Dataset Information

Mining genomic patterns in Mycobacterium tuberculosis H37Rv using a web server Tuber-Gene.

ABSTRACT: Mycobacterium tuberculosis (MTB), causative agent of tuberculosis, is one of the most dreaded diseases of the century. It has long been studied by researchers throughout the world using various wet-lab and dry-lab techniques. In this study, we focus on mining useful patterns at genomic level that can be applied for in silico functional characterization of genes from the MTB complex. The model developed on the basis of the patterns found in this study can correctly identify 99.77% of the input genes from the genome of MTB strain H37Rv. The model was tested against four other MTB strains and the homologue M. bovis to further evaluate its generalization capability. The mean prediction accuracy was 85.76%. It was also observed that the GC content remained fairly constant throughout the genome, implicating the absence of any pathogenicity island transferred from other organisms. This study reveals that dinucleotide composition is an efficient functional class discriminator for MTB complex. To facilitate the application of this model, a web server Tuber-Gene has been developed, which can be freely accessed at http://www.bifmanit.org/tb2/.

SUBMITTER: Rishishwar L

PROVIDER: S-EPMC5054438 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature

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Mining genomic patterns in Mycobacterium tuberculosis H37Rv using a web server Tuber-Gene.

Rishishwar Lavanya L Pant Bhasker B Pant Kumud K Pardasani Kamal R KR

Genomics, proteomics & bioinformatics 20111001 4-5

Mycobacterium tuberculosis (MTB), causative agent of tuberculosis, is one of the most dreaded diseases of the century. It has long been studied by researchers throughout the world using various wet-lab and dry-lab techniques. In this study, we focus on mining useful patterns at genomic level that can be applied for in silico functional characterization of genes from the MTB complex. The model developed on the basis of the patterns found in this study can correctly identify 99.77% of the input ge ...[more]

PMID: 22196360

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Project description:Mycobacterium tuberculosis H37Ra is an attenuated tubercle bacillus closely related to the virulent type strain M. tuberculosis H37Rv. Despite extensive study, the reason for the decreased virulence of M. tuberculosis H37Ra has not been determined. A genomic approach was therefore initiated to identify genetic differences between M. tuberculosis H37Rv and M. tuberculosis H37Ra as a means of pinpointing the attenuating mutation(s). Digestion with the rare-cutting restriction endonuclease DraI revealed two polymorphisms between the strains: a 480-kb fragment in M. tuberculosis H37Rv was replaced by two fragments of 220 and 260 kb in M. tuberculosis H37Ra, while there was a approximately 7.9-kb DraI fragment in M. tuberculosis H37Ra that had no counterpart in M. tuberculosis H37Rv. As the M. tuberculosis insertion sequence IS6110 contains a single DraI restriction site, it was considered possible that these polymorphisms were the result of IS6110 transposition events in M. tuberculosis H37Ra, events that may have inactivated virulence genes. The 7.9-kb polymorphism was found to be due to the presence of the previously described H37Rv RvD2 deletion in M. tuberculosis H37Ra, with sequence analysis suggesting an IS6110-mediated deletion mechanism for loss of RvD2. Three other IS6110-catalyzed deletions from the M. tuberculosis H37Rv chromosome (RvD3 to RvD5) were also identified, suggesting that this mechanism plays an important role in genome plasticity in the tubercle bacilli. Comparative mapping and sequencing revealed that the 480-kb polymorphism was due to an IS6110 insertion in M. tuberculosis H37Ra near oriC. Complementation of M. tuberculosis H37Ra with a 2.9-kb restriction fragment from M. tuberculosis H37Rv that encompassed the IS6110 insertion did not increase the survival of recombinant M. tuberculosis H37Ra in mice. In conclusion, this study describes the presence and mechanisms of genomic variation between M. tuberculosis H37Ra and M. tuberculosis H37Rv, although the role that they play in the attenuation of M. tuberculosis H37Ra is unclear.

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/.

Project description:The ability to adapt to different conditions is key for Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), to successfully infect human hosts. Adaptations allow the organism to evade the host immune responses during acute infections and persist for an extended period of time during the latent infectious stage. In latently infected individuals, estimated to include one-third of the human population, the organism exists in a variety of metabolic states, which impedes the development of a simple strategy for controlling or eradicating this disease. Direct knowledge of the metabolic states of M. tuberculosis in patients would aid in the management of the disease as well as in forming the basis for developing new drugs and designing more efficacious drug cocktails. Here, we propose an in silico approach to create state-specific models based on readily available gene expression data. The coupling of differential gene expression data with a metabolic network model allowed us to characterize the metabolic adaptations of M. tuberculosis H37Rv to hypoxia. Given the microarray data for the alterations in gene expression, our model predicted reduced oxygen uptake, ATP production changes, and a global change from an oxidative to a reductive tricarboxylic acid (TCA) program. Alterations in the biomass composition indicated an increase in the cell wall metabolites required for cell-wall growth, as well as heightened accumulation of triacylglycerol in preparation for a low-nutrient, low metabolic activity life style. In contrast, the gene expression program in the deletion mutant of dosR, which encodes the immediate hypoxic response regulator, failed to adapt to low-oxygen stress. Our predictions were compatible with recent experimental observations of M. tuberculosis activity under hypoxic and anaerobic conditions. Importantly, alterations in the flow and accumulation of a particular metabolite were not necessarily directly linked to differential gene expression of the enzymes catalyzing the related metabolic reactions.

Project description:Seven-transmembrane region-containing receptors (7TMRs) play central roles in eukaryotic signal transduction. Due to their biomedical importance, thorough mining of 7TMRs from diverse genomes has been an active target of bioinformatics and pharmacogenomics research. The need for new and accurate 7TMR/GPCR prediction tools is paramount with the accelerated rate of acquisition of diverse sequence information. Currently available and often used protein classification methods (e.g., profile hidden Markov Models) are highly accurate for identifying their membership information among already known 7TMR subfamilies. However, these alignment-based methods are less effective for identifying remote similarities, e.g., identifying proteins from highly divergent or possibly new 7TMR families. In this regard, more sensitive (e.g., alignment-free) methods are needed to complement the existing protein classification methods. A better strategy would be to combine different classifiers, from more specific to more sensitive methods, to identify a broader spectrum of 7TMR protein candidates.We developed a Web server, 7TMRmine, by integrating alignment-free and alignment-based classifiers specifically trained to identify candidate 7TMR proteins as well as transmembrane (TM) prediction methods. This new tool enables researchers to easily assess the distribution of GPCR functionality in diverse genomes or individual newly-discovered proteins. 7TMRmine is easily customized and facilitates exploratory analysis of diverse genomes. Users can integrate various alignment-based, alignment-free, and TM-prediction methods in any combination and in any hierarchical order. Sixteen classifiers (including two TM-prediction methods) are available on the 7TMRmine Web server. Not only can the 7TMRmine tool be used for 7TMR mining, but also for general TM-protein analysis. Users can submit protein sequences for analysis, or explore pre-analyzed results for multiple genomes. The server currently includes prediction results and the summary statistics for 68 genomes.7TMRmine facilitates the discovery of 7TMR proteins. By combining prediction results from different classifiers in a multi-level filtering process, prioritized sets of 7TMR candidates can be obtained for further investigation. 7TMRmine can be also used as a general TM-protein classifier. Comparisons of TM and 7TMR protein distributions among 68 genomes revealed interesting differences in evolution of these protein families among major eukaryotic phyla.

Dataset Information

Mining genomic patterns in Mycobacterium tuberculosis H37Rv using a web server Tuber-Gene.

Publications

Mining genomic patterns in Mycobacterium tuberculosis H37Rv using a web server Tuber-Gene.

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