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. 2015 Oct 14;18(4):489-500.
doi: 10.1016/j.chom.2015.09.008.

Inflammation, Antibiotics, and Diet as Environmental Stressors of the Gut Microbiome in Pediatric Crohn's Disease

James D Lewis  1 , Eric Z Chen  2 , Robert N Baldassano  3 , Anthony R Otley  4 , Anne M Griffiths  5 , Dale Lee  6 , Kyle Bittinger  7 , Aubrey Bailey  7 , Elliot S Friedman  8 , Christian Hoffmann  7 , Lindsey Albenberg  3 , Rohini Sinha  7 , Charlene Compher  9 , Erin Gilroy  2 , Lisa Nessel  2 , Amy Grant  4 , Christel Chehoud  7 , Hongzhe Li  2 , Gary D Wu  10 , Frederic D Bushman  11
Affiliations

Inflammation, Antibiotics, and Diet as Environmental Stressors of the Gut Microbiome in Pediatric Crohn's Disease

James D Lewis et al. Cell Host Microbe. .

Erratum in

Abstract

Abnormal composition of intestinal bacteria--"dysbiosis"-is characteristic of Crohn's disease. Disease treatments include dietary changes and immunosuppressive anti-TNFα antibodies as well as ancillary antibiotic therapy, but their effects on microbiota composition are undetermined. Using shotgun metagenomic sequencing, we analyzed fecal samples from a prospective cohort of pediatric Crohn's disease patients starting therapy with enteral nutrition or anti-TNFα antibodies and reveal the full complement and dynamics of bacteria, fungi, archaea, and viruses during treatment. Bacterial community membership was associated independently with intestinal inflammation, antibiotic use, and therapy. Antibiotic exposure was associated with increased dysbiosis, whereas dysbiosis decreased with reduced intestinal inflammation. Fungal proportions increased with disease and antibiotic use. Dietary therapy had independent and rapid effects on microbiota composition distinct from other stressor-induced changes and effectively reduced inflammation. These findings reveal that dysbiosis results from independent effects of inflammation, diet, and antibiotics and shed light on Crohn disease treatments.

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Figures

Figure 1
Figure 1
Bacterial composition in samples from children with Crohn’s disease and healthy controls. (A) A heatmap demonstrating relative abundance of bacterial taxa prior to therapy according to presence or absence of Crohn’s disease, cluster assignment, use of corticosteroids and antibiotics, FCP concentration, and response to therapy. Metadata is indicated by the color code at the top of the figure. White cells indicate missing data. Taxa that were statistically different in abundance between Crohn’s disease and healthy controls are identified by *; taxa that were statistically different in abundance between the two Crohn’s disease clusters are identified by † (q<0.05). FCP in this and subsequent figures indicates Fecal Calprotectin. Samples were ordered by the metadata (healthy versus Crohn’s samples, and cluster 1 versus cluster 2, then other forms of metadata). (B) Multidimensional scaling (MDS) analysis of samples from children with Crohn’s disease and healthy controls. Bacterial taxa present were quantified by MetaPhlAn, distances were calculated using binary Jaccard Index, and samples were plotted based on MDS. Samples from healthy controls are shown by the filled circles, Crohn’s disease as open circles. Clusters were defined by partitioning around medoids with estimation of number of clusters (PAMK), and are colored blue (healthy associated) and red (dysbiotic). The size of the dot is scaled by the proportion of human DNA in the sample. (C) Percentage of human DNA reads in each metagenomic sequence sample. Near cluster (blue, associated with healthy controls) and far cluster (red, dysbiotic) refer to the groups shown in 1B.
Figure 2
Figure 2
Comparison of gene pathways present in samples from Crohn’s disease subjects and healthy controls at baseline assessed by shotgun metagenomics. (A) Heatmap of pathways that differed significantly (q value <0.05) between healthy controls and Crohn’s disease subjects at baseline. Each row was normalized by z-score. B) Cluster analysis based on multidimensional scaling using the pathway data. Colors indicate samples that are members of the near cluster (overlapping healthy controls) or far cluster (dysbiotic) as defined by the analysis of bacterial taxa. Controls are defined by filled circles, Crohn’s disease samples by open circles. (C) Analysis using the machine learning algorithm Random Forest. Gene pathways that most strongly distinguish patients with Crohn’s disease from healthy controls were identified. Importance scores were derived from the loss in accuracy measured when each indicated pathway was removed from the analysis. The units on the x-axis indicate mean decrease in accuracy.
Figure 3
Figure 3
Fungal representation in samples from Crohn’s disease subjects and healthy controls at baseline. (A) Heatmap summarizing the abundance of fungal taxa present in healthy controls (left side of figure) and children with Crohn’s disease (right side of figure). Metadata is indicated by the color code at the top of the figure. White cells indicate missing data. Bar graphs (B)–(F) show the relative abundance of the five main taxa detected. The y-axis shows reads per kilobase of target DNA per million reads in the sample (RPKM). (G) Graph showing correlation between human DNA % and fungal DNA %. Points are colored by membership in the near or far clusters based on the bacterial taxonomic data. Samples from healthy subjects are shown filled, Crohn’s disease by open circles.
Figure 4
Figure 4
Change in the microbiota composition among children with Crohn’s disease treated with EEN, PEN, and anti-TNF therapy. (A) Characterization of the bacterial taxonomic composition based on distance from the centroid of healthy controls. Boxes show median and first and third quartile. The x-axis shows the group and time point, the y-axis shows the distance from the centroid of the healthy controls. The groups compared are shown to the right. (B) Plot of regression coefficients and their confidence intervals. The dependent variable used is distance to the healthy centroid. Covariates included antibiotic use, response to therapy defined as reduction in FCP to less than 250mcg/g, and the starting distance from the healthy centroid. Regression coefficients are shown as dots, one standard deviation is shown as the thin lines, and two standard deviations as the thick lines.
Figure 5
Figure 5
Bacterial and fungal genera associated with environmental stressors. Microbial genera are shown that differed in four comparisons: Crohn’s disease versus healthy controls at baseline (“Disease”); antibiotic use at baseline in the Crohn’s disease cohort (“Antibiotics”); diet (EEN) or anti-TNF therapy at week 1 (Diet”); and reduction of inflammation or not at the end of the study at week 8 (“Inflammation”). The time line is shown along the bottom (yellow). Bacterial lineages are shown in light blue, fungal lineages in pink. Data for the diagrams are in Tables S1I–L, and S1 N–Q. Taxa shown were significantly associated after adjustment for multiple comparisons for Crohn’s disease versus healthy controls, for antibiotic use comparisons, and for all fungal comparisons. The bacterial taxa shown for the effect of EEN and resolution of inflammation were significant at a nominal p value<0.05 (i. e. without correction for multiple comparisons).
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