CD11c+ T-bet+ B Cells Require IL-21 and IFN-γ from Type 1 T Follicular Helper Cells and Intrinsic Bcl-6 Expression but Develop Normally in the Absence of T-bet
Bibliographic record
Abstract
Abstract CD11c+ T-bet+ B cells generated during ehrlichial infection require CD4+ T cell help and IL-21 signaling for their development, but the exact T cell subset required had not been known. In this study, we show in a mouse model of Ehrlichia muris that type 1 T follicular helper (TFH1) cells provide help to CD11c+ T-bet+ B cells via the dual secretion of IL-21 and IFN-γ in a CD40/CD40L-dependent manner. TFH1 cell help was delivered in two phases: IFN-γ signals were provided early in infection, whereas CD40/CD40L help was provided late in infection. In contrast to T-bet+ T cells, T-bet+ B cells did not develop in the absence of B cell–intrinsic Bcl-6 but were generated in the absence of T-bet. T-bet–deficient memory B cells were largely indistinguishable from their wild-type counterparts, although they no longer underwent switching to IgG2c. These data suggest that a primary function of T-bet in B cells during ehrlichial infection is to promote appropriate class switching, not lineage specification. Thus, CD11c+ memory B cells develop normally without T-bet but require Bcl-6 and specialized help from dual cytokine-producing TFH1 cells.
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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) | 0.001 | 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".