1
|
Banerjee U, Chedere A, Padaki R, Mohan A, Sambaturu N, Singh A, Chandra N. PathTracer Comprehensively Identifies Hypoxia-Induced Dormancy Adaptations in Mycobacterium tuberculosis. J Chem Inf Model 2023; 63:6156-6167. [PMID: 37756209 DOI: 10.1021/acs.jcim.3c00845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/29/2023]
Abstract
Mining large-scale data to discover biologically relevant information remains a challenge despite the rapid development of bioinformatics tools. Here, we have developed a new tool, PathTracer, to identify biologically relevant information flows by mining genome-wide protein-protein interaction networks following integration of gene expression data. PathTracer successfully mines interactions between genes and traces the most perturbed paths of perceived activities under the conditions of the study. We further demonstrated the utility of this tool by identifying adaptation mechanisms of hypoxia-induced dormancy in Mycobacterium tuberculosis (Mtb).
Collapse
Affiliation(s)
- Ushashi Banerjee
- Department of Biochemistry, Indian Institute of Science, Bangalore 560012, Karnataka, India
| | - Adithya Chedere
- Department of Biochemistry, Indian Institute of Science, Bangalore 560012, Karnataka, India
| | - Raksha Padaki
- Department of Biochemistry, Indian Institute of Science, Bangalore 560012, Karnataka, India
| | - Abhilash Mohan
- Department of Biochemistry, Indian Institute of Science, Bangalore 560012, Karnataka, India
| | - Narmada Sambaturu
- IISc Mathematics Initiative, Indian Institute of Science, Bangalore 560012, Karnataka, India
| | - Amit Singh
- Center for Infectious Disease Research, Indian Institute of Science, Bangalore 560012, Karnataka, India
| | - Nagasuma Chandra
- Department of Biochemistry, Indian Institute of Science, Bangalore 560012, Karnataka, India
- IISc Mathematics Initiative, Indian Institute of Science, Bangalore 560012, Karnataka, India
- BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, Karnataka, India
| |
Collapse
|
2
|
Agrawal P, Sambaturu N, Olgun G, Hannenhalli S. A Path-Based Analysis of Infected Cell Line and COVID-19 Patient Transcriptome Reveals Novel Potential Targets and Drugs Against SARS-CoV-2. Front Immunol 2022; 13:918817. [PMID: 35844595 PMCID: PMC9284228 DOI: 10.3389/fimmu.2022.918817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 06/07/2022] [Indexed: 11/13/2022] Open
Abstract
Most transcriptomic studies of SARS-CoV-2 infection have focused on differentially expressed genes, which do not necessarily reveal the genes mediating the transcriptomic changes. In contrast, exploiting curated biological network, our PathExt tool identifies central genes from the differentially active paths mediating global transcriptomic response. Here we apply PathExt to multiple cell line infection models of SARS-CoV-2 and other viruses, as well as to COVID-19 patient-derived PBMCs. The central genes mediating SARS-CoV-2 response in cell lines were uniquely enriched for ATP metabolic process, G1/S transition, leukocyte activation and migration. In contrast, PBMC response reveals dysregulated cell-cycle processes. In PBMC, the most frequently central genes are associated with COVID-19 severity. Importantly, relative to differential genes, PathExt-identified genes show greater concordance with several benchmark anti-COVID-19 target gene sets. We propose six novel anti-SARS-CoV-2 targets ADCY2, ADSL, OCRL, TIAM1, PBK, and BUB1, and potential drugs targeting these genes, such as Bemcentinib, Phthalocyanine, and Conivaptan.
Collapse
Affiliation(s)
- Piyush Agrawal
- Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Narmada Sambaturu
- IISc Mathematics Initiative, Indian Institute of Science, Bangalore, India
| | - Gulden Olgun
- Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Sridhar Hannenhalli
- Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| |
Collapse
|
3
|
Thakur C, Tripathi A, Ravichandran S, Shivananjaiah A, Chakraborty A, Varadappa S, Chikkavenkatappa N, Nagarajan D, Lakshminarasimhaiah S, Singh A, Chandra N. A new blood-based RNA signature (R 9), for monitoring effectiveness of tuberculosis treatment in a South Indian longitudinal cohort. iScience 2022; 25:103745. [PMID: 35118358 PMCID: PMC8800112 DOI: 10.1016/j.isci.2022.103745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Revised: 03/31/2021] [Accepted: 01/06/2022] [Indexed: 11/17/2022] Open
Abstract
Tuberculosis (TB) treatment involves a multidrug regimen for six months, and until two months, it is unclear if treatment is effective. This delay can lead to the evolution of drug resistance, lung damage, disease spread, and transmission. We identify a blood-based 9-gene signature using a computational pipeline that constructs and interrogates a genome-wide transcriptome-integrated protein-interaction network. The identified signature is able to determine treatment response at week 1-2 in three independent public datasets. Signature-based R9-score correctly detected treatment response at individual timepoints (204 samples) from a newly developed South Indian longitudinal cohort involving 32 patients with pulmonary TB. These results are consistent with conventional clinical metrics and can discriminate good from poor treatment responders at week 2 (AUC 0.93(0.81-1.00)). In this work, we provide proof of concept that the R9-score can determine treatment effectiveness, making a case for designing a larger clinical study.
Collapse
Affiliation(s)
- Chandrani Thakur
- Department of Biochemistry, Indian Institute of Science, Bangalore, India
| | - Ashutosh Tripathi
- Department of Microbiology and Cell Biology, Indian Institute of Science, Bangalore, India
- Centre for Infectious Disease Research, Indian Institute of Science, Bangalore, India
| | | | - Akshatha Shivananjaiah
- SDS Tuberculosis Research Centre and Rajiv Gandhi Institute of Chest Diseases, Bangalore, India
| | - Anushree Chakraborty
- SDS Tuberculosis Research Centre and Rajiv Gandhi Institute of Chest Diseases, Bangalore, India
| | - Sreekala Varadappa
- SDS Tuberculosis Research Centre and Rajiv Gandhi Institute of Chest Diseases, Bangalore, India
| | | | - Deepesh Nagarajan
- Department of Biochemistry, Indian Institute of Science, Bangalore, India
| | | | - Amit Singh
- Department of Microbiology and Cell Biology, Indian Institute of Science, Bangalore, India
- Centre for Infectious Disease Research, Indian Institute of Science, Bangalore, India
| | - Nagasuma Chandra
- Department of Biochemistry, Indian Institute of Science, Bangalore, India
- National Mathematics Initiative, Indian Institute of Science, Bangalore, India
- Centre for Biosystems Science and Engineering, Indian Institute of Science, Bangalore, India
| |
Collapse
|
4
|
Sambaturu N, Pusadkar V, Hannenhalli S, Chandra N. PathExt: a general framework for path-based mining of omics-integrated biological networks. Bioinformatics 2021; 37:1254-1262. [PMID: 33305329 PMCID: PMC8599850 DOI: 10.1093/bioinformatics/btaa941] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 09/24/2020] [Accepted: 10/27/2020] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Transcriptomes are routinely used to prioritize genes underlying specific phenotypes. Current approaches largely focus on differentially expressed genes (DEGs), despite the recognition that phenotypes emerge via a network of interactions between genes and proteins, many of which may not be differentially expressed. Furthermore, many practical applications lack sufficient samples or an appropriate control to robustly identify statistically significant DEGs. RESULTS We provide a computational tool-PathExt, which, in contrast to differential genes, identifies differentially active paths when a control is available, and most active paths otherwise, in an omics-integrated biological network. The sub-network comprising such paths, referred to as the TopNet, captures the most relevant genes and processes underlying the specific biological context. The TopNet forms a well-connected graph, reflecting the tight orchestration in biological systems. Two key advantages of PathExt are (i) it can extract characteristic genes and pathways even when only a single sample is available, and (ii) it can be used to study a system even in the absence of an appropriate control. We demonstrate the utility of PathExt via two diverse sets of case studies, to characterize (i) Mycobacterium tuberculosis response upon exposure to 18 antibacterial drugs where only one transcriptomic sample is available for each exposure; and (ii) tissue-relevant genes and processes using transcriptomic data for 39 human tissues. Overall, PathExt is a general tool for prioritizing context-relevant genes in any omics-integrated biological network for any condition(s) of interest, even with a single sample or in the absence of appropriate controls. AVAILABILITYAND IMPLEMENTATION The source code for PathExt is available at https://github.com/NarmadaSambaturu/PathExt. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Narmada Sambaturu
- IISc Mathematics Initiative, Indian Institute of Science, Bangalore, Karnataka 560012, India
| | - Vaidehi Pusadkar
- Department of Biochemistry, Indian Institute of Science, Bangalore, Karnataka 560012, India
| | - Sridhar Hannenhalli
- Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Nagasuma Chandra
- IISc Mathematics Initiative, Indian Institute of Science, Bangalore, Karnataka 560012, India.,Department of Biochemistry, Indian Institute of Science, Bangalore, Karnataka 560012, India
| |
Collapse
|
5
|
Banerjee U, Baloni P, Singh A, Chandra N. Immune Subtyping in Latent Tuberculosis. Front Immunol 2021; 12:595746. [PMID: 33897680 PMCID: PMC8059438 DOI: 10.3389/fimmu.2021.595746] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Accepted: 03/16/2021] [Indexed: 12/13/2022] Open
Abstract
Latent tuberculosis infection (LTBI) poses a major roadblock in the global effort to eradicate tuberculosis (TB). A deep understanding of the host responses involved in establishment and maintenance of TB latency is required to propel the development of sensitive methods to detect and treat LTBI. Given that LTBI individuals are typically asymptomatic, it is challenging to differentiate latently infected from uninfected individuals. A major contributor to this problem is that no clear pattern of host response is linked with LTBI, as molecular correlates of latent infection have been hard to identify. In this study, we have analyzed the global perturbations in host response in LTBI individuals as compared to uninfected individuals and particularly the heterogeneity in such response, across LTBI cohorts. For this, we constructed individualized genome-wide host response networks informed by blood transcriptomes for 136 LTBI cases and have used a sensitive network mining algorithm to identify top-ranked host response subnetworks in each case. Our analysis indicates that despite the high heterogeneity in the gene expression profiles among LTBI samples, clear patterns of perturbation are found in the immune response pathways, leading to grouping LTBI samples into 4 different immune-subtypes. Our results suggest that different subnetworks of molecular perturbations are associated with latent tuberculosis.
Collapse
Affiliation(s)
- Ushashi Banerjee
- Department of Biochemistry, Indian Institute of Science, Bangalore, India
| | - Priyanka Baloni
- Department of Biochemistry, Indian Institute of Science, Bangalore, India
| | - Amit Singh
- Centre for Infectious Disease Research, Indian Institute of Science, Bangalore, India
| | - Nagasuma Chandra
- Department of Biochemistry, Indian Institute of Science, Bangalore, India.,Center for Biosystems Science and Engineering, Indian Institute of Science, Bangalore, India
| |
Collapse
|
6
|
Sarmah DT, Bairagi N, Chatterjee S. Tracing the footsteps of autophagy in computational biology. Brief Bioinform 2020; 22:5985288. [PMID: 33201177 PMCID: PMC8293817 DOI: 10.1093/bib/bbaa286] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2020] [Revised: 09/29/2020] [Accepted: 09/30/2020] [Indexed: 12/11/2022] Open
Abstract
Autophagy plays a crucial role in maintaining cellular homeostasis through the degradation of unwanted materials like damaged mitochondria and misfolded proteins. However, the contribution of autophagy toward a healthy cell environment is not only limited to the cleaning process. It also assists in protein synthesis when the system lacks the amino acids’ inflow from the extracellular environment due to diet consumptions. Reduction in the autophagy process is associated with diseases like cancer, diabetes, non-alcoholic steatohepatitis, etc., while uncontrolled autophagy may facilitate cell death. We need a better understanding of the autophagy processes and their regulatory mechanisms at various levels (molecules, cells, tissues). This demands a thorough understanding of the system with the help of mathematical and computational tools. The present review illuminates how systems biology approaches are being used for the study of the autophagy process. A comprehensive insight is provided on the application of computational methods involving mathematical modeling and network analysis in the autophagy process. Various mathematical models based on the system of differential equations for studying autophagy are covered here. We have also highlighted the significance of network analysis and machine learning in capturing the core regulatory machinery governing the autophagy process. We explored the available autophagic databases and related resources along with their attributes that are useful in investigating autophagy through computational methods. We conclude the article addressing the potential future perspective in this area, which might provide a more in-depth insight into the dynamics of autophagy.
Collapse
Affiliation(s)
| | - Nandadulal Bairagi
- Centre for Mathematical Biology and Ecology, Department of Mathematics, Jadavpur University, Kolkata, India
| | - Samrat Chatterjee
- Translational Health Science and Technology Institute, Faridabad, India
| |
Collapse
|
7
|
Sharma A, Chattopadhyay G, Chopra P, Bhasin M, Thakur C, Agarwal S, Ahmed S, Chandra N, Varadarajan R, Singh R. VapC21 Toxin Contributes to Drug-Tolerance and Interacts With Non-cognate VapB32 Antitoxin in Mycobacterium tuberculosis. Front Microbiol 2020; 11:2037. [PMID: 33042034 PMCID: PMC7517352 DOI: 10.3389/fmicb.2020.02037] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 07/31/2020] [Indexed: 12/13/2022] Open
Abstract
The prokaryotic ubiquitous Toxin-antitoxin (TA) modules encodes for a stable toxin and an unstable antitoxin. VapBC subfamily is the most abundant Type II TA system in M. tuberculosis genome. However, the exact physiological role for most of these Type II TA systems are still unknown. Here, we have comprehensively characterized the VapBC21 TA locus from M. tuberculosis. The overexpression of VapC21 inhibited mycobacterial growth in a bacteriostatic manner and as expected, growth inhibition was abrogated upon co-expression of the cognate antitoxin, VapB21. We observed that the deletion of vapC21 had no noticeable influence on the in vitro and in vivo growth of M. tuberculosis. Using co-expression and biophysical studies, we observed that in addition to VapB21, VapC21 is also able to interact with non-cognate antitoxin, VapB32. The strength of interaction varied between the cognate and non-cognate TA pairs. The overexpression of VapC21 resulted in differential expression of approximately 435 transcripts in M. tuberculosis. The transcriptional profiles obtained upon ectopic expression of VapC21 was similar to those reported in M. tuberculosis upon exposure to stress conditions such as nutrient starvation and enduring hypoxic response. Further, VapC21 overexpression also led to increased expression of WhiB7 regulon and bacterial tolerance to aminoglycosides and ethambutol. Taken together, these results indicate that a complex network of interactions exists between non-cognate TA pairs and VapC21 contributes to drug tolerance in vitro.
Collapse
Affiliation(s)
- Arun Sharma
- Tuberculosis Research Laboratory, Translational Health Science and Technology Institute, Faridabad, India
| | | | - Pankaj Chopra
- Tuberculosis Research Laboratory, Translational Health Science and Technology Institute, Faridabad, India
| | - Munmun Bhasin
- Molecular Biophysics Unit, Indian Institute of Science, Bengaluru, India
| | - Chandrani Thakur
- Department of Biochemistry, Indian Institute of Science, Bengaluru, India
| | - Sakshi Agarwal
- Tuberculosis Research Laboratory, Translational Health Science and Technology Institute, Faridabad, India
| | - Shahbaz Ahmed
- Molecular Biophysics Unit, Indian Institute of Science, Bengaluru, India
| | - Nagasuma Chandra
- Department of Biochemistry, Indian Institute of Science, Bengaluru, India
| | - Raghavan Varadarajan
- Molecular Biophysics Unit, Indian Institute of Science, Bengaluru, India.,Jawaharlal Nehru Centre for Advanced Scientific Research, Bengaluru, India
| | - Ramandeep Singh
- Tuberculosis Research Laboratory, Translational Health Science and Technology Institute, Faridabad, India
| |
Collapse
|
8
|
Mishra S, Shukla P, Bhaskar A, Anand K, Baloni P, Jha RK, Mohan A, Rajmani RS, Nagaraja V, Chandra N, Singh A. Efficacy of β-lactam/β-lactamase inhibitor combination is linked to WhiB4-mediated changes in redox physiology of Mycobacterium tuberculosis. eLife 2017; 6:e25624. [PMID: 28548640 PMCID: PMC5473688 DOI: 10.7554/elife.25624] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2017] [Accepted: 05/24/2017] [Indexed: 12/15/2022] Open
Abstract
Mycobacterium tuberculosis (Mtb) expresses a broad-spectrum β-lactamase (BlaC) that mediates resistance to one of the highly effective antibacterials, β-lactams. Nonetheless, β-lactams showed mycobactericidal activity in combination with β-lactamase inhibitor, clavulanate (Clav). However, the mechanistic aspects of how Mtb responds to β-lactams such as Amoxicillin in combination with Clav (referred as Augmentin [AG]) are not clear. Here, we identified cytoplasmic redox potential and intracellular redox sensor, WhiB4, as key determinants of mycobacterial resistance against AG. Using computer-based, biochemical, redox-biosensor, and genetic strategies, we uncovered a functional linkage between specific determinants of β-lactam resistance (e.g. β-lactamase) and redox potential in Mtb. We also describe the role of WhiB4 in coordinating the activity of β-lactamase in a redox-dependent manner to tolerate AG. Disruption of WhiB4 enhances AG tolerance, whereas overexpression potentiates AG activity against drug-resistant Mtb. Our findings suggest that AG can be exploited to diminish drug-resistance in Mtb through redox-based interventions.
Collapse
Affiliation(s)
- Saurabh Mishra
- Microbiology and Cell Biology, Centre for Infectious Disease Research, Indian Institute of Science, Bangalore, India
| | - Prashant Shukla
- Microbiology and Cell Biology, Centre for Infectious Disease Research, Indian Institute of Science, Bangalore, India
- International Centre for Genetic Engineering and Biotechnology, New Delhi, India
| | | | - Kushi Anand
- Microbiology and Cell Biology, Centre for Infectious Disease Research, Indian Institute of Science, Bangalore, India
| | - Priyanka Baloni
- Department of Biochemistry, Indian Institute of Science, Bangalore, India
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | - Rajiv Kumar Jha
- Microbiology and Cell Biology, Centre for Infectious Disease Research, Indian Institute of Science, Bangalore, India
| | - Abhilash Mohan
- Department of Biochemistry, Indian Institute of Science, Bangalore, India
| | - Raju S Rajmani
- Microbiology and Cell Biology, Centre for Infectious Disease Research, Indian Institute of Science, Bangalore, India
| | - Valakunja Nagaraja
- Microbiology and Cell Biology, Centre for Infectious Disease Research, Indian Institute of Science, Bangalore, India
- Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore, India
| | - Nagasuma Chandra
- Department of Biochemistry, Indian Institute of Science, Bangalore, India
| | - Amit Singh
- Microbiology and Cell Biology, Centre for Infectious Disease Research, Indian Institute of Science, Bangalore, India
| |
Collapse
|
9
|
Abstract
Tuberculosis remains a major global health challenge worldwide, causing more than a million deaths annually. To determine newer methods for detecting and combating the disease, it is necessary to characterise global host responses to infection. Several high throughput omics studies have provided a rich resource including a list of several genes differentially regulated in tuberculosis. An integrated analysis of these studies is necessary to identify a unified response to the infection. Such data integration is met with several challenges owing to platform dependency, patient heterogeneity, and variability in the extent of infection, resulting in little overlap among different datasets. Network-based approaches offer newer alternatives to integrate and compare diverse data. In this study, we describe a meta-analysis of host's whole blood transcriptomic profiles that were integrated into a genome-scale protein-protein interaction network to generate response networks in active tuberculosis, and monitor their behaviour over treatment. We report the emergence of a highly active common core in disease, showing partial reversals upon treatment. The core comprises 380 genes in which STAT1, phospholipid scramblase 1 (PLSCR1), C1QB, OAS1, GBP2 and PSMB9 are prominent hubs. This network captures the interplay between several biological processes including pro-inflammatory responses, apoptosis, complement signalling, cytoskeletal rearrangement, and enhanced cytokine and chemokine signalling. The common core is specific to tuberculosis, and was validated on an independent dataset from an Indian cohort. A network-based approach thus enables the identification of common regulators that characterise the molecular response to infection, providing a platform-independent foundation to leverage maximum insights from available clinical data.
Collapse
|
10
|
Sambarey A, Devaprasad A, Mohan A, Ahmed A, Nayak S, Swaminathan S, D'Souza G, Jesuraj A, Dhar C, Babu S, Vyakarnam A, Chandra N. Unbiased Identification of Blood-based Biomarkers for Pulmonary Tuberculosis by Modeling and Mining Molecular Interaction Networks. EBioMedicine 2016; 15:112-126. [PMID: 28065665 PMCID: PMC5233809 DOI: 10.1016/j.ebiom.2016.12.009] [Citation(s) in RCA: 59] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2016] [Revised: 12/16/2016] [Accepted: 12/16/2016] [Indexed: 02/06/2023] Open
Abstract
Efficient diagnosis of tuberculosis (TB) is met with multiple challenges, calling for a shift of focus from pathogen-centric diagnostics towards identification of host-based multi-marker signatures. Transcriptomics offer a list of differentially expressed genes, but cannot by itself identify the most influential contributors to the disease phenotype. Here, we describe a computational pipeline that adopts an unbiased approach to identify a biomarker signature. Data from RNA sequencing from whole blood samples of TB patients were integrated with a curated genome-wide molecular interaction network, from which we obtain a comprehensive perspective of variations that occur in the host due to TB. We then implement a sensitive network mining method to shortlist gene candidates that are most central to the disease alterations. We then apply a series of filters that include applicability to multiple publicly available datasets as well as additional validation on independent patient samples, and identify a signature comprising 10 genes - FCGR1A, HK3, RAB13, RBBP8, IFI44L, TIMM10, BCL6, SMARCD3, CYP4F3 and SLPI, that can discriminate between TB and healthy controls as well as distinguish TB from latent tuberculosis and HIV in most cases. The signature has the potential to serve as a diagnostic marker of TB.
Collapse
Affiliation(s)
| | | | - Abhilash Mohan
- Department of Biochemistry, IISc, Bangalore 560012, India
| | - Asma Ahmed
- Centre for Infectious Disease Research (CIDR), IISc, Bangalore 560012, India
| | - Soumya Nayak
- Centre for Infectious Disease Research (CIDR), IISc, Bangalore 560012, India
| | - Soumya Swaminathan
- National Institute for Research in Tuberculosis, Mayor Sathiyamoorthy Road, Chetpet, Chennai 600031, India
| | - George D'Souza
- St John's Research Institute, St. John's National Academy of Health Sciences, 560034 Bangalore, India
| | - Anto Jesuraj
- St John's Research Institute, St. John's National Academy of Health Sciences, 560034 Bangalore, India
| | - Chirag Dhar
- St John's Research Institute, St. John's National Academy of Health Sciences, 560034 Bangalore, India
| | - Subash Babu
- NIH-NIRT-ICER, Mayor Sathiyamoorthy Road, Chetpet, Chennai 600031, India
| | - Annapurna Vyakarnam
- Centre for Infectious Disease Research (CIDR), IISc, Bangalore 560012, India; Department of Infectious Diseases, King's College London School of Medicine, Guy's Hospital, Great Maze Pond, London, UK
| | | |
Collapse
|