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Prediction and verification of the AD-FTLD common pathomechanism based on dynamic molecular network analysis
Meihua Jin1, Xiaocen Jin1, Hidenori Homma2
1Department of Neuropathology, Medical Research Institute, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo, Japan.
Abstract:
Multiple gene mutations cause familial frontotemporal lobar degeneration (FTLD) while no single gene mutations exists in sporadic FTLD. Various proteins aggregate in variable regions of the brain, leading to multiple pathological and clinical prototypes. The heterogeneity of FTLD could be one of the reasons preventing development of disease-modifying therapy. We newly develop a mathematical method to analyze chronological changes of PPI networks with sequential big data from comprehensive phosphoproteome of four FTLD knock-in (KI) mouse models (PGRNR504X-KI, TDP43N267S-KI, VCPT262A-KI and CHMP2BQ165X-KI mice) together with four transgenic mouse models of Alzheimer's disease (AD) and with APPKM670/671NL-KI mice at multiple time points. The new method reveals the common core pathological network across FTLD and AD, which is shared by mouse models and human postmortem brains. Based on the prediction, we performed therapeutic intervention of the FTLD models, and confirmed amelioration of pathologies and symptoms of four FTLD mouse models by interruption of the core molecule HMGB1, verifying the new mathematical method to predict dynamic molecular networks.
Insights
A new mathematical method identified a common core pathological network in frontotemporal lobar degeneration (FTLD) and Alzheimer's disease (AD). Targeting this network, specifically HMGB1, ameliorated FTLD pathologies and symptoms in mouse models.
Area of Science:
- Neuroscience
- Computational Biology
- Genetics
Background:
- Familial frontotemporal lobar degeneration (FTLD) is caused by multiple gene mutations, leading to diverse protein aggregates and clinical presentations.
- The heterogeneity of FTLD presents a significant challenge for developing effective disease-modifying therapies.
- Sporadic FTLD lacks a single causative gene mutation, further complicating therapeutic strategies.
Purpose of the Study:
- To develop a novel mathematical method for analyzing dynamic changes in protein-protein interaction (PPI) networks.
- To identify a common core pathological network underlying both FTLD and Alzheimer's disease (AD).
- To validate the therapeutic potential of targeting identified network components in FTLD models.
Main Methods:
- Utilized a new mathematical approach to analyze comprehensive phosphoproteome data from multiple FTLD and AD mouse models over time.
- Integrated sequential big data from knock-in (KI) and transgenic mouse models, including PGRNR504X-KI, TDP43N267S-KI, VCPT262A-KI, CHMP2BQ165X-KI, and APPKM670/671NL-KI mice.
- Performed therapeutic interventions based on network predictions in FTLD models.
Main Results:
- The developed method revealed a conserved core pathological network common to FTLD and AD mouse models and human postmortem brains.
- Interruption of the core molecule High Mobility Group Box 1 (HMGB1) led to the amelioration of pathologies and symptoms in four distinct FTLD mouse models.
- The study successfully validated the predictive power of the mathematical method for dynamic molecular networks.
Conclusions:
- A novel mathematical framework can effectively predict dynamic molecular networks relevant to neurodegenerative diseases.
- HMGB1 is identified as a critical node in a common pathological network shared by FTLD and AD.
- Targeting HMGB1 represents a promising therapeutic strategy for FTLD, potentially applicable to other neurodegenerative conditions.
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