Uncovering a novel role of focal adhesion and interferon-gamma in cellular rejection of kidney allografts at single cell resolution
Bibliographic record
Abstract
Background: Kidney transplant recipients are currently treated with nonspecific immunosuppressants that cause severe systemic side effects. Current immunosuppressants were developed based on their effect on T-cell activation rather than the underlying mechanisms driving alloimmune responses. Thus, understanding the role of the intragraft microenvironment will help us identify more directed therapies with lower side effects. Methods: To understand the role of the alloimmune response and the intragraft microenvironment in cellular rejection progression, we conducted a Single nucleus RNA sequencing (snRNA-seq) on one human non-rejecting kidney allograft sample, one borderline sample, and T-cell mediated rejection (TCMR) sample (Banff IIa). We studied the differential gene expression and enriched pathways in different conditions, in addition to ligand-receptor (L-R) interactions. Results: Pathway analysis of T-cells in borderline sample showed enrichment for allograft rejection pathway, suggesting that the borderline sample reflects an early rejection. Hence, this allows for studying the early stages of cellular rejection. Moreover, we showed that focal adhesion (FA), IFNg pathways, and endomucin (EMCN) were significantly upregulated in endothelial cell clusters (ECs) of borderline compared to ECs TCMR. Furthermore, we found that pericytes in TCMR seem to favor endothelial permeability compared to borderline. Similarly, T-cells interaction with ECs in borderline differs from TCMR by involving DAMPS-TLRs interactions. Conclusion: Our data revealed novel roles of T-cells, ECs, and pericytes in cellular rejection progression, providing new clues on the pathophysiology of allograft 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 | 0.001 | 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".