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Arya R, Shakya H, Chaurasia R, Kumar S, Vinetz JM, Kim JJ. Computational reassessment of RNA-seq data reveals key genes in active tuberculosis. PLoS One 2024; 19:e0305582. [PMID: 38935691 PMCID: PMC11210783 DOI: 10.1371/journal.pone.0305582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 05/31/2024] [Indexed: 06/29/2024] Open
Abstract
BACKGROUND Tuberculosis is a serious life-threatening disease among the top global health challenges and rapid and effective diagnostic biomarkers are vital for early diagnosis especially given the increasing prevalence of multidrug resistance. METHODS Two human whole blood microarray datasets, GSE42826 and GSE42830 were retrieved from publicly available gene expression omnibus (GEO) database. Deregulated genes (DEGs) were identified using GEO2R online tool and Gene Ontology (GO), protein-protein interaction (PPI) network analysis was performed using Metascape and STRING databases. Significant genes (n = 8) were identified using T-test/ANOVA and Molecular Complex Detection (MCODE) score ≥10, which was validated in GSE34608 dataset. The diagnostic potential of three biomarkers was assessed using Area Under Curve (AUC) of Receiver Operating Characteristic (ROC) plot. The transcriptional levels of these genes were also examined in a separate dataset GSE31348, to monitor the patterns of variation during tuberculosis treatment. RESULTS A total of 62 common DEGs (57 upregulated, 7 downregulated genes) were identified in two discovery datasets. GO functions and pathway enrichment analysis shed light on the functional roles of these DEGs in immune response and type-II interferon signaling. The genes in Module-1 (n = 18) were linked to innate immune response, interferon-gamma signaling. The common genes (n = 8) were validated in GSE34608 dataset, that corroborates the results obtained from discovery sets. The gene expression levels demonstrated responsiveness to Mtb infection during anti-TB therapy in GSE31348 dataset. In GSE34608 dataset, the expression levels of three specific genes, GBP5, IFITM3, and EPSTI1, emerged as potential diagnostic makers. In combination, these genes scored remarkable diagnostic performance with 100% sensitivity and 89% specificity, resulting in an impressive Area Under Curve (AUC) of 0.958. However, GBP5 alone showed the highest AUC of 0.986 with 100% sensitivity and 89% specificity. CONCLUSIONS The study presents valuable insights into the critical gene network perturbed during tuberculosis. These genes are determinants for assessing the effectiveness of an anti-TB response and distinguishing between active TB and healthy individuals. GBP5, IFITM3 and EPSTI1 emerged as candidate core genes in TB and holds potential as novel molecular targets for the development of interventions in the treatment of TB.
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Affiliation(s)
- Rakesh Arya
- Department of Biotechnology, Yeungnam University, Gyeongsan, Gyeongbuk, South Korea
| | - Hemlata Shakya
- Department of Biomedical Engineering, Shri G. S. Institute of Technology and Science, Indore, Madhya Pradesh, India
| | - Reetika Chaurasia
- Department of Internal Medicine, Section of Infectious Diseases, Yale University School of Medicine, New Haven, CT, United States of America
| | - Surendra Kumar
- Department of Orthopaedic Surgery, The Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
| | - Joseph M. Vinetz
- Department of Internal Medicine, Section of Infectious Diseases, Yale University School of Medicine, New Haven, CT, United States of America
| | - Jong Joo Kim
- Department of Biotechnology, Yeungnam University, Gyeongsan, Gyeongbuk, South Korea
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Wang X, VanValkenberg A, Odom AR, Ellner JJ, Hochberg NS, Salgame P, Patil P, Johnson WE. Comparison of gene set scoring methods for reproducible evaluation of tuberculosis gene signatures. BMC Infect Dis 2024; 24:610. [PMID: 38902649 PMCID: PMC11191245 DOI: 10.1186/s12879-024-09457-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Accepted: 05/31/2024] [Indexed: 06/22/2024] Open
Abstract
BACKGROUND Blood-based transcriptional gene signatures for tuberculosis (TB) have been developed with potential use to diagnose disease. However, an unresolved issue is whether gene set enrichment analysis of the signature transcripts alone is sufficient for prediction and differentiation or whether it is necessary to use the original model created when the signature was derived. Intra-method comparison is complicated by the unavailability of original training data and missing details about the original trained model. To facilitate the utilization of these signatures in TB research, comparisons between gene set scoring methods cross-data validation of original model implementations are needed. METHODS We compared the performance of 19 TB gene signatures across 24 transcriptomic datasets using both rrebuilt original models and gene set scoring methods. Existing gene set scoring methods, including ssGSEA, GSVA, PLAGE, Singscore, and Zscore, were used as alternative approaches to obtain the profile scores. The area under the ROC curve (AUC) value was computed to measure performance. Correlation analysis and Wilcoxon paired tests were used to compare the performance of enrichment methods with the original models. RESULTS For many signatures, the predictions from gene set scoring methods were highly correlated and statistically equivalent to the results given by the original models. In some cases, PLAGE outperformed the original models when considering signatures' weighted mean AUC values and the AUC results within individual studies. CONCLUSION Gene set enrichment scoring of existing gene sets can distinguish patients with active TB disease from other clinical conditions with equivalent or improved accuracy compared to the original methods and models. These data justify using gene set scoring methods of published TB gene signatures for predicting TB risk and treatment outcomes, especially when original models are difficult to apply or implement.
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Affiliation(s)
- Xutao Wang
- Department of Biostatistics, Boston University, Boston, MA, USA
- Division of Computational Biomedicine and Bioinformatics Program, Boston University, Boston, MA, USA
| | - Arthur VanValkenberg
- Division of Infectious Disease, Center for Data Science, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Aubrey R Odom
- Division of Computational Biomedicine and Bioinformatics Program, Boston University, Boston, MA, USA
| | - Jerrold J Ellner
- Department of Medicine, Center for Emerging Pathogens, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Natasha S Hochberg
- Boston Medical Center, Boston, MA, USA
- Section of Infectious Diseases, Boston University School of Medicine, Boston, MA, USA
| | - Padmini Salgame
- Department of Medicine, Center for Emerging Pathogens, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Prasad Patil
- Department of Biostatistics, Boston University, Boston, MA, USA
| | - W Evan Johnson
- Division of Infectious Disease, Center for Data Science, Rutgers New Jersey Medical School, Newark, NJ, USA.
- Department of Medicine, Center for Emerging Pathogens, Rutgers New Jersey Medical School, Newark, NJ, USA.
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Li L, Wang T, Chen Z, Liang J, Ding H. Multi-cohort analysis reveals immune subtypes and predictive biomarkers in tuberculosis. Sci Rep 2024; 14:13345. [PMID: 38858405 PMCID: PMC11164950 DOI: 10.1038/s41598-024-63365-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Accepted: 05/28/2024] [Indexed: 06/12/2024] Open
Abstract
Tuberculosis (TB) remains a significant global health threat, necessitating effective strategies for diagnosis, prognosis, and treatment. This study employs a multi-cohort analysis approach to unravel the immune microenvironment of TB and delineate distinct subtypes within pulmonary TB (PTB) patients. Leveraging functional gene expression signatures (Fges), we identified three PTB subtypes (C1, C2, and C3) characterized by differential immune-inflammatory activity. These subtypes exhibited unique molecular features, functional disparities, and cell infiltration patterns, suggesting varying disease trajectories and treatment responses. A neural network model was developed to predict PTB progression based on a set of biomarker genes, achieving promising accuracy. Notably, despite both genders being affected by PTB, females exhibited a relatively higher risk of deterioration. Additionally, single-cell analysis provided insights into enhanced major histocompatibility complex (MHC) signaling in the rapid clearance of early pathogens in the C3 subgroup. This comprehensive approach offers valuable insights into PTB pathogenesis, facilitating personalized treatment strategies and precision medicine interventions.
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Affiliation(s)
- Ling Li
- The Eighth Medical Center of the PLA General Hospital, Beijing, 100091, People's Republic of China
| | - Tao Wang
- The Eighth Medical Center of the PLA General Hospital, Beijing, 100091, People's Republic of China
| | - Zhi Chen
- The Eighth Medical Center of the PLA General Hospital, Beijing, 100091, People's Republic of China
| | - Jianqin Liang
- The Eighth Medical Center of the PLA General Hospital, Beijing, 100091, People's Republic of China
| | - Hong Ding
- The Eighth Medical Center of the PLA General Hospital, Beijing, 100091, People's Republic of China.
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Biswas VK, Sen K, Ahad A, Ghosh A, Verma S, Pati R, Prusty S, Nayak SP, Podder S, Kumar D, Gupta B, Raghav SK. NCoR1 controls Mycobacterium tuberculosis growth in myeloid cells by regulating the AMPK-mTOR-TFEB axis. PLoS Biol 2023; 21:e3002231. [PMID: 37590294 PMCID: PMC10465006 DOI: 10.1371/journal.pbio.3002231] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 08/29/2023] [Accepted: 07/04/2023] [Indexed: 08/19/2023] Open
Abstract
Mycobacterium tuberculosis (Mtb) defends host-mediated killing by repressing the autophagolysosome machinery. For the first time, we report NCoR1 co-repressor as a crucial host factor, controlling Mtb growth in myeloid cells by regulating both autophagosome maturation and lysosome biogenesis. We found that the dynamic expression of NCoR1 is compromised in human peripheral blood mononuclear cells (PBMCs) during active Mtb infection, which is rescued upon prolonged anti-mycobacterial therapy. In addition, a loss of function in myeloid-specific NCoR1 considerably exacerbates the growth of M. tuberculosis in vitro in THP1 differentiated macrophages, ex vivo in bone marrow-derived macrophages (BMDMs), and in vivo in NCoR1MyeKO mice. We showed that NCoR1 depletion controls the AMPK-mTOR-TFEB signalling axis by fine-tuning cellular adenosine triphosphate (ATP) homeostasis, which in turn changes the expression of proteins involved in autophagy and lysosomal biogenesis. Moreover, we also showed that the treatment of NCoR1 depleted cells by Rapamycin, Antimycin-A, or Metformin rescued the TFEB activity and LC3 levels, resulting in enhanced Mtb clearance. Similarly, expressing NCoR1 exogenously rescued the AMPK-mTOR-TFEB signalling axis and Mtb killing. Overall, our data revealed a central role of NCoR1 in Mtb pathogenesis in myeloid cells.
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Affiliation(s)
- Viplov Kumar Biswas
- Immuno-genomics & Systems Biology Laboratory, Institute of Life Sciences (ILS), Bhubaneswar, India
- School of Biotechnology, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, India
| | - Kaushik Sen
- Immuno-genomics & Systems Biology Laboratory, Institute of Life Sciences (ILS), Bhubaneswar, India
- Regional Centre for Biotechnology, Faridabad, India
| | - Abdul Ahad
- Immuno-genomics & Systems Biology Laboratory, Institute of Life Sciences (ILS), Bhubaneswar, India
| | - Arup Ghosh
- Immuno-genomics & Systems Biology Laboratory, Institute of Life Sciences (ILS), Bhubaneswar, India
- School of Biotechnology, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, India
| | - Surbhi Verma
- Molecular Medicine: Cellular Immunology, International Centre for Genetic Engineering and Biotechnology (ICGEB), New Delhi, India
| | - Rashmirekha Pati
- Immuno-genomics & Systems Biology Laboratory, Institute of Life Sciences (ILS), Bhubaneswar, India
| | - Subhasish Prusty
- Immuno-genomics & Systems Biology Laboratory, Institute of Life Sciences (ILS), Bhubaneswar, India
- Regional Centre for Biotechnology, Faridabad, India
| | - Sourya Prakash Nayak
- Immuno-genomics & Systems Biology Laboratory, Institute of Life Sciences (ILS), Bhubaneswar, India
| | - Sreeparna Podder
- Immuno-genomics & Systems Biology Laboratory, Institute of Life Sciences (ILS), Bhubaneswar, India
- School of Biotechnology, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, India
| | - Dhiraj Kumar
- Molecular Medicine: Cellular Immunology, International Centre for Genetic Engineering and Biotechnology (ICGEB), New Delhi, India
| | - Bhawna Gupta
- School of Biotechnology, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, India
| | - Sunil Kumar Raghav
- Immuno-genomics & Systems Biology Laboratory, Institute of Life Sciences (ILS), Bhubaneswar, India
- School of Biotechnology, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, India
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Vargas R, Abbott L, Bower D, Frahm N, Shaffer M, Yu WH. Gene signature discovery and systematic validation across diverse clinical cohorts for TB prognosis and response to treatment. PLoS Comput Biol 2023; 19:e1010770. [PMID: 37471455 PMCID: PMC10393163 DOI: 10.1371/journal.pcbi.1010770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 06/15/2023] [Indexed: 07/22/2023] Open
Abstract
While blood gene signatures have shown promise in tuberculosis (TB) diagnosis and treatment monitoring, most signatures derived from a single cohort may be insufficient to capture TB heterogeneity in populations and individuals. Here we report a new generalized approach combining a network-based meta-analysis with machine-learning modeling to leverage the power of heterogeneity among studies. The transcriptome datasets from 57 studies (37 TB and 20 viral infections) across demographics and TB disease states were used for gene signature discovery and model training and validation. The network-based meta-analysis identified a common 45-gene signature specific to active TB disease across studies. Two optimized random forest regression models, using the full or partial 45-gene signature, were then established to model the continuum from Mycobacterium tuberculosis infection to disease and treatment response. In model validation, using pooled multi-cohort datasets to mimic the real-world setting, the model provides robust predictive performance for incipient to active TB risk over a 2.5-year period with an AUROC of 0.85, 74.2% sensitivity, and 78.3% specificity, which approximates the minimum criteria (>75% sensitivity and >75% specificity) within the WHO target product profile for prediction of progression to TB. Moreover, the model strongly discriminates active TB from viral infection (AUROC 0.93, 95% CI 0.91-0.94). For treatment monitoring, the TB scores generated by the model statistically correlate with treatment responses over time and were predictive, even before treatment initiation, of standard treatment clinical outcomes. We demonstrate an end-to-end gene signature model development scheme that considers heterogeneity for TB risk estimation and treatment monitoring.
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Affiliation(s)
- Roger Vargas
- Bill & Melinda Gates Medical Research Institute, Cambridge, Massachusetts, United States of America
- Harvard University, Cambridge, Massachusetts, United States of America
| | - Liam Abbott
- Bill & Melinda Gates Medical Research Institute, Cambridge, Massachusetts, United States of America
| | - Daniel Bower
- Bill & Melinda Gates Medical Research Institute, Cambridge, Massachusetts, United States of America
| | - Nicole Frahm
- Bill & Melinda Gates Medical Research Institute, Cambridge, Massachusetts, United States of America
| | - Mike Shaffer
- Bill & Melinda Gates Medical Research Institute, Cambridge, Massachusetts, United States of America
| | - Wen-Han Yu
- Bill & Melinda Gates Medical Research Institute, Cambridge, Massachusetts, United States of America
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Wang X, VanValkenberg A, Odom-Mabey AR, Ellner JJ, Hochberg NS, Salgame P, Patil P, Johnson WE. Comparison of gene set scoring methods for reproducible evaluation of multiple tuberculosis gene signatures. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.19.520627. [PMID: 36711818 PMCID: PMC9882404 DOI: 10.1101/2023.01.19.520627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Rationale Many blood-based transcriptional gene signatures for tuberculosis (TB) have been developed with potential use to diagnose disease, predict risk of progression from infection to disease, and monitor TB treatment outcomes. However, an unresolved issue is whether gene set enrichment analysis (GSEA) of the signature transcripts alone is sufficient for prediction and differentiation, or whether it is necessary to use the original statistical model created when the signature was derived. Intra-method comparison is complicated by the unavailability of original training data, missing details about the original trained model, and inadequate publicly-available software tools or source code implementing models. To facilitate these signatures' replicability and appropriate utilization in TB research, comprehensive comparisons between gene set scoring methods with cross-data validation of original model implementations are needed. Objectives We compared the performance of 19 TB gene signatures across 24 transcriptomic datasets using both re-rebuilt original models and gene set scoring methods to evaluate whether gene set scoring is a reasonable proxy to the performance of the original trained model. We have provided an open-access software implementation of the original models for all 19 signatures for future use. Methods We considered existing gene set scoring and machine learning methods, including ssGSEA, GSVA, PLAGE, Singscore, and Zscore, as alternative approaches to profile gene signature performance. The sample-size-weighted mean area under the curve (AUC) value was computed to measure each signature's performance across datasets. Correlation analysis and Wilcoxon paired tests were used to analyze the performance of enrichment methods with the original models. Measurement and Main Results For many signatures, the predictions from gene set scoring methods were highly correlated and statistically equivalent to the results given by the original diagnostic models. PLAGE outperformed all other gene scoring methods. In some cases, PLAGE outperformed the original models when considering signatures' weighted mean AUC values and the AUC results within individual studies. Conclusion Gene set enrichment scoring of existing blood-based biomarker gene sets can distinguish patients with active TB disease from latent TB infection and other clinical conditions with equivalent or improved accuracy compared to the original methods and models. These data justify using gene set scoring methods of published TB gene signatures for predicting TB risk and treatment outcomes, especially when original models are difficult to apply or implement.
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Affiliation(s)
- Xutao Wang
- Department of Biostatistics, Boston University, Boston, MA, USA
- Division of Computational Biomedicine and Bioinformatics Program, Boston University, Boston, MA, USA
| | - Arthur VanValkenberg
- Division of Computational Biomedicine and Bioinformatics Program, Boston University, Boston, MA, USA
| | - Aubrey R. Odom-Mabey
- Division of Computational Biomedicine and Bioinformatics Program, Boston University, Boston, MA, USA
| | - Jerrold J. Ellner
- Department of Medicine, Center for Emerging Pathogens, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Natasha S. Hochberg
- Boston Medical Center, Boston, MA, USA
- Section of Infectious Diseases, Boston University School of Medicine, Boston, MA, USA
| | - Padmini Salgame
- Department of Medicine, Center for Emerging Pathogens, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Prasad Patil
- Department of Biostatistics, Boston University, Boston, MA, USA
| | - W. Evan Johnson
- Division of Infectious Disease, Center for Data Science, Rutgers New Jersey Medical School, Newark, NJ, USA
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Hu W, Xu K. Research progress on genetic control of host susceptibility to tuberculosis. Zhejiang Da Xue Xue Bao Yi Xue Ban 2022; 51:679-690. [PMID: 36915969 PMCID: PMC10262011 DOI: 10.3724/zdxbyxb-2022-0484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 10/11/2022] [Indexed: 02/16/2023]
Abstract
The "Lübeck disaster", twins studies, adoptees studies, and other epidemiological observational studies have shown that host genetic factors play a significant role in determining the host susceptibility to Mycobacterium tuberculosis infection and pathogenesis of tuberculosis. From linkage analyses to genome-wide association studies, it has been discovered that human leucocyte antigen (HLA) genes as well as non-HLA genes (such as SLC11A1, VDR, ASAP1 as well as genes encoding cytokines and pattern recognition receptors) are associated with tuberculosis susceptibility. To provide ideas for subsequent studies about risk prediction of MTB infection and the diagnosis and treatment of tuberculosis, we review the research progress on tuberculosis susceptibility related genes in recent years, focusing on the correlation of HLA genes and non-HLA genes with the pathogenesis of tuberculosis. We also report the results of an enrichment analysis of the genes mentioned in the article. Most of these genes appear to be involved in the regulation of immune system and inflammation, and are also closely related to autoimmune diseases.
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Kaipilyawar V, Zhao Y, Wang X, Joseph NM, Knudsen S, Prakash Babu S, Muthaiah M, Hochberg NS, Sarkar S, Horsburgh CR, Ellner JJ, Johnson WE, Salgame P. Development and Validation of a Parsimonious Tuberculosis Gene Signature Using the digital NanoString nCounter Platform. Clin Infect Dis 2022; 75:1022-1030. [PMID: 35015839 PMCID: PMC9522394 DOI: 10.1093/cid/ciac010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Blood-based biomarkers for diagnosing active tuberculosis (TB), monitoring treatment response, and predicting risk of progression to TB disease have been reported. However, validation of the biomarkers across multiple independent cohorts is scarce. A robust platform to validate TB biomarkers in different populations with clinical end points is essential to the development of a point-of-care clinical test. NanoString nCounter technology is an amplification-free digital detection platform that directly measures mRNA transcripts with high specificity. Here, we determined whether NanoString could serve as a platform for extensive validation of candidate TB biomarkers. METHODS The NanoString platform was used for performance evaluation of existing TB gene signatures in a cohort in which signatures were previously evaluated on an RNA-seq dataset. A NanoString codeset that probes 107 genes comprising 12 TB signatures and 6 housekeeping genes (NS-TB107) was developed and applied to total RNA derived from whole blood samples of TB patients and individuals with latent TB infection (LTBI) from South India. The TBSignatureProfiler tool was used to score samples for each signature. An ensemble of machine learning algorithms was used to derive a parsimonious biomarker. RESULTS Gene signatures present in NS-TB107 had statistically significant discriminative power for segregating TB from LTBI. Further analysis of the data yielded a NanoString 6-gene set (NANO6) that when tested on 10 published datasets was highly diagnostic for active TB. CONCLUSIONS The NanoString nCounter system provides a robust platform for validating existing TB biomarkers and deriving a parsimonious gene signature with enhanced diagnostic performance.
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Affiliation(s)
- Vaishnavi Kaipilyawar
- Department of Medicine, Center for Emerging Pathogens, Rutgers-New Jersey Medical School, Newark, New Jersey, USA
| | - Yue Zhao
- Department of Medicine, Division of Computational Biomedicine, Boston University School of Medicine, Boston, Massachusetts, USA
| | - Xutao Wang
- Department of Medicine, Division of Computational Biomedicine, Boston University School of Medicine, Boston, Massachusetts, USA
| | - Noyal M Joseph
- Department of Microbiology, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
| | | | - Senbagavalli Prakash Babu
- Department of Preventive and Social Medicine, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
| | - Muthuraj Muthaiah
- Department of Microbiology, State TB Training and Demonstration Center, Government Hospital for Chest Disease, Gorimedu, Puducherry, India
| | - Natasha S Hochberg
- Boston Medical Center, Boston, Massachusetts, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, USA
- Department of Medicine, Section of Infectious Diseases, Boston University School of Medicine, Boston, Massachusetts, USA
| | - Sonali Sarkar
- Department of Preventive and Social Medicine, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
| | - Charles R Horsburgh
- Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Jerrold J Ellner
- Department of Medicine, Center for Emerging Pathogens, Rutgers-New Jersey Medical School, Newark, New Jersey, USA
| | - W Evan Johnson
- Department of Medicine, Division of Computational Biomedicine, Boston University School of Medicine, Boston, Massachusetts, USA
- Bioinformatics Program, Boston University, Boston, Massachusetts, USA
| | - Padmini Salgame
- Department of Medicine, Center for Emerging Pathogens, Rutgers-New Jersey Medical School, Newark, New Jersey, USA
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VanValkenburg A, Kaipilyawar V, Sarkar S, Lakshminarayanan S, Cintron C, Prakash Babu S, Knudsen S, Joseph NM, Horsburgh CR, Sinha P, Ellner JJ, Narasimhan PB, Johnson WE, Hochberg NS, Salgame P. Malnutrition leads to increased inflammation and expression of tuberculosis risk signatures in recently exposed household contacts of pulmonary tuberculosis. Front Immunol 2022; 13:1011166. [PMID: 36248906 PMCID: PMC9554585 DOI: 10.3389/fimmu.2022.1011166] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 09/05/2022] [Indexed: 11/17/2022] Open
Abstract
Background Most individuals exposed to Mycobacterium tuberculosis (Mtb) develop latent tuberculosis infection (LTBI) and remain at risk for progressing to active tuberculosis disease (TB). Malnutrition is an important risk factor driving progression from LTBI to TB. However, the performance of blood-based TB risk signatures in malnourished individuals with LTBI remains unexplored. The aim of this study was to determine if malnourished and control individuals had differences in gene expression, immune pathways and TB risk signatures. Methods We utilized data from 50 tuberculin skin test positive household contacts of persons with TB - 18 malnourished participants (body mass index [BMI] < 18.5 kg/m2) and 32 controls (individuals with BMI ≥ 18.5 kg/m2). Whole blood RNA-sequencing was conducted to identify differentially expressed genes (DEGs). Ingenuity Pathway Analysis was applied to the DEGs to identify top canonical pathways and gene regulators. Gene enrichment methods were then employed to score the performance of published gene signatures associated with progression from LTBI to TB. Results Malnourished individuals had increased activation of inflammatory pathways, including pathways involved in neutrophil activation, T-cell activation and proinflammatory IL-1 and IL-6 cytokine signaling. Consistent with known association of inflammatory pathway activation with progression to TB disease, we found significantly increased expression of the RISK4 (area under the curve [AUC] = 0.734) and PREDICT29 (AUC = 0.736) progression signatures in malnourished individuals. Conclusion Malnourished individuals display a peripheral immune response profile reflective of increased inflammation and a concomitant increased expression of risk signatures predicting progression to TB. With validation in prospective clinical cohorts, TB risk biomarkers have the potential to identify malnourished LTBI for targeted therapy.
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Affiliation(s)
- Arthur VanValkenburg
- Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA, United States
- Bioinformatics Program, Boston University, Boston, MA, United States
| | - Vaishnavi Kaipilyawar
- Department of Medicine, Center for Emerging Pathogens, Rutgers-New Jersey Medical School, Newark, NJ, United States
| | - Sonali Sarkar
- Department of Preventive and Social Medicine, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
| | - Subitha Lakshminarayanan
- Department of Preventive and Social Medicine, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
| | - Chelsie Cintron
- Department of Medicine, Boston Medical Center, Boston, MA, United States
| | - Senbagavalli Prakash Babu
- Department of Preventive and Social Medicine, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
| | - Selby Knudsen
- Department of Medicine, Boston Medical Center, Boston, MA, United States
| | - Noyal Mariya Joseph
- Department of Microbiology, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
| | - C. Robert Horsburgh
- Section of Infectious Diseases, Boston University School of Medicine, Boston, MA, United States
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, United States
| | - Pranay Sinha
- Department of Medicine, Boston Medical Center, Boston, MA, United States
| | - Jerrold J. Ellner
- Department of Medicine, Center for Emerging Pathogens, Rutgers-New Jersey Medical School, Newark, NJ, United States
| | - Prakash Babu Narasimhan
- Department of Clinical Immunology, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
| | - W. Evan Johnson
- Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA, United States
- Bioinformatics Program, Boston University, Boston, MA, United States
| | - Natasha S. Hochberg
- Department of Medicine, Boston Medical Center, Boston, MA, United States
- Section of Infectious Diseases, Boston University School of Medicine, Boston, MA, United States
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, United States
| | - Padmini Salgame
- Department of Medicine, Center for Emerging Pathogens, Rutgers-New Jersey Medical School, Newark, NJ, United States
- *Correspondence: Padmini Salgame,
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10
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Kalesinskas L, Gupta S, Khatri P. Increasing reproducibility, robustness, and generalizability of biomarker selection from meta-analysis using Bayesian methodology. PLoS Comput Biol 2022; 18:e1010260. [PMID: 35759523 PMCID: PMC9269905 DOI: 10.1371/journal.pcbi.1010260] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 07/08/2022] [Accepted: 05/29/2022] [Indexed: 01/07/2023] Open
Abstract
A major limitation of gene expression biomarker studies is that they are not reproducible as they simply do not generalize to larger, real-world, heterogeneous populations. Frequentist multi-cohort gene expression meta-analysis has been frequently used as a solution to this problem to identify biomarkers that are truly differentially expressed. However, the frequentist meta-analysis framework has its limitations-it needs at least 4-5 datasets with hundreds of samples, is prone to confounding from outliers and relies on multiple-hypothesis corrected p-values. To address these shortcomings, we have created a Bayesian meta-analysis framework for the analysis of gene expression data. Using real-world data from three different diseases, we show that the Bayesian method is more robust to outliers, creates more informative estimates of between-study heterogeneity, reduces the number of false positive and false negative biomarkers and selects more generalizable biomarkers with less data. We have compared the Bayesian framework to a previously published frequentist framework and have developed a publicly available R package for use.
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Affiliation(s)
- Laurynas Kalesinskas
- Institute for Immunity, Transplantation and Infection, School of Medicine, Stanford University, Stanford, California, United States of America
- Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, California, United States of America
- Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, California, United States of America
| | - Sanjana Gupta
- Institute for Immunity, Transplantation and Infection, School of Medicine, Stanford University, Stanford, California, United States of America
- Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, California, United States of America
| | - Purvesh Khatri
- Institute for Immunity, Transplantation and Infection, School of Medicine, Stanford University, Stanford, California, United States of America
- Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, California, United States of America
- * E-mail:
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11
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Siddhi P, Raveendranath R, Pulgari P, Chinnaswamy A, Song R, Welch S. A systematic review on Correlates of Risk of TB disease in children and adults. Indian J Tuberc 2022; 70:197-213. [PMID: 37100577 DOI: 10.1016/j.ijtb.2022.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 05/16/2022] [Indexed: 10/18/2022]
Abstract
BACKGROUND Tuberculosis (TB) remains one of the leading causes of death in the world. Targeted treatment to prevent progression from TB exposure and infection to disease is a key element of WHO End-TB strategy. A systematic review to identify and develop correlates of risk (COR) of TB disease is timely. METHOD EMBASE, MEDLINE, PUBMED were searched using relevant keywords and MeSH terms published between 2000 and 2020 on COR of TB disease in children and adults. Preferred Reporting Items for Systematic reviews and Meta-analysis (PRISMA) framework was used for structuring and reporting of outcomes. Risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies tool-2 (QUADAS-2). RESULTS 4105 studies were identified. Following eligibility screening, 27 studies were quality assessed. Risk of bias was high in all studies. Broad variations in COR type, study population, methodology and result reporting were observed. Tuberculin skin test (TST) and interferon gamma release essays (IGRA) are poor COR. Transcriptomic signatures although promising require validation studies to assess wider applicability. Performance consistency of other CORs-cell marker, cytokines and metabolites are much needed. CONCLUSION This review identifies the need for a standardized approach to identify a universally applicable COR signature to achieve the WHO END-TB targets.
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12
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Cintron C, Narasimhan PB, Locks L, Babu S, Sinha P, Rajkumari N, Kaipilyawar V, Bhargava A, Maloomian K, Chandrasekaran P, Verma S, Joseph N, Johnson WE, Wanke C, Horsburgh CR, Ellner JJ, Sarkar S, Salgame P, Lakshminarayanan S, Hochberg NS. Tuberculosis-Learning the Impact of Nutrition (TB LION): protocol for an interventional study to decrease TB risk in household contacts. BMC Infect Dis 2021; 21:1058. [PMID: 34641820 PMCID: PMC8506078 DOI: 10.1186/s12879-021-06734-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 09/23/2021] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Comorbidities such as undernutrition and parasitic infections are widespread in India and other tuberculosis (TB)-endemic countries. This study examines how these conditions as well as food supplementation and parasite treatment might alter immune responses to Mycobacterium tuberculosis (Mtb) infection and risk of progression to TB disease. METHODS This is a 5-year prospective clinical trial at Jawaharlal Institute of Post Graduate Medical Education and Research in Puducherry, Tamil Nadu, India. We aim to enroll 760 household contacts (HHC) of adults with active TB in order to identify 120 who are followed prospectively for 2 years: Thirty QuantiFERON-TB Gold Plus (QFT-Plus) positive HHCs ≥ 18 years of age in four proposed groups: (1) undernourished (body mass index [BMI] < 18.5 kg/m2); (2) participants with a BMI ≥ 18.5 kg/m2 who have a parasitic infection (3) undernourished participants with a parasitic infection and (4) controls-participants with BMI ≥ 18.5 kg/m2 and without parasitic infection. We assess immune response at baseline and after food supplementation (for participants with BMI < 18.5 kg/m2) and parasite treatment (for participants with parasites). Detailed nutritional assessments, anthropometry, and parasite testing through polymerase chain reaction (PCR) and microscopy are performed. In addition, at serial time points, these samples will be further analyzed using flow cytometry and whole blood transcriptomics to elucidate the immune mechanisms involved in disease progression. CONCLUSIONS This study will help determine whether undernutrition and parasite infection are associated with gene signatures that predict risk of TB and whether providing nutritional supplementation and/or treating parasitic infections improves immune response towards this infection. This study transcends individual level care and presents the opportunity to benefit the population at large by analyzing factors that affect disease progression potentially reducing the overall burden of people who progress to TB disease. Trial registration ClinicalTrials.gov; NCT03598842; Registered on July 26, 2018; https://clinicaltrials.gov/ct2/show/NCT03598842.
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Affiliation(s)
- Chelsie Cintron
- Department of Medicine, Section of Infectious Diseases, Boston Medical Center, Boston, MA, USA
| | - Prakash Babu Narasimhan
- Department of Preventive and Social Medicine, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
| | - Lindsey Locks
- Department of Health Sciences, Boston University College of Health and Rehabilitation Sciences Sargent College, Boston, MA, USA
| | - Senbagavalli Babu
- Department of Preventive and Social Medicine, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
| | - Pranay Sinha
- Department of Medicine, Section of Infectious Diseases, Boston Medical Center, Boston, MA, USA
| | - Nonika Rajkumari
- Department of Preventive and Social Medicine, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
| | - Vaishnavi Kaipilyawar
- Department of Medicine, Center for Emerging Pathogens, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Anurag Bhargava
- Department of Internal Medicine, Yenepoya Medical College, Mangalore, Karnataka, India
| | | | - Padma Chandrasekaran
- Department of Clinical Research, National Institute for Research in Tuberculosis, Chennai, India
| | - Sheetal Verma
- Department of Medicine, Center for Emerging Pathogens, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Noyal Joseph
- Department of Preventive and Social Medicine, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
| | - W Evan Johnson
- Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA
| | - Christine Wanke
- Department of Public Health & Community Medicine, Tufts University School of Medicine, Boston, MA, USA
| | - C Robert Horsburgh
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Jerrold J Ellner
- Department of Medicine, Center for Emerging Pathogens, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Sonali Sarkar
- Department of Preventive and Social Medicine, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
| | - Padmini Salgame
- Department of Medicine, Center for Emerging Pathogens, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Subitha Lakshminarayanan
- Department of Preventive and Social Medicine, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
| | - Natasha S Hochberg
- Department of Medicine, Section of Infectious Diseases, Boston Medical Center, Boston, MA, USA.
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA.
- Department of Medicine, Section of Infectious Diseases, Boston University, School of Medicine, Boston, MA, USA.
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13
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Devalraju KP, Tripathi D, Neela VSK, Paidipally P, Radhakrishnan RK, Singh KP, Ansari MS, Jaeger M, Netea-Maier RT, Netea MG, Park S, Cheng SY, Valluri VL, Vankayalapati R. Reduced thyroxine production in young household contacts of tuberculosis patients increases active tuberculosis disease risk. JCI Insight 2021; 6:e148271. [PMID: 34236051 PMCID: PMC8410087 DOI: 10.1172/jci.insight.148271] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 05/26/2021] [Indexed: 12/03/2022] Open
Abstract
In the current study, we followed 839 household contacts (HHCs) of tuberculosis (TB) patients for 2 years and identified the factors that enhanced the development of TB. Fourteen of the 17 HHCs who progressed to TB were in the 15- to 30-year-old age group. At baseline (the “0“ time point, when all the individuals were healthy), the concentration of the thyroid hormone thyroxine (T4) was lower, and there were increased numbers of Tregs in PBMCs of TB progressors. At baseline, PBMCs from TB progressors stimulated with early secretory antigenic target 6 (ESAT-6) and 10 kDa culture filtrate antigen (CFP-10) produced less IL-1α. Thyroid hormones inhibited Mycobacterium tuberculosis (Mtb) growth in macrophages in an IL-1α–dependent manner. Mtb-infected Thra1PV/+ (mutant thyroid hormone receptor) mice had increased mortality and reduced IL-1α production. Our findings suggest that young HHCs who exhibit decreased production of thyroid hormones are at high risk of developing active TB disease.
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Affiliation(s)
- Kamakshi Prudhula Devalraju
- Immunology and Molecular Biology Department, Bhagwan Mahavir Medical Research Centre, Hyderabad, Telangana, India
| | - Deepak Tripathi
- Department of Pulmonary Immunology, Center for Biomedical Research, University of Texas Health Science Center, Tyler, Texas, USA
| | - Venkata Sanjeev Kumar Neela
- Immunology and Molecular Biology Department, Bhagwan Mahavir Medical Research Centre, Hyderabad, Telangana, India
| | - Padmaja Paidipally
- Department of Pulmonary Immunology, Center for Biomedical Research, University of Texas Health Science Center, Tyler, Texas, USA
| | - Rajesh Kumar Radhakrishnan
- Department of Pulmonary Immunology, Center for Biomedical Research, University of Texas Health Science Center, Tyler, Texas, USA
| | - Karan P Singh
- Department of Epidemiology and Biostatistics, School of Community and Rural Health, University of Texas Health Science Center, Tyler, Texas, USA
| | - Mohammad Soheb Ansari
- Immunology and Molecular Biology Department, Bhagwan Mahavir Medical Research Centre, Hyderabad, Telangana, India
| | - Martin Jaeger
- Department of Internal Medicine, Division of Endocrinology, and.,Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, Netherlands
| | | | - Mihai G Netea
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, Netherlands
| | - Sunmi Park
- Laboratory of Molecular Biology, National Cancer Institute, NIH, Bethesda, Maryland, USA
| | - Sheue-Yann Cheng
- Laboratory of Molecular Biology, National Cancer Institute, NIH, Bethesda, Maryland, USA
| | - Vijaya Lakshmi Valluri
- Immunology and Molecular Biology Department, Bhagwan Mahavir Medical Research Centre, Hyderabad, Telangana, India
| | - Ramakrishna Vankayalapati
- Department of Pulmonary Immunology, Center for Biomedical Research, University of Texas Health Science Center, Tyler, Texas, USA
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14
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Bouton TC, Jacobson KR. Symptom Screens Are Not Sufficient: The Fight Against Tuberculosis Needs Better Weapons. Clin Infect Dis 2021; 73:121-123. [PMID: 32296819 PMCID: PMC8246807 DOI: 10.1093/cid/ciaa440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 04/14/2020] [Indexed: 11/13/2022] Open
Affiliation(s)
- Tara C Bouton
- Section of Infectious Diseases, Boston University School of Medicine, Boston, Massachusetts, USA
| | - Karen R Jacobson
- Section of Infectious Diseases, Boston University School of Medicine, Boston, Massachusetts, USA
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15
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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.
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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
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16
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Perumal P, Abdullatif MB, Garlant HN, Honeyborne I, Lipman M, McHugh TD, Southern J, Breen R, Santis G, Ellappan K, Kumar SV, Belgode H, Abubakar I, Sinha S, Vasan SS, Joseph N, Kempsell KE. Validation of Differentially Expressed Immune Biomarkers in Latent and Active Tuberculosis by Real-Time PCR. Front Immunol 2021; 11:612564. [PMID: 33841389 PMCID: PMC8029985 DOI: 10.3389/fimmu.2020.612564] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 12/23/2020] [Indexed: 12/18/2022] Open
Abstract
Tuberculosis (TB) remains a major global threat and diagnosis of active TB ((ATB) both extra-pulmonary (EPTB), pulmonary (PTB)) and latent TB (LTBI) infection remains challenging, particularly in high-burden countries which still rely heavily on conventional methods. Although molecular diagnostic methods are available, e.g., Cepheid GeneXpert, they are not universally available in all high TB burden countries. There is intense focus on immune biomarkers for use in TB diagnosis, which could provide alternative low-cost, rapid diagnostic solutions. In our previous gene expression studies, we identified peripheral blood leukocyte (PBL) mRNA biomarkers in a non-human primate TB aerosol-challenge model. Here, we describe a study to further validate select mRNA biomarkers from this prior study in new cohorts of patients and controls, as a prerequisite for further development. Whole blood mRNA was purified from ATB patients recruited in the UK and India, LTBI and two groups of controls from the UK (i) a low TB incidence region (CNTRLA) and (ii) individuals variably-domiciled in the UK and Asia ((CNTRLB), the latter TB high incidence regions). Seventy-two mRNA biomarker gene targets were analyzed by qPCR using the Roche Lightcycler 480 qPCR platform and data analyzed using GeneSpring™ 14.9 bioinformatics software. Differential expression of fifty-three biomarkers was confirmed between MTB infected, LTBI groups and controls, seventeen of which were significant using analysis of variance (ANOVA): CALCOCO2, CD52, GBP1, GBP2, GBP5, HLA-B, IFIT3, IFITM3, IRF1, LOC400759 (GBP1P1), NCF1C, PF4V1, SAMD9L, S100A11, TAF10, TAPBP, and TRIM25. These were analyzed using receiver operating characteristic (ROC) curve analysis. Single biomarkers and biomarker combinations were further assessed using simple arithmetic algorithms. Minimal combination biomarker panels were delineated for primary diagnosis of ATB (both PTB and EPTB), LTBI and identifying LTBI individuals at high risk of progression which showed good performance characteristics. These were assessed for suitability for progression against the standards for new TB diagnostic tests delineated in the published World Health Organization (WHO) technology product profiles (TPPs).
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Affiliation(s)
- Prem Perumal
- Public Health England, Porton Down, Salisbury, Wiltshire, United Kingdom
| | | | - Harriet N. Garlant
- Public Health England, Porton Down, Salisbury, Wiltshire, United Kingdom
| | - Isobella Honeyborne
- Centre for Clinical Microbiology, University College London, Royal Free Campus, London, United Kingdom
| | - Marc Lipman
- UCL Respiratory, University College London, Royal Free Campus, London, United Kingdom
| | - Timothy D. McHugh
- Centre for Clinical Microbiology, University College London, Royal Free Campus, London, United Kingdom
| | - Jo Southern
- Institute for Global Health, University College London, London, United Kingdom
| | - Ronan Breen
- Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - George Santis
- Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Kalaiarasan Ellappan
- Jawaharlal Institute of Postgraduate Medical Education and Research, Dhanvantri Nagar, Gorimedu, Puducherry, India
| | - Saka Vinod Kumar
- Jawaharlal Institute of Postgraduate Medical Education and Research, Dhanvantri Nagar, Gorimedu, Puducherry, India
| | - Harish Belgode
- Jawaharlal Institute of Postgraduate Medical Education and Research, Dhanvantri Nagar, Gorimedu, Puducherry, India
| | - Ibrahim Abubakar
- Institute for Global Health, University College London, London, United Kingdom
| | - Sanjeev Sinha
- Department of Medicine, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, India
| | - Seshadri S. Vasan
- Public Health England, Porton Down, Salisbury, Wiltshire, United Kingdom
- Department of Health Sciences, University of York, York, United Kingdom
| | - Noyal Joseph
- Jawaharlal Institute of Postgraduate Medical Education and Research, Dhanvantri Nagar, Gorimedu, Puducherry, India
| | - Karen E. Kempsell
- Public Health England, Porton Down, Salisbury, Wiltshire, United Kingdom
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17
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Kanabalan RD, Lee LJ, Lee TY, Chong PP, Hassan L, Ismail R, Chin VK. Human tuberculosis and Mycobacterium tuberculosis complex: A review on genetic diversity, pathogenesis and omics approaches in host biomarkers discovery. Microbiol Res 2021; 246:126674. [PMID: 33549960 DOI: 10.1016/j.micres.2020.126674] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 12/09/2020] [Accepted: 12/16/2020] [Indexed: 12/16/2022]
Abstract
Mycobacterium tuberculosis complex (MTBC) refers to a group of mycobacteria encompassing nine members of closely related species that causes tuberculosis in animals and humans. Among the nine members, Mycobacterium tuberculosis (M. tuberculosis) remains the main causative agent for human tuberculosis that results in high mortality and morbidity globally. In general, MTBC species are low in diversity but exhibit distinctive biological differences and phenotypes among different MTBC lineages. MTBC species are likely to have evolved from a common ancestor through insertions/deletions processes resulting in species speciation with different degrees of pathogenicity. The pathogenesis of human tuberculosis is complex and remains poorly understood. It involves multi-interactions or evolutionary co-options between host factors and bacterial determinants for survival of the MTBC. Granuloma formation as a protection or survival mechanism in hosts by MTBC remains controversial. Additionally, MTBC species are capable of modulating host immune response and have adopted several mechanisms to evade from host immune attack in order to survive in humans. On the other hand, current diagnostic tools for human tuberculosis are inadequate and have several shortcomings. Numerous studies have suggested the potential of host biomarkers in early diagnosis of tuberculosis, in disease differentiation and in treatment monitoring. "Multi-omics" approaches provide holistic views to dissect the association of MTBC species with humans and offer great advantages in host biomarkers discovery. Thus, in this review, we seek to understand how the genetic variations in MTBC lead to species speciation with different pathogenicity. Furthermore, we also discuss how the host and bacterial players contribute to the pathogenesis of human tuberculosis. Lastly, we provide an overview of the journey of "omics" approaches in host biomarkers discovery in human tuberculosis and provide some interesting insights on the challenges and directions of "omics" approaches in host biomarkers innovation and clinical implementation.
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Affiliation(s)
- Renuga Devi Kanabalan
- Department of Community Health, Faculty of Medicine, Universiti Kebangsaan Malaysia Medical Centre, Jalan Yaacob Latiff, Bandar Tun Razak, Kuala Lumpur, 56000, Malaysia
| | - Le Jie Lee
- Prima Nexus Sdn. Bhd., Menara CIMB, Jalan Stesen Sentral 2, Kuala Lumpur, Malaysia
| | - Tze Yan Lee
- Perdana University School of Liberal Arts, Science and Technology (PUScLST), Suite 9.2, 9th Floor, Wisma Chase Perdana, Changkat Semantan Damansara Heights, Kuala Lumpur, 50490, Malaysia
| | - Pei Pei Chong
- School of Biosciences, Faculty of Health and Medical Sciences, Taylor's University Lakeside Campus, Subang Jaya, 47500, Malaysia
| | - Latiffah Hassan
- Department of Veterinary Laboratory Diagnostics, Faculty of Veterinary Medicine, Universiti Putra Malaysia, Serdang, Selangor, 43400 UPM, Malaysia
| | - Rosnah Ismail
- Department of Community Health, Faculty of Medicine, Universiti Kebangsaan Malaysia Medical Centre, Jalan Yaacob Latiff, Bandar Tun Razak, Kuala Lumpur, 56000, Malaysia.
| | - Voon Kin Chin
- Department of Medical Microbiology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, 43400 UPM, Malaysia; Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA, Puncak Alam Campus, Bandar Puncak Alam, Selangor, 42300, Malaysia.
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18
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Johnson WE, Odom A, Cintron C, Muthaiah M, Knudsen S, Joseph N, Babu S, Lakshminarayanan S, Jenkins DF, Zhao Y, Nankya E, Horsburgh CR, Roy G, Ellner J, Sarkar S, Salgame P, Hochberg NS. Comparing tuberculosis gene signatures in malnourished individuals using the TBSignatureProfiler. BMC Infect Dis 2021; 21:106. [PMID: 33482742 PMCID: PMC7821401 DOI: 10.1186/s12879-020-05598-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 11/09/2020] [Indexed: 12/12/2022] Open
Abstract
Background Gene expression signatures have been used as biomarkers of tuberculosis (TB) risk and outcomes. Platforms are needed to simplify access to these signatures and determine their validity in the setting of comorbidities. We developed a computational profiling platform of TB signature gene sets and characterized the diagnostic ability of existing signature gene sets to differentiate active TB from LTBI in the setting of malnutrition. Methods We curated 45 existing TB-related signature gene sets and developed our TBSignatureProfiler software toolkit that estimates gene set activity using multiple enrichment methods and allows visualization of single- and multi-pathway results. The TBSignatureProfiler software is available through Bioconductor and on GitHub. For evaluation in malnutrition, we used whole blood gene expression profiling from 23 severely malnourished Indian individuals with TB and 15 severely malnourished household contacts with latent TB infection (LTBI). Severe malnutrition was defined as body mass index (BMI) < 16 kg/m2 in adults and based on weight-for-height Z scores in children < 18 years. Gene expression was measured using RNA-sequencing. Results The comparison and visualization functions from the TBSignatureProfiler showed that TB gene sets performed well in malnourished individuals; 40 gene sets had statistically significant discriminative power for differentiating TB from LTBI, with area under the curve ranging from 0.662–0.989. Three gene sets were not significantly predictive. Conclusion Our TBSignatureProfiler is a highly effective and user-friendly platform for applying and comparing published TB signature gene sets. Using this platform, we found that existing gene sets for TB function effectively in the setting of malnutrition, although differences in gene set applicability exist. RNA-sequencing gene sets should consider comorbidities and potential effects on diagnostic performance. Supplementary Information The online version contains supplementary material available at 10.1186/s12879-020-05598-z.
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Affiliation(s)
- W Evan Johnson
- Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA. .,Bioinformatics Program, Boston University, Boston, MA, USA. .,Division of Computational Biomedicine and Bioinformatics Program, Boston University, Boston, MA, USA.
| | - Aubrey Odom
- Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA.,Bioinformatics Program, Boston University, Boston, MA, USA.,Division of Computational Biomedicine and Bioinformatics Program, Boston University, Boston, MA, USA
| | | | | | | | - Noyal Joseph
- Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
| | - Senbagavalli Babu
- Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
| | | | - David F Jenkins
- Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA.,Bioinformatics Program, Boston University, Boston, MA, USA.,Division of Computational Biomedicine and Bioinformatics Program, Boston University, Boston, MA, USA
| | - Yue Zhao
- Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA.,Bioinformatics Program, Boston University, Boston, MA, USA.,Division of Computational Biomedicine and Bioinformatics Program, Boston University, Boston, MA, USA
| | - Ethel Nankya
- Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA.,Bioinformatics Program, Boston University, Boston, MA, USA.,Division of Computational Biomedicine and Bioinformatics Program, Boston University, Boston, MA, USA
| | - C Robert Horsburgh
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Gautam Roy
- Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
| | - Jerrold Ellner
- Department of Medicine, Center for Emerging Pathogens, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Sonali Sarkar
- Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
| | - Padmini Salgame
- Department of Medicine, Center for Emerging Pathogens, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Natasha S Hochberg
- Boston Medical Center, Boston, MA, USA.,Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA.,Section of Infectious Diseases, Boston University School of Medicine, Boston, MA, USA
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19
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Acuña-Villaorduña C, Jones-López EC, Salgame P, Dietze R, Ellner JJ. Clinical variables and gene signatures in tuberculosis. THE LANCET. INFECTIOUS DISEASES 2020; 20:1227-1229. [PMID: 33098773 DOI: 10.1016/s1473-3099(20)30702-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Accepted: 08/19/2020] [Indexed: 11/24/2022]
Affiliation(s)
- Carlos Acuña-Villaorduña
- Section of Infectious Diseases, Department of Medicine, Boston Medical Center and Boston University School of Medicine, Boston, MA 02118, USA; Section of Infectious Diseases, Lemuel Shattuck Hospital, Jamaica Plain, MA, USA.
| | - Edward C Jones-López
- Section of Infectious Diseases, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Padmini Salgame
- Centre for Emerging Pathogens, Department of Medicine, Rutgers-New Jersey Medical School, Newark, NJ, USA
| | - Reynaldo Dietze
- Núcleo de Doenças Infecciosas, Universidade Federal do Espírito Santo, Vitória, Brazil; Global Health and Tropical Medicine, Instituto de Higiene e Medicina Tropical, Universidade Nova de Lisboa, Lisbon, Portugal
| | - Jerrold J Ellner
- Centre for Emerging Pathogens, Department of Medicine, Rutgers-New Jersey Medical School, Newark, NJ, USA
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Ault RC, Headley CA, Hare AE, Carruthers BJ, Mejias A, Turner J. Blood RNA signatures predict recent tuberculosis exposure in mice, macaques and humans. Sci Rep 2020; 10:16873. [PMID: 33037303 PMCID: PMC7547102 DOI: 10.1038/s41598-020-73942-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 09/18/2020] [Indexed: 11/18/2022] Open
Abstract
Tuberculosis (TB) is the leading cause of death due to a single infectious disease. Knowing when a person was infected with Mycobacterium tuberculosis (M.tb) is critical as recent infection is the strongest clinical risk factor for progression to TB disease in immunocompetent individuals. However, time since M.tb infection is challenging to determine in routine clinical practice. To define a biomarker for recent TB exposure, we determined whether gene expression patterns in blood RNA correlated with time since M.tb infection or exposure. First, we found RNA signatures that accurately discriminated early and late time periods after experimental infection in mice and cynomolgus macaques. Next, we found a 6-gene blood RNA signature that identified recently exposed individuals in two independent human cohorts, including adult household contacts of TB cases and adolescents who recently acquired M.tb infection. Our work supports the need for future longitudinal studies of recent TB contacts to determine whether biomarkers of recent infection can provide prognostic information of TB disease risk in individuals and help map recent transmission in communities.
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Affiliation(s)
- Russell C Ault
- Texas Biomedical Research Institute, San Antonio, TX, USA
- Department of Microbial Infection and Immunity, Ohio State University, Columbus, OH, USA
- Biomedical Sciences Graduate Program, Ohio State University, Columbus, OH, USA
- Medical Scientist Training Program, Ohio State University, Columbus, OH, USA
| | - Colwyn A Headley
- Texas Biomedical Research Institute, San Antonio, TX, USA
- Department of Microbial Infection and Immunity, Ohio State University, Columbus, OH, USA
- Biomedical Sciences Graduate Program, Ohio State University, Columbus, OH, USA
| | - Alexander E Hare
- Biomedical Sciences Graduate Program, Ohio State University, Columbus, OH, USA
- Medical Scientist Training Program, Ohio State University, Columbus, OH, USA
| | - Bridget J Carruthers
- Department of Microbial Infection and Immunity, Ohio State University, Columbus, OH, USA
| | - Asuncion Mejias
- Center for Vaccines and Immunity, Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, OH, USA
| | - Joanne Turner
- Texas Biomedical Research Institute, San Antonio, TX, USA.
- Department of Microbial Infection and Immunity, Ohio State University, Columbus, OH, USA.
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