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. 2013 Oct 4;12(10):4642-9.
doi: 10.1021/pr4007359. Epub 2013 Sep 10.

Application of (1)h NMR spectroscopy-based metabolomics to sera of tuberculosis patients

Aiping Zhou  1 , Jinjing Ni, Zhihong Xu, Ying Wang, Shuihua Lu, Wei Sha, Petros C Karakousis, Yu-Feng Yao
Affiliations

Application of (1)h NMR spectroscopy-based metabolomics to sera of tuberculosis patients

Aiping Zhou et al. J Proteome Res. .

Abstract

Nuclear magnetic resonance (NMR) spectroscopy is an ideal platform for the metabolic analysis of biofluids due to its high reproducibility, nondestructiveness, nonselectivity in metabolite detection, and the ability to simultaneously quantify multiple classes of metabolites. Tuberculosis (TB) is a chronic wasting inflammatory disease characterized by multisystem involvement, which can cause metabolic derangements in afflicted patients. In this study, we combined multivariate pattern recognition (PR) analytical techniques with (1)H NMR spectroscopy to explore the metabolic profile of sera from TB patients. A total of 77 serum samples obtained from patients with TB (n = 38) and healthy controls (n = 39) were investigated. Orthogonal partial least-squares discriminant analysis (OPLS-DA) was capable of distinguishing TB patients from controls and establishing a TB-specific metabolite profile. A total of 17 metabolites differed significantly in concentration between the two groups. Serum samples from TB patients were characterized by increased concentrations of 1-methylhistidine, acetoacetate, acetone, glutamate, glutamine, isoleucine, lactate, lysine, nicotinate, phenylalanine, pyruvate, and tyrosine, accompanied by reduced concentrations of alanine, formate, glycine, glycerolphosphocholine, and low-density lipoproteins relative to control subjects. Our study reveals the metabolic profile of sera from TB patients and indicates that NMR-based methods can distinguish TB patients from healthy controls. NMR-based metabolomics has the potential to be developed into a novel clinical tool for TB diagnosis or therapeutic monitoring and could contribute to an improved understanding of disease mechanisms.

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Figures

Figure 1
Figure 1. Representation of 600 MHz 1H NMR CPMG spectrum (δ0.5–5.6 and δ 5.6–9.5) of serum obtained from (A) TB patients and (B) control subjects
The region of δ5.6–9.5 (in the box) was magnified 16 times compared with the corresponding region of δ0.5–5.6 for the purpose of clarity. Keys: 1-MH: 1-Methylhistidene; AA: Acetoacetate; Ace: Acetate; Act: Acetone; Ala: Alanine; Cr: Creatine; Eth: Ethanol; For: Formate; Gln: Glutamine; Glu: Glutamate; Gly: Glycine; GPC: Glycerolphosphocholine; Ileu: Isoleucine; L1: LDL, CH3- (CH2)n-; L2: VLDL, CH3- (CH2)n-; L3: LDL, CH3- (CH2)n-; L4: VLDL, CH3- (CH2)n-; L5: VLDL, -CH2-CH2-C=O; L6: Lipid, -CH2-CH=CH-; L7: Lipid, -CH2-C=O; L8: Lipid, =CH-CH2-CH=; L9: Lipid, -CH=CH-; Lac: Lactate; Leu: Leucine; Lys: Lysine; MA: Methylamine; NAG: N-acetyl glycoprotein signals; PC: Phosphocholine: Phe: Phenylalanine; Py: Pyruvate; Tyr: Tyrosine; Val: Valine; α-Glc: α-Glucose; β-Glc: β-Glucose.
Figure 2
Figure 2. OPLS-DA scores plots derived from 1H NMR spectra of sera (A) and corresponding coefficient loading plots (B) obtained from control and TB groups
The color map shows the significance of metabolite variations between the two groups. Peaks in the positive direction indicate metabolites that are more abundant in the control group. Consequently, metabolites that are more abundant in the TB group are presented as peaks in the negative direction. Keys of the assignments are shown in Figure 1.
Figure 3
Figure 3. Metabolic differences between TB patients and controls
Differences in metabolic pathways noted between TB patients and healthy controls are shown in this map. The horizontal bar graph summarizes the most significant metabolite sets identified during the analysis. The most significant change is protein biosynthesis according to fold enrichment (>15) followed by alanine metabolism and phenylanine tyrosine metabolism.
See this image and copyright information in PMC

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