Metabolomic fingerprinting of pig seminal plasma identifies in vivo fertility biomarkers

Abstract

Background Metabolomic approaches, which include the study of low molecular weight molecules, are an emerging -omics technology useful for identification of biomarkers. In this field, nuclear magnetic resonance (NMR) spectroscopy has already been used to uncover (in) fertility biomarkers in the seminal plasma (SP) of several mammalian species. However, NMR studies profiling the porcine SP metabolome to uncover in vivo fertility biomarkers are yet to be carried out. Thus, this study aimed to evaluate the putative relationship between SP-metabolites and in vivo fertility outcomes. To this end, 24 entire ejaculates (three ejaculates per boar) were collected from artificial insemination (AI)-boars throughout a year (one ejaculate every 4 months). Immediately after collection, ejaculates were centrifuged to obtain SP-samples, which were stored for subsequent metabolomic analysis by NMR spectroscopy. Fertility outcomes from 1525 inseminations were recorded over a year, including farrowing rate, litter size, stillbirths per litter and the duration of pregnancy. Results A total of 24 metabolites were identified and quantified in all SP-samples. Receiver operating characteristic (ROC) curve analysis showed that lactate levels in SP had discriminative capacity for farrowing rate (area under the curve [AUC] = 0.764) while carnitine (AUC = 0.847), hypotaurine (AUC = 0.819), sn-glycero-3-phosphocholine (AUC = 0.833), glutamate (AUC = 0.799) and glucose (AUC = 0.750) showed it for litter size. Similarly, citrate (AUC = 0.743), creatine (AUC = 0.812), phenylalanine (AUC = 0.750), tyrosine (AUC = 0.753) and malonate (AUC = 0.868) levels had discriminative capacity for stillbirths per litter; and malonate (AUC = 0.767) and fumarate (AUC = 0.868) levels for gestation length. Conclusions The assessment of selected SP-metabolites in ejaculates through NMR spectroscopy could be considered as a promising non-invasive tool to predict in vivo fertility outcomes in pigs. Moreover, supplementing AI-doses with specific metabolites should also be envisaged as a way to improve their fertility potential.

Sperm physiology and in vitro fertilising ability rely on basal metabolic activity: insights from the pig model

“…The utility of metabolomic technologies to predict in vivo fertility from SP [40][41][42][43][44][45][46][47] or sperm 5,48 has widely been proved in several mammalian species. To the best of the authors' knowledge, nevertheless, no study in any mammalian species has looked into the relationship between the sperm metabolome and IVF outcomes.…”

Exploring the metabolome of seminal plasma in two different horse types: Light versus draft stallions

“…Other molecules such as hippurate, fumarate, 2‐hydroxyisobutyrate, cystine and valine have also been found in other species and their roles on fertility and offsprings are currently under investigation (Mateo‐Otero et al, 2021 ; Vyvial et al, 2019 ; Zhu et al, 2022 ).…”

“…This approach would also permit the identification of new biomarkers that could be used to diagnose infertile subjects with increased sensitivity and specificity (Engel et al, 2019 ). Studies have been performed on seminal plasma from different species, including bull (Kosior et al, 2020 ; Kumar et al, 2015 ), ram (Sousa et al, 2020 ), stallion (Magistrini et al, 2000 ), boar (Mateo‐Otero et al, 2021 ) and man (Engel et al, 2019 ; Paiva et al, 2015 ; Wang et al, 2019 ), providing interesting insights into the metabolites contained in seminal plasma and its associations with sperm functions and fertility. Metabolomics applied to animal breeding aims to improve prediction of the breeding values of the animals to cope with traditional and new objectives of the selection programs (Kalaiselvi et al, 2019 ).…”

Serum metabolomics reveal pathways associated with protective effect of ginsenoside Rg3 on immune stress

“…Correlation analysis between serum metabolites and clinical parameters ( Bi et al, 2022 ) was performed using Spearman's rank correlation, and P value < 0.05 was considered as significantly associated metabolites. The correlation heatmap was generated using R program “heatmap” package and then was visualized by Cytoscape (Version 3.2.1) ( Mateo-Otero et al, 2021 ).…”