Functional mapping of whole-exome data in metabolic disease
Inborn Errors of Metabolism (IEM) are defects in enzymes or proteins involved in metabolic pathways. Identification of the underlying gene defects is currently dependent on a characteristic clinical and biochemical phenotype, from which the defective gene can be predicted in a limited number of cases. However, the clinical symptoms of IEM are highly variable and biochemical assays mostly do not lead directly to the causative gene. In two major IEM groups, involving the Golgi and mitochondrial organelles, only about 40% of patients with evident biochemical abnormalities can be solved at the genetic level at this moment. For both organelles, at least 1000 proteins are predicted to be involved, while less than 10% of the underlying genes have been causally related to IEMs.
In this project we will revolutionize the disease gene identification and validation process for these important metabolic diseases by combining a whole genome approach followed by targeted functional assays.
First, to validate the genetic approach we will apply whole exome sequencing in a blinded fashion to a limited number of genetically characterized IEM families. Next, we will study new patients for which homozygosity mapping and biochemical data is available. After bioinformatical prioritization, the 20 most promising pathogenic variants will be studied by siRNA and complementation assays in model cells and patient fibroblasts. These proof-of-principle studies will educate us how to apply exome sequencing and select functional models for efficient identification of the causative gene. Finally, the suitability of this approach will be demonstrated by solving the genetic cause in 20 to 40 unresolved singleton IEM patients with a proven biochemical abnormality in mitochondria or Golgi. This project will provide important insights in the genetic causes of two major groups of metabolic diseases and will learn us how to implement whole genome sequencing in the diagnostic setting of IEM.