Network dynamics-based subtyping of Alzheimer’s disease with microglial genetic risk factors
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...
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| Format: | Article |
| Language: | English |
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BMC
2024-10-01
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| Series: | Alzheimer’s Research & Therapy |
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| Online Access: | https://doi.org/10.1186/s13195-024-01583-9 |
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| author | Jae Hyuk Choi Jonghoon Lee Uiryong Kang Hongjun Chang Kwang-Hyun Cho |
| author_facet | Jae Hyuk Choi Jonghoon Lee Uiryong Kang Hongjun Chang Kwang-Hyun Cho |
| author_sort | Jae Hyuk Choi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3a125418e4df4c7bab43eecefa1d8ae8 |
| institution | OA Journals |
| issn | 1758-9193 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | BMC |
| record_format | Article |
| series | Alzheimer’s Research & Therapy |
| spelling | doaj-art-3a125418e4df4c7bab43eecefa1d8ae82025-08-20T02:17:34ZengBMCAlzheimer’s Research & Therapy1758-91932024-10-0116112010.1186/s13195-024-01583-9Network dynamics-based subtyping of Alzheimer’s disease with microglial genetic risk factorsJae Hyuk Choi0Jonghoon Lee1Uiryong Kang2Hongjun Chang3Kwang-Hyun Cho4Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST)Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST)Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST)Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST)Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST)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.https://doi.org/10.1186/s13195-024-01583-9Alzheimer’s diseaseMicrogliaNetwork dynamicsGenetic risk factorsPatient subtypingSystems biology |
| spellingShingle | Jae Hyuk Choi Jonghoon Lee Uiryong Kang Hongjun Chang Kwang-Hyun Cho Network dynamics-based subtyping of Alzheimer’s disease with microglial genetic risk factors Alzheimer’s Research & Therapy Alzheimer’s disease Microglia Network dynamics Genetic risk factors Patient subtyping Systems biology |
| title | Network dynamics-based subtyping of Alzheimer’s disease with microglial genetic risk factors |
| title_full | Network dynamics-based subtyping of Alzheimer’s disease with microglial genetic risk factors |
| title_fullStr | Network dynamics-based subtyping of Alzheimer’s disease with microglial genetic risk factors |
| title_full_unstemmed | Network dynamics-based subtyping of Alzheimer’s disease with microglial genetic risk factors |
| title_short | Network dynamics-based subtyping of Alzheimer’s disease with microglial genetic risk factors |
| title_sort | network dynamics based subtyping of alzheimer s disease with microglial genetic risk factors |
| topic | Alzheimer’s disease Microglia Network dynamics Genetic risk factors Patient subtyping Systems biology |
| url | https://doi.org/10.1186/s13195-024-01583-9 |
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