1
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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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2
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Rocha EF, Vinhaes CL, Araújo-Pereira M, Mota TF, Gupte AN, Kumar NP, Arriaga MB, Sterling TR, Babu S, Gaikwad S, Karyakarte R, Mave V, Kulkarni V, Paradkar M, Viswanathan V, Kornfeld H, Gupta A, Andrade BB, Queiroz ATLD. The sound of silent RNA in tuberculosis and the lncRNA role on infection. iScience 2024; 27:108662. [PMID: 38205253 PMCID: PMC10777062 DOI: 10.1016/j.isci.2023.108662] [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: 09/05/2023] [Revised: 11/27/2023] [Accepted: 12/05/2023] [Indexed: 01/12/2024] Open
Abstract
Tuberculosis (TB) is one of the leading causes of death worldwide, and Diabetes Mellitus is one of the major comorbidities (TB/DM) associated with the disease. A total of 103 differentially expressed ncRNAs have been identified in the TB and TB/DM comparisons. A machine learning algorithm was employed to identify the most informative lncRNAs: ADM-DT, LINC02009, LINC02471, SOX2-OT, and GK-AS1. These lncRNAs presented substantial accuracy in classifying TB from HC (AUCs >0.85) and TB/DM from HC (AUCs >0.90) in the other three countries. Genes with significant correlations with the five lncRNAs enriched common pathways in Brazil and India for both TB and TB/DM. This suggests that lncRNAs play an important role in the regulation of genes related to the TB immune response.
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Affiliation(s)
- Eduardo Fukutani Rocha
- Centro de Integração de Dados e Conhecimentos para Saúde, Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil
- Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative, Salvador, Brazil
| | - Caian Leal Vinhaes
- Laboratório de Inflamação e Biomarcadores, Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil
- Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative, Salvador, Brazil
- Escola Bahiana de Medicina e Saúde Pública (EBMSP), Salvador 40290-150, Brazil
| | - Mariana Araújo-Pereira
- Laboratório de Inflamação e Biomarcadores, Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil
- Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative, Salvador, Brazil
- Escola Bahiana de Medicina e Saúde Pública (EBMSP), Salvador 40290-150, Brazil
- Faculdade de Tecnologia e Ciências, Instituto de Pesquisa Clínica e Translacional, Salvador, Brazil
| | - Tiago Feitosa Mota
- Centro de Integração de Dados e Conhecimentos para Saúde, Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil
- Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative, Salvador, Brazil
| | | | | | - Maria Belen Arriaga
- Laboratório de Inflamação e Biomarcadores, Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil
- Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative, Salvador, Brazil
| | - Timothy R. Sterling
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN USA
| | - Subash Babu
- National Institutes of Health- NIRT - International Center for Excellence in Research, Chennai, India
| | - Sanjay Gaikwad
- Department of Pulmonary Medicine, Byramjee-Jeejeebhoy Government Medical College and Sassoon General Hospitals, Pune, India
| | - Rajesh Karyakarte
- Department of Microbiology, Byramjee-Jeejeebhoy Government Medical College and Sassoon General Hospitals, Pune, India
| | - Vidya Mave
- Byramjee-Jeejeebhoy Government Medical College-Johns Hopkins University Clinical Research Site, Pune, India
- Johns Hopkins Center for Infectious Diseases in India, Pune, India
| | - Vandana Kulkarni
- Byramjee-Jeejeebhoy Government Medical College-Johns Hopkins University Clinical Research Site, Pune, India
- Johns Hopkins Center for Infectious Diseases in India, Pune, India
| | - Mandar Paradkar
- Byramjee-Jeejeebhoy Government Medical College-Johns Hopkins University Clinical Research Site, Pune, India
- Johns Hopkins Center for Infectious Diseases in India, Pune, India
| | | | - Hardy Kornfeld
- Department of Medicine, University of Massachusetts Medical School, Worcester, MA USA
- UMass Chan Medical School, Worcester, MA USA
| | - Amita Gupta
- Byramjee-Jeejeebhoy Government Medical College-Johns Hopkins University Clinical Research Site, Pune, India
| | - Bruno Bezerril Andrade
- Laboratório de Inflamação e Biomarcadores, Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil
- Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative, Salvador, Brazil
- Escola Bahiana de Medicina e Saúde Pública (EBMSP), Salvador 40290-150, Brazil
- Faculdade de Tecnologia e Ciências, Instituto de Pesquisa Clínica e Translacional, Salvador, Brazil
| | - Artur Trancoso Lopo de Queiroz
- Centro de Integração de Dados e Conhecimentos para Saúde, Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil
- Laboratório de Inflamação e Biomarcadores, Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil
- Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative, Salvador, Brazil
| | - RePORT Brazil
- Centro de Integração de Dados e Conhecimentos para Saúde, Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil
- Laboratório de Inflamação e Biomarcadores, Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil
- Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative, Salvador, Brazil
- Escola Bahiana de Medicina e Saúde Pública (EBMSP), Salvador 40290-150, Brazil
- Boston University School of Public Health, Boston, MA USA
- National Institutes of Health- NIRT - International Center for Excellence in Research, Chennai, India
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN USA
- Department of Pulmonary Medicine, Byramjee-Jeejeebhoy Government Medical College and Sassoon General Hospitals, Pune, India
- Department of Microbiology, Byramjee-Jeejeebhoy Government Medical College and Sassoon General Hospitals, Pune, India
- Byramjee-Jeejeebhoy Government Medical College-Johns Hopkins University Clinical Research Site, Pune, India
- Johns Hopkins Center for Infectious Diseases in India, Pune, India
- Prof. M. Viswanathan Diabetes Research Centre, Chennai, India
- Faculdade de Tecnologia e Ciências, Instituto de Pesquisa Clínica e Translacional, Salvador, Brazil
- Department of Medicine, University of Massachusetts Medical School, Worcester, MA USA
- UMass Chan Medical School, Worcester, MA USA
- ICMR-National Institute for Research in Tuberculosis, Chennai, India
| | - RePORT India Consortia
- Centro de Integração de Dados e Conhecimentos para Saúde, Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil
- Laboratório de Inflamação e Biomarcadores, Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil
- Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative, Salvador, Brazil
- Escola Bahiana de Medicina e Saúde Pública (EBMSP), Salvador 40290-150, Brazil
- Boston University School of Public Health, Boston, MA USA
- National Institutes of Health- NIRT - International Center for Excellence in Research, Chennai, India
- Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN USA
- Department of Pulmonary Medicine, Byramjee-Jeejeebhoy Government Medical College and Sassoon General Hospitals, Pune, India
- Department of Microbiology, Byramjee-Jeejeebhoy Government Medical College and Sassoon General Hospitals, Pune, India
- Byramjee-Jeejeebhoy Government Medical College-Johns Hopkins University Clinical Research Site, Pune, India
- Johns Hopkins Center for Infectious Diseases in India, Pune, India
- Prof. M. Viswanathan Diabetes Research Centre, Chennai, India
- Faculdade de Tecnologia e Ciências, Instituto de Pesquisa Clínica e Translacional, Salvador, Brazil
- Department of Medicine, University of Massachusetts Medical School, Worcester, MA USA
- UMass Chan Medical School, Worcester, MA USA
- ICMR-National Institute for Research in Tuberculosis, Chennai, India
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3
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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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4
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Mousavian Z, Folkesson E, Fröberg G, Foroogh F, Correia-Neves M, Bruchfeld J, Källenius G, Sundling C. A protein signature associated with active tuberculosis identified by plasma profiling and network-based analysis. iScience 2022; 25:105652. [PMID: 36561889 PMCID: PMC9763869 DOI: 10.1016/j.isci.2022.105652] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 09/19/2022] [Accepted: 11/18/2022] [Indexed: 11/23/2022] Open
Abstract
Annually, approximately 10 million people are diagnosed with active tuberculosis (TB), and 1.4 million die of the disease. If left untreated, each person with active TB will infect 10-15 new individuals. The lack of non-sputum-based diagnostic tests leads to delayed diagnoses of active pulmonary TB cases, contributing to continued disease transmission. In this exploratory study, we aimed to identify biomarkers associated with active TB. We assessed the plasma levels of 92 proteins associated with inflammation in individuals with active TB (n = 20), latent TB (n = 14), or healthy controls (n = 10). Using co-expression network analysis, we identified one module of proteins with strong association with active TB. We removed proteins from the module that had low abundance or were associated with non-TB diseases in published transcriptomic datasets, resulting in a 12-protein plasma signature that was highly enriched in individuals with pulmonary and extrapulmonary TB and was further associated with disease severity.
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Affiliation(s)
- Zaynab Mousavian
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran
| | - Elin Folkesson
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
| | - Gabrielle Fröberg
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Microbiology, Karolinska University Laboratory, Karolinska University Hospital, Stockholm, Sweden
| | - Fariba Foroogh
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
| | - Margarida Correia-Neves
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Life and Health Sciences Research Institute, School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B’s, PT Government Associate Laboratory, Braga, Portugal
| | - Judith Bruchfeld
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
| | - Gunilla Källenius
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
| | - Christopher Sundling
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
- Corresponding author
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5
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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: 0] [Impact Index Per Article: 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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6
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Long NP, Anh NK, Yen NTH, Phat NK, Park S, Thu VTA, Cho YS, Shin JG, Oh JY, Kim DH. Comprehensive lipid and lipid-related gene investigations of host immune responses to characterize metabolism-centric biomarkers for pulmonary tuberculosis. Sci Rep 2022; 12:13395. [PMID: 35927287 PMCID: PMC9352691 DOI: 10.1038/s41598-022-17521-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 07/26/2022] [Indexed: 12/04/2022] Open
Abstract
Despite remarkable success in the prevention and treatment of tuberculosis (TB), it remains one of the most devastating infectious diseases worldwide. Management of TB requires an efficient and timely diagnostic strategy. In this study, we comprehensively characterized the plasma lipidome of TB patients, then selected candidate lipid and lipid-related gene biomarkers using a data-driven, knowledge-based framework. Among 93 lipids that were identified as potential biomarker candidates, ether-linked phosphatidylcholine (PC O–) and phosphatidylcholine (PC) were generally upregulated, while free fatty acids and triglycerides with longer fatty acyl chains were downregulated in the TB group. Lipid-related gene enrichment analysis revealed significantly altered metabolic pathways (e.g., ether lipid, linolenic acid, and cholesterol) and immune response signaling pathways. Based on these potential biomarkers, TB patients could be differentiated from controls in the internal validation (random forest model, area under the curve [AUC] 0.936, 95% confidence interval [CI] 0.865–0.992). PC(O-40:4), PC(O-42:5), PC(36:0), and PC(34:4) were robust biomarkers able to distinguish TB patients from individuals with latent infection and healthy controls, as shown in the external validation. Small changes in expression were identified for 162 significant lipid-related genes in the comparison of TB patients vs. controls; in the random forest model, their utilities were demonstrated by AUCs that ranged from 0.829 to 0.956 in three cohorts. In conclusion, this study introduced a potential framework that can be used to identify and validate metabolism-centric biomarkers.
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Affiliation(s)
- Nguyen Phuoc Long
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea.,Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea
| | - Nguyen Ky Anh
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea.,Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea
| | - Nguyen Thi Hai Yen
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea.,Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea
| | - Nguyen Ky Phat
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea.,Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea
| | - Seongoh Park
- School of Mathematics, Statistics and Data Science, Sungshin Women's University, Seoul, Republic of Korea
| | - Vo Thuy Anh Thu
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea.,Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea
| | - Yong-Soon Cho
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea.,Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea
| | - Jae-Gook Shin
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea.,Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea.,Department of Clinical Pharmacology, Inje University Busan Paik Hospital, Busan, Republic of Korea
| | - Jee Youn Oh
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Internal Medicine, Korea University Guro Hospital, Seoul, Republic of Korea.
| | - Dong Hyun Kim
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea.
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7
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Kaforou M, Broderick C, Vito O, Levin M, Scriba TJ, Seddon JA. Transcriptomics for child and adolescent tuberculosis. Immunol Rev 2022; 309:97-122. [PMID: 35818983 PMCID: PMC9540430 DOI: 10.1111/imr.13116] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Tuberculosis (TB) in humans is caused by Mycobacterium tuberculosis (Mtb). It is estimated that 70 million children (<15 years) are currently infected with Mtb, with 1.2 million each year progressing to disease. Of these, a quarter die. The risk of progression from Mtb infection to disease and from disease to death is dependent on multiple pathogen and host factors. Age is a central component in all these transitions. The natural history of TB in children and adolescents is different to adults, leading to unique challenges in the development of diagnostics, therapeutics, and vaccines. The quantification of RNA transcripts in specific cells or in the peripheral blood, using high-throughput methods, such as microarray analysis or RNA-Sequencing, can shed light into the host immune response to Mtb during infection and disease, as well as understanding treatment response, disease severity, and vaccination, in a global hypothesis-free manner. Additionally, gene expression profiling can be used for biomarker discovery, to diagnose disease, predict future disease progression and to monitor response to treatment. Here, we review the role of transcriptomics in children and adolescents, focused mainly on work done in blood, to understand disease biology, and to discriminate disease states to assist clinical decision-making. In recent years, studies with a specific pediatric and adolescent focus have identified blood gene expression markers with diagnostic or prognostic potential that meet or exceed the current sensitivity and specificity targets for diagnostic tools. Diagnostic and prognostic gene expression signatures identified through high-throughput methods are currently being translated into diagnostic tests.
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Affiliation(s)
- Myrsini Kaforou
- Department of Infectious DiseaseImperial College LondonLondonUK
| | | | - Ortensia Vito
- Department of Infectious DiseaseImperial College LondonLondonUK
| | - Michael Levin
- Department of Infectious DiseaseImperial College LondonLondonUK
| | - Thomas J. Scriba
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine and Division of Immunology, Department of PathologyUniversity of Cape TownCape TownSouth Africa
| | - James A. Seddon
- Department of Infectious DiseaseImperial College LondonLondonUK
- Desmond Tutu TB Centre, Department of Paediatrics and Child HealthStellenbosch UniversityCape TownSouth Africa
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8
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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] [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)
| | | | | | - 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
| | | | - Padmini Salgame
- Correspondence: Padmini Salgame, Rutgers–New Jersey Medical School, International Center for Public Health, 225 Warren St, Room W250H, Newark, NJ 07103 ()
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9
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A 10-gene biosignature of tuberculosis treatment monitoring and treatment outcome prediction. Tuberculosis (Edinb) 2021; 131:102138. [PMID: 34801869 DOI: 10.1016/j.tube.2021.102138] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 09/30/2021] [Accepted: 10/07/2021] [Indexed: 11/23/2022]
Abstract
The clinical utility of blood transcriptomic biosignatures for the treatment monitoring and outcome prediction of tuberculosis (TB) remains limited. In this study, we aimed to discover and validate biomarkers for pulmonary TB treatment monitoring and outcome prediction based on kinetic responses of gene expression during treatment. In particular, differentially expressed genes (DEGs) were identified by time-series comparison. Subsequently, DEGs with the monotonic expression alterations during the treatment were selected. Ten consistently down-regulated genes (CD274, KIF1B, IL15, TLR1, TLR5, FCGR1A, GBP1, NOD2, GBP2, EGF) exhibited significant potential in treatment monitoring, demonstrated via biological and technical validation. Additionally, the biosignature showed potential in predicting the cured versus relapsed patients. Furthermore, the biosignature could be utilized for TB diagnosis, latent tuberculosis infection/active TB differential diagnosis, and risk of progression to active TB. Benchmarking analysis of the 10-gene biosignature with other biosignatures showed equivalent performance in tested data sets. In conclusion, we established a 10-gene transcriptomic biosignature that represents the kinetic responses of TB treatment. Subsequent studies are warranted to validate, refine and translate the biosignature into a precise assay to assist clinical decisions in a broad spectrum of TB management.
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10
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Sinha P, Lönnroth K, Bhargava A, Heysell SK, Sarkar S, Salgame P, Rudgard W, Boccia D, Van Aartsen D, Hochberg NS. Food for thought: addressing undernutrition to end tuberculosis. THE LANCET. INFECTIOUS DISEASES 2021; 21:e318-e325. [PMID: 33770535 PMCID: PMC8458477 DOI: 10.1016/s1473-3099(20)30792-1] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 07/18/2020] [Accepted: 09/04/2020] [Indexed: 01/15/2023]
Abstract
Tuberculosis is the leading cause of deaths from an infectious disease worldwide. WHO's End TB Strategy is falling short of several 2020 targets. Undernutrition is the leading population-level risk factor for tuberculosis. Studies have consistently found that undernutrition is associated with increased tuberculosis incidence, increased severity, worse treatment outcomes, and increased mortality. Modelling studies support implementing nutritional interventions for people living with tuberculosis and those at risk of tuberculosis disease to ensure the success of the End TB Strategy. In this Personal View, we highlight nutrition-related immunocompromisation, implications of undernutrition for tuberculosis treatment and prevention, the role of nutritional supplementation, pharmacokinetics and pharmacodynamics of antimycobacterial medications in undernourished people with tuberculosis, and the role of social protection interventions in addressing undernutrition as a tuberculosis risk factor. To catalyse action on this insufficiently addressed accelerant of the global tuberculosis epidemic, research should be prioritised to understand the immunological pathways that are impaired by nutrient deficiencies, develop tools to diagnose clinical and subclinical tuberculosis in people who are undernourished, and understand how nutritional status affects the efficacy of tuberculosis vaccine and therapy. Through primary research, modelling, and implementation research, policy change should also be accelerated, particularly in countries with a high burden of tuberculosis.
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Affiliation(s)
- Pranay Sinha
- Section of Infectious Diseases, Department of Medicine, Boston University School of Medicine, Boston University, MA, USA.
| | - Knut Lönnroth
- Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden
| | - Anurag Bhargava
- Department of Medicine, Yenepoya Medical College, and Center for Nutrition Studies, Yenepoya (Deemed to be University), Mangalore, India; Department of Medicine, McGill University, Montreal, QC, Canada
| | - Scott K Heysell
- Division of Infectious Diseases and International Health, University of Virginia, VA, USA
| | - Sonali Sarkar
- Department of Preventive and Social Medicine, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
| | - Padmini Salgame
- Center for Emerging Pathogens, Department of Medicine, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - William Rudgard
- Department of Social Policy and Intervention, University of Oxford, Oxford, UK
| | - Delia Boccia
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Daniel Van Aartsen
- Division of Infectious Diseases and International Health, University of Virginia, VA, USA
| | - Natasha S Hochberg
- Section of Infectious Diseases, Department of Medicine, Boston University School of Medicine, Boston University, MA, USA; Department of Epidemiology, Boston University School of Public Health, Boston University, MA, USA
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