Pre–Germinal Center Interactions with T Cells Are Natural Checkpoints to Limit Autoimmune B Cell Responses
Bibliographic record
Abstract
Interactions with Ag-specific T cells drive B cell activation and fate choices that ultimately determine the quality of high-affinity Ab responses. As such, these interactions, and especially the long-lived interactions that occur before germinal center formation, may be important checkpoints to regulate undesirable responses. Using mouse model Ag systems, we directly observed interactions between T and B cells responding to the self-antigen myelin oligodendrocyte glycoprotein (MOG) and found that they are of lower quality compared with interactions between cells responding to the model foreign Ag nitrophenyl-haptenated OVA. This was associated with reduced expression of molecules that facilitate these interactions on the B cells, but not on T cells. B cell expression of these molecules was not dictated by the T cell partner, nor could the relative lack of expression on MOG-specific (MOG-sp.) B cells be reversed by a multivalent Ag. Instead, MOG-sp. B cells were inherently less responsive to BCR stimulation than MOG-non-sp. cells. However, the phenotype of MOG-sp. B cells was not consistent with previous descriptions of autoimmune B cells that had been tolerized via regular exposure to systemically expressed self-antigen. This suggests that alternate anergy pathways may exist to limit B cell responses to tissue-restricted self-antigens.
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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 | 0.001 | 0.001 |
| 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".