Dendritic Cells Recruit T Cell Exosomes via Exosomal LFA-1 Leading to Inhibition of CD8+ CTL Responses through Downregulation of Peptide/MHC Class I and Fas Ligand-Mediated Cytotoxicity
Bibliographic record
Abstract
Active T cells release bioactive exosomes (EXOs). However, its potential modulation in immune responses is elusive. In this study, we in vitro generated active OVA-specific CD8(+) T cells by cultivation of OVA-pulsed dendritic cells (DC(OVA)) with naive CD8(+) T cells derived from OVA-specific TCR transgenic OTI mice and purified EXOs from CD8(+) T cell culture supernatant by differential ultracentrifugation. We then investigated the suppressive effect of T cell EXOs on DC(OVA)-mediated CD8(+) CTL responses and antitumor immunity. We found that DC(OVA) uptake OTI T cell EXOs expressing OVA-specific TCRs and Fas ligand via peptide/MHC Ag I-TCR and CD54-LFA-1 interactions leading to downregulation of peptide/MHC Ag I expression and induction of apoptosis of DC(OVA) via Fas/Fas ligand pathway. We demonstrated that OVA-specific OTI T cell EXOs, but not lymphocytic choriomeningitis virus-specific TCR transgenic mouse CD8(+) T cell EXOs, can inhibit DC(OVA)-stimulated CD8(+) CTL responses and antitumor immunity against OVA-expressing B16 melanoma. In addition, these T cell EXOs can also inhibit DC(OVA)-mediated CD8(+) CTL-induced diabetes in transgenic rat insulin promoter-mOVA mice. Interestingly, the anti-LFA-1 Ab treatment significantly reduces T cell EXO-induced inhibition of CD8(+) CTL responses in both antitumor immunity and autoimmunity. EXOs released from T cell hybridoma RF3370 cells expressing OTI CD8(+) TCRs have a similar inhibitory effect as T cell EXOs in DC(OVA)-stimulated CTL responses and antitumor immunity. Therefore, our data indicate that Ag-specific CD8(+) T cells can modulate immune responses via T cell-released EXOs, and T cell EXOs may be useful for treatment of autoimmune diseases.
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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 | 0.001 |
| 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".