TMEM16F regulates bystander TCR-CD3 membrane binding at the immunological synapse
Bibliographic record
Abstract
Abstract The signaling cascade induced by T cell receptor (TCR) triggering has been extensively studied, but the steps separating peptide recognition from TCR phosphorylation remain elusive. In resting T cells, the TCR CD3ɛ and ζ chains dynamically associate with the plasma membrane (PM) through electrostatic interactions with anionic lipids such as phosphatidylserine (PS). Upon ligand recognition, the CD3 chains dissociate from the PM, enabling robust signaling initiation. However, the mechanisms regulating PM dissociation remain obscure. Recent studies have highlighted the role of TMEM16F, a PS-specific scramblase, in regulating TCR signaling, but the molecular steps involved in this activity have not been identified. To study the role of TMEM16F in regulating TCR activation, we knocked-down its expression using shRNA targeting. Our results show that TMEM16F knockdown inhibited PS redistribution, which significantly reduced TCR activation. To showcase TMEM16F activity in relation to TCR signaling, we transduced T cells with a mutant form of the scramblase that constitutively redistributes PS. TMEM16F-mutant T cells showed a significant increase in proximal TCR signaling. This was directly linked with an increase in PS redistribution upon TCR engagement, which enabled the dissociation of bystander CD3ɛ chains and their participation in signaling. Our results show that PS redistribution by way of TMEM16F activity is critical for TCR signaling. Our results also establish our ability to modulate PS redistribution by targeting TMEM16F activity in order to regulate T cell activation. These findings may ultimately lead to novel approaches enabling the modulation of TCR and other immune receptor activity in T cell-based immunotherapies.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".