Genome-scale metabolic network reconstruction of model animals as a platform for translational research
Artikel i vetenskaplig tidskrift, 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

Författare

Hao Wang

Chalmers, Biologi och bioteknik, Systembiologi, CSBI

Wallenberg Lab.

Jonathan Robinson

Chalmers, Biologi och bioteknik, Systembiologi, CSBI

Pinar Kocabas

Chalmers, Biologi och bioteknik, Systembiologi

Johan Gustafsson

Chalmers, Biologi och bioteknik, Systembiologi

Petre Mihail Anton

Chalmers, Biologi och bioteknik, Systembiologi, CSBI

Pierre-Etienne Cholley

Chalmers, Biologi och bioteknik, Systembiologi, CSBI

Shan Huang

Chalmers, Biologi och bioteknik, Systembiologi

Johan Gobom

Göteborgs universitet

Thomas Svensson

Chalmers, Biologi och bioteknik, Systembiologi, CSBI

Mathias Uhlen

Danmarks Tekniske Universitet (DTU)

Kungliga Tekniska Högskolan (KTH)

Henrik Zetterberg

Göteborgs universitet

University College London (UCL)

Sahlgrenska universitetssjukhuset

Jens B Nielsen

Chalmers, Biologi och bioteknik, Systembiologi

BioInnovation Institute

Danmarks Tekniske Universitet (DTU)

Proceedings of the National Academy of Sciences of the United States of America

0027-8424 (ISSN) 1091-6490 (eISSN)

Vol. 118 30 e2102344118

Ämneskategorier

Farmaceutisk vetenskap

Bioinformatik (beräkningsbiologi)

Bioinformatik och systembiologi

DOI

10.1073/pnas.2102344118

PubMed

34282017

Mer information

Senast uppdaterat

2021-08-05