The novel potential biomarkers for multidrug-resistance tuberculosis using UPLC-Q-TOF-MS

Abstract

The lack of rapid and efficient diagnostics impedes largely the epidemic control of multidrug-resistant tuberculosis, and might misguide the therapeutic strategies as well. This study aimed to identify novel multidrug-resistant tuberculosis biomarkers to improve the early intervention, symptomatic treatment and control of the prevalence of multidrug-resistant tuberculosis. The serum small molecule metabolites in healthy controls, patients with drug-susceptible tuberculosis, and patients with multidrug-resistant tuberculosis were screened using ultra-high-performance liquid chromatography combined with quadrupole-time-of-flight mass spectrometry (UPLC-Q-TOF-MS). The differentially abundant metabolites were filtered out through multidimensional statistical analysis and bioinformatics analysis. Compared with drug-susceptible tuberculosis patients and healthy controls, the levels of 13 metabolites in multidrug-resistant tuberculosis patients altered. Among them, the most significant changes were found in N1-Methyl-2-pyridone-5-carboxamide (N1M2P5C), 1-Myristoyl-sn-glycerol-3-phosphocholine (MG3P), Caprylic acid (CA), and D-Xylulose (DX). And a multidrug-resistant tuberculosis/drug-susceptible tuberculosis differential diagnostic model was built based on these four metabolites, achieved the accuracy, sensitivity, and specificity of 0.928, 86.7%, and 86.7%, respectively. The enrichment analysis of metabolic pathways showed that the phospholipid remodeling of cell membranes was active in multidrug-resistant tuberculosis patients. In addition, in patients with tuberculosis, the metabolites of dipalmitoyl phosphatidylcholine (DPPC), a major component of pulmonary surfactant, were down-regulated. N1M2P5C, MG3P, CA, and DX may have the potential to serve as novel multidrug-resistant tuberculosis biomarkers. This research provides a preliminary experimental basis to further investigate potential multidrug-resistant tuberculosis biomarkers.

Impact statement: The MDR-TB incidence remains high, making the effective control of TB epidemic yet challenging. Rapid and accurate diagnosis is vitally important for improving the therapeutic efficacy and controlling the prevalence of drug resistance TB. Metabolomics has dramatic potential to distinguish MDR-TB and DS-TB. N1M2P5C, MG3P, CA, and DX that we identified in this study might have potential as novel MDR-TB biomarkers. The phospholipid remodeling of cell membranes was highly active in MDR-TB. The DPPC metabolites in TB were significantly down-regulated. This work aimed to investigate potential MDR-TB biomarkers to enhance the clinical diagnostic efficacy. The metabolic pathway distinctly altered in MDR-TB might provide novel targets to develop new anti-TB drugs.

Keywords: Multidrug-resistance tuberculosis; drug-sensitive tuberculosis; metabolomics; phospholipid remodeling; potential biomarkers; serum.

Figure 1.

The OPLS-DA model and volcano map of MDR-TB/DS-TB group in HILIC column. The OPLS-DA score scatter plots of ion mode: (a) positive, (c) negative. The permutation test results of ion mode: (b) positive, (d) negative. The differential metabolite volcano maps of ion mode: (e) positive, (f) negative. The upward trend in metabolites was indicated by red scatters, the downward trend in metabolites was indicated by blue scatters, and the non-significant trend in metabolites was indicated by black scatters. (A color version of this figure is available in the online journal.)

Figure 2.

The OPLS-DA model and volcano map of MDR-TB/DS-TB group in T3 column. The OPLS-DA score scatter plots of ion mode: (a) positive, (c) negative. The permutation test results of ion mode: (b) positive, (d) negative. The differential metabolite volcano maps of ion mode: (e) positive, (f) negative. The upward trend in metabolites was indicated by red scatters, the downward trend in metabolites was indicated by blue scatters, and the non-significant trend in metabolites was indicated by black scatters. (A color version of this figure is available in the online journal.)

Figure 3.

The metabolic pathway map of MDR-TB/DS-TB group. Metabolic pathway map of HILIC column (a), metabolic pathway map of T3 column (b). (A color version of this figure is available in the online journal.)

Figure 4.

Diagram of differential metabolite enrichment pathways. The upward trend in metabolites was indicated by red rectangles, the downward trend in metabolites was indicated by blue rectangles, and the non-significant trend in metabolites was indicated by white rectangles. (A color version of this figure is available in the online journal.)

Figure 5.

Relative quantitative values of differential metabolites N1M2P5C, MG3P, CA and DX. The P-value was tested by Student’s t-test. *P < 0.05; **P < 0.01; ***P < 0.001.

Figure 6.

ROC analysis for differential metabolites N1M2P5C, MG3P, CA, DX and combinations. (a) the ROC analysis of MDR-TB/DS-TB group, (b) the ROC analysis of MDR-TB/HC group. (A color version of this figure is available in the online journal.)