Computational Tools and Methods for the Study of Systemic Amyloidosis at the Clinical and Molecular Level
Dario Di Silvestre1, Francesca Brambilla2, Giampaolo Merlini3
1Institute for Biomedical Technologies - National Research Council (ITB-CNR), Segrate, Milan, Italy. dario.disilvestre@itb.cnr.it.
Abstract:
Amyloidosis diseases are characterized by protein misfolding, which forms insoluble beta-sheet fibrils progressively deposited in tissues. Deposition in the form of amyloid aggregates can occur in various organs, damaging their structure and function. The hallmark of amyloidosis is aberrant interactions leading to protein aggregation and proteotoxicity. Accordingly, amyloidosis-related samples represent a valuable source of information to generate new knowledge useful for diagnostic, prognostic, and therapeutic purposes. In this scenario, we outline the path to apply computational methods and strategies based on the combination of proteomics and systems biology approaches. In addition to algorithms useful for subtyping amyloid deposits or assessing proteome recovery after drug treatment, our chapter provides workflows based on protein-protein interaction and protein co-expression network models. In particular, the main steps to reconstruct and analyze them at both functional and topological levels are described. Our chapter aims to provide tools and instructions to identify and monitor prognostic, diagnostic, and therapeutic markers and to shed light on the processes, pathways, and functions affected by amyloidogenic proteins.
Insights
This study introduces computational proteomics and systems biology for amyloidosis research. These methods help identify diagnostic, prognostic, and therapeutic markers for protein misfolding diseases.
Area of Science:
- Biochemistry and Molecular Biology
- Computational Biology and Bioinformatics
- Systems Biology
Background:
- Amyloidosis diseases involve protein misfolding, leading to beta-sheet fibril deposition in tissues.
- Amyloid aggregate deposition damages organ structure and function, driven by aberrant protein interactions and proteotoxicity.
- Amyloidosis samples are crucial for advancing diagnostic, prognostic, and therapeutic strategies.
Purpose of the Study:
- To outline computational methods combining proteomics and systems biology for amyloidosis research.
- To provide workflows for analyzing protein-protein interaction and co-expression networks.
- To offer tools for identifying and monitoring diagnostic, prognostic, and therapeutic markers in amyloidosis.
Main Methods:
- Application of computational methods integrating proteomics data.
- Utilizing systems biology approaches, including protein-protein interaction networks.
- Employing protein co-expression network models for functional and topological analysis.
Main Results:
- Development of algorithms for subtyping amyloid deposits.
- Establishment of methods for assessing proteome recovery post-drug treatment.
- Reconstruction and analysis of functional and topological network models.
Conclusions:
- Computational proteomics and systems biology offer powerful tools for amyloidosis research.
- These approaches facilitate the identification of key markers for disease management.
- The study provides a framework to understand pathways affected by amyloidogenic proteins.
