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 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 | 0.002 | 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) | 0.000 | 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".