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Bioinformatic analysis reveals the expression of unique transcriptomic signatures in Zika virus infected human neural stem cells

Overview of attention for article published in Cell & Bioscience, June 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#49 of 1,035)
  • High Attention Score compared to outputs of the same age (86th percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

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1 blog
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10 X users

Citations

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47 Dimensions

Readers on

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193 Mendeley
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Title
Bioinformatic analysis reveals the expression of unique transcriptomic signatures in Zika virus infected human neural stem cells
Published in
Cell & Bioscience, June 2016
DOI 10.1186/s13578-016-0110-x
Pubmed ID
Authors

Alyssa J. Rolfe, Dale B. Bosco, Jingying Wang, Richard S. Nowakowski, Jianqing Fan, Yi Ren

Abstract

The single-stranded RNA Flavivirus, Zika virus (ZIKV), has recently re-emerged and spread rapidly across the western hemisphere's equatorial countries, primarily through Aedes mosquito transmission. While symptoms in adult infections appear to be self-limiting and mild, severe birth defects, such as microcephaly, have been linked to infection during early pregnancy. Recently, Tang et al. (Cell Stem Cell 2016, doi: 10.1016/j.stem.2016.02.016) demonstrated that ZIKV efficiently infects induced pluripotent stem cell (iPSC) derived human neural progenitor cells (hNPCs), resulting in cell cycle abnormalities and apoptosis. Consequently, hNPCs are a suggested ZIKV target. We analyzed the transcriptomic sequencing (RNA-seq) data (GEO: GSE78711) of ZIKV (Strain: MR766) infected hNPCs. For comparison to the ZIKV-infected hNPCs, the expression data from hNPCs infected with human cytomegalovirus (CMV) (Strain: AD169) was used (GEO: GSE35295). Utilizing a combination of Gene Ontology, database of human diseases, and pathway analysis, we generated a putative systemic model of infection supported by known molecular pathways of other highly related viruses. We analyzed RNA-sequencing data for transcript expression alterations in ZIKV-infected hNPCs, and then compared them to expression patterns of iPSC-derived hNPCs infected with CMV, a virus that can also induce severe congenital neurological defects in developing fetuses. We demonstrate for the first time that many of cellular pathways correlate with clinical pathologies following ZIKV infection such as microcephaly, congenital nervous system disorders and epilepsy. Furthermore, ZIKV activates several inflammatory signals within infected hNPCs that are implicated in innate and acquired immune responses, while CMV-infected hNPCs showed limited representation of these pathways. Moreover, several genes related to pathogen responses are significantly upregulated upon ZIKV infection, but not perturbed in CMV-infected hNPCs. The presented study is the first to report enrichment of numerous pro-inflammatory pathways in ZIKV-infected hNPCs, indicating that hNPCs are capable of signaling through canonical pro-inflammatory pathways following viral infection. By defining gene expression profiles, new factors in the pathogenesis of ZIKV were identified which could help develop new therapeutic strategies.

X Demographics

X Demographics

The data shown below were collected from the profiles of 10 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 193 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 1%
Sri Lanka 1 <1%
Unknown 190 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 31 16%
Student > Ph. D. Student 29 15%
Student > Bachelor 28 15%
Researcher 27 14%
Student > Doctoral Student 22 11%
Other 33 17%
Unknown 23 12%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 36 19%
Agricultural and Biological Sciences 35 18%
Medicine and Dentistry 32 17%
Immunology and Microbiology 19 10%
Neuroscience 13 7%
Other 24 12%
Unknown 34 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 19 June 2016.
All research outputs
#2,607,035
of 23,867,274 outputs
Outputs from Cell & Bioscience
#49
of 1,035 outputs
Outputs of similar age
#47,123
of 349,829 outputs
Outputs of similar age from Cell & Bioscience
#2
of 16 outputs
Altmetric has tracked 23,867,274 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,035 research outputs from this source. They receive a mean Attention Score of 3.7. This one has done particularly well, scoring higher than 95% 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 349,829 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 16 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 93% of its contemporaries.