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X Demographics
Mendeley readers
Attention Score in Context
| Title |
TNF-α and IGF1 modify the microRNA signature in skeletal muscle cell differentiation
|
|---|---|
| Published in |
Cell Communication and Signaling, January 2015
|
| DOI | 10.1186/s12964-015-0083-0 |
| Pubmed ID | |
| Authors |
Swanhild U Meyer, Christian Thirion, Anna Polesskaya, Stefan Bauersachs, Sebastian Kaiser, Sabine Krause, Michael W Pfaffl |
| Abstract |
BackgroundElevated levels of the inflammatory cytokine TNF-¿ are common in chronic diseases or inherited or degenerative muscle disorders and can lead to muscle wasting. By contrast, IGF1 has a growth promoting effect on skeletal muscle. The molecular mechanisms mediating the effect of TNF-¿ and IGF1 on muscle cell differentiation are not completely understood. Muscle cell proliferation and differentiation are regulated by microRNAs (miRNAs) which play a dominant role in this process. This study aims at elucidating how TNF-¿ or IGF1 regulate microRNA expression to affect myoblast differentiation and myotube formation.ResultsIn this study, we analyzed the impact of TNF-¿ or IGF1 treatment on miRNA expression in myogenic cells. Results reveal that i) TNF-¿ and IGF1 regulate miRNA expression during skeletal muscle cell differentiation in vitro, ii) microRNA targets can mediate the negative effect of TNF-¿ on fusion capacity of skeletal myoblasts by targeting genes associated with axon guidance, MAPK signalling, focal adhesion, and neurotrophin signalling pathway, iii) inhibition of miR-155 in combination with overexpression of miR-503 partially abrogates the inhibitory effect of TNF-¿ on myotube formation, and iv) MAPK/ERK inhibition might participate in modulating the effect of TNF-¿ and IGF1 on miRNA abundance.ConclusionsThe inhibitory effects of TNF-¿ or the growth promoting effects of IGF1 on skeletal muscle differentiation include the deregulation of known muscle-regulatory miRNAs as well as miRNAs which have not yet been associated with skeletal muscle differentiation or response to TNF-¿ or IGF1. This study indicates that miRNAs are mediators of the inhibitory effect of TNF-¿ on myoblast differentiation. We show that intervention at the miRNA level can ameliorate the negative effect of TNF-¿ by promoting myoblast differentiation. Moreover, we cautiously suggest that TNF-¿ or IGF1 modulate the miRNA biogenesis of some miRNAs via MAPK/ERK signalling. Finally, this study identifies indicative biomarkers of myoblast differentiation and cytokine influence and points to novel RNA targets. |
X Demographics
The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 14% |
| Unknown | 6 | 86% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 6 | 86% |
| Science communicators (journalists, bloggers, editors) | 1 | 14% |
Mendeley readers
The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 2% |
| Sweden | 1 | 2% |
| Unknown | 55 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 17 | 30% |
| Student > Ph. D. Student | 13 | 23% |
| Student > Doctoral Student | 7 | 12% |
| Student > Postgraduate | 4 | 7% |
| Student > Bachelor | 3 | 5% |
| Other | 8 | 14% |
| Unknown | 5 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 17 | 30% |
| Agricultural and Biological Sciences | 17 | 30% |
| Medicine and Dentistry | 10 | 18% |
| Engineering | 2 | 4% |
| Nursing and Health Professions | 2 | 4% |
| Other | 3 | 5% |
| Unknown | 6 | 11% |
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 23 September 2015.
All research outputs
#7,778,510
of 25,374,647 outputs
Outputs from Cell Communication and Signaling
#251
of 1,499 outputs
Outputs of similar age
#98,871
of 361,629 outputs
Outputs of similar age from Cell Communication and Signaling
#2
of 9 outputs
Altmetric has tracked 25,374,647 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 1,499 research outputs from this source. They receive a mean Attention Score of 3.8. This one has done well, scoring higher than 82% 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 361,629 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 72% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 7 of them.