Naturally transmitted segmented filamentous bacteria segregate with diabetes protection in nonobese diabetic mice
Bibliographic record
Abstract
Vertebrates typically harbor a rich gastrointestinal microbiota, which has coevolved with the host over millennia and is essential for several host 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 cell type 17 (Th17) 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 cosegregation 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, probably reflecting their variable dependence on different Th subsets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".