Project

Unraveling Neurodevelopmental Disorders (NDDs): Identification and functional characterization of novel NDD genes and regulatory elements using multi-omics and several model systems

Code
bof/baf/1y/2024/01/028
Duration
01 January 2024 → 31 December 2024
Funding
Regional and community funding: Special Research Fund
Research disciplines
  • Natural sciences
    • Computational transcriptomics and epigenomics
    • Developmental genetics
  • Medical and health sciences
    • Genetics
    • Bio-informatics and computational biology not elsewhere classified
    • Development of bioinformatics software, tools and databases
Keywords
induced pluripotent stem cells 3D genome CRISPR neural organoids neurodevelopmental disorders multi-omics enhancers
 
Project description

Neurodevelopmental disorders (NDDs) are a heterogenous group of early onset disorders affecting central nervous system (CNS) development that affect approximately 2-5% of children worldwide. The clinical and genetic heterogeneity of NDDs, make it challenging to find a molecular diagnosis for individual cases. Although recent technological improvements have led to a significant increase in diagnostic yield and identification of several novel NDD associated genes, many associated genes have yet to be discovered and, in addition, the noncoding part of the genome has not yet been addressed.

Therefore, in this project we are trying to identify and characterize new candidate genes associated with NDDs on the one hand and trying to unravel regulatory elements that are crucial during neurodevelopment on the other hand.

To this end, we make use of a wide range of model systems such as 2D and 3D in vitro models, model organisms such as Drosophila melanogaster (fruit fly) and Danio rerio (zebrafish). We aim to characterize the function of (novel) NDD genes and to identify and validate important regulatory elements of NDD genes by using a multi-omics strategy consisting of a combination of functional genomics, transcriptomics and proteomics in the above mentioned model systems.