Metabolic alterations in the sera of Chinese patients with mild persistent asthma: a GC-MS-based metabolomics analysis

Abstract

Aim: To character the specific metabolomics profiles in the sera of Chinese patients with mild persistent asthma and to explore potential metabolic biomarkers.

Methods: Seventeen Chinese patients with mild persistent asthma and age- and sex-matched healthy controls were enrolled. Serum samples were collected, and serum metabolites were analyzed using GC-MS coupled with a series of multivariate statistical analyses.

Results: Clear intergroup separations existed between the asthmatic patients and control subjects. A list of differential metabolites and several top altered metabolic pathways were identified. The levels of succinate (an intermediate in tricarboxylic acid cycle) and inosine were highly upregulated in the asthmatic patients, suggesting a greater effort to breathe during exacerbation and hypoxic stress due to asthma. Other differential metabolites, such as 3,4-dihydroxybenzoic acid and phenylalanine, were also identified. Furthermore, the differential metabolites possessed higher values of area under the ROC curve (AUC), suggesting an excellent clinical ability for the prediction of asthma.

Conclusion: Metabolic activity is significantly altered in the sera of Chinese patients with mild persistent asthma. The data might be helpful for identifying novel biomarkers and therapeutic targets for asthma.

Figure 1

PCA analysis of GC-MS metabolite profiles. (A) The PCA score plot showed that the asthma samples and control subjects were scattered into two different regions. The X-axis, t[1], and Y-axis, t[2], indicated the first and second principal components, respectively. (B) 3-D score plot of the PCA analysis.

Figure 2

PLS-DA and OPLS-DA analysis of GC-MS metabolite profiles. (A) The PLS-DA score plot showed that the asthma samples and control subjects were scattered into two different regions. The X-axis, t[1], and Y-axis, t[2], indicated the first and second principal components, respectively. (B) A permutation test was performed to validate the PLS-DA model. The R² and Q² intercept values were 0.726 and -0.17, respectively after 200 permutations. (C) The OPLS-DA score plot showed that the asthma samples and control subjects were scattered into two different regions. The X-axis, t[1], and Y-axis, t[2], indicated the first and second principal components, respectively. (D) The OPLS-DA loading plot was constructed to display the relationship between the X-variables and the Y-variables for the first predictive component and the first Y-orthogonal component. The horizontal axis represented the X-loadings p and the Y-loadings q of the predictive component. The vertical axis represented the X-loadings p(o) and the Y-loadings s(o) for the Y-orthogonal component. X-variables situated in the vicinity of the dummy Y-variables have the highest discriminatory power between the classes.

Figure 3

Expression levels of the significantly changed metabolites. (A) 2-ketovaleric acid; (B) 3,4-dihydroxybenzoic acid; (C) 5-aminovaleric acid; (D) ascorbate; (E) dehydroascorbic acid; (F) inosine; (G) phenylalanine; (H) succinic acid; (I) β-glycerophosphoric acid; (J) maleamate; (K) maleic acid; (L) monoolein; (M) ribose; (N) trans-4-hydroxy-L-proline; (O) ranking of VIP values.

Figure 4

ROC graphs of metabolites with the highest AUC values.

Figure 5

Pathway analysis of metabolomics alterations associated with asthma. The KEGG database was used to search for each differential metabolites. The illustration was generated using the reference maps from KEGG to construct the altered TCA cycle, urea cycle and amino acid metabolic pathway in asthmatic patients.