BCR ligation selectively inhibits IgE class switch recombination
Bibliographic record
Abstract
BACKGROUND: Mechanisms that restrict class switch recombination (CSR) to IgE may limit the subsequent production of IgE antibodies in allergic diseases. A role for B-cell receptor (BCR) signaling in IgE regulation was revealed in mice and in cultured B cells with altered BCR signaling. While prior work has focused on BCR signaling in IgE-switched cells, BCR signaling also reportedly inhibits CSR. OBJECTIVE: We sought to determine whether BCR signaling selectively inhibits IgE CSR. METHODS: We assessed whether BCR signaling strength affected IgE responses in immunized mice. For mechanistic evaluation, primary mouse or human B cells were induced to switch to IgE in cell culture and were perturbed with antibodies or cognate antigen to ligate the BCR, pharmacologic inhibitors of signaling proteins, and/or additional cytokines. Primary readouts were flow cytometry and RNA analysis. RESULTS: In immunized mice, BCR signaling strength inversely correlated with the relative frequencies of IgE-switched germinal center B cells and plasma cells. In mouse B-cell cultures, BCR signaling selectively inhibited IgE CSR in a manner dependent on ligand concentration, affinity, and avidity. This inhibition required Syk, whereas blockade of the PI3K subunit p110δ increased IgE cell frequencies independently of BCR ligation. The cytokines IL-21 or TGF-β1, in combination with BCR ligation, cooperatively inhibited IgE CSR. Similar results were observed in cultures of human tonsillar B cells. CONCLUSION: IgE CSR is uniquely susceptible to inhibition by BCR signaling in mouse and human B cells, with important implications for the regulation and pathogenesis of allergic disease.
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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) | 0.005 | 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".