RNA based biomarkers for prediction of the endometrial window of implantation

buir.advisorGüre, Ali Osmay
dc.contributor.authorDedeoğlu, Ege
dc.date.accessioned2021-02-01T06:39:56Z
dc.date.available2021-02-01T06:39:56Z
dc.date.copyright2020-12
dc.date.issued2020-12
dc.date.submitted2021-01-28
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (M.S.): Bilkent University, Department of Molecular Biology and Genetics, İhsan Doğramacı Bilkent University, 2020.en_US
dc.descriptionIncludes bibliographical references (leaves 74-82).en_US
dc.description.abstractEarly reproductive failure is the most common issue related to successful pregnancies, as around 30% of all conceptions reach live birth. The path to a successful pregnancy is reliant on the successful implantation of the embryo to the endometrium. This event requires three major components; a viable embryo ready for implantation, a receptive endometrium in which the implantation will occur, and healthy crosstalk between the embryo and receptive endometrium. It is estimated that two out of all three implantation failures are related to endometrial origin. This has led many researchers to attempt to elucidate the mechanism behind endometrial receptivity and generate a prediction of successful implantation of endometrial origin. Although there have been plenty of articles on this subject, there is still no consensus regarding standard endometrial receptivity biomarkers. Additionally, most of these articles’ findings cannot find their way into clinics. This is highlighted by the fact that the success rate of embryo implantation in ART applied in clinics is only around 10%. This study aimed to identify novel methods and biomarkers to predict the endometrium's receptivity, which could be applied in clinics easier and faster than the current kits in the market. We took several different approaches to achieve this aim. The first was to identify and validate particular miRNAs that showed a change in expression levels of the different days of the endometrial cycle in a healthy women’s serum. Our bioinformatical analysis has yielded ten miRNAs that show statistical differences in the human endometrium and being expressed in the serum. Downstream RNA-Seq and qPCR experiments have validated specific miRNAs previously predicted and identified novel miRNAs used for this purpose. The second was using in silico methods, identifying novel genes present in the endometrium that can predict the optimal point of receptivity. If considered and validated in vitro, this novel gene-list will be a cheaper but still as powerful alternative to the current endometrial test kit used in clinics today. Further validation RNA-Seq experiments on healthy and infertile females will elucidate our novel biomarkers' strength, designed to be used in ART clinics worldwide. Furthermore, upon building on these findings, it is possible to uncover previously overlooked mechanisms leading to women's implantation success.en_US
dc.description.provenanceSubmitted by Betül Özen (ozen@bilkent.edu.tr) on 2021-02-01T06:39:56Z No. of bitstreams: 1 ED_AOG_MSThesis_Final.pdf: 3682473 bytes, checksum: 044d1702b1092465f0c1d60c6a208a70 (MD5)en
dc.description.provenanceMade available in DSpace on 2021-02-01T06:39:56Z (GMT). No. of bitstreams: 1 ED_AOG_MSThesis_Final.pdf: 3682473 bytes, checksum: 044d1702b1092465f0c1d60c6a208a70 (MD5) Previous issue date: 2021-01en
dc.description.statementofresponsibilityby Ege Dedeoğluen_US
dc.embargo.release2021-07-28
dc.format.extentxvii, 92 leaves : color charts, graphics ; 30 cm.en_US
dc.identifier.itemidB150722
dc.identifier.urihttp://hdl.handle.net/11693/54965
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectEndometrial receptivityen_US
dc.subjectmiRNAen_US
dc.subjectSerumen_US
dc.subjectImplantationen_US
dc.subjectEmbryoen_US
dc.subjectIn silicoen_US
dc.subjectRNA-Seqen_US
dc.titleRNA based biomarkers for prediction of the endometrial window of implantationen_US
dc.title.alternativeEndometriyal implantasyon penceresinin RNA temelli biyobelirteçler ile tahmin edilmesien_US
dc.typeThesisen_US
thesis.degree.disciplineMolecular Biology and Genetics
thesis.degree.grantorBilkent University
thesis.degree.levelMaster's
thesis.degree.nameMS (Master of Science)

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