Unveiling a Protective Role of Endomucin in Endothelial Cells During Kidney Allograft Rejection
Bibliographic record
Abstract
Background: Understanding the role of endothelial cells in autoimmune and alloimmune mediated kidney diseases promises to develop more targeted therapies. Our single nuclei RNA sequencing (snRNA seq) identified an unexpected role for endothelial cells in cellular rejection. Notably, we reported a novel role of endomucin (EMCN), a molecule known for its anti-adhesive and anti-inflammatory properties, in kidney allograft rejection. Methods: We performed snRNA seq of human kidney allografts with non-rejection, borderline rejection, and T-cell mediated rejection (TCMR) based on Banff criteria. We performed pathway analysis focusing on immune and endothelial cells. As validation for our endothelial cells findings, we isolated and cultured blood outgrowth endothelial cells (BOECs) from different patients using our optimized protocol (S Beland et al., JASN 2023). We stimulated BOECs with INFg, TNFa, and allogeneic PBMCs. We studied the expression of EMCN and ICAM1 over time under different stimulation using Flow cytometry. Results: Our snRNA seq showed that TCMR samples had enrichment for allograft rejection pathway, suggesting that our borderline sample reflects an early rejection. Hence, this allows for studying the early stages of rejection. Pathway analysis of endothelial cells (ECs) of borderline and non-rejecting samples showed that focal adhesion and IFN-gamma pathways were significantly enriched compared to TCMR. Major genes related to focal adhesion were upregulated in borderline, suggesting a role of focal adhesion as a physical obstacle to immune migration. ECs upregulated EMCN in early rejection, suggesting a critical role in protecting against T cell adhesion and infiltration in the allograft. EMCN was then downregulated when rejection advanced. Similarly, in our in vitro model, activated human ECs downregulated the constitutive expression of EMCN when stimulated with allogeneic PBMCs, contrary to ICAM1, which was significantly upregulated. This observation was not seen when ECs were stimulated by IFNg and TNFa, suggesting that the effect of immune cells on ECs is not mediated by these cytokines. Conclusions: Our data showed that late during rejection, focal adhesion and EMCN are downregulated in endothelial cells suggesting a possible role in protecting the graft from immune invasion and rejection.
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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".