Multiscale networks in multiple sclerosis.

Area of Science:

• Neuroimmunology
• Systems Biology
• Computational Biology

Background:

• Complex diseases like Multiple Sclerosis (MS) involve intricate interactions across multiple biological scales, from molecular to organismal levels.
• Understanding these interconnected scales is crucial for deciphering disease phenotypes and progression.

Purpose of the Study:

• To investigate the multilayer network of biological interactions in Multiple Sclerosis (MS) using a comprehensive multi-omics approach.
• To identify key molecular and cellular pathways connecting different biological scales and explaining clinical phenotypes in MS.

Main Methods:

• Multilayer network analysis and deep phenotyping integrating genomics, phosphoproteomics, cytomics, brain and retinal imaging, and clinical data from 328 MS patients and 90 controls.
• Topological analysis using mutual information and Boolean simulations based on Pearson correlation to identify significant biological paths.
• Single-cell level validation using flow cytometry for specific identified pathways.

Main Results:

• A commonly identified path linked protein MK03 to total T cells, retinal nerve fiber layer (RNFL) thickness, and walking speed.
• This path involves nodes related to protein phosphorylation, glial cell differentiation, and stress-activated MAPK cascade.
• Specific combinations of proteins (GSK3AB, HSBP1, RS6) and immune cells (Th17, CD8, Treg, B memory) were found to explain clinical phenotypes.

Conclusions:

• The identified Boolean simulation paths effectively bridge molecular insights with higher-level biological scales, such as retinal damage and patient disability in MS.
• These findings offer a novel framework for connecting the molecular underpinnings of MS to its overall clinical presentation.
• The study highlights the potential of multilayer network analysis for understanding complex diseases.