Vascular Injury-Derived Exosomes Trigger Renal Tertiary Lymphoid Structures and Accelerate Lupus Nephritis
Bibliographic record
Abstract
Background: Microvascular damage is an emerging contributing factor to Lupus Nephritis (LN) leading to end stage renal disease. We have demonstrated that apoptotic exosomes derived from vascular injury (ApoExo) trigger the production of SLE-associated antibodies in wild-type mice. ApoExo infusion induces autantibody production and tertiary lymphoid structure (TLS) formation in a murine vascular allograft rejection model. We hypothesize that ApoExo induce an autoimmune response that accelerates the development of lupus nephritis. Methods: 20 weeks old NZB/WF1 mice were infused with ApoExo or vehicle every second day for 3 weeks. Circulating anti-LG3 and ApoExo levels were measured by ELISA and hs-FCM respectively. Kidneys were collected and renal histology and 3D in vivo micro-computed tomography (MicroCT) analyses were performed. Results: NZB/WF1 mice infused with ApoExo show higher levels of circulating anti-LG3 compared with the vehicle group (p=0.0034). MicroCT data suggest microvascular involution associated with LN development. In addition, ApoExo infused NZB/WF1 mice demonstrate significant renal inflammatory infiltration compared to mice infused with vehicle. ApoExo triggered the recruitment to the renal interstitium of CD3+, CD20+, and AID+ lymphocytes into nodules reminiscent of TLS (p=0.0342) associated with increased Lyve 1+ suggesting lymphoangiogenisis. Finally, heightened renal tubular damage (p<0.05), blood urea nitrogen levels (p<0.05) and decreased survival (p=0.0055) were observed in ApoExo infused NZB/WF1 mice compared to the ones infused with vehicle. Conclusions: ApoExo infusion increases renal nodular lymphocyte infiltration, autoantibody production increase renal damage in lupus prone mice. This project is, to our knowledge, the first to evaluate the contribution of vascular injury derived extracellular vesicles to LN. Funding: Private Foundation Support, Government Support - Non-U.S.
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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".