Mutation spectrum in the gene encoding methyl-CpG-binding protein 2 in Egyptian patients with Rett syndrome

Sharaf-Eldin, Wessam E.; Soliman, Hala; Abdel-Aziz, Nahla N.; Elbendary, Hasnaa M; Issa, Mahmoud Y.; Zaki, Maha S.

doi:10.1016/j.mgene.2019.100620

Cited by 1 publication

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“…Studied variants were detected during molecular analysis of MECP2 gene in females with provisional diagnosis of Rett syndrome (RTT) as previously mentioned in Sharaf-Eldin et al [17]. Data for human MECP2 gene sequence and variants were collected from NCBI (http:// www.…”

Section: Detection Of Variantsmentioning

confidence: 99%

Variable predicted pathogenic mechanisms for novel MECP2 variants in RTT patients

Sharaf-Eldin

Issa

Zaki

et al. 2022

Journal of Genetic Engineering and Biotechnology

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Background Methyl CpG binding protein 2 (MeCP2) is essential for the normal function of mature neurons. Mutations in the MECP2 gene are the main cause of Rett syndrome (RTT). Gene mutations have been identified throughout the gene and the mutation effect is mainly correlated with its type and location. Methods In this study, a series of in silico algorithms were applied for analyzing the functional consequences of 3 novel gene missense mutations (D121A, S359Y, and P403S) and a rarely reported one with suspicious effect (R133H) on RettBASE. Besides, a ROC curve analysis was performed to investigate the critical factors affecting variant pathogenicity. Results (1) The ROC curve analysis for a retrieved set of MeCP2 variants showed that physicochemical characters do not significantly affect variant pathogenicity; (2) PREM PDI tool revealed that both D121A and R133H mainly contribute to disease progression via reducing MeCP2 affinity to DNA; (3) GPS v5.0 software indicated that P403S may correlate with altered protein phosphorylation; however, no defective protein interaction has been already documented. (4) The applied computational algorithms failed to explore any informative pathogenic mechanism for the S359Y variant. Conclusion The conducted approach might provide an efficient prediction model for the effect of MECP2 variants that are located in MBD and CTD.

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Section: Detection Of Variantsmentioning

confidence: 99%