MyD88-Dependent SHIP1 Regulates Proinflammatory Signaling Pathways in Dendritic Cells after Monophosphoryl Lipid A Stimulation of TLR4
Bibliographic record
Abstract
We previously showed that monophosphoryl lipid A (MLA) activates TLR4 in dendritic cells (DCs) in a Toll/IL-1R domain-containing adaptor inducing IFN-β (TRIF)-biased manner: MLA produced from Salmonella minnesota Re595 induced signaling events and expression of gene products that were primarily TRIF dependent, whereas MyD88-dependent signaling was impaired. Moreover, when tested in TRIF-intact/MyD88-deficient DCs, synthetic MLA of the Escherichia coli chemotype (sMLA) showed the same activity as its diphosphoryl, inflammatory counterpart (synthetic diphosphoryl lipid A), indicating that TRIF-mediated signaling is fully induced by sMLA. Unexpectedly, we found that the transcript level of one proinflammatory cytokine was increased in sMLA-treated cells by MyD88 deficiency to the higher level induced by synthetic diphosphoryl lipid A, which suggested MyD88 may paradoxically help restrain proinflammatory signaling by TRIF-biased sMLA. In this article, we demonstrate that sMLA induces MyD88 recruitment to TLR4 and activates the anti-inflammatory lipid phosphatase SHIP1 in an MyD88-dependent manner. At the same time, MyD88-dependent signaling activity at the level of IL-1R-associated kinase 1 is markedly reduced. Increased SHIP1 activity is associated with reductions in sMLA-induced IκB kinase α/β and IFN regulatory factor 3 activation and with restrained expression of their downstream targets, endothelin-1 and IFN-β, respectively. Results of this study identify a pattern that is desirable in the context of vaccine adjuvant design: TRIF-biased sMLA can stimulate partial MyD88 activity, with MyD88-dependent SHIP1 helping to reduce proinflammatory signaling in DCs.
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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) | 0.001 | 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 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".