Plant metabolomics: towards biological function and mechanism

Metabolite profiling is a fast growing technology and is useful for phenotyping and diagnostic analyses of plants. It is also rapidly becoming a key tool in functional annotation of genes and in the comprehensive understanding of the cellular response to biological conditions. Metabolomics approaches have recently been used to assess the natural variance in metabolite content between individual plants, an approach with great potential for the improvement of the compositional quality of crops. Here, we assess the contribution of metabolite profiling to these areas.     

Identification of novel lignans in the whole grain rye bran by non-targeted LC–MS metabolite profiling

Rye (Secale cereale) is among the richest dietary sources of lignan phytochemicals. Lignans are one of the suggested metabolite groups to contribute to the beneficial health effects of whole grain products evidenced in epidemiological studies. So far, the complete repertoire of lignan derivatives in rye, especially in the bran, has not been fully described. In this study, ten novel oligomeric sesqui- and dilignans were identified in rye bran by the use of high resolution LC?CMS analysis (i.e., UPLC-qTOF-MS/MS). Putative identification of lignan components in the bran was performed by combining: (i) detailed inspection of the fragmentation behavior of available standard compounds belonging to different lignan types, (ii) interpretation of MS/MS data obtained from unknown metabolites in the samples. This combined analysis, particularly detailed MS/MS characterization, is most valuable for non-targeted assays in metabolite-rich matrices such as plant extracts, in which the verification of identity with authentic standards for each detected metabolite is normally not possible. Metabolomics analysis will increasingly aid in deciphering the active compounds in dietary products as part of studies aiming at elucidating the link between human health and nutrition.     

Comprehensive optimization of the metabolomic methodology for metabolite profiling of Corynebacterium glutamicum

Applied Microbiology and Biotechnology - Metabolomics has been a potential tool for strain improvement through analyzing metabolite changes in the context of different conditions. However, the...     

Analytical metabolomics: nutritional opportunities for personalized health

Nutrition is the cornerstone of health; survival depends on acquiring essential nutrients, and dietary components can both prevent and promote disease. Metabolomics, the study of all small molecule metabolic products in a system, has been shown to provide a detailed snapshot of the body's processes at any particular point in time, opening up the possibility of monitoring health and disease, prevention and treatment. Metabolomics has the potential to fundamentally change clinical chemistry and, by extension, the fields of nutrition, toxicology and medicine. Technological advances, combined with new knowledge of the human genome and gut microbiome, have made and will continue to make possible earlier, more accurate, less invasive diagnoses, all while enhancing our understanding of the root causes of disease and leading to a generation of dietary recommendations that enable optimal health. This article reviews the recent contributions of metabolomics to the fields of nutrition, toxicology and medicine. It is expected that these fields will eventually blend together through development of new technologies in metabolomics and genomics into a new area of clinical chemistry: personalized medicine.     

Associations between thyroid hormones and serum metabolite profiles in an euthyroid population

Metabolomics - The aim was to characterise associations between circulating thyroid hormones—free thyroxine (FT4) and thyrotropin (TSH)—and the metabolite profiles in serum samples from...     

代谢物组学方法及其在植物学研究中的应用

代谢物是生物体受遗传控制和环境影响的最终表达产物,以全体代谢物(代谢物组)为研究对象的代谢物组学是继基因组学和蛋白质组学后必然出现的又一门"组学"技术。该文综述了代谢物组的检测、数据的处理和分析等以及这些技术在植物目标分析、基因功能、代谢途径和代谢工程、整合植物学、信号转导等研究中的应用和前景。     

Differential metabolomics reveals ophthalmic acid as an oxidative stress biomarker indicating hepatic glutathione consumption

Metabolomics is an emerging tool that can be used to gain insights into cellular and physiological responses. Here we present a metabolome differential display method based on capillary electrophoresis time-of-flight mass spectrometry to profile liver metabolites following acetaminophen-induced hepatotoxicity. We globally detected 1,859 peaks in mouse liver extracts and highlighted multiple changes in metabolite levels, including an activation of the ophthalmate biosynthesis pathway. We confirmed that ophthalmate was synthesized from 2-aminobutyrate through consecutive reactions with gamma-glutamylcysteine and glutathione synthetase. Changes in ophthalmate level in mouse serum and liver extracts were closely correlated and ophthalmate levels increased significantly in conjunction with glutathione consumption. Overall, our results provide a broad picture of hepatic metabolite changes following acetaminophen treatment. In addition, we specifically found that serum ophthalmate is a sensitive indicator of hepatic GSH depletion, and may be a new biomarker for oxidative stress. Our method can thus pinpoint specific metabolite changes and provide insights into the perturbation of metabolic pathways on a large scale and serve as a powerful new tool for discovering low molecular weight biomarkers.     

Optimization of a sample preparation method for the metabolomic analysis of clinically relevant bacteria

Metabolomics, or metabolite profiling, is an approach that is increasingly used to study the metabolism of diverse organisms, elucidate biological processes and/or find characteristic biomarkers of physiological states. Here, we describe the optimization of a method for global metabolomic analysis of bacterial cultures, with the following steps. Cells are grown to log-phase, starting from an overnight culture and bacterial concentrations are monitored by measuring the optical density of the cultures at 600 nm. At an appropriate density they are harvested by centrifugation, washed three times with NaCl solution and metabolites are extracted using methanol and a bead-mill. Dried extracts are methoxymated and derivatized with methyltrimethylsilyltrifluoroacetamide (MSTFA) then analyzed using gas chromatography coupled to time-of-flight mass spectrometry (GC-MS/TOF). Finally, patterns in the acquired data are examined by multivariate data modeling. This method enabled us to obtain reproducible metabolite profiles of Yersinia pseudotuberculosis, with about 25% compound identification, based on comparison with entries in available GC-MS libraries. To assess the potential utility of the method for comparative analysis of other bacterial species we analyzed cultures of Pseudomonas aeruginosa, Salmonella typhimurium, Escherichia coli and methicillin-sensitive Staphylococcus aureus (MSSA). Multivariate analysis of the acquired data showed that it was possible to differentiate the species according to their metabolic profiles. Our results show that the presented procedure can be used for metabolomic analysis of a wide range of bacterial species of clinical interest.     

Toward better annotation in plant metabolomics: isolation and structure elucidation of 36 specialized metabolites from Oryza sativa (rice) by using MS/MS and NMR analyses

Metabolomics plays an important role in phytochemical genomics and crop breeding; however, metabolite annotation is a significant bottleneck in metabolomic studies. In particular, in liquid chromatography–mass spectrometry (MS)-based metabolomics, which has become a routine technology for the profiling of plant-specialized metabolites, a substantial number of metabolites detected as MS peaks are still not assigned properly to a single metabolite. Oryza sativa (rice) is one of the most important staple crops in the world. In the present study, we isolated and elucidated the structures of specialized metabolites from rice by using MS/MS and NMR. Thirty-six compounds, including five new flavonoids and eight rare flavonolignan isomers, were isolated from the rice leaves. The MS/MS spectral data of the isolated compounds, with a detailed interpretation of MS fragmentation data, will facilitate metabolite annotation of the related phytochemicals by enriching the public mass spectral data depositories, including the plant-specific MS/MS-based database, ReSpect.     

Fungal metabolite analysis in genomics and phenomics

Metabolomics consists of strategies to quantitatively identify cellular metabolites and to understand how trafficking of these biochemical messengers through the metabolic network influences phenotype. The application of metabolomics to fungi has been strongly pursued because these organisms are widely used for the production of chemicals, are well known for their diverse metabolic landscape and serve as excellent eukaryotic model organisms for studying metabolism and systems biology. Within the context of fungal systems, recent progress has been made in the development of analytical tools and mathematical strategies used in metabolite analysis that have enhanced our ability to crack the code underpinning the cellular inventory, regulatory schemes and communication mechanisms that dictate cellular function. Metabolomics has played a key role in functional genomics and strain classification.     

Metabolomics meets lipidomics: Assessing the small molecule component of metabolism

Metabolomics, including lipidomics, is emerging as a quantitative biology approach for the assessment of energy flow through metabolism and information flow through metabolic signaling; thus, providing novel insights into metabolism and its regulation, in health, healthy ageing and disease. In this forward-looking review we provide an overview on the origins of metabolomics, on its role in this postgenomic era of biochemistry and its application to investigate metabolite role and (bio)activity, from model systems to human population studies. We present the challenges inherent to this analytical science, and approaches and modes of analysis that are used to resolve, characterize and measure the infinite chemical diversity contained in the metabolome (including lipidome) of complex biological matrices. In the current outbreak of metabolic diseases such as cardiometabolic disorders, cancer and neurodegenerative diseases, metabolomics appears to be ideally situated for the investigation of disease pathophysiology from a metabolite perspective.     

Effects of dietary macronutrient composition on the fasted plasma metabolome of healthy adult cats

Metabolomics assays have recently been used in humans for the identification of biomarkers for dietary assessment and diseases. The application of metabolomics to feline nutrition, however, has been very limited. The objective of this study was to identify how the feline blood metabolome changed in response to dietary macronutrient composition. Twelve adult domestic cats were fed four nutritionally complete diets [control, high-fat (HF), high-protein (HP), high-carbohydrate (HC)] at amounts to maintain ideal body weight and body condition score for 16 days. Overnight fasted plasma samples were collected on day 16 and subjected to liquid/gas chromatography and mass spectrometry. Principal component analysis showed that metabolite profiles of cats fed HP, HF, and HC dietary regimes formed distinct clusters. Cats fed the HP diet had a metabolite profile associated with decreased nucleotide catabolism, but increased amino acid metabolism and ketone bodies, indicating a greater use of protein and fat for energy. Cats fed the HP diet had a significant increase in metabolites associated with gut microbial metabolism. Cats fed the HF diet had metabolites indicative of increased lipid metabolism, including free fatty acids, monoacylglycerols, glycerol-3-phosphate, cholesterol, ketone bodies, and markers of oxidative stress. γ-glutamylleucine, 3-hydroxyisobutyrate, and 3-indoxyl sulfate were identified by random forest analysis to distinguish cats fed the three macronutrient-rich diets. In conclusion, macronutrient-rich diets primarily altered markers of amino acid and lipid metabolism, with little changes in markers of carbohydrate and energy metabolism. Moreover, the HP diet influenced several metabolites originating from gut microbial metabolism.     

Effect of milk replacer and concentrate intake on growth rate,feeding behaviour and systemic metabolite concentrations of pre-weaned bull calves of two dairy breeds

Early-life nutrition affects calf development and thus subsequent performance. The aim of this study was to examine the effect plane of nutrition on growth, feeding behaviour and systemic metabolite concentrations of artificially reared dairy bull calves. Holstein-Friesian (F; n=42) and Jersey (J; n=25) bull calves with a mean±SD age (14±4.7 v. 27±7.2 days) and BW (47±5.5 v. 33±4.7 kg) were offered a high, medium or low plane of nutrition for 8 weeks using an electronic feeding system which recorded a range of feed-related events. Calves were weighed weekly and plasma samples were collected via jugular venipuncture on weeks 1, 4 and 7 relative to the start of the trial period. The calves offered a high plane of nutrition had the greatest growth rate. However, the increased consumption of milk replacer led to a reduction in feed efficiency. Holstein-Friesian calves offered a low plane of nutrition had the greatest number of daily unrewarded visits to the feeder (P<0.001). β-hydroxybutyrate (BHB) concentrations were greater in F calves on a low plane of nutrition (P<0.001). Although there was no effect of plane of nutrition, BHB concentrations in F calves increased before weaning, concomitant with an increase in concentrate consumption. Urea concentrations were unaffected by plane of nutrition within either breed. Jersey calves on a low plane of nutrition tended to have lower triglycerides than those on a high plane (P=0.08), but greater than those on a medium plane (P=0.08). Holstein-Friesian calves offered a high plane of nutrition tended to have greater triglyceride concentrations than those on a medium plane (P=0.08). Triglycerides increased from the start to the end of the feeding period (P<0.05), across both breeds. A medium plane of nutrition resulted in a growth, feeding behaviour and metabolic response comparable with a high plane of nutrition in pre-weaned bull calves of both F and J breeds.     

Metabolite signal identification in accurate mass metabolomics data with MZedDB, an interactive m/z annotation tool utilising predicted ionisation behaviour 'rules'

Background  

Metabolomics experiments using Mass Spectrometry (MS) technology measure the mass to charge ratio (m/z) and intensity of ionised molecules in crude extracts of complex biological samples to generate high dimensional metabolite 'fingerprint' or metabolite 'profile' data. High resolution MS instruments perform routinely with a mass accuracy of < 5 ppm (parts per million) thus providing potentially a direct method for signal putative annotation using databases containing metabolite mass information. Most database interfaces support only simple queries with the default assumption that molecules either gain or lose a single proton when ionised. In reality the annotation process is confounded by the fact that many ionisation products will be not only molecular isotopes but also salt/solvent adducts and neutral loss fragments of original metabolites. This report describes an annotation strategy that will allow searching based on all potential ionisation products predicted to form during electrospray ionisation (ESI).     

Understanding mouse models of disease through metabolomics

Metabolomics is widely applicable to a number of fields including toxicology, plant metabolism and functional genomics. In the area of functional genomics, a number of studies have demonstrated the potential of this approach, which combines high-throughput metabolite profiling with computer-assisted pattern recognition approaches. In this review, recent applications of metabolomics to understanding mouse models of disease are considered. This includes studies on the impact of mouse strain on disease models, as well as metabolic profiling of cardiovascular, metabolic and neurodegenerative diseases. This versatile tool is set to increase in popularity as functional genomic approaches produce more mouse models for phenotyping.     

Mass appeal: metabolite identification in mass spectrometry-focused untargeted metabolomics

Metabolomics has advanced significantly in the past 10 years with important developments related to hardware, software and methodologies and an increasing complexity of applications. In discovery-based investigations, applying untargeted analytical methods, thousands of metabolites can be detected with no or limited prior knowledge of the metabolite composition of samples. In these cases, metabolite identification is required following data acquisition and processing. Currently, the process of metabolite identification in untargeted metabolomic studies is a significant bottleneck in deriving biological knowledge from metabolomic studies. In this review we highlight the different traditional and emerging tools and strategies applied to identify subsets of metabolites detected in untargeted metabolomic studies applying various mass spectrometry platforms. We indicate the workflows which are routinely applied and highlight the current limitations which need to be overcome to provide efficient, accurate and robust identification of metabolites in untargeted metabolomic studies. These workflows apply to the identification of metabolites, for which the structure can be assigned based on entries in databases, and for those which are not yet stored in databases and which require a de novo structure elucidation.

     

Plant metabolomics: large-scale phytochemistry in the functional genomics era

Metabolomics or the large-scale phytochemical analysis of plants is reviewed in relation to functional genomics and systems biology. A historical account of the introduction and evolution of metabolite profiling into today's modern comprehensive metabolomics approach is provided. Many of the technologies used in metabolomics, including optical spectroscopy, nuclear magnetic resonance, and mass spectrometry are surveyed. The critical role of bioinformatics and various methods of data visualization are summarized and the future role of metabolomics in plant science assessed.