DNA Methylation Changes in Nucleated Red Blood Cells from Patients with Sickle Cell Disease: A Potential Marker of Engraftment after Stem Cell Transplantation
Notice bibliographique
Résumé
Hematopoietic cell transplant (HCT) is the only established cure for sickle cell disease (SCD). Engraftment (chimerism) of donor red blood cells (RBCs) determines HCT success but current methods measure only white blood cell chimerism and may be inaccurate or fail to indicate graft rejection in a timely manner. Measurement of differentially methylated regions (DMRs) in nucleated red blood cells (nRBCs) to distinguish individuals with sickle cell anemia (genotype HbSS) nRBCs from HbAA and HbAS nRBCs to quantify RBC chimerism could improve post-HCT monitoring by increasing sensitivity to detect rejection and allow for early intervention. Cell-free DNA derived from nRBCs represents a potential method to specifically measure RBC chimerism. Methylation of cytosine nucleotides that are followed by a guanine (methylated CpG sites) is a major means of cell-specific gene expression, and measuring regions of multiple differentially methylated cytosines (DMRs) enables the identification of cell-of-origin of DNA fragments. Cell-free DNA is released into the circulation from all body tissues and cells, including nRBCs, and retains their cell-specific methylation signatures (figure 1). We hypothesized that DMRs unique to healthy and/or SCD nRBC DNA could be identified and later quantified to assess RBC donor chimerism. We generated genome-wide methylomes of nRBC DNA from pediatric SCD (n=2) and non-SCD control (n=3) patients using enzymatic methyl-sequencing (figure 2). From these 5 novel nRBC methylomes and publicly available methylomes and hydroxymethylomes from 35 different tissues and hematopoietic cells we computationally identified 105 DMRs uniquely found in nRBCs compared to other tissues and cells. There were 34 DMRs that specifically distinguished control nRBCs from SCD nRBCs. The 105 DMRs unique to nRBCs were all relatively hypomethylated. These DMRs were intergenic and not associated with CpG islands or shores. DMRs that distinguished SCD nRBCs from control nRBCs were predominantly hypermethylated (76%) in SCD nRBCs and were commonly (21%-24%) located at promoter, exon or intron sequences while 47% were intergenic. 24% were in CpG islands and 5% were in CpG shores. The most significant gene ontology terms in SCD-specific nRBC DMRs were cardiac-related, including heart contraction and conduction. Other highly significant terms were related to cell signaling, transmembrane transporters, and ion channels. Enriched pathway analysis identified terms related to cell signaling, though these were associated with neuronal and immune signaling pathways. To validate our in silico findings, we amplified 8 DMRs from genomic DNA isolated from 23 different cells and tissues and compared methylation patterns. Four DMRs were relatively hypomethylated in control nRBCs compared to SCD nRBCs and other tissues and cells. Amplification of these DMRs from cell-free DNA in patient plasma before and after HCT will validate them as potential unique markers of successful RBC chimerism. Quantification in recipients with HbAA and HbAS donors will be performed. The successful identification of nRBC-specific DMRs in plasma cell-free DNA will enable novel methodology for the quantification of RBC chimerism after HCT for SCD that can be compared to conventional methods of chimerism measurement in future clinical trials.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».