Advances in mass spectrometry applied to pharmaceutical metabolomics

Exploring machine learning for untargeted metabolomics using molecular fingerprints

BACKGROUND

Metabolomics, the study of substrates and products of cellular metabolism, offers valuable insights into an organism's state under specific conditions and has the potential to revolutionise preventive healthcare and pharmaceutical research. However, analysing large metabolomics datasets remains challenging, with available methods relying on limited and incompletely annotated metabolic pathways.

METHODS

This study, inspired by well-established methods in drug discovery, employs machine learning on metabolite fingerprints to explore the relationship of their structure with responses in experimental conditions beyond known pathways, shedding light on metabolic processes. It evaluates fingerprinting effectiveness in representing metabolites, addressing challenges like class imbalance, data sparsity, high dimensionality, duplicate structural encoding, and interpretable features. Feature importance analysis is then applied to reveal key chemical configurations affecting classification, identifying related metabolite groups.

RESULTS

The approach is tested on two datasets: one on Ataxia Telangiectasia and another on endothelial cells under low oxygen. Machine learning on molecular fingerprints predicts metabolite responses effectively, and feature importance analysis aligns with known metabolic pathways, unveiling new affected metabolite groups for further study.

CONCLUSION

In conclusion, the presented approach leverages the strengths of drug discovery to address critical issues in metabolomics research and aims to bridge the gap between these two disciplines. This work lays the foundation for future research in this direction, possibly exploring alternative structural encodings and machine learning models.

From Pipeline to Plant Protection Products: Using New Approach Methodologies (NAMs) in Agrochemical Safety Assessment

The human population will be approximately 9.7 billion by 2050, and food security has been identified as one of the key issues facing the global population. Agrochemicals are an important tool available to farmers that enable high crop yields and continued access to healthy foods, but the average new agrochemical active ingredient takes more than ten years, 350 million dollars, and 20,000 animals to develop and register. The time, monetary, and animal costs incentivize the use of New Approach Methodologies (NAMs) in early-stage screening to prioritize chemical candidates. This review outlines NAMs that are currently available or can be adapted for use in early-stage screening agrochemical programs. It covers new in vitro screens that are on the horizon in key areas of regulatory concern. Overall, early-stage screening with NAMs enables the prioritization of development for agrochemicals without human and environmental health concerns through a more directed, agile, and iterative development program before animal-based regulatory testing is even considered.

Lipidomics Analysis in Ferroptosis

Ferroptosis is a form of regulated cell death that occurs due to abnormal lipid metabolism. Lipids, which have been identified in over 45,000 different molecular species, play essential roles in modulating basic life processes. The process of ferroptosis is highly reliant on various lipid species, with polyunsaturated fatty acids (PUFAs) playing a central role in driving this process. Recent advances in mass spectrometry-based lipidomics have led to a surge in studies on ferroptosis. To explore the mechanism of lipid homeostasis in ferroptosis, the development of lipidomics techniques is critical. Currently, liquid chromatography-tandem mass spectrometry (LC-MS/MS) and gas chromatography-mass spectrometry (GC-MS) are the most widely used analytical techniques in lipidomics. These techniques offer deeper insights into the complex lipid mechanisms that underlie ferroptosis.

Machine Learning Using Neural Networks for Metabolomic Pathway Analyses

Elucidating the mechanisms of metabolic pathways helps us understand the cascade of enzyme-catalyzed reactions that lead to the conversion of substances into final products. This has implications for predicting how newly synthesized compounds will affect a person's metabolism and, hence, the development of novel treatments to improve one's health. The study of metabolomic pathways, together with protein engineering, may also aid in the extraction, at a scale, of natural products to be used as drugs and drug precursors. Several approaches have been used to correlate protein annotations to metabolic pathways in order to derive pathways directly related to specific organisms. These could range from association rule-mining techniques to machine learning methods such as decision trees, naïve Bayes, logistic regression, and ensemble methods.In this chapter, we will be reviewing the use of machine learning for metabolic pathway analyses, with a step-by-step focus on the use of deep learning to predict the association of compounds (metabolites) to their respective metabolomic pathway classes. This prediction could help explain interactions of small molecules in organisms. Inspired by the work of Baranwal et al. (2019), we demonstrate how to build and train a deep learning neural network model to perform a multi-label prediction. We considered two different types of fingerprints as features (inputs to the model). The output of the model is the set of metabolic pathway classes (from the KEGG dataset) in which the input molecule participates. We will walk through the various steps of this process, including data collection, feature engineering, model selection, training, and evaluation. This model-building and evaluation process may be easily transferred to other domains of interest. All the source code used in this chapter is made publicly available at https://github.com/jp-um/machine_learning_for_metabolomic_pathway_analyses .

The study of human serum metabolome on the health effects of glyphosate and early warning of potential damage

Glyphosate is one of the most widely used herbicide with high efficiency, low toxicity and broad-spectrum. In recent decades, increasing evidence suggests that glyphosate may cause adverse health effects on human beings. However, until now, there is little data on the human metabolic changes. Since occupational workers are under greater health risks than ordinary people, the understanding regarding the health effects of glyphosate on occupational workers is very important for the early warning of potential damage. In this study, serum metabolic alterations in workers from three chemical factories were analyzed by gas chromatography-mass spectrometry (GC-MS) to assess the potential health risks caused by glyphosate at the molecular level. It was found that the levels of 27 metabolites changed significantly in the exposed group compared to the controls. The altered metabolic pathways, including amino acid metabolism, energy metabolism (glycolysis and TCA cycle) and glutathione metabolism (oxidative stress), etc., indicated a series of changes occur in health profile of the human body after glyphosate exposure, and the suboptimal health status of human may further evolve into various diseases, such as Parkinson's disease, renal and liver dysfunction, hepatocellular carcinoma, and colorectal cancer. Subsequently, 4 biomarkers (i.e., benzoic acid, 2-ketoisocaproic acid, alpha-ketoglutarate, and monoolein) were identified as potential biomarkers related to glyphosate exposure based on the partial correlation analyses, linear regression analyses, and FDR correction. Receiver-operating curve (ROC) analyses manifested that these potential biomarkers and their combinational pattern had good performance and potential clinical value to assess the potential health risk associated with glyphosate exposure while retaining high accuracy. Our findings provided new insights on mechanisms of health effects probably induced by glyphosate, and may be valuable for the health risk assessment of glyphosate exposure.

Mass recalibration for desorption electrospray ionization mass spectrometry imaging using endogenous reference ions

BACKGROUND

Mass spectrometry imaging (MSI) data often consist of tens of thousands of mass spectra collected from a sample surface. During the time necessary to perform a single acquisition, it is likely that uncontrollable factors alter the validity of the initial mass calibration of the instrument, resulting in mass errors of magnitude significantly larger than their theoretical values. This phenomenon has a two-fold detrimental effect: (a) it reduces the ability to interpret the results based on the observed signals, (b) it can affect the quality of the observed signal spatial distributions.

RESULTS

We present a post-acquisition computational method capable of reducing the observed mass drift by up to 60 ppm in biological samples, exploiting the presence of typical molecules with a known mass-to-charge ratio. The procedure, tested on time-of-flight and Orbitrap mass spectrometry analyzers interfaced to a desorption electrospray ionization (DESI) source, improves the molecular annotation quality and the spatial distributions of the detected ions.

CONCLUSION

The presented method represents a robust and accurate tool for performing post-acquisition mass recalibration of DESI-MSI datasets and can help to increase the reliability of the molecular assignment and the data quality.

Mass recalibration for desorption electrospray ionization mass spectrometry imaging using endogenous reference ions

BACKGROUND

Mass spectrometry imaging (MSI) data often consist of tens of thousands of mass spectra collected from a sample surface. During the time necessary to perform a single acquisition, it is likely that uncontrollable factors alter the validity of the initial mass calibration of the instrument, resulting in mass errors of magnitude significantly larger than their theoretical values. This phenomenon has a two-fold detrimental effect: (a) it reduces the ability to interpret the results based on the observed signals, (b) it can affect the quality of the observed signal spatial distributions.

RESULTS

We present a post-acquisition computational method capable of reducing the observed mass drift by up to 60 ppm in biological samples, exploiting the presence of typical molecules with a known mass-to-charge ratio. The procedure, tested on time-of-flight and Orbitrap mass spectrometry analyzers interfaced to a desorption electrospray ionization (DESI) source, improves the molecular annotation quality and the spatial distributions of the detected ions.

CONCLUSION

The presented method represents a robust and accurate tool for performing post-acquisition mass recalibration of DESI-MSI datasets and can help to increase the reliability of the molecular assignment and the data quality.

Effect of Dietary Protein Levels on Dynamic Changes and Interactions of Ruminal Microbiota and Metabolites in Yaks on the Qinghai-Tibetan Plateau

To improve performance and optimize rumen function in yaks (Bos grunniens), further knowledge on the appropriate dietary protein levels for ruminal microbiota and the metabolite profiles of yaks in feedlot feeding is necessary. Current understanding of dietary protein requirements, ruminal microbiota, and metabolites is limited. In this study, yaks were fed a low-protein diet (L; 9.64%), middle low-protein diet (ML; 11.25%), middle high-protein diet (MH; 12.48%), or a high-protein diet (H; 13.87%), and the effects of those diets on changes and interactions in ruminal microbiota and metabolites were investigated. Twenty-four female yaks were selected, and the effects on ruminal microbiota and metabolites were investigated using 16s rRNA gene sequencing and gas chromatography time-of-flight/mass spectrometry (GC-TOF/MS). Diets containing different protein levels changed the composition of the rumen bacterial community, the H group significantly reduced the diversity of ruminal microbiota (p < 0.05), and the number of shared amplicon sequence variants (ASVs) between the H group and the other three groups was lower, suggesting that the ruminal microbiota community fluctuated more with a high-protein diet. In rumen, Bacteroidetes, Firmicutes, and Proteobacteria were the most abundant bacteria at the phylum level, and Bacteroidetes was significantly less abundant in the MH group than in the L and ML groups (p < 0.05). Prevotella_1, Rikenellaceae_RC9_gut_group, and Christensenellaceae_R-7_group had the highest abundance at the genus level. Prevotellaceae was enriched in the low-protein groups, whereas Bacteroidales_BS11_gut_group was enriched in the high-protein groups. Rumen metabolite concentrations and metabolic patterns were altered by dietary protein levels: organic acid metabolites, antioxidant-related metabolites, and some plant-derived metabolites showed variation between the groups. Enrichment analysis revealed that significant changes were concentrated in six pathways, including the citrate cycle (TCA cycle), glyoxylate and dicarboxylate metabolism, and butanoate metabolism. Network analysis showed promotion or restraint relationships between different rumen microbiota and metabolites. Overall, the rumen function was higher in the MH group. This study provides a reference for appropriate dietary protein levels and improves understanding of rumen microbes and metabolites.

Metabolomics of Oral/Head and Neck Cancer

Oral/head and neck cancer is the sixth most common human malignancies in the world. Despite the treatment advances in surgery, chemotherapy, and radiotherapy, the patient survival has not been significantly improved in the past several decades. As a new methodological approach, metabolomics may help reveal the metabolic reprogramming mechanisms underlying head and neck cancer cell proliferation, invasion, and metastasis and may be used to identify metabolite biomarkers for clinical applications of the disease. In this chapter, we briefly review recent metabolomic applications in head and neck cancer.

Application of omics technologies in dermatological research and skin management

BACKGROUND

"Omics" are usually based on the use of high-throughput analysis methods for global analysis of biological samples and the discovery of biomarkers, and may provide new insights into biological phenomena. Over the last few years, the development of omics technologies has considerably accelerated the pace of dermatological research.

AIMS

The purpose of this article was to review the development of omics in recent decades and their application in dermatological research.

METHODS

An extensive literature search was conducted on omics technologies since the first research on omics.

RESULTS

This article summarizes the history and main research methods of the six omics technologies, including genomics, transcriptomics, proteomics, metabolomics, lipidomics, and microbiomics. Their application in certain skin diseases and cosmetics research and development are also summarized.

CONCLUSIONS

This information will help to understand the mechanism of some skin diseases and the discovery of potential biomarkers, and provide new insights for skin health management and cosmetics research and development.

Alterations in Serum-Free Amino Acid Profiles in Childhood Asthma

Asthma often begins in childhood, although making an early diagnosis is difficult. Clinical manifestations, the exclusion of other causes of bronchial obstruction, and responsiveness to anti-inflammatory therapy are the main tool of diagnosis. However, novel, precise, and functional biochemical markers are needed in the differentiation of asthma phenotypes, endotypes, and creating personalized therapy. The aim of the study was to search for metabolomic-based asthma biomarkers among free amino acids (AAs). A wide panel of serum-free AAs in asthmatic children, covering both proteinogenic and non-proteinogenic AAs, were analyzed. The examination included two groups of individuals between 3 and 18 years old: asthmatic children and the control group consisted of children with neither asthma nor allergies. High-performance liquid chromatography combined with tandem mass spectrometry (LC-MS/MS technique) was used for AA measurements. The data were analyzed by applying uni- and multivariate statistical tests. The obtained results indicate the decreased serum concentration of taurine, L-valine, DL-β-aminoisobutyric acid, and increased levels of ƴ-amino-n-butyric acid and L-arginine in asthmatic children when compared to controls. The altered concentration of these AAs can testify to their role in the pathogenesis of childhood asthma. The authors' results should contribute to the future introduction of new diagnostic markers into clinical practice.

Analysis of differential metabolites in lung cancer patients based on metabolomics and bioinformatics

Aim: Based on metabonomics, the metabolic markers of lung cancer patients were analyzed, combined with bioinformatics to explore the underlying disease mechanism. Materials & methods: Based on case-control design, using UPLC-Q-TOF/MS, urine metabolites were detected in discovery and validation set. Multivariate statistical analysis were performed to identify potential markers for lung cancer. A network analysis was constructed to integrate lung cancer disease targets with the above metabolic markers, and its possible mechanism and biological significance were explained. Results: A total of 35 potential markers were identified, 11 of which overlapped. Five key markers have a good linear correlation with serum biochemical indicators. Conclusion: The occurrence and development of lung cancer are closely related to disturbance of D-Glutamine and D-glutamate metabolism, amino acid imbalance. This test was registered on China clinical trial registration center (www.chictr.org.cn/index.aspx), registration number was ChiCTR1900025543.

Applicability of Supercritical fluid chromatography-Mass spectrometry to metabolomics. II-Assessment of a comprehensive library of metabolites and evaluation of biological matrices

In this work, the impact of biological matrices, such as plasma and urine, was evaluated under SFCHRMS in the field of metabolomics. For this purpose, a representative set of 49 metabolites were selected. The assessment of the matrix effects (ME), the impact of biological fluids on the quality of MS/MS spectra and the robustness of the SFCHRMS method were each taken into consideration. The results have highlighted a limited presence of ME in both plasma and urine, with 30% of the metabolites suffering from ME in plasma and 25% in urine, demonstrating a limited sensitivity loss in the presence of matrices. Subsequently, the MS/MS spectra evaluation was performed for further peak annotation. Their analyses have highlighted three different scenarios: 63% of the tested metabolites did not suffer from any interference regardless of the matrix; 21% were negatively impacted in only one matrix and the remaining 16% showed the presence of matrix-belonging compounds interfering in both urine and plasma. Finally, the assessment of retention times stability in the biological samples, has brought into evidence a remarkable robustness of the SFCHRMS method. Average RSD (%) values of retention times for spiked metabolites were equal or below 0.5%, in the two biological fluids over a period of three weeks. In the second part of the work, the evaluation of the Sigma Mass Spectrometry Metabolite Library of Standards containing 597 metabolites, under SFCHRMS conditions was performed. A total detectability of the commercial library up to 66% was reached. Among the families of detected metabolites, large percentages were met for some of them. Highly polar metabolites such as amino acids (87%), nucleosides (85%) and carbohydrates (71%) have demonstrated important success rates, equally for hydrophobic analytes such as steroids (78%) and lipids (71%). On the negative side, very poor performance was found for phosphorylated metabolites, namely phosphate-containing compounds (14%) and nucleotides (31%).

Improving diagnosis of genitourinary cancers: Biomarker discovery strategies through mass spectrometry-based metabolomics

The genitourinary oncology field needs integration of results from basic science, epidemiological studies, clinical and translational research to improve the current methods for diagnosis. MS-based metabolomics can be transformative for disease diagnosis and contribute to global health parity. Metabolite panels are promising to translate metabolomic findings into the clinics, changing the current diagnosis paradigm based on single biomarker analysis. This review article describes capabilities of the MS-based oncometabolomics field for improving kidney, prostate, and bladder cancer detection, early diagnosis, risk stratification, and outcome. Published works are critically discussed based on the study design; type and number of samples analyzed; data quality assessment through quality assurance and quality control practices; data analysis workflows; confidence levels reported for identified metabolites; validation attempts; the overlap of discriminant metabolites for the different genitourinary cancers; and the translation capability of findings into clinical settings. Ongoing challenges are discussed, and future directions are delineated.

A Metabolomics Approach to Pharmacotherapy Personalization

The optimization of drug therapy according to the personal characteristics of patients is a perspective direction in modern medicine. One of the possible ways to achieve such personalization is through the application of "omics" technologies, including current, promising metabolomics methods. This review demonstrates that the analysis of pre-dose metabolite biofluid profiles allows clinicians to predict the effectiveness of a selected drug treatment for a given individual. In the review, it is also shown that the monitoring of post-dose metabolite profiles could allow clinicians to evaluate drug efficiency, the reaction of the host to the treatment, and the outcome of the therapy. A comparative description of pharmacotherapy personalization (pharmacogenomics, pharmacoproteomics, and therapeutic drug monitoring) and personalization based on the analysis of metabolite profiles for biofluids (pharmacometabolomics) is also provided.

Application of Metabolomic Tools for Studying Low Molecular-Weight Fraction of Animal Venoms and Poisons

Both venoms and poisonous secretions are complex mixtures that assist in defense, predation, communication, and competition in the animal world. They consist of variable bioactive molecules, such as proteins, peptides, salts and also metabolites. Metabolomics opens up new perspectives for the study of venoms and poisons as it gives an opportunity to investigate their previously unexplored low molecular-weight components. The aim of this article is to summarize the available literature where metabolomic technologies were used for examining the composition of animal venoms and poisons. The paper discusses only the low molecular-weight components of venoms and poisons collected from snakes, spiders, scorpions, toads, frogs, and ants. An overview is given of the analytical strategies used in the analysis of the metabolic content of the samples. We paid special attention to the classes of compounds identified in various venoms and poisons and potential applications of the small molecules (especially bufadienolides) discovered. The issues that should be more effectively addressed in the studies of animal venoms and poisons include challenges related to sample collection and preparation, species-related chemical diversity of compounds building the metabolome and a need of an online database that would enhance identification of small molecule components of these secretions.

Serum Metabonomics Analysis of Liver Failure Treated by Nonbioartificial Liver Support Systems

OBJECTIVE

To analyze the small molecular metabolic compounds of nonbioartificial liver for treatment of hepatic failure and make further efforts to study the clinical efficacy, mechanism of action, and pathogenesis of hepatic failure.

METHODS

52 patients who met the standard of artificial liver treatment for liver failure were enrolled; these patients included 6 cases of acute liver failure (11.54%), 3 cases of subacute liver failure (5.77%), acute-on-chronic liver failure in 10 cases (19.23%), and 33 cases of chronic liver failure (63.46%). Treatment modes included plasma exchange in 34 patients (65.38%), bilirubin adsorption in 9 patients (17.31%), and hemofiltration in 9 patients (17.31%). The clinical efficacy of artificial liver was assessed by monitoring the effects in the near future. Significant changes in metabolic compounds of liver failure in the treatment before and after artificial liver were screened by using Ultra-Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS). Related metabolic pathways were analyzed by MetaboAnalyst.

RESULTS

After artificial liver treatment, the liver function and coagulation function of liver failure patients were significantly improved (P < 0.01), the Meld score was lower than that before treatment, and the difference was statistically significant (P < 0.05). Serum metabolomics identified 29 small metabolic compounds and 12 metabolic pathways with variable projection importance (VIP) greater than 1 before and after artificial liver treatment. There were 11 metabolic compounds of VIP over 1 and 7 metabolic pathways in the different modes of artificial liver treatment for chronic liver failure. Among them, bile acid metabolism, fatty acid metabolism, and amino acid metabolism are the main sources.

CONCLUSION

Artificial liver treatment can effectively improve liver function and blood coagulation function and Meld score, clinical symptoms and signs in patients with liver failure; the curative effect of artificial liver was verified, which reflected the clinical value of artificial liver in the treatment of liver failure. Artificial liver treatment of liver failure on fatty acids and primary bile acid synthesis pathway was the most significant. The difference of fatty acid, primary bile acid synthesis pathway, and phenylalanine metabolic pathway in different artificial liver patterns of chronic liver failure was the most significant. This provides a new basis for understanding the mechanism of hepatic failure and the mechanism of liver failure by artificial liver treatment.

Investigation on the combined effect of cocaine and ethanol administration through a liquid chromatography-mass spectrometry metabolomics approach

Alcohol is the most widely consumed legal drug, whereas cocaine is the illicit psychostimulant most commonly used in Europe. The combined use of alcohol and cocaine is frequent among drug-abuse consumers and leads to further exacerbation of health consequences compared to individual consumption. The pharmacokinetic and metabolic interactions leading to an increase in their combined toxicity still remains poorly understood. Here, the first metabolomics study of combined cocaine and ethanol chronic exposure effects is reported. A Liquid Chromatography strategy based on sample derivatization with 9-fluorenylmethyloxycarbonyl chloride and using a C18 column coupled to high resolution Mass Spectrometry (time of flight analyzer) was employed to analyze plasma from rats exposed intravenously to these drugs in a 52-min analysis. Using a combination of non-supervised and supervised multivariate analysis the metabolic differences between our experimental groups were explored and unraveled. A comparative analysis of the individual models and their variable importance in the projection values have shown that every experiment intervention includes a subset of specific metabolites. Eleven of these metabolites were annotated, where eight were unequivocally identified using standards and three were tentatively identified by matching the MS/MS spectra to libraries. The results demonstrated that the affected metabolic pathways were mainly those related to the metabolism of different amino acids.

The Effect of Chinese Herbal Medicine Formula mKG on Allergic Asthma by Regulating Lung and Plasma Metabolic Alternations

Asthma is a chronic inflammatory disorder of the airway and is characterized by airway remodeling, hyperresponsiveness, and shortness of breath. Modified Kushen Gancao Formula (mKG), derived from traditional Chinese herbal medicines (TCM), has been demonstrated to have good therapeutic effects on experimental allergic asthma. However, its anti-asthma mechanism remains currently unknown. In the present work, metabolomics studies of biochemical changes in the lung tissue and plasma of ovalbumin (OVA)-induced allergic asthma mice with mKG treatment were performed using ultra high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS). Partial least squares–discriminate analysis (PLS−DA) indicated that the metabolic perturbation induced by OVA was reduced after mKG treatment. A total of twenty-four metabolites involved in seven metabolic pathways were identified as potential biomarkers in the development of allergic asthma. Among them, myristic acid (L3 or P2), sphinganine (L6 or P4), and lysoPC(15:0) (L12 or P16) were detected both in lung tissue and plasma. Additionally, l-acetylcarnitine (L1), thromboxane B2 (L2), 10-HDoHE (L10), and 5-HETE (L11) were first reported to be potential biomarkers associated with allergic asthma. The treatment of mKG mediated all of those potential biomarkers except lysoPC(15:0) (P16). The anti-asthma mechanism of mKG can be achieved through the comprehensive regulation of multiple perturbed biomarkers and metabolic pathways.

Metabonomics Research Progress on Liver Diseases

Metabolomics as the new omics technique develops after genomics, transcriptomics, and proteomics and has rapid development at present. Liver diseases are worldwide public health problems. In China, chronic hepatitis B and its secondary diseases are the common liver diseases. They can be diagnosed by the combination of history, virology, liver function, and medical imaging. However, some patients seldom have relevant physical examination, so the diagnosis may be delayed. Many other liver diseases, such as drug-induced liver injury (DILI), alcoholic liver disease (ALD) and nonalcoholic fatty liver disease (NAFLD), and autoimmune liver diseases, still do not have definite diagnostic markers; the diagnosis consists of history, medical imaging, and the relevant score. As a result, the clinical work becomes very complex. So it has broad prospects to explore the specific and sensitive biomarkers of liver diseases with metabolomics. In this paper, there are several summaries which are related to the current research progress and application of metabolomics on biomarkers of liver diseases.

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.

Metabolic analysis of osteoarthritis subchondral bone based on UPLC/Q-TOF-MS

Osteoarthritis (OA), one of the most widespread musculoskeletal joint diseases among the aged, is characterized by the progressive loss of articular cartilage and continuous changes in subchondral bone. The exact pathogenesis of osteoarthritis is not completely clear. In this work, ultra-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry (UPLC/Q-TOF-MS) in combination with multivariate statistical analysis was applied to analyze the metabolic profiling of subchondral bone from 42 primary osteoarthritis patients. This paper described a modified two-step method for extracting the metabolites of subchondral bone from primary osteoarthritis patients. Finally, 68 metabolites were identified to be significantly changed in the sclerotic subchondral bone compared with the non-sclerotic subchondral bone. Taurine and hypotaurine metabolism and beta-alanine metabolism were probably relevant to the sclerosis of subchondral bone. Taurine, L-carnitine, and glycerophospholipids played a vital regulation role in the pathological process of sclerotic subchondral bone. In the sclerotic process, beta-alanine and L-carnitine might be related to the increase of energy consumption. In addition, our findings suggested that the intra-cellular environment of sclerotic subchondral bone might be more acidotic and hypoxic compared with the non-sclerotic subchondral bone. In conclusion, this study provided a new insight into the pathogenesis of subchondral bone sclerosis. Our results indicated that metabolomics could serve as a promising approach for elucidating the pathogenesis of subchondral bone sclerosis in primary osteoarthritis. Graphical Abstract Metabolic analysis of osteoarthritis subchondral bone.

Metabolomics and its potential in diagnosis, prognosis and treatment of rheumatic diseases

The main aim of metabolomics is to make a comprehensive study of metabolites, the intermediates of biochemical processes in living organisms. Any pathophysiological mechanism caused by disease will inevitably lead to related changes in the concentrations of specific metabolites. In line with this, metabolomics offers a promising laboratory tool for the analysis of potential diagnostic biomarkers that may be used to assess susceptibility to a disease and to evaluate the prognosis and therapeutic response to treatment. Recent data have shown that metabolomics analysis in rheumatoid arthritis has made possible more efficient diagnosis, discrimination between patients with regard to disease activity, prediction of the response to a particular treatment approach, differentiation between rheumatic disease subtypes and greater understanding of the pathophysiology of this disease. Here we characterize metabolomics as a comprehensive laboratory tool and review its potential in the diagnosis, prognosis and treatment of rheumatic diseases such as rheumatoid arthritis.

Mass accuracy and isotopic abundance measurements for HR-MS instrumentation: capabilities for non-targeted analyses

The development of automated non-targeted workflows for small molecule analyses is highly desirable in many areas of research and diagnostics. Sufficient mass and chromatographic resolution is necessary for the detectability of compounds and subsequent componentization and interpretation of ions. The mass accuracy and relative isotopic abundance are critical in correct molecular formulae generation for unknown compounds. While high-resolution instrumentation provides accurate mass information, sample complexity can greatly influence data quality and the measurement of compounds of interest. Two high-resolution instruments, an Orbitrap and a Q-TOF, were evaluated for mass accuracy and relative isotopic abundance with various concentrations of a standard mixture in four complex sample matrices. The overall average ± standard deviation of the mass accuracy was 1.06 ± 0.76 ppm and 1.62 ± 1.88 ppm for the Orbitrap and the Q-TOF, respectively; however, individual measurements were ± 5 ppm for the Orbitrap and greater than 10 ppm for the Q-TOF. Relative isotopic abundance measurements for A + 1 were within 5% of the theoretical value if the intensity of the monoisotopic peak was greater than 1E7 for the Orbitrap and 1E5 for the Q-TOF, where an increase in error is observed with a decrease in intensity. Furthermore, complicating factors were found in the data that would impact automated data analysis strategies, including coeluting species that interfere with detectability and relative isotopic abundance measurements. The implications of these findings will be discussed with an emphasis on reasonable expectations from these instruments, guidelines for experimental workflows, data analysis considerations, and software design for non-targeted analyses.

Transmission mode direct analysis in real time mass spectrometry for fast untargeted metabolic fingerprinting

RATIONALE

Untargeted metabolic fingerprinting is a discovery tool to better understand biochemical processes involved in detecting and characterizing disease states and responses to environmental stressors. Although current mass spectrometric (MS) methods are very powerful, there is a clear need for more rapid, high-throughput MS approaches for metabolomics studies.

METHODS

A rapid metabolic fingerprinting method for human blood sera that utilizes a new transmission mode direct analysis in real time (TM-DART) sampling technique coupled with quadrupole time of flight mass spectrometry (QTOFMS) is presented. In this approach, the sample is deposited directly on a stainless steel mesh that is held in the ionization region by a custom-built module. As a result, the DART plasma gas stream interacts with the sample in a flow-through fashion, which maximizes the interaction between the sample and ionizing species and minimizes variance in sample positioning.

RESULTS

The optimization of TM-DART parameters directly affecting metabolite desorption and ionization, such as sample position and ionizing gas desorption temperature, was critical in achieving high sensitivity and detecting a broad mass range of metabolites. Ramping the ionizing gas desorption temperature further enhanced analysis by adding a simple separation dimension to this ambient approach. In terms of reproducibility, TM-DART compared favorably with traditional probe mode (PM-) DART analysis, with coefficients of variation as low as 16%. The longer-lasting TM-DART signals enabled the acquisition of metabolite full scan and product ion accurate mass spectra in a single experiment, resulting in greater confidence in metabolite identification.

CONCLUSIONS

TM-DART MS proved to be a powerful analytical technique for rapid metabolome analysis of human blood sera.

Simultaneous extraction of metabolome and lipidome with methyl tert-butyl ether from a single small tissue sample for ultra-high performance liquid chromatography/mass spectrometry

A common challenge for scientists working with animal tissue or human biopsy samples is the limitation of material and consequently, the difficulty to perform comprehensive metabolic profiling within one experiment. Here, we present a novel approach to simultaneously perform targeted and non-targeted metabolomics as well as lipidomics from one small piece of liver or muscle tissue by ultra-high performance liquid chromatography/mass spectrometry (UHPLC/MS) following a methyl tert-butyl ether (MTBE)-based extraction. Equal relative amounts of the resulting polar and non-polar fractions were pooled, evaporated and reconstituted in the appropriate solvent for UHPLC/MS analysis. This mix was comparable or superior in yield and reproducibility to a standard 80% methanol extraction for the profiling of polar and lipophilic metabolites (free carnitine, acylcarnitines and FFA). The mix was also suitable for non-targeted metabolomics, an easy measure to increase the metabolite coverage by 30% relative to using the polar fraction alone. Lipidomics was performed from an aliquot of the non-polar fraction. This novel strategy could successfully be applied to one mouse soleus muscle with a dry weight of merely 2.5 mg. By enabling a simultaneous profiling of lipids and metabolites with mixed polarity while saving material for molecular, biochemical or histological analyses, our approach may open up new perspectives toward a comprehensive investigation of small, valuable tissue samples.

Use of brightness wavelet transformation for automated analysis of serum metallothioneins- and zinc-containing proteins by Western blots to subclassify childhood solid tumours

In this study, we determined serum levels of metallothioneins (MTs) and zinc in children with solid tumours (neuroblastoma, Hodgkin lymphoma, medulloblastoma, osteosarcoma, Ewing sarcoma and nephroblastoma) by differential pulse voltammetry Brdicka reaction and ELISA. Zn(II) level in patients sera was 40% compared to controls, contrariwise, MT level was 4.2 × higher in patients. No significant differences among single diagnoses were found both for Zn(II) and MT. When determined Zn(II)/MT ratio, in controls its value was 24.6, but it was 2.6 in patients. After Western-blotting with anti-MT and anti-Zn chicken antibodies, variable intensities of the bands within the samples were observed. The brightness curve obtained for each sample both for MT- and Zn blots was further analysed to produce a list of band positions together with some complementary information related to the intensity of the observed bands by the optimised algorithm. We constructed from those profiles decision trees that enable to distinguish different groups of tumours. The blood samples were heat-treated, in which we supposed mainly MT, but samples contained other thermostable Zn-containing proteins that were helpful for identification of embryonal tumours with 88% accuracy and for identification of sarcomas with 78% accuracy. In MT blots the accuracies were 53 and 45%, respectively. Simultaneous analysis of MT and Zn blots did not increased accuracy of identification neither in embryonal tumours (80%) nor in sarcomas. Those results are promising not only from diagnostic point of view but particularly in the area of studying of individual MT isoforms and their aggregates in malignant tumours.

Metabolomics guides rational development of a simplified cell culture medium for drug screening against Trypanosoma brucei

In vitro culture methods underpin many experimental approaches to biology and drug discovery. The modification of established cell culture methods to make them more biologically relevant or to optimize growth is traditionally a laborious task. Emerging metabolomic technology enables the rapid evaluation of intra- and extracellular metabolites and can be applied to the rational development of cell culture media. In this study, untargeted semiquantitative and targeted quantitative metabolomic analyses of fresh and spent media revealed the major nutritional requirements for the growth of bloodstream form Trypanosoma brucei. The standard culture medium (HMI11) contained unnecessarily high concentrations of 32 nutrients that were subsequently removed to make the concentrations more closely resemble those normally found in blood. Our new medium, Creek's minimal medium (CMM), supports in vitro growth equivalent to that in HMI11 and causes no significant perturbation of metabolite levels for 94% of the detected metabolome (<3-fold change; α = 0.05). Importantly, improved sensitivity was observed for drug activity studies in whole-cell phenotypic screenings and in the metabolomic mode of action assays. Four-hundred-fold 50% inhibitory concentration decreases were observed for pentamidine and methotrexate, suggesting inhibition of activity by nutrients present in HMI11. CMM is suitable for routine cell culture and offers important advantages for metabolomic studies and drug activity screening.

Novel methodologies in metabolic profiling with a focus on molecular diagnostic applications

The metabolome contains all the biological end points of genomic, transcriptomic and proteomic perturbations, also including the influence of gut microbiota and the environment, giving a direct picture of an organism's ongoing metabolic state. Metabolomics thus has the potential to be an effective tool for early diagnosis of disease, and also to be a predictor of treatment response and survival. In recent years, the development of instrumental systems has enabled more comprehensive coverage of the metabolome. Advances in mass spectrometry and chromatography have particularly improved both the efficiency of nontargeted metabolic profiling as well as the sensitivity and reliability of targeted analyses. Mass spectrometric techniques are also increasingly becoming accepted as a routine diagnostic tool in clinical laboratories. This review summarizes the most recent advances and current challenges in metabolomics, with a focus on mass spectrometric methods utilized in biomarker research, highlighted with selected examples.

In situ mass spectrometry imaging and ex vivo characterization of renal crystalline deposits induced in multiple preclinical drug toxicology studies

Drug toxicity observed in animal studies during drug development accounts for the discontinuation of many drug candidates, with the kidney being a major site of tissue damage. Extensive investigations are often required to reveal the mechanisms underlying such toxicological events and in the case of crystalline deposits the chemical composition can be problematic to determine. In the present study, we have used mass spectrometry imaging combined with a set of advanced analytical techniques to characterize such crystalline deposits in situ. Two potential microsomal prostaglandin E synthase 1 inhibitors, with similar chemical structure, were administered to rats over a seven day period. This resulted in kidney damage with marked tubular degeneration/regeneration and crystal deposits within the tissue that was detected by histopathology. Results from direct tissue section analysis by matrix-assisted laser desorption ionization mass spectrometry imaging were combined with data obtained following manual crystal dissection analyzed by liquid chromatography mass spectrometry and nuclear magnetic resonance spectroscopy. The chemical composition of the crystal deposits was successfully identified as a common metabolite, bisulphonamide, of the two drug candidates. In addition, an un-targeted analysis revealed molecular changes in the kidney that were specifically associated with the area of the tissue defined as pathologically damaged. In the presented study, we show the usefulness of combining mass spectrometry imaging with an array of powerful analytical tools to solve complex toxicological problems occurring during drug development.

Capillary electrophoresis/mass spectrometry relevant to pharmaceutical and biotechnological applications

Advanced analytical techniques play a crucial role in the pharmaceutical and biotechnological field. In this context, capillary electrophoresis/mass spectrometry (CE/MS) has attracted attention due to efficient and selective separation in combination with powerful detection allowing identification and detailed characterization. Method developments and applications of CE/MS have been focused on questions not easily accessible by liquid chromatography/mass spectrometry (LC/MS) as the analysis of intact proteins, carbohydrates, and various small molecules, including peptides. Here, recent approaches and applications of CE/MS relevant to (bio)pharmaceuticals are reviewed and discussed to show actual developments and future prospects. Based on other reviews on related subjects covering large parts of previous works, the paper is focused on general ideas and contributions of the last 2 years; for the analysis of glycans, the period is extended back to 2006.

Untargeted plasma metabolite profiling reveals the broad systemic consequences of xanthine oxidoreductase inactivation in mice

A major challenge in systems biology is integration of molecular findings for individual enzyme activities into a cohesive high-level understanding of cellular metabolism and physiology/pathophysiology. However, meaningful prediction for how a perturbed enzyme activity will globally impact metabolism in a cell, tissue or intact organisms is precluded by multiple unknowns, including in vivo enzymatic rates, subcellular distribution and pathway interactions. To address this challenge, metabolomics offers the potential to simultaneously survey changes in thousands of structurally diverse metabolites within complex biological matrices. The present study assessed the capability of untargeted plasma metabolite profiling to discover systemic changes arising from inactivation of xanthine oxidoreductase (XOR), an enzyme that catalyzes the final steps in purine degradation. Using LC-MS coupled with a multivariate statistical data analysis platform, we confidently surveyed >3,700 plasma metabolites (50-1,000 Da) for differential expression in XOR wildtype vs. mice with inactivated XOR, arising from gene deletion or pharmacological inhibition. Results confirmed the predicted derangements in purine metabolism, but also revealed unanticipated perturbations in metabolism of pyrimidines, nicotinamides, tryptophan, phospholipids, Krebs and urea cycles, and revealed kidney dysfunction biomarkers. Histochemical studies confirmed and characterized kidney failure in xor-nullizygous mice. These findings provide new insight into XOR functions and demonstrate the power of untargeted metabolite profiling for systemic discovery of direct and indirect consequences of gene mutations and drug treatments.

Exploring the metabolic state of microorganisms using metabolomics

Microorganisms depend on their ability to modulate their metabolic composition according to specific circumstances, such as different phases of the growth cycle and circadian rhythms, fluctuations in environmental conditions, as well as experimental perturbations. A thorough understanding of these metabolic adaptations requires the ability to comprehensively identify and quantify the metabolome of bacterial cells in different states. In this review, we present an overview of the diverse metabolomics approaches recently adopted to explore the metabolism of a wide variety of microorganisms. Focusing on a selection of illustrative case studies, we assess the different experimental designs used and explore the major achievements and remaining challenges in the field. We conclude by discussing the important complementary information provided by computational methods such as genome-scale metabolic modeling, which enable an integrated analysis of metabolic state changes in the context of overall cellular physiology.

Mass spectrometry-based quantitative analysis and biomarker discovery

Mass spectrometry-based quantitative analysis and biomarker discovery using metabolomics approach represent one of the major platforms in clinical fields including for the prognosis or diagnosis, assessment of severity and response to therapy in a number of clinical disease states as well as therapeutic drug monitoring (TDM). This review first summarizes our mass spectrometry-based research strategy and some results on relationship between cysteinyl leukotriene (cysLT), thromboxane (TX), 12-hydroxyeicosatetraenoic acid (12-HETE) and other metabolites of arachidonic acid and diseases such as atopic dermatitis, rheumatoid arthritis and diabetes mellitus. For the purpose of evaluating the role of these metabolites of arachidonic acid in disease status, we have developed sensitive determination methods with simple solid-phase extraction and applied in clinical settings. In addition to these endogenous compounds, using mass spectrometry, we have developed actually applicable quantitative methods for TDM. Representative example was a method of TDM for sirolimus, one of the immunosuppressant agents for a recipient of organ transplant, which requires rigorous monitoring of blood level. As we recognized great potential in mass spectrometry during these researches, we have become interested in metabolomics as the non-targeted analysis of metabolites. Now, established strategy for the metabolomics investigation applies to samples from cells, animals and humans to separate groups based on altered patterns of metabolites in biological fluids and to identify metabolites as potential biomarkers discriminating groups. We would be honored if our research using mass spectrometry would contribute to provide useful information in the field of medical pharmacy.

The Use of High-Throughput Technologies to Investigate Vascular Inflammation and Atherosclerosis

The greatest challenge of scientific research is to understand the causes and consequences of disease. In recent years, great efforts have been devoted to unraveling the basic mechanisms of atherosclerosis (the underlying pathology of cardiovascular disease), which remains a major cause of morbidity and mortality worldwide. Because of the complex and multifactorial pathophysiology of cardiovascular disease, different research techniques have increasingly been combined to unravel genetic aspects, molecular pathways, and cellular functions involved in atherogenesis, vascular inflammation, and dyslipidemia to gain a multifaceted picture addressing this complexity. Thanks to the rapid evolution of high-throughput technologies, we are now able to generate large-scale data on the DNA, RNA, and protein levels. With the help of sophisticated computational tools, these data sets are integrated to enhance information extraction and are being increasingly used in a systems biology approach to model biological processes as interconnected and regulated networks. This review exemplifies the use of high-throughput technologies—such as genomics, transcriptomics, proteomics, and epigenomics—and systems biology to explore pathomechanisms of vascular inflammation and atherosclerosis.