Why Has Metabolomics So Far Not Managed to Efficiently Contribute to the Improvement of Assisted Reproduction Outcomes? The Answer through a Review of the Best Available Current Evidence

A critical review of the recent concept of regulatory performance of DNA Methylations, and DNA methyltransferase enzymes alongside the induction of immune microenvironment elements in recurrent pregnancy loss

Recurrent pregnancy Loss (RPL)is a frequent and upsetting condition. Besides the prevalent cause of RPL including chromosomal defects in the embryo,the effect of translational elements like alterations of epigenetics are of great importance. The emergence of epigenetics has offered a fresh outlook on the causes and treatment of RPL by focusing on the examination of DNA methylation. RPL may arise as a result of aberrant DNA methylation of imprinted genes, placenta-specific genes, immune-related genes, and sperm DNA, which may have a direct or indirect impact on embryo implantation, growth, and development. Moreover, the distinct immunological tolerogenic milieu established at the interface between the mother and fetus plays a crucial role in sustaining pregnancy. Given this, there has been a great deal of interest in the regulation of DNA methylation and alterations in the cellular components of the maternal-fetal immunological milieu. The research on DNA methylation's role in RPL incidence and the control of the mother-fetal immunological milieu is summed up in this review.

The prospect of artificial intelligence to personalize assisted reproductive technology

Infertility affects 1-in-6 couples, with repeated intensive cycles of assisted reproductive technology (ART) required by many to achieve a desired live birth. In ART, typically, clinicians and laboratory staff consider patient characteristics, previous treatment responses, and ongoing monitoring to determine treatment decisions. However, the reproducibility, weighting, and interpretation of these characteristics are contentious, and highly operator-dependent, resulting in considerable reliance on clinical experience. Artificial intelligence (AI) is ideally suited to handle, process, and analyze large, dynamic, temporal datasets with multiple intermediary outcomes that are generated during an ART cycle. Here, we review how AI has demonstrated potential for optimization and personalization of key steps in a reproducible manner, including: drug selection and dosing, cycle monitoring, induction of oocyte maturation, and selection of the most competent gametes and embryos, to improve the overall efficacy and safety of ART.

Metabolomics Integration in Assisted Reproductive Technologies for Enhanced Embryo Selection beyond Morphokinetic Analysis

Embryo quality evaluation during in vitro development is a crucial factor for the success of assisted reproductive technologies (ARTs). However, the subjectivity inherent in the morphological evaluation by embryologists can introduce inconsistencies that impact the optimal embryo choice for transfer. To provide a more comprehensive evaluation of embryo quality, we undertook the integration of embryo metabolomics alongside standardized morphokinetic classification. The culture medium of 55 embryos (derived from 21 couples undergoing ICSI) was collected at two timepoints (days 3 and 5). Samples were split into Good (n = 29), Lagging (n = 19), and Bad (n = 10) according to embryo morphokinetic evaluation. Embryo metabolic performance was assessed by monitoring the variation in specific metabolites (pyruvate, lactate, alanine, glutamine, acetate, formate) using 1H-NMR. Adjusted metabolite differentials were observed during the first 3 days of culture and found to be discriminative of embryo quality at the end of day 5. Pyruvate, alanine, glutamine, and acetate were major contributors to this discrimination. Good and Lagging embryos were found to export and accumulate pyruvate and glutamine in the first 3 days of culture, while Bad embryos consumed them. This suggests that Bad embryos have less active metabolic activity than Good and Lagging embryos, and these two metabolites are putative biomarkers for embryo quality. This study provides a more comprehensive evaluation of embryo quality and can lead to improvements in ARTs by enabling the selection of the best embryos. By combining morphological assessment and metabolomics, the selection of high-quality embryos with the potential to result in successful pregnancies may become more accurate and consistent.

Ram sperm cryopreservation disrupts metabolism of unsaturated fatty acids

In ram sperm, metabolites are important components of the plasma membrane, energy metabolism cycle, and precursors for other membrane lipids, and they may have important roles in maintaining plasma membrane integrity, energy metabolism, and regulation of cryotolerance. In this study, the ejaculates from 6 Dorper rams were pooled and sperm were systematically investigated by metabolomics at various steps of cryopreservation (37 °C, fresh [F]; from 37 to 4 °C, cooling [C]; and from 4 to -196 to 37 °C, frozen-thawed [FT]) to identify differential metabolites (DM). There were 310 metabolites identified, of which 86 were considered DMs. Regarding the DMs, there were 23 (0 up and 23 down), 25 (12 up and 13 down), and 38 (7 up and 31 down) identified during cooling (C vs F), freezing (FT vs C), and cryopreservation (FT vs F), respectively. Furthermore, some key polyunsaturated fatty acids (FAs), particularly, linoleic acid (LA), docosahexaenoic acid (DHA), and arachidonic acid (AA) were down-regulated during cooling and cryopreservation. Significant DMs were enriched in several metabolic pathways including biosynthesis of unsaturated FAs, LA metabolism, mammalian target of rapamycin (mTOR), forkhead box transcription factors (FoxO), adenosine monophosphate-activated protein kinase (AMPK), phosphatidylinositol 3-kinase/protein kinase B (PI3K-Akt) signaling pathways, regulation of lipolysis in adipocytes, and FA biosynthesis. This was apparently the first report to compare metabolomics profiles of ram sperm during cryopreservation and provided new knowledge to improve this process.

Combining Machine Learning with Metabolomic and Embryologic Data Improves Embryo Implantation Prediction

This study investigated whether combining metabolomic and embryologic data with machine learning (ML) models improve the prediction of embryo implantation potential. In this prospective cohort study, infertile couples (n=56) undergoing day-5 single blastocyst transfer between February 2019 and August 2021 were included. After day-5 single blastocyst transfer, spent culture medium (SCM) was subjected to metabolite analysis using nuclear magnetic resonance (NMR) spectroscopy. Derived metabolite levels and embryologic parameters between successfully implanted and failed groups were incorporated into ML models to explore their predictive potential regarding embryo implantation. The SCM of blastocysts that resulted in successful embryo implantation had significantly lower pyruvate (p<0.05) and threonine (p<0.05) levels compared to medium control but not compared to SCM related to embryos that failed to implant. Notably, the prediction accuracy increased when classical ML algorithms were combined with metabolomic and embryologic data. Specifically, the custom artificial neural network (ANN) model with regularized parameters for metabolomic data provided 100% accuracy, indicating the efficiency in predicting implantation potential. Hence, combining ML models (specifically, custom ANN) with metabolomic and embryologic data improves the prediction of embryo implantation potential. The approach could potentially be used to derive clinical benefits for patients in real-time.

Updates in Assisted Reproduction

There are multiple reasons for which the “updates in assisted reproduction” topic is and will be in the center of scientific attention—both clinical and laboratory—during the next decades. In this editorial, we present and discuss some of them.