A review of strategies for untargeted urinary metabolomic analysis using gas chromatography-mass spectrometry

Urine organic acid metabolomic profiling by gas chromatography mass spectrometry: Assessment of solvent extract evaporation parameters on the recovery of key diagnostic metabolites

BACKGROUND

Analysis of urinary organic acids (UOAs) by gas chromatography mass-spectrometry (GC-MS) is widely used in metabolomic studies. It is a complex test with many limitations and pitfalls yet there is limited evidence in the literature to support best practice. This study investigated the impact of drying down time and temperature on the recovery of 16 key analytes from solvent extracts.

METHODS

Pooled urine specimens were enriched with organic acids. Urine aliquots (n = 3) were acidified and extracted into diethyl ether and ethyl acetate. Extracts were dried under nitrogen at ambient temperature (25 °C); 40 °C; 60 °C then left for 0; +5; +15 min. Dried extracts were derivatised with N,O,-bis-(trimethylsilyl)trifluoroacetamide prior to analysis by GC-MS. Urine specimens from individuals with biotinidase deficiency, maple syrup urine disease (MSUD) and ketotic hypoglycemia were analysed to demonstrate the potential clinical impact.

RESULTS

Recovery of shorter chain hydroxycarboxylic acids decreased significantly when extracts were dried above 25 °C (mean recovery 89 % at 60 °C, p < 0.01) or left under nitrogen post-drying (mean recovery at ambient + 15 min, 40 °C + 15mins and 60 °C + 15mins was 56 %, 12 % and 2 %, respectively, p < 0.01). Whilst dicarboxylic acids/medium chain fatty acids were unaffected by temperature (mean recovery 100 %), prolonged drying reduced recovery (mean recovery 85 % at 60 °C + 15mins, p < 0.01).

CONCLUSIONS

Evaporation of solvent extracts with heat and/or prolonged drying under nitrogen results in significant losses of the shorter chain hydroxycarboxylic acids. The evaporation protocol must be carefully controlled to ensure accurate and reproducible results, preventing misdiagnoses and/or misinterpretation of results.

Understanding LC/MS-Based Metabolomics: A Detailed Reference for Natural Product Analysis

Liquid chromatography, when used in conjunction with mass spectrometry (LC/MS), is a powerful tool for conducting accurate and reproducible investigations of numerous metabolites in natural products (NPs). LC/MS has gained prominence in metabolomic research due to its high throughput, the availability of multiple ionization techniques and its ability to provide comprehensive metabolite coverage. This unique method can significantly influence various scientific domains. This review offers a comprehensive overview of the current state of LC/MS-based metabolomics in the investigation of NPs. This review provides a thorough overview of the state of the art in LC/MS-based metabolomics for the investigation of NPs. It covers the principles of LC/MS, various aspects of LC/MS-based metabolomics such as sample preparation, LC modes, method development, ionization techniques and data pre-processing. Moreover, it presents the applications of LC/MS-based metabolomics in numerous fields of NPs research such as including biomarker discovery, the agricultural research, food analysis, the study of marine NPs and microbiological research. Additionally, this review discusses the challenges and limitations of LC/MS-based metabolomics, as well as emerging trends and developments in this field.

Advances and challenges in non-targeted analysis: An insight into sample preparation and detection by liquid chromatography-mass spectrometry

Unknown impurities, metabolites and harmful pollutants present in pharmaceutical products, biological and environmental samples, respectively are of high concern in terms of their detection and quantification. The targeted analysis aims to quantify known chemical entities, but it lacks the ability to identify unknown components present in a sample. Non-targeted analysis is an analytical approach that can be made applicable to various disciplines of science to effectively search for unknown chemical, biological, or environmental entities that can answer various baffling mysteries of research. It employs various high-end analytical techniques that can specifically screen out multiple unknown compounds from complex mixtures. Non-targeted analysis is also applicable for complex studies such as metabolomics to search unidentified metabolites of new chemical entities. This review critically discusses the current advancements in non-targeted analysis related to the analysis of pharmaceutical, biological, and environmental samples. Various steps like sample collection, handling, preparation, extraction, its analysis using advanced techniques like high-resolution mass spectrometry, liquid chromatography mass spectrometry, and lastly interpretation of the huge amounts of complex data obtained upon analysis of complex matrices have been discussed broadly in this article. Besides the advantages of non-targeted analysis over targeted analysis, limitations, bioinformatics tools, sources of error, and research gaps have been critically analyzed.

Innovative molecular networking analysis of steroids and characterisation of the urinary steroidome

Steroids are cholesterol-derived biomolecules that play an essential role in biological processes. These substances used as growth promoters in animals are strictly regulated worldwide. Targeted assays are the conventional methods of monitoring steroid abuse, with limitations: only detect known metabolites. Metabolism leads to many potential compounds (isomers), which complicates the analysis. Thus, to overcome these limitations, non-targeted analysis offers new opportunities for a deeper understanding of metabolites related to steroid metabolism. Molecular networking (MN) appears to be an innovative strategy combining high-resolution mass spectrometry and specific data processing to study metabolic pathways. In the present study, two databases and networks of steroids were constructed to lay the foundations for the implementation of the GNPS-MN approach. Steroids of the same family were grouped together, nandrolone and testosterone were linked to other analogues. This network and associated database were then applied to a few urine samples in order to demonstrate the annotation capacity in steroidome study. The results show that MN strategy could be used to study steroid metabolism and highlight biomarkers.

Determination of pesticide residues in urine by chromatography-mass spectrometry: methods and applications

Introduction

Pollution has emerged as a significant threat to humanity, necessitating a thorough evaluation of its impacts. As a result, various methods for human biomonitoring have been proposed as vital tools for assessing, managing, and mitigating exposure risks. Among these methods, urine stands out as the most commonly analyzed biological sample and the primary matrix for biomonitoring studies.

Objectives

This review concentrates on exploring the literature concerning residual pesticide determination in urine, utilizing liquid and gas chromatography coupled with mass spectrometry, and its practical applications.

Method

The examination focused on methods developed since 2010. Additionally, applications reported between 2015 and 2022 were thoroughly reviewed, utilizing Web of Science as a primary resource.

Synthesis

Recent advancements in chromatography-mass spectrometry technology have significantly enhanced the development of multi-residue methods. These determinations are now capable of simultaneously detecting numerous pesticide residues from various chemical and use classes. Furthermore, these methods encompass analytes from a variety of environmental contaminants, offering a comprehensive approach to biomonitoring. These methodologies have been employed across diverse perspectives, including toxicological studies, assessing pesticide exposure in the general population, occupational exposure among farmers, pest control workers, horticulturists, and florists, as well as investigating consequences during pregnancy and childhood, neurodevelopmental impacts, and reproductive disorders.

Future directions

Such strategies were essential in examining the health risks associated with exposure to complex mixtures, including pesticides and other relevant compounds, thereby painting a broader and more accurate picture of human exposure. Moreover, the implementation of integrated strategies, involving international research initiatives and biomonitoring programs, is crucial to optimize resource utilization, enhancing efficiency in health risk assessment.

Decoding active compounds and molecular targets of herbal medicine by high-throughput metabolomics technology: A systematic review

Clinical experiences of herbal medicine (HM) have been used to treat a variety of human intractable diseases. As the treatment of diseases using HM is characterized by multi-components and multi-targets, it is difficult to determine the bio-active components, explore the molecular targets and reveal the mechanisms of action. Metabolomics is frequently used to characterize the effect of external disturbances on organisms because of its unique advantages on detecting changes in endogenous small-molecule metabolites. Its systematicity and integrity are consistent with the effective characteristics of HM. After HM intervention, metabolomics can accurately capture and describe the behavior of endogenous metabolites under the disturbance of functional compounds, which will be used to decode the bioactive ingredients of HM and expound the molecular targets. Metabolomics can provide an approach for explaining HM, addressing unclear clinical efficacy and undefined mechanisms of action. In this review, the metabolomics strategy and its applications in HM are systematically introduced, which offers valuable insights for metabolomics methods to characterizing the pharmacological effects and molecular targets of HM.

Urinary Metabolomic Differentiation of Infants Fed on Human Breastmilk and Formulated Milk

Human breastmilk is an invaluable nutritional and pharmacological resource with a highly diverse metabolite profile, which can directly affect the metabolism of infants. Application of metabolomics can discriminate the complex relationship between such nutrients and infant health. As the most common biological fluid in metabolomic study, infant urinary metabolomics may provide the physiological impacts of different nutritional resources, namely human breastmilk and formulated milk. In this study, we aimed to identify possible differences in the urine metabolome of 30 infants (1-14 days after birth) fed with breast milk (n = 15) or formulated milk (n = 15). From metabolomic analysis with gas chromatography-mass spectrometry, 163 metabolites from single mass spectrometry (GC-MS), and 383 metabolites from tandem mass spectrometry (GC-MS/MS) were confirmed in urinary samples. Various multivariate statistical analysis were performed to discriminate the differences originating from physiological/nutritional variables, including human breastmilk/formulate milk feeding, sex, and duration of feeding. Both unsupervised and supervised discriminant analyses indicated that feeding resources (human breastmilk/formulated milk) gave marginal but significant differences in urinary metabolomes, while other factors (sex, duration of feeding) did not show notable discrimination between groups. According to the biomarker analyses, several organic acid and amino acids showed statistically significant differences between different feeding resources, such as 2-hydroxyhippurate.

Impact of exposure to atmospheric particulate matter in human skin-derived fibroblast cells: A metabolomics approach for the class of amino acids based on GC×GC-Q-TOFMS/MS

The particulate matter (PM) in the air comprises particles containing a complex mixture of pollutants associated with various environmental and public health disturbances. However, studies related to the effects of PM on the skin are still incipient. In this work, the toxicity of particulate material to fibroblast cells derived from the human dermis was investigated using metabolomic analysis for the class of amino acids. For the analysis of amino acids, a new method with high selectivity and resolution based on comprehensive two-dimensional gas chromatography coupled to mass spectrometry (GC×GC-Q-TOFMS/MS) was developed and validated. The exposure impact of PM up to 2.5 µm (PM2.5) on fibroblast cells was shown to be dose-dependent. Metabolomics results indicated that amino acid levels and metabolic pathways in fibroblasts were significantly affected by PM2.5. Given the results, it was possible to correlate these effects to a series of responses, including decreased cellular energy, dysregulation of cellular homeostasis, decreased collagen synthesis, interference with wound healing, and suppression of protein biosynthesis. ENVIRONMENTAL IMPLICATION: Although some progress has been made in air pollution control, the health risk related to PM2.5 exposure remains important. The effects of air pollution on the skin have been extensively studied. However, few studies are related to the impact of PM2.5 on the skin. This study determines the profile of amino acids from fibroblast cells exposed to PM2.5, providing new insight into the damage to skin cells from atmospheric pollution.

Carotenoids in Health as Studied by Omics-Related Endpoints

Carotenoids have been associated with risk reduction for several chronic diseases, including the association of their dietary intake/circulating levels with reduced incidence of obesity, type 2 diabetes, certain types of cancer, and even lower total mortality. In addition to some carotenoids constituting vitamin A precursors, they are implicated in potential antioxidant effects and pathways related to inflammation and oxidative stress, including transcription factors such as nuclear factor κB and nuclear factor erythroid 2-related factor 2. Carotenoids and metabolites may also interact with nuclear receptors, mainly retinoic acid receptor/retinoid X receptor and peroxisome proliferator-activated receptors, which play a role in the immune system and cellular differentiation. Therefore, a large number of downstream targets are likely influenced by carotenoids, including but not limited to genes and proteins implicated in oxidative stress and inflammation, antioxidation, and cellular differentiation processes. Furthermore, recent studies also propose an association between carotenoid intake and gut microbiota. While all these endpoints could be individually assessed, a more complete/integrative way to determine a multitude of health-related aspects of carotenoids includes (multi)omics-related techniques, especially transcriptomics, proteomics, lipidomics, and metabolomics, as well as metagenomics, measured in a variety of biospecimens including plasma, urine, stool, white blood cells, or other tissue cellular extracts. In this review, we highlight the use of omics technologies to assess health-related effects of carotenoids in mammalian organisms and models.

Optimising a urinary extraction method for non-targeted GC-MS metabolomics

Urine is ideal for non-targeted metabolomics, providing valuable insights into normal and pathological cellular processes. Optimal extraction is critical since non-targeted metabolomics aims to analyse various compound classes. Here, we optimised a low-volume urine preparation procedure for non-targeted GC-MS. Five extraction methods (four organic acid [OA] extraction variations and a "direct analysis" [DA] approach) were assessed based on repeatability, metabolome coverage, and metabolite recovery. The DA method exhibited superior repeatability, and achieved the highest metabolome coverage, detecting 91 unique metabolites from multiple compound classes comparatively. Conversely, OA methods may not be suitable for all non-targeted metabolomics applications due to their bias toward a specific compound class. In accordance, the OA methods demonstrated limitations, with lower compound recovery and a higher percentage of undetected compounds. The DA method was further improved by incorporating an additional drying step between two-step derivatization but did not benefit from urease sample pre-treatment. Overall, this study establishes an improved low-volume urine preparation approach for future non-targeted urine metabolomics applications using GC-MS. Our findings contribute to advancing the field of metabolomics and enable efficient, comprehensive analysis of urinary metabolites, which could facilitate more accurate disease diagnosis or biomarker discovery.

Role of untargeted omics biomarkers of exposure and effect for tobacco research

Tobacco research remains a clear priority to improve individual and population health, and has recently become more complex with emerging combustible and noncombustible tobacco products. The use of omics methods in prevention and cessation studies are intended to identify new biomarkers for risk, compared risks related to other products and never use, and compliance for cessation and reinitation. to assess the relative effects of tobacco products to each other. They are important for the prediction of reinitiation of tobacco use and relapse prevention. In the research setting, both technical and clinical validation is required, which presents a number of complexities in the omics methodologies from biospecimen collection and sample preparation to data collection and analysis. When the results identify differences in omics features, networks or pathways, it is unclear if the results are toxic effects, a healthy response to a toxic exposure or neither. The use of surrogate biospecimens (e.g., urine, blood, sputum or nasal) may or may not reflect target organs such as the lung or bladder. This review describes the approaches for the use of omics in tobacco research and provides examples of prior studies, along with the strengths and limitations of the various methods. To date, there is little consistency in results, likely due to small number of studies, limitations in study size, the variability in the analytic platforms and bioinformatic pipelines, differences in biospecimen collection and/or human subject study design. Given the demonstrated value for the use of omics in clinical medicine, it is anticipated that the use in tobacco research will be similarly productive.

Metabolomics Analysis of DRG and Serum in the CCI Model of Mice

Neuropathic pain (NP) is a chronic and intractable disease that is widely present in the general population. It causes painful behavior and even mood changes such as anxiety and depression by altering the metabolism of substances. However, there have been limited metabolomics studies conducted in relation to neuropathic pain. Therefore, in this study, the effects of NP on metabolites in serum and the dorsal root ganglion (DRG) were investigated using a non-targeted metabolomics approach detected by gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) to uncover differential metabolites and affected metabolic pathways associated with NP. Sixty mice were divided into the following two groups: a chronic constriction injury (CCI) of the sciatic nerve group and a sham group (n = 30, each). After 7 days of CCI modeling, the metabolite profiles of serum and the DRG were analyzed using GC/LC-MS for both the CCI and sham groups of mice. Multivariate analysis revealed differential metabolites and altered metabolic pathways between the CCI and sham groups. In the CCI group, our findings provided insights into the complex phospholipid, amino acid and acylcarnitine metabolic perturbations of DRG metabolism. In addition, phospholipid metabolic disorders and impaired glucose metabolism were observed in the serum. Moreover, the metabolic differences in the DRG and serum were correlated with each other. The results from this untargeted metabolomics study provide a perspective on the metabolic impact of NP on serum and the DRG.

Denoising Autoencoder Normalization for Large-Scale Untargeted Metabolomics by Gas Chromatography-Mass Spectrometry

Large-scale metabolomics assays are widely used in epidemiology for biomarker discovery and risk assessments. However, systematic errors introduced by instrumental signal drifting pose a big challenge in large-scale assays, especially for derivatization-based gas chromatography-mass spectrometry (GC-MS). Here, we compare the results of different normalization methods for a study with more than 4000 human plasma samples involved in a type 2 diabetes cohort study, in addition to 413 pooled quality control (QC) samples, 413 commercial pooled plasma samples, and a set of 25 stable isotope-labeled internal standards used for every sample. Data acquisition was conducted across 1.2 years, including seven column changes. In total, 413 pooled QC (training) and 413 BioIVT samples (validation) were used for normalization comparisons. Surprisingly, neither internal standards nor sum-based normalizations yielded median precision of less than 30% across all 563 metabolite annotations. While the machine-learning-based SERRF algorithm gave 19% median precision based on the pooled quality control samples, external cross-validation with BioIVT plasma pools yielded a median 34% relative standard deviation (RSD). We developed a new method: systematic error reduction by denoising autoencoder (SERDA). SERDA lowered the median standard deviations of the training QC samples down to 16% RSD, yielding an overall error of 19% RSD when applied to the independent BioIVT validation QC samples. This is the largest study on GC-MS metabolomics ever reported, demonstrating that technical errors can be normalized and handled effectively for this assay. SERDA was further validated on two additional large-scale GC-MS-based human plasma metabolomics studies, confirming the superior performance of SERDA over SERRF or sum normalizations.

The overshadowed role of electron ionization-mass spectrometry in analytical biotechnology

Target and untargeted analysis of several compounds are crucial methods in important areas such as omics sciences. Gas chromatography coupled to mass spectrometry (GC-MS) is widely used for volatile and thermally stable compounds. In this case, the electron ionization technique (EI) is preferable as it produces highly fragmented and reproducible spectra comparable to spectral libraries. However, only a fraction of target compounds is analyzable by GC without chemical derivatization. Therefore, liquid chromatography (LC) coupled with MS is the most used technique. Contrary to EI, electrospray ionization does not produce reproducible spectra. That is why researchers have been working on interfaces between LC and EI-MS to bridge the gap between those techniques. This short review will discuss advancements, applications, and perspectives on biotechnological analysis.

Compound-Specific 1D 1H NMR Pulse Sequence Selection for Metabolomics Analyses

NMR-based metabolomics approaches have been used in a wide range of applications, for example, with medical, plant, and marine samples. One-dimensional (1D) ¹H NMR is routinely used to find out biomarkers in biofluids such as urine, blood plasma, and serum. To mimic biological conditions, most NMR studies have been carried out in an aqueous solution where the high intensity of the water peak is a major problem in obtaining a meaningful spectrum. Different methods have been used to suppress the water signal, including 1D Carr-Purcell-Meiboom-Gill (CPMG) presat, consisting of a T₂ filter to suppress macromolecule signals and reduce the humped curve in the spectrum. 1D nuclear Overhauser enhancement spectroscopy (NOESY) is another method for water suppression that is used routinely in plant samples with fewer macromolecules than in biofluid samples. Other common 1D ¹H NMR methods such as 1D ¹H presat and 1D ¹H ES have simple pulse sequences; their acquisition parameters can be set easily. The proton with presat has just one pulse and the presat block causes water suppression, while other 1D ¹H NMR methods including those mentioned above have more pulses. However, it is not well known in metabolomics studies because it is used only occasionally and in a few types of samples by metabolomics experts. Another effective method is excitation sculpting to suppress water. Herein, we evaluate the effect of method selection on signal intensities of commonly detected metabolites. Different classes of samples including biofluid, plant, and marine samples were investigated, and recommendations on the advantages and limitations of each method are presented.

Urine Metabolomic Signature of People Diagnosed with Balkan Endemic Nephropathy and Other Types of Chronic Kidney Disease Compared with Healthy Subjects in Romania

Metabolomic analysis methods were employed to determine biomarkers for various chronic kidney diseases (CKDs). Modern analytical methods were developed and applied successfully to find a specific metabolomic profile in urine samples from CKD and Balkan endemic nephropathy (BEN) patients. The aim was to explore a specific metabolomic profile defined by feasible/easy-to-identify molecular markers. Urine samples were collected from patients with CKDs and BEN, and from healthy subjects from endemic and nonendemic areas in Romania. Metabolomic analysis of urine samples, extracted by the liquid-liquid extraction (LLE) method, was performed by gas chromatography-mass spectrometry (GC-MS). The statistical exploration of the results was performed through a principal component analysis (PCA) evaluation. Urine samples were statistically analyzed using a classification based on six types of metabolites. Most urinary metabolites are distributed in the center of a loading plot, meaning that these compounds do not represent significant markers for BEN. One of the most frequent and higher-concentration urinary metabolites in BEN patients was p-Cresol, a phenolic compound that implies a severe injury of the renal filtration function. The presence of p-Cresol was associated with protein-bound uremic toxins, which have specific functional groups such as indole and phenyl. In prospective studies for future investigation, prevention, and disease treatment, we suggest a larger sample size, sample extraction using other methods, and analysis using other chromatography techniques coupled with mass spectrometry, which can generate a more significant data set for statistical analysis.

An Exploratory Study of the Metabolite Profiling from Pesticides Exposed Workers

Pesticides constitute a category of chemical products intended specifically for the control and mitigation of pests. With their constant increase in use, the risk to human health and the environment has increased proportionally due to occupational and environmental exposure to these compounds. The use of these chemicals is associated with several toxic effects related to acute and chronic toxicity, such as infertility, hormonal disorders and cancer. The present work aimed to study the metabolic profile of individuals occupationally exposed to pesticides, using a metabolomics tool to identify potential new biomarkers. Metabolomics analysis was carried out on plasma and urine samples from individuals exposed and non-exposed occupationally, using liquid chromatography coupled with mass spectrometry (UPLC-MS). Non-targeted metabolomics analysis, using principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) or partial least squares discriminant orthogonal analysis (OPLS-DA), demonstrated good separation of the samples and identified 21 discriminating metabolites in plasma and 17 in urine. The analysis of the ROC curve indicated the compounds with the greatest potential for biomarkers. Comprehensive analysis of the metabolic pathways influenced by exposure to pesticides revealed alterations, mainly in lipid and amino acid metabolism. This study indicates that the use of metabolomics provides important information about complex biological responses.

Towards inferring absolute concentrations from relative abundance in time-course GC-MS metabolomics data

Metabolomics, the large-scale study of metabolites, has significant appeal as a source of information for metabolic modeling and other scientific applications. One common approach for measuring metabolomics data is gas chromatography-mass spectrometry (GC-MS). However, GC-MS metabolomics data are typically reported as relative abundances, precluding their use with approaches and tools where absolute concentrations are necessary. While chemical standards can be used to help provide quantification, their use is time-consuming, expensive, or even impossible due to their limited availability. The ability to infer absolute concentrations from GC-MS metabolomics data without chemical standards would have significant value. We hypothesized that when analyzing time-course metabolomics datasets, the mass balances of metabolism and other biological information could provide sufficient information towards inference of absolute concentrations. To demonstrate this, we developed and characterized MetaboPAC, a computational framework that uses two approaches-one based on kinetic equations and another using biological heuristics-to predict the most likely response factors that allow translation between relative abundances and absolute concentrations. When used to analyze noiseless synthetic data generated from multiple types of kinetic rate laws, MetaboPAC performs significantly better than negative control approaches when 20% of kinetic terms are known a priori. Under conditions of lower sampling frequency and high noise, MetaboPAC is still able to provide significant inference of concentrations in 3 of 4 models studied. This provides a starting point for leveraging biological knowledge to extract concentration information from time-course intracellular GC-MS metabolomics datasets, particularly for systems that are well-studied and have partially known kinetic structures.

Metabolic profiling reveals metabolic features of consolidation therapy in pediatric acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) and its treatment continue to pose substantial risks. To understand ALL more deeply, the metabolome in fasting plasma of 27 ALL patients before and after high-dose methotrexate therapies (consolidation therapy) including methotrexate and 6-mercaptopurine (6-MP) was investigated. Plasma metabolites were analyzed using liquid chromatography-tandem mass spectrometry (LC-MS). Orthogonal projections to latent structures discriminant analysis and significance analysis of microarrays were used to evaluate the metabolic changes. Pathway enrichment and co-expression network analyses were performed to identify clusters of molecules, and 2826 metabolites were identified. Among them, 38 metabolites were identified by univariate analysis, and 7 metabolites that were altered by conditioning therapy were identified by multivariate analysis. The Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used for pathway enrichment analysis. Among the enriched KEGG pathways, the 3 significantly altered metabolic pathways were pyrimidine metabolism; phenylalanine, tyrosine, and tryptophan biosynthesis; and phenylalanine metabolism. In addition, L-phenylalanine was significantly correlated with blood urea nitrogen (BUN), and palmitoylcarnitine was correlated with aspartate aminotransferase (AST). In summary, consolidation therapy significantly affected pyrimidine- and phenylalanine-associated metabolic pathways in pediatric ALL patients. These findings may provide an insight into the role of metabolic profiling in consolidation treatment and as a potential for pediatric ALL patients.

Current State and Future Perspectives on Personalized Metabolomics

Metabolomics is one of the most promising 'omics' sciences for the implementation in medicine by developing new diagnostic tests and optimizing drug therapy. Since in metabolomics, the end products of the biochemical processes in an organism are studied, which are under the influence of both genetic and environmental factors, the metabolomics analysis can detect any changes associated with both lifestyle and pathological processes. Almost every case-controlled metabolomics study shows a high diagnostic accuracy. Taking into account that metabolomics processes are already described for most nosologies, there are prerequisites that a high-speed and comprehensive metabolite analysis will replace, in near future, the narrow range of chemical analyses used today, by the medical community. However, despite the promising perspectives of personalized metabolomics, there are currently no FDA-approved metabolomics tests. The well-known problem of complexity of personalized metabolomics data analysis and their interpretation for the end-users, in addition to a traditional need for analytical methods to address the quality control, standardization, and data treatment are reported in the review. Possible ways to solve the problems and change the situation with the introduction of metabolomics tests into clinical practice, are also discussed.

The untargeted urine volatilome for biomedical applications: methodology and volatilome database

Chemically diverse in compounds, urine can give us an insight into metabolic breakdown products from foods, drinks, drugs, environmental contaminants, endogenous waste metabolites, and bacterial by-products. Hundreds of them are volatile compounds; however, their composition has never been provided in detail, nor has the methodology used for urine volatilome untargeted analysis. Here, we summarize key elements for the untargeted analysis of urine volatilome from a comprehensive compilation of literature, including the latest reports published. Current achievements and limitations on each process step are discussed and compared. 34 studies were found retrieving all information from the urine treatment to the final results obtained. In this report, we provide the first specific urine volatilome database, consisting of 841 compounds from 80 different chemical classes.

Quantitation of ethanol in UTI assay for volatile organic compound detection by electronic nose using the validated headspace GC-MS method

Disease detection through gas analysis has long been the topic of many studies because of its potential as a rapid diagnostic technique. In particular, the pathogens that cause urinary tract infection (UTI) have been shown to generate different profiles of volatile organic compounds, thus enabling the discrimination of causative agents using an electronic nose. While past studies have performed data collection on either agar culture or jellified urine culture, this study measures the headspace volume of liquid urine culture samples. Evaporation of the liquid and the presence of background compounds during electronic nose (e-nose) device operation could introduce variability to the collected data. Therefore, a headspace gas chromatography-mass spectrometry method was developed and validated for quantitating ethanol in the headspace of the urine samples. By leveraging the new method to characterize the sample stability during e-nose measurement, it was revealed that ethanol concentration dropped more than 15% after only three measurement cycles, which equal 30 minutes for this study. It was further shown that by using only data within the first three cycles, better accuracies for between-day classification were achieved, which was 73.7% and 97.0%, compared to using data from within the first nine cycles, which resulted in 65.0% and 81.1% accuracies. Therefore, the newly developed method provides better quality control for data collection, paving ways for the future establishment of a training data library for UTI.

Comparison of Different Drying Methods for Asparagus [Asparagus cochinchinensis (Lour.) Merr.] Root Volatile Compounds as Revealed Using Gas Chromatography Ion Mobility Spectrometry

Asparagus [Asparagus cochinchinensis (Lour.) Merr.] is a traditional herbal medicine plant commonly used to nourish yin, moisten dryness, and clear fire cough symptoms. Drying is an excellent option to conserve food materials, i.e., grains, fruits, vegetables, and herbs, reducing the raw materials volume and weight. This study aims to evaluate different drying approaches that could increase the value of asparagus, particularly as an ingredient in fast foods or as nutraceutical byproducts. The volatile components of asparagus roots were analyzed by using headspace-gas chromatography-ion mobility spectroscopy under different drying conditions, i.e., natural drying (ND) at ambient air temperature in the dark, well-ventilated room, temperature range 28-32°C, blast or oven drying at 50°C, heat pump or hot-air drying at temperature 50°C and air velocity at 1.5 ms-1 and vacuum freeze-drying at the temperature of -45°C and vacuum pressure of 10-30 Pa for 24 h. The findings revealed that the various drying processes had multiple effects on the color, odor index, and volatile compounds of the asparagus roots. As a result of the investigations, multiple characteristics of components, therefore, exploitation and comparison of various flavors; a total of 22 compounds were identified, such as alcohols, ketones, aldehydes, acids, esters, heterocyclic, and terpene. The present findings may help understand the flavor of the processed asparagus roots and find a better option for drying and processing.

Non-Invasive and Mechanism-Based Molecular Assessment of Endometrial Receptivity During the Window of Implantation: Current Concepts and Future Prospective Testing Directions

Suboptimal endometrial receptivity and altered embryo-endometrial crosstalk account for approximately two-thirds of human implantation failures. Current tests of the window of implantation, such as endometrial thickness measurements and the endometrial receptivity assay, do not consistently improve clinical outcomes as measured by live birth rates. Understanding the mechanisms regulating the endometrial receptivity during the window of implantation is a critical step toward developing clinically meaningful tests. In this narrative review, the available literature is evaluated regarding mechanisms that regulate the endometrial receptivity during the window of implantation and the current tests developed. Overall, both animal and human studies point to five possible and interrelated mechanisms regulating the endometrial window of implantation: suitable synchrony between endometrial cells, adequate synchrony between the endometrium and the embryo, standard progesterone signaling and endometrial responses to progesterone, silent genetic variations, and typical morphological characteristics of the endometrial glands. The biological basis of current clinical markers or tests of window of implantation is poor. Future studies to elucidate the mechanisms shaping the window of implantation and to investigate the potential markers based on these mechanisms are required. In addition, molecular testing of the endometrium at single-cell resolution should be an initial step toward developing clinically meaningful tests for the optimal window of implantation. As understanding of the optimal window of implantation continues to evolve, one can envision the future development of non-invasive, mechanism-based testing of the window of implantation.

Metabolomics Research in Periodontal Disease by Mass Spectrometry

Periodontology is a newer field relative to other areas of dentistry. Remarkable progress has been made in recent years in periodontology in terms of both research and clinical applications, with researchers worldwide now focusing on periodontology. With recent advances in mass spectrometry technology, metabolomics research is now widely conducted in various research fields. Metabolomics, which is also termed metabolomic analysis, is a technology that enables the comprehensive analysis of small-molecule metabolites in living organisms. With the development of metabolite analysis, methods using gas chromatography–mass spectrometry, liquid chromatography–mass spectrometry, capillary electrophoresis–mass spectrometry, etc. have progressed, making it possible to analyze a wider range of metabolites and to detect metabolites at lower concentrations. Metabolomics is widely used for research in the food, plant, microbial, and medical fields. This paper provides an introduction to metabolomic analysis and a review of the increasing applications of metabolomic analysis in periodontal disease research using mass spectrometry technology.

Acylcarnitines: Can They Be Biomarkers of Diabetic Nephropathy?

Diabetic nephropathy (DN), one of the most serious microvascular complications of diabetes mellitus (DM), may progress to end-stage renal disease (ESRD). Current biochemical biomarkers, such as urinary albumin excretion rate (UAER), have limitations for early screening and monitoring of DN. Recent studies have identified some metabolites as candidate biomarkers for early detection of DN. In this review, we summarize the role of dysregulated acylcarnitines (AcylCNs) in DN pathophysiology. Lower abundance of short- and medium-chain AcylCNs and higher long-chain AcylCNs often occurred in DM with normal albuminuria and microalbuminuria, compared with advanced stages of DN. The increase of long-chain AcylCNs was supposed to be an adaptive compensation in fat acids (FAs) oxidation in the early stage of DN. Conversely, the decrease of long-chain AcylCNs was due to incomplete oxidation of FAs in advanced stage of DN. Thus, AcylCNs may serve as sensitive biomarkers in predicting the risk of DN.

Structure Elucidation and Mitigation of Endogenous Interferences in LC-MS-Based Metabolic Profiling of Urine

Liquid chromatography-mass spectrometry (LC-MS) is the main workhorse of metabolomics owing to its high degree of analytical sensitivity and specificity when measuring diverse chemistry in complex biological samples. LC-MS-based metabolic profiling of human urine, a biofluid of primary interest for clinical and biobank studies, is not widely considered to be compromised by the presence of endogenous interferences and is often accomplished using a simple "dilute-and-shoot" approach. Yet, it is our experience that broad obscuring signals are routinely observed in LC-MS metabolic profiles and represent interferences that lack consideration in the relevant metabolomics literature. In this work, we chromatographically isolated the interfering metabolites from human urine and unambiguously identified them via de novo structure elucidation as two separate proline-containing dipeptides: N,N,N-trimethyl-l-alanine-l-proline betaine (l,l-TMAP) and N,N-dimethyl-l-proline-l-proline betaine (l,l-DMPP), the latter reported here for the first time. Offline LC-MS/MS, magnetic resonance mass spectrometry (MRMS), and nuclear magnetic resonance (NMR) spectroscopy were essential components of this workflow for the full chemical and spectroscopic characterization of these metabolites and for establishing the coexistence of cis and trans isomers of both dipeptides in solution. Analysis of these definitive structures highlighted intramolecular ionic interactions as responsible for slow interconversion between these isomeric forms resulting in their unusually broad elution profiles. Proposed mitigation strategies, aimed at increasing the quality of LC-MS-based urine metabolomics data, include modification of column temperature and mobile-phase pH to reduce the chromatographic footprint of these dipeptides, thereby reducing their interfering effect on the underlying metabolic profiles. Alternatively, sample dilution and internal standardization methods may be employed to reduce or account for the observed effects of ionization suppression on the metabolic profile.

Optimization of metabolomic data processing using NOREVA

A typical output of a metabolomic experiment is a peak table corresponding to the intensity of measured signals. Peak table processing, an essential procedure in metabolomics, is characterized by its study dependency and combinatorial diversity. While various methods and tools have been developed to facilitate metabolomic data processing, it is challenging to determine which processing workflow will give good performance for a specific metabolomic study. NOREVA, an out-of-the-box protocol, was therefore developed to meet this challenge. First, the peak table is subjected to many processing workflows that consist of three to five defined calculations in combinatorially determined sequences. Second, the results of each workflow are judged against objective performance criteria. Third, various benchmarks are analyzed to highlight the uniqueness of this newly developed protocol in (1) evaluating the processing performance based on multiple criteria, (2) optimizing data processing by scanning thousands of workflows, and (3) allowing data processing for time-course and multiclass metabolomics. This protocol is implemented in an R package for convenient accessibility and to protect users' data privacy. Preliminary experience in R language would facilitate the usage of this protocol, and the execution time may vary from several minutes to a couple of hours depending on the size of the analyzed data.

Parahydrogen hyperpolarization of minimally altered urine samples for sensitivity enhanced NMR metabolomics

Parahydrogen hyperpolarization has been shown to enhance NMR sensitivity in urine analysis by several orders of magnitude if urine samples are prepared by solid phase extraction (SPE). We present a different approach, developed for minimal sample alteration before analysis. Removing SPE from the workflow allows to retain a wider range of metabolites and paves the way towards more universal hyperpolarized NMR metabolomics of low abundance metabolites.

Thermal degradation of metabolites in urine using multiple isotope-labelled internal standards for off-line GC metabolomics - effects of injector and oven temperatures

Thermal processes are widely used in small molecule chemical analysis and metabolomics for derivatization, vaporization, chromatography, and ionization, especially in gas chromatography mass spectrometry (GC/MS). An optimized derivatization protocol has been successfully applied using multiple isotope labelled analytical internal standards of selected deuterated and ¹³C selected compounds, covering a range of different groups of metabolites for non-automated GC metabolomics (off-line). Moreover, the study was also realized in a pooled urine sample, following metabolic profiling. A study of thermal degradation of metabolites due to GC inlet and oven programs (fast, slow) was performed, where the results indicated that both GC oven programs (fast and slow) negatively affected the thermal stability of the metabolites, while the fast-ramp GC program also suppressed MS signals. However, the use of multiple internal standards can overcome this drawback. The application of extended temperature ramp GC program presented identical behaviour on metabolite stability and better chromatographic separation combined with much lower signal suppression, compared to a short temperature ramp program. No effects were observed for organic acids, fatty acids, sugars and sugar alcohols, while significant differences were observed for amino acids. GC metabolomics is a strong tool that can facilitate analysis, but special attention is required for sampling handling and heating, before and during the GC analysis. The use and application of multiple multi-group internal standards is highly recommended.

Deep Learning Based Prediction of Gas Chromatographic Retention Indices for a Wide Variety of Polar and Mid-Polar Liquid Stationary Phases

Prediction of gas chromatographic retention indices based on compound structure is an important task for analytical chemistry. The predicted retention indices can be used as a reference in a mass spectrometry library search despite the fact that their accuracy is worse in comparison with the experimental reference ones. In the last few years, deep learning was applied for this task. The use of deep learning drastically improved the accuracy of retention index prediction for non-polar stationary phases. In this work, we demonstrate for the first time the use of deep learning for retention index prediction on polar (e.g., polyethylene glycol, DB-WAX) and mid-polar (e.g., DB-624, DB-210, DB-1701, OV-17) stationary phases. The achieved accuracy lies in the range of 16–50 in terms of the mean absolute error for several stationary phases and test data sets. We also demonstrate that our approach can be directly applied to the prediction of the second dimension retention times (GC × GC) if a large enough data set is available. The achieved accuracy is considerably better compared with the previous results obtained using linear quantitative structure-retention relationships and ACD ChromGenius software. The source code and pre-trained models are available online.

High-Throughput Non-targeted Chemical Structure Identification Using Gas-Phase Infrared Spectra

The high-throughput identification of unknown metabolites in biological samples remains challenging. Most current non-targeted metabolomics studies rely on mass spectrometry, followed by computational methods that rank thousands of candidate structures based on how closely their predicted mass spectra match the experimental mass spectrum of an unknown. We reasoned that the infrared (IR) spectra could be used in an analogous manner and could add orthologous structure discrimination; however, this has never been evaluated on large data sets. Here, we present results of a high-throughput computational method for predicting IR spectra of candidate compounds obtained from the PubChem database. Predicted spectra were ranked based on their similarity to gas-phase experimental IR spectra of test compounds obtained from the NIST. Our computational workflow (IRdentify) consists of a fast semiempirical quantum mechanical method for initial IR spectra prediction, ranking, and triaging, followed by a final IR spectra prediction and ranking using density functional theory. This approach resulted in the correct identification of 47% of 258 test compounds. On average, there were 2152 candidate structures evaluated for each test compound, giving a total of approximately 555,200 candidate structures evaluated. We discuss several variables that influenced the identification accuracy and then demonstrate the potential application of this approach in three areas: (1) combining IR and mass spectra rankings into a single composite rank score, (2) identifying the precursor and fragment ions using cryogenic ion vibrational spectroscopy, and (3) the incorporation of a trimethylsilyl derivatization step to extend the method compatibility to less-volatile compounds. Overall, our results suggest that matching computational with experimental IR spectra is a potentially powerful orthogonal option for adding significant high-throughput chemical structure discrimination when used with other non-targeted chemical structure identification methods.

Assessing the antioxidant and metabolic effect of an alpha-lipoic acid and acetyl-L-carnitine nutraceutical

Personalized nutrition (PN) is seen as a potentially effective and affordable strategy for the prevention of non-communicable diseases (NCDs). In this study we aimed to evaluate the antioxidant and metabolic effect of a dietary supplement based on alpha-lipoic acid (ALA) and acetyl-L-carnitine (ALC) in order to include this product in a novel PN service. The antioxidant properties of the commercial nutraceutical were investigated at physiological conditions (through in vitro digestion) and at mitochondrial conditions. The metabolic activity was assessed in a human pilot study using a Gas Chromatography-Mass Spectrometry (GC-MS) methodology in dried urine samples. The nutraceutical exerted an elevated antiradical activity and reducing capacity, especially at mitochondrial conditions, after in vitro digestion. This increase in mitochondrial activity was also evidenced in vivo by a significant increase in the urinary phosphate concentration (p ​= ​0.004). As pro-oxidant effect was reached with the concentration of 4 capsules, 2 capsules at the same time could be a reasonable dose. No adverse effects were recorded in vivo with this dose. Thus, although its metabolic effect was not so conclusive, ALA ​+ ​ALC combination might be beneficial as a dietary supplement for the prevention of the oxidative stress and an interesting dietary supplement to consider in large scale studies.

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The nutritional supplement showed a high in vitro antioxidant-reducing capacity.

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The antioxidant capacity increase after digestion in contrast to other antioxidants.

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A pro-oxidant effect was reached with the concentration of 4 capsules.

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2 capsules at the same time are safe in humans and may exert some metabolic changes.

Advances in high resolution GC-MS technology: a focus on the application of GC-Orbitrap-MS in metabolomics and exposomics for FAIR practices

Gas chromatography-mass spectrometry (GC-MS) provides a complementary analytical platform for capturing volatiles, non-polar and (derivatized) polar metabolites and exposures from a diverse array of matrixes. High resolution (HR) GC-MS as a data generation platform can capture data on analytes that are usually not detectable/quantifiable in liquid chromatography mass-spectrometry-based solutions. With the rise of high-resolution accurate mass (HRAM) GC-MS systems such as GC-Orbitrap-MS in the last decade after the time-of-flight (ToF) renaissance, numerous applications have been found in the fields of metabolomics and exposomics. In a short span of time, a multitude of studies have used GC-Orbitrap-MS to generate exciting new high throughput data spanning from diverse basic to applied research areas. The GC-Orbitrap-MS has found application in both targeted and untargeted efforts for capturing metabolomes and exposomes across diverse studies. In this review, I capture and summarize all the reported studies to date, and provide a snapshot of the milieu of commercial and open-source software solutions, spectral libraries, and informatics solutions available to a GC-Orbitrap-MS system instrument user or a data analyst dealing with these datasets. Lastly, but importantly, I provide an account on data sharing and meta-data capturing solutions that are available to make HRAM GC-MS based metabolomics and exposomics studies findable, accessible, interoperable, and reproducible (FAIR). These FAIR practices would allow data generators and users of GC-HRMS instruments to help the community of GC-MS researchers to collaborate and co-develop exciting tools and algorithms in the future.

Urinary metabolomic profiling reveals difference between two traditional Chinese medicine subtypes of coronary heart disease

The World Health Organization has shown that coronary heart disease (CHD) is a more common cause of death than cancer. In traditional Chinese medicine (TCM), CHD is classified as a form of thoracic obstruction that can be divided in different subtypes including Qi stagnation with blood stasis (QS) and Qi deficiency with blood stasis (QD). Different treatment strategies are used based on this subtyping. Owing to the lack of scientific markers in the diagnosis of these subtypes, subjective judgments made by clinicians have limited the objective manner for utility of TCM in the treatment of CHD. Untargeted (UHPLC-QTOF-MS) and targeted (UHPLC-MS/MS) metabolomics approaches were employed to search significantly different metabolites related to the QS or QD subtypes of CHD with angina pectoris in this study. A total of 42 metabolites were obtained in the untargeted metabolomics analysis and 34 amino acids were detected in the targeted metabolomics analysis. In total, 16 metabolites were found significantly different among different groups. The results showed distinct metabolic profiles of urine samples not only between CHD patients and healthy controls, but also between the two subtypes of CHD. Pathway analysis of the significantly varied metabolites revealed that there were subtype-related differences in the activity of pathways. Therefore, urinary metabolomics can reveal the pathological changes of CHD in different subtypes, make the diagnosis of CHD in different subtypes in an objective manner and comprehensive and contribute to personalized treatment by providing scientific evidence.

Effect of Drying Methods on Volatile Compounds of Burdock (Arctium lappa L.) Root Tea as Revealed by Gas Chromatography Mass Spectrometry-Based Metabolomics

Burdock (Arctium lappa L.) is one of the nutritional foods widely planted in many countries. Dried burdock root (BR) is available as a herbal tincture and tea in many Asian countries with good flavor and taste. In this study, the volatile components in dried BR were identified and the effects of different drying methods on the volatile components were investigated by HS-GC-MS method. A total of 49 compounds were identified. Different drying methods including hot-air drying (HD, at 50, 60, 70, and 80 °C), vacuum drying (VD, at 50, 60, 70, and 80 °C), sunlight drying (SD), natural drying (ND), and vacuum freeze drying (VFD) were evaluated by HS-GC-MS-based metabolomics method. Results showed that different drying methods produced different effects on the volatile compounds. It was observed that 2,3-pentanedione, 1-(1H-pyrrol-2-yl)-ethanone, furfural, and heptanal were detected at higher concentrations in HD 80 and VD 70. The traditional HD and SD methods produced more flavor substances than VFD. The BR treated by the VFD method could maintain the shape of the fresh BR pieces while HD50 and VD80 methods could maintain the color of fresh BR pieces. These findings could help better understand the flavor of the corresponding processed BR and provide a guide for the drying and processing of BR tea.

Analytical Methods for the Determination of Neuroactive Steroids

Neuroactive steroids are a family of all steroid-based compounds, of both natural and synthetic origin, which can affect the nervous system functions. Their biosynthesis occurs directly in the nervous system (so-called neurosteroids) or in peripheral endocrine tissues (hormonal steroids). Steroid hormone levels may fluctuate due to physiological changes during life and various pathological conditions affecting individuals. A deeper understanding of neuroactive steroids' production, in addition to reliable monitoring of their levels in various biological matrices, may be useful in the prevention, diagnosis, monitoring, and treatment of some neurodegenerative and psychiatric diseases. The aim of this review is to highlight the most relevant methods currently available for analysis of neuroactive steroids, with an emphasis on immunoanalytical methods and gas, or liquid chromatography combined with mass spectrometry.

Optimisation of Urine Sample Preparation for Headspace-Solid Phase Microextraction Gas Chromatography-Mass Spectrometry: Altering Sample pH, Sulphuric Acid Concentration and Phase Ratio

Headspace-solid phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) can be used to measure volatile organic compounds (VOCs) in human urine. However, there is no widely adopted standardised protocol for the preparation of urine samples for analysis resulting in an inability to compare studies reliably between laboratories. This paper investigated the effect of altering urine sample pH, volume, and vial size for optimising detection of VOCs when using HS-SPME-GC-MS. This is the first, direct comparison of H₂SO₄, HCl, and NaOH as treatment techniques prior to HS-SPME-GC-MS analysis. Altering urine sample pH indicates that H₂SO₄ is more effective at optimising detection of VOCs than HCl or NaOH. H₂SO₄ resulted in a significantly larger mean number of VOCs being identified per sample (on average, 33.5 VOCs to 24.3 in HCl or 12.2 in NaOH treated urine) and more unique VOCs, produced a more diverse range of classes of VOCs, and led to less HS-SPME-GC-MS degradation. We propose that adding 0.2 mL of 2.5 M H₂SO₄ to 1 mL of urine within a 10 mL headspace vial is the optimal sample preparation prior to HS-SPME-GC-MS analysis. We hope the use of our optimised method for urinary HS-SPME-GC-MS analysis will enhance our understanding of human disease and bolster metabolic biomarker identification.

Application of Metabolomics in Diagnosis and Treatment of Chronic Liver Diseases

Chronic liver disease represents stepwise destruction of the liver parenchyma after chronic liver injury, which is often difficult to be diagnosed accurately. Thus, the development of specific biomarkers of chronic liver disease is important. Metabolomics is a powerful tool for biomarker exploration, which enables the exploration of disease pathogenesis or drug action mechanisms at the global metabolic level. The metabolomics workflow generally includes collection, preparation, and analysis of samples, and data processing and bioinformatics. A metabolomics study can simultaneously detect the dysfunctions in the glucose, lipid, amino-acid, and nucleotide metabolisms. Hence, it facilitates the obtaining of a better understanding of the pathogenesis of chronic liver disease and its diagnosis. Many effective drugs could reverse the change of comprehensive biochemical phenotypes induced by chronic liver disease. They can even potentially restore the normal metabolic signatures of patients. Increasingly more researchers have begun to apply metabolomics technologies to diagnose chronic liver disease and investigate the mechanism of action of effective drugs or the variations in drug responses. We are convinced that deepening the understanding of the metabolic alterations could extend their use as powerful biomarkers, promoting the more effective clinical diagnosis and treatment of chronic liver disease in the future.