Tracking main environmental factors masking a minor steroidal doping effect using metabolomic analysis of horse urine by liquid chromatography-high-resolution mass spectrometry

Comparison of muscle metabolomics between two Chinese horse breeds

With their enormous muscle mass and athletic ability, horses are well-positioned as model organisms for understanding muscle metabolism. There are two different types of horse breeds-Guanzhong (GZ) horses, an athletic breed with a larger body height (~148.7 cm), and the Ningqiang pony (NQ) horses, a lower height breed generally used for ornamental purposes-both inhabited in the same region of China with obvious differences in muscle content. The main objective of this study was to evaluate the breed-specific mechanisms controlling muscle metabolism. In this study, we observed muscle glycogen, enzyme activities, and LC-MS/MS untargeted metabolomics in the gluteus medius muscle of six, each of GZ and NQ horses, to explore differentiated metabolites that are related to the development of two muscles. As expected, the glycogen content, citrate synthase, and hexokinase activity of muscle were significantly higher in GZ horses. To alleviate the false positive rate, we used both MS1 and MS2 ions for metabolite classification and differential analysis. As a result, a total of 51,535 MS1 and 541 MS2 metabolites were identified, and these metabolites can separate these two groups from each other. Notably, 40% of these metabolites were clustered into lipids and lipid-like molecules. Furthermore, 13 significant metabolites were differentially detected between GZ and NQ horses (fold change [FC] value ≥ 2, variable important in projection value ≥1, and Q value ≤ 0.05). They are primarily clustered into glutathione metabolism (GSH, p = 0.01), taurine, and hypotaurine metabolism (p < 0.05) pathways. Seven of the 13 metabolites were also found in thoroughbred racing horses, suggesting that metabolites related to antioxidants, amino acids, and lipids played a key role in the development of skeleton muscle in horses. Those metabolites related to muscle development shed a light on racing horses' routine maintenance and improvement of athletic performance.

Performance assessment of an equine metabolomics model for screening a range of anabolic agents

INTRODUCTION

Despite their ban, Anabolic Androgenic Steroids (AAS) are considered as the most important threat for equine doping purposes. In the context of controlling such practices in horse racing, metabolomics has emerged as a promising alternative strategy to study the effect of a substance on metabolism and to discover new relevant biomarkers of effect. Based on the monitoring of 4 metabolomics derived candidate biomarkers in urine, a prediction model to screen for testosterone esters abuse was previously developed. The present work focuses on assessing the robustness of the associated method and define its scope of application.

MATERIALS AND METHODS

Several hundred urine samples were selected from 14 different horses of ethically approved administration studies involving various doping agents' (AAS, SARMS, β-agonists, SAID, NSAID) (328 urine samples). In addition, 553 urine samples from untreated horses of doping control population were included in the study. Samples were characterized with the previously described LC-HRMS/MS method, with the objective of assessing both its biological and analytical robustness.

RESULTS

The study concluded that the measurement of the 4 biomarkers involved in the model was fit for purpose. Further, the classification model confirmed its effectiveness in screening for testosterone esters use; and it demonstrated its ability to screen for the misuse of other anabolic agents, allowing the development of a global screening tool dedicated to this class of substances. Finally, the results were compared to a direct screening method targeting anabolic agents demonstrating complementary performances of traditional and omics approaches in the screening of anabolic agents in horses.

FROM A NON-TARGETED METABOLOMICS APPROACH TO A TARGETED BIOMARKERS STRATEGY TO HIGHLIGHT TESTOSTERONE ABUSE IN EQUINE. ILLUSTRATION OF A METHODOLOGICAL TRANSFER BETWEEN PLATFORMS AND LABORATORIES

In order to overcome the challenge associated with the screening of Anabolic-Androgenic Steroids (AAS) abuses in animal competitions, a non-targeted LC-HRMS based metabolomics approach was implemented on equine urine samples to highlight potential biomarkers associated with the administration of such compounds, using testosterone esters as model steroids. A statistical model relying on 4 potential biomarkers intensity could be defined to predict the status of the samples. With a routine application perspective, the monitoring of the highlighted potential biomarkers was first transferred into high-throughput LC-SRM. The model's performances and robustness of the approach were preserved and providing a first demonstration of metabolomics-based biomarkers integration within a targeted workflow using common benchtop MS instrumentation. In addition, with a view to the widespread implementation of such biomarker-based tools, we have transferred the method to a second laboratory with similar instrumentation. This proof of concept allows the development and application of biomarker-based strategies to meet current doping control needs.

Novel Algorithms for Comprehensive Untargeted Detection of Doping Agents in Biological Samples

To address the limitations of current targeted analytical methods that can only detect known doping agents, a novel methodology that permits untargeted drug detection (UDD) has been developed to help in the fight against doping in sports. Fifty-seven drugs were spiked into blank equine plasma and were treated as unknowns since their exact masses and chromatographic retention times were not utilized for detection. The spiked drugs were extracted from the plasma samples and were analyzed using liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). The acquired LC-HRMS raw data files were processed using metabolomic software for compound detection and identification. For UDD with the resultant data, a mathematical model was created, and two algorithms were generated to calculate the ratio of the mean (ROM) and outlier index (OLI). Using ROM and OLI, the majority of the 57 drugs were accurately detected by name (52 of 57) or chemical formula (1 of 57). The limit of detection for the drugs was from tens of picograms to nanograms per milliliter. Xenobiotics and endogenous substances relevant to doping control were also identified using this untargeted approach following their extraction from real-world race samples, thus validating the UDD methodology. To the authors' knowledge, this is the first completely UDD methodological approach and represents significant advance toward using artificial intelligence for the detection of both known and emerging doping agents in sports.