Current and Future Perspectives on the Structural Identification of Small Molecules in Biological Systems

Implementation of multiomic mass spectrometry approaches for the evaluation of human health following environmental exposure

Omics analyses collectively refer to the possibility of profiling genetic variants, RNA, epigenetic markers, proteins, lipids, and metabolites. The most common analytical approaches used for detecting molecules present within biofluids related to metabolism are vibrational spectroscopy techniques, represented by infrared, Raman, and nuclear magnetic resonance (NMR) spectroscopies and mass spectrometry (MS). Omics-based assessments utilizing MS are rapidly expanding and being applied to various scientific disciplines and clinical settings. Most of the omics instruments are operated by specialists in dedicated laboratories; however, the development of miniature portable omics has made the technology more available to users for field applications. Variations in molecular information gained from omics approaches are useful for evaluating human health following environmental exposure and the development and progression of numerous diseases. As MS technology develops so do statistical and machine learning methods for the detection of molecular deviations from personalized metabolism, which are correlated to altered health conditions, and they are intended to provide a multi-disciplinary overview for researchers interested in adding multiomic analysis to their current efforts. This includes an introduction to mass spectrometry-based omics technologies, current state-of-the-art capabilities and their respective strengths and limitations for surveying molecular information. Furthermore, we describe how knowledge gained from these assessments can be applied to personalized medicine and diagnostic strategies.

New sensitive tools to characterize meta-metabolome response to short- and long-term cobalt exposure in dynamic river biofilm communities

Untargeted metabolomics is a non-a priori analysis of biomolecules that characterizes the metabolome variations induced by short- and long-term exposures to stressors. Even if the metabolite annotation remains lacunar due to database gaps, the global metabolomic fingerprint allows for trend analyses of dose-response curves for hundreds of cellular metabolites. Analysis of dose/time-response curve trends (biphasic or monotonic) of untargeted metabolomic features would thus allow the use of all the chemical signals obtained in order to determine stress levels (defense or damage) in organisms. To develop this approach in a context of time-dependent microbial community changes, mature river biofilms were exposed for 1 month to four cobalt (Co) concentrations (from background concentration to 1 × 10-6 M) in an open system of artificial streams. The meta-metabolomic response of biofilms was compared against a multitude of biological parameters (including bioaccumulation, biomass, chlorophyll a content, composition and structure of prokaryotic and eukaryotic communities) monitored at set exposure times (from 1 h to 28 d). Cobalt exposure induced extremely rapid responses of the meta-metabolome, with time range inducing defense responses (TRIDeR) of around 10 s, and time range inducing damage responses (TRIDaR) of several hours. Even in biofilms whose structure had been altered by Co bioaccumulation (reduced biomass, chlorophyll a contents and changes in the composition and diversity of prokaryotic and eukaryotic communities), concentration range inducing defense responses (CRIDeR) with similar initiation thresholds (1.41 ± 0.77 × 10-10 M Co2+ added in the exposure medium) were set up at the meta-metabolome level at every time point. In contrast, the concentration range inducing damage responses (CRIDaR) initiation thresholds increased by 10 times in long-term Co exposed biofilms. The present study demonstrates that defense and damage responses of biofilm meta-metabolome exposed to Co are rapidly and sustainably impacted, even within tolerant and resistant microbial communities.

Proposal and confirmation of N-(2-carboxyethyl)proline as a human endogenous metabolite

RATIONALE

Malondialdehyde, one of the peroxidation products of polyunsaturated fatty acids, has been widely reported as an oxidative stress biomarker in many diseases. However, malondialdehyde is inherently unstable in biological matrices, which renders its measurement unreliable with all the reported analytical methods. To find an alternative oxidative stress biomarker, we envisioned that N-(2-carboxyethyl)proline, a modified conjugate of malondialdehyde and proline, could be a stable candidate for this purpose.

METHODS

The proposed compound was chemically synthesized, and liquid chromatography-mass spectrometry methods were developed and used to search for the compound in human biological samples.

RESULTS

An endogenous metabolite in human feces and urine samples was found to match the synthetic N-(2-carboxyethyl)proline by chromatographic retention and the fragmentation pattern of its molecular ion.

CONCLUSION

The results confirmed that N-(2-carboxyethyl)proline was a new metabolite in human feces and urine samples. In addition, our results demonstrated a case of successful identification of true unknown metabolite by knowledge-based hypothesis of possible metabolites followed by experimental confirmation with a synthetic standard.

Meta-metabolomic responses of river biofilms to cobalt exposure and use of dose-response model trends as an indicator of effects

The response of the meta-metabolome is rarely used to characterize the effects of contaminants on a whole community. Here, the meta-metabolomic fingerprints of biofilms were examined after 1, 3 and 7 days of exposure to five concentrations of cobalt (from background concentration to 1 × 10-5 M) in aquatic microcosms. The untargeted metabolomic data were processed using the DRomics tool to build dose-response models and to calculate benchmark-doses. This approach made it possible to use 100% of the chemical signal instead of being limited to the very few annotated metabolites (7%). These benchmark-doses were further aggregated into an empirical cumulative density function. A trend analysis of the untargeted meta-metabolomic feature dose-response curves after 7 days of exposure suggested the presence of a concentration range inducing defense responses between 1.7 × 10-9 and 2.7 × 10-6 M, and of a concentration range inducing damage responses from 2.7 × 10-6 M and above. This distinction was in good agreement with changes in the other biological parameters studied (biomass and chlorophyll content). This study demonstrated that the molecular defense and damage responses can be related to contaminant concentrations and represents a promising approach for environmental risk assessment of metals.

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.

How do biomarkers dance? Specific moves of defense and damage biomarkers for biological interpretation of dose-response model trends

Omics studies are currently increasingly used in ecotoxicology to highlight the induction of known or novel biomarkers when organisms are exposed to contaminants. Although it is virtually impossible to identify all biomarkers from all organisms, biomarkers can be grouped as defense or damage biomarkers, exhibiting a limited number of response trends. Our working hypothesis is that defense and damage biomarkers follow different dose-response patterns. A meta-analysis of 156 articles and 2595 observations of dose-response curves of defense and damage biomarkers was carried out in order to characterize the response trends of these biological parameters in a large panel of living organisms (18 phyla) exposed to inorganic or organic contaminants (176 in total). Using multinomial logistic regression models, defense biomarkers were found to describe biphasic responses (bell- and U-shaped) to a greater extent (2.5 times) than damage biomarkers. In contrast, damage biomarkers varied mainly monotonically (decreasing or increasing), representing 85% of the observations. Neither the nature of the contaminant nor the type of organisms belonging to 4 kingdoms, influence these specific responses. This result suggests that cellular defense and damage mechanisms are not specific to stressors and are conserved throughout life. Trend analysis of dose-response models as a biological interpretation of biomarkers could thus be a valuable way to exploit large omics datasets.

Microbiome metabolite quantification methods enabling insights into human health and disease

Many of the health-associated impacts of the microbiome are mediated by its chemical activity, producing and modifying small molecules (metabolites). Thus, microbiome metabolite quantification has a central role in efforts to elucidate and measure microbiome function. In this review, we cover general considerations when designing experiments to quantify microbiome metabolites, including sample preparation, data acquisition and data processing, since these are critical to downstream data quality. We then discuss data analysis and experimental steps to demonstrate that a given metabolite feature is of microbial origin. We further discuss techniques used to quantify common microbial metabolites, including short-chain fatty acids (SCFA), secondary bile acids (BAs), tryptophan derivatives, N-acyl amides and trimethylamine N-oxide (TMAO). Lastly, we conclude with challenges and future directions for the field.

Chemical Composition of Commercial Cannabis

Cannabis is widely used for medicinal and recreational purposes. As a result, there is increased interest in its chemical components and their physiological effects. However, current information on cannabis chemistry is often outdated or scattered across many books and journals. To address this issue, we used modern metabolomics techniques and modern bioinformatics techniques to compile a comprehensive list of >6000 chemical constituents in commercial cannabis. The metabolomics methods included a combination of high- and low-resolution liquid chromatography-mass spectrometry (MS), gas chromatography-MS, and inductively coupled plasma-MS. The bioinformatics methods included computer-aided text mining and computational genome-scale metabolic inference. This information, along with detailed compound descriptions, physicochemical data, known physiological effects, protein targets, and referential compound spectra, has been made available through a publicly accessible database called the Cannabis Compound Database (https://cannabisdatabase.ca). Such a centralized, open-access resource should prove to be quite useful for the cannabis community.

Tripogon loliiformis tolerates rapid desiccation after metabolic and transcriptional priming during initial drying

Crop plants and undomesticated resilient species employ different strategies to regulate their energy resources and growth. Most crop species are sensitive to stress and prioritise rapid growth to maximise yield or biomass production. In contrast, resilient plants grow slowly, are small, and allocate their resources for survival in challenging environments. One small group of plants, termed resurrection plants, survive desiccation of their vegetative tissue and regain full metabolic activity upon watering. However, the precise molecular mechanisms underlying this extreme tolerance remain unknown. In this study, we employed a transcriptomics and metabolomics approach, to investigate the mechanisms of desiccation tolerance in Tripogon loliiformis, a modified desiccation-tolerant plant, that survives gradual but not rapid drying. We show that T. loliiformis can survive rapid desiccation if it is gradually dried to 60% relative water content (RWC). Furthermore, the gene expression data showed that T. loliiformis is genetically predisposed for desiccation in the hydrated state, as evidenced by the accumulation of MYB, NAC, bZIP, WRKY transcription factors along with the phytohormones, abscisic acid, salicylic acid, amino acids (e.g., proline) and TCA cycle sugars during initial drying. Through network analysis of co-expressed genes, we observed differential responses to desiccation between T. loliiformis shoots and roots. Dehydrating shoots displayed global transcriptional changes across broad functional categories, although no enrichment was observed during drying. In contrast, dehydrating roots showed distinct network changes with the most significant differences occurring at 40% RWC. The cumulative effects of the early stress responses may indicate the minimum requirements of desiccation tolerance and enable T. loliiformis to survive rapid drying. These findings potentially hold promise for identifying biotechnological solutions aimed at developing drought-tolerant crops without growth and yield penalties.

GC-MS, quantum mechanics calculation and the antifungal activity of river red gum essential oil when applied to four natural textiles

The most important uses of old fabrics include clothing, mummification, and bookbinding. However, because they are predominantly constructed of natural materials, they are particularly susceptible to physical and chemical deterioration brought on by fungi. The treatments that are typically used to preserve old textiles focus on the use of synthetic fungicides, which have the potential to be dangerous for both human health and the environment. Essential oils (EOs), which are safe for the environment and have no negative effects on human health, have been widely advocated as an alternative to conventional antifungals. Four natural fabrics-linen, cotton, wool, and silk-were utilized in the current work. The extracted EO from leaves of river red gum (Eucalyptus camaldulensis Dehnh.) were prepared at 125, 250, and 500 µL/L. Aspergillus flavus, Fusarium culmorum and Aspergillus niger were inoculated separately into the treated four fabrics with the EO at concentrations of 125, 250, and 500 µL/L or the main compounds (spathulenol and eucalyptol) at the concentrations of 6, 12, 25, and 50 µL/L and were then compared to the un-treated samples. GC-MS was used to analyze the EO chemical composition, while visual observations and scanning electron microscopic (SEM) were used to study the fungal growth inhibition. Spathulenol (26.56%), eucalyptol (14.91%), and p-cymene (12.40%) were the principal chemical components found in E. camaldulensis EO by GC-MS. Spathulenol molecule displayed the highest electrostatic potential (ESP) compared with the other primary compound, as calculated by quantum mechanics. In the untreated textile samples, SEM analysis revealed substantial proliferation of hyphae from A. flavus, F. culmorum, and A. niger. The fungal growth was completely inhibited at a concentration of 500 µL/L from the EO. Both eucalyptol and spathulenol completely inhibited the formation of the fungal spores at a concentration of 50 µL/L, although eucalyptol was more effective than spathulenol across the board for all four textiles. The results support E. camaldulensis EO functionalized textiles as an effective active antifungal agent.

Intracellular metabolic profiling of drug resistant cells by surface enhanced Raman scattering

BACKGROUND

Intracellular metabolic profiling reveals real-time metabolic information useful for the study of underlying mechanisms of cells in particular conditions such as drug resistance. However, mass spectrometry (MS), one of the leading metabolomics technologies, usually requires a large number of cells and complex pretreatments. Surface enhanced Raman scattering (SERS) has an ultrahigh detection sensitivity and specificity, favorable for metabolomics analysis. However, some targeted SERS methods focus on very limited metabolite without global bioprofiling, and some label-free approaches try to fingerprint the metabolic response based on whole SERS spectral classification, but comprehensive interpretation of biological mechanisms was lacking. (95) RESULTS: We proposed a label-free SERS technique for intracellular metabolic profiling in complex cellular lysates within 3 min. We first compared three kinds of cellular lysis methods and sonication lysis shows the highest extraction efficiency of metabolites. To obtain comprehensive metabolic information, we collected a spectral set for each sample and further qualified them by the Pearson correlation coefficient (PCC) to calculate how many spectra should be acquired at least to gain the adequate information from a statistical and global view. In addition, according to our measurements with 10 pure metabolites, we can understand the spectra acquired from complex cellular lysates of different cell lines more precisely. Finally, we further disclosed the variations of 22 SERS bands in enzalutamide-resistant prostate cancer cells and some are associated with the androgen receptor signaling activity and the methionine salvage pathway in the drug resistance process, which shows the same metabolic trends as MS. (149) SIGNIFICANCE: Our technique has the capability to capture the intracellular metabolic fingerprinting with the optimized lysis approach and spectral set collection, showing high potential in rapid, sensitive and global metabolic profiling in complex biosamples and clinical liquid biopsy. This gives a new perspective to the study of SERS in insightful understanding of relevant biological mechanisms. (54).

DeepSAT: Learning Molecular Structures from Nuclear Magnetic Resonance Data

The identification of molecular structure is essential for understanding chemical diversity and for developing drug leads from small molecules. Nevertheless, the structure elucidation of small molecules by Nuclear Magnetic Resonance (NMR) experiments is often a long and non-trivial process that relies on years of training. To achieve this process efficiently, several spectral databases have been established to retrieve reference NMR spectra. However, the number of reference NMR spectra available is limited and has mostly facilitated annotation of commercially available derivatives. Here, we introduce DeepSAT, a neural network-based structure annotation and scaffold prediction system that directly extracts the chemical features associated with molecular structures from their NMR spectra. Using only the 1H-13C HSQC spectrum, DeepSAT identifies related known compounds and thus efficiently assists in the identification of molecular structures. DeepSAT is expected to accelerate chemical and biomedical research by accelerating the identification of molecular structures.

Advanced Methods for Natural Products Discovery: Bioactivity Screening, Dereplication, Metabolomics Profiling, Genomic Sequencing, Databases and Informatic Tools, and Structure Elucidation

Natural Products (NP) are essential for the discovery of novel drugs and products for numerous biotechnological applications. The NP discovery process is expensive and time-consuming, having as major hurdles dereplication (early identification of known compounds) and structure elucidation, particularly the determination of the absolute configuration of metabolites with stereogenic centers. This review comprehensively focuses on recent technological and instrumental advances, highlighting the development of methods that alleviate these obstacles, paving the way for accelerating NP discovery towards biotechnological applications. Herein, we emphasize the most innovative high-throughput tools and methods for advancing bioactivity screening, NP chemical analysis, dereplication, metabolite profiling, metabolomics, genome sequencing and/or genomics approaches, databases, bioinformatics, chemoinformatics, and three-dimensional NP structure elucidation.

Potential of in vitro nuclear magnetic resonance of biofluids and tissues in clinical research

Body fluids, cells, and tissues contain a wide variety of metabolites that consist of a mixture of various low-molecular-weight compounds, including amino acids, peptides, lipids, nucleic acids, and organic acids, which makes comprehensive analysis more difficult. Quantitative nuclear magnetic resonance (NMR) spectroscopy is a well-established analytical technique for analyzing the metabolic profiles of body fluids, cells, and tissues. It enables fast and comprehensive detection, characterization, a high level of experimental reproducibility, minimal sample preparation, and quantification of various endogenous metabolites. In recent times, NMR-based metabolomics has been appreciably utilized in diverse branches of medicine, including microbiology, toxicology, pathophysiology, pharmacology, nutritional intervention, and disease diagnosis/prognosis. In this review, the utility of NMR-based metabolomics in clinical studies is discussed. The significance of in vitro NMR-based metabolomics as an effective tool for detecting metabolites and their variations in different diseases are discussed, together with the possibility of identifying specific biomarkers that can contribute to early detection and diagnosis of disease.

Application of vibrational spectroscopy and nuclear magnetic resonance methods for drugs pharmacokinetics research

OBJECTIVES

The development of new methods for determining the concentration of drugs is an actual topic today. The article contains a detailed review on vibrational spectroscopy and nuclear magnetic resonance methods using for pharmacokinetic research. This study is devoted to the possibility of using vibrational spectroscopy and 1H nuclear magnetic resonance spectroscopy to determine the concentration of drugs and the use of these groups of techniques for therapeutic drug monitoring.

CONTENT

The study was conducted by using scientific libraries (Scopus, Web of Science Core Collection, Medline, GoogleScholar, eLIBRARY, PubMed) and reference literature. A search was conducted for the period from 2011 to 2021 in Russian and English, by combinations of words: 1H nuclear magnetic resonance (¹H NMR), vibrational spectroscopy, Surface-Enhanced Raman spectroscopy, drug concentration, therapeutic drug monitoring. These methods have a number of advantages and are devoid of some of the disadvantages of classical therapeutic drug monitoring (TDM) methods - high performance liquid chromatography and mass spectrometry. This review considers the possibility of using the methods of surface-enhanced Raman scattering (SERS) and ¹H NMR-spectroscopy to assess the concentration of drugs in various biological media (blood, urine), as well as to study intracellular metabolism and the metabolism of ophthalmic drugs. ¹Н NMR-spectroscopy can be chosen as a TDM method, since it allows analyzing the structure and identifying metabolites of various drugs. ¹Н NMR-based metabolomics can provide information on the side effects of drugs, predict response to treatment, and provide key information on the mechanisms of action of known and new drug compounds.

SUMMARY AND OUTLOOK

SERS and ¹Н NMR-spectroscopy have great potential for further study and the possibility of introducing them into clinical practice, including for evaluating the efficacy and safety of drugs.

Biochemical metabolomic profiling of the Crown-of-Thorns Starfish (Acanthaster): New insight into its biology for improved pest management

The Crown-of-Thorns Starfish (COTS), Acanthaster species, is a voracious coral predator that destroys coral reefs when in outbreak status. The baseline metabolite and lipid biomolecules of 10 COTS tissues, including eggs from gravid females, were investigated in this study to provide insight into their biology and identify avenues for control. Targeted and untargeted metabolite- and lipidomics-based mass spectrometry approaches were used to obtain tissue-specific metabolite and lipid profiles. Across all COTS tissues, 410 metabolites and 367 lipids were identified. Most abundant were amino acids and peptides (18.7%) and wax esters (17%). There were 262 metabolites and 192 lipids identified in COTS eggs. Wax esters were more abundant in the eggs (30%) followed by triacylglycerols (TG), amino acids, and peptides. The diversity of asterosaponins in eggs (34) was higher than in tissues (2). Several asterosaponins known to modulate sperm acrosome reaction were putatively identified, including glycoside B, asterosaponin-4 (Co-Aris III), and regularoside B (asterosaponin A). The saponins saponin A, thornasteroside A, hillaside B, and non-saponins dictyol J and axinellamine B which have been shown to possess defensive properties, were found in abundance in gonads, skin, and radial nerve tissues. Inosine and 2-hexyldecanoic acid are the most abundant metabolites in tissues and eggs. As a secondary metabolite of purine degradation, inosine plays an important role in purine biosynthesis, while 2-hexyldecanoic acid is known to suppress side-chain crystallization during the synthesis of amphiphilic macromolecules (i.e., phospholipids). These significant spatial changes in metabolite, lipid, and asterosaponin profiles enabled unique insights into key biological tissue-specific processes that could be manipulated to better control COTS populations. Our findings highlight COTS as a novel source of molecules with therapeutic and cosmetic properties (ceramides, sphingolipids, carnosine, and inosine). These outcomes will be highly relevant for the development of strategies for COTS management including chemotaxis-based biocontrol and exploitation of COTS carcasses for the extraction of commercial molecules.

Advances in Ultra-High-Resolution Mass Spectrometry for Pharmaceutical Analysis

Pharmaceutical analysis refers to an area of analytical chemistry that deals with active compounds either by themselves (drug substance) or when formulated with excipients (drug product). In a less simplistic way, it can be defined as a complex science involving various disciplines, e.g., drug development, pharmacokinetics, drug metabolism, tissue distribution studies, and environmental contamination analyses. As such, the pharmaceutical analysis covers drug development to its impact on health and the environment. Moreover, due to the need for safe and effective medications, the pharmaceutical industry is one of the most heavily regulated sectors of the global economy. For this reason, powerful analytical instrumentation and efficient methods are required. In the last decades, mass spectrometry has been increasingly used in pharmaceutical analysis both for research aims and routine quality controls. Among different instrumental setups, ultra-high-resolution mass spectrometry with Fourier transform instruments, i.e., Fourier transform ion cyclotron resonance (FTICR) and Orbitrap, gives access to valuable molecular information for pharmaceutical analysis. In fact, thanks to their high resolving power, mass accuracy, and dynamic range, reliable molecular formula assignments or trace analysis in complex mixtures can be obtained. This review summarizes the principles of the two main types of Fourier transform mass spectrometers, and it highlights applications, developments, and future perspectives in pharmaceutical analysis.

Metabolomics of plant root exudates: From sample preparation to data analysis

Plants release a set of chemical compounds, called exudates, into the rhizosphere, under normal conditions and in response to environmental stimuli and surrounding soil organisms. Plant root exudates play indispensable roles in inhibiting the growth of harmful microorganisms, while also promoting the growth of beneficial microbes and attracting symbiotic partners. Root exudates contain a complex array of primary and specialized metabolites. Some of these chemicals are only found in certain plant species for shaping the microbial community in the rhizosphere. Comprehensive understanding of plant root exudates has numerous applications from basic sciences to enhancing crop yield, production of stress-tolerant crops, and phytoremediation. This review summarizes the metabolomics workflow for determining the composition of root exudates, from sample preparation to data acquisition and analysis. We also discuss recent advances in the existing analytical methods and future perspectives of metabolite analysis.

A review of omics-based PFAS exposure studies reveals common biochemical response pathways

Per and Polyfluoroalkyl Substances (PFAS) are a diverse group of man-made chemicals with a range of industrial applications and which are widespread in the environment. They are structurally diverse but comprise a common chemical feature of at least one (though usually more) perfluorocarbon moiety (-C_nF_2n-) attached to a functional group such as a carboxylic or sulphonic acid. The strength of the Carbon-Fluorine bond means the compounds do not break down easily and can thus bioaccumulate. PFAS are of high concern to regulators and the public due to their potential toxicity and high persistence. At high exposure levels, PFAS have been implicated in a range of harmful effects on human and environmental health, particularly problems in/with development, cholesterol and endocrine disruption, immune system function, and oncogenesis. However, most environmental toxicology studies use far higher levels of PFAS than are generally found in the environment. Additionally, since the type of exposure, the PFAS used, and the organisms tested all vary between studies, so do the results. Traditional ecotoxicology studies may thus not identify PFAS effects at environmentally relevant exposures. Here we conduct a review of omics-based PFAS exposure studies using laboratory ecotoxicological methodologies and environmentally relevant exposure levels and show that common biochemical response pathways are identified in multiple studies. A major pathway identified was the pentose phosphate shunt pathway. Such molecular markers of sublethal PFAS exposure will greatly benefit accurate and effective risk assessments to ensure that new PFAS regulations can consider the full effects of PFAS exposure on environmental and human health receptors.

LC-HRMS-Based Profiling: Antibacterial and Lipase Inhibitory Activities of Some Medicinal Plants for the Remedy of Obesity

Globally, obesity is a serious health concern that causes numerous diseases, including type 2 diabetes, hypertension, cardiovascular diseases, etc. Medicinal plants have been used to aid in weight loss since ancient times. Thus, this research is focused on the exploration of pancreatic lipase inhibitory activity and secondary metabolite profiling of Bergenia ciliata, Mimosa pudica, and Phyllanthus emblica, selected based on an ethnobotanical survey. The lipase inhibition was investigated using 4-nitrophenyl butyrate (p-NPB) as a substrate. To uncover further therapeutic potentials of these medicinal plants, antimicrobial activity and minimum inhibitory concentration (MIC) of the extracts were also determined. The ethyl acetate plant extracts showed higher antimicrobial activity against Staphylococcus aureus, Escherichia coli, Salmonella typhi, and Shigella sonnei. The MIC of ethyl acetate extracts of medicinal plants considered in this study ranges from 1.56 to 6.25 mg/mL. The hexane fraction of Mimosa pudica and Phyllanthus emblica showed a higher lipase inhibitory activity as compared to others, with IC50 values of 0.49 ± 0.02 and 2.45 ± 0.003 mg/mL, respectively. In the case of Bergenia ciliata, the methanolic extract inhibited lipase more effectively than others, with an IC50 value of 1.55 ± 0.02 mg/mL (IC50 value of orlistat was 179.70 ± 3.60 µg/mL). A mass spectrometry analysis of various solvent/solvent partition fractions (extracts) revealed 29 major secondary metabolites. The research offers a multitude of evidence for using medicinal plants as antiobesity and antimicrobial agents.

Multi-Omic Profiling of Multi-Biosamples Reveals the Role of Amino Acid and Nucleotide Metabolism in Endometrial Cancer

Background

Endometrial cancer (EC) is one of the most common gynecological cancers. The traditional diagnosis of EC relies on histopathology, which, however, is invasive and may arouse tumor spread. There have been many studies aiming to find the metabolomic biomarkers of EC to improve the early diagnosis of cancer in a non-invasive or minimally invasive way, which can also provide valuable information for understanding the disease. However, most of these studies only analyze a single type of sample by metabolomics, and cannot provide a comprehensive view of the altered metabolism in EC patients. Our study tries to gain a pathway-based view of multiple types of samples for understanding metabolomic disorders in EC by combining metabolomics and proteomics.

Methods

Forty-four EC patients and forty-three controls were recruited for the research. We collected endometrial tissue, urine, and intrauterine brushing samples. Untargeted metabolomics and untargeted proteomics were both performed on the endometrial tissue samples, while only untargeted metabolomics was performed on the urine and intrauterine brushing samples.

Results

By integrating the differential metabolites and proteins between EC patients and controls detected in the endometrial tissue samples, we identified several EC-related significant pathways, such as amino acid metabolism and nucleotide metabolism. The significance of these pathways and the potential of metabolite biomarker-based diagnosis were then further verified by using urine and intrauterine brushing samples. It was found that the regulation of metabolites involved in the significant pathways showed similar trends in the intrauterine brushings and the endometrial tissue samples, while opposite trends in the urine and the endometrial tissue samples.

Conclusions

With multi-omics characterization of multi-biosamples, the metabolomic changes related to EC are illustrated in a pathway-based way. The network of altered metabolites and related proteins provides a comprehensive view of altered metabolism in the endometrial tissue samples. The verification of these critical pathways by using urine and intrauterine brushing samples provides evidence for the possible non-invasive or minimally invasive biopsy for EC diagnosis in the future.

Omics-based ecosurveillance for the assessment of ecosystem function, health, and resilience

Current environmental monitoring efforts often focus on known, regulated contaminants ignoring the potential effects of unmeasured compounds and/or environmental factors. These specific, targeted approaches lack broader environmental information and understanding, hindering effective environmental management and policy. Switching to comprehensive, untargeted monitoring of contaminants, organism health, and environmental factors, such as nutrients, temperature, and pH, would provide more effective monitoring with a likely concomitant increase in environmental health. However, even this method would not capture subtle biochemical changes in organisms induced by chronic toxicant exposure. Ecosurveillance is the systematic collection, analysis, and interpretation of ecosystem health-related data that can address this knowledge gap and provide much-needed additional lines of evidence to environmental monitoring programs. Its use would therefore be of great benefit to environmental management and assessment. Unfortunately, the science of ‘ecosurveillance’, especially omics-based ecosurveillance is not well known. Here, we give an overview of this emerging area and show how it has been beneficially applied in a range of systems. We anticipate this review to be a starting point for further efforts to improve environmental monitoring via the integration of comprehensive chemical assessments and molecular biology-based approaches. Bringing multiple levels of omics technology-based assessment together into a systems-wide ecosurveillance approach will bring a greater understanding of the environment, particularly the microbial communities upon which we ultimately rely to remediate perturbed ecosystems.

Metabolic Footprinting of Microbial Systems Based on Comprehensive In Silico Predictions of MS/MS Relevant Data

Metabolic footprinting represents a holistic approach to gathering large-scale metabolomic information of a given biological system and is, therefore, a driving force for systems biology and bioprocess development. The ongoing development of automated cultivation platforms increases the need for a comprehensive and rapid profiling tool to cope with the cultivation throughput. In this study, we implemented a workflow to provide and select relevant metabolite information from a genome-scale model to automatically build an organism-specific comprehensive metabolome analysis method. Based on in-house literature and predicted metabolite information, the deduced metabolite set was distributed in stackable methods for a chromatography-free dilute and shoot flow-injection analysis multiple-reaction monitoring profiling approach. The workflow was used to create a method specific for Saccharomyces cerevisiae, covering 252 metabolites with 7 min/sample. The method was validated with a commercially available yeast metabolome standard, identifying up to 74.2% of the listed metabolites. As a first case study, three commercially available yeast extracts were screened with 118 metabolites passing quality control thresholds for statistical analysis, allowing to identify discriminating metabolites. The presented methodology provides metabolite screening in a time-optimised way by scaling analysis time to metabolite coverage and is open to other microbial systems simply starting from genome-scale model information.

Evaluation of ORAC, IR and NMR metabolomics for predicting ripening stage and variety in melon (Cucumis melo L.)

A combination of FTIR- and NMR-based metabolomics approaches coupled to multivariate data analysis techniques was used for the first time to characterize and explore the metabolic changes along the ripening of two Spanish melon landraces (Jimbee N and Jimbee XL). Furthermore, the fatty acids profile and the antioxidant capacity were investigated by GC-FID and ORAC method, respectively. Despite FTIR was able to identify changes in the sugars content between fruits of different maturity stages, it failed to discriminate between both varieties, being exceeded by NMR in both situations. Increased fruit maturation led to an increase of sucrose, fatty acids and β-carotene, accompanied by a decrease of glucose, fructose, citrate, amino acids, and polyphenols (which were positively correlated with fruits antioxidant activity).

13C NMR Spectroscopic Studies of Intra- and Intermolecular Interactions of Amino Acid Derivatives and Peptide Derivatives in Solutions

13C NMR spectroscopic investigations were conducted for various amino acid derivatives and peptides. It was observed that 13C NMR chemical shifts of the carbonyl carbons are correlated with the solvent polarities, but the extent depends on the structures. The size of the functional groups and inter- and intra-molecular hydrogen bonding appear to be the major contributors for this tendency.

Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite-Protein Physical Interaction Subnetworks Altered in Cancer

Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.

METABOLOMICS REVEALS BIOMARKERS IN HUMAN URINE AND PLASMA TO PREDICT CYP2D6 ACTIVITY

Background and Purpose

Individualized assessment of cytochrome P450 2D6 (CYP2D6) activity is usually performed through phenotyping following administration of a probe drug to measure the enzyme's activity. To avoid any iatrogenic harm (allergic drug reaction, dosing error) related to the probe drug, the development of non‐burdensome tools for real‐time phenotyping of CYP2D6 could significantly contribute to precision medicine. This study focuses on the identification of markers of the CYP2D6 enzyme in human biofluids using an LC‐high‐resolution mass spectrometry‐based metabolomic approach.

Experimental Approach

Plasma and urine samples from healthy volunteers were analysed before and after intake of a daily dose of paroxetine 20 mg over 7 days. CYP2D6 genotyping and phenotyping, using single oral dose of dextromethorphan 5 mg, were also performed in all participants.

Key Results

We report four metabolites of solanidine and two unknown compounds as possible novel CYP2D6 markers. Mean relative intensities of these features were significantly reduced during the inhibition session compared with the control session (n = 37). Semi‐quantitative analysis showed that the largest decrease (−85%) was observed for the ion m/z 432.3108 normalized to solanidine (m/z 398.3417). Mean relative intensities of these ions were significantly higher in the CYP2D6 normal–ultrarapid metabolizer group (n = 37) compared with the poor metabolizer group (n = 6). Solanidine intensity was more than 15 times higher in CYP2D6‐deficient individuals compared with other volunteers.

Conclusion and Implications

The applied untargeted metabolomic strategy identified potential novel markers capable of semi‐quantitatively predicting CYP2D6 activity, a promising discovery for personalized medicine.

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

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

Tissue/Biofluid Specific Molecular Cartography of Leishmania donovani Infected BALB/c Mice: Deciphering Systemic Reprogramming

Pathophysiology of visceral leishmaniasis (VL) is not fully understood and it has been widely accepted that the parasitic components and host immune response both contribute to the perpetuation of the disease. Host alterations during leishmaniasis is a feebly touched area that needs to be explored more to better understand the VL prognosis and diagnosis, which are vital to reduce mortality and post-infection sequelae. To address this, we performed untargeted metabolomics of Leishmania donovani (Ld) infected, uninfected and treated BALB/c mice’s tissues and biofluids to elucidate the host metabolome changes using gas chromatography–mass spectrometry. Univariate and multivariate data treatments provided numerous significant differential hits in several tissues like the brain, liver, spleen and bone marrow. Differential modulations were also observed in serum, urine and fecal samples of Ld-infected mice, which could be further targeted for biomarker and diagnostic validations. Several metabolic pathways were found to be upregulated/downregulated in infected (TCA, glycolysis, fatty acids, purine and pyrimidine, etcetera) and treated (arginine, fumaric acid, orotic acid, choline succinate, etcetera) samples. Results also illustrated several metabolites with different pattern of modulations in control, infected and treated samples as well as in different tissues/biofluids; for e.g. glutamic acid identified in the serum samples of infected mice. Identified metabolites include a range of amino acids, saccharides, energy-related molecules, etcetera. Furthermore, potential biomarkers have been identified in various tissues—arginine and fumaric acid in brain, choline in liver, 9-(10) EpOME in spleen and bone marrow, N-acetyl putrescine in bone marrow, etcetera. Among biofluids, glutamic acid in serum, hydrazine and deoxyribose in urine and 3-Methyl-2-oxo pentanoic acid in feces are some of the potential biomarkers identified. These metabolites could be further looked into for their role in disease complexity or as a prognostic marker. The presented profiling approach allowed us to attain a metabolic portrait of the individual tissue/biofluid modulations during VL in the host and represent a valuable system readout for further studies. Our outcomes provide an improved understanding of perturbations of the host metabolome interface during VL, including identification of many possible potential diagnostic and therapeutic targets.

NMR-Based Quantum Mechanical Analysis Builds Trust and Orthogonality in Structural Analysis: The Case of a Bisdesmosidic Triglycoside as Withania somnifera Aerial Parts Marker

The present study demonstrates the relationship between conventional and quantum mechanical (QM) NMR spectroscopic analyses, shown here to assist in building a convincingly orthogonal platform for the solution and documentation of demanding structures. Kaempferol-3-O-robinoside-7-O-glucoside, a bisdesmosidic flavonol triglycoside and botanical marker for the aerial parts of Withania somnifera, served as an exemplary case. As demonstrated, QM-based 1H iterative full spin analysis (HiFSA) advances the understanding of both individual nuclear resonance spin patterns and the entire 1H NMR spectrum of a molecule and establishes structurally determinant, numerical HiFSA profiles. The combination of HiFSA with regular 1D 1H NMR spectra allows for simplified yet specific identification tests via comparison of high-quality experimental with QM-calculated spectra. HiFSA accounts for all features encountered in 1H NMR spectra: nonlinear high-order effects, complex multiplets, and their usually overlapped signals. As HiFSA replicates spectrum patterns from field-independent parameters with high accuracy, this methodology can be ported to low-field NMR instruments (40-100 MHz). With its reliance on experimental NMR evidence, the QM approach builds up confidence in structural characterization and potentially reduces identity analyses to simple 1D 1H NMR experiments. This approach may lead to efficient implementation of conclusive identification tests in pharmacopeial and regulatory analyses: from simple organics to complex natural products.

Metabolomics and Lipidomics: Expanding the Molecular Landscape of Exercise Biology

Dynamic changes in circulating and tissue metabolites and lipids occur in response to exercise-induced cellular and whole-body energy demands to maintain metabolic homeostasis. The metabolome and lipidome in a given biological system provides a molecular snapshot of these rapid and complex metabolic perturbations. The application of metabolomics and lipidomics to map the metabolic responses to an acute bout of aerobic/endurance or resistance exercise has dramatically expanded over the past decade thanks to major analytical advancements, with most exercise-related studies to date focused on analyzing human biofluids and tissues. Experimental and analytical considerations, as well as complementary studies using animal model systems, are warranted to help overcome challenges associated with large human interindividual variability and decipher the breadth of molecular mechanisms underlying the metabolic health-promoting effects of exercise. In this review, we provide a guide for exercise researchers regarding analytical techniques and experimental workflows commonly used in metabolomics and lipidomics. Furthermore, we discuss advancements in human and mammalian exercise research utilizing metabolomic and lipidomic approaches in the last decade, as well as highlight key technical considerations and remaining knowledge gaps to continue expanding the molecular landscape of exercise biology.

Database Independent Automated Structure Elucidation of Organic Molecules Based on IR, 1H NMR, 13C NMR, and MS Data

Herein, we report a computational algorithm that follows a spectroscopist-driven elucidation process of the structure of an organic molecule based on IR, 1H and 13C NMR, and MS tabular data. The algorithm is independent from database searching and is based on a bottom-up approach, building the molecular structure from small structural fragments visible in spectra. It employs an analytical combinatorial approach with a graph search technique to determine the connectivity of structural fragments that is based on the analysis of the NMR spectra, to connect the identified structural fragments into a molecular structure. After the process is completed, the interface lists the compound candidates, which are visualized by the WolframAlpha computational knowledge engine within the interface. The candidates are ranked according to the predefined rules for analyzing the spectral data. The developed elucidator has a user-friendly web interface and is publicly available (http://schmarnica.si).

Evaluation of the use of untargeted metabolomics in the safety assessment of genetically modified crops

BACKGROUND

The safety assessment of foods and feeds from genetically modified (GM) crops includes the comparison of key characteristics, such as crop composition, agronomic phenotype and observations from animal feeding studies compared to conventional counterpart varieties that have a history of safe consumption, often including a near isogenic variety. The comparative compositional analysis of GM crops has been based on targeted, validated, quantitative analytical methods for the key food and feed nutrients and antinutrients for each crop, as identified by Organization of Economic Co-operation and Development (OCED). As technologies for untargeted metabolomic methods have evolved, proposals have emerged for their use to complement or replace targeted compositional analytical methods in regulatory risk assessments of GM crops to increase the number of analyzed metabolites.

AIM OF REVIEW

The technical opportunities, challenges and strategies of including untargeted metabolomics analysis in the comparative safety assessment of GM crops are reviewed. The results from metabolomics studies of GM and conventional crops published over the last eight years provide context to enable the discussion of whether metabolomics can materially improve the risk assessment of food and feed from GM crops beyond that possible by the Codex-defined practices used worldwide for more than 25 years.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Published studies to date show that environmental and genetic factors affect plant metabolomics profiles. In contrast, the plant biotechnology process used to make GM crops has little, if any consequence, unless the inserted GM trait is intended to alter food or feed composition. The nutritional value and safety of food and feed from GM crops is well informed by the quantitative, validated compositional methods for list of key analytes defined by crop-specific OECD consensus documents. Untargeted metabolic profiling has yet to provide data that better informs the safety assessment of GM crops than the already rigorous Codex-defined quantitative comparative assessment. Furthermore, technical challenges limit the implementation of untargeted metabolomics for regulatory purposes: no single extraction method or analytical technique captures the complete plant metabolome; a large percentage of metabolites features are unknown, requiring additional research to understand if differences for such unknowns affect food/feed safety; and standardized methods are needed to provide reproducible data over time and laboratories.

NMR as a "Gold Standard" Method in Drug Design and Discovery

Studying disease models at the molecular level is vital for drug development in order to improve treatment and prevent a wide range of human pathologies. Microbial infections are still a major challenge because pathogens rapidly and continually evolve developing drug resistance. Cancer cells also change genetically, and current therapeutic techniques may be (or may become) ineffective in many cases. The pathology of many neurological diseases remains an enigma, and the exact etiology and underlying mechanisms are still largely unknown. Viral infections spread and develop much more quickly than does the corresponding research needed to prevent and combat these infections; the present and most relevant outbreak of SARS-CoV-2, which originated in Wuhan, China, illustrates the critical and immediate need to improve drug design and development techniques. Modern day drug discovery is a time-consuming, expensive process. Each new drug takes in excess of 10 years to develop and costs on average more than a billion US dollars. This demonstrates the need of a complete redesign or novel strategies. Nuclear Magnetic Resonance (NMR) has played a critical role in drug discovery ever since its introduction several decades ago. In just three decades, NMR has become a "gold standard" platform technology in medical and pharmacology studies. In this review, we present the major applications of NMR spectroscopy in medical drug discovery and development. The basic concepts, theories, and applications of the most commonly used NMR techniques are presented. We also summarize the advantages and limitations of the primary NMR methods in drug development.

Metabolomic Approaches to Study Chemical Exposure-Related Metabolism Alterations in Mammalian Cell Cultures

Biological organisms are constantly exposed to an immense repertoire of molecules that cover environmental or food-derived molecules and drugs, triggering a continuous flow of stimuli-dependent adaptations. The diversity of these chemicals as well as their concentrations contribute to the multiplicity of induced effects, including activation, stimulation, or inhibition of physiological processes and toxicity. Metabolism, as the foremost phenotype and manifestation of life, has proven to be immensely sensitive and highly adaptive to chemical stimuli. Therefore, studying the effect of endo- or xenobiotics over cellular metabolism delivers valuable knowledge to apprehend potential cellular activity of individual molecules and evaluate their acute or chronic benefits and toxicity. The development of modern metabolomics technologies such as mass spectrometry or nuclear magnetic resonance spectroscopy now offers unprecedented solutions for the rapid and efficient determination of metabolic profiles of cells and more complex biological systems. Combined with the availability of well-established cell culture techniques, these analytical methods appear perfectly suited to determine the biological activity and estimate the positive and negative effects of chemicals in a variety of cell types and models, even at hardly detectable concentrations. Metabolic phenotypes can be estimated from studying intracellular metabolites at homeostasis in vivo, while in vitro cell cultures provide additional access to metabolites exchanged with growth media. This article discusses analytical solutions available for metabolic phenotyping of cell culture metabolism as well as the general metabolomics workflow suitable for testing the biological activity of molecular compounds. We emphasize how metabolic profiling of cell supernatants and intracellular extracts can deliver valuable and complementary insights for evaluating the effects of xenobiotics on cellular metabolism. We note that the concepts and methods discussed primarily for xenobiotics exposure are widely applicable to drug testing in general, including endobiotics that cover active metabolites, nutrients, peptides and proteins, cytokines, hormones, vitamins, etc.

Resources and tools for the high-throughput, multi-omic study of intestinal microbiota

The human gut microbiome impacts several aspects of human health and disease, including digestion, drug metabolism and the propensity to develop various inflammatory, autoimmune and metabolic diseases. Many of the molecular processes that play a role in the activity and dynamics of the microbiota go beyond species and genic composition and thus, their understanding requires advanced bioinformatics support. This article aims to provide an up-to-date view of the resources and software tools that are being developed and used in human gut microbiome research, in particular data integration and systems-level analysis efforts. These efforts demonstrate the power of standardized and reproducible computational workflows for integrating and analysing varied omics data and gaining deeper insights into microbe community structure and function as well as host-microbe interactions.

"notame": Workflow for Non-Targeted LC-MS Metabolic Profiling

Metabolomics analysis generates vast arrays of data, necessitating comprehensive workflows involving expertise in analytics, biochemistry and bioinformatics in order to provide coherent and high-quality data that enable discovery of robust and biologically significant metabolic findings. In this protocol article, we introduce notame, an analytical workflow for non-targeted metabolic profiling approaches, utilizing liquid chromatography-mass spectrometry analysis. We provide an overview of lab protocols and statistical methods that we commonly practice for the analysis of nutritional metabolomics data. The paper is divided into three main sections: the first and second sections introducing the background and the study designs available for metabolomics research and the third section describing in detail the steps of the main methods and protocols used to produce, preprocess and statistically analyze metabolomics data and, finally, to identify and interpret the compounds that have emerged as interesting.

The integration of LC-MS and NMR for the analysis of low molecular weight trace analytes in complex matrices

This review discusses the integration of liquid chromatography (LC), mass spectrometry (MS), and nuclear magnetic resonance (NMR) in the comprehensive analysis of small molecules from complex matrices. We first discuss the steps taken toward making the three technologies compatible, so as to create an efficient analytical platform. The development of online LC-MS-NMR, highlighted by successful applications in the profiling of highly concentrated analytes (LODs 10 μg) is discussed next. This is followed by a detailed overview of the alternative approaches that have been developed to overcome the challenges associated with online LC-MS-NMR that primarily stem from the inherently low sensitivity of NMR. These alternative approaches include the use of stop-flow LC-MS-NMR, loop collection of LC peaks, LC-MS-SPE-NMR, and offline NMR. The potential and limitations of all these approaches is discussed in the context of applications in various fields, including metabolomics and natural product discovery.

Oligosaccharides and Complex Carbohydrates: A New Paradigm for Cranberry Bioactivity

Cranberry is a well-known functional food, but the compounds directly responsible for many of its reported health benefits remain unidentified. Complex carbohydrates, specifically xyloglucan and pectic oligosaccharides, are the newest recognized class of biologically active compounds identified in cranberry materials. Cranberry oligosaccharides have shown similar biological properties as other dietary oligosaccharides, including effects on bacterial adhesion, biofilm formation, and microbial growth. Immunomodulatory and anti-inflammatory activity has also been observed. Oligosaccharides may therefore be significant contributors to many of the health benefits associated with cranberry products. Soluble oligosaccharides are present at relatively high concentrations (~20% w/w or greater) in many cranberry materials, and yet their possible contributions to biological activity have remained unrecognized. This is partly due to the inherent difficulty of detecting these compounds without intentionally seeking them. Inconsistencies in product descriptions and terminology have led to additional confusion regarding cranberry product composition and the possible presence of oligosaccharides. This review will present our current understanding of cranberry oligosaccharides and will discuss their occurrence, structures, ADME, biological properties, and possible prebiotic effects for both gut and urinary tract microbiota. Our hope is that future investigators will consider these compounds as possible significant contributors to the observed biological effects of cranberry.

A complex matrix characterization approach, applied to cigarette smoke, that integrates multiple analytical methods and compound identification strategies for non-targeted liquid chromatography with high-resolution mass spectrometry

RATIONALE

For the characterization of the chemical composition of complex matrices such as tobacco smoke, containing more than 6000 constituents, several analytical approaches have to be combined to increase compound coverage across the chemical space. Furthermore, the identification of unknown molecules requiring the implementation of additional confirmatory tools in the absence of reference standards, such as tandem mass spectrometry spectra comparisons and in silico prediction of mass spectra, is a major bottleneck.

METHODS

We applied a combination of four chromatographic/ionization techniques (reversed-phase (RP) - heated electrospray ionization (HESI) in both positive (+) and negative (-) modes, RP - atmospheric pressure chemical ionization (APCI) in positive mode, and hydrophilic interaction liquid chromatography (HILIC) - HESI positive) using a Thermo Q Exactive™ liquid chromatography/high-resolution accurate mass spectrometry (LC/HRAM-MS) platform for the analysis of 3R4F-derived smoke. Compound identification was performed by using mass spectral libraries and in silico predicted fragments from multiple integrated databases.

RESULTS

A total of 331 compounds with semi-quantitative estimates ≥100 ng per cigarette were identified, which were distributed within the known chemical space of tobacco smoke. The integration of multiple LC/HRAM-MS-based chromatographic/ionization approaches combined with complementary compound identification strategies was key for maximizing the number of amenable compounds and for strengthening the level of identification confidence. A total of 50 novel compounds were identified as being present in tobacco smoke. In the absence of reference MS2 spectra, in silico MS2 spectra prediction gave a good indication for compound class and was used as an additional confirmatory tool for our integrated non-targeted screening (NTS) approach.

CONCLUSIONS

This study presents a powerful chemical characterization approach that has been successfully applied for the identification of novel compounds in cigarette smoke. We believe that this innovative approach has general applicability and a huge potential benefit for the analysis of any complex matrices.

Utilization of GC-MS untargeted metabolomics to assess the delayed response of glufosinate treatment of transgenic herbicide resistant (HR) buffalo grasses (Stenotaphrum secundatum L.)

INTRODUCTION

Herbicide resistant (HR) buffalo grasses were genetically engineered to resist the non-selective herbicide, glufosinate in order to facilitate a modern, 'weeding program' which is highly effective in terms of minimizing costs and labor. The resistant trait was conferred by an insertion of the pat gene to allow for the production of the enzyme phosphinothricin acetyltransferase (PAT) to detoxify the glufosinate inhibitive effect. To date, there are only a few reports using metabolomics as well as molecular characterizations published for glufosinate-resistant crops with no reports on HR turfgrass. Therefore, for the first time, this study examines the metabolome of glufosinate-resistant buffalo grasses which not only will be useful to future growers but also the scientific community.

OBJECTIVE

A major aim of this present work is to characterize and evaluate the metabolic alterations which may arise from a genetic transformation of HR buffalo grasses by comprehensively using gas chromatography-mass spectrometry (GC-MS) based untargeted metabolomics.

METHODS

Eight-week old plants of 4 HR buffalo grasses, (93-1A, 93-2B, 93-3C and 93-5A) and 3 wild type varieties (WT 8-4A, WT 9-1B and WT 9-1B) were selected for physiological, molecular and metabolomics experiments. Plants were either sprayed with 1, 5, 10 and 15% v/v of glufosinate to evaluate the visual injuries or submerged in 5% v/v of glufosinate 3 days prior to a GC-MS based untargeted metabolomics analysis. In contrast, the control group was treated with distilled water. Leaves were extracted in 1:1 methanol:water and then analysed, using an in-house GC-MS untargeted workflow.

RESULTS

Results identified 199 metabolites with only 6 of them (cis-aconitic acid, allantoin, cellobiose, glyceric acid, maltose and octadecanoic acid) found to be statistically significant (p < 0.05) between the HR and wild type buffalo grass varieties compared to the control experiment. Among these metabolites, unusual accumulation of allantoin was prominent and was an unanticipated effect of the pat gene insertion. As expected, glufosinate treatment caused significant metabolic alterations in the sensitive wild type, with the up-regulation of several amino acids (e.g. phenylalanine and isoleucine) which was likely due to glufosinate-induced senescence. The aminoacyl-tRNA biosynthetic pathway was identified as the most significant enriched pathway as a result of glufosinate effects because a number of its intermediates were amino acids.

CONCLUSION

HR buffalo grasses were very similar to its wild type comparator based on a comprehensive GC-MS based untargeted metabolomics and therefore, should guarantee the safe use of these HR buffalo grasses. The current metabolomics analyses not only confirmed the effects of glufosinate to up-regulate free amino acid pools in the sensitive wild type but also several alterations in sugar, sugar phosphate and organic acid metabolism have been reported.

From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data

Untargeted metabolomics (including lipidomics) is a holistic approach to biomarker discovery and mechanistic insights into disease onset and progression, and response to intervention. Each step of the analytical and statistical pipeline is crucial for the generation of high-quality, robust data. Metabolite identification remains the bottleneck in these studies; therefore, confidence in the data produced is paramount in order to maximize the biological output. Here, we outline the key steps of the metabolomics workflow and provide details on important parameters and considerations. Studies should be designed carefully to ensure appropriate statistical power and adequate controls. Subsequent sample handling and preparation should avoid the introduction of bias, which can significantly affect downstream data interpretation. It is not possible to cover the entire metabolome with a single platform; therefore, the analytical platform should reflect the biological sample under investigation and the question(s) under consideration. The large, complex datasets produced need to be pre-processed in order to extract meaningful information. Finally, the most time-consuming steps are metabolite identification, as well as metabolic pathway and network analysis. Here we discuss some widely used tools and the pitfalls of each step of the workflow, with the ultimate aim of guiding the reader towards the most efficient pipeline for their metabolomics studies.

Emerging Insights into the Metabolic Alterations in Aging Using Metabolomics

Metabolomics is the latest 'omics' technology and systems biology science that allows for comprehensive profiling of small-molecule metabolites in biological systems at a specific time and condition. Metabolites are cellular intermediate products of metabolic reactions, which reflect the ultimate response to genomic, transcriptomic, proteomic, or environmental changes in a biological system. Aging is a complex biological process that is characterized by a gradual and progressive decline in molecular, cellular, tissue, organ, and organismal functions, and it is influenced by a combination of genetic, environmental, diet, and lifestyle factors. The precise biological mechanisms of aging remain unknown. Metabolomics has emerged as a powerful tool to characterize the organism phenotypes, identify altered metabolites, pathways, novel biomarkers in aging and disease, and offers wide clinical applications. Here, I will provide a comprehensive overview of our current knowledge on metabolomics led studies in aging with particular emphasis on studies leading to biomarker discovery. Based on the data obtained from model organisms and humans, it is evident that metabolites associated with amino acids, lipids, carbohydrate, and redox metabolism may serve as biomarkers of aging and/or longevity. Current challenges and key questions that should be addressed in the future to advance our understanding of the biological mechanisms of aging are discussed.

Metabolic Fingerprint of Chronic Obstructive Lung Diseases: A New Diagnostic Perspective

Chronic obstructive lung disease (COLD) is a group of airway diseases, previously known as emphysema and chronic bronchitis. The heterogeneity of COLD does not allow early diagnosis and leads to increased morbidity and mortality. The increasing number of COLD incidences stresses the need for precision medicine approaches that are specific to the patient. Metabolomics is an emerging technology that allows for the discrimination of metabolic changes in the cell as a result of environmental factors and specific genetic background. Thus, quantification of metabolites in human biofluids can provide insights into the metabolic state of the individual in real time and unravel the presence of, or predisposition to, a disease. In this article, the advantages of and potential barriers to putting metabolomics into clinical practice for COLD are discussed. Today, metabolomics is mostly lab-based, and research studies with novel COLD-specific biomarkers are continuously being published. Several obstacles in the research and the market field hamper the translation of these data into clinical practice. However, technological and computational advances will facilitate the clinical interpretation of data and provide healthcare professionals with the tools to prevent, diagnose, and treat COLD with precision in the coming decades.