↓ Skip to main content

High-throughput mRNA and miRNA profiling of epithelial-mesenchymal transition in MDCK cells

Overview of attention for article published in BMC Genomics, November 2015
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#26 of 7,251)
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

news
8 news outlets
blogs
2 blogs
twitter
1 tweeter

Citations

dimensions_citation
19 Dimensions

Readers on

mendeley
68 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
High-throughput mRNA and miRNA profiling of epithelial-mesenchymal transition in MDCK cells
Published in
BMC Genomics, November 2015
DOI 10.1186/s12864-015-2036-9
Pubmed ID
Authors

Priyank Shukla, Claus Vogl, Barbara Wallner, Doris Rigler, Mathias Müller, Sabine Macho-Maschler

Abstract

Epithelial-mesenchymal transition (EMT) is an important process in embryonic development, especially during gastrulation and organ formation. Furthermore EMT is widely observed in pathological conditions, e.g., fibrosis, tumor progression and metastasis. Madin-Darby Canine Kidney (MDCK) cells are widely used for studies of EMT and epithelial plasticity. MDCK cells show an epithelial phenotype, while oncogenic Ras-transformed MDCK (MDCK-Ras) cells undergo EMT and show a mesenchymal phenotype. RNA-Seq and miRNA-Seq analyses were performed on MDCK and MDCK-Ras cells. Data were validated by qRT-PCR. Gene signature analyses were carried out to identify pathways and gene ontology terms. For selected miRNAs target prediction was performed. With RNA-Seq, mRNAs of approximately half of the genes known for dog were detected. These were screened for differential regulation during Ras-induced EMT. We went further and performed gene signature analyses and found Gene Ontology (GO) terms and pathways important for epithelial polarity and implicated in EMT. Among the identified pathways, TGFβ1 emerged as a central signaling factor in many EMT related pathways and biological processes. With miRNA-Seq, approximately half of the known canine miRNAs were found expressed in MDCK and MDCK-Ras cells. Furthermore, among differentially expressed miRNAs, miRNAs that are known to be important regulators of EMT were detected and new candidates were predicted. New dog miRNAs were discovered after aligning our reads to that of other species in miRBase. Importantly, we could identify 25 completely novel miRNAs with a stable hairpin structure. Two of these novel miRNAs were differentially expressed. We validated the two novel miRNAs with the highest read counts by RT-qPCR. Target prediction of a particular novel miRNA highly expressed in mesenchymal MDCK-Ras cells revealed that it targets components of epithelial cell junctional complexes. Combining target prediction for the most upregulated miRNAs and validation of the targets in MDCK-Ras cells with pathway analysis allowed us to identify two novel pathways, e.g., JAK/STAT signaling and pancreatic cancer pathways. These pathways could not be detected solely by gene set enrichment analyses of RNA-Seq data. With deep sequencing data of mRNAs and miRNAs of MDCK cells and of Ras-induced EMT in MDCK cells, differentially regulated mRNAs and miRNAs are identified. Many of the identified genes are within pathways known to be involved in EMT. Novel differentially upregulated genes in MDCK cells are interferon stimulated genes and genes involved in Slit and Netrin signaling. New pathways not yet linked to these processes were identified. A central pathway in Ras induced EMT is TGFβ signaling, which leads to differential regulation of many target genes, including miRNAs. With miRNA-Seq we identified miRNAs involved in either epithelial cell biology or EMT. Finally, we describe completely novel miRNAs and their target genes.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 68 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 21%
Researcher 11 16%
Student > Bachelor 8 12%
Student > Master 7 10%
Student > Doctoral Student 5 7%
Other 12 18%
Unknown 11 16%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 26 38%
Agricultural and Biological Sciences 13 19%
Medicine and Dentistry 5 7%
Immunology and Microbiology 4 6%
Veterinary Science and Veterinary Medicine 2 3%
Other 3 4%
Unknown 15 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 73. 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 08 December 2015.
All research outputs
#193,396
of 12,378,687 outputs
Outputs from BMC Genomics
#26
of 7,251 outputs
Outputs of similar age
#7,439
of 320,913 outputs
Outputs of similar age from BMC Genomics
#2
of 474 outputs
Altmetric has tracked 12,378,687 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,251 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done particularly well, scoring higher than 99% 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 320,913 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 474 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.