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Shaik J, Pillay M, Jeena P. A Review Of Host-Specific Diagnostic And Surrogate Biomarkers In Children With Pulmonary Tuberculosis. Paediatr Respir Rev 2024:S1526-0542(24)00018-6. [PMID: 38521643 DOI: 10.1016/j.prrv.2024.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 01/23/2024] [Accepted: 01/23/2024] [Indexed: 03/25/2024]
Abstract
BACKGROUND Tuberculosis (TB) is one of the most common causes of mortality globally with a steady rise in paediatric cases in the past decade. Laboratory methods of diagnosing TB and monitoring response to treatment have limitations. Current research focuses on interrogating host- and/or pathogen-specific biomarkers to address this problem. METHODS We reviewed the literature on host-specific biomarkers in TB to determine their value in diagnosis and treatment response in TB infected and HIV/TB co-infected children on anti-tuberculosis treatment. RESULTS AND CONCLUSION While no single host-specific biomarker has been identified for diagnosis or treatment responses in children, several studies suggest predictive biosignatures for disease activity. Alarmingly, current data on host-specific biomarkers for diagnosing and assessing anti-tuberculosis treatment in TB/HIV co-infected children is inadequate. Various factors affecting host-specific biomarker responses should be considered in interpreting findings and designing future studies within specific clinical settings.
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Affiliation(s)
- Junaid Shaik
- Department of Paediatrics and Child Health, School of Clinical Medicine, College of Health Sciences, University of KwaZulu-Natal, 719 Umbilo Road, Durban, 4000, South Africa; Faculty of Health Sciences, Durban University of Technology, Steve Biko Road, Berea, Durban, 4000, South Africa.
| | - Manormoney Pillay
- Medical Microbiology, School of Laboratory Medicine and Medical Sciences, College of Health Sciences, University of KwaZulu-Natal, 719 Umbilo Road, Durban, 4000, South Africa
| | - Prakash Jeena
- Department of Paediatrics and Child Health, School of Clinical Medicine, College of Health Sciences, University of KwaZulu-Natal, 719 Umbilo Road, Durban, 4000, South Africa
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Sivakumaran D, Jenum S, Srivastava A, Steen VM, Vaz M, Doherty TM, Ritz C, Grewal HMS. Host blood-based biosignatures for subclinical TB and incipient TB: A prospective study of adult TB household contacts in Southern India. Front Immunol 2023; 13:1051963. [PMID: 36713386 PMCID: PMC9876034 DOI: 10.3389/fimmu.2022.1051963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 12/21/2022] [Indexed: 01/13/2023] Open
Abstract
A large proportion of the global tuberculosis (TB) burden is asymptomatic and not detectable by symptom-based screening, driving the TB epidemic through continued M. tuberculosis transmission. Currently, no validated tools exist to diagnose incipient and subclinical TB. Nested within a large prospective study in household contacts of pulmonary TB cases in Southern India, we assessed 35 incipient TB and 12 subclinical TB cases, along with corresponding household active TB cases (n=11), and household controls (n=39) using high throughput methods for transcriptional and protein profiling. We split the data into training and test sets and applied a support vector machine classifier followed by a Lasso regression model to identify signatures. The Lasso regression model identified an 11-gene signature (ABLIM2, C20orf197, CTC-543D15.3, CTD-2503O16.3, HLADRB3, METRNL, RAB11B-AS1, RP4-614C10.2, RNA5SP345, RSU1P1, and UACA) that distinguished subclinical TB from incipient TB with a very good discriminatory power by AUCs in both training and test sets. Further, we identified an 8-protein signature comprising b-FGF, IFNγ, IL1RA, IL7, IL12p70, IL13, PDGF-BB, and VEGF that differentiated subclinical TB from incipient TB with good and moderate discriminatory power by AUCs in the training and test sets, respectively. The identified 11-gene signature discriminated well between the distinct stages of the TB disease spectrum, with very good discriminatory power, suggesting it could be useful for predicting TB progression in household contacts. However, the high discriminatory power could partly be due to over-fitting, and validation in other studies is warranted to confirm the potential of the immune biosignatures for identifying subclinical TB.
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Affiliation(s)
- Dhanasekaran Sivakumaran
- Department of Clinical Science, Bergen Integrated Diagnostic Stewardship Cluster, Faculty of Medicine, University of Bergen, Bergen, Norway
- Department of Microbiology, Haukeland University Hospital, University of Bergen, Bergen, Norway
| | - Synne Jenum
- Department of Infectious Diseases, Oslo University Hospital, Oslo, Norway
| | - Aashish Srivastava
- Genome Core Facility, Clinical Laboratory (K2), Haukeland University Hospital, University of Bergen, Bergen, Norway
| | - Vidar M. Steen
- Genome Core Facility, Clinical Laboratory (K2), Haukeland University Hospital, University of Bergen, Bergen, Norway
| | - Mario Vaz
- Department of Physiology, St. John’s Medical College and Division of Health and Humanities, St. John’s Research Institute, Koramangala, Bangalore, India
| | | | - Christian Ritz
- Department of Clinical Science, Bergen Integrated Diagnostic Stewardship Cluster, Faculty of Medicine, University of Bergen, Bergen, Norway
- National Institute of Public Health, University of Southern Denmark, Copenhagen, Denmark
| | - Harleen M. S. Grewal
- Department of Clinical Science, Bergen Integrated Diagnostic Stewardship Cluster, Faculty of Medicine, University of Bergen, Bergen, Norway
- Department of Microbiology, Haukeland University Hospital, University of Bergen, Bergen, Norway
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Luo Y, Xue Y, Liu W, Song H, Huang Y, Tang G, Wang F, Wang Q, Cai Y, Sun Z. Development of diagnostic algorithm using machine learning for distinguishing between active tuberculosis and latent tuberculosis infection. BMC Infect Dis 2022; 22:965. [PMID: 36581808 PMCID: PMC9798640 DOI: 10.1186/s12879-022-07954-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 12/19/2022] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND The discrimination between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) remains challenging. The present study aims to investigate the value of diagnostic models established by machine learning based on multiple laboratory data for distinguishing Mycobacterium tuberculosis (Mtb) infection status. METHODS T-SPOT, lymphocyte characteristic detection, and routine laboratory tests were performed on participants. Diagnostic models were built according to various algorithms. RESULTS A total of 892 participants (468 ATB and 424 LTBI) and another 263 participants (125 ATB and 138 LTBI), were respectively enrolled at Tongji Hospital (discovery cohort) and Sino-French New City Hospital (validation cohort). Receiver operating characteristic (ROC) curve analysis showed that the value of individual indicator for differentiating ATB from LTBI was limited (area under the ROC curve (AUC) < 0.8). A total of 28 models were successfully established using machine learning. Among them, the AUCs of 25 models were more than 0.9 in test set. It was found that conditional random forests (cforest) model, based on the implementation of the random forest and bagging ensemble algorithms utilizing conditional inference trees as base learners, presented best discriminative power in segregating ATB from LTBI. Specially, cforest model presented an AUC of 0.978, with the sensitivity of 93.39% and the specificity of 91.18%. Mtb-specific response represented by early secreted antigenic target 6 (ESAT-6) and culture filtrate protein 10 (CFP-10) spot-forming cell (SFC) in T-SPOT assay, as well as global adaptive immunity assessed by CD4 cell IFN-γ secretion, CD8 cell IFN-γ secretion, and CD4 cell number, were found to contribute greatly to the cforest model. Superior performance obtained in the discovery cohort was further confirmed in the validation cohort. The sensitivity and specificity of cforest model in validation set were 92.80% and 89.86%, respectively. CONCLUSIONS Cforest model developed upon machine learning could serve as a valuable and prospective tool for identifying Mtb infection status. The present study provided a novel and viable idea for realizing the clinical diagnostic application of the combination of machine learning and laboratory findings.
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Affiliation(s)
- Ying Luo
- grid.412793.a0000 0004 1799 5032Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Road 1095, Wuhan, 430030 China
| | - Ying Xue
- grid.33199.310000 0004 0368 7223Department of Immunology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, Wuhan, China
| | - Wei Liu
- grid.412793.a0000 0004 1799 5032Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Road 1095, Wuhan, 430030 China
| | - Huijuan Song
- grid.412793.a0000 0004 1799 5032Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Road 1095, Wuhan, 430030 China
| | - Yi Huang
- grid.412793.a0000 0004 1799 5032Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Road 1095, Wuhan, 430030 China
| | - Guoxing Tang
- grid.412793.a0000 0004 1799 5032Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Road 1095, Wuhan, 430030 China
| | - Feng Wang
- grid.412793.a0000 0004 1799 5032Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Road 1095, Wuhan, 430030 China
| | - Qi Wang
- Télécom Physique Strasbourg, Illkirch-Graffenstaden, France
| | - Yimin Cai
- grid.33199.310000 0004 0368 7223Department of Epidemiology and Biostatistics, Key Laboratory of Environmental Health of Ministry of Education, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong Road 13, Wuhan, China
| | - Ziyong Sun
- grid.412793.a0000 0004 1799 5032Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Road 1095, Wuhan, 430030 China
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Host biomarker-based quantitative rapid tests for detection and treatment monitoring of tuberculosis and COVID-19. iScience 2022; 26:105873. [PMID: 36590898 PMCID: PMC9791715 DOI: 10.1016/j.isci.2022.105873] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 10/24/2022] [Accepted: 12/21/2022] [Indexed: 12/27/2022] Open
Abstract
Diagnostic services for tuberculosis (TB) are not sufficiently accessible in low-resource settings, where most cases occur, which was aggravated by the COVID-19 pandemic. Early diagnosis of pulmonary TB can reduce transmission. Current TB-diagnostics rely on detection of Mycobacterium tuberculosis (Mtb) in sputum requiring costly, time-consuming methods, and trained staff. In this study, quantitative lateral flow (LF) assays were used to measure levels of seven host proteins in sera from pre-COVID-19 TB patients diagnosed in Europe and latently Mtb-infected individuals (LTBI), and from COVID-19 patients and healthy controls. Analysis of host proteins showed significantly lower levels in LTBI versus TB (AUC:0 · 94) and discriminated healthy individuals from COVID-19 patients (0 · 99) and severe COVID-19 from TB. Importantly, these host proteins allowed treatment monitoring of both respiratory diseases. This study demonstrates the potential of non-sputum LF assays as adjunct diagnostics and treatment monitoring for COVID-19 and TB based on quantitative detection of multiple host biomarkers.
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Ndiaye MDB, Ranaivomanana P, Rasoloharimanana LT, Rasolofo V, Ratovoson R, Herindrainy P, Rakotonirina J, Schoenhals M, Hoffmann J, Rakotosamimanana N. Plasma host protein signatures correlating with Mycobacterium tuberculosis activity prior to and during antituberculosis treatment. Sci Rep 2022; 12:20640. [PMID: 36450921 PMCID: PMC9712643 DOI: 10.1038/s41598-022-25236-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 11/28/2022] [Indexed: 12/03/2022] Open
Abstract
There is a need for rapid non-sputum-based tests to identify and treat patients infected with Mycobacterium tuberculosis (Mtb). The overall objective of this study was to measure and compare the expression of a selected panel of human plasma proteins in patients with active pulmonary tuberculosis (ATB) throughout anti-TB treatment (from baseline to the end of treatment), in Mtb-infected individuals (TBI) and healthy donors (HD) to identify a putative host-protein signature useful for both TB diagnosis and treatment monitoring. A panel of seven human host proteins CLEC3B, SELL, IGFBP3, IP10, CD14, ECM1 and C1Q were measured in the plasma isolated from an HIV-negative prospective cohort of 37 ATB, 24 TBI and 23 HD. The protein signatures were assessed using a Luminex xMAP® to quantify the plasmatic levels in unstimulated blood of the different clinical group as well as the protein levels at baseline and at three timepoints during the 6-months ATB treatment, to compare the plasma protein levels between culture slow and fast converters that may contribute to monitor the TB treatment outcome. Protein signatures were defined using the CombiROC algorithm and multivariate models. The studied plasma host proteins showed different levels between the clinical groups and during the TB treatment. Six of the plasma proteins (CLEC3B, SELL, IGFBP3, IP10, CD14 and C1Q) showed significant differences in normalised median fluorescence intensities when comparing ATB vs HD or TBI groups while ECM1 revealed a significant difference between fast and slow sputum culture converters after 2 months following treatment (p = 0.006). The expression of a four-host protein markers (CLEC3B-ECM1-IP10-SELL) was significantly different between ATB from HD or TBI groups (respectively, p < 0.05). The expression of the same signature was significantly different between the slow vs the fast sputum culture converters after 2 months of treatment (p < 0.05). The results suggest a promising 4 host-plasma marker signature that would be associated with both TB diagnostic and treatment monitoring.
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Affiliation(s)
| | - Paulo Ranaivomanana
- grid.418511.80000 0004 0552 7303Institut Pasteur de Madagascar, Antananarivo, Madagascar
| | | | - Voahangy Rasolofo
- grid.418511.80000 0004 0552 7303Institut Pasteur de Madagascar, Antananarivo, Madagascar
| | - Rila Ratovoson
- grid.418511.80000 0004 0552 7303Institut Pasteur de Madagascar, Antananarivo, Madagascar
| | - Perlinot Herindrainy
- United States Agency for International Development (USAID), Antananarivo, Madagascar
| | - Julio Rakotonirina
- Centre Hospitalier Universitaire de Soins et Santé Publique Analakely (CHUSSPA), Antananarivo, Madagascar
| | - Matthieu Schoenhals
- grid.418511.80000 0004 0552 7303Institut Pasteur de Madagascar, Antananarivo, Madagascar
| | - Jonathan Hoffmann
- grid.434215.50000 0001 2106 3244Medical and Scientific Department, Fondation Mérieux, Lyon, France
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Abstract
The current diagnostic abilities for the detection of pediatric tuberculosis are suboptimal. Multiple factors contribute to the under-diagnosis of intrathoracic tuberculosis in children, namely the absence of pathognomonic features of the disease, low bacillary loads in respiratory specimens, challenges in sample collection, and inadequate access to diagnostic tools in high-burden settings. Nonetheless, the 2020s have witnessed encouraging progress in the area of novel diagnostics. Recent WHO-endorsed rapid molecular assays hold promise for use in service decentralization strategies, and new policy recommendations include stools as an alternative, child-friendly specimen for testing with the GeneXpert assay. The pipeline of promising assays in mid/late-stage development is expanding, and novel pediatric candidate biomarkers based on the host immune response are being identified for use in diagnostic and triage tests. For a new test to meet the pediatric target product profiles prioritized by the WHO, it is key that the peculiarities and needs of the hard-to-reach pediatric population are considered in the early planning phases of discovery, validation, and implementation studies.
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Affiliation(s)
| | - Pamela Nabeta
- FIND, the global alliance for diagnostics, Chemin des Mines 9, 1202 Geneva, Switzerland
| | - Morten Ruhwald
- FIND, the global alliance for diagnostics, Chemin des Mines 9, 1202 Geneva, Switzerland
| | - Rinn Song
- Oxford Vaccine Group, Department of Paediatrics, University of Oxford, Oxford, UK
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Luo Y, Xue Y, Song H, Tang G, Liu W, Bai H, Yuan X, Tong S, Wang F, Cai Y, Sun Z. Machine learning based on routine laboratory indicators promoting the discrimination between active tuberculosis and latent tuberculosis infection. J Infect 2022; 84:648-657. [PMID: 34995637 DOI: 10.1016/j.jinf.2021.12.046] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 12/18/2021] [Accepted: 12/26/2021] [Indexed: 12/26/2022]
Abstract
BACKGROUND Discriminating active tuberculosis (ATB) from latent tuberculosis infection (LTBI) remains challenging. The present study aims to evaluate the performance of diagnostic models established using machine learning based on routine laboratory indicators in differentiating ATB from LTBI. METHODS Participants were respectively enrolled at Tongji Hospital (discovery cohort) and Sino-French New City Hospital (validation cohort). Diagnostic models were established based on routine laboratory indicators using machine learning. RESULTS A total of 2619 participants (1025 ATB and 1594 LTBI) were enrolled in discovery cohort and another 942 subjects (388 ATB and 554 LTBI) were recruited in validation cohort. ATB patients had significantly higher levels of tuberculosis-specific antigen/phytohemagglutinin ratio and coefficient variation of red blood cell volume distribution width, and lower levels of albumin and lymphocyte count than those of LTBI individuals. Six models were built and the optimal performance was obtained from GBM model. GBM model derived from training set (n = 1965) differentiated ATB from LTBI in the test set (n = 654) with a sensitivity of 84.38% (95% CI, 79.42%-88.31%) and a specificity of 92.71% (95% CI, 89.73%-94.88%). Further validation by an independent cohort confirmed its encouraging value with a sensitivity of 87.63% (95% CI, 83.98%-90.54%) and specificity of 91.34% (95% CI, 88.70%-93.40%), respectively. CONCLUSIONS We successfully developed a model with promising diagnostic value based on machine learning for the first time. Our study proposed that GBM model may be of great benefit served as a tool for the accurate identification of ATB.
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Affiliation(s)
- Ying Luo
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang road 1095, Wuhan 430030, China.
| | - Ying Xue
- Department of Immunology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Huijuan Song
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang road 1095, Wuhan 430030, China
| | - Guoxing Tang
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang road 1095, Wuhan 430030, China
| | - Wei Liu
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang road 1095, Wuhan 430030, China
| | - Huan Bai
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang road 1095, Wuhan 430030, China
| | - Xu Yuan
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang road 1095, Wuhan 430030, China
| | - Shutao Tong
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang road 1095, Wuhan 430030, China.
| | - Feng Wang
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang road 1095, Wuhan 430030, China.
| | - Yimin Cai
- Department of Epidemiology and Biostatistics, Key Laboratory of Environmental Health of Ministry of Education, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hangkong road 13, Wuhan, China.
| | - Ziyong Sun
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang road 1095, Wuhan 430030, China.
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Kumar NP, Hissar S, Thiruvengadam K, Banurekha VV, Balaji S, Elilarasi S, Gomathi NS, Ganesh J, Aravind MA, Baskaran D, Tripathy S, Swaminathan S, Babu S. Plasma chemokines as immune biomarkers for diagnosis of pediatric tuberculosis. BMC Infect Dis 2021; 21:1055. [PMID: 34635070 PMCID: PMC8504024 DOI: 10.1186/s12879-021-06749-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 09/10/2021] [Indexed: 01/18/2023] Open
Abstract
Background Diagnosing tuberculosis (TB) in children is challenging due to paucibacillary disease, and lack of ability for microbiologic confirmation. Hence, we measured the plasma chemokines as biomarkers for diagnosis of pediatric tuberculosis. Methods We conducted a prospective case control study using children with confirmed, unconfirmed and unlikely TB. Multiplex assay was performed to examine the plasma CC and CXC levels of chemokines. Results Baseline levels of CCL1, CCL3, CXCL1, CXCL2 and CXCL10 were significantly higher in active TB (confirmed TB and unconfirmed TB) in comparison to unlikely TB children. Receiver operating characteristics curve analysis revealed that CCL1, CXCL1 and CXCL10 could act as biomarkers distinguishing confirmed or unconfirmed TB from unlikely TB with the sensitivity and specificity of more than 80%. In addition, combiROC exhibited more than 90% sensitivity and specificity in distinguishing confirmed and unconfirmed TB from unlikely TB. Finally, classification and regression tree models also offered more than 90% sensitivity and specificity for CCL1 with a cutoff value of 28 pg/ml, which clearly classify active TB from unlikely TB. The levels of CCL1, CXCL1, CXCL2 and CXCL10 exhibited a significant reduction following anti-TB treatment. Conclusion Thus, a baseline chemokine signature of CCL1/CXCL1/CXCL10 could serve as an accurate biomarker for the diagnosis of pediatric tuberculosis. Supplementary Information The online version contains supplementary material available at 10.1186/s12879-021-06749-6.
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Affiliation(s)
| | - Syed Hissar
- ICMR-National Institute for Research in Tuberculosis, Chennai, India.
| | | | | | - Sarath Balaji
- Institute of Child Health and Hospital for Children, Chennai, India
| | - S Elilarasi
- Institute of Child Health and Hospital for Children, Chennai, India
| | - N S Gomathi
- ICMR-National Institute for Research in Tuberculosis, Chennai, India
| | - J Ganesh
- Government Stanley Medical College and Hospital, Chennai, India
| | - M A Aravind
- Government Stanley Medical College and Hospital, Chennai, India
| | - Dhanaraj Baskaran
- ICMR-National Institute for Research in Tuberculosis, Chennai, India
| | - Srikanth Tripathy
- ICMR-National Institute for Research in Tuberculosis, Chennai, India
| | - Soumya Swaminathan
- ICMR-National Institute for Research in Tuberculosis, Chennai, India.,World Health Organisation, Geneva, Switzerland
| | - Subash Babu
- International Center for Excellence in Research, National Institute for Research in Tuberculosis , Chennai, India.,LPD, NIAID, NIH, Bethesda, MD, USA
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Luo Y, Xue Y, Mao L, Lin Q, Tang G, Song H, Liu W, Tong S, Hou H, Huang M, Ouyang R, Wang F, Sun Z. Activation Phenotype of Mycobacterium tuberculosis-Specific CD4 + T Cells Promoting the Discrimination Between Active Tuberculosis and Latent Tuberculosis Infection. Front Immunol 2021; 12:721013. [PMID: 34512645 PMCID: PMC8426432 DOI: 10.3389/fimmu.2021.721013] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Accepted: 07/29/2021] [Indexed: 12/13/2022] Open
Abstract
Background Rapid and effective discrimination between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) remains a challenge. There is an urgent need for developing practical and affordable approaches targeting this issue. Methods Participants with ATB and LTBI were recruited at Tongji Hospital (Qiaokou cohort) and Sino-French New City Hospital (Caidian cohort) based on positive T-SPOT results from June 2020 to January 2021. The expression of activation markers including HLA-DR, CD38, CD69, and CD25 was examined on Mycobacterium tuberculosis (MTB)-specific CD4+ T cells defined by IFN-γ, TNF-α, and IL-2 expression upon MTB antigen stimulation. Results A total of 90 (40 ATB and 50 LTBI) and another 64 (29 ATB and 35 LTBI) subjects were recruited from the Qiaokou cohort and Caidian cohort, respectively. The expression patterns of Th1 cytokines including IFN-γ, TNF-α, and IL-2 upon MTB antigen stimulation could not differentiate ATB patients from LTBI individuals well. However, both HLA-DR and CD38 on MTB-specific cells showed discriminatory value in distinguishing between ATB patients and LTBI individuals. As for developing a single candidate biomarker, HLA-DR had the advantage over CD38. Moreover, HLA-DR on TNF-α+ or IL-2+ cells had superiority over that on IFN-γ+ cells in differentiating ATB patients from LTBI individuals. Besides, HLA-DR on MTB-specific cells defined by multiple cytokine co-expression had a higher ability to discriminate patients with ATB from LTBI individuals than that of MTB-specific cells defined by one kind of cytokine expression. Specially, HLA-DR on TNF-α+IL-2+ cells produced an AUC of 0.901 (95% CI, 0.833–0.969), with a sensitivity of 93.75% (95% CI, 79.85–98.27%) and specificity of 72.97% (95% CI, 57.02–84.60%) as a threshold of 44% was used. Furthermore, the performance of HLA-DR on TNF-α+IL-2+ cells for differential diagnosis was obtained with validation cohort data: 90.91% (95% CI, 72.19–97.47%) sensitivity and 68.97% (95% CI, 50.77–82.73%) specificity. Conclusions We demonstrated that HLA-DR on MTB-specific cells was a potentially useful biomarker for accurate discrimination between ATB and LTBI.
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Affiliation(s)
- Ying Luo
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ying Xue
- Department of Immunology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Liyan Mao
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qun Lin
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Guoxing Tang
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Huijuan Song
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wei Liu
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shutao Tong
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hongyan Hou
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Min Huang
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Renren Ouyang
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Feng Wang
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ziyong Sun
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Luo Y, Xue Y, Tang G, Cai Y, Yuan X, Lin Q, Song H, Liu W, Mao L, Zhou Y, Chen Z, Zhu Y, Liu W, Wu S, Wang F, Sun Z. Lymphocyte-Related Immunological Indicators for Stratifying Mycobacterium tuberculosis Infection. Front Immunol 2021; 12:658843. [PMID: 34276653 PMCID: PMC8278865 DOI: 10.3389/fimmu.2021.658843] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 06/10/2021] [Indexed: 12/16/2022] Open
Abstract
Background Easily accessible tools that reliably stratify Mycobacterium tuberculosis (MTB) infection are needed to facilitate the improvement of clinical management. The current study attempts to reveal lymphocyte-related immune characteristics of active tuberculosis (ATB) patients and establish immunodiagnostic model for discriminating ATB from latent tuberculosis infection (LTBI) and healthy controls (HC). Methods A total of 171 subjects consisted of 54 ATB, 57 LTBI, and 60 HC were consecutively recruited at Tongji hospital from January 2019 to January 2021. All participants were tested for lymphocyte subsets, phenotype, and function. Other examination including T-SPOT and microbiological detection for MTB were performed simultaneously. Results Compared with LTBI and HC, ATB patients exhibited significantly lower number and function of lymphocytes including CD4+ T cells, CD8+ T cells and NK cells, and significantly higher T cell activation represented by HLA-DR and proportion of immunosuppressive cells represented by Treg. An immunodiagnostic model based on the combination of NK cell number, HLA-DR+CD3+ T cells, Treg, CD4+ T cell function, and NK cell function was built using logistic regression. Based on receiver operating characteristic curve analysis, the area under the curve (AUC) of the diagnostic model was 0.920 (95% CI, 0.867-0.973) in distinguishing ATB from LTBI, while the cut-off value of 0.676 produced a sensitivity of 81.48% (95% CI, 69.16%-89.62%) and specificity of 91.23% (95% CI, 81.06%-96.20%). Meanwhile, AUC analysis between ATB and HC according to the diagnostic model was 0.911 (95% CI, 0.855-0.967), with a sensitivity of 81.48% (95% CI, 69.16%-89.62%) and a specificity of 90.00% (95% CI, 79.85%-95.34%). Conclusions Our study demonstrated that the immunodiagnostic model established by the combination of lymphocyte-related indicators could facilitate the status differentiation of MTB infection.
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Affiliation(s)
- Ying Luo
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ying Xue
- Department of Immunology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Guoxing Tang
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yimin Cai
- Department of Epidemiology and Biostatistics, Key Laboratory of Environmental Health of Ministry of Education, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xu Yuan
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qun Lin
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Huijuan Song
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wei Liu
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Liyan Mao
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yu Zhou
- Department of Laboratory Medicine, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Zhongju Chen
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yaowu Zhu
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Weiyong Liu
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shiji Wu
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Feng Wang
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ziyong Sun
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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11
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Kontsevaya I, Lange C, Comella-Del-Barrio P, Coarfa C, DiNardo AR, Gillespie SH, Hauptmann M, Leschczyk C, Mandalakas AM, Martinecz A, Merker M, Niemann S, Reimann M, Rzhepishevska O, Schaible UE, Scheu KM, Schurr E, Abel Zur Wiesch P, Heyckendorf J. Perspectives for systems biology in the management of tuberculosis. Eur Respir Rev 2021; 30:30/160/200377. [PMID: 34039674 DOI: 10.1183/16000617.0377-2020] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 01/28/2021] [Indexed: 12/18/2022] Open
Abstract
Standardised management of tuberculosis may soon be replaced by individualised, precision medicine-guided therapies informed with knowledge provided by the field of systems biology. Systems biology is a rapidly expanding field of computational and mathematical analysis and modelling of complex biological systems that can provide insights into mechanisms underlying tuberculosis, identify novel biomarkers, and help to optimise prevention, diagnosis and treatment of disease. These advances are critically important in the context of the evolving epidemic of drug-resistant tuberculosis. Here, we review the available evidence on the role of systems biology approaches - human and mycobacterial genomics and transcriptomics, proteomics, lipidomics/metabolomics, immunophenotyping, systems pharmacology and gut microbiomes - in the management of tuberculosis including prediction of risk for disease progression, severity of mycobacterial virulence and drug resistance, adverse events, comorbidities, response to therapy and treatment outcomes. Application of the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach demonstrated that at present most of the studies provide "very low" certainty of evidence for answering clinically relevant questions. Further studies in large prospective cohorts of patients, including randomised clinical trials, are necessary to assess the applicability of the findings in tuberculosis prevention and more efficient clinical management of patients.
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Affiliation(s)
- Irina Kontsevaya
- Research Center Borstel, Borstel, Germany.,German Center for Infection Research, Hamburg-Lübeck-Borstel-Riems, Borstel, Germany.,International Health/Infectious Diseases, University of Lübeck, Lübeck, Germany
| | - Christoph Lange
- Research Center Borstel, Borstel, Germany.,German Center for Infection Research, Hamburg-Lübeck-Borstel-Riems, Borstel, Germany.,International Health/Infectious Diseases, University of Lübeck, Lübeck, Germany
| | - Patricia Comella-Del-Barrio
- Research Institute Germans Trias i Pujol, CIBER Respiratory Diseases, Universitat Autònoma de Barcelona, Badalona, Spain
| | - Cristian Coarfa
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA.,Molecular and Cellular Biology, Center for Precision Environmental health, Baylor College of Medicine, Houston, TX, USA
| | - Andrew R DiNardo
- The Global Tuberculosis Program, Texas Children's Hospital, Dept of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | | | - Matthias Hauptmann
- Research Center Borstel, Borstel, Germany.,German Center for Infection Research, Hamburg-Lübeck-Borstel-Riems, Borstel, Germany
| | - Christoph Leschczyk
- Research Center Borstel, Borstel, Germany.,German Center for Infection Research, Hamburg-Lübeck-Borstel-Riems, Borstel, Germany
| | - Anna M Mandalakas
- The Global Tuberculosis Program, Texas Children's Hospital, Dept of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Antal Martinecz
- Dept of Biology, Pennsylvania State University, University Park, PA, USA.,Center for Infectious Disease Dynamics, Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA, USA.,Dept of Pharmacy, Faculty of Health Sciences, UiT, Arctic University of Norway, Tromsø, Norway
| | - Matthias Merker
- Research Center Borstel, Borstel, Germany.,German Center for Infection Research, Hamburg-Lübeck-Borstel-Riems, Borstel, Germany
| | - Stefan Niemann
- Research Center Borstel, Borstel, Germany.,German Center for Infection Research, Hamburg-Lübeck-Borstel-Riems, Borstel, Germany
| | - Maja Reimann
- Research Center Borstel, Borstel, Germany.,German Center for Infection Research, Hamburg-Lübeck-Borstel-Riems, Borstel, Germany.,International Health/Infectious Diseases, University of Lübeck, Lübeck, Germany
| | - Olena Rzhepishevska
- Dept of Chemistry, Umeå University, Umeå, Sweden.,Dept of Clinical Microbiology, Umeå University, Umeå, Sweden
| | - Ulrich E Schaible
- Research Center Borstel, Borstel, Germany.,German Center for Infection Research, Hamburg-Lübeck-Borstel-Riems, Borstel, Germany
| | | | - Erwin Schurr
- Infectious Diseases and Immunity in Global Health Program, Research Institute of the McGill University Health Centre, Montréal, Canada
| | - Pia Abel Zur Wiesch
- Dept of Biology, Pennsylvania State University, University Park, PA, USA.,Center for Infectious Disease Dynamics, Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA, USA
| | - Jan Heyckendorf
- Research Center Borstel, Borstel, Germany .,German Center for Infection Research, Hamburg-Lübeck-Borstel-Riems, Borstel, Germany.,International Health/Infectious Diseases, University of Lübeck, Lübeck, Germany
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12
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Sivakumaran D, Ritz C, Gjøen JE, Vaz M, Selvam S, Ottenhoff THM, Doherty TM, Jenum S, Grewal HMS. Host Blood RNA Transcript and Protein Signatures for Sputum-Independent Diagnostics of Tuberculosis in Adults. Front Immunol 2021; 11:626049. [PMID: 33613569 PMCID: PMC7891042 DOI: 10.3389/fimmu.2020.626049] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 12/21/2020] [Indexed: 12/21/2022] Open
Abstract
To achieve the ambitious targets for tuberculosis (TB) prevention, care, and control stated by the End TB Strategy, new health care strategies, diagnostic tools are warranted. Host-derived biosignatures are explored for their TB diagnostic potential in accordance with the WHO target product profiles (TPPs) for point-of-care (POC) testing. We aimed to identify sputum-independent TB diagnostic signatures in newly diagnosed adult pulmonary-TB (PTB) patients recruited in the context of a prospective household contact cohort study conducted in Andhra Pradesh, India. Whole-blood mRNA samples from 158 subjects (PTB, n = 109; age-matched household controls, n = 49) were examined by dual-color Reverse-Transcriptase Multiplex Ligation-dependent Probe-Amplification (dcRT-MLPA) for the expression of 198 pre-defined genes and a Mesoscale discovery assay for the concentration of 18 cytokines/chemokines in TB-antigen stimulated QuantiFERON supernatants. To identify signatures, we applied a two-step approach; in the first step, univariate filtering was used to identify and shortlist potentially predictive biomarkers; this step may be seen as removing redundant biomarkers. In the second step, a logistic regression approach was used such that group membership (PTB vs. household controls) became the binary response in a Lasso regression model. We identified an 11-gene signature that distinguished PTB from household controls with AUCs of ≥0.98 (95% CIs: 0.94–1.00), and a 4-protein signature (IFNγ, GMCSF, IL7 and IL15) that differentiated PTB from household controls with AUCs of ≥0.87 (95% CIs: 0.75–1.00), in our discovery cohort. Subsequently, we evaluated the performance of the 11-gene signature in two external validation data sets viz, an independent cohort at the Glenfield Hospital, University Hospitals of Leicester NHS Trust, Leicester, UK (GSE107994 data set), and the Catalysis treatment response cohort (GSE89403 data set) from South Africa. The 11-gene signature validated and distinguished PTB from healthy and asymptomatic M. tuberculosis infected household controls in the GSE107994 data set, with an AUC of 0.95 (95% CI: 0.91–0.98) and 0.94 (95% CI: 0.89–0.98). More interestingly in the GSE89403 data set, the 11-gene signature distinguished PTB from household controls and patients with other lung diseases with an AUC of 0.93 (95% CI: 0.87–0.99) and 0.73 (95% CI: 0.56–0.89). These criteria meet the WHO TTP benchmarks for a non–sputum-based triage test for TB diagnosis. We suggest that further validation is required before clinical implementation of the 11-gene signature we have identified markers will be possible.
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Affiliation(s)
- Dhanasekaran Sivakumaran
- Department of Clinical Science, Faculty of Medicine, University of Bergen, Bergen, Norway.,Department of Microbiology, Haukeland University Hospital, University of Bergen, Bergen, Norway
| | - Christian Ritz
- Department of Clinical Science, Faculty of Medicine, University of Bergen, Bergen, Norway.,Department of Nutrition, Exercise and Sports, University of Copenhagen, Copenhagen, Denmark
| | - John Espen Gjøen
- Department of Paediatrics, Haukeland University Hospital, Bergen, Norway
| | - Mario Vaz
- Department of Physiology, St. John's Medical College and Division of Health and Humanities, St. John's Research Institute, Bangalore, India
| | - Sumithra Selvam
- Division of Infectious Diseases, St. John's Research Institute, Bangalore, India
| | - Tom H M Ottenhoff
- Department of Infectious Diseases, Leiden University Medical Center, Leiden, Netherlands
| | | | - Synne Jenum
- Department of Infectious Diseases, Oslo University Hospital, Oslo, Norway
| | - Harleen M S Grewal
- Department of Clinical Science, Faculty of Medicine, University of Bergen, Bergen, Norway.,Department of Microbiology, Haukeland University Hospital, University of Bergen, Bergen, Norway
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13
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Khambati N, Olbrich L, Ellner J, Salgame P, Song R, Bijker EM. Host-Based Biomarkers in Saliva for the Diagnosis of Pulmonary Tuberculosis in Children: A Mini-Review. Front Pediatr 2021; 9:756043. [PMID: 34760853 PMCID: PMC8575443 DOI: 10.3389/fped.2021.756043] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 09/27/2021] [Indexed: 12/28/2022] Open
Abstract
The diagnosis of pulmonary tuberculosis (TB) in children remains a significant challenge due to its paucibacillary nature, non-specificity of symptoms and suboptimal sensitivity of available diagnostic methods. In young children particularly, it is difficult to obtain high-quality sputum specimens for testing, with this group the least likely to be diagnosed, while most at risk of severe disease. The World Health Organization (WHO) has prioritized research into rapid biomarker-based tests for TB using easily obtainable non-sputum samples, such as saliva. However, the role of biomarkers in saliva for diagnosing TB in children has not been fully explored. In this mini-review, we discuss the value of saliva as a diagnostic specimen in children given its ready availability and non-invasive nature of collection, and review the literature on the use of host-based biomarkers in saliva for diagnosing active pulmonary TB in adults and children. Based on available data from adult studies, we highlight that combinations of cytokines and other proteins show promise in reaching WHO-endorsed target product profiles for new TB triage tests. Given the lack of pediatric research on host biomarkers in saliva and the differing immune response to TB infection between children and adults, we recommend that pediatric studies are now performed to discover and validate salivary host biosignatures for diagnosing pulmonary TB in children. Future directions for pediatric saliva studies are discussed, with suggestions for technologies that can be applied for salivary biomarker discovery and point-of-care test development.
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Affiliation(s)
- Nisreen Khambati
- Oxford Vaccine Group, Department of Paediatrics, University of Oxford, Oxford, United Kingdom
| | - Laura Olbrich
- Oxford Vaccine Group, Department of Paediatrics, University of Oxford, Oxford, United Kingdom.,Division of Infectious Diseases and Tropical Medicine, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany.,German Center for Infection Research (DZIF), Partner Site Munich, Munich, Germany
| | - Jerrold Ellner
- Department of Medicine, New Jersey Medical School, Rutgers, The State University of New Jersey, Newark, NJ, United States
| | - Padmini Salgame
- Department of Medicine, New Jersey Medical School, Rutgers, The State University of New Jersey, Newark, NJ, United States
| | - Rinn Song
- Oxford Vaccine Group, Department of Paediatrics, University of Oxford, Oxford, United Kingdom.,Division of Infectious Diseases, Boston Children's Hospital, Boston, MA, United States
| | - Else Margreet Bijker
- Oxford Vaccine Group, Department of Paediatrics, University of Oxford, Oxford, United Kingdom
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14
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Affiliation(s)
- Pierre Nauleau
- EBioMedicine, 230 Park Avenue, #800, New York, NY 10169, United States.
| | - Hannah Ralph
- EBioMedicine, 125 London Wall, London EC2Y 5AS, UK
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