Homeostatic Signature of Anabolic Steroids in Cattle Using1H−13C HMBC NMR Metabonomics

Comparative Studies on the Structure and Biological Activities of Two New Polysaccharides from Tricholoma sinoportentosum (TS-P) and Termitomyces albuminosus (TA-P)

Polysaccharides are important active ingredients of living organisms. In this study, two new polysaccharides, Tricholoma sinoportentosum polysaccharide (TS-P) and Termitomyces albuminosus (TA-P), were extracted and purified using anion exchange column chromatography. The structure of each polysaccharide was identified by HPGPC, FT-IR, HPLC, GC-MS and NMR, and the biological activities were also investigated. The results of the structure identification showed that TS-P was composed of arabinose, mannose, glucose and galactose at a ratio of 1:1:3:2 and its main chain was composed of (1→4)-Arap residues, (1→4,6)-D-Manp residues and two (1→6)-Galp residues. The TA-P was composed of arabinose, glucose and galactose at a ratio of 2:4:8. Its main chain was composed of two (1→4)-β-L-Arap residues, one (1→4)-Glcp residues, three (1→2,6)-Galp residues and five (1→6)-Galp residues. The immunoassay showed that TS-P and TA-P could significantly promote the proliferation of T cells, B cells and RAW264.7 cells. The cell cycle results showed that for B cells and macrophages, TS-P and TA-P mainly affected the G0/G1 phases of the cell cycle; for T cells, TS-P affected G2/M phase, while TA-P mainly affected the G0/G1 phases. TS-P could significantly promote B cells to secrete IgA, IgG and IgD (p < 0.01), while TA-P could significantly promote the secretion of IgA and IgG (p < 0.01). The chemical structure and biological activity of TS-P and TA-P were first studied and compared to lay a theoretical foundation for the application of fungal polysaccharide.

Metabolomic signatures in elite cyclists: differential characterization of a seeming normal endocrine status regarding three serum hormones

INTRODUCTION

Serum phenotyping of elite cyclists regarding cortisol, IGF1 and testosterone is a way to detect endocrine disruptions possibly explained by exercise overload, non-balanced diet or by doping. This latter disruption-driven approach is supported by fundamental physiology although without any evidence of any metabolic markers.

OBJECTIVES

Serum samples were distributed through Low, High or Normal endocrine classes according to hormone concentration. A ¹H NMR metabolomic study of 655 serum obtained in the context of the longitudinal medical follow-up of 253 subjects was performed to discriminate the three classes for every endocrine phenotype.

METHODS

An original processing algorithm was built which combined a partial-least squares-based orthogonal correction of metabolomic signals and a shrinkage discriminant analysis (SDA) to get satisfying classifications. An extended validation procedure was used to plan in larger size cohorts a minimal size to get a global prediction rate (GPR), i.e. the product of the three class prediction rates, higher than 99.9%.

RESULTS

Considering the 200 most SDA-informative variables, a sigmoidal fitting of the GPR gave estimates of a minimal sample size to 929, 2346 and 1408 for cortisol, IGF1 and testosterone, respectively. Analysis of outliers from cortisol and testosterone Normal classes outside the 97.5%-confidence limit of score prediction revealed possibly (i) an inadequate protein intake for outliers or (ii) an intake of dietary ergogenics, glycine or glutamine, which might explain the significant presence of heterogeneous metabolic profiles in a supposedly normal cyclists subgroup.

CONCLUSION

In a next validation metabolomics study of a so-sized cohort, anthropological, clinical and dietary metadata should be recorded in priority at the blood collection time to confirm these functional hypotheses.

Biomarker Discovery in Animal Health and Disease: The Application of Post-Genomic Technologies

The causes of many important diseases in animals are complex and multifactorial, which present unique challenges. Biomarkers indicate the presence or extent of a biological process, which is directly linked to the clinical manifestations and outcome of a particular disease. Identifying biomarkers or biomarker profiles will be an important step towards disease characterization and management of disease in animals. The emergence of post-genomic technologies has led to the development of strategies aimed at identifying specific and sensitive biomarkers from the thousands of molecules present in a tissue or biological fluid. This review will summarize the current developments in biomarker discovery and will focus on the role of transcriptomics, proteomics and metabolomics in biomarker discovery for animal health and disease.

Analysis of metabolomic patterns in thoroughbreds before and after exercise

Objective

Evaluation of exercise effects in racehorses is important in horseracing industry and animal health care. In this study, we compared metabolic patterns between before and after exercise to screen metabolic biomarkers for exercise effects in thoroughbreds.

Methods

The concentration of metabolites in muscle, plasma, and urine was measured by ¹H nuclear magnetic resonance (NMR) spectroscopy analysis and the relative metabolite levels in the three samples were compared between before and after exercise. Subsequently, multivariate data analysis based on the metabolic profiles was performed using orthogonal partial least square discriminant analysis (OPLS-DA) and variable important plots and t-test was used for basic statistical analysis.

Results

From ¹H NMR spectroscopy analysis, 35, 25, and 34 metabolites were detected in the muscle, plasma, and urine. Aspartate, betaine, choline, cysteine, ethanol, and threonine were increased over 2-fold in the muscle; propionate and trimethylamine were increased over 2-fold in the plasma; and alanine, glycerol, inosine, lactate, and pyruvate were increased over 2-fold whereas acetoacetate, arginine, citrulline, creatine, glutamine, glutarate, hippurate, lysine, methionine, phenylacetylglycine, taurine, trigonelline, trimethylamine, and trimethylamine N-oxide were decreased below 0.5-fold in the urine. The OPLS-DA showed clear separation of the metabolic patterns before and after exercise in the muscle, plasma, and urine. Statistical analysis showed that after exercise, acetoacetate, arginine, glutamine, hippurate, phenylacetylglycine trimethylamine, trimethylamine N-oxide, and trigonelline were significantly decreased and alanine, glycerol, inosine, lactate, and pyruvate were significantly increased in the urine (p<0.05).

Conclusion

In conclusion, we analyzed integrated metabolic patterns in the muscle, plasma, and urine before and after exercise in racehorses. We found changed patterns of metabolites in the muscle, plasma, and urine of racehorses before and after exercise.

The strengths and weaknesses of NMR spectroscopy and mass spectrometry with particular focus on metabolomics research

Mass spectrometry (MS) and nuclear magnetic resonance (NMR) have evolved as the most common techniques in metabolomics studies, and each brings its own advantages and limitations. Unlike MS spectrometry, NMR spectroscopy is quantitative and does not require extra steps for sample preparation, such as separation or derivatization. Although the sensitivity of NMR spectroscopy has increased enormously and improvements continue to emerge steadily, this remains a weak point for NMR compared with MS. MS-based metabolomics provides an excellent approach that can offer a combined sensitivity and selectivity platform for metabolomics research. Moreover, different MS approaches such as different ionization techniques and mass analyzer technology can be used in order to increase the number of metabolites that can be detected. In this chapter, the advantages, limitations, strengths, and weaknesses of NMR and MS as tools applicable to metabolomics research are highlighted.

Evaluation of the discriminative potential of a novel biomarker for estradiol treatments in bovine animals

The endogenous occurrence of natural hormones obstructs the application of classical targeted methods as confirmatory options. In the case of estradiol, the ultimate confirmation of its exogenous administration relies on gas chromatography coupled to combustion/isotope ratio mass spectrometry (GC-C/IRMS). A serum dipeptide composed of pyroglutamic acid and phenylalanine was identified as a potential biomarker of estradiol treatments in adult cows. To evaluate its potential to pinpoint suspicious samples, samples from prepubertal females under different estrogenic treatments have been analyzed. The results confirmed the up-regulation of the dipeptide in adult bovines. The 2-week-old females exhibited short-lasting responses only in a few animals. The 6-month-old female showed a delayed but clear increase on the biomarker level. The composition of the anabolic preparations, the dose, and/or the administration route are possible additional reasons for the reduced response in young animals. A comparison to previous results reported by various researchers is included.

Molecular effects of supraphysiological doses of doping agents on health

Supraphysiological doses of doping agents, such as T/DHT and GH/IGF-1, affect cellular pathways associated with apoptosis and inflammation.

Phenotypic prediction based on metabolomic data for growing pigs from three main European breeds

Predicting phenotypes is a statistical and biotechnical challenge, both in medicine (predicting an illness) and animal breeding (predicting the carcass economical value on a young living animal). High-throughput fine phenotyping is possible using metabolomics, which describes the global metabolic status of an individual, and is the closest to the terminal phenotype. The purpose of this work was to quantify the prediction power of metabolomic profiles for commonly used production phenotypes from a single blood sample from growing pigs. Several statistical approaches were investigated and compared on the basis of cross validation: raw data vs. signal preprocessing (wavelet transformation), with a single-feature selection method. The best results in terms of prediction accuracy were obtained when data were preprocessed using wavelet transformations on the Daubechies basis. The phenotypes related to meat quality were not well predicted because the blood sample was taken some time before slaughter, and slaughter is known to have a strong influence on these traits. By contrast, phenotypes of potential economic interest (e.g., lean meat percentage and ADFI) were well predicted (R(2) = 0.7; P < 0.0001) using metabolomic data.

Quantitative analysis of blood plasma metabolites using isotope enhanced NMR methods

NMR spectroscopy is a powerful analytical tool for both qualitative and quantitative analysis. However, accurate quantitative analysis in complex fluids such as human blood plasma is challenging, and analysis using one-dimensional NMR is limited by signal overlap. It is impractical to use heteronuclear experiments involving natural abundance (13)C on a routine basis due to low sensitivity, despite their improved resolution. Focusing on circumventing such bottlenecks, this study demonstrates the utility of a combination of isotope enhanced NMR experiments to analyze metabolites in human blood plasma. (1)H-(15)N HSQC and (1)H-(13)C HSQC experiments on the isotope tagged samples combined with the conventional (1)H one-dimensional and (1)H-(1)H TOCSY experiments provide quantitative information on a large number of metabolites in plasma. The methods were first tested on a mixture of 28 synthetic analogues of metabolites commonly present in human blood; 27 metabolites in a standard NIST (National Institute of Standards and Technology) human blood plasma were then identified and quantified with an average coefficient of variation of 2.4% for 17 metabolites and 5.6% when all the metabolites were considered. Carboxylic acids and amines represent a majority of the metabolites in body fluids, and their analysis by isotope tagging enables a significant enhancement of the metabolic pool for biomarker discovery applications. Improved sensitivity and resolution of NMR experiments imparted by (15)N and (13)C isotope tagging are attractive for both the enhancement of the detectable metabolic pool and accurate analysis of plasma metabolites. The approach can be easily extended to many additional metabolites in almost any biological mixture.

Analytical and statistical approaches to metabolomics research

Metabolomics, the global profiling of metabolites in different living systems, has experienced a rekindling of interest partially due to the improved detection capabilities of the instrumental techniques currently being used in this area of biomedical research. The analytical methods of choice for the analysis of metabolites in search of disease biomarkers in biological specimens, and for the study of various low molecular weight metabolic pathways include NMR spectroscopy, GC/MS, CE/MS, and HPLC/MS. Global metabolite analysis and profiling of two different sets of data results in a plethora of data that is difficult to manage or interpret manually because of their subtle differences. Multivariate statistical methods and pattern-recognition programs were developed to handle the acquired data and to search for the discriminating features between data acquired from two sample sets, healthy and diseased. Metabolomics have been used in toxicology, plant physiology, and biomedical research. In this paper, we discuss various aspects of metabolomic research including sample collection, handling, storage, requirements for sample analysis, peak alignment, data interpretation using statistical approaches, metabolite identification, and finally recommendations for successful analysis.

Metabolomics-based methods for early disease diagnostics

The emerging field of metabolomics, in which a large number of small-molecule metabolites from body fluids or tissues are detected quantitatively in a single step, promises immense potential for early diagnosis, therapy monitoring and for understanding the pathogenesis of many diseases. Metabolomics methods are mostly focused on the information-rich analytical techniques of NMR spectroscopy and mass spectrometry (MS). Analysis of the data from these high-resolution methods using advanced chemometric approaches provides a powerful platform for translational and clinical research and diagnostic applications. In this review, the current trends and recent advances in NMR- and MS-based metabolomics are described with a focus on the development of advanced NMR and MS methods, improved multivariate statistical data analysis and recent applications in the area of cancer, diabetes, inborn errors of metabolism and cardiovascular diseases.

Application of NMR-based metabonomics in the investigation of myocardial ischemia-reperfusion, ischemic preconditioning and antioxidant intervention in rabbits

NMR based metabonomics was applied in rabbit plasma samples during myocardial ischemia-reperfusion injury, with the following interventions: (1) Control (no intervention); (2) ischemic preconditioning (IpC); (3) administration of melatonin; (4) IpC+administration of melatonin; (5) treatment of the indole derivative C6458. The (1)H NMR signal intensity ratio of lactate/glucose was found to increase in Control samples during reperfusion compared to baseline, while lactate+alanine/acetate was decreased suggesting impairment of aerobic glycolysis and concomitant lipid utilization. In contrast, after IpC or treatment with C6458, the lactate/glucose ratio was similar to baseline in accordance with the previously reported decrease in infarct size. Multivariate statistical methods such as Principal Component Analysis (PCA), and Discriminant Analysis (DA) were used for the discrimination of samples. The use of ANOVA variable preselection prior to PCA was advantageous in producing adequate models. PCA could classify the Control group in three clusters according to the condition of the heart (baseline-ischemia-reperfusion) while in the IpC groups no classification was evident. PCA discrimination upon treatment with melatonin and C6458 provided further evidence of their effect on the metabolic profile. The supervised DA resulted in fine discrimination between the different subgroups. Plasma NMR spectra in combination with pattern recognition techniques proved to be an efficient and simple method to depict the metabolic changes produced upon ischemia-reperfusion of the myocardium.

Metabolic profiling technologies for biomarker discovery in biomedicine and drug development

The state-of-the-art of nuclear magnetic resonance spectroscopy, mass spectrometry and statistical tools for the acquisition and evaluation of complex multidimensional spectroscopic data in metabolic profiling is reviewed in this article. The continuous evolution of the sensitivity, precision and throughput has made these technologies powerful and extremely robust tools for application in systems biology, pharmaceutical and diagnostics research. Particular emphasis is also given to the collection and storage of biological samples that are subjected to metabolite profiling. Selected examples from preclinical and clinical applications are paradigmatically shown. These illustrate the power of the profiling technologies for characterizing the metabolic phenotype of healthy, diseased and treated subjects. The complexity of disease and drug treatment is asking for an adequate response by integrated and comprehensive metabolite profiling approaches that allow the discovery of new combinations of metabolic biomarkers.