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Embryo transfer in cattle and other species is a key reproductive technology to improve genetic merit. However, pregnancy loss after embryo transfer is still a major barrier to optimal utilization of the technology. Furthermore, the lack of a method to objectively quantify embryonic competence hinders investigations aimed at improving the competence of an embryo. Based on the knowledge that bovine embryos have an inherent molecular signature that determines their ability for pregnancy establishment which can result in distinct gene expression profiles, we have previously integrated transcriptomic data from independent experiments to identify eight genes capable of predicting embryo competence for survival with high accuracy. In this study, we developed a function for the R software containing a mathematical formula based on the model coefficients to yield an embryonic competence index (ECI) according to the expression of those eight critical genes. Application of the function to a gene expression dataset generates a quantitative ECI value for each embryo that can be employed in statistical analyses when performing an experiment. The folder with the R project and required datasets can be found in https://zenodo.org/records/12515587.

Early pregnancy loss markedly impacts reproductive efficiency in cattle. The objectives were to model a biologically relevant gene signature predicting embryonic competence for survival after integrating transcriptomic data from blastocysts and elongating conceptuses with different developmental capacities and to validate the potential biomarkers with independent embryonic data sets through the application of machine-learning algorithms. First, two data sets from in vivo-produced blastocysts competent or not to sustain a pregnancy were integrated with a data set from long and short day-15 conceptuses. A statistical contrast determined differentially expressed genes (DEG) increasing in expression from a competent blastocyst to a long conceptus and vice versa; these were enriched for KEGG pathways related to glycolysis/gluconeogenesis and RNA processing, respectively. Next, the most discriminative DEG between blastocysts that resulted or did not in pregnancy were selected by linear discriminant analysis. These eight putative biomarker genes were validated by modeling their expression in competent or noncompetent blastocysts through Bayesian logistic regression or neural networks and predicting embryo developmental fate in four external data sets consisting of in vitro-produced blastocysts (i) competent or not, or (ii) exposed or not to detrimental conditions during culture, and elongated conceptuses (iii) of different length, or (iv) developed in the uteri of high- or subfertile heifers. Predictions for each data set were more than 85% accurate, suggesting that these genes play a key role in embryo development and pregnancy establishment. In conclusion, this study integrated transcriptomic data from seven independent experiments to identify a small set of genes capable of predicting embryonic competence for survival.

In cattle, the in vitro production (IVP) of embryos is becoming more relevant than embryos produced in vivo, i.e. after multiple ovulation and embryo transfer (MOET). However, the effects of IVP on the developmental programming of specific organs in the postnatal calves are yet unknown. Previously, we reported an epigenomic and transcriptomic profile of the hypothalamus–pituitary–testicular axis compatible with its earlier activation in IVP calves compared to MOET animals. Here, we studied the hepatic and muscular epigenome and transcriptome of those same male dairy calves (n = 4 per group). Tissue samples from liver and semitendinosus muscle were obtained at 3 months of age, and the extracted gDNA and RNA were sequenced through whole-genome bisulfite sequencing and RNA-sequencing, respectively. Next, bioinformatic analyses determined differentially methylated cytosines or differentially expressed genes [false discovery rate (FDR) < 0.05] for each Omic dataset; and nonparametrically combined genes (NPCG) for both integrated omics (P < 0.05). KEGG pathways enrichment analysis showed that NPCG upregulated in the liver and the muscle of the IVP calves were involved in oxidative phosphorylation and the tricarboxylic acid cycle. In contrast, ribosome and translation were upregulated in the liver but downregulated in the muscle of the IVP calves compared to the MOET calves (FDR < 0.05). A model considering the effect of the methylation levels and the group on the expression of all the genes involved in these pathways confirmed these findings. In conclusion, the multiomics data integration approach indicated an altered hepatic and muscular energy regulation in phenotypically normal IVP calves compared to MOET calves.

Transcriptomic data is often expensive and difficult to generate in large cohorts in comparison to genomic data and therefore is often important to integrate multiple transcriptomic datasets from both microarray and next generation sequencing (NGS) based transcriptomic data across similar experiments or clinical trials to improve analytical power and discovery of novel transcripts and genes. However, transcriptomic data integration presents a few challenges including re-annotation and batch effect removal. We developed the Gene Expression Data Integration (GEDI) R package to enable transcriptomic data integration by combining already existing R packages. With just four functions, the GEDI R package makes constructing a transcriptomic data integration pipeline straightforward. Together, the functions overcome the complications in transcriptomic data integration by automatically re-annotating the data and removing the batch effect. The removal of the batch effect is verified with Principal Component Analysis and the data integration is verified using a logistic regression model with forward stepwise feature selection. To demonstrate the functionalities of the GEDI package, we integrated five bovine endometrial transcriptomic datasets from the NCBI Gene Expression Omnibus. The datasets included Affymetrix, Agilent and RNA-sequencing data. Furthermore, we compared the GEDI package to already existing tools and found that GEDI is the only tool that provides a full transcriptomic data integration pipeline including verification of both batch effect removal and data integration.

The main goal was to apply machine learning (ML) methods on integrated multi-transcriptomic data, to identify endometrial genes capable of predicting uterine receptivity according to their expression patterns in the cow. Public data from five studies were re-analyzed. In all of them, endometrial samples were obtained at day 6–7 of the estrous cycle, from cows or heifers of four different European breeds, classified as pregnant (n = 26) or not (n = 26). First, gene selection was performed through supervised and unsupervised ML algorithms. Then, the predictive ability of potential key genes was evaluated through support vector machine as classifier, using the expression levels of the samples from all the breeds but one, to train the model, and the samples from that one breed, to test it. Finally, the biological meaning of the key genes was explored. Fifty genes were identified, and they could predict uterine receptivity with an overall 96.1% accuracy, despite the animal’s breed and category. Genes with higher expression in the pregnant cows were related to circadian rhythm, Wnt receptor signaling pathway, and embryonic development. This novel and robust combination of computational tools allowed the identification of a group of biologically relevant endometrial genes that could support pregnancy in the cattle.

The objective was to identify the transcriptomic profile of in vitro-derived embryos with high competence to establish and maintain gestation. Embryos produced with X-sorted sperm were cultured from day 5 to day 7 in serum-free medium containing 10 ng/ml recombinant bovine CSF2 or vehicle. The CSF2 was administered because this molecule can increase blastocyst competence for survival after embryo transfer. Blastocysts were harvested on day 7 of culture and manually bisected. One demi-embryo from a single blastocyst was transferred into a synchronized recipient and the other half was used for RNA-seq analysis. Using P 2-fold or