Human embryos secrete microRNAs into culture media--a potential biomarker for implantation

Evan M Rosenbluth1, Dawne N Shelton1, Lindsay M Wells1

  • 1Department of Obstetrics and Gynecology, University of Iowa Carver College of Medicine, Iowa City, Iowa.

Abstract

Insights

Human blastocysts release microRNAs (miRNAs) into culture media, which can indicate embryo ploidy and predict in vitro fertilization success. These findings suggest miRNAs are potential biomarkers for improving IVF outcomes.

Area of Science:

  • Reproductive biology
  • Molecular biology
  • Genetics

Background:

  • Human blastocysts secrete molecules into their surrounding environment.
  • MicroRNAs (miRNAs) are small non-coding RNAs with regulatory functions.
  • Predicting in vitro fertilization (IVF) success remains a challenge.

Purpose of the Study:

  • To investigate if human blastocysts secrete miRNAs into culture media.
  • To determine if these secreted miRNAs reflect embryonic ploidy status.
  • To assess the potential of these miRNAs as biomarkers for predicting IVF outcomes.

Main Methods:

  • Analysis of miRNA expression in IVF culture media from donated human embryos.
  • Chromosomal comparative genomic hybridization (CGH) to assess embryo ploidy.
  • Quantitative real-time polymerase chain reaction (qPCR) array analysis for miRNA detection.
  • Correlation of miRNA expression with fertilization method and pregnancy outcomes.

Main Results:

  • Ten miRNAs were detected in the culture media, with miR-191 and miR-372 being specific to spent media.
  • Higher concentrations of miR-191 were found in media from aneuploid embryos.
  • Specific miRNAs (miR-191, miR-372, miR-645) were elevated in media from failed IVF cycles and after intracytoplasmic sperm injection (ICSI).

Conclusions:

  • MicroRNAs are detectable in IVF culture media.
  • Differential expression of certain miRNAs correlates with fertilization method, chromosomal status, and pregnancy outcomes.
  • These miRNAs show promise as non-invasive biomarkers for predicting IVF success.

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