A synergy between mechanosensitive calcium- and membrane-binding mediates tension-sensing by C2-like domains Journal Article


Authors: Shen, Z.; Belcheva, K. T.; Jelcic, M.; Hui, K. L.; Katikaneni, A.; Niethammer, P.
Article Title: A synergy between mechanosensitive calcium- and membrane-binding mediates tension-sensing by C2-like domains
Abstract: When nuclear membranes are stretched, the peripheral membrane enzyme cytosolic phospholipase A2 (cPLA2) binds via its calcium-dependent C2 domain (cPLA2-C2) and initiates bioactive lipid signaling and tissue inflammation. More than 150 C2-like domains are encoded in vertebrate genomes. How many of them are mechanosensors and quantitative relationships between tension and membrane recruitment remain unexplored, leaving a knowledge gap in the mechanotransduction field. In this study, we imaged the mechanosensitive adsorption of cPLA2 and its C2 domain to nuclear membranes and artificial lipid bilayers, comparing it to related C2-like motifs. Stretch increased the Ca2+ sensitivity of all tested domains, promoting half-maximal binding of cPLA2 at cytoplasmic resting-Ca2+ concentrations. cPLA2-C2 bound up to 50 times tighter to stretched than to unstretched membranes. Our data suggest that a synergy of mechanosensitive Ca2+ interactions and deep, hydrophobic membrane insertion enables cPLA2-C2 to detect stretched membranes with antibody-like affinity, providing a quantitative basis for understanding mechanotransduction by C2-like domains. © 2022 National Academy of Sciences. All rights reserved.
Keywords: calcium; mechanotransduction; membrane; nucleus; cpla2
Journal Title: Proceedings of the National Academy of Sciences of the United States of America
Volume: 119
Issue: 1
ISSN: 0027-8424
Publisher: National Academy of Sciences  
Date Published: 2022-01-04
Start Page: e2112390119
Language: English
DOI: 10.1073/pnas.2112390119
PUBMED: 34969839
PROVIDER: scopus
PMCID: PMC8740744
DOI/URL:
Notes: Article -- Export Date: 1 February 2022 -- Source: Scopus
Altmetric
Citation Impact
BMJ Impact Analytics
MSK Authors
  1. Mark Joseph Jelcic
    8 Jelcic
  2. King Lam Hui
    6 Hui
  3. Zhouyang Shen
    4 Shen