Carmen Argmann, PhD

Background: Inborn errors of metabolism (IEM) are increasingly viewed as complex diseases as they often present as a spectrum of disease phenotypes with a clear disconnect between the severity of mutation at the primary affected locus and the phenotype. The lack of genotype and phenotype correlation greatly impacts the ability to predict a patient’s disease course. It also illustrates the existence of a fundamental gap in our knowledge of disease pathophysiology. The era of one-gene one-disease is being abandoned, and the contribution of modifying factors considered. However, identifying the modifying factors is not trivial, as rare diseases have rare data.

Hypothesis: We hypothesize that by embracing the concept of IEM as complex diseases that 1. datasets and 2. network approaches generated in populations with common disorders can be used to study disease modifying biology in IEM thereby overcoming the rare disease rare data drawback. Our strategy is based on observing that differentially expressed genes from IEM experimental models highlight highly connected subnetworks in the molecular networks established in common disease populations.

Aim:  Our lab’s aim is to use our innovative data-driven multi-scale computational approach to derive and then wet-lab validate novel candidate modifying genes and their associated biology related to the screenable inborn errors of fatty acid oxidation (FAO) the lysosomal storage disorder, Gaucher disease (GD). This is a key collaboration between myself and a faculty expert in IEM, Dr Sander Houten, also of the Department of Genetics and Genomics Sciences at ICAHN.

Impact: Combined these two aims will break new ground for FAO and GD and rare diseases in general  by overcoming inherent limitations of rare data through combining novel methodologies with existing data. We furthermore perform validations and these methods which could point the IEM research field into multiple new directions. These novel insights are needed to propel the IEM field into the next generation of understanding.