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Integration of metabolomics and transcriptomics reveals novel biomarkers in the blood for tuberculosis diagnosis in children.

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.

SUBMITTER: Dutta NK

PROVIDER: S-EPMC7658223 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Integration of metabolomics and transcriptomics reveals novel biomarkers in the blood for tuberculosis diagnosis in children.

Dutta Noton K NK Tornheim Jeffrey A JA Fukutani Kiyoshi F KF Paradkar Mandar M Tiburcio Rafael T RT Kinikar Aarti A Valvi Chhaya C Kulkarni Vandana V Pradhan Neeta N Shivakumar Shri Vijay Bala Yogendra SVBY Kagal Anju A Gupte Akshay A Gupte Nikhil N Mave Vidya V Gupta Amita A Andrade Bruno B BB Karakousis Petros C PC

Scientific reports 20201111 1

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 thro ...[more]

PMID: 33177551

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Project description:Pulmonary tuberculosis (PTB) and diabetes mellitus (DM) are common chronic diseases that threaten human health. Patients with DM are susceptible to PTB, an important factor that aggravates the complications of diabetes. However, the molecular regulatory mechanism underlying the susceptibility of patients with DM to PTB infection remains unknown. In this study, healthy subjects, patients with primary PTB, and patients with primary PTB complicated by DM were recruited according to inclusion and exclusion criteria. Peripheral whole blood was collected, and alteration profiles and potential molecular mechanisms were further analyzed using integrated bioinformatics analysis of metabolomics and transcriptomics. Transcriptional data revealed that lipocalin 2 (LCN2), defensin alpha 1 (DEFA1), peptidoglycan recognition protein 1 (PGLYRP1), and integrin subunit alpha 2b (ITGA2B) were significantly upregulated, while chloride intracellular channel 3 (CLIC3) was significantly downregulated in the group with PTB and DM (PTB_DM) in contrast to the healthy control (HC) group. Additionally, the interleukin 17 (IL-17), phosphatidylinositol 3-kinase (PI3K)-AKT, and peroxisome proliferator-activated receptor (PPAR) signaling pathways are important for PTB infection and regulation of PTB-complicated diabetes. Metabolomic data showed that glycerophospholipid metabolism, carbon metabolism, and fat digestion and absorption processes were enriched in the differential metabolic analysis. Finally, integrated analysis of both metabolomic and transcriptomic data indicated that the NOTCH1/JAK/STAT signaling pathway is important in PTB complicated by DM. In conclusion, PTB infection altered the transcriptional and metabolic profiles of patients with DM. Metabolomic and transcriptomic changes were highly correlated in PTB patients with DM. Peripheral metabolite levels may be used as biomarkers for PTB management in patients with DM. IMPORTANCE The comorbidity of diabetes mellitus (DM) significantly increases the risk of tuberculosis infection and adverse tuberculosis treatment outcomes. Most previous studies have focused on the relationship between the effect of blood glucose control and the outcome of antituberculosis treatment in pulmonary tuberculosis (PTB) with DM (PTB_DM); however, early prediction and the underlying molecular mechanism of susceptibility to PTB infection in patients with DM remain unclear. In this study, transcriptome sequencing and untargeted metabolomics were performed to elucidate the key molecules and signaling pathways involved in PTB infection and the susceptibility of patients with diabetes to PTB. Our findings contribute to the development of vital diagnostic biomarkers for PTB or PTB_DM and provide a comprehensive understanding of molecular regulation during disease progression.

Project description:Nutrient acquisition from the host environment is crucial for the survival of intracellular pathogens, but conceptual and technical challenges limit our knowledge of pathogen diets. To overcome some of these technical roadblocks, we exploited an experimentally accessible model for early infection of human macrophages by Mycobacterium tuberculosis, the etiological agent of tuberculosis, to study host-pathogen interactions with a multi-omics approach. We collected metabolomics and complete transcriptome RNA sequencing (dual RNA-seq) data of the infected macrophages, integrated them in a genome-wide reaction pair network, and identified metabolic subnetworks in host cells and M. tuberculosis that are modularly regulated during infection. Up- and downregulation of these metabolic subnetworks suggested that the pathogen utilizes a wide range of host-derived compounds, concomitant with the measured metabolic and transcriptional changes in both bacteria and host. To quantify metabolic interactions between the host and intracellular pathogen, we used a combined genome-scale model of macrophage and M. tuberculosis metabolism constrained by the dual RNA-seq data. Metabolic flux balance analysis predicted coutilization of a total of 33 different carbon sources and enabled us to distinguish between the pathogen's substrates directly used as biomass precursors and the ones further metabolized to gain energy or to synthesize building blocks. This multiple-substrate fueling confers high robustness to interventions with the pathogen's metabolism. The presented approach combining multi-omics data as a starting point to simulate system-wide host-pathogen metabolic interactions is a useful tool to better understand the intracellular lifestyle of pathogens and their metabolic robustness and resistance to metabolic interventions. IMPORTANCE The nutrients consumed by intracellular pathogens are mostly unknown. This is mainly due to the challenge of disentangling host and pathogen metabolism sharing the majority of metabolic pathways and hence metabolites. Here, we investigated the metabolic changes of Mycobacterium tuberculosis, the causative agent of tuberculosis, and its human host cell during early infection. To this aim, we combined gene expression data of both organisms and metabolite changes during the course of infection through integration into a genome-wide metabolic network. This led to the identification of infection-specific metabolic alterations, which we further exploited to model host-pathogen interactions quantitatively by flux balance analysis. These in silico data suggested that tubercle bacilli consume up to 33 different nutrients during early macrophage infection, which the bacteria utilize to generate energy and biomass to establish intracellular growth. Such multisubstrate fueling strategy renders the pathogen's metabolism robust toward perturbations, such as innate immune responses or antibiotic treatments.

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Integration of metabolomics and transcriptomics reveals novel biomarkers in the blood for tuberculosis diagnosis in children.

Publications

Integration of metabolomics and transcriptomics reveals novel biomarkers in the blood for tuberculosis diagnosis in children.

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