Evidence map›Paper›PMID 32080222›Full record

ArticleScientific reports2020

Giant Endoplasmic Reticulum vesicles (GERVs), a novel model membrane tool.

Mona Grimmer, Kirsten Bacia

Abstract read
In one paragraph

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Mona GrimmerBiophysical Chemistry, Institute of Chemistry, Charles-Tanford Protein Center, University of Halle, Kurt-Mothes-Str. 3 A, 06120, Halle, Germany.
Kirsten BaciaBiophysical Chemistry, Institute of Chemistry, Charles-Tanford Protein Center, University of Halle, Kurt-Mothes-Str. 3 A, 06120, Halle, Germany. kirsten.bacia@chemie.uni-halle.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial giant vesicles have proven highly useful as membrane models in a large variety of biophysical and biochemical studies. They feature accessibility for manipulation and detection, but lack the compositional complexity needed to reconstitute complicated cellular processes. For the plasma membrane (PM), this gap was bridged by the establishment of giant PM vesicles (GPMVs). These native membranes have facilitated studies of protein and lipid diffusion, protein interactions, electrophysiology, fluorescence analysis of lateral domain formation and protein and lipid partitioning as well as mechanical membrane properties and remodeling. The endoplasmic reticulum (ER) is key to a plethora of biological processes in any eukaryotic cell. However, its intracellular location and dynamic and intricate tubular morphology makes it experimentally even less accessible than the PM. A model membrane, which will allow the afore-mentioned types of studies on GPMVs to be performed on ER membranes outside the cell, is therefore genuinely needed. Here, we introduce the formation of giant ER vesicles, termed GERVs, as a new tool for biochemistry and biophysics. To obtain GERVs, we have isolated ER membranes from Saccharomyces cerevisiae and fused them by exploiting the atlastin-like fusion protein Sey1p. We demonstrate the production of GERVs and their utility for further studies.

Indexed as

Cytological TechniquesApyraseCell MembraneDiffusionEndoplasmic ReticulumLipid MetabolismMicroscopy, ConfocalMicrosomesReproducibility of ResultsSaccharomyces cerevisiaeSaccharomyces cerevisiae ProteinsSEC Translocation ChannelsApyraseSaccharomyces cerevisiae ProteinsSEC61 protein, S cerevisiaeSEC Translocation Channels

Identifiers

PMID32080222
PMCPMC7033103

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.