Evidence map›Paper›PMID 34536389›Full record

ArticleBiophysical journal2021

Single-virus content-mixing assay reveals cholesterol-enhanced influenza membrane fusion efficiency.

Katherine N Liu, Steven G Boxer

Open access · bronzeAbstract read
In one paragraph

Article in Biophysical journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
2.3field-weighted citation impact, top 11% of its field
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

13 citing papers in PubMed, 36 citations in OpenAlex.

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  11. Recent Developments in Single-Virus Fusion Assay.The Journal of membrane biology · 2022
    Review
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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 at 2 institutions in 1 country.

Katherine N LiuDepartment of Chemistry, Stanford University, Stanford, California.
Steven G BoxerDepartment of Chemistry, Stanford University, Stanford, California. Electronic address: sboxer@stanford.edu.
Stanford Medicine · USStanford University · US

Funding

Biophysical Studies of Macromolecules and Molecular AssembliesR35GM118044 · NIGMS · STANFORD UNIVERSITY · PI STEVEN G. BOXER · 2016 to 2026
$7.2M
NIGMS NIH HHS R35 GM118044
6 · The paper itself

Abstract

To infect a cell, enveloped viruses must first undergo membrane fusion, which proceeds through a hemifusion intermediate, followed by the formation of a fusion pore through which the viral genome is transferred to a target cell. Single-virus fusion studies to elucidate the dynamics of content mixing typically require extensive fluorescent labeling of viral contents. The labeling process must be optimized depending on the virus identity and strain and can potentially be perturbative to viral fusion behavior. Here, we introduce a single-virus assay in which content-labeled vesicles are bound to unlabeled influenza A virus (IAV) to eliminate the problematic step of content-labeling virions. We use fluorescence microscopy to observe individual, pH-triggered content mixing and content-loss events between IAV and target vesicles of varying cholesterol compositions. We show that target membrane cholesterol increases the efficiency of IAV content mixing and decreases the fraction of content-mixing events that result in content loss. These results are consistent with previous findings that cholesterol stabilizes pore formation in IAV entry and limits leakage after pore formation. We also show that content loss due to hemagglutinin fusion peptide engagement with the target membrane is independent of composition. This approach is a promising strategy for studying the single-virus content-mixing kinetics of other enveloped viruses.

Indexed as

Influenza A virusInfluenza, HumanCholesterolHumansMembrane FusionVirus InternalizationCholesterol

Identifiers

PMID34536389
PMCPMC8595734
OpenAlexW3200630293

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.