Dataset Information

Mining for Peaks in LC-HRMS Datasets Using Finnee - A Case Study with Exhaled Breath Condensates from Healthy, Asthmatic, and COPD Patients.

ABSTRACT: Separation techniques hyphenated to high-resolution mass spectrometry are essential in untargeted metabolomic analyses. Due to the complexity and size of the resulting data, analysts rely on computer-assisted tools to mine for features that may represent a chromatographic signal. However, this step remains problematic, and a high number of false positives are often obtained. This work reports a novel approach where each step is carefully controlled to decrease the likelihood of errors. Datasets are first corrected for baseline drift and background noise before the MS scans are converted from profile to centroid. A new alignment strategy that includes purity control is introduced, and features are quantified using the original data with scans recorded as profile, not the extracted features. All the algorithms used in this work are part of the Finnee Matlab toolbox that is freely available. The approach was validated using metabolites in exhaled breath condensates to differentiate individuals diagnosed with asthma from patients with chronic obstructive pulmonary disease. With this new pipeline, twice as many markers were found with Finnee in comparison to XCMS-online, and nearly 50% more than with MS-Dial, two of the most popular freeware for untargeted metabolomics analysis.

SUBMITTER: Erny GL

PROVIDER: S-EPMC7346274 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Mining for Peaks in LC-HRMS Datasets Using Finnee - A Case Study with Exhaled Breath Condensates from Healthy, Asthmatic, and COPD Patients.

Erny Guillaume L GL Gomes Ricardo A RA Santos Mónica S F MSF Santos Lúcia L Neuparth Nuno N Carreiro-Martins Pedro P Marques João Gaspar JG Guerreiro Ana C L ACL Gomes-Alves Patrícia P

ACS omega 20200623 26

Separation techniques hyphenated to high-resolution mass spectrometry are essential in untargeted metabolomic analyses. Due to the complexity and size of the resulting data, analysts rely on computer-assisted tools to mine for features that may represent a chromatographic signal. However, this step remains problematic, and a high number of false positives are often obtained. This work reports a novel approach where each step is carefully controlled to decrease the likelihood of errors. Datasets ...[more]

PMID: 32656431

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Project description:Asthma etiopathology is still not fully determined. One of its possible causes can be found in airway microbiome dysbiosis. The study’s purpose was to determine whether there are any significant differences in the bacterial microbiome diversity of lower airways microbiota of asthmatic children, since knowledge of this topic is very scarce. To the authors’ knowledge, this is the first research using exhaled breath condensates in children’s lower airways for bacterial assessment. Exhaled breath condensates (EBC) and oropharyngeal swabs were obtained from pediatric asthmatic patients and a healthy group (n = 38, 19 vs. 19). The microbial assessment was conducted through genetic material PCR amplification, followed by bacterial 16S rRNA amplicon sequencing. Collected data were analyzed, in terms of taxonomy and alpha and beta diversity between assessed groups. Swab samples are characterized by higher species richness compared to exhaled breath condensates (Shannon diversity index (mean 4.11 vs. 2.867, p = 9.108 × 10−8), observed features (mean 77.4 vs. 17.3, p = 5.572 × 10−11), and Faith’s phylogenetic diversity (mean 7.686 vs. 3.280 p = 1.296 × 10−10)). Asthmatic children had a higher abundance of bacterial species (Shannon diversity index, mean 3.029 vs. 2.642, p = 0.026) but more even distribution (Pielou’s evenness, mean 0.742 vs. 0.648, p = 0.002) in EBC than healthy ones; the same results were observed within pediatric patients born naturally within EBC samples. In children with a positive family history of allergic diseases, alpha diversity of lower airway material was increased (Shannon’s diversity index p = 0.026, Faith’s phylogenetic diversity p = 0.011, observed features p = 0.003). Class Gammaproteobacteria and Bacilli were less abundant among asthmatics in the exhaled breath samples. The most dominant bacteria on a phylum level in both sample types were Firmicutes, followed by Proteobacteria and Actinobacteriota. The obtained outcome of higher bacterial diversity of lower airways among asthmatic patients indicates a further need for future studies of microbiota connection with disease pathogenesis.

Dataset Information

Mining for Peaks in LC-HRMS Datasets Using Finnee - A Case Study with Exhaled Breath Condensates from Healthy, Asthmatic, and COPD Patients.

Publications

Mining for Peaks in LC-HRMS Datasets Using Finnee - A Case Study with Exhaled Breath Condensates from Healthy, Asthmatic, and COPD Patients.

Similar Datasets

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