Network Analysis of Integrin Adhesion Complexes

• Published in: Methods in molecular biology (Clifton, N.J.) Vol. 2217; p. 149
• Main Authors: Li Mow Chee, Frederic, Byron, Adam
• Format: Journal Article
• Language: English
• Published: United States 2021
• Subjects: Actin Cytoskeleton - chemistry; Actin Cytoskeleton - metabolism; Animals; Cell Adhesion; Cell Movement; Computational Biology - methods; Extracellular Matrix - chemistry; Extracellular Matrix - metabolism; Humans; Integrins - genetics; Integrins - metabolism; Multiprotein Complexes - genetics; Multiprotein Complexes - metabolism; Neural Networks, Computer; Protein Binding; Protein Interaction Mapping - methods; Proteome - genetics; Proteome - metabolism; Software; Interaction networks; Data analysis; Network analysis; Cell adhesion; Proteomics; Bioinformatics; Cell signaling; Integrins
• ISSN: 1940-6029
• DOI: 10.1007/978-1-0716-0962-0_10

Cell-surface adhesion receptors mediate interactions with the extracellular matrix (ECM) to control many fundamental aspects of cell behavior, including cell migration, survival, and proliferation. Integrin adhesion receptors recruit structural and signaling proteins to form multimolecular adhesion complexes that link the plasma membrane to the actomyosin cytoskeleton. The assembly and turnover of adhesion complexes are tightly regulated, governed in part by the networks of physical protein interactions and functional signaling associations between components of the adhesome. Proteomic profiling of adhesion complexes has begun to reveal their molecular complexity and diversity. To interrogate the composition of cell-ECM adhesions, we detail herein an approach for the network analysis of adhesion complex proteomes. Integration of these proteomic data with adhesome databases in the context of predicted protein interactions enables the mapping of experimentally defined adhesion complex networks. Computational analysis of resultant network models can identify subnetworks of putative functionally linked adhesion protein communities. This approach provides a framework to predict functional adhesion protein relationships and generate new mechanistic hypotheses for further experimental testing.