Menke-Hennekam syndrome; delineation of domain-specific subtypes with distinct clinical and DNA methylation profiles

CREB-binding protein (CBP, encoded by CREBBP) and its paralog E1A-associated protein (p300, encoded by EP300) are involved in histone acetylation and transcriptional regulation. Variants that produce a null allele or disrupt the catalytic domain of either protein cause Rubinstein-Taybi syndrome (RSTS), while pathogenic missense and in-frame indel variants in parts of exons 30 and 31 cause phenotypes recently described as Menke-Hennekam syndrome (MKHK). To distinguish MKHK subtypes and define their characteristics, molecular and extended clinical data on 82 individuals (54 unpublished) with variants affecting CBP (n = 71) or p300 (n = 11) (NP_004371.2 residues 1,705-1,875 and NP_001420.2 residues 1,668-1,833, respectively) were summarized. Additionally, genome-wide DNA methylation profiles were assessed in DNA extracted from whole peripheral blood from 54 individuals. Most variants clustered closely around the zinc-binding residues of two zinc-finger domains (ZZ and TAZ2) and within the first α helix of the fourth intrinsically disordered linker (ID4) of CBP/p300. Domain-specific methylation profiles were discerned for the ZZ domain in CBP/p300 (found in nine out of 10 tested individuals) and TAZ2 domain in CBP (in 14 out of 20), while a domain-specific diagnostic episignature was refined for the ID4 domain in CBP/p300 (in 21 out of 21). Phenotypes including intellectual disability of varying degree and distinct physical features were defined for each of the regions. These findings demonstrate existence of at least three MKHK subtypes, which are domain specific (MKHK-ZZ, MKHK-TAZ2, and MKHK-ID4) rather than gene specific (CREBBP/EP300). DNA methylation episignatures enable stratification of molecular pathophysiologic entities within a gene or across a family of paralogous genes.

Integrative omics approaches to advance rare disease diagnostics

Over the past decade high-throughput DNA sequencing approaches, namely whole exome and whole genome sequencing became a standard procedure in Mendelian disease diagnostics. Implementation of these technologies greatly facilitated diagnostics and shifted the analysis paradigm from variant identification to prioritisation and evaluation. The diagnostic rates vary widely depending on the cohort size, heterogeneity and disease and range from around 30% to 50% leaving the majority of patients undiagnosed. Advances in omics technologies and computational analysis provide an opportunity to increase these unfavourable rates by providing evidence for disease-causing variant validation and prioritisation. This review aims to provide an overview of the current application of several omics technologies including RNA-sequencing, proteomics, metabolomics and DNA-methylation profiling for diagnostics of rare genetic diseases in general and inborn errors of metabolism in particular.

Generation of DNA Methylation Signatures and Classification of Variants in Rare Neurodevelopmental Disorders Using EpigenCentral

There are more than 700 genes that encode proteins that function in epigenetic regulation and chromatin modification. Germline variants in these genes (typically heterozygous) are associated with rare neurodevelopmental disorders (NDDs) characterized by growth abnormalities and intellectual and developmental delay. Advancements in next-generation sequencing have dramatically increased the detection of pathogenic sequence variants in genes encoding epigenetic machinery associated with NDDs and, concurrently, the number of clinically uninterpretable variants classified as variants of uncertain significance (VUS). Recently, DNA methylation (DNAm) signatures, disorder-specific patterns of DNAm change, have emerged as a functional tool that provides insights into disorder pathophysiology and can classify pathogenicity of variants in NDDs. To date, our group and others have identified DNAm signatures for more than 60 Mendelian neurodevelopmental disorders caused by variants in genes encoding epigenetic machinery. There is broad interest in both the research and clinical communities to develop and catalog DNAm signatures in rare NDDs, but there are challenges in optimizing study design considerations and availability of platforms that integrate bioinformatics tools with the appropriate statistical framework required to analyze genome-wide DNAm data. We previously published EpigenCentral, a platform for analysis of DNAm data in rare NDDs. In this article, we utilize the published Weaver syndrome dataset to provide step-by-step protocols for using EpigenCentral for exploratory analysis to identify DNAm signatures and for classification of NDD variants. We also provide important considerations for experimental design and interpretation of DNAm results. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Exploratory analysis to identify disorder-specific DNAm signatures Basic Protocol 2: Classification of variants associated with neurodevelopmental disorders.