Global phosphoproteomic analysis of PD-1 signaling reveals T cell subset specific PD-1 functions
Bibliographic record
Abstract
Abstract PD-1 is a cell surface receptor with immunoregulatory properties expressed in activated T cells. PD-1 binds to either PD-L1 or PD-L2 and mediates downstream tyrosine dephosphorylation of key proteins critical for TCR signaling. This prevents excessive inflammation as well as T cell mediated anti-tumor immunity. Anti-PD-1 therapy is effective in ~30% of cancer patients, of which up to 25% develop off-target immune inflammation. Little is known about downstream signaling triggered by PD-1 and further analysis will provide better rationales for safer and more effective PD-1 targeting therapies. We performed tandem mass tag spectrometry and examined protein phosphorylation in Jurkat T cells following PD-1 ligation by PD-L2. Computational analysis revealed the PD-1/PD-L2 phosphoproteomic landscape. Our results confirm previous reports that PD-1 ligation dephosphorylates key proteins in the TCR signaling cascade. In addition, compared to unstimulated cells and to cells stimulated via the TCR alone, αCD3 stimulation in the presence of PD-1 ligation led to significant dephosphorylation of proteins involved in cytoskeletal organization and cellular adhesion. Surprisingly, we observed increased S/T phosphorylation of proteins associated with cell cycle regulation, chromosomal organization and negative regulation of gene expression triggered by PD-1 ligation. Further analysis in primary human cells revealed that these PD-1 triggered pathways were T cell subset specific, which is key for developing T cell subset specific targeted therapies. Our study provides a comprehensive system wide view of the signaling cascade downstream of PD-1 and may serve as a springboard for future therapeutic approaches targeting the PD-1 pathway.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.000 |
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 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".