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

Pharmacometabolomics in Drug Disposition, Toxicity, and Precision Medicine

The precision medicine initiative has driven a substantial change in the way scientists and health care practitioners think about diagnosing and treating disease. While it has long been recognized that drug response is determined by the intersection of genetic, environmental, and disease factors, improvements in technology have afforded precision medicine guided dosing of drugs to improve efficacy and reduce toxicity. Pharmacometabolomics aims to evaluate small molecule metabolites in plasma and/or urine to help evaluate mechanisms that predict and/or reflect drug efficacy and toxicity. In this mini review, we provide an overview of pharmacometabolomic approaches and methodologies. Relevant examples where metabolomic techniques have been used to better understand drug efficacy and toxicity in major depressive disorder and cancer chemotherapy are discussed. In addition, the utility of metabolomics in drug development and understanding drug metabolism, transport, and pharmacokinetics is reviewed. Pharmacometabolomic approaches can help describe factors mediating drug disposition, efficacy, and toxicity. While important advancements in this area have been made, there remain several challenges that must be overcome before this approach can be fully implemented into clinical drug therapy. SIGNIFICANCE STATEMENT: Pharmacometabolomics has emerged as an approach to identify metabolites that allow for implementation of precision medicine approaches to pharmacotherapy. This review article provides an overview of pharmacometabolomics including highlights of important examples.

Diagnosis of epilepsy by machine learning of high-performance plasma metabolic fingerprinting

Epilepsy is a chronic neurological disorder that causes a major threat to public health and the burden of disease worldwide. High-performance diagnostic tools for epilepsy need to be developed to improve diagnostic accuracy and efficiency while still missing. Herein, we utilized nanoparticle-enhanced laser desorption/ionization mass spectrometry (NELDI MS) to acquire plasma metabolic fingerprints (PMFs) from epileptic and healthy individuals for timely and accurate screening of epilepsy. The NELDI MS enabled high detection speed (∼30 s per sample), high throughput (up to 384 samples per run), and favorable reproducibility (coefficients of variation <15 %), acquiring high-performed PMFs. We next constructed an epilepsy diagnostic model by machine learning of PMFs, achieving desirable diagnostic capability with the area under the curve (AUC) value of 0.941 for the validation set. Furthermore, four metabolites were identified as a diagnostic biomarker panel for epilepsy, with an AUC value of 0.812-0.860. Our approach provides a high-performed and high-throughput platform for epileptic diagnostics, promoting the development of metabolic diagnostic tools in precision medicine.

The Metabolomic Footprint of Liver Fibrosis

Both experimental and clinical liver fibrosis leave a metabolic footprint that can be uncovered and defined using metabolomic approaches. Metabolomics combines pattern recognition algorithms with analytical chemistry, in particular, 1H and 13C nuclear magnetic resonance spectroscopy (NMR), gas chromatography-mass spectrometry (GC-MS) and various liquid chromatography-mass spectrometry (LC-MS) platforms. The analysis of liver fibrosis by each of these methodologies is reviewed separately. Surprisingly, there was little general agreement between studies within each of these three groups and also between groups. The metabolomic footprint determined by NMR (two or more hits between studies) comprised elevated lactate, acetate, choline, 3-hydroxybutyrate, glucose, histidine, methionine, glutamine, phenylalanine, tyrosine and citrate. For GC-MS, succinate, fumarate, malate, ascorbate, glutamate, glycine, serine and, in agreement with NMR, glutamine, phenylalanine, tyrosine and citrate were delineated. For LC-MS, only β-muricholic acid, tryptophan, acylcarnitine, p-cresol, valine and, in agreement with NMR, phosphocholine were identified. The metabolomic footprint of liver fibrosis was upregulated as regards glutamine, phenylalanine, tyrosine, citrate and phosphocholine. Several investigators employed traditional Chinese medicine (TCM) treatments to reverse experimental liver fibrosis, and a commentary is given on the chemical constituents that may possess fibrolytic activity. It is proposed that molecular docking procedures using these TCM constituents may lead to novel therapies for liver fibrosis affecting at least one-in-twenty persons globally, for which there is currently no pharmaceutical cure. This in-depth review summarizes the relevant literature on metabolomics and its implications in addressing the clinical problem of liver fibrosis, cirrhosis and its sequelae.

Exploring the Chemical Space of the Exposome: How Far Have We Gone?

Around two-thirds of chronic human disease can not be explained by genetics alone. The Lancet Commission on Pollution and Health estimates that 16% of global premature deaths are linked to pollution. Additionally, it is now thought that humankind has surpassed the safe planetary operating space for introducing human-made chemicals into the Earth System. Direct and indirect exposure to a myriad of chemicals, known and unknown, poses a significant threat to biodiversity and human health, from vaccine efficacy to the rise of antimicrobial resistance as well as autoimmune diseases and mental health disorders. The exposome chemical space remains largely uncharted due to the sheer number of possible chemical structures, estimated at over 1060 unique forms. Conventional methods have cataloged only a fraction of the exposome, overlooking transformation products and often yielding uncertain results. In this Perspective, we have reviewed the latest efforts in mapping the exposome chemical space and its subspaces. We also provide our view on how the integration of data-driven approaches might be able to bridge the identified gaps.

Identification of Predictive Biomarkers of Lameness in Transition Dairy Cows

The aim of this study was to identify with a high level of confidence metabolites previously identified as predictors of lameness and understand their biological relevance by carrying out pathway analyses. For the dairy cattle sector, lameness is a major challenge with a large impact on animal welfare and farm economics. Understanding metabolic alterations during the transition period associated with lameness before the appearance of clinical signs may allow its early detection and risk prevention. The annotation with high confidence of metabolite predictors of lameness and the understanding of interactions between metabolism and immunity are crucial for a better understanding of this condition. Using liquid chromatography-tandem mass spectrometry (LC-MS/MS) with authentic standards to increase confidence in the putative annotations of metabolites previously determined as predictive for lameness in transition dairy cows, it was possible to identify cresol, valproic acid, and gluconolactone as L1, L2, and L1, respectively which are the highest levels of confidence in identification. The metabolite set enrichment analysis of biological pathways in which predictors of lameness are involved identified six significant pathways (p < 0.05). In comparison, over-representation analysis and topology analysis identified two significant pathways (p < 0.05). Overall, our LC-MS/MS analysis proved to be adequate to confidently identify metabolites in urine samples previously found to be predictive of lameness, and understand their potential biological relevance, despite the challenges of metabolite identification and pathway analysis when performing untargeted metabolomics. This approach shows potential as a reliable method to identify biomarkers that can be used in the future to predict the risk of lameness before calving. Validation with a larger cohort is required to assess the generalization of these findings.

Assessing antibiotic residues in sediments from mangrove ecosystems: A review

Antibiotics' widespread and abusive use in aquaculture and livestock leads to extensive environmental dissemination and dispersion, consequently increasing antibiotic-resistant bacteria in marine ecosystems. Hence, there is an increased need for efficient methods for identifying and quantifying antibiotic residues in soils and sediments. From a review of the last 20 years, we propose and compare different chromatographic techniques for detecting and quantifying antibiotics in sediment samples from marine ecosystems, particularly in mangrove forest sediments. The methods typically include three stages: extraction of antibiotics from the solid matrix, cleaning, and concentration of samples before quantification. We address the leading causes of the occurrence of antibiotics in marine ecosystem sediments and analyze the most appropriate methods for each analytical stage. Ultimately, selecting a method for identifying antibiotic residues depends on multiple factors, ranging from the nature and physicochemical properties of the analytes to the availability of the necessary equipment and the available resources.

Stable Isotope Tracing Analysis in Cancer Research: Advancements and Challenges in Identifying Dysregulated Cancer Metabolism and Treatment Strategies

Metabolic reprogramming is a hallmark of cancer, driving the development of therapies targeting cancer metabolism. Stable isotope tracing has emerged as a widely adopted tool for monitoring cancer metabolism both in vitro and in vivo. Advances in instrumentation and the development of new tracers, metabolite databases, and data analysis tools have expanded the scope of cancer metabolism studies across these scales. In this review, we explore the latest advancements in metabolic analysis, spanning from experimental design in stable isotope-labeling metabolomics to sophisticated data analysis techniques. We highlight successful applications in cancer research, particularly focusing on ongoing clinical trials utilizing stable isotope tracing to characterize disease progression, treatment responses, and potential mechanisms of resistance to anticancer therapies. Furthermore, we outline key challenges and discuss potential strategies to address them, aiming to enhance our understanding of the biochemical basis of cancer metabolism.

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

BACKGROUND

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

RESULT

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

SIGNIFICANCE

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

The hidden treasures in endophytic fungi: a comprehensive review on the diversity of fungal bioactive metabolites, usual analytical methodologies, and applications

This review provides a comprehensive overview of the key aspects of the natural metabolite production by endophytic fungi, which has attracted significant attention due to its diverse biological activities and wide range of applications. Synthesized by various fungal species, these metabolites encompass compounds with therapeutic, agricultural, and commercial significance. We delved into strategies and advancements aimed at optimizing fungal metabolite production. Fungal cultivation, especially by Aspergillus, Penicillium, and Fusarium, plays a pivotal role in metabolite biosynthesis, and researchers have explored both submerged and solid-state cultivation processes to harness the full potential of fungal species. Nutrient optimization, pH, and temperature control are critical factors in ensuring high yields of the targeted bioactive metabolites especially for scaling up processes. Analytical methods that includes High-Performance Liquid Chromatography (HPLC), Liquid Chromatography-Mass Spectrometry (LC-MS), Gas Chromatography-Mass Spectrometry (GC-MS), Nuclear Magnetic Resonance (NMR), and Mass Spectrometry (MS), are indispensable for the identification and quantification of the compounds. Moreover, genetic engineering and metabolic pathway manipulation have emerged as powerful tools to enhance metabolite production and develop novel fungal strains with increased yields. Regulation and control mechanisms at the genetic, epigenetic, and metabolic levels are explored to fine-tune the biosynthesis of fungal metabolites. Ongoing research aims to overcome the complexity of the steps involved to ensure the efficient production and utilization of fungal metabolites.

Decoding active compounds and molecular targets of herbal medicine by high-throughput metabolomics technology: A systematic review

Clinical experiences of herbal medicine (HM) have been used to treat a variety of human intractable diseases. As the treatment of diseases using HM is characterized by multi-components and multi-targets, it is difficult to determine the bio-active components, explore the molecular targets and reveal the mechanisms of action. Metabolomics is frequently used to characterize the effect of external disturbances on organisms because of its unique advantages on detecting changes in endogenous small-molecule metabolites. Its systematicity and integrity are consistent with the effective characteristics of HM. After HM intervention, metabolomics can accurately capture and describe the behavior of endogenous metabolites under the disturbance of functional compounds, which will be used to decode the bioactive ingredients of HM and expound the molecular targets. Metabolomics can provide an approach for explaining HM, addressing unclear clinical efficacy and undefined mechanisms of action. In this review, the metabolomics strategy and its applications in HM are systematically introduced, which offers valuable insights for metabolomics methods to characterizing the pharmacological effects and molecular targets of HM.

Mobilize a Proton to Transform the Collision-Induced Dissociation Spectral Pattern of a Cyclic Peptide

The collision-induced dissociation (CID) behaviors of protonated molecules of anabaenopeptins, a group of cyanobacterial cyclic peptides, were investigated in detail using liquid chromatography-tandem mass spectrometry. Although anabaenopeptin A and B share a macrocyclic peptide structure, they give strikingly different fragmentation patterns; the former gives a variety of product ions including cleavages in the cyclic peptide structure, which is useful for structural analysis; whereas the latter gives far fewer product ions and no fragmentation in the cyclic moiety. Energy-resolved CID experiments clarified the mechanism behind the striking difference attributable to the difference in exocyclic amino acid residues, Tyr or Arg. The guanidino group in Arg-containing analogue, anabaenopeptin B, should be by far the most preferred protonation site; the proton would be sequestered at the guanidino group in the protonated molecule, with the lack of proton mobility prohibiting opening of the charge-directed fragmentation channels in the cyclic moiety. Enzymatic hydrolysis of the guanidino group to give citrullinated-anabaenopeptin B restored proton mobility. The fragmentation pattern of the citrullinated peptide became almost identical to that of anabaenopeptin A. The observed fragmentation behaviors of these cyclic peptides were consistent with those of linear peptides, which have been well understood based on the mobile proton model.

IMN4NPD: An Integrated Molecular Networking Workflow for Natural Product Dereplication

Molecular networking has emerged as a standard approach for natural product (NP) discovery. However, the current pipeline based on molecular networks tends to prioritize larger clusters comprising multiple nodes. To address this issue, we present the integrated molecular networking workflow for NP dereplication (IMN4NPD). This approach not only expedites the rapid dereplication of extensive clusters within the molecular network but also places specific emphasis on self-looped or pairs of nodes, which are often overlooked by the current methods. By amalgamating the outputs from various computational tools, we efficiently dereplicate compounds falling into specific categories and provide annotations for both large cluster nodes and self-looped or pair of nodes within the molecular network. Furthermore, we have incorporated several fundamentally distinct similarity algorithms, namely, Spec2Vec and MS2DeepScore, for constructing the t-SNE network. Through comparison with modified cosine similarity, we have observed that integrating additional diverse spectral similarity measures, the resulting t-SNE network enhanced the ability to dereplicate NPs. Demonstrating the use case of an ethanol extract of Plumula nelumbinis, we illustrate that an integration of multiple computational solutions with IMN4NPD aids the dereplication, especially self-looped nodes, and in the discovery of novel compounds in NPs.

Metabolomic biomarkers in liquid biopsy: accurate cancer diagnosis and prognosis monitoring

Liquid biopsy, a novel detection method, has recently become an active research area in clinical cancer owing to its unique advantages. Studies on circulating free DNA, circulating tumor cells, and exosomes obtained by liquid biopsy have shown great advances and they have entered clinical practice as new cancer biomarkers. The metabolism of the body is dynamic as cancer originates and progresses. Metabolic abnormalities caused by cancer can be detected in the blood, sputum, urine, and other biological fluids via systemic or local circulation. A considerable number of recent studies have focused on the roles of metabolic molecules in cancer. The purpose of this review is to provide an overview of metabolic markers from various biological fluids in the latest clinical studies, which may contribute to cancer screening and diagnosis, differentiation of cancer typing, grading and staging, and prediction of therapeutic response and prognosis.

Improving quality of analysis by suppression of unwanted signals through band-selective excitation in NMR spectroscopy for metabolomics studies

INTRODUCTION

Nuclear Magnetic Resonance (NMR) spectroscopy stands as a preeminent analytical tool in the field of metabolomics. Nevertheless, when it comes to identifying metabolites present in scant amounts within various types of complex mixtures such as plants, honey, milk, and biological fluids and tissues, NMR-based metabolomics presents a formidable challenge. This predicament arises primarily from the fact that the signals emanating from metabolites existing in low concentrations tend to be overshadowed by the signals of highly concentrated metabolites within NMR spectra.

OBJECTIVES

The aim of this study is to tackle the issue of intense sugar signals overshadowing the desired metabolite signals, an optimal pulse sequence with band-selective excitation has been proposed for the suppression of sugar's moiety signals (SSMS). This sequence serves the crucial purpose of suppressing unwanted signals, with a particular emphasis on mitigating the interference caused by sugar moieties' signals.

METHODS

We have implemented this comprehensive approach to various NMR techniques, including 1D 1H presaturation (presat), 2D J-resolved (RES), 2D 1H-1H Total Correlation Spectroscopy (TOCSY), and 2D 1H-13C Heteronuclear Single Quantum Coherence (HSQC) for the samples of dates-flesh, honey, a standard stock solution of glucose, and nine amino acids, and commercial fetal bovine serum (FBS).

RESULTS

The outcomes of this approach were significant. The suppression of the high-intensity sugar signals has considerably enhanced the visibility and sensitivity of the signals emanating from the desired metabolites.

CONCLUSION

This, in turn, enables the identification of a greater number of metabolites. Additionally, it streamlines the experimental process, reducing the time required for the comparative quantification of metabolites in statistical studies in the field of metabolomics.

Metabolomics and mitochondrial dysfunction in cardiometabolic disease

Circulating metabolites are indicators of systemic metabolic dysfunction and can be detected through contemporary techniques in metabolomics. These metabolites are involved in numerous mitochondrial metabolic processes including glycolysis, fatty acid β-oxidation, and amino acid catabolism, and changes in the abundance of these metabolites is implicated in the pathogenesis of cardiometabolic diseases (CMDs). Epigenetic regulation and direct metabolite-protein interactions modulate metabolism, both within cells and in the circulation. Dysfunction of multiple mitochondrial components stemming from mitochondrial DNA mutations are implicated in disease pathogenesis. This review will summarize the current state of knowledge regarding: i) the interactions between metabolites found within the mitochondrial environment during CMDs, ii) various metabolites' effects on cellular and systemic function, iii) how harnessing the power of metabolomic analyses represents the next frontier of precision medicine, and iv) how these concepts integrate to expand the clinical potential for translational cardiometabolic medicine.

Impact of thermal processing on dietary flavonoids

Flavonoids are a class of polyphenols which are widely distributed in natural products and foods. They have diverse bioactivities, including anti-inflammatory, anti-aging, and antioxidant activities. Generally, the foods rich in flavonoids are usually consumed after thermal processing. However, thermal stability of flavonoids is usually low, and thermal processing could cause either positive or negative influences on their stability and bioactivities. In this review, the effects of thermal processing on thermal stability and bioactivity of dietary flavonoids from different food sources are summarized. Then, strategies to improve thermal stability of dietary flavonoids are discussed and the effect of some promising thermal technologies are also preliminary clarified. The promising thermal technologies may be alternative to conventional thermal processing technologies.

Structural Diversity and Biological Activity of Cyanopeptolins Produced by Nostoc edaphicum CCNP1411

Cyanopeptolins (CPs) are one of the most commonly occurring class of cyanobacterial nonribosomal peptides. For the majority of these compounds, protease inhibition has been reported. In the current work, the structural diversity of cyanopeptolins produced by Nostoc edaphicum CCNP1411 was explored. As a result, 93 CPs, including 79 new variants, were detected and structurally characterized based on their mass fragmentation spectra. CPs isolated in higher amounts were additionally characterized by NMR. To the best of our knowledge, this is the highest number of cyanopeptides found in one strain. The biological assays performed with the 34 isolated CPs confirmed the significance of the amino acid located between Thr and the unique 3-amino-6-hydroxy-2-piperidone (Ahp) on the activity of the compounds against serine protease and HeLa cancer cells.

Identification of metabolites from complex mixtures by 3D correlation of 1H NMR, MS and LC data using the SCORE-metabolite-ID approach

Not only in metabolomics studies, but also in natural product chemistry, reliable identification of metabolites usually requires laborious steps of isolation and purification and remains a bottleneck in many studies. Direct metabolite identification from a complex mixture without individual isolation is therefore a preferred approach, but due to the large number of metabolites present in natural products, this approach is often hampered by signal overlap in the respective 1H NMR spectra. This paper presents a method for the three-dimensional mathematical correlation of NMR with MS data over the third dimension of the time course of a chromatographic fractionation. The MATLAB application SCORE-metabolite-ID (Semi-automatic COrrelation analysis for REliable metabolite IDentification) provides semi-automatic detection of correlated NMR and MS data, allowing NMR signals to be related to associated mass-to-charge ratios from ESI mass spectra. This approach enables fast and reliable dereplication of known metabolites and facilitates the dynamic analysis for the identification of unknown compounds in any complex mixture. The strategy was validated using an artificial mixture and further tested on a polar extract of a pine nut sample. Straightforward identification of 40 metabolites could be shown, including the identification of β-D-glucopyranosyl-1-N-indole-3-acetyl-N-L-aspartic acid (1) and Nα-(2-hydroxy-2-carboxymethylsuccinyl)-L-arginine (2), the latter being identified in a food sample for the first time.

An epidemiological introduction to human metabolomic investigations

Metabolomics holds great promise for uncovering insights around biological processes impacting disease in human epidemiological studies. Metabolites can be measured across biological samples, including plasma, serum, saliva, urine, stool, and whole organs and tissues, offering a means to characterize metabolic processes relevant to disease etiology and traits of interest. Metabolomic epidemiology studies face unique challenges, such as identifying metabolites from targeted and untargeted assays, defining standards for quality control, harmonizing results across platforms that often capture different metabolites, and developing statistical methods for high-dimensional and correlated metabolomic data. In this review, we introduce metabolomic epidemiology to the broader scientific community, discuss opportunities and challenges presented by these studies, and highlight emerging innovations that hold promise to uncover new biological insights.

Dysregulated lipid metabolism in TMZ-resistant glioblastoma: pathways, proteins, metabolites and therapeutic opportunities

Glioblastoma (GBM) is a highly aggressive and lethal brain tumor with limited treatment options, such as the chemotherapeutic agent, temozolomide (TMZ). However, many GBM tumors develop resistance to TMZ, which is a major obstacle to effective therapy. Recently, dysregulated lipid metabolism has emerged as an important factor contributing to TMZ resistance in GBM. The dysregulation of lipid metabolism is a hallmark of cancer and alterations in lipid metabolism have been linked to multiple aspects of tumor biology, including proliferation, migration, and resistance to therapy. In this review, we aimed to summarize current knowledge on lipid metabolism in TMZ-resistant GBM, including key metabolites and proteins involved in lipid synthesis, uptake, and utilization, and recent advances in the application of metabolomics to study lipid metabolism in GBM. We also discussed the potential of lipid metabolism as a target for novel therapeutic interventions. Finally, we highlighted the challenges and opportunities associated with developing these interventions for clinical use, and the need for further research to fully understand the role of lipid metabolism in TMZ resistance in GBM. Our review suggests that targeting dysregulated lipid metabolism may be a promising approach to overcome TMZ resistance and improve outcomes in patients with GBM.

Compound-Specific 1D 1H NMR Pulse Sequence Selection for Metabolomics Analyses

NMR-based metabolomics approaches have been used in a wide range of applications, for example, with medical, plant, and marine samples. One-dimensional (1D) ¹H NMR is routinely used to find out biomarkers in biofluids such as urine, blood plasma, and serum. To mimic biological conditions, most NMR studies have been carried out in an aqueous solution where the high intensity of the water peak is a major problem in obtaining a meaningful spectrum. Different methods have been used to suppress the water signal, including 1D Carr-Purcell-Meiboom-Gill (CPMG) presat, consisting of a T₂ filter to suppress macromolecule signals and reduce the humped curve in the spectrum. 1D nuclear Overhauser enhancement spectroscopy (NOESY) is another method for water suppression that is used routinely in plant samples with fewer macromolecules than in biofluid samples. Other common 1D ¹H NMR methods such as 1D ¹H presat and 1D ¹H ES have simple pulse sequences; their acquisition parameters can be set easily. The proton with presat has just one pulse and the presat block causes water suppression, while other 1D ¹H NMR methods including those mentioned above have more pulses. However, it is not well known in metabolomics studies because it is used only occasionally and in a few types of samples by metabolomics experts. Another effective method is excitation sculpting to suppress water. Herein, we evaluate the effect of method selection on signal intensities of commonly detected metabolites. Different classes of samples including biofluid, plant, and marine samples were investigated, and recommendations on the advantages and limitations of each method are presented.

The Crosstalk between Gut Microbiota and Nervous System: A Bidirectional Interaction between Microorganisms and Metabolome

Several studies have shown that the gut microbiota influences behavior and, in turn, changes in the immune system associated with symptoms of depression or anxiety disorder may be mirrored by corresponding changes in the gut microbiota. Although the composition/function of the intestinal microbiota appears to affect the central nervous system (CNS) activities through multiple mechanisms, accurate epidemiological evidence that clearly explains the connection between the CNS pathology and the intestinal dysbiosis is not yet available. The enteric nervous system (ENS) is a separate branch of the autonomic nervous system (ANS) and the largest part of the peripheral nervous system (PNS). It is composed of a vast and complex network of neurons which communicate via several neuromodulators and neurotransmitters, like those found in the CNS. Interestingly, despite its tight connections to both the PNS and ANS, the ENS is also capable of some independent activities. This concept, together with the suggested role played by intestinal microorganisms and the metabolome in the onset and progression of CNS neurological (neurodegenerative, autoimmune) and psychopathological (depression, anxiety disorders, autism) diseases, explains the large number of investigations exploring the functional role and the physiopathological implications of the gut microbiota/brain axis.

Unusual Biotransformation Reactions of Drugs and Drug Candidates

Detailed assessment of the fate of drugs in nonclinical test species and humans is essential to ensure the safety and efficacy of medicines in patients. In this context, biotransformation of drugs and drug candidates has been an area of keen interest over many decades in the pharmaceutical industry as well as academia. Although many of the enzymes and biotransformation pathways involved in the metabolism of xenobiotics and more specifically drugs have been well characterized, each drug molecule is unique and constitutes specific challenges for the biotransformation scientist. In this mini-review written for the special issue on the occasion of the 50th Anniversary celebration of Drug Metabolism and Disposition and to celebrate contributions of F. Peter Guengerich, one of the pioneers of the drug metabolism field, recently reported "unusual" biotransformation reactions are presented. Scientific and technological advances in the "toolbox" of the biotransformation scientists are summarized. As the pharmaceutical industry continues to explore therapeutic modalities different from the traditional small molecule drugs, the new challenges confronting the biotransformation scientist as well as future opportunities are discussed. SIGNIFICANCE STATEMENT: For the biotransformation scientists, it is essential to share and be aware of unexpected biotransformation reactions so that they can increase their confidence in predicting metabolites of drugs in humans to ensure the safety and efficacy of these metabolites before the medicines reach large numbers of patients. The purpose of this review is to highlight recent observations of "unusual" metabolites so that the scientists working in the area of drug metabolism can strengthen their readiness in expecting the unexpected.

Efficacy evaluation, active ingredients, and multitarget exploration of herbal medicine

Evidence shows that herbal medicine (HM) could be beneficial for the treatment of various diseases. However, complexities present in HM due to the unclear bioactive compounds, mechanisms of action, undetermined targets for therapy, and nonspecific features for metabolism, are currently an obstacle for the progression of novel drug discovery. Metabolomics could be a potential tool to overcome these issues and for the understanding of HM from a small-molecule metabolism level. The chinmedomics-based metabolomics method assesses the overall metabolism of organisms with a holistic view and shows great potential for understanding metabolic pathways, evaluating curative effects, clarifying mechanisms, discovering active ingredients, and precision medicine. This review focuses on the efficacy evaluation, active ingredient discovery, and target exploration of HM based on metabolomics and chinmedomics.

Engineering the biosynthesis of fungal nonribosomal peptides

Covering: 2011 up to the end of 2021.Fungal nonribosomal peptides (NRPs) and the related polyketide-nonribosomal peptide hybrid products (PK-NRPs) are a prolific source of bioactive compounds, some of which have been developed into essential drugs. The synthesis of these complex natural products (NPs) utilizes nonribosomal peptide synthetases (NRPSs), multidomain megaenzymes that assemble specific peptide products by sequential condensation of amino acids and amino acid-like substances, independent of the ribosome. NRPSs, collaborating polyketide synthase modules, and their associated tailoring enzymes involved in product maturation represent promising targets for NP structure diversification and the generation of small molecule unnatural products (uNPs) with improved or novel bioactivities. Indeed, reprogramming of NRPSs and recruiting of novel tailoring enzymes is the strategy by which nature evolves NRP products. The recent years have witnessed a rapid development in the discovery and identification of novel NRPs and PK-NRPs, and significant advances have also been made towards the engineering of fungal NRP assembly lines to generate uNP peptides. However, the intrinsic complexities of fungal NRP and PK-NRP biosynthesis, and the large size of the NRPSs still present formidable conceptual and technical challenges for the rational and efficient reprogramming of these pathways. This review examines key examples for the successful (and for some less-successful) re-engineering of fungal NRPS assembly lines to inform future efforts towards generating novel, biologically active peptides and PK-NRPs.

Targeted Per- and Polyfluoroalkyl substances (PFAS) assessments for high throughput screening: Analytical and testing considerations to inform a PFAS stock quality evaluation framework

Per- and polyfluoroalkyl substances (PFAS) represent a large chemical class lacking hazard, toxicokinetic, and exposure information. To accelerate PFAS hazard evaluation, new approach methodologies (NAMs) comprised of in vitro high-throughput toxicity screening, toxicokinetic data, and computational modeling are being employed in read across strategies to evaluate the larger PFAS landscape. A critical consideration to ensure robust evaluations is a parallel assessment of the quality of the screening stock solutions, where dimethyl sulfoxide (DMSO) is often the diluent of choice. Challenged by the lack of commercially available reference standards for many of the selected PFAS and reliance on mass spectrometry approaches for such an evaluation, we developed a high-throughput framework to evaluate the quality of screening stocks for 205 PFAS selected for these NAM efforts. Using mass spectrometry coupled with either liquid or gas chromatography, a quality scoring system was developed that incorporated observations during mass spectral examination to provide a simple pass or fail notation. Informational flags were used to further describe findings regarding parent analyte presence through accurate mass identification, evidence of contaminants and/or degradation, or further describe characteristics such as isomer presence. Across the PFAS-DMSO stocks tested, 148 unique PFAS received passing quality scores to allow for further in vitro testing whereas 57 received a failing score primarily due to detection issues or confounding effects of DMSO. Principle component analysis indicated vapor pressure and Henry's Law Constant as top indicators for a failed quality score for those analyzed by gas chromatography. Three PFAS in the hexafluoropropylene oxide family failed due to degradation in DMSO. As the PFAS evaluated spanned over 20 different structural categories, additional commentary describes analytical observations across specific groups related to PFAS stock composition, detection, stability, and methodologic considerations that will be useful for informing future analytical assessment and downstream HTS efforts. The high-throughput stock quality scoring workflow presented holds value as a tool to evaluate chemical presence and quality efficiently and for informing data inclusion in PFAS or other NAM screening efforts.

Preclinical and Clinical Applications of Metabolomics and Proteomics in Glioblastoma Research

Glioblastoma (GB) is a primary malignancy of the central nervous system that is classified by the WHO as a grade IV astrocytoma. Despite decades of research, several aspects about the biology of GB are still unclear. Its pathogenesis and resistance mechanisms are poorly understood, and methods to optimize patient diagnosis and prognosis remain a bottle neck owing to the heterogeneity of the malignancy. The field of omics has recently gained traction, as it can aid in understanding the dynamic spatiotemporal regulatory network of enzymes and metabolites that allows cancer cells to adjust to their surroundings to promote tumor development. In combination with other omics techniques, proteomic and metabolomic investigations, which are a potent means for examining a variety of metabolic enzymes as well as intermediate metabolites, might offer crucial information in this area. Therefore, this review intends to stress the major contribution these tools have made in GB clinical and preclinical research and highlights the crucial impacts made by the integrative "omics" approach in reducing some of the therapeutic challenges associated with GB research and treatment. Thus, our study can purvey the use of these powerful tools in research by serving as a hub that particularly summarizes studies employing metabolomics and proteomics in the realm of GB diagnosis, treatment, and prognosis.

Drug discovery inspired by bioactive small molecules from nature

Natural products (NPs) have greatly contributed to the development of novel treatments for human diseases such as cancer, metabolic disorders, and infections. Compared to synthetic chemical compounds, primary and secondary metabolites from medicinal plants, fungi, microorganisms, and our bodies are promising resources with immense chemical diversity and favorable properties for drug development. In addition to the well-validated significance of secondary metabolites, endogenous small molecules derived from central metabolism and signaling events have shown great potential as drug candidates due to their unique metabolite-protein interactions. In this short review, we highlight the values of NPs, discuss recent scientific and technological advances including metabolomics tools, chemoproteomics approaches, and artificial intelligence-based computation platforms, and explore potential strategies to overcome the current challenges in NP-driven drug discovery.

Millettia isoflavonoids: a comprehensive review of structural diversity, extraction, isolation, and pharmacological properties

There are approximately 260 known species in the genus Millettia, many of which are used in traditional medicine to treat human and other animal ailments in various parts of the world. Being in the Leguminosae (Fabaceae) family, Millettia species are rich sources of isoflavonoids. In the past three decades alone, several isoflavonoids originating from Millettia have been isolated, and their pharmacological activities have been evaluated against major diseases, such as cancer, inflammation, and diabetes. Despite such extensive research, no recent and comprehensive review of the phytochemistry and pharmacology of Millettia isoflavonoids is available. Furthermore, the structural diversity of isoflavonoids in Millettia species has rarely been reported. In this review, we comprehensively summarized the structural diversity of Millettia isoflavonoids, the methods used for their extraction and isolation protocols, and their pharmacological properties. According to the literature, 154 structurally diverse isoflavonoids were isolated and reported from the various tissues of nine well-known Millettia species. Prenylated isoflavonoids and rotenoids were the most dominant subclasses of isoflavonoids reported. Other subclasses of reported isoflavonoids include isoflavans, aglycone isoflavones, glycosylated isoflavones, geranylated isoflavonoids, phenylcoumarins, pterocarpans and coumaronochromenes. Although some isolated molecules showed promising pharmacological properties, such as anticancer, anti-inflammatory, estrogenic, and antibacterial activities, others remained untested. In general, this review highlights the potential of Millettia isoflavonoids and could improve their utilization in drug discovery and medicinal use processes.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11101-022-09845-w.

A Bio-Guided Screening for Antioxidant, Anti-Inflammatory and Hypolipidemic Potential Supported by Non-Targeted Metabolomic Analysis of Crepis spp

This study was designed to evaluate the chemical fingerprints and the antioxidant, anti-inflammatory and hypolipidemic activity of selected Crepis species collected in Greece, namely, C. commutata, C. dioscoridis, C. foetida, C. heldreichiana, C. incana, C. rubra, and Phitosia crocifolia (formerly known as Crepis crocifolia). For the phytochemical analyses, sample measurements were carried out by using nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography coupled with mass spectrometry (LC-MS). Τhe extracts were evaluated both in vitro (radical scavenging activity: DPPH assay and total phenolic content: Folin–Ciocalteu) and in vivo (paw edema reduction and hypolipidemic activity: experimental mouse protocols). Among the tested extracts, C. incana presented the highest gallic acid equivalents (GAE) (0.0834 mg/mL) and the highest antioxidant activity (IC₅₀ = 0.07 mg/mL) in vitro, as well as the highest anti-inflammatory activity with 32% edema reduction in vivo. Moreover, in the hypolipidemic protocol, the same extract increased plasma total antioxidant capacity (TAC) by 48.7%, and decreased cholesterol (41.3%) as well as triglycerides (37.2%). According to fractionation of the extract and the phytochemical results, this biological effect may be associated with the rich phenolic composition; caffeoyl tartaric acid derivatives (cichoric and caftaric acid) are regarded as the most prominent bioactive specialized metabolites. The present study contributes to the knowledge regarding the phytochemical and pharmacological profile of Crepis spp.

Fecal amine metabolite analysis before onset of severe necrotizing enterocolitis in preterm infants: a prospective case-control study

Infants developing necrotizing enterocolitis (NEC) have a different metabolomic profile compared to controls. The potential of specific metabolomics, i.e. amino acids and amino alcohols (AAA), as early diagnostic biomarkers for NEC is largely unexplored. In this multicenter prospective case-control study, longitudinally collected fecal samples from preterm infants (born <30 weeks of gestation) from 1-3 days before diagnosis of severe NEC (Bell's stage IIIA/IIIB), were analyzed by targeted high-performance liquid chromatography (HPLC). Control samples were collected from gestational and postnatal age-matched infants. Thirty-one NEC cases (15 NEC IIIA;16 NEC IIIB) with 1:1 matched controls were included. Preclinical samples of infants with NEC were characterized by five increased essential amino acids-isoleucine, leucine, methionine, phenylalanine and valine. Lysine and ethanolamine ratios were lower prior to NEC, compared to control samples. A multivariate model was rendered based on isoleucine, lysine, ethanolamine, tryptophan and ornithine, modestly discriminating cases from controls (AUC 0.67; p < 0.001). Targeted HPLC pointed to several specific AAA alterations in samples collected 1-3 days before NEC onset, compared to controls. Whether this reflects metabolic alterations and has a role in early biomarker development for NEC, has yet to be elucidated.

α-glucosidase inhibitors from Syzygium polyanthum (Wight) Walp leaves as revealed by metabolomics and in silico approaches

ETHNOPHARMACOLOGICAL RELEVANCE

Syzygium polyanthum (Wight) Walp leaves are traditionally used to cure diabetes in many regions of Indonesia. Traditional use involves boiling the leaves until the water is reduced to half volume, and then the decoction is taken 1-2 times daily. Despite several studies reporting the antidiabetic activity of this plant, bioactive compounds have not been well identified.

AIM OF THE STUDY

Indonesia is one of the countries with the highest diabetes cases, particularly type 2 diabetes mellitus (T2DM). Few people have access to modern medicinal treatment; thus, the role of antidiabetic traditional medicine has become increasingly important. This research aimed to identify α-glucosidase inhibitors from S. polyathum leaves using a metabolomics approach. When the active compounds of S. polyathum are properly identified, the quality of the herb can be more easily controlled.

MATERIALS AND METHODS

The dried leaves of S. polyanthum were extracted by a comprehensive extraction method using a solvent combination of n-hexane, acetone, and water in a gradient, resulting in a total of 42 fractions. All fractions were subjected to an in vitro α-glucosidase inhibition test and chemical profile analysis using Nuclear Magnetic Resonance (NMR) and high performance liquid chromatography (HPLC). Orthogonal projection least square (OPLS) analysis was used to correlate the two data to identify NMR signals, and HPLC chromatogram peaks correlated to the activity. 2D NMR and ultra-high-performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS) analyses were also used to give more precise compound identification. The activity of the identified active compounds was confirmed by an in silico technique.

RESULTS AND DISCUSSION

The results of the α-glucosidase activity test showed that the most active fractions were obtained from solvents with medium polarity: Fractions 9 and 10 (F9 and F10), obtained from gradient acetone-water 4:1 and 3:2, respectively. The IC50 values of F9 and F10 were 24.8 and 31.8 μg/mL, respectively. NMR data showed that F9 had more intense and diverse signals in the aromatic region than F10. OPLS analysis results showed that some typical flavonoid signals abundant in F9 positively correlated with α-glucosidase activity. 2D NMR and UHPLC-HRMS analysis of F9 led to the conclusion that these signals could be attributed to myricetin-3-O-rhamnoside (myricitrin) and epigallocatechin-3-gallate (EGCG). In silico analysis confirmed these results, as myricitrin and EGCG had binding energies resembling acarbose as a positive control (-8.47, -8.19, and -10.13, respectively).

CONCLUSIONS

NMR and HPLC-metabolomics successfully identified myricitrin and EGCG as α-glucosidase inhibitors from S. polyanthum leaves, and docking analysis validated their inhibitory activity. The results of this study justified the traditional use of S. polyanthum as an antidiabetes herbal.

Accurate detection of perchlorate in epoxy resins via chlorine-35 quantitative quadrupolar NMR (qQNMR)

We present herein the application of a qQNMR method that uses a quadrupolar nucleus such as chlorine-35 for the quantification of perchlorate in epoxy resins.

Monitoring Retinoblastoma by Machine Learning of Aqueous Humor Metabolic Fingerprinting

The most common intraocular pediatric malignancy, retinoblastoma (RB), accounts for ≈10% of cancer in children. Efficient monitoring can enhance living quality of patients and 5-year survival ratio of RB up to 95%. However, RB monitoring is still insufficient in regions with limited resources and the mortality may even reach over 70% in such areas. Here, an RB monitoring platform by machine learning of aqueous humor metabolic fingerprinting (AH-MF) is developed, using nanoparticle enhanced laser desorption/ionization mass spectrometry (LDI MS). The direct AH-MF of RB free of sample pre-treatment is recorded, with both high reproducibility (coefficient of variation < 10%) and sensitivity (low to 0.3 pmol) at sample volume down to 40 nL only. Further, early and advanced RB patients with area-under-the-curve over 0.9 and accuracy over 80% are differentiated, through machine learning of AH-MF. Finally, a metabolic biomarker panel of 7 metabolites through accurate MS and tandem MS (MS/MS) with pathway analysis to monitor RB is identified. This work can contribute to advanced metabolic analysis of eye diseases including but not limited to RB and screening of new potential metabolic targets toward therapeutic intervention.

The online coupling of liquid chromatography to Fourier transform infrared spectroscopy using a solute-deposition interface: A proof of concept

Hyphenated techniques combining chromatographic and spectroscopic methods are the gold standard to effectively handle the emerging challenges in the analysis of unknown chemical components in mixtures, and in this regard the coupling of liquid chromatography to Fourier transform infrared spectroscopy (LC-FTIR) is no exception. While earlier attempts to couple LC to IR spectroscopy relied almost entirely on offline techniques, clear motivations for implementing online LC-FTIR instrumentation emerged from the need for shorter analysis time, a higher degree of automation and sample throughput, better reproducibility, and reduced contamination. Most recent designs of LC-FTIR concepts have aimed to combine the advantages of both approaches by means of a solvent-elimination interface. The hyphenated instrumentation and method presented in this research are based on a pneumatically assisted LC-FTIR interface, relying on a small-scale self-regulating spray dryer to attain desolvation of the LC eluent stream while retaining the spatial and temporal resolution of the dissolved substrates. Focused deposition of the dried analytes occurs onto a ZnSe disc for continuous transmission mid-IR analysis at a resolution of 4 cm^-1. The optimization of the LC-FTIR technique is discussed in terms of interface parameters, limits of detection, and limits of quantification for a pair of furanocoumarin isomers differing in the position (linear or angular type) of the furan ring fused to coumarin. Finally, confident discrimination between the two closely related molecules was attained by matching the experimental FTIR spectra in a dedicated library. The quality match factors obtained were higher than 99% for both molecules. The limit of identification (LOI) was determined experimentally as the minimum amount of substance yielding a library-searchable IR spectrum (affording a quality match factor higher than 90%). Specifically, LOI of 0.6 μg and 1.25 μg was determined for psoralen and angelicin, respectively.

NanoUPLC-QTOF-MS/MS Determination of Major Rosuvastatin Degradation Products Generated by Gamma Radiation in Aqueous Solution

Rosuvastatin, a member of the statin family of drugs, is used to regulate high cholesterol levels in the human body. Moreover, rosuvastatin and other statins demonstrate a protective role against free radical-induced oxidative stress. Our research aimed to investigate the end-products of free radical-induced degradation of rosuvastatin. To induce the radical degradation, an aqueous solution of rosuvastatin was irradiated using different doses of gamma radiation (50-1000 Gy) under oxidative conditions. Rosuvastatin and related degradation products were separated on nanoC18 column under gradient elution, and identification was carried out on hyphenated nanoUPLC and nanoESI-QTOF mass spectrometer system. Elemental composition analysis using highly accurate mass measurements together with isotope fitting algorithm identified nine major degradation products. This is the first study of gamma radiation-induced degradation of rosuvastatin, where chemical structures, MS/MS fragmentation pathways and formation mechanisms of the resulting degradation products are detailly described. The presented results contribute to the understanding of the degradation pathway of rosuvastatin and possibly other statins under gamma radiation conditions.

Nuclear Magnetic Resonance Spectroscopy for Quantitative Analysis: A Review for Its Application in the Chemical, Pharmaceutical and Medicinal Domains

Nuclear magnetic resonance (NMR) is a rapid and accurate analytical tool for qualification and quantification. The capacity of NMR of being quantitative can also justify the calibration of other analytical methods. In pharmaceutical domain, quantitative NMR (qNMR) can be applied in the identification and quantification of drug simultaneously. The early drug development stage requires a minimum sample for analysis. Thus, priority should be given to utilize this technique to attain results with least investment, rapid analysis time and minimum sample consumption. This technique is a significant phenomenon to identify impurities, drug substance, residual solvents of in-process control (IPC) samples and characterizing the formulations. From an analyst's perspective, qNMR proved to be a routine practice in pharmaceutical industry to qualify any drug product. The absolute and relative methods offer great help in quantifying the component of interest in the process control samples and finished products. This review highlights the evolution of NMR application in the pharmaceutical industry, where determining the purity of drug substance, drug product and establishing the identity of impurities and its level are the challenging aspects. NMR in medicinal field emerging as a numero uno for Covid-19 severity detection and its dire consequences, accelerated vaccine development and the mapping of SAR-COV-2 RNA and proteins via chemical shift assignments.

LC-Q-TOF-MS/MS detection of food flavonoids: principle, methodology, and applications

Flavonoids have been attracting increasing research interest because of their multiple health promoting effects. However, many flavonoids with similar structures are present in foods, often at low concentrations, which increases the difficulty of their separation and identification. Liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (LC-Q-TOF-MS/MS) has become one of the most widely used techniques for flavonoid detection. LC-Q-TOF-MS/MS can achieve highly efficient separation by LC; it also provides structural information regarding flavonoids by Q-TOF-MS/MS. This review presents a comprehensive summary of the scientific principles and detailed methodologies (e.g., qualitative determination, quantitative determination, and data processing) of LC-Q-TOF-MS/MS specifically for food flavonoids. It also discusses the recent applications of LC-Q-TOF-MS/MS in determination of flavonoid types and contents in agricultural products, changes in their structures and contents during food processing, and metabolism in vivo after consumption. Moreover, it proposes necessary technological improvements and potential applications. This review would facilitate the scientific understanding of theory and technique of LC-Q-TOF-MS/MS for flavonoid detection, and promote its applications in food and health industry.

IBD metabonomics predicts phenotype, disease course, and treatment response

Metabonomics in inflammatory bowel disease (IBD) characterizes the effector molecules of biological systems and thus aims to describe the molecular phenotype, generate insight into the pathology, and predict disease course and response to treatment. Nuclear magnetic resonance (NMR) spectroscopy, mass spectrometry (MS), and integrated NMR and MS platforms coupled with multivariate analyses have been applied to create such metabolic profiles. Recent advances have identified quiescent ulcerative colitis as a distinct molecular phenotype and demonstrated metabonomics as a promising clinical tool for predicting relapse and response to treatment with biologics as well as fecal microbiome transplantation, thus facilitating much needed precision medicine. However, understanding this complex research field and how it translates into clinical settings is a challenge. This review aims to describe the current workflow, analytical strategies, and associated bioinformatics, and translate current IBD metabonomic knowledge into new potential clinically applicable treatment strategies, and outline future key translational perspectives.

Combining HPLC-DAD-QTOF-MS and HPLC-SPE-NMR to Monitor In Vitro Vitetrifolin D Phase I and II Metabolism

By combining HPLC-DAD-QTOF-MS and HPLC-SPE-NMR, the in vitro metabolism of vitetrifolin D, a pharmacologically active key molecule from Vitex agnus-castus in liver cell fractions, was investigated. Twenty-seven phase I and phase II metabolites were tentatively identified from the culture broth by HPLC-DAD-QTOF-MS. The subsequent HPLC-SPE-NMR analysis allowed for the unequivocal structural characterization of nine phase I metabolites. Since the preparative isolation of the metabolites was avoided, the substance input was much lower than in conventional strategies. The study did prove that the use of hyphenated instrumental analysis methodologies allows for the successful performance of in vitro metabolism studies, even if the availability of substances is very limited.

Ultra-high-performance liquid chromatography high-resolution mass spectrometry variants for metabolomics research

Ultra-high-performance liquid chromatography high-resolution mass spectrometry (UHPLC-HRMS) variants currently represent the best tools to tackle the challenges of complexity and lack of comprehensive coverage of the metabolome. UHPLC offers flexible and efficient separation coupled with high-sensitivity detection via HRMS, allowing for the detection and identification of a broad range of metabolites. Here we discuss current common strategies for UHPLC-HRMS-based metabolomics, with a focus on expanding metabolome coverage.

Methoxy terminated poly dimethylsiloxane bonded stationary phase for reversed-phase liquid chromatography

In this work, a methoxy terminated poly dimethylsiloxane modified silica gel material was proposed as a novel stationary phase for reversed-phase liquid chromatography. With 5 μm silica gel as matrix, methoxy terminated poly dimethylsiloxane polymer was grafted by one step chemical bonding reaction. The obtained stationary phase was characterized by scanning electron microscopy, fourier transform infrared spectroscopy and element analysis. To our knowledge, this is the novel methoxy terminated poly dimethylsiloxane bonded stationary phase with good separation efficiency (42107-46988 plates/m for benzene homologues) and high stability (RSD is 0.08-5.09%). Comparing to other traditional columns of the same type, the proposed stationary phase has a wider polarity separation scale and shorter analysis time. In addition to the rapid separation of hydrophobic compound, such column also exhibited great potential in the separation of hydrophilic analytes.

Ultra-durable, multi-template molecularly imprinted polymers for ultrasensitive monitoring and multicomponent quantification of trace sulfa antibiotics

Traditional analysis methods are susceptible to interference caused by the complexity of sample matrices, and detector surface fouling arising from nonspecific adsorption of microorganisms (in biological samples) which leads in particular to a gradual loss of sensitivity. Imprinted materials can be used to effectively reduce interference originating in the matrices. However, the poor reproducibility and multicomponent quantification of trace antibiotics represent significant challenges to the detection process. Meanwhile, the high biological risk presented by bacterial antibiotic immunity and the persistence of antibiotics in foodstuffs, especially meat, both caused by the overuse of sulfonamide antibiotics, remain urgent issues. Here, we present the first example of a method for the accurate quantification of trace sulfa antibiotics (SAs) based on multi-template imprinted polymers (MMIPs). Levels of multiple SAs have been simultaneously successfully quantified by applying MMIP extraction coupled with UPLC-MS/MS analysis. This method shows excellent linearity of detection in the range of 0.1-500 μg L^-1, and ultrasensitivity with low limits of detection of 0.03 μg L^-1. The maximum SA residue recovered from sample tissues by using MMIPs was 5.48 μg g^-1. MMIP-coupled UPLC-MS/MS quantification of SAs is an accurate and repeatable method for the monitoring of SA accumulation in mouse tissue samples. It also provides an effective strategy for the tracking and quantification of drugs in other biological samples.

Plasmonic Alloys Reveal a Distinct Metabolic Phenotype of Early Gastric Cancer

Gastric cancer (GC) is a multifactorial process, accompanied by alterations in metabolic pathways. Non-invasive metabolic profiling facilitates GC diagnosis at early stage leading to an improved prognostic outcome. Herein, mesoporous PdPtAu alloys are designed to characterize the metabolic profiles in human blood. The elemental composition is optimized with heterogeneous surface plasmonic resonance, offering preferred charge transfer for photoinduced desorption/ionization and enhanced photothermal conversion for thermally driven desorption. The surface structure of PdPtAu is further tuned with controlled mesopores, accommodating metabolites only, rather than large interfering compounds. Consequently, the optimized PdPtAu alloy yields direct metabolic fingerprints by laser desorption/ionization mass spectrometry in seconds, consuming 500 nL of native plasma. A distinct metabolic phenotype is revealed for early GC by sparse learning, resulting in precise GC diagnosis with an area under the curve of 0.942. It is envisioned that the plasmonic alloy will open up a new era of minimally invasive blood analysis to improve the surveillance of cancer patients in the clinical setting.

Natural products as pharmacological modulators of mitochondrial dysfunctions for the treatments of Alzheimer's disease: A comprehensive review

Alzheimer's disease (AD) is the most common progressive neurodegenerative disorder characterized by neuronal loss and cognitive impairment that harshly affect the elderly individuals. Currently, the available anti-AD pharmacological approaches are purely symptomatic to alleviate AD symptoms, and the curative effects of novel anti-AD drugs focused on Aβ target are disappointing. Hence, there is a tremendous need to adjust AD therapeutic targets and discover novel anti-AD agents. In AD, mitochondrial dysfunction gradually triggers neuronal death from different aspects and worsens the occurrence and progress of AD. Consequently, it has been proposed that the intervention of impaired mitochondria represents an attractive breakthrough point for AD treatments. Due to chemical diversity, poly-pharmacological activities, few adverse effects and multiple targeting, natural products (NPs) have been identified as a valuable treasure for drug discovery and development. Multiple lines of studies have scientifically proven that NPs display ameliorative benefits in AD treatment in relation to mitochondrial dysfunction. This review surveys the complicated implications for mitochondrial dysregulation and AD, and then summarizes the potentials of NPs and their underlying molecular mechanisms against AD via reducing or improving mitochondrial dysfunction. It is expected that this work may open the window to speed up the development of innovative anti-AD drugs originated from NPs and improve upcoming AD therapeutics.

Metabolomics for Biomarker Discovery: Key Signatory Metabolic Profiles for the Identification and Discrimination of Oat Cultivars

The first step in crop introduction-or breeding programmes-requires cultivar identification and characterisation. Rapid identification methods would therefore greatly improve registration, breeding, seed, trade and inspection processes. Metabolomics has proven to be indispensable in interrogating cellular biochemistry and phenotyping. Furthermore, metabolic fingerprints are chemical maps that can provide detailed insights into the molecular composition of a biological system under consideration. Here, metabolomics was applied to unravel differential metabolic profiles of various oat (Avena sativa) cultivars (Magnifico, Dunnart, Pallinup, Overberg and SWK001) and to identify signatory biomarkers for cultivar identification. The respective cultivars were grown under controlled conditions up to the 3-week maturity stage, and leaves and roots were harvested for each cultivar. Metabolites were extracted using 80% methanol, and extracts were analysed on an ultra-high performance liquid chromatography (UHPLC) system coupled to a quadrupole time-of-flight (qTOF) high-definition mass spectrometer analytical platform. The generated data were processed and analysed using multivariate statistical methods. Principal component analysis (PCA) models were computed for both leaf and root data, with PCA score plots indicating cultivar-related clustering of the samples and pointing to underlying differential metabolic profiles of these cultivars. Further multivariate analyses were performed to profile differential signatory markers, which included carboxylic acids, amino acids, fatty acids, phenolic compounds (hydroxycinnamic and hydroxybenzoic acids, and associated derivatives) and flavonoids, among the respective cultivars. Based on the key signatory metabolic markers, the cultivars were successfully distinguished from one another in profiles derived from both leaves and roots. The study demonstrates that metabolomics can be used as a rapid phenotyping tool for cultivar differentiation.