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Effects of combined progesterone and 17β-estradiol treatment on the transcriptome of cultured human myometrial smooth muscle cells

Overview of attention for article published in Physiological Genomics, January 2016
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Title
Effects of combined progesterone and 17β-estradiol treatment on the transcriptome of cultured human myometrial smooth muscle cells
Published in
Physiological Genomics, January 2016
DOI 10.1152/physiolgenomics.00021.2015
Pubmed ID
Authors

Sreenath Chandran, Michael T. Cairns, Margaret O'Brien, Enda O'Connell, Kaveh Mashayekhi, Terry J. Smith

Abstract

A transcriptomic analysis of cultured human uterine smooth muscle cells (hUtSMCs) was performed in order to examine gene expression profiles in smooth muscle in an environment containing the two major steroid hormones that regulate the human myometrium in physiological states associated with estrous, pregnancy, labor, and pathophysiological states such as leiomyoma and endometrial cancer. hUtSMCs were treated with progesterone (P4) and 17β-estradiol (E2) individually and in combination, in the presence and absence of RU486 (mifepristone). Transcription of many genes was modulated in the presence of P4 or E2 alone but almost six times more genes were transcriptionally modulated in the presence of the P4/E2 hormone combination. In total 796 annotated genes were significantly differentially expressed in the presence of both P4 and E2 relative to their expression in untreated cells. Functional withdrawal of progesterone by addition of RU486 effectively reversed almost all transcriptional changes caused by P4/E2 treatment. Gene ontology analysis of differentially expressed genes revealed a strong association between P4/E2 treatment and down-regulated expression of genes involved in cell communication, signal transduction, channel activity, inflammatory response and differentiation. Up-regulated processes included cell survival, gene transcription, steroid hormone biosynthesis, muscle development, insulin receptor signalling and cell growth.

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 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 20%
Other 3 20%
Professor 2 13%
Student > Ph. D. Student 2 13%
Student > Doctoral Student 1 7%
Other 2 13%
Unknown 2 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 47%
Nursing and Health Professions 1 7%
Pharmacology, Toxicology and Pharmaceutical Science 1 7%
Agricultural and Biological Sciences 1 7%
Social Sciences 1 7%
Other 1 7%
Unknown 3 20%

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 05 November 2015.
All research outputs
#11,065,030
of 12,446,654 outputs
Outputs from Physiological Genomics
#704
of 792 outputs
Outputs of similar age
#225,653
of 273,627 outputs
Outputs of similar age from Physiological Genomics
#24
of 27 outputs
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So far Altmetric has tracked 792 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 27 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.