Machine learning approach to integrated endometrial transcriptomic datasets reveals biomarkers predicting uterine receptivity in cattle at seven days after estrous

Abstract

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.

Genes and Mechanisms Associated With Experimentally Induced Bovine Respiratory Disease Identified With Supervised Machine Learning Methodology on Integrated Transcriptomic Datasets

“…Fortunately, publicly available gene expression repositories, such as the NCBI Gene Expression Omnibus, make it possible to acquire, integrate, and analyze datasets related to a particular eld or disease. Such studies have been performed in mammalian species, including cattle, to better characterize genomic mechanisms and protein production related to a particular disease or condition 49,78,79 . Additionally, with the dynamic capacity for analysis that supervised ML models allow, it is possible to explore and characterize gene expression patterns associated with clinical BRD with profound sensitivity 42,79 .…”

“…Between all testing groups and the six models utilized in this study, the support vector machines (SVM) model typically performed the best in terms of classi cation capacity. While originally utilized in microarray experiments, this algorithm is popular for genomic classi cation research in RNA-Seq, as it has been used to discover cancer biomarkers in humans, classify genes related to early conception in cattle, and automate single-cell RNA-Seq identi cation 49,83,84 . While this algorithm was capable of accurately classifying BRSV and IBR challenged datasets, compared to controls, this model is a nonsparse classi er and therefore does not have a built-in process for feature selection and gene extraction within MLSEq.…”

“…Supervised machine learning (ML) models used in biological research aid in the discover of molecules and establishment of dynamic models that recognize, classify, and predict disease outcomes 40,41,42,43,44 . In recent years, studies have employed the use of ML framework to identify candidate biomarkers for disease classi cation, cell and tumor expression signatures, and novel protein mechanisms within publicly available RNA-Seq datasets 45,46,47,48,49 . However, to our knowledge, the use of ML-based methodology has not been explored with BRD-associated datasets.…”

GEDI: an R package for integration of transcriptomic data from multiple high-throughput platforms

“…The fourth and final function, VerifyGEDI, verifies the transcriptomic data integration using a logistic regression model. These functions were demonstrated on a case inspired by a transcriptomic integration study by (Rabaglino and Kadarmideen, 2020).…”

Transcriptomic Analysis of STAT1/3 in the Goat Endometrium During Embryo Implantation

“…Implantation failure is the major cause of pregnancy loss in cattle, accounting for 30%-50% of all cases (3, 4). Although many studies have been conducted to identify the gene network in the endometrium or conceptus during embryo implantation (5)(6)(7)(8), the precise molecular mechanism has not been well-characterized.…”