Th Cells Promote CTL Survival and Memory via Acquired pMHC-I and Endogenous IL-2 and CD40L Signaling and by Modulating Apoptosis-Controlling Pathways
Bibliographic record
Abstract
Involvement of CD4(+) helper T (Th) cells is crucial for CD8(+) cytotoxic T lymphocyte (CTL)-mediated immunity. However, CD4(+) Th's signals that govern CTL survival and functional memory are still not completely understood. In this study, we assessed the role of CD4(+) Th cells with acquired antigen-presenting machineries in determining CTL fates. We utilized an adoptive co-transfer into CD4(+) T cell-sufficient or -deficient mice of OTI CTLs and OTII Th cells or Th cells with various gene deficiencies pre-stimulated in vitro by ovalbumin (OVA)-pulsed dendritic cell (DCova). CTL survival was kinetically assessed in these mice using FITC-anti-CD8 and PE-H-2K(b)/OVA257-264 tetramer staining by flow cytometry. We show that by acting via endogenous CD40L and IL-2, and acquired peptide-MHC-I (pMHC-I) complex signaling, CD4(+) Th cells enhance survival of transferred effector CTLs and their differentiation into the functional memory CTLs capable of protecting against highly-metastasizing tumor challenge. Moreover, RT-PCR, flow cytometry and Western blot analysis demonstrate that increased survival of CD4(+) Th cell-helped CTLs is matched with enhanced Akt1/NF-κB activation, down-regulation of TRAIL, and altered expression profiles with up-regulation of prosurvival (Bcl-2) and down-regulation of proapoptotic (Bcl-10, Casp-3, Casp-4, Casp-7) molecules. Taken together, our results reveal a previously unexplored mechanistic role for CD4(+) Th cells in programming CTL survival and memory recall responses. This knowledge could also aid in the development of efficient adoptive CTL cancer therapy.
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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) | 0.001 | 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 | 0.000 |
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".