Targeted metabolomics and mass spectrometry

Spatial multi-omics: deciphering technological landscape of integration of multi-omics and its applications

The emergence of spatial multi-omics has helped address the limitations of single-cell sequencing, which often leads to the loss of spatial context among cell populations. Integrated analysis of the genome, transcriptome, proteome, metabolome, and epigenome has enhanced our understanding of cell biology and the molecular basis of human diseases. Moreover, this approach offers profound insights into the interactions between intracellular and intercellular molecular mechanisms involved in the development, physiology, and pathogenesis of human diseases. In this comprehensive review, we examine current advancements in multi-omics technologies, focusing on their evolution and refinement over the past decade, including improvements in throughput and resolution, modality integration, and accuracy. We also discuss the pivotal contributions of spatial multi-omics in revealing spatial heterogeneity, constructing detailed spatial atlases, deciphering spatial crosstalk in tumor immunology, and advancing translational research and cancer therapy through precise spatial mapping.

Unraveling Protein-Metabolite Interactions in Precision Nutrition: A Case Study of Blueberry-Derived Metabolites Using Advanced Computational Methods

Metabolomics, the study of small-molecule metabolites within biological systems, has become a potent instrument for understanding cellular processes. Despite its profound insights into health, disease, and drug development, identifying the protein partners for metabolites, especially dietary phytochemicals, remains challenging. In the present study, we introduced an innovative in silico, structure-based target prediction approach to efficiently predict protein targets for metabolites. We analyzed 27 blood serum metabolites from nutrition intervention studies' blueberry-rich diets, known for their health benefits, yet with elusive mechanisms of action. Our findings reveal that blueberry-derived metabolites predominantly interact with Carbonic Anhydrase (CA) family proteins, which are crucial in acid-base regulation, respiration, fluid balance, bone metabolism, neurotransmission, and specific aspects of cellular metabolism. Molecular docking showed that these metabolites bind to a common pocket on CA proteins, with binding energies ranging from -5.0 kcal/mol to -9.0 kcal/mol. Further molecular dynamics (MD) simulations confirmed the stable binding of metabolites near the Zn binding site, consistent with known compound interactions. These results highlight the potential health benefits of blueberry metabolites through interaction with CA proteins.

Multiomics analysis of platelet-rich plasma promoting biological performance of mesenchymal stem cells

Mesenchymal Stem Cells are ideal seed cells for tissue repair and cell therapy and have promising applications in regenerative medicine and tissue engineering. Using Platelet-Rich Plasma as an adjuvant to create and improve the microenvironment for Mesenchymal Stem Cells growth can enhance the biological properties of Mesenchymal Stem Cells and improve the efficacy of cell therapy. However, the mechanism by which Platelet-Rich Plasma improves the biological performance of Mesenchymal Stem Cells is still unknown. In this study, by examining the effects of Platelet-Rich Plasma on the biological performance of Mesenchymal Stem Cells, combined with multiomics analysis (Transcriptomics, Proteomics and Metabolomics) and related tests, we analyzed the specific pathways, related mechanisms and metabolic pathways of Platelet-Rich Plasma to improve the biological performance of Mesenchymal Stem Cells. In an in vitro cell culture system, the biological performance of Mesenchymal Stem Cells was significantly improved after replacing Foetal Bovine Serum with Platelet-Rich Plasma, and the genes (ESM1, PDGFB, CLEC7A, CCR1 and ITGA6 et al.) related to cell proliferation, adhesion, growth, migration and signal transduction were significantly upregulated. Platelet-Rich Plasma can enhance the secretion function of MSC exosomes, significantly upregulate many proteins related to tissue repair, immune regulation and anti-infection, and enhance the repair effect of exosomes on skin injury. After replacing Foetal Bovine Serum with Platelet-Rich Plasma, Mesenchymal Stem Cells underwent metabolic reprogramming, the metabolism of amino acids and fatty acids and various signaling pathways were changed, the anabolic pathways of various proteins were enhanced. These results provide a theoretical and technical reference for optimizing the Mesenchymal Stem Cells culture system, improving the biological characteristics and clinical application effects of Mesenchymal Stem Cells.

Comparison of Cu+, Ag+, and Au+ Ions as Ionization Agents of Volatile Organic Compounds at Subatmospheric Pressure

Ionization of volatile organic compounds (VOCs) by coinage metal ions (Cu+, Ag+, and Au+) generated by laser desorption and ionization (LDI) of a metal nanolayer in subatmospheric conditions is explored. The study was performed in a commercial subatmospheric dual MALDI/ESI ion source. Five compounds representing different VOC classes were chosen for a detailed study of the metal ionization mechanism: ethanol, acetone, acetic acid, xylene, and cyclohexane. In the gas phase, ion molecular complexes of all three metal ions were formed, typically with two ligand molecules. The successful detection of the metal complexes with VOCs strongly depended on the applied voltages across the ion source, minimizing the in-source fragmentation. The employed orbital trap with ultrahigh resolving power and sub-parts-per-million mass accuracy allowed unambiguous identification of the formed complexes based on their molecular formulas. The detection limits of the studied compounds in the gas were in the range 0.1-1.4 nmol/L. Compared to Cu+ and Ag+ ions, Au+ ions exhibited the highest reactivity, often complicating spectra by side products of reactions. On the other hand, they also allowed detecting saturated hydrocarbons, which did not produce any signals with Ag+ and Cu+.

Mass Spectrometry Rearrangement Ions and Metabolic Pathway-Based Discovery of Indole Derivatives during the Aging Process in Citrus reticulata 'Chachi'

The rapid analysis and characterization of compounds using mass spectrometry (MS) may overlook trace compounds. Although targeted analysis methods can significantly improve detection sensitivity, it is hard to discover novel scaffold compounds in the trace. This study developed a strategy for discovering trace compounds in the aging process of traditional Chinese medicine based on MS fragmentation and known metabolic pathways. Specifically, we found that the characteristic component of C. reticulata 'Chachi', methyl N-methyl anthranilate (MMA), fragmented in electrospray ionization coupled with collision-induced dissociation (CID) to produce the rearrangement ion 3-hydroxyindole, which was proven to exist in trace amounts in C. reticulata 'Chachi' based on comparison with the reference substance using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Combining the known metabolic pathways of 3-hydroxyindole and the possible methylation reactions that may occur during aging, a total of 10 possible indole derivatives were untargeted predicted. These compounds were confirmed to originate from MMA using purchased or synthesized reference substances, all of which were detected in C. reticulata 'Chachi' through LC-MS/MS, achieving trace compound analysis from untargeted to targeted. These results may contribute to explaining the aging mechanism of C. reticulata 'Chachi', and the strategy of using the CID-induced special rearrangement ion-binding metabolic pathway has potential application value for discovering trace compounds.

Comparative analysis of bile metabolic profile in patients with biliary obstruction complicated by Clonorchis sinensis infection

Background

Clonorchiasis is an important foodborne parasitic disease. However, eggs of Clonorchis sinensis (C. sinensis) cannot be detected in feces during biliary obstruction. Moreover, many diseases can cause biliary obstruction, such as gallstones, adenocarcinoma, cholangiocarcinoma and Ascaris lumbricoides infection. Therefore, it is of great significance to distinguish between patients of biliary obstruction and biliary obstruction with C. sinensis infection.

Methods

A total of 48 biliary obstruction patients were enrolled, including 23 infected with C. sinensis (C. sinensis) (OB+C.s) and 25 non-infected subjects (OB). The bile samples were collected by endoscopic retrograde cholangiopancreatography and analyzed using ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-QTOF MS). Additionally, multivariate statistical analysis methods were employed to identify differential metabolites. Next, bile amino acid levels were determined by targeted metabolomics analysis.

Result

A total of 146 and 132 significant metabolites were identified in electrospray ionization (ESI)+ and ESI- modes, respectively. The levels of amino acids (asparagine, glutamate, ornithine) and polyamines (spermidine and spermine) were significantly changed. Targeted analysis showed that the levels of amino acids (such as L-arginine, L-glutamine, L-lysine, L-propionic, and L-tyrosine) were lower in OB+C.s patients compared to those in OB patients. Marked metabolic pathways were involved in "Glutathione metabolism", "Caffeine metabolism", "Alanine, aspartate and glutamate metabolism", "Arginine and proline metabolism", "Purine metabolism", "Beta-Alanine metabolism", and "D-glutamine and D-glutamate metabolism".

Conclusion

These results show that there were significant differences between OB+C.s and OB patients, especially in amino acids. The metabolic signature and perturbations in metabolic pathways may help to better distinguish OB+C.s and OB patients.

Implication of metabolism in the polarization of tumor-associated-macrophages: the mass spectrometry-based point of view

Tumor-associated macrophages (TAMs) represent one of the main tumor-infiltrating immune cell types and are generally categorized into either of two functionally contrasting subtypes, namely classical activated M1 macrophages and alternatively activated M2 macrophages. TAMs showed different activation states that can be represent by the two extremes of the complex profile of macrophages biology, the M1-like phenotype (pro-inflammatory activity) and the M2-like phenotype (anti-inflammatory activity). Based on the tumor type, and grades, TAMs can acquire different functions and properties; usually, the M1-like phenotype is typical of early tumor stages and is associated to an anti-tumor activity, while M2-like phenotype has a pro-inflammatory activity and is related to a poor patients' prognosis. The classification of macrophages into M1/M2 groups based on well-defined stimuli does not model the infinitely more complex tissue milieu where macrophages (potentially of different origin) would be exposed to multiple signals in different sequential order. This review aims to summarize the recent mass spectrometry-based (MS-based) metabolomics findings about the modifications of metabolism in TAMs polarization in different tumors. The published data shows that MS-based metabolomics is a promising tool to help better understanding TAMs metabolic phenotypes, although it is still poorly applied for TAMs metabolism. The knowledge of key metabolic alterations in TAMs is an essential step for discovering TAMs polarization novel biomarkers and developing novel therapeutic approaches targeting TAM metabolism to repolarize TAMs towards their anti-tumor phenotype.

Simultaneous Quantitation and Discovery (SQUAD) Analysis: Combining the Best of Targeted and Untargeted Mass Spectrometry-Based Metabolomics

Untargeted and targeted approaches are the traditional metabolomics workflows acquired for a wider understanding of the metabolome under focus. Both approaches have their strengths and weaknesses. The untargeted, for example, is maximizing the detection and accurate identification of thousands of metabolites, while the targeted is maximizing the linear dynamic range and quantification sensitivity. These workflows, however, are acquired separately, so researchers compromise either a low-accuracy overview of total molecular changes (i.e., untargeted analysis) or a detailed yet blinkered snapshot of a selected group of metabolites (i.e., targeted analysis) by selecting one of the workflows over the other. In this review, we present a novel single injection simultaneous quantitation and discovery (SQUAD) metabolomics that combines targeted and untargeted workflows. It is used to identify and accurately quantify a targeted set of metabolites. It also allows data retro-mining to look for global metabolic changes that were not part of the original focus. This offers a way to strike the balance between targeted and untargeted approaches in one single experiment and address the two approaches' limitations. This simultaneous acquisition of hypothesis-led and discovery-led datasets allows scientists to gain more knowledge about biological systems in a single experiment.

Reprogramming of Plant Central Metabolism in Response to Abiotic Stresses: A Metabolomics View

Abiotic stresses rewire plant central metabolism to maintain metabolic and energy homeostasis. Metabolites involved in the plant central metabolic network serve as a hub for regulating carbon and energy metabolism under various stress conditions. In this review, we introduce recent metabolomics techniques used to investigate the dynamics of metabolic responses to abiotic stresses and analyze the trend of publications in this field. We provide an updated overview of the changing patterns in central metabolic pathways related to the metabolic responses to common stresses, including flooding, drought, cold, heat, and salinity. We extensively review the common and unique metabolic changes in central metabolism in response to major abiotic stresses. Finally, we discuss the challenges and some emerging insights in the future application of metabolomics to study plant responses to abiotic stresses.

Capturing the microbial volatilome: an oft overlooked 'ome'

Among the diverse metabolites produced by microbial communities, some are volatile. Volatile organic compounds (VOCs) are vigorously cycled by microbes as metabolic substrates and products and as signaling molecules. Yet, current microbial metabolomic studies predominantly focus on nonvolatile metabolites and overlook VOCs, which therefore represent a missing component of the metabolome. Advances in VOC detection now allow simultaneous observation of the numerous VOCs constituting the 'volatilome' of microbial systems. We present a roadmap for integrating and advancing VOC and other 'omics approaches and highlight the potential for realtime VOC measurements to help overcome limitations in discrete 'omics sampling. Including volatile metabolites in metabolomics, both conceptually and in practice, will build a more comprehensive understanding of microbial processes across ecological communities.

Metabolomics facilitate the personalized management in inflammatory bowel disease

Inflammatory bowel disease (IBD) is a gastrointestinal disorder characterized by chronic relapsing inflammation and mucosal lesions. Reliable biomarkers for monitoring disease activity, predicting therapeutic response, and disease relapse are needed in the personalized management of IBD. Given the alterations in metabolomic profiles observed in patients with IBD, metabolomics, a new and developing technique for the qualitative and quantitative study of small metabolite molecules, offers another possibility for identifying candidate markers and promising predictive models. With increasing research on metabolomics, it is gradually considered that metabolomics will play a significant role in the management of IBD. In this review, we summarize the role of metabolomics in the assessment of disease activity, including endoscopic activity and histological activity, prediction of therapeutic response, prediction of relapse, and other aspects concerning disease management in IBD. Furthermore, we describe the limitations of metabolomics and highlight some solutions.

Unveiling Metabolic Phenotype Alterations in Anorexia Nervosa through Metabolomics

Anorexia nervosa (AN) is a mental disorder characterized by an intense fear of weight gain that affects mainly young women. It courses with a negative body image leading to altered eating behaviors that have devastating physical, metabolic, and psychological consequences for the patients. Although its origin is postulated to be multifactorial, the etiology of AN remains unknown, and this increases the likelihood of chronification and relapsing. Thus, expanding the available knowledge on the pathophysiology of AN is of enormous interest. Metabolomics is proposed as a powerful tool for the elucidation of disease mechanisms and to provide new insights into the diagnosis, treatment, and prognosis of AN. A review of the literature related to studies of AN patients by employing metabolomic strategies to characterize the main alterations associated with the metabolic phenotype of AN during the last 10 years is described. The most common metabolic alterations are derived from chronic starvation, including amino acid, lipid, and carbohydrate disturbances. Nonetheless, recent findings have shifted the attention to gut-microbiota metabolites as possible factors contributing to AN development, progression, and maintenance. We have identified the areas of ongoing research in AN and propose further perspectives to improve our knowledge and understanding of this disease.

Current progress in metabolomics of gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is one of the most common metabolic disorders of pregnancy and can cause short- and long-term adverse effects in both pregnant women and their offspring. However, the etiology and pathogenesis of GDM are still unclear. As a metabolic disease, GDM is well suited to metabolomics study, which can monitor the changes in small molecular metabolites induced by maternal stimuli or perturbations in real time. The application of metabolomics in GDM can be used to discover diagnostic biomarkers, evaluate the prognosis of the disease, guide the application of diet or drugs, evaluate the curative effect, and explore the mechanism. This review provides comprehensive documentation of metabolomics research methods and techniques as well as the current progress in GDM research. We anticipate that the review will contribute to identifying gaps in the current knowledge or metabolomics technology, provide evidence-based information, and inform future research directions in GDM.

Metabolomics in asthma: A platform for discovery

Asthma, characterized by airway hyperresponsiveness, inflammation and remodeling, is a chronic airway disease with complex etiology. Severe asthma is characterized by frequent exacerbations and poor therapeutic response to conventional asthma therapy. A clear understanding of cellular and molecular mechanisms of asthma is critical for the discovery of novel targets for optimal therapeutic control of asthma. Metabolomics is emerging as a powerful tool to elucidate novel disease mechanisms in a variety of diseases. In this review, we summarize the current status of knowledge in asthma metabolomics at systemic and cellular levels. The findings demonstrate that various metabolic pathways, related to energy metabolism, macromolecular biosynthesis and redox signaling, are differentially modulated in asthma. Airway smooth muscle cell plays pivotal roles in asthma by contributing to airway hyperreactivity, inflammatory mediator release and remodeling. We posit that metabolomic profiling of airway structural cells, including airway smooth muscle cells, will shed light on molecular mechanisms of asthma and airway hyperresponsiveness and help identify novel therapeutic targets.

Plant Responses to Abiotic Stresses and Rhizobacterial Biostimulants: Metabolomics and Epigenetics Perspectives

In response to abiotic stresses, plants mount comprehensive stress-specific responses which mediate signal transduction cascades, transcription of relevant responsive genes and the accumulation of numerous different stress-specific transcripts and metabolites, as well as coordinated stress-specific biochemical and physiological readjustments. These natural mechanisms employed by plants are however not always sufficient to ensure plant survival under abiotic stress conditions. Biostimulants such as plant growth-promoting rhizobacteria (PGPR) formulation are emerging as novel strategies for improving crop quality, yield and resilience against adverse environmental conditions. However, to successfully formulate these microbial-based biostimulants and design efficient application programs, the understanding of molecular and physiological mechanisms that govern biostimulant-plant interactions is imperatively required. Systems biology approaches, such as metabolomics, can unravel insights on the complex network of plant-PGPR interactions allowing for the identification of molecular targets responsible for improved growth and crop quality. Thus, this review highlights the current models on plant defence responses to abiotic stresses, from perception to the activation of cellular and molecular events. It further highlights the current knowledge on the application of microbial biostimulants and the use of epigenetics and metabolomics approaches to elucidate mechanisms of action of microbial biostimulants.

Metabolomics Analysis of the Effect of Glutamic Acid on Monacolin K Synthesis in Monascus purpureus

Monacolin K is a secondary metabolite produced by Monascus with beneficial effects on health, including the ability to lower cholesterol. We previously showed that the yield of monacolin K was significantly improved when glutamic acid was added to the fermentation broth of Monascus purpureus M1. In this study, we analyzed M. purpureus in media with and without glutamic acid supplementation using a metabolomic profiling approach to identify key metabolites and metabolic pathway differences. A total of 817 differentially expressed metabolites were identified between the two fermentation broths on day 8 of fermentation. Pathway analysis of these metabolites using the KEGG database indicated overrepresentation of the citric acid cycle; biotin metabolism; and alanine, aspartate, and glutamate metabolic pathways. Six differentially expressed metabolites were found to be related to the citric acid cycle. The effect of citric acid as an exogenous additive on the synthesis of monacolin K was examined. These results provide technical support and a theoretical basis for further studies of the metabolic regulatory mechanisms underlying the beneficial effects of monacolin K and medium optimization, as well as genetic engineering of Monascus M1 for efficient monacolin K production.

Advanced cell culture techniques for cancer research

The incessant increase number of cancer cases, motivates scientists to constantly develop and search for new therapies. Along with the dynamic development of anti-cancer drugs and therapies, we are witnessing huge progress in the world of science - the development of personalized medicine. An inseparable element is also a very strong trend in the development of new in vitro animal models for chemotherapeutic research. Cell cultures are commonly undertaken by research models before animal testing. They are the basis for the development of new diagnostic and cancer treatments. It should be emphasized that basic research is a strong foundation for any therapy introduced. This chapter provides an overview of the modern cell culture techniques that are currently developing, which allow the introduction of modern models that reflect the organs and physiological system. Currently available cell culture methods are a key aspect of studying these interactions, however, a method that eliminates the limitations of standard methods is still being sought.

Concepts and Software Package for Efficient Quality Control in Targeted Metabolomics Studies: MeTaQuaC

Targeted quantitative mass spectrometry metabolite profiling is the workhorse of metabolomics research. Robust and reproducible data are essential for confidence in analytical results and are particularly important with large-scale studies. Commercial kits are now available which use carefully calibrated and validated internal and external standards to provide such reliability. However, they are still subject to processing and technical errors in their use and should be subject to a laboratory's routine quality assurance and quality control measures to maintain confidence in the results. We discuss important systematic and random measurement errors when using these kits and suggest measures to detect and quantify them. We demonstrate how wider analysis of the entire data set alongside standard analyses of quality control samples can be used to identify outliers and quantify systematic trends to improve downstream analysis. Finally, we present the MeTaQuaC software which implements the above concepts and methods for Biocrates kits and other target data sets and creates a comprehensive quality control report containing rich visualization and informative scores and summary statistics. Preliminary unsupervised multivariate analysis methods are also included to provide rapid insight into study variables and groups. MeTaQuaC is provided as an open source R package under a permissive MIT license and includes detailed user documentation.

An Integrated Multi-Disciplinary Perspectivefor Addressing Challenges of the Human Gut Microbiome

Our understanding of the human gut microbiome has grown exponentially. Advances in genome sequencing technologies and metagenomics analysis have enabled researchers to study microbial communities and their potential function within the context of a range of human gut related diseases and disorders. However, up until recently, much of this research has focused on characterizing the gut microbiological community structure and understanding its potential through system wide (meta) genomic and transcriptomic-based studies. Thus far, the functional output of these microbiomes, in terms of protein and metabolite expression, and within the broader context of host-gut microbiome interactions, has been limited. Furthermore, these studies highlight our need to address the issues of individual variation, and of samples as proxies. Here we provide a perspective review of the recent literature that focuses on the challenges of exploring the human gut microbiome, with a strong focus on an integrated perspective applied to these themes. In doing so, we contextualize the experimental and technical challenges of undertaking such studies and provide a framework for capitalizing on the breadth of insight such approaches afford. An integrated perspective of the human gut microbiome and the linkages to human health will pave the way forward for delivering against the objectives of precision medicine, which is targeted to specific individuals and addresses the issues and mechanisms in situ.

Active surveillance as a management strategy for papillary thyroid microcarcinoma

Active surveillance (AS) can be considered as a treatment strategy for low risk papillary thyroid microcarcinoma (PTMC), with the absence of clinically apparent lymph nodes, extrathyroidal extensions, and distant metastasis. After reviewing the reports on AS of low risk PTMCs worldwide, we introduced AS, and discussed the selection criteria for active surveillance candidates based on different guidelines and the follow-up schedules. Moreover, the requirement of cytological diagnosis, progression evaluation methods, necessity of thyrotropin suppression, and medical costs were issues that both clinicians and patients considered. The usefulness of AS for low risk PTMC patients depended on accurate and confidential evaluation of patient risk. Clinicians may adopt measures like dynamic monitoring, risk stratification, and making personal follow-up schedules to minimize these potential risks. By appropriately selecting PTMC patients, AS can be an effective alternative treatment to immediate surgery.

Framework nucleic acids as programmable carrier for transdermal drug delivery

DNA nanostructures are promising drug carriers with their intrinsic biocompatibility, uniformity and versatility. However, rapid serum disintegration leads to low bioavailability at targeted sites following systemic administration, hindering their biomedical applications. Here we demonstrate transdermal delivery of framework nucleic acids (FNAs) through topical applications. By designing FNAs with distinct shapes and sizes, we interrogate their penetration on mice and human skin explant. Skin histology reveals size-dependent penetration, with FNAs ≤75 nm effectively reaching dermis layer. 17 nm-tetrahedral FNAs show greatest penetration to 350 µm from skin periphery. Importantly, structural integrity is maintained during the skin penetration. Employing a mouse melanoma model, topical application of doxorubicin-loaded FNAs accommodates ≥2-fold improvement in drug accumulation and tumor inhibition relative to topically-applied free doxorubicin, or doxorubicin loaded in liposomes and polymeric nanoparticles. Programmable penetration with minimal systemic biodistribution underlines FNA potential as localized transdermal drug delivery carriers.

Effects of chronic stress on intestinal amino acid pathways

Major depressive disorder (MDD) is a debilitating mental disorder with a high prevalence and severe impacts on quality of life. However, the pathophysiological mechanisms underlying MDD remain poorly understood. Here, we used high-performance liquid chromatography with ultraviolet detection-based targeted metabolomics to identify amino acid changes in the small intestine, in a rat model of chronic unpredictable mild stress (CUMS). Pearson's correlation analysis was conducted to investigate the correlations between amino acid changes and behavioral outcomes. Western blot analysis was employed to verify intestinal amino acid transport function. Moreover, we performed an integrated analysis of related differential amino acids in the hippocampus, peripheral blood mononuclear cells (PBMCs), urine and cerebellum identified in our previous studies using the CUMS rat model to further our understanding of amino acid metabolism in depression. Decreased concentrations of glutamine and glycine and upregulation of aspartic acid were found in CUMS model rats. These changes were significantly correlated with depressive-like behaviors. Western blot analysis revealed that CUMS rats exhibited a reduction in the expression levels of amino acid transporters ASCT2 and B⁰AT1, as well as an increase in LAT1 expression. Impaired transport of glycine and glutamine into the small intestine may contribute to a central deficiency. The current findings suggest that the glycine and glutamine uptake systems may be potential therapeutic targets for depression. The integrated analysis strategy used in the current study may provide new insight into the cellular and molecular mechanisms underlying the gut-brain axis, and help to elucidate the pathophysiological changes in central and peripheral systems in depression.

A High-Throughput Targeted Metabolomics Workflow for the Detection of 200 Polar Metabolites in Central Carbon Metabolism

Targeted metabolomics aims to analyze a set of pre-selected metabolites from biologically relevant metabolic pathways. The triple quadrupole mass spectrometry (QqQ-MS) based multiple reaction monitoring (MRM) technique is the most widely approach used for targeted metabolomics, and features high selectivity and sensitivity, good reproducibility and wide dynamic range in quantitative analysis. Here, we describe an MRM based targeted metabolomics workflow for the quantitative analysis of 200 polar metabolites in central carbon metabolic pathways, including the data acquisition method and the automated data processing procedures using our in-house R package MRMAnalyzer. The workflow described in this chapter combines a hydrophilic interaction liquid chromatography (HILIC) separation and positive/negative ion polarity switching based MS detection, and is able to acquire data from multiple types of biological samples such as bacteria, cultured mammalian cells, animal tissues and biofluids (e.g., serum and urine). Finally, the MRMAnalyzer software can automatically process the generated large-scale data set with high efficiency. We hope it is a valuable and efficient workflow for researchers to facilitate the respective biological studies using targeted metabolomics.

Metabolomics of mammalian brain reveals regional differences

Background

The mammalian brain is organized into regions with specific biological functions and properties. These regions have distinct transcriptomes, but little is known whether they may also differ in their metabolome. The metabolome, a collection of small molecules or metabolites, is at the intersection of the genetic background of a given cell or tissue and the environmental influences that affect it. Thus, the metabolome directly reflects information about the physiologic state of a biological system under a particular condition. The objective of this study was to investigate whether various brain regions have diverse metabolome profiles, similarly to their genetic diversity. The answer to this question would suggest that not only the genome but also the metabolome may contribute to the functional diversity of brain regions.

Methods

We investigated the metabolome of four regions of the mouse brain that have very distinct functions: frontal cortex, hippocampus, cerebellum, and olfactory bulb. We utilized gas- and liquid- chromatography mass spectrometry platforms and identified 215 metabolites.

Results

Principal component analysis, an unsupervised multivariate analysis, clustered each brain region based on its metabolome content, thus providing the unique metabolic profile of each region. A pathway-centric analysis indicated that olfactory bulb and cerebellum had most distinct metabolic profiles, while the cortical parenchyma and hippocampus were more similar in their metabolome content. Among the notable differences were distinct oxidative-anti-oxidative status and region-specific lipid profiles. Finally, a global metabolic connectivity analysis using the weighted correlation network analysis identified five hub metabolites that organized a unique metabolic network architecture within each examined brain region. These data indicate the diversity of global metabolome corresponding to specialized regional brain function and provide a new perspective on the underlying properties of brain regions.

Conclusion

In summary, we observed many differences in the metabolome among the various brain regions investigated. All four brain regions in our study had a unique metabolic signature, but the metabolites came from all categories and were not pathway-centric.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0644-0) contains supplementary material, which is available to authorized users.

Circulating tumour DNA, microRNA and metabolites in cerebrospinal fluid as biomarkers for central nervous system malignancies

Central nervous system (CNS) malignancies can be difficult to diagnose and many do not respond satisfactorily to existing therapies. Monitoring patients with CNS malignancies for treatment response and tumour recurrence can be challenging because of the difficulty and risks of brain biopsies, and the low specificity and sensitivity of the less invasive methodologies that are currently available. Uncertainty about tumour diagnosis or whether a tumour has responded to treatment or has recurred can cause delays in therapeutic decisions that can impact patient outcome. Therefore, there is an urgent need to develop and validate reliable and minimally invasive biomarkers for CNS tumours that can be used alone or in combination with current clinical practices. Blood-based biomarkers can be informative in the diagnosis and monitoring of various types of cancer. However, blood-based biomarkers have proven suboptimal for analysis of CNS tumours. In contrast, circulating biomarkers in cerebrospinal fluid (CSF), including circulating tumour DNA, microRNAs and metabolites, hold promise for accurate and minimally invasive assessment of CNS tumours. This review summarises the current understanding of these three types of CSF biomarkers and their potential use in neuro-oncologic clinical practice.

Web of microbes (WoM): a curated microbial exometabolomics database for linking chemistry and microbes

Background

As microbiome research becomes increasingly prevalent in the fields of human health, agriculture and biotechnology, there exists a need for a resource to better link organisms and environmental chemistries. Exometabolomics experiments now provide assertions of the metabolites present within specific environments and how the production and depletion of metabolites is linked to specific microbes. This information could be broadly useful, from comparing metabolites across environments, to predicting competition and exchange of metabolites between microbes, and to designing stable microbial consortia. Here, we introduce Web of Microbes (WoM; freely available at: http://webofmicrobes.org), the first exometabolomics data repository and visualization tool.

Description

WoM provides manually curated, direct biochemical observations on the changes to metabolites in an environment after exposure to microorganisms. The web interface displays a number of key features: (1) the metabolites present in a control environment prior to inoculation or microbial activation, (2) heatmap-like displays showing metabolite increases or decreases resulting from microbial activities, (3) a metabolic web displaying the actions of multiple organisms on a specified metabolite pool, (4) metabolite interaction scores indicating an organism’s interaction level with its environment, potential for metabolite exchange with other organisms and potential for competition with other organisms, and (5) downloadable datasets for integration with other types of -omics datasets.

Conclusion

We anticipate that Web of Microbes will be a useful tool for the greater research community by making available manually curated exometabolomics results that can be used to improve genome annotations and aid in the interpretation and construction of microbial communities.

Electronic supplementary material

The online version of this article (10.1186/s12866-018-1256-y) contains supplementary material, which is available to authorized users.

Validation and Automation of a High-Throughput Multitargeted Method for Semiquantification of Endogenous Metabolites from Different Biological Matrices Using Tandem Mass Spectrometry

The use of metabolomics profiling to understand the metabolism under different physiological states has increased in recent years, which created the need for robust analytical platforms. Here, we present a validated method for targeted and semiquantitative analysis of 102 polar metabolites that cover major metabolic pathways from 24 classes in a single 17.5-min assay. The method has been optimized for a wide range of biological matrices from various organisms, and involves automated sample preparation and data processing using an inhouse developed R-package. To ensure reliability, the method was validated for accuracy, precision, selectivity, specificity, linearity, recovery, and stability according to European Medicines Agency guidelines. We demonstrated an excellent repeatability of retention times (CV < 4%), calibration curves (R² ≥ 0.980) in their respective wide dynamic concentration ranges (CV < 3%), and concentrations (CV < 25%) of quality control samples interspersed within 25 batches analyzed over a period of one year. The robustness was demonstrated through a high correlation between metabolite concentrations measured using our method and the NIST reference values (R² = 0.967), including cross-platform comparability against the BIOCRATES AbsoluteIDQp180 kit (R² = 0.975) and NMR analyses (R² = 0.884). We have shown that our method can be successfully applied in many biomedical research fields and clinical trials, including epidemiological studies for biomarker discovery. In summary, a thorough validation demonstrated that our method is reproducible, robust, reliable, and suitable for metabolomics studies.

An integrated strategy to improve data acquisition and metabolite identification by time-staggered ion lists in UHPLC/Q-TOF MS-based metabolomics

The narrow linear range and the limited scan time of the given ion make the quantification of the features challenging in liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics with the full-scan mode. And metabolite identification is another bottleneck of untargeted analysis owing to the difficulty of acquiring MS/MS information of most metabolites detected. In this study, an integrated workflow was proposed using the newly established multiple ion monitoring mode with time-staggered ion lists (tsMIM) and target-directed data-dependent acquisition with time-staggered ion lists (tsDDA) to improve data acquisition and metabolite identification in UHPLC/Q-TOF MS-based untargeted metabolomics. Compared to the conventional untargeted metabolomics, the proprosed workflow exhibited the better repeatability before and after data normalization. After selecting features with the significant change by statistical analysis, MS/MS information of all these features can be obtained by tsDDA analysis to facilitate metabolite identification. Using time-staggered ion lists, the workflow is more sensitive in data acquisition, especially for the low-abundant features. Moreover, the metabolites with low abundance tend to be wrongly integrated and triggered by full scan-based untargeted analysis with MS^E acquisition mode, which can be greatly improved by the proposed workflow. The integrated workflow was also successfully applied to discover serum biosignatures for the genetic modification of fat-1 in mice, which indicated its practicability and great potential in future metabolomics studies.

A review of metabolomics approaches and their application in identifying causal pathways of childhood asthma

Because asthma is a disease that results from host-environment interactions, an approach that allows assessment of the effect of the environment on the host is needed to understand the disease. Metabolomics has appealing potential as an application to study pathways to childhood asthma development. The objective of this review is to provide an overview of metabolomics methods and their application to understanding host-environment pathways in asthma development. We reviewed recent literature on advances in metabolomics and their application to study pathways to childhood asthma development. We highlight the (1) potential of metabolomics in understanding the pathogenesis of disease and the discovery of biomarkers; (2) choice of metabolomics techniques, biospecimen handling, and data analysis; (3) application to studying the role of the environment on asthma development; (4) review of metabolomics applied to the outcome of asthma; (5) recommendations for application of metabolomics-based -omics data integration in understanding disease pathogenesis; and (6) limitations. In conclusion, metabolomics allows use of biospecimens to identify useful biomarkers and pathways involved in disease development and subsequently to inform a greater understanding of disease pathogenesis and endotypes and prediction of the clinical course of childhood asthma phenotypes.

Subacute oral toxicity assessment of benalaxyl in mice based on metabolomics methods

In this study, the metabolic responses of mice after 30 days of exposure to benalaxyl were assessed using NMR-based untargeted metabolomics and LC-MS-based targeted profiling of 20 amino acids. Urinary ¹H NMR analyses revealed alterations in energy metabolism, lipid metabolism, vitamin B metabolism, the urea cycle and amino acid metabolism, and targeted analyses indicated that the serum levels of asparagine, histidine, lysine and aspartic acid were significantly altered. Additionally, significant oxidative stress was observed in the liver and kidney, although no apparent histopathological injury was observed. The tissue distribution indicated a significant stereoselectivity in the brain, where (-)-R-benalaxyl was enriched. These data provide a comprehensive picture of the subacute toxic effects of benalaxyl in mice. The results of this study suggested that, for a toxicity evaluation, metabolomics analysis is much more sensitive than traditional toxicological methods. The results also highlight the combined use of untargeted and targeted metabolomics approaches in evaluating the health risks of xenobiotics.

Integration of metabolomics and transcriptomics in nanotoxicity studies

Biomedical research involving nanoparticles has produced useful products with medical applications. However, the potential toxicity of nanoparticles in biofluids, cells, tissues, and organisms is a major challenge. The '-omics' analyses provide molecular profiles of multifactorial biological systems instead of focusing on a single molecule. The 'omics' approaches are necessary to evaluate nanotoxicity because classical methods for the detection of nanotoxicity have limited ability in detecting miniscule variations within a cell and do not accurately reflect the actual levels of nanotoxicity. In addition, the 'omics' approaches allow analyses of in-depth changes and compensate for the differences associated with high-throughput technologies between actual nanotoxicity and results from traditional cytotoxic evaluations. However, compared with a single omics approach, integrated omics provides precise and sensitive information by integrating complex biological conditions. Thus, these technologies contribute to extended safety evaluations of nanotoxicity and allow the accurate diagnoses of diseases far earlier than was once possible in the nanotechnology era. Here, we review a novel approach for evaluating nanotoxicity by integrating metabolomics with metabolomic profiling and transcriptomics, which is termed "metabotranscriptomics". [BMB Reports 2018; 51(1): 14-20].

Direct and indirect approaches to identify drug modes of action

Phenotypic assays are becoming increasingly more common among drug discovery practices, expanding drug target diversity as lead compounds identified through such screens are not limited to known targets. While increasing diversity is beneficial to the drug discovery process and the fight against disease, the unknown modes of action of new lead compounds can hamper drug discovery as, in most cases, the process of lead compound optimization is made difficult due to the unknown nature of the target; blindly changing substituents can prove fruitless due to the inexhaustible number of potential combinations, and it is therefore desirable to rapidly identify the targets of lead compounds developed through phenotypic screening. In addition, leads identified through target-based screening often have off-target effects that contribute towards drug toxicity, and by identifying those secondary targets, the drugs can be improved. However, the identification of a leads mode of action is far from trivial and now represents a major bottleneck in the drug discovery pipeline. This review looks at some of the recent developments in the identification of drug modes of action, focusing on phenotype-based methods using metabolomics, proteomics, transcriptomics, and genomics to detect changes in phenotype in response to the presence of the drug, and affinity-based methods using modified/unmodified drug as bait to capture and identify targets. © 2017 IUBMB Life, 70(1):9-22, 2018.

Metabolomic signature of brain cancer

Despite advances in surgery and adjuvant therapy, brain tumors represent one of the leading causes of cancer-related mortality and morbidity in both adults and children. Gliomas constitute about 60% of all cerebral tumors, showing varying degrees of malignancy. They are difficult to treat due to dismal prognosis and limited therapeutics. Metabolomics is the untargeted and targeted analyses of endogenous and exogenous small molecules, which charact erizes the phenotype of an individual. This emerging "omics" science provides functional readouts of cellular activity that contribute greatly to the understanding of cancer biology including brain tumor biology. Metabolites are highly informative as a direct signature of biochemical activity; therefore, metabolite profiling has become a promising approach for clinical diagnostics and prognostics. The metabolic alterations are well-recognized as one of the key hallmarks in monitoring disease progression, therapy, and revealing new molecular targets for effective therapeutic intervention. Taking advantage of the latest high-throughput analytical technologies, that is, nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), metabolomics is now a promising field for precision medicine and drug discovery. In the present report, we review the application of metabolomics and in vivo metabolic profiling in the context of adult gliomas and paediatric brain tumors. Analytical platforms such as high-resolution (HR) NMR, in vivo magnetic resonance spectroscopic imaging and high- and low-resolution MS are discussed. Moreover, the relevance of metabolic studies in the development of new therapeutic strategies for treatment of gliomas are reviewed.

Chemical Methods for Probing Virus-Host Proteomic Interactions

Interactions between host and pathogen proteins constitute an important aspect of both infectivity and the host immune response. Different viruses have evolved complex mechanisms to hijack host-cell machinery and metabolic pathways to redirect resources and energy flow toward viral propagation. These interactions are often critical to the virus, and thus understanding these interactions at a molecular level gives rise to opportunities to develop novel antiviral strategies for therapeutic intervention. This review summarizes current advances in chemoproteomic methods for studying these molecular altercations between different viruses and their hosts.

PLS-Based and Regularization-Based Methods for the Selection of Relevant Variables in Non-targeted Metabolomics Data

Non-targeted metabolomics constitutes a part of the systems biology and aims at determining numerous metabolites in complex biological samples. Datasets obtained in the non-targeted metabolomics studies are high-dimensional due to sensitivity of mass spectrometry-based detection methods as well as complexity of biological matrices. Therefore, a proper selection of variables which contribute into group classification is a crucial step, especially in metabolomics studies which are focused on searching for disease biomarker candidates. In the present study, three different statistical approaches were tested using two metabolomics datasets (RH and PH study). The orthogonal projections to latent structures-discriminant analysis (OPLS-DA) without and with multiple testing correction as well as the least absolute shrinkage and selection operator (LASSO) with bootstrapping, were tested and compared. For the RH study, OPLS-DA model built without multiple testing correction selected 46 and 218 variables based on the VIP criteria using Pareto and UV scaling, respectively. For the PH study, 217 and 320 variables were selected based on the VIP criteria using Pareto and UV scaling, respectively. In the RH study, OPLS-DA model built after correcting for multiple testing, selected 4 and 19 variables as in terms of Pareto and UV scaling, respectively. For the PH study, 14 and 18 variables were selected based on the VIP criteria in terms of Pareto and UV scaling, respectively. In the RH and PH study, the LASSO selected 14 and 4 variables with reproducibility between 99.3 and 100%, respectively. In the light of PLS-based models, the larger the search space the higher the probability of developing models that fit the training data well with simultaneous poor predictive performance on the validation set. The LASSO offers potential improvements over standard linear regression due to the presence of the constrain, which promotes sparse solutions. This paper is the first one to date utilizing the LASSO penalized logistic regression in untargeted metabolomics studies.

The use of mass spectrometry for analysing metabolite biomarkers in epidemiology: methodological and statistical considerations for application to large numbers of biological samples

Data quality is critical for epidemiology, and as scientific understanding expands, the range of data available for epidemiological studies and the types of tools used for measurement have also expanded. It is essential for the epidemiologist to have a grasp of the issues involved with different measurement tools. One tool that is increasingly being used for measuring biomarkers in epidemiological cohorts is mass spectrometry (MS), because of the high specificity and sensitivity of MS-based methods and the expanding range of biomarkers that can be measured. Further, the ability of MS to quantify many biomarkers simultaneously is advantageously compared to single biomarker methods. However, as with all methods used to measure biomarkers, there are a number of pitfalls to consider which may have an impact on results when used in epidemiology. In this review we discuss the use of MS for biomarker analyses, focusing on metabolites and their application and potential issues related to large-scale epidemiology studies, the use of MS "omics" approaches for biomarker discovery and how MS-based results can be used for increasing biological knowledge gained from epidemiological studies. Better understanding of the possibilities and possible problems related to MS-based measurements will help the epidemiologist in their discussions with analytical chemists and lead to the use of the most appropriate statistical tools for these data.

A combined non-targeted and targeted metabolomics approach to study the stereoselective metabolism of benalaxyl enantiomers in mouse hepatic microsomes

Understanding of xenobiotic metabolism is necessary for risk assessment as well as toxicological research. In the present study, nanoLC/LTQ-Orbitrap mass based non-targeted metabolomics method coupled with ultra-performance liquid chromatography (UPLC)/triple quadrupole mass based targeted metabolomics method was carried out to investigate the stereoselective metabolism of benalaxyl in mouse hepatic microsomes. As a result, 7 metabolites of benalaxyl were identified, including 5 previously reported and 2 newly identified metabolites in present work. Hydroxylation, oxidation and esterolysis were major biotransformation reactions of benalaxyl in mouse hepatic microsomes. For stereoselective metabolism study, (-)-R-benalaxyl degraded much faster than its antipode with the t1/2 of 81.24 and 190.38 min for (-)-R- and (+)-S-benalaxyl, respectively. More importantly, stereoselectivity was also observed in the formation of the identified metabolites. In conclusion, the combined use of the mass spectrometry based targeted and non-targeted metabolomics provided a new approach to investigate stereoselective metabolism and identify novel metabolites of chiral pesticides. This study highlights the stereoselective metabolic profile of benalaxyl enantiomers and provides reliable data for benalaxyl toxicological risk assessment in mammal.

Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases

Complex and dynamic networks of molecules are involved in human diseases. High-throughput technologies enable omics studies interrogating thousands to millions of makers with similar biochemical properties (eg, transcriptomics for RNA transcripts). However, a single layer of "omics" can only provide limited insights into the biological mechanisms of a disease. In the case of genome-wide association studies, although thousands of single nucleotide polymorphisms have been identified for complex diseases and traits, the functional implications and mechanisms of the associated loci are largely unknown. Additionally, the genomic variants alone are not able to explain the changing disease risk across the life span. DNA, RNA, protein, and metabolite often have complementary roles to jointly perform a certain biological function. Such complementary effects and synergistic interactions between omic layers in the life course can only be captured by integrative study of multiple molecular layers. Building upon the success in single-omics discovery research, population studies started adopting the multi-omics approach to better understanding the molecular function and disease etiology. Multi-omics approaches integrate data obtained from different omic levels to understand their interrelation and combined influence on the disease processes. Here, we summarize major omics approaches available in population research, and review integrative approaches and methodologies interrogating multiple omic layers, which enhance the gene discovery and functional analysis of human diseases. We seek to provide analytical recommendations for different types of multi-omics data and study designs to guide the emerging multi-omic research, and to suggest improvement of the existing analytical methods.

Metabolomics, Lipidomics and Pharmacometabolomics of Human Hypertension

Hypertension is a common but complex human disease, which can lead to a heart attack, stroke, kidney disease or other complications. Since the pathogenesis of hypertension is heterogeneous and multifactorial, it is crucial to establish a comprehensive metabolomic approach to elucidate the molecular mechanism of hypertension. Although there have been limited metabolomic, lipidomic and pharmacometabolomic studies investigating this disease to date, metabolomic studies on hypertension have provided greater insights into the identification of disease-specific biomarkers, predicting treatment outcome and monitor drug safety and efficacy. Therefore, we discuss recent updates on the applications of metabolomics technology in human hypertension with a focus on metabolic biomarker discovery.

Analytical challenges for measuring steroid responses to stress, neurodegeneration and injury in the central nervous system

Levels of steroids in the adult central nervous system (CNS) show marked changes in response to stress, degenerative disorders and injury. However, their analysis in complex matrices such as fatty brain and spinal cord tissues, and even in plasma, requires accurate and precise analytical methods. Radioimmunoassays (RIA) and enzyme-linked immunosorbent assays, even with prepurification steps, do not provide sufficient specificity, and they are at the origin of many inconsistent results in the literature. The analysis of steroids by mass spectrometric methods has become the gold standard for accurate and sensitive steroid analysis. However, these technologies involve multiple purification steps prone to errors, and they only provide accurate reference values when combined with careful sample workup. In addition, the interpretation of changes in CNS steroid levels is not an easy task because of their multiple sources: the endocrine glands and the local synthesis by neural cells. In the CNS, decreased steroid levels may reflect alterations of their biosynthesis, as observed in the case of chronic stress, post-traumatic stress disorders or depressive episodes. In such cases, return to normalization by administering exogenous hormones or by stimulating their endogenous production may have beneficial effects. On the other hand, increases in CNS steroids in response to acute stress, degenerative processes or injury may be part of endogenous protective or rescue programs, contributing to the resistance of neural cells to stress and insults. The aim of this review is to encourage a more critical reading of the literature reporting steroid measures, and to draw attention to the absolute need for well-validated methods. We discuss reported findings concerning changing steroid levels in the nervous system by insisting on methodological issues. An important message is that even recent mass spectrometric methods have their limits, and they only become reliable tools if combined with careful sample preparation.

Mass spectral similarity for untargeted metabolomics data analysis of complex mixtures

While in nucleotide sequencing, the analysis of DNA from complex mixtures of organisms is common, this is not yet true for mass spectrometric data analysis of complex mixtures. The comparative analyses of mass spectrometry data of microbial communities at the molecular level is difficult to perform, especially in the context of a host. The challenge does not lie in generating the mass spectrometry data, rather much of the difficulty falls in the realm of how to derive relevant information from this data. The informatics based techniques to visualize and organize datasets are well established for metagenome sequencing; however, due to the scarcity of informatics strategies in mass spectrometry, it is currently difficult to cross correlate two very different mass spectrometry data sets from microbial communities and their hosts. We highlight that molecular networking can be used as an organizational tool of tandem mass spectrometry data, automated database search for rapid identification of metabolites, and as a workflow to manage and compare mass spectrometry data from complex mixtures of organisms. To demonstrate this platform, we show data analysis from hard corals and a human lung associated with cystic fibrosis.

The utility of metabolomics in natural product and biomarker characterization

BACKGROUND

Metabolomics is a well-established rapidly developing research field involving quantitative and qualitative metabolite assessment within biological systems. Recent improvements in metabolomics technologies reveal the unequivocal value of metabolomics tools in natural products discovery, gene-function analysis, systems biology and diagnostic platforms.

SCOPE OF REVIEW

We review here some of the prominent metabolomics methodologies employed in data acquisition and analysis of natural products and disease-related biomarkers.

MAJOR CONCLUSIONS

This review demonstrates that metabolomics represents a highly adaptable technology with diverse applications ranging from environmental toxicology to disease diagnosis. Metabolomic analysis is shown to provide a unique snapshot of the functional genetic status of an organism by examining its biochemical profile, with relevance toward resolving phylogenetic associations involving horizontal gene transfer and distinguishing subgroups of genera possessing high genetic homology, as well as an increasing role in both elucidating biosynthetic transformations of natural products and detecting preclinical biomarkers of numerous disease states.

GENERAL SIGNIFICANCE

This review expands the interest in multiplatform combinatorial metabolomic analysis. The applications reviewed range from phylogenetic assignment, biosynthetic transformations of natural products, and the detection of preclinical biomarkers.

Metabolomic strategies to map functions of metabolic pathways

Genome sequencing efforts have revealed a strikingly large number of unannotated and uncharacterized genes that fall into metabolic enzymes classes, likely indicating that our current knowledge of biochemical pathways in normal physiology, let alone in disease states, remains largely incomplete. This realization presents a daunting challenge for post-genomic-era scientists in deciphering the biochemical and (patho)physiological roles of these enzymes and their metabolites and metabolic networks. This is further complicated by many recent studies showing a rewiring of normal metabolic networks in disease states to give rise to unique pathophysiological functions of enzymes, metabolites, and metabolic pathways. This review focuses on recent discoveries made using metabolic mapping technologies to uncover novel pathways and metabolite-mediated posttranslational modifications and epigenetic alterations and their impact on physiology and disease.

Sample preparation issues in NMR-based plant metabolomics: optimisation for Vitis wood samples

INTRODUCTION

Nuclear magnetic resonance (NMR) is one of the most commonly used analytical techniques in plant metabolomics. Although this technique is very reproducible and simple to implement, sample preparation procedures have a great impact on the quality of the metabolomics data.

OBJECTIVE

Investigation of different sample preparation methods and establishment of an optimised protocol for untargeted NMR-based metabolomics of Vitis vinifera L. wood samples.

METHODS

Wood samples from two different cultivars of V. vinifera with well-defined phenotypes (Gamaret and 2091) were selected as reference materials. Different extraction solvents (successively, dichloromethane, methanol and water, as well as ethyl acetate and 7:3 methanol-water (v/v)) and deuterated solvents (methanol-d4, 7:3 chloroform-d-methanol-d4 (v/v), dimethylsulphoxide-d6 and 9:1 dimethylsulphoxide-d6-water-d2 (v/v)) were evaluated for NMR acquisition, and the spectral quality was compared. The optimal extract concentration, chemical shift stability and peak area repeatability were also investigated.

RESULTS

Ethyl acetate was found to be the most satisfactory solvent for the extraction of all representative chemical classes of secondary metabolites in V. vinifera wood. The optimal concentration of dried extract was 10 mg/mL and 7:3 chloroform-d-methanol-d4 (v/v) was the most suitable solvent system for NMR analysis. Multivariate data analysis was used to estimate the biological variation and clustering between different cultivars.

CONCLUSION

Close attention should be paid to all required procedures before NMR analysis, especially to the selection of an extraction solvent and a deuterated solvent system to perform an extensive metabolomic survey of the specific matrix.