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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".