scholarly journals Genome-wide DNA Methylation Changes in a Mouse Model of Infection-Mediated Neurodevelopmental Disorders

2017 ◽  
Vol 81 (3) ◽  
pp. 265-276 ◽  
Author(s):  
Juliet Richetto ◽  
Renaud Massart ◽  
Ulrike Weber-Stadlbauer ◽  
Moshe Szyf ◽  
Marco A. Riva ◽  
...  
2021 ◽  
Vol 22 (16) ◽  
pp. 8611
Author(s):  
Kathleen Rooney ◽  
Michael A. Levy ◽  
Sadegheh Haghshenas ◽  
Jennifer Kerkhof ◽  
Daniela Rogaia ◽  
...  

The 22q11.2 deletion syndrome (22q11.2DS) is the most common genomic disorder in humans and is the result of a recurrent 1.5 to 2.5 Mb deletion, encompassing approximately 20–40 genes, respectively. The clinical presentation of the typical deletion includes: Velocardiofacial, Di George, Opitz G/BBB and Conotruncalanomaly face syndromes. Atypical deletions (proximal, distal or nested) are rare and characterized mainly by normal phenotype or mild intellectual disability and variable clinical features. The pathogenetic mechanisms underlying this disorder are not completely understood. Because the 22q11.2 region harbours genes coding for transcriptional factors and chromatin remodelers, in this study, we performed analysis of genome-wide DNA methylation of peripheral blood from 49 patients with 22q11.2DS using the Illumina Infinium Methylation EPIC bead chip arrays. This cohort comprises 43 typical, 2 proximal and 4 distal deletions. We demonstrated the evidence of a unique and highly specific episignature in all typical and proximal 22q11.2DS. The sensitivity and specificity of this signature was further confirmed by comparing it to over 1500 patients with other neurodevelopmental disorders with known episignatures. Mapping the 22q11.2DS DNA methylation episignature provides both novel insights into the molecular pathogenesis of this disorder and an effective tool in the molecular diagnosis of 22q11.2DS.


2020 ◽  
Vol 22 (3) ◽  
pp. 1709-1716
Author(s):  
Joong‑Sun Kim ◽  
In‑Sik Shin ◽  
Na‑Rae Shin ◽  
Jae‑Yong Nam ◽  
Chul Kim

2019 ◽  
Vol 63 (6) ◽  
pp. 785-795 ◽  
Author(s):  
David E. Godler ◽  
David J. Amor

Abstract DNA methylation (mDNA) plays an important role in the pathogenesis of neurodevelopmental disorders (NDDs), however its use in diagnostic testing has been largely restricted to a handful of methods for locus-specific analysis in monogenic syndromes. Recent studies employing genome-wide methylation analysis (GWMA) have explored utility of a single array-based test to detect methylation changes in probands negative by exome sequencing, and to diagnose different monogenic NDDs with defined epigenetic signatures. While this may be a more efficient approach, several significant barriers remain. These include non-uniform and low coverage of regulatory regions that may have CG-rich sequences, and lower analytical sensitivity as compared with locus-specific analyses that may result in methylation mosaicism not being detected. A major challenge associated with the above technologies, regardless of whether the analysis is locus specific or genome wide, is the technical bias introduced by indirect analysis of methylation. This review summarizes evidence from the most recent studies in this field and discusses future directions, including direct analysis of methylation using long-read technologies and detection of 5-methylcytosine (5-mC or total mDNA) and 5-hydroxymethylacytosine (5-hmC) as biomarkers of NDDs.


2020 ◽  
Vol 21 (23) ◽  
pp. 9303
Author(s):  
Sadegheh Haghshenas ◽  
Pratibha Bhai ◽  
Erfan Aref-Eshghi ◽  
Bekim Sadikovic

Mendelian neurodevelopmental disorders customarily present with complex and overlapping symptoms, complicating the clinical diagnosis. Individuals with a growing number of the so-called rare disorders exhibit unique, disorder-specific DNA methylation patterns, consequent to the underlying gene defects. Besides providing insights to the pathophysiology and molecular biology of these disorders, we can use these epigenetic patterns as functional biomarkers for the screening and diagnosis of these conditions. This review summarizes our current understanding of DNA methylation episignatures in rare disorders and describes the underlying technology and analytical approaches. We discuss the computational parameters, including statistical and machine learning methods, used for the screening and classification of genetic variants of uncertain clinical significance. Describing the rationale and principles applied to the specific computational models that are used to develop and adapt the DNA methylation episignatures for the diagnosis of rare disorders, we highlight the opportunities and challenges in this emerging branch of diagnostic medicine.


Sign in / Sign up

Export Citation Format

Share Document