Alteration of the fecal microbiota and serum metabolite profiles in dogs with idiopathic inflammatory bowel disease

Abstract

Idiopathic inflammatory bowel disease (IBD) is a common cause of chronic gastrointestinal (GI) disease in dogs. The combination of an underlying host genetic susceptibility, an intestinal dysbiosis, and dietary/environmental factors are suspected as main contributing factors in the pathogenesis of canine IBD. However, actual mechanisms of the host-microbe interactions remain elusive. The aim of this study was to compare the fecal microbiota and serum metabolite profiles between healthy dogs (n = 10) and dogs with IBD before and after 3 weeks of medical therapy (n = 12). Fecal microbiota and metabolite profiles were characterized by 454-pyrosequencing of 16 S rRNA genes and by an untargeted metabolomics approach, respectively. Significantly lower bacterial diversity and distinct microbial communities were observed in dogs with IBD compared to the healthy control dogs. While Gammaproteobacteria were overrepresented, Erysipelotrichia, Clostridia, and Bacteroidia were underrepresented in dogs with IBD. The functional gene content was predicted from the 16 S rRNA gene data using PICRUSt, and revealed overrepresented bacterial secretion system and transcription factors, and underrepresented amino acid metabolism in dogs with IBD. The serum metabolites 3-hydroxybutyrate, hexuronic acid, ribose, and gluconic acid lactone were significantly more abundant in dogs with IBD. Although a clinical improvement was observed after medical therapy in all dogs with IBD, this was not accompanied by significant changes in the fecal microbiota or in serum metabolite profiles. These results suggest the presence of oxidative stress and a functional alteration of the GI microbiota in dogs with IBD, which persisted even in the face of a clinical response to medical therapy.

Keywords: 16 S rRNA, 16 S ribosomal RNA; ANOSIM, analysis of similarities; CIBDAI, canine IBD activity index; FDR, false discovery rate; Faecalibacterium; GC-TOF/MS, gas chromatography coupled with time-of-flight mass spectrometry; GI, gastrointestinal; IBD; IBD, idiopathic inflammatory bowel disease; KEGG, Kyoto Encyclopedia of Genes and Genomes; LEfSe, linear discriminant analysis (LDA) effect size; PCA, principal component analysis; PCoA, principal coordinates analysis; PICRUSt, Phylogenetic Investigation of Communities by Reconstruction of Unobserved States; ROC, receiver operating characteristic; dog; dysbiosis; feces; metabolomics; microbiome.

Figure 1.

Bacterial diversity measures. β diversity: (A) principal coordinates analysis (PCoA) of unweighted UniFrac distances of 16S rRNA genes. PCoA plots along with Principal Coordinates (PC) 1 and PC 3. Analysis of similarity (ANOSIM) revealed clustering between healthy control dogs and dogs with IBD (p = 0.01), but not between IBD pre-treatment and IBD post-treatment groups (p = 0.34). Alpha diversity measures: (B) rarefaction analysis (number of observed species) of 16 S rRNA gene sequences. Lines represent the mean of each group, while the error bars represent the standard deviations. (C) Comparisons of α diversity. Samples from dogs with IBD post-treatment were divided into 2 groups based on antibiotic administration status during 3 weeks of medical intervention. Red lines represent the median for each measure. HC, healthy control dogs; IBD-PRE, dogs with IBD pre-treatment; IBD-POST_NON AB, dogs with IBD post-treatment that did not receive antibiotic; IBD-POST_AB, dogs with IBD post-treatment that received antibiotic *p < 0.05; and **P < 0.01

Figure 2.

Linear discriminant analysis (LDA) effect size (LEfSe) of 454-pyrosequencing data sets based on 16 S rRNA gene sequences. (A) Histogram of the LDA scores computed for differentially abundant bacterial taxa between healthy control dogs and dogs with IBD (pre-treatment). (B) Taxonomic distribution of bacterial groups significant for IBD. A total of 22 differentially abundant bacterial taxa were detected (α = 0.01, LDA score > 3.0). Of those, 7 bacterial taxa were significantly overrepresented in pretreatment samples from dogs with IBD (green) and 15 bacterial taxa were overrepresented in samples from healthy control dogs (red).

Figure 3.

The abundances of selected bacterial groups in healthy dogs and dogs with IBD (pre- and post-treatment) based on qPCR. Samples from dogs with IBD post-treatment were divided into 2 groups based on antibiotic administration status during the 3 weeks of medical intervention. Red lines represent the median of log DNA. HC, healthy control dogs; IBD-PRE, dogs with IBD pre-treatment; IBD-POST_NON AB, dogs with IBD post-treatment that did not receive antibiotic; IBD-POST_AB, dogs with IBD post-treatment that received antibiotic *q < 0.05

Figure 4.

Predicted functional composition of metagenomes based on 16 S rRNA gene sequencing data. (A) Relative abundances of predicted functions (second level of the Kyoto Encyclopedia of Genes and Genomes (KEGG) Ortholog (KO) hierarchy) based on phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) data set. Each stacked bar represents relative abundances of the predicted functions of each dog. (B) LEfSe based on the PICRUSt data set (third level of the KO hierarchy) revealed a total of 4 differentially enriched bacterial functions (enriched in healthy controls: amino acid metabolism, enriched in IBD dogs: secretion system, transcription factors, and a pathway with unknown function) between healthy control dogs and dogs with IBD (pre-treatment; α = 0.01, LDA score > 3.0). None of the bacterial functions were differentially expressed between IBD pre-treatment and IBD post-treatment. (C) Univariate analysis of major metabolisms. Red lines represent the median of relative abundance. HC, healthy control dogs; IBD-PRE, dogs with IBD pre-treatment; IBD-POST, dogs with IBD post-treatment; NS, no significance *metabolic functions that differed significantly between healthy control dogs and dogs with IBD **q < 0.01.

Figure 5.

Effect of antibiotic administration on the fecal microbiota. (A) PCoA of unweighted UniFrac distances of 16 S rRNA genes from dogs with IBD pre-treatment and post-treatment. Dogs in the IBD post-treatment group were divided into 2 groups based on the antibiotic administration status during 3 weeks of medical intervention. ANOSIM revealed no clustering either between IBD-PRE and IBD-POST_AB, or IBD-PRE and IBD POST-NON_AB (ANOSIM p = 0.26, p = 0.51, respectively). Dashed lines connect the pre-treatment and the post-treatment samples from each dog. The pre-treatment sample from one dog with IBD was excluded due to insufficient sequencing depth. (B) Median unweighted UniFrac distance between pre- and post-treatment samples from dogs with and without antibiotic treatment. Whiskers represent interquartile ranges. IBD-PRE, dogs with IBD pre-treatment; IBD-POST_NON AB, dogs with IBD post-treatment that did not receive antibiotic; IBD-POST_AB, dogs with IBD post-treatment that received antibiotic; NS, no significance

Figure 6.

Serum metabolite profiles. (A) PCA score plots of metabolites in serum from healthy control dogs, dogs with IBD pre-treatment, and dogs with IBD post-treatment. Ellipses represent the 95% confidence interval of metabolite profiles for each group. No clear separations between groups were identified. (B) Hierarchical clustering and heatmap of the top 75 metabolites that were different in their peak intensity between groups. The 75 most abundant metabolites are provided on the x-axis (see detail in Table S4). Each row represents the serum metabolite profile of each dog and sorted by group (colored bars on the x-axis represent groups (blue, healthy control dogs; red, dogs with IBD post-treatment; green, dogs with IBD pre-treatment) (C) Comparisons of the peak intensity for differentially expressed serum metabolites. Samples from dogs with IBD post-treatment were divided into 2 groups based on antibiotic administration status during 3 weeks of medical intervention. Gluconic acid lactone was the only identified metabolite that differed significantly between dogs with IBD pre-treatment and post-treatment. Red lines represent the median of peak intensity. HC, healthy control dogs; IBD-PRE, dogs with IBD pre-treatment; IBD-POST_NON AB, dogs with IBD post-treatment that did not receive antibiotic; IBD-POST_AB, dogs with IBD post-treatment that received antibiotic *q < 0.05

Figure 7.

Principal component analysis (PCA) score plots of serum metabolomics data from dogs with IBD pre-treatment, dogs with IBD post-treatment that received antibiotic, and dogs with IBD post-treatment that did not received antibiotic. Ellipses represent the 95% confidence interval of the metabolite profile of each group. No clear separations between groups were identified. IBD-PRE, dogs with IBD pre-treatment; IBD-POST_NON AB, dogs with IBD post-treatment that did not receive antibiotic; IBD-POST_AB, dogs with IBD post-treatment that received antibiotic

Figure 8.

Receiver operating characteristic (ROC) analysis for gluconic acid lactone. (A) Discrimination ability between healthy control dogs and dogs with IBD. (B) Discrimination ability between dogs with IBD pre- and post-treatment. AUC, area under the curve

Figure 9.

Network analysis of serum metabolites. (A) Overview of the network of relationships among metabolites. (B) Clustered network detected by network analysis. White nodes indicate metabolites that were not detected in this study, light blue nodes indicate metabolites that were detected but were not different (q > 0.25) between groups, dark blue nodes indicate metabolites that were detected and were different among groups. The edges indicate biochemical relationship among the metabolites.

Figure 10.

Correlation analysis of serum metabolites. Metabolic networks associated with (A) healthy control dogs, (B) dogs with IBD pre-treatment, and (C) dogs with IBD post-treatment. The IBD groups had 2 clusters of correlated metabolites. One of the clusters, highlighted in green, was centered around citric acid and its metabolites. The second cluster, highlighted in blue, was composed of saturated, monounsaturated, and polyunsaturated fatty acids. The healthy group had only one minor cluster which was composed of fatty acids.