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Definition of novel cell envelope associated proteins in Triton X-114 extracts of Mycobacterium tuberculosis H37Rv.

ABSTRACT: BACKGROUND: Membrane- and membrane-associated proteins are important for the pathogenicity of bacteria. We have analysed the content of these proteins in virulent Mycobacterium tuberculosis H37Rv using Triton X-114 detergent-phase separation for extraction of lipophilic proteins, followed by their identification with high resolution mass spectrometry. RESULTS: In total, 1417 different proteins were identified. In silico analysis of the identified proteins revealed that 248 proteins had at least one predicted trans-membrane region. Also, 64 of the identified proteins were predicted lipoproteins, and 54 proteins were predicted as outer membrane proteins. Three-hundred-and-ninety-five of the observed proteins, including 91 integral membrane proteins were described for the first time. Comparison of abundance levels of the identified proteins was performed using the exponentially modified protein abundance index (emPAI) which takes into account the number of the observable peptides to the number of experimentally observed peptide ions for a given protein. The outcome showed that among the membrane-and membrane-associated proteins several proteins are present with high relative abundance. Further, a close examination of the lipoprotein LpqG (Rv3623) which is only detected in the membrane fractions of M. tuberculosis but not in M. bovis, revealed that the homologous gene in M. bovis lack the signal peptide and lipobox motif, suggesting impaired export to the membrane. CONCLUSIONS: Altogether, we have identified a substantial proportion of membrane- and membrane-associated proteins of M. tuberculosis H37Rv, compared the relative abundance of the identified proteins and also revealed subtle differences between the different members of the M. tuberculosis complex.

SUBMITTER: Malen H

PROVIDER: S-EPMC2874799 | biostudies-other | 2010

REPOSITORIES: biostudies-other

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Definition of novel cell envelope associated proteins in Triton X-114 extracts of Mycobacterium tuberculosis H37Rv.

Målen Hiwa H Pathak Sharad S Søfteland Tina T de Souza Gustavo A GA Wiker Harald G HG

BMC microbiology 20100429

<h4>Background</h4>Membrane- and membrane-associated proteins are important for the pathogenicity of bacteria. We have analysed the content of these proteins in virulent Mycobacterium tuberculosis H37Rv using Triton X-114 detergent-phase separation for extraction of lipophilic proteins, followed by their identification with high resolution mass spectrometry.<h4>Results</h4>In total, 1417 different proteins were identified. In silico analysis of the identified proteins revealed that 248 proteins ...[more]

PMID: 20429878

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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.

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Definition of novel cell envelope associated proteins in Triton X-114 extracts of Mycobacterium tuberculosis H37Rv.

Publications

Definition of novel cell envelope associated proteins in Triton X-114 extracts of Mycobacterium tuberculosis H37Rv.

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