LC-NMR-MS in drug discovery

Acacia senegal Budmunchiamines as a Potential Adjuvant for Rejuvenating Phenicol Activities towards Escherichia coli-Resistant Strains

The continuous emergence of bacterial resistance alters the activities of different antibiotic families and requires appropriate strategies to solve therapeutic impasses. Medicinal plants are an attractive source for researching alternative and original therapeutic molecules. In this study, the fractionation of natural extracts from A. senegal and the determination of antibacterial activities are associated with molecular networking and tandem mass spectrometry (MS/MS) data used to characterize active molecule(s). The activities of the combinations, which included various fractions plus an antibiotic, were investigated using the "chessboard" test. Bio-guided fractionation allowed the authors to obtain individually active or synergistic fractions with chloramphenicol activity. An LC-MS/MS analysis of the fraction of interest and molecular array reorganization showed that most identified compounds are Budmunchiamines (macrocyclic alkaloids). This study describes an interesting source of bioactive secondary metabolites structurally related to Budmunchiamines that are able to rejuvenate a significant chloramphenicol activity in strains that produce an AcrB efflux pump. They will pave the way for researching new active molecules for restoring the activity of antibiotics that are substrates of efflux pumps in enterobacterial-resistant strains.

Microbial Natural Products with Antiviral Activities, Including Anti-SARS-CoV-2: A Review

The SARS-CoV-2 virus, which caused the COVID-19 infection, was discovered two and a half years ago. It caused a global pandemic, resulting in millions of deaths and substantial damage to the worldwide economy. Currently, only a few vaccines and antiviral drugs are available to combat SARS-CoV-2. However, there has been an increase in virus-related research, including exploring new drugs and their repurposing. Since discovering penicillin, natural products, particularly those derived from microbes, have been viewed as an abundant source of lead compounds for drug discovery. These compounds treat bacterial, fungal, parasitic, and viral infections. This review incorporates evidence from the available research publications on isolated and identified natural products derived from microbes with anti-hepatitis, anti-herpes simplex, anti-HIV, anti-influenza, anti-respiratory syncytial virus, and anti-SARS-CoV-2 properties. About 131 compounds with in vitro antiviral activity and 1 compound with both in vitro and in vivo activity have been isolated from microorganisms, and the mechanism of action for some of these compounds has been described. Recent reports have shown that natural products produced by the microbes, such as aurasperone A, neochinulin A and B, and aspulvinone D, M, and R, have potent in vitro anti-SARS-CoV-2 activity, targeting the main protease (M^pro). In the near and distant future, these molecules could be used to develop antiviral drugs for treating infections and preventing the spread of disease.

Natural products dereplication by diffusion ordered NMR spectroscopy (DOSY)

Diffusion-ordered NMR spectroscopy (DOSY) can be used to analyze mixtures of compounds since resonances deriving from different compounds are distinguished by their diffusion coefficients (D). Previously, DOSY has mostly been used for organometallic and polymer analysis, we have now applied DOSY to investigate diffusion coefficients of structurally diverse organic compounds such as natural products (NP). The experimental Ds derived from 55 diverse NPs has allowed us to establish a power law relationship between D and molecular weight (MW) and therefore predict MW from experimental D. We have shown that D is also affected by factors such as hydrogen bonding, molar density and molecular shape of the compound and we have generated new models that incorporate experimentally derived variables for these factors so that more accurate predictions of MW can be calculated from experimental D. The recognition that multiple physicochemical properties affect D has allowed us to generate a polynomial equation based on multiple linear regression analysis of eight calculated physicochemical properties from 63 compounds to accurately correlate predicted D with experimental D for any known organic compound. This equation has been used to calculate predicted D for 217 043 compounds present in a publicly available natural product database (DEREP-NP) and to dereplicate known NPs in a mixture based on matching of experimental D and structural features derived from NMR analysis with predicted D and calculated structural features in the database. These models have been validated by the dereplication of a mixture of two known sesquiterpenes obtained from Tasmannia xerophila and the identification of new alkaloids from the bryozoan Amathia lamourouxi. These new methodologies allow the MW of compounds in mixtures to be predicted without the need for MS analysis, the dereplication of known compounds and identification of new compounds based solely on parameters derived by DOSY NMR.

We report accurate DOSY NMR based molecular weight and diffusion coefficient prediction tools. These tools can be used to dereplicate known natural products from databases using structurally rich NMR data as a surrogate for mass spectrometric data.

ViscY NMR experiments in phosphoric acid as a viscous solvent for individualization of small molecules within mixtures by spin diffusion

The analysis of small molecules within complex mixtures is a particularly difficult task when dealing with the study of metabolite mixtures or chemical reaction media. This issue has fostered in recent years an active search for effective and practical solutions. In this context, the ViscY NMR approach has been recently proposed. ViscY collectively designates the NMR experiments that take advantage of spin diffusion in highly viscous solvents or solvent blends for the individualization of the NMR spectra of small molecule mixture components. Two viscous media were prepared from ortho-phosphoric acid (85%) solution by dilution with either D2O or DMSO-d6, thus providing solvent blends with slightly different polarities in which all liquid-state NMR experiments can be carried out easily. Two mixtures, one of four structurally close dipeptides and one of four low-polarity phosphorus-containing compounds, were used for the method assessment, using ViscY experiments such as homonuclear selective 1D and 2D 1H NOESY experiments, heteronuclear 2D 1H-15N/1H-31P HSQC-NOESY and 1H-13C/1H-15N/1H-31P NOAH experiments.

Tailoring the nuclear Overhauser effect for the study of small and medium-sized molecules by solvent viscosity manipulation

The nuclear Overhauser effect (NOE) is a consequence of cross-relaxation between nuclear spins mediated by dipolar coupling. Its sensitivity to internuclear distances has made it an increasingly important tool for the determination of through-space atom proximity relationships within molecules of sizes ranging from the smallest systems to large biopolymers. With the support of sophisticated FT-NMR techniques, the NOE plays an essential role in structure elucidation, conformational and dynamic investigations in liquid-state NMR. The efficiency of magnetization transfer by the NOE depends on the molecular rotational correlation time, whose value depends on solution viscosity. The magnitude of the NOE between ¹H nuclei varies from +50% when molecular tumbling is fast to -100% when it is slow, the latter case corresponding to the spin diffusion limit. In an intermediate tumbling regime, the NOE may be vanishingly small. Increasing the viscosity of the solution increases the motional correlation time, and as a result, otherwise unobservable NOEs may be revealed and brought close to the spin diffusion limit. The goal of this review is to report the resolution of structural problems that benefited from the manipulation of the negative NOE by means of viscous solvents, including examples of molecular structure determination, conformation elucidation and mixture analysis (the ViscY method).

The utility of nuclear magnetic resonance spectroscopy in assisted reproduction

Infertility affects approximately 15-20% of individuals of reproductive age worldwide. Over the last 40 years, assisted reproductive technology (ART) has helped millions of childless couples. However, ART is limited by a low success rate and risk of multiple gestations. Devising methods for selecting the best gamete or embryo that increases the ART success rate and prevention of multiple gestation has become one of the key goals in ART today. Special emphasis has been placed on the development of non-invasive approaches, which do not require perturbing the embryonic cells, as the current morphology-based embryo selection approach has shortcomings in predicting the implantation potential of embryos. An observed association between embryo metabolism and viability has prompted researchers to develop metabolomics-based biomarkers. Nuclear magnetic resonance (NMR) spectroscopy provides a non-invasive approach for the metabolic profiling of tissues, gametes and embryos, with the key advantage of having a minimal sample preparation procedure. Using NMR spectroscopy, biologically important molecules can be identified and quantified in intact cells, extracts or secretomes. This, in turn, helps to map out the active metabolic pathways in a system. The present review covers the contribution of NMR spectroscopy in assisted reproduction at various stages of the process.

New approaches for antituberculosis leads from Actinobacteria

Bioactive metabolites derived from the phylum Actinobacteria represent many of the existing antimicrobial drugs. Compared with other bacterial pathogens, direct preliminary screening by diffusion assays is a limiting factor against Mycobacterium tuberculosis (Mtb) and different methodologies have been used to improve the search for new molecules. However, the concern remains that most of the previously discovered molecules replicate by conventional procedures. The combination of multidisciplinary approaches with new technologies could advance the discovery of new leads against Mtb like considering the unexplored Actinobacteria jointly with selective and integrative procedures.

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.

Circulating Metabolites as Potential Biomarkers for Neurological Disorders-Metabolites in Neurological Disorders

There are, still, limitations to predicting the occurrence and prognosis of neurological disorders. Biomarkers are molecules that can change in different conditions, a feature that makes them potential tools to improve the diagnosis of disease, establish a prognosis, and monitor treatments. Metabolites can be used as biomarkers, and are small molecules derived from the metabolic process found in different biological media, such as tissue samples, cells, or biofluids. They can be identified using various strategies, targeted or untargeted experiments, and by different techniques, such as high-performance liquid chromatography, mass spectrometry, or nuclear magnetic resonance. In this review, we aim to discuss the current knowledge about metabolites as biomarkers for neurological disorders. We will present recent developments that show the need and the feasibility of identifying such biomarkers in different neurological disorders, as well as discuss relevant research findings in the field of metabolomics that are helping to unravel the mechanisms underlying neurological disorders. Although several relevant results have been reported in metabolomic studies in patients with neurological diseases, there is still a long way to go for the clinical use of metabolites as potential biomarkers in these disorders, and more research in the field is needed.

Characterization and analysis of antioxidant activity of walnut-derived pentapeptide PW5 via nuclear magnetic resonance spectroscopy

The antioxidative activity of natural products has commonly been studied by free radical scavenging methods. However, the mechanisms by which antioxidation is explored by free radical scavenging methods remain largely unknown. This study analyzed the composition of walnut-derived pentapeptides PW5 with potential biological activity and its oxidation reaction products in 2,2'-Azinobis-(3-ethylbenzthiazoline-6-sulphonate) (ABTS) test by nuclear magnetic resonance (NMR) spectroscopy. The amino acid sequence of PW5 peptide successfully characterized as Proline-Proline-Lysine-Asparagine-Tryptophan exhibited significant antioxidant activity with lower IC₅₀ value (0.2210 ± 0.0032 mM) compared to glutathione (GSH, 0.2567 ± 0.0023 mM, p < 0.001). Furthermore, we found that the tryptophan residue was the only residue in PW5 with obvious alteration after treatment with ABTS free radicals, which was linked to its potential antioxidant properties. These findings revealed how NMR-characterized structures and oxidation reaction products may be used to explore the antioxidative mechanisms of food-derived peptides as well as other natural products.

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.

Current status and contemporary approaches to the discovery of antitumor agents from higher plants

Higher plant constituents have afforded clinically available anticancer drugs. These include both chemically unmodified small molecules and their synthetic derivatives currently used or those in clinical trials as antineoplastic agents, and an updated summary is provided. In addition, botanical dietary supplements, exemplified by mangosteen and noni constituents, are also covered as potential cancer chemotherapeutic agents. Approaches to metabolite purification, rapid dereplication, and biological evaluation including analytical hyphenated techniques, molecular networking, and advanced cellular and animal models are discussed. Further, enhanced and targeted drug delivery systems for phytochemicals, including micelles, nanoparticles and antibody drug conjugates (ADCs) are described herein.

In depth natural product discovery - Myxobacterial strains that provided multiple secondary metabolites

In recognition of many microorganisms ability to produce a variety of secondary metabolites in parallel, Zeeck and coworkers introduced the term "OSMAC" (one strain many compounds) around the turn of the century. Since then, additional efforts focused on the systematic characterization of a single bacterial species ability to form multiple secondary metabolite scaffolds. With the beginning of the genomic era mainly initiated by a dramatic reduction of sequencing costs, investigations of the genome encoded biosynthetic potential and especially the exploitation of biosynthetic gene clusters of undefined function gained attention. This was seen as a novel means to extend range and diversity of bacterial secondary metabolites. Genome analyses showed that even for well-studied bacterial strains, like the myxobacterium Myxococcus xanthus DK1622, many biosynthetic gene clusters are not yet assigned to their corresponding hypothetical secondary metabolites. In contrast to the results from emerging genome and metabolome mining techniques that show the large untapped biosynthetic potential per strain, many newly isolated bacterial species are still used for the isolation of only one target compound class and successively abandoned in the sense that no follow up studies are published from the same species. This work provides an overview about myxobacterial bacterial strains, from which not just one but multiple different secondary metabolite classes were successfully isolated. The underlying methods used for strain prioritization and natural product discovery such as biological characterization of crude extracts against a panel of pathogens, in-silico prediction of secondary metabolite abundance from genome data and state of the art instrumental analytics required for new natural product scaffold discovery in comparative settings are summarized and classified according to their output. Furthermore, for each approach selected studies performed with actinobacteria are shown to underline especially innovative methods used for natural product discovery.

Therapeutic value of steroidal alkaloids in cancer: Current trends and future perspectives

Discovery and development of new potentially selective anticancer agents are necessary to prevent a global cancer health crisis. Currently, alternative medicinal agents derived from plants have been extensively investigated to develop anticancer drugs with fewer adverse effects. Among them, steroidal alkaloids are conventional secondary metabolites that comprise an important class of natural products found in plants, marine organisms and invertebrates, and constitute a judicious choice as potential anti-cancer leads. Traditional medicine and modern science have shown that representatives from this compound group possess potential antimicrobial, analgesic, anticancer and anti-inflammatory effects. Therefore, systematic and recapitulated information about the bioactivity of these compounds, with special emphasis on the molecular or cellular mechanisms, is of high interest. In this review, we methodically discuss the in vitro and in vivo potential of the anticancer activity of natural steroidal alkaloids and their synthetic and semi-synthetic derivatives. This review focuses on cumulative and comprehensive molecular mechanisms, which will help researchers understand the molecular pathways involving steroid alkaloids to generate a selective and safe new lead compound with improved therapeutic applications for cancer prevention and therapy. In vitro and in vivo studies provide evidence about the promising therapeutic potential of steroidal alkaloids in various cancer cell lines, but advanced pharmacokinetic and clinical experiments are required to develop more selective and safe drugs for cancer treatment.

Understanding glycaemic control and current approaches for screening antidiabetic natural products from evidence-based medicinal plants

Type 2 Diabetes Mellitus has reached epidemic proportions as a result of over-nutrition and increasingly sedentary lifestyles. Current therapies, although effective, are not without limitations. These limitations, the alarming increase in the prevalence of diabetes, and the soaring cost of managing diabetes and its complications underscores an urgent need for safer, more efficient and affordable alternative treatments. Over 1200 plant species are reported in ethnomedicine for treating diabetes and these represents an important and promising source for the identification of novel antidiabetic compounds. Evaluating medicinal plants for desirable bioactivity goes hand-in-hand with methods in analytical biochemistry for separating and identifying lead compounds. This review aims to provide a comprehensive summary of current methods used in antidiabetic plant research to form a useful resource for researchers beginning in the field. The review summarises the current understanding of blood glucose regulation and the general mechanisms of action of current antidiabetic medications, and combines knowledge on common experimental approaches for screening plant extracts for antidiabetic activity and currently available analytical methods and technologies for the separation and identification of bioactive natural products. Common in vivo animal models, in vitro models, in silico methods and biochemical assays used for testing the antidiabetic effects of plants are discussed with a particular emphasis on in vitro methods such as cell-based bioassays for screening insulin secretagogues and insulinomimetics. Enzyme inhibition assays and molecular docking are also highlighted. The role of metabolomics, metabolite profiling, and dereplication of data for the high-throughput discovery of novel antidiabetic agents is reviewed. Finally, this review also summarises sample preparation techniques such as liquid-liquid extraction, solid phase extraction, and supercritical fluid extraction, and the critical function of nuclear magnetic resonance and high resolution liquid chromatography-mass spectrometry for the dereplication, putative identification and structure elucidation of natural compounds from evidence-based medicinal plants.

Non-targeted Screening in Environmental Monitoring Programs

Contaminant monitoring programs have been tasked with understanding the fate and transport of toxic chemicals in the environment. Mass spectrometry based methods have traditionally been developed to maximize sensitivity and accuracy of a select set of target compounds. As mass spectrometry methods have advanced, so has the breadth of questions proposed by environmental chemists. Incorporating these methods in chemical monitoring programs provides large data sets to explore the effects of complex mixtures on environmental systems.

NMR Spectroscopy for Metabolomics Research

Over the past two decades, nuclear magnetic resonance (NMR) has emerged as one of the three principal analytical techniques used in metabolomics (the other two being gas chromatography coupled to mass spectrometry (GC-MS) and liquid chromatography coupled with single-stage mass spectrometry (LC-MS)). The relative ease of sample preparation, the ability to quantify metabolite levels, the high level of experimental reproducibility, and the inherently nondestructive nature of NMR spectroscopy have made it the preferred platform for long-term or large-scale clinical metabolomic studies. These advantages, however, are often outweighed by the fact that most other analytical techniques, including both LC-MS and GC-MS, are inherently more sensitive than NMR, with lower limits of detection typically being 10 to 100 times better. This review is intended to introduce readers to the field of NMR-based metabolomics and to highlight both the advantages and disadvantages of NMR spectroscopy for metabolomic studies. It will also explore some of the unique strengths of NMR-based metabolomics, particularly with regard to isotope selection/detection, mixture deconvolution via 2D spectroscopy, automation, and the ability to noninvasively analyze native tissue specimens. Finally, this review will highlight a number of emerging NMR techniques and technologies that are being used to strengthen its utility and overcome its inherent limitations in metabolomic applications.

A robust intracellular metabolite extraction protocol for human neutrophil metabolic profiling

Neutrophils are phagocytic innate immune cells that play essential roles in host defence, but are also implicated in inflammatory diseases such as rheumatoid arthritis (RA) where they contribute to systemic inflammation and joint damage. Transcriptomic analysis of neutrophils has revealed significant changes in gene expression in neutrophils activated in vitro by cytokines and in vivo during inflammation in RA. However, there are no reports on the global metabolomic changes that occur as a consequence of this activation. The aim of this study was to establish protocols for the study of changes in the metabolome of human neutrophils using 1H NMR spectroscopy. Sample preparation and spectral analysis protocols were optimised using neutrophils isolated by Ficoll-Paque, with decreased washing steps and inclusion of a heat-shock step to quench metabolite turnover. Cells were incubated ± PMA for 15 min in HEPES-free media and samples were analysed by NMR using a 700 MHz NMR Avance IIIHD Bruker NMR spectrometer equipped with a TCI cryoprobe. Chenomx, Bruker TopSpin and AMIX software were used to process spectra and identify metabolites. Principal Component Analysis (PCA) and signalling pathway analysis was carried out using Metaboanalyst. Cell number and number of scans (NS) were optimised as >3.6 million cells and 512 NS. 327 spectral bins were defined in the neutrophil spectra, of which 287 (87.7%) were assigned to 110 metabolites that included: amino acids, peptides and analogues; carbohydrates, carbonyls and alcohols; nucleotides, nucleosides and analogues; lipids and lipid-like molecules; benzenoids; and other organic compounds. 43 metabolites changed at least 1.5 fold (increase or decrease) after the addition of PMA for 5 or 15 min. Pathway analysis revealed that PMA affected nicotinate and nicotinamide metabolism, aminoacyl-tRNA biosynthesis and glycolysis, suggesting a redirection of glucose metabolism from glycolysis to the pentose phosphate pathway and production of NADPH for activation of the NADPH oxidase and subsequent respiratory burst. We have developed protocols for the study of human neutrophils by 1H NMR spectroscopy. Importantly, this methodology has sufficient sensitivity and reproducibility to detect changes in metabolite abundance from cell numbers typically collected from clinical samples or experiments with multiple assay conditions.

Recent (2000-2015) developments in the analysis of minor unknown natural products based on characteristic fragment information using LC-MS

Liquid chromatography-Mass Spectrometry (LC-MS) has been widely used in natural product analysis. Global detection and identification of nontargeted components are desirable in natural product research, for example, in quality control of Chinese herbal medicine. Nontargeted components analysis continues to expand to exciting life science application domains such as metabonomics. With this background, the present review summarizes recent developments in the analysis of minor unknown natural products using LC-MS and mainly focuses on the determination of the molecular formulae, selection of precursor ions, and characteristic fragmentation patterns of the known compounds. This review consists of three parts. Firstly, the methods used to determine unique molecular formula of unknown compounds such as accurate mass measurements, MSⁿ spectra, or relative isotopic abundance information, are introduced. Secondly, the methods improving signal-to-noise ratio of MS/MS spectra by manual-MS/MS or workflow targeting-only signals were elucidated; pure precursor ions can be selected by changing the precursor ion isolated window. Lastly, characteristic fragmentation patterns such as Retro-Diels-Alder (RDA), McLafferty rearrangements, "internal residue loss," and so on, occurring in the molecular ions of natural products are summarized. Classical application of characteristic fragmentation patterns in identifying unknown compounds in extracts and relevant fragmentation mechanisms are presented (RDA reactions occurring readily in the molecular ions of flavanones or isoflavanones, McLafferty-type fragmentation reactions of some natural products such as epipolythiodioxopiperazines; fragmentation by "internal residue loss" possibly involving ion-neutral complex intermediates). © 2016 Wiley Periodicals, Inc. Mass Spec Rev 37:202-216, 2018.

An efficient computer-aided structural elucidation strategy for mixtures using an iterative dynamic programming algorithm

The identification of chemical structures in natural product mixtures is an important task in drug discovery but is still a challenging problem, as structural elucidation is a time-consuming process and is limited by the available mass spectra of known natural products. Computer-aided structure elucidation (CASE) strategies seek to automatically propose a list of possible chemical structures in mixtures by utilizing chromatographic and spectroscopic methods. However, current CASE tools still cannot automatically solve structures for experienced natural product chemists. Here, we formulated the structural elucidation of natural products in a mixture as a computational problem by extending a list of scaffolds using a weighted side chain list after analyzing a collection of 243,130 natural products and designed an efficient algorithm to precisely identify the chemical structures. The complexity of such a problem is NP-complete. A dynamic programming (DP) algorithm can solve this NP-complete problem in pseudo-polynomial time after converting floating point molecular weights into integers. However, the running time of the DP algorithm degrades exponentially as the precision of the mass spectrometry experiment grows. To ideally solve in polynomial time, we proposed a novel iterative DP algorithm that can quickly recognize the chemical structures of natural products. By utilizing this algorithm to elucidate the structures of four natural products that were experimentally and structurally determined, the algorithm can search the exact solutions, and the time performance was shown to be in polynomial time for average cases. The proposed method improved the speed of the structural elucidation of natural products and helped broaden the spectrum of available compounds that could be applied as new drug candidates. A web service built for structural elucidation studies is freely accessible via the following link (http://csccp.cmdm.tw/).

Using Accurate Mass Gas Chromatography-Mass Spectrometry with the MINE Database for Epimetabolite Annotation

Mass spectrometry-based untargeted metabolomics often detects statistically significant metabolites that cannot be readily identified. Without defined chemical structure, interpretation of the biochemical relevance is not feasible. Epimetabolites are produced from canonical metabolites by defined enzymatic reactions and may represent a large fraction of the structurally unidentified metabolome. We here present a systematic workflow for annotating unknown epimetabolites using high resolution gas chromatography-accurate mass spectrometry with multiple ionization techniques and stable isotope labeled derivatization methods. We first determine elemental formulas, which are then used to query the "metabolic in-silico expansion" database (MINE DB) to obtain possible molecular structures that are predicted by enzyme promiscuity from canonical pathways. Accurate mass fragmentation rules are combined with in silico spectra prediction programs CFM-ID and MS-FINDER to derive the best candidates. We validated the workflow by correctly identifying 10 methylated nucleosides and 6 methylated amino acids. We then employed this strategy to annotate eight unknown compounds from cancer studies and other biological systems.

G.A.M.E.: GPU-accelerated mixture elucidator

GPU acceleration is useful in solving complex chemical information problems. Identifying unknown structures from the mass spectra of natural product mixtures has been a desirable yet unresolved issue in metabolomics. However, this elucidation process has been hampered by complex experimental data and the inability of instruments to completely separate different compounds. Fortunately, with current high-resolution mass spectrometry, one feasible strategy is to define this problem as extending a scaffold database with sidechains of different probabilities to match the high-resolution mass obtained from a high-resolution mass spectrum. By introducing a dynamic programming (DP) algorithm, it is possible to solve this NP-complete problem in pseudo-polynomial time. However, the running time of the DP algorithm grows by orders of magnitude as the number of mass decimal digits increases, thus limiting the boost in structural prediction capabilities. By harnessing the heavily parallel architecture of modern GPUs, we designed a “compute unified device architecture” (CUDA)-based GPU-accelerated mixture elucidator (G.A.M.E.) that considerably improves the performance of the DP, allowing up to five decimal digits for input mass data. As exemplified by four testing datasets with verified constitutions from natural products, G.A.M.E. allows for efficient and automatic structural elucidation of unknown mixtures for practical procedures.Graphical abstract

Differential Attenuation of NMR Signals by Complementary Ion-Exchange Resin Beads for De Novo Analysis of Complex Metabolomics Mixtures

A primary goal of metabolomics is the characterization of a potentially very large number of metabolites that are part of complex mixtures. Application to biofluids and tissue samples offers insights into biochemical metabolic pathways and their role in health and disease. 1D ¹ H and 2D ¹³ C-¹ H HSQC NMR spectra are most commonly used for this purpose. They yield quantitative information about each proton of the mixture, but do not tell which protons belong to the same molecule. Interpretation requires the use of NMR spectral databases, which naturally limits these investigations to known metabolites. Here, a new method is presented that uses complementary ion exchange resin beads to differentially attenuate 2D NMR cross-peaks that belong to different metabolites. Based on their characteristic attenuation patterns, cross-peaks could be clustered and assigned to individual molecules, including unknown metabolites with multiple spin systems, as demonstrated for a metabolite model mixture and E. coli cell lysate.

Assessing 2D electrophoretic mobility spectroscopy (2D MOSY) for analytical applications

Electrophoretic displacement of charged entity phase modulates the spectrum acquired in electrophoretic NMR experiments, and this modulation can be presented via 2D FT as 2D mobility spectroscopy (MOSY) spectra. We compare in various mixed solutions the chemical selectivity provided by 2D MOSY spectra with that provided by 2D diffusion-ordered spectroscopy (DOSY) spectra and demonstrate, under the conditions explored, a superior performance of the former method. 2D MOSY compares also favourably with closely related LC-NMR methods. The shape of 2D MOSY spectra in complex mixtures is strongly modulated by the pH of the sample, a feature that has potential for areas such as in drug discovery and metabolomics. Copyright © 2016 The Authors. Magnetic Resonance in Chemistry published by John Wiley & Sons Ltd.

Beyond the paradigm: Combining mass spectrometry and nuclear magnetic resonance for metabolomics

Metabolomics is undergoing tremendous growth and is being employed to solve a diversity of biological problems from environmental issues to the identification of biomarkers for human diseases. Nuclear magnetic resonance (NMR) and mass spectrometry (MS) are the analytical tools that are routinely, but separately, used to obtain metabolomics data sets due to their versatility, accessibility, and unique strengths. NMR requires minimal sample handling without the need for chromatography, is easily quantitative, and provides multiple means of metabolite identification, but is limited to detecting the most abundant metabolites (⩾1μM). Conversely, mass spectrometry has the ability to measure metabolites at very low concentrations (femtomolar to attomolar) and has a higher resolution (∼10³-10⁴) and dynamic range (∼10³-10⁴), but quantitation is a challenge and sample complexity may limit metabolite detection because of ion suppression. Consequently, liquid chromatography (LC) or gas chromatography (GC) is commonly employed in conjunction with MS, but this may lead to other sources of error. As a result, NMR and mass spectrometry are highly complementary, and combining the two techniques is likely to improve the overall quality of a study and enhance the coverage of the metabolome. While the majority of metabolomic studies use a single analytical source, there is a growing appreciation of the inherent value of combining NMR and MS for metabolomics. An overview of the current state of utilizing both NMR and MS for metabolomics will be presented.

Coupling and optimisation of online nuclear magnetic resonance spectroscopy and mass spectrometry for process monitoring to cover the broad range of process concentration

Real time online monitoring of chemical processes can be carried out by a number of analytical techniques, including optical and vibrational spectroscopies, nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS). As each technique has unique advantages and challenges, combinations are an attractive option. The combination of a 500-MHz ¹ H NMR and a small footprint mass spectrometer to monitor a batch reaction at process concentration was investigated. The mass spectrometer was coupled into the flow path of an online reaction monitoring NMR. Reaction mixture was pumped from a 100-ml vessel to an NMR flow tube before returning to the vessel. Small aliquots were diverted into a sampling make-up flow using an active flow splitter and passed to the mass spectrometer. Advantages of the combination were observed. ¹ H NMR was ideal for quantitation of high level components, whereas MS showed a greater capability for detecting those at low level. In preliminary experiments MS produced a limited linear relationship with concentration (0.02% to 2% relative concentration, 0.01 mg/ml-1.25 mg/ml), because of signal saturation at the higher concentrations. NMR was unable to detect components below 0.1% relative to concentration maximum. Optimisation of sample transfer to the MS extended the linearity to 10% relative to the concentration maximum. Therefore, the combination of online NMR and MS allows both qualitative and quantitative analysis of reaction components over the full process range. The application of the combination was demonstrated by monitoring a batch chemical reaction and this is described. Copyright © 2016 John Wiley & Sons, Ltd.

Extraction and Quantification of Bioactive Tyrian Purple Precursors: A Comparative and Validation Study from the Hypobranchial Gland of a Muricid Dicathais orbita

Muricidae are marine molluscs known for the production of Tyrian purple and bioactive precursor compounds. A validation study for the extraction and analysis of secondary metabolites found in the hypobranchial gland of the muricid Dicathais orbita is reported, using high performance liquid chromatography–mass spectrometry (HPLC-MS) with diode array detector (DAD). Quantification of the dominant secondary metabolites from D. orbita is described, followed by a comparison of solvent extraction procedures and stability studies. The intra- and inter-day relative standard deviation (RSD) for tyrindoxyl sulphate was 0.46% and 0.17%, respectively. The quantification was linear for standards murexine, 6-bromoisatin, and tyrindoxyl sulphate. The limits of detection were 0.03, 0.004, and 0.07 mg/mL, respectively, and the limits of quantification were 0.09, 0.01, and 0.22 mg/mL, respectively. The results showed that alcoholic solvents were better for extracting choline ester and indoxyl sulphate ultimate precursors, while chloroform was more suitable for the extraction of the intermediate precursors. Multivariate analysis revealed significant differences in extract composition according to the solvent used. Stability testing showed an increase of the oxidative compounds 6-bromoisatin and putative tyrindoxyl S-oxide sulphate in the ethanol extracts while more degradation products were seen in the chloroform extracts after months of cold storage. The validated method was found to be simple, reproducible, precise, and suitable for quantification of the secondary metabolites of muricid molluscs for dye precursor and nutraceutical quality control, as well as applications in marine chemical ecology.

Nuclear Magnetic Resonance (NMR) Spectroscopy For Metabolic Profiling of Medicinal Plants and Their Products

NMR spectroscopy has multidisciplinary applications, including excellent impact in metabolomics. The analytical capacity of NMR spectroscopy provides information for easy qualitative and quantitative assessment of both endogenous and exogenous metabolites present in biological samples. The complexity of a particular metabolite and its contribution in a biological system are critically important for understanding the functional state that governs the organism's phenotypes. This review covers historical aspects of developments in the NMR field, its applications in chemical profiling, metabolomics, and quality control of plants and their derived medicines, foods, and other products. The bottlenecks of NMR in metabolic profiling are also discussed, keeping in view the future scope and further technological interventions.

Dose-dependent targeted knockout methodology combined with deep structure elucidation strategies for Chinese licorice chemical profiling

One of the limitations with regards to the chemical profiling of Chinese herbs is that low-level compounds are masked by high-level structures. Here, we established a novel methodology based on a dose-dependent targeted knockout (DDTK) technique combined with deep structure elucidation strategies to allow the chemical profiling of Chinese licorice. We employed ultra-performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (UPLC-Q/TOF MS) incorporated with the DDTK technique to identify the compounds in different concentration samples and found that the compounds at the high- or medium-level were detected readily in the sample at a low concentration; subsequently, minor or trace-level constituents were identified in the sample at a high concentration by rejecting high-level constituents detected in the sample at a low concentration based on a heart-cutting technique during analysis. In this study, among the 232 compounds detected, 27 compounds were unequivocally identified and 165 compounds, including 29 new compounds and two new natural products, were tentatively characterized. The novel methodology established in this work paves the way the further identification of compounds from complicated mixtures, especially traditional Chinese medicines.

NMR/MS Translator for the Enhanced Simultaneous Analysis of Metabolomics Mixtures by NMR Spectroscopy and Mass Spectrometry: Application to Human Urine

A novel metabolite identification strategy is presented for the combined NMR/MS analysis of complex metabolite mixtures. The approach first identifies metabolite candidates from 1D or 2D NMR spectra by NMR database query, which is followed by the determination of the masses (m/z) of their possible ions, adducts, fragments, and characteristic isotope distributions. The expected m/z ratios are then compared with the MS(1) spectrum for the direct assignment of those signals of the mass spectrum that contain information about the same metabolites as the NMR spectra. In this way, the mass spectrum can be assigned with very high confidence, and it provides at the same time validation of the NMR-derived metabolites. The method was first demonstrated on a model mixture, and it was then applied to human urine collected from a pool of healthy individuals. A number of metabolites could be detected that had not been reported previously, further extending the list of known urine metabolites. The new analysis approach, which is termed NMR/MS Translator, is fully automated and takes only a few seconds on a computer workstation. NMR/MS Translator synergistically uses the power of NMR and MS, enhancing the accuracy and efficiency of the identification of those metabolites compiled in databases.

Metabolomics beyond spectroscopic databases: a combined MS/NMR strategy for the rapid identification of new metabolites in complex mixtures

A novel strategy is introduced that combines high-resolution mass spectrometry (MS) with NMR for the identification of unknown components in complex metabolite mixtures encountered in metabolomics. The approach first identifies the chemical formulas of the mixture components from accurate masses by MS and then generates all feasible structures (structural manifold) that are consistent with these chemical formulas. Next, NMR spectra of each member of the structural manifold are predicted and compared with the experimental NMR spectra in order to identify the molecular structures that match the information obtained from both the MS and NMR techniques. This combined MS/NMR approach was applied to Escherichia coli extract, where the approach correctly identified a wide range of different types of metabolites, including amino acids, nucleic acids, polyamines, nucleosides, and carbohydrate conjugates. This makes this approach, which is termed SUMMIT MS/NMR, well suited for high-throughput applications for the discovery of new metabolites in biological and biomedical mixtures, overcoming the need of experimental MS and NMR metabolite databases.

Limit of detection studies for application to natural product identification using high performance liquid chromatography coupled to nuclear magnetic resonance spectroscopy

In the pursuit of new natural products, the demand to rapidly identify compounds present, in ever decreasing amounts, in complex crude extracts has become a limiting factor. Despite improvements in HPLC-NMR hardware and pulse sequences, no extensive limit of detection (LOD) investigations have been reported for the acquisition of 2D NMR spectroscopic experiments acquired through HPLC-NMR. In this study the LOD for five key 1D and 2D NMR spectroscopic experiments have been established, using two reference compounds, including the on-flow (WET 1D proton), stop-flow (WET1D proton), gCOSY, HSQCAD and gHMBCAD NMR experiments. The LOD for all of the NMR experiments were within the range of 700ng to 1mg for the set of fixed experimental parameters implemented. For principle components in a complex multi-component mixture, this would allow for in situ compound identification. HPLC-NMR analysis was employed to investigate the principle components present in a marine brown alga crude extract, Cystophora subfarcinata.

A novel strategy for screening new natural products by a combination of reversed-phase liquid chromatography fractionation and 13C NMR pattern recognition: the discovery of new anti-cancer flavone dimers from Dysosma versipellis (Hance)

A new screening strategy for the discovery of new natural products by a combination of reversed-phase liquid chromatography (RPLC) and ¹³C NMR pattern recognition.

A Metabolomic Strategy to Screen the Prototype Components and Metabolites of Shuang-Huang-Lian Injection in Human Serum by Ultra Performance Liquid Chromatography Coupled with Quadrupole Time-of-Flight Mass Spectrometry

Shuang-huang-lian injection (SHLI) is a famous Chinese patent medicine, which has been wildly used in clinic to treat acute respiratory tract infection, pneumonia, influenza, and so forth. Despite the widespread clinical application, the prototype components and metabolites of SHLI have not been fully elucidated, especially in human body. To discover and screen the constituents or metabolites of Chinese medicine in biofluids tends to be more and more difficult due to the complexity of chemical compositions, metabolic reactions and matrix effects. In this work, a metabolomic strategy to comprehensively elucidate the prototype components and metabolites of SHLI in human serum conducted by UPLC-Q-TOF/MS was developed. Orthogonal partial least squared discriminant analysis (OPLS-DA) was applied to distinguish the exogenous, namely, drug-induced constituents, from endogenous in human serum. In the S-plot, 35 drug-induced constituents were found, including 23 prototype compounds and 12 metabolites which indicated that SHLI in human body mainly caused phase II metabolite reactions. It was concluded that the metabolomic strategy for identification of herbal constituents and metabolites in biological samples was successfully developed. This identification and structural elucidation of the chemical compounds provided essential data for further pharmacological and pharmacokinetics study of SHLI.

Ultrafast 2D NMR: an emerging tool in analytical spectroscopy

Two-dimensional nuclear magnetic resonance (2D NMR) spectroscopy is widely used in chemical and biochemical analyses. Multidimensional NMR is also witnessing increased use in quantitative and metabolic screening applications. Conventional 2D NMR experiments, however, are affected by inherently long acquisition durations, arising from their need to sample the frequencies involved along their indirect domains in an incremented, scan-by-scan nature. A decade ago, a so-called ultrafast (UF) approach was proposed, capable of delivering arbitrary 2D NMR spectra involving any kind of homo- or heteronuclear correlation, in a single scan. During the intervening years, the performance of this subsecond 2D NMR methodology has been greatly improved, and UF 2D NMR is rapidly becoming a powerful analytical tool experiencing an expanded scope of applications. This review summarizes the principles and main developments that have contributed to the success of this approach and focuses on applications that have been recently demonstrated in various areas of analytical chemistry--from the real-time monitoring of chemical and biochemical processes, to extensions in hyphenated techniques and in quantitative applications.

Mass spectrometry and NMR spectroscopy: modern high-end detectors for high resolution separation techniques--state of the art in natural product HPLC-MS, HPLC-NMR, and CE-MS hyphenations

Current natural product research is unthinkable without the use of high resolution separation techniques as high performance liquid chromatography or capillary electrophoresis (HPLC or CE respectively) combined with mass spectrometers (MS) or nuclear magnetic resonance (NMR) spectrometers. These hyphenated instrumental analysis platforms (CE-MS, HPLC-MS or HPLC-NMR) are valuable tools for natural product de novo identification, as well as the authentication, distribution, and quantification of constituents in biogenic raw materials, natural medicines and biological materials obtained from model organisms, animals and humans. Moreover, metabolic profiling and metabolic fingerprinting applications can be addressed as well as pharmacodynamic and pharmacokinetic issues. This review provides an overview of latest technological developments, discusses the assets and drawbacks of the available hyphenation techniques, and describes typical analytical workflows.

The importance of hyphenated techniques in the discovery of new lead compounds from nature

Nature represents an extraordinary reservoir of novel molecules and there is currently a resurgence of interest in natural products as a possible source of new lead compounds for introduction into therapeutical screening programmes. To discover new bioactive natural products, the dereplication of crude extracts performed prior to isolation work is of crucial importance for avoiding the tedious isolation of known constituents. In this respect, chemical screening strategies based on hyphenated techniques such as liquid chromatography-ultraviolet photodiode array detection, liquid chromatography-mass spectrometry, liquid chromatography tandom mass spectrometry and liquid chromatography-nuclear magnetic resonance (LC-NMR) are more and more extensively used. In the laboratory of Hostettmann's group, these analytical methods have been fully integrated into the isolation process and are used for the chemical screening of crude plant extracts, in complement with online or at-line bioassays, for rapid localisation and identification of new bioactive compounds. In this paper, possibilities and limitations of hyphenated techniques for de novo online natural product identification are discussed. As LC-NMR is playing a key role in this respect, the main part of the paper is dedicated to this technique. In particular, various ways of integrating NMR in the dereplication process are illustrated and strategies involving either direct or indirect hyphenation are presented.

Liquid chromatography-nuclear magnetic resonance coupling as alternative to liquid chromatography-mass spectrometry hyphenations: curious option or powerful and complementary routine tool?

Combining the most powerful separation techniques, i.e. liquid chromatography (LC) or capillary electrophoresis (CE) with a information rich detection system - the mass spectrometer or the nuclear magnetic resonance (NMR) spectrometer - has been pursued for more than three decades. This compilation shall provide an overview of the advantages and limitations of the LC-NMR hyphenation in the light of its most valued application-the unequivocal analyte identification. Especially the post LC trapping of analytes with an in-line solid phase extraction (SPE) device prior to transferring the analyte of interest to the NMR spectrometer (LC-SPE-NMR) proved to be a robust installation allowing a significant cut-down of the amount of analyte needed for the generation of high quality heteronuclear NMR shift correlation data. Different available technical realizations will be discussed and typical application examples from natural product research and from industrial settings will be given.