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  • Title: Comparison of methylation episignatures in KMT2B- and KMT2D-related human disorders.
    Author: Lee S, Ochoa E, Barwick K, Cif L, Rodger F, Docquier F, Pérez-Dueñas B, Clark G, Martin E, Banka S, Kurian MA, Maher ER.
    Journal: Epigenomics; 2022 May; 14(9):537-547. PubMed ID: 35506254.
    Abstract:
    Aim & methods: To investigate peripheral blood methylation episignatures in KMT2B-related dystonia (DYT-KMT2B), the authors undertook genome-wide methylation profiling of ∼2 M CpGs using a next-generation sequencing-based assay and compared the findings with those in controls and patients with KMT2D-related Kabuki syndrome type 1 (KS1). Results: A total of 1812 significantly differentially methylated CpG positions (false discovery rate < 0.05) were detected in DYT-KMT2B samples compared with controls. Multi-dimensional scaling analysis showed that the 10 DYT-KMT2B samples clustered together and separately from 29 controls and 10 with pathogenic variants in KMT2D. The authors found that most differentially methylated CpG positions were specific to one disorder and that all (DYT-KMT2B) and most (Kabuki syndrome type 1) methylation alterations in CpG islands were gain of methylation events. Conclusion: Using sensitive methylation profiling methodology, the authors replicated recent reports of a methylation episignature for DYT-KMT2B. These findings will facilitate the development of episignature-based assays to improve diagnostic accuracy. The authors compared the DNA methylation patterns in blood from individuals with two rare neurodevelopmental disorders (childhood-onset dystonia [DYT-KMT2B] and Kabuki syndrome type 1) and healthy control samples. These two disorders are associated with pathogenic variants in KMT2B and KMT2D, which encode proteins with related functions but cause distinct inherited disorders. Comparison of the methylation patterns in the two disorders showed that most DNA regions with altered methylation patterns differed between the two disorders and controls. These findings suggest that analyzing DNA methylation patterns could improve diagnostic testing for these disorders and might provide insights into how the clinical features of these disorders are caused.
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