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α-catenin middle- and actin-binding domain unfolding mutants differentially impact epithelial strength and sheet migration.

Jeanne M QuinnYuou WangMegan WoodAnnette S FlozakPhuong M LeAlex YemelyanovPatrick W OakesCara J Gottardi
Published in: Molecular biology of the cell (2024)
α-catenin (α-cat) displays force-dependent unfolding and binding to actin filaments through direct and indirect means, but features of adherens junction structure and function most vulnerable to loss of these allosteric mechanisms have not been directly compared. By reconstituting an α-cat F-actin-binding domain unfolding mutant known to exhibit enhanced binding to actin (α-cat-H0-FABD + ) into α-cat knockout Madin Darby Canine Kidney (MDCK) cells, we show that partial loss of the α-cat catch bond mechanism (via an altered H0 α-helix) leads to stronger epithelial sheet integrity with greater colocalization between the α-cat-H0-FABD + mutant and actin. α-cat-H0-FABD + -expressing cells are less efficient at closing scratch-wounds, suggesting reduced capacity for more dynamic cell-cell coordination. Evidence that α-cat-H0-FABD + is equally accessible to the conformationally sensitive α18 antibody epitope as WT α-cat and shows similar vinculin recruitment suggests this mutant engages lower tension cortical actin networks, as its M-domain is not persistently open. Conversely, α-cat-M-domain salt-bridge mutants with persistent recruitment of vinculin and phosphorylated myosin light chain show only intermediate monolayer adhesive strengths, but display less directionally coordinated and thereby slower migration speeds during wound-repair. These data show α-cat M- and FABD-unfolding mutants differentially impact cell-cell cohesion and migration properties, and suggest signals favoring α-cat-cortical actin interaction without persistent M-domain opening may improve epithelial monolayer strength through enhanced coupling to lower tension actin networks.
Keyphrases
  • single cell
  • cell migration
  • cell therapy
  • induced apoptosis
  • wild type
  • stem cells
  • machine learning
  • signaling pathway
  • transcription factor
  • oxidative stress
  • dna binding
  • big data