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, CSBI

Wallenberg Lab.

Jonathan Robinson

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology, CSBI

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, CSBI

Pierre-Etienne Cholley

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology, CSBI

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, CSBI

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 e2102344118

Subject Categories

Pharmaceutical Sciences

Bioinformatics (Computational Biology)

Bioinformatics and Systems Biology

DOI

10.1073/pnas.2102344118

PubMed

34282017

More information

Latest update

8/5/2021 7