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Dissecting Gq/11-Mediated Plasma Membrane Translocation of Sphingosine Kinase-1.

Kira Vanessa BlankenbachRalf Frederik ClaasNatalie Judith AsterAnna Katharina SpohnerSandra TrautmannNerea FerreirósJustin L BlackJohn J G TesmerStefan OffermannsThomas WielandDagmar Meyer Zu Heringdorf
Published in: Cells (2020)
Diverse extracellular signals induce plasma membrane translocation of sphingosine kinase-1 (SphK1), thereby enabling inside-out signaling of sphingosine-1-phosphate. We have shown before that Gq-coupled receptors and constitutively active Gαq/11 specifically induced a rapid and long-lasting SphK1 translocation, independently of canonical Gq/phospholipase C (PLC) signaling. Here, we further characterized Gq/11 regulation of SphK1. SphK1 translocation by the M3 receptor in HEK-293 cells was delayed by expression of catalytically inactive G-protein-coupled receptor kinase-2, p63Rho guanine nucleotide exchange factor (p63RhoGEF), and catalytically inactive PLCβ3, but accelerated by wild-type PLCβ3 and the PLCδ PH domain. Both wild-type SphK1 and catalytically inactive SphK1-G82D reduced M3 receptor-stimulated inositol phosphate production, suggesting competition at Gαq. Embryonic fibroblasts from Gαq/11 double-deficient mice were used to show that amino acids W263 and T257 of Gαq, which interact directly with PLCβ3 and p63RhoGEF, were important for bradykinin B2 receptor-induced SphK1 translocation. Finally, an AIXXPL motif was identified in vertebrate SphK1 (positions 100-105 in human SphK1a), which resembles the Gαq binding motif, ALXXPI, in PLCβ and p63RhoGEF. After M3 receptor stimulation, SphK1-A100E-I101E and SphK1-P104A-L105A translocated in only 25% and 56% of cells, respectively, and translocation efficiency was significantly reduced. The data suggest that both the AIXXPL motif and currently unknown consequences of PLCβ/PLCδ(PH) expression are important for regulation of SphK1 by Gq/11.
Keyphrases
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  • cell proliferation
  • oxidative stress
  • cell cycle arrest
  • machine learning
  • induced pluripotent stem cells
  • data analysis