Opportunities and Limitations for Untargeted Mass Spectrometry Metabolomics to Identify Biologically Active Constituents in Complex Natural Product Mixtures

Bacterial metabolomics: current applications for human welfare and future aspects

An imbalanced microbiome is linked to several diseases, such as cancer, inflammatory bowel disease, obesity, and even neurological disorders. Bacteria and their by-products are used for various industrial and clinical purposes. The metabolites under discussion were chosen based on their biological impacts on host and gut microbiota interactions as established by metabolome research. The separation of bacterial metabolites by using statistics and machine learning analysis creates new opportunities for applications of bacteria and their metabolites in the environmental and medical sciences. Thus, the metabolite production strategies, methodologies, and importance of bacterial metabolites for human well-being are discussed in this review.

Multidisciplinary approach combining food metabolomics and epidemiology identifies meglutol as an important bioactive metabolite in tempe, an Indonesian fermented food

This study introduces a multidisciplinary approach to investigate bioactive food metabolites often overlooked due to their low concentrations. We integrated an in-house food metabolite library (n = 494), a human metabolite library (n = 891) from epidemiological studies, and metabolite pharmacological databases to screen for food metabolites with potential bioactivity. We identified six potential metabolites, including meglutol (3-hydroxy-3-methylglutarate), an understudied low-density lipoprotein (LDL)-lowering compound. We further focused on meglutol as a case study to showcase the range of characterizations achievable with this approach. Green pea tempe was identified to contain the highest meglutol concentration (21.8 ± 4.6 mg/100 g). Furthermore, we identified a significant cross-sectional association between plasma meglutol (per 1-standard deviation) and lower LDL cholesterol in two Hispanic adult cohorts (n = 1,628) (β [standard error]: -5.5 (1.6) mg/dl, P = 0.0005). These findings highlight how multidisciplinary metabolomics can serve as a systematic tool for discovering and enhancing bioactive metabolites in food, such as meglutol, with potential applications in personalized dietary approaches for disease prevention.

Chemometric-based drug discovery approaches from natural origins using hyphenated chromatographic techniques

INTRODUCTION

Isolation and characterization of bioactive components from complex matrices of marine or terrestrial biological origins are the most challenging issues for natural product chemists. Biochemometric is a new potential scope in natural product analytical science, and it is a methodology to find the compound's correlation to their bioactivity with the help of hyphenated chromatographic techniques and chemometric tools.

OBJECTIVES

The present review aims to evaluate the application of chemometric tools coupled to chromatographic techniques for drug discovery from natural resources.

METHODS

The searching keywords "biochemometric," "chemometric," "chromatography," "natural products bioassay," and "bioassay" were selected to search the published articles between 2010-2023 using different search engines including "Pubmed", "Web of Science," "ScienceDirect," and "Google scholar."

RESULTS

An initial stage in natural product analysis is applying the chromatographic hyphenated techniques in conjunction with biochemometric approaches. Among the applied chromatographic techniques, liquid chromatography (LC) techniques, have taken up more than half (53%) and also, mass spectroscopy (MS)-based chromatographic techniques such as LC-MS are the most widely used techniques applied in combination with chemometric methods for natural products bioassay. Considering the complexity of dataset achieved from chromatographic hyphenated techniques, chemometric tools have been increasingly employed for phytochemical studies in the context of determining botanicals geographical origin, quality control, and detection of bioactive compounds.

CONCLUSION

Biochemometric application is expected to be further improved with advancing in data acquisition methods, new efficient preprocessing, model validation and variable selection methods which would guarantee that the applied model to have good prediction ability in compound relation to its bioactivity.

Heavy metal exposure and metabolomics analysis: an emerging frontier in environmental health

Exposure to heavy metals in various populations can lead to extensive damage to different organs, as these metals infiltrate and bioaccumulate in the human body, causing metabolic disruptions in various organs. To comprehensively understand the metal homeostasis, inter-organ "traffic," and extensive metabolic alterations resulting from heavy metal exposure, employing complementary analytical methods is crucial. Metabolomics is pivotal in unraveling the intricacies of disease vulnerability by furnishing thorough understandings of metabolic changes linked to different metabolic diseases. This field offers exciting prospects for enhancing the disease prevention, early detection, and tailoring treatment approaches to individual needs. This article consolidates the existing knowledge on disease-linked metabolic pathways affected by the exposure of diverse heavy metals providing concise overview of the underlying impact mechanisms. The main aim is to investigate the connection between the altered metabolic pathways and long-term complex health conditions induced by heavy metals such as diabetes mellitus, cardiovascular diseases, renal disorders, inflammation, neurodegenerative diseases, reproductive risks, and organ damage. Further exploration of common pathways may unveil the shared targets for treating associated pathological conditions. In this article, the role of metabolomics in disease susceptibility is emphasized that metabolomics is expected to be routinely utilized for the diagnosis and monitoring of diseases and practical value of biomarkers derived from metabolomics, as well as determining their appropriate integration into extensive clinical settings.

Towards non-target proactive food safety: identification of active compounds in convenience tomato products by ten-dimensional hyphenation with integrated simulated gastrointestinal digestion

Current strategies for non-target food screening focus mainly on known hazardous chemicals (adulterants, residues, contaminants, packaging migrants, etc.) instead of bioactive constituents in general and exclude the biological effect detection. To widen the perspective, a more proactive non-target effect-directed strategy is introduced to complement food safety in order to detect not only known but also unknown bioactive compounds. The developed 10-dimensional hyphenation included on-surface digestion (1D), planar chromatographic separation (2D), visualization using white light (3D), UV light (4D), fluorescence light (5D), effect-directed assay analysis (6D), heart-cut zone elution to an orthogonal reversed phase column chromatography including online desalting (7D) with subsequent diode array detection (8D), high-resolution mass spectrometry (9D), and fragmentation (10D). Metabolism, i.e., intestinal digestion of each sample, was simulated and integrated on the same adsorbent surface to study any changes in the compound profiles. As proof of principle, nine convenience tomato products and a freshly prepared tomato soup were screened via five different planar assays in a non-targeted mode. Non-digested and digested samples were compared side by side. In their effect-directed profiles, 14 bioactive compounds from classes of lipids, plant hormones, spices, and pesticides were identified. In particular, bioactive compounds coming from the lipid class were increased by gastrointestinal digestion, while spices and pesticides remained unaffected. With regard to food safety, the determination of the two dinitrophenol herbicides dinoterb and dinoseb in highly processed tomato products should be given special attention. The hyphenation covered a broad analyte spectrum and showed robust and reliable results.

Metabolomics of natural samples: A tutorial review on the latest technologies

Metabolomics is the study of metabolites present in a living system. It is a rapidly growing field aimed at discovering novel compounds, studying biological processes, diagnosing diseases, and ensuring the quality of food products. Recently, the analysis of natural samples has become important to explore novel bioactive compounds and to study how environment and genetics affect living systems. Various metabolomics techniques, databases, and data analysis tools are available for natural sample metabolomics. However, choosing the right method can be a daunting exercise because natural samples are heterogeneous and require untargeted approaches. This tutorial review aims to compile the latest technologies to guide an early-career scientist on natural sample metabolomics. First, different extraction methods and their pros and cons are reviewed. Second, currently available metabolomics databases and data analysis tools are summarized. Next, recent research on metabolomics of milk, honey, and microbial samples is reviewed. Finally, after reviewing the latest trends in technologies, a checklist is presented to guide an early-career researcher on how to design a metabolomics project. In conclusion, this review is a comprehensive resource for a researcher planning to conduct their first metabolomics analysis. It is also useful for experienced researchers to update themselves on the latest trends in metabolomics.

Anti-Inflammatory Activity of Lauraceae Plant Species and Prediction Models Based on Their Metabolomics Profiling Data

The Lauraceae is a botanical family known for its anti-inflammatory potential. However, several species have not yet been studied. Thus, this work aimed to screen the anti-inflammatory activity of this plant family and to build statistical prediction models. The methodology was based on the statistical analysis of high-resolution liquid chromatography coupled with mass spectrometry data and the ex vivo anti-inflammatory activity of plant extracts. The ex vivo results demonstrated significant anti-inflammatory activity for several of these plants for the first time. The sample data were applied to build anti-inflammatory activity prediction models, including the partial least square acquired, artificial neural network, and stochastic gradient descent, which showed adequate fitting and predictive performance. Key anti-inflammatory markers, such as aporphine and benzylisoquinoline alkaloids were annotated with confidence level 2. Additionally, the validated prediction models proved to be useful for predicting active extracts using metabolomics data and studying their most bioactive metabolites.

Improvement of the functional value of green soybean (edamame) using germination and tempe fermentation: A comparative metabolomics study

Green soybean, also known as edamame, is a legume with high nutritional and functional value. Despite its growing popularity and potential health benefits, the functionality of green soybean has not been thoroughly studied. Previous research on the functionality of green soybean has largely focused on a limited number of specific, well-studied, bioactive metabolites, without comprehensively investigating the metabolome of this legume. Additionally, very few studies have explored the improvement of the functional value of green soybean. This study aimed to investigate the metabolome profile of green soybean, identify bioactive metabolites, and to further explore the potential improvement of the identified bioactive metabolites using germination and tempe fermentation. A total of 80 metabolites were annotated from green soybean using GC-MS and HPLC-PDA-MS. Among them, 16 important bioactive metabolites were identified: soy isoflavones daidzin, glycitin, genistin, malonyl daidzin, malonyl genistin, malonyl glycitin, acetyl daidzin, acetyl genistin, acetyl glycitin, daidzein, glycitein, and genistein, as well as other metabolites including 3,4-dihydroxybenzoic acid, 3-hydroxyanthranillic acid, 3-hydroxy-3-methylglutaric acid (meglutol), and 4-aminobutyric acid (GABA). Germination and tempe fermentation techniques were employed to potentially improve the concentrations of these bioactive metabolites. While showing improvements in amino acid contents, germination process did not improve bioactive metabolites significantly. In contrast, tempe fermentation was found to significantly increase the concentrations of daidzein, genistein, glycitein, acetyl genistin, acetyl daidzin, 3-hydroxyanthranillic acid, and meglutol (>2-fold increase with p < 0.05) while also improving amino acid levels. This study highlights the potentials of germination and fermentation to improve the functionality of legumes, particularly green soybean.

Mass spectrometry-based techniques for identification of compounds in milk and meat matrix

Food including milk and meat is often viewed as the mixture of different components such as fat, protein, carbohydrates, moisture and ash, which are estimated using well-established protocols and techniques. However, with the advent of metabolomics, low-molecular weight substances, also known as metabolites, have been recognized as one of the major factors influencing the production, quality and processing. Therefore, different separation and detection techniques have been developed for the rapid, robust and reproducible separation and identification of compounds for efficient control in milk and meat production and supply chain. Mass-spectrometry based techniques such as GC-MS and LC-MS and nuclear magnetic resonance spectroscopy techniques have been proven successful in the detailed food component analysis owing to their associated benefits. Different metabolites extraction protocols, derivatization, spectra generated, data processing followed by data interpretation are the major sequential steps for these analytical techniques. This chapter deals with not only the detailed discussion of these analytical techniques but also sheds light on various applications of these analytical techniques in milk and meat products.

Kvalheim O, Cech NB. Interaction Metabolomics to Discover Synergists in Natural Product Mixtures

Mass spectrometry metabolomics has become increasingly popular as an integral aspect of studies to identify active compounds from natural product mixtures. Classical metabolomics data analysis approaches do not consider the possibility that interactions (such as synergy) could occur between mixture components. With this study, we developed "interaction metabolomics" to overcome this limitation. The innovation of interaction metabolomics is the inclusion of compound interaction terms (CITs), which are calculated as the product of the intensities of each pair of features (detected ions) in the data matrix. Herein, we tested the utility of interaction metabolomics by spiking known concentrations of an antimicrobial compound (berberine) and a synergist (piperine) into a set of inactive matrices. We measured the antimicrobial activity for each of the resulting mixtures against Staphylococcus aureus and analyzed the mixtures with liquid chromatography coupled to high-resolution mass spectrometry. When the data set was processed without CITs (classical metabolomics), statistical analysis yielded a pattern of false positives. However, interaction metabolomics correctly identified berberine and piperine as the compounds responsible for the synergistic activity. To further validate the interaction metabolomics approach, we prepared mixtures from extracts of goldenseal (Hydrastis canadensis) and habañero pepper (Capsicum chinense) and correctly correlated synergistic activity of these mixtures to the combined action of berberine and several capsaicinoids. Our results demonstrate the utility of a conceptually new approach for identifying synergists in mixtures that may be useful for applications in natural products research and other research areas that require comprehensive mixture analysis.

Modulation of in Vitro SARS-CoV-2 Infection by Stephania tetrandra and Its Alkaloid Constituents

Botanical natural products have been widely consumed for their purported usefulness against COVID-19. Here, six botanical species from multiple sources and 173 isolated natural product compounds were screened for blockade of wild-type (WT) SARS-CoV-2 infection in human 293T epithelial cells overexpressing ACE-2 and TMPRSS2 protease (293TAT). Antiviral activity was demonstrated by an extract from Stephania tetrandra. Extract fractionation, liquid chromatography-mass spectrometry (LC-MS), antiviral assays, and computational analyses revealed that the alkaloid fraction and purified alkaloids tetrandrine, fangchinoline, and cepharanthine inhibited WT SARS-CoV-2 infection. The alkaloids and alkaloid fraction also inhibited the delta variant of concern but not WT SARS-CoV-2 in VeroAT cells. Membrane permeability assays demonstrate that the alkaloids are biologically available, although fangchinoline showed lower permeability than tetrandrine. At high concentrations, the extract, alkaloid fractions, and pure alkaloids induced phospholipidosis in 293TAT cells and less so in VeroAT cells. Gene expression profiling during virus infection suggested that alkaloid fraction and tetrandrine displayed similar effects on cellular gene expression and pathways, while fangchinoline showed distinct effects on cells. Our study demonstrates a multifaceted approach to systematically investigate the diverse activities conferred by complex botanical mixtures, their cell-context specificity, and their pleiotropic effects on biological systems.

Developmental programming: Preconceptional and gestational exposure of sheep to a real-life environmental chemical mixture alters maternal metabolome in a fetal sex-specific manner

BACKGROUND

Everyday, humans are exposed to a mixture of environmental chemicals some of which have endocrine and/or metabolism disrupting actions which may contribute to non-communicable diseases. The adverse health impacts of real-world chemical exposure, characterized by chronic low doses of a mixture of chemicals, are only recently emerging. Biosolids derived from human waste represent the environmental chemical mixtures humans are exposed to in real life. Prior studies in sheep have shown aberrant reproductive and metabolic phenotypes in offspring after maternal biosolids exposure.

OBJECTIVE

To determine if exposure to biosolids perturbs the maternal metabolic milieu of pregnant ewes, in a fetal sex-specific manner.

METHODS

Ewes were grazed on inorganic fertilizer (Control) or biosolids-treated pastures (BTP) from before mating and throughout gestation. Plasma from pregnant ewes (Control n = 15, BTP n = 15) obtained mid-gestation were analyzed by untargeted metabolomics. Metabolites were identified using Agilent MassHunter. Multivariate analyses were done using MetaboAnalyst 5.0 and confirmed using SIMCA.

RESULTS

Univariate and multivariate analysis of 2301 annotated metabolites identified 193 differentially abundant metabolites (DM) between control and BTP sheep. The DM primarily belonged to the super-class of lipids and organic acids. 15-HeTrE, oleamide, methionine, CAR(3:0(OH)) and pyroglutamic acid were the top DM and have been implicated in the regulation of fetal growth and development. Fetal sex further exacerbated differences in metabolite profiles in the BTP group. The organic acids class of metabolites was abundant in animals with male fetuses. Prenol lipid, sphingolipid, glycerolipid, alkaloid, polyketide and benzenoid classes showed fetal sex-specific responses to biosolids.

DISCUSSION

Our study illustrates that exposure to biosolids significantly alters the maternal metabolome in a fetal sex-specific manner. The altered metabolite profile indicates perturbations to fatty acid, arginine, branched chain amino acid and one‑carbon metabolism. These factors are consistent with, and likely contribute to, the adverse phenotypic outcomes reported in the offspring.

Indole Alkaloids from Psychoactive Mushrooms: Chemical and Pharmacological Potential as Psychotherapeutic Agents

Neuropsychiatric diseases such as depression, anxiety, and post-traumatic stress represent a substantial long-term challenge for the global health systems because of their rising prevalence, uncertain neuropathology, and lack of effective pharmacological treatments. The approved existing studies constitute a piece of strong evidence whereby psychiatric drugs have shown to have unpleasant side effects and reduction of sustained tolerability, impacting patients' quality of life. Thus, the implementation of innovative strategies and alternative sources of bioactive molecules for the search for neuropsychiatric agents are required to guarantee the success of more effective drug candidates. Psychotherapeutic use of indole alkaloids derived from magic mushrooms has shown great interest and potential as an alternative to the synthetic drugs currently used on the market. The focus on indole alkaloids is linked to their rich history, their use as pharmaceuticals, and their broad range of biological properties, collectively underscoring the indole heterocycle as significant in drug discovery. In this review, we aim to report the physicochemical and pharmacological characteristics of indole alkaloids, particularly those derived from magic mushrooms, highlighting the promising application of such active ingredients as safe and effective therapeutic agents for the treatment of neuropsychiatric disorders.

Metabolomics- and systems-biology-guided discovery of metabolite lead compounds and druggable targets

Metabolomics enables the comprehensive and unbiased analysis of metabolites and lipids in biological systems. In conjunction with high-throughput activity screening, big data and synthetic biology, metabolomics can guide the discovery of lead compounds with pharmacological activity from natural sources and the gut microbiome. In combination with other omics, metabolomics can further unlock the elucidation of compound toxicity, the mode of action and novel druggable targets of disease. Here, we discuss the workflows, limitations and future opportunities to leverage metabolomics and big data in conjunction with systems and synthetic biology for streamlining the discovery and development of molecules of pharmaceutical interest.

Synergistic Inhibiting Effect of Phytochemicals in Rheum palmatum on Tyrosinase Based on Metabolomics and Isobologram Analyses

Tyrosinase (TYR) plays a key role in the enzymatic reaction that is responsible for a range of unwanted discoloration effects, such as food browning and skin hyperpigmentation. TYR inhibitors could, therefore, be candidates for skin care products that aim to repair pigmentation problems. In this study, we used a metabolomics approach combined with the isobologram analysis to identify anti-TYR compounds within natural resources, and evaluate their possible synergism with each other. Rheum palmatum was determined to be a model plant for observing the effect, of which seven extracts with diverse phytochemicals were prepared by way of pressurized solvent extraction. Each Rheum palmatum extract (RPE) was profiled using nuclear magnetic resonance spectroscopy and its activity of tyrosinase inhibition was evaluated. According to the orthogonal partial least square analysis used to correlate phytochemicals in RPE with the corresponding activity, the goodness of fit of the model (R₂ = 0.838) and its predictive ability (Q₂ = 0.711) were high. Gallic acid and catechin were identified as the active compounds most relevant to the anti-TYR effect of RPE. Subsequently, the activity of gallic acid and catechin were evaluated individually, and when combined in various ratios by using isobologram analysis. The results showed that gallic acid and catechin in the molar ratios of 9:5 and 9:1 exhibited a synergistic inhibition on TYR, with a combination index lower than 0.77, suggesting that certain combinations of these compounds may prove effective for use in cosmetic, pharmaceutical, and food industries.

Discovering New Natural Products Using Metabolomics-Based Approaches

The incessant search for new natural molecules with biological activities has forced researchers in the field of chemistry of natural products to seek different approaches for their prospection studies. In particular, researchers around the world are turning to approaches in metabolomics to avoid high rates of re-isolation of certain compounds, something recurrent in this branch of science. Thanks to the development of new technologies in the analytical instrumentation of spectroscopic and spectrometric techniques, as well as the advance in the computational processing modes of the results, metabolomics has been gaining more and more space in studies that involve the prospection of natural products. Thus, this chapter summarizes the precepts and good practices in the metabolomics of microbial natural products using mass spectrometry and nuclear magnetic resonance spectroscopy, and also summarizes several examples where this approach has been applied in the discovery of bioactive molecules.

How Low Can You Go? Selecting Intensity Thresholds for Untargeted Metabolomics Data Preprocessing

Untargeted mass spectrometry (MS) metabolomics is an increasingly popular approach for characterizing complex mixtures. Recent studies have highlighted the impact of data preprocessing for determining the quality of metabolomics data analysis. The first step in data processing with untargeted metabolomics requires that signal thresholds be selected for which features (detected ions) are included in the dataset. Analysts face the challenge of knowing where to set these thresholds; setting them too high could mean missing relevant features, but setting them too low could result in a complex and unwieldy dataset. This study compared data interpretation for an example metabolomics dataset when intensity thresholds were set at a range of feature heights. The main observations were that low signal thresholds (1) improved the limit of detection, (2) increased the number of features detected with an associated isotope pattern and/or an MS-MS fragmentation spectrum, and (3) increased the number of in-source clusters and fragments detected for known analytes of interest. When the settings of parameters differing in intensities were applied on a set of 39 samples to discriminate the samples through principal component analyses (PCA), similar results were obtained with both low- and high-intensity thresholds. We conclude that the most information-rich datasets can be obtained by setting low-intensity thresholds. However, in the cases where only a qualitative comparison of samples with PCA is to be performed, it may be sufficient to set high thresholds and thereby reduce the complexity of the data processing and amount of computational time required.

Artificial intelligence in microbial natural product drug discovery: current and emerging role

Covering: up to the end of 2022Microorganisms are exceptional sources of a wide array of unique natural products and play a significant role in drug discovery. During the golden era, several life-saving antibiotics and anticancer agents were isolated from microbes; moreover, they are still widely used. However, difficulties in the isolation methods and repeated discoveries of the same molecules have caused a setback in the past. Artificial intelligence (AI) has had a profound impact on various research fields, and its application allows the effective performance of data analyses and predictions. With the advances in omics, it is possible to obtain a wealth of information for the identification, isolation, and target prediction of secondary metabolites. In this review, we discuss drug discovery based on natural products from microorganisms with the help of AI and machine learning.

Mechanism and Active Components of Qingre Lidan Tablets Alleviate Intrahepatic Cholestasis by Activating the Farnesoid X Receptor

Background

Qingre Lidan tablets (QLTs) are a compound preparation of Chinese medicine that have long been used clinically to treat poor bile circulation caused by the inflammation and obstruction of the gallbladder and bile duct and to relieve jaundice and other symptoms. However, its material basis and mechanism are still unclear. The purpose of this study was to investigate the mechanism and active components of QLTs for treating intrahepatic cholestasis (IHC) in rat models.

Methods

In vivo experiments verified the effect of QLTs on alpha-naphthyl isothiocyanate (ANIT)-induced IHC models in rats. The mRNA and protein expression levels of farnesoid X receptor (FXR), bile salt export pump (BSEP), and multidrug-associated protein 2 (MRP2) in the rat liver were detected. UPLC/Q-TOF-MS was used to separate and identify the monomers in QLTs, and a dual-luciferase reporter assay was used to select effective the monomers that stimulate FXR. Among the selected monomers, baicalein was used as a representative to verify the effect on rat IHC models.

Results

QLTs and baicalein significantly reduced the serum biochemical indicators reflecting the changes in liver function among IHC rats and remitted the ANIT-induced liver histopathological changes. The expression levels of FXR, BSEP, and MRP2 in the liver were significantly increased after QLT treatment in a dose-dependent manner. Moreover, six types of active components that activate FXR were selected in QLTs, namely baicalein, wogonin, baicalein II, emodin, dibutyl phthalate, and diisooctyl phthalate.

Conclusions

QLTs and the active component, baicalein, can alleviate IHC in model rats.

Innovative Application of Metabolomics on Bioactive Ingredients of Foods

Metabolomics, as a new omics technology, has been widely accepted by researchers and has shown great potential in the field of nutrition and health in recent years. This review briefly introduces the process of metabolomics analysis, including sample preparation and extraction, derivatization, separation and detection, and data processing. This paper focuses on the application of metabolomics in food-derived bioactive ingredients. For example, metabolomics techniques are used to analyze metabolites in food to find bioactive substances or new metabolites in food materials. Moreover, bioactive substances have been tested in vitro and in vivo, as well as in humans, to investigate the changes of metabolites and the underlying metabolic pathways, among which metabolomics is used to find potential biomarkers and targets. Metabolomics provides a new approach for the prevention and regulation of chronic diseases and the study of the underlying mechanisms. It also provides strong support for the development of functional food or drugs. Although metabolomics has some limitations such as low sensitivity, poor repeatability, and limited detection range, it is developing rapidly in general, and also in the field of nutrition and health. At the end of this paper, we put forward our own insights on the development prospects of metabolomics in the application of bioactive ingredients in food.

Interactions between Medical Plant-Derived Bioactive Compounds: Focus on Antimicrobial Combination Effects

It is accepted that the medicinal use of complex mixtures of plant-derived bioactive compounds is more effective than purified bioactive compounds due to beneficial combination interactions. However, synergy and antagonism are very difficult to study in a meticulous fashion since most established methods were designed to reduce the complexity of mixtures and identify single bioactive compounds. This study represents a critical review of the current scientific literature on the combined effects of plant-derived extracts/bioactive compounds. A particular emphasis is provided on the identification of antimicrobial synergistic or antagonistic combinations using recent metabolomics methods and elucidation of approaches identifying potential mechanisms that underlie their interactions. Proven examples of synergistic/antagonistic antimicrobial activity of bioactive compounds are also discussed. The focus is also put on the current challenges, difficulties, and problems that need to be overcome and future perspectives surrounding combination effects. The utilization of bioactive compounds from medicinal plant extracts as appropriate antimicrobials is important and needs to be facilitated by means of new metabolomics technologies to discover the most effective combinations among them. Understanding the nature of the interactions between medicinal plant-derived bioactive compounds will result in the development of new combination antimicrobial therapies.

Investigating the Antituberculosis Activity of Selected Commercial Essential Oils and Identification of Active Constituents Using a Biochemometrics Approach and In Silico Modeling

Tuberculosis (TB) is a disease caused by Mycobacterium tuberculosis which has become prevalent due to the emergence of resistant M. tuberculosis strains. The use of essential oils (EOs) as potential anti-infective agents to treat microbial infections, including TB, offers promise due to their long historical use and low adverse effects. The current study aimed to investigate the in vitro anti-TB activity of 85 commercial EOs, and identify compounds responsible for the activity, using a biochemometrics approach. A microdilution assay was used to determine the antimycobacterial activity of the EOs towards some non-pathogenic Mycobacterium strains. In parallel, an Alamar blue assay was used to investigate antimycobacterial activity towards the pathogenic M. tuberculosis strain. Chemical profiling of the EOs was performed using gas chromatography-mass spectrometry (GC-MS) analysis. Biochemometrics filtered out putative biomarkers using orthogonal projections to latent structures discriminant analysis (OPLS-DA). In silico modeling was performed to identify potential therapeutic targets of the active biomarkers. Broad-spectrum antimycobacterial activity was observed for Cinnamomum zeylanicum (bark) (MICs = 1.00, 0.50, 0.25 and 0.008 mg/mL) and Levisticum officinale (MICs = 0.50, 0.5, 0.5 and 0.004 mg/mL) towards M. smegmatis, M. fortuitum, M. gordonae and M. tuberculosis, respectively. Biochemometrics predicted cinnamaldehyde, thymol and eugenol as putative biomarkers. Molecular docking demonstrated that cinnamaldehyde could serve as a scaffold for developing a novel class of antimicrobial compounds by targeting FtsZ and PknB from M. tuberculosis.

Identification of Anti-Proliferative Compounds from Genista monspessulana Seeds through Covariate-Based Integration of Chemical Fingerprints and Bioactivity Datasets

Genista monspessulana (L.) L.A.S. Johnson (Fabaceae) is a Mediterranean plant introduced to South America and other regions for ornamental purposes. However, it is considered an invasive shrub due to its reproductive vigor in many areas. Unlike other Genista plants, G. monspessulana has few studies disclosing its biologically active components, particularly cytotoxic agents against cancer cells. Thus, as part of our research on anti-proliferative bioactives, a set of ethanolic seed extracts from ten accessions of G. monspessulana, collected in the Bogotá plateau, were evaluated against four cell lines: PC-3 (prostate adenocarcinoma), SiHa (cervical carcinoma), A549 (lung carcinoma), and L929 (normal mouse fibroblasts). Extracts were also analyzed through liquid chromatography coupled with mass spectrometry (LC/MS) to record chemical fingerprints and determine the composition and metabolite variability between accessions. Using multiple covariate statistics, chemical and bioactivity datasets were integrated to recognize patterns and identify bioactive compounds among studied extracts. G. monspessulana seed-derived extracts exhibited dose-dependent antiproliferative activity on PC-3 and SiHa cell lines (>500 µg/mL < IC50 < 26.3 µg/mL). Seven compounds (1−7) were inferred as the compounds most likely responsible for the observed anti-proliferative activity and subsequently isolated and identified by spectroscopic techniques. A tricyclic quinolizidine (1) and a pyranoisoflavone (2) were found to be the most active compounds, exhibiting selectivity against PC-3 cell lines (IC50 < 18.6 µM). These compounds were used as precursors to obtain a quinolizidine-pyranoisoflavone adduct via Betti reaction, improving the activity against PC-3 and comparable to curcumin as the positive control. Results indicated that this composition−activity associative approach is advantageous to finding those bioactive principles efficiently within active extracts. This correlative association can be employed in further studies focused on the targeted isolation of anti-proliferative compounds from Genista plants and accessions.

Carbon-nitrogen bond formation to construct novel polyketide-indole hybrids from the indole-3-carbinol exposed culture of Daldinia eschscholzii

A plenty of cytochrome P450s have been annotated in the Daldinia eschosholzii genome. Inspired by the fact that some P450s have been reported to catalyze the carbon-nitrogen (C–N) bond formation, we were curious about whether hybrids through C–N bond formation could be generated in the indole-3-carbinol (I3C) exposed culture of D. eschscholzii. As expected, two skeletally undescribed polyketide-indole hybrids, designated as indolpolyketone A and B (1 and 2), were isolated and assigned to be constructed through C–N bond formation. Their structures were elucidated by 1D and 2D NMR spectra. The absolute configurations of 1 and 2 were determined by comparing the recorded and calculated electronic circular dichroism (ECD) spectra. Furthermore, the plausible biosynthetic pathways for 1 and 2 were proposed. Compounds 1 and 2 exhibited significant antiviral activity against H1N1 with IC₅₀ values of 45.2 and 31.4 μM, respectively. In brief, compounds 1 and 2 were reported here for the first time and were the first example of polyketide-indole hybrids pieced together through C–N bond formation in the I3C-exposed culture of D. eschscholzii. Therefore, this study expands the knowledge about the chemical production of D. eschscholzii through precursor-directed biosynthesis (PDB).

Composition and Antifungal Activity of the Alkaloidal Fraction of Lupinus mirabilis Leaves: A Biochemometrics-Based Exploration

Lupinus plants are well-recognized due to their significant alkaloid content, which has made them the subject of several studies. However, the lack of chemical and biological information on the Colombian Lupinus species remains a fact. Therefore, the alkaloidal fractions from the leaves of L. mirabilis obtained by conventional solvent and ultrasound-assisted extraction (CSE and UAE, respectively) at different time frames were analyzed. Sparteine (2) was the main component in all cases; however, its relative abundance showed large variability, ranging from 64.7% to 80.6%. Minor constituents were also affected by the extraction conditions. In general, prolonged times gave a higher proportion of alkaloids under CSE, while only a slight decrease was observed under UAE. Both the method and extraction time appeared to equally affect the ratios of particular alkaloids, leading to variations in their effect on the mycelial growth of Fusarium oxysporum. Holistic analysis through multiple-covariate statistical methods as an approach to integrating chemical and bioactivity datasets allowed inferring the compounds most likely responsible for the changes in mycelial growth inhibition. 13α-Hydroxylupanine (12) might represent a promising compound to be included in further studies against this phytopathogen.

Identification of Antifungal Compounds from Piper Plants Against Fusarium oxysporum: An Untargeted Metabolite Profiling-Based Approach

The phytopathogen Fusarium oxysporum produces considerable losses in economically important crops, making alternative control measures urgently required. Piper plants are widely distributed in tropical regions, and they are also known to produce metabolites with biological activity against infectious agents. As part of our continuous search for antifungals, 18 Piper-derived ethanolic extracts were evaluated by their in vitro effect on F oxysporum mycelial growth inhibition. The total content of phenol and flavonoid measurements and liquid chromatography-electrospray ionization-mass spectrometry analysis served as the chemical characterization of the investigated extracts. Piper pulchrum, Piper barcoense, and Piper tuberculatum exhibited the highest mycelial growth inhibition (>74%). The integration of chemical fingerprints and bioactivity datasets led to recognizing 4 bioactive candidates among extracts through single- Y orthogonal partial least squares regression and univariate statistics. These candidates were 2 amides (1,3), an alkyl lactone (2), and a prenylated benzoquinone (4), subsequently isolated and identified by nuclear magnetic resonance spectroscopy. These isolated compounds exhibited reasonable antifungal activity (IC50 < 50 µM). The findings indicated that the correlative association is advantageous for identifying bioactive metabolites within active extracts.

Characterization of Constituents with Potential Anti-Inflammatory Activity in Chinese Lonicera Species by UHPLC-HRMS Based Metabolite Profiling

This study centered on detecting potentially anti-inflammatory active constituents in ethanolic extracts of Chinese Lonicera species by taking an UHPLC-HRMS-based metabolite profiling approach. Extracts from eight different Lonicera species were subjected to both UHPLC-HRMS analysis and to pharmacological testing in three different cellular inflammation-related assays. Compounds exhibiting high correlations in orthogonal projections to latent structures discriminant analysis (OPLS-DA) of pharmacological and MS data served as potentially activity-related candidates. Of these candidates, 65 were tentatively or unambiguously annotated. 7-Hydroxy-5,3′,4′,5′-tetramethoxyflavone and three bioflavonoids, as well as three C₃₂- and one C₃₄-acetylated polyhydroxy fatty acid, were isolated from Lonicera hypoglauca leaves for the first time, and their structures were fully or partially elucidated. Of the potentially active candidate compounds, 15 were subsequently subjected to pharmacological testing. Their activities could be experimentally verified in part, emphasizing the relevance of Lonicera species as a source of anti-inflammatory active constituents. However, some compounds also impaired the cell viability. Overall, the approach was found useful to narrow down the number of potentially bioactive constituents in the complex extracts investigated. In the future, the application of more refined concepts, such as extract prefractionation combined with bio-chemometrics, may help to further enhance the reliability of candidate selection.

Evaluation of Ion Mobility Spectrometry for Improving Constitutional Assignment in Natural Product Mixtures

The comprehensive chemical characterization of biological samples remains a central challenge in the field of natural products. Conventional workflows using liquid chromatography (LC)-coupled high-resolution tandem mass spectrometry (MS/MS or MS2) allow the detection of relevant small molecules while providing diagnostic fragment ions for their structural assignment. Still, many natural product extracts are of a molecular complexity that challenges the resolving power of modern LC-MS2 pipelines. In this study, we examined the effect of integrating ion mobility spectrometry (IMS) to our LC-MS2 platform for the characterization of natural product mixtures. IMS provides an additional axis of separation in the gas phase as well as experimental collision cross-sectional (CCS) values. We analyzed a mixture of 20 commercial standards at 2 concentration ranges, either solubilized in solvent or spiked into an actinobacterial extract. Data were acquired in positive ion mode using both data-dependent acquisition (DDA) and data-independent acquisition (DIA) MS2 fragmentation approaches and assessed for both chemical coverage and spectral quality. IMS-DIA identified the largest number of standards in the spiked extract at the lower concentration of standards (17), followed by IMS-DDA (10), DDA (8), and DIA (6). In addition, we examined how these data sets performed in the Global Natural Products Social Molecular Networking (GNPS) platform. Overall, integrating IMS increased both metabolite detection and the quality of MS2 spectra, particularly for samples analyzed in DIA mode.

Comprehensive characterization of naturally occurring antioxidants from the twigs of mulberry (Morus alba) using on-line high-performance liquid chromatography coupled with chemical detection and high-resolution mass spectrometry

INTRODUCTION

The mulberry tree (Morus alba L.) is a prolific source of biologically active compounds. There is considerable growing interest in probing M. alba twigs as a source of disruptive antioxidant lead candidates for cosmetic skin care product development.

OBJECTIVE

An integrated approach using high-performance liquid chromatography (HPLC) coupled with either chemical detection (CD) or high-resolution mass spectrometry (HRMS) was applied to the hydroalcoholic extract of M. alba to detect and identify lead antioxidant compounds, respectively.

MATERIAL AND METHODS

The twigs were weighed, powdered and homogenized using a mill and the extract was prepared using 70% aqueous ethanol. The antioxidant metabolites were detected with HPLC coupled with CD (based on the ORAC assay) and their structural identification was carried out using a Q-Exactive Orbitrap MS instrument.

RESULTS

Using this approach, 13 peaks were detected as overall contributors to the antioxidant activity of M. alba, i.e. mulberrosides (A & E), oxyresveratrol & its derivatives, moracin & its derivatives and a dihydroxy-octadecadienoic acid, which together accounted for >90% of the antioxidant activity, highlighting the effectiveness of the integrated approach based on HPLC-CD and HPLC-HRMS. Additionally, a (3,4-dimethoxyphenyl-1-O-β-D-apiofuranosyl-(1″ → 6')-O-β-D-glucopyranoside was also discovered for the first time from the twig extract and is presented here.

CONCLUSION

To our knowledge, this is the first report from M. alba twigs using HPLC-CD and HPLC-HRMS that identifies key compounds responsible for the antioxidant property of this native Chinese medicinal plant.

Is Our Natural Food Our Homeostasis? Array of a Thousand Effect-Directed Profiles of 68 Herbs and Spices

The beneficial effects of plant-rich diets and traditional medicines are increasingly recognized in the treatment of civilization diseases due to the abundance and diversity of bioactive substances therein. However, the important active portion of natural food or plant-based medicine is presently not under control. Hence, a paradigm shift from quality control based on marker compounds to effect-directed profiling is postulated. We investigated 68 powdered plant extracts (botanicals) which are added to food products in food industry. Among them are many plants that are used as traditional medicines, herbs and spices. A generic strategy was developed to evaluate the bioactivity profile of each botanical as completely as possible and to straightforwardly assign the most potent bioactive compounds. It is an 8-dimensional hyphenation of normal-phase high-performance thin-layer chromatography with multi-imaging by ultraviolet, visible and fluorescence light detection as well as effect-directed assay and heart-cut of the bioactive zone to orthogonal reversed-phase high-performance liquid chromato-graphy-photodiode array detection-heated electrospray ionization mass spectrometry. In the non-target, effect-directed screening via 16 different on-surface assays, we tentatively assigned more than 60 important bioactive compounds in the studied botanicals. These were antibacterials, estrogens, antiestrogens, androgens, and antiandrogens, as well as acetylcholinesterase, butyrylcholinesterase, α-amylase, α-glucosidase, β-glucosidase, β-glucuronidase, and tyrosinase inhibitors, which were on-surface heart-cut eluted from the bioautogram or enzyme inhibition autogram to the next dimension for further targeted characterization. This biological-physicochemical hyphenation is able to detect and control active mechanisms of traditional medicines or botanicals as well as the essentials of plant-based food. The array of 1,292 profiles (68 samples × 19 detections) showed the versatile bioactivity potential of natural food. It reveals how efficiently and powerful our natural food contributes to our homeostasis.

Metabolomics Insights into Chemical Convergence in Xanthomonas perforans and Metabolic Changes Following Treatment with the Small Molecule Carvacrol

Microbes are natural chemical factories and their metabolome comprise diverse arrays of chemicals. The genus Xanthomonas comprises some of the most important plant pathogens causing devastating yield losses globally and previous studies suggested that species in the genus are untapped chemical minefields. In this study, we applied an untargeted metabolomics approach to study the metabolome of a globally spread important xanthomonad, X. perforans. The pathogen is difficult to manage, but recent studies suggest that the small molecule carvacrol was efficient in disease control. Bacterial strains were treated with carvacrol, and samples were taken at time intervals (1 and 6 h). An untreated control was also included. There were five replicates for each sample and samples were prepared for metabolomics profiling using the standard procedure. Metabolomics profiling was carried out using a thermo Q-Exactive orbitrap mass spectrometer with Dionex ultra high-performance liquid chromatography (UHPLC) and an autosampler. Annotation of significant metabolites using the Metabolomics Standards Initiative level 2 identified an array of novel metabolites that were previously not reported in Xanthomonas perforans. These metabolites include methoxybrassinin and cyclobrassinone, which are known metabolites of brassicas; sarmentosin, a metabolite of the Passiflora-heliconiine butterfly system; and monatin, a naturally occurring sweetener found in Sclerochiton ilicifolius. To our knowledge, this is the first report of these metabolites in a microbial system. Other significant metabolites previously identified in non-Xanthomonas systems but reported in this study include maculosin; piperidine; β-carboline alkaloids, such as harman and derivatives; and several important medically relevant metabolites, such as valsartan, metharbital, pirbuterol, and ozagrel. This finding is consistent with convergent evolution found in reported biological systems. Analyses of the effect of carvacrol in time-series and associated pathways suggest that carvacrol has a global effect on the metabolome of X. perforans, showing marked changes in metabolites that are critical in energy biosynthesis and degradation pathways, amino acid pathways, nucleic acid pathways, as well as the newly identified metabolites whose pathways are unknown. This study provides the first insight into the X. perforans metabolome and additionally lays a metabolomics-guided foundation for characterization of novel metabolites and pathways in xanthomonad systems.

Antifungal Activity against Fusarium oxysporum of Botanical End-Products: An Integration of Chemical Composition and Antifungal Activity Datasets to Identify Antifungal Bioactives

Plants produce various compounds as defensive barriers to naturally control fungal diseases. Among them, vascular wilt caused by Fusarium oxysporum is one of the most destructive diseases in crops, causing relevant economic losses. The application of synthetic fungicides is the most used management for this disease. However, this kind of method also involves adverse environmental impacts. Therefore, alternative methods are continuously being developed as a strategy to be involved in integrated pest management programs. Thus, as part of our research on antifungals of plant origin, a group of botanical extracts was assessed for the respective inhibitory effect on mycelium and conidia of F. oxysporum. Mycelial growth inhibition was measured in 12-well plates containing amended semi-solid medium, whereas conidial susceptibility was determined through microdilution. The identification of the bioactive compounds among test extracts was performed using an indirect approach, consisting of the integration of chemical composition and antifungal activity datasets through single-Y orthogonal partial least squares (OPLS) regression. Results showed that Piper aduncum extract was the most potent mycelial growth inhibitor whereas P. elongatum exhibited the best effect on conidia susceptibility. The active compounds identified through statistical integration and subsequent isolation were piperaduncin C, asebogenin and (-)-methyllinderatin. These findings indicated that the integrative, indirect approach is useful for the identification of bioactive metabolites from botanical extracts to be further used as biological protective agents against this phytopathogen.

Metabolomics and genomics in natural products research: complementary tools for targeting new chemical entities

Covering: 2010 to 2021Organisms in nature have evolved into proficient synthetic chemists, utilizing specialized enzymatic machinery to biosynthesize an inspiring diversity of secondary metabolites. Often serving to boost competitive advantage for their producers, these secondary metabolites have widespread human impacts as antibiotics, anti-inflammatories, and antifungal drugs. The natural products discovery field has begun a shift away from traditional activity-guided approaches and is beginning to take advantage of increasingly available metabolomics and genomics datasets to explore undiscovered chemical space. Major strides have been made and now enable -omics-informed prioritization of chemical structures for discovery, including the prospect of confidently linking metabolites to their biosynthetic pathways. Over the last decade, more integrated strategies now provide researchers with pipelines for simultaneous identification of expressed secondary metabolites and their biosynthetic machinery. However, continuous collaboration by the natural products community will be required to optimize strategies for effective evaluation of natural product biosynthetic gene clusters to accelerate discovery efforts. Here, we provide an evaluative guide to scientific literature as it relates to studying natural product biosynthesis using genomics, metabolomics, and their integrated datasets. Particular emphasis is placed on the unique insights that can be gained from large-scale integrated strategies, and we provide source organism-specific considerations to evaluate the gaps in our current knowledge.

The isolation of water-soluble natural products - challenges, strategies and perspectives

Covering period: up to 2019Water-soluble natural products constitute a relevant group of secondary metabolites notably known for presenting potent biological activities. Examples are aminoglycosides, β-lactam antibiotics, saponins of both terrestrial and marine origin, and marine toxins. Although extensively investigated in the past, particularly during the golden age of antibiotics, hydrophilic fractions have been less scrutinized during the last few decades. This review addresses the possible reasons on why water-soluble metabolites are now under investigated and describes approaches and strategies for the isolation of these natural compounds. It presents examples of several classes of hydrosoluble natural products and how they have been isolated. Novel stationary phases and chromatography techniques are also reviewed, providing a perspective towards a renaissance in the investigation of water-soluble natural products.

Synthetic biology approaches to actinomycete strain improvement

Their biochemical versatility and biotechnological importance make actinomycete bacteria attractive targets for ambitious genetic engineering using the toolkit of synthetic biology. But their complex biology also poses unique challenges. This mini review discusses some of the recent advances in synthetic biology approaches from an actinomycete perspective and presents examples of their application to the rational improvement of industrially relevant strains.

Non-target bioanalytical eight-dimensional hyphenation including bioassay, heart-cut trapping, online desalting, orthogonal separations and mass spectrometry

It is still a challenge to discover and identify individual bioactive compounds directly in multicomponent mixtures. Current workflows are too tedious for routine use. Hence, the hyphenation of separation and detection techniques is a powerful tool to maximize the information obtained by a single sample run. A robust eight-dimensional (8D) hyphenation was developed. Orthogonal separations, biological assay detection, analyte trapping, desalting, and physico-chemical detections were arranged in the following order, i.e. 1) normal phase high-performance thin-layer chromatography (NP-HPTLC) separation, 2) Vis detection, 3) UV detection, 4) fluorescence detection (FLD), 5) bioassay for effect-directed analysis (EDA), 6) heart-cut trapping/desalting/elution to reversed phase high-performance liquid chromatography (RP-HPLC) separation, 7) photodiode array (PDA) and 8) mass spectrometry (MS) detection. For the first time, the hyphenation exploited online analyte trapping to desalt the eluted bioactive zone from the plate containing highly salted bioassay media. Subsequent valve switching guided the trapped analyte(s) to the main column, followed by multiple detection. As proof-of-principle, cinnamon samples were analyzed by NP-HPTLC-UV/Vis/FLD-EDA-RP-HPLC-PDA-MS, whereby a bioactive zone was separated into two distinct peaks detected by PDA and MS to be 2-methoxy cinnamaldehyde and cinnamaldehyde. The developed 8D hyphenation is applicable for routine, allowing the non-target high-throughput screening of complex samples for individual bioactive compounds.

Predictive Modeling for Metabolomics Data

In recent years, mass spectrometry (MS)-based metabolomics has been extensively applied to characterize biochemical mechanisms, and study physiological processes and phenotypic changes associated with disease. Metabolomics has also been important for identifying biomarkers of interest suitable for clinical diagnosis. For the purpose of predictive modeling, in this chapter, we will review various supervised learning algorithms such as random forest (RF), support vector machine (SVM), and partial least squares-discriminant analysis (PLS-DA). In addition, we will also review feature selection methods for identifying the best combination of metabolites for an accurate predictive model. We conclude with best practices for reproducibility by including internal and external replication, reporting metrics to assess performance, and providing guidelines to avoid overfitting and to deal with imbalanced classes. An analysis of an example data will illustrate the use of different machine learning methods and performance metrics.

Composition of the metabolomic bio-coronas isolated from Ocimum sanctum and Rubia tinctorum

Objective

Nanoharvesting from intact plants, organs, and cultured cells is a method in which nanoparticles are co-incubated with the target tissue, which leads to the internalization of nanoparticles. Internalized nanoparticles are coated in situ with specific metabolites that form a dynamic surface layer called a bio-corona. Our previous study showed that metabolites that form the bio-corona around anatase TiO₂ nanoparticles incubated with leaves of the model plant Arabidopsis thaliana are enriched for flavonoids and lipids. The present study focused on the identification of metabolites isolated by nanoharvesting from two medicinal plants, Ocimum sanctum (Tulsi) and Rubia tinctorum (common madder).

Results

To identify metabolites that form the bio-corona, Tulsi leaves and madder roots were incubated with ultra-small anatase TiO₂ nanoparticles, the coated nanoparticles were collected, and the adsorbed molecules were released from the nanoparticle surface and analyzed using an untargeted metabolomics approach. Similar to the results in which Arabidopsis tissue was used as a source of metabolites, TiO₂ nanoparticle bio-coronas from Tulsi and madder were enriched for flavonoids and lipids, suggesting that nanoharvesting has a wide-range application potential. The third group of metabolites enriched in bio-coronas isolated from both plants were small peptides with C-terminal arginine and lysine residues.

Precision-characterization and quantitative determination of main compounds in Si-Ni-San with UHPLC-MS/MS based targeted-profiling method

Mass spectrometry based precision identification of natural products requires the validation of the reference compounds. This study attempted to develop a LC-QTOF MS combined with LC-TQ MS method to precisely characterize the chemicals of Si-Ni-San (SNS), a classic traditional Chinese medicine formula, which is composed of four medicinal plants, and widely used for the treatments of liver disorders. 74 compounds in SNS were provisionally identified by acquiring MS spectra and MS/MS spectra of the possible chemical features, as well as retrieving small-molecule database. By comparing with the accurate MS/MS spectra of reference compounds, 37 compounds in SNS were precisely identified for the first time. In addition, our effort also successfully assigned the origin of each identified compounds against four medicinal plants. Furthermore, we developed a UHPLC-TQ MS based quantitative-profiling method for simultaneous determination of 37 targeted compounds in the different extracts of the raw SNS and commercial lyophilized powders, enabling to facilitate overall quality control of SNS and associated commercial products. Collectively, our finding precisely characterized the main chemicals in SNS, which also provides a new strategy with LC-MS/MS based chemical profiling to precisely identify a diversity of chemicals in Chinese medicinal plants and associated formulae.

From Treatise to Test: Evaluating Traditional Remedies for Anti-Biofilm Potential

Traditional plant-based remedies hold vast potential as novel antimicrobial agents, particularly for recalcitrant infection states such as biofilms. To explore their potential, it is important to bring these remedies out of historical treatises, and into present-day scientific evaluation. Using an example of Indian traditional medicine (Ayurveda), we present a perspective toward evaluating historical remedies for anti-biofilm potential. Across compendia, we identified three plant-based remedies (of Kalanchoe pinnata, Cynodon dactylon, and Ocimum tenuiflorum) recommended for wounds. The remedies were reconstituted in accordance with historical practices, and tested for their effects on biofilm formation and eradication assays of wound pathogens, Pseudomonas aeruginosa and Staphylococcus aureus. Based on our approach and the results obtained, we provide insights into the considerations and challenges related to identifying potential remedies in historical texts, and testing them in the laboratory with standard biofilm assays. We believe this will be relevant for future studies exploring anti-biofilm approaches at the interface of historical medicine and present-day scientific practices.

Thrombin-based discovery strategy of bioactive-chemical quality marker combination for pollen of Typha orientalis by metabolomics coupled with chemometrics

BACKGROUND

It is of utmost significance to choose the bioactive components as quality markers for ensuring the effectiveness of traditional Chinese medicine (TCM). Nonetheless, some markers are able to assess effectively the quality of TCM without considering the pharmacological mechanisms and intrinsic chemical complexities.

OBJECTIVE

This underscores the need to discover new and efficient markers which can assess both quality and mechanism of action. Herein, a strategy of bioactive-chemical quality marker combination was proposed to improve the level of the quality control of TCM by metabolomics coupled with chemometrics.

METHODS

A four-step plan was followed. Firstly, acquisition of metabolic features and component characterization of different batches of pollen of Typha orientalis C.Presl were performed using UHPLC-Q-TOF/MS. Secondly, the direct inhibitory effects of pollen of T. orientalis on thrombin was assessed by using chromogenic substrate method together with HPLC. Thereafter, bioactive-chemical marker combination associated with anti-thrombin segregation was screened using supervised classifiers. Finally, quantitative assay and prediction-model of selected markers were established for guarantying the quality of pollen of T. orientalis.

RESULTS

A total of 22 compounds were annotated based on comparison with previous work from pollen of T. orientalis by UHPLC-Q-TOF/MS. Citric acid and linolenic acid inhibited the thrombin activity with IC₅₀ values, 0.52 ± 0.02 and 0.51 ± 0.02 mg/mL, respectively. A bioactive-chemical marker combination including citric acid, linolenic acid, typhaneoside, and isorhamnetin-3-O-neohesperidoside were discovered and selected as quality markers for evaluation of pollen of T. orientalis according to their capacity for inhibiting thrombin.

CONCLUSION

The thrombin-based discovery strategy of bioactive-chemical marker combination was a powerful tool for screening the quality markers for evaluation of pollen of T. orientalis.

Application of High Resolution Mass Spectrometric methods coupled with chemometric techniques in olive oil authenticity studies - A review

Extra Virgin Olive Oil (EVOO), the emblematic food of the Mediterranean diet, is recognized for its nutritional value and beneficial health effects. The main authenticity issues associated with EVOO's quality involve the organoleptic properties (EVOO or defective), mislabeling of production type (organic or conventional), variety and geographical origin, and adulteration. Currently, there is an emerging need to characterize EVOOs and evaluate their genuineness. This can be achieved through the development of analytical methodologies applying advanced "omics" technologies and the investigation of EVOOs chemical fingerprints. The objective of this review is to demonstrate the analytical performance of High Resolution Mass Spectrometry (HRMS) in the field of food authenticity assessment, allowing the determination of a wide range of food constituents with exceptional identification capabilities. HRMS-based workflows used for the investigation of critical olive oil authenticity issues are presented and discussed, combined with advanced data processing, comprehensive data mining and chemometric tools. The use of unsupervised classification tools, such as Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA), as well as supervised classification techniques, including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Partial Least Square Discriminant Analysis (PLS-DA), Orthogonal Projection to Latent Structure-Discriminant Analysis (OPLS-DA), Counter Propagation Artificial Neural Networks (CP-ANNs), Self-Organizing Maps (SOMs) and Random Forest (RF) is summarized. The combination of HRMS methodologies with chemometrics improves the quality and reliability of the conclusions from experimental data (profile or fingerprints), provides valuable information suggesting potential authenticity markers and is widely applied in food authenticity studies.

Targeted and untargeted analysis of secondary metabolites to monitor growth and quorum sensing inhibition for methicillin-resistant Staphylococcus aureus (MRSA)

Drug resistant infections are an increasing problem world-wide, responsible for an estimated 700,000 annual mortalities. The use of antibiotics to treat such infections has resulted in the development of resistant bacterial pathogens such as methicillin-resistant Staphylococcus aureus (MRSA). One potential alternative strategy for treating drug resistant bacterial infections is to inhibit the production of toxins, thereby making the bacteria less harmful to the host, a so called "anti-virulence" approach. In MRSA, the agr quorum sensing system is one of the major regulators of toxin production, and quorum sensing inhibitors that target this system are a promising anti-virulence strategy. With this study, we developed a method that enables the activity of quorum sensing inhibitors to be measured using ultra-performance liquid chromatography coupled to mass spectrometry (UPLC-MS). This method is an improvement over existing methods because it can be employed to distinguish antimicrobial activity from quorum sensing inhibition activity based on the UPLC-MS data. This is possible by simultaneously tracking production of metabolites regulated by the agr quorum sensing system (AIP-I and formylated δ-toxin) and a metabolite that appears not to be agr regulated under the conditions of this study (aureusimine B). The newly developed method provides more nuanced indication of how metabolite production changes over time and in response to quorum sensing or growth inhibition than is possible with commonly employed spectroscopic methods.

Pharmacodynamic and Metabolomics Studies on the Effect of Kouyanqing Granule in the Treatment of Phenol-Induced Oral Ulcer Worsened by Sleep Deprivation

Oral ulcers are the most prevalent oral mucosal diseases globally, and no specific treatment schemes are currently available due to the complexity of oral ulcer diseases. Sleep deprivation increases the risk of a deterioration in oral health. Kouyanqing Granule (KYQG) has been used for decades in China to treat inflammatory diseases of the mouth and throat associated with the hyperactivity of fire due to yin deficiency syndrome. However, the mechanisms underlying the effects of KYQG in the treatment of oral ulcers are still unclear. The aims of this study were to investigate whether KYQG treatment could attenuate the symptoms of oral ulcers worsened by sleep deprivation and identify the involved metabolic pathways. First, we conducted chemical profiling of KYQG via UPLC–MS analysis. We then combined pharmacological and metabolomics approaches in a phenol-induced rat model of oral ulcers worsened by sleep deprivation. A total of 79 compounds were initially identified. Our observations showed that KYQG treatment induced a significantly higher healing rate in oral ulcers worsened by sleep deprivation. KYQG significantly reduced the levels of 5-HT and GABA in serum, and only decreased the 5-HT level in brain tissue after phenol injury followed by sleep deprivation. Moreover, KYQG administration significantly suppressed systemic inflammation by inhibiting TNF-α, IL-1β, IL-6, IL-18, and MCP-1. Immunohistochemical analysis further revealed that KYQG inhibited IL-6 expression in buccal mucosa tissues. KYQG treatment also significantly decreased the serum levels of ACTH, CORT, IgM, and 8-OHdG. Serum metabolomics analysis showed that a total of 30 metabolites showed significant differential abundances under KYQG intervention, while metabolic pathway analysis suggested that the altered metabolites were associated with the dysregulation of eight metabolic pathways. Taken together, our results indicated that KYQG attenuates the symptoms of oral ulcers worsened by sleep deprivation probably through the regulation of the neuroimmunoendocrine system, oxidative stress levels, and tryptophan metabolism. This study also provides a novel approach for addressing the increased health risks resulting from sleep deficiency using an herbal medicine formula.

Authentication of Greek PDO Kalamata Table Olives: A Novel Non-Target High Resolution Mass Spectrometric Approach

Food science continually requires the development of novel analytical methods to prevent fraudulent actions and guarantee food authenticity. Greek table olives, one of the most emblematic and valuable Greek national products, are often subjected to economically motivated fraud. In this work, a novel ultra-high-performance liquid chromatography–quadrupole time of flight tandem mass spectrometry (UHPLC-QTOF-MS) analytical method was developed to detect the mislabeling of Greek PDO Kalamata table olives, and thereby establish their authenticity. A non-targeted screening workflow was applied, coupled to advanced chemometric techniques such as Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA) in order to fingerprint and accurately discriminate PDO Greek Kalamata olives from Kalamata (or Kalamon) type olives from Egypt and Chile. The method performance was evaluated using a target set of phenolic compounds and several validation parameters were calculated. Overall, 65 table olive samples from Greece, Egypt, and Chile were analyzed and processed for the model development and its accuracy was validated. The robustness of the chemometric model was tested using 11 Greek Kalamon olive samples that were produced during the following crop year, 2018, and they were successfully classified as Greek Kalamon olives from Kalamata. Twenty-six characteristic authenticity markers were indicated to be responsible for the discrimination of Kalamon olives of different geographical origins.

Bioactive Molecular Networking for Mapping the Antimicrobial Constituents of the Baltic Brown Alga Fucus vesiculosus

The brown alga Fucus vesiculosus is common to the intertidal zones of the Baltic Sea, where it is exposed to high fouling pressures by microorganisms. Our previous studies showed, repeatedly, the consistent antimicrobial activity of F. vesiculosus crude extracts against human pathogens, while untargeted metabolomics analyses have revealed a variety of metabolites. In this study, we applied the UPLC-QToF-MS/MS-based "bioactive molecular networking" (BMN) concept on the most bioactive n-hexane and n-butanol subextracts of Baltic F. vesiculosus coupled with in silico dereplication tools to identify the compounds responsible for antimicrobial activity. The first antimicrobial cluster identified by BMN was galactolipids. Our targeted isolation efforts for this class led to the isolation of six monogalactosyldiacylglycerol (MGDG) derivatives (1-6) and one digalactosyldiacylglycerol (DGDG, 7). The MGDGs 5 and 6 and the DGDG 7 exhibited activity against Staphylococcus aureus. The second compound class with high bioactivity was phlorotannins. In particular, phlorethol-type phlorotannins showed high correlations with antimicrobial activity based on the BMN approach, and two phlorotannins (8-9) were isolated. This study shows that antimicrobial components of F. vesiculosus reside in the algal cell walls and membranes and that BMN provides a complementary tool for the targeted isolation of bioactive metabolites.

Chemical Evaluation of the Effects of Storage Conditions on the Botanical Goldenseal using Marker-based and Metabolomics Approaches

Hydrastis canadensis, commonly known as goldenseal, is a botanical native to the southeastern United States that has been used for the treatment of infection. The activity of goldenseal is often attributed to the presence of alkaloids (cyclic, nitrogen-containing compounds) present within its roots. Chemical components of botanical supplements like goldenseal may face degradation if not stored properly. The purpose of the research was to analyze the stability of known and unknown metabolites of H. canadensis during exposure to different storage conditions using mass spectrometry. Three abundant metabolites of H. canadensis, berberine, canadine, and hydrastine, were chosen for targeted analysis, and the stability of unknown metabolites was evaluated using untargeted metabolomics. The analysis and evaluation of H. canadensis samples were performed utilizing LC-MS and Principal Component Analysis (PCA). The research project focused on identifying the chemical changes in the metabolite content of H. canadensis under different temperature conditions (40°C ± 5°C, 20°C ± 5°C , and 4°C ± 5°C), different light:dark (hr:hr) cycles (16:8, 12:12, and 0:24), and different sample conditions (powdered roots versus whole roots) over a six month period. The results of this 6-month study revealed that the storage conditions evaluated had no significant effects on the chemical composition of H. canadensis roots. Hence, as long as H. canadensis roots are stored within the storage conditions tested in the study, no significant changes in chemical compositions of metabolites are expected.

Differences in airway microbiome and metabolome of single lung transplant recipients

Background

Recent studies suggest that alterations in lung microbiome are associated with occurrence of chronic lung diseases and transplant rejection. To investigate the host-microbiome interactions, we characterized the airway microbiome and metabolome of the allograft (transplanted lung) and native lung of single lung transplant recipients.

Methods

BAL was collected from the allograft and native lungs of SLTs and healthy controls. 16S rRNA microbiome analysis was performed on BAL bacterial pellets and supernatant used for metabolome, cytokines and acetylated proline-glycine-proline (Ac-PGP) measurement by liquid chromatography-high-resolution mass spectrometry.

Results

In our cohort, the allograft airway microbiome was distinct with a significantly higher bacterial burden and relative abundance of genera Acinetobacter & Pseudomonas. Likewise, the expression of the pro-inflammatory cytokine VEGF and the neutrophil chemoattractant matrikine Ac-PGP in the allograft was significantly higher. Airway metabolome distinguished the native lung from the allografts and an increased concentration of sphingosine-like metabolites that negatively correlated with abundance of bacteria from phyla Proteobacteria.

Conclusions

Allograft lungs have a distinct microbiome signature, a higher bacterial biomass and an increased Ac-PGP compared to the native lungs in SLTs compared to the native lungs in SLTs. Airway metabolome distinguishes the allografts from native lungs and is associated with distinct microbial communities, suggesting a functional relationship between the local microbiome and metabolome.