ERK signals that promote γδ T cell development require ERK interaction with DEF domain-containing targets (HEM3P.276)
Bibliographic record
Abstract
Abstract Differential induction of ERK signals has been implicated in numerous fate decisions; however, the molecular basis by which gradations in ERK signaling specify alternate fates remains poorly understood. We report here that divergence of the αβ and γδ T cell fates is dependent upon differences in the extent of T cell receptor (TCR) induced activation of ERK signaling. Adoption of the γδ fate is linked to greater amplitude and duration of ERK activation, but ERK activation does not promote γδ development by phosphorylation of substrates like Rsk. Instead, ERK promotes adoption of the γδ fate by physically interacting with DEF domain containing targets through its DEF binding pocket (DBP). The DEF domain-containing targets include immediate early genes (IEG) such as the transcription factor early growth response gene 1 (Egr1). Egr1 protein is normally unstable, but is stabilized by DEF-DBP mediated interaction with ERK. Thus, ERK signals promote γδ development by stabilizing IEG proteins, including transcription factors, thereby enabling them to transactivate targets not possible in the absence of this increase in stability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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".