The Proteasome, Active within Apoptotic Exosome-like Vesicles Triggers γδT-17 Activation, Tertiary Lymphoid Structure Formation and Accelerate Rejection
Bibliographic record
Abstract
Ischemia-reperfusion injury (IRI) is a major risk factor of progressive organ failure in solid organ transplant recipients. Our group recently identified exosome-like membrane vesicles (apoExo) released during IRI by apoptotic endothelial cells (apoEC) as an important trigger for the production of anti-perlecan/LG3 antibodies and acceleration of rejection. Here we aim to characterize the key molecular components controlling the autoimmune and inflammatory responses triggered by ApoExo. Naïve WT and aortic allografted WT and γδTcells KO mice were injected every other day with purified ApoExo, bortezomib treated ApoExo, Apoptotic bodies or vehicle until sacrifice at 3 weeks. Serum cytokine 36plex assay was performed at sacrifice. Graft histology was evaluated by immunohistochemistry. In all models, various autoantibodies productions were monitored by ELISA. In naive mice, injection of ApoExo (but not of ApoBodies) triggered an increase in circulating levels of IL-23, IL-17, CXCL-1 and TNF-alpha (p<0.01), hallmarks of the IL-23/IL-17 autoimmune axis. Strong anti-LG3 IgG (p<0.001) and ANA responses (p<0.001) were also noted while anti-vimentin, anti-AT1R and anti-fibronectin levels were not affected by ApoExo injection. Similarly, the injection of ApoExo (but not of ApoBodies) to allografted WT mice did not affect anti-vimentin, anti-AT1R and anti-fibronectin levels but increased anti-LG3 (p<0.01) and ANA (p<0.005) levels and heightened vascular remodelling (p<0.01). IL-17 rich lymphoid-like structures (TLS) formation was observed within vascular allografts in apoExo injected WT recipient but not in γδT-KO recipient or in WT recipient injected with Bortezo treated apoExo, showing that the proteasome, active within apoExo triggers γδT-17 dependent TLS formation. In conclusion, these results identify IRI derived apoExo as inducers of γδT-17 dependent autoimmune pathways leading to the formation of TLS and accelerated rejection. These results provide novel insights into the cellular and molecular pathways regulating autoimmune responses in association with chronic vascular injury. Canadian Institutes of Health Research (CIHR). Shire Chair in Nephrology, Transplantation and Renal Regeneration of the Université de Montréal. J.-L. Lévesque Foundation. Fonds de Recherche du Québec - Santé (FRQS).
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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".