Large-scale Metabolite Profiling Research Articles

Dynamic Inventory of Intermediate Metabolites of Cyanobacteria in a Diurnal Cycle.

SummaryCyanobacteria are gaining importance both as hosts for photoautotrophic production of chemicals and as model systems for studies of diurnal lifestyle. The proteome and transcriptome of cyanobacteria have been closely examined under diurnal growth, whereas the downstream effects on the intermediary metabolism have not received sufficient attention. The present study focuses on identifying the cellular metabolites whose inventories undergo dramatic changes in a fast-growing cyanobacterium, Synechococcus elongatus PCC 11801. We identified and quantified 67 polar metabolites, whose inventory changes significantly during diurnal growth, with some metabolites changing by 100-fold. The Calvin-Benson-Bassham cycle intermediates peak at midday to support fast growth. The hitherto unexplored γ-glutamyl peptides act as reservoirs of amino acids. Interestingly, several storage molecules or their precursors accumulate during the dark phase, dispelling the notion that all biosynthetic activity takes place in the light phase. Our results will guide metabolic modeling and strain engineering of cyanobacteria.

Piper anisum as a promising new source of bioactive metabolites

Piper species are commonly used by indigenous communities to treat several gastrointestinal diseases. In China, they are also used as an active ingredient in formulae to treat cancer. The objective of the study was to perform a large-scale metabolite profiling analysis to identify bioactive compounds in Piper anisum. Antioxidant capacity was assessed by the DPPH assay and total phenolics were assessed by Folin–Ciocalteu’s method. Antimicrobial activity was assessed against several Gram-positive and Gram-negative bacteria, whereas cytotoxicity was assessed against tumor cell lines MCF-7, HCT116, HepG2 and HL-60, and non-tumor cell line MRC-5. The multiplatform metabolite profiling approach encompassed NMR, GC–MS and LC–MS analyses. P. anisum root extract showed the greatest antioxidant capacity and total phenolic content, followed by the stem and leaf extracts. P. anisum extracts showed a highly selective antimicrobial profile, being specifically active against C. albicans (MIC of 500 μg mL−1). Additionally, the root extract (50 μg mL−1) showed the highest cell inhibition percentages against tumor cell lines MCF-7 (59.5%), HCT116 (49.2%), and HepG2 (61.0%). Forty-eight metabolites were annotated by GC–MS and 27 by LC–MS. These included alkaloids, carbohydrates, fatty acids, hydrocarbons, organic acids, phenolic compounds, and terpenes. Taken together, these results showed that P. anisum root extract is a promising source of bioactive compounds.

Profiling Metabolites and Biological Activities of Sugarcane (Saccharum officinarum Linn.) Juice and its Product Molasses via a Multiplex Metabolomics Approach.

Sugarcane (Saccharum officinarum L.) is an important perennial grass in the Poaceae family cultivated worldwide due to its economical and medicinal value. In this study, a combined approach using mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy was employed for the large-scale metabolite profiling of sugarcane juice and its by-product molasses. The polyphenols were analysed via UPLC-UV-ESI-MS, whereas the primary metabolites such as sugars and organic and amino acids were profiled using NMR spectroscopy and gas chromatography/mass spectrometry (GC/MS). UPLC/MS was more effective than NMR spectroscopy or GC/MS for determining differences among the metabolite compositions of the products. Under the optimized conditions, UPLC/MS led to the identification of 42 metabolites, including nine flavonoids, nine fatty acids, and two sterols. C/O Flavone glycosides were the main subclass detected, with tricin-7-O-deoxyhexosyl glucuronide being detected in sugarcane and molasses for the first time. Based on GC/MS analysis, disaccharides were the predominant species in the sugarcane juice and molasses, with sucrose accounting for 66% and 59%, respectively, by mass of all identified metabolites. The phenolic profiles of sugarcane and molasses were further investigated in relation to their in vitro antioxidant activities using free radical scavenging assays such as 2,2-Diphenyl-1-picrylhydrazyl free radical-scavenging ability (DPPH), Trolox equivalent antioxidant capacity (TEAC) and ferric reducing antioxidant power (FRAP). In view of its higher total phenolic content (TPC) (196 ± 2.1 mg GAE/100 g extract) compared to that of sugarcane juice (93 ± 2.9 mg GAE/100 g extract), molasses exhibited a substantially higher antioxidant effect. Interestingly, both extracts were also found to inhibit α-glucosidase and α-amylase enzymes, suggesting a possible antihyperglycaemic effect. These findings suggest molasses may be a new source of natural antioxidants for functional foods.

Pharmacometabolomics in Endogenous Drugs: A New Approach for Predicting the Individualized Pharmacokinetics of Cholic Acid.

The evaluation of individual variability in endogenous drugs' metabolism and disposition is a very challenging task. We developed and validated a metabotype to pharmacokinetics (PK) matching approach by taking cholic acid as an example to predict the individualized PK of endogenous drugs. The stable isotope-labeled cholic acid was selected as the substitute analyte of cholic acid to ensure the accurate measurement of blood concentration. First, large-scale metabolite profiling studies were performed on the predose urine samples of 28 rats. Then, to examine the individualized PK of deuterium 4-cholic acid (d4-cholic acid) in these rats, we determined its plasma concentrations and calculated the differential AUC values. Subsequently, we conducted a two-stage partial least-squares analysis in which 31 baseline metabolites were screened initially for predicting the individualized AUC values of d4-cholic acid using the data of predose urine metabolites. Finally, network biology analysis was applied to give the biological interpretation of the individual variances in cholic acid metabolism and disposition, and the result further narrowed the selection of baseline metabolites from 31 to 2 (sarcosine and S-adenosyl-l-homocysteine) for such prediction. Collectively, this pharmacometabolomics research provided a new strategy for predicting individualized PK of endogenous drugs.

Metabolomics for Plant Improvement: Status and Prospects.

Post-genomics era has witnessed the development of cutting-edge technologies that have offered cost-efficient and high-throughput ways for molecular characterization of the function of a cell or organism. Large-scale metabolite profiling assays have allowed researchers to access the global data sets of metabolites and the corresponding metabolic pathways in an unprecedented way. Recent efforts in metabolomics have been directed to improve the quality along with a major focus on yield related traits. Importantly, an integration of metabolomics with other approaches such as quantitative genetics, transcriptomics and genetic modification has established its immense relevance to plant improvement. An effective combination of these modern approaches guides researchers to pinpoint the functional gene(s) and the characterization of massive metabolites, in order to prioritize the candidate genes for downstream analyses and ultimately, offering trait specific markers to improve commercially important traits. This in turn will improve the ability of a plant breeder by allowing him to make more informed decisions. Given this, the present review captures the significant leads gained in the past decade in the field of plant metabolomics accompanied by a brief discussion on the current contribution and the future scope of metabolomics to accelerate plant improvement.

Metabolomic Profiles of Body Mass Index in the Framingham Heart Study Reveal Distinct Cardiometabolic Phenotypes.

BackgroundAlthough obesity and cardiometabolic traits commonly overlap, underlying pathways remain incompletely defined. The association of metabolite profiles across multiple cardiometabolic traits may lend insights into the interaction of obesity and metabolic health. We sought to investigate metabolic signatures of obesity and related cardiometabolic traits in the community using broad-based metabolomic profiling.Methods and ResultsWe evaluated the association of 217 assayed metabolites and cross-sectional as well as longitudinal changes in cardiometabolic traits among 2,383 Framingham Offspring cohort participants. Body mass index (BMI) was associated with 69 of 217 metabolites (P<0.00023 for all), including aromatic (tyrosine, phenylalanine) and branched chain amino acids (valine, isoleucine, leucine). Additional metabolic pathways associated with BMI included the citric acid cycle (isocitrate, alpha-ketoglutarate, aconitate), the tryptophan pathway (kynurenine, kynurenic acid), and the urea cycle. There was considerable overlap in metabolite profiles between BMI, abdominal adiposity, insulin resistance [IR] and dyslipidemia, modest overlap of metabolite profiles between BMI and hyperglycemia, and little overlap with fasting glucose or elevated blood pressure. Metabolite profiles were associated with longitudinal changes in fasting glucose, but the involved metabolites (ornithine, 5-HIAA, aminoadipic acid, isoleucine, cotinine) were distinct from those associated with baseline glucose or other traits. Obesity status appeared to “modify” the association of 9 metabolites with IR. For example, bile acid metabolites were strongly associated with IR among obese but not lean individuals, whereas isoleucine had a stronger association with IR in lean individuals.ConclusionsIn this large-scale metabolite profiling study, body mass index was associated with a broad range of metabolic alterations. Metabolite profiling highlighted considerable overlap with abdominal adiposity, insulin resistance, and dyslipidemia, but not with fasting glucose or blood pressure traits.

Discriminating precursors of common fragments for large-scale metabolite profiling by triple quadrupole mass spectrometry.

The goal of large-scale metabolite profiling is to compare the relative concentrations of as many metabolites extracted from biological samples as possible. This is typically accomplished by measuring the abundances of thousands of ions with high-resolution and high mass accuracy mass spectrometers. Although the data from these instruments provide a comprehensive fingerprint of each sample, identifying the structures of the thousands of detected ions is still challenging and time intensive. An alternative, less-comprehensive approach is to use triple quadrupole (QqQ) mass spectrometry to analyze predetermined sets of metabolites (typically fewer than several hundred). This is done using authentic standards to develop QqQ experiments that specifically detect only the targeted metabolites, with the advantage that the need for ion identification after profiling is eliminated. Here, we propose a framework to extend the application of QqQ mass spectrometers to large-scale metabolite profiling. We aim to provide a foundation for designing QqQ multiple reaction monitoring (MRM) experiments for each of the 82 696 metabolites in the METLIN metabolite database. First, we identify common fragmentation products from the experimental fragmentation data in METLIN. Then, we model the likelihoods of each precursor structure in METLIN producing each common fragmentation product. With these likelihood estimates, we select ensembles of common fragmentation products that minimize our uncertainty about metabolite identities. We demonstrate encouraging performance and, based on our results, we suggest how our method can be integrated with future work to develop large-scale MRM experiments. Our predictions, Supplementary results, and the code for estimating likelihoods and selecting ensembles of fragmentation reactions are made available on the lab website at http://pattilab.wustl.edu/FragPred.

Applications of Circadian Metabolomics

Behavioral and physiologic rhythms are temporally coordinated with daily changes in the environment. This is achieved by the circadian timing system, which synchronizes the body’s rhythms with the 24-hour solar day and feeding cycles. To better understand the role of circadian rhythms in metabolism and disease processes, an increasing number of studies have used large-scale metabolite profiling (metabolomics) to analyze biological specimens collected over the diurnal/ circadian cycle. Here, we review recent progress in the application of metabolomics to circadian rhythms research in mammals. Based on studies of the liver metabolome in mice, the circadian clock plays a key role in regulating carbohydrate, lipid, and nucleotide metabolism. Circadian metabolomics has also revealed marked changes in liver function in response to the timing of food consumption, and has led to the discovery of novel signaling pathways underlying fat metabolism. In humans, circadian profiling of metabolites in plasma has confirmed an important role for the clock in regulating steroid hormone metabolism and lipid homeostasis. A method for estimating internal body time has also been developed using plasma metabolomics, which could potentially be used to optimize the timing of drug delivery to improve patient outcomes and reduce unwanted side effects. Looking forward, metabolomics approaches can be used to evaluate the impact of genetic and environmental factors on circadian-regulated metabolic pathways, while providing valuable insight into the role of the circadian clock in regulating complex biomolecular networks. Keywords: Chronobiology, circadian, disease, metabolism, rhythm, sleep.

Genetic mapping of semi-polar metabolites in pepper fruits (Capsicum sp.): towards unravelling the molecular regulation of flavonoid quantitative trait loci

Untargeted LCMS profiling of semi-polar metabolites followed by metabolite quantitative trait locus (mQTL) analysis was performed in ripe pepper fruits of 113 F2 plants derived from a cross between Capsicum annuum AC1979 (no. 19) and Capsicum chinense No. 4661 Selection (no. 18). The parental accessions were selected based on their variation in fruit morphological characteristics and fruit content of some target phytonutrients. Clear segregation of fruit colour and fruit metabolite profiles was observed in the F2 population. The F2 plants formed three clusters based on their metabolite profiles. Of the total of 542 metabolites, 52 could be annotated, including a range of flavonoids, such as flavone C-glycosides, flavonol O-glycosides and naringenin chalcone, as well as several phenylpropanoids, a capsaicin analogue, fatty acid derivatives and amino acid derivatives. Interval mapping revealed 279 mQTLs in total. Two mQTL hotspots were found on chromosome 9. These two chromosomal regions regulated the relative levels of 35 and 103 metabolites, respectively. Analysis also revealed an mQTL for a capsaicin analogue, located on chromosome 7. Confirmation of flavonoid mQTLs using a set of six flavonoid candidate gene markers and their corresponding expression data (expression QTLs) indicated the Ca-MYB12 transcription factor gene on chromosome 1 and the gene encoding flavone synthase (FS-2) on chromosome 6 as likely causative genes determining the variation in naringenin chalcone and flavone C-glycosides, respectively, in this population. The combination of large-scale metabolite profiling and QTL analysis provided valuable insight into the genomic regions and genes important for the production of (secondary) metabolites in pepper fruit. This will impact breeding strategies aimed at optimising the content of specific metabolites in pepper fruit.Electronic supplementary materialThe online version of this article (doi:10.1007/s11032-013-9967-0) contains supplementary material, which is available to authorized users.

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.

An ultrasonication-assisted extraction and derivatization protocol for GC/TOFMS-based metabolite profiling

Conventional chemical derivatization of metabolites in biological specimens is time-consuming, which limits the throughput and efficiency of metabolite profiling using a gas chromatography/time-of-flight mass spectrometry (GC/TOFMS) platform. We report an ultrasonication-assisted protocol which reduces the derivatization time from hours to about 30 min and significantly enhances the derivatization efficiency prior to a GC/TOFMS analysis. The protocol was evaluated using 40 compounds representing different classes of human metabolites, and demonstrated good analytical precision and accuracy. In comparison with the conventional method, the new protocol was able to increase the intensity of most of the identified peaks (71.0%) in the GC/TOFMS chromatograms of human serum samples. The detected compounds with increased intensity include most amino acids, keto-containing organic acids, carbonyl-containing carbohydrates, and unsaturated fatty acids. We applied this protocol in a metabolomic study of human serum samples obtained from 34 patients diagnosed with hypertension and 29 age- and gender-matched healthy subjects. Metabolite markers associated with hypertension, including glucosamine, D-sorbitol, 1-stearoylglycerol, and homocysteine, were identified and validated by statistical methods and use of reference standards. Our work highlights the potential of this novel approach for the large-scale metabolite profiling of samples generated from plant, animal, and clinical and epidemiological studies.

Hypoxia-induced metabolic shifts in cancer cells: Moving beyond the Warburg effect

Hypoxia has been recognized to play a role in promoting the invasive and metastatic behaviour of cancer cells. Largely via the transcription factor, hypoxia-induced factor 1, hypoxia exerts significant effects on cellular metabolism, with numerous downstream consequences. Energetically there is a significant shift away from oxidative phosphorylation in mitochondria towards glycolysis, a state also involved in the ‘Warburg effect’. The proteins involved in mediating the altered metabolic pathways seen in tumour cells thus represent new targets for potential therapeutic intervention. Hypoxia has been associated with the development of aggressive phenotypes in cancer cells, and can be accompanied by changes in carbohydrate and lipid metabolism that impact tumour cell proliferation, adhesion, and angiogenesis. Herein, we examine glycolytic and other less investigated metabolic pathways in relation to cancer and hypoxia, with a focus on emerging tools for large-scale metabolite profiling (‘metabolomics’). Metabolomic technologies permit the measurement of a wide range of metabolites in an untargeted manner, however, to date, this technology has been relatively under utilized for studying cellular responses to hypoxia. We detail some of the common experimental approaches employed in metabolomics experiments, including nuclear magnetic resonance and new mass spectrometry-based methods of analysis. Selected examples of the application of these technologies to the study of metabolic alterations brought about by hypoxia are provided, particularly as they relate to energy, carbohydrate, and lipid metabolism. Finally, the potential for therapeutic targeting of metabolic processes activated by hypoxia is presented.

Widely Targeted Metabolomics Based on Large-Scale MS/MS Data for Elucidating Metabolite Accumulation Patterns in Plants

Metabolomics is an ‘omics’ approach that aims to analyze all metabolites in a biological sample comprehensively. The detailed metabolite profiling of thousands of plant samples has great potential for directly elucidating plant metabolic processes. However, both a comprehensive analysis and a high throughput are difficult to achieve at the same time due to the wide diversity of metabolites in plants. Here, we have established a novel and practical metabolomics methodology for quantifying hundreds of targeted metabolites in a high-throughput manner. Multiple reaction monitoring (MRM) using tandem quadrupole mass spectrometry (TQMS), which monitors both the specific precursor ions and product ions of each metabolite, is a standard technique in targeted metabolomics, as it enables high sensitivity, reproducibility and a broad dynamic range. In this study, we optimized the MRM conditions for specific compounds by performing automated flow injection analyses with TQMS. Based on a total of 61,920 spectra for 860 authentic compounds, the MRM conditions of 497 compounds were successfully optimized. These were applied to high-throughput automated analysis of biological samples using TQMS coupled with ultra performance liquid chromatography (UPLC). By this analysis, approximately 100 metabolites were quantified in each of 14 plant accessions from Brassicaceae, Gramineae and Fabaceae. A hierarchical cluster analysis based on the metabolite accumulation patterns clearly showed differences among the plant families, and family-specific metabolites could be predicted using a batch-learning self-organizing map analysis. Thus, the automated widely targeted metabolomics approach established here should pave the way for large-scale metabolite profiling and comparative metabolomics.

Comprehensive Two-Dimensional Gas Chromatography - Mass Spectrometry and its Use in High-Resolution Metabolomics

Background Metabolomics is a comprehensive analysis strategy that aims to identify and quantify the entire suite of metabolites in an organism. The majority of large-scale metabolite profiling studies performed to date [ 1 ] have applied multidimensional analysis utilizing chromatography and mass spectrometry (MS). While NMR spectroscopy is an alternative approach for metabolomic analysis, [ 2 ] MS-based approaches are generally more sensitive. Moreover, the chromatographic step reduces the number of overlapping signals, so these approaches result in higher numbers of absolute metabolite identifications. Gas chromatography-mass spectrometry (GC-MS) is a tried-and-tested technique for metabolomics; [ 3 , 4 ] however, the quality of the GC-MS information can be improved by using comprehensive two-dimensional GC to enhance the chromatographic resolution.