Combinatorial proteomic analysis of intercellular signaling applied to the CD28 T-cell costimulatory receptor
Bibliographic record
Abstract
Systematic characterization of intercellular signaling approximating the physiological conditions of stimulation that involve direct cell-cell contact is challenging. We describe a proteomic strategy to analyze physiological signaling mediated by the T-cell costimulatory receptor CD28. We identified signaling pathways activated by CD28 during direct cell-cell contact by global analysis of protein phosphorylation. To define immediate CD28 targets, we used phosphorylated forms of the CD28 cytoplasmic region to obtain the CD28 interactome. The interaction profiles of selected CD28-interacting proteins were further characterized in vivo for amplifying the CD28 interactome. The combination of the global phosphorylation and interactome analyses revealed broad regulation of CD28 and its interactome by phosphorylation. Among the cellular phosphoproteins influenced by CD28 signaling, CapZ-interacting protein (CapZIP), a regulator of the actin cytoskeleton, was implicated by functional studies. The combinatorial approach applied herein is widely applicable for characterizing signaling networks associated with membrane receptors with short cytoplasmic tails.
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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 | 0.001 | 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) | 0.001 | 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".