IgM+IgD+CD27+ B cells are markedly reduced in IRAK-4–, MyD88-, and TIRAP- but not UNC-93B–deficient patients
Bibliographic record
Abstract
We studied the distribution of peripheral B-cell subsets in patients deficient for key factors of the TLR-signaling pathways (MyD88, TIRAP/MAL, IL-1 receptor-associated kinase 4 [IRAK-4], TLR3, UNC-93B, TRIF). All TLRs, except TLR3, which signals through the TRIF adaptor, require MyD88 and IRAK-4 to mediate their function. TLR4 and the TLR2 heterodimers (with TLR1, TLR6, and possibly TLR10) require in addition the adaptor TIRAP, whereas UNC-93B is needed for the proper localization of intracellular TLR3, TLR7, TLR8, and TLR9. We found that IgM(+)IgD(+)CD27(+) but not switched B cells were strongly reduced in MyD88-, IRAK-4-, and TIRAP-deficient patients. This defect did not appear to be compensated with age. However, somatic hypermutation of Ig genes and heavy-chain CDR3 size distribution of IgM(+)IgD(+)CD27(+) B cells were not affected in these patients. In contrast, the numbers of IgM(+)IgD(+)CD27(+) B cells were normal in the absence of TLR3, TRIF, and UNC-93B, suggesting that UNC-93B-dependent TLRs, and notably TLR9, are dispensable for the presence of this subset in peripheral blood. Interestingly, TLR10 was found to be expressed at greater levels in IgM(+)IgD(+)CD27(+) compared with switched B cells in healthy patients. Hence, we propose a role for TIRAP-dependent TLRs, possibly TLR10 in particular, in the development and/or maintenance of IgM(+)IgD(+)CD27(+) B cells in humans.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".