ABSTRACT: Vertebrates typically harbor a rich gastrointestinal microbiota, which has co-evolved with the host over millennia and is essential for several of its physiological functions, in particular maturation of the immune system. Recent studies have highlighted the importance of a single bacterial species, segmented filamentous bacteria (SFB), in inducing a robust T helper (Th)17 population in the small intestinal lamina propria (SI-LP) of the mouse gut. Consequently, SFB can promote IL-17-dependent immune and autoimmune responses, gut-associated as well as systemic, including inflammatory arthritis and experimental autoimmune encephalomyelitis. Here, we exploit the incomplete penetrance of SFB colonization of NOD mice in our animal facility to explore its impact on the incidence and course of type-1 diabetes in this prototypical, spontaneous model. There was a strong co-segregation of SFB-positivity and diabetes protection in females, but not in males, which remained relatively disease-free regardless of the SFB status. In contrast, insulitis did not depend on SFB colonization. SFB-positive, but not SFB-negative, females had a substantial population of Th17 cells in the SI-LP, which was the only significant, repeatable difference in the examined T cell compartments of the gut, pancreas or systemic lymphoid tissues. Th17 signature transcripts dominated the very limited SFB-induced molecular changes detected in SI-LP CD4+ T cells. Thus, a single bacterium, and the gut immune system alterations associated with it, can either promote or protect from autoimmunity in predisposed mouse models, likely reflecting their variable dependence on different Th subsets. All gene expression profiles were obtained from highly purified T cell populations sorted by flow cytometry. Cells were sorted from individual mice with at least four replicates generated for all groups. RNA from 1-5 x 104 cells was amplified, labeled, and hybridized to Affymetrix Mo GENE 1.0ST microarrays. Raw data were preprocessed with the RMA algorithm in GenePattern, and averaged expression values were used for analysis.
