MyD88 is essential to sustain mTOR activation necessary to promote T helper 17 cell proliferation by linking IL-1 and IL-23 signaling
Bibliographic record
Abstract
Myeloid differentiation primary response protein 88 (MyD88) is classically known as an adaptor, linking TLR and IL-1R to downstream signaling pathways in the innate immune system. In addition to its role in innate immune cells, MyD88 has been shown to play an important role in T cells. How MyD88 regulates helper T-cell differentiation remains largely unknown, however. Here we demonstrate that MyD88 is an important regulator of IL-17-producing CD4(+) T helper cells (Th17) cell proliferation. MyD88-deficient CD4(+) T cells showed a defect in Th17 cell differentiation, but not in Th1 cell or Th2 cell differentiation. The impaired IL-17 production from MyD88-deficient CD4(+) T cells is not a result of defective RAR-related orphan receptor γt (RORγt) expression. Instead, MyD88 is essential for sustaining the mammalian target of rapamycin (mTOR) activation necessary to promote Th17 cell proliferation by linking IL-1 and IL-23 signaling. MyD88-deficient CD4(+) T cells showed impaired mTOR activation and, consequently, reduced Th17 cell proliferation. Importantly, the absence of MyD88 in T cells ameliorated disease in the experimental autoimmune encephalomyelitis model. Taken together, our results demonstrate that MyD88 has a dual function in Th17 cells by delivering IL-1 signaling during the early differentiation stage and integrating IL-23 signaling to the mTOR complex to expand committed Th17 cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".