Associations between host gene expression, the mucosal microbiome, and clinical outcome in the pelvic pouch of patients with inflammatory bowel disease

Abstract

Background: Pouchitis is common after ileal pouch-anal anastomosis (IPAA) surgery for ulcerative colitis (UC). Similar to inflammatory bowel disease (IBD), both host genetics and the microbiota are implicated in its pathogenesis. We use the IPAA model of IBD to associate mucosal host gene expression with mucosal microbiomes and clinical outcomes. We analyze host transcriptomic data and 16S rRNA gene sequencing data from paired biopsies from IPAA patients with UC and familial adenomatous polyposis. To achieve power for a genome-wide microbiome-transcriptome association study, we use principal component analysis for transcript and clade reduction, and identify significant co-variation between clades and transcripts.

Results: Host transcripts co-vary primarily with biopsy location and inflammation, while microbes co-vary primarily with antibiotic use. Transcript-microbe associations are surprisingly modest, but the most strongly microbially-associated host transcript pattern is enriched for complement cascade genes and for the interleukin-12 pathway. Activation of these host processes is inversely correlated with Sutterella, Akkermansia, Bifidobacteria, and Roseburia abundance, and positively correlated with Escherichia abundance.

Conclusions: This study quantifies the effects of inflammation, antibiotic use, and biopsy location upon the microbiome and host transcriptome during pouchitis. Understanding these effects is essential for basic biological insights as well as for well-designed and adequately-powered studies. Additionally, our study provides a method for profiling host-microbe interactions with appropriate statistical power using high-throughput sequencing, and suggests that cross-sectional changes in gut epithelial transcription are not a major component of the host-microbiome regulatory interface during pouchitis.

Figure 1

Overview of data analysis. (A) Data were acquired from a cohort of 265 UC and FAP patients who had IPAA surgery at least 1 year previously. Biopsies were collected from each patient from both the pre-pouch ileum and j-pouch. The host transcriptome was profiled using cDNA microarrays, and the microbiome was profiled by sequencing the V4 region of the 16S gene. Data were then subjected to unsupervised reduction and linear modeling (B), and to supervised reduction and linear discriminant analysis (C). (B) After quality control, data dimensionality was reduced to maximize statistical power prior to linear modeling. After filtering low-variance transcripts, principal component analysis was used to create nine gene principal components (gPCs) to account for 50% of the variance in the transcriptome data. OTUs were filtered for minimum abundance and for presence in at least three samples. PCA was then used to create nine clade principal components (cPCs) explaining 50% of the variance in OTU data. Multivariate association with linear modeling was then used to test for associations between clades and transcripts that were significant after adjusting for metadata (inflammation, antibiotic use, and outcome). (C) In an alternative data reduction approach, a list of 449 genes was curated from IBD genome-wide association studies [4] and host genes that physically interact with bacteria [22]. The expression profiles of these 449 genes were further reduced by k-medoid clustering into 75 medoids, each representing a cluster of genes with similar expression profiles. Abundant microbial clades were hierarchically clustered, and one representative from each cluster was chosen. Linear discriminant analysis was used to measure which genes and clades were most discriminant between clinical outcomes. (See also Additional file 1, Additional file 2, and Additional file 3A to C).

Figure 2

Biplot of clades, genes, and study metadata. Non-metric multidimensional scaling (NMDS) of clade abundances was used to position samples and show samples relatively enriched in specific clades (purple). Arrows represent host transcripts (brown) and metadata (blue), which include antibiotic use and clinical outcome. Arrow coordinates are determined by averaging the coordinates of each sample containing a specific metadata, and show the central tendency of the metadata. Samples are color-coded according to inflammation, which ranges from none (green) to high (red). This figure was created with PPI-only samples.

Figure 3

The relationship between clades and metadata in univariate analysis. The major metadata in the cohort were antibiotic use, inflammation, tissue (pouch or PPI), and outcome (AP, NP, CP, FAP, or CDL). Univariate linear discriminant analysis effect size analysis was performed on each of these variables. Antibiotic use was associated the greatest number of perturbations in the microbiome, causing broad decreases in the Clostridia, Bacteroides, Tenericutes, and Betaproteobacteria, and increases in the Lactobacilliales, Actinobacteria, and Gammaproteobacteria. Because the antibiotic effect size was very large and affected most clades, LDA effects for inflammation (ring 2), tissue types (ring 3), and outcomes (rings 4, 5, and 6) were calculated after stratifying for antibiotic use. Color intensity of ring corresponds to the taxonomic level at which the LDA effect is significant (P <0.05), from phylum (least intense) to genus (most intense).

Figure 4

Results of multivariate linear modeling. Principal component analysis was used to reduce the data into nine gPCs and cPCs that explained 50% of total transcriptional and microbial variation. The top six loadings for each cPC (left) and cPC (middle) are shown; orange and blue indicate increases or decreases in expression, respectively. (Right) MaAsLin [5,28] was used for multivariate linear analysis of associations between cPCs and gPCs while controlling for the effects of inflammation, tissue location, and antibiotic use. Black/gray scale corresponds to the significance of the association, while blue / orange corresponds to the direction. See also Additional file 5.

Figure 5

Linear discriminant analysis for clinical outcome. Linear discriminant analysis was used to determine which genes and clades were most discriminant between clinical outcomes after controlling for antibiotic use. All samples with antibiotic use were removed prior to analysis, and an LDA fitting model with leave-one-out cross-validation was used. (A, B) The separation of clinical outcomes by LD1 and LD2. See also (Additional file 6).