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. 2015 Mar 9;10(3):e0119362.
doi: 10.1371/journal.pone.0119362. eCollection 2015.

VSports - Alcohol induced alterations to the human fecal VOC metabolome

Robin D Couch  1 , Allyson Dailey  1 , Fatima Zaidi  1 , Karl Navarro  1 , Christopher B Forsyth  2 , Ece Mutlu  3 , Phillip A Engen  3 , Ali Keshavarzian  4
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Alcohol induced alterations to the human fecal VOC metabolome

VSports手机版 - Robin D Couch et al. PLoS One. .

Abstract

Studies have shown that excessive alcohol consumption impacts the intestinal microbiota composition, causing disruption of homeostasis (dysbiosis) VSports手机版. However, this observed change is not indicative of the dysbiotic intestinal microbiota function that could result in the production of injurious and toxic products. Thus, knowledge of the effects of alcohol on the intestinal microbiota function and their metabolites is warranted, in order to better understand the role of the intestinal microbiota in alcohol associated organ failure. Here, we report the results of a differential metabolomic analysis comparing volatile organic compounds (VOC) detected in the stool of alcoholics and non-alcoholic healthy controls. We performed the analysis with fecal samples collected after passage as well as with samples collected directly from the sigmoid lumen. Regardless of the approach to fecal collection, we found a stool VOC metabolomic signature in alcoholics that is different from healthy controls. The most notable metabolite alterations in the alcoholic samples include: (1) an elevation in the oxidative stress biomarker tetradecane; (2) a decrease in five fatty alcohols with anti-oxidant property; (3) a decrease in the short chain fatty acids propionate and isobutyrate, important in maintaining intestinal epithelial cell health and barrier integrity; (4) a decrease in alcohol consumption natural suppressant caryophyllene; (5) a decrease in natural product and hepatic steatosis attenuator camphene; and (6) decreased dimethyl disulfide and dimethyl trisulfide, microbial products of decomposition. Our results showed that intestinal microbiota function is altered in alcoholics which might promote alcohol associated pathologies. .

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Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Metabolite composition and abundance.
The pooled analytes present in the alcoholic and healthy cohorts were distributed among the listed chemical classes and then tallied. A and B) The bar graphs indicate the total number of analytes in each chemical class for the endoscopy (A) or home collected (B) fecal VOC metabolomes. C and D) The relative abundance (peak height) of the metabolites present in each cohort were distributed among the indicated chemical classes and then summed. The bar graphs indicate the relative abundance of each class for the endoscopy (C) or the home collected (D) fecal VOC metabolomes.
Fig 2
Fig 2. Metabolite distribution within the endoscopy collected fecal samples.
A) The VOCs identified in the fecal samples were sorted according to the indicated chemical classes and then further arranged by their unique association with either the healthy or alcoholic cohorts, or their appearance in both cohorts. The percent distribution relates to the total number of metabolites within the chemical class. B) The number of identified VOCs as a function of frequency of appearance among the total number of fecal samples analyzed. A large number of analytes appear in a small number of fecal samples, likely a reflection of dietary variation among the study participants. C) The plot was prepared as described in A), but with the exclusion of the low frequency metabolites (≤20%) identified in B). Consequently, there are no longer any metabolites exclusive to either the healthy or alcoholic cohorts.
Fig 3
Fig 3. Principal component analysis (PCA) of the VOC metabolomes derived from the healthy and alcoholic human fecal samples.
Infrequent metabolites were disregarded by restricting the analysis to analytes that appear in a minimum of 21% of all samples in each cohort (see Fig. 2). Hence, the endoscopy collected dataset contains 525 metabolites while the home collected dataset contains 641. A) PCA plots of the endoscopy collected fecal VOC metabolome. The first (PC1) through sixth (PC6) principal components are shown, relative to one another. Healthy samples are identified as blue spheres, while alcoholic samples are denoted as yellow spheres. The two cohorts clearly segregate in all plots involving PC1, while little to no difference is observed among the cohorts in the combinatorial plots with PC2 through PC6. B) PCA plots of the home collected fecal VOC metabolomes. The samples are colored as described in A). The two cohorts segregate in all plots involving PC1, PC2, and PC3 (to differing degrees), while segregation is not observed in the combinatorial plots of PC4, PC5, and PC6. C) A three dimensional PCA plot of the endoscopy collected fecal VOC metabolome (depicting the first, second and third principal components) clearly illustrates the distinctiveness of the healthy and alcoholic cohorts. Samples are colored as in A). Unique sample identifiers are shown adjacent to each data point. D) A three dimensional PCA plot of the home collected fecal VOC metabolome clearly differentiates the healthy and alcoholic cohorts. E and F) Metabolite contribution to the principal components. For clarity, each graph is restricted to the first three principal components and the top 104 contributing metabolites. Metabolites were arranged by descending contributions to each principal component, and the values plotted in the bar graph. The plots indicate that numerous metabolites collectively contribute to cohort segregation in the endoscopy collected (E) and home collected (F) fecal VOC metabolomes. See text for further discussion.
Fig 4
Fig 4. Heat map showing the unsupervised hierarchical clustering of the fecal samples according to the similarity of metabolome composition.
The endoscopy collected fecal metabolomes are compared in (A) while the home collected fecal metabolomes are compared in (B). The samples are arranged in rows, the metabolites in columns, and shades of red represent elevation of a metabolite while shades of green represent decrease of a metabolite, relative to the median metabolite levels (see color scale). In the dendrograms, the clustering clearly differentiates the alcoholic and healthy fecal samples.
Fig 5
Fig 5. Metabolite correlation network of the endoscopy collected (A) and home collected (B) fecal VOC metabolomes.
Pearson’s correlation coefficients were calculated for metabolites present in 80% or greater of the total fecal samples. A Pearson correlation value greater than 0.95 is depicted as a green line between metabolites (negative correlations are not shown, as correlation values less than-0.95 were not obtained). To facilitate comparison of the networks, metabolites are numerically represented and their placement around the circumference of each network is fixed among the paired plots. Regardless of the fecal collection method used, the fecal samples from the alcoholic participants have a notably different correlation network than that seen in the fecal samples from non-alcoholics. This difference is even more apparent in correlation networks derived using metabolites present in ≥21% of all fecal samples (S2 Fig.).
Fig 6
Fig 6. Fold change analysis of the metabolite abundance between the healthy and alcoholic fecal samples.
The fold change (FC) is calculated as the log transformation of the ratio between the mean metabolite abundance in the alcoholic cohort relative to the healthy cohort. The analysis was performed with both the endoscopy (A) and home passage collected (B) metabolomes. A log2(FC) greater than 1.5 is deemed significant (equivalent to a threefold or greater change in metabolite abundance). A fold change analysis comparing median analyte values produces similar results (not shown).
Fig 7
Fig 7. Metabolites with a statistically significant difference in median abundance levels.
The metabolites listed in Table 2 were compared among the healthy and alcoholic cohorts. Box plots are shown, depicting the interquartile range of Z-score normalized abundance values, with whiskers extending from minimum to maximum. The median value is identified by a horizontal line within the box. Metabolites from the endoscopy collected samples are shown in A), and are numerically coded as follows; 1) tetradecane, 2) 2-tetradecen-1-ol, 3) 1-undecanol, 4) propanoic acid, 5) cyclopropane, nonyl-, 6) 6-pentadecen-1-ol, 7) 8-Tetradecen-1-yl acetate, 8) 1,15-Pentadecanediol, and 9) Eicosen-1-ol. Metabolites from the home collected samples are shown in B), and are numerically coded as follows; 1) caryophyllene, 2) 1-naphthalenol, 3) phellandrene, 4) dimethyl disulfide, 5) dimethyl trisulfide, 6) camphene, 7) 2,5-pyrrolidinedione, 1-(benzoyloxy)-, 8) 5-hepten-2-one, 6-methyl-, and 9) (2-aziridinylethyl)amine. See text for further discussion.
Fig 8
Fig 8. Comparison of SCFA abundance among the cohorts.
Box plots are presented, depicted as described in Fig.8. A) Among the endoscopy collected samples, while most of the SCFAs are comparable in abundance within the healthy and alcoholic cohorts, propanoic acid demonstrates a statistically significant reduction in median abundance in the alcoholic cohort relative to the healthy cohort (p = 0.03, fold change = 2.32). B) In the home collected samples, butyrate has a 1.6 fold reduction in median abundance, while the remaining SCFAs are very similar between the healthy and alcoholic fecal samples. See text for further discussion.
Fig 9
Fig 9. Comparison of protein putrefaction products among the cohorts.
Box plots depicting relative metabolite abundance are shown, as described in Fig. 8. A) Among the endoscopy collected samples, with the exception of methyl indole, the metabolites are comparable in abundance within the healthy and alcoholic cohorts. Note however, the difference observed with methyl indole is not statistically significant (p = 0.519). B) In the home collected samples, metabolite abundance is very similar between the healthy and alcoholic fecal samples.
Fig 10
Fig 10. ROC curve of dimethyl disulfide and dimethyl trisulfide.
The area under the curve is 0.80 and 0.77, respectively, which are indicative of a fairly good diagnostic test (albeit not excellent).
See this image and copyright information in PMC

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