Machine‐learning methods applied to integrated transcriptomic data from bovine blastocysts and elongating conceptuses to identify genes predictive of embryonic competence

Abstract

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 vivoproduced 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

Proteomic analysis to identification of hypoxia related markers in spinal tuberculosis: a study based on weighted gene co-expression network analysis and machine learning

“…Subsequently, we integrated the genes from the LASSO, SVM-REF, and MCODE modules to obtain three important genes. Finally, a diagnostic model was developed using five machine learning techniques, including logistic regression [ 33 ], Bayesian logistic regression [ 34 ], decision tree [ 35 ], random forest [ 36 ], and extreme gradient boosting [ 37 ], to evaluate the diagnostic value of these three genes in TB disease.…”

Maternal contributions to pregnancy success: from gamete quality to uterine environment

“…Determining markers of embryo competence to establish a pregnancy has been the focus of much research as it has the potential to improve the efficiency of ART by enabling the early prediction of which embryos are more likely to survive to term. Recently, Rabaglino et al (2023) combined the integration of transcriptomic datasets with the use of machine learning to predict embryo competence and survival. The authors reported a subset of eight genes, namely GSTO1, CHSY1, TPI1, YWHAG, CCNA2, LSM4, CDK7, and EIF4A3, which predicted with high accuracy the competence of embryos in different datasets, indicating that these genes might be important biomarkers of embryo competence.…”

Understanding conceptus–maternal interactions: what tools do we need to develop?

“…ML approaches have recently become popular as a method of analysing large, complex omics-based datasets. Rabaglino et al (2023) combined Bayesian logistic regression and neural network models with bovine transcriptomic data to identify eight genes whose expression was predictive of conceptus competence. In addition, the development of ML approaches, such as in Christmas et al (2023), have led to finding enhancer regions associated with phenotypic differences in mammals, with many of the most highly conserved regions associated with embryonic development.…”