Epigenomic And Transcriptomic Analyses Reveal Early Activation Of The Hpg Axis In In Vitro-Produced Male Dairy Calves

FASEB JOURNAL(2021)

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摘要
In cattle, several calves born after IVP ("in vitro" embryo production) present similar birthweight to those generated after MOET (multiple ovulation and embryo transfer). However, the underlying molecular patterns in organs involved in the developmental process are unknown and could indicate physiological programming. The objectives of this study were: (1) to compare epigenomic and transcriptomic modifications in the hypothalamus, pituitary, gonadal and adrenal organs between 3 months old ovum pick-up-IVP and MOET male calves (n = 4 per group) and (2) to use blood epigenomic data to proxy methylation of the inner organs. Extracted gDNA and RNA were sequenced through whole-genome bisulfite sequencing and RNA sequencing, respectively. Next, bioinformatic analyses determined differentially methylated cytosines (DMC) and differentially expressed genes (DEG) (FDR < 0.05) in IVP versus MOET samples and the KEGG pathways that were overrepresented by genes associated with DMC or DEG (FDR < 0.1). Pathways related to hypothalamus, pituitary, gonadal (HPG) axis activation (GnRH secretion in the hypothalamus, GnRH signaling in the pituitary, and steroidogenesis in the testicle) were enriched in IVP calves. Modeling the effect of the methylation levels and the group on the expression of all the genes involved in these pathways confirmed their upregulation in HPG organs in IVP calves. The application of the DIABLO method allowed the identification of 15 epigenetic and five transcriptomic biomarkers, which were able to predict the embryo origin using the epigenomic data from the blood. In conclusion, the use of an integrated epigenomic-transcriptomic approach suggested an early activation of the HPG axis in male IVP calves compared to MOET counterparts, and the identification of potential biomarkers allowed the use of blood samples to proxy methylation levels of the relevant internal organs.
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computational biology, machine learning, reproductive techniques, system biology
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