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High-resolution plasma metabolomics analysis to detect Mycobacterium tuberculosis-associated metabolites that distinguish active pulmonary tuberculosis in humans.

ABSTRACT: INTRODUCTION:Pulmonary tuberculosis (TB) is a major worldwide health problem that lacks robust blood-based biomarkers for detection of active disease. High-resolution metabolomics (HRM) is an innovative method to discover low-abundance metabolites as putative blood biomarkers to detect TB disease, including those known to be produced by the causative organism, Mycobacterium tuberculosis (Mtb). METHODS:We used HRM profiling to measure the plasma metabolome for 17 adults with active pulmonary TB disease and 16 of their household contacts without active TB. We used a suspect screening approach to identify metabolites previously described in cell culture studies of Mtb based on retention time and accurate mass matches. RESULTS:The association of relative metabolite abundance in active TB disease subjects compared to their household contacts predicted three Mtb-associated metabolites that were significantly increased in the active TB patients based on accurate mass matches: phosphatidylglycerol (PG) (16:0_18:1), lysophosphatidylinositol (Lyso-PI) (18:0) and acylphosphatidylinositol mannoside (Ac1PIM1) (56:1) (p<0.001 for all). These three metabolites provided excellent classification accuracy for active TB disease (AUC = 0.97). Ion dissociation spectra (tandem MS/MS) supported the identification of PG (16:0_18:1) and Lyso-PI (18:0) in the plasma of patients with active TB disease, though the identity of Ac1PIM1 could not be definitively confirmed. CONCLUSIONS:Presence of the Mtb-associated lipid metabolites PG (16:0_18:1) and Lyso-PI (18:0) in plasma accurately identified patients with active TB disease. Consistency of in vitro and in vivo data suggests suitability for exploring these in future studies for possible development as TB disease biomarkers.

SUBMITTER: Collins JM

PROVIDER: S-EPMC6181350 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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High-resolution plasma metabolomics analysis to detect Mycobacterium tuberculosis-associated metabolites that distinguish active pulmonary tuberculosis in humans.

Collins Jeffrey M JM Walker Douglas I DI Jones Dean P DP Tukvadze Nestani N Liu Ken H KH Tran ViLinh T VT Uppal Karan K Frediani Jennifer K JK Easley Kirk A KA Shenvi Neeta N Khadka Manoj M Ortlund Eric A EA Kempker Russell R RR Blumberg Henry M HM Ziegler Thomas R TR

PloS one 20181011 10

<h4>Introduction</h4>Pulmonary tuberculosis (TB) is a major worldwide health problem that lacks robust blood-based biomarkers for detection of active disease. High-resolution metabolomics (HRM) is an innovative method to discover low-abundance metabolites as putative blood biomarkers to detect TB disease, including those known to be produced by the causative organism, Mycobacterium tuberculosis (Mtb).<h4>Methods</h4>We used HRM profiling to measure the plasma metabolome for 17 adults with active ...[more]

PMID: 30308073

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Project description:Although previous studies have reported the use of metabolomics for Mycobacterium species differentiation, little is known about the potential of extracellular metabolites of Mycobacterium tuberculosis (MTB) as specific biomarkers. Using an optimized ultrahigh performance liquid chromatography-electrospray ionization-quadruple time of flight-mass spectrometry (UHPLC-ESI-Q-TOF-MS) platform, we characterized the extracellular metabolomes of culture supernatant of nine MTB strains and nine non-tuberculous Mycobacterium (NTM) strains (four M. avium complex, one M. bovis Bacillus Calmette-Guérin (BCG), one M. chelonae, one M. fortuitum and two M. kansasii). Principal component analysis readily distinguished the metabolomes between MTB and NTM. Using multivariate and univariate analysis, 24 metabolites with significantly higher levels in MTB were identified. While seven metabolites were identified by tandem mass spectrometry (MS/MS), the other 17 metabolites were unidentified by MS/MS against database matching, suggesting that they may be potentially novel compounds. One metabolite was identified as dexpanthenol, the alcohol analog of pantothenic acid (vitamin B5), which was not known to be produced by bacteria previously. Four metabolites were identified as 1-tuberculosinyladenosine (1-TbAd), a product of the virulence-associated enzyme Rv3378c, and three previously undescribed derivatives of 1-TbAd. Two derivatives differ from 1-TbAd by the ribose group of the nucleoside while the other likely differs by the base. The remaining two metabolites were identified as a tetrapeptide, Val-His-Glu-His, and a monoacylglycerophosphoglycerol, phosphatidylglycerol (PG) (16?0/0?0), respectively. Further studies on the chemical structure and biosynthetic pathway of these MTB-specific metabolites would help understand their biological functions. Studies on clinical samples from tuberculosis patients are required to explore for their potential role as diagnostic biomarkers.

Project description:RationaleNew approaches to define factors underlying the immunopathogenesis of pulmonary diseases including sarcoidosis and tuberculosis are needed to develop new treatments and biomarkers. Comparing the blood transcriptional response of tuberculosis to other similar pulmonary diseases will advance knowledge of disease pathways and help distinguish diseases with similar clinical presentations.ObjectivesTo determine the factors underlying the immunopathogenesis of the granulomatous diseases, sarcoidosis and tuberculosis, by comparing the blood transcriptional responses in these and other pulmonary diseases.MethodsWe compared whole blood genome-wide transcriptional profiles in pulmonary sarcoidosis, pulmonary tuberculosis, to community acquired pneumonia and primary lung cancer and healthy controls, before and after treatment, and in purified leucocyte populations.Measurements and main resultsAn Interferon-inducible neutrophil-driven blood transcriptional signature was present in both sarcoidosis and tuberculosis, with a higher abundance and expression in tuberculosis. Heterogeneity of the sarcoidosis signature correlated significantly with disease activity. Transcriptional profiles in pneumonia and lung cancer revealed an over-abundance of inflammatory transcripts. After successful treatment the transcriptional activity in tuberculosis and pneumonia patients was significantly reduced. However the glucocorticoid-responsive sarcoidosis patients showed a significant increase in transcriptional activity. 144-blood transcripts were able to distinguish tuberculosis from other lung diseases and controls.ConclusionsTuberculosis and sarcoidosis revealed similar blood transcriptional profiles, dominated by interferon-inducible transcripts, while pneumonia and lung cancer showed distinct signatures, dominated by inflammatory genes. There were also significant differences between tuberculosis and sarcoidosis in the degree of their transcriptional activity, the heterogeneity of their profiles and their transcriptional response to treatment.

Project description:CoMet, a fully automated Computational Metabolomics method to predict changes in metabolite levels in cancer cells compared to normal references has been developed and applied to Jurkat T leukemia cells with the goal of testing the following hypothesis: up or down regulation in cancer cells of the expression of genes encoding for metabolic enzymes leads to changes in intracellular metabolite concentrations that contribute to disease progression. Nine metabolites predicted to be lowered in Jurkat cells with respect to normal lymphoblasts were examined: riboflavin, tryptamine, 3-sulfino-L-alanine, menaquinone, dehydroepiandrosterone, Î±-hydroxystearic acid, hydroxyacetone, seleno-L-methionine and 5,6-dimethylbenzimidazole. All, alone or in combination, exhibited antiproliferative activity. Of eleven metabolites predicted to be increased or unchanged in Jurkat cells, only two (bilirubin and androsterone) exhibited significant antiproliferative activity. These results suggest that cancer cell metabolism may be regulated to reduce the intracellular concentration of certain antiproliferative metabolites, resulting in uninhibited cellular growth and have the implication that many other endogenous metabolites with important roles in carcinogenesis are awaiting discovery. Experiment Overall Design: The first step of the CoMet approach consists of the classification of each enzyme-coding human gene into four possible groups: G1) upregulated in cancer cells, G2) downregulated in cancer cells, G3) expressed in both, normal and cancer cells, at levels that are statistically indistinguishable, and G4) not expressed in both, normal and cancer cells. We used two types of data for the classification: the log base 2 signal intensities and the presence calls of the corresponding probe sets, as reported by the Affymetrix Microarray Suite Software 5.0 (MAS 5.0). First, an â??offâ?? status is provisionally assigned to each gene in each of the two studied conditions (normal and cancer) if the mean fraction of presence calls labeled as â??marginalâ?? or â??absentâ?? in the corresponding probe sets is at least 80%; otherwise, an â??onâ?? status is assigned. Then, each gene is temporarily classified into the G1, G2, G3 or G4 group, according to its on/off status in normal and cancer conditions. Finally, genes in the temporary G3 or G4 groups are transferred to the G1 or G2 groups if they fulfill the following criterion for differential expression: the signal intensities in normal and cancer samples exhibit a statistically significant difference in at least 40% of the corresponding probe sets, as evaluated by an ANOVA two-tailed test with P < 0.005. Experiment Overall Design: RNA extraction, amplification and microarray data processing. Total RNA was extracted from cell lines using Trizol (Invitrogen) and processed using the RiboAmp OA or HS kit (Arcturus) in conjunction with the IVT Labeling Kit from Affymetrix, to produce an amplified, biotin-labeled mRNA suitable for hybridizing to GeneChip Probe Arrays (Affymetrix). Labeled mRNA was hybridized to GeneChip Human Genome U133 Plus 2.0 Arrays in the GeneChip Hybridization oven 640, further processed with the GeneChip Fluidics Station 450 and scanned with the GeneChip Scanner. Affymetrix .CEL files were processed using the Affymetrix Expression Console (EC) Software Version 1.1. Files were processed using the default MAS5 3â?? expression workflow which includes scaling all probes to a target intensity (TGT) of 500. Spiked in report controls used were AFFX-BioB, AFFX-BioC, AFFX-BioDn, and AFFX-CreX. Affymetrix .CEL files for three normal lymphoblast samples used as a normal reference to compare Jurkat cells expression data were directly retrieved from the Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/, samples GSM113678, GSM113802 and GSM113803 of untreated GM15851 cells from the Series GSE5040).

Dataset Information

High-resolution plasma metabolomics analysis to detect Mycobacterium tuberculosis-associated metabolites that distinguish active pulmonary tuberculosis in humans.

Publications

High-resolution plasma metabolomics analysis to detect Mycobacterium tuberculosis-associated metabolites that distinguish active pulmonary tuberculosis in humans.

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