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Cited by 7Alzheimer's disease research and treatmentsBioinformatics and Genomic NetworksDementia and Cognitive Impairment Research

Systems biology approaches to unravel the molecular and genetic architecture of Alzheimer's disease and related tauopathies

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Miyoshi, Emily, Morabito, Samuel, Swarup, Vivek·2021·Neurobiology of Disease
DOI 10.1016/j.nbd.2021.105530PMC PMC8616667
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Abstract

Over the years, genetic studies have identified multiple genetic risk variants associated with neurodegenerative disorders and helped reveal new biological pathways and genes of interest. However, genetic risk variants commonly reside in non-coding regions and may regulate distant genes rather than the nearest gene, as well as a gene's interaction partners in biological networks. Systems biology and functional genomics approaches provide the framework to unravel the functional significance of genetic risk variants in disease. In this review, we summarize the genetic and transcriptomic studies of Alzheimer's disease and related tauopathies and focus on the advantages of performing systems-level analyses to interrogate the biological pathways underlying neurodegeneration. Finally, we highlight new avenues of multi-omics analysis with single-cell approaches, which provide unparalleled opportunities to systematically explore cellular heterogeneity, and present an example of how to integrate publicly available single-cell datasets. Systems-level analysis has illuminated the function of many disease risk genes, but much work remains to study tauopathies and to understand spatiotemporal gene expression changes of specific cell types.

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  • Linking genetic variation to downstream protein networks and metabolic processes has led to the identification of novel therapeutic targets.

    “Linking genetic variation to downstream protein networks and metabolic processes has led to the identification of novel therapeutic targets137,160,205.”

    From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [205]

    In “Surveying the diseased brain using co-expression network analysis” (page unavailable), the review describes studies relating genetic risk to protein-expression networks and identifying dysregulated pathways. But “Future Directions” (page unavailable) presents therapeutics targeting novel regulators as an opportunity, not as targets already identified through this linkage; the cited passages also do not establish the claimed link to metabolic processes. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • AI-driven multi-omics integration enables linking microbial genes with host immune responses and amyloid processing pathways.

    “AI-driven multi-omics integration enables linking microbial genes with host immune responses and amyloid processing pathways205.”

    From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [205]

    The 'Integrating single-cell datasets for meta-analysis' section describes integration across omics modalities and an AD analysis using a deep neural network. The paper also discusses immune-related AD genes, but the passages do not link microbial genes to host immune responses or amyloid-processing pathways; searches for microbial genes and microbiome found no such link. No page numbers are available in our copy. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Miyoshi, E., Morabito, S., & Swarup, V.
Affiliations
University of California, Irvine (US)
Corresponding author
Swarup, Vivek
ORCID
Venue
Neurobiology of Disease
Publisher
Elsevier BV
Published
2021-12
Volume
160
Pages
105530
ISSN
0969-9961 (ISSN-L)
DOI
10.1016/j.nbd.2021.105530
PMC
PMC8616667
OpenAlex
W3207299060
Cited by
7
References
31
Citations by year
2025: 3 · 2024: 2 · 2023: 2
FWCI
0.13
Type
journal article
Language
en
Open access
Licence
Locations
  • Neurobiology of Disease (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • eScholarship (California Digital Library) (submittedVersion, cc-by-nc-nd)
Funders
National Institute on Aging (RF1AG071683, R01AG071683)
Topics
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
Keywords
Alzheimer's disease, tauopathy, genetic risk variants, non-coding variants, transcriptomics, multi-omics integration, single-cell RNA sequencing, cellular heterogeneity, gene regulatory networks, neurodegeneration, spatiotemporal gene expression
MeSH terms
Alzheimer Disease, Humans, Genomics, Tauopathies, Systems Biology, Transcriptome

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