| Autor: |
Camila A. B. Garcia, Simone C. S. Carvalho, Xiaoxu Yang, Laurel L. Ball, Renee D. George, Kiely N. James, Valentina Stanley, Martin W. Breuss, Ursula Thomé, Marcelo V. Santos, Fabiano P. Saggioro, Luciano Neder Serafini, Wilson A. Silva Jr, Joseph G. Gleeson, Hélio R. Machado |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
Epilepsia Open, Vol 5, Iss 1, Pp 97-106 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2470-9239 |
| DOI: |
10.1002/epi4.12377 |
| Popis: |
Abstract Objectives Recently, defects in the protein kinase mTOR (mammalian target of rapamycin) and its associated pathway have been correlated with hemimegalencephaly (HME). mTOR acts as a central regulator of important physiological cellular functions such as growth and proliferation, metabolism, autophagy, death, and survival. This study was aimed at identifying specific variants in mTOR signaling pathway genes in patients diagnosed with HME. Methods Using amplicon and whole exome sequencing (WES) of resected brain and paired blood samples from five HME patients, we were able to identify pathogenic mosaic variants in the mTOR pathway genes MTOR, PIK3CA, and DEPDC5. Results These results strengthen the hypothesis that somatic variants in PI3K‐Akt‐mTOR pathway genes contribute to HME. We also describe one patient presenting with a pathogenic variant on DEPDC5 gene, which reinforces the role of DEPDC5 on cortical structural changes due to mTORC1 hyperactivation. These findings also provide insights into when in brain development these variants occurred. An early developmental variant is expected to affect a larger number of cells and to result in a larger malformation, whereas the same variant occurring later in development would cause a minor malformation. Significance In the future, numerous somatic variants in known or new genes will undoubtedly be revealed in resected brain samples, making it possible to draw correlations between genotypes and phenotypes and allow for a genetic clinical diagnosis that may help to predict a given patient's outcome. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
Plný text