Mass spectrometry strategies in metabolomics

Hollow Cathode Discharge Ionization Mass Spectrometry: Detection, Quantification and Gas Phase Ion-Molecule Reactions of Explosives and Related Compounds

Mass spectrometry (MS) has become an essential analytical method in every sector of science and technology. Because of its unique ability to provide direct molecular structure information on analytes, an extra method is rarely required. This review describes fabrication of a variable-pressure hollow cathode discharge (HCD) ion source for MS in detection, quantification and investigation of gas-phase ion molecule reactions of explosives and related compounds using air as a carrier gas. The HCD ion source has been designed in such a way that by altering the ion source pressures, the system can generate both HCD and conventional GD. This design enables for the selective detection and quantification of explosives at trace to ultra-trace levels. The pressure-dependent HCD ion source has also been used to investigate ion-molecule reactions in the gas phase of explosives and related compounds. The mechanism of ion formation in explosive reactions is also discussed.

Method Development for Omics Analyses using Schirmer Strips

PURPOSE

The aim of this article was to investigate whether Schirmer strips gathered during clinical dry eye examinations can be prepared for omics analyses in a standardized way, to adjust for variations in tear volume and enable two separate omics analyses from the same sample. In addition, the intention was to investigate whether fluorescein dye instillation in the eyes gave bias effects on metabolomic analysis.

METHODS

Twelve samples from six individuals, with normal or reduced tear production, were collected. Half of the samples were harvested after instillation of fluorescein in the eye. Each strip was divided in half along the length and prepared with a new method for extracting tear content from the Schirmer strip. The new method was established to compensate for different dilutions of metabolites in varying Schirmer strip wetting levels when using identical extraction volume for all samples. Metabolomic data were compared in samples with and without fluorescein dye and Schirmer strips ranging from 1 to 35 mm wetting levels using a global LC-MS method.

RESULTS

All samples were successfully analyzed with an average of ∼350 relevant features detected per sample after using both positive and negative electrospray ionization mode, despite low tear volumes in some samples and that only one half of the Schirmer strips were used. Principal component analysis plots and heatmaps revealed no bias effects of fluorescein dye presence or different Schirmer strip values when using the proposed method.

CONCLUSION

A high number of relevant metabolomic features can be extracted from longitudinally cut halves of Schirmer strips, which may enable analyses with more than one omics modality from the same sample. With the pre-analytical method described, Schirmer strips can be used for metabolomic analyses even in cases of very low or high tear volume with or without fluorescence.

WTV2.0: A high-coverage plant volatilomics method with a comprehensive selective ion monitoring acquisition mode

Volatilomics is essential for understanding the biological functions and fragrance contributions of plant volatiles. However, the annotation coverage achieved using current untargeted and widely targeted volatomics (WTV) methods has been limited by low sensitivity and/or low acquisition coverage. Here, we introduce WTV 2.0, which enabled the construction of a high-coverage library containing 2111 plant volatiles, and report the development of a comprehensive selective ion monitoring (cSIM) acquisition method, including the selection of characteristic qualitative ions with the minimal ion number for each compound and an optimized segmentation method, that can acquire the smallest but sufficient number of ions for most plant volatiles, as well as the automatic qualitative and semi-quantitative analysis of cSIM data. Importantly, the library and acquisition method we developed can be self-expanded by incorporating compounds not present in the library, utilizing the obtained cSIM data. We showed that WTV 2.0 increases the median signal-to-noise ratio by 7.6-fold compared with the untargeted method, doubled the annotation coverage compared with the untargeted and WTV 1.0 methods in tomato fruit, and led to the discovery of menthofuran as a novel flavor compound in passion fruit. WTV 2.0 is a Python library with a user-friendly interface and is applicable to profiling of volatiles and primary metabolites in any species.

Identification of potential chemical biomarkers of hexaconazole using in vitro metabolite profiling in rat and human liver microsomes and in vivo confirmation through urinary excretion study in rats

Hexaconazole (HEX) is an azole fungicide widely used in agricultural practices across various countries and numerous studies have reported the toxic effects of HEX, such as endocrine disruption, immunotoxicity, neurotoxicity and carcinogenicity. Despite its widespread agricultural use and toxic effects, the metabolism of HEX is not completely understood, and information on urinary elimination of HEX or its metabolites is limited. Therefore, in the present study, we aimed to identify HEX metabolites in rat and human liver microsomes followed by their in vivo confirmation using a urinary excretion study in rats to identify potential candidate for exposure biomarkers for human biomonitoring studies. From the in vitro assay, a total of 12 metabolites were observed, where the single oxidation metabolites (M5 and M6) were the most abundant metabolites in both rat and human liver microsomes. The triple oxidation followed by dehydration metabolite, M8 (which could also be hexaconazole acid or hydroxy keto-hexaconazole), and the double oxidation metabolite (M9) were the major metabolites found in rat urine and were detectable in rat urine longer than the parent. These metabolites increased with decreasing concentrations of HEX in the rat urine samples. Therefore, metabolites M8, M9 and M5 could be pursued further as potential biomarkers for assessing and monitoring human exposure to HEX.

Metabolomics facilitates differential diagnosis in common inherited retinal degenerations by exploring their profiles of serum metabolites

The diagnosis of inherited retinal degeneration (IRD) is challenging owing to its phenotypic and genotypic complexity. Clinical information is important before a genetic diagnosis is made. Metabolomics studies the entire picture of bioproducts, which are determined using genetic codes and biological reactions. We demonstrated that the common diagnoses of IRD, including retinitis pigmentosa (RP), cone-rod dystrophy (CRD), Stargardt disease (STGD), and Bietti's crystalline dystrophy (BCD), could be differentiated based on their metabolite heatmaps. Hundreds of metabolites were identified in the volcano plot compared with that of the control group in every IRD except BCD, considered as potential diagnosing markers. The phenotypes of CRD and STGD overlapped but could be differentiated by their metabolomic features with the assistance of a machine learning model with 100% accuracy. Moreover, EYS-, USH2A-associated, and other RP, sharing considerable similar characteristics in clinical findings, could also be diagnosed using the machine learning model with 85.7% accuracy. Further study would be needed to validate the results in an external dataset. By incorporating mass spectrometry and machine learning, a metabolomics-based diagnostic workflow for the clinical and molecular diagnoses of IRD was proposed in our study.

Comprehensive Metabolomic Analysis of Human Heart Tissue Enabled by Parallel Metabolite Extraction and High-Resolution Mass Spectrometry

The heart contracts incessantly and requires a constant supply of energy, utilizing numerous metabolic substrates, such as fatty acids, carbohydrates, lipids, and amino acids, to supply its high energy demands. Therefore, a comprehensive analysis of various metabolites is urgently needed for understanding cardiac metabolism; however, complete metabolome analyses remain challenging due to the broad range of metabolite polarities, which makes extraction and detection difficult. Herein, we implemented parallel metabolite extractions and high-resolution mass spectrometry (MS)-based methods to obtain a comprehensive analysis of the human heart metabolome. To capture the diverse range of metabolite polarities, we first performed six parallel liquid-liquid extractions (three monophasic, two biphasic, and one triphasic) of healthy human donor heart tissue. Next, we utilized two complementary MS platforms for metabolite detection: direct-infusion ultrahigh-resolution Fourier-transform ion cyclotron resonance (DI-FTICR) and high-resolution liquid chromatography quadrupole time-of-flight tandem MS (LC-Q-TOF-MS/MS). Using DI-FTICR MS, 9644 metabolic features were detected where 7156 were assigned a molecular formula and 1107 were annotated by accurate mass assignment. Using LC-Q-TOF-MS/MS, 21,428 metabolic features were detected where 285 metabolites were identified based on fragmentation matching against publicly available libraries. Collectively, 1340 heart metabolites were identified in this study, which span a wide range of polarities including polar (benzenoids, carbohydrates, and nucleosides) as well as nonpolar (phosphatidylcholines, acylcarnitines, and fatty acids) compounds. The results from this study will provide critical knowledge regarding the selection of appropriate extraction and MS detection methods for the analysis of the diverse classes of human heart metabolites.

Metabolomics Analysis and Diagnosis of Lung Cancer: Insights from Diverse Sample Types

Lung cancer is a highly fatal disease that poses a significant global health burden. The absence of characteristic clinical symptoms frequently results in the diagnosis of most patients at advanced stages of lung cancer. Although low-dose computed tomography (LDCT) screening has become increasingly prevalent in clinical practice, its high rate of false positives continues to present a significant challenge. In addition to LDCT screening, tumor biomarker detection represents a critical approach for early diagnosis of lung cancer; unfortunately, no tumor marker with optimal sensitivity and specificity is currently available. Metabolomics has recently emerged as a promising field for developing novel tumor biomarkers. In this paper, we introduce metabolic pathways, instrument platforms, and a wide variety of sample types for lung cancer metabolomics. Specifically, we explore the strengths, limitations, and distinguishing features of various sample types employed in lung cancer metabolomics research. Additionally, we present the latest advances in lung cancer metabolomics research that utilize diverse sample types. We summarize and enumerate research studies that have investigated lung cancer metabolomics using different metabolomic sample types. Finally, we provide a perspective on the future of metabolomics research in lung cancer. Our discussion of the potential of metabolomics in developing new tumor biomarkers may inspire further study and innovation in this dynamic field.

Metabolomics: An Emerging "Omics" Platform for Systems Biology and Its Implications for Huntington Disease Research

Huntington's disease (HD) is a progressive, fatal neurodegenerative disease characterized by motor, cognitive, and psychiatric symptoms. The precise mechanisms of HD progression are poorly understood; however, it is known that there is an expansion of the trinucleotide cytosine-adenine-guanine (CAG) repeat in the Huntingtin gene. Important new strategies are of paramount importance to identify early biomarkers with predictive value for intervening in disease progression at a stage when cellular dysfunction has not progressed irreversibly. Metabolomics is the study of global metabolite profiles in a system (cell, tissue, or organism) under certain conditions and is becoming an essential tool for the systemic characterization of metabolites to provide a snapshot of the functional and pathophysiological states of an organism and support disease diagnosis and biomarker discovery. This review briefly highlights the historical progress of metabolomic methodologies, followed by a more detailed review of the use of metabolomics in HD research to enable a greater understanding of the pathogenesis, its early prediction, and finally the main technical platforms in the field of metabolomics.

Raman Multi-Omic Snapshots of Koshihikari Rice Kernels Reveal Important Molecular Diversities with Potential Benefits in Healthcare

This study exploits quantitative algorithms of Raman spectroscopy to assess, at the molecular scale, the nutritional quality of individual kernels of the Japanese short-grain rice cultivar Koshihikari in terms of amylose-to-amylopectin ratio, fractions of phenylalanine and tryptophan aromatic amino acid residues, protein-to-carbohydrate ratio, and fractions of protein secondary structures. Statistical assessments on a large number of rice kernels reveal wide distributions of the above nutritional parameters over nominally homogeneous kernel batches. This demonstrates that genetic classifications cannot catch omic fluctuations, which are strongly influenced by a number of extrinsic factors, including the location of individual grass plants within the same rice field and the level of kernel maturation. The possibility of collecting nearly real-time Raman "multi-omic snapshots" of individual rice kernels allows for the automatic (low-cost) differentiation of groups of kernels with restricted nutritional characteristics that could be used in the formulation of functional foods for specific diseases and in positively modulating the intestinal microbiota for protection against bacterial infection and cancer prevention.

Comprehensive Metabolomic Analysis of Human Heart Tissue Enabled by Parallel Metabolite Extraction and High-Resolution Mass Spectrometry

The heart contracts incessantly and requires a constant supply of energy, utilizing numerous metabolic substrates such as fatty acids, carbohydrates, lipids, and amino acids to supply its high energy demands. Therefore, a comprehensive analysis of various metabolites is urgently needed for understanding cardiac metabolism; however, complete metabolome analyses remain challenging due to the broad range of metabolite polarities which makes extraction and detection difficult. Herein, we implemented parallel metabolite extractions and high-resolution mass spectrometry (MS)-based methods to obtain a comprehensive analysis of the human heart metabolome. To capture the diverse range of metabolite polarities, we first performed six parallel liquid-liquid extractions (three monophasic, two biphasic, and one triphasic extractions) of healthy human donor heart tissue. Next, we utilized two complementary MS platforms for metabolite detection - direct-infusion ultrahigh-resolution Fourier-transform ion cyclotron resonance (DI-FTICR) and high-resolution liquid chromatography quadrupole time-of-flight tandem MS (LC-Q-TOF MS/MS). Using DI-FTICR MS, 9,521 metabolic features were detected where 7,699 were assigned a chemical formula and 1,756 were assigned an annotated by accurate mass assignment. Using LC-Q-TOF MS/MS, 21,428 metabolic features were detected where 626 metabolites were identified based on fragmentation matching against publicly available libraries. Collectively, 2276 heart metabolites were identified in this study which span a wide range of polarities including polar (benzenoids, alkaloids and derivatives and nucleosides) as well as non-polar (phosphatidylcholines, acylcarnitines, and fatty acids) compounds. The results of this study will provide critical knowledge regarding the selection of appropriate extraction and MS detection methods for the analysis of the diverse classes of human heart metabolites.

Protocol for a multicentre cross-sectional, longitudinal ambulatory clinical trial in rheumatoid arthritis and Parkinson's disease patients analysing the relation between the gut microbiome, fasting and immune status in Germany (ExpoBiome)

INTRODUCTION

Chronic inflammatory diseases like rheumatoid arthritis (RA) and neurodegenerative disorders like Parkinson's disease (PD) have recently been associated with a decreased diversity in the gut microbiome, emerging as key driver of various diseases. The specific interactions between gut-borne microorganisms and host pathophysiology remain largely unclear. The microbiome can be modulated by interventions comprising nutrition.The aim of our clinical study is to (1) examine effects of prolonged fasting (PF) and time-restricted eating (TRE) on the outcome parameters and the immunophenotypes of RA and PD with (2) special consideration of microbial taxa and molecules associated with changes expected in (1), and (3) identify factors impacting the disease course and treatment by in-depth screening of microorganisms and molecules in personalised HuMiX gut-on-chip models, to identify novel targets for anti-inflammatory therapy.

METHODS AND ANALYSIS

This trial is an open-label, multicentre, controlled clinical trial consisting of a cross-sectional and a longitudinal study. A total of 180 patients is recruited. For the cross-sectional study, 60 patients with PD, 60 patients with RA and 60 healthy controls are recruited at two different, specialised clinical sites. For the longitudinal part, 30 patients with PD and 30 patients with RA undergo 5-7 days of PF followed by TRE (16:8) for a period of 12 months. One baseline visit takes place before the PF intervention and 10 follow-up visits will follow over a period of 12 months (April 2021 to November 2023).

ETHICS AND DISSEMINATION

Ethical approval was obtained to plan and conduct the trial from the institutional review board of the Charité-Universitätsmedizin Berlin (EA1/204/19), the ethics committee of the state medical association (Landesärztekammer) of Hessen (2021-2230-zvBO) and the Ethics Review Panel (ERP) of the University of Luxembourg (ERP 21-001 A ExpoBiome). The results of this study will be disseminated through peer-reviewed publications, scientific presentations and social media.

TRIAL REGISTRATION NUMBER

NCT04847011.

Species phylogeny, ecology, and root traits as predictors of root exudate composition

Root traits including root exudates are key factors affecting plant interactions with soil and thus play an important role in determining ecosystem processes. The drivers of their variation, however, remain poorly understood. We determined the relative importance of phylogeny and species ecology in determining root traits and analyzed the extent to which root exudate composition can be predicted by other root traits. We measured different root morphological and biochemical traits (including exudate profiles) of 65 plant species grown in a controlled system. We tested phylogenetic conservatism in traits and disentangled the individual and overlapping effects of phylogeny and species ecology on traits. We also predicted root exudate composition using other root traits. Phylogenetic signal differed greatly among root traits, with the strongest signal in phenol content in plant tissues. Interspecific variation in root traits was partly explained by species ecology, but phylogeny was more important in most cases. Species exudate composition could be partly predicted by specific root length, root dry matter content, root biomass, and root diameter, but a large part of variation remained unexplained. In conclusion, root exudation cannot be easily predicted based on other root traits and more comparative data on root exudation are needed to understand their diversity.

Metabolic biomarkers in lung cancer screening and early diagnosis (Review)

Late diagnosis is one of the major contributing factors to the high mortality rate of lung cancer, which is now the leading cause of cancer-associated mortality worldwide. At present, low-dose CT (LDCT) screening in the high-risk population, in which lung cancer incidence is higher than that of the low-risk population is the predominant diagnostic strategy. Although this has efficiently reduced lung cancer mortality in large randomized trials, LDCT screening has high false-positive rates, resulting in excessive subsequent follow-up procedures and radiation exposure. Complementation of LDCT examination with biofluid-based biomarkers has been documented to increase efficacy, and this type of preliminary screening can potentially reduce potential radioactive damage to low-risk populations and the burden of hospital resources. Several molecular signatures based on components of the biofluid metabolome that can possibly discriminate patients with lung cancer from healthy individuals have been proposed over the past two decades. In the present review, advancements in currently available technologies in metabolomics were reviewed, with particular focus on their possible application in lung cancer screening and early detection.

Phosphorus and Serendipita indica synergism augments arsenic stress tolerance in rice by regulating secondary metabolism related enzymatic activity and root metabolic patterns

The multifarious problems created by arsenic (As), for collective environment and human health, serve a cogent case for searching integrative agricultural approaches to attain food security. Rice (Oryza sativa L.) acts as a sponge for heavy metal(loid)s accretion, specifically As, due to anaerobic flooded growth conditions facilitating its uptake. Acclaimed for their positive impact on plant growth, development and phosphorus (P) nutrition, 'mycorrhizas' are able to promote stress tolerance. Albeit, the metabolic alterations underlying Serendipita indica (S. indica; S.i) symbiosis-mediated amelioration of As stress along with nutritional management of P are still understudied. By using biochemical, RT-qPCR and LC-MS/MS based untargeted metabolomics approach, rice roots of ZZY-1 and GD-6 colonized by S. indica, which were later treated with As (10 µM) and P (50 µM), were compared with non-colonized roots under the same treatments with a set of control plants. The responses of secondary metabolism related enzymes, especially polyphenol oxidase (PPO) activities in the foliage of ZZY-1 and GD-6 were enhanced 8.5 and 12-fold, respectively, compared to their respective control counterparts. The current study identified 360 cationic and 287 anionic metabolites in rice roots, and the commonly enriched pathway annotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was biosynthesis of phenylalanine, tyrosine and tryptophan, which validated the results of biochemical and gene expression analyses associated with secondary metabolic enzymes. Particularly under As+S.i+P comparison, both genotypes exhibited an upregulation of key detoxification and defense related metabolites, including fumaric acid, L-malic acid, choline, 3,4-dihydroxybenzoic acid, to name a few. The results of this study provided the novel insights into the promising role of exogenous P and S. indica in alleviating As stress.

Amino acid analysis as a method of discovering biomarkers for diagnosis of diabetes and its complications

Diabetes mellitus (DM) is a severe chronic diseases with a global prevalence of 9%, leading to poor health and high health care costs, and is a direct cause of millions of deaths each year. The rising epidemic of diabetes and its complications, such as retinal and peripheral nerve disease, is a huge burden globally. A better understanding of the molecular pathways involved in the development and progression of diabetes and its complications can facilitate individualized prevention and treatment. High diabetes mellitus incidence rate is caused mainly by lack of non-invasive and reliable methods for early diagnosis, such as plasma biomarkers. The incidence of diabetes and its complications in the world still grows so it is crucial to develop a new, faster, high specificity and more sensitive diagnostic technologies. With the advancement of analytical techniques, metabolomics can identify and quantify multiple biomarkers simultaneously in a high-throughput manner, and effective biomarkers can greatly improve the efficiency of diabetes and its complications. By providing information on potential metabolic pathways, metabolomics can further define the mechanisms underlying the progression of diabetes and its complications, help identify potential therapeutic targets, and improve the prevention and management of T2D and its complications. The application of amino acid metabolomics in epidemiological studies has identified new biomarkers of diabetes mellitus (DM) and its complications, such as branched-chain amino acids, phenylalanine and arginine metabolites. This study focused on the analysis of metabolic amino acid profiling as a method for identifying biomarkers for the detection and screening of diabetes and its complications. The results presented are all from recent studies, and in all cases analyzed, there were significant changes in the amino acid profile of patients in the experimental group compared to the control group. This study demonstrates the potential of amino acid profiles as a detection method for diabetes and its complications.

Molecular, Metabolic, and Nutritional Changes after Metabolic Surgery in Obese Diabetic Patients (MoMen): A Protocol for a Multicenter Prospective Cohort Study

Metabolic surgery is an essential option in the treatment of obese patients with type 2 diabetes (T2D). Despite its known advantages, this surgery still needs to be introduced in Malaysia. In this prospective study, the pathophysiological mechanisms at the molecular level will be studied and the metabolomics pathways of diabetes remission will be explored. The present study aims to evaluate the changes in the anthropometric measurements, body composition, phase angle, diet intake, biochemistry parameters, adipokines, microRNA, and metabolomics, both pre- and post-surgery, among obese diabetic patients in Malaysia. This is a multicenter prospective cohort study that will involve obese patients (n = 102) with a body mass index (BMI) of ≥25 kg/m2 (Asian BMI categories: WHO/IASO/IOTF, 2000) who will undergo metabolic surgery. They will be categorized into three groups: non-diabetes, prediabetes, and diabetes. Their body composition will be measured using a bioimpedance analyzer (BIA). The phase angle (PhA) data will be analyzed. Venous blood will be collected from each patient for glycated hemoglobin (HbA1c), lipids, liver, renal profile, hormones, adipokines, and molecular and metabolomics analyses. The serum microRNA will be measured. A gene expression study of the adipose tissue of different groups will be conducted to compare the groups. The relationship between the 1HNMR-metabolic fingerprint and the patients’ lifestyles and dietary practices will be determined. The factors responsible for the excellent remission of T2D will be explored in this study.

Partial compression increases acidity, but decreases phenolics in jujube fruit: Evidence from targeted metabolomics

Jujube fruit (Ziziphus jujuba Mill.) is extremely susceptible to mechanical injury by extrusion and collision during storage, transportation and processing. In this study, we examined the morphology and endogenous metabolism of jujubes at three developmental stages after applying partial compression (PC) to mimic mechanical injury. Generally, PC did not affect the total soluble solids content, but increased the acidity and decreased the amount of phenolics in the jujube fruit. Targeted metabolomics analysis further confirmed that acid and phenolics content were differentially altered in response to PC. To our knowledge, this is the first study to characterize metabolic variations in ready-to-eat fruit that occur in response to physical damage. The results will provide insight into the understanding the consequences of mechanical injury on fruit nutrition and health benefits.

Acute effects of moderate vs. vigorous endurance exercise on urinary metabolites in healthy, young, physically active men-A multi-platform metabolomics approach

Introduction: Endurance exercise alters whole-body as well as skeletal muscle metabolism and physiology, leading to improvements in performance and health. However, biological mechanisms underlying the body's adaptations to different endurance exercise protocols are not entirely understood. Methods: We applied a multi-platform metabolomics approach to identify urinary metabolites and associated metabolic pathways that distinguish the acute metabolic response to two endurance exercise interventions at distinct intensities. In our randomized crossover study, 16 healthy, young, and physically active men performed 30 min of continuous moderate exercise (CME) and continuous vigorous exercise (CVE). Urine was collected during three post-exercise sampling phases (U01/U02/U03: until 45/105/195 min post-exercise), providing detailed temporal information on the response of the urinary metabolome to CME and CVE. Also, fasting spot urine samples were collected pre-exercise (U00) and on the following day (U04). While untargeted two-dimensional gas chromatography-mass spectrometry (GC×GC-MS) led to the detection of 608 spectral features, 44 metabolites were identified and quantified by targeted nuclear magnetic resonance (NMR) spectroscopy or liquid chromatography-mass spectrometry (LC-MS). Results: 104 urinary metabolites showed at least one significant difference for selected comparisons of sampling time points within or between exercise trials as well as a relevant median fold change >1.5 or <0. 6 ¯ (NMR, LC-MS) or >2.0 or <0.5 (GC×GC-MS), being classified as either exercise-responsive or intensity-dependent. Our findings indicate that CVE induced more profound alterations in the urinary metabolome than CME, especially at U01, returning to baseline within 24 h after U00. Most differences between exercise trials are likely to reflect higher energy requirements during CVE, as demonstrated by greater shifts in metabolites related to glycolysis (e.g., lactate, pyruvate), tricarboxylic acid cycle (e.g., cis-aconitate, malate), purine nucleotide breakdown (e.g., hypoxanthine), and amino acid mobilization (e.g., alanine) or degradation (e.g., 4-hydroxyphenylacetate). Discussion: To conclude, this study provided first evidence of specific urinary metabolites as potential metabolic markers of endurance exercise intensity. Future studies are needed to validate our results and to examine whether acute metabolite changes in urine might also be partly reflective of mechanisms underlying the health- or performance-enhancing effects of endurance exercise, particularly if performed at high intensities.

Creation of a Plant Metabolite Spectral Library for Untargeted and Targeted Metabolomics

Large-scale high throughput metabolomic technologies are indispensable components of systems biology in terms of discovering and defining the metabolite parts of the system. However, the lack of a plant metabolite spectral library limits the metabolite identification of plant metabolomic studies. Here, we have created a plant metabolite spectral library using 544 authentic standards, which increased the efficiency of identification for untargeted metabolomic studies. The process of creating the spectral library was described, and the mzVault library was deposited in the public repository for free download. Furthermore, based on the spectral library, we describe a process of creating a pseudo-targeted method, which was applied to a proof-of-concept study of Arabidopsis leaf extracts. As authentic standards become available, more metabolite spectra can be easily incorporated into the spectral library to improve the mzVault package.

Normalization Approach by a Reference Material to Improve LC-MS-Based Metabolomic Data Comparability of Multibatch Samples

Large cohorts of samples from multiple batches are usually required for global metabolomic studies to characterize the metabolic state of human disease. As such, it is critical to eliminate systematic variation and truly reveal the biologically associated alterations. In this study, we proposed a reference material-based approach (Ref-M) for data correction by liquid chromatography-mass spectrometry and represented by an analysis of multibatch human serum samples. The reference material was generated by mixing serum from healthy donors and distributed to each extraction batch of subject samples. Pooled quality control samples and isotopic internal standards were then applied in each acquisition batch for data quality control. Finally, each metabolite in subject samples was normalized by its counterpart in the reference serum. We demonstrated that Ref-M significantly enhanced the numbers of efficient features and effectively eliminated the batch variation of 522 serum samples of healthy individuals, benign pulmonary nodules, and lung cancer patients. Twenty differential metabolites were identified to distinguish lung cancer from healthy controls in the training set. The discriminant model was validated in an independent data set with an area under the receiver operating characteristics (ROC) curve (AUC) of 0.853. Another 40 serum samples further tested with Ref-M were achieved an AUC of 0.843 by the established model. Our results showed that the reference material-based approach presents the potential to improve the data comparability and precision for biomarker discovery in large-scale metabolomic studies.

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.

Mass spectrometry-based strategies for single-cell metabolomics

Single cell analysis has drawn increasing interest from the research community due to its capability to interrogate cellular heterogeneity, allowing refined tissue classification and facilitating novel biomarker discovery. With the advancement of relevant instruments and techniques, it is now possible to perform multiple omics including genomics, transcriptomics, metabolomics or even proteomics at single cell level. In comparison with other omics studies, single-cell metabolomics (SCM) represents a significant challenge since it involves many types of dynamically changing compounds with a wide range of concentrations. In addition, metabolites cannot be amplified. Although difficult, considerable progress has been made over the past decade in mass spectrometry (MS)-based SCM in terms of processing technologies and biochemical applications. In this review, we will summarize recent progress in the development of promising MS platforms, sample preparation methods and SCM analysis of various cell types (including plant cell, cancer cell, neuron, embryo cell, and yeast cell). Current limitations and future research directions in the field of SCM will also be discussed.

Microbiome and Metabolome Insights into the Role of the Gastrointestinal-Brain Axis in Parkinson's and Alzheimer's Disease: Unveiling Potential Therapeutic Targets

Neurodegenerative diseases such as Parkinson's (PD) and Alzheimer's disease (AD), the prevalence of which is rapidly rising due to an aging world population and westernization of lifestyles, are expected to put a strong socioeconomic burden on health systems worldwide. Clinical trials of therapies against PD and AD have only shown limited success so far. Therefore, research has extended its scope to a systems medicine point of view, with a particular focus on the gastrointestinal-brain axis as a potential main actor in disease development and progression. Microbiome and metabolome studies have already revealed important insights into disease mechanisms. Both the microbiome and metabolome can be easily manipulated by dietary and lifestyle interventions, and might thus offer novel, readily available therapeutic options to prevent the onset as well as the progression of PD and AD. This review summarizes our current knowledge on the interplay between microbiota, metabolites, and neurodegeneration along the gastrointestinal-brain axis. We further illustrate state-of-the art methods of microbiome and metabolome research as well as metabolic modeling that facilitate the identification of disease pathomechanisms. We conclude with therapeutic options to modulate microbiome composition to prevent or delay neurodegeneration and illustrate potential future research directions to fight PD and AD.

LC-TOF-MS/MS and GC-MS based phytochemical profiling and evaluation of wound healing activity of Oroxylum Indicum (L.) Kurz (Beka)

Background: Beka (Oroxylum indicum (L.) Kurz) has been used as a culinary herb and natural remedy by the local communities in Malaysia. The leaf of O. indicum is traditionally used for the treatment of diarrhea, high blood pressure, and improving digestive health. Objectives: The present study was conducted to evaluate the phytochemical constituents and wound healing properties (in vitro and in vivo models) of aqueous and ethanol extracts of O. indicum leaves. Methods: The total phenolic (TPC) and total flavonoid (TFC) contents in the plant extracts were determined by the spectrophotometric methods. Further, the extract was characterized by Liquid Chromatography Time-of-Flight Mass Spectrometry (LC-TOF-MS/MS) and Gas Chromatography-Mass Spectrometry (GC-MS). The wound healing activity was assessed using the in vitro scratch wound-healing assay and in vivo excisional wound model. Results: The results show the ethanol leaves extract had the higher TPC (164 mg GAE/g) when compared with the aqueous leaves extract (30 mg gallic acid equivalents/g). The ethanol leaves extract was also found to have higher TFC (101 mg Catechin equivalents/g) than the aqueous leaves extract (76 mg Catechin equivalents/g). The ethanol leaves extract was then used for further chemical analysis. The LC-TOF-MS/MS analysis showed that the leaves extracts of O. indicum contains many important compounds such as Orientin, Chrysin, Pinoquercetin, Cupressuflavone, Puerarin xyloside, Forsythiaside and Paederoside. In GC-MS analysis, 19 compounds were identified in ethanolic leaves extract. The wound healing studies shows that O. indicum has promising wound healing activity by increasing the rate of wound contraction significantly (p < 0.05). Conclusion: In conclusion, the present study showed that O. indicum leaf contains important phytochemicals and the wound healing potential of the O. indicum extract may probably be as a result of the presence of various phytoconstituents.

Metabolome profiling in the study of aging processes

Aging of a living organism is closely related to systemic metabolic changes. But due to the multilevel and network nature of metabolic pathways, it is difficult to understand these connections. Today, this problem is solved using one of the main approaches of metabolomics — untargeted metabolome profiling. The purpose of this publication is to systematize the results of metabolomic studies based on such profiling, both in animal models and in humans.

Metabolome Profiling in Aging Studies

Organism aging is closely related to systemic metabolic changes. However, due to the multilevel and network nature of metabolic pathways, it is difficult to understand these connections. Today, scientists are trying to solve this problem using one of the main approaches of metabolomics-untargeted metabolome profiling. The purpose of this publication is to review metabolomic studies based on such profiling, both in animal models and in humans. This review describes metabolites that vary significantly across age groups and include carbohydrates, amino acids, carnitines, biogenic amines, and lipids. Metabolic pathways associated with the aging process are also shown, including those associated with amino acid, lipid, and energy metabolism. The presented data reveal the mechanisms of aging and can be used as a basis for monitoring biological age and predicting age-related diseases in the early stages of their development.

Bioactive Compounds from Marine Sponges and Algae: Effects on Cancer Cell Metabolome and Chemical Structures

Metabolomics represent the set of small organic molecules generally called metabolites, which are located within cells, tissues or organisms. This new “omic” technology, together with other similar technologies (genomics, transcriptomics and proteomics) is becoming a widely used tool in cancer research, aiming at the understanding of global biology systems in their physiologic or altered conditions. Cancer is among the most alarming human diseases and it causes a considerable number of deaths each year. Cancer research is one of the most important fields in life sciences. In fact, several scientific advances have been made in recent years, aiming to illuminate the metabolism of cancer cells, which is different from that of healthy cells, as suggested by Otto Warburg in the 1950s. Studies on sponges and algae revealed that these organisms are the main sources of the marine bioactive compounds involved in drug discovery for cancer treatment and prevention. In this review, we analyzed these two promising groups of marine organisms to focus on new metabolomics approaches for the study of metabolic changes in cancer cell lines treated with chemical extracts from sponges and algae, and for the classification of the chemical structures of bioactive compounds that may potentially prove useful for specific biotechnological applications.

Extraction of High-Value Chemicals from Plants for Technical and Medical Applications

Plants produce a variety of high-value chemicals (e.g., secondary metabolites) which have a plethora of biological activities, which may be utilised in many facets of industry (e.g., agrisciences, cosmetics, drugs, neutraceuticals, household products, etc.). Exposure to various different environments, as well as their treatment (e.g., exposure to chemicals), can influence the chemical makeup of these plants and, in turn, which chemicals will be prevalent within them. Essential oils (EOs) usually have complex compositions (>300 organic compounds, e.g., alkaloids, flavonoids, phenolic acids, saponins and terpenes) and are obtained from botanically defined plant raw materials by dry/steam distillation or a suitable mechanical process (without heating). In certain cases, an antioxidant may be added to the EO (EOs are produced by more than 17,500 species of plants, but only ca. 250 EOs are commercially available). The interesting bioactivity of the chemicals produced by plants renders them high in value, motivating investment in their production, extraction and analysis. Traditional methods for effectively extracting plant-derived biomolecules include cold pressing and hydro/steam distillation; newer methods include solvent/Soxhlet extractions and sustainable processes that reduce waste, decrease processing times and deliver competitive yields, examples of which include microwave-assisted extraction (MAE), ultrasound-assisted extraction (UAE), subcritical water extraction (SWE) and supercritical CO2 extraction (scCO2). Once extracted, analytical techniques such as chromatography and mass spectrometry may be used to analyse the contents of the high-value extracts within a given feedstock. The bioactive components, which can be used in a variety of formulations and products (e.g., displaying anti-aging, antibacterial, anticancer, anti-depressive, antifungal, anti-inflammatory, antioxidant, antiparasitic, antiviral and anti-stress properties), are biorenewable high-value chemicals.

Antibacterial Activity from Momordica charantia L. Leaves and Flavones Enriched Phase

Momordica charantia L. (Cucurbitaceae) is a plant known in Brazil as “melão de São Caetano”, which has been related to many therapeutic applications in folk medicine. Herein, we describe antibacterial activities and related metabolites for an extract and fractions obtained from the leaves of that species. An ethanolic extract and its three fractions were used to perform in vitro antibacterial assays. In addition, liquid chromatography coupled to mass spectrometry and the molecular networking approach were used for the metabolite annotation process. Overall, 25 compounds were annotated in the ethanolic extract from M. charantia leaves, including flavones, terpenes, organic acids, and inositol pyrophosphate derivatives. The ethanolic extract exhibited low activity against Proteus mirabilis (MIC 312.5 µg·mL⁻¹) and Klebsiella pneumoniae (MIC 625 µg·mL⁻¹). The ethyl acetate phase showed interesting antibacterial activity (MIC 156.2 µg·mL⁻¹) against Klebsiella pneumoniae, and it was well justified by the high content of glycosylated flavones. Therefore, based on the ethyl acetate phase antibacterial result, we suggest that M. charantia leaves could be considered as an alternative antibacterial source against K. pneumoniae and can serve as a pillar for future studies as well as pharmacological application against the bacteria.

Metabolomics of Non-Saccharomyces Yeasts in Fermented Beverages

Fermented beverages have been consumed for millennia and today support a global industry producing diverse products. Saccharomyces yeasts currently dominate the fermented beverage industry, but consumer demands for alternative products with a variety of sensory profiles and actual or perceived health benefits are driving the diversification and use of non-Saccharomyces yeasts. The diversity of flavours, aromas, and other sensory characteristics that can be obtained by using non-Saccharomyces yeasts in fermentation is, in large part, due to the diverse secondary metabolites they produce compared to conventional Saccharomyces yeast. Here, we review the use of metabolomic analyses of non-Saccharomyces yeasts to explore their impact on the sensory characteristics of fermented beverages. We highlight several key species currently used in the industry, including Brettanomyces, Torulaspora, Lachancea, and Saccharomycodes, and emphasize the future potential for the use of non-Saccharomyces yeasts in the production of diverse fermented beverages.

Ecotype Division and Chemical Diversity of Cynomorium songaricum from Different Geographical Regions

Cynomorium songaricum is an important endangered plant with significant medicinal and edible values. However, the lack of resources and quality variation have limited the comprehensive developments and sustainable utilization of C. songaricum. Here, we evaluated the chemical and genetic traits of C. songaricum from the highly suitable habitat regions simulated with species distribution models. The PCA and NJ tree analyses displayed intraspecific variation in C. songaricum, which could be divided into two ecotypes: ecotype I and ecotype II. Furthermore, the LC-MS/MS-based metabolomic was used to identify and analyze the metabolites of two ecotypes. The results indicated that a total of 589 compounds were detected, 236 of which were significantly different between the two ecotypes. Specifically, the relative content and the kind of flavonoids were more abundant in ecotype I, which were closely associated with the medicinal activities. In contrast, amino acids and organic acids were more enriched in ecotype II, which may provide better nutritional quality and unique flavor. In summary, our findings demonstrate the ecotype division and chemical diversity of C. songaricum in China from different geographical regions and provide a reference for the development of germplasm and directed plant breeding of endangered medicinal plants.

The ratio of nicotinic acid to nicotinamide as a microbial biomarker for assessing cell therapy product sterility

Controlling microbial risks in cell therapy products (CTPs) is important for product safety. Here, we identified the nicotinic acid (NA) to nicotinamide (NAM) ratio as a biomarker that detects a broad spectrum of microbial contaminants in cell cultures. We separately added six different bacterial species into mesenchymal stromal cell and T cell culture and found that NA was uniquely present in these bacteria-contaminated CTPs due to the conversion from NAM by microbial nicotinamidases, which mammals lack. In cells inoculated with 1 × 10⁴ CFUs/mL of different microorganisms, including USP <71> defined organisms, the increase in NA to NAM ratio ranged from 72 to 15,000 times higher than the uncontaminated controls after 24 h. Importantly, only live microorganisms caused increases in this ratio. In cells inoculated with 18 CFUs/mL of Escherichia coli, 20 CFUs/mL of Bacillus subtilis, and 10 CFUs/mL of Candida albicans, significant increase of NA to NAM ratio was detected using LC-MS after 18.5, 12.5, and 24.5 h, respectively. In contrast, compendial sterility test required >24 h to detect the same amount of these three organisms. In conclusion, the NA to NAM ratio is a useful biomarker for detection of early-stage microbial contaminations in CTPs.

MAFFIN: Metabolomics Sample Normalization Using Maximal Density Fold Change with High-Quality Metabolic Features and Corrected Signal Intensities

MOTIVATION

Post-acquisition sample normalization is a critical step in comparative metabolomics to remove the variation introduced by sample amount or concentration difference. Previously reported approaches are either specific to one sample type or built on strong assumptions on data structure, which are limited to certain levels. This encouraged us to develop MAFFIN, an accurate and robust post-acquisition sample normalization workflow that works universally for metabolomics data collected on mass spectrometry (MS) platforms.

RESULTS

MAFFIN calculates normalization factors using maximal density fold change (MDFC) computed by a kernel density-based approach. Using both simulated data and 20 metabolomics data sets, we showcased that MDFC outperforms four commonly used normalization methods in terms of reducing the intragroup variation among samples. Two essential steps, overlooked in conventional methods, were also examined and incorporated into MAFFIN. (1) MAFFIN uses multiple orthogonal criteria to select high-quality features for normalization factor calculation, which minimizes the bias caused by abiotic features or metabolites with poor quantitative performance. (2) MAFFIN corrects the MS signal intensities of high-quality features using serial quality control (QC) samples, which guarantees the accuracy of fold change calculations. MAFFIN was applied to a human saliva metabolomics study and led to better data separation in principal component analysis (PCA) and more confirmed significantly altered metabolites.

AVAILABILITY AND IMPLEMENTATION

The MAFFIN algorithm was implemented in an R package named MAFFIN. Package installation, user instruction, and demo data are available at https://github.com/HuanLab/MAFFIN.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Challenges in Metabolomics-Based Tests, Biomarkers Revealed by Metabolomic Analysis, and the Promise of the Application of Metabolomics in Precision Medicine

Metabolomics helps identify metabolites to characterize/refine perturbations of biological pathways in living organisms. Pre-analytical, analytical, and post-analytical limitations that have hampered a wide implementation of metabolomics have been addressed. Several potential biomarkers originating from current targeted metabolomics-based approaches have been discovered. Precision medicine argues for algorithms to classify individuals based on susceptibility to disease, and/or by response to specific treatments. It also argues for a prevention-based health system. Because of its ability to explore gene-environment interactions, metabolomics is expected to be critical to personalize diagnosis and treatment. Stringent guidelines have been applied from the very beginning to design studies to acquire the information currently employed in precision medicine and precision prevention approaches. Large, prospective, expensive and time-consuming studies are now mandatory to validate old, and discover new, metabolomics-based biomarkers with high chances of translation into precision medicine. Metabolites from studies on saliva, sweat, breath, semen, feces, amniotic, cerebrospinal, and broncho-alveolar fluid are predicted to be needed to refine information from plasma and serum metabolome. In addition, a multi-omics data analysis system is predicted to be needed for omics-based precision medicine approaches. Omics-based approaches for the progress of precision medicine and prevention are expected to raise ethical issues.

A Review of Metabolomic Profiling in Rheumatoid Arthritis: Bringing New Insights in Disease Pathogenesis, Treatment and Comorbidities

Metabolomic analysis provides a wealth of information that can be predictive of distinctive phenotypes of pathogenic processes and has been applied to better understand disease development. Rheumatoid arthritis (RA) is an autoimmune disease with the establishment of chronic synovial inflammation that affects joints and peripheral tissues such as skeletal muscle and bone. There is a lack of useful disease biomarkers to track disease activity, drug response and follow-up in RA. In this review, we describe potential metabolic biomarkers that might be helpful in the study of RA pathogenesis, drug response and risk of comorbidities. TMAO (choline and trimethylamine oxide) and TCA (tricarboxylic acid) cycle products have been suggested to modulate metabolic profiles during the early stages of RA and are present systemically, which is a relevant characteristic for biomarkers. Moreover, the analysis of lipids such as cholesterol, FFAs and PUFAs may provide important information before disease onset to predict disease activity and treatment response. Regarding therapeutics, TNF inhibitors may increase the levels of tryptophan, valine, lysine, creatinine and alanine, whereas JAK/STAT inhibitors may modulate exclusively fatty acids. These observations indicate that different disease modifying antirheumatic drugs have specific metabolic profiles and can reveal differences between responders and non-responders. In terms of comorbidities, physical impairment represented by higher fatigue scores and muscle wasting has been associated with an increase in urea cycle, FFAs, tocopherols and BCAAs. In conclusion, synovial fluid, blood and urine samples from RA patients seem to provide critical information about the metabolic profile related to drug response, disease activity and comorbidities.

Untargeted Metabolomics Coupled with Chemometrics for Leaves and Stem Barks of Dioecious Morus alba L

The differences in metabolites in male and female individuals of dioecious Morus alba L. (Moraceae) are usually ignored and lack study. In the present study, 58 leaves and 61 stem barks from male and female individuals were analyzed by untargeted metabolomics via headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) coupled with chemometrics, including principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA). A total of 66 and 44 metabolites were identified from leaves and stem barks, respectively. Four and eight differential metabolites among candidate metabolites in leaves and stem barks from male and female individuals were identified. Moreover, females possessed stronger antioxidant activity than males. This is the first report where untargeted metabolomics coupled with chemometrics was used to analyze the different metabolites and to discriminate the gender of leaves and stem barks of dioecious M. alba. It provided the basis for further study of M. alba and reference value for researching dioecious plants.

Metabolomics: Paving the path for personalized periodontics – A literature review

The pathogenesis of periodontal disease is governed by a multitude of factors ranging from the macroscopic to the microscopic scale. Among the factors that constitute the etiological agents of the disease, a major element is the role played by the body's metabolome–i.e., the complete collection of microscopic molecules and metabolic products of cells and tissues in the body. Being of a regulatory nature, the interplay of these molecules exerts a considerable effect on the development as well as the progression of disease, which differs in each individual based on their phenotype. Exploring this connection and application into the field of diagnostic as well as prediction of risk for periodontitis will ultimately result in a personalized standard of care for patients in the future.

Recent Developments in Metabolomics Studies of Endophytic Fungi

Endophytic fungi are microorganisms that colonize living plants' tissues without causing any harm. They are known as a natural source of bioactive metabolites with diverse pharmacological functions. Many structurally different chemical metabolites were isolated from endophytic fungi. Recently, the increasing trends in human health problems and diseases have escalated the search for bioactive metabolites from endophytic fungi. The conventional bioassay-guided study is known as laborious due to chemical complexity. Thus, metabolomics studies have attracted extensive research interest owing to their potential in dealing with a vast number of metabolites. Metabolomics coupled with advanced analytical tools provides a comprehensive insight into systems biology. Despite its wide scientific attention, endophytic fungi metabolomics are relatively unexploited. This review highlights the recent developments in metabolomics studies of endophytic fungi in obtaining the global metabolites picture.

Streptozotocin-induced hyperglycemia alters the cecal metabolome and exacerbates antibiotic-induced dysbiosis

It is well established in the microbiome field that antibiotic (ATB) use and metabolic disease both impact the structure and function of the gut microbiome. But how host and microbial metabolism interacts with ATB susceptibility to affect the resulting dysbiosis remains poorly understood. In a streptozotocin-induced model of hyperglycemia (HG), we use a combined metagenomic, metatranscriptomic, and metabolomic approach to profile changes in microbiome taxonomic composition, transcriptional activity, and metabolite abundance both pre- and post-ATB challenge. We find that HG impacts both microbiome structure and metabolism, ultimately increasing susceptibility to amoxicillin. HG exacerbates drug-induced dysbiosis and increases both phosphotransferase system activity and energy catabolism compared to controls. Finally, HG and ATB co-treatment increases pathogen susceptibility and reduces survival in a Salmonella enterica infection model. Our data demonstrate that induced HG is sufficient to modify the cecal metabolite pool, worsen the severity of ATB dysbiosis, and decrease colonization resistance.

Review on in vivo profiling of drug metabolites with LC-MS/MS in the past decade

Metabolite profiling is an indispensable part of drug discovery and development, enabling a comprehensive understanding of the drug's metabolic behavior. Liquid chromatography-mass spectrometry facilitates metabolite profiling by reducing sample complexity and providing high sensitivity. This review discusses the in vivo metabolite profiling involving LC-MS/MS and the utilization of QTOF, QQQ mass analyzers with a particular emphasis on a mass filter. Further, a summary of sample extraction procedures in biological matrices such as plasma, urine, feces, serum and hair as in vivo samples are outlined. toward the end, we present 15 case studies in biological matrices and their LC-MS/MS conditions to understand the metabolic disposition.

Accurate Identification of Bioactive Meliaceae Limonoids by UHPLC-MS/MS Based Structure-Fragment Relationships (SFRs)

Limonoids are bioactive plant specialized metabolites found in the Meliaceae family. The basic limonoids, i.e., azadiradione, epoxyazadiradione, and gedunin have been exploited for various bioactivities and therefore are the potential drug leads for tomorrow. However, their low abundance, structural similarity, and lack of adequate mass fragmentation data have hampered their accurate identification and quantification from various sources. In the present study, basic limonoids such as azadirone, azadiradione, epoxyazadiradione, and gedunin isolated from Neem were utilized for the synthesis of their derivatives and isotopologs. A total of 30 one compounds were used in this study among which five were isolated, two were biotransformed, and 24 were synthesized. Among the synthesized compounds nine are novel compounds including six deuterated analogs/isotopologs which are (1,3-²H)-1,2-dihydro-3β-hydroxyazadiradione (9), (1,3,16-²H)-1,2-dihydro-3β-16β-dihydroxyazadiradione (10), 3β-hydroxyazadiradione (11), 3β-16β-dihydroxyazadiradione (12), (3-²H)-3β-hydroxyazadiradione (13), (3,16-²H)-3β-16β-dihydroxyazadiradione (14), (1,3,7-²H)-1,2-dihydro-3β-hydroxy-7-deacetylazadiradione (15), 1,2,20,21,22,23-hexahydroazadiradione (17), and (1,3-²H)-1,2-dihydro-3β-hydroxygedunin (29). These limonoids along with their semisynthesized derivatives were subjected to ultra high performance liquid chromatography mass spectrometry (UHPLC-MS/MS) and the fragmentation pathway was established based on structure-fragment relationships (SFRs), utilizing high resolution MS/MS data. We have developed a most reliable and easily reproducible protocol describing in depth analysis of SFRs based on the structural modifications and synthesis of isotopologs. Also, the MS/MS fragment library of these basic limonoids generated in this study acts as a fingerprint for accurate identification and quantification of limonoids by MS/MS analysis in various plant tissue extracts, phytopharmaceutical formulations and biological samples.

Taste compounds, affecting factors, and methods used to evaluate chicken soup: A review

The taste of chicken soup is dependent upon various taste substances and human senses. More than 300 nonvolatile compounds reportedly exist in chicken/chicken soup. The primary purpose of this review was to elaborate on the prominent taste substances, the taste evaluation methods, and the factors affecting the taste of chicken soup. Most taste-active compounds with taste descriptions and thresholds in chicken soup were summarized. The application of sensory evaluation, liquid chromatography, electronic tongue, and other evaluation methods in chicken soup taste analysis were elaborated. The effects of genetic constitution, preslaughter, processing, and storage on chicken soup taste had been discussed. Nucleotides (especially inosine 5'-monophosphate), amino acids and their derivatives, organic acids, sugars, and peptides play a vital role in the taste attributes of chicken soup. Combining of liquid chromatography and mass spectrometry enables qualitative and quantitative analysis of taste-active compounds in chicken soup, aiding the exploration of key taste-active compounds. The electronic tongue application helps the overall taste perception of the soluble taste-active compounds present in chicken soup samples. Postmortem aging and stewing for a prolonged duration are effective techniques for improving the taste quality of chicken soup. The washing of preprocessing, the cooking temperature of processing, and the storage conditions also exert a significant impact on the taste of chicken soup.

The potential for mitigation of methane emissions in ruminants through the application of metagenomics, metabolomics, and other -OMICS technologies

Ruminant supply chains contribute 5.7 gigatons of CO_2-eq per annum, which represents approximately 80% of the livestock sector emissions. One of the largest sources of emission in the ruminant sector is methane (CH₄), accounting for approximately 40% of the sectors total emissions. With climate change being a growing concern, emphasis is being put on reducing greenhouse gas emissions, including those from ruminant production. Various genetic and environmental factors influence cattle CH₄ production, such as breed, genetic makeup, diet, management practices, and physiological status of the host. The influence of genetic variability on CH₄ yield in ruminants indicates that genomic selection for reduced CH₄ emissions is possible. Although the microbiology of CH₄ production has been studied, further research is needed to identify key differences in the host and microbiome genomes and how they interact with one another. The advancement of “-omics” technologies, such as metabolomics and metagenomics, may provide valuable information in this regard. Improved understanding of genetic mechanisms associated with CH₄ production and the interaction between the microbiome profile and host genetics will increase the rate of genetic progress for reduced CH₄ emissions. Through a systems biology approach, various “-omics” technologies can be combined to unravel genomic regions and genetic markers associated with CH₄ production, which can then be used in selective breeding programs. This comprehensive review discusses current challenges in applying genomic selection for reduced CH₄ emissions, and the potential for “-omics” technologies, especially metabolomics and metagenomics, to minimize such challenges. The integration and evaluation of different levels of biological information using a systems biology approach is also discussed, which can assist in understanding the underlying genetic mechanisms and biology of CH₄ production traits in ruminants and aid in reducing agriculture’s overall environmental footprint.

Chromatographic methods coupled to mass spectrometry for the determination of oncometabolites in biological samples-A review

It is now well-established that dysregulation of the tricarboxylic acid (TCA) cycle enzymes succinate dehydrogenase, fumarate hydratase, and isocitrate dehydrogenase leads to the abnormal cellular accumulation of succinate, fumarate, and 2-hydroxyglutarate, respectively, which contribute to the formation and malignant progression of numerous types of cancers. Thus, these metabolites, called oncometabolites, could potentially be useful as tumour-specific biomarkers and as therapeutic targets. For this reason, the development of analytical methodologies for the accurate identification and determination of their levels in biological matrices is an important task in the field of cancer research. Currently, hyphenated gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) techniques are the most powerful analytical tools in what concerns high sensitivity and selectivity to achieve such difficult task. In this review, we first provide a brief description of the biological formation of oncometabolites and their oncogenic properties, and then we present an overview and critical assessment of the GC-MS and LC-MS based analytical approaches that are reported in the literature for the determination of oncometabolites in biological samples, such as biofluids, cells, and tissues. Advantages and drawbacks of these approaches will be comparatively discussed. We believe that the present review represents the first attempt to summarize the applications of these hyphenated techniques in the context of oncometabolite analysis, which may be useful to new and existing researchers in this field.

Gut Microbiota in Adipose Tissue Dysfunction Induced Cardiovascular Disease: Role as a Metabolic Organ

The gut microbiome has emerged as a key regulator of host metabolism. Accumulating evidence has indicated that the gut microbiota is involved in the development of various human diseases. This association relies on the structure and metabolites of the gut microbiota. The gut microbiota metabolizes the diet ingested by the host into a series of metabolites, including short chain fatty acids, secondary bile acids, trimethylamine N-oxide, and branched-chain amino acids, which affects the physiological processes of the host by activating numerous signaling pathways. In this review, we first summarize the various mechanisms through which the gut microbiota influences adipose tissue dysfunction and metabolic processes that subsequently cause cardiovascular diseases, highlighting the complex interactions between gut microbes, their metabolites, and the metabolic activity of the host. Furthermore, we investigated the current status of clinical therapies for adipose tissue dysfunction directed at the gut microbiota. Finally, we discuss the challenges that remain to be addressed before this field of research can be translated to everyday clinical practice.

Discovery of donor age markers from bloodstain by LC-MS/MS using a metabolic approach

Bloodstains are frequently encountered at crime scenes and they provide important evidence about the incident, such as information about the victim or suspect and the time of death or other events. Efforts have been made to identify the age of the bloodstain's donor through genomic approaches, but there are some limitations, such as the availability of databases and the quality dependence of DNA. There is a need for the development of a tool that can obtain information at once from a small blood sample. The aim of this study is to identify bloodstain metabolite candidates that can be used to determine donor age. We prepared bloodstain samples and analyzed metabolites using high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS). Eighteen molecular features (MFs) were selected as candidates using volcano plots and multivariate analysis. Based on the MS/MS spectrum of the MFs, the following nine metabolites were identified from the METaboliteLINk database: Δ2-cis eicosenoic acid, ergothioneine, adenosine 5'-monophosphate, benzaldehyde, phenacylamine, myristic acid ethyl ester, p-coumaric acid, niacinamide, and N-arachidonoyl-L-alanine. These nine age markers at high or low abundances could be used to estimate the age of a bloodstain's donor. This study was the first to develop metabolite age markers that can be used to analyze crime scene bloodstains.

Emerging application of metabolomics on Chinese herbal medicine for depressive disorder

Depressive disorder is a kind of emotional disorder that is mainly manifested with spontaneous and persistent low mood. Its etiology is complex and still not fully understood. Metabolomics, an important part of system biology characterized by its integrity and systematicness, analyzes endogenous metabolites of small molecules in vivo and examines the metabolic status of the organism. It is widely used in the field of disease research for its unique advantage in the disease molecular marker discovering Due to fewer adverse reactions and high safety, Chinese herbal medicine (CHM) has great advantages in the treatment of chronic diseases including depression. Metabolomics has been gradually applied to the efficacy evaluation of CHM in treatment of depression and the metabolomics analysis exhibits a systemic metabolic shift in amino acids (such as alanine, glutamic acid, valine, etc.), organic acids (lactic acid, citric acid, stearic acid, palmitic acid, etc.), and sugars, amines, etc. These differential metabolites are mainly involved in energy metabolism, amino acid metabolism, lipid metabolism, etc. In this review, we have exemplified the study of CHM in animals or clinics on the depression, and revealed that CHM treatment has significantly changed the metabolic disorders associated with depression, promoting metabolic network reorganization through restoring of key metabolites, and metabolic pathways, which may be the main mechanism basis of CHM's treatment on depression. Besides, we further envisioned the future application of metabolomics in the study of CHM treatment of depression.

Potential Metabolic Biomarkers in Adult Asthmatics

Asthma is the most common chronic airway inflammation, with multiple phenotypes caused by complicated interactions of genetic, epigenetic, and environmental factors. To date, various determinants have been suggested for asthma pathogenesis by a new technology termed omics, including genomics, transcriptomics, proteomics, and metabolomics. In particular, the systematic analysis of all metabolites in a biological system, such as carbohydrates, amino acids, and lipids, has helped identify a novel pathway related to complex diseases. These metabolites are involved in the regulation of hypermethylation, response to hypoxia, and immune reactions in the pathogenesis of asthma. Among them, lipid metabolism has been suggested to be related to lung dysfunction in mild-to-moderate asthma. Sphingolipid metabolites are an important mediator contributing to airway inflammation in obese asthma and aspirin-exacerbated respiratory disease. Although how these molecular variants impact the disease has not been completely determined, identification of new causative factors may possibly lead to more-personalized and precise pathway-specific approaches for better diagnosis and treatment of asthma. In this review, perspectives of metabolites related to asthma and clinical implications have been highlighted according to various phenotypes.

Metabolomics in Prenatal Medicine: A Review

Pregnancy is a complicated and insidious state with various aspects to consider, including the well-being of the mother and child. Developing better non-invasive tests that cover a broader range of disorders with lower false-positive rates is a fundamental necessity in the prenatal medicine field, and, in this sense, the application of metabolomics could be extremely useful. Metabolomics measures and analyses the products of cellular biochemistry. As a biomarker discovery tool, the integrated holistic approach of metabolomics can yield new diagnostic or therapeutic approaches. In this review, we identify and summarize prenatal metabolomics studies and identify themes and controversies. We conducted a comprehensive search of PubMed and Google Scholar for all publications through January 2020 using combinations of the following keywords: nuclear magnetic resonance, mass spectrometry, metabolic profiling, prenatal diagnosis, pregnancy, chromosomal or aneuploidy, pre-eclampsia, fetal growth restriction, pre-term labor, and congenital defect. Metabolite detection with high throughput systems aided by advanced bioinformatics and network analysis allowed for the identification of new potential prenatal biomarkers and therapeutic targets. We took into consideration the scientific papers issued between the years 2000-2020, thus observing that the larger number of them were mainly published in the last 10 years. Initial small metabolomics studies in perinatology suggest that previously unidentified biochemical pathways and predictive biomarkers may be clinically useful. Although the scientific community is considering metabolomics with increasing attention for the study of prenatal medicine as well, more in-depth studies would be useful in order to advance toward the clinic world as the obtained results appear to be still preliminary. Employing metabolomics approaches to understand fetal and perinatal pathophysiology requires further research with larger sample sizes and rigorous testing of pilot studies using various omics and traditional hypothesis-driven experimental approaches.