DNA methylation status of bovine blastocysts obtained from peripubertal oocyte donors

Abstract

In the dairy industry, the high selection pressure combined with the increased efficiency of assisted reproduction technologies (ART) are leading toward the use of younger females for reproduction purposes, with the aim to reduce the interval between generations. This situation could impair embryo quality, decreasing the success rate of the ART procedures and the values of resulting offspring. Young Holstein heifers (n = 10) were subjected to ovarian stimulation and oocyte collection at 8, 11, and 14 months of age. All the oocytes were fertilized in vitro with semen from one adult bull, generating three pools of embryos per animal. Each animal was its own control for the evaluation of the effects of age. The EmbryoGENE platform was used to compare the DNA methylation status of blastocysts obtained from oocytes collected at 8 versus 14 and 11 versus 14 months of age. Age‐related contrast analysis identified 5,787 and 3,658 differentially methylated regions (DMRs) in blastocysts from heifers at 8 versus 14 and 11 versus 14 months of age, respectively. For both contrasts, the DMRs were distributed nonrandomly in the different DNA regions. The DNA from embryos from 8‐month‐old donors was more hypermethylated, while the DNA from embryos from 11‐month‐old donors displayed an intermediate phenotype. According to Ingenuity Pathway Analysis, the upstream regulator genes cellular tumor antigen p53, transforming growth factor β1, tumor necrosis factor, and hepatocyte nuclear factor 4α are particularly associated with methylation sensitive targets, which were more hypermethylated in embryos from younger donors.

IGF2R, KCNQ1, PLAGL1, and SNRPN DNA methylation is completed in bovine by the early antral follicle stage

“…Maternal ART procedures such as OS and IVM were linked to a higher frequency of imprinting defects in embryos (Kindsfather et al, 2019; Market‐Velker et al, 2010; Santos et al, 2010); however, evidence in the mouse (Anckaert et al, 2013; Denomme et al, 2011; Kindsfather et al, 2019) and the bovine (Heinzmann et al, 2011) models suggest that oocyte methylation is not affected by these procedures. Similarly, methylation differences were observed in blastocysts derived from bovine oocytes of prepubertal donors of varying ages (Morin‐Doré et al, 2020). The present study provides new evidence to more accurately describe the acquisition of DNA methylation at IGF2R, KCNQ1, PLAGL1, and SNRPN bovine maternally imprinted genes and its relationship to oocyte development.…”

“…Differences in follicular development dynamics (Currin et al, 2021; Landry, Labrecque, et al, 2018; Landry, Rossi‐Perazza, et al, 2018; Warzych et al, 2017) result in oocytes collected from prepubertal donors to enter atresia faster than their mature donor counterparts when left in the follicle longer before OPU (Landry, Labrecque, et al, 2018; Landry, Rossi‐Perazza, et al, 2018). Moreover, molecular evidence highlighted transcriptomic (Morin‐Doré et al, 2017) and epigenetic (Morin‐Doré et al, 2020) differences between embryos derived from mature and immature oocyte donors. In a previous study, we also observed that the donor's age coupled with ART affected maternally imprinted genes in embryonic and trophoblastic cells of Days 7 and 12 embryos (Lafontaine et al, 2020).…”

Pre-Implantation Bovine Embryo Evaluation—From Optics to Omics and Beyond

“…In addition to the methods described above, pre-implantation bovine embryos have been studied using a variety of other tools too (Table 10). Ribosome profile High-resolution ribosome fractionation, polysome profiling, RNA-seq [198] DNA methylation landscape Anti-5-methylcytosine (5-MeC) antibody, methylation-sensitive high-resolution melting analysis (MS-HRM), bisulfite sequencing [75,[242][243][244][245][246][247][248] mRNA localization In situ hybridization [63,80,249,250] Chromosomal abnormalities 5% Giemsa staining (1250× magnification) [251] Automation/artificial intelligence (AI)-based microscopy Use of genetic algorithms, artificial neural networks, automatic feature extraction from images and supervised learning to formulate AI-based computer-assisted scoring systems (CASS)/predictive models [1,[252][253][254][255][256] Gene knockdown Cytosine base editing, CRISPR/Cas-9 gene editing, siRNA-mediated gene knockdown [79,182,196,197,257] Characterization of extracellular vesicles Nanoparticle tracking analysis, electron microscopy, small RNA sequencing [258][259][260] Multi-omics approaches Integrating transcriptomic and epigenetic data and integrating metabolomics and epigenetic data [261,262]…”

DOHaD: A Menagerie of Adaptations and Perspectives: The interplay between early embryo metabolism and mitoepigenetic programming of development

“…In mice (Zhang et al 2019) and bovine (Ispada et al 2020), the supplementation of culture media with α-KG during in vitro development interferes with DNA methylation/demethylation dynamics. Still in bovine, the analysis of the effect of the paternal and maternal age (very young bulls and cows) (Morin-Doré et al 2020, Wu et al 2020, as well as the exposure to ketone bodies (β-hydroxybutyrate) in post-partum cows, presented evidence that these three conditions share a common metabolic stress (an energy deficit) and have a similar impact on the early embryos: epigenetic alterations, reduction of their own metabolism, and induction of mitochondrial dysfunction (Wu & Sirard 2020). Surprisingly, in vitro culture seems to create a similar picture of embryos being forced into an 'economy mode' as an adaptation to suboptimal metabolic environment.…”