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Indira Nagar, India

Kumar M.,Tuberculosis Research Center | Meenakshi N.,Government Thiruvotteeswarar Hospital of Thoracic Medicine | Sundaramurthi J.C.,Biomedical Informatics Center | Kaur G.,All India Institute of Medical Sciences | And 2 more authors.
Tuberculosis | Year: 2010

The 6-kDa early secreted antigenic target (ESAT-6) is a T-cell antigen recognized by individuals infected with Mycobacterium tuberculosis. The aim of the study was to identify "protective epitopes" of ESAT-6 protein in the south Indian population. Proliferative and Interferon gamma (IFN-γ) responses to ESAT-6 peptides were studied by flow cytometry and Enzyme linked immunosorbent assay (ELISA). Healthy household contacts (HHC) recognized Esp1 (10/17) and Esp6 (9/17) peptides. Among pulmonary tuberculosis patients (PTB), Esp1 (3/11) and Esp6 (5/11) were recognized. Maximal response (7/10) was found for Esp1 and Esp8 in treated patients (TR). Median values for the responding subjects gave the following results: Esp1 (76 pg/ml), Esp6 (64 pg/ml), induced IFN-γ production in HHC; PTB gave low IFN-γ responses for the peptides. TR responded to the peptides Esp1 (141 pg/ml), Esp8 (102 pg/ml). The proliferation of CD4 cells was similar in both PTB and TR for all peptides; but HHC showed an increase for Esp1 (p < 0.05) and Esp6 (p < 0.01). Esp1 (amino acids aa 1-20) and Esp6 (aa 51-70) were the immunogenic peptides recognized by the alleles HLA DRB1*04 and HLA DRB1*10 among HHC. But the association of the alleles with ESAT-6 peptide presentation needs to be confirmed in a large cohort of subjects. We speculate that ESAT-6 can be used along with other immune-eliciting proteins for vaccine design strategies in south Indian population. © 2009 Elsevier Ltd. All rights reserved. Source

Jacob S.,National Health Research Institute | Nayak S.,National Health Research Institute | Kakar R.,National Health Research Institute | Chaudhari U.K.,National Health Research Institute | And 6 more authors.
Cancer Biology and Therapy | Year: 2016

Telomerase activation is one of the key mechanisms that allow cells to bypass replicative senescence. Telomerase activity is primarily regulated at the level of transcription of its catalytic unit- hTERT. Prostate cancer (PCa), akin to other cancers, is characterized by high telomerase activity. Existing data suggest that hTERT expression and telomerase activity are positively regulated by androgenic stimuli in androgen-dependent prostate cancer (ADPC) cells. A part of the present study reaffirmed this by demonstrating a decline in the hTERT expression and telomerase activity on “loss of AR” in ADPC cells. The study further addressed 2 unresolved queries, i) whether AR-mediated signaling is of any relevance to hTERT expression in castration-resistant prostate cancer (CRPC) and ii) whether this signaling involves EGR1. Our data suggest that AR-mediated signaling negatively regulates hTERT expression in CRPC cells. Incidental support for the possibility of EGR1 being a regulator of hTERT expression in PCa was provided by i) immunolocalization of hTERT and EGR1 proteins in the same cell type (secretory epithelium) of PCa and BPH tissues; ii) significantly (p< 0.001) higher levels of both these proteins in CRPC (PC3 and DU145), compared with ADPC (LNCaP) cells. A direct evidence for the role of EGR1 in hTERT expression was evident by a significant (p<0.0001) decrease in the hTERT transcript levels in the EGR1-silenced CRPC cells. Further, “gain of AR” led to a significant reduction in the levels of hTERT and EGR1 in CRPC cells. However, restoration of EGR1 levels prevented the decline in the hTERT transcript levels in these cells. Taken together, our data indicate that AR regulates the expression of EGR1, which in turn acts as a positive regulator of hTERT expression in CRPC cells. Thus, AR exerts an inhibitory effect on hTERT expression and telomerase activity by modulating EGR1 levels in CRPC cells. © 2016 National Research In Reproductive Health, Indian Council Of Medical Research. Source

Kumar M.,Tuberculosis Research Center | Sundaramurthi J.C.,Biomedical Informatics Center | Mehra N.K.,All India Institute of Medical Sciences | Kaur G.,All India Institute of Medical Sciences | Raja A.,Tuberculosis Research Center
Medical Microbiology and Immunology | Year: 2010

The Mycobacterium tuberculosis (M. tuberculosis)-specific culture filtrate protein-10 (CFP-10) is highly recognized by M. tuberculosis infected subjects. In the present study, the proliferative response and IFN-γ secretion was found for C-terminal peptides of the protein (Cfp651-70, Cfp7 61-80, Cfp871-90, and Cfp981-100). The alleles HLA DRB104 and HLA DRB110 recognized the C-terminal peptides Cfp7, Cfp8, and Cfp9 in HHC. Cfp6 was predominantly recognized by the alleles HLA DRB103 and HLA DRB115 by PTB. The minimal nonameric epitopes from the C-terminal region were CFP-1056-64 and CFP-1076-84. These two peptides deserve attention for inclusion in a vaccine against tuberculosis in this region. © 2009 Springer-Verlag. Source

Sundaramurthi J.C.,Biomedical Informatics Center | Brindha S.,University of Madras | Reddy T.B.K.,Stanford University | Hanna L.E.,Biomedical Informatics Center
Tuberculosis | Year: 2012

Integration of biological data on gene sequence, genome annotation, gene expression, metabolic pathways, protein structure, drug target prioritization and selection, has resulted in several online bioinformatics databases and tools for Mycobacterium tuberculosis. Alongside there has been a growth in the list of cheminformatics databases for small molecules and tools to facilitate drug discovery. In spite of these efforts there is a noticeable lag in the drug discovery process which is an urgent need in the case of emerging and re-emerging infectious diseases. For example, more than 25 online databases are available freely for tuberculosis and yet these resources have not been exploited optimally. Informatics-centered drug discovery based on the integration and analysis of both bioinformatics and cheminformatics data could fill in the gap and help to accelerate the process of drug discovery. This article aims to review the current standing of developments in tuberculosis-bioinformatics and highlight areas where integration of existing resources could lead to acceleration of drug discovery against tuberculosis. Such an approach could be adapted for other diseases as well. © 2011 Elsevier Ltd. All rights reserved. Source

Malik A.,Biomedical Informatics Center | Malik A.,Jamia Millia Islamia University | Firoz A.,Biomedical Informatics Center | Jha V.,Biomedical Informatics Center | Ahmad S.,Japan National Institute of Biomedical Innovation
Advances in Bioinformatics | Year: 2010

Understanding of the three-dimensional structures of proteins that interact with carbohydrates covalently (glycoproteins) as well as noncovalently (protein-carbohydrate complexes) is essential to many biological processes and plays a significant role in normal and disease-associated functions. It is important to have a central repository of knowledge available about these protein-carbohydrate complexes as well as preprocessed data of predicted structures. This can be significantly enhanced by tools de novo which can predict carbohydrate-binding sites for proteins in the absence of structure of experimentally known binding site. PROCARB is an open-access database comprising three independently working components, namely, (i) Core PROCARB module, consisting of three-dimensional structures of protein-carbohydrate complexes taken from Protein Data Bank (PDB), (ii) Homology Models module, consisting of manually developed three-dimensional models of N-linked and O-linked glycoproteins of unknown three-dimensional structure, and (iii) CBS-Pred prediction module, consisting of web servers to predict carbohydrate-binding sites using single sequence or server-generated PSSM. Several precomputed structural and functional properties of complexes are also included in the database for quick analysis. In particular, information about function, secondary structure, solvent accessibility, hydrogen bonds and literature reference, and so forth, is included. In addition, each protein in the database is mapped to Uniprot, Pfam, PDB, and so forth. © 2010 Adeel Malik et al. Source

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