scholarly journals Mutations in DNMT3B Modify Epigenetic Repression of the D4Z4 Repeat and the Penetrance of Facioscapulohumeral Dystrophy

2016 ◽  
Vol 98 (5) ◽  
pp. 1020-1029 ◽  
Author(s):  
Marlinde L. van den Boogaard ◽  
Richard J.L.F. Lemmers ◽  
Judit Balog ◽  
Mariëlle Wohlgemuth ◽  
Mari Auranen ◽  
...  
2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Rianne J M Goselink ◽  
Vivian Schreur ◽  
Caroline R van Kernebeek ◽  
George W Padberg ◽  
Silvère M van der Maarel ◽  
...  

Abstract Ophthalmological abnormalities in facioscapulohumeral dystrophy may lead to treatable vision loss, facilitate diagnostics, could help unravelling the pathophysiology and serve as biomarkers. In this study, we provide a detailed description of the ophthalmological findings in a well-defined cohort of patients with facioscapulohumeral dystrophy using state of the art retina imaging techniques. Thirty-three genetically confirmed patients (aged 7–80 years) and 24 unrelated healthy controls (aged 6–68 years) underwent clinical ophthalmological examination, fundus photography, optical coherence tomography/angiography, genotyping and neurological examination. All patients had normal corrected visual acuity and normal intraocular pressure. In 27 of the 33 patients, weakness of the orbicularis oculi was observed. Central retinal pathology, only seen in patients and not in healthy controls, included twisting (tortuosity) of the retinal arteries in 25 of the 33 patients and retinal pigment epithelium defects in 4 of the 33 patients. Asymmetrical foveal hypoplasia was present in three patients, and exudative abnormalities were observed in one patient. There was a correlation between the severity of retinal tortuosity and the D4Z4 repeat array size (R2 = 0.44, P < 0.005). Follow-up examination in a subgroup of six patients did not show any changes after 2 years. To conclude, retinal abnormalities were frequent but almost always subclinical in patients with facioscapulohumeral dystrophy and consisted primarily of arterial tortuosity and foveal abnormalities. Retinal tortuosity was seen in the retinal arterioles and correlated with the D4Z4 repeat array size, thereby providing clinical evidence for an underlying genetic linkage between the retina and facioscapulohumeral dystrophy.


2007 ◽  
Vol 34 (S 2) ◽  
Author(s):  
K Traufeller ◽  
K Eger ◽  
J Hobohm ◽  
M Deschauer ◽  
S Zierz

2020 ◽  
Vol 15 ◽  
Author(s):  
Dicle Yalcin ◽  
Hasan H. Otu

Background: Epigenetic repression mechanisms play an important role in gene regulation, specifically in cancer development. In many cases, a CpG island’s (CGI) susceptibility or resistance to methylation are shown to be contributed by local DNA sequence features. Objective: To develop unbiased machine learning models–individually and combined for different biological features–that predict the methylation propensity of a CGI. Methods: We developed our model consisting of CGI sequence features on a dataset of 75 sequences (28 prone, 47 resistant) representing a genome-wide methylation structure. We tested our model on two independent datasets that are chromosome (132 sequences) and disease (70 sequences) specific. Results: We provided improvements in prediction accuracy over previous models. Our results indicate that combined features better predict the methylation propensity of a CGI (area under the curve (AUC) ~0.81). Our global methylation classifier performs well on independent datasets reaching an AUC of ~0.82 for the complete model and an AUC of ~0.88 for the model using select sequences that better represent their classes in the training set. We report certain de novo motifs and transcription factor binding site (TFBS) motifs that are consistently better in separating prone and resistant CGIs. Conclusion: Predictive models for the methylation propensity of CGIs lead to a better understanding of disease mechanisms and can be used to classify genes based on their tendency to contain methylation prone CGIs, which may lead to preventative treatment strategies. MATLAB and Python™ scripts used for model building, prediction, and downstream analyses are available at https://github.com/dicleyalcin/methylProp_predictor.


Neoplasia ◽  
2008 ◽  
Vol 10 (9) ◽  
pp. 908-IN2 ◽  
Author(s):  
Michael W.Y. Chan ◽  
Yi-Wen Huang ◽  
Corinna Hartman-Frey ◽  
Chieh-Ti Kuo ◽  
Daniel Deatherage ◽  
...  

2011 ◽  
Vol 32 (6) ◽  
pp. 812-821 ◽  
Author(s):  
Benjamin A.T. Rodriguez ◽  
Yu-I Weng ◽  
Ta-Ming Liu ◽  
Tao Zuo ◽  
Pei-Yin Hsu ◽  
...  

PLoS ONE ◽  
2015 ◽  
Vol 10 (3) ◽  
pp. e0118813 ◽  
Author(s):  
Eugénie Ansseau ◽  
Jacqueline S. Domire ◽  
Lindsay M. Wallace ◽  
Jocelyn O. Eidahl ◽  
Susan M. Guckes ◽  
...  

2010 ◽  
Vol 27 ◽  
pp. S7-S8
Author(s):  
J. Kjems ◽  
E.D. Wiklund ◽  
J.B. Bramsen ◽  
T. Hulf ◽  
L. Dyrskjøt ◽  
...  

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