Transcriptional Profiling and Deriving a Seven-Gene Signature That Discriminates Active and Latent Tuberculosis: An Integrative Bioinformatics Approach

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (M.tb.). Our integrative analysis aims to identify the transcriptional profiling and gene expression signature that distinguish individuals with active TB (ATB) disease, and those with latent tuberculosis infection (LTBI). In the present study, we reanalyzed a microarray dataset (GSE37250) from GEO database and explored the data for differential gene expression analysis between those with ATB and LTBI derived from Malawi and South African cohorts. We used BRB array tool to distinguish DEGs (differentially expressed genes) between ATB and LTBI. Pathway enrichment analysis of DEGs was performed using DAVID bioinformatics tool. The protein–protein interaction (PPI) network of most upregulated genes was constructed using STRING analysis. We have identified 375 upregulated genes and 152 downregulated genes differentially expressed between ATB and LTBI samples commonly shared among Malawi and South African cohorts. The constructed PPI network was significantly enriched with 76 nodes connected to 151 edges. The enriched GO term/pathways were mainly related to expression of IFN stimulated genes, interleukin-1 production, and NOD-like receptor signaling pathway. Downregulated genes were significantly enriched in the Wnt signaling, B cell development, and B cell receptor signaling pathways. The short-listed DEGs were validated in a microarray data from an independent cohort (GSE19491). ROC curve analysis was done to assess the diagnostic accuracy of the gene signature in discrimination of active and latent tuberculosis. Thus, we have derived a seven-gene signature, which included five upregulated genes FCGR1B, ANKRD22, CARD17, IFITM3, TNFAIP6 and two downregulated genes FCGBP and KLF12, as a biomarker for discrimination of active and latent tuberculosis. The identified genes have a sensitivity of 80–100% and specificity of 80–95%. Area under the curve (AUC) value of the genes ranged from 0.84 to 1. This seven-gene signature has a high diagnostic accuracy in discrimination of active and latent tuberculosis

“Vaai Ganam” - a screening program for early detection of oral potentially malignant disorders and oral cancer among truck drivers in Chennai – a cross-sectional survey

Introduction: Truck drivers, though forming an integral part of a vital trade link for the Indian population, lack basic life insurance and health care benefits offered by other organized sectors in Indian Industries. This paper aims to present the initial findings of the “VaaiGanam” program which proposes to identify tobacco use and the prevalence of Oral potentially malignant disorders (OPMDs) among truck drivers who are stationed or passing via Chennai and provide cessation services by behavioral therapy. Methods: This cross-sectional study was conducted by a dental screening team who were involved in data collection and screening of the 747 truck drivers who fulfilled the inclusion and exclusion criteria between Jan to Oct 2022. After data collection, oral examinations were done and suspicious lesions were sought for expert opinion. A standard punch biopsy was taken from those lesions requiring confirmation. Results: Among the 747 subjects who participated in this program, 704 (94.2%) were current users of various tobacco products, with 235 (31.4%) preferring smoking and the rest 469(62.8%) using smokeless tobacco products. Oral mucosal lesions were recorded in 49 (6.5%) of the study population, mostly among tobacco users. Punch/incisional biopsies were taken among 17 of the 49 subjects and oral dysplasia was histopathologically confirmed in 9 (mild epithelial dysplasia = 5; moderate epithelial dysplasia = 4) subjects.  Conclusion: Truck drivers with tobacco and substance abuse are at high risk of developing oral cancer and hence this study emphasizes the importance of periodic oral cancer screening programs for this vulnerable population to identify potentially malignant oral lesions at an early stage

Identification and Characterization of Genetic Determinants of Isoniazid and Rifampicin Resistance in Mycobacterium tuberculosis in Southern India

Abstract: Drug-resistant tuberculosis (TB), one of the leading causes of death worldwide, arises mainly from spontaneous mutations in the genome of Mycobacterium tuberculosis. There is an urgent need to understand the mechanisms by which the mutations confer resistance in order to identify new drug targets and to design new drugs. Previous studies have reported numerous mutations that confer resistance to anti-TB drugs, but there has been little systematic analysis to understand their genetic background and the potential impacts on the drug target stability and/or interactions. Here, we report the analysis of whole-genome sequence data for 98 clinical M. tuberculosis isolates from a city in southern India. The collection was screened for phenotypic resistance and sequenced to mine the genetic mutations conferring resistance to isoniazid and rifampicin. The most frequent mutation among isoniazid and rifampicin isolates was S315T in katG and S450L in rpoB respectively. The impacts of mutations on protein stability, protein-protein interactions and protein-ligand interactions were analysed using both statistical and machine-learning approaches. Drug-resistant mutations were predicted not only to target active sites in an orthosteric manner, but also to act through allosteric mechanisms arising from distant sites, sometimes at the protein-protein interface

Genome Sequencing of Polydrug-, Multidrug-, and Extensively Drug-Resistant Mycobacterium tuberculosis Strains from South India.

The genomes of 16 clinical Mycobacterium tuberculosis isolates were subjected to whole-genome sequencing to identify mutations related to resistance to one or more anti-Mycobacterium drugs. The sequence data will help in understanding the genomic characteristics of M. tuberculosis isolates and their resistance mutations prevalent in South India.This publication presents research supported by the MRC-DBT-funded partnership between the National Institute for Research in Tuberculosis, Chennai, India (Indian Council of Medical Research, New Delhi 5/2-8/LDCE/2014 for S.K., Department of Biotechnology [BT/IN/DBT-MRC (UK)/12/SS/2015-2016] for D.N., M.N., S.P.T., S.S., and U.D.R.) and the University of Cambridge (UK Medical Research Council [MR/N501864/1] for N.K. and S.P.)

Exploring the design space of metadata-focused file management systems

Operating systems both old and new are reliant on the venerable hierarchical file system. For some time now, however, attempts have been made to either define new file systems or to bolt on applications that offer much improved functionality to attach and use metadata. This is because researchers have shown that traditional file systems are not able to meet users' needs in terms of organising large numbers of files effectively, and to support expeditious retrieval of those files when they are needed at a later time. Numerous proposals for post-hierarchical file management systems have been described in the literature; researchers focus on different dimensions of such systems in order to solve or reduce identified limitations. In some cases this leads to significantly different file system architectures, while in other cases new functionality is added on top of a traditional system through special purpose user-space applications. Orthogonally, some proposals focus on tags while others favour named attribute-value pairs. Still other choices are, seemingly, made in an ad hoc and often implicit manner. This paper investigates the different dimensions and associated choices that participate in the proposal of new approaches and that affect their ability to improve on current systems. The Cartesian product of those dimensions and options forms a large design space; we map some of the existing literature onto that design space and discuss approaches to evaluate new proposals

Environmental Response and Genomic Regions Correlated with Rice Root Growth and Yield under Drought in the OryzaSNP Panel across Multiple Study Systems

Funding: This research was funded by the Generation Challenge Program (GCP) project G3008.06, “Targeting Drought-Avoidance Root Traits to Enhance Rice Productivity under Water-Limited Environments". The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

Human Protein Reference Database—2009 update

Human Protein Reference Database (HPRD—http://www.hprd.org/), initially described in 2003, is a database of curated proteomic information pertaining to human proteins. We have recently added a number of new features in HPRD. These include PhosphoMotif Finder, which allows users to find the presence of over 320 experimentally verified phosphorylation motifs in proteins of interest. Another new feature is a protein distributed annotation system—Human Proteinpedia (http://www.humanproteinpedia.org/)—through which laboratories can submit their data, which is mapped onto protein entries in HPRD. Over 75 laboratories involved in proteomics research have already participated in this effort by submitting data for over 15 000 human proteins. The submitted data includes mass spectrometry and protein microarray-derived data, among other data types. Finally, HPRD is also linked to a compendium of human signaling pathways developed by our group, NetPath (http://www.netpath.org/), which currently contains annotations for several cancer and immune signaling pathways. Since the last update, more than 5500 new protein sequences have been added, making HPRD a comprehensive resource for studying the human proteome

Using matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling in order to predict clinical outcomes of patients with heart failure

Background Current risk prediction models in heart failure (HF) including clinical characteristics and biomarkers only have moderate predictive value. The aim of this study was to use matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling to determine if a combination of peptides identified with MALDI-MS will better predict clinical outcomes of patients with HF. Methods A cohort of 100 patients with HF were recruited in the biomarker discovery phase (50 patients who died or had a HF hospital admission vs. 50 patients who did not have an event). The peptide extraction from plasma samples was performed using reversed phase C18. Then samples were analysed using MALDI-MS. A multiple peptide biomarker model was discovered that was able to predict clinical outcomes for patients with HF. Finally, this model was validated in an independent cohort with 100 patients with HF. Results After normalisation and alignment of all the processed spectra, a total of 11,389 peptides (m/z) were detected using MALDI-MS. A multiple biomarker model was developed from 14 plasma peptides that was able to predict clinical outcomes in HF patients with an area under the receiver operating characteristic curve (AUC) of 1.000 (p = 0.0005). This model was validated in an independent cohort with 100 HF patients that yielded an AUC of 0.817 (p = 0.0005) in the biomarker validation phase. Addition of this model to the BIOSTAT risk prediction model increased the predictive probability for clinical outcomes of HF from an AUC value of 0.643 to an AUC of 0.823 (p = 0.0021). Moreover, using the prediction model of fourteen peptides and the composite model of the multiple biomarker of fourteen peptides with the BIOSTAT risk prediction model achieved a better predictive probability of time-to-event in prediction of clinical events in patients with HF (p = 0.0005). Conclusions The results obtained in this study suggest that a cluster of plasma peptides using MALDI-MS can reliably predict clinical outcomes in HF that may help enable precision medicine in HF

Proteomic analysis of human synovial fluid reveals potential diagnostic biomarkers for ankylosing spondylitis

Background Ankylosing spondylitis (AS) is a chronic inflammatory rheumatic disease affecting the axial skeleton and peripheral joints. The etiology of this disease remains poorly understood, but interactions between genetic and environmental factors have been implicated. The present study identified differentially expressed proteins in the synovial fluid (SF) of AS patients to elucidate the underlying cause of AS. Methods A cohort of 40 SF samples from 10 AS and 10 each of rheumatoid arthritis (RA), gout, and osteoarthritis (OA) patients were analyzed by liquid chromatography tandem mass spectrometry (LC–MS/MS) to identify differentially expressed proteins specific to AS. The label-free LC–MS/MS results were verified by western blotting. Results We identified 8 proteins that were > 1.5-fold upregulated in the SF of AS patients compared to that of the disease control groups, including HP, MMP1, MMP3, serum amyloid P-component (APCS), complement factor H-related protein 5 (CFHR5), mannose-binding lectin 2 (MBL2), complement component C9 (C9), and complement C4-A (C4A). CFHR5 and C9 were previously found in serum from AS patients, while APCS was previously found in SF as well as in serum. However, the present study has identified C4A, and MBL2 as potential AS biomarkers for the first time. The expression levels of MMP3, C9, and CFHR5 were verified in AS SF using western blotting. Conclusion We performed quantitative comparative proteomic analysis using by LC–MS/MS of the SF from four disease states: RA, gout, and OA. This systematic comparison revealed novel differentially expressed proteins in AS SF, as well as two previously reported candidate biomarkers. We further verified the expression of MMP3, C9 and CFHR5 by western blot. These proteins may serve as diagnostic or prognostic biomarkers in patients with AS, and may thus improve the clinical outcomes of this serious disease.This work was supported by NFR-2017R1C1B5017278 (CNS) and NRF2018M3C1B7020722 (SHK) of the National Research Foundation, and IBSR008-D1 (JSK) of Institute for Basic Science from the Ministry of Science and ICT of Korea