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Identification of microRNAs associated with allergic airway disease using a genetically diverse mouse population

Overview of attention for article published in BMC Genomics, August 2015
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  • Above-average Attention Score compared to outputs of the same age and source (61st percentile)

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Title
Identification of microRNAs associated with allergic airway disease using a genetically diverse mouse population
Published in
BMC Genomics, August 2015
DOI 10.1186/s12864-015-1732-9
Pubmed ID
Authors

Holly Rutledge, Jeanette Baran-Gale, Fernando Pardo-Manuel de Villena, Elissa J. Chesler, Gary A. Churchill, Praveen Sethupathy, Samir N. P. Kelada

Abstract

Allergic airway diseases (AADs) such as asthma are characterized in part by granulocytic airway inflammation. The gene regulatory networks that govern granulocyte recruitment are poorly understood, but evidence is accruing that microRNAs (miRNAs) play an important role. To identify miRNAs that may underlie AADs, we used two complementary approaches that leveraged the genotypic and phenotypic diversity of the Collaborative Cross (CC) mouse population. In the first approach, we sought to identify miRNA expression quantitative trait loci (eQTL) that overlap QTL for AAD-related phenotypes. Specifically, CC founder strains and incipient lines of the CC were sensitized and challenged with house dust mite allergen followed by measurement of granulocyte recruitment to the lung. Total lung RNA was isolated and miRNA was measured using arrays for CC founders and qRT-PCR for incipient CC lines. Among CC founders, 92 miRNAs were differentially expressed. We measured the expression of 40 of the most highly expressed of these 92 miRNAs in the incipient lines of the CC and identified 18 eQTL corresponding to 14 different miRNAs. Surprisingly, half of these eQTL were distal to the corresponding miRNAs, and even on different chromosomes. One of the largest-effect local miRNA eQTL was for miR-342-3p, for which we identified putative causal variants by bioinformatic analysis of the effects of single nucleotide polymorphisms on RNA structure. None of the miRNA eQTL co-localized with QTL for eosinophil or neutrophil recruitment. In the second approach, we constructed putative miRNA/mRNA regulatory networks and identified three miRNAs (miR-497, miR-351 and miR-31) as candidate master regulators of genes associated with neutrophil recruitment. Analysis of a dataset from human keratinocytes transfected with a miR-31 inhibitor revealed two target genes in common with miR-31 targets correlated with neutrophils, namely Oxsr1 and Nsf. miRNA expression in the allergically inflamed murine lung is regulated by genetic loci that are smaller in effect size compared to mRNA eQTL and often act in trans. Thus our results indicate that the genetic architecture of miRNA expression is different from mRNA expression. We identified three miRNAs, miR-497, miR-351 and miR-31, that are candidate master regulators of genes associated with neutrophil recruitment. Because miR-31 is expressed in airway epithelia and is predicted to target genes with known links to neutrophilic inflammation, we suggest that miR-31 is a potentially novel regulator of airway inflammation.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 43 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 21%
Researcher 6 14%
Student > Master 4 9%
Student > Doctoral Student 3 7%
Student > Bachelor 3 7%
Other 9 21%
Unknown 9 21%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 28%
Biochemistry, Genetics and Molecular Biology 9 21%
Medicine and Dentistry 4 9%
Immunology and Microbiology 4 9%
Engineering 2 5%
Other 3 7%
Unknown 9 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 03 May 2016.
All research outputs
#7,727,791
of 24,792,414 outputs
Outputs from BMC Genomics
#3,412
of 11,067 outputs
Outputs of similar age
#85,367
of 273,219 outputs
Outputs of similar age from BMC Genomics
#99
of 258 outputs
Altmetric has tracked 24,792,414 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 11,067 research outputs from this source. They receive a mean Attention Score of 4.8. 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 273,219 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.
We're also able to compare this research output to 258 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.