Defective humoral immunity disrupts bile acid homeostasis which promotes inflammatory disease of the small bowel

Mucosal antibodies maintain gut homeostasis by promoting spatial segregation between host tissues and luminal microbes. Whether and how mucosal antibody responses influence gut health through modulation of microbiota composition is unclear. Here, we use a CD19−/− mouse model of antibody-deficiency to demonstrate that a relationship exists between dysbiosis, defects in bile acid homeostasis, and gluten-sensitive enteropathy of the small intestine. The gluten-sensitive small intestine enteropathy that develops in CD19−/− mice is associated with alterations to luminal bile acid composition in the SI, marked by significant reductions in the abundance of conjugated bile acids. Manipulation of bile acid availability, adoptive transfer of functional B cells, and ablation of bacterial bile salt hydrolase activity all influence the severity of small intestine enteropathy in CD19−/− mice. Collectively, results from our experiments support a model whereby mucosal humoral immune responses limit inflammatory disease of the small bowel by regulating bacterial BA metabolism.

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For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors/reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). Basic univariate and bi-variate analyses were conducted using PRISM8.0 software. R-studio was utilized to perform gene enrichment analyses for RNAseq experiments. The QIIME2.0 analysis pipeline was utilized for 16S rRNA gene sequencing analyses. For RNAseq datasets, sequences were aligned to the Mus Musculus genome GRCm38.p5 (GCA_000001635.7, ensemble release-88) using STAR v2.4. Samtools (v1.2) was used to convert aligned sam files to bam files and reads were counted using the featureCounts function of the Subreads package with Gencode.vM19.basic.annotation.gtf annotation file. Differential expression analysis was performed in R using the edgeR package. Raw counts were normalized using the Trimmed Mean of M-values (TMM) method. The normalized read counts were fitted to a quasi-likelihood negative binomial generalized log-linear model using the function glmQLFit. Genewise statistical tests for significant differential expression were conducted with empirical Bayes quasi-likelihood F-tests using the function glmQLFTest.

October 2018
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All studies must disclose on these points even when the disclosure is negative.  Tables T1-T6 that are referenced in the main document are provided as supplementary source data alongside the published paper (filename 'Bcells_and_BileAcids_Source_Data'). Supplementary Tables T7 and T8 have been provided within the Supplemental File associated with this paper. Source data for datasets summarized in this manuscript have been deposited and made publicly available through the Dryad Data Repository (https://doi.org/10.5061/dryad.rxwdbrv9h). Raw sequence data and relevant metadata for all 16S analyses shown in this manuscript have been deposited in the NCBI short read archive (SRA) under Bioproject ID#PRJNA773874. Raw sequence data and relevant metadata for ileal and liver RNAseq datasets have been deposited into the NCBI Gene Expression Omnibus (GEO) under the GEO identifiers GSE186435 and GSE186436, respectively.
Power analyses were not performed to pre-determine experimental group sizes for animal studies. Instead, appropriate group sizes were estimated based on prior effect sizes observed for similar models in a previous study by our group (PUBMED#31708923) as well as animal availability. Animal numbers are provided throughout in the figure legends.
Data exclusion was pre-established in all experiments and based on statistical criteria. Specifically, using the ROUT method of outlier identification, a false discovery rate of 0.1 was applied to relevant data-sets. This q-value results in a 99% likelihood of correctly identifying statistical outliers and this was the maximum q-value threshold applied to all data-sets. Outlier analysis was performed on data-sets shown in Figure 1A, 1B, and 6C using Prism8.0 software.
2-3 replicate experiments were conducted for all datasets shown. Datasets represent pooled data across replicates. All attempts at replication were successful.
For adoptive transfer experiments and mono-colonization studies of CD19 littermates, male and female mice of appropriate ages were randomly assigned to experimental groups. Randomization involved allocating mice derived from different litters equally between experimental treatments.
For pathology scoring, our Pathologist (Dr. Ioulia Chatzistamou) was blinded to animal treatment group. Blinding was not relevant to any other aspect of our study because those involved in data collection and analysis (the first-and senior-author) designed all experiments and needed to know the specific genotype/treatment.