Genome-scale metabolic network reconstruction of model animals as a platform for translational research.
Hao WangJonathan L RobinsonPinar KocabasJohan GustafssonPetre Mihail AntonPierre-Etienne CholleyShan HuangJohan GobomL Thomas SvenssonMattias UhlenHenrik ZetterbergJens B NielsenPublished in: Proceedings of the National Academy of Sciences of the United States of America (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.
Keyphrases
- endothelial cells
- single cell
- end stage renal disease
- electronic health record
- high throughput
- chronic kidney disease
- newly diagnosed
- mouse model
- gene expression
- drosophila melanogaster
- cell proliferation
- oxidative stress
- cerebrospinal fluid
- ejection fraction
- peritoneal dialysis
- artificial intelligence
- machine learning
- mild cognitive impairment
- dna methylation
- induced pluripotent stem cells
- white matter
- deep learning
- subarachnoid hemorrhage
- amino acid
- affordable care act
- genetic diversity