B cell antigen receptor-induced activation of an IRAK4-dependent signaling pathway revealed by a MALT1-IRAK4 double knockout mouse model
Bibliographic record
Abstract
BACKGROUND: The B cell antigen receptor (BCR) and pathogen recognition receptors, such as Toll-like receptor 4 (TLR4), act in concert to control adaptive B cell responses. However, little is known about the signaling pathways that integrate BCR activation with intrinsic TLR4 stimulation. Antigen receptors initialize activation of the inducible transcription factor nuclear factor-κB (NF-κB) via recruitment of the membrane-associated guanylate kinase caspase recruitment domain protein 11 (CARD11), the adapter molecule B cell CLL/lymphoma 10 (BCL10), and the "paracaspase" mucosa-associated lymphoid tissue lymphoma translocation gene 1 (MALT1) into lipid rafts. Upon BCR triggering, this activation strictly depends on BCL10, but not on MALT1, leading to the hypothesis that a MALT1-independent NF-κB activation pathway contributes to BCR-induced NF-κB activation downstream of BCL10. The identity of this pathway has remained elusive. RESULTS: Using genetic and biochemical approaches, we demonstrate that the IRAK4- and IRAK1-dependent TLR signaling branch is activated upon BCR triggering to induce partial NF-κB activation. BCR-induced MALT1-independent IκB degradation and B cell proliferation were inhibited in MALT1/IRAK4 double knockout B cells. Moreover, IRAK1 was recruited into lipid rafts upon BCR stimulation and activated following transient recruitment of IRAK4. CONCLUSION: We propose that the observed crosstalk between BCR and TLR signaling components may contribute to the discrimination of signals that emanate from single and dual receptor engagement to control adaptive B cell responses.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".