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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. [ABSTRACT FROM AUTHOR]

Microarray data of chorionic villous samples (CVSs) obtained from women of ~11.5 gestational weeks who developed preeclampsia with severe features (sPE; PE-CVS) revealed a molecular signature of impaired endometrial maturation (decidualization) before and during early pregnancy. Because endometrial disorders are also associated with aberrant decidualization, we asked whether they share molecular features with sPE. We employed microarray data integration to compare the molecular pathologies of PE-CVS and endometrial disorders, as well as decidua obtained postpartum from women with sPE. Eight public databases were reanalyzed with R software to determine differentially expressed genes (DEGs) in pathologic tissues relative to normal controls. DEGs were then compared to explore overlap. Shared DEGs were examined for enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Principal component and network analyses were subsequently applied to selected DEGs. There was significant overlap of DEGs changing in the same direction for PE-CVS and endometrial disorders, suggesting common molecular pathways. Shared DEGs were enriched for cytokine-cytokine receptor interaction. Genes in this pathway revealed expression patterns forming 2 distinct clusters, one for normal and the other pathologic endometrium. The most affected hub genes were related to decidualization and NK cell function. Few DEGs were shared by PE-CVS, and PE decidua obtained postpartum. sPE may be part of a biologic continuum of "endometrial spectrum disorders.". [ABSTRACT FROM AUTHOR]