Genome-scale metabolic network reconstruction of model animals as a platform for translational research
Wang H, Robinson JL, Kocabas P, Gustafsson J, Anton M, Cholley PE, Huang S, Gobom J et al.. Proceedings of the National Academy of Sciences of the United States of America, 2021-07-01
Topic: Brain proteins, microglia and lab models
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.
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APA 7th
Wang, H., Robinson, J. L., Kocabas, P., Gustafsson, J., Anton, M., Cholley, P. E., et al. (2021). Genome-scale metabolic network reconstruction of model animals as a platform for translational research. Proceedings of the National Academy of Sciences of the United States of America. https://doi.org/10.1073/pnas.2102344118
Vancouver
Wang H, Robinson JL, Kocabas P, Gustafsson J, Anton M, Cholley PE, et al. Genome-scale metabolic network reconstruction of model animals as a platform for translational research. Proceedings of the National Academy of Sciences of the United States of America. 2021. doi:10.1073/pnas.2102344118. PMID: 34282017.
BibTeX
@article{wang2021-34282017,
author = {Wang, H. and Robinson, J. L. and Kocabas, P. and Gustafsson, J. and Anton, M. and Cholley, P. E. and Huang, S. and Gobom, J. and others},
title = {{Genome-scale metabolic network reconstruction of model animals as a platform for translational research}},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
year = {2021},
doi = {10.1073/pnas.2102344118},
pmid = {34282017},
url = {https://doi.org/10.1073/pnas.2102344118}
}
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Key findings
- 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.
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Study details
- Evidence strengthLow
How this study scored 43/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 25 of the 58 points we could judge it on, which is 43 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Proceedings of the National Academy of Sciences of the United States of America) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 3 / 5 | 2021 — within 6 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 34282017) |
| Free to read | 2 / 2 | Free to read |
| Points | 25 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 25 of the 58 points we could judge it on is 43 out of 100. |
| Total | 43 / 100 | — |
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