Complement-Activating Anti-HLA Antibodies in Kidney Transplantation: Allograft Gene Expression Profiling and Response to Treatment
Bibliographic record
Abstract
Export Complement-activating anti-HLA donor-specific antibodies (DSAs) are associated with impaired kidney transplant outcome; however, whether these antibodies induce a specific rejection phenotype and influence response to therapy remains undetermined. We prospectively screened 931 kidney recipients for complement-activating DSAs and used histopathology, immunostaining, and allograft gene expression to assess rejection phenotypes. Effector cells were evaluated using in vitro human cell cultures. Additionally, we assessed the effect of complement inhibition on kidney allograft rejection phenotype and the clinical response to complement inhibition in 116 independent kidney recipients with DSAs at transplant receiving rejection prophylaxis with eculizumab or standard of care (plasma exchange and intravenous Ig) at ten international centers. The histomolecular rejection phenotype associated with complement-activating DSA was characterized by complement deposition and accumulation of natural killer cells and monocytes/macrophages in capillaries and increased expression of five biologically relevant genes (CXCL11, CCL4, MS4A7, MS4A6A, and FCGR3A) indicative of endothelial activation, IFNγ response, CD16-mediated natural killer cell activation, and monocyte/macrophage activation. Compared with standard of care, eculizumab specifically abrogated this histomolecular rejection phenotype and associated with a decreased 3-month rejection incidence rate in patients with complement-activating DSAs (56%; 95% confidence interval [95% CI], 38% to 74% versus 19%; 95% CI, 8% to 35%; P=0.001) but not in those with noncomplement-activating DSAs (9%; 95% CI, 2% to 25% versus 13%; 95% CI, 2% to 40%; P=0.65). In conclusion, circulating complement-activating anti-HLA DSAs are associated with a specific histomolecular kidney allograft rejection phenotype that can be abrogated by complement inhibition.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".