Evidence map›Paper›PMID 28984199›Full record

ArticleBMC systems biology2017

Roles of alternative splicing in modulating transcriptional regulation.

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

Open access · diamondAbstract read
In one paragraph

Article in BMC systems biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
1.3field-weighted citation impact, top 19% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

23 citing papers in PubMed, 33 citations in OpenAlex.

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  7. Floral Homeotic Factors: A Question of Specificity.Plants (Basel, Switzerland) · 2023
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  8. DAZAP1 overexpression promotes growth of HCC cell lines: a primary study using CEUS.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2022
    Article
  9. ABA Mediates Plant Development and Abiotic Stress via Alternative Splicing.International journal of molecular sciences · 2022
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 2 countries.

Jin LiCollege of Automation, Harbin Engineering University, Harbin, Heilongjiang, 150001, China.
Yang WangCollege of Automation, Harbin Engineering University, Harbin, Heilongjiang, 150001, China.
Xi RaoDepartment of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
Yue WangDepartment of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
Weixing FengCollege of Automation, Harbin Engineering University, Harbin, Heilongjiang, 150001, China.
Hong LiangCollege of Automation, Harbin Engineering University, Harbin, Heilongjiang, 150001, China. lh@hrbeu.edu.cn.
Yunlong LiuCollege of Automation, Harbin Engineering University, Harbin, Heilongjiang, 150001, China. yunliu@iu.edu.
Harbin Engineering University · CNIndiana University – Purdue University Indianapolis · USIndiana University School of Medicine

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe 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.

resultsIn 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.

conclusionsOur 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.

Indexed as

Alternative SplicingModels, GeneticDatabases, GeneticGene Expression RegulationHumansProto-Oncogene Proteins c-mdm2Receptors, GlucocorticoidRegression AnalysisTranscription, GeneticTumor Suppressor Protein p53Proto-Oncogene Proteins c-mdm2Receptors, GlucocorticoidTumor Suppressor Protein p53Alternative splicingGRKidney cancerLinear regressionMDM2TP53Transcriptional regulation

Identifiers

PMID28984199
PMCPMC5629561
OpenAlexW2762529831

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.