Extracellular vesicles derived from injured vascular tissue promote the formation of tertiary lymphoid structures in vascular allografts
Bibliographic record
Abstract
Tertiary lymphoid structures (TLS) accumulate at sites of chronic injury where they function as an ectopic germinal center, fostering local autoimmune responses. Vascular injury leads to the release of endothelial-derived apoptotic exosome-like vesicles (ApoExo) that contribute to rejection in transplanted organs. The purpose of the study was to evaluate the impact of ApoExo on TLS formation in a model of vascular allograft rejection. Mice transplanted with an allogeneic aortic transplant were injected with ApoExo. The formation of TLS was significantly increased by ApoExo injection along with vascular remodeling and increased levels of antinuclear antibodies and anti-perlecan/LG3 autoantibodies. ApoExo also enhanced allograft infiltration by γδT17 cells. Recipients deficient in γδT cells showed reduced TLS formation and lower autoantibodies levels following ApoExo injection. ApoExo are characterized by proteasome activity, which can be blocked by bortezomib. Bortezomib treated ApoExo reduced the recruitment of γδT17 cells to the allograft, lowered TLS formation, and reduced autoantibody production. This study identifies vascular injury-derived extracellular vesicles (ApoExo), as initiators of TLS formation and demonstrates the pivotal role of γδT17 in coordinating TLS formation and autoantibody production. Finally, our results suggest proteasome inhibition with bortezomib as a potential option for controlling TLS formation in rejected allografts.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".