Important insights from discrete details : Gaining insight in pathophysiology of inborn errors of metabolism through deep metabolic phenotyping

Abstract

For many rare genetic diseases, including metabolic diseases, we do not understand how a clerical error in a gene can lead to symptoms in a patient. Despite the limited number of patients per disease, the recent development of new technologies by which big data can be collected, offers promising opportunities to better understand these diseases. These opportunities come along with new challenges: how to gain valuable lessons from this amount of data? We demonstrate four new ways for delineating the patients’ symptoms and systematically comparing these to patients with the same disease but another genetic clerical error, or to patients with another genetic disease. In addition, we describe the development of a new method by which we can analyze over 1800 metabolites in only a few drops of blood or cerebrospinal fluid. Using this method, we can rapidly and reliably determine the effects of the genetic clerical error on the different metabolites in blood and cerebrospinal fluid. This creates not only better understanding of how a genetic clerical error can lead to symptoms, but the analysis can also improve the diagnostic process of metabolic diseases. Finally, we describe how these strategies were used for two rare metabolic diseases, that were added to the heel prick per October 1st 2019 in the Netherlands: propionic acidemia and methylmalonic acidemia. The majority of the new insights that were gained, can directly be used in the clinical management of children that are now diagnosed with these diseases via the heel prick at a young age.