The LFA-1 signal for cell migration influences Notch and TGF-β pathways with functional consequences for T-cells
Bibliographic record
Abstract
Abstract The integrin LFA-1 plays a key role in T-cell motility, which is critical for host defense and is also main driver of autoimmune diseases. However, whether and how LFA-1 signalling can modulate T-cell effector functions remain to be understood. We performed gene expression analysis of LFA-1-stimulated human T-cells and identified genomic signatures associated with Notch and TGF-β pathways, both involved in T-cell differentiation. The activation of Notch pathway in motile T-cells was confirmed by the nuclear translocation of cleaved Notch intracellular domain and up-regulation of target genes Hey1 and Hes1. LFA-1 stimulation of naïve peripheral blood mononuclear cells was found to promote T-cell Th1 polarization through a GSK3β signalling-dependent Notch pathway. We further detected that LFA-1 signalling up-regulated Stat3 and/or JNK activation-dependent expression of Smad7, Smurf2 and Ski causing T-cell TGF-β unresponsiveness. LFA-1-stimulated naïve T-cells were refractory to TGF-β-mediated induction of Foxp3+ iTreg or RORγt+ Th17 differentiation. Of note, peripheral or splenic T-cells isolated from patients with rheumatoid arthritis, type1 diabetes or thyroiditis showed constitutively high expression of Tbet. Pretreatment of cells with blocking anti-LFA-1 antibody, specific inhibitors or siRNA against identified molecules restored LFA-1-mediated modulation of T-cell functional phenotypes. Thus, LFA-1-mediated signalling is crucial for T-cell immunoregulation concurrent with T-cell motility, involving both Notch and TGF-β pathways. These findings have implications for normal immunologic functions and also have therapeutic relevance for inflammatory diseases.
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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) | 0.003 | 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".