4.6 Article

TRP_2, a Lipid/Trafficking Domain That Mediates Diacylglycerol-induced Vesicle Fusion

期刊

JOURNAL OF BIOLOGICAL CHEMISTRY
卷 283, 期 49, 页码 34384-34392

出版社

AMER SOC BIOCHEMISTRY MOLECULAR BIOLOGY INC
DOI: 10.1074/jbc.M804707200

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资金

  1. Searle Young Investigators Award
  2. NCSA [MCB070027N]
  3. T. O. Magnetti Fund
  4. ML4 Foundation [MH18501]
  5. Research Scientist Award [DA00074]
  6. Eberly College of Sciences
  7. Huck Institutes of the Life Sciences

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We recently modeled transient receptor potential (TRP) channels using the Gestalt Domain Detection Algorithm-Basic Local Alignment Tool (GDDA-BLAST), which derives structural, functional, and evolutionary information from primary amino acid sequences using phylogenetic profiles (Ko, K. D., Hong, Y., Chang, G. S., Bhardwaj, G., van Rossum, D. B., and Patterson, R. L. (2008) Physics Arch. Quant. Methods arXiv:0806.2394v1). Herein we test our functional predictions for the TRP_2 domain of TRPC3; a domain of unknown function that is conserved in all TRPC channels. Our functional models of this domain identify both lipid binding and trafficking activities. In this study, we reveal: (i) a novel structural determinant of ion channel sensitivity to lipids, (ii) a molecular mechanism for the difference between diacylglycerol (DAG)-sensitive and DAG-insensitive TRPC subfamilies, and (iii) evidence that TRPC3 can comprise part of the vesicle fusion machinery. Indeed, the TRPC3 TRP_2 domain mediates channel trafficking to the plasma membrane and binds to plasma membrane lipids. Further, mutations in TRP_2, which alter lipid binding, also disrupt the DAG-mediated fusion of TRPC3-containing vesicles with the plasma membrane without disrupting SNARE interactions. Importantly, these data agree with the known role of DAG in membrane destabilization, which facilitates SNARE-dependent synaptic vesicle fusion (Villar, A. V., Goni, F. M., and Alonso, A. (2001) FEBS Lett. 494, 117-120 and Goni, F. M., and Alonso, A. (1999) Prog. Lipid Res. 38, 1-48). Taken together, functional models generated by GDDA-BLAST provide a computational platform for deriving domain functionality, which can have in vivo and mechanistic relevance.

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