Virus Infection Causes Dysbiosis to Promote Type 1 Diabetes Onset
Bibliographic record
Abstract
Abstract Autoimmune disorders like type 1 diabetes (T1D) are complex diseases caused by numerous factors including both genetic variance and environmental influences. Two such exogenous factors, intestinal microbial composition and enterovirus infection, have been independently associated with T1D onset in both humans and animal models. Since environmental factors rarely work in isolation, we examined the cross-talk between the microbiome and Coxsackievirus B4 (CVB4), an enterovirus that accelerates T1D onset in non-obese diabetic (NOD) mice. We demonstrate that CVB4-infection induced restructuring of the intestinal microbiome prior to T1D onset that was associated with thinning of the mucosal barrier, bacterial translocation to the pancreatic lymph node, and increased detection of circulating and intestinal commensal-reactive antibodies. Notably, the CVB4-induced change in community composition was strikingly similar to that of uninfected NOD mice that spontaneously developed diabetes, thus implying a mutual “diabetogenic” microbiome. Furthermore, fecal microbiome transfer (FMT) of the diabetogenic microbiota from CVB4-infected mice was sufficient to enhance T1D susceptibility in naïve NOD recipients. These findings support a model whereby CVB infection disrupts the microbiome and intestinal homeostasis in a way that promotes activation of autoreactive immune cells and T1D.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".