221.1: ABO-incompatible transplantation following enzymatic A-antigen removal in a mouse model: A-antigen re-expression and prevention of early antibody-mediated rejection.
Bibliographic record
Abstract
Introduction: The ABO histo-blood group barrier challenges equitable organ allocation due to high risk of rapid antibody-mediated rejection (AMR) of ABO-incompatible transplants (ABOi Tx). Enzymatic reduction of graft ABO-A-antigen (Ag) using FpGalNAc deacetylase and FpGalactosaminidase, termed Azymes, has demonstrated efficacy in converting human ABO-A lungs to ABO-O via ex vivo perfusion. The timing of A-Ag re-expression in the donor organ and the effectiveness of Azymes treatment in preventing early AMR remain unclear. A-transgenic (A-Tg) mice constitutively express A-Ag on vascular endothelium and can be used to model ABOi Tx and AMR. Herein, we used this model to evaluate A-Ag re-expression kinetics after Azyme treatment and determine whether early AMR is prevented by pre-transplant donor organ Azyme treatment. Methods: After i.v. administration of 0.8mg/kg Azyme (or control), heart and lung from A-Tg C57BL/6 (B6) mice (male/female, n=5/6; 15-19 weeks of age) were harvested. To assess A-Ag removal, organs were stored in organ preservation (UW) solution at 4°C for 4 or 24 hrs then placed in fixative for microscopy. To analyze A-Ag re-expression, hearts were stored in UW solution at 4°C for 4 hrs then heterotopically Tx’d into sex-matched wild-type (WT) B6 recipients and harvested 1, 4 or 7 days later. To model AMR, WT recipients were Tx’d with A-Tg heart (±Azyme treatment) followed by i.v. administration of mouse IgM anti-A monoclonal antibody and rabbit complement. Graft function was assessed by palpation 24 hrs post-Tx; A- and H-Ag expression and C4d deposition were assessed by immunohistochemistry. Results: After in vivo Azyme/4 hrs cold storage, vascular endothelium of A-Tg heart and lung showed marked reduction in A-Ag and were H-Ag positive (Fig 1). Further reduction in A-Ag was observed after 24 hrs cold storage. 24 hours post-Tx, Azyme-treated A-Tg grafts remained H-antigen positive with only very weak/diffuse A-Ag staining (Fig 2A). Azyme-treated hearts at 4 days post-Tx showed weak A-Ag staining primarily localized to outer myocardial microvasculature; at 7 days post-Tx, A-Ag expression was similar to non-Azyme-treated grafts. In the AMR model, the Azyme-treated graft showed no evidence of AMR after 24 hrs, whereas grafts without treatment had reduced graft function, C4d deposition (Fig 2B), and lymphocyte infiltration.Conclusion: Azyme treatment rapidly depleted A-Ag in A-Tg mouse heart and lung. A-Ag re-expression was minimal in heart grafts 4 days post-Tx, but fully visible 7 days post-Tx. Preliminary findings showed that A-Ag removal by Azyme treatment prevented early AMR. Longer term AMR studies will offer further insight into the effectiveness of temporary A-Ag depletion in avoiding AMR. Clinical application of Azymes may permit additional ABOi Tx, offering lifesaving treatment for individuals for whom compatible organs may not be found and utilizing organs otherwise discarded. This work was funded by the Collaborative Health Research Project funds from the Canadian Institutes of Health Research (CIHR) and the Natural Sciences and Engineering Research Council of Canada (NSERC). TE was supported through a NSERC Undergraduate Student Research Award.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".