Inositol polyphosphate multikinase regulates Th1 and Th17 cell differentiation by controlling Akt-mTOR signaling
Bibliographic record
Abstract
Abstract Activated proinflammatory T helper (Th) cells, such as Th1 and Th17 cells, mediate immune responses against intra- and extra-cellular pathogens as well as cause the development of various autoimmune diseases. Inositol polyphosphate multikinase (IPMK) is a key enzyme essential for inositol phosphate and phosphoinositide metabolism, which is known to control major biological events such as growth; however, its role in the function of Th cells remains unclear. Here we show that the expression of IPMK is highly induced in distinct Th1 and Th17 subsets. Further, while conditional deletion of IPMK in CD4 + T cells is dispensable for Th2-dependent immune responses, both Th1- and Th17-mediated immune responses are markedly diminished when this enzyme is absent resulting in reduced resistance to Leishmania major infection and attenuation of experimental autoimmune encephalomyelitis (EAE), an animal model of multiple sclerosis. In addition, IPMK-deficient naive CD4 + T cells display aberrant T cell activation and impaired differentiation into Th17 cells, which is associated with reduced activation of Akt, mechanistic target of rapamycin (mTOR), and STAT3. Mechanistically, IPMK as a phosphatidylinositol 3-kinase (PI3-kinase) controls the production of phosphatidylinositol (3,4,5)-trisphosphate, thereby promoting T cell activation, differentiation, and effector functions. Our findings suggest that IPMK acts as a critical regulator of Th1 and Th17 differentiation, highlighting the physiological importance of IPMK in Th1- and Th17-mediated immune homeostasis.
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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) | 0.002 | 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".