Authors
Cristina Bertocchi, Juan José Alegría, Sebastián Vásquez-Sepúlveda, Rosario Ibanez-Prat, Aishwarya Srinivasan, Ignacio Arrano-Valenzuela, Barbara Castro-Pereira, Catalina Soto-Montandon, Alejandra Trujillo-Espergel, Ignacio Montenegro-Rojas, Shinji Deguchi, Gareth I Owen, Pakorn Kanchanawong, Mauricio Cerda, Giovanni Motta, Ronen Zaidel-Bar, Andrea Ravasio
Published in
Cell communication and signaling : CCS. Jul 03, 2026. Epub Jul 03, 2026.
Abstract
Cadherin-mediated adhesions serve as key mechanical and signaling hubs in epithelial tissues, linking the actin cytoskeleton of adjacent cells. Their disruption is a hallmark of cancer progression. The "cadhesome" network comprises over 170 proteins involved in cadherin-mediated adhesion and force transmission, yet its complexity hampers functional understanding. We developed a high-throughput platform combining gene silencing, imaging, and AI-based analysis to profile the role of each cadhesome component in monolayer formation and mechanical integrity. Using EpH4 epithelial cells, we analyzed phenotypes under vehicle and nocodazole-challenge conditions. Machine learning enabled classification of monolayer disruption, junctional organization, and contractile state. Beyond confirming known mechanotransduction hubs centered on E-cadherin, EGFR, and RAC1, our approach systematically uncovered candidate regulators of monolayer contractile state and stress adaptation, identified condition-specific roles of poorly characterized proteins, and organized them into annotated mechanobiological subnetworks that serve as a basis for hypothesis generation. Presented as a prioritized discovery resource, this work establishes a scalable strategy to decode mechano-molecular networks and provides a blueprint for hypothesis-driven investigation of epithelial mechanics with potential translational relevance.
PMID:
42399739
Bibliographic data and abstract were imported from PubMed on 04 Jul 2026.
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