Coupled IL-2–Dependent Extracellular Feedbacks Govern Two Distinct Consecutive Phases of CD4 T Cell Activation
Bibliographic record
Abstract
T cells integrate cell-specific Ag receptor signaling with shared signals mediated by secreted cytokines, which often involve regulatory feedback loops. IL-2 signaling, for example, reduces the synthesis of IL-2 and increases the synthesis of IL-2Rα-chain, whereas both genes require TCR signaling for their activation. The ways by which T cells dynamically integrate these private and public signals during activation are not well understood. We combined robotics, multiparameter flow cytometry, and real-time quantitative PCR to analyze T cell activation at high temporal resolution over several days. Two distinct temporal phases of T cell activation were evident. First, Ag-dependent signals activated low IL-2Rα and high IL-2 production, independent of IL-2 signaling. Subsequently, secreted IL-2 acted as a shared resource driving high IL-2Rα expression, reduced IL-2 synthesis, and cell proliferation. This transition was independent of continued TCR signaling. Our data allowed the determination of the parameters of the IL-2-mediated extracellular positive and negative feedback circuits and demonstrated that the two loops are coupled and become activated at a similar level of IL-2 signaling. We propose that temporal separation of private and shared signals allows T cells to first integrate Ag-specific responses and subsequently share information leading to collective decision making.
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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 | 0.001 | 0.001 |
| 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".