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Dual RNA-seq reveals viral infections in asthmatic children without respiratory illness which are associated with changes in the airway transcriptome

Overview of attention for article published in Genome Biology, January 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • Average Attention Score compared to outputs of the same age and source

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12 X users

Citations

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112 Mendeley
Title
Dual RNA-seq reveals viral infections in asthmatic children without respiratory illness which are associated with changes in the airway transcriptome
Published in
Genome Biology, January 2017
DOI 10.1186/s13059-016-1140-8
Pubmed ID
Authors

Agata Wesolowska-Andersen, Jamie L. Everman, Rebecca Davidson, Cydney Rios, Rachelle Herrin, Celeste Eng, William J. Janssen, Andrew H. Liu, Sam S. Oh, Rajesh Kumar, Tasha E. Fingerlin, Jose Rodriguez-Santana, Esteban G. Burchard, Max A. Seibold

Abstract

Respiratory illness caused by viral infection is associated with the development and exacerbation of childhood asthma. Little is known about the effects of respiratory viral infections in the absence of illness. Using quantitative PCR (qPCR) for common respiratory viruses and for two genes known to be highly upregulated in viral infections (CCL8/CXCL11), we screened 92 asthmatic and 69 healthy children without illness for respiratory virus infections. We found 21 viral qPCR-positive and 2 suspected virus-infected subjects with high expression of CCL8/CXCL11. We applied a dual RNA-seq workflow to these subjects, together with 25 viral qPCR-negative subjects, to compare qPCR with sequencing-based virus detection and to generate the airway transcriptome for analysis. RNA-seq virus detection achieved 86% sensitivity when compared to qPCR-based screening. We detected additional respiratory viruses in the two CCL8/CXCL11-high subjects and in two of the qPCR-negative subjects. Viral read counts varied widely and were used to stratify subjects into Virus-High and Virus-Low groups. Examination of the host airway transcriptome found that the Virus-High group was characterized by immune cell airway infiltration, downregulation of cilia genes, and dampening of type 2 inflammation. Even the Virus-Low group was differentiated from the No-Virus group by 100 genes, some involved in eIF2 signaling. Respiratory virus infection without illness is not innocuous but may determine the airway function of these subjects by driving immune cell airway infiltration, cellular remodeling, and alteration of asthmogenic gene expression.

X Demographics

X Demographics

The data shown below were collected from the profiles of 12 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 112 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 <1%
United Kingdom 1 <1%
Unknown 110 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 25 22%
Researcher 23 21%
Student > Master 12 11%
Student > Bachelor 10 9%
Student > Doctoral Student 8 7%
Other 16 14%
Unknown 18 16%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 23 21%
Agricultural and Biological Sciences 21 19%
Medicine and Dentistry 19 17%
Immunology and Microbiology 12 11%
Computer Science 6 5%
Other 8 7%
Unknown 23 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 18 October 2018.
All research outputs
#4,706,721
of 25,377,790 outputs
Outputs from Genome Biology
#2,766
of 4,467 outputs
Outputs of similar age
#85,908
of 420,495 outputs
Outputs of similar age from Genome Biology
#36
of 61 outputs
Altmetric has tracked 25,377,790 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one is in the 38th percentile – i.e., 38% of its peers scored the same or lower than it.
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 420,495 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 79% of its contemporaries.
We're also able to compare this research output to 61 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.