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Characterizing Outdoor Air Using Microbial Volatile Organic Compounds (MVOCs)
Published in Raquel Cumeras, Xavier Correig, Volatile organic compound analysis in biomedical diagnosis applications, 2018
Sonia Garcia-Alcega, Frédéric Coulon
The schematic procedure for the analysis of the samples is represented in Figure 6.5. The procedure of the analysis of MVOCs from environmental air starts with the noise removal of the chromatograms (using SNIP baseline detector for example) followed by peak deconvolution, which allows accurate mass spectra identification from complex chromatograms (e.g., AMDIS). Then, it proceeds with the identification of all the peaks within the chromatograms of the samples, and this can be done with NIST or free databases such as mzCloud or METLIN. The mzCloud uses a new third generation spectra correlation algorithm to search and identify the compounds. METLIN is a metabolomics database useful for the identification of metabolites, which are linked to the KEGG database (Kanehisa Laboratories, 2016) to see the metabolic pathways and the microorganisms producers. The MVOCs analysis for ambient air is complex because not all MVOCs have solely microbial origin; these also can be anthropogenic or produced by fruits or vegetables. To discriminate MVOCs from VOCs there is an approach available looking at metabolical databases such as KEGG database (Kanehisa Laboratories, 2016) or mVOC database (Lemfack et al., 2014). After identifying the chromatogram peaks, the m/z spectra of the MVOCs are analyzed statistically by multivariate analysis (Schenkel et al., 2015), hierarchical Cluster Analysis (HCA) or multidimensional scaling analysis (MDS) and/or principal component analysis (PCA) to study the MVOCs patterns and trends across the samples (Schenkel et al., 2015; Sun et al., 2014). These analysis can be done using chemometric software such as among others SpectConnect, ACD/MS Manager, OpenChrom, Mass Profiler Professional, or commercial statistical software (MATLAB, ADAPT, etc.) (Murphy et al., 2012). These statistical analyses help us to identify the key and more representative compounds per site, giving us a preliminary idea of the potential markers for each site. Then, the microbial identity of these potentially species-specific markers should be verified by correlation with DNA sequencing analysis. Specific microbial markers could be used in the future for the identification of microbes in air ideally with a sampling device that gives real time data.
Efficacy of bile acid profiles in diagnosing and staging of alcoholic liver disease
Published in Scandinavian Journal of Clinical and Laboratory Investigation, 2023
Gaixia Zhang, Haizhen Chen, Wenbo Ren, Jing Huang
The small molecule metabolites in the serum were detected in the four groups including healthy control, ALD, NAFLD and VLD groups using a non-targeted metabolomics method. A typical total ion current diagram is shown in Supplementary Figure S1. Figure 1(A,B) represents the positive and negative ion PCA score plots, respectively, showing the evident clusters among the four groups. OPLS-DA positive ion model (R2Y= 0.973, Q2 = 0.748; Figure 1(C)), negative ion model (R2Y= 0.847, Q2 = 0.708; Figure 1(D)) and the 200 ordination tests showed no signs of overfitting (Figure 1(E,F)), indicating that the model was excellent for discovering differential metabolite. A total of 40 differential metabolites were identified by METLIN database (Supplementary Table S1), including 5 BAs, 8 amino acids, 18 lipids and 9 other substances. A heatmap was showing the differences among groups (Figure 2(A)). Metabolic pathway enrichment analysis showed that the main altered pathways were primary BA biosynthesis, unsaturated fatty acid biosynthesis, fatty acid elongation, arginine biosynthesis, pantothenic acid and CoA biosynthesis and glutathione metabolism (Figure 2(B)). A targeted metabolomic approach was used to detect specific changes in BA profiles since the biosynthesis pathway of primary BAs included the most differential metabolites.