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.

Authors

Institutions

Publication Details

Journal
Journal of Inherited Metabolic Disease
Published
2025-05-30
DOI
https://doi.org/10.1002/jimd.70045
Primary Topic
Metabolism and Genetic Disorders
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Pablo Ranea‐Robles, Johan Auwerx, Aaron Bender, Christiaan N. Levelt et al.
Journal of Inherited Metabolic Disease
Metabolism and Genetic Disorders
article

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

Pablo Ranea‐Robles, Johan Auwerx, Aaron Bender, Christiaan N. Levelt, Johannes M. F. G. Aerts, Mina Mirzaian, J. Alexander Heimel, Sander M. Houten, Ronald J. A. Wanders, Carmen Argmann, Jun Zhu, Evan G. Williams
article en

Abstract

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.

Journal of Inherited Metabolic DiseaseVol. 48(4)
Leiden University (NL), Netherlands Institute for Neuroscience (NL), Universidad de Granada (ES), University of Luxembourg (LU), Erasmus MC (NL), Emma Kinderziekenhuis (NL), Instituto de Investigación Biosanitaria de Granada (ES), Amsterdam University Medical Centers (NL), École Polytechnique Fédérale de Lausanne (CH), University of Amsterdam (NL), Icahn School of Medicine at Mount Sinai (US)
Leona M. and Harry B. Helmsley Charitable Trust, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, National Institutes of Health, European Research Council, National Institute of Diabetes and Digestive and Kidney Diseases
Good health and well-being
Openalex Percentile: Top 86%
Metabolism and Genetic Disorders
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.