Virus-mediated dysbiosis alters immune populations to promote type 1 diabetes onset
Bibliographic record
Abstract
Abstract In combination with genetic determinants, susceptibility to autoimmune diseases such as Type 1 Diabetes (T1D) is established by various environmental factors including infection, microbial dysbiosis, antibiotic use, and vitamin D deficiency. Studies have implicated infection with certain viruses such as coxsackievirus B (CVB) to be an important cofactor associated with diabetes development and pathogenesis. Infections may be an instigating factor to alter the microbiome and this microbial change may be sufficient to skew immune populations and promote autoimmunity. Mucosa-associated invariant T (MAIT) cell populations have been shown to be altered leading up to diabetes onset in patients and mice. These cells are activated by microbial products in the gut to promote intestinal integrity, but they can also take on a more inflammatory phenotype and participate in autoimmune responses in the pancreas. Ultimately, there exists a significant potential for cross-talk between CVB infection, the microbiome, and gut-resident immune cells impacting T1D susceptibility. We have found CVB infection not only promotes onset of T1D in non-obese diabetic (NOD) mice but also causes dysbiosis which resembles that of a spontaneously diabetic NOD mouse. Introducing this new infection-induced microbial composition into naïve mice through the use of fecal microbiome transfers (FMTs) can accelerate T1D onset and alter immune profiles in the gut as well as the pancreas. Furthermore, MAIT populations are altered by this “diabetogenic” microbiome and also respond directly to CVB infection. Together our data highlights the role of virus infection and its ability to affect the gut microbiome and immune homeostasis to contribute to T1D development.
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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) | 0.002 | 0.000 |
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".