The 20 <i>S</i> proteasome core, active within apoptotic exosome-like vesicles, induces autoantibody production and accelerates rejection
Bibliographic record
Abstract
Autoantibodies to components of apoptotic cells, such as anti-perlecan antibodies, contribute to rejection in organ transplant recipients. However, mechanisms of immunization to apoptotic components remain largely uncharacterized. We used large-scale proteomics, with validation by electron microscopy and biochemical methods, to compare the protein profiles of apoptotic bodies and apoptotic exosome-like vesicles, smaller extracellular vesicles released by endothelial cells downstream of caspase-3 activation. We identified apoptotic exosome-like vesicles as a central trigger for production of anti-perlecan antibodies and acceleration of rejection. Unlike apoptotic bodies, apoptotic exosome-like vesicles triggered the production of anti-perlecan antibodies in naïve mice and enhanced anti-perlecan antibody production and allograft inflammation in mice transplanted with an MHC (major histocompatibility complex)-incompatible aortic graft. The 20S proteasome core was active within apoptotic exosome-like vesicles and controlled their immunogenic activity. Finally, we showed that proteasome activity in circulating exosome-like vesicles increased after vascular injury in mice. These findings open new avenues for predicting and controlling maladaptive humoral responses to apoptotic cell components that enhance the risk of rejection after transplantation.
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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".