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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-04-16

AUTHORS

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

ABSTRACT

BACKGROUND: 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. RESULTS: 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. CONCLUSION: 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. More... »

PAGES

258

References to SciGraph publications

  • 2006-04-12. Oocyte-granulosa cell interactions during mouse follicular development: regulation of kit ligand expression and its role in oocyte growth in REPRODUCTIVE BIOLOGY AND ENDOCRINOLOGY
  • 2005-01-01. limma: Linear Models for Microarray Data in BIOINFORMATICS AND COMPUTATIONAL BIOLOGY SOLUTIONS USING R AND BIOCONDUCTOR
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s12864-018-4642-9

    DOI

    http://dx.doi.org/10.1186/s12864-018-4642-9

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1103386586

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/29661134


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