Explanatory signal interpretation and metabolite identification strategies for nominal mass FIE-MS metabolite fingerprints

A chemical analysis of the Pelargonium species: P. odoratissimum, P. graveolens, and P. zonale identifies secondary metabolites with activity against gram-positive bacteria with multidrug-resistance

The Pelargonium genus encompasses around 280 species, most of which are used for medicinal purposes. While P. graveolens, P. odoratissimum, and P. zonale are known to exhibit antimicrobial activity, there is an evident absence of studies evaluating all three species to understand their chemical differences and biological effects. Through the analysis of the hydroalcoholic extracts of P. graveolens, P. odoratissimum, and P. zonale, using HPLC-DAD-MS/MS, quercetin and kaempferol derivatives were identified in these three species. Conversely, gallotannins and anthocyanins were uniquely detected in P. zonale. P. graveolens stood out due to the various types of myricetin derivatives that were not detected in P. odoratissimum and P. zonale extracts. Evaluation of their biological activities revealed that P. zonale displayed superior antibacterial and antibiofilm activities in comparison to the other two species. The antibacterial efficacy of P. zonale was observed towards the clinically relevant strains of Staphylococcus aureus ATCC 25923, Methicillin-resistant Staphylococcus aureus (MRSA) 333, Enterococcus faecalis ATCC 29212, and the Vancomycin-resistant E. faecalis INSPI 032. Fractionation analysis of P. zonale suggested that the antibacterial activity attributed to this plant is due to the presence of quercetin derivatives and kaempferol and its derivatives, alongside their synergistic interaction with gallotannins and anthocyanins. Lastly, the three Pelargonium species exhibited notable antioxidant activity, which may be attributed to their high content of total phenolic compounds.

Metabolomic Changes in Naturally MAP-Infected Holstein-Friesian Heifers Indicate Immunologically Related Biochemical Reprogramming

Johne’s disease, caused by Mycobacterium avium subsp. paratuberculosis (MAP), causes weight loss, diarrhoea, and reduced milk yields in clinically infected cattle. Asymptomatic, subclinically infected cattle shed MAP bacteria but are frequently not detected by diagnostic tests. Herein, we compare the metabolite profiles of sera from subclinically infected Holstein–Friesian heifers and antibody binding to selected MAP antigens. The study used biobanked serum samples from 10 naturally MAP-infected and 10 control heifers, sampled monthly from ~1 to 19 months of age. Sera were assessed using flow infusion electrospray–high-resolution mass spectrometry (FIE–HRMS) on a Q Exactive hybrid quadrupole–Orbitrap mass spectrometer for high-throughput, sensitive, non-targeted metabolite fingerprinting. Partial least-squares discriminant analyses (PLS-DA) and hierarchical cluster analysis (HCA) of the data discriminated between naturally MAP-infected and control heifers. In total, 33 metabolites that differentially accumulated in naturally MAP-infected heifers compared to controls were identified. Five were significantly elevated within MAP-infected heifers throughout the study, i.e., leukotriene B4, bicyclo prostaglandin E2 (bicyclo PGE2), itaconic acid, 2-hydroxyglutaric acid and N6-acetyl-L-lysine. These findings highlight the potential of metabolomics in the identification of novel MAP diagnostic markers and particular biochemical pathways, which may provide insights into the bovine immune response to MAP.

From untargeted metabolomics to the multiple reaction monitoring-based quantification of polyphenols in chocolates from different geographical areas

Plants, including cocoa bean, are the main source of metabolites with multiple biological functions. Polyphenol extracts are widely used as a nutraceutical supplement for their well-known health-promoting role. In this paper, a preliminary untargeted metabolic screening was carried out by matrix-assisted laser desorption/ionization (MALDI)-time of flight (TOF)/TOF on a pool of chocolate samples made by cocoa beans of different geographical areas. Then, a targeted approach was developed for polyphenol quantification by an optimized Liquid chromatography (LC)-tandem mass spectrometry (MS/MS) method multiple reaction monitoring (MRM) ion mode. Detection limit of polyphenol standard ranged between 1 and 25 pg/μl with variation coefficient lower than 15%. External calibration curves were used for quantification of polyphenols in 18 samples. Fifty polyphenols were detected in a single LC-MRM/MS run and quantified by monitoring almost 90 transitions in a 5-minute run. The polyphenols content of different cocoa beans from several countries was finally compared by principal component analysis (PCA) statistical analysis suggesting that the chocolate made by Ecuador cocoa beans showed the highest level of polyphenols.

Advances in lung cancer biomarkers: The role of (metal-) metabolites and selenoproteins

Lung cancer (LC) is the second most common cause of death in men after prostate cancer, and the third most recurrent type of tumor in women after breast and colon cancers. Unfortunately, when LC symptoms begin to appear, the disease is already in an advanced stage and the survival rate only reaches 2%. Thus, there is an urgent need for early diagnosis of LC using specific biomarkers, as well as effective therapies and strategies against LC. On the other hand, the influence of metals on more than 50% of proteins is responsible for their catalytic properties or structure, and their presence in molecules is determined in many cases by the genome. Research has shown that redox metal dysregulation could be the basis for the onset and progression of LC disease. Moreover, metals can interact between them through antagonistic, synergistic and competitive mechanisms, and for this reason metals ratios and correlations in LC should be explored. One of the most studied antagonists against the toxic action of metals is selenium, which plays key roles in medicine, especially related to selenoproteins. The study of potential biomarkers able to diagnose the disease in early stage is conditioned by the development of new analytical methodologies. In this sense, omic methodologies like metallomics, proteomics and metabolomics can greatly assist in the discovery of biomarkers for LC early diagnosis.

Co-Culture Systems for the Production of Secondary Metabolites: Current and Future Prospects

Microorganisms are the great sources of Natural Products (NPs); these are imperative to their survival apart from conferring competitiveness amongst each other within their environmental niches. Primary and secondary metabolites are the two major classes of NPs that help in cell development, where antimicrobial activity is closely linked with secondary metabolites. To capitalize on the effects of secondary metabolites, co-culture methods have been often used to develop an artificial microbial community that promotes the action of these metabolites. Different analytical techniques will subsequently be employed based on the metabolite specificity and sensitivity to further enhance the metabolite induction. Liquid Chromatography-Mass Spectrometry (LC-MS) and Gas Chromatography (GC)-MS are commonly used for metabolite separation while Nuclear Magnetic Resonance (NMR) and Mass Spectrometry (MS) have been used as tools to elucidate the structure of compounds. This review intends to discuss current systems in use for co-culture in addition to its advantages, with discourse into the investigation of specific techniques in use for the detailed study of secondary metabolites. Further advancements and focus on co-culture technologies are required to fully realize the massive potential in synthetic biological systems.

Defining Metabolic Rewiring in Lung Squamous Cell Carcinoma

Metabolomics based on untargeted flow infusion electrospray ionization high-resolution mass spectrometry (FIE-HRMS) can provide a snap-shot of metabolism in living cells. Lung Squamous Cell Carcinoma (SCC) is one of the predominant subtypes of Non-Small Cell Lung Cancers (NSCLCs), which usually shows a poor prognosis. We analysed lung SCC samples and matched histologically normal lung tissues from eight patients. Metabolites were profiled by FIE-HRMS and assessed using t-test and principal component analysis (PCA). Differentially accumulating metabolites were mapped to pathways using the mummichog algorithm in R, and biologically meaningful patterns were indicated by Metabolite Set Enrichment Analysis (MSEA). We identified metabolic rewiring networks, including the suppression of the oxidative pentose pathway and found that the normal tricarboxylic acid (TCA) cycle were decoupled from increases in glycolysis and glutamine reductive carboxylation. Well-established associated effects on nucleotide, amino acid and thiol metabolism were also seen. Novel aspects in SCC tissue were increased in Vitamin B complex cofactors, serotonin and a reduction of γ-aminobutyric acid (GABA). Our results show the value of FIE-HRMS as a high throughput screening method that could be exploited in clinical contexts.

Untargeted metabolomics reveals a new mode of action of pretomanid (PA-824)

Pretomanid is a promising anti-tubercular drug currently at clinical phase III, but its mechanisms of action are currently unclear. This study aimed to: (i) reveal the metabolome of Mycobacterium smegmatis under pretomanid treatment; (ii) compare major sources of metabolite variation in bacteria treated with pretomanid treatment and other antibiotics; and (iii) to target metabolites responsible for the killing activity of pretomanid in mycobacteria. Untargeted high-resolution metabolite profiling was carried out using flow infusion electrospray ion high resolution mass spectrometry (FIE-HRMS) to identify and quantify metabolites. The identification of key metabolites was independently confirmed by gas-chromatography time-of flight mass spectrometry (GC-tofMS) in comparison to standards. Pretomanid treatments generated a unique distinctive metabolite profile when compared to ampicillin, ethambutol, ethionamide, isoniazid, kanamycin, linezolid, rifampicin and streptomycin. Metabolites which differed significantly only with pretomanid treatment were identified and mapped on to bacterial metabolic pathways. This targeted the pentose phosphate pathway with significant accumulation seen with fructose-6-phosphate, ribose-5-phosphate and glyceraldehyde-3-phosphate. These effects were linked to the accumulation of a toxic metabolite methylglyoxal. This compound showed significant antimicrobial activity (MIC 0.65 mM) against M. smegmatis.

Direct infusion mass spectrometry for metabolomic phenotyping of diseases

Metabolomics based on direct mass spectrometry (MS) analysis, either by direct infusion or flow injection of crude sample extracts, shows a great potential for metabolic fingerprinting because of its high-throughput screening capability, wide metabolite coverage and reduced time of analysis. Considering that numerous metabolic pathways are significantly perturbed during the initiation and progression of diseases, these metabolomic tools can be used to get a deeper understanding about disease pathogenesis and discover potential biomarkers for early diagnosis. In this work, we describe the most common metabolomic platforms used in biomedical research, with special focus on strategies based on direct MS analysis. Then, a comprehensive review on the application of direct MS fingerprinting in clinical issues is provided.

Dereplication: racing to speed up the natural products discovery process

Covering: 1993-2014 (July)To alleviate the dereplication holdup, which is a major bottleneck in natural products discovery, scientists have been conducting their research efforts to add tools to their "bag of tricks" aiming to achieve faster, more accurate and efficient ways to accelerate the pace of the drug discovery process. Consequently dereplication has become a hot topic presenting a huge publication boom since 2012, blending multidisciplinary fields in new ways that provide important conceptual and/or methodological advances, opening up pioneering research prospects in this field.

High resolution mass spectrometry based techniques at the crossroads of metabolic pathways

The metabolome is the set of small molecular mass compounds found in biological media, and metabolomics, which refers to as the analysis of metabolome in a given biological condition, deals with the large scale detection and quantification of metabolites in biological media. It is a data driven and multidisciplinary approach combining analytical chemistry for data acquisition, and biostatistics, informatics and biochemistry for mining and interpretation of these data. Since the middle of the 2000s, high resolution mass spectrometry is widely used in metabolomics, mainly because the detection and identification of metabolites are improved compared to low resolution instruments. As the field of HRMS is quickly and permanently evolving, the aim of this work is to review its use in different aspects of metabolomics, including data acquisition, metabolite annotation, identification and quantification. At last, we would like to show that, thanks to their versatility, HRMS instruments are the most appropriate to achieve optimal metabolome coverage, at the border of other omics fields such as lipidomics and glycomics.

The root extract of the medicinal plant Pelargonium sidoides is a potent HIV-1 attachment inhibitor

Global HIV-1 treatment would benefit greatly from safe herbal medicines with scientifically validated novel anti-HIV-1 activities. The root extract from the medicinal plant Pelargonium sidoides (PS) is licensed in Germany as the herbal medicine EPs®7630, with numerous clinical trials supporting its safety in humans. Here we provide evidence from multiple cell culture experiments that PS extract displays potent anti-HIV-1 activity. We show that PS extract protects peripheral blood mononuclear cells and macrophages from infection with various X4 and R5 tropic HIV-1 strains, including clinical isolates. Functional studies revealed that the extract from PS has a novel mode-of-action. It interferes directly with viral infectivity and blocks the attachment of HIV-1 particles to target cells, protecting them from virus entry. Analysis of the chemical footprint of anti-HIV activity indicates that HIV-1 inhibition is mediated by multiple polyphenolic compounds with low cytotoxicity and can be separated from other extract components with higher cytotoxicity. Based on our data and its excellent safety profile, we propose that PS extract represents a lead candidate for the development of a scientifically validated herbal medicine for anti-HIV-1 therapy with a mode-of-action different from and complementary to current single-molecule drugs.

Systematization of the protein sequence diversity in enzymes related to secondary metabolic pathways in plants, in the context of big data biology inspired by the KNApSAcK motorcycle database

Biology is increasingly becoming a data-intensive science with the recent progress of the omics fields, e.g. genomics, transcriptomics, proteomics and metabolomics. The species-metabolite relationship database, KNApSAcK Core, has been widely utilized and cited in metabolomics research, and chronological analysis of that research work has helped to reveal recent trends in metabolomics research. To meet the needs of these trends, the KNApSAcK database has been extended by incorporating a secondary metabolic pathway database called Motorcycle DB. We examined the enzyme sequence diversity related to secondary metabolism by means of batch-learning self-organizing maps (BL-SOMs). Initially, we constructed a map by using a big data matrix consisting of the frequencies of all possible dipeptides in the protein sequence segments of plants and bacteria. The enzyme sequence diversity of the secondary metabolic pathways was examined by identifying clusters of segments associated with certain enzyme groups in the resulting map. The extent of diversity of 15 secondary metabolic enzyme groups is discussed. Data-intensive approaches such as BL-SOM applied to big data matrices are needed for systematizing protein sequences. Handling big data has become an inevitable part of biology.

Data Mining Methods for Omics and Knowledge of Crude Medicinal Plants toward Big Data Biology

Molecular biological data has rapidly increased with the recent progress of the Omics fields, e.g., genomics, transcriptomics, proteomics and metabolomics that necessitates the development of databases and methods for efficient storage, retrieval, integration and analysis of massive data. The present study reviews the usage of KNApSAcK Family DB in metabolomics and related area, discusses several statistical methods for handling multivariate data and shows their application on Indonesian blended herbal medicines (Jamu) as a case study. Exploration using Biplot reveals many plants are rarely utilized while some plants are highly utilized toward specific efficacy. Furthermore, the ingredients of Jamu formulas are modeled using Partial Least Squares Discriminant Analysis (PLS-DA) in order to predict their efficacy. The plants used in each Jamu medicine served as the predictors, whereas the efficacy of each Jamu provided the responses. This model produces 71.6% correct classification in predicting efficacy. Permutation test then is used to determine plants that serve as main ingredients in Jamu formula by evaluating the significance of the PLS-DA coefficients. Next, in order to explain the role of plants that serve as main ingredients in Jamu medicines, information of pharmacological activity of the plants is added to the predictor block. Then N-PLS-DA model, multiway version of PLS-DA, is utilized to handle the three-dimensional array of the predictor block. The resulting N-PLS-DA model reveals that the effects of some pharmacological activities are specific for certain efficacy and the other activities are diverse toward many efficacies. Mathematical modeling introduced in the present study can be utilized in global analysis of big data targeting to reveal the underlying biology.

Fourier Transform Ion Cyclotron Resonance mass spectrometry for plant metabolite profiling and metabolite identification

Mass spectrometry (MS) is usually the technique of choice for metabolomic studies where the volume of sample material is too limited for applications employing nuclear magnetic resonance (NMR) spectroscopy. With the advent of ultra-high accuracy mass spectrometers such as the Orbitrap (resolution ∼ 10(5)) and the Fourier Transform Ion Cyclotron Resonance (FT-ICR) analysers (resolution potentially in excess of 10(6)) there is the opportunity to generate an accurate mass fingerprint (often referred to as a profile since the variables are considered as effectively discrete) of an infused sample extract. In such data representations mass "peaks" are detected in the raw data and the centroid mass intensity calculated. The resolving power and sensitivity of these ultra-high accuracy mass analysers is such that metabolite signals from molecules containing naturally abundant elemental isotopes (e.g. (13)C, (41)K, (15)N, (17)O, (34)S, and (37)Cl) are visible in the data. Such is the instruments precision that it allows for the calculation of highly accurate elemental compositions for the unknown signals, thus aiding greatly in the selection of potential metabolite candidates for the annotation of unknowns prior to their confirmation by comparisons to analytical standards. The application of FT-ICR-MS to plant metabolomics has thus far been limited to a few studies and clear step-by-step methodologies are as yet unavailable. This chapter presents a rigorous method for the extraction and FT-ICR-MS analysis of plant leaf tissues as well as downstream data processing.

Dereplication of microbial natural products by LC-DAD-TOFMS

Dereplication, the rapid identification of known compounds present in a mixture, is crucial to the fast discovery of novel natural products. Determining the elemental composition of compounds in mixtures and tentatively identifying natural products using MS/MS and UV/vis spectra is becoming easier with advances in analytical equipment and better compound databases. Here we demonstrate the use of LC-UV/vis-MS-based dereplication using data from UV/vis diode array detection and ESI+/ESI- time-of-flight MS for assignment of 719 microbial natural product and mycotoxin reference standards. ESI+ was the most versatile ionization method, detecting 93% of the compounds, although with 12% ionizing poorly. Using ESI+ alone, 56.1% of the compounds could be unambiguously assigned based on characteristic patterns of multiple adduct ions. Using ESI-, 36.4% of the compounds could have their molecular mass assigned unambiguously using multiple adduct ions, while a further 41% of the compounds were detected only as [M - H]-. The most reliable interpretations of conflicting ESI+ and ESI- data on a chromatographic peak were from the ionization polarity with the most intense ionization. Poor ionization was most common with small molecules (<200 Da). In ESI-, these were often polar and basic, while in ESI+ they were small aromatic acids or anthraquinones. No single ion-source settings could be applied over a m/z 60-2000 range. However, continuous switching among three settings (e.g., for 0.5 s each) during the chromatographic run allowed MS of both small labile molecules and large peptides, and pseudo MS/MS data on labile molecules since the settings for large molecules often induce fragmentation into small molecules.

Mass spectrometry-based holistic analytical approaches for metabolite profiling in systems biology studies

Metabonomics and metabolomics represent one of the three major platforms in systems biology. To perform metabolomics it is necessary to generate comprehensive "global" metabolite profiles from complex samples, for example, biological fluids or tissue extracts. Analytical technologies based on mass spectrometry (MS), and in particular on liquid chromatography-MS (LC-MS), have become a major tool providing a significant source of global metabolite profiling data. In the present review we describe and compare the utility of the different analytical strategies and technologies used for MS-based metabolomics with a particular focus on LC-MS. Both the advantages offered by the technology and also the challenges and limitations that need to be addressed for the successful application of LC-MS in metabolite analysis are described. Data treatment and approaches resulting in the detection and identification of biomarkers are considered. Special emphasis is given to validation issues, instrument stability, and QA/quality control (QC) procedures.

Metabolomics and human nutrition

The present report summarises a workshop convened by the UK Food Standards Agency (Agency) on 25 March 2010 to discuss the current Agency's funded research on the use of metabolomics technologies in human nutrition research. The objectives of this workshop were to review progress to date, to identify technical challenges and ways of overcoming them, and to discuss future research priorities and the application of metabolomics in public health nutrition research and surveys. Results from studies nearing completion showed that by using carefully designed dietary and sampling regimens, it is possible to identify novel biomarkers of food intake that could not have been predicted from current knowledge of food composition. These findings provide proof-of-principle that the metabolomics approach can be used to develop new putative biomarkers of dietary intake. The next steps will be to validate these putative biomarkers, to develop rapid and inexpensive assays for biomarkers of food intake of high public health relevance, and to test their utility in population cohort studies and dietary surveys.

Mass spectral analyses of corn stover prehydrolysates to assess conditioning processes

Flow injection electrospray (FIE) and LC-tandem mass spectrometry techniques were used to characterize corn stover acid hydrolysates before and after overliming and ammonia conditioning steps. Analyses were performed on samples without fractionation (dilution only) in an effort provide an inventory of ionizable substances. Statistical evaluation of the results indicates that the ammonia-treated and crude hydrolysates were more similar to one another than any other pairing, with conditioning leading to a decrease in malate levels. LC-tandem mass spectrometry studies were also developed to characterize the oligosaccharides present in each hydrolysate utilizing a hydrophilic interaction chromatographic separation method. Neutral and acidic pentose-based oligosaccharides (xylodextrins) with degrees of polymerization between 2 and 5 were quantified with 4-O-methyl glucuronic acid-containing dimer and trimers predominating. Conditioning had little effect on the quantified oligosaccharide pool.

Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy

The study of biological systems in a holistic manner (systems biology) is increasingly being viewed as a necessity to provide qualitative and quantitative descriptions of the emergent properties of the complete system. Systems biology performs studies focussed on the complex interactions of system components; emphasising the whole system rather than the individual parts. Many perturbations to mammalian systems (diet, disease, drugs) are multi-factorial and the study of small parts of the system is insufficient to understand the complete phenotypic changes induced. Metabolomics is one functional level tool being employed to investigate the complex interactions of metabolites with other metabolites (metabolism) but also the regulatory role metabolites provide through interaction with genes, transcripts and proteins (e.g. allosteric regulation). Technological developments are the driving force behind advances in scientific knowledge. Recent advances in the two analytical platforms of mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy have driven forward the discipline of metabolomics. In this critical review, an introduction to metabolites, metabolomes, metabolomics and the role of MS and NMR spectroscopy will be provided. The applications of metabolomics in mammalian systems biology for the study of the health-disease continuum, drug efficacy and toxicity and dietary effects on mammalian health will be reviewed. The current limitations and future goals of metabolomics in systems biology will also be discussed (374 references).

The metabolic transition during disease following infection of Arabidopsis thaliana by Pseudomonas syringae pv. tomato

The outcome of bacterial infection in plants is determined by the ability of the pathogen to successfully occupy the apoplastic space and deliver a constellation of effectors that collectively suppress basal and effector-triggered immune responses. In this study, we examined the metabolic changes associated with establishment of disease using analytical techniques that interrogated a range of chemistries. We demonstrated clear differences in the metabolome of Arabidopsis thaliana leaves infected with virulent Pseudomonas syringae within 8 h of infection. In addition to confirmation of changes in phenolic and indolic compounds, we identified rapid alterations in the abundance of amino acids and other nitrogenous compounds, specific classes of glucosinolates, disaccharides, and molecules that influence the prevalence of reactive oxygen species. Our data illustrate that, superimposed on defence suppression, pathogens reconfigure host metabolism to provide the sustenance required to support exponentially growing populations of apoplastically localized bacteria. We performed a detailed baseline study reporting the metabolic dynamics associated with bacterial infection. Moreover, we have integrated these data with the results of transcriptome profiling to distinguish metabolomic pathways that are transcriptionally activated from those that are post-transcriptionally regulated.

The complex links between dietary phytochemicals and human health deciphered by metabolomics

A large variety of phytochemicals commonly consumed with the human diet, influence health and may contribute to the prevention of diseases. However, it is still difficult to make nutritional recommendations for these bioactive compounds. Current studies of phytochemicals are generally focused on specific compounds and their effects on a limited number of markers. New approaches are needed to take into account both the diversity of phytochemicals found in the diet and the complexity of their biological effects. Recent progress in high-throughput analytical technologies and in bioinformatics now allows the simultaneous analysis of the hundreds or more metabolites constituting the metabolome in urine or plasma. These analyses give complex metabolic fingerprints characteristic of a given phenotype. The exploitation of the wealth of information it contains, in randomized controlled trials and cohort studies, should lead to the discovery of new markers of intake for phytochemicals and new markers of effects. In this paper, we briefly review the current methods used to evaluate intake of phytochemicals and their effects on health. We then describe the applications of metabolomics in this field. Recent metabolomics studies illustrate the potential of such a global approach to explore the complex relationships linking phytochemical intake and metabolism and health.

Metabolite fingerprinting of urine suggests breed-specific dietary metabolism differences in domestic dogs

Selective breeding of dogs has culminated in a large number of modern breeds distinctive in terms of size, shape and behaviour. Inadvertently, a range of breed-specific genetic disorders have become fixed in some pure-bred populations. Several inherited conditions confer chronic metabolic defects that are influenced strongly by diet, but it is likely that many less obvious breed-specific differences in physiology exist. Using Labrador retrievers and miniature Schnauzers maintained in a simulated domestic setting on a controlled diet, an experimental design was validated in relation to husbandry, sampling and sample processing for metabolomics. Metabolite fingerprints were generated from 'spot' urine samples using flow injection electrospray MS (FIE-MS). With class based on breed, urine chemical fingerprints were modelled using Random Forest (a supervised data classification technique), and metabolite features (m/z) explanatory of breed-specific differences were putatively annotated using the ARMeC database (http://www.armec.org). GC-MS profiling to confirm FIE-MS predictions indicated major breed-specific differences centred on the metabolism of diet-related polyphenols. Metabolism of further diet components, including potentially prebiotic oligosaccharides, animal-derived fats and glycerol, appeared significantly different between the two breeds. Analysis of the urinary metabolome of young male dogs representative of a wider range of breeds from animals maintained under domestic conditions on unknown diets provided preliminary evidence that many breeds may indeed have distinctive metabolic differences, with significant differences particularly apparent in comparisons between large and smaller breeds.

Metabolomic analysis reveals a common pattern of metabolic re-programming during invasion of three host plant species by Magnaporthe grisea

The mechanisms by which biotrophic and hemi-biotrophic fungal pathogens simultaneously subdue plant defences and sequester host nutrients are poorly understood. Using metabolite fingerprinting, we show that Magnaporthe grisea, the causal agent of rice blast disease, dynamically re-programmes host metabolism during plant colonization. Identical patterns of metabolic change occurred during M. grisea infections in barley, rice and Brachypodium distachyon. Targeted metabolite profiling by GC-MS confirmed the modulation of a conserved set of metabolites. In pre-symptomatic tissues, malate and polyamines accumulated, rather than being utilized to generate defensive reactive oxygen species, and the levels of metabolites associated with amelioration of redox stress in various cellular compartments increased dramatically. The activity of NADP-malic enzyme and generation of reactive oxygen species were localized to pathogen penetration sites, and both appeared to be suppressed in compatible interactions. Early diversion of the shikimate pathway to produce quinate was observed, as well as accumulation of non-polymerized lignin precursors. These data are consistent with modulation of defensive phenylpropanoid metabolism by M. grisea and the inability of susceptible hosts to mount a hypersensitive reaction or produce lignified papillae (both involving reactive oxygen species) to restrict pathogen invasion. Rapid proliferation of M. grisea hyphae in plant tissue after 3 days was associated with accelerated nutrient acquisition and utilization by the pathogen. Conversion of photoassimilate into mannitol and glycerol for carbon sequestration and osmolyte production appear to drive hyphal growth. Taken together, our results suggest that fungal pathogens deploy a common metabolic re-programming strategy in diverse host species to suppress plant defence and colonize plant tissue.

Metabolomics in evaluation of glucose disorders

PURPOSE OF REVIEW

Recent advances in metabolomic tools now permit to characterize dysregulated metabolic pathways in various diseases associated with the identification of sensitive and specific early responding biomarkers that are critical both for the diagnosis of the type of insult as well as for the selection and evaluation of therapy.

RECENT FINDINGS

This short review describes progresses made in analytical science and their applications in the field of glucose disorders. Recent studies focused mainly on type 2 diabetes both in human and animal models in order to validate early biomarkers and effects of drugs on disease progression. The potential of using the metabolomic approach was also demonstrated for diagnosing diabetic complications such as diabetic nephropathy.

SUMMARY

In addition to its application in the discovery of disease biomarkers, metabolomics can contribute to the elucidation of pathophysiological mechanisms.

MS/MS spectral tag-based annotation of non-targeted profile of plant secondary metabolites

The MS/MS spectral tag (MS2T) library-based peak annotation procedure was developed for informative non-targeted metabolic profiling analysis using LC-MS. An MS2T library of Arabidopsis metabolites was created from a set of MS/MS spectra acquired using the automatic data acquisition function of the mass spectrometer. By using this library, we obtained structural information for the detected peaks in the metabolic profile data without performing additional MS/MS analysis; this was achieved by searching for the corresponding MS2T accession in the library. In the case of metabolic profile data for Arabidopsis tissues containing more than 1000 peaks, approximately 50% of the peaks were tagged by MS2Ts, and 90 peaks were identified or tentatively annotated with metabolite information by searching the metabolite databases and manually interpreting the MS2Ts. A comparison of metabolic profiles among the Arabidopsis tissues revealed that many unknown metabolites accumulated in a tissue-specific manner, some of which were deduced to be unusual Arabidopsis metabolites based on the MS2T data. Candidate genes responsible for these biosyntheses could be predicted by projecting the results to the transcriptome data. The method was also used for metabolic phenotyping of a subset of Ds transposon-inserted lines of Arabidopsis, resulting in clarification of the functions of reported genes involved in glycosylation of flavonoids. Thus, non-targeted metabolic profiling analysis using MS2T annotation methods could prove to be useful for investigating novel functions of secondary metabolites in plants.

Preprocessing, classification modeling and feature selection using flow injection electrospray mass spectrometry metabolite fingerprint data

Metabolome analysis by flow injection electrospray mass spectrometry (FIE-MS) fingerprinting generates measurements relating to large numbers of m/z signals. Such data sets often exhibit high variance with a paucity of replicates, thus providing a challenge for data mining. We describe data preprocessing and modeling methods that have proved reliable in projects involving samples from a range of organisms. The protocols interact with software resources specifically for metabolomics provided in a Web-accessible data analysis package FIEmspro (http://users.aber.ac.uk/jhd) written in the R environment and requiring a moderate knowledge of R command-line usage. Specific emphasis is placed on describing the outcome of modeling experiments using FIE-MS data that require further preprocessing to improve quality. The salient features of both poor and robust (i.e., highly generalizable) multivariate models are outlined together with advice on validating classifiers and avoiding false discovery when seeking explanatory variables.

High-throughput, nontargeted metabolite fingerprinting using nominal mass flow injection electrospray mass spectrometry

Flow injection electrospray-mass spectrometry (FIE-MS) is finding utility as a first-pass metabolite fingerprinting tool in many research fields. We provide a protocol that has proved reliable in large-scale research projects involving diverse sample matrices originating from plants, microbes and mammalian biofluids. Using Brachypodium leaf material as an example matrix all steps are summarized from sample extraction to data quality assessment. Alternative procedures for dealing with other common matrices such as bloods and urine are included. With little sample pretreatment, no chromatography and instrument cycle times of <5 min it is feasible to analyze >1,000 samples per week. Analysis of a typical batch of 240 samples (including first-pass data analysis) can be accomplished within 48 h.