Metabolite profiling and fingerprinting of commercial cultivars of Humulus lupulus L. (hop): a comparison of MS and NMR methods in metabolomics

Hop (Humulus lupulus L. Cannabaceae) is an economically important crop. In addition to its role in beer brewing, its pharmaceutical applications have been of increasing importance in recent years. Bitter acids (prenylated polyketides), prenylflavonoids and essential oils, are the primary phytochemical components that account for hop medicinal value. An integrated approach utilizing nuclear magnetic resonance (NMR) and mass spectrometry (MS) techniques was used for the first large-scale metabolite profiling in Humulus lupulus. Resins and extracts prepared from 13 hop cultivars were analysed using NMR, liquid chromatography (LC)-MS and fourier transform ion cyclotron resonance (FTICR)-MS in parallel and subjected to principal component analysis (PCA). A one pot extraction method, compatible with both MS and NMR measurement was developed to help rule out effects due to differences in extraction protocols. Under optimised conditions, we were able to simultaneously quantify and identify 46 metabolites including 18 bitter acids, 12 flavonoids, 3 terpenes, 3 fatty acids and 2 sugars. Cultivars segregation in PCA plots generated from both LC-MS and NMR data were found comparable and mostly influenced by differences in bitter acids composition among cultivars. FTICR-MS showed inconsistent PCA loading plot results which are likely due to preferential ionisation and also point to the presence of novel isoprenylated metabolites in hop. This comparative metabolomic approach provided new insights for the complementariness and coincidence for these different technology platform applications in hop and similar plant metabolomics projects.     

The human serum metabolome

Continuing improvements in analytical technology along with an increased interest in performing comprehensive, quantitative metabolic profiling, is leading to increased interest pressures within the metabolomics community to develop centralized metabolite reference resources for certain clinically important biofluids, such as cerebrospinal fluid, urine and blood. As part of an ongoing effort to systematically characterize the human metabolome through the Human Metabolome Project, we have undertaken the task of characterizing the human serum metabolome. In doing so, we have combined targeted and non-targeted NMR, GC-MS and LC-MS methods with computer-aided literature mining to identify and quantify a comprehensive, if not absolutely complete, set of metabolites commonly detected and quantified (with today's technology) in the human serum metabolome. Our use of multiple metabolomics platforms and technologies allowed us to substantially enhance the level of metabolome coverage while critically assessing the relative strengths and weaknesses of these platforms or technologies. Tables containing the complete set of 4229 confirmed and highly probable human serum compounds, their concentrations, related literature references and links to their known disease associations are freely available at http://www.serummetabolome.ca.     

An overview of NMR-based metabolomics to identify secondary plant compounds involved in host plant resistance

Secondary metabolites provide a potential source for the generation of host plant resistance and development of biopesticides. This is especially important in view of the rapid and vast spread of agricultural and horticultural pests worldwide. Multiple pests control tactics in the framework of an integrated pest management (IPM) programme are necessary. One important strategy of IPM is the use of chemical host plant resistance. Up to now the study of chemical host plant resistance has, for technical reasons, been restricted to the identification of single compounds applying specific chemical analyses adapted to the compound in question. In biological processes however, usually more than one compound is involved. Metabolomics allows the simultaneous detection of a wide range of compounds, providing an immediate image of the metabolome of a plant. One of the most universally used metabolomic approaches comprises nuclear magnetic resonance spectroscopy (NMR). It has been NMR which has been applied as a proof of principle to show that metabolomics can constitute a major advancement in the study of host plant resistance. Here we give an overview on the application of NMR to identify candidate compounds for host plant resistance. We focus on host plant resistance to western flower thrips (Frankliniella occidentalis) which has been used as a model for different plant species.     

Illuminating the dark metabolome to advance the molecular characterisation of biological systems

Background

The latest version of the Human Metabolome Database (v4.0) lists 114,100 individual entries. Typically, however, metabolomics studies identify only around 100 compounds and many features identified in mass spectra are listed only as ‘unknown compounds’. The lack of ability to detect all metabolites present, and fully identify all metabolites detected (the dark metabolome) means that, despite the great contribution of metabolomics to a range of areas in the last decade, a significant amount of useful information from publically funded studies is being lost or unused each year. This loss of data limits our potential gain in knowledge and understanding of important research areas such as cell biology, environmental pollution, plant science, food chemistry and health and biomedical research. Metabolomics therefore needs to develop new tools and methods for metabolite identification to advance as a field.

Aim of review

In this critical review, some potential issues with metabolite identification are identified and discussed. New and novel emerging technologies and tools which may contribute to expanding the number of compounds identified in metabolomics studies (thus illuminating the dark metabolome) are reviewed. The aim is to stimulate debate and research in the molecular characterisation of biological systems to drive forward metabolomic research.

Key scientific concepts of review

The work specifically discusses dynamic nuclear polarisation nuclear magnetic resonance spectroscopy (DNP-NMR), non-proton NMR active nuclei, two-dimensional liquid chromatography (2DLC) and Raman spectroscopy (RS). It is suggested that developing new methods for metabolomics with these techniques could lead to advances in the field and better characterisation of biological systems.

     

Prediction of complex phenotypes using the Drosophila melanogaster metabolome

Understanding the genotype–phenotype map and how variation at different levels of biological organization is associated are central topics in modern biology. Fast developments in sequencing technologies and other molecular omic tools enable researchers to obtain detailed information on variation at DNA level and on intermediate endophenotypes, such as RNA, proteins and metabolites. This can facilitate our understanding of the link between genotypes and molecular and functional organismal phenotypes. Here, we use the Drosophila melanogaster Genetic Reference Panel and nuclear magnetic resonance (NMR) metabolomics to investigate the ability of the metabolome to predict organismal phenotypes. We performed NMR metabolomics on four replicate pools of male flies from each of 170 different isogenic lines. Our results show that metabolite profiles are variable among the investigated lines and that this variation is highly heritable. Second, we identify genes associated with metabolome variation. Third, using the metabolome gave better prediction accuracies than genomic information for four of five quantitative traits analyzed. Our comprehensive characterization of population-scale diversity of metabolomes and its genetic basis illustrates that metabolites have large potential as predictors of organismal phenotypes. This finding is of great importance, e.g., in human medicine, evolutionary biology and animal and plant breeding.Subject terms: Quantitative trait, Genetic association study     

Metabolomic Analysis of Rat Brain by High Resolution Nuclear Magnetic Resonance Spectroscopy of Tissue Extracts

Studies of gene expression on the RNA and protein levels have long been used to explore biological processes underlying disease. More recently, genomics and proteomics have been complemented by comprehensive quantitative analysis of the metabolite pool present in biological systems. This strategy, termed metabolomics, strives to provide a global characterization of the small-molecule complement involved in metabolism. While the genome and the proteome define the tasks cells can perform, the metabolome is part of the actual phenotype. Among the methods currently used in metabolomics, spectroscopic techniques are of special interest because they allow one to simultaneously analyze a large number of metabolites without prior selection for specific biochemical pathways, thus enabling a broad unbiased approach. Here, an optimized experimental protocol for metabolomic analysis by high-resolution NMR spectroscopy is presented, which is the method of choice for efficient quantification of tissue metabolites. Important strengths of this method are (i) the use of crude extracts, without the need to purify the sample and/or separate metabolites; (ii) the intrinsically quantitative nature of NMR, permitting quantitation of all metabolites represented by an NMR spectrum with one reference compound only; and (iii) the nondestructive nature of NMR enabling repeated use of the same sample for multiple measurements. The dynamic range of metabolite concentrations that can be covered is considerable due to the linear response of NMR signals, although metabolites occurring at extremely low concentrations may be difficult to detect. For the least abundant compounds, the highly sensitive mass spectrometry method may be advantageous although this technique requires more intricate sample preparation and quantification procedures than NMR spectroscopy. We present here an NMR protocol adjusted to rat brain analysis; however, the same protocol can be applied to other tissues with minor modifications.     

Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism

With the rapid progress in metabolomics and sequencing technologies, more data on the metabolome of single microbes and their communities become available, revealing the potential of microorganisms to metabolize a broad range of chemical compounds. The analysis of microbial metabolomics datasets remains challenging since it inherits the technical challenges of metabolomics analysis, such as compound identification and annotation, while harboring challenges in data interpretation, such as distinguishing metabolite sources in mixed samples. This review outlines the recent advances in computational methods to analyze primary microbial metabolism: knowledge-based approaches that take advantage of metabolic and molecular networks and data-driven approaches that employ machine/deep learning algorithms in combination with large-scale datasets. These methods aim at improving metabolite identification and disentangling reciprocal interactions between microbes and metabolites. We also discuss the perspective of combining these approaches and further developments required to advance the investigation of primary metabolism in mixed microbial samples.     

Assessment of 1H NMR spectroscopy and multivariate analysis as a technique for metabolite fingerprinting of Arabidopsis thaliana

An approach to metabolite fingerprinting of crude plant extracts that utilizes 1H nuclear magnetic resonance (NMR) spectroscopy and multivariate statistics has been tested. Using ecotypes of Arabidopsis thaliana as experimental material, a method has been developed for the rapid analysis of unfractionated polar plant extracts, enabling the creation of reproducible metabolite fingerprints. These fingerprints could be readily stored and compared by a variety of chemometric methods. Comparison by principal component analysis using SIMCA-P allowed the generation of residual NMR spectra of the compounds that contributed significantly to the differences between samples. From these plots, conclusions were drawn with respect to the identity and relative levels of metabolites differing between samples.     

New insights into cardiovascular and lipid metabolomics

Metabolomics is the study of metabolite profiles in biological samples, particularly urine, saliva, blood plasma and fat biopsies. The metabolome is now considered by some to be the most predictive phenotype: consequently, the comprehensive and quantitative study of metabolites is a desirable tool for diagnosing disease, identifying new therapeutic targets and enabling appropriate treatments. A wealth of information about metabolites has been accumulated with global profiling tools and several candidate technologies for metabolomic studies are now available. Many high-throughput metabolomics methodologies are currently under development and have yet to be applied in clinical practice on a routine basis. In the cardiovascular field, few recent metabolomic studies have been reported so far. This minireview provides an updated overview of alternative technical approaches for metabolomics studies and reviews initial applications of metabolomics that relate to both cardiovascular disease and lipid metabolism research.     

Web-based resources for mass-spectrometry-based metabolomics: A user’s guide

In recent years, a plethora of web-based tools aimed at supporting mass-spectrometry-based metabolite profiling and metabolomics applications have appeared. Given the huge hurdles presented by the chemical diversity and dynamic range of the metabolites present in the plant kingdom, profiling the levels of a broad range of metabolites is highly challenging. Given the scale and costs involved in defining the plant metabolome, it is imperative that data are effectively shared between laboratories pursuing this goal. However, ensuring accurate comparison of samples run on the same machine within the same laboratory, let alone cross-machine and cross-laboratory comparisons, requires both careful experimentation and data interpretation. In this review, we present an overview of currently available software that aids either in peak identification or in the related field of peak alignment as well as those with utility in defining structural information of compounds and metabolic pathways.     

植物代谢组学的研究方法及其应用

代谢组是指某一生物或细胞在一特定生理时期内所有的低分子量代谢产物。植物代谢组学是指对植物抽提物中代谢组进行高通量、无偏差全面分析的技术。近年来, 植物代谢组学研究取得了很大进展。本文介绍了其含义、历史沿革及研究方法, 并用典型实例阐释了它的应用方向。     

Metabolomics

Metabolomics is the systematic identification and quantitation of all metabolites in a given organism or biological sample. The enhanced resolution provided by nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), along with powerful chemometric software, allows the simultaneous determination and comparison of thousands of chemical entities, which has lead to an expansion of small molecule biochemistry studies in bacteria, plants, and mammals. Continued development of these analytical platforms will accelerate the widespread use of metabolomics and allow further integration of small molecules into systems biology. Here, recent studies using metabolomics in xenobiotic metabolism and genetically modified mice are highlighted.     

Rhynchophorus ferrugineus attack affects a group of compounds rather than rearranging Phoenix canariensis metabolic pathways

The red palm weevil（RPW; Rhynchophorus ferrugineus） is spreading worldwide and severely harming many palm species. However, most studies on RPW focused on insect biology, and little information is available about the plant response to the attack. In the present experiment, we used metabolomics to study the alteration of the leaf metabolome of Phoenix canariensis at initial(1^st stage) or advanced(2^nd stage)attack by RPW compared with healthy（unattacked） plants.The leaf metabolome significantly varied among treatments. At the 1^st stage of attack, plants showed a reprogramming of carbohydrate and organic acid metabolism; in contrast, peptides and lipid metabolic pathways underwent more changes during the 2^nd than 1^st stage of attack. Enrichment metabolomics analysis indicated that RPW attack mostly affected a particular group of compounds rather than rearranging plant metabolic pathways. Some compounds selectively affected during the 1^st rather than 2^nd stage（e.g. phenylalanine; tryptophan; cellobiose;xylose; quinate; xylonite; idonate; and iso-threonate; cellobiotol and arbutine） are upstream events in the phenylpropanoid,terpenoid and alkaloid biosynthesis. These compounds could be designated as potential markers of initial RPW attack. However,further investigation is needed to determine efficient early screening methods of RPW attack based on the concentrations of these molecules.     

Stable isotope-resolved metabolomics (SIRM) in cancer research with clinical application to nonsmall cell lung cancer

Metabolomics provides a readout of the state of metabolism in cells or tissue and their responses to external perturbations. For this reason, the approach has great potential in clinical diagnostics. Clinical metabolomics using stable isotope resolved metabolomics (SIRM) for pathway tracing represents an important new approach to obtaining metabolic parameters in human cancer subjects in situ. Here we provide an overview of the technology development of labeling from cells in culture and mouse models. The high throughput analytical methods NMR and mass spectrometry, especially Fourier transform ion cyclotron resonance, for analyzing the resulting metabolite isotopomers and isotopologues are described with examples of applications in cancer biology. Special technical considerations for clinical applications of metabolomics using stable isotope tracers are described. The whole process from concept to analysis will be exemplified by our on-going study of nonsmall cell lung cancer (NSCLC) metabolomics. This powerful new approach has already provided important new insights into metabolic adaptations in lung cancer cells, including the upregulation of anaplerosis via pyruvate carboxylation in NSCLC.     

植物代谢组学的研究方法及其应用

代谢组是指某一生物或细胞在一特定生理时期内所有的低分子量代谢产物.植物代谢组学是指对植物抽提物中代谢组进行高通量、无偏差全面分析的技术.近年来,植物代谢组学研究取得了很大进展.本文介绍了其含义、历史沿革及研究方法,并用典型实例阐释了它的应用方向.