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Transcriptome profiling of human oocytes experiencing recurrent total fertilization failure

There exist some patients who face recurrent total fertilization failure during assisted reproduction treatment, but the pathological mechanism underlying is elusive. Here, by using sc-RNA-seq method, the transcriptome profiles of ten abnormally fertilized zygotes were assessed, including five zygot...

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Detalles Bibliográficos
Autores principales: Suo, Lun, Zhou, Yu xiao, Jia, Li ling, Wu, Hai bo, Zheng, Jin, Lyu, Qi feng, Sun, Li hua, Sun, Han, Kuang, Yan ping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6297154/
https://www.ncbi.nlm.nih.gov/pubmed/30559372
http://dx.doi.org/10.1038/s41598-018-36275-6
Descripción
Sumario:There exist some patients who face recurrent total fertilization failure during assisted reproduction treatment, but the pathological mechanism underlying is elusive. Here, by using sc-RNA-seq method, the transcriptome profiles of ten abnormally fertilized zygotes were assessed, including five zygotes from one patient with recurrent Poly-PN zygotes, and five zygotes from a patient with pronuclear fusion failure. Four zygotes with three pronuclear (Tri-PN) were collected from four different patients as controls. After that, we identified 951 and 1697 significantly differentially expressed genes (SDEGs) in Poly-PN and PN arrest zygotes, respectively as compared with the control group. KEGG analyses indicated down regulated genes in the Poly-PN group included oocyte meiosis related genes, such as PPP2R1B, YWHAZ, MAD2L1, SPDYC, SKP1 and CDC27, together with genes associated with RNA processing, such as SF3B1, LOC645691, MAGOHB, PHF5A, PRPF18, DDX5, THOC1 and BAT1. In contrast, down regulated genes in the PN arrest group, included cell cycle genes, such as E2F4, DBF4, YWHAB, SKP2, CDC23, SMC3, CDC25A, CCND3, BUB1B, MDM2, CCNA2 and CDC7, together with homologous recombination related genes, such as NBN, XRCC3, SHFM1, RAD54B and RAD51. Thus, our work provides a better understanding of transcriptome profiles underlying RTFF, although it based on a limited number of patients.