Advances in mass spectrometry-based metabolomics for investigation of metabolites

The impact of the human gut microbiome on the treatment of autoimmune disease

Autoimmune (or rheumatic) diseases are increasing in prevalence but selecting the best therapy for each patient proceeds in trial-and-error fashion. This strategy can lead to ineffective therapy resulting in irreversible damage and suffering; thus, there is a need to bring the promise of precision medicine to patients with autoimmune disease. While host factors partially determine the therapeutic response to immunosuppressive drugs, these are not routinely used to tailor therapy. Thus, non-host factors likely contribute. Here, we consider the impact of the human gut microbiome in the treatment of autoimmunity. We propose that the gut microbiome can be manipulated to improve therapy and to derive greater benefit from existing therapies. We focus on the mechanisms by which the human gut microbiome impacts treatment response, provide a framework to interrogate these mechanisms, review a case study of a widely-used anti-rheumatic drug, and discuss challenges with studying multiple complex systems: the microbiome, the human immune system, and autoimmune disease. We consider open questions that remain in the field and speculate on the future of drug-microbiome-autoimmune disease interactions. Finally, we present a blue-sky vision for how the microbiome can be used to bring the promise of precision medicine to patients with rheumatic disease.

Applications of chromatographic methods in metabolomics: A review

Chromatography is a robust and reliable separation method that can use various stationary phases to separate complex mixtures commonly seen in metabolomics. This review examines the types of chromatography and stationary phases that have been used in targeted or untargeted metabolomics with methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. General considerations for sample pretreatment and separations in metabolomics are considered, along with the various supports and separation formats for chromatography that have been used in such work. The types of liquid chromatography (LC) that have been most extensively used in metabolomics will be examined, such as reversed-phase liquid chromatography and hydrophilic liquid interaction chromatography. In addition, other forms of LC that have been used in more limited applications for metabolomics (e.g., ion-exchange, size-exclusion, and affinity methods) will be discussed to illustrate how these techniques may be utilized for new and future research in this field. Multidimensional LC methods are also discussed, as well as the use of gas chromatography and supercritical fluid chromatography in metabolomics. In addition, the roles of chromatography in NMR- vs. MS-based metabolomics are considered. Applications are given within the field of metabolomics for each type of chromatography, along with potential advantages or limitations of these separation methods.

Metabolomic and proteomic applications to exercise biomedicine

Objectives

'OMICs encapsulates study of scaled data acquisition, at the levels of DNA, RNA, protein, and metabolite species. The broad objectives of OMICs in biomedical exercise research are multifarious, but commonly relate to biomarker development and understanding features of exercise adaptation in health, ageing and metabolic diseases.

Methods

This field is one of exponential technical (i.e., depth of feature coverage) and scientific (i.e., in health, metabolic conditions and ageing, multi-OMICs) progress adopting targeted and untargeted approaches.

Results

Key findings in exercise biomedicine have led to the identification of OMIC features linking to heritability or adaptive responses to exercise e.g., the forging of GWAS/proteome/metabolome links to cardiovascular fitness and metabolic health adaptations. The recent addition of stable isotope tracing to proteomics ('dynamic proteomics') and metabolomics ('fluxomics') represents the next phase of state-of-the-art in 'OMICS.

Conclusions

These methods overcome limitations associated with point-in-time 'OMICs and can be achieved using substrate-specific tracers or deuterium oxide (D2O), depending on the question; these methods could help identify how individual protein turnover and metabolite flux may explain exercise responses. We contend application of these methods will shed new light in translational exercise biomedicine.

Spatial metabolomics and its application in the liver

Hepatocytes work in highly structured, repetitive hepatic lobules. Blood flow across the radial axis of the lobule generates oxygen, nutrient, and hormone gradients, which result in zoned spatial variability and functional diversity. This large heterogeneity suggests that hepatocytes in different lobule zones may have distinct gene expression profiles, metabolic features, regenerative capacity, and susceptibility to damage. Here, we describe the principles of liver zonation, introduce metabolomic approaches to study the spatial heterogeneity of the liver, and highlight the possibility of exploring the spatial metabolic profile, leading to a deeper understanding of the tissue metabolic organization. Spatial metabolomics can also reveal intercellular heterogeneity and its contribution to liver disease. These approaches facilitate the global characterization of liver metabolic function with high spatial resolution along physiological and pathological time scales. This review summarizes the state of the art for spatially resolved metabolomic analysis and the challenges that hinder the achievement of metabolome coverage at the single-cell level. We also discuss several major contributions to the understanding of liver spatial metabolism and conclude with our opinion on the future developments and applications of these exciting new technologies.

Streamlining regular liquid chromatography with MALDI-TOF MS and NMR spectroscopy using automatic full-contact splitless spotting interface and flash-tap fractioning collection

BACKGROUND

High-resolution matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) and nuclear magnetic resonance (NMR) spectroscopy are powerful tools to identify unknown psychoactive substances. However, in complex matrices, trace levels of unknown substances usually require additional fractionation and concentration. Specialized liquid chromatography systems are necessary for both techniques. The small flow rate of nano LC, typically paired with MALDI-TOF MS, often results in prolonged fractionation times. Conversely, the larger flow rate of semi-preparative LC, used for NMR analysis, can be time-consuming and labor-intensive when concentrating samples. To address these issues, we developed an integrated automatic system that integrated to regular LC.

RESULT

Automatic spot collector (ASC) and automatic fraction collector (AFC) were present in this study. The ASC utilized in-line matrix mixing, full-contact spotting and real time heating (50 °C), achieving great capacity of 5 μL droplet on MALDI plate, high recovery (76-116%) and rapid evaporation in 2 min. The analytes were concentrated 4-8 times, forming even crystallization, reaching the detection limit at the concentration of 50 μg L-1 for 12 psychoactive substances in urine. The AFC utilizes flexible tubing which flash-tapped the microtube's upper rim (3 mm depth) instead of reaching the bottom. This method prevents sample loss and minimizes the robotic arm's movement, providing a high fractionating speed at 6 s 12 psychoactive compounds were fractionated in a single round analysis (recovery: 81%-114%). Methamphetamine and nitrazepam obtained from drug-laced coffee samples were successful analyzed with photodiode array (PDA) after one AFC round and NMR after five rounds.

SIGNIFICANCE

The ASC device employed real-time heating, in-line matrix mixing, and full-contact spotting to facilitate the samples spotting onto the MALDI target plate, thereby enhancing detection sensitivity in low-concentration and complex samples. The AFC device utilized the novel flash-tapping method to achieve rapid fractionation and high recovery rate. These devices were assembled using commercially available components, making them affordable (400 USD) for most laboratories while still meeting the required performance for advanced commercialized systems.

Understanding PFAS toxicity through cell culture metabolomics: Current applications and future perspectives

Per- and polyfluoroalkyl substances (PFAS), ubiquitous environmental contaminants, pose significant challenges to ecosystems and human health. While cell cultures have emerged as new approach methodologies (NAMs) in ecotoxicity research, metabolomics is an emerging technique used to characterize the small-molecule metabolites present in cells and to understand their role in various biological processes. Integration of metabolomics with cell cultures, known as cell culture metabolomics, provides a novel and robust tool to unravel the complex molecular responses induced by PFAS exposure. In vitro testing also reduces reliance on animal testing, aligning with ethical and regulatory imperatives. The current review summarizes key findings from recent studies utilizing cell culture metabolomics to investigate PFAS toxicity, highlighting alterations in metabolic pathways, biomarker identification, and the potential linkages between metabolic perturbations. Additionally, the paper discusses different types of cell cultures and metabolomics methods used for studies of environmental contaminants and particularly PFAS. Future perspectives on the combination of metabolomics with other advanced technologies, such as single-cell metabolomics (SCM), imaging mass spectrometry (IMS), extracellular flux analysis (EFA), and multi-omics are also explored, which offers a holistic understanding of environmental contaminants. The synthesis of current knowledge and identification of research gaps provide a foundation for future investigations that aim to elucidate the complexities of PFAS-induced cellular responses and contribute to the development of effective strategies for mitigating their adverse effects on human health.

Description of the fasted serum metabolomic signature of lean and obese cats at maintenance and of obese cats under energy restriction

This study aimed to investigate the serum metabolomic profile of obese and lean cats as well as obese cats before and after energy restriction for weight loss. Thirty cats, 16 obese (body condition score 8 to 9/9) and 14 lean (body condition score 4 to 5/9), were fed a veterinary weight loss food during a 4-week period of weight maintenance (L-MAINT and O-MAINT). The 16 obese cats were then energy restricted by a 60% energy intake reduction with the same food for a 10-week period (O-RESTRICT). Fasted serum metabolites were measured using nuclear magnetic resonance and direct infusion mass spectrometry after the maintenance period for L-MAINT and O-MAINT cats and after the energy restriction period for O-RESTRICT and compared between groups using a two-sided t-test. Obese cats lost 672 g ± 303 g over the 10-week restriction period, representing a weight loss rate of 0.94 ± 0.28% per week. Glycine, l-alanine, l-histidine, l-glutamine, 2-hydroxybutyrate, isobutryric acid, citric acid, creatine, and methanol were greater in O-RESTRICT compared to O-MAINT. There was a greater concentration of long-chain acylcarnitines in O-RESTRICT compared to both O-MAINT and L-MAINT, and greater total amino acids compared to O-MAINT. Glycerol and 3-hydroxybutyric acid were greater in O-MAINT compared to L-MAINT, as were several lysophosphatidylcholines. Thus, energy restriction resulted in increased dispensable amino acids in feline serum which could indicate alterations in amino acid partitioning. An increase in lipolysis was not evident, though greater circulating acylcarnitines were observed, suggesting that fatty acid oxidation rates may have been greater under calorie restriction. More research is needed to elucidate energy metabolism and substrate utilization, specifically fatty acid oxidation and methyl status, during energy restriction in strict carnivorous cats to optimize weight loss.

AutonoMS: Automated Ion Mobility Metabolomic Fingerprinting

Automation is dramatically changing the nature of laboratory life science. Robotic lab hardware that can perform manual operations with greater speed, endurance, and reproducibility opens an avenue for faster scientific discovery with less time spent on laborious repetitive tasks. A major bottleneck remains in integrating cutting-edge laboratory equipment into automated workflows, notably specialized analytical equipment, which is designed for human usage. Here we present AutonoMS, a platform for automatically running, processing, and analyzing high-throughput mass spectrometry experiments. AutonoMS is currently written around an ion mobility mass spectrometry (IM-MS) platform and can be adapted to additional analytical instruments and data processing flows. AutonoMS enables automated software agent-controlled end-to-end measurement and analysis runs from experimental specification files that can be produced by human users or upstream software processes. We demonstrate the use and abilities of AutonoMS in a high-throughput flow-injection ion mobility configuration with 5 s sample analysis time, processing robotically prepared chemical standards and cultured yeast samples in targeted and untargeted metabolomics applications. The platform exhibited consistency, reliability, and ease of use while eliminating the need for human intervention in the process of sample injection, data processing, and analysis. The platform paves the way toward a more fully automated mass spectrometry analysis and ultimately closed-loop laboratory workflows involving automated experimentation and analysis coupled to AI-driven experimentation utilizing cutting-edge analytical instrumentation. AutonoMS documentation is available at https://autonoms.readthedocs.io.

Mass Spectrometry Strategies for O-Glycoproteomics

Glycoproteomics has accelerated in recent decades owing to numerous innovations in the analytical workflow. In particular, new mass spectrometry strategies have contributed to inroads in O-glycoproteomics, a field that lags behind N-glycoproteomics due to several unique challenges associated with the complexity of O-glycosylation. This review will focus on progress in sample preparation, enrichment strategies, and MS/MS techniques for the identification and characterization of O-glycoproteins.

Evaluation of normalization strategies for GC-based metabolomics

INTRODUCTION

For many samples studied by GC-based metabolomics applications, extensive sample preparation involving extraction followed by a two-step derivatization procedure of methoximation and trimethylsilylation (TMS) is typically required to expand the metabolome coverage. Performing normalization is critical to correct for variations present in samples and any biases added during the sample preparation steps and analytical runs. Addressing the totality of variations with an adequate normalization method increases the reliability of the downstream data analysis and interpretation of the results.

OBJECTIVES

Normalizing to sample mass is one of the most commonly employed strategies, while the total peak area (TPA) as a normalization factor is also frequently used as a post-acquisition technique. Here, we present a new normalization approach, total derivatized peak area (TDPA), where data are normalized to the intensity of all derivatized compounds. TDPA relies on the benefits of silylation as a universal derivatization method for GC-based metabolomics studies.

METHODS

Two sample classes consisting of systematically incremented sample mass were simulated, with the only difference between the groups being the added amino acid concentrations. The samples were TMS derivatized and analyzed using comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC × GC-TOFMS). The performance of five normalization strategies (no normalization, normalized to sample mass, TPA, total useful peak area (TUPA), and TDPA) were evaluated on the acquired data.

RESULTS

Of the five normalization techniques compared, TUPA and TDPA were the most effective. On PCA score space, they offered a clear separation between the two classes.

CONCLUSION

TUPA and TDPA carry different strengths: TUPA requires peak alignment across all samples, which depends upon the completion of the study, while TDPA is free from the requirement of alignment. The findings of the study would enhance the convenient and effective use of data normalization strategies and contribute to overcoming the data normalization challenges that currently exist in the metabolomics community.

Study on the accumulation pattern of anthocyanins, sugars and organic acids in medicinal Vitis vinifera 'SuoSuo' during ripening

In this study, targeted metabolomics technology was used to accurately and quantitatively analyze the metabolic pathways of anthocyanin, sugars and organic acid metabolites during the ripening of 'SuoSuo' grape berries. Results, 33, 10 and 36 metabolites of anthocyanins, sugars and organic acids, respectively, were detected. The anthocyanin with the highest content was cyanidin-3-O-glucoside (136.343 ng/g), which reached a maximum at 135 days after full bloom. The highest fructose content in sugar was 167.69 ng/g (135 days after full bloom). Among the organic acids, tartaric acid exhibited the highest content (37,196.67 mg/kg, 105 days after full bloom). The content of oleanolic acid (230.064 mg/kg, 135 days after full bloom) was higher in organic acids. These results clarify how anthocyanin, sugar and organic acid metabolites accumulate and change as 'SuoSuo' grapes ripen and provide a reference for the development and utilization of 'SuoSuo'.

Plasma metabolomics for diagnostic biomarkers on ectopic pregnancy

Metabolomics is a relatively novel omics tool to provide potential biomarkers for early diagnosis of the diseases and to insight the pathophysiology not having discussed ever before. In the present study, an ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) was employed to the plasma samples of Group T1: Patients with ectopic pregnancy diagnosed using ultrasound, and followed-up with beta-hCG level (n = 40), Group T2: Patients with ectopic pregnancy diagnosed using ultrasound, underwent surgical treatment and confirmed using histopathology (n = 40), Group P: Healthy pregnant women (n = 40) in the first prenatal visit of pregnancy, Group C: Healthy volunteers (n = 40) scheduling a routine gynecological examination. Metabolite extraction was performed using 3 kDa pores - Amicon® Ultra 0.5 mL Centrifugal Filters. A gradient elution program (mobile phase composition was water and acetonitrile consisting of 0.1% formic acid) was applied using a C18 column (Agilent Zorbax 1.8 μM, 100 x 2.1 mm). Total analysis time was 25 min when the flow rate was 0.2 mL/min. The raw data was processed through XCMS - R program language edition where the optimum parameters detected using Isotopologue Parameter Optimization (IPO). The potential metabolites were identified using MetaboAnalyst 5.0 and finally 27 metabolites were evaluated to be proposed as potential biomarkers to be used for the diagnosis of ectopic pregnancy.

Study on the anti-mitochondrial apoptosis mechanism of Erigeron breviscapus injection based on UPLC-Q-TOF-MS metabolomics and molecular docking in rats with cerebral ischemia-reperfusion injury

ETHNOPHARMACOLOGICAL RELEVANCE

Erigeron breviscapus is a common medicine of eight ethnic minorities, including Miao, Naxi, and Yi. As early as the Ming Dynasty (AD 1368-1644), Lanmao's Materia Medica of Southern Yunnan (AD 1436) recorded that the medicine is used for the treatment of "Zuo tan you huan." In modern pharmacological research, Erigeron breviscapus injection is the most commonly used preparation in the treatment of ischemic stroke caused by acute cerebral infarction, but its mechanism of action in the treatment of ischemic stroke is not well understood.

AIM OF THE STUDY

In this study, a metabonomics study based on ultraperformance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (UPLC-QTOF-MS) was used in investigating the effect of a traditional Chinese medicine preparation Erigeron breviscapus injection on the rat model of focal cerebral ischemia-reperfusion and the affinity of its main components with the targets of mitochondrial apoptotic pathways.

MATERIALS AND METHODS

This study used molecular docking technology to verify the effective binding ability of main effective components of Erigeron breviscapus injection to target proteins related to mitochondrial apoptosis pathway. This study developed a metabonomics method based on the ultra-performance liquid chromatography combined with quadrupole time-of-flight tandem mass spectrometry (UPLC Q-TOF MS) to evaluate the efficacy and study the mechanism of traditional Chinese medicine preparation. With pattern recognition analysis (principal component analysis and partial least squares-discriminate analysis) of urinary metabolites, a clear separation of focal cerebral ischemia-reperfusion model group and healthy control group was achieved.

RESULTS

Erigeron breviscapus injection can significantly reduce the area of cerebral infarction, improve tissue morphological lesion in rats, and can increase the number of Nissl bodies. It may be a promoting factor by inhibiting hippocampal nerve cell apoptosis and Bax protein expression and by exerting effects against ischemia reperfusion after the induction of apoptosis. Thus, it plays a role in brain protection. Moreover, it can considerably promote the recovery of neurological deficiency signs in advance. Meanwhile, Erigeron breviscapus decreased malondialdehyde content and T-NOS activity. Its curative effect from strong to weak order: low dose > high dose > medium dose. The representative components of Erigeron breviscapus have good affinity with the active sites of mitochondrial apoptosis-related proteins. Metabolomics found that the potential biomarkers regulated by breviscapine are kynurequinolinic acid, succinylornithine, and leucine proline. It is speculated that it may participate in TRP-kynurequinolinic acid and succinylornithine-urea cycle-NO metabolic pathways.

CONCLUSIONS

This paper revealed the potential biomarkers and metabolic pathways regulated by Erigeron breviscapus. It was speculated that the mechanism is related to its inhibition of mitochondrion-mediated apoptosis. Erigeron breviscapus could restore the metabolic profiles of the model animals to normal animal levels. The mechanism may be related to the potential biomarkers of quinolinic acid, succinylornithine, and leucine proline and the metabolic pathways involved. However, the exact mechanism by which Erigeron breviscapus inhibits mitochondrion-mediated apoptosis remains to be further explored.

Molecular Biomarkers in Thyroid Eye Disease: A Literature Review

PURPOSE

Thyroid eye disease (TED) is the most common extrathyroidal manifestation of Graves disease. Patients may be severely affected with eyelid retraction, exophthalmos, diplopia, pain, and threatened vision. Autoantibodies against thyroid-stimulating hormone receptor and insulin-like growth factor 1 receptor have shown associations with pathophysiological and clinical traits. Autoantibodies against thyroid-stimulating hormone receptor is in current clinical use as biomarker, but not with unambiguous diagnostic performance. A biomarker with high diagnostic accuracy and/or prognostic capability would be of immense value in diagnosing TED, especially in subclinical cases or when TED precedes the thyroid dysfunction. This article is a literature review on molecular biomarkers of TED.

METHODS

A literature search was performed using PubMed and Embase. Studies on molecular biomarkers in blood, tear fluid, and urine were included in the review.

RESULTS

Forty-six papers were included, of which 30, 14, and 2 studies on biomarkers in blood, tears, and urine, respectively. Fourteen of the papers evaluated the diagnostic performance of various biomarkers, 12 in blood and 2 in tears. Most studies evaluated single biomarkers, but 3 tested a panel of several markers. Except for autoantibodies against thyroid-stimulating hormone receptor, the reported diagnostic performances for the biomarkers were not confirmed in independent cohorts. In 32 studies, no or insufficient performance data were given, but the findings indicated involvement of various biologic mechanisms in TED including inflammation, oxidative stress, fibrosis, lipid metabolism, and ocular surface microflora.

CONCLUSIONS

Currently, serum autoantibodies against thyroid-stimulating hormone receptor is the only molecular biomarker with clinical utility in patients with TED. Several potential biomarkers have been investigated, and particularly panels of multiple biomarkers in tears are promising. To improve patient care, biomarkers in TED should be studied further.

Optimization of heteronuclear ultrafast 2D NMR for the study of complex mixtures hyperpolarized by dynamic nuclear polarization

Hyperpolarized 13C NMR at natural abundance, based on dissolution dynamic nuclear polarization (d-DNP), provides rich, sensitive and repeatable 13C NMR fingerprints of complex mixtures. However, the sensitivity enhancement is associated with challenges such as peak overlap and the difficulty to assign hyperpolarized 13C signals. Ultrafast (UF) 2D NMR spectroscopy makes it possible to record heteronuclear 2D maps of d-DNP hyperpolarized samples. Heteronuclear UF 2D NMR can provide correlation peaks that link quaternary carbons and protons through long-range scalar couplings. Here, we report the analytical assessment of an optimized UF long-range HETCOR pulse sequence, applied to the detection of metabolic mixtures at natural abundance and hyperpolarized by d-DNP, based on repeatability and sensitivity considerations. We show that metabolite-dependent limits of quantification in the range of 1-50 mM (in the sample before dissolution) can be achieved, with a repeatability close to 10% and a very good linearity. We provide a detailed comparison of such analytical performance in two different dissolution solvents, D2O and MeOD. The reported pulse sequence appears as an useful analytical tool to facilitate the assignment and integration of metabolite signals in hyperpolarized complex mixtures.

Emerging trends and applications of metabolomics in food science and nutrition

The study of all chemical processes involving metabolites is known as metabolomics. It has been developed into an essential tool in several disciplines, such as the study of plant physiology, drug development, human diseases, and nutrition. The field of food science, diagnostic biomarker research, etiological analysis in the field of medical therapy, and raw material quality, processing, and safety have all benefited from the use of metabolomics recently. Food metabolomics includes the use of metabolomics in food production, processing, and human diets. As a result of changing consumer habits and the rising of food industries all over the world, there is a remarkable increase in interest in food quality and safety. It requires the employment of various technologies for the food supply chain, processing of food, and even plant breeding. This can be achieved by understanding the metabolome of food, including its biochemistry and composition. Additionally, Food metabolomics can be used to determine the similarities and differences across crop kinds, as an indicator for tracking the process of ripening to increase crops' shelf life and attractiveness, and identifying metabolites linked to pathways responsible for postharvest disorders. Moreover, nutritional metabolomics is used to investigate the connection between diet and human health through detection of certain biomarkers. This review assessed and compiled literature on food metabolomics research with an emphasis on metabolite extraction, detection, and data processing as well as its applications to the study of food nutrition, food-based illness, and phytochemical analysis. Several studies have been published on the applications of metabolomics in food but further research concerning the use of standard reproducible procedures must be done. The results published showed promising uses in the food industry in many areas such as food production, processing, and human diets. Finally, metabolome-wide association studies (MWASs) could also be a useful predictor to detect the connection between certain diseases and low molecular weight biomarkers.

Chemical Exposure Highlighted without Any A Priori Information in an Epidemiological Study by Metabolomic FT-ICR-MS Fingerprinting at High Throughput and High Resolution

Epidemiological studies aim to assess associations between diseases and risk factors. Such investigations involve a large sample size and require powerful analytical methods to measure the effects of risk factors, resulting in a long analysis time. In this study, chemical exposure markers were detected as the main variables strongly affecting two components coming from a principal component analysis (PCA) exploration of the metabolomic data generated from urinary samples collected on a cohort of about 500 individuals using direct introduction coupled with a Fourier-transform ion cyclotron resonance instrument. The assignment of their chemical identity was first achieved based on their isotopic fine structures detected at very high resolution (Rp > 900,000). Their identification as dimethylbiguanide and sotalol was obtained at level 1, thanks to the available authentic chemical standards, tandem mass spectrometry (MS/MS) experiments, and collision cross section measurements. Epidemiological data confirmed that the subjects discriminated by PCA had declared to be prescribed these drugs for either type II diabetes or cardiac arrhythmia. Concentrations of these drugs in urine samples of interest were also estimated by rapid quantification using an external standard calibration method, direct introduction, and MS/MS experiments. Regression analyses showed a good correlation between the estimated drug concentrations and the scores of individuals distributed on these specific PCs. The detection of these chemical exposure markers proved the potential of the proposed high-throughput approach without any prior drug exposure knowledge as a powerful emerging tool for rapid and large-scale phenotyping of subjects enrolled in epidemiological studies to rapidly characterize the chemical exposome and adherence to medical prescriptions.

Mass spectrometry-based approaches to assess the botanical authenticity of dietary supplements

Dietary supplements are legally considered foods despite frequently including medicinal plants as ingredients. Currently, the consumption of herbal dietary supplements, also known as plant food supplements (PFS), is increasing worldwide and some raw botanicals, highly demanded due to their popularity, extensive use, and/or well-established pharmacological effects, have been attaining high prices in the international markets. Therefore, botanical adulteration for profit increase can occur along the whole PFS industry chain, from raw botanicals to plant extracts, until final PFS. Besides the substitution of high-value species, unintentional mislabeling can happen in morphologically similar species. Both cases represent a health risk for consumers, prompting the development of numerous works to access botanical adulterations in PFS. Among different approaches proposed for this purpose, mass spectrometry (MS)-based techniques have often been reported as the most promising, particularly when hyphenated with chromatographic techniques. Thus, this review aims at describing an overview of the developments in this field, focusing on the applications of MS-based techniques to targeted and untargeted analysis to detect botanical adulterations in plant materials, extracts, and PFS.

Approach strategies and application of metabolomics to biotechnology in plants

Metabolomics refers to the technology for the comprehensive analysis of metabolites and low-molecular-weight compounds in a biological system, such as cells or tissues. Metabolites play an important role in biological phenomena through their direct involvement in the regulation of physiological mechanisms, such as maintaining cell homeostasis or signal transmission through protein-protein interactions. The current review aims provide a framework for how the integrated analysis of metabolites, their functional actions and inherent biological information can be used to understand biological phenomena related to the regulation of metabolites and how this information can be applied to safety assessments of crops created using biotechnology. Advancement in technology and analytical instrumentation have led new ways to examine the convergence between biology and chemistry, which has yielded a deeper understanding of complex biological phenomena. Metabolomics can be utilized and applied to safety assessments of biotechnology products through a systematic approach using metabolite-level data processing algorithms, statistical techniques, and database development. The integration of metabolomics data with sequencing data is a key step towards improving additional phenotypical evidence to elucidate the degree of environmental affects for variants found in genome associated with metabolic processes. Moreover, information analysis technology such as big data, machine learning, and IT investment must be introduced to establish a system for data extraction, selection, and metabolomic data analysis for the interpretation of biological implications of biotechnology innovations. This review outlines the integrity of metabolomics assessments in determining the consequences of genetic engineering and biotechnology in plants.

Hollow Crystallization COF Capsuled MOF Hybrids Depict Serum Metabolic Profiling for Precise Early Diagnosis and Risk Stratification of Acute Coronary Syndrome

Acute coronary syndrome (ACS), comprising unstable angina (UA) and acute myocardial infarction (AMI), is the leading cause of death worldwide. Currently, lacking effective strategies for classifying ACS hinders the prognosis improvement of ACS patients. Disclosing the nature of metabolic disorders holds the potential to reflect disease progress and high-throughput mass spectrometry-based metabolic analysis is a promising tool for large-scale screening. Herein, a hollow crystallization COF capsuled MOF hybrids (UiO-66@HCOF) assisted serum metabolic analysis is developed for the early diagnosis and risk stratification of ACS. UiO-66@HCOF exhibits unrivaled chemical and structural stability as well as endowing satisfying desorption/ionization efficiency in the detection of metabolites. Paired with machine learning algorithms, early diagnosis of ACS is achieved with the area under the curve (AUC) value of 0.945 for validation sets. Besides, a comprehensive ACS risk stratification method is established, and the AUC value for the discrimination of ACS from healthy controls, and AMI from UA are 0.890, and 0.928. Moreover, the AUC value of the subtyping of AMI is 0.964. Finally, the potential biomarkers exhibit high sensitivity and specificity. This study makes metabolic molecular diagnosis a reality and provided new insight into the progress of ACS.

Development and application of lipidomics for food research

Lipidomics is an emerging and promising omics derived from metabolomics to comprehensively analyze all of lipid molecules in biological matrices. The purpose of this chapter is to introduce the development and application of lipidomics for food research. First, three aspects of sample preparation are introduced: food sampling, lipid extraction, and transportation and storage. Second, five types of instruments for data acquisition are summarized: direct infusion-mass spectrometry (MS), chromatographic separation-MS, ion mobility-MS, MS imaging, and nuclear magnetic resonance spectroscopy. Third, data acquisition and analysis software are described for the lipidomics software development. Fourth, the application of lipidomics for food research is discussed such as food origin and adulteration analysis, food processing research, food preservation research, and food nutrition and health research. All the contents suggest that lipidomics is a powerful tool for food research based on its ability of lipid component profile analysis.

Hyperpolarized NMR metabolomics

Hyperpolarized NMR is a promising approach to address the sensitivity limits of conventional NMR metabolomics approaches, which currently fails to detect minute metabolite concentrations in biological samples. This review describes how tremendous signal enhancement offered by dissolution-dynamic nuclear polarization and parahydrogen-based techniques can be fully exploited for molecular omics sciences. Recent developments, including the combination of hyperpolarization techniques with fast multi-dimensional NMR implementation and quantitative workflows are described, and a comprehensive comparison of existing hyperpolarization techniques is proposed. High-throughput, sensitivity, resolution and other relevant challenges that should be tackled for a general application of hyperpolarized NMR in metabolomics are discussed.

NMR-based metabolomics applied to ecotoxicology with zebrafish (Danio rerio) as a prominent model for metabolic profiling and biomarker discovery: Overviewing the most recent approaches

Metabolomics is an innovative approach used in the medical, toxicological, and biological sciences. As an interdisciplinary topic, metabolomics and its relation with the environment and toxicological research are extensive. The use of substances, such as drugs and pesticides, contributes to the continuous releasing of xenobiotics into the environment, harming organisms and their habitats. In this context, fish are important bioindicators of the environmental condition and have often been used as model species. Among them, zebrafish (Danio rerio) presents itself as a versatile and straightforward option due to its unique attributes for research. Zebrafish proves to be a valuable model for toxicity assays and also for metabolomics profiling by analytical tools. Thus, NMR-based metabolomics associated with statistical analysis can reasonably assist researchers in critical factors related to discovering and validating biomarkers through accurate diagnosis. Therefore, this review aimed to report the studies that applied zebrafish as a model for (eco)toxicological assays and essentially utilized NMR-based metabolomics analysis to assess the biochemical profile and thus suggest the potential biological marker.

High-throughput metabolomics for the design and validation of a diauxic shift model

Saccharomyces cerevisiae is a very well studied organism, yet ∼20% of its proteins remain poorly characterized. Moreover, recent studies seem to indicate that the pace of functional discovery is slow. Previous work has implied that the most probable path forward is via not only automation but fully autonomous systems in which active learning is applied to guide high-throughput experimentation. Development of tools and methods for these types of systems is of paramount importance. In this study we use constrained dynamical flux balance analysis (dFBA) to select ten regulatory deletant strains that are likely to have previously unexplored connections to the diauxic shift. We then analyzed these deletant strains using untargeted metabolomics, generating profiles which were then subsequently investigated to better understand the consequences of the gene deletions in the metabolic reconfiguration of the diauxic shift. We show that metabolic profiles can be utilised to not only gaining insight into cellular transformations such as the diauxic shift, but also on regulatory roles and biological consequences of regulatory gene deletion. We also conclude that untargeted metabolomics is a useful tool for guidance in high-throughput model improvement, and is a fast, sensitive and informative approach appropriate for future large-scale functional analyses of genes. Moreover, it is well-suited for automated approaches due to relative simplicity of processing and the potential to make massively high-throughput.

To metabolomics and beyond: a technological portfolio to investigate cancer metabolism

Tumour cells have exquisite flexibility in reprogramming their metabolism in order to support tumour initiation, progression, metastasis and resistance to therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic and redox status to sustain the increased energetic demand of the cells. Over the last decades, the cancer metabolism field has seen an explosion of new biochemical technologies giving more tools than ever before to navigate this complexity. Within a cell or a tissue, the metabolites constitute the direct signature of the molecular phenotype and thus their profiling has concrete clinical applications in oncology. Metabolomics and fluxomics, are key technological approaches that mainly revolutionized the field enabling researchers to have both a qualitative and mechanistic model of the biochemical activities in cancer. Furthermore, the upgrade from bulk to single-cell analysis technologies provided unprecedented opportunity to investigate cancer biology at cellular resolution allowing an in depth quantitative analysis of complex and heterogenous diseases. More recently, the advent of functional genomic screening allowed the identification of molecular pathways, cellular processes, biomarkers and novel therapeutic targets that in concert with other technologies allow patient stratification and identification of new treatment regimens. This review is intended to be a guide for researchers to cancer metabolism, highlighting current and emerging technologies, emphasizing advantages, disadvantages and applications with the potential of leading the development of innovative anti-cancer therapies.

Filling gaps in animal welfare assessment through metabolomics

Sustainability has become a central issue in Italian livestock systems driving food business operators to adopt high standards of production concerning animal husbandry conditions. Meat sector is largely involved in this ecological transition with the introduction of new label claims concerning the defense of animal welfare (AW). These new guarantees referred to AW provision require new tools for the purpose of authenticity and traceability to assure meat supply chain integrity. Over the years, European Union (EU) Regulations, national, and international initiatives proposed provisions and guidelines for assuring AW introducing requirements to be complied with and providing tools based on scoring systems for a proper animal status assessment. However, the comprehensive and objective assessment of the AW status remains challenging. In this regard, phenotypic insights at molecular level may be investigated by metabolomics, one of the most recent high-throughput omics techniques. Recent advances in analytical and bioinformatic technologies have led to the identification of relevant biomarkers involved in complex clinical phenotypes of diverse biological systems suggesting that metabolomics is a key tool for biomarker discovery. In the present review, the Five Domains model has been employed as a vademecum describing AW. Starting from the individual Domains-nutrition (I), environment (II), health (III), behavior (IV), and mental state (V)-applications and advances of metabolomics related to AW setting aimed at investigating phenotypic outcomes on molecular scale and elucidating the biological routes most perturbed from external solicitations, are reviewed. Strengths and weaknesses of the current state-of-art are highlighted, and new frontiers to be explored for AW assessment throughout the metabolomics approach are argued. Moreover, a detailed description of metabolomics workflow is provided to understand dos and don'ts at experimental level to pursue effective results. Combining the demand for new assessment tools and meat market trends, a new cross-strategy is proposed as the promising combo for the future of AW assessment.

Serum metabolomic analysis of the dose-response effect of dietary choline in overweight male cats fed at maintenance energy requirements

Choline participates in methyl group metabolism and has been recognized for its roles in lipid metabolism, hepatic health and muscle function in various species. Data regarding the impacts of choline on feline metabolic pathways are scarce. The present study investigated how choline intake affects the metabolomic profile of overweight cats fed at maintenance energy. Overweight (n = 14; body condition score:6-8/9) male adult cats were supplemented with five doses of choline in a 5x5 Latin Square design. Cats received a daily dose of choline on extruded food (3620 mg choline/kg diet) for three weeks at maintenance energy requirements (130 kcal/kgBW0.4). Doses were based on body weight (BW) and the daily recommended allowance (RA) for choline for adult cats (63 mg/kg BW0.67). Treatment groups included: Control (no additional choline, 1.2 x NRC RA, 77 mg/kg BW0.67), 2 x NRC RA (126 mg/kg BW0.67), 4 x NRC RA (252 mg/kg BW0.67), 6 x RA (378 mg/kg BW0.67), and 8 x NRC RA (504 mg/kg BW0.67). Serum was collected after an overnight fast at the end of each treatment period and analyzed for metabolomic parameters through nuclear magnetic resonance (NMR) spectroscopy and direct infusion mass spectrometry (DI-MS). Data were analyzed using GLIMMIX, with group and period as random effects, and dose as the fixed effect. Choline up to 8 x NRC RA was well-tolerated. Choline at 6 and 8 x NRC RA resulted in greater concentrations of amino acids and one-carbon metabolites (P < 0.05) betaine, dimethylglycine and methionine. Choline at 6 x NRC RA also resulted in greater phosphatidylcholine and sphingomyelin concentrations (P < 0.05). Supplemental dietary choline may be beneficial for maintaining hepatic health in overweight cats, as it may increase hepatic fat mobilization and methyl donor status. Choline may also improve lean muscle mass in cats. More research is needed to quantify how choline impacts body composition.

Understanding the Chemical Exposome During Fetal Development and Early Childhood: A Review

Early human life is considered a critical window of susceptibility to external exposures. Infants are exposed to a multitude of environmental factors, collectively referred to as the exposome. The chemical exposome can be summarized as the sum of all xenobiotics that humans are exposed to throughout a lifetime. We review different exposure classes and routes that impact fetal and infant metabolism and the potential toxicological role of mixture effects. We also discuss the progress in human biomonitoring and present possiblemodels for studying maternal-fetal transfer. Data gaps on prenatal and infant exposure to xenobiotic mixtures are identified and include natural biotoxins, in addition to commonly reported synthetic toxicants, to obtain a more holistic assessment of the chemical exposome. We highlight the lack of large-scale studies covering a broad range of xenobiotics. Several recommendations to advance our understanding of the early-life chemical exposome and the subsequent impact on health outcomes are proposed.

Advances in Mass Spectrometry-Metabolomics Based Approaches

Highly selective and sensitive analytical techniques are necessary for microbial metabolomics due to the complexity of the microbial sample matrix. Hence, mass spectrometry (MS) has been successfully applied in microbial metabolomics due to its high precision, versatility, sensitivity, and wide dynamic range. The different analytical tools using MS have been employed in microbial metabolomics investigations and can contribute to the discovery or accelerate the search for bioactive substances. The coupling with chromatographic and electrophoretic separation techniques has resulted in more efficient technologies for the analysis of microbial compounds occurring in trace levels. This book chapter describes the current advances in the application of mass spectrometry-based metabolomics in the search for new biologically active agents from microbial sources; the development of new approaches for in silico annotation of natural products; the different technologies employing mass spectrometry imaging to deliver more comprehensive analysis and elucidate the metabolome involved in ecological interactions as they enable visualization of the spatial dispersion of small molecules. We also describe other ambient ionization techniques applied to the fingerprint of microbial natural products and modern techniques such as ion mobility mass spectrometry used to microbial metabolomic analyses and the dereplication of natural microbial products through MS.

Metabolomics during the spontaneous fermentation in cocoa (Theobroma cacao L.): An exploraty review

Spontaneous fermentation is a process that depends on substrates' physical characteristics, crop variety, and postharvest practices; it induces variations in the metabolites that are responsible for the taste, aroma, and quality. Metabolomics makes it possible to detect key metabolites using chemometrics and makes it possible to establish patterns or identify biomarker behaviors under certain conditions at a given time. Therefore, sensitive and highly efficient analytical techniques allow for studying the metabolomic fingerprint changes during fermentation; which identify and quantify metabolites related to taste and aroma formation of an adequate processing time. This review shows that studying metabolomics in spontaneous fermentation permits the characterization of spontaneous fermentation in different stages. Also, it demonstrates the possibility of modulating the quality of cocoa by improving the spontaneous fermentation time (because of volatile aromatic compounds formation), thus standardizing the process to obtain attributes and quality that will later impact the chocolate quality.

Mitochondrial encephalomyopathy

Mitochondrial dysfunction, especially perturbation of oxidative phosphorylation and adenosine triphosphate (ATP) generation, disrupts cellular homeostasis and is a surprisingly frequent cause of central and peripheral nervous system pathology. Mitochondrial disease is an umbrella term that encompasses a host of clinical syndromes and features caused by in excess of 300 different genetic defects affecting the mitochondrial and nuclear genomes. Patients with mitochondrial disease can present at any age, ranging from neonatal onset to late adult life, with variable organ involvement and neurological manifestations including neurodevelopmental delay, seizures, stroke-like episodes, movement disorders, optic neuropathy, myopathy, and neuropathy. Until relatively recently, analysis of skeletal muscle biopsy was the focus of diagnostic algorithms, but step-changes in the scope and availability of next-generation sequencing technology and multiomics analysis have revolutionized mitochondrial disease diagnosis. Currently, there is no specific therapy for most types of mitochondrial disease, although clinical trials research in the field is gathering momentum. In that context, active management of epilepsy, stroke-like episodes, dystonia, brainstem dysfunction, and Parkinsonism are all the more important in improving patient quality of life and reducing mortality.

Recent Advances in Gas-phase Ion/Ion Chemistry for Lipid Analysis

Gas-phase ion/ion reactions can be used to alter analyte ion-types for subsequent dissociation both quickly and efficiently without the need for altering analyte ionization conditions. This capability can be particularly useful when the ion-type that is most efficiently generated by the ionization method at hand does not provide the structural information of interest using available dissociation methods. This situation often arises in the analysis of lipids, which constitute a diverse array of chemical species with many possibilities for isomers. Gas-phase ion/ion reactions have been demonstrated to be capable of enhancing the ability of tandem mass spectrometry to characterize the structures of various lipid classes. This review summarizes progress to date in the application of gas-phase ion/ion reactions to lipid structural characterization.

Tapping into Plant-Microbiome Interactions through the Lens of Multi-Omics Techniques

This review highlights the pivotal role of root exudates in the rhizosphere, especially the interactions between plants and microbes and between plants and plants. Root exudates determine soil nutrient mobilization, plant nutritional status, and the communication of plant roots with microbes. Root exudates contain diverse specialized signaling metabolites (primary and secondary). The spatial behavior of these metabolites around the root zone strongly influences rhizosphere microorganisms through an intimate compatible interaction, thereby regulating complex biological and ecological mechanisms. In this context, we reviewed the current understanding of the biological phenomenon of allelopathy, which is mediated by phytotoxic compounds (called allelochemicals) released by plants into the soil that affect the growth, survival, development, ecological infestation, and intensification of other plant species and microbes in natural communities or agricultural systems. Advances in next-generation sequencing (NGS), such as metagenomics and metatranscriptomics, have opened the possibility of better understanding the effects of secreted metabolites on the composition and activity of root-associated microbial communities. Nevertheless, understanding the role of secretory metabolites in microbiome manipulation can assist in designing next-generation microbial inoculants for targeted disease mitigation and improved plant growth using the synthetic microbial communities (SynComs) tool. Besides a discussion on different approaches, we highlighted the advantages of conjugation of metabolomic approaches with genetic design (metabolite-based genome-wide association studies) in dissecting metabolome diversity and understanding the genetic components of metabolite accumulation. Recent advances in the field of metabolomics have expedited comprehensive and rapid profiling and discovery of novel bioactive compounds in root exudates. In this context, we discussed the expanding array of metabolomics platforms for metabolome profiling and their integration with multivariate data analysis, which is crucial to explore the biosynthesis pathway, as well as the regulation of associated pathways at the gene, transcript, and protein levels, and finally their role in determining and shaping the rhizomicrobiome.

Metabolomic Study of Urine from Workers Exposed to Low Concentrations of Benzene by UHPLC-ESI-QToF-MS Reveals Potential Biomarkers Associated with Oxidative Stress and Genotoxicity

Benzene is a human carcinogen whose exposure to concentrations below 1 ppm (3.19 mg·m^-3) is associated with myelotoxic effects. The determination of biomarkers such as trans-trans muconic acid (AttM) and S-phenylmercapturic acid (SPMA) show exposure without reflecting the toxic effects of benzene. For this reason, in this study, the urinary metabolome of individuals exposed to low concentrations of benzene was investigated, with the aim of understanding the biological response to exposure to this xenobiotic and identifying metabolites correlated with the toxic effects induced by it. Ultra-efficient liquid chromatography coupled to a quadrupole-time-of-flight mass spectrometer (UHPLC-ESI-Q-ToF-MS) was used to identify metabolites in the urine of environmentally (n = 28) and occupationally exposed (n = 32) to benzene (mean of 22.1 μg·m^-3 and 31.8 μg·m^-3, respectively). Non-targeted metabolomics analysis by PLS-DA revealed nine urinary metabolites discriminating between groups and statistically correlated with oxidative damage (MDA, thiol) and genetic material (chromosomal aberrations) induced by the hydrocarbon. The analysis of metabolic pathways revealed important alterations in lipid metabolism. These results point to the involvement of alterations in lipid metabolism in the mechanisms of cytotoxic and genotoxic action of benzene. Furthermore, this study proves the potential of metabolomics to provide relevant information to understand the biological response to exposure to xenobiotics and identify early effect biomarkers.

Metabolomics and modelling approaches for systems metabolic engineering

Metabolic engineering involves the manipulation of microbes to produce desirable compounds through genetic engineering or synthetic biology approaches. Metabolomics involves the quantitation of intracellular and extracellular metabolites, where mass spectrometry and nuclear magnetic resonance based analytical instrumentation are often used. Here, the experimental designs, sample preparations, metabolite quenching and extraction are essential to the quantitative metabolomics workflow. The resultant metabolomics data can then be used with computational modelling approaches, such as kinetic and constraint-based modelling, to better understand underlying mechanisms and bottlenecks in the synthesis of desired compounds, thereby accelerating research through systems metabolic engineering. Constraint-based models, such as genome scale models, have been used successfully to enhance the yield of desired compounds from engineered microbes, however, unlike kinetic or dynamic models, constraint-based models do not incorporate regulatory effects. Nevertheless, the lack of time-series metabolomic data generation has hindered the usefulness of dynamic models till today. In this review, we show that improvements in automation, dynamic real-time analysis and high throughput workflows can drive the generation of more quality data for dynamic models through time-series metabolomics data generation. Spatial metabolomics also has the potential to be used as a complementary approach to conventional metabolomics, as it provides information on the localization of metabolites. However, more effort must be undertaken to identify metabolites from spatial metabolomics data derived through imaging mass spectrometry, where machine learning approaches could prove useful. On the other hand, single-cell metabolomics has also seen rapid growth, where understanding cell-cell heterogeneity can provide more insights into efficient metabolic engineering of microbes. Moving forward, with potential improvements in automation, dynamic real-time analysis, high throughput workflows, and spatial metabolomics, more data can be produced and studied using machine learning algorithms, in conjunction with dynamic models, to generate qualitative and quantitative predictions to advance metabolic engineering efforts.

Balancing Trade-Offs Imposed by Growth Media and Mass Spectrometry for Bacterial Exometabolomics

The bacterial exometabolome consists of a vast array of specialized metabolites, many of which are only produced in response to specific environmental stimuli. For this reason, it is desirable to control the extracellular environment with a defined growth medium composed of pure ingredients. However, complex (undefined) media are expected to support the robust growth of a greater variety of microorganisms than defined media. Here, we investigate the trade-offs inherent to a range of complex and defined solid media for the growth of soil microorganisms, production of specialized metabolites, and detection of these compounds using direct infusion mass spectrometry. We find that complex media support growth of more soil microorganisms, as well as allowing for the detection of more previously discovered natural products as a fraction of total m/z features detected in each sample. However, the use of complex media often caused mass spectrometer injection failures and poor-quality mass spectra, which in some cases resulted in over a quarter of samples being removed from analysis. Defined media, while more limiting in growth, generated higher quality spectra and yielded more m/z features after background subtraction. These results inform future exometabolomic experiments requiring a medium that supports the robust growth of many soil microorganisms. IMPORTANCE Bacteria are capable of producing and secreting a rich diversity of specialized metabolites. Yet, much of their exometabolome remains hidden due to challenges associated with eliciting specialized metabolite production, labor-intensive sample preparation, and time-consuming analysis techniques. Using our versatile three-dimensional (3D)-printed culturing platform, SubTap, we demonstrate that rapid exometabolomic data collection from a diverse set of environmental bacteria is feasible. We optimized our platform by surveying Streptomyces isolated from soil on a variety of media types to assess viability, degree of specialized metabolite production, and compatibility with downstream LESA-DIMS analysis. Ultimately, this will enable data-rich experimentation, allowing for a better understanding of bacterial exometabolomes.

A narrative review of metabolomics in the era of "-omics": integration into clinical practice for inborn errors of metabolism

BACKGROUND AND OBJECTIVE

Traditional targeted metabolomic investigations identify a pre-defined list of analytes in samples and have been widely used for decades in the diagnosis and monitoring of inborn errors of metabolism (IEMs). Recent technological advances have resulted in the development and maturation of untargeted metabolomics: a holistic, unbiased, analytical approach to detecting metabolic disturbances in human disease. We aim to provide a summary of untargeted metabolomics [focusing on tandem mass spectrometry (MS-MS)] and its application in the field of IEMs.

METHODS

Data for this review was identified through a literature search using PubMed, Google Scholar, and personal repositories of articles collected by the authors. Findings are presented within several sections describing the metabolome, the current use of targeted metabolomics in the diagnostic pathway of patients with IEMs, the more recent integration of untargeted metabolomics into clinical care, and the limitations of this newly employed analytical technique.

KEY CONTENT AND FINDINGS

Untargeted metabolomic investigations are increasingly utilized in screening for rare disorders, improving understanding of cellular and subcellular physiology, discovering novel biomarkers, monitoring therapy, and functionally validating genomic variants. Although the untargeted metabolomic approach has some limitations, this "next generation metabolic screening" platform is becoming increasingly affordable and accessible.

CONCLUSIONS

When used in conjunction with genomics and the other promising "-omic" technologies, untargeted metabolomics has the potential to revolutionize the diagnostics of IEMs (and other rare disorders), improving both clinical and health economic outcomes.

Development of a mass spectrometry-based metabolomics workflow for traceability of wild and cultivated Cordyceps sinensis

Cordyceps sinensis, as an expensive traditional Chinese medicine and edible fungus mycelium, lacks an effective quality evaluation method, especially and cultivated Cordyceps sinensis. In this study, a feasible workflow method was developed for traceability evaluation of wild and cultivated Cordyceps sinensis, based on mass spectrometry-based metabolomics. Mass spectrometry data were firstly acquired from Cordyceps sinensis, samples by liquid chromatography-quadrupole and time of flight mass spectrometry. Characteristic mass spectrometry peaks were extracted by applying the MZmine. Then significant markers were obtained from Cordyceps sinensis samples by orthogonal partial least square discriminant analysis. Then, identification of significant markers were identified by MS-FINDER data analytics. The results showed that Changdu, the other four wild origins (Naqu, Xinghai, Yushu and Guoluo) and cultivated samples could be significantly distinguished. This identified significant markers of Cordyceps sinensis, including 174 special significant markers for the wild samples, 204 special significant markers for the cultivated samples and 87 share significant markers. Number of 87 shared significant markers were identified in the wild and cultivated Cordyceps sinensis, especially 28 confident significant compounds, such as adenosine, riboflavin, tyrosine, arginine and glutamine. These shared significant markers might support the quality control of multi-targets of Cordyceps sinensis, compared with a single target in the Chinese Pharmacopoeia. The special significant markers indicated that cultivated Cordyceps sinensis was different from the wild based on mass spectrometry-based metabolomics. In the comparison of chromatographic fingerprint technology, it was found that the established feasible workflow method was easy to acquire significant markers and traceability of Cordyceps sinensis. This feasible workflow method has great potential to be successful for comprehensive and traceability evaluation of the wild and cultivated Cordyceps sinensis.

NPvis: An Interactive Visualizer of Peptidic Natural Product–MS/MS Matches

Peptidic natural products (PNPs) represent a medically important class of secondary metabolites that includes antibiotics, anti-inflammatory and antitumor agents. Advances in tandem mass spectra (MS/MS) acquisition and in silico database search methods have enabled high-throughput PNP discovery. However, the resulting spectra annotations are often error-prone and their validation remains a bottleneck. Here, we present NPvis, a visualizer suitable for the evaluation of PNP–MS/MS matches. The tool interactively maps annotated spectrum peaks to the corresponding PNP fragments and allows researchers to assess the match correctness. NPvis accounts for the wide chemical diversity of PNPs that prevents the use of the existing proteomics visualizers. Moreover, NPvis works even if the exact chemical structure of the matching PNP is unknown. The tool is available online and as a standalone application. We hope that it will benefit the community by streamlining PNP data analysis and validation.

Mass spectrometry in organic and bio-organic catalysis: Using thermochemical properties to lend insight into mechanism

In this review, we discuss gas phase experimentation centered on the measurement of acidity and proton affinity of substrates that are useful for understanding catalytic mechanisms. The review is divided into two parts. The first covers examples of organocatalysis, while the second focuses on biological catalysis. The utility of gas phase acidity and basicity values for lending insight into mechanisms of catalysis is highlighted.

What are we imaging? Software tools and experimental strategies for annotation and identification of small molecules in mass spectrometry imaging

Mass spectrometry imaging (MSI) has become a widespread analytical technique to perform nonlabeled spatial molecular identification. The Achilles' heel of MSI is the annotation and identification of molecular species due to intrinsic limitations of the technique (lack of chromatographic separation and the difficulty to apply tandem MS). Successful strategies to perform annotation and identification combine extra analytical steps, like using orthogonal analytical techniques to identify compounds; with algorithms that integrate the spectral and spatial information. In this review, we discuss different experimental strategies and bioinformatics tools to annotate and identify compounds in MSI experiments. We target strategies and tools for small molecule applications, such as lipidomics and metabolomics. First, we explain how sample preparation and the acquisition process influences annotation and identification, from sample preservation to the use of orthogonal techniques. Then, we review twelve software tools for annotation and identification in MSI. Finally, we offer perspectives on two current needs of the MSI community: the adaptation of guidelines for communicating confidence levels in identifications; and the creation of a standard format to store and exchange annotations and identifications in MSI.

A network pharmacology-based study on the quality control markers of antithrombotic herbs: Using Salvia miltiorrhiza - Ligusticum chuanxiong as an example

ETHNOPHARMACOLOGICAL RELEVANCE

Salvia miltiorrhiza (Danshen, DS), the dried root and rhizome of Salvia miltiorrhiza Bunge and Ligusticum chuanxiong (Chuanxiong, CX), the dried rhizomes of Ligusticum striatum DC are effective in invigorating blood circulation and eliminating stasis which is highly related with cardiovascular disease (CVD).

AIM OF STUDY

The identification of activity-based chemical markers is very important, but the complex mechanism of "multi-component, multi-target, and multi-effect" within traditional Chinese medicine (TCM) poses a great challenge to this work. In this study, we combined network pharmacological prediction with experimental validation of the DS and CX to explore an effective method for discovering quality control (QC) of antithrombotic herbs by clarifying the intermediate layer "module/cluster" between the whole complex system and a single component.

MATERIALS AND METHODS

Based on structural similarity analysis of compound and the thrombosis network published before, we firstly modularized two layers called chemical cluster (CC) network and functional module (FM) network respectively and linked them into one bilayer modularized compound target (BMCT) network. "Two-step" calculation was applied on identifying the significant compounds as the potential QC markers from CC. The in vitro inhibitory activity of selected QC marker compounds on thrombin was evaluated to partially verify their pharmacological activities. HPLC was used to determine contents.

RESULTS

According to the network-based analysis, nine compounds with great importance in the BMCT network were identified as QC markers of DS-CX, including tanshinone I, tanshinone IIA, cryptotanshinone, salvianolic acid B, ferulic acid, salvianolic acid A, rosmarinic acid, chlorogenic acid, and coniferyl ferulate. Enzyme inhibitory test partially verified the activity of tanshinone I and tanshinone IIA. Chemical profiling indicated that the nine marker compounds are the main components in the herbal pair.

CONCLUSIONS

This study identified activity-based QC markers of DS-CX herbal pair and provided a new methodology that can be used in the QC of other herbs, herbal pairs, or formulas.

Metabolomics-based mass spectrometry methods to analyze the chemical content of 3D organoid models

Metabolomics, the study of metabolites present in biological samples, can provide a global view of sample state as well as insights into biological changes caused by disease or environmental interactions. Mass spectrometry (MS) is commonly used for metabolomics analysis given its high-throughput capabilities, high sensitivity, and capacity to identify multiple compounds in complex samples simultaneously. MS can be coupled to separation methods that can handle small volumes, making it well suited for analyzing the metabolome of organoids, miniaturized three-dimensional aggregates of stem cells that model in vivo organs. Organoids are being used in research efforts to study human disease and development, and in the design of personalized drug treatments. For organoid models to be useful, they need to recapitulate morphological and chemical aspects, such as the metabolome, of the parent tissue. This review highlights the separation- and imaging-based MS-based metabolomics methods that have been used to analyze the chemical contents of organoids. Future perspectives on how MS techniques can be optimized to determine the accuracy of organoid models and expand the field of organoid research are also discussed.

Characterizing human mesenchymal stromal cells' immune-modulatory potency using targeted lipidomic profiling of sphingolipids

Cell therapies are expected to increase over the next decade owing to increasing demand for clinical applications. Mesenchymal stromal cells (MSCs) have been explored to treat a number of diseases, with some successes in early clinical trials. Despite early successes, poor MSC characterization results in lessened therapeutic capacity once in vivo. Here, we characterized MSCs derived from bone marrow (BM), adipose tissue and umbilical cord tissue for sphingolipids (SLs), a class of bioactive lipids, using liquid chromatography/tandem mass spectrometry. We found that ceramide levels differed based on the donor's sex in BM-MSCs. We detected fatty acyl chain variants in MSCs from all three sources. Linear discriminant analysis revealed that MSCs separated based on tissue source. Principal component analysis showed that interferon-γ-primed and unstimulated MSCs separated according to their SL signature. Lastly, we detected higher ceramide levels in low indoleamine 2,3-dioxygenase MSCs, indicating that sphingomyelinase or ceramidase enzymatic activity may be involved in their immune potency.

Plant metabolomics: a new strategy and tool for quality evaluation of Chinese medicinal materials

The present quality control method of Chinese medicinal materials (CMM) has obvious deficiency, which cannot be compatible with the multi-target and multi-component characteristics and production process of CMM. Plant metabolomics with a huge impetus to comprehensively characterize the metabolites and clarify the complexity and integrity of CMM, has been widely used in the research of CMM. This article comprehensively reviewed the application of plant metabolomics in the quality control of CMM. It introduced the concept, technique, and application examples, discussed the prospects, limitations, improvements of plant metabolomics. MS and NMR, as important techniques for plant metabolomics, are mainly highlighted in the case references. The purpose of this article is to clarify the advantage of plants metabolomics for promoting the optimization of the CMM quality control system and proposing a system approach to realize the overall quality control of CMM based on plant metabolomics combined with multidisciplinary method.

Amino acids in inflammatory bowel diseases: Modern diagnostic tools and methodologies

Amino acids are crucial building blocks of living organisms. Together with their derivatives, they participate in many intracellular processes to act as hormones, neuromodulators, and neurotransmitters. For several decades amino acids have been studied for their potential as markers of various diseases, including inflammatory bowel diseases. Subsequent improvements in sample pretreatment, separation, and detection methods have enabled the specific and very sensitive determination of these molecules in multicomponent matrices-biological fluids and tissues. The information obtained from targeted amino acid analysis (biomarker-based analytical strategy) can be further used for early diagnostics, to monitor the course of the disease or compliance of the patients. This review will provide an insight into current knowledge about inflammatory bowel diseases, the role of proteinogenic amino acids in intestinal inflammation and modern analytical techniques used in its diagnosis and disease activity monitoring. Current advances in the analysis of amino acids focused on sample pretreatment, separation strategy, or detection methods are highlighted, and their potential in clinical laboratories is discussed. In addition, the latest clinical data obtained from the metabolomic profiling of patients suffering from inflammatory bowel diseases are summarized with a focus on proteinogenic amino acids.