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A programmable DNA-origami platform for studying lipid transfer between bilayers.

Xin Bian | Zhao Zhang | Qiancheng Xiong | Pietro De Camilli | Chenxiang Lin
Nature chemical biology | 2019

Non-vesicular lipid transport between bilayers at membrane contact sites plays important physiological roles. Mechanistic insight into the action of lipid-transport proteins localized at these sites requires determination of the distance between bilayers at which this transport can occur. Here we developed DNA-origami nanostructures to organize size-defined liposomes at precise distances and used them to study lipid transfer by the synaptotagmin-like mitochondrial lipid-binding protein (SMP) domain of extended synaptotagmin 1 (E-Syt1). Pairs of DNA-ring-templated donor and acceptor liposomes were docked through DNA pillars, which determined their distance. The SMP domain was anchored to donor liposomes via an unstructured linker, and lipid transfer was assessed via a Förster resonance energy transfer (FRET)-based assay. We show that lipid transfer can occur over distances that exceed the length of an SMP dimer, which is compatible with the shuttle model of lipid transport. The DNA nanostructures developed here can also be adapted to study other processes occurring where two membranes are closely apposed to each other.

Pubmed ID: 31320758

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Associated grants

  • Agency: NIGMS NIH HHS, United States
    Id: DP2 GM114830
  • Agency: NIDA NIH HHS, United States
    Id: P30 DA018343
  • Agency: NINDS NIH HHS, United States
    Id: R01 NS036251
  • Agency: NINDS NIH HHS, United States
    Id: R37 NS036251

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PRISM (tool)

RRID:SCR_005375

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5,2022.Tool that predicts interactions between transcription factors and their regulated genes from binding motifs. Understanding vertebrate development requires unraveling the cis-regulatory architecture of gene regulation. PRISM provides accurate genome-wide computational predictions of transcription factor binding sites for the human and mouse genomes, and integrates the predictions with GREAT to provide functional biological context. Together, accurate computational binding site prediction and GREAT produce for each transcription factor: 1. putative binding sites, 2. putative target genes, 3. putative biological roles of the transcription factor, and 4. putative cis-regulatory elements through which the factor regulates each target in each functional role.

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