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

Development of a formula for scoring competence of bovine embryos to sustain pregnancy

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.

Machine learning to identify endometrial biomarkers predictive of pregnancy success following artificial insemination in dairy cows†

The objective was to identify a set of genes whose transcript abundance is predictive of a cow's ability to become pregnant following artificial insemination. Endometrial epithelial cells from the uterine body were collected for RNA sequencing using the cytobrush method from 193 first-service Holstein cows at estrus prior to artificial insemination (day 0). A group of 253 first-service cows not used for cytobrush collection were controls. There was no effect of cytobrush collection on pregnancy outcomes at day 30 or 70 or on pregnancy loss between days 30 and 70. There were 2 upregulated and 214 downregulated genes (false discovery rate < 0.05, absolute fold change >2-fold) for cows pregnant at day 30 versus those that were not pregnant. Functional terms overrepresented in the downregulated genes included those related to immune and inflammatory responses. Machine learning for fertility biomarkers with the R package BORUTA resulted in identification of 57 biomarkers that predicted pregnancy outcome at day 30 with an average accuracy of 77%. Thus, machine learning can identify predictive biomarkers of pregnancy in endometrium with high accuracy. Moreover, sampling of endometrial epithelium using the cytobrush can help understand functional characteristics of the endometrium at artificial insemination without compromising cow fertility. Functional characteristics of the genes comprising the set of biomarkers is indicative that a major determinant of cow fertility, at least for first insemination after calving, is immune status of the uterus, which, in turn, is likely to reflect the previous history of uterine disease.

Artificial intelligence and porcine breeding

Livestock management is evolving into a new era, characterized by the analysis of vast quantities of data (Big Data) collected from both traditional breeding methods and new technologies such as sensors, automated monitoring system, and advanced analytics. Artificial intelligence (A-In), which refers to the capability of machines to mimic human intelligence, including subfields like machine learning and deep learning, is playing a pivotal role in this transformation. A wide array of A-In techniques, successfully employed in various industrial and scientific contexts, are now being integrated into mainstream livestock management practices. In the case of swine breeding, while traditional methods have yielded considerable success, the increasing amount of information requires the adoption of new technologies such as A-In to drive productivity, enhance animal welfare, and reduce environmental impact. Current findings suggest that these techniques have the potential to match or exceed the performance of traditional methods, often being more scalable in terms of efficiency and sustainability within the breeding industry. This review provides insights into the application of A-In in porcine breeding, from the perspectives of both sows (including welfare and reproductive management) and boars (including semen quality and health), and explores new approaches which are already being applied in other species.

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

Communication between the maternal endometrium and developing embryo/conceptus is critical to support successful pregnancy to term. Studying the peri-implantation period of pregnancy is critical as this is when most pregnancy loss occurs in cattle. Our current understanding of these interactions is limited, due to the lack of appropriate in vitro models to assess these interactions. The endometrium is a complex and heterogeneous tissue that is regulated in a transcriptional and translational manner throughout the oestrous cycle. While there are in vitro models to study endometrial function, they are static and 2D in nature or explant models and are limited in how well they recapitulate the in vivo endometrium. Recent developments in organoid systems, microfluidic approaches, extracellular matrix biology, and in silico approaches provide a new opportunity to develop in vitro systems that better model the in vivo scenario. This will allow us to investigate in a more high-throughput manner the fundamental molecular interactions that are required for successful pregnancy in cattle.

Molecular and cellular programs underlying the development of bovine pre-implantation embryos

Early embryonic mortality is a major cause of infertility in cattle, yet the underlying molecular causes remain a mystery. Over the past half century, assisted reproductive technologies such as in vitro fertilisation and somatic cell nuclear transfer have been used to improve cattle reproductive efficiency; however, reduced embryo developmental potential is seen compared to their in vivo counterparts. Recent years have seen exciting progress across bovine embryo research, including genomic profiling of embryogenesis, new methods for improving embryo competence, and experimenting on building bovine embryos from stem cell cultures. These advances are beginning to define bovine embryo molecular and cellular programs and could potentially lead to improved embryo health. Here, I highlight the current status of molecular determinants and cellular programs of bovine embryo development and new opportunities to improve the bovine embryo health.

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

The establishment and maintenance of a pregnancy that goes to term is sine qua non for the long-term sustainability of dairy and beef cattle operations. The oocyte plays a critical role in providing the factors necessary for preimplantation embryonic development. Furthermore, the female, or maternal, environment where oocytes and embryos develop is crucial for the establishment and maintenance of a pregnancy to term. During folliculogenesis, the oocyte must sequentially acquire meiotic and developmental competence, which are the results of a series of molecular events preparing the highly specialized gamete to return to totipotency after fertilization. Given that folliculogenesis is a lengthy process in the cow, the occurrence of disease, metabolic imbalances, heat stress, or other adverse events can make it challenging to maintain oocyte quality. Following fertilization, the newly formed embryo must execute a tightly planned program that includes global DNA remodeling, activation of the embryonic genome, and cell fate decisions to form a blastocyst within a few days and cell divisions. The increasing use of assisted reproductive technologies creates an additional layer of complexity to ensure the highest oocyte and embryo quality given that in vitro systems do not faithfully recreate the physiological maternal environment. In this review, we discuss cellular and molecular factors and events known to be crucial for proper oocyte development and maturation, as well as adverse events that may negatively affect the oocyte; and the importance of the uterine environment, including signaling proteins in the maternal-embryonic interactions that ensure proper embryo development. We also discuss the impact of assisted reproductive technologies in oocyte and embryo quality and developmental potential, and considerations when looking into the prospects for developing systems that allow for in vitro gametogenesis as a tool for assisted reproduction in cattle.

Applications of livestock monitoring devices and machine learning algorithms in animal production and reproduction: an overview

Some sectors of animal production and reproduction have shown great technological advances due to the development of research areas such as Precision Livestock Farming (PLF). PLF is an innovative approach that allows animals to be monitored, through the adoption of cutting-edge technologies that continuously collect real-time data by combining the use of sensors with advanced algorithms to provide decision tools for farmers. Artificial Intelligence (AI) is a field that merges computer science and large datasets to create expert systems that are able to generate predictions and classifications similarly to human intelligence. In a simplified manner, Machine Learning (ML) is a branch of AI, and can be considered as a broader field that encompasses Deep Learning (DL, a Neural Network formed by at least three layers), generating a hierarchy of subsets formed by AI, ML and DL, respectively. Both ML and DL provide innovative methods for analyzing data, especially beneficial for large datasets commonly found in livestock-related activities. These approaches enable the extraction of valuable insights to address issues related to behavior, health, reproduction, production, and the environment, facilitating informed decision-making. In order to create the referred technologies, studies generally go through five steps involving data processing: acquisition, transferring, storage, analysis and delivery of results. Although the data collection and analysis steps are usually thoroughly reported by the scientific community, a good execution of each step is essential to achieve good and credible results, which impacts the degree of acceptance of the proposed technologies in real life practical circumstances. In this context, the present work aims to describe an overview of the current implementations of ML/DL in livestock reproduction and production, as well to identify potential challenges and critical points in each of the five steps mentioned, which can affect results and application of AI techniques by farmers in practical situations.

Pre-Implantation Bovine Embryo Evaluation-From Optics to Omics and Beyond

Approximately 80% of the ~1.5 million bovine embryos transferred in 2021 were in vitro produced. However, only ~27% of the transferred IVP embryos will result in live births. The ~73% pregnancy failures are partly due to transferring poor-quality embryos, a result of erroneous stereomicroscopy-based morphological evaluation, the current method of choice for pre-transfer embryo evaluation. Numerous microscopic (e.g., differential interference contrast, electron, fluorescent, time-lapse, and artificial-intelligence-based microscopy) and non-microscopic (e.g., genomics, transcriptomics, epigenomics, proteomics, metabolomics, and nuclear magnetic resonance) methodologies have been tested to find an embryo evaluation technique that is superior to morphologic evaluation. Many of these research tools can accurately determine embryo quality/viability; however, most are invasive, expensive, laborious, technically sophisticated, and/or time-consuming, making them futile in the context of in-field embryo evaluation. However accurate they may be, using complex methods, such as RNA sequencing, SNP chips, mass spectrometry, and multiphoton microscopy, at thousands of embryo production/collection facilities is impractical. Therefore, future research is warranted to innovate field-friendly, simple benchtop tests using findings already available, particularly from omics-based research methodologies. Time-lapse monitoring and artificial-intelligence-based automated image analysis also have the potential for accurate embryo evaluation; however, further research is warranted to innovate economically feasible options for in-field applications.

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

OBJECTIVE

This article aims at exploring the role of hypoxia-related genes and immune cells in spinal tuberculosis and tuberculosis involving other organs.

METHODS

In this study, label-free quantitative proteomics analysis was performed on the intervertebral discs (fibrous cartilaginous tissues) obtained from five spinal tuberculosis (TB) patients. Key proteins associated with hypoxia were identified using molecular complex detection (MCODE), weighted gene co-expression network analysis(WGCNA), least absolute shrinkage and selection operator (LASSO), and support vector machine recursive feature Elimination (SVM-REF) methods, and their diagnostic and predictive values were assessed. Immune cell correlation analysis was then performed using the Single Sample Gene Set Enrichment Analysis (ssGSEA) method. In addition, a pharmaco-transcriptomic analysis was also performed to identify targets for treatment.

RESULTS

The three genes, namely proteasome 20 S subunit beta 9 (PSMB9), signal transducer and activator of transcription 1 (STAT1), and transporter 1 (TAP1), were identified in the present study. The expression of these genes was found to be particularly high in patients with spinal TB and other extrapulmonary TB, as well as in TB and multidrug-resistant TB (p-value < 0.05). They revealed high diagnostic and predictive values and were closely related to the expression of multiple immune cells (p-value < 0.05). It was inferred that the expression of PSMB9, STAT 1, and TAP1 could be regulated by different medicinal chemicals.

CONCLUSION

PSMB9, STAT1, and TAP1, might play a key role in the pathogenesis of TB, including spinal TB, and the protein product of the genes can be served as diagnostic markers and potential therapeutic target for TB.

Review: Overview of the transcriptomic landscape in bovine blastocysts and elongated conceptuses driving developmental competence

In cattle, pregnancy loss due to early embryonic mortality is a major concern that significantly impacts reproductive efficiency. Given the economic importance of cattle in livestock productivity, much research has been carried out to comprehend the regulatory mechanisms underlying this early embryo loss. Thus, understanding the molecular principles behind the reciprocal communication between the maternal uterus and the developing conceptus is paramount. Measurement of mRNA expression through a variety of techniques is widely used to unravel the complex and dynamic interaction between these two players. Development of high-throughput technologies, such as microarrays and RNA sequencing, have allowed global quantification of the full range of expressed mRNA, or transcriptome, of a biological sample. Therefore, numerous investigators have applied one or the other method to study the bovine embryo transcriptome at different developmental checkpoints and under different conditions. The goal of this article was to review studies involving the use of high-throughput techniques to study the transcriptome of the bovine embryo from the blastocyst (∼day 7) to the elongating conceptus stage (∼days 13-16) in terms of developmental capacity and the impact of procedures for in vitro embryo production. Furthermore, the differentially expressed genes reported by each study and enriched pathways were compared to determine common terms. The studies described here highlight differences in the transcriptome (i) between blastocysts with divergent ability to sustain a pregnancy, (ii) between age-matched elongated conceptuses with divergent developmental fates, and (iii) between blastocysts and elongated conceptuses produced in vitro or in vivo. Comparison between these works, supported by other studies involving transcriptomic data integration presented at the end of this review, highlights the involvement of pathways related to energy metabolism in embryonic competence, which may be altered because of the procedures involved in the in vitro production of embryos.