3-Methoxytyrosine as an indicator of dopaminergic manipulation in equine plasma

The use of catechol-O-methyltransferase inhibitors may mask doping agents, primarily levodopa, administered to racehorses and prolong the stimulating effects of dopaminergic compounds such as dopamine. It is known that 3-methoxytyramine is a metabolite of dopamine and 3-methoxytyrosine is a metabolite of levodopa thus these compounds are proposed to be potential biomarkers of interest. Previous research established a urinary threshold of 4,000 ng/mL for 3-methoxytyramine to monitor misuse of dopaminergic agents. However, there is no equivalent biomarker in plasma. To address this deficiency a rapid protein precipitation method was developed and validated to isolate target compounds from 100 µL equine plasma. A liquid chromatography-high resolution accurate mass (LC-HRAM) method using an IMTAKT Intrada amino acid column provided quantitative analysis of 3-methoxytyrosine (3-MTyr) with lower limit of quantification of 5 ng/mL. Reference population profiling (n = 1129) investigated the expected basal concentrations for raceday samples from equine athletes and showed a right-skewed distribution (skewness = 2.39, kurtosis = 10.65) which resulted from large variation (RSD = 71%) within the data. Logarithmic transformation of the data provided a normal distribution (skewness = 0.26, kurtosis = 3.23) resulting in the proposal of a conservative threshold for plasma 3-MTyr of 1,000 ng/mL at a 99.995% confidence level. A 12-horse administration study of Stalevo® (800 mg L-DOPA, 200 mg carbidopa, 1600 mg entacapone) revealed elevated 3-MTyr concentrations for 24-hours post-administration.

Towards Non-Targeted Screening of Lipid Biomarkers for Improved Equine Anti-Doping

The current approach to equine anti-doping is focused on the targeted detection of prohibited substances. However, as new substances are rapidly being developed, the need for complimentary methods for monitoring is crucial to ensure the integrity of the racing industry is upheld. Lipidomics is a growing field involved in the characterisation of lipids, their function and metabolism in a biological system. Different lipids have various biological effects throughout the equine system including platelet aggregation and inflammation. A certain class of lipids that are being reviewed are the eicosanoids (inflammatory markers). The use of eicosanoids as a complementary method for monitoring has become increasingly popular with various studies completed to highlight their potential. Studies including various corticosteroids, non-steroidal anti-inflammatories and cannabidiol have been reviewed to highlight the progress lipidomics has had in contributing to the equine anti-doping industry. This review has explored the techniques used to prepare and analyse samples for lipidomic investigations in addition to the statistical analysis and potential for lipidomics to be used for a longitudinal assessment in the equine anti-doping industry.

Metabolomics in clinical and forensic toxicology, sports anti-doping and veterinary residues

Metabolomics is a multidisciplinary field providing workflows for complementary approaches to conventional analytical determinations. It allows for the study of metabolically related groups of compounds or even the study of novel pathways within the biological system. The procedural stages of metabolomics; experimental design, sample preparation, analytical determinations, data processing and statistical analysis, compound identification and validation strategies are explored in this review. The selected approach will depend on the type of study being conducted. Experimental design influences the whole metabolomics workflow and thus needs to be properly assessed to ensure sufficient sample size, minimal introduced and biological variation and appropriate statistical power. Sample preparation needs to be simple, yet potentially global in order to detect as many compounds as possible. Analytical determinations need to be optimised either for the list of targeted compounds or a universal approach. Data processing and statistical analysis approaches vary widely and need to be better harmonised for review and interpretation. This includes validation strategies that are currently deficient in many presented workflows. Common compound identification approaches have been explored in this review. Metabolomics applications are discussed for clinical and forensic toxicology, human and equine sports anti-doping and veterinary residues.

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

There is an urgent need to implement holistic and untargeted doping control protocols with improved discriminatory power, compared to conventional methods that only target doping agents. Metabolomics, which aims to characterize all metabolites present in biological matrices, could fulfill this need. In this context, the aim of this study was to evaluate the impact of environmental factors on the ability to obtain a metabolic signature of stanozolol administration in horse doping situation. Urine samples from 16 horses breeded in two different places were collected over a one-year period, before, during and seven months after the administration of stanozolol, a horse doping agent. Metabolomic analysis was performed using ultra-high pressure reverse phase liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry (MS). Results showed a major impact of the nutritional regimen, drug administration (for de-worming purpose) and breeding place on the metabolite profiles of horse urines, which hampered the detection of metabolic perturbations induced by stanozolol administration. After having used MS/MS experiments to characterize some MS features related to these environmental factors, we showed that highlighting and then removing the features impacted by these confounding factors before performing supervised multivariate statistical analyses could address this issue. In conclusion, adequate consideration should be given to environmental and physiological factors; otherwise, they can emerge as confounding factors and conceal doping administration.

Method selectivity evaluation using the co-feature ratio in LC/MS metabolomics: Comparison of HILIC stationary phase performance for the analysis of plasma, urine and cell extracts

Evaluation of the chromatographic separation in metabolomics studies has primarily been done using preselected sets of standards or by counting the number of detected features. An alternative approach is to calculate each feature's co-feature ratio, which is a combined selectivity measurement for the separation (i.e. extent of co-elution) and the MS-signal (i.e. adduct formation and in-source fragmentation). The aim of this study was to demonstrate how the selectivity of different HILIC stationary phases can be evaluated using the co-feature ratio approach. The study was based on three sample types; plasma, urine and cell extracts. Samples were analyzed on an UHPLC-ESI-Q-ToF system using an amide, a bare silica and a sulfobetaine stationary phase. For each feature, a co-feature ratio was calculated and used for multivariate analysis of the selectivity differences between the three stationary phases. Unsupervised PCA models indicated that the co-feature ratios were highly dependent on type of stationary phase. For several metabolites a 15-30 fold difference in the co-feature ratio were observed between the stationary phases. Observed selectivity differences related primarily to the retention patterns of unwanted matrix components such as inorganic salts (detected as salt clusters), glycerophospholipids, and polyethylene glycols. These matrix components affected the signal intensity of co-eluting metabolites by interfering with the ionization efficiency and/or their adduct formation. Furthermore, the retention pattern of these matrix components had huge influence on the number of detected features. The co-feature ratio approach has successfully been applied for evaluation of the selectivity performance of three HILIC stationary phases. The co-feature ratio could therefore be used in metabolomics for developing selective methods fit for their purpose, thereby avoiding generic analytical approaches, which are often biased, as type and amount of interfering matrix components are metabolome dependent.

Baseline urine metabolic phenotype in patients with severe alcoholic hepatitis and its association with outcome

Severe alcoholic hepatitis (SAH) has a high mortality rate, and corticosteroid therapy is effective in 60% patients. This study aimed to investigate a baseline metabolic phenotype that could help stratify patients not likely to respond to steroid therapy and to have an unfavorable outcome. Baseline urine metabolome was studied in patients with SAH using ultra‐high performance liquid chromatography and high‐resolution mass spectrometry. Patients were categorized as responders (Rs, n = 52) and nonresponders (NRs, n = 8) at day 7 according to the Lille score. Multivariate projection analysis identified metabolites in the discovery cohort (n = 60) and assessed these in a validation cohort of 80 patients (60 Rs, 20 NRs). A total of 212 features were annotated by using metabolomic/biochemical/spectral databases for metabolite identification. After a stringent selection procedure, a total of nine urinary metabolites linked to mitochondrial functions significantly discriminated nonresponders, most importantly by increased acetyl‐L‐carnitine (12‐fold), octanoylcarnitine (4‐fold), decanoylcarnitine (4‐fold), and alpha‐ketoglutaric acid (2‐fold) levels. Additionally, urinary acetyl‐L‐carnitine and 3‐hydroxysebasic acid discriminated nonsurvivors (P < 0.01). These urinary metabolites significantly correlated to severity indices and mortality (r > 0.3; P < 0.01) and were associated with nonresponse (odds ratio >3.0; P < 0.001). In the validation cohort, baseline urinary acetyl‐L‐carnitine documented an area under the receiver operating curve of 0.96 (0.85‐0.99) for nonresponse prediction and a hazard ratio of 3.5 (1.5‐8.3) for the prediction of mortality in patients with SAH. Acetyl‐L‐carnitine at a level of >2,500 ng/mL reliably segregated survivors from nonsurvivors (P < 0.01, log‐rank test) in our study cohort. Conclusion: Urinary metabolome signatures related to mitochondrial functions can predict pretherapy steroid response and disease outcome in patients with SAH. (Hepatology Communications 2018;2:628‐643)

Serial-omics characterization of equine urine

Horse urine is easily collected and contains molecules readily measurable using mass spectrometry that can be used as biomarkers representative of health, disease or drug tampering. This study aimed at analyzing microliter levels of horse urine to purify, identify and quantify proteins, polar metabolites and non-polar lipids. Urine from a healthy 12 year old quarter horse mare on a diet of grass hay and vitamin/mineral supplements with limited pasture access was collected for serial-omics characterization. The urine was treated with methyl tert-butyl ether (MTBE) and methanol to partition into three distinct layers for protein, non-polar lipid and polar metabolite content from a single liquid-liquid extraction and was repeated two times. Each layer was analyzed by high performance liquid chromatography—high resolution tandem mass spectrometry (LC-MS/MS) to obtain protein sequence and relative protein levels as well as identify and quantify small polar metabolites and lipids. The results show 46 urine proteins, many related to normal kidney function, structural and circulatory proteins as well as 474 small polar metabolites but only 10 lipid molecules. Metabolites were mostly related to urea cycle and ammonia recycling as well as amino acid related pathways, plant diet specific molecules, etc. The few lipids represented triglycerides and phospholipids. These data show a complete mass spectrometry based—omics characterization of equine urine from a single 333 μL mid-stream urine aliquot. These omics data help serve as a baseline for healthy mare urine composition and the analyses can be used to monitor disease progression, health status, monitor drug use, etc.

Applications of Fourier Transform Ion Cyclotron Resonance (FT-ICR) and Orbitrap Based High Resolution Mass Spectrometry in Metabolomics and Lipidomics

Metabolomics, along with other "omics" approaches, is rapidly becoming one of the major approaches aimed at understanding the organization and dynamics of metabolic networks. Mass spectrometry is often a technique of choice for metabolomics studies due to its high sensitivity, reproducibility and wide dynamic range. High resolution mass spectrometry (HRMS) is a widely practiced technique in analytical and bioanalytical sciences. It offers exceptionally high resolution and the highest degree of structural confirmation. Many metabolomics studies have been conducted using HRMS over the past decade. In this review, we will explore the latest developments in Fourier transform mass spectrometry (FTMS) and Orbitrap based metabolomics technology, its advantages and drawbacks for using in metabolomics and lipidomics studies, and development of novel approaches for processing HRMS data.