Description | 1Center for Tuberculosis Research, Department of Medicine, Johns Hopkins University School of Medicine,
1551 East Jefferson Street, Room 110, Baltimore, MD 21287, USA. 2Center for Clinical Global Health Education,
Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA. 3Laboratório de
Inflamação E Biomarcadores, Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Brazil. 4Multinational
Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative,
Salvador, Brazil. 5Curso de Medicina, Faculdade de Tecnologia E Ciências, Salvador, Brazil. 6Byramjee
Jeejeebhoy Government Medical College, Johns Hopkins University Clinical Research Site, Pune, Maharashtra,
India. 7Byramjee Jeejeebhoy Government Medical College, Pune, Maharashtra, India. 8Johns Hopkins University
- India Office (CCGHE), Pune, Maharashtra, India. 9Universidade Salvador (UNIFACS), Laureate Universities,
Salvador, Brazil. 10Escola Bahiana de Medicina E Saúde Pública (EBMSP), Salvador, Brazil. 11Department of
International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. 12These authors
contributed equally: Noton K. Dutta, Jeffrey A. Tornheim, Kiyoshi F. Fukutani, Mandar Paradkar, Bruno B. Andrade
and Petros C. Karakousis. *email: notonkumardutta@gmail.com; petros@jhmi.edu | pt_BR |
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 (gammaglutamylalanine,
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. | pt_BR |