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Integrative Deep Sequencing of the Mouse Lung Transcriptome Reveals Differential Expression of Diverse Classes of Small RNAs in Response to Respiratory Virus Infection

Overview of attention for article published in mBio, November 2011
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

Mentioned by

news
1 news outlet
twitter
2 X users
patent
1 patent

Citations

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74 Dimensions

Readers on

mendeley
134 Mendeley
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Title
Integrative Deep Sequencing of the Mouse Lung Transcriptome Reveals Differential Expression of Diverse Classes of Small RNAs in Response to Respiratory Virus Infection
Published in
mBio, November 2011
DOI 10.1128/mbio.00198-11
Pubmed ID
Authors

Xinxia Peng, Lisa Gralinski, Martin T. Ferris, Matthew B. Frieman, Matthew J. Thomas, Sean Proll, Marcus J. Korth, Jennifer R. Tisoncik, Mark Heise, Shujun Luo, Gary P. Schroth, Terrence M. Tumpey, Chengjun Li, Yoshihiro Kawaoka, Ralph S. Baric, Michael G. Katze

Abstract

We previously reported widespread differential expression of long non-protein-coding RNAs (ncRNAs) in response to virus infection. Here, we expanded the study through small RNA transcriptome sequencing analysis of the host response to both severe acute respiratory syndrome coronavirus (SARS-CoV) and influenza virus infections across four founder mouse strains of the Collaborative Cross, a recombinant inbred mouse resource for mapping complex traits. We observed differential expression of over 200 small RNAs of diverse classes during infection. A majority of identified microRNAs (miRNAs) showed divergent changes in expression across mouse strains with respect to SARS-CoV and influenza virus infections and responded differently to a highly pathogenic reconstructed 1918 virus compared to a minimally pathogenic seasonal influenza virus isolate. Novel insights into miRNA expression changes, including the association with pathogenic outcomes and large differences between in vivo and in vitro experimental systems, were further elucidated by a survey of selected miRNAs across diverse virus infections. The small RNAs identified also included many non-miRNA small RNAs, such as small nucleolar RNAs (snoRNAs), in addition to nonannotated small RNAs. An integrative sequencing analysis of both small RNAs and long transcripts from the same samples showed that the results revealing differential expression of miRNAs during infection were largely due to transcriptional regulation and that the predicted miRNA-mRNA network could modulate global host responses to virus infection in a combinatorial fashion. These findings represent the first integrated sequencing analysis of the response of host small RNAs to virus infection and show that small RNAs are an integrated component of complex networks involved in regulating the host response to infection.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 134 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 4%
India 1 <1%
Netherlands 1 <1%
Japan 1 <1%
United Kingdom 1 <1%
Unknown 125 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 34 25%
Student > Ph. D. Student 31 23%
Student > Master 14 10%
Professor 10 7%
Professor > Associate Professor 9 7%
Other 19 14%
Unknown 17 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 56 42%
Biochemistry, Genetics and Molecular Biology 22 16%
Medicine and Dentistry 11 8%
Immunology and Microbiology 11 8%
Veterinary Science and Veterinary Medicine 2 1%
Other 6 4%
Unknown 26 19%
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 02 December 2021.
All research outputs
#2,721,508
of 25,374,647 outputs
Outputs from mBio
#2,106
of 6,508 outputs
Outputs of similar age
#14,070
of 152,916 outputs
Outputs of similar age from mBio
#4
of 25 outputs
Altmetric has tracked 25,374,647 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 6,508 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 25.0. This one has gotten more attention than average, scoring higher than 67% 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 152,916 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 90% of its contemporaries.
We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.