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. 2024 Dec;56(1):2399320.
doi: 10.1080/07853890.2024.2399320. Epub 2024 Sep 16.

Serum metabolomics profile identifies patients with community-acquired pneumonia infected by bacteria, fungi, and viruses

Li Chen  1   2 , Jianbo Xue  2 , Yukun He  2 , Lili Zhao  2 , Ying Zhang  2 , Lu Yin  2 , Shining Fu  3 , Wenyi Yu  2 , Xinqian Ma  2 , Yu Wang  1 , Yanfen Tang  1 , Zhancheng Gao  2
Affiliations

Serum metabolomics profile identifies patients with community-acquired pneumonia infected by bacteria, fungi, and viruses

Li Chen et al. Ann Med. 2024 Dec.

Abstract

Purpose: Patients with bacterial, fungal, and viral community-acquired pneumonia (CAP) were studied to determine their metabolic profiles.

Methods: Loop-mediated isothermal amplification technology and nucleic acid sequence-dependent amplification combined with microfluidic chip technology were applied to screen multiple pathogens from respiratory tract samples. Eighteen patients with single bacterial infection (B-CAP), fifteen with single virus infection (V-CAP), twenty with single fungal infection (F-CAP), and twenty controls were enrolled. UHPLC-MS/MS analysis of untargeted serum samples for metabolic profiles. Multiple linear regression and Spearman's rank correlation analysis were used to determine associations between metabolites and clinical parameters. The sensitivity and specificity of the screened metabolites were also examined, along with their area under the curve.

Results: The metabolic signatures of patients with CAP infected by bacteria, viruses, and fungi were markedly different from those of controls. The abundances of 45, 56, and 79 metabolites were significantly unbalanced. Among these differential metabolites, 11, 13, and 29 were unique to the B-CAP, V-CAP, and F-CAP groups, respectively. Bacterial infections were the only known causes of disturbances in the pentose and glucuronate and aldarate and ascorbate metabolism interconversions metabolic pathway.

Conclusions: Serum metabolomic techniques based on UHPLC-MS/MS may identify differences between individuals with CAP who have been infected by various pathogens, and they can also build a metabolite signature for early detection of the origin of infection and prompt care.

Keywords: Metabolomics profiles; bacterial infection; community-acquired pneumonia; diagnosis; fungal infection; viral infection.

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Conflict of interest statement

No potential conflict of interest was reported by the authors.

Figures

Figure 1.
Figure 1.
PCA scores plot and orthogonal partial least squares discriminant analysis (OPLS-DA) of metabolic profiles in CAP group (including B-CAP, V-CAP, F-CAP) and HC. The Venn diagram of the relationship between the data sets of differential metabolites in the three groups of patients with CAP compared to the HC group (a) PCA scores plot of metabolic profiles in CAP group (including B-CAP, V-CAP, F-CAP) and HC. (b) OPLS-DA score plot discriminates B-CAP versus HC (c) OPLS-DA score plots of V-CAP versus HC group. (d) OPLS-DA score plots of F-CAP versus HC. The model of OPLS-DA reflect good separation trends among CAP and HC. B-CAP, patients with single bacterial infection; V-CAP, patients with single virus infection; F-CAP, patients with single fungal infection. Red, B-CAP; orange, V-CAP; blue, F-CAP; green, healthy controls (HC). No sample was placed outside the ellipse that describes the 95% CI of hotelling’s T-squared distribution. (e) The Venn diagram visualizes the relationship between the data sets of differential metabolites in the three groups of patients with CAP compared to the HC group.
Figure 2.
Figure 2.
Hierarchical cluster heatmap of different metabolites unique to B-CAP (a), V-CAP (b), and F-CAP (c). Row represents metabolites and column represents individual samples. Red, blue, yellow and green represent B-CAP, V-CAP, F-CAP and HC, respectively. Greater brown indicates higher relative intensity of metabolites, while light blue indicates lower intensity.
Figure 3.
Figure 3.
Metabolic pathway analysis and metabolic pathway network of different metabolites unique to three groups in CAP. Node colour based on p value and node radius determined based on pathway impact values. (a) Metabolic pathway analysis of different metabolites unique to B-CAP. (b) Metabolic pathway analysis of different metabolites unique to V-CAP. (c) Metabolic pathway analysis of different metabolites unique to F-CAP.
Figure 4.
Figure 4.
The area under the curve (AUC) of the ROC analysis and box plots of the selected different metabolites. (a-c) The area under the curves (AUCs) of the ROC analysis and box plot of the selected metabolites with excellent ability to distinguish B-CAP from other groups. (d-g) The AUCs and box plots of the selected metabolites with excellent ability to distinguish F-CAP from other groups.
Figure 5.
Figure 5.
Correlation analysis between different metabolites unique to three groups and clinical parameters. (a) Correlation analysis of differential metabolites unique to B-CAP group and clinical parameters. (b-d) Correlation analysis of differential metabolites unique to V-CAP group and clinical parameters. (e-g) Correlation analysis of differential metabolites unique to F-CAP group and clinical parameters. Solid black line, the fitted regression line. Area within the dotted line lines, the 95% confidence intervals.
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Grants and funding

The research was funded by the Beijing Science and Technology planning project (Z211100002521020) and Research and Development Fund of Peking University People’s Hospital (Scientific Research) (Grant No. RDGS2022-11).

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