The Molecular Phenotype of Antibody-Mediated Rejection in Heart Transplants.
Bibliographic record
Abstract
Background ABMR is a major cause of organ transplant failure. Recently, the molecular landscape of ABMR has been defined in kidney transplantation. We hypothesized that there are common molecular signatures and overlapping rejection mechanisms across these two organs. Methods We enrolled heart transplant recipients from four French centers with a diagnosis of biopsy proven ABMR and adequateendomyocardial (EMB) material for microarray processing (n=21). ABMR diagnosis was based on the presence of circulating DSA using Luminex SA together with histopathology evidence of intra-vascular mononuclear cells and/or C4d/CD68 IHC positivity (ISHLT 2013). We used the Molecular Microscope system of diagnostic classifier equations (ABMR Score) and pathogenesis-based transcript sets (PBTs) derived from literature and validated in kidney transplantation: endothelial DSA-selective transcripts (eDSAST), macrophage transcripts (QCMAT), gamma-interferon response (GRIT), injury-repair response transcripts (IRRAT) and the ABMR Score. We compared the gene expression in EMB in the ABMR group to a matched control group of heart recipients with normal EMB (n=37). Results All patients with ABMR EMB had DSA at the time of biopsy: class I in 4 (19%), class II in 10 (47.6%) and class I and II in 7 (33.3%). The mean class I or II immunodominant DSA MFI was 4940 ± 718. The histopathological features of ABMR revealed 15 cases of pAMR1 (71%), 5 cases of pAMR2 (24%) and 1 case of pAMR3 (5%). Eight patients had positive C4d/CD68 staining. The gene expression analysis revealed that as compared EMB from heart transplant patients without rejection, EMB with ABMR showed distinct molecular signal characterized by higher expression of eDSAST (p<0.0001), QCMAT (p<0.0001), GRIT (p<0.0001), IRRAT (p=0.0001) and increase of the ABMR Score that reflects interferon-effects, microcirculation stress and NK burden (p<0.0001). Conclusion The molecular signature of ABMR in heart shows common features with relevant gene scores and PBTs identified in kidney transplant biopsies. This suggest common mechanisms involved in ABMR - endothelial response to injury, NK transcripts, IFNG effects - and may provide basis for improving diagnosis and identifying adapted therapeutic strategies in heart transplantation. DISCLOSURE:Halloran, P.: Other, Astellas, Lecturing, Novartis, Lecturing, One Lambda, Lecturing.
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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 | 0.001 | 0.001 |
| 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".