LFA-1/ICAM-1 Ligation in Human T Cells Promotes Th1 Polarization through a GSK3β Signaling–Dependent Notch Pathway
Bibliographic record
Abstract
In this study, we report that the integrin LFA-1 cross-linking with its ligand ICAM-1 in human PBMCs or CD4(+) T cells promotes Th1 polarization by upregulating IFN-γ secretion and T-bet expression. LFA-1 stimulation in PBMCs, CD4(+) T cells, or the T cell line HuT78 activates the Notch pathway by nuclear translocation of cleaved Notch1 intracellular domain (NICD) and upregulation of target molecules Hey1 and Hes1. Blocking LFA-1 by a neutralizing Ab or specific inhibition of Notch1 by a γ-secretase inhibitor substantially inhibits LFA-1/ICAM-1-mediated activation of Notch signaling. We further demonstrate that the Notch pathway activation is dependent on LFA-1/ICAM-1-induced inactivation of glycogen synthase kinase 3β (GSK3β), which is mediated via Akt and ERK. Furthermore, in silico analysis in combination with coimmunoprecipitation assays show an interaction between NICD and GSK3β. Thus, there exists a molecular cross-talk between LFA-1 and Notch1 through the Akt/ERK-GSK3β signaling axis that ultimately enhances T cell differentiation toward Th1. Although clinical use of LFA-1 antagonists is limited by toxicity related to immunosuppression, these findings support the concept that Notch inhibitors could be attractive for prevention or treatment of Th1-related immunologic disorders and have implications at the level of local inflammatory responses.
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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 | 0.000 |
| 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".