Discover the network mechanisms underlying the connections between aging and age-related diseases
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Although our knowledge of aging has greatly expanded in the past decades, it remains elusive why and how aging contributes to the development of age-related diseases (ARDs). In particular, a global mechanistic understanding of the connections between aging and ARDs is yet to be established. We rely on a network modelling named “GeroNet” to study the connections between aging and more than a hundred diseases. By evaluating topological connections between aging genes and disease genes in over three thousand subnetworks corresponding to various biological processes, we show that aging has stronger connections with ARD genes compared to non-ARD genes in subnetworks corresponding to “response to decreased oxygen levels”, “insulin signalling pathway”, “cell cycle”, etc. Based on subnetwork connectivity, we can correctly “predict” if a disease is age-related and prioritize the biological processes that are involved in connecting to multiple ARDs. Using Alzheimer’s disease (AD) as an example, GeroNet identifies meaningful genes that may play key roles in connecting aging and ARDs. The top modules identified by GeroNet in AD significantly overlap with modules identified from a large scale AD brain gene expression experiment, supporting that GeroNet indeed reveals the underlying biological processes involved in the disease.
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Several researches proved that coding genes, noncoding RNAs, and proteins are related to diseases, including AD.
“Several researches proved that coding genes/noncoding RNAs/proteins were related to diseases, including AD [29,30,31,32,33,34,35,36].”
From An Efficient Classifier for Alzheimer’s Disease Genes Identification (2018) · cites this paper as [34]
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- Authors
- Yang, J., Huang, T., Song, W.-M., Petralia, F., Mobbs, C. V., Zhang, B., Zhao, Y., Schadt, E. E., Zhu, J., & Tu, Z.
- Affiliations
- Icahn School of Medicine at Mount Sinai (US)
- Corresponding author
- Tu, Zhidong
- ORCID
- Venue
- Scientific Reports
- Published
- 2016-09-01
- Volume
- 6
- Issue
- 1
- Pages
- 32566-32566
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC5007654
- OpenAlex
- W2516546691
- Cited by
- 46
- References
- 64
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 2 · 2023: 3 · 2022: 9 · 2021: 9 · 2020: 4 · 2019: 8 · 2018: 3 · 2017: 5
- FWCI
- 2.77
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Icahn School of Medicine at Mount Sinai
- Fondation Leducq
- National Institutes of Health (1s10od018522-01)
- National Institute on Aging (U01AG046170)
- National Human Genome Research Institute (U01HG008451)
- National Institute of Diabetes and Digestive and Kidney Diseases (P30DK020541)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- aging, age-related diseases, aging-related genes, disease-associated genes, hypoxia response, insulin signaling pathway, cell cycle, Alzheimer's disease, gene modules, disease prediction
- MeSH terms
- Aging, Alzheimer Disease, Brain, Cell Cycle, Humans, Signal Transduction, Gene Expression Profiling, Systems Biology, Gene Regulatory Networks, Molecular Sequence Annotation, Gene Ontology
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