Enrichment of Lck in Lipid Rafts Regulates Colocalized Fyn Activation and the Initiation of Proximal Signals through TCRαβ
Bibliographic record
Abstract
Recent results provide insight into the temporal and spatial relationship governing lck-dependent fyn activation and demonstrate TCR/CD4-induced activation and translocation of lck into lipid rafts and the ensuing activation of colocalized fyn. The prediction follows that directly targeting lck to lipid rafts will bypass the requirement for juxtaposing TCR and CD4-lck, and rescue cellular activation mediated by Ab specific for the constant region of TCRbeta chain. The present study uses a family of murine IL-2-dependent CD4(+) T cell clonal variants in which anti-TCRCbeta signaling is impaired in an lck-dependent fashion. Importantly, these variants respond to Ag- and mAb-mediated TCR-CD4 coaggregation, both of which enable the coordinated interaction of CD4-associated lck with the TCR/CD3 complex. We have previously demonstrated that anti-TCRCbeta responsiveness in this system correlates with the presence of kinase-active, membrane-associated lck and preformed hypophosphorylated TCRzeta:zeta-associated protein of 70 kDa complexes, a phenotype recapitulated in primary resting CD4(+) T cells. We show in this study that forced expression of wild-type lck achieved the same basal composition of the TCR/CD3 complex and yet did not rescue anti-TCRCbeta signaling. In contrast, forced expression of C20S/C23S-mutated lck (double-cysteine lck), unable to bind CD4, rescues anti-TCRCbeta proximal signaling and cellular growth. Double-cysteine lck targets lipid rafts, colocalizes with >98% of cellular fyn, and results in a 7-fold increase in basal fyn kinase activity. Coaggregation of CD4 and TCR achieves the same outcome. These results underscore the critical role of lipid rafts in spatially coordinating the interaction between lck and fyn that predicates proximal TCR/CD3 signaling.
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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 | 0.000 |
| 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".