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Updated: Jun 10, 2025
Quantitative 3D In Silico Modeling q3DISM of Cerebral Amyloid-beta Phagocytosis in Rodent Models of Alzheimer's Disease
Published on: December 26, 2016
Network dynamics-based subtyping of Alzheimer's disease with microglial genetic risk factors
Jae Hyuk Choi1, Jonghoon Lee1, Uiryong Kang1
1Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.
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.
Insights
This study identifies nine Alzheimer's disease (AD) subtypes using microglia molecular networks and genetic data. These findings enable personalized AD treatments by targeting specific genetic risk factors and clarifying complex disease mechanisms.
Area of Science:
- Neuroscience
- Systems Biology
- Genetics
Background:
- Microglia are promising therapeutic targets for Alzheimer's disease (AD).
- Genetic factors create significant diversity within microglia, complicating AD treatment.
- Existing AD subtyping methods do not fully address genetic complexities.
Purpose of the Study:
- To develop a systems biology approach for AD subtyping based on microglial molecular networks.
- To identify patient-specific subtypes and potential therapeutic targets for AD.
- To clarify conflicting experimental findings in AD research.
Main Methods:
- Constructed patient-specific microglial molecular regulatory network models.
- Utilized single-cell RNA sequencing data and literature.
- Employed large-scale computer simulations and dynamic network analysis for subtyping.
Main Results:
- Identified nine distinct AD subtypes based on molecular regulatory mechanisms.
- Incorporated 20 risk factors and key microglial pathways (inflammation, phagocytosis, autophagy).
- Discovered common therapeutic targets (PICALM, MEF2C, LAT2) and explained contradictory findings regarding AKT/INPP5D.
Conclusions:
- Developed a method to classify AD patients by genetic risk factors.
- Advanced understanding of AD pathogenesis and microglial network regulation.
- Paved the way for genotype-tailored AD treatments.
