SHIP negatively regulates Flt3L-derived dendritic cell generation and positively regulates MyD88-independent TLR-induced maturation
Bibliographic record
Abstract
We demonstrate herein that SHIP negatively regulates the proliferation, differentiation, and survival of FL-DCs from BM precursors, as shown by a more rapid appearance and higher numbers of CD11c(+) DCs from SHIP-/- cultures as well as increased survival of mature FL-DCs in the absence of Flt3L. This increased survival, which is lost with low levels of the PI3K inhibitor, LY, correlates with an enhanced constitutive activation of the Akt pathway. Interestingly, however, these SHIP-/- FL-DCs display a less-mature phenotype after TLR ligand stimulation, as far as MHCII, CD40, and CD86 are concerned. Unexpectedly, SHIP-/- FL-DCs activated with TLR ligands, which use MyD88-independent pathways, are markedly impaired in their ability to stimulate Ag-specific T cell proliferation, and SHIP-/- FL-DCs activated by TLRs, which exclusively use the MyD88-dependent pathway, are as capable as WT FL-DCs. There is also a more pronounced T(H)1 skewing by the SHIP-/- FL-DCs than by WT FL-DCs, which is consistent with our finding that SHIP-/- FL-DCs secrete higher levels of IL-12 and TNF-α in response to LPS or dsRNA than their WT counterparts. These results suggest that SHIP negatively regulates FL-DC generation but positively regulates the maturation and function of FL-DCs induced by TLRs, which operate via MyD88-independent pathways.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".