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<p>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.</p>

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Systematic AI-assisted screening of the cadhesome to map epithelial monolayer mechanics

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月3日

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  • 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

摘要

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.