What goes up must come down: A tripartite Dok‐3/Grb2/SHIP1 inhibitory module limits BCR signaling
Bibliographic record
Abstract
Properly regulated immunity requires precise integration of activating and inhibitory signals. As for other lymphocytes, B cells express an antigen‐specific activating receptor, the B‐cell antigen receptor (BCR), and inhibitory receptors (e.g. FcγRIIb) that exercise checkpoint control on B‐cell activation. Moreover, following BCR engagement, CD19 recruits proteins that amplify BCR signaling, while CD22 initiates a negative feedback loop by recruiting proteins that inhibit BCR signaling. Initial BCR signaling is mediated by protein tyrosine kinases and lipid kinases; inhibitory receptors directly antagonize the actions of these enzymes by recruiting protein tyrosine phosphatases and lipid phosphatases and positioning them close to actively signaling BCRs. Previously it was thought that inhibitory receptors such as FcγRIIb and CD22 were essential for bringing these phosphatases near the BCR. In this issue of the European Journal of Immunology, Manno et al. show that a tripartite inhibitory module consisting of the adaptor proteins Dok‐3 and Grb2 and the lipid phosphatase SHIP1 binds directly to activated BCRs and limits the Ca2+ mobilization that is required for B lymphocyte activation. This reveals that the BCR can be both an activating and inhibitory receptor, one that activates signaling enzymes while initiating a negative feedback loop that prevents excessive signaling.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".