1
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Sharma N, Upadhyay D, Gautam H, Sharma U, Lodha R, Kabra SK, Das BK, Kapil A, Mohan A, Jagannathan NR, Guleria R, Singh UB. Small molecule bio-signature in childhood intra-thoracic tuberculosis identified by metabolomics. NMR IN BIOMEDICINE 2023:e4941. [PMID: 36999218 DOI: 10.1002/nbm.4941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 02/27/2023] [Accepted: 03/21/2023] [Indexed: 06/19/2023]
Abstract
The diagnosis of pediatric tuberculosis (TB) remains a major challenge, hence the evaluation of new tools for improved diagnostics is urgently required. We investigated the serum metabolic profile of children with culture-confirmed intra-thoracic TB (ITTB) (n = 23) and compared it with those of non-TB controls (NTCs) (n = 13) using proton NMR spectroscopy-based targeted and untargeted metabolomics approaches. In targeted metabolic profiling, five metabolites (histidine, glycerophosphocholine, creatine/phosphocreatine, acetate, and choline) differentiated TB children from NTCs. Additionally, seven discriminatory metabolites (N-α-acetyl-lysine, polyunsaturated fatty acids, phenylalanine, lysine, lipids, glutamate + glutamine, and dimethylglycine) were identified in untargeted metabolic profiling. The pathway analysis revealed alterations in six metabolic pathways. The altered metabolites were associated with impaired protein synthesis, hindered anti-inflammatory and cytoprotective mechanisms, abnormalities in energy generation processes and membrane metabolism, and deregulated fatty acid and lipid metabolisms in children with ITTB. The diagnostic significance of the classification models obtained from significantly distinguishing metabolites showed sensitivity, specificity, and area under the curve of 78.2%, 84.6%, and 0.86, respectively, in the targeted profiling and 92.3%, 100%, and 0.99, respectively, in the untargeted profiling. Our findings highlight detectable metabolic changes in childhood ITTB; however, further validation is warranted in a large cohort of the pediatric population.
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Affiliation(s)
- Nupur Sharma
- Department of Microbiology, All India Institute of Medical Sciences, New Delhi, India
| | - Deepti Upadhyay
- Department of Nuclear Magnetic Resonance, All India Institute of Medical Sciences, New Delhi, India
| | - Hitender Gautam
- Department of Microbiology, All India Institute of Medical Sciences, New Delhi, India
| | - Uma Sharma
- Department of Nuclear Magnetic Resonance, All India Institute of Medical Sciences, New Delhi, India
| | - Rakesh Lodha
- Department of Pediatrics, All India Institute of Medical Sciences, New Delhi, India
| | - Sushil Kumar Kabra
- Department of Pediatrics, All India Institute of Medical Sciences, New Delhi, India
| | - Bimal Kumar Das
- Department of Microbiology, All India Institute of Medical Sciences, New Delhi, India
| | - Arti Kapil
- Department of Microbiology, All India Institute of Medical Sciences, New Delhi, India
| | - Anant Mohan
- Department of Pulmonary Medicine & Sleep Disorders, All India Institute of Medical Sciences, New Delhi, India
| | - Naranamangalam Raghunathan Jagannathan
- Department of Nuclear Magnetic Resonance, All India Institute of Medical Sciences, New Delhi, India
- Department of Radiology, Chettinad Academy of Research & Education, Kelambakkam, Tamil Nadu, India
| | - Randeep Guleria
- Department of Pulmonary Medicine & Sleep Disorders, All India Institute of Medical Sciences, New Delhi, India
- Department of Pulmonary Medicine, Medanta, Gurgaon, Haryana, India
| | - Urvashi Balbir Singh
- Department of Microbiology, All India Institute of Medical Sciences, New Delhi, India
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2
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Exploration of Lipid Metabolism Alterations in Children with Active Tuberculosis Using UHPLC-MS/MS. J Immunol Res 2023; 2023:8111355. [PMID: 36815950 PMCID: PMC9936505 DOI: 10.1155/2023/8111355] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 11/09/2022] [Accepted: 11/24/2022] [Indexed: 02/11/2023] Open
Abstract
Metabolic profiling using nonsputum samples has demonstrated excellent performance in diagnosing infectious diseases. But little is known about the lipid metabolism alternation in children with tuberculosis (TB). Therefore, the study was performed to explore lipid metabolic changes caused by Mycobacterium tuberculosis infection and identify specific lipids as diagnostic biomarkers in children with TB using UHPLC-MS/MS. Plasma samples obtained from 70 active TB children, 21 non-TB infectious disease children, and 21 healthy controls were analyzed by a partial least-squares discriminant analysis model in the training set, and 12 metabolites were identified that can separate children with TB from non-TB controls. In the independent testing cohort with 49 subjects, three of the markers, PC (15:0/17:1), PC (17:1/18:2), and PE (18:1/20:3), presented with high diagnostic values. The areas under the curve of the three metabolites were 0.904, 0.833, and 0.895, respectively. The levels of the altered lipid metabolites were found to be associated with the severity of the TB disease. Taken together, plasma lipid metabolites are potentially useful for diagnosis of active TB in children and would provide insights into the pathogenesis of the disease.
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3
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Yu Y, Jiang XX, Li JC. Biomarker discovery for tuberculosis using metabolomics. Front Mol Biosci 2023; 10:1099654. [PMID: 36891238 PMCID: PMC9986447 DOI: 10.3389/fmolb.2023.1099654] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 02/06/2023] [Indexed: 02/22/2023] Open
Abstract
Tuberculosis (TB) is the leading cause of death among infectious diseases, and the ratio of cases in which its pathogen Mycobacterium tuberculosis (Mtb) is drug resistant has been increasing worldwide, whereas latent tuberculosis infection (LTBI) may develop into active TB. Thus it is important to understand the mechanism of drug resistance, find new drugs, and find biomarkers for TB diagnosis. The rapid progress of metabolomics has enabled quantitative metabolite profiling of both the host and the pathogen. In this context, we provide recent progress in the application of metabolomics toward biomarker discovery for tuberculosis. In particular, we first focus on biomarkers based on blood or other body fluids for diagnosing active TB, identifying LTBI and predicting the risk of developing active TB, as well as monitoring the effectiveness of anti-TB drugs. Then we discuss the pathogen-based biomarker research for identifying drug resistant TB. While there have been many reports of potential candidate biomarkers, validations and clinical testing as well as improved bioinformatics analysis are needed to further substantiate and select key biomarkers before they can be made clinically applicable.
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Affiliation(s)
- Yi Yu
- Center for Analyses and Measurements, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Xin-Xin Jiang
- Clinical Research Laboratory, Shaoxing Seventh People's Hospital, Shaoxing, China
| | - Ji-Cheng Li
- Clinical Research Laboratory, Shaoxing Seventh People's Hospital, Shaoxing, China.,Institute of Cell Biology, Zhejiang University Medical School, Hangzhou, China
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4
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Combining metabolome and clinical indicators with machine learning provides some promising diagnostic markers to precisely detect smear-positive/negative pulmonary tuberculosis. BMC Infect Dis 2022; 22:707. [PMID: 36008772 PMCID: PMC9403968 DOI: 10.1186/s12879-022-07694-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 08/22/2022] [Indexed: 11/30/2022] Open
Abstract
Background Tuberculosis (TB) had been the leading lethal infectious disease worldwide for a long time (2014–2019) until the COVID-19 global pandemic, and it is still one of the top 10 death causes worldwide. One important reason why there are so many TB patients and death cases in the world is because of the difficulties in precise diagnosis of TB using common detection methods, especially for some smear-negative pulmonary tuberculosis (SNPT) cases. The rapid development of metabolome and machine learning offers a great opportunity for precision diagnosis of TB. However, the metabolite biomarkers for the precision diagnosis of smear-positive and smear-negative pulmonary tuberculosis (SPPT/SNPT) remain to be uncovered. In this study, we combined metabolomics and clinical indicators with machine learning to screen out newly diagnostic biomarkers for the precise identification of SPPT and SNPT patients. Methods Untargeted plasma metabolomic profiling was performed for 27 SPPT patients, 37 SNPT patients and controls. The orthogonal partial least squares-discriminant analysis (OPLS-DA) was then conducted to screen differential metabolites among the three groups. Metabolite enriched pathways, random forest (RF), support vector machines (SVM) and multilayer perceptron neural network (MLP) were performed using Metaboanalyst 5.0, “caret” R package, “e1071” R package and “Tensorflow” Python package, respectively. Results Metabolomic analysis revealed significant enrichment of fatty acid and amino acid metabolites in the plasma of SPPT and SNPT patients, where SPPT samples showed a more serious dysfunction in fatty acid and amino acid metabolisms. Further RF analysis revealed four optimized diagnostic biomarker combinations including ten features (two lipid/lipid-like molecules and seven organic acids/derivatives, and one clinical indicator) for the identification of SPPT, SNPT patients and controls with high accuracy (83–93%), which were further verified by SVM and MLP. Among them, MLP displayed the best classification performance on simultaneously precise identification of the three groups (94.74%), suggesting the advantage of MLP over RF/SVM to some extent. Conclusions Our findings reveal plasma metabolomic characteristics of SPPT and SNPT patients, provide some novel promising diagnostic markers for precision diagnosis of various types of TB, and show the potential of machine learning in screening out biomarkers from big data. Supplementary Information The online version contains supplementary material available at 10.1186/s12879-022-07694-8.
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5
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Sholeye AR, Williams AA, Loots DT, Tutu van Furth AM, van der Kuip M, Mason S. Tuberculous Granuloma: Emerging Insights From Proteomics and Metabolomics. Front Neurol 2022; 13:804838. [PMID: 35386409 PMCID: PMC8978302 DOI: 10.3389/fneur.2022.804838] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 02/24/2022] [Indexed: 12/24/2022] Open
Abstract
Mycobacterium tuberculosis infection, which claims hundreds of thousands of lives each year, is typically characterized by the formation of tuberculous granulomas — the histopathological hallmark of tuberculosis (TB). Our knowledge of granulomas, which comprise a biologically diverse body of pro- and anti-inflammatory cells from the host immune responses, is based mainly upon examination of lungs, in both human and animal studies, but little on their counterparts from other organs of the TB patient such as the brain. The biological heterogeneity of TB granulomas has led to their diverse, relatively uncoordinated, categorization, which is summarized here. However, there is a pressing need to elucidate more fully the phenotype of the granulomas from infected patients. Newly emerging studies at the protein (proteomics) and metabolite (metabolomics) levels have the potential to achieve this. In this review we summarize the diverse nature of TB granulomas based upon the literature, and amplify these accounts by reporting on the relatively few, emerging proteomics and metabolomics studies on TB granulomas. Metabolites (for example, trimethylamine-oxide) and proteins (such as the peptide PKAp) associated with TB granulomas, and knowledge of their localizations, help us to understand the resultant phenotype. Nevertheless, more multidisciplinary ‘omics studies, especially in human subjects, are required to contribute toward ushering in a new era of understanding of TB granulomas – both at the site of infection, and on a systemic level.
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Affiliation(s)
- Abisola Regina Sholeye
- Department of Biochemistry, Human Metabolomics, Faculty of Natural and Agricultural Sciences, North-West University, Potchefstroom, South Africa
| | - Aurelia A. Williams
- Department of Biochemistry, Human Metabolomics, Faculty of Natural and Agricultural Sciences, North-West University, Potchefstroom, South Africa
| | - Du Toit Loots
- Department of Biochemistry, Human Metabolomics, Faculty of Natural and Agricultural Sciences, North-West University, Potchefstroom, South Africa
| | - A. Marceline Tutu van Furth
- Department of Pediatric Infectious Diseases and Immunology, Pediatric Infectious Diseases and Immunology, Amsterdam University Medical Center, Emma Children's Hospital, Amsterdam, Netherlands
| | - Martijn van der Kuip
- Department of Pediatric Infectious Diseases and Immunology, Pediatric Infectious Diseases and Immunology, Amsterdam University Medical Center, Emma Children's Hospital, Amsterdam, Netherlands
| | - Shayne Mason
- Department of Biochemistry, Human Metabolomics, Faculty of Natural and Agricultural Sciences, North-West University, Potchefstroom, South Africa
- *Correspondence: Shayne Mason
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6
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Magdalena D, Michal S, Marta S, Magdalena KP, Anna P, Magdalena G, Rafał S. Targeted metabolomics analysis of serum and Mycobacterium tuberculosis antigen-stimulated blood cultures of pediatric patients with active and latent tuberculosis. Sci Rep 2022; 12:4131. [PMID: 35260782 PMCID: PMC8904507 DOI: 10.1038/s41598-022-08201-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 03/03/2022] [Indexed: 12/28/2022] Open
Abstract
Profound tuberculosis (TB)-induced metabolic changes reflected in the blood metabolomic profile may provide an opportunity to identify specific markers of Mycobacterium tuberculosis infection. Using targeted liquid chromatography tandem mass spectrometry, we compared the levels of 30 small metabolites, including amino acids and derivatives, and small organic compounds in serum and M.tb antigen-stimulated whole blood cultures of active TB children, latent TB (LTBI) children, nonmycobacterial pneumonia (NMP) children, and healthy controls (HCs) to assess their potential as biomarkers of childhood TB. We found elevated levels of leucine and kynurenine combined with reduced concentrations of citrulline and glutamine in serum and blood cultures of TB and LTBI groups. LTBI status was additionally associated with a decrease in valine levels in blood cultures. The NMP metabolite profile was characterized by an increase in citrulline and glutamine and a decrease in leucine, kynurenine and valine concentrations. The highest discriminatory potential for identifying M.tb infection was observed for leucine detected in serum and kynurenine in stimulated blood cultures. The use of targeted metabolomics may reveal metabolic changes in M.tb-infected children, and the obtained results are a proof of principle of the usefulness of metabolites in the auxiliary diagnosis of TB in children.
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Affiliation(s)
- Druszczynska Magdalena
- Department of Immunology and Infectious Biology, Institute of Microbiology, Biotechnology and Immunology, Faculty of Biology and Environmental Protection, University of Lodz, Banacha 12/16, 90-237, Lodz, Poland.
| | - Seweryn Michal
- Biobank Lab, Department of Molecular Biophysics, Faculty of Biology and Environmental Protection, University of Lodz, Banacha 12/16, 90-237, Lodz, Poland
| | | | - Kowalewska-Pietrzak Magdalena
- Regional Specialized Hospital of Tuberculosis, Lung Diseases, and Rehabilitation in Lodz, Okolna 181, 91-520, Lodz, Poland
| | - Pankowska Anna
- Regional Specialized Hospital of Tuberculosis, Lung Diseases, and Rehabilitation in Lodz, Okolna 181, 91-520, Lodz, Poland
| | - Godkowicz Magdalena
- Department of Immunology and Infectious Biology, Institute of Microbiology, Biotechnology and Immunology, Faculty of Biology and Environmental Protection, University of Lodz, Banacha 12/16, 90-237, Lodz, Poland
| | - Szewczyk Rafał
- , Labexperts sp z o. o. Piekarnicza 5, 80-126, Gdansk, Poland
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7
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Ruiz-Cabello J, Sevilla IA, Olaizola E, Bezos J, Miguel-Coello AB, Muñoz-Mendoza M, Beraza M, Garrido JM, Izquierdo-García JL. Benchtop nuclear magnetic resonance-based metabolomic approach for the diagnosis of bovine tuberculosis. Transbound Emerg Dis 2021; 69:e859-e870. [PMID: 34717039 DOI: 10.1111/tbed.14365] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 09/23/2021] [Accepted: 10/18/2021] [Indexed: 11/28/2022]
Abstract
Even though enormous efforts and control strategies have been implemented, bovine tuberculosis (TB) remains a significant source of health and socioeconomic concern. The standard method used in TB eradication programs for in vivo detection is the tuberculin skin test. However, the specificity of the tuberculin skin test is affected by infection with non-tuberculous mycobacteria or by vaccination. Thus, some animals are not correctly diagnosed. This study aimed first to identify a plasma metabolic TB profile by high-field (HF) nuclear magnetic resonance (NMR) spectroscopy and second measure this characteristic TB metabolic profile using low-field benchtop (LF) NMR as an affordable molecular technology for TB diagnosis. Plasma samples from cattle diagnosed with TB (derivation set, n = 11), diagnosed with paratuberculosis (PTB, n = 10), PTB-vaccinated healthy control (n = 10) and healthy PTB-unvaccinated control (n = 10) were analyzed by NMR. Unsupervised Principal Component Analysis (PCA) was used to identify metabolic differences between groups. We identified 14 metabolites significantly different between TB and control animals. The second group of TB animals was used to validate the results (validation set, n = 14). Predictive models based on metabolic fingerprint acquired by both HF and LF NMR spectroscopy successfully identified TB versus control subjects (Area under the curve of Receiver Operating Characteristic over 0.92, in both models; Confidence Interval 0.77-1). In summary, plasma fingerprinting using HF and LF-NMR differentiated TB subjects from uninfected animals, and PTB and PTB-vaccinated subjects who may provide a TB-false positive, highlighting the use of LF-NMR-based metabolomics as a complementary or alternative diagnostic tool to the current diagnostic methods.
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Affiliation(s)
- Jesús Ruiz-Cabello
- BRTA Basque Research and Technology Alliance, CIC biomaGUNE Center for Cooperative Research in Biomaterials, Donostia, Gipuzkoa, Spain.,Departamento de Química en Ciencias Farmacéuticas, Universidad Complutense de Madrid. Facultad de Farmacia, Madrid, Spain.,Instituto de Salud Carlos III, CIBER de enfermedades respiratorias (CIBERES), Madrid, Spain.,Basque Foundation for Science, IKERBASQUE, Bilbao, Vizcaya, Spain
| | - Iker A Sevilla
- Basque Research and Technology Alliance (BRTA), Animal Health Department, NEIKER-Basque Institute for Agricultural Research and Development, Derio, Bizkaia, Spain
| | - Ekine Olaizola
- BRTA Basque Research and Technology Alliance, CIC biomaGUNE Center for Cooperative Research in Biomaterials, Donostia, Gipuzkoa, Spain
| | - Javier Bezos
- Departamento de Sanidad Animal y Centro de Vigilancia Sanitaria Veterinaria (VISAVET), Universidad Complutense de Madrid. Facultad de Veterinaria, Madrid, Spain
| | - Ana B Miguel-Coello
- BRTA Basque Research and Technology Alliance, CIC biomaGUNE Center for Cooperative Research in Biomaterials, Donostia, Gipuzkoa, Spain
| | - Marta Muñoz-Mendoza
- Servicio de Sanidad Animal, Xunta de Galicia, Consellería de Medio Rural, Santiago de Compostela, Spain
| | - Marta Beraza
- BRTA Basque Research and Technology Alliance, CIC biomaGUNE Center for Cooperative Research in Biomaterials, Donostia, Gipuzkoa, Spain
| | - Joseba M Garrido
- Basque Research and Technology Alliance (BRTA), Animal Health Department, NEIKER-Basque Institute for Agricultural Research and Development, Derio, Bizkaia, Spain
| | - Jose L Izquierdo-García
- Departamento de Química en Ciencias Farmacéuticas, Universidad Complutense de Madrid. Facultad de Farmacia, Madrid, Spain.,Instituto de Salud Carlos III, CIBER de enfermedades respiratorias (CIBERES), Madrid, Spain.,Instituto Pluridisciplinar, Universidad Complutense de Madrid, Madrid, Spain
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8
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Conde R, Laires R, Gonçalves LG, Rizvi A, Barroso C, Villar M, Macedo R, Simões MJ, Gaddam S, Lamosa P, Puchades-Carrasco L, Pineda-Lucena A, Patel AB, Mande SC, Barnejee S, Matzapetakis M, Coelho AV. Discovery of serum biomarkers for diagnosis of tuberculosis by NMR metabolomics including cross-validation with a second cohort. Biomed J 2021; 45:654-664. [PMID: 34314900 PMCID: PMC9486122 DOI: 10.1016/j.bj.2021.07.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 06/14/2021] [Accepted: 07/19/2021] [Indexed: 11/24/2022] Open
Abstract
Background Tuberculosis (TB) is a disease with worldwide presence and a major cause of death in several developing countries. Current diagnostic methodologies often lack specificity and sensitivity, whereas a long time is needed to obtain a conclusive result. Methods In an effort to develop better diagnostic methods, this study aimed at the discovery of a biomarker signature for TB diagnosis using a Nuclear Magnetic Resonance based metabolomics approach. In this study, we acquired 1H NMR spectra of blood serum samples of groups of healthy subjects, individuals with latent TB and of patients with pulmonary and extra-pulmonary TB. The resulting data were treated with uni- and multivariate statistical analysis. Results Six metabolites (inosine, hypoxanthine, mannose, asparagine, aspartate and glutamate) were validated by an independent cohort, all of them related with metabolic processes described as associated with TB infection. Conclusion The findings of the study are according with the WHO Target Product Profile recommendations for a triage test to rule-out active TB.
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Affiliation(s)
- R Conde
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, 2780-157 Oeiras, Portugal.
| | - R Laires
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, 2780-157 Oeiras, Portugal.
| | - L G Gonçalves
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, 2780-157 Oeiras, Portugal.
| | - A Rizvi
- Department of Biochemistry, School of Life Sciences, University of Hyderabad, Hyderabad, India.
| | - C Barroso
- CDP Almada-Seixal, ARSLVT, Portugal.
| | - M Villar
- CDP Venda Nova, ARSLVT, Portugal.
| | | | | | - S Gaddam
- Department of Immunology, Bhagwan Mahavir Medical Research Center, Hyderabad, India; Department of Genetics, Osmania University, Hyderabad, India.
| | - P Lamosa
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, 2780-157 Oeiras, Portugal.
| | - L Puchades-Carrasco
- Drug Discovery Unit, Instituto de Investigación Sanitaria La Fe, Hospital Universitario y Politécnico La Fe, Valencia, Spain.
| | - A Pineda-Lucena
- Drug Discovery Unit, Instituto de Investigación Sanitaria La Fe, Hospital Universitario y Politécnico La Fe, Valencia, Spain; Molecular Therapeutics Program, Centro de Investigación Médica Aplicada, University of Navarra, Pamplona, Spain.
| | - A B Patel
- CSIR- Centre for Cellular Molecular Biology, Hyderabad, India.
| | - S C Mande
- National Centre For Cell Science, Pune, India; Present address: Council of Scientific and Industrial Research, New Delhi, India.
| | - S Barnejee
- Department of Biochemistry, School of Life Sciences, University of Hyderabad, Hyderabad, India.
| | - M Matzapetakis
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, 2780-157 Oeiras, Portugal.
| | - A V Coelho
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, 2780-157 Oeiras, Portugal.
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9
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Comella-del-Barrio P, Izquierdo-Garcia JL, Gautier J, Doresca MJC, Campos-Olivas R, Santiveri CM, Muriel-Moreno B, Prat-Aymerich C, Abellana R, Pérez-Porcuna TM, Cuevas LE, Ruiz-Cabello J, Domínguez J. Urine NMR-based TB metabolic fingerprinting for the diagnosis of TB in children. Sci Rep 2021; 11:12006. [PMID: 34099838 PMCID: PMC8184981 DOI: 10.1038/s41598-021-91545-0] [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: 12/22/2020] [Accepted: 05/25/2021] [Indexed: 02/07/2023] Open
Abstract
Tuberculosis (TB) is a major cause of morbidity and mortality in children, and early diagnosis and treatment are crucial to reduce long-term morbidity and mortality. In this study, we explore whether urine nuclear magnetic resonance (NMR)-based metabolomics could be used to identify differences in the metabolic response of children with different diagnostic certainty of TB. We included 62 children with signs and symptoms of TB and 55 apparently healthy children. Six of the children with presumptive TB had bacteriologically confirmed TB, 52 children with unconfirmed TB, and 4 children with unlikely TB. Urine metabolic fingerprints were identified using high- and low-field proton NMR platforms and assessed with pattern recognition techniques such as principal components analysis and partial least squares discriminant analysis. We observed differences in the metabolic fingerprint of children with bacteriologically confirmed and unconfirmed TB compared to children with unlikely TB (p = 0.041 and p = 0.013, respectively). Moreover, children with unconfirmed TB with X-rays compatible with TB showed differences in the metabolic fingerprint compared to children with non-pathological X-rays (p = 0.009). Differences in the metabolic fingerprint in children with different diagnostic certainty of TB could contribute to a more accurate characterisation of TB in the paediatric population. The use of metabolomics could be useful to improve the prediction of TB progression and diagnosis in children.
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Affiliation(s)
- Patricia Comella-del-Barrio
- grid.7080.fInstitut d’Investigació Germans Trias i Pujol, Departament de Genètica i Microbiologia, Universitat Autònoma de Barcelona, Badalona, Barcelona, Spain ,grid.413448.e0000 0000 9314 1427CIBER de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
| | - José Luis Izquierdo-Garcia
- grid.413448.e0000 0000 9314 1427CIBER de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain ,grid.4795.f0000 0001 2157 7667Departamento de Química en Ciencias Farmacéuticas, Facultad de Farmacia, Universidad Complutense de Madrid, Madrid, Spain ,grid.424269.f0000 0004 1808 1283Cooperative Research in Biomaterials (CIC biomaGUNE), Basque Research and Technology Alliance (BRTA), Donostia, Spain
| | - Jacqueline Gautier
- Department of Pediatrics, Division of Tuberculosis, Hôpital Saint-Damien, Nos Petits-Frères Et Sœurs, Tabarre, Haiti
| | - Mariette Jean Coute Doresca
- Department of Pediatrics, Division of Tuberculosis, Hôpital Saint-Damien, Nos Petits-Frères Et Sœurs, Tabarre, Haiti
| | - Ramón Campos-Olivas
- grid.7719.80000 0000 8700 1153Spectroscopy and Nuclear Magnetic Resonance Unit, CNIO Centro Nacional de Investigaciones Oncológicas, Madrid, Spain
| | - Clara M. Santiveri
- grid.7719.80000 0000 8700 1153Spectroscopy and Nuclear Magnetic Resonance Unit, CNIO Centro Nacional de Investigaciones Oncológicas, Madrid, Spain
| | - Beatriz Muriel-Moreno
- grid.7080.fInstitut d’Investigació Germans Trias i Pujol, Departament de Genètica i Microbiologia, Universitat Autònoma de Barcelona, Badalona, Barcelona, Spain
| | - Cristina Prat-Aymerich
- grid.7080.fInstitut d’Investigació Germans Trias i Pujol, Departament de Genètica i Microbiologia, Universitat Autònoma de Barcelona, Badalona, Barcelona, Spain ,grid.413448.e0000 0000 9314 1427CIBER de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain ,grid.7692.a0000000090126352Julius Centre for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Rosa Abellana
- grid.5841.80000 0004 1937 0247Department of Basic Clinical Practice, Faculty of Medicine, University of Barcelona, Barcelona, Spain
| | - Tomas M. Pérez-Porcuna
- grid.414875.b0000 0004 1794 4956Servei de Pediatria, Atenció Primària, Unitat de Investigació Fundació Mútua Terrassa, Hospital Universitari Mútua Terrassa, Terrassa, Spain
| | - Luis E. Cuevas
- grid.48004.380000 0004 1936 9764Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK
| | - Jesús Ruiz-Cabello
- grid.413448.e0000 0000 9314 1427CIBER de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain ,grid.4795.f0000 0001 2157 7667Departamento de Química en Ciencias Farmacéuticas, Facultad de Farmacia, Universidad Complutense de Madrid, Madrid, Spain ,grid.424269.f0000 0004 1808 1283Cooperative Research in Biomaterials (CIC biomaGUNE), Basque Research and Technology Alliance (BRTA), Donostia, Spain ,grid.424810.b0000 0004 0467 2314IKERBASQUE, Basque Foundation for Science, Bilbao, Spain
| | - José Domínguez
- grid.7080.fInstitut d’Investigació Germans Trias i Pujol, Departament de Genètica i Microbiologia, Universitat Autònoma de Barcelona, Badalona, Barcelona, Spain ,grid.413448.e0000 0000 9314 1427CIBER de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
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10
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Izquierdo-Garcia JL, Comella-Del-Barrio P, Campos-Olivas R, Villar-Hernández R, Prat-Aymerich C, De Souza-Galvão ML, Jiménez-Fuentes MA, Ruiz-Manzano J, Stojanovic Z, González A, Serra-Vidal M, García-García E, Muriel-Moreno B, Millet JP, Molina-Pinargote I, Casas X, Santiago J, Sabriá F, Martos C, Herzmann C, Ruiz-Cabello J, Domínguez J. Discovery and validation of an NMR-based metabolomic profile in urine as TB biomarker. Sci Rep 2020; 10:22317. [PMID: 33339845 PMCID: PMC7749110 DOI: 10.1038/s41598-020-78999-4] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 12/02/2020] [Indexed: 11/16/2022] Open
Abstract
Despite efforts to improve tuberculosis (TB) detection, limitations in access, quality and timeliness of diagnostic services in low- and middle-income countries are challenging for current TB diagnostics. This study aimed to identify and characterise a metabolic profile of TB in urine by high-field nuclear magnetic resonance (NMR) spectrometry and assess whether the TB metabolic profile is also detected by a low-field benchtop NMR spectrometer. We included 189 patients with tuberculosis, 42 patients with pneumococcal pneumonia, 61 individuals infected with latent tuberculosis and 40 uninfected individuals. We acquired the urine spectra from high and low-field NMR. We characterised a TB metabolic fingerprint from the Principal Component Analysis. We developed a classification model from the Partial Least Squares-Discriminant Analysis and evaluated its performance. We identified a metabolic fingerprint of 31 chemical shift regions assigned to eight metabolites (aminoadipic acid, citrate, creatine, creatinine, glucose, mannitol, phenylalanine, and hippurate). The model developed using low-field NMR urine spectra correctly classified 87.32%, 85.21% and 100% of the TB patients compared to pneumococcal pneumonia patients, LTBI and uninfected individuals, respectively. The model validation correctly classified 84.10% of the TB patients. We have identified and characterised a metabolic profile of TB in urine from a high-field NMR spectrometer and have also detected it using a low-field benchtop NMR spectrometer. The models developed from the metabolic profile of TB identified by both NMR technologies were able to discriminate TB patients from the rest of the study groups and the results were not influenced by anti-TB treatment or TB location. This provides a new approach in the search for possible biomarkers for the diagnosis of TB.
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Affiliation(s)
- José Luis Izquierdo-Garcia
- CIC biomaGUNE Center for Cooperative Research in Biomaterials, BRTA Basque Research and Technology Alliance, Donostia, Donostia, Gipuzkoa, Spain
- CIBER de enfermedades respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Facultad de Farmacia, Universidad Complutense de Madrid, Madrid, Spain
| | - Patricia Comella-Del-Barrio
- CIBER de enfermedades respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Servei de Microbiologia, Hospital Universitari Germans Trias i Pujol, Institut d'Investigació Germans Trias i Pujol, Badalona, Barcelona, Spain
- Departament de Genètica i Microbiologia, Universitat Autònoma de Barcelona, Barcelona, Spain
| | | | - Raquel Villar-Hernández
- CIBER de enfermedades respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Servei de Microbiologia, Hospital Universitari Germans Trias i Pujol, Institut d'Investigació Germans Trias i Pujol, Badalona, Barcelona, Spain
- Departament de Genètica i Microbiologia, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Cristina Prat-Aymerich
- CIBER de enfermedades respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Servei de Microbiologia, Hospital Universitari Germans Trias i Pujol, Institut d'Investigació Germans Trias i Pujol, Badalona, Barcelona, Spain
- Departament de Genètica i Microbiologia, Universitat Autònoma de Barcelona, Barcelona, Spain
- Julius Centre for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Maria Luiza De Souza-Galvão
- Unitat de Tuberculosi de Drassanes, Servei de Pneumologia, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | | | - Juan Ruiz-Manzano
- CIBER de enfermedades respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Servei de Pneumologia, Hospital Universitari Germans Trias i Pujol, Barcelona, Spain
| | - Zoran Stojanovic
- CIBER de enfermedades respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Servei de Pneumologia, Hospital Universitari Germans Trias i Pujol, Barcelona, Spain
| | - Adela González
- CIBER de enfermedades respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Servei de Pneumologia, Hospital Universitari Germans Trias i Pujol, Barcelona, Spain
| | - Mar Serra-Vidal
- Servei de Microbiologia, Hospital Universitari Germans Trias i Pujol, Institut d'Investigació Germans Trias i Pujol, Badalona, Barcelona, Spain
| | - Esther García-García
- Servei de Microbiologia, Hospital Universitari Germans Trias i Pujol, Institut d'Investigació Germans Trias i Pujol, Badalona, Barcelona, Spain
| | - Beatriz Muriel-Moreno
- Servei de Microbiologia, Hospital Universitari Germans Trias i Pujol, Institut d'Investigació Germans Trias i Pujol, Badalona, Barcelona, Spain
| | - Joan Pau Millet
- Serveis Clínics, Unitat Clínica de Tractament Directament Observat de la Tuberculosi, Barcelona, Spain
- CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
| | - Israel Molina-Pinargote
- Serveis Clínics, Unitat Clínica de Tractament Directament Observat de la Tuberculosi, Barcelona, Spain
| | - Xavier Casas
- Serveis Clínics, Unitat Clínica de Tractament Directament Observat de la Tuberculosi, Barcelona, Spain
| | - Javier Santiago
- Serveis Clínics, Unitat Clínica de Tractament Directament Observat de la Tuberculosi, Barcelona, Spain
| | - Fina Sabriá
- Servei de Pneumologia, Hospital Sant Joan Despí Moises Broggi, Sant Joan Despi, Barcelona, Spain
| | - Carmen Martos
- Servei de Pneumologia, Hospital Sant Joan Despí Moises Broggi, Sant Joan Despi, Barcelona, Spain
| | | | - Jesús Ruiz-Cabello
- CIC biomaGUNE Center for Cooperative Research in Biomaterials, BRTA Basque Research and Technology Alliance, Donostia, Donostia, Gipuzkoa, Spain
- CIBER de enfermedades respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Facultad de Farmacia, Universidad Complutense de Madrid, Madrid, Spain
- IKERBASQUE, Basque Foundation for Science, Bilbao, Vizcaya, Spain
| | - José Domínguez
- CIBER de enfermedades respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain.
- Servei de Microbiologia, Hospital Universitari Germans Trias i Pujol, Institut d'Investigació Germans Trias i Pujol, Badalona, Barcelona, Spain.
- Departament de Genètica i Microbiologia, Universitat Autònoma de Barcelona, Barcelona, Spain.
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11
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Diray-Arce J, Conti MG, Petrova B, Kanarek N, Angelidou A, Levy O. Integrative Metabolomics to Identify Molecular Signatures of Responses to Vaccines and Infections. Metabolites 2020; 10:E492. [PMID: 33266347 PMCID: PMC7760881 DOI: 10.3390/metabo10120492] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 11/24/2020] [Accepted: 11/30/2020] [Indexed: 12/16/2022] Open
Abstract
Approaches to the identification of metabolites have progressed from early biochemical pathway evaluation to modern high-dimensional metabolomics, a powerful tool to identify and characterize biomarkers of health and disease. In addition to its relevance to classic metabolic diseases, metabolomics has been key to the emergence of immunometabolism, an important area of study, as leukocytes generate and are impacted by key metabolites important to innate and adaptive immunity. Herein, we discuss the metabolomic signatures and pathways perturbed by the activation of the human immune system during infection and vaccination. For example, infection induces changes in lipid (e.g., free fatty acids, sphingolipids, and lysophosphatidylcholines) and amino acid pathways (e.g., tryptophan, serine, and threonine), while vaccination can trigger changes in carbohydrate and bile acid pathways. Amino acid, carbohydrate, lipid, and nucleotide metabolism is relevant to immunity and is perturbed by both infections and vaccinations. Metabolomics holds substantial promise to provide fresh insight into the molecular mechanisms underlying the host immune response. Its integration with other systems biology platforms will enhance studies of human health and disease.
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Affiliation(s)
- Joann Diray-Arce
- Precision Vaccines Program, Division of Infectious Diseases, Boston Children’s Hospital, Boston, MA 02115, USA; (M.G.C.); (A.A.)
- Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA; (B.P.); (N.K.)
| | - Maria Giulia Conti
- Precision Vaccines Program, Division of Infectious Diseases, Boston Children’s Hospital, Boston, MA 02115, USA; (M.G.C.); (A.A.)
- Department of Maternal and Child Health, Sapienza University of Rome, 5, 00185 Rome, Italy
| | - Boryana Petrova
- Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA; (B.P.); (N.K.)
- Department of Pathology, Boston Children’s Hospital, Boston, MA 02115, USA
| | - Naama Kanarek
- Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA; (B.P.); (N.K.)
- Department of Pathology, Boston Children’s Hospital, Boston, MA 02115, USA
| | - Asimenia Angelidou
- Precision Vaccines Program, Division of Infectious Diseases, Boston Children’s Hospital, Boston, MA 02115, USA; (M.G.C.); (A.A.)
- Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA; (B.P.); (N.K.)
- Department of Neonatology, Beth Israel Deaconess Medical Center, Boston, MA 02115, USA
| | - Ofer Levy
- Precision Vaccines Program, Division of Infectious Diseases, Boston Children’s Hospital, Boston, MA 02115, USA; (M.G.C.); (A.A.)
- Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA; (B.P.); (N.K.)
- Broad Institute of MIT & Harvard, Cambridge, MA 02142, USA
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12
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Integration of metabolomics and transcriptomics reveals novel biomarkers in the blood for tuberculosis diagnosis in children. Sci Rep 2020; 10:19527. [PMID: 33177551 PMCID: PMC7658223 DOI: 10.1038/s41598-020-75513-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 10/13/2020] [Indexed: 01/11/2023] Open
Abstract
Pediatric tuberculosis (TB) remains a major global health problem. Improved pediatric diagnostics using readily available biosources are urgently needed. We used liquid chromatography-mass spectrometry to analyze plasma metabolite profiles of Indian children with active TB (n = 16) and age- and sex-matched, Mycobacterium tuberculosis-exposed but uninfected household contacts (n = 32). Metabolomic data were integrated with whole blood transcriptomic data for each participant at diagnosis and throughout treatment for drug-susceptible TB. A decision tree algorithm identified 3 metabolites that correctly identified TB status at distinct times during treatment. N-acetylneuraminate achieved an area under the receiver operating characteristic curve (AUC) of 0.66 at diagnosis. Quinolinate achieved an AUC of 0.77 after 1 month of treatment, and pyridoxate achieved an AUC of 0.87 after successful treatment completion. A set of 4 metabolites (gamma-glutamylalanine, gamma-glutamylglycine, glutamine, and pyridoxate) identified treatment response with an AUC of 0.86. Pathway enrichment analyses of these metabolites and corresponding transcriptional data correlated N-acetylneuraminate with immunoregulatory interactions between lymphoid and non-lymphoid cells, and correlated pyridoxate with p53-regulated metabolic genes and mitochondrial translation. Our findings shed new light on metabolic dysregulation in children with TB and pave the way for new diagnostic and treatment response markers in pediatric TB.
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13
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Nicol MP, Zar HJ. Advances in the diagnosis of pulmonary tuberculosis in children. Paediatr Respir Rev 2020; 36:52-56. [PMID: 32624357 PMCID: PMC7686111 DOI: 10.1016/j.prrv.2020.05.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Accepted: 05/19/2020] [Indexed: 10/24/2022]
Abstract
Major challenges still exist in the accurate diagnosis of tuberculosis in children. Algorithms based on clinical and radiological features remain in widespread use despite poor performance. Newer molecular diagnostics allow for rapid identification of TB and detection of drug-resistance in a subset of children, but lack sensitivity. Molecular testing of multiple specimens, including non-traditional specimen types, such as nasopharyngeal aspirates and stool and urine, may improve sensitivity, but the optimal combination of specimens requires further research. Novel tests under development or evaluation include a urine lipoarabinomannan test with improved sensitivity and a range of biomarkers measured from stimulated or unstimulated peripheral blood.
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Affiliation(s)
- Mark P Nicol
- Division of Infection and Immunity, School of Biomedical Sciences, University of Western Australia, Perth, Australia.
| | - Heather J Zar
- Department of Paediatrics and Child Health, and SA-MRC Unit on Child & Adolescent Health, University of Cape Town and Red Cross War Memorial Children's Hospital, Cape Town, South Africa
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14
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Andreas NJ, Basu Roy R, Gomez-Romero M, Horneffer-van der Sluis V, Lewis MR, Camuzeaux SSM, Jiménez B, Posma JM, Tientcheu L, Egere U, Sillah A, Togun T, Holmes E, Kampmann B. Performance of metabonomic serum analysis for diagnostics in paediatric tuberculosis. Sci Rep 2020; 10:7302. [PMID: 32350385 PMCID: PMC7190829 DOI: 10.1038/s41598-020-64413-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Accepted: 03/13/2020] [Indexed: 12/31/2022] Open
Abstract
We applied a metabonomic strategy to identify host biomarkers in serum to diagnose paediatric tuberculosis (TB) disease. 112 symptomatic children with presumptive TB were recruited in The Gambia and classified as bacteriologically-confirmed TB, clinically diagnosed TB, or other diseases. Sera were analysed using 1H nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS). Multivariate data analysis was used to distinguish patients with TB from other diseases. Diagnostic accuracy was evaluated using Receiver Operating Characteristic (ROC) curves. Model performance was tested in a validation cohort of 36 children from the UK. Data acquired using 1H NMR demonstrated a sensitivity, specificity and Area Under the Curve (AUC) of 69% (95% confidence interval [CI], 56-73%), 83% (95% CI, 73-93%), and 0.78 respectively, and correctly classified 20% of the validation cohort from the UK. The most discriminatory MS data showed a sensitivity of 67% (95% CI, 60-71%), specificity of 86% (95% CI, 75-93%) and an AUC of 0.78, correctly classifying 83% of the validation cohort. Amongst children with presumptive TB, metabolic profiling of sera distinguished bacteriologically-confirmed and clinical TB from other diseases. This novel approach yielded a diagnostic performance for paediatric TB comparable to that of Xpert MTB/RIF and interferon gamma release assays.
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Affiliation(s)
- Nicholas J Andreas
- Centre for International Child Health, Department of Paediatrics, Imperial College London, St. Mary's Hospital, Praed Street, London, W2 1NY, United Kingdom
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, Sir Alexander Fleming Building, South Kensington, London, SW7 2AZ, United Kingdom
| | - Robindra Basu Roy
- Centre for International Child Health, Department of Paediatrics, Imperial College London, St. Mary's Hospital, Praed Street, London, W2 1NY, United Kingdom
- MRC Unit The Gambia at the London School of Hygiene and Tropical Medicine, Vaccines and Immunity Theme, Atlantic Road, Fajara, The Gambia
- The Vaccine Centre, Department of Clinical Research, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, United Kingdom
| | - Maria Gomez-Romero
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, Sir Alexander Fleming Building, South Kensington, London, SW7 2AZ, United Kingdom
- MRC-NIHR National Phenome Centre, Department of Surgery and Cancer, Imperial College London, IRDB Building, Du Cane Road, London, W12 0NN, United Kingdom
- Clinical Phenotyping Centre, Department of Surgery and Cancer, Imperial College London, Sir Alexander Fleming Building, South Kensington, London, SW7 2AZ, United Kingdom
| | - Verena Horneffer-van der Sluis
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, Sir Alexander Fleming Building, South Kensington, London, SW7 2AZ, United Kingdom
- MRC-NIHR National Phenome Centre, Department of Surgery and Cancer, Imperial College London, IRDB Building, Du Cane Road, London, W12 0NN, United Kingdom
| | - Matthew R Lewis
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, Sir Alexander Fleming Building, South Kensington, London, SW7 2AZ, United Kingdom
- MRC-NIHR National Phenome Centre, Department of Surgery and Cancer, Imperial College London, IRDB Building, Du Cane Road, London, W12 0NN, United Kingdom
- Clinical Phenotyping Centre, Department of Surgery and Cancer, Imperial College London, Sir Alexander Fleming Building, South Kensington, London, SW7 2AZ, United Kingdom
| | - Stephane S M Camuzeaux
- MRC-NIHR National Phenome Centre, Department of Surgery and Cancer, Imperial College London, IRDB Building, Du Cane Road, London, W12 0NN, United Kingdom
| | - Beatriz Jiménez
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, Sir Alexander Fleming Building, South Kensington, London, SW7 2AZ, United Kingdom
- MRC-NIHR National Phenome Centre, Department of Surgery and Cancer, Imperial College London, IRDB Building, Du Cane Road, London, W12 0NN, United Kingdom
- Clinical Phenotyping Centre, Department of Surgery and Cancer, Imperial College London, Sir Alexander Fleming Building, South Kensington, London, SW7 2AZ, United Kingdom
| | - Joram M Posma
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, Sir Alexander Fleming Building, South Kensington, London, SW7 2AZ, United Kingdom
| | - Leopold Tientcheu
- MRC Unit The Gambia at the London School of Hygiene and Tropical Medicine, Vaccines and Immunity Theme, Atlantic Road, Fajara, The Gambia
| | - Uzochukwu Egere
- MRC Unit The Gambia at the London School of Hygiene and Tropical Medicine, Vaccines and Immunity Theme, Atlantic Road, Fajara, The Gambia
| | - Abdou Sillah
- MRC Unit The Gambia at the London School of Hygiene and Tropical Medicine, Vaccines and Immunity Theme, Atlantic Road, Fajara, The Gambia
| | - Toyin Togun
- MRC Unit The Gambia at the London School of Hygiene and Tropical Medicine, Vaccines and Immunity Theme, Atlantic Road, Fajara, The Gambia
- The Vaccine Centre, Department of Clinical Research, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, United Kingdom
| | - Elaine Holmes
- Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, Sir Alexander Fleming Building, South Kensington, London, SW7 2AZ, United Kingdom
| | - Beate Kampmann
- Centre for International Child Health, Department of Paediatrics, Imperial College London, St. Mary's Hospital, Praed Street, London, W2 1NY, United Kingdom.
- MRC Unit The Gambia at the London School of Hygiene and Tropical Medicine, Vaccines and Immunity Theme, Atlantic Road, Fajara, The Gambia.
- The Vaccine Centre, Department of Clinical Research, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, United Kingdom.
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15
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Identification of serum biomarkers for active pulmonary tuberculosis using a targeted metabolomics approach. Sci Rep 2020; 10:3825. [PMID: 32123207 PMCID: PMC7052258 DOI: 10.1038/s41598-020-60669-0] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Accepted: 02/13/2020] [Indexed: 12/13/2022] Open
Abstract
Although tuberculosis (TB) is a severe health problem worldwide, the current diagnostic methods are far from optimal. Metabolomics is increasingly being used in the study of infectious diseases. We performed metabolome profiling to identify potential biomarkers in patients with active TB. Serum samples from 21 patients with active pulmonary TB, 20 subjects with latent TB infection (LTBI), and 28 healthy controls were analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS) followed by multivariate and univariate analyses. Metabolic profiles indicated higher serum levels of glutamate, sulfoxy methionine, and aspartate and lower serum levels of glutamine, methionine, and asparagine in active TB patients than in LTBI subjects or healthy controls. The ratios between metabolically related partners (glutamate/glutamine, sulfoxy methionine/methionine, and aspartate/asparagine) were also elevated in the active TB group. There was no significant difference in the serum concentration of these metabolites according to the disease extent or risk of relapse in active TB patients. Novel serum biomarkers such as glutamate, sulfoxy methionine, aspartate, glutamine, methionine, and asparagine are potentially useful for adjunctive, rapid, and noninvasive pulmonary TB diagnosis.
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16
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Song Z, Wang H, Yin X, Deng P, Jiang W. Application of NMR metabolomics to search for human disease biomarkers in blood. Clin Chem Lab Med 2019; 57:417-441. [PMID: 30169327 DOI: 10.1515/cclm-2018-0380] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2018] [Accepted: 07/16/2018] [Indexed: 02/05/2023]
Abstract
Recently, nuclear magnetic resonance spectroscopy (NMR)-based metabolomics analysis and multivariate statistical techniques have been incorporated into a multidisciplinary approach to profile changes in small molecules associated with the onset and progression of human diseases. The purpose of these efforts is to identify unique metabolite biomarkers in a specific human disease so as to (1) accurately predict and diagnose diseases, including separating distinct disease stages; (2) provide insights into underlying pathways in the pathogenesis and progression of the malady and (3) aid in disease treatment and evaluate the efficacy of drugs. In this review we discuss recent developments in the application of NMR-based metabolomics in searching disease biomarkers in human blood samples in the last 5 years.
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Affiliation(s)
- Zikuan Song
- Molecular Medicine Research Center, West China Hospital, Sichuan University, Chengdu, Sichuan, P.R. China.,West China School of Basic Medical Science and Forensic Medicine, Sichuan University, Chengdu, Sichuan, P.R. China
| | - Haoyu Wang
- Molecular Medicine Research Center, West China Hospital, Sichuan University, Chengdu, Sichuan, P.R. China.,West China School of Basic Medical Science and Forensic Medicine, Sichuan University, Chengdu, Sichuan, P.R. China
| | - Xiaotong Yin
- Molecular Medicine Research Center, West China Hospital, Sichuan University, Chengdu, Sichuan, P.R. China.,West China School of Basic Medical Science and Forensic Medicine, Sichuan University, Chengdu, Sichuan, P.R. China
| | - Pengchi Deng
- Analytical and Testing Center, Sichuan University, Chengdu, Sichuan, P.R. China
| | - Wei Jiang
- Molecular Medicine Research Center, West China Hospital, Sichuan University, Chengdu, Sichuan, P.R. China
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17
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Zhang Q, Liu X, Yan L, Zhao R, An J, Liu C, Yang H. Danshen extract (Salvia miltiorrhiza Bunge) attenuate spinal cord injury in a rat model: A metabolomic approach for the mechanism study. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2019; 62:152966. [PMID: 31132751 DOI: 10.1016/j.phymed.2019.152966] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Revised: 04/25/2019] [Accepted: 05/19/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUD Spinal cord injury (SCI) is a devastating neurological disorder caused by trauma. To date, SCI treatment is still a significant challenge in clinic and research around the world. Danshen (dried roots and rhizomes of Salvia miltiorrhiza), a commonly used Chinese medicinal herb, has been attracting attention in SCI treatment. PURPOSE Aim of this study was to evaluate the potential beneficial effects of danshen extract in a SCI rat model, as well as investigate possible mechanism of action and potential biomarkers. METHODS Here, a rat SCI model was established with weight-drop method, and danshen extract was administered by oral gavage (12.5 g/kg). Recovery of motor function and histomorphological changes were evaluated by Basso, Beattie and Bresnahan score and hematoxylin-eosin staining, respectively. In addition, neurofilament 200 (NF-H), brain-derived neurotrophic factor (BDNF), glial fibrillary acidic protein (GFAP) and CD11b expressions were assayed by immunofluorescence and western blot analysis. Furthermore, a metabolomics analysis based on ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) approach was conducted. RESULTS The results demonstrated that danshen extract could significantly ameliorated histopathology changes and improved recovery of motor function after SCI. Moreover, NF-H, BDNF and CD11b expression were progressively increased until 4 weeks post-injury after administrated danshen extract. Furthermore, a good separation was observed among different groups using OPLS-DA. Trajectory analysis showed the gradual shift from position of model group toward normal group with increasing time after administration of danshen extract. Meanwhile, 51 significantly altered metabolites were identified, while metabolic pathway analysis suggested that 6 metabolic pathways were disturbed by the altered metabolites. CONCLUSION In summary, this study provides an overview of neuroprotective effects and investigates possible mechanism of danshen extract in SCI treatment. However, further research is needed to uncover its regulatory mechanisms more clearly.
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Affiliation(s)
- Qian Zhang
- Translational Medicine Center, Hong Hui Hospital, Xi'an Jiaotong University, Xi'an 710054, China; College of Pharmacy, Shaanxi University of Chinese Medicine, Xianyang, China.
| | - Xifang Liu
- Department of Chinese Medicine Orthopaedic, Hong Hui Hospital, Xi'an Jiaotong University, Xi'an, China
| | - Liang Yan
- Department of Spine Surgery, Hong Hui Hospital, Xi'an Jiaotong University, Xi'an, China
| | - Rui Zhao
- Translational Medicine Center, Hong Hui Hospital, Xi'an Jiaotong University, Xi'an 710054, China; College of Pharmacy, Shaanxi University of Chinese Medicine, Xianyang, China
| | - Jing An
- Translational Medicine Center, Hong Hui Hospital, Xi'an Jiaotong University, Xi'an 710054, China
| | - Ciucui Liu
- Translational Medicine Center, Hong Hui Hospital, Xi'an Jiaotong University, Xi'an 710054, China
| | - Hao Yang
- Translational Medicine Center, Hong Hui Hospital, Xi'an Jiaotong University, Xi'an 710054, China
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18
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Togun TO, MacLean E, Kampmann B, Pai M. Biomarkers for diagnosis of childhood tuberculosis: A systematic review. PLoS One 2018; 13:e0204029. [PMID: 30212540 PMCID: PMC6136789 DOI: 10.1371/journal.pone.0204029] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Accepted: 08/31/2018] [Indexed: 12/14/2022] Open
Abstract
Introduction As studies of biomarkers of tuberculosis (TB) disease provide hope for a simple, point-of-care test, we aimed to synthesize evidence on biomarkers for diagnosis of TB in children and compare their accuracy to published target product profiles (TPP). Methods We conducted a systematic review of biomarkers for diagnosis of pulmonary TB in exclusively paediatric populations, defined as age less than 15 years. PubMed, EMBASE and Web of Science were searched for relevant publications from January 1, 2000 to November 27, 2017. Studies using mixed adult and paediatric populations or reporting biomarkers for extrapulmonary TB were excluded. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies—2 (QUADAS-2) framework. No meta-analysis was done because the published childhood TB biomarkers studies were mostly early stage studies and highly heterogeneous. Results The 29 studies included in this systematic review comprise 20 case-control studies, six cohort studies and three cross-sectional studies. These studies reported diverse and heterogeneous forms of biomarkers requiring different types of clinical specimen and laboratory assays. Majority of the studies (27/29 [93%]) either did not meet the criteria in at least one of the four domains of the QUADAS-2 reporting framework or the assessment was unclear. However, the diagnostic performance of biomarkers reported in 22 studies met one or both of the WHO-recommended minimal targets of 66% sensitivity and 98% specificity for a new diagnostic test for TB disease in children, and/or 90% sensitivity and 70% specificity for a triage test. Conclusion We found that majority of the biomarkers for diagnosis of TB in children are promising but will need further refining and optimization to improve their performances. As new data are emerging, stronger emphasis should be placed on improving the design, quality and general reporting of future studies investigating TB biomarkers in children.
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Affiliation(s)
- Toyin Omotayo Togun
- McGill International TB Centre, and Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
- * E-mail:
| | - Emily MacLean
- McGill International TB Centre, and Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
| | - Beate Kampmann
- Vaccines and Immunity Theme, Medical Research Council Unit The Gambia at the London School of Hygiene & Tropical Medicine, Atlantic Boulevard, Fajara, The Gambia
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London, United Kingdom
| | - Madhukar Pai
- McGill International TB Centre, and Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
- Manipal McGill Centre for Infectious Diseases, Manipal University, Manipal, India
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Salgado-Bustamante M, Rocha-Viggiano AK, Rivas-Santiago C, Magaña-Aquino M, López JA, López-Hernández Y. Metabolomics applied to the discovery of tuberculosis and diabetes mellitus biomarkers. Biomark Med 2018; 12:1001-1013. [PMID: 30043640 DOI: 10.2217/bmm-2018-0050] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Tuberculosis (TB) and diabetes mellitus Type 2 (DM2) are two diseases as ancient as they are harmful to human health. The outcome for both diseases in part depends on immune and metabolic individual responses. DM2 is increasing yearly, mainly due to environmental, genetic and lifestyle habits. There are multiple evidence that DM2 is one of the most important risk factor of becoming infected with TB or reactivating latent TB. Mass spectrometry-based metabolomics is an important tool for elucidating the metabolites and metabolic pathways that influence the immune responses to M. tuberculosis infection during diabetes. We provide an up-to-date review highlighting the importance and benefit of metabolomics for identifying biomarkers as candidate molecules for diagnosis, disease activity or prognosis.
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Affiliation(s)
- Mariana Salgado-Bustamante
- Biochemistry Department, Medicine Faculty, Universidad Autonoma de San Luis Potosi, San Luis Potosi, Mexico
| | - Ana K Rocha-Viggiano
- Biochemistry Department, Medicine Faculty, Universidad Autonoma de San Luis Potosi, San Luis Potosi, Mexico
| | - César Rivas-Santiago
- CONACyT, Unidad Academica de Ciencias Biologicas, Universidad Autonoma de Zacatecas, Zacatecas, Mexico
| | - Martín Magaña-Aquino
- Infectology Department, Hospital Central Ignacio Morones Prieto, San Luis Potosi, Mexico
| | - Jesús A López
- MicroRNAs Laboratory, Unidad Academica de Ciencias Biologicas, Universidad Autonoma de Zacatecas, Zacatecas, Mexico
| | - Yamilé López-Hernández
- CONACyT, Unidad Academica de Ciencias Biologicas, Universidad Autonoma de Zacatecas, Zacatecas, Mexico
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20
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Rockel JS, Zhang W, Shestopaloff K, Likhodii S, Sun G, Furey A, Randell E, Sundararajan K, Gandhi R, Zhai G, Kapoor M. A classification modeling approach for determining metabolite signatures in osteoarthritis. PLoS One 2018; 13:e0199618. [PMID: 29958292 PMCID: PMC6025859 DOI: 10.1371/journal.pone.0199618] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2017] [Accepted: 04/27/2018] [Indexed: 11/18/2022] Open
Abstract
Multiple factors can help predict knee osteoarthritis (OA) patients from healthy individuals, including age, sex, and BMI, and possibly metabolite levels. Using plasma from individuals with primary OA undergoing total knee replacement and healthy volunteers, we measured lysophosphatidylcholine (lysoPC) and phosphatidylcholine (PC) analogues by metabolomics. Populations were stratified on demographic factors and lysoPC and PC analogue signatures were determined by univariate receiver-operator curve (AUC) analysis. Using signatures, multivariate classification modeling was performed using various algorithms to select the most consistent method as measured by AUC differences between resampled training and test sets. Lists of metabolites indicative of OA [AUC > 0.5] were identified for each stratum. The signature from males age > 50 years old encompassed the majority of identified metabolites, suggesting lysoPCs and PCs are dominant indicators of OA in older males. Principal component regression with logistic regression was the most consistent multivariate classification algorithm tested. Using this algorithm, classification of older males had fair power to classify OA patients from healthy individuals. Thus, individual levels of lysoPC and PC analogues may be indicative of individuals with OA in older populations, particularly males. Our metabolite signature modeling method is likely to increase classification power in validation cohorts.
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Affiliation(s)
- Jason S. Rockel
- Arthritis Program, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
- Krembil Research Institute, University Health Network, Toronto, Ontario, Canada
| | - Weidong Zhang
- Discipline of Genetics, Faculty of Medicine, Memorial University of Newfoundland, St John’s, Newfoundland, Canada
- School of Pharmaceutical Sciences, Jilin University, Changchun, P.R. China
| | - Konstantin Shestopaloff
- Arthritis Program, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
- Krembil Research Institute, University Health Network, Toronto, Ontario, Canada
| | - Sergei Likhodii
- Department of Laboratory Medicine, Faculty of Medicine, Memorial University of Newfoundland, St John’s, Newfoundland, Canada
| | - Guang Sun
- Discipline of Medicine, Faculty of Medicine, Memorial University of Newfoundland, St John’s, Newfoundland, Canada
| | - Andrew Furey
- Department of Surgery, Faculty of Medicine, Memorial University of Newfoundland, St John’s, Newfoundland, Canada
| | - Edward Randell
- Department of Laboratory Medicine, Faculty of Medicine, Memorial University of Newfoundland, St John’s, Newfoundland, Canada
| | - Kala Sundararajan
- Arthritis Program, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
- Krembil Research Institute, University Health Network, Toronto, Ontario, Canada
| | - Rajiv Gandhi
- Arthritis Program, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
- Krembil Research Institute, University Health Network, Toronto, Ontario, Canada
- Department of Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Guangju Zhai
- Discipline of Genetics, Faculty of Medicine, Memorial University of Newfoundland, St John’s, Newfoundland, Canada
- Menzies Research Institute, University of Tasmania, Hobart, Tasmania, Australia
- * E-mail: (GZ); (MK)
| | - Mohit Kapoor
- Arthritis Program, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
- Krembil Research Institute, University Health Network, Toronto, Ontario, Canada
- Department of Surgery, University of Toronto, Toronto, Ontario, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Ontario, Canada
- * E-mail: (GZ); (MK)
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