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. 2022 Oct 17;12(10):980.
doi: 10.3390/metabo12100980.

Identification of Exhaled Metabolites in Children with Cystic Fibrosis

Ronja Weber  1 , Nathan Perkins  2 , Tobias Bruderer  3 , Srdjan Micic  1 , Alexander Moeller  1   4
Affiliations

Identification of Exhaled Metabolites in Children with Cystic Fibrosis

Ronja Weber et al. Metabolites. .

Abstract

The early detection of inflammation and infection is important to prevent irreversible lung damage in cystic fibrosis. Novel and non-invasive monitoring tools would be of high benefit for the quality of life of patients. Our group previously detected over 100 exhaled mass-to-charge (m/z) features, using on-line secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS), which distinguish children with cystic fibrosis from healthy controls. The aim of this study was to annotate as many m/z features as possible with putative chemical structures. Compound identification was performed by applying a rigorous workflow, which included the analysis of on-line MS2 spectra and a literature comparison. A total of 49 discriminatory exhaled compounds were putatively identified. A group of compounds including glycolic acid, glyceric acid and xanthine were elevated in the cystic fibrosis group. A large group of acylcarnitines and aldehydes were found to be decreased in cystic fibrosis. The proposed compound identification workflow was used to identify signatures of volatile organic compounds that discriminate children with cystic fibrosis from healthy controls, which is the first step for future non-invasive and personalized applications.

Keywords: SESI-HRMS; breath analysis; children; cystic fibrosis; infection; inflammation; putative compound identification.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Schematic overview of the applied compound identification workflow. Details of the individual steps are described below.
Figure 2
Figure 2
Correlation matrix heatmap of all the 45 identified compounds, with the dendrogram (right and top) depicting the relatedness among the compounds. Cell colors correspond to the Pearson correlation coefficients, ranging from blue (negative correlation) to red (positive correlation).
Figure 3
Figure 3
Exemplary box plots of 4 acylcarnitines (red = CF patients, blue = healthy controls), *: false discovery rate (FDR) adjusted p < 0.05 taken from our previous study [30]) and correlation network plot of all 16 acylcarnitines elevated in the healthy group. (A) Carnitine (m/z + 162.1123), (B) Acetylcarnitine (m/z + 204.123), (C) Propionylcarnitine (m/z + 218.1388, (D) Butyrylcarnitine (m/z + 232.154), (E) Correlation network plot of the 16 acylcarnitines. Edges indicate correlations between compounds, calculated using Pearson’s correlation coefficients. Red color indicates positive correlation ranging, from lighter color and thinner edges (lower correlation, minimal value = 0.78) to darker color and thicker edges (higher correlation, maximum value = 0.97). Only significant correlations (p < 0.05 after Bonferroni correction [46,47]) are displayed.
Figure 4
Figure 4
Box plots of a selection of compounds elevated in the CF group. Red = CF patients, blue = healthy controls (*: p < 0.05, **: p < 0.01, ***: p < 0.001 with FDR adjusted p-values taken from our previous work [30]). (A) Glycolic acid (m/z − 75.0085), (B) glyceric acid (m/z − 105.018), (C) xanthine (m/z − 151.0247), (D) Diethanolamine (m/z + 106.0858), (E) polysiloxane [M + H] + (m/z + 445.12), (F) polysiloxane [M-CH4 + H]+ (m/z + 429.088).
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