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Biomedical subjects

Kaya Bilguvar

Publications and source records attributed to Kaya Bilguvar.

2 recordsLinked to original sources

Dysregulation of mTOR signalling is a converging mechanism in lissencephaly.

Cerebral cortex development in humans is a highly complex and orchestrated process that is under tight genetic regulation. Rare mutations that alter gene expression or function can disrupt the structure of the cerebral cortex, resulting in a range of neurological conditions1. Lissencephaly ('smooth brain') spectrum disorders comprise a group of rare, genetically heterogeneous congenital brain malformations commonly associated with epilepsy and intellectual disability2. However, the molecular mechanisms underlying disease pathogenesis remain unknown. Here we establish hypoactivity of the mTOR pathway as a clinically relevant molecular mechanism in lissencephaly spectrum disorders. We characterized two types of cerebral organoid derived from individuals with genetically distinct lissencephalies with a recessive mutation in p53-induced death domain protein 1 (PIDD1) or a heterozygous chromosome 17p13.3 microdeletion leading to Miller-Dieker lissencephaly syndrome (MDLS). PIDD1-mutant organoids and MDLS organoids recapitulated the thickened cortex typical of human lissencephaly and demonstrated dysregulation of protein translation, metabolism and the mTOR pathway. A brain-selective activator of mTOR complex 1 prevented and reversed cellular and molecular defects in the lissencephaly organoids. Our findings show that a converging molecular mechanism contributes to two genetically distinct lissencephaly spectrum disorders.

Humans

A Comprehensive Bioinformatics Approach to Analysis of Variants: Variant Calling, Annotation, and Prioritization.

Next-Generation Sequencing (NGS), also known as high-throughput sequencing technologies, has enabled rapid and efficient sequencing of large amounts of DNA and RNA. These technologies have revolutionized the field of genomics, transcriptomics, and proteomics and have been widely used in cancer research, leading to advances in clinical diagnosis and treatment. Improvements in the NGS technologies enabled millions of fragments to be sequenced simultaneously in a time- and cost-effective manner and resulted in large amount of genomic data which require efficient analysis methods. Analysis of the genomic data requires both efficient computer resources and bioinformatics approaches. This chapter details a comprehensive computational approach and analysis steps for genomic data analysis.

Computational Biology