TLR9 Deficiency in B Cells Promotes Immune Tolerance via Interleukin-10 in a Type 1 Diabetes Mouse Model
Bibliographic record
Abstract
Toll-like receptor 9 (TLR9) is highly expressed in B cells, and B cells are important in the pathogenesis of type 1 diabetes (T1D) development. However, the intrinsic effect of TLR9 in B cells on β-cell autoimmunity is not known. To fill this knowledge gap, we generated NOD mice with a B-cell–specific deficiency of TLR9 (TLR9fl/fl/CD19-Cre+ NOD). The B-cell–specific deletion of TLR9 resulted in near-complete protection from T1D development. Diabetes protection was accompanied by an increased proportion of interleukin-10 (IL-10)–producing B cells. We also found that TLR9-deficient B cells were hyporesponsive to both innate and adaptive immune stimuli. This suggested that TLR9 in B cells modulates T1D susceptibility in NOD mice by changing the frequency and function of IL-10–producing B cells. Molecular analysis revealed a network of TLR9 with matrix metalloproteinases, tissue inhibitor of metalloproteinase-1, and CD40, all of which are interconnected with IL-10. Our study has highlighted an important connection of an innate immune molecule in B cells to the immunopathogenesis of T1D. Thus, targeting the TLR9 pathway, specifically in B cells, may provide a novel therapeutic strategy for T1D treatment.
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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) | 0.001 | 0.001 |
| 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".