Dynamic self-guiding analysis of Alzheimer's disease

Area of Science:

• Computational biology
• Systems biology
• Neuroscience

Background:

• Alzheimer's disease (AD) is linked to amyloid-beta precursor protein (APP) signaling dysregulation.
• Understanding the complex data and literature surrounding AD is challenging.

Purpose of the Study:

• To develop a novel computational model for Alzheimer's disease using evolutionary algorithms.
• To investigate the role of cytoskeletal connectivity and cellular microenvironments in AD pathogenesis.

Main Methods:

• Applied a self-guiding evolutionary algorithm to synthesize AD-related data and literature.
• Utilized an APP protein interaction network and a seed model as guides and filters for data analysis.
• Analyzed data across sub-disciplines and system-scale discovery platforms.

Main Results:

• Identified dynamic cytoskeletal connectivity as crucial for multicellular network stability and plasticity.
• Linked chronic impairment of cell adhesions and cytoskeletal networks to disordered, wound-like microenvironments.
• Showed these microenvironments support pro-inflammatory, de-differentiated cellular phenotypes.

Conclusions:

• Chronic cellular disconnection and wound-like microenvironments may drive degenerative disorders like Alzheimer's disease and cancer.
• Epigenetic modifications and mutations stabilize degenerated cellular phenotypes in these environments.
• The study highlights the importance of cellular architecture and microenvironment in neurodegenerative disease progression.