An Integrated Metabolomic and Microbiome Analysis Identified Specific Gut Microbiota Associated with Fecal Cholesterol and Coprostanol in Clostridium difficile Infection

Abstract

Clostridium difficile infection (CDI) is characterized by dysbiosis of the intestinal microbiota and a profound derangement in the fecal metabolome. However, the contribution of specific gut microbes to fecal metabolites in C. difficile-associated gut microbiome remains poorly understood. Using gas-chromatography mass spectrometry (GC-MS) and 16S rRNA deep sequencing, we analyzed the metabolome and microbiome of fecal samples obtained longitudinally from subjects with Clostridium difficile infection (n = 7) and healthy controls (n = 6). From 155 fecal metabolites, we identified two sterol metabolites at >95% match to cholesterol and coprostanol that significantly discriminated C. difficile-associated gut microbiome from healthy microbiota. By correlating the levels of cholesterol and coprostanol in fecal extracts with 2,395 bacterial operational taxonomic units (OTUs) determined by 16S rRNA sequencing, we identified 63 OTUs associated with high levels of coprostanol and 2 OTUs correlated with low coprostanol levels. Using indicator species analysis (ISA), 31 of the 63 coprostanol-associated bacteria correlated with health, and two Veillonella species were associated with low coprostanol levels that correlated strongly with CDI. These 65 bacterial taxa could be clustered into 12 sub-communities, with each community containing a consortium of organisms that co-occurred with one another. Our studies identified 63 human gut microbes associated with cholesterol-reducing activities. Given the importance of gut bacteria in reducing and eliminating cholesterol from the GI tract, these results support the recent finding that gut microbiome may play an important role in host lipid metabolism.

Fig 1. Flow-chart of integrative scheme between genomics and metabolomics to identify bacterial OTUs associated with cholesterol and coprostanol.

Genomic DNA (gDNA) from longitudinal fecal samples emanating from Healthy or CDI subjects (over 90 days) was isolated and deep sequenced on the V1V3 hypervariable 16s rRNA gene before being classified to 2395 refOTUs (Right). The same longitudinal fecal sample was extracted with dichloromethane and injected on a GC-MS instrument where the retention time of discriminatory peaks were determined based on PLS-DA VIP scores (Left). Discriminatory peaks cholesterol and coprostanol were Spearman correlated to refOTUs based on NMDS and ANOVA. As a further step, ISA was used to determine whether refOTUs associated with high coprostanol or cholesterol were enriched in Healthy or CDI cohorts. Red arrows represent feedback and integration between chart items whereas black arrows are directional flow of the pipeline. Abbreviations: ANOVA: analysis of variance, ISA: indicator species analysis, NMDS: non-metric multidimensional scaling, PLS-DA: partial least squares discriminant analysis refOTUs: reference operational taxonomic units, RT: retention time, VIP scores: Variable importance in projection scores, CH2Cl2: dichloromethane.

Fig 2. PLS-DA plots using a (left) two-state model, and (right) 4-state model.

Antibiotic therapy for CDI (Met: Metronidazole, Vanc: Vancomycin) and antibiotic exposure history (HAbx: antibiotic exposure; Healthy: no antibiotic exposure) were used to distinguish groups. A matrix of retention time intensities were sum normalized and auto-scaled to generate both plots using a metabolomics pipeline established by Xia, et al. Each sphere represents a fecal chromatographic sample.

Fig 3. Inverse relationship of cholesterol and coprostanol levels in fecal extracts from subjects with CDI and Healthy controls.

(A) The relationship between retention times for cholesterol and coprostanol as determined by mass spectrometry (x-axis) and their relative abundance (y-axis). The inverse relationship between the two compounds based on fold change in fecal composition is highlighted in blue and red circles. (B) Box-plots showing distribution of average total ion current of coprostanol (left) and cholesterol (right) for all fecal samples from the Healthy or the CDI group. The TIC of the two metabolites was normalized by auto-scaling before plotting. (C) Percentage of coprostanol TIC relative to the sum of coprostanol and cholesterol TIC for each subgroup. ANOVA on the ranked Coprostanol TIC values indicated a significant difference among the four cohorts (F_{3, 9} = 9.797, p < 0.01). For the 13 subjects, ranks were highest for Healthy (10) and HAbx (10), followed by Met (6) and Vanc (2); numbers in parentheses indicate mean ranks. Letters above whiskers indicate similar groups based on ranks according to the Tukey HSD test. Fecal samples from a Healthy, HAbx, or Metronidazole origin could be grouped together according to coprostanol levels. Likewise, Metronidazole and Vancomycin treated fecal derived samples could be grouped together based on coprostanol levels.

Fig 4. Correlation between coprostanol total ion current (TIC) and 16S rRNA taxonomic sequences.

(A): Spearman’s rank of 65 bacteria significantly correlated to coprostanol and cholesterol total ion current. Each taxon was grouped according to an indicator cohort (HAbx, Healthy, or Vancomycin) using indicator species analysis. No phylotypes were identified as an indicator for the Metronidazole (Met) cohort. (B) Nonmetric Multidimensional Scaling (NMDS) analysis of bacterial OTUs and relative coprostanol TICs. Fecal samples were assigned as either “High” or “Low” coprostanol formers. Data was reduced by the NMDS approach using Bray-Curtis distances, followed by Spearman rank correlation to identify OTUs associated with coprostanol TIC levels. Dimension 1 represents coprostanol levels; Dimension 2 represents CDI treatment or antibiotics exposure for each subject.

Fig 5. Relationship between the abundance of coprostanol-associated bacteria and coprostanol levels for each subject.

(A) The mean abundance of coprostanol-associated bacteria for all samples within each subject is shown in x-axis. The mean proportion of coprostanol relative to the sum of cholesterol and coprostanol in TIC (total ion current) is shown on the y-axis. Both an exponential and a linear model fit well to the correlation between coprostanol TIC and 16S rRNA sequence abundance (see Results section). H: Healthy volunteers without prior antibiotic use, HA: Healthy volunteers with prior antibiotic exposure, M: Subjects with CDI who received metronidazole therapy, and V: subjects with CDI who received oral vancomycin therapy (V). (B): Hierarchical clustering of all samples using Ward’s clustering on a Manhattan distance matrix. Each row indicates a sample, color-coded according to cohort: HAbx (yellow), Healthy (blue), Metronidazole group (gray), and Vancomycin group (red). The abundance of coprostanol, cholesterol TIC, and 16S rRNA sequence reads that mapped to coprostanol-associated bacteria for each sample is shown per each column.

Fig 6. Agglomerative hierarchical clustering of 63 OTUs correlated with coprostanol and two correlated with high cholesterol.

The dendrogram shows communities of bacteria more likely to co-localize with each other based on rank coprostanol and cholesterol GC-MS levels. Communities are shown in red boxes and are numbered from community 1 to 12 (C1-C12) (B) Boxplots for ranks of the abundances of community clusters (C1-C12). Highest abundances were given the lowest rankings (i.e., 1 = most abundant). The rank abundances were determined based on disease-drug combination (i.e., cohort). Cohort abbreviations are health (H, n = 3), healthy with prior antibiotics (HA, n = 3), CDI with metronidazole treatment (M, n = 4), and CDI with vancomycin treatment (V). Clusters are defined as in Fig 6A in red boxes. (C) Heatmaps for subject by community clusters scaled by column (left) and by row (right). Color scale goes from green (low value) to red (high value). Column scaling (left) indicates in which subject(s) a particular community cluster is dominant. Row scaling indicates which community clusters any given subject was dominated by, if any. Heatmap on the left indicates clustering based on the two-level model (CDI vs Health) and heatmap on the right is clustering based on a 4-level model (H vs. HA vs. M vs. V).