Evidence map›Paper›PMID 28815417›Full record

ArticleVirus genes2017

Cellular interactome analysis of vaccinia virus K7 protein identifies three transport machineries as binding partners for K7.

Ye Li, Leiliang Zhang, Youyang Ke

Abstract read
PubMed Publisher
In one paragraph

Article in Virus genes, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.8field-weighted citation impact, top 32% 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

8 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Review
  6. Article
  7. Article
  8. 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

3 authors at 1 institution in 1 country.

Ye LiMOH Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China.
Leiliang ZhangMOH Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China. armzhang@hotmail.com.
Youyang KeDepartment of Emergency, 171st Hospital of PLA, Jiujiang, 332000, Jiangxi, China. keyouyang001@163.com.
Chinese Academy of Medical Sciences & Peking Union Medical College · CN

Funding

National Key Plan for Research and Development of China 2016YFD0500300National Natural Science Foundation of China 81471955Program for Changjiang Scholars and Innovative Research Team in University IRT13007
6 · The paper itself

Abstract

Identification of viral-host interacting proteins will contribute to understanding of how poxvirus exploits the host cellular machinery. The vaccinia virus gene K7R encodes a conserved protein K7 in most orthopoxviruses. To gain insight into the biology of K7, we investigated the cellular interactome of K7 by GST pulldown coupled with mass spectrometry. The top categories of identified proteins contained components of trafficking machineries. We selected key components of three transport machineries including coatomer, retromer, and CHEVI to further confirm their binding abilities to K7. Di-lysine motif of K7 is required for its interaction with coatomer, while C terminal leucines in K7 are critical for association of retromer. Our study uncovers the viral-host interactome of vaccinia K7 and reveals three host transport machineries as binding partners of K7, which might have important roles in poxvirus' life cycles.

Indexed as

Cell Line, TumorHeLa CellsHost-Pathogen InteractionsHumansProtein BindingVacciniaVaccinia virusViral ProteinsK7R protein, Vaccinia virusViral ProteinsCHEVICoatomerK7RetromerVaccinia virus

Identifiers

PMID28815417
OpenAlexW2748252917

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.