QLS Seminar Series - Matthew Akamatsu
Mechanisms of load adaptation by endocytic actin networks
Matthew Akamatsu, University of Washington
Tuesday March 24, 12-1pm
Zoom Link:听
In Person: 550 Sherbrooke, Room 189
Abstract:听Self-organization is a hallmark of biological processes wherein local (neighboring) interactions constrained by the geometrical environment and physical laws give rise to emergent architectures and adaptive behaviors. In clathrin-mediated endocytosis, branched actin cytoskeletal networks deform the cell鈥檚 plasma membrane against tension to internalize cargo. Characteristic of self-organizing systems, these networks adapt their size and architecture under high load to increase force-producing capability. However, the mechanisms that govern this mechano-adaptation remain poorly understood. Our lab combines biophysical modeling, quantitative microscopy of gene-edited human stem cells, and high-resolution image analysis to investigate cytoskeletal self-organization and load adaptation in mammalian endocytosis.
A minimal biophysical model recapitulated the mechano-adaptive behavior of these endocytic cytoskeletal networks. Simulations predicted that under elevated load, endocytic actin networks respond by nucleating actin filaments and shifting their orientations to a more load-bearing arrangement. To test this prediction, we used quantitative lattice light-sheet microscopy of gene-edited human induced pluripotent stem cells and transient osmotic shocks to increase membrane tension. Indeed, elevating membrane tension increased branched actin assembly and the fraction of endocytic sites assembling an actin cytoskeleton, specifically at the basal cellular surface. With simulations, we identified several putative factors that contribute to load adaptation, including the transient stalling of endocytic sites and force-dependent association rates between capping protein and actin filaments. These adaptive behaviors allow a dynamic actin cytoskeleton to robustly and reliably deform cellular membranes in the stochastic cellular environment.