| Title |
microRNA-128 regulates the proliferation and differentiation of bovine skeletal muscle satellite cells by repressing Sp1
|
|---|---|
| Published in |
Molecular and Cellular Biochemistry, February 2016
|
| DOI | 10.1007/s11010-016-2656-7 |
| Pubmed ID | |
| Authors |
Yang Dai, Wei Ran Zhang, Yi Min Wang, Xin Feng Liu, Xin Li, Xiang Bin Ding, Hong Guo |
| Abstract |
MicroRNAs (miRNAs) play essential roles in muscle cell proliferation and differentiation. The muscle-specific miRNAs miR-1 and miR-206 have been shown to regulate muscle development and promote myogenic differentiation; however, it is likely that a number of other miRNAs play important roles in regulating myogenesis as well. microRNA-128 (miR-128) has been reported to be highly expressed in brain and skeletal muscle, and we found that miR-128 is also up-regulated during bovine skeletal muscle satellite cell differentiation using microarray analysis and qRT-PCR. However, little is known about the functions of miR-128 in bovine skeletal muscle satellite cell development. In this study, we investigated the biological functions of miR-128 in bovine skeletal muscle cell development. Using a dual-luciferase reporter assay, we confirmed that miR-128 regulates the Sp1 gene. Over-expression of miR-128 reduced Sp1 protein levels and inhibited muscle satellite cell proliferation and differentiation. Inhibition of miR-128 increased Sp1 protein levels and promoted muscle satellite cell differentiation but also suppressed proliferation. Changes in miR-128 and Sp1 expression levels also affected the protein levels of MyoD and CDKN1A. Sp1, an activator of MyoD and a suppressor of CDKN1A, plays an important role in bovine muscle cell proliferation and differentiation. The results of our study reveal a mechanism by which miR-128 regulates bovine skeletal muscle satellite cell proliferation and myogenic differentiation via Sp1. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 26 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Doctoral Student | 5 | 19% |
| Student > Ph. D. Student | 5 | 19% |
| Student > Bachelor | 3 | 12% |
| Researcher | 2 | 8% |
| Professor > Associate Professor | 2 | 8% |
| Other | 2 | 8% |
| Unknown | 7 | 27% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 7 | 27% |
| Biochemistry, Genetics and Molecular Biology | 6 | 23% |
| Immunology and Microbiology | 2 | 8% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 4% |
| Veterinary Science and Veterinary Medicine | 1 | 4% |
| Other | 2 | 8% |
| Unknown | 7 | 27% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 04 February 2016.
All research outputs
#18,437,241
of 22,842,950 outputs
Outputs from Molecular and Cellular Biochemistry
#1,564
of 2,307 outputs
Outputs of similar age
#287,662
of 397,369 outputs
Outputs of similar age from Molecular and Cellular Biochemistry
#14
of 29 outputs
Altmetric has tracked 22,842,950 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,307 research outputs from this source. They receive a mean Attention Score of 3.9. This one is in the 21st percentile – i.e., 21% 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 397,369 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
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 is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.