Nanotubes Connect CD4+ T Cells to Airway Smooth Muscle Cells: Novel Mechanism of T Cell Survival
Bibliographic record
Abstract
Contact between airway smooth muscle (ASM) cells and activated CD4(+) T cells, a key interaction in diseases such as asthma, triggers ASM cell proliferation and enhances T cell survival. We hypothesized that direct contact between ASM and CD4(+) T cells facilitated the transfer of anti-apoptotic proteins via nanotubes, resulting in increased survival of activated CD4(+) T cells. CD4(+) T cells, isolated from PBMCs of healthy subjects, when activated and cocultured with ASM cells for 24 h, formed nanotubes that were visualized by immunofluorescence and atomic force microscopy. Cell-to-cell transfer of the fluorescent dye calcein-AM confirmed cytoplasmic communication via nanotubes. Immunoreactive B cell lymphoma 2 (Bcl-2) and induced myeloid leukemia cell differentiation protein (Mcl-1), two major anti-apoptotic proteins, were present within the nanotubes. Downregulation of Mcl-1 by small interfering RNA in ASM cells significantly increased T cell apoptosis, whereas downregulation of Bcl-2 had no effect. Transfer of GFP-tagged Mcl-1 from ASM cells to CD4(+) T cells via the nanotubes confirmed directionality of transfer. In conclusion, activated T cells communicate with ASM cells via nanotube formation. Direct transfer of Mcl-1 from ASM to CD(+) T cells via nanotubes is involved in T cell survival. This study provides a novel mechanism of survival of CD4(+) T cells that is dependent on interaction with a structural cell.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".