Mice Lacking Endogenous IL-10–Producing Regulatory B Cells Develop Exacerbated Disease and Present with an Increased Frequency of Th1/Th17 but a Decrease in Regulatory T Cells
Bibliographic record
Abstract
IL-10-producing B cells, also known as regulatory B cells (Bregs), play a key role in controlling autoimmunity. In this study, we report that chimeric mice specifically lacking IL-10-producing B cells (IL-10(-/-)B cell) developed an exacerbated arthritis compared with chimeric wild-type (WT) B cell mice. A significant decrease in the absolute numbers of Foxp3 regulatory T cells (Tregs), in their expression level of Foxp3, and a marked increase in inflammatory Th1 and Th17 cells were detected in IL-10(-/-) B cell mice compared with WT B cell mice. Reconstitution of arthritic B cell deficient (μMT) mice with different B cell subsets revealed that the ability to modulate Treg frequencies in vivo is exclusively restricted to transitional 2 marginal zone precursor Bregs. Moreover, transfer of WT transitional 2 marginal zone precursor Bregs to arthritic IL-10(-/-) mice increased Foxp3(+) Tregs and reduced Th1 and Th17 cell frequencies to levels measured in arthritic WT mice and inhibited inflammation. In vitro, IL-10(+/+) B cells established longer contact times with arthritogenic CD4(+)CD25(-) T cells compared with IL-10(-/-) B cells in response to Ag stimulation, and using the same culture conditions, we observed upregulation of Foxp3 on CD4(+) T cells. Thus, IL-10-producing B cells restrain inflammation by promoting differentiation of immunoregulatory over proinflammatory T cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".