Practical metabolomics in drug discovery

Targeted metabolomics in human and animal biofluids and tissues using liquid chromatography coupled with tandem mass spectrometry

Here, we present a targeted polar metabolomics protocol for the analysis of biofluids and frozen tissue biopsies using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). We describe steps for sample pretreatment, liquid-liquid extraction, and isolation of polar metabolites. We then detail procedures for target LC-MS/MS analysis. In this protocol, we focus on the analysis of plasma and serum samples. We also provide brief instructions on how to process other biological matrices as supplemental information. For complete details on the use and execution of this protocol, please refer to Coskun et al. (2022).1.

Integrative Analysis of the Gut Microbiota and Metabolome for In Vitro Human Gut Fermentation Modeling

This study aimed to find the best in vitro fermentation method by integrative analysis of the gut microbiota and metabolome. We selected five different media: brain heart infusion broth, Luria-Bertani broth, Mueller-Hinton broth, anaerobe basal broth, and anaerobic medium base (AMB). After in vitro fermentation, the gut microbiota and metabolites were analyzed at different culture times. The results showed that different culture media have different effects on the bacterial community structure and metabolites. The integrative analysis of gut microbiota and metabolism also proved that AMB medium is effective in keeping a stable bacterial community structure and producing less metabolites and short-chain fatty acids by simulating the nutrient-poor microenvironment in the human gut during in vitro fermentation. Thus, culturing with AMB medium for 48 h is the most suitable in vitro model for human gut microbiota fermentation, which provides an alternative approach for diet and health research.

Deciphering the mechanism of action of antitubercular compounds with metabolomics

Tuberculosis (TB), one of the oldest and deadliest bacterial diseases, continues to cause serious global economic, health, and social problems. Current TB treatments are lengthy, expensive, and routinely ineffective against emerging drug resistant strains. Thus, there is an urgent need for the identification and development of novel TB drugs possessing comprehensive and specific mechanisms of action (MoAs). Metabolomics is a valuable approach to elucidating the MoA, toxicity, and potency of promising chemical leads, which is a critical step of the drug discovery process. Recent advances in metabolomics methodologies for deciphering MoAs include high-throughput screening techniques, the integration of multiple omics methods, mass spectrometry imaging, and software for automated analysis. This review describes recently introduced metabolomics methodologies and techniques for drug discovery, highlighting specific applications to the discovery of new antitubercular drugs and the elucidation of their MoAs.

Applying metabolomics to veterinary pharmacology and therapeutics

Metabolomics is the large-scale study of low-molecular-weight substances in a biological system in a given physiological state at a given time point. Metabolomics can be applied to identify predictors of inter-individual variability in drug response, provide clinicians with data useful for decision-making processes in drug selection, and inform about the pharmacokinetics and pharmacodynamics of a drug. It is, therefore, an exceptional approach for gaining new understanding effects in the field of comparative veterinary pharmacology. However, the incorporation of metabolomics into veterinary pharmacology and toxicology is not yet widespread, and this is probably, at least in part, a result of its highly multidisciplinary nature. This article reviews the potential applications of metabolomics in veterinary pharmacology and therapeutics. It integrates key concepts for designing metabolomics studies and analyzing and interpreting metabolomics data, providing solid foundations for applying metabolomics to the study of drugs in all veterinary species.

1H NMR and UHPLC/Q-Orbitrap-MS-Based Metabolomics Combined with 16S rRNA Gut Microbiota Analysis Revealed the Potential Regulation Mechanism of Nuciferine in Hyperuricemia Rats

Hyperuricemia seriously jeopardizes human health by increasing the risk of several diseases, such as gout and stroke. Nuciferine is able to alleviate hyperuricemia significantly. However, the underlying metabolic regulation mechanism remains unknown. To understand the metabolic effects of nuciferine on hyperuricemia by establishing a rat model of rapid hyperuricemia, ¹H NMR and liquid chromatography-mass spectrometry were used to conduct nontargeted metabolomics studies. A total of 21 metabolites were authenticated in plasma and urine to be closely related with hyperuricemia, which were mainly correlated to the six metabolic pathways. Moreover, 16S rRNA analysis indicated that diversified intestinal microorganisms are closely related to changes in differential metabolites, especially bacteria from Firmicutes and Bacteroidetes. We propose that indoxyl sulfate and N-acetylglutamate in urine may be the potential biomarkers besides uric acid for early diagnosis and prevention of hyperuricemia. Gut microbiological analysis found that changes in the gut microbiota are closely related to these metabolites.

Metabolomics technology and bioinformatics for precision medicine

Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.

Inter-Laboratory Comparison of Metabolite Measurements for Metabolomics Data Integration

BACKGROUND

One of the current problems in the field of metabolomics is the difficulty in integrating data collected using different equipment at different facilities, because many metabolomic methods have been developed independently and are unique to each laboratory.

METHODS

In this study, we examined whether different analytical methods among 12 different laboratories provided comparable relative quantification data for certain metabolites. Identical samples extracted from two cell lines (HT-29 and AsPc-1) were distributed to each facility, and hydrophilic and hydrophobic metabolite analyses were performed using the daily routine protocols of each laboratory.

RESULTS

The results indicate that there was no difference in the relative quantitative data (HT-29/AsPc-1) for about half of the measured metabolites among the laboratories and assay methods. Data review also revealed that errors in relative quantification were derived from issues such as erroneous peak identification, insufficient peak separation, a difference in detection sensitivity, derivatization reactions, and extraction solvent interference.

CONCLUSION

The results indicated that relative quantification data obtained at different facilities and at different times would be integrated and compared by using a reference materials shared for data normalization.

Metabolomic profile of systemic sclerosis patients

Systemic sclerosis (SSc) is an autoimmune disease of unknown aetiology characterized by vascular lesions, immunological alterations and diffuse fibrosis of the skin and internal organs. Since recent evidence suggests that there is a link between metabolomics and immune mediated disease, serum metabolic profile of SSc patients and healthy controls was investigated by ¹H-NMR and GC-MS techniques. The results indicated a lower level of aspartate, alanine, choline, glutamate, and glutarate in SSc patients compared with healthy controls. Moreover, comparing patients affected by limited SSc (lcSSc) and diffuse SSc (dcSSc), 6 discriminant metabolites were identified. The multivariate analysis performed using all the metabolites significantly different revealed glycolysis, gluconeogenesis, energetic pathways, glutamate metabolism, degradation of ketone bodies and pyruvate metabolism as the most important networks. Aspartate, alanine and citrate yielded a high area under receiver-operating characteristic (ROC) curves (AUC of 0.81; CI 0.726–0.93) for discriminating SSc patients from controls, whereas ROC curve generated with acetate, fructose, glutamate, glutamine, glycerol and glutarate (AUC of 0.84; CI 0.7–0.98) discriminated between lcSSc and dcSSc. These results indicated that serum NMR-based metabolomics profiling method is sensitive and specific enough to distinguish SSc from healthy controls and provided a feasible diagnostic tool for the diagnosis and classification of the disease.

Effects of Pu-erh ripened tea on hyperuricemic mice studied by serum metabolomics

To evaluate effects of Pu-erh ripened tea in hyperuricemic mice, a mouse hyperuricemia model was developed by oral administration of potassium oxonate for 7 d. Serum metabolomics, based on gas chromatography-mass spectrometry, was used to generate metabolic profiles from normal control, hyperuricemic and allopurinol-treated hyperuricemic mice, as well as hyperuricemic mice given Pu-erh ripened tea at three doses. Pu-erh ripened tea significantly lowered serum uric acid levels. Twelve potential biomarkers associated with hyperuricemia were identified. Pu-erh ripened tea and allopurinol differed in their metabolic effects in the hyperuricemic mice. Levels of glutamic acid, indolelactate, L-allothreonine, nicotinoylglycine, isoleucine, l-cysteine and glycocyamine, all involved in amino acid metabolism, were significantly changed in hyperuricemic mice treated Pu-erh ripened tea. Thus, modulating amino acid metabolism might be the primary mechanism of anti-hyperuricemia by Pu-erh ripened tea.

Metabolomics for phytochemical discovery: development of statistical approaches using a cranberry model system

Metabolomics is the qualitative and quantitative analysis of all of the small molecules in a biological sample at a specific time and influence. Technologies for metabolomics analysis have developed rapidly as new analytical tools for chemical separations, mass spectrometry, and NMR spectroscopy have emerged. Plants have one of the largest metabolomes, and it is estimated that the average plant leaf can contain upward of 30 000 phytochemicals. In the past decade, over 1200 papers on plant metabolomics have been published. A standard metabolomics data set contains vast amounts of information and can either investigate or generate hypotheses. The key factors in using plant metabolomics data most effectively are the experimental design, authentic standard availability, extract standardization, and statistical analysis. Using cranberry (Vaccinium macrocarpon) as a model system, this review will discuss and demonstrate strategies and tools for analysis and interpretation of metabolomics data sets including eliminating false discoveries and determining significance, metabolite clustering, and logical algorithms for discovery of new metabolites and pathways. Together these metabolomics tools represent an entirely new pipeline for phytochemical discovery.

The application of metabolomics in traditional Chinese medicine opens up a dialogue between Chinese and Western medicine

Metabolomics provides an opportunity to develop the systematic analysis of the metabolites and has been applied to discovering biomarkers and perturbed pathways which can clarify the action mechanism of traditional Chinese medicines (TCM). TCM is a comprehensive system of medical practice that has been used to diagnose, treat and prevent illnesses more than 3000 years. Metabolomics represents a powerful approach that provides a dynamic picture of the phenotype of biosystems through the study of endogenous metabolites, and its methods resemble those of TCM. Recently, metabolomics tools have been used for facilitating interactional effects of both Western medicine and TCM. We describe a protocol for investigating how metabolomics can be used to open up 'dialogue' between Chinese and Western medicine, and facilitate lead compound discovery and development from TCM. Metabolomics will bridge the cultural gap between TCM and Western medicine and improve development of integrative medicine, and maximally benefiting the human.

Comparative lipidomics of mouse brain exposed to enriched environment

Several studies have shown that housing conditions and environmental exposure to a series of stimuli lead to behavior improvement in several species. While more works have been focused on illustrating changes of the proteome and transcriptome following enriched environment exposure in mice, little has been done to understand changes in the brain metabolome in this paradigm due to the complexity of this type of analysis. In this paper, lipidomics focused on phospholipids and gangliosides were conducted for brain tissues of mice exposed to enriched or impoverished conditions. We optimized previously reported method and established a reliable relative comparison method for phospholipids and gangliosides in brain tissue using prefractionation with weak anion exchange cartridge. We used liquid chromatography mass spectrometry to explore metabolic signatures of the cerebral cortex and hippocampus after confirming the animals had significant memory differences using the fear conditioning paradigm and brain immunohistochemistry. Although both cerebral cortex and hippocampus regions did not show major alterations in ganglioside composition, we found significant differences in a series of phospholipids containing 22:6 fatty acid in the prefrontal cortex, indicating that environmental enrichment and impoverished housing conditions might be a relevant paradigm to study aberrant lipid metabolism of docosahexaenoic acid consumption. Our study highlights the hypothesis-generating potential of lipidomics and identifies novel region-specific lipid changes possibly linked not only to change of memory function in these models, but also to help us better understand how lipid changes may contribute to memory disorders.

Metabolomic changes in follicular fluid induced by soy isoflavones administered to rats from weaning until sexual maturity

Female Wistar rats at 21 days of age were treated with one of three concentrations of soy isoflavones (SIF) (50, 100 or 200mg/kg body weight, orally, once per day) from weaning until sexual maturity (3 months) in order to evaluate the influence of SIF on ovarian follicle development. After treatment, the serum sex hormone levels and enumeration of ovarian follicles of the ovary were measured. The metabolic profile of follicular fluid was determined using HPLC-MS. Principal component analysis (PCA) and partial least-squares-discriminant analysis (PLS-DA) was used to identify differences in metabolites and reveal useful toxic biomarkers. The results indicated that modest doses of SIF affect ovarian follicle development, as demonstrated by decreased serum estradiol levels and increases in both ovarian follicle atresia and corpora lutea number in the ovary. SIF treatment-related metabolic alterations in follicular fluid were also found in the PCA and PLS-DA models. The 24 most significantly altered metabolites were identified, including primary sex hormones, amino acids, fatty acids and metabolites involved in energy metabolism. These findings may indicate that soy isoflavones affect ovarian follicle development by inducing metabolomic variations in the follicular fluid.

Multivariate Analysis in Metabolomics

Metabolomics aims to provide a global snapshot of all small-molecule metabolites in cells and biological fluids, free of observational biases inherent to more focused studies of metabolism. However, the staggeringly high information content of such global analyses introduces a challenge of its own; efficiently forming biologically relevant conclusions from any given metabolomics dataset indeed requires specialized forms of data analysis. One approach to finding meaning in metabolomics datasets involves multivariate analysis (MVA) methods such as principal component analysis (PCA) and partial least squares projection to latent structures (PLS), where spectral features contributing most to variation or separation are identified for further analysis. However, as with any mathematical treatment, these methods are not a panacea; this review discusses the use of multivariate analysis for metabolomics, as well as common pitfalls and misconceptions.

Identification of a new plasma biomarker of Alzheimer's disease using metabolomics technology

We performed unbiased analysis of steroid-related compounds to identify novel Alzheimer's disease (AD) plasma biomarkers using liquid chromatography-atmospheric pressure chemical ionization-mass spectroscopy. The analysis revealed that desmosterol was found to be decreased in AD plasma versus controls. To precisely quantify variations in desmosterol, we established an analytical method to measure desmosterol and cholesterol. Using this LC-based method, we discovered that desmosterol and the desmosterol/cholesterol ratio are significantly decreased in AD. Finally, the validation of this assay using 109 clinical samples confirmed the decrease of desmosterol in AD as well as a change in the desmosterol/cholesterol ratio in AD. Interestingly, we could also observe a difference between mild cognitive impairment and control. In addition, the decrease of desmosterol was somewhat more significant in females. Receiver operating characteristic (ROC) analysis between controls and AD, using plasma desmosterol shows a score of 0.80, indicating a good discrimination power for this marker in the two reference populations and confirms the potential usefulness of measuring plasma desmosterol levels for diagnosing AD. Further analysis showed a significant correlation of plasma desmosterol with Mini-Mental State Examination scores. Although larger sample populations will be needed to confirm this diagnostic marker sensitivity, our studies demonstrate a sensitive and accurate method of detecting plasma desmosterol concentration and suggest that plasma desmosterol could become a powerful new specific biomarker for early and easy AD diagnosis.

Metabolomics study of alcohol-induced liver injury and hepatocellular carcinoma xenografts in mice

Alcohol abuse is one of the major causes of liver injury and a promoter for hepatocellular carcinoma (HCC). To understand the disease-associated metabolic changes, we investigated and compared the profiles of metabolites in nude mice with alcohol-induced liver injury or bearing a HCC xenograft (HCCX). Alcohol-induced liver injury was achieved by daily administration of grain liquor, and HCC xenografts were generated by subcutaneous inoculation of HepG2 cells in nude mice. Metabolites in serum samples were profiled by ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC/Q-TOF MS). The acquired data was analyzed by principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) to identify potential disease-specific biomarkers. Results showed that the phosphatidylcholine (PC) levels were significantly higher in both liver injury and HCCX mice compared with the control. Interestingly, lysophosphatidylcholines (LPCs) that contain saturated or monounsaturated fatty acids were reduced in both liver injury and HCCX mice, but polyunsaturated fatty acids LPCs were elevated in liver injury mice only. These data delineated the disease-related metabolic alterations of LPCs in liver injury and HCC, suggesting that the LPC profile in serum may be biomarkers for these two common liver diseases.