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Transcriptome analysis of granulosa cells after conventional vs long FSH-induced superstimulation in cattle

Overview of attention for article published in BMC Genomics, April 2018
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Title
Transcriptome analysis of granulosa cells after conventional vs long FSH-induced superstimulation in cattle
Published in
BMC Genomics, April 2018
DOI 10.1186/s12864-018-4642-9
Pubmed ID
Authors

F. C. F. Dias, M. I. R. Khan, M. A. Sirard, G. P. Adams, J. Singh

Abstract

Prolongation of superstimulatory treatment appears to be associated with a greater superovulatory response and with greater oocyte maturation in cattle. A genome-wide bovine oligo-microarray was used to compare the gene expression of granulosa cells collected from ovarian follicles after differing durations of the growing phase induced by exogenous FSH treatment. Cows were given a conventional (4-day) or long (7-day) superstimulatory treatment (25 mg FSH im at 12-h intervals; n = 6 per group), followed by prostaglandin treatment with last FSH and LH treatment 24 h later. Granulosa cells were harvested 24 h after LH treatment. The expression of 416 genes was down-regulated and 615 genes was up-regulated in the long FSH group compared to the conventional FSH group. Quantification by RT-PCR of 7 genes (NTS, PTGS2, PTX3, RGS2, INHBA, CCND2 and LRP8) supported the microarrays data. Multigene bioinformatic analysis indicates that markers of fertility and follicle maturity were up-regulated in the long FSH group. Using the large gene expression dataset generated by the genomic analysis and our previous associated with the growth phase and gene expression changes post LH, we can conclude that a prolonged FSH-induced growing phase is associated with transcriptomic characteristics of greater follicular maturity and may therefore be more appropriate for optimizing the superovulatory response and developmental competence of oocytes in cattle.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 32%
Researcher 4 16%
Student > Bachelor 3 12%
Student > Master 3 12%
Professor 2 8%
Other 2 8%
Unknown 3 12%
Readers by discipline Count As %
Veterinary Science and Veterinary Medicine 4 16%
Agricultural and Biological Sciences 4 16%
Medicine and Dentistry 3 12%
Biochemistry, Genetics and Molecular Biology 2 8%
Immunology and Microbiology 1 4%
Other 1 4%
Unknown 10 40%

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 18 April 2018.
All research outputs
#11,396,406
of 12,818,993 outputs
Outputs from BMC Genomics
#6,610
of 7,538 outputs
Outputs of similar age
#234,482
of 270,423 outputs
Outputs of similar age from BMC Genomics
#16
of 21 outputs
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