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RNAs competing for microRNAs mutually influence their fluctuations in a highly non-linear microRNA-dependent manner in single cells

Overview of attention for article published in Genome Biology, February 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 (76th percentile)
  • Average Attention Score compared to outputs of the same age and source

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10 X users
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1 Wikipedia page

Citations

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45 Dimensions

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50 Mendeley
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Title
RNAs competing for microRNAs mutually influence their fluctuations in a highly non-linear microRNA-dependent manner in single cells
Published in
Genome Biology, February 2017
DOI 10.1186/s13059-017-1162-x
Pubmed ID
Authors

Carla Bosia, Francesco Sgrò, Laura Conti, Carlo Baldassi, Davide Brusa, Federica Cavallo, Ferdinando Di Cunto, Emilia Turco, Andrea Pagnani, Riccardo Zecchina

Abstract

Distinct RNA species may compete for binding to microRNAs (miRNAs). This competition creates an indirect interaction between miRNA targets, which behave as miRNA sponges and eventually influence each other's expression levels. Theoretical predictions suggest that not only the mean expression levels of targets but also the fluctuations around the means are coupled through miRNAs. This may result in striking effects on a broad range of cellular processes, such as cell differentiation and proliferation. Although several studies have reported the functional relevance of this mechanism of interaction, detailed experiments are lacking that study this phenomenon in controlled conditions by mimicking a physiological range. We used an experimental design based on two bidirectional plasmids and flow cytometry measurements of cotransfected mammalian cells. We validated a stochastic gene interaction model that describes how mRNAs can influence each other's fluctuations in a miRNA-dependent manner in single cells. We show that miRNA-target correlations eventually lead to either bimodal cell population distributions with high and low target expression states, or correlated fluctuations across targets when the pool of unbound targets and miRNAs are in near-equimolar concentration. We found that there is an optimal range of conditions for the onset of cross-regulation, which is compatible with 10-1000 copies of targets per cell. Our results are summarized in a phase diagram for miRNA-mediated cross-regulation that links experimentally measured quantities and effective model parameters. This phase diagram can be applied to in vivo studies of RNAs that are in competition for miRNA binding.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Denmark 1 2%
Unknown 49 98%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 12 24%
Researcher 9 18%
Student > Ph. D. Student 8 16%
Student > Master 5 10%
Professor > Associate Professor 3 6%
Other 2 4%
Unknown 11 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 11 22%
Agricultural and Biological Sciences 11 22%
Engineering 4 8%
Pharmacology, Toxicology and Pharmaceutical Science 2 4%
Computer Science 2 4%
Other 6 12%
Unknown 14 28%
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 11 October 2017.
All research outputs
#4,574,148
of 25,382,440 outputs
Outputs from Genome Biology
#2,718
of 4,468 outputs
Outputs of similar age
#75,576
of 323,273 outputs
Outputs of similar age from Genome Biology
#38
of 63 outputs
Altmetric has tracked 25,382,440 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 4,468 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 39th percentile – i.e., 39% 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 323,273 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 76% of its contemporaries.
We're also able to compare this research output to 63 others from the same source and published within six weeks on either side of this one. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.