Genome-scale metabolic network reconstruction of model animals as a platform for translational research
Journal article, 2021
Genome-scale metabolic models (GEMs) are used extensively for analysis of mechanisms underlying human diseases and metabolic malfunctions. However, the lack of comprehensive and high-quality GEMs for model organisms restricts translational utilization of omics data accumulating from the use of various disease models. Here we present a unified platform of GEMs that covers five major model animals, including Mouse1 (Mus musculus), Rat1 (Rattus norvegicus), Zebrafish1 (Danio rerio), Fruitfly1 (Drosophila melanogaster), and Worm1 (Caenorhabditis elegans). These GEMs represent the most comprehensive coverage of the metabolic network by considering both orthology-based pathways and species-specific reactions. All GEMs can be interactively queried via the accompanying web portal Metabolic Atlas. Specifically, through integrative analysis of Mouse1 with RNA-sequencing data from brain tissues of transgenic mice we identified a coordinated up-regulation of lysosomal GM2 ganglioside and peptide degradation pathways which appears to be a signature metabolic alteration in Alzheimer’s disease (AD) mouse models with a phenotype of amyloid precursor protein overexpression. This metabolic shift was further validated with proteomics data from transgenic mice and cerebrospinal fluid samples from human patients. The elevated lysosomal enzymes thus hold potential to be used as a biomarker for early diagnosis of AD. Taken together, we foresee that this evolving open-source platform will serve as an important resource to facilitate the development of systems medicines and translational biomedical applications.
Translational medicine
Alzheimer’s disease
Animal model
Genome-scale model
Aβ deposition
Author
Hao Wang
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Wallenberg Lab.
Jonathan Robinson
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Pinar Kocabas
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Johan Gustafsson
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Petre Mihail Anton
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Pierre-Etienne Cholley
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Shan Huang
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Johan Gobom
University of Gothenburg
Thomas Svensson
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Mathias Uhlen
Technical University of Denmark (DTU)
Royal Institute of Technology (KTH)
Henrik Zetterberg
University of Gothenburg
University College London (UCL)
Sahlgrenska University Hospital
Jens B Nielsen
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
BioInnovation Institute
Technical University of Denmark (DTU)
Proceedings of the National Academy of Sciences of the United States of America
0027-8424 (ISSN) 1091-6490 (eISSN)
Vol. 118 30 e2102344118Subject Categories
Pharmaceutical Sciences
Bioinformatics (Computational Biology)
Bioinformatics and Systems Biology
DOI
10.1073/pnas.2102344118
PubMed
34282017