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Roles of alternative splicing in modulating transcriptional regulation

Overview of attention for article published in BMC Systems Biology, October 2017
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  • High Attention Score compared to outputs of the same age and source (83rd percentile)

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Title
Roles of alternative splicing in modulating transcriptional regulation
Published in
BMC Systems Biology, October 2017
DOI 10.1186/s12918-017-0465-6
Pubmed ID
Authors

Jin Li, Yang Wang, Xi Rao, Yue Wang, Weixing Feng, Hong Liang, Yunlong Liu

Abstract

The ability of a transcription factor to regulate its targets is modulated by a variety of genetic and epigenetic mechanisms. Alternative splicing can modulate gene function by adding or removing certain protein domains, and therefore affect the activity of protein. Reverse engineering of gene regulatory networks using gene expression profiles has proven valuable in dissecting the logical relationships among multiple proteins during the transcriptional regulation. However, it is unclear whether alternative splicing of certain proteins affects the activity of other transcription factors. In order to investigate the roles of alternative splicing during transcriptional regulation, we constructed a statistical model to infer whether the alternative splicing events of modulator proteins can affect the ability of key transcription factors in regulating the expression levels of their transcriptional targets. We tested our strategy in KIRC (Kidney Renal Clear Cell Carcinoma) using the RNA-seq data downloaded from TCGA (the Cancer Genomic Atlas). We identified 828of modulation relationships between the splicing levels of modulator proteins and activity levels of transcription factors. For instance, we found that the activity levels of GR (glucocorticoid receptor) protein, a key transcription factor in kidney, can be influenced by the splicing status of multiple proteins, including TP53, MDM2 (mouse double minute 2 homolog), RBM14 (RNA-binding protein 14) and SLK (STE20 like kinase). The influenced GR-targets are enriched by key cancer-related pathways, including p53 signaling pathway, TR/RXR activation, CAR/RXR activation, G1/S checkpoint regulation pathway, and G2/M DNA damage checkpoint regulation pathway. Our analysis suggests, for the first time, that exon inclusion levels of certain regulatory proteins can affect the activities of many transcription factors. Such analysis can potentially unravel a novel mechanism of how splicing variation influences the cellular function and provide important insights for how dysregulation of splicing outcome can lead to various diseases.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 48 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 33%
Student > Bachelor 5 10%
Other 3 6%
Researcher 3 6%
Student > Master 3 6%
Other 6 13%
Unknown 12 25%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 15 31%
Agricultural and Biological Sciences 6 13%
Computer Science 4 8%
Engineering 4 8%
Neuroscience 2 4%
Other 5 10%
Unknown 12 25%
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 07 October 2017.
All research outputs
#7,174,980
of 23,881,329 outputs
Outputs from BMC Systems Biology
#254
of 1,126 outputs
Outputs of similar age
#111,588
of 325,064 outputs
Outputs of similar age from BMC Systems Biology
#4
of 18 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 1,126 research outputs from this source. They receive a mean Attention Score of 3.6. This one has done well, scoring higher than 77% 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 325,064 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 65% of its contemporaries.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.