Identification of Unknown Metabolomics Mixture Compounds by Combining NMR, MS, and Cheminformatics

Combining metabolomics with network pharmacology to reveal the therapeutic mechanism of Dingchuan Decoction in rats with OVA-induced allergic asthma

Dingchuan Decoction (DCD) is a traditional Chinese medicine prescription commonly used in the treatment of asthma, but the mechanism of DCD in treating asthma has not yet been determined. In this study, we employed a combination of metabolomics and network pharmacology to investigate the mechanism of DCD in treating asthma. An allergic asthma rat model was induced by ovalbumin (OVA). Metabolomics based on 1H NMR and UHPLC-MS was used to identify differential metabolites and obtain the major metabolic pathways and potential targets. Network pharmacology was utilized to explore potential targets of DCD for asthma treatment. Finally, the results of metabolomics and network pharmacology were integrated to obtain the key targets and metabolic pathways of DCD for the therapy of asthma, and molecular docking was utilized to validate the key targets. A total of 76 important metabolites and 231 potential targets were identified through metabolomics. Using network pharmacology, 184 potential therapeutic targets were obtained. These 184 targets were overlaid with the 231 potential targets obtained through metabolomics and were analyzed in conjunction with metabolic pathways. Ultimately, the key targets were identified as aldehyde dehydrogenase 2 (ALDH2) and amine oxidase copper-containing 3 (AOC3), and the relevant metabolic pathways affected were glycolysis and gluconeogenesis as well as arginine and proline metabolism. Molecular docking showed that the key targets had high affinity with the relevant active ingredients in DCD, which further demonstrated that DCD may exert therapeutic effects by acting on the key targets. The present study demonstrated that DCD can alleviate OVA-induced allergic asthma and that DCD may have a therapeutic effect by regulating intestinal flora and polyamine metabolism through its effects on ALDH2 and AOC3.

ToF-SIMS evaluation of PEG-related mass peaks and applications in PEG detection in cosmetic products

Polyethylene glycols (PEGs) are used in industrial, medical, health care, and personal care applications. The cycling and disposal of synthetic polymers like PEGs pose significant environmental concerns. Detecting and monitoring PEGs in the real world calls for immediate attention. This study unveils the efficacy of time-of-flight secondary ion mass spectrometry (ToF-SIMS) as a reliable approach for precise analysis and identification of reference PEGs and PEGs used in cosmetic products. By comparing SIMS spectra, we show remarkable sensitivity in pinpointing distinctive ion peaks inherent to various PEG compounds. Moreover, the employment of principal component analysis effectively discriminates compositions among different samples. Notably, the application of SIMS two-dimensional image analysis visually portrays the spatial distribution of various PEGs as reference materials. The same is observed in authentic cosmetic products. The application of ToF-SIMS underscores its potential in distinguishing PEGs within intricate environmental context. ToF-SIMS provides an effective solution to studying emerging environmental challenges, offering straightforward sample preparation and superior detection of synthetic organics in mass spectral analysis. These features show that SIMS can serve as a promising alternative for evaluation and assessment of PEGs in terms of the source, emission, and transport of anthropogenic organics.

Applications of chromatographic methods in metabolomics: A review

Chromatography is a robust and reliable separation method that can use various stationary phases to separate complex mixtures commonly seen in metabolomics. This review examines the types of chromatography and stationary phases that have been used in targeted or untargeted metabolomics with methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. General considerations for sample pretreatment and separations in metabolomics are considered, along with the various supports and separation formats for chromatography that have been used in such work. The types of liquid chromatography (LC) that have been most extensively used in metabolomics will be examined, such as reversed-phase liquid chromatography and hydrophilic liquid interaction chromatography. In addition, other forms of LC that have been used in more limited applications for metabolomics (e.g., ion-exchange, size-exclusion, and affinity methods) will be discussed to illustrate how these techniques may be utilized for new and future research in this field. Multidimensional LC methods are also discussed, as well as the use of gas chromatography and supercritical fluid chromatography in metabolomics. In addition, the roles of chromatography in NMR- vs. MS-based metabolomics are considered. Applications are given within the field of metabolomics for each type of chromatography, along with potential advantages or limitations of these separation methods.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Present and future of pure shift NMR in metabolomics

NMR is one of the most powerful techniques for the analysis of biological samples in the field of metabolomics. However, the high complexity of fluids, tissues, or other biological materials taken from living organisms is still a challenge for state-of-the-art pulse sequences, thereby limiting the detection, the identification, and the quantification of metabolites. In this context, the resolution enhancement provided by broadband homonuclear decoupling methods, which allows for simplifying 1 H multiplet patterns into singlets, has placed this so-called pure shift technique as a promising approach to perform metabolic profiling with unparalleled level of detail. In recent years, the many advances achieved in the design of pure shift experiments has paved the way to the analysis of a wide range of biological samples with ultra-high resolution. This review leads the reader from the early days of the main pure shift methods that have been successfully developed over the last decades to address complex samples, to the most recent and promising applications of pure shift NMR to the field of NMR-based metabolomics.

NMR metabolomics study of chronic low-dose exposure to a cocktail of persistent organic pollutants

Nowadays, exposure to endocrine-disrupting chemicals (EDCs), including persistent organic pollutants (POPs), is one of the most critical threats to public health. EDCs are chemicals that mimic, block, or interfere with hormones in the body's endocrine system and have been associated with a wide range of health issues. This innovative, untargeted metabolomics study investigates chronic low-dose internal exposure to a cocktail of POPs on multiple tissues that are known to accumulate these lipophilic compounds. Interestingly, the metabolic response differs among selected tissues/organs in mice. In the liver, we observed a dynamic effect according to the exposure time and the doses of POPs. In the brain tissue, the situation is the opposite, leading to the conclusion that the presence of POPs immediately gives a saturated effect that is independent of the dose and the duration of exposure studied. By contrast, for the adipose tissues, nearly no effect is observed. This metabolic profiling leads to a holistic and dynamic overview of the main metabolic pathways impacted in lipophilic tissues by a cocktail of POPs.

Advances in Microbial NMR Metabolomics

Confidently, nuclear magnetic resonance (NMR) is the most informative technique in analytical chemistry and its use as an analytical platform in metabolomics is well proven. This chapter aims to present NMR as a viable tool for microbial metabolomics discussing its fundamental aspects and applications in metabolomics using some chosen examples.

Practical Aspects of NMR-Based Metabolomics

While NMR-based metabolomics is only about 20 years old, NMR has been a key part of metabolic and metabolism studies for >40 years. Historically, metabolic researchers used NMR because of its high level of reproducibility, superb instrument stability, facile sample preparation protocols, inherently quantitative character, non-destructive nature, and amenability to automation. In this chapter, we provide a short history of NMR-based metabolomics. We then provide a detailed description of some of the practical aspects of performing NMR-based metabolomics studies including sample preparation, pulse sequence selection, and spectral acquisition and processing. The two different approaches to metabolomics data analysis, targeted vs. untargeted, are briefly outlined. We also describe several software packages to help users process NMR spectra obtained via these two different approaches. We then give several examples of useful or interesting applications of NMR-based metabolomics, ranging from applications to drug toxicology, to identifying inborn errors of metabolism to analyzing the contents of biofluids from dairy cattle. Throughout this chapter, we will highlight the strengths and limitations of NMR-based metabolomics. Additionally, we will conclude with descriptions of recent advances in NMR hardware, methodology, and software and speculate about where NMR-based metabolomics is going in the next 5-10 years.

A pilot study for fragment identification using 2D NMR and deep learning

This paper presents a proof of concept of a method to identify substructures in 2D NMR spectra of mixtures using a bespoke image-based convolutional neural network application. This is done using HSQC and HMBC spectra separately and in combination. The application can reliably detect substructures in pure compounds, using a simple network. Results indicate that it can work for mixtures when trained on pure compounds only. HMBC data and the combination of HMBC and HSQC show better results than HSQC alone in this pilot study.

Absolute Metabolite Quantification Using Pure Shift NMR: Toward Quantitative Metabolic Profiling of Aqueous Biological Samples

Accurate quantification of metabolites by nuclear magnetic resonance (NMR) is of prime importance in the field of health sciences for understanding the metabolic pathways of the investigated system, to address the mechanisms of action of diseases, and improving their diagnosis, treatment, and prognosis. Unfortunately, the absolute quantitative analysis of complex samples is still limited by sensitivity and resolution issues that are intrinsic to this technique. Ultrahigh-resolution pure shift methods have especially shown to be suitable for interpreting mixtures of metabolites in biological samples. Here, we introduce a robust analytical protocol based on the use of a pure shift library of calibration reference spectra to fit the fingerprint of each metabolite of interest and determine its concentration. The approach based on the SAPPHIRE pulse sequence enhanced with a block for solvent suppression has been validated through the results of a series of model mixtures, exhibiting excellent trueness (slope values in the range of 0.93-1.02) and linearity (R² > 0.996) in a total time (a few hours) that is fully compatible with metabolomics studies. Furthermore, we have successfully applied our method to determine the absolute metabolite concentrations in a lymphoma extracellular medium, which improves metabolomic protocols reported to date by providing a quantitative and highly resolved vision of metabolic processes at play.

Differential metabolism between biofilm and suspended Pseudomonas aeruginosa cultures in bovine synovial fluid by 2D NMR-based metabolomics

Total joint arthroplasty is a common surgical procedure resulting in improved quality of life; however, a leading cause of surgery failure is infection. Periprosthetic joint infections often involve biofilms, making treatment challenging. The metabolic state of pathogens in the joint space and mechanism of their tolerance to antibiotics and host defenses are not well understood. Thus, there is a critical need for increased understanding of the physiological state of pathogens in the joint space for development of improved treatment strategies toward better patient outcomes. Here, we present a quantitative, untargeted NMR-based metabolomics strategy for Pseudomonas aeruginosa suspended culture and biofilm phenotypes grown in bovine synovial fluid as a model system. Significant differences in metabolic pathways were found between the suspended culture and biofilm phenotypes including creatine, glutathione, alanine, and choline metabolism and the tricarboxylic acid cycle. We also identified 21 unique metabolites with the presence of P. aeruginosa in synovial fluid and one uniquely present with the biofilm phenotype in synovial fluid. If translatable in vivo, these unique metabolite and pathway differences have the potential for further development to serve as targets for P. aeruginosa and biofilm control in synovial fluid.

COLMARq: A Web Server for 2D NMR Peak Picking and Quantitative Comparative Analysis of Cohorts of Metabolomics Samples

Highly quantitative metabolomics studies of complex biological mixtures are facilitated by the resolution enhancement afforded by 2D NMR spectra such as 2D ¹³C-¹H HSQC spectra. Here, we describe a new public web server, COLMARq, for the semi-automated analysis of sets of 2D HSQC spectra of cohorts of samples. The workflow of COLMARq includes automated peak picking using the deep neural network DEEP Picker, quantitative cross-peak volume extraction by numerical fitting using Voigt Fitter, the matching of corresponding cross-peaks across cohorts of spectra, peak volume normalization between different spectra, database query for metabolite identification, and basic univariate and multivariate statistical analyses of the results. COLMARq allows the analysis of cross-peaks that belong to both known and unknown metabolites. After a user has uploaded cohorts of 2D ¹³C-¹H HSQC and optionally 2D ¹H-¹H TOCSY spectra in their preferred format, all subsequent steps on the web server can be performed fully automatically, allowing manual editing if needed and the sessions can be saved for later use. The accuracy, versatility, and interactive nature of COLMARq enables quantitative metabolomics analysis, including biomarker identification, of a broad range of complex biological mixtures as is illustrated for cohorts of samples from bacterial cultures of Pseudomonas aeruginosa in both its biofilm and planktonic states.

NMR Spectroscopy Identifies Chemicals in Cigarette Smoke Condensate That Impair Skeletal Muscle Mitochondrial Function

Tobacco smoke-related diseases such as chronic obstructive pulmonary disease (COPD) are associated with high healthcare burden and mortality rates. Many COPD patients were reported to have muscle atrophy and weakness, with several studies suggesting intrinsic muscle mitochondrial impairment as a possible driver of this phenotype. Whereas much information has been learned about muscle pathology once a patient has COPD, little is known about how active tobacco smoking might impact skeletal muscle physiology or mitochondrial health. In this study, we examined the acute effects of cigarette smoke condensate (CSC) on muscle mitochondrial function and hypothesized that toxic chemicals present in CSC would impair mitochondrial respiratory function. Consistent with this hypothesis, we found that acute exposure of muscle mitochondria to CSC caused a dose-dependent decrease in skeletal muscle mitochondrial respiratory capacity. Next, we applied an analytical nuclear magnetic resonance (NMR)-based approach to identify 49 water-soluble and 12 lipid-soluble chemicals with high abundance in CSC. By using a chemical screening approach in the Seahorse XF96 analyzer, several CSC-chemicals, including nicotine, o-Cresol, phenylacetate, and decanoic acid, were found to impair ADP-stimulated respiration in murine muscle mitochondrial isolates significantly. Further to this, several chemicals, including nicotine, o-Cresol, quinoline, propylene glycol, myo-inositol, nitrosodimethylamine, niacinamide, decanoic acid, acrylonitrile, 2-naphthylamine, and arsenic acid, were found to significantly decrease the acceptor control ratio, an index of mitochondrial coupling efficiency.

Cadaverine Is a Switch in the Lysine Degradation Pathway in Pseudomonas aeruginosa Biofilm Identified by Untargeted Metabolomics

There is a critical need to accurately diagnose, prevent, and treat biofilms in humans. The biofilm forming P. aeruginosa bacteria can cause acute and chronic infections, which are difficult to treat due to their ability to evade host defenses along with an inherent antibiotic-tolerance. Using an untargeted NMR-based metabolomics approach, we identified statistically significant differences in 52 metabolites between P. aeruginosa grown in the planktonic and lawn biofilm states. Among them, the metabolites of the cadaverine branch of the lysine degradation pathway were systematically decreased in biofilm. Exogenous supplementation of cadaverine caused significantly increased planktonic growth, decreased biofilm accumulation by 49% and led to altered biofilm morphology, converting to a pellicle biofilm at the air-liquid interface. Our findings show how metabolic pathway differences directly affect the growth mode in P. aeruginosa and could support interventional strategies to control biofilm formation.

Investigating metabolism by mass spectrometry: From steady state to dynamic view

Metabolism is the set of life-sustaining reactions in organisms. These biochemical reactions are organized in metabolic pathways, in which one metabolite is converted through a series of steps catalyzed by enzymes in another chemical compound. Metabolic reactions are categorized as catabolic, the breaking down of metabolites to produce energy, and/or anabolic, the synthesis of compounds that consume energy. The balance between catabolism of the preferential fuel substrate and anabolism defines the overall metabolism of a cell or tissue. Metabolomics is a powerful tool to gain new insights contributing to the identification of complex molecular mechanisms in the field of biomedical research, both basic and translational. The enormous potential of this kind of analyses consists of two key aspects: (i) the possibility of performing so-called targeted and untargeted experiments through which it is feasible to verify or formulate a hypothesis, respectively, and (ii) the opportunity to run either steady-state analyses to have snapshots of the metabolome at a given time under different experimental conditions or dynamic analyses through the use of labeled tracers. In this review, we will highlight the most important practical (e.g., different sample extraction approaches) and conceptual steps to consider for metabolomic analysis, describing also the main application contexts in which it is used. In addition, we will provide some insights into the most innovative approaches and progress in the field of data analysis and processing, highlighting how this part is essential for the proper extrapolation and interpretation of data.

Quantitative NMR-Based Biomedical Metabolomics: Current Status and Applications

Nuclear Magnetic Resonance (NMR) spectroscopy is a quantitative analytical tool commonly utilized for metabolomics analysis. Quantitative NMR (qNMR) is a field of NMR spectroscopy dedicated to the measurement of analytes through signal intensity and its linear relationship with analyte concentration. Metabolomics-based NMR exploits this quantitative relationship to identify and measure biomarkers within complex biological samples such as serum, plasma, and urine. In this review of quantitative NMR-based metabolomics, the advancements and limitations of current techniques for metabolite quantification will be evaluated as well as the applications of qNMR in biomedical metabolomics. While qNMR is limited by sensitivity and dynamic range, the simple method development, minimal sample derivatization, and the simultaneous qualitative and quantitative information provide a unique landscape for biomedical metabolomics, which is not available to other techniques. Furthermore, the non-destructive nature of NMR-based metabolomics allows for multidimensional analysis of biomarkers that facilitates unambiguous assignment and quantification of metabolites in complex biofluids.

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

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

NMR-TS: de novo molecule identification from NMR spectra

Nuclear magnetic resonance (NMR) spectroscopy is an effective tool for identifying molecules in a sample. Although many previously observed NMR spectra are accumulated in public databases, they cover only a tiny fraction of the chemical space, and molecule identification is typically accomplished manually based on expert knowledge. Herein, we propose NMR-TS, a machine-learning-based python library, to automatically identify a molecule from its NMR spectrum. NMR-TS discovers candidate molecules whose NMR spectra match the target spectrum by using deep learning and density functional theory (DFT)-computed spectra. As a proof-of-concept, we identify prototypical metabolites from their computed spectra. After an average 5451 DFT runs for each spectrum, six of the nine molecules are identified correctly, and proximal molecules are obtained in the other cases. This encouraging result implies that de novo molecule generation can contribute to the fully automated identification of chemical structures. NMR-TS is available at https://github.com/tsudalab/NMR-TS.

Virgin olive oil metabolomics: A review

Metabolomics involvement in the study of foods is steadily growing. Such a rise is a consequence of the increasing demand in the food sector to address challenges regarding the issues of food safety, quality, and authenticity in a more comprehensive way. Virgin olive oil (VOO) is a key product of the Mediterranean diet, with a globalized consumer interest as it may be associated with various nutritional and health benefits. Despite the strict legislation to protect this high added-value agricultural commodity and offer guarantees to consumers and honest producers, there are still analytical issues needing to be further addressed. Thus, this review aims to present the efforts made using targeted and untargeted metabolomics approaches, namely nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry-based techniques (mainly LC/GC-MS) combined with multivariate statistical analysis. Case-studies focusing on geographical/varietal classification and detection of adulteration are discussed with regards to the identification of possible markers. The advantages and limitations of each of the aforementioned techniques applied to VOO analysis are also highlighted.

Lipidomic characteristics and clinical findings of epileptic patients treated with valproic acid

Our early study has found valproic acid (VPA)-induced lipid dysmetabolism in animal model, however, the details of lipid profiling of VPA-treated epileptic patients remain unknown. Therefore, in this study, the blood samples of VPA-treated epileptic patients and VPA-free controls were collected for lipidomic and biochemical assays. As results, clinical data showed the changes of some blood lipid molecules in VPA-treated epileptic patients. In lipidomic assays, all 3797 annotated positive ions were identified prior to the data validation. In addition, the number of differentially expressed lipids were identified. And the 133 lipid molecules in VPA-treated cases were significantly up-regulated when compared to those in controls, while other 250 lipid metabolites were down-regulated. Further, these lipid metabolites were mainly constituted with glycerolipids, glycerophopholipids, fatty acyls, sterol lipids. In addition, the most significant elevations of metabolite molecules of triglyceride, sphingomyelin, phosphorylcholine, ceramides, phenolic phthiocerol, as well as topped reductions of phosphoethanolamines, diradylglycerols, 1α,25-dihydroxy-24-oxo-22-oxavitamin D3, 2-deoxy-20-hydroxy-5alpha-ecdysone 3-acetate, dolichyl-4 phosphate were identified respectively. Taken together, these clinical findings demonstrate that negative impacts of exposure to VPA on expression of lipid mediators, progressively disrupting the functions of lipid molecules. Interestingly, these differentially expressed metabolites may be potential biomarkers for screening VPA-induced dyslipidemia.