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. 2016 Jul 2;2(6):750-766.
doi: 10.1016/j.jcmgh.2016.06.004. eCollection 2016 Nov.

"V体育官网" A Disease-Associated Microbial and Metabolomics State in Relatives of Pediatric Inflammatory Bowel Disease Patients

Jonathan P Jacobs  1 , Maryam Goudarzi  2 , Namita Singh  3 , Maomeng Tong  4 , Ian H McHardy  4 , Paul Ruegger  5 , Miro Asadourian  4 , Bo-Hyun Moon  2 , Allyson Ayson  4 , James Borneman  5 , Dermot P B McGovern  6 , Albert J Fornace Jr  2 , Jonathan Braun  4 , Marla Dubinsky  7
Affiliations

A Disease-Associated Microbial and Metabolomics State in Relatives of Pediatric Inflammatory Bowel Disease Patients

Jonathan P Jacobs et al. Cell Mol Gastroenterol Hepatol. .

"VSports在线直播" Abstract

Background & aims: Microbes may increase susceptibility to inflammatory bowel disease (IBD) by producing bioactive metabolites that affect immune activity and epithelial function VSports手机版. We undertook a family based study to identify microbial and metabolic features of IBD that may represent a predisease risk state when found in healthy first-degree relatives. .

Methods: Twenty-one families with pediatric IBD were recruited, comprising 26 Crohn's disease patients in clinical remission, 10 ulcerative colitis patients in clinical remission, and 54 healthy siblings/parents. Fecal samples were collected for 16S ribosomal RNA gene sequencing, untargeted liquid chromatography-mass spectrometry metabolomics, and calprotectin measurement. Individuals were grouped into microbial and metabolomics states using Dirichlet multinomial models. Multivariate models were used to identify microbes and metabolites associated with these states. V体育安卓版.

Results: Individuals were classified into 2 microbial community types. One was associated with IBD but irrespective of disease status, had lower microbial diversity, and characteristic shifts in microbial composition including increased Enterobacteriaceae, consistent with dysbiosis. This microbial community type was associated similarly with IBD and reduced microbial diversity in an independent pediatric cohort. Individuals also clustered bioinformatically into 2 subsets with shared fecal metabolomics signatures. One metabotype was associated with IBD and was characterized by increased bile acids, taurine, and tryptophan. The IBD-associated microbial and metabolomics states were highly correlated, suggesting that they represented an integrated ecosystem. Healthy relatives with the IBD-associated microbial community type had an increased incidence of elevated fecal calprotectin. V体育ios版.

Conclusions: Healthy first-degree relatives can have dysbiosis associated with an altered intestinal metabolome that may signify a predisease microbial susceptibility state or subclinical inflammation VSports最新版本. Longitudinal prospective studies are required to determine whether these individuals have a clinically significant increased risk for developing IBD. .

Keywords: AUC, area under the curve; CD, Crohn’s disease; Family Cohort; IBD, inflammatory bowel disease; Inflammatory Bowel Disease; LC/MS, liquid chromatography/mass spectrometry; Metabolomics; Microbiome; OTU, operational taxonomic unit; PCR, polymerase chain reaction; PCoA, principal coordinates analysis; ToFMS, time-of-flight mass spectrometry; UC, ulcerative colitis; UPLC, ultra-performance liquid chromatography; rRNA, ribosomal RNA. V体育平台登录.

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Figures

Figure 1
Figure 1
A low-diversity microbial community type is associated with IBD but also is present in healthy relatives. (A) PCoA plots were used to visualize differences in microbial composition across samples (β diversity) as measured by the square root of the Jensen–Shannon divergence. Dots represent fecal samples colored by IBD status (left 2 panels), Chao1 (a measure of microbial richness), or OTU type (determined using Dirichlet multinomial mixture models). Dot size corresponds to fecal calprotectin level in the second panel from the left. P values for the association of the indicated variable (IBD status, calprotectin, Chao1, and OTU type) with microbial composition were calculated using Adonis. (B) Contingency table of IBD status by OTU type. P value was calculated using the Fisher exact test. (C) Chao1 by IBD status and OTU type. P values were calculated using the Mann–Whitney U test. *P < .05, **P < .005, ***P < .0001. (D) Venn diagram indicating differential OTUs for CD (vs non-IBD), UC (vs non-IBD), and OTU type (2 vs 1) in multivariate DESeq2 models including sex, Jewish ancestry, current anti–tumor necrosis factor therapy, mode of delivery, family group, IBD diagnosis, and OTU type as covariates. (E) OTUs with a statistically significant difference in abundance between OTU types 2 and 1 in multivariate DESeq2 models are shown. Effect size is represented as the log2 fold-change (FC) between OTU types 2 and 1. Dot size is proportional to the abundance of that OTU after normalization in DESeq2. Only OTUs with a mean normalized abundance greater than 10-4 are included. Color signifies whether the OTU also is differentially abundant in CD, UC, or both. The genus of each OTU is shown on the horizontal axis, ordered by the log2 FC between OTU types 2 and 1 of the genus as a whole. OTUs are grouped into families (f) where indicated if they did not have an assigned genus. OTUs putatively identified at the species level in the Greengenes database are annotated.
Figure 2
Figure 2
Microbial composition and metabolomics vary across families. (A) Box plots showing unweighted UniFrac distances for all pairwise combinations of individuals across families or within the same family were generated in QIIME using the function make_distance_boxplots. Lower values indicate a greater similarity of microbial communities. Increased similarity when comparing pairs of samples within families relative to the similarity of pairs of samples from different families implies an effect of family on microbial composition. This approach has been used in studies comparing monozygotic and dizygotic twins to show a role for genes in regulating microbial composition. Statistical significance was calculated in QIIME using t tests with 100,000 Monte Carlo simulations. (B) PCoA plot showing microbial composition divided by family (each symbol/color combination represents 1 of the 21 families in this cohort). P value was calculated using Adonis. (C) Box plots showing root square Jensen–Shannon divergence (distance) of the fecal metabolome for all pairwise combinations of individuals across and within IBD groups or families. Statistical significance was calculated using t tests with 100,000 Monte Carlo simulations. (D) PCoA plot showing metabolomics data by family.
Figure 3
Figure 3
Dirichlet multinomial mixture models support the presence of 2 OTU types and 2 metabotypes. (A and B) The model fit is shown for the given number of (A) OTU types and (B) metabotypes estimated from Dirichlet multinomial mixture models using the Laplace approximation. The exponent of the difference in model fit is the estimated probability of one model over another. (C) No statistically significant difference in Bacteroides or Prevotella abundance was observed between the 2 OTU types in our cohort. Ruminococcus was increased in OTU type 1 but this was not the exclusive driver of OTU type because 2 individuals with OTU type 2 had greater Ruminococcus levels than all individuals with OTU type 1. **P < .005.
Figure 4
Figure 4
Microbial genera associated with IBD status and OTU type. Fold-change is shown for genera associated with CD (vs non-IBD), UC (vs non-IBD), or OTU type (2 vs 1) with q less than .05 in multivariate DESeq2 models including sex, Jewish ancestry, current anti–tumor necrosis factor therapy, mode of delivery, family group, IBD diagnosis, and OTU type as covariates. Some taxa represent unclassified members of the indicated family (f) or order (o).
Figure 5
Figure 5
Microbial community types were predicted in an independent cohort of pediatric CD patients using a random forest classifier. (A) A classifier was created to predict OTU type in the family cohort using 57 OTUs that were differentially abundant in DESeq2 models and were present in at least 30% of samples. The receiver operating characteristic curve of this classifier based on 10-fold cross-validation is shown. (B) Log2 FC between OTU types 2 and 1 in the family cohort for OTUs included in the random forest classifier. Size is proportional to the importance score of the OTU in the classifier, which measures the loss in accuracy of the classifier when the OTU is permutated randomly. (C) PCoA plots visualizing microbial composition in the RISK cohort of pediatric CD patients and controls with gastrointestinal symptoms but no evidence of inflammatory disease. Samples are colored by IBD status, Chao1, or OTU type predicted from the classifier shown in panel A. P values were calculated using Adonis. (D) Contingency table of IBD status by predicted OTU type in the RISK cohort. P value was calculated using the Fisher exact test. (E) Chao1 was determined by IBD status and predicted OTU type in the RISK cohort. P values were calculated using the Mann–Whitney U test. ***P < .0001.
Figure 6
Figure 6
Metabolomic features of CD also were seen in a subset of healthy relatives. (A) PCoA plots were used to visualize differences in metabolomics profiles across samples as measured by the square root of the Jensen–Shannon divergence. Dots represent fecal samples, colored by IBD status. Dot size corresponds to fecal calprotectin level in the lower panel. P values were calculated using Adonis. Separate calculations were made for the whole cohort and for just the CD subset because increased calprotectin was seen primarily in a subset of CD. (B) A random forest classifier was created to distinguish CD from non-IBD using fecal metabolomics profiles. The receiver operating characteristic curve of this classifier based on 10-fold cross-validation is shown. (C) Venn diagram of the differential spectral features for CD or UC vs non-IBD in multivariate DESeq2 models including sex, Jewish ancestry, current anti–tumor necrosis factor therapy, mode of delivery, family group, and IBD diagnosis as covariates. The heat map shows intensity values for the 123 spectral features with differential abundance in CD vs non-IBD. Intensity is represented on a color scale spanning 3 standard deviations above and below the mean across all samples. Samples and spectral features were clustered hierarchically by Pearson correlation coefficient with the average linkage method. IBD status of samples is indicated by colored boxes above the heat map. (D) Spectral features differentially abundant in CD or UC (as summarized in the Venn diagram in panel C) are shown, arranged by retention time. Effect size is represented as the log2 FC in CD or UC compared with non-IBD in multivariate DESeq2 models. Features associated with both CD and UC are plotted by the log2 FC in CD vs non-IBD. Size is proportional to the importance score of the feature in the random forest classifier for CD. The names of validated metabolites are shown. Lower panel: the density of detected spectral features by retention time on the UPLC column.
Figure 7
Figure 7
Fecal metabotypes are associated with IBD status. (A) PCoA plots of the metabolomics profile of fecal samples colored by metabotype (determined using Dirichlet multinomial mixture models). (B) Contingency table of IBD status by metabotype. The P value was calculated using the Fisher exact test. (C) A random forest classifier was created to predict metabotype. The receiver operating characteristic (ROC) curve of this classifier based on 10-fold cross-validation is shown. (D) Venn diagram showing the number of differential spectral features for metabotypes 2 vs 1, CD vs non-IBD, and UC vs non-IBD in multivariate DESeq2 models including sex, Jewish ancestry, current anti–tumor necrosis factor therapy, mode of delivery, family group, IBD diagnosis, and metabotype as covariates. (E) Metabotype-associated spectral features are shown, plotted by retention time. Effect size is represented as the log2 FC of metabotypes 2 vs 1 in multivariate DESeq2 models. Size is proportional to the importance score of the feature in the random forest classifier for metabotype. The names of validated metabolites are shown, color-coded by metabolite category.
Figure 8
Figure 8
Microbe–metabolite interactions between the IBD-associated microbial community type and metabotype. (A) The PCoA plot for the metabolome (visualized in Figure 6A) was rotated and rescaled using Procrustes, then superimposed on the PCoA plot for the microbiome (visualized in Figure 1A). Points represent 16S rRNA and UPLC/ToFMS data, color-coded by IBD status, OTU type, or metabotype. Each line connects the microbial and metabolomics data from one individual. (B) Contingency table of OTU type and metabotype for the full cohort and only non-IBD individuals. P values were calculated using the Fisher exact test. (C) An inter’omic network was constructed with nodes representing OTU-type–associated microbes and metabotype-associated spectral features with an importance score greater than 2 in random forest classifiers. Microbe (circle) and metabolite (triangle) nodes are outlined in red if they are decreased in OTU type 2 or metabotype 2, respectively. Node size reflects inter’omic degree centrality—the number of connections to nodes of the opposite data type (ie, microbe–metabolite pairs). Edges represent statistically significant correlations (q < 0.05) between microbe–microbe, metabolite–metabolite, or microbe–metabolite pairs. These correlations were made using residuals from multivariate DESeq2 models adjusting for sex, Jewish ancestry, current anti–tumor necrosis factor therapy, mode of delivery, family group, IBD diagnosis, and OTU type/metabotype. This approach highlights correlations that cannot be explained by these factors. Selected microbes and metabolites are indicated by fill color, numbers, or letters.
Figure 9
Figure 9
Healthy first-degree relatives with the IBD-associated microbial community type are at higher risk for increased fecal calprotectin. (A) Unadjusted odds ratios (ORs) and 95% confidence intervals were calculated for IBD risk using univariate logistic regression. Adjusted ORs were obtained from a multivariate logistic regression model incorporating OTU type, metabotype, parent/child status, and Jewish ethnicity. (B) Dot plot showing fecal calprotectin in the 54 healthy first-degree relatives, stratified by OTU type. The lowest 2 rows of dots represent samples below or near the limit of detection of 15.6 μg/g. The P value was calculated from univariate logistic regression for increased calprotectin level (cut-off value is indicated by the dashed line). (C) Pedigrees are shown for 5 families with healthy children who carry an IBD-associated OTU type. Two of these children had an increased fecal calprotectin level.
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