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. 2021 Dec 6;11(12):847.
doi: 10.3390/metabo11120847.

Metabolomics Diagnosis of COVID-19 from Exhaled Breath Condensate

Elettra Barberis  1   2 , Elia Amede  1   2 , Shahzaib Khoso  1   2 , Luigi Castello  1   3   4 , Pier Paolo Sainaghi  1   3 , Mattia Bellan  1   3 , Piero Emilio Balbo  3 , Giuseppe Patti  1   3 , Diego Brustia  3 , Mara Giordano  3   5 , Roberta Rolla  3   5 , Annalisa Chiocchetti  2   5 , Giorgia Romani  3 , Marcello Manfredi  1   2 , Rosanna Vaschetto  1   3
Affiliations

Metabolomics Diagnosis of COVID-19 from Exhaled Breath Condensate

Elettra Barberis et al. Metabolites. .

Abstract

Infection from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can lead to severe respiratory tract damage and acute lung injury. Therefore, it is crucial to study breath-associated biofluids not only to investigate the breath's biochemical changes caused by SARS-CoV-2 infection, but also to discover potential biomarkers for the development of new diagnostic tools. In the present study, we performed an untargeted metabolomics approach using a bidimensional gas chromatography mass spectrometer (GCxGC-TOFMS) on exhaled breath condensate (EBC) from COVID-19 patients and negative healthy subjects to identify new potential biomarkers for the noninvasive diagnosis and monitoring of the COVID-19 disease. The EBC analysis was further performed in patients with acute or acute-on-chronic cardiopulmonary edema (CPE) to assess the reliability of the identified biomarkers. Our findings demonstrated that an abundance of EBC fatty acids can be used to discriminate COVID-19 patients and that they may have a protective effect, thus suggesting their potential use as a preventive strategy against the infection.

Keywords: COVID-19; GCxGC-MS; breath analysis; metabolomics; noninvasive analysis.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Experimental design of the study: exhaled breath condensate molecules were analyzed using untargeted metabolomics. Statistical analysis performed on quantified molecules was used to identify potential biomarkers and breath-biochemical changes associated with SARS-CoV-2 infection.
Figure 2
Figure 2
Partial least squares discriminant analysis (PLS-DA) of EBC metabolome from COVID-19 (red dots) and healthy subjects (green dots). The two groups are well-separated (a). Important features identified by PLS-DA (b): colored boxes indicate the most predictive or discriminative features in each group (red, high; blue, low).
Figure 3
Figure 3
EBC-modulated metabolites in the comparison between healthy subjects versus SARS-CoV-2 patients. Hierarchical clustering heatmap of molecules in COVID-19 patients (red) and healthy subjects (green) (a). Volcano plot reporting 26 regulated small molecules with a p-value less than 0.05 and a fold change greater than 1.3 (b).
Figure 4
Figure 4
Boxplots and ROC curves for the best potential biomarkers identified with metabolomics analysis (red dots: COVID-19 patients, green dots: healthy subjects). 1-monomyristin (a); 2-monomyristin (b); heptadecanoic acid, glycerine-(1)-monoester (c); monolaurin (d); 2,3-dihydroxypropylicosanoate (e); pentadecanoic acid, glycerine-(1)-monoester (f); 2-tert-butyl-4-ethylphenol (g); nonadecanoic acid, glycerine-(1)-monoester (h). ***, p-value < 0.001; ****, p-value < 0.0001.
Figure 5
Figure 5
Combined ROC curve of the two best metabolites: 1-monomyristin and monolaurin.
See this image and copyright information in PMC

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