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Cited by 7Neuroinflammation and Neurodegeneration MechanismsAlzheimer's disease research and treatmentsDementia and Cognitive Impairment Research

Network Dynamics‐based Subtyping of Alzheimer's Disease With Microglial Genetic Risk Factors

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Choi, Jae Hyuk, Lee, Jonghoon, Kang, Uiryong, Chang, Hongjun, Cho, Kwang-Hyun·2024·Alzheimer's Research & Therapy
DOI 10.1186/s13195-024-01583-9PMC PMC11481771
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Abstract

BACKGROUND: The potential of microglia as a target for Alzheimer's disease (AD) treatment is promising, yet the clinical and pathological diversity within microglia, driven by genetic factors, poses a significant challenge. Subtyping AD is imperative to enable precise and effective treatment strategies. However, existing subtyping methods fail to comprehensively address the intricate complexities of AD pathogenesis, particularly concerning genetic risk factors. To address this gap, we have employed systems biology approaches for AD subtyping and identified potential therapeutic targets. METHODS: We constructed patient-specific microglial molecular regulatory network models by utilizing existing literature and single-cell RNA sequencing data. The combination of large-scale computer simulations and dynamic network analysis enabled us to subtype AD patients according to their distinct molecular regulatory mechanisms. For each identified subtype, we suggested optimal targets for effective AD treatment. RESULTS: To investigate heterogeneity in AD and identify potential therapeutic targets, we constructed a microglia molecular regulatory network model. The network model incorporated 20 known risk factors and crucial signaling pathways associated with microglial functionality, such as inflammation, anti-inflammation, phagocytosis, and autophagy. Probabilistic simulations with patient-specific genomic data and subsequent dynamics analysis revealed nine distinct AD subtypes characterized by core feedback mechanisms involving SPI1, CASS4, and MEF2C. Moreover, we identified PICALM, MEF2C, and LAT2 as common therapeutic targets among several subtypes. Furthermore, we clarified the reasons for the previous contradictory experimental results that suggested both the activation and inhibition of AKT or INPP5D could activate AD through dynamic analysis. This highlights the multifaceted nature of microglial network regulation. CONCLUSIONS: These results offer a means to classify AD patients by their genetic risk factors, clarify inconsistent experimental findings, and advance the development of treatments tailored to individual genotypes for AD.

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  • Some Alzheimer's disease risk loci are highly enriched within or near genes encoding microglia-specific transcription factors.

    “Interestingly, some Alzheimer's disease risk loci are highly enriched within or near genes encoding microglia‐specific transcription factors, highlighting their crucial roles in the pathogenesis of Alzheimer's disease (Table 1) [5, 7, 20, 21].”

    From Targeting Microglial Transcriptional Reprogramming as a Therapy Strategy for Alzheimer's Disease (2026) · cites this paper as [21]

    The Discussion identifies SPI1 and MEF2C as Alzheimer’s disease risk factors and transcription factors related to microglial function; the Results also report risk variants in SPI1. Neither passage establishes that risk loci are highly enriched within or near genes encoding microglia-specific transcription factors. The enrichment and microglia-specificity claims therefore go beyond what this paper says. (LLM, conf=0.84)

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Sorted by citations

Authors
Choi, J. H., Lee, J., Kang, U., Chang, H., & Cho, K.-H.
Affiliations
Korea Advanced Institute of Science and Technology (KR)
Corresponding author
Cho, Kwang-Hyun
ORCID
Venue
Alzheimer's Research & Therapy
Publisher
Springer Science and Business Media LLC
Published
2024-10-16
Volume
16
Issue
1
Pages
229-229
ISSN
1758-9193 (ISSN-L)
DOI
10.1186/s13195-024-01583-9
PMC
PMC11481771
OpenAlex
W4403461600
Cited by
7
References
16
Citations by year
2026: 4 · 2025: 3
FWCI
1.09
Type
journal article
Language
en
Open access
Licence
Locations
  • Alzheimer s Research & Therapy (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Korea Dementia Research Center (HU21C0060)
  • National Research Foundation of Korea (2023R1A2C3002619)
Topics
  • Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
Keywords
Alzheimer's disease, disease subtyping, microglia, genetic risk factors, molecular regulatory networks, network dynamics, therapeutic target, PICALM, MEF2C, LAT2, PU.1, Akt, INPP5D, phagocytosis, autophagy, inflammation, personalized medicine
MeSH terms
Alzheimer Disease, Computer Simulation, Humans, Risk Factors, Microglia, Genetic Predisposition to Disease, Gene Regulatory Networks

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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