β-adrenergic signaling modulates cancer cell mechanotype through a RhoA-ROCK-myosin II axis
Kim, T.-H.; Vazquez-Hidalgo, E.; Abdou, A.; Tan, X. H. M.; Christodoulides, A.; Farris, C. M.; Chiou, P. Y.; Sloan, E. K.; Katira, P.; Rowat, A. C.
Show abstract
The ability of cells to deform and generate forces are key mechanical properties that are implicated in metastasis. While various soluble and mechanical cues are known to regulate cancer cell mechanical phenotype or mechanotype, our knowledge of how cells translate external signals into changes in mechanotype is still emerging. We previously discovered that activation of {beta}-adrenergic signaling, which results from soluble stress hormone cues, causes cancer cells to be stiffer or less deformable; this stiffer mechanotype was associated with increased cell motility and invasion. Here, we characterize how {beta}-adrenergic activation is translated into changes in cellular mechanotype by identifying molecular mediators that regulate key components of mechanotype including cellular deformability, traction forces, and non-muscle myosin II (NMII) activity. Using a micropillar assay and computational modelling, we determine that {beta}AR activation increases cellular force generation by increasing the number of actin-myosin binding events; this mechanism is distinct from how cells increase force production in response to matrix stiffness, suggesting that cells regulate their mechanotype using a complementary mechanism in response to stress hormone cues. To identify the molecules that modulate cellular mechanotype with {beta}AR activation, we use a high throughput filtration platform to screen the effects of pharmacologic and genetic perturbations on {beta}AR regulation of whole cell deformability. Our results indicate that {beta}AR activation decreases cancer cell deformability and increases invasion by signaling through RhoA, ROCK, and NMII. Our findings establish {beta}AR-RhoA-ROCK-NMII as a primary signaling axis that mediates cancer cell mechanotype, which provides a foundation for future interventions to stop metastasis.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Caveolin-1 protects endothelial cells from extensive expansion of transcellular tunnel by stiffening the plasma membrane 95%
- ADF and cofilin-1 collaborate to promote cortical actin flow and the leader bleb-based migration of confined cells 95%
- De novo identification of universal cell mechanics gene signatures 95%
Similar papers in this journal
- Nuclear lamin isoforms differentially contribute to LINC complex-dependent nucleocytoskeletal coupling and whole cell mechanics 95%
- Contractile forces direct the chiral swirling of minimal cell collectives 94%
- Elucidating Chiral Myosin-Induced Actin Dynamics: From Single Filament Behavior to Collective Structures 94%
Similar papers in this journal
- Matrix obstructions cause multiscale disruption in collective epithelial migration by suppressing physical function of leader cells 96%
- Ventral Stress Fibers Induce Plasma Membrane Deformation in Human Fibroblasts 96%
- TGFβ1-TNFα regulated secretion of neutrophil chemokines is independent of epithelial-mesenchymal transitions in breast tumor cells 95%
Similar papers in this journal
- Oncogenic RAS instructs morphological transformation of human epithelia via differential tissue mechanics. 95%
- Activated I-BAR IRSp53 clustering controls the formation of VASP-actin-based membrane protrusions 95%
- Myosin-I Synergizes with Arp2/3 Complex to Enhance Pushing Forces of Branched Actin Networks 94%
Similar papers in this journal
- Optogenetic control of small GTPases reveals RhoA-mediated intracellular calcium signaling 94%
- Receptor binding domain of SARS-CoV-2 is a functional αv-integrin agonist 94%
- S2Tag, a novel affinity tag for the capture and immobilization of coiled-coil proteins: application to the study of human β-cardiac myosin 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.