常见食源性致病菌代谢组学研究进展

食源性致病菌是一类以食品为传播媒介,可引起食物中毒的致病性微生物,是目前食源性疾病的主要诱因。食源性致病菌代谢组学通过研究致病菌代谢小分子物质产生的规律性和特征性,寻找新的检测靶标和建立基于代谢组学方法的食源性致病菌鉴别技术。随着目前代谢组学研究技术逐渐成熟和微生物挥发性代谢产物数据库的建立,食源性致病菌挥发性代谢产物分析已被建议作为临床和食品中致病菌鉴别的替代方法。本文综述了目前常见食源性致病菌代谢组学主要研究技术及其在临床和食品检测中的研究进展,以期为进一步建立食源性致病菌代谢产物数据库和研发基于代谢组学方法的食源性致病菌检测技术提供参考。     

Basics of mass spectrometry based metabolomics

The emerging field of metabolomics, aiming to characterize small molecule metabolites present in biological systems, promises immense potential for different areas such as medicine, environmental sciences, agronomy, etc. The purpose of this article is to guide the reader through the history of the field, then through the main steps of the metabolomics workflow, from study design to structure elucidation, and help the reader to understand the key phases of a metabolomics investigation and the rationale underlying the protocols and techniques used. This article is not intended to give standard operating procedures as several papers related to this topic were already provided, but is designed as a tutorial aiming to help beginners understand the concept and challenges of MS‐based metabolomics. A real case example is taken from the literature to illustrate the application of the metabolomics approach in the field of doping analysis. Challenges and limitations of the approach are then discussed along with future directions in research to cope with these limitations. This tutorial is part of the International Proteomics Tutorial Programme (IPTP18).     

Recent advances in mass spectrometry-based computational metabolomics

The computational metabolomics field brings together computer scientists, bioinformaticians, chemists, clinicians, and biologists to maximize the impact of metabolomics across a wide array of scientific and medical disciplines. The field continues to expand as modern instrumentation produces datasets with increasing complexity, resolution, and sensitivity. These datasets must be processed, annotated, modeled, and interpreted to enable biological insight. Techniques for visualization, integration (within or between omics), and interpretation of metabolomics data have evolved along with innovation in the databases and knowledge resources required to aid understanding. In this review, we highlight recent advances in the field and reflect on opportunities and innovations in response to the most pressing challenges. This review was compiled from discussions from the 2022 Dagstuhl seminar entitled “Computational Metabolomics: From Spectra to Knowledge”.     

Untargeted metabolomics: an overview of its usefulness and future potential in prenatal diagnosis

ABSTRACT

Introduction: Metabolomics opens up new avenues for biomarker discovery in different branches of medicine, including perinatology. Chromosomal aberration, preterm delivery (PTD), congenital heart defects, spina bifida, chorioamnionitis, and low birth weight are the main perinatal pathologies. Investigations using untargeted metabolomics have found the candidate metabolites for diagnostic biomarkers.

Areas covered: This review describes areas of prenatal diagnosis in which untargeted metabolomics has been used. Data on the disease, type of sample, techniques used, number of samples used in the study, and metabolites obtained including the sign of their regulation are summarized.

Expert commentary: Untargeted metabolomics is a powerful tool which can shed a new light on prenatal diagnostics. It helps to discover affected metabolic pathways what may help to reveal disease pathogenesis and propose potential biomarkers. Among others, glycerol and 2- and 3-hydroxybutyrate were proposed as markers of chromosomal aberration. Serum metabolic signature of PTD was characterized by increased lipids and decreased levels of hypoxanthine, tryptophane, and pyroglutamic acid. Lower level lipids and vitamin D3 metabolites together with increased bilirubin level in maternal serum were associated with macrosomia. However, to give a real value to those assays and allow their clinical application multicenter, large cohort validation studies are necessary.     

The potential biomarkers of drug addiction: proteomic and metabolomics challenges

Drug addiction places a significant burden on society and individuals. Proteomics and metabolomics approaches pave the road for searching potential biomarkers to assist the diagnosis and treatment. This review summarized putative drug addiction-related biomarkers in proteomics and metabolomics studies and discussed challenges and prospects in future studies. Alterations of several hundred proteins and metabolites were reported when exposure to abused drug, which enriched in energy metabolism, oxidative stress response, protein modification and degradation, synaptic function and neurotrasmission, etc. Hsp70, peroxiredoxin-6 and α- and β-synuclein, as well as n-methylserotonin and purine metabolites, were promising as potential biomarker for drug addiction.     

Elucidating tumor-stromal metabolic crosstalk in colorectal cancer through integration of constraint-based models and LC-MS metabolomics

Colorectal cancer (CRC) is a major cause of morbidity and mortality in the United States. Tumor-stromal metabolic crosstalk in the tumor microenvironment promotes CRC development and progression, but exactly how stromal cells, in particular cancer-associated fibroblasts (CAFs), affect the metabolism of tumor cells remains unknown. Here we take a data-driven approach to investigate the metabolic interactions between CRC cells and CAFs, integrating constraint-based modeling and metabolomic profiling. Using metabolomics data, we perform unsteady-state parsimonious flux balance analysis to infer flux distributions for central carbon metabolism in CRC cells treated with or without CAF-conditioned media. We find that CAFs reprogram CRC metabolism through stimulation of glycolysis, the oxidative arm of the pentose phosphate pathway (PPP), and glutaminolysis, as well as inhibition of the tricarboxylic acid cycle. To identify potential therapeutic targets, we simulate enzyme knockouts and find that CAF-treated CRC cells are especially sensitive to inhibitions of hexokinase and glucose-6-phosphate, the rate limiting steps of glycolysis and oxidative PPP. Our work gives mechanistic insights into the metabolic interactions between CRC cells and CAFs and provides a framework for testing hypotheses towards CRC-targeted therapies.     

Annotation of plant gene function via combined genomics, metabolomics and informatics

Given the ever expanding number of model plant species for which complete genome sequences are available and the abundance of bio-resources such as knockout mutants, wild accessions and advanced breeding populations, there is a rising burden for gene functional annotation. In this protocol, annotation of plant gene function using combined co-expression gene analysis, metabolomics and informatics is provided (Figure 1). This approach is based on the theory of using target genes of known function to allow the identification of non-annotated genes likely to be involved in a certain metabolic process, with the identification of target compounds via metabolomics. Strategies are put forward for applying this information on populations generated by both forward and reverse genetics approaches in spite of none of these are effortless. By corollary this approach can also be used as an approach to characterise unknown peaks representing new or specific secondary metabolites in the limited tissues, plant species or stress treatment, which is currently the important trial to understanding plant metabolism.     

Metabolic engineering of flavonoids in tomato (Solanum lycopersicum): the potential for metabolomics

Flavonoids comprise a large and diverse group of polyphenolic plant secondary metabolites. In plants, flavonoids play important roles in many biological processes such as pigmentation of flowers, fruits and vegetables, plant-pathogen interactions, fertility and protection against UV light. Being natural plant compounds, flavonoids are an integral part of the human diet and there is increasing evidence that dietary polyphenols are likely candidates for the observed beneficial effects of a diet rich in fruits and vegetables on the prevention of several chronic diseases. Within the plant kingdom, and even within a single plant species, there is a large variation in the levels and composition of flavonoids. This variation is often due to specific mutations in flavonoid-related genes leading to quantitative and qualitative differences in metabolic profiles. The use of such specific flavonoid mutants with easily scorable, visible phenotypes has led to the isolation and characterisation of many structural and regulatory genes involved in the flavonoid biosynthetic pathway from different plant species. These genes have been used to engineer the flavonoid biosynthetic pathway in both model and crop plant species, not only from a fundamental perspective, but also in order to alter important agronomic traits, such as flower and fruit colour, resistance, nutritional value. This review describes the advances made in engineering the flavonoid pathway in tomato (Solanum lycopersicum). Three different approaches will be described; (I) Increasing endogenous tomato flavonoids using structural or regulatory genes; (II) Blocking specific steps in the flavonoid pathway by RNA interference strategies; and (III) Production of novel tomato flavonoids by introducing novel branches of the flavonoid pathway. Metabolite profiling is an essential tool to analyse the effects of pathway engineering approaches, not only to analyse the effect on the flavonoid composition itself, but also on other related or unrelated metabolic pathways. Metabolomics will therefore play an increasingly important role in revealing a more complete picture of metabolic perturbation and will provide additional novel insights into the effect of the introduced genes and the role of flavonoids in plant physiology and development.     

Tools and ingredients for the biocatalytic synthesis of metabolites

Metabolic networks have been an interesting starting point not only for the design of synthetic routes in a similar sequence of reactions, e.g., in biomimetic syntheses, but also for assembling a number of biocatalytic steps by preparing the required enzymes and auxiliary reagents. Retrosynthetic analysis involving multiple biocatalytic reactions steps therefore needs to consider the practically realized biocatalytic single steps. The opportunities for route selection are enlarged if novel synthetic reactions connecting easily available starting materials and products are found, and/or both biocatalytic and classical reactions of organic chemistry are utilized. Tools and ingredients for biocatalytic synthesis are of special interest for reactions difficult to achieve by classical organic synthesis. Densely and differentially functionalized small molecules do not allow much space for protecting or activating groups. Biocatalytic reactions have therefore performed well for a number of useful metabolites in enantiopure form to achieve full functionality. Although many well-known metabolites from classical biochemistry have only been prepared in racemic form, it is of fundamental interest to have these available in enantiomerically pure form. Biocatalytic reactions with nature's privileged chiral catalysts appear to be a promising synthetic strategy towards these metabolites, especially when sensitive or stable-isotope-labeled metabolites are to be prepared. The main applications for these metabolites are as references materials in metabolomics, as enzyme substrates for the characterization of metabolic enzyme activities and as potential pharmaceuticals in biomedical research. The use of stable-isotope-labeled metabolites can thereby simplify in vivo applications and metabolic flux analyses.     

1H NMR metabolomics analysis of glioblastoma subtypes: correlation between metabolomics and gene expression characteristics

Glioblastoma multiforme (GBM) is the most common form of malignant glioma, characterized by unpredictable clinical behaviors that suggest distinct molecular subtypes. With the tumor metabolic phenotype being one of the hallmarks of cancer, we have set upon to investigate whether GBMs show differences in their metabolic profiles. (1)H NMR analysis was performed on metabolite extracts from a selection of nine glioblastoma cell lines. Analysis was performed directly on spectral data and on relative concentrations of metabolites obtained from spectra using a multivariate regression method developed in this work. Both qualitative and quantitative sample clustering have shown that cell lines can be divided into four groups for which the most significantly different metabolites have been determined. Analysis shows that some of the major cancer metabolic markers (such as choline, lactate, and glutamine) have significantly dissimilar concentrations in different GBM groups. The obtained lists of metabolic markers for subgroups were correlated with gene expression data for the same cell lines. Metabolic analysis generally agrees with gene expression measurements, and in several cases, we have shown in detail how the metabolic results can be correlated with the analysis of gene expression. Combined gene expression and metabolomics analysis have shown differential expression of transporters of metabolic markers in these cells as well as some of the major metabolic pathways leading to accumulation of metabolites. Obtained lists of marker metabolites can be leveraged for subtype determination in glioblastomas.     

Non-targeted metabolomics of saliva to explore potential biomarkers for gastric ulceration in pigs fed hemp

Gastric ulceration is a common disease in pig production worldwide and is associated with economic losses as well as animal health and welfare issues. The aim of this study was to explore potential salivary biomarkers for gastric ulceration in pigs. In addition, the aim was to study the effect of hemp on the incidence of gastric ulcers. Approximately 440 growing-finishing pigs in the period from 30 to 110 kg BW were allocated to four different diets: meal feed (Meal); pelleted feed (Pellets); pelleted feed added 4% hempseed cake (Hemp Cake); pelleted feed added 4% hempseed hulls (Hemp Hulls). The day before slaughter, saliva samples from each pig were collected. After slaughter, the stomachs were emptied to assess the consistency of the stomach content and examined for gastric ulceration using an index scale (0–10). Noticeable changes of the gastric mucosa (total index score ≥ 6) were observed in 291 pigs. The odds of having index scores 0–5 relative to index scores 6–8 and 9–10, respectively, were higher (P < 0.001) for pigs fed Meal compared to pigs fed Pellets. The odds of suffering from severe gastric ulcers tended (P = 0.08) to be lower in pigs fed Hemp Hulls compared to pigs fed Pellets. A non-targeted liquid chromatography mass spectrometry based metabolomics analysis was performed on saliva samples to determine any separation between pigs with healthy stomachs and those with gastric ulcers and to examine a possible correlation between gastric ulcer index and potential biomarkers. Partial least-squares discriminant analysis showed a separation between pigs with ulcers and those with healthy stomachs/hyperkeratosis (HK). Metabolites contributing to the separation between groups were identified. Levels of oxylipins deriving from linoleic acid were lower (P < 0.001) in pigs with ulcers compared to healthy/HK pigs. This may indicate a shift in the metabolic pathways towards more pro-inflammatory arachidonic acid-derived eicosanoids, which might reflect an increased inflammatory response. Thus, reduced levels of oxylipins derived from linoleic acid seemed to be associated with active gastric ulcers, and thereby they might function as biomarkers for gastric ulceration in pigs. In addition, supplementation of hempseed hulls had a beneficial effect on severe gastric ulcers, as hempseed hulls changed the consistency of the gastric content by conferring more solidness. However, it was not possible to observe any reliable separation between pigs fed pellets supplemented with hemp products and pigs fed non-supplemented pellets according to the identified salivary metabolites.     

A novel application of metabolomics in vertebrate development

Many studies have demonstrated the functions of individual genes associated with embryogenesis and have determined the genome sequences of several organisms. Despite the availability of enormous amount of genetic information, dynamic changes that occur during embryogenesis have not yet been completely understood. In order to understand the dynamic processes involved in embryogenesis, we employed the metabolomic approach. The results of our study indicated that there is a close correlation between metabolomes and developmental stages. Our method enables the identification of embryonic stages using metabolomes as “fingerprints.” In this manner, we could successfully predict embryonic development on the basis of metabolomic fingerprints. This is the first report describing a model for predicting vertebrate development by using metabolomics.     

Dark-Field Imaging: Recent developments and potential clinical applications

This paper describes an X-ray phase contrast imaging technique using analyzer-based optics called X-ray Dark-Field Imaging that has been under development for the past 10 years. We describe the theory behind XDFI, the X-ray optics required for implementing it in practice, and algorithms used for 2D, 2.5D, and 3D image reconstruction. The XDFI optical chain consists of an asymmetrically cut, Bragg-type monochromator-collimator that provides a planar monochromatic X-ray beam, a positioning stage for the specimens, a Laue-case angle analyzer, and one or two cameras to capture the dark and bright field images. We demonstrate the soft-tissue discrimination capabilities of XDFI by reconstructing images with absorption and phase contrast. By using a variety of specimens such as breast tissue with cancer, joints with articular cartilage, ex-vivo human eye specimen, and others, we show that refraction-based contrast derived from XDFI is more effective in characterizing anatomical features, articular pathology, and neoplastic disease than conventional absorption-based images. For example, XDFI of breast tissue can discriminate between the normal and diseased terminal duct lobular unit, and between invasive and in-situ cancer. The final section of this paper is devoted to potential future developments to enable clinical and histo-pathological applications of this technique.     

Complexity and pitfalls of mass spectrometry-based targeted metabolomics in brain research

Current quantitative metabolomic research in brain tissue is challenged by several analytical issues. To compare data of metabolite pattern, ratios of individual metabolite concentrations and composed classifiers characterizing a distinct state, standardized workup conditions, and extraction medium are crucial. Differences in physicochemical properties of individual compounds and compound classes such as polarity determine extraction yields and, thus, ratios of compounds with varying properties. Also, variations in suppressive effects related to coextracted matrix components affect standards or references and their concentration-dependent responses.The selection of a common tissue extraction protocol is an ill-posed problem because it can be regarded as a multiple objective decision depending on factors such as sample handling practicability, measurement precision, control of matrix effects, and relevance of the chemical assay. This study systematically evaluates the impact of extraction solvents and the impact of the complex brain tissue on measured metabolite levels, taking into account ionization efficiency as well as challenges encountered in the trace-level quantification of the analytes in brain matrices. In comparison with previous studies that relied on nontargeted platforms, consequently emphasizing the global behavior of the metabolomic fingerprint, here we focus on several series of metabolites spanning over extensive polarity, concentration, and molecular mass ranges.     

Metabolomic profile in hyperthyroid patients before and after antithyroid drug treatment: Correlation with thyroid hormone and TSH concentration

Hyperthyroidism (HT) is characterized by an intense metabolic impact which affects the lipid, carbohydrate and amino acids metabolism, with increased resting energy expenditure and thermogenesis. Metabolomics is a new comprehensive technique that allows to capture an instant metabolic picture of an organism, reflecting peculiar molecular and pathophysiological states. The aim of the present prospective study was to identify a distinct metabolomic profile in HT patients using ¹H NMR spectroscopy before and after antithyroid drug treatment. This prospective study included 15 patients (10 female, 5 male) who were newly diagnosed hyperthyroidism. A nuclear magnetic resonance (¹H NMR) based analysis was performed on plasma samples from the same patients at diagnosis (HypT₀) and when they achieved euthyroidism (HypT₁). The case groups were compared with a control group of 26 healthy volunteers (C). Multivariate statistical analysis was performed with Partial Least Squares-Discriminant Analysis (PLS-DA). PLS-DA identified a distinct metabolic profile between C and untreated hyperthyroid patients (R²X 0.638, R²Y 0.932, Q² 0.783). Interestingly, a significant difference was also found between C and euthyroid patients after treatment (R²X 0.510, R²Y 0.838, Q² 0.607), while similar cluster emerged comparing HypT₀ vs HypT₁ patients. This study shows that metabolomic profile is deeply influenced by hyperthyroidism and this alteration persists after normalization of thyrotropin (TSH) and free thyroid hormone (FT3, FT4) concentration. This suggests that TSH, FT3 and FT4 assays may not be insufficient to detect long lasting peripheral effects of the thyroid hormones action. Further studies are needed to clarify whether and to what extent the evaluation of metabolomics profile may provide relevant information in the clinical management of hyperthyroidism.     

Integration of wavelet transform with PCA and ANN for metabolomics data-mining

PCA (principal components analysis) and ANN (artificial neural network) are two broadly used pattern recognition methods in metabolomics data-mining. Yet their limitations sometimes are great obstacles for researchers. In this paper the wavelet transform (WT) method was used to integrate with PCA and ANN to improve their performance in manipulating metabolomics data. A dataset was decomposed by wavelets and then reconstructed. The "hard thresholding" algorithm was used, through which the detail information was discarded, and the entire "metabolomics image" reconstructed on the significant information. It was supposed that the most relevant information was captured after this process. It was found that, thanks to its ability in denoising data, the WT method could significantly improve the performance of the non-linear essence-extracting method ANN in classifying samples; further integration of WT with PCA showed that WT could greatly enhance the ability of PCA in distinguishing one group of samples from another and also its ability in identifying potential biomarkers. The results highlighted WT as a promising resolution in bridging the gap between huge bytes of data and the instructive biological information.     

Nanostructure Initiator Mass Spectrometry for tissue imaging in metabolomics: future prospects and perspectives

Mass spectrometry-based metabolomics provides a new approach to interrogate mechanistic biochemistry related to natural processes such as health and disease. Physiological and pathological conditions, however, are characterized not only by the identities and concentrations of metabolites present, but also by the location of metabolites within a tissue. Unfortunately, most relevant MS platforms in metabolomics can only measure samples in solution, therefore metabolites are typically extracted by tissue homogenization. Recent developments of imaging-MS technologies, however, have allowed particular metabolites to be spatially localized within biological tissues. In this context, Nanostructure-Initiator Mass Spectrometry (NIMS), a matrix-free technique for surface-based analysis, has proven an alternative approach for tissue imaging of metabolites. Here we review the basic principles of NIMS for tissue imaging and show applications that can complement LC/MS and GC/MS-based metabolomic studies investigating the mechanisms of fundamental biological processes. In addition, the new surface modifications and nanostructured materials herein presented demonstrate the versatility of NIMS surface to expand the range of detectable metabolites.     

Identification of anthocyanins in the fruits of Kadsura coccinea using UPLC-MS/MS-based metabolomics

Fruit color is thought to be an adaptation to different animal pollinators, the fruits of Kadsura coccinea present diverse colors but the metabolic mechanism of the differences in color still needs further clarification. Here, we performed ultra-performance liquid chromatography-tandem mass spectrometry based on targeted metabolome analysis of the fruits of two K. coccinea cultivars, ‘Dahong No. 1’ (red-peel) and ‘Jinhu’ (yellow-peel). A total of seventeen anthocyanins were identified in the fruit peels of ‘Dahong No. 1’, whereas no anthocyanins were detected in ‘Jinhu’. Our results suggest that the color differences between the two cultivars can be explained by variations in abundance of cyanidin 3-O-rutinoside, cyanidin 3-O-glucoside and delphinidin 3-O-glucoside, accounting for 91.64% of the content of total anthocyanins. This study provides new insights into the underlying metabolic causes of color variation in K. coccinea fruits, and thus provides a theoretical basis for the development and utilization of K. coccinea fruits in the future.     

Recent developments in adenosine receptor ligands and their potential as novel drugs

Medicinal chemical approaches have been applied to all four of the adenosine receptor (AR) subtypes (A(1), A(2A), A(2B), and A(3)) to create selective agonists and antagonists for each. The most recent class of selective AR ligands to be reported is the class of A(2B)AR agonists. The availability of these selective ligands has facilitated research on therapeutic applications of modulating the ARs and in some cases has provided clinical candidates. Prodrug approaches have been developed which improve the bioavailability of the drugs, reduce side-effects, and/or may lead to site-selective effects. The A(2A) agonist regadenoson (Lexiscan?), a diagnostic drug for myocardial perfusion imaging, is the first selective AR agonist to be approved. Other selective agonists and antagonists are or were undergoing clinical trials for a broad range of indications, including capadenoson and tecadenoson (A(1) agonists) for atrial fibrillation, or paroxysmal supraventricular tachycardia, respectively, apadenoson and binodenoson (A(2A) agonists) for myocardial perfusion imaging, preladenant (A(2A) antagonist) for the treatment of Parkinson's disease, and CF101 and CF102 (A(3) agonists) for inflammatory diseases and cancer, respectively.