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KLRD1-expressing natural killer cells predict influenza susceptibility

Overview of attention for article published in Genome Medicine, June 2018
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#10 of 1,581)
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

news
69 news outlets
blogs
3 blogs
twitter
44 X users
facebook
1 Facebook page

Citations

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56 Dimensions

Readers on

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70 Mendeley
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Title
KLRD1-expressing natural killer cells predict influenza susceptibility
Published in
Genome Medicine, June 2018
DOI 10.1186/s13073-018-0554-1
Pubmed ID
Authors

Erika Bongen, Francesco Vallania, Paul J. Utz, Purvesh Khatri

Abstract

Influenza infects tens of millions of people every year in the USA. Other than notable risk groups, such as children and the elderly, it is difficult to predict what subpopulations are at higher risk of infection. Viral challenge studies, where healthy human volunteers are inoculated with live influenza virus, provide a unique opportunity to study infection susceptibility. Biomarkers predicting influenza susceptibility would be useful for identifying risk groups and designing vaccines. We applied cell mixture deconvolution to estimate immune cell proportions from whole blood transcriptome data in four independent influenza challenge studies. We compared immune cell proportions in the blood between symptomatic shedders and asymptomatic nonshedders across three discovery cohorts prior to influenza inoculation and tested results in a held-out validation challenge cohort. Natural killer (NK) cells were significantly lower in symptomatic shedders at baseline in both discovery and validation cohorts. Hematopoietic stem and progenitor cells (HSPCs) were higher in symptomatic shedders at baseline in discovery cohorts. Although the HSPCs were higher in symptomatic shedders in the validation cohort, the increase was statistically nonsignificant. We observed that a gene associated with NK cells, KLRD1, which encodes CD94, was expressed at lower levels in symptomatic shedders at baseline in discovery and validation cohorts. KLRD1 expression in the blood at baseline negatively correlated with influenza infection symptom severity. KLRD1 expression 8 h post-infection in the nasal epithelium from a rhinovirus challenge study also negatively correlated with symptom severity. We identified KLRD1-expressing NK cells as a potential biomarker for influenza susceptibility. Expression of KLRD1 was inversely correlated with symptom severity. Our results support a model where an early response by KLRD1-expressing NK cells may control influenza infection.

X Demographics

X Demographics

The data shown below were collected from the profiles of 44 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 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 70 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 13 19%
Student > Ph. D. Student 12 17%
Researcher 12 17%
Student > Master 5 7%
Other 4 6%
Other 8 11%
Unknown 16 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 15 21%
Medicine and Dentistry 10 14%
Immunology and Microbiology 7 10%
Agricultural and Biological Sciences 5 7%
Engineering 3 4%
Other 9 13%
Unknown 21 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 590. 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 14 February 2024.
All research outputs
#38,958
of 25,388,353 outputs
Outputs from Genome Medicine
#10
of 1,581 outputs
Outputs of similar age
#838
of 335,539 outputs
Outputs of similar age from Genome Medicine
#2
of 24 outputs
Altmetric has tracked 25,388,353 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 1,581 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.8. 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 335,539 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 24 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 95% of its contemporaries.