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Differential long noncoding RNA/mRNA expression profiling and functional network analysis during osteogenic differentiation of human bone marrow mesenchymal stem cells

Overview of attention for article published in Stem Cell Research & Therapy, February 2017
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Title
Differential long noncoding RNA/mRNA expression profiling and functional network analysis during osteogenic differentiation of human bone marrow mesenchymal stem cells
Published in
Stem Cell Research & Therapy, February 2017
DOI 10.1186/s13287-017-0485-6
Pubmed ID
Authors

Wenyuan Zhang, Rui Dong, Shu Diao, Juan Du, Zhipeng Fan, Fu Wang

Abstract

Mesenchymal stem cells (MSCs) are the most promising cell types for bone regeneration and repair due to their osteogenic potential. MSC differentiation is precisely regulated and orchestrated by the mechanical and molecular signals from the extracellular environment, involving complex pathways regulated at both the transcriptional and post-transcriptional levels. However, the potential role of long noncoding RNA (lncRNA) in the osteogenic differentiation of human MSCs remains largely unclear. Here, we undertook the survey of differential coding and noncoding transcript expression profiling and functional network analysis during osteogenic differentiation of human bone marrow mesenchymal stem cells (BMSCs) using human whole transcriptome microarray. The key pathways, mRNAs, and lncRNAs controlling osteogenic differentiation of BMSCs were identified by further bioinformatic analysis. The role of lncRNA in the osteogenic differentiation of MSCs was verified by lncRNA overexpression or knockdown methods. A total of 1269 coding transcripts with 648 genes significantly upregulated and 621 genes downregulated, and 1408 lncRNAs with 785 lncRNAs significantly upregulated and 623 lncRNAs downregulated were detected along with osteogenic differentiation. Bioinformatic analysis identified that several pathways may be associated with osteogenic differentiation potentials of BMSCs, such as the MAPK signaling pathway, the Jak-STAT signaling pathway, the Toll-like receptor signaling pathway, and the TGF-beta signaling pathway, etc. Bioinformatic analysis also revealed 13 core regulatory genes including seven mRNAs (GPX3, TLR2, BDKRB1, FBXO5, BRCA1, MAP3K8, and SCARB1), and six lncRNAs (XR_111050, NR_024031, FR374455, FR401275, FR406817, and FR148647). Based on the analysis, we identified one lncRNA, XR_111050, that could enhance the osteogenic differentiation potentials of MSCs. The potential regulatory mechanisms were identified using bioinformatic analyses. We further predicted the interactions of differentially expressed coding and noncoding genes, and identified core regulatory factors by co-expression networks during osteogenic differentiation of BMSCs. Our results could lead to a better understanding of the molecular mechanisms of genes and lncRNAs, and their cooperation underlying MSC osteogenic differentiation and bone formation. We identified that one lncRNA, XR_111050, could be a potential target for bone tissue engineering.

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The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 63 100%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 11 17%
Student > Master 9 14%
Researcher 7 11%
Student > Ph. D. Student 7 11%
Student > Bachelor 3 5%
Other 4 6%
Unknown 22 35%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 18 29%
Medicine and Dentistry 10 16%
Agricultural and Biological Sciences 5 8%
Materials Science 2 3%
Immunology and Microbiology 1 2%
Other 4 6%
Unknown 23 37%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 23 March 2017.
All research outputs
#14,918,889
of 22,952,268 outputs
Outputs from Stem Cell Research & Therapy
#1,211
of 2,428 outputs
Outputs of similar age
#242,158
of 420,202 outputs
Outputs of similar age from Stem Cell Research & Therapy
#27
of 48 outputs
Altmetric has tracked 22,952,268 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,428 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one is in the 44th percentile – i.e., 44% 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 420,202 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 48 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.