A Multiomic Network Approach to Uncover Disease Modifying Mechanisms of Inborn Errors of Metabolism.

For many inborn errors of metabolism (IEM) the understanding of disease mechanisms remains limited, in part explaining their unmet medical needs. The expressivity of IEM disease phenotypes is affected by disease-modifying factors, including rare and common polygenic variation. We hypothesize that we can identify these modulating pathways using molecular signatures of IEM in combination with multiomic data and gene regulatory networks generated from non-IEM animal and human populations. We tested this approach by identifying and subsequently validating glucocorticoid signaling as a candidate modifier of mitochondrial fatty acid oxidation disorders, and recapitulating complement signaling as a modifier of inflammation in Gaucher disease. Our work describes a novel approach that can overcome the rare disease-rare data dilemma and reveal new IEM pathophysiology and potential drug targets using multiomics data in seemingly healthy populations.