Gut bacteria from multiple sclerosis patients modulate human T cells and exacerbate symptoms in mouse models
Baranzini, Sergio (2017), Gut bacteria from multiple sclerosis patients modulate human T cells and exacerbate symptoms in mouse models, v2, UC San Francisco Dash, Dataset, https://doi.org/10.7272/Q6WQ01ZB
The gut microbiota regulates T cell functions throughout the body. We hypothesized that intestinal bacteria impact the pathogenesis of multiple sclerosis (MS), an autoimmune disorder of the central nervous system, and thus analyzed the microbiomes of 71 MS patients not undergoing treatment and 71 healthy controls. Although no major shifts in microbial community structure were found, we identified specific bacterial taxa that were significantly associated with MS. Akkermansia muciniphila and Acinetobacter calcoaceticus, both increased in MS patients, induced pro-inflammatory responses in human PBMCs and in mono-colonized mice. In contrast, Parabacteroides distasonis, which was reduced in MS patients, stimulated anti-inflammatory interleukin-10 (IL-10)-expressing human CD4+CD25+ T cells, and IL-10+FoxP3+ regulatory T cells (Tregs) in mice. Finally, microbiota transplants from MS patients into germ-free mice resulted in more severe symptoms of experimental autoimmune encephalomyelitis (EAE) and reduced proportions of IL-10+ Tregs compared to mice “humanized” with microbiota from healthy controls. This study identifies specific human gut bacteria that regulate adaptive autoimmune responses, suggesting therapeutic targeting of the microbiota as a novel treatment for MS.
Human fecal sample collection
Fecal samples were collected from 71 adult patients with relapsing-remitting multiple sclerosis that had not received treatment for at least 3 months prior to the time of collection and 71 controls without autoimmune disorders at the University of California, San Francisco (UCSF) and the Icahn School of Medicine at Mt Sinai (New York, NY). The inclusion criteria specified no use of antibiotics or cancer therapeutics in 3 months prior to the study. Detailed patient information is available in Supplementary Table 1. Samples were collected using culture swabs (BD #220135) and stored at -80C until DNA extraction or bacterial isolation.
16S rRNA amplicon sequencing and computational analysis of human and mouse microbiome samples
DNA was extracted from samples using MoBio Power Fecal DNA extraction kit (MoBio #12830) and amplicons of V4 region of the prokaryotic 16S rRNA gene were sequenced using the Earth Microbiome Project standard protocol (1). Analysis was performed using QIIME v1.9 as described (2). Essentially, amplicon sequences were quality-filtered and grouped to “species-level” OTUs using SortMeRNA method (3) using GreenGenes version 13.8 97% dataset for closed reference. Sequences that did not match reference sequences in the GreenGenes database were dropped from the analysis. Taxonomy was assigned to the retained OTUs based on the GreenGenes reference sequence, and the GreenGenes tree was used for all downstream phylogenetic community comparisons. Samples were filtered to at least 10000 sequences per sample, and OTUs were filtered to retain only OTUs present in at least 5% of samples and covering at least 100 total reads. After filtering samples were rarefied to 10000 sequences per sample. We identified 129 total genera and 1462 total operational taxonomic units (OTUs) in our samples. We systematically compared relative abundances of individual microbial taxa between MS patients and controls at the genus and OTU levels by negative binomial Wald test using Benjamini-Hochberg correction for multiple comparisons (4). Alpha diversity was calculated using the Chao1 method (5). For analysis of beta diversity, pairwise distance matrices were generated using the phylogenetic metric unweighted UniFrac (6) and used for principal coordinate analysis (PCoA). For comparison of individual taxa, samples were not rarefied. Instead, OTU abundances were normalized using variance-stabilizing transformation and taxa distributions were compared using Wald negative binomial test from R software package DESeq2 as described previously (4, 7) with Benjamini-Hochberg correction for multiple comparisons. All statistical analyses of differences between individual bacterial species were performed using QIIME v.1.9 or R (packages DESeq2 and phyloseq).
Two files are uploaded. The dataset contains both human and mice samples.
5set_map_for_EBI.txt: Contains the sample metadata
5set_otus_for_EBI.txt: contains the normalized OTU abundances for each individual
OTU abundances were normalized using variance-stabilizing transformation and taxa distributions were compared using the Wald negative binomial test from the R software package DESeq2 (as described in (4, 5) with Benjamini-Hochberg correction for multiple comparisons.
US Department of Defense,
Valhalla Charitable foundation,
National multiple sclerosis Foundation,
National multiple sclerosis Foundation,
- This dataset is cited by https://doi.org/10.1073/pnas.1711235114