Dataset Information

Identification of Exhaled Metabolites in Children with Cystic Fibrosis.

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.

SUBMITTER: Weber R

PROVIDER: S-EPMC9611656 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Identification of Exhaled Metabolites in Children with Cystic Fibrosis.

Weber Ronja R Perkins Nathan N Bruderer Tobias T Micic Srdjan S Moeller Alexander A

Metabolites 20221017 10

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 (<i>m</i>/<i>z</i>) 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 ...[more]

PMID: 36295881

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Project description:Nontuberculous mycobacterial (NTM) pulmonary infections in people with cystic fibrosis (CF) are associated with significant morbidity and mortality and are increasing in prevalence. Host risk factors for NTM infection in CF are largely unknown. We hypothesize that the airway microbiota represents a host risk factor for NTM infection. In this study, 69 sputum samples were collected from 59 people with CF; 42 samples from 32 subjects with NTM infection (14 samples collected before incident NTM infection and 28 samples collected following incident NTM infection) were compared to 27 samples from 27 subjects without NTM infection. Sputum samples were analyzed with 16S rRNA gene sequencing and metabolomics. A supervised classification and correlation analysis framework (sparse partial least-squares discriminant analysis [sPLS-DA]) was used to identify correlations between the microbial and metabolomic profiles of the NTM cases compared to the NTM-negative controls. Several metabolites significantly differed in the NTM cases compared to controls, including decreased levels of tryptophan-associated and branched-chain amino acid metabolites, while compounds involved in phospholipid metabolism displayed increased levels. When the metabolome and microbiome data were integrated by sPLS-DA, the models and component ordinations showed separation between the NTM and control samples. While this study could not determine if the observed differences in sputum metabolites between the cohorts reflect metabolic changes that occurred as a result of the NTM infection or metabolic features that contributed to NTM acquisition, it is hypothesis generating for future work to investigate host and bacterial community factors that may contribute to NTM infection risk in CF. IMPORTANCE Host risk factors for nontuberculous mycobacterial (NTM) infection in people with cystic fibrosis (CF) are largely unclear. The goal of this study was to help identify potential host and bacterial community risk factors for NTM infection in people with CF, using microbiome and metabolome data from CF sputum samples. The data obtained in this study identified several metabolic profile differences in sputum associated with NTM infection in CF, including 2-methylcitrate/homocitrate and selected ceramides. These findings represent potential risk factors and therapeutic targets for preventing and/or treating NTM infections in people with CF.

Project description:BackgroundCystic Fibrosis (CF) is characterized by chronically inflamed airways, and inflammation even increases during pulmonary exacerbations. These adverse events have an important influence on the well-being, quality of life, and lung function of patients with CF. Prediction of exacerbations by inflammatory markers in exhaled breath condensate (EBC) combined with early treatment may prevent these pulmonary exacerbations and may improve the prognosis.AimTo investigate the diagnostic accuracy of a set of inflammatory markers in EBC to predict pulmonary exacerbations in children with CF.MethodsIn this one-year prospective observational study, 49 children with CF were included. During study visits with an interval of 2 months, a symptom questionnaire was completed, EBC was collected, and lung function measurements were performed. The acidity of EBC was measured directly after collection. Inflammatory markers interleukin (IL)-6, IL-8, tumor necrosis factor α (TNF-α), and macrophage migration inhibitory factor (MIF) were measured using high sensitivity bead based flow immunoassays. Pulmonary exacerbations were recorded during the study and were defined in two ways. The predictive power of inflammatory markers and the other covariates was assessed using conditionally specified models and a receiver operating characteristic curve (SAS version 9.2). In addition, k-nearest neighbors (KNN) algorithm was applied (SAS version 9.2).ResultsSixty-five percent of the children had one or more exacerbations during the study. The conditionally specified models showed an overall correct prediction rate of 55%. The area under the curve (AUC) was equal to 0.62. The results obtained with the KNN algorithm were very similar.ConclusionAlthough there is some evidence indicating that the predictors outperform random guessing, the general diagnostic accuracy of EBC acidity and the EBC inflammatory markers IL-6, IL-8, TNF-α and MIF is low. At present it is not possible to predict pulmonary exacerbations in children with CF with the chosen biomarkers and the method of EBC analysis. The biochemical measurements of EBC markers should be improved and other techniques should be considered.

Dataset Information

Identification of Exhaled Metabolites in Children with Cystic Fibrosis.

Publications

Identification of Exhaled Metabolites in Children with Cystic Fibrosis.

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