↓ Skip to main content

ASBMB

Article Metrics

Global interactomics uncovers extensive organellar targeting by Zika virus

Overview of attention for article published in Molecular and Cellular Proteomics, July 2018
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#1 of 2,231)
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

news
47 news outlets
twitter
9 tweeters
facebook
1 Facebook page

Readers on

mendeley
6 Mendeley
citeulike
2 CiteULike
Title
Global interactomics uncovers extensive organellar targeting by Zika virus
Published in
Molecular and Cellular Proteomics, July 2018
DOI 10.1074/mcp.tir118.000800
Pubmed ID
Authors

Etienne Coyaud, Charlene Ranadheera, Derrick T. Cheng, Joao Goncalves, Boris Dyakov, Estelle Laurent, Jonathan R. St-Germain, Laurence Pelletier, Anne-Claude Gingras, John Hunter Brumell, Peter K Kim, David Safronetz, Brian Raught

Abstract

Zika virus (ZIKV) is a membrane enveloped Flavivirus with a positive strand RNA genome, transmitted by Aedes mosquitoes (1). The geographical range of ZIKV has dramatically expanded in recent decades resulting in increasing numbers of infected individuals, and the spike in ZIKV infections has been linked to significant increases in both Guillain-Barré syndrome and microcephaly (1). While a large number of host proteins have been physically and/or functionally linked to other Flaviviruses, very little is known about the virus-host protein interactions established by ZIKV. Here we map host cell protein interaction profiles for each of the ten polypeptides encoded in the ZIKV genome, generating a protein topology network comprising 3033 interactions amongst 1224 unique human polypeptides. The interactome is enriched in proteins with roles in polypeptide processing and quality control, vesicle trafficking, RNA processing and lipid metabolism. >60% of the network components have been previously implicated in other types of viral infections; the remaining interactors comprise hundreds of new putative ZIKV functional partners. Mining this rich dataset, we highlight several examples of how ZIKV may usurp or disrupt the function of host cell organelles, and uncover an important role for peroxisomes in ZIKV infection.

Twitter Demographics

The data shown below were collected from the profiles of 9 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 50%
Student > Bachelor 1 17%
Other 1 17%
Student > Doctoral Student 1 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 3 50%
Agricultural and Biological Sciences 3 50%

Attention Score in Context

This research output has an Altmetric Attention Score of 369. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 31 August 2018.
All research outputs
#23,323
of 11,727,438 outputs
Outputs from Molecular and Cellular Proteomics
#1
of 2,231 outputs
Outputs of similar age
#1,002
of 222,710 outputs
Outputs of similar age from Molecular and Cellular Proteomics
#1
of 35 outputs
Altmetric has tracked 11,727,438 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,231 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done particularly well, scoring higher than 99% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 222,710 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 35 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.