT: The transcription regulatory system of Mycobacterium tuberculosis (M. tb) remains incompletely understood. In this study, we have applied the eGLECLUBS algorithm to a group of related prokaryotic genomes for de novo genome-wide prediction of cis-regulatory binding sites (CRBSs) in M. tb H37Rv. The top 250 clusters from our prediction recovered 83.3% (50/60) of all known CRBSs in extracted inter-operonic sequences of this strain. We further demonstrated that the integration of our prediction results with the ChIP-Seq datasets is very effective in identifying true binding sites of TFs. Using electrophoretic mobility shift assays and real-time RT-PCR, we experimentally verified our prediction of CRBSs for Rv0081, an important transcription factor thought to be involved in regulation of M. tb under hypoxia.

SUBMITTER: Wu W

PROVIDER: S-EPMC4757040 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Genome-Wide De Novo Prediction of Cis-Regulatory Binding Sites in Mycobacterium tuberculosis H37Rv.

Wu Wei W Sun Xian X Gao Yun Y Jiang Jun J Cui Zhenling Z Ge Baoxue B Wu Hai H Zhang Lu L Li Yao Y

PloS one 20160217 2

The transcription regulatory system of Mycobacterium tuberculosis (M. tb) remains incompletely understood. In this study, we have applied the eGLECLUBS algorithm to a group of related prokaryotic genomes for de novo genome-wide prediction of cis-regulatory binding sites (CRBSs) in M. tb H37Rv. The top 250 clusters from our prediction recovered 83.3% (50/60) of all known CRBSs in extracted inter-operonic sequences of this strain. We further demonstrated that the integration of our prediction resu ...[more]

PMID: 26886880

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Project description:BackgroundIn eukaryotes, transcriptional regulation is usually mediated by interactions of multiple transcription factors (TFs) with their respective specific cis-regulatory elements (CREs) in the so-called cis-regulatory modules (CRMs) in DNA. Although the knowledge of CREs and CRMs in a genome is crucial to elucidate gene regulatory networks and understand many important biological phenomena, little is known about the CREs and CRMs in most eukaryotic genomes due to the difficulty to characterize them by either computational or traditional experimental methods. However, the exponentially increasing number of TF binding location data produced by the recent wide adaptation of chromatin immunoprecipitation coupled with microarray hybridization (ChIP-chip) or high-throughput sequencing (ChIP-seq) technologies has provided an unprecedented opportunity to identify CRMs and CREs in genomes. Nonetheless, how to effectively mine these large volumes of ChIP data to identify CREs and CRMs at nucleotide resolution is a highly challenging task.ResultsWe have developed a novel graph-theoretic based algorithm DePCRM for genome-wide de novo predictions of CREs and CRMs using a large number of ChIP datasets. DePCRM predicts CREs and CRMs by identifying overrepresented combinatorial CRE motif patterns in multiple ChIP datasets in an effective way. When applied to 168 ChIP datasets of 56 TFs from D. melanogaster, DePCRM identified 184 and 746 overrepresented CRE motifs and their combinatorial patterns, respectively, and predicted a total of 115,932 CRMs in the genome. The predictions recover 77.9% of known CRMs in the datasets and 89.3% of known CRMs containing at least one predicted CRE. We found that the putative CRMs as well as CREs as a whole in a CRM are more conserved than randomly selected sequences.ConclusionOur results suggest that the CRMs predicted by DePCRM are highly likely to be functional. Our algorithm is the first of its kind for de novo genome-wide prediction of CREs and CRMs using larger number of transcription factor ChIP datasets. The algorithm and predictions will hopefully facilitate the elucidation of gene regulatory networks in eukaryotes. All the predicted CREs, CRMs, and their target genes are available at http://bioinfo.uncc.edu/mniu/pcrms/www/.

Project description:BackgroundMycobacterium tuberculosis (M.tb) is the causative agent of tuberculosis, killing ~1.7 million people annually. The remarkable capacity of this pathogen to escape the host immune system for decades and then to cause active tuberculosis disease, makes M.tb a successful pathogen. Currently available anti-mycobacterial therapy has poor compliance due to requirement of prolonged treatment resulting in accelerated emergence of drug resistant strains. Hence, there is an urgent need to identify new chemical entities with novel mechanism of action and potent activity against the drug resistant strains.ResultsThis study describes novel computational models developed for predicting inhibitors against both replicative and non-replicative phase of drug-tolerant M.tb under carbon starvation stage. These models were trained on highly diverse dataset of 2135 compounds using four classes of binary fingerprint namely PubChem, MACCS, EState, SubStructure. We achieved the best performance Matthews correlation coefficient (MCC) of 0.45 using the model based on MACCS fingerprints for replicative phase inhibitor dataset. In case of non-replicative phase, Hybrid model based on PubChem, MACCS, EState, SubStructure fingerprints performed better with maximum MCC value of 0.28. In this study, we have shown that molecular weight, polar surface area and rotatable bond count of inhibitors (replicating and non-replicating phase) are significantly different from non-inhibitors. The fragment analysis suggests that substructures like hetero_N_nonbasic, heterocyclic, carboxylic_ester, and hetero_N_basic_no_H are predominant in replicating phase inhibitors while hetero_O, ketone, secondary_mixed_amine are preferred in the non-replicative phase inhibitors. It was observed that nitro, alkyne, and enamine are important for the molecules inhibiting bacilli residing in both the phases. In this study, we introduced a new algorithm based on Matthews correlation coefficient called MCCA for feature selection and found that this algorithm is better or comparable to frequency based approach.ConclusionIn this study, we have developed computational models to predict phase specific inhibitors against drug resistant strains of M.tb grown under carbon starvation. Based on simple molecular properties, we have derived some rules, which would be useful in robust identification of tuberculosis inhibitors. Based on these observations, we have developed a webserver for predicting inhibitors against drug tolerant M.tb H37Rv available at http://crdd.osdd.net/oscadd/mdri/.

Dataset Information

Genome-Wide De Novo Prediction of Cis-Regulatory Binding Sites in Mycobacterium tuberculosis H37Rv.

Publications

Genome-Wide De Novo Prediction of Cis-Regulatory Binding Sites in Mycobacterium tuberculosis H37Rv.

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