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Regulatory networks in retinal ischemia-reperfusion injury

Overview of attention for article published in BMC Genomic Data, April 2015
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  • Above-average Attention Score compared to outputs of the same age (54th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

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Title
Regulatory networks in retinal ischemia-reperfusion injury
Published in
BMC Genomic Data, April 2015
DOI 10.1186/s12863-015-0201-4
Pubmed ID
Authors

Kalina Andreeva, Maha M Soliman, Nigel GF Cooper

Abstract

Retinal function is ordered by interactions between transcriptional and posttranscriptional regulators at the molecular level. These regulators include transcription factors (TFs) and posttranscriptional factors such as microRNAs (miRs). Some studies propose that miRs predominantly target the TFs rather than other types of protein coding genes and such studies suggest a possible interconnection of these two regulators in co-regulatory networks. Our lab has generated mRNA and miRNA microarray expression data to investigate time-dependent changes in gene expression, following induction of ischemia-reperfusion (IR) injury in the rat retina. Data from different reperfusion time points following retinal IR-injury were analyzed. Paired expression data for miRNA-target gene (TG), TF-TG, miRNA-TF were used to identify regulatory loop motifs whose expressions were altered by the IR injury paradigm. These loops were subsequently integrated into larger regulatory networks and biological functions were assayed. Systematic analyses of the networks have provided new insights into retinal gene regulation in the early and late periods of IR. We found both overlapping and unique patterns of molecular expression at the two time points. These patterns can be defined by their characteristic molecular motifs as well as their associated biological processes. We highlighted the regulatory elements of miRs and TFs associated with biological processes in the early and late phases of ischemia-reperfusion injury. The etiology of retinal ischemia-reperfusion injury is orchestrated by complex and still not well understood gene networks. This work represents the first large network analysis to integrate miRNA and mRNA expression profiles in context of retinal ischemia. It is likely that an appreciation of such regulatory networks will have prognostic potential. In addition, the computational framework described in this study can be used to construct miRNA-TF interactive systems networks for various diseases/disorders of the retina and other tissues.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 13%
Student > Postgraduate 2 13%
Student > Doctoral Student 2 13%
Student > Bachelor 2 13%
Professor 1 6%
Other 5 31%
Unknown 2 13%
Readers by discipline Count As %
Medicine and Dentistry 5 31%
Agricultural and Biological Sciences 3 19%
Biochemistry, Genetics and Molecular Biology 2 13%
Engineering 2 13%
Computer Science 1 6%
Other 0 0%
Unknown 3 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 12 October 2015.
All research outputs
#8,535,684
of 25,374,917 outputs
Outputs from BMC Genomic Data
#316
of 1,204 outputs
Outputs of similar age
#97,562
of 279,710 outputs
Outputs of similar age from BMC Genomic Data
#11
of 29 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,204 research outputs from this source. They receive a mean Attention Score of 4.3. This one has gotten more attention than average, scoring higher than 65% 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 279,710 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 54% of its contemporaries.
We're also able to compare this research output to 29 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 62% of its contemporaries.