The Role of Dynamic DNA Methylation in Liver Transplant Rejection in Children
Bibliographic record
Abstract
Background. Transcriptional regulation of liver transplant (LT) rejection may reveal novel predictive and therapeutic targets. The purpose of this article is to test the role of differential DNA methylation in children with biopsy-proven acute cellular rejection after LT. Methods. Paired peripheral blood DNA samples were obtained before and after LT from 17 children, including 4 rejectors (Rs) and 13 nonrejectors (NRs), and assayed with MethylC capture sequencing approach covering 5 million CpGs in immune-cell–specific regulatory elements. Differentially methylated CpGs (DMCs) were identified using generalized linear regression models adjusting for sex and age and merged into differentially methylated regions (DMRs) comprising 3 or more DMCs. Results. Contrasting Rs versus NRs, we identified 2238 DMCs in post-LT and 2620 DMCs in pre-LT samples, which clustered in 216 and 282 DMRs, respectively. DMCs associated with R were enriched in enhancers and depleted in promoters. Among DMRs, the proportion of hypomethylated DMRs increased from 61/282 (22%) in pre-LT to 103/216 (48%, P < 0.0001) in post-LT samples. The highest-ranked biological processes enriched in post-LT DMCs were antigen processing and presentation via major histocompatibility complex (MHC) class I, MHC class I complex, and peptide binding ( P < 7.92 × 10 −17 ), respectively. Top-ranked DMRs mapped to genes that mediate B-cell receptor signaling ( ADAP1 ) or regulate several immune cells (ARRB2 ) ( P < 3.75 × 10 −08 ). DMRs in MHC class I genes were enriched for single nucleotide polymorphisms (SNPs), which bind transcription factors, affect gene expression and splicing, or alter peptide-binding amino acid sequences. Conclusions. Dynamic methylation in distal regulatory regions reveals known transplant-relevant MHC-dependent rejection pathways and identifies novel loci for future mechanistic evaluations in pediatric transplant subcohorts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".