http://www.mdpi.com/journal/metabolites Latest open access articles published in Metabolites at http://www.mdpi.com/journal/metabolites http://www.mdpi.com/2218-1989/3/2/373 There has been a recent shift in how cancers are defined, where tumors are no longer simply classified by their tissue origin, but also by their molecular characteristics. Furthermore, personalized medicine has become a popular term and it could start to play an important role in future medical care. However, today, a “one size fits all” approach is still the most common form of cancer treatment. In this mini-review paper, we report on the role of nuclear magnetic resonance (NMR) metabolomics in drug development and in personalized medicine. NMR spectroscopy has successfully been used to evaluate current and potential therapies, both single-agents and combination therapies, to analyze toxicology, optimal dose, resistance, sensitivity, and biological mechanisms. It can also provide biological insight on tumor subtypes and their different responses to drugs, and indicate which patients are most likely to experience off-target effects and predict characteristics for treatment efficacy. Identifying pre-treatment metabolic profiles that correlate to these events could significantly improve how we view and treat tumors. We also briefly discuss several targeted cancer drugs that have been studied by metabolomics. We conclude that NMR technology provides a key platform in metabolomics that is well-positioned to play a crucial role in realizing the ultimate goal of better tailored cancer medicine. Metabolites 2013-05-17 3 2 Review 10.3390/metabo3020373 373 396 2218-1989 2013-05-17 doi: 10.3390/metabo3020373 Marie Palmnas Hans Vogel http://www.mdpi.com/2218-1989/3/2/347 Soybean (Glycine max) seeds are an important source of seed storage compounds, including protein, oil, and sugar used for food, feed, chemical, and biofuel production. We assessed detailed temporal transcriptional and metabolic changes in developing soybean embryos to gain a systems biology view of developmental and metabolic changes and to identify potential targets for metabolic engineering. Two major developmental and metabolic transitions were captured enabling identification of potential metabolic engineering targets specific to seed filling and to desiccation. The first transition involved a switch between different types of metabolism in dividing and elongating cells. The second transition involved the onset of maturation and desiccation tolerance during seed filling and a switch from photoheterotrophic to heterotrophic metabolism. Clustering analyses of metabolite and transcript data revealed clusters of functionally related metabolites and transcripts active in these different developmental and metabolic programs. The gene clusters provide a resource to generate predictions about the associations and interactions of unknown regulators with their targets based on “guilt-by-association” relationships. The inferred regulators also represent potential targets for future metabolic engineering of relevant pathways and steps in central carbon and nitrogen metabolism in soybean embryos and drought and desiccation tolerance in plants. Metabolites 2013-05-14 3 2 Article 10.3390/metabo3020347 347 372 2218-1989 2013-05-14 doi: 10.3390/metabo3020347 Eva Collakova Delasa Aghamirzaie Yihui Fang Curtis Klumas Farzaneh Tabataba Akshay Kakumanu Elijah Myers Lenwood Heath Ruth Grene http://www.mdpi.com/2218-1989/3/2/325 Diatoms are heterokont algae derived from a secondary symbiotic event in which a eukaryotic host cell acquired an eukaryotic red alga as plastid. The multiple endosymbiosis and horizontal gene transfer processes provide diatoms unusual opportunities for gene mixing to establish distinctive biosynthetic pathways and metabolic control structures. Diatoms are also known to have significant impact on global ecosystems as one of the most dominant phytoplankton species in the contemporary ocean. As such their metabolism and growth regulating factors have been of particular interest for many years. The publication of the genomic sequences of two independent species of diatoms and the advent of an enhanced experimental toolbox for molecular biological investigations have afforded far greater opportunities than were previously apparent for these species and re-invigorated studies regarding the central carbon metabolism of diatoms. In this review we discuss distinctive features of the central carbon metabolism of diatoms and its response to forthcoming environmental changes and recent advances facilitating the possibility of industrial use of diatoms for oil production. Although the operation and importance of several key pathways of diatom metabolism have already been demonstrated and determined, we will also highlight other potentially important pathways wherein this has yet to be achieved. Metabolites 2013-05-07 3 2 Review 10.3390/metabo3020325 325 346 2218-1989 2013-05-07 doi: 10.3390/metabo3020325 Toshihiro Obata Alisdair Fernie Adriano Nunes-Nesi http://www.mdpi.com/2218-1989/3/2/312 The “Critical Assessment of Small Molecule Identification” (CASMI) contest was aimed in testing strategies for small molecule identification that are currently available in the experimental and computational mass spectrometry community. We have applied tandem mass spectral library search to solve Category 2 of the CASMI Challenge 2012 (best identification for high resolution LC/MS data). More than 230,000 tandem mass spectra part of four well established libraries (MassBank, the collection of tandem mass spectra of the “NIST/NIH/EPA Mass Spectral Library 2012”, METLIN, and the ‘Wiley Registry of Tandem Mass Spectral Data, MSforID’) were searched. The sample spectra acquired in positive ion mode were processed. Seven out of 12 challenges did not produce putative positive matches, simply because reference spectra were not available for the compounds searched. This suggests that to some extent the limited coverage of chemical space with high-quality reference spectra is still a problem encountered in tandem mass spectral library search. Solutions were submitted for five challenges. Three compounds were correctly identified (kanamycin A, benzyldiphenylphosphine oxide, and 1-isopropyl-5-methyl-1H-indole-2,3-dione). In the absence of any reference spectrum, a false positive identification was obtained for 1-aminoanthraquinone by matching the corresponding sample spectrum to the structurally related compounds N-phenylphthalimide and 2-aminoanthraquinone. Another false positive result was submitted for 1H-benz[g]indole; for the 1H-benz[g]indole-specific sample spectra provided, carbazole was listed as the best matching compound. In this case, the quality of the available 1H-benz[g]indole-specific reference spectra was found to hamper unequivocal identification. Metabolites 2013-04-26 3 2 Article 10.3390/metabo3020312 312 324 2218-1989 2013-04-26 doi: 10.3390/metabo3020312 Herbert Oberacher http://www.mdpi.com/2218-1989/3/2/294 Amino acids are not only building blocks for proteins but serve as precursors for the synthesis of many metabolites with multiple functions in growth and other biological processes of a living organism. The biosynthesis of amino acids is tightly connected with central carbon, nitrogen and sulfur metabolism. Recent publication of genome sequences for two diatoms Thalassiosira pseudonana and Phaeodactylum tricornutum created an opportunity for extensive studies on the structure of these metabolic pathways. Based on sequence homology found in the analyzed diatomal genes, the biosynthesis of amino acids in diatoms seems to be similar to higher plants. However, one of the most striking differences between the pathways in plants and in diatomas is that the latter possess and utilize the urea cycle. It serves as an important anaplerotic pathway for carbon fixation into amino acids and other N-containing compounds, which are essential for diatom growth and contribute to their high productivity. Metabolites 2013-04-18 3 2 Review 10.3390/metabo3020294 294 311 2218-1989 2013-04-18 doi: 10.3390/metabo3020294 Mariusz Bromke http://www.mdpi.com/2218-1989/3/2/277 Ion mobility spectrometry with pre-separation by multi-capillary columns (MCC/IMS) has become an established inexpensive, non-invasive bioanalytics technology for detecting volatile organic compounds (VOCs) with various metabolomics applications in medical research. To pave the way for this technology towards daily usage in medical practice, different steps still have to be taken. With respect to modern biomarker research, one of the most important tasks is the automatic classification of patient-specific data sets into different groups, healthy or not, for instance. Although sophisticated machine learning methods exist, an inevitable preprocessing step is reliable and robust peak detection without manual intervention. In this work we evaluate four state-of-the-art approaches for automated IMS-based peak detection: local maxima search, watershed transformation with IPHEx, region-merging with VisualNow, and peak model estimation (PME).We manually generated Metabolites 2013, 3 278 a gold standard with the aid of a domain expert (manual) and compare the performance of the four peak calling methods with respect to two distinct criteria. We first utilize established machine learning methods and systematically study their classification performance based on the four peak detectors’ results. Second, we investigate the classification variance and robustness regarding perturbation and overfitting. Our main finding is that the power of the classification accuracy is almost equally good for all methods, the manually created gold standard as well as the four automatic peak finding methods. In addition, we note that all tools, manual and automatic, are similarly robust against perturbations. However, the classification performance is more robust against overfitting when using the PME as peak calling preprocessor. In summary, we conclude that all methods, though small differences exist, are largely reliable and enable a wide spectrum of real-world biomedical applications. Metabolites 2013-04-16 3 2 Article 10.3390/metabo3020277 277 293 2218-1989 2013-04-16 doi: 10.3390/metabo3020277 Anne-Christin Hauschild Dominik Kopczynski Marianna D'Addario Jörg Baumbach Sven Rahmann Jan Baumbach http://www.mdpi.com/2218-1989/3/2/259 One of the most significant challenges in the comparative analysis of Nuclear Magnetic Resonance (NMR) metabolome profiles is the occurrence of shifts between peaks across different spectra, for example caused by fluctuations in pH, temperature, instrument factors and ion content. Proper alignment of spectral peaks is therefore often a crucial preprocessing step prior to downstream quantitative analysis. Various alignment methods have been developed specifically for this purpose. Other methods were originally developed to align other data types (GC, LC, SELDI-MS, etc.), but can also be applied to NMR data. This review discusses the available methods, as well as related problems such as reference determination or the evaluation of alignment quality. We present a generic alignment framework that allows for comparison and classification of different alignment approaches according to their algorithmic principles, and we discuss their performance. Metabolites 2013-04-15 3 2 Review 10.3390/metabo3020259 259 276 2218-1989 2013-04-15 doi: 10.3390/metabo3020259 Trung Vu Kris Laukens http://www.mdpi.com/2218-1989/3/2/243 It is consensus in the metabolomics community that standardized protocols should be followed for sample handling, storage and analysis, as it is of utmost importance to maintain constant measurement conditions to identify subtle biological differences. The aim of this work, therefore, was to systematically investigate the influence of freezing procedures and storage temperatures and their effect on NMR spectra as a potentially disturbing aspect for NMR-based metabolomics studies. Urine samples were collected from two healthy volunteers, centrifuged and divided into aliquots. Urine aliquots were frozen either at −20 °C, on dry ice, at −80 °C or in liquid nitrogen and then stored at −20 °C, −80 °C or in liquid nitrogen vapor phase for 1–5 weeks before NMR analysis. Results show spectral changes depending on the freezing procedure, with samples frozen on dry ice showing the largest deviations. The effect was found to be based on pH differences, which were caused by variations in CO2 concentrations introduced by the freezing procedure. Thus, we recommend that urine samples should be frozen at −20 °C and transferred to lower storage temperatures within one week and that freezing procedures should be part of the publication protocol. Metabolites 2013-04-09 3 2 Article 10.3390/metabo3020243 243 258 2218-1989 2013-04-09 doi: 10.3390/metabo3020243 Manuela Rist Claudia Muhle-Goll Benjamin Görling Achim Bub Stefan Heissler Bernhard Watzl Burkhard Luy http://www.mdpi.com/2218-1989/3/2/223 Plants produce various volatile organic compounds (VOCs), which are thought to be a crucial factor in their interactions with harmful insects, plants and animals. Composition of VOCs may differ when plants are grown under different nutrient conditions, i.e., macronutrient-deficient conditions. However, in plants, relationships between macronutrient assimilation and VOC composition remain unclear. In order to identify the kinds of VOCs that can be emitted when plants are grown under various environmental conditions, we established a conventional method for VOC profiling in Arabidopsis thaliana (Arabidopsis) involving headspace-solid-phase microextraction-gas chromatography-time-of-flight-mass spectrometry (HS-SPME-GC-TOF-MS). We grew Arabidopsis seedlings in an HS vial to directly perform HS analysis. To maximize the analytical performance of VOCs, we optimized the extraction method and the analytical conditions of HP-SPME-GC-TOF-MS. Using the optimized method, we conducted VOC profiling of Arabidopsis seedlings, which were grown under two different nutrition conditions, nutrition-rich and nutrition-deficient conditions. The VOC profiles clearly showed a distinct pattern with respect to each condition. This study suggests that HS-SPME-GC-TOF-MS analysis has immense potential to detect changes in the levels of VOCs in not only Arabidopsis, but other plants grown under various environmental conditions. Metabolites 2013-04-09 3 2 Article 10.3390/metabo3020223 223 242 2218-1989 2013-04-09 doi: 10.3390/metabo3020223 Miyako Kusano Yumiko Iizuka Makoto Kobayashi Atsushi Fukushima Kazuki Saito http://www.mdpi.com/2218-1989/3/2/204 Milk is a key component in infant nutrition worldwide and, in the Western parts of the world, also in adult nutrition. Milk of bovine origin is both consumed fresh and processed into a variety of dairy products including cheese, fermented milk products, and infant formula. The nutritional quality and processing capabilities of bovine milk is closely associated to milk composition. Metabolomics is ideal in the study of the low-molecular-weight compounds in milk, and this review focuses on the recent nuclear magnetic resonance (NMR)-based metabolomics trends in milk research, including applications linking the milk metabolite profiling with nutritional aspects, and applications which aim to link the milk metabolite profile to various technological qualities of milk. The metabolite profiling studies encompass the identification of novel metabolites, which potentially can be used as biomarkers or as bioactive compounds. Furthermore, metabolomics applications elucidating how the differential regulated genes affects milk composition are also reported. This review will highlight the recent advances in NMR-based metabolomics on milk, as well as give a brief summary of when NMR spectroscopy can be useful for gaining a better understanding of how milk composition is linked to nutritional or quality traits. Metabolites 2013-04-02 3 2 Review 10.3390/metabo3020204 204 222 2218-1989 2013-04-02 doi: 10.3390/metabo3020204 Ulrik Sundekilde Lotte Larsen Hanne Bertram http://www.mdpi.com/2218-1989/3/1/185 Proanthocyanidins (PAs) are fundamental nutritional metabolites in different types of grape products consumed by human beings. Although the biosynthesis of PAs in berry of Vitis vinifera has gained intensive investigations, the understanding of PAs in other Vitis species is limited. In this study, we report PA formation and characterization of gene expression involved in PA biosynthesis in leaves of V. bellula, a wild edible grape species native to south and south-west China. Leaves are collected at five developmental stages defined by sizes ranging from 0.5 to 5 cm in length. Analyses of thin layer chromatography (TLC) and high performance liquid chromatography-photodiode array detector (HPLC-PAD) show the formation of (+)-catechin, (−)-epicatechin, (+)-gallocatechin and (−)-epigallocatechin during the entire development of leaves. Analyses of butanol-HCl boiling cleavage coupled with spectrometry measurement at 550 nm show a temporal trend of extractable PA levels, which is characterized by an increase from 0.5 cm to 1.5 cm long leaves followed by a decrease in late stages. TLC and HPLC-PAD analyses identify cyanidin, delphinidin and pelargonidin produced from the cleavage of PAs in the butanol-HCl boiling, showing that the foliage PAs of V. bellula include three different types of extension units. Four cDNAs, which encode VbANR, VbDFR, VbLAR1 and VbLAR2, respectively, are cloned from young leaves. The expression patterns of VbANR and VbLAR2 but not VbLAR1 and VbDFR follow a similar trend as the accumulation patterns of PAs. Two cDNAs encoding VbMYBPA1 and VbMYB5a, the homologs of which have been demonstrated to regulate the expression of both ANR and LAR in V. vinifera, are also cloned and their expression profiles are similar to those of VbANR and VbLAR2. In contrast, the expression profiles of MYBA1 and 2 homologs involved in anthocyanin biosynthesis are different from those of VbANR and VbLAR2. Our data show that both ANR and LAR branches are involved in PA biosynthesis in leaves of V. bellula. Metabolites 2013-03-19 3 1 Article 10.3390/metabo3010185 185 203 2218-1989 2013-03-19 doi: 10.3390/metabo3010185 Yue Zhu Qing-Zhong Peng Ci Du Ke-Gang Li De-Yu Xie http://www.mdpi.com/2218-1989/3/1/168 Algae are divergent organisms having a wide variety of evolutional histories. Although most of them share photosynthetic activity, their pathways of primary carbon metabolism are rather diverse among species. Here we developed a method for gas chromatography-mass spectroscopy (GC-MS) based metabolite profiling for the coccolithophorid alga Emiliania huxleyi, which is one of the most abundant microalgae in the ocean, in order to gain an overview of the pathway of primary metabolism within this alga. Following method optimization, twenty-six metabolites could be detected by this method. Whilst most proteogenic amino acids were detected, no peaks corresponding to malate and fumarate were found. The metabolite profile of E. huxleyi was, however, characterized by a prominent accumulation of mannitol reaching in excess of 14 nmol 106 cells−1. Similarly, the accumulation of the 13C label during short term H13CO3− feeding revealed a massive redistribution of label into mannitol as well as rapid but saturating label accumulation into glucose and several amino acids including aspartate, glycine and serine. These results provide support to previous work suggesting that this species adopts C3 photosynthesis and that mannitol functions as a carbon store in E. huxleyi. Metabolites 2013-03-11 3 1 Article 10.3390/metabo3010168 168 184 2218-1989 2013-03-11 doi: 10.3390/metabo3010168 Toshihiro Obata Steffi Schoenefeld Ina Krahnert Susan Bergmann André Scheffel Alisdair Fernie http://www.mdpi.com/2218-1989/3/1/155 In the last decade, complex networks have widely been applied to the study of many natural and man-made systems, and to the extraction of meaningful information from the interaction structures created by genes and proteins. Nevertheless, less attention has been devoted to metabonomics, due to the lack of a natural network representation of spectral data. Here we define a technique for reconstructing networks from spectral data sets, where nodes represent spectral bins, and pairs of them are connected when their intensities follow a pattern associated with a disease. The structural analysis of the resulting network can then be used to feed standard data-mining algorithms, for instance for the classification of new (unlabeled) subjects. Furthermore, we show how the structure of the network is resilient to the presence of external additive noise, and how it can be used to extract relevant knowledge about the development of the disease. Metabolites 2013-03-11 3 1 Article 10.3390/metabo3010155 155 167 2218-1989 2013-03-11 doi: 10.3390/metabo3010155 Massimiliano Zanin David Papo José Solís Juan Espinosa Claudio Frausto-Reyes Pascual Anda Ricardo Sevilla-Escoboza Rider Jaimes-Reategui Stefano Boccaletti Ernestina Menasalvas Pedro Sousa http://www.mdpi.com/2218-1989/3/1/144 In animals, cobalamin (Cbl) is a cofactor for methionine synthase and methylmalonyl-CoA mutase (MCM), which utilizes methylcobalamin and 5′-deoxyadenosylcobalamin (AdoCbl), respectively. The cblA complementation class of inborn errors of Cbl metabolism in humans is one of three known disorders that affect AdoCbl synthesis. The gene responsible for cblA has been identified in humans (MMAA) as well as its homolog (meaB) in Methylobacterium extorquens. Recently, it has been reported that human MMAA plays an important role in the protection and reactivation of MCM in vitro. However, the physiological function of MMAA is largely unknown. In the present study, we isolated the cDNA encoding MMAA from Euglena gracilis Z, a photosynthetic flagellate. The deduced amino acid sequence of the cDNA shows 79%, 79%, 79% and 80% similarity to human, mouse, Danio rerio MMAAs and M. extorquens MeaB, respectively. The level of the MCM transcript was higher in Cbl-deficient cultures of E. gracilis than in those supplemented with Cbl. In contrast, no significant differences were observed in the levels of the MMAA transcript under the same two conditions. No significant difference in MCM activity was observed between Escherichia coli that expressed either MCM together with MMAA or expressed MCM alone. Metabolites 2013-02-18 3 1 Article 10.3390/metabo3010144 144 154 2218-1989 2013-02-18 doi: 10.3390/metabo3010144 Yukinori Yabuta Ryota Takamatsu Satoshi Kasagaki Fumio Watanabe http://www.mdpi.com/2218-1989/3/1/119 Lignocellulosic biomass is the future feedstock for the production of biofuel and bio-based chemicals. The pretreatment-hydrolysis product of biomass, so-called hydrolysate, contains not only fermentable sugars, but also compounds that inhibit its fermentability by microbes. To reduce the toxicity of hydrolysates as fermentation media, knowledge of the identity of inhibitors and their dynamics in hydrolysates need to be obtained. In the past decade, various studies have applied targeted metabolomics approaches to examine the composition of biomass hydrolysates. In these studies, analytical methods like HPLC, RP-HPLC, CE, GC-MS and LC-MS/MS were used to detect and quantify small carboxylic acids, furans and phenols. Through applying targeted metabolomics approaches, inhibitors were identified in hydrolysates and their dynamics in fermentation processes were monitored. However, to reveal the overall composition of different hydrolysates and to investigate its influence on hydrolysate fermentation performance, a non-targeted metabolomics study needs to be conducted. In this review, a non-targeted and generic metabolomics approach is introduced to explore inhibitor identification in biomass hydrolysates, and other similar metabolomics questions. Metabolites 2013-02-11 3 1 Review 10.3390/metabo3010119 119 143 2218-1989 2013-02-11 doi: 10.3390/metabo3010119 Ying Zha Peter Punt http://www.mdpi.com/2218-1989/3/1/101 Polyhydroxybutyrate (PHB) is a common carbon storage polymer among heterotrophic bacteria. It is also accumulated in some photoautotrophic cyanobacteria; however, the knowledge of how PHB accumulation is regulated in this group is limited. PHB synthesis in Synechocystis sp. PCC 6803 is initiated once macronutrients like phosphorus or nitrogen are limiting. We have previously reported a mutation in the gene sll0783 that impairs PHB accumulation in this cyanobacterium upon nitrogen starvation. In this study we present data which explain the observed phenotype. We investigated differences in intracellular localization of PHB synthase, metabolism, and the NADPH pool between wild type and mutant. Localization of PHB synthase was not impaired in the sll0783 mutant; however, metabolome analysis revealed a difference in sorbitol levels, indicating a more oxidizing intracellular environment than in the wild type. We confirmed this by directly measuring the NADPH/NADP ratio and by altering the intracellular redox state of wild type and sll0783 mutant. We were able to physiologically complement the mutant phenotype of diminished PHB synthase activity by making the intracellular environment more reducing. Our data illustrate that the NADPH pool is an important factor for regulation of PHB biosynthesis and metabolism, which is also of interest for potential biotechnological applications. Metabolites 2013-02-06 3 1 Article 10.3390/metabo3010101 101 118 2218-1989 2013-02-06 doi: 10.3390/metabo3010101 Waldemar Hauf Maximilian Schlebusch Jan Hüge Joachim Kopka Martin Hagemann Karl Forchhammer http://www.mdpi.com/2218-1989/3/1/72 Our knowledge on cyanobacterial molecular biology increased tremendously by the application of the “omics” techniques. Only recently, metabolomics was applied systematically to model cyanobacteria. Metabolomics, the quantitative estimation of ideally the complete set of cellular metabolites, is particularly well suited to mirror cellular metabolism and its flexibility under diverse conditions. Traditionally, small sets of metabolites are quantified in targeted metabolome approaches. The development of separation technologies coupled to mass-spectroscopy- or nuclear-magnetic-resonance-based identification of low molecular mass molecules presently allows the profiling of hundreds of metabolites of diverse chemical nature. Metabolome analysis was applied to characterize changes in the cyanobacterial primary metabolism under diverse environmental conditions or in defined mutants. The resulting lists of metabolites and their steady state concentrations in combination with transcriptomics can be used in system biology approaches. The application of stable isotopes in fluxomics, i.e. the quantitative estimation of carbon and nitrogen fluxes through the biochemical network, has only rarely been applied to cyanobacteria, but particularly this technique will allow the making of kinetic models of cyanobacterial systems. The further application of metabolomics in the concert of other “omics” technologies will not only broaden our knowledge, but will also certainly strengthen the base for the biotechnological application of cyanobacteria. Metabolites 2013-02-04 3 1 Review 10.3390/metabo3010072 72 100 2218-1989 2013-02-04 doi: 10.3390/metabo3010072 Doreen Schwarz Isabel Orf Joachim Kopka Martin Hagemann http://www.mdpi.com/2218-1989/3/1/47 1,8-Cineole is a widely distributed odorant that also shows physiological effects, but whose human metabolism has hitherto not been extensively investigated. The aim of the present study was, thus, to characterise the metabolites of 1,8-cineole, identified previously in human milk, after the oral intake of 100 mg of this substance. Special emphasis was placed on the enantiomeric composition of the metabolites since these data may provide important insights into potential biotransformation pathways, as well as potential biological activities of these substances, for example on the breastfed child. The volatile fraction of the human milk samples was therefore isolated via Solvent Assisted Flavour Evaporation (SAFE) and subjected to gas chromatography-mass spectrometry (GC-MS). The absolute concentrations of each metabolite were determined by matrix calibration with an internal standard, and the ratios of enantiomers were analysed on chiral capillaries. The concentrations varied over a broad range, from traces in the upper ng/kg region up to 40 µg/kg milk, with the exception of the main metabolite α2-hydroxy-1,8-cineole that showed concentrations of 100–250 µg/kg. Also, large inter- and intra-individual variations were recorded for the enantiomers, with nearly enantiomerically pure α2-hydroxy- and 3-oxo-1,8-cineole, while all other metabolites showed ratios of ~30:70 to 80:20. Metabolites 2013-01-30 3 1 Article 10.3390/metabo3010047 47 71 2218-1989 2013-01-30 doi: 10.3390/metabo3010047 Frauke Kirsch Andrea Buettner http://www.mdpi.com/2218-1989/3/1/33 In the present study, proton NMR-based metabonomics was applied on femoral arterial plasma samples collected from young male subjects (milk protein n = 12 in a crossover design; non-caloric control n = 8) at different time intervals (70, 220, 370 min) after heavy resistance training and intake of either a whey or calcium caseinate protein drink in order to elucidate the impact of the protein source on post-exercise metabolism, which is important for muscle hypertrophy. Dynamic changes in the post-exercise plasma metabolite profile consisted of fluctuations in alanine, beta-hydroxybutyrate, branched amino acids, creatine, glucose, glutamine, glutamate, histidine, lipids and tyrosine. In comparison with the intake of a non-caloric drink, the same pattern of changes in low-molecular weight plasma metabolites was found for both whey and caseinate intake. However, the study indicated that whey and caseinate protein intake had a different impact on low-density and very-low-density lipoproteins present in the blood, which may be ascribed to different effects of the two protein sources on the mobilization of lipid resources during energy deficiency. In conclusion, no difference in the effects on low-molecular weight metabolites as measured by proton NMR-based metabonomics was found between the two protein sources. Metabolites 2013-01-30 3 1 Article 10.3390/metabo3010033 33 46 2218-1989 2013-01-30 doi: 10.3390/metabo3010033 Christian Yde Ditte Ditlev Søren Reitelseder Hanne Bertram http://www.mdpi.com/2218-1989/3/1/24 The marine sponge Haliclona fulva (previously described as Reniera fulva) is widespread in the Mediterranean Sea. The chemical study of the sponge led to the isolation and identification of a new compound: 3-epi-cladocroic acid (1) alongside the previously reported cladocroic acid (2) and some other known compounds previously isolated. The structure was fully determined on the basis of extensive analysis by 1D and 2D NMR, as well as GC-MS/MS. The absolute configuration was determined by comparison of the experimental electronic circular dichroism (ECD) spectra with theoretically calculated spectra; these results may be extended to other asymetric cyclopropane carboxylic acids. Metabolites 2013-01-14 3 1 Article 10.3390/metabo3010024 24 32 2218-1989 2013-01-14 doi: 10.3390/metabo3010024 Grégory Genta-Jouve Olivier Thomas http://www.mdpi.com/2218-1989/3/1/1 Plant diseases caused by pathogenic bacteria or fungi cause major economic damage every year and destroy crop yields that could feed millions of people. Only by a thorough understanding of the interaction between plants and phytopathogens can we hope to develop strategies to avoid or treat the outbreak of large-scale crop pests. Here, we studied the interaction of plant-pathogen pairs at the metabolic level. We selected five plant-pathogen pairs, for which both genomes were fully sequenced, and constructed the corresponding genome-scale metabolic networks. We present theoretical investigations of the metabolic interactions and quantify the positive and negative effects a network has on the other when combined into a single plant-pathogen pair network. Merged networks were examined for both the native plant-pathogen pairs as well as all other combinations. Our calculations indicate that the presence of the parasite metabolic networks reduce the ability of the plants to synthesize key biomass precursors. While the producibility of some precursors is reduced in all investigated pairs, others are only impaired in specific plant-pathogen pairs. Interestingly, we found that the specific effects on the host’s metabolism are largely dictated by the pathogen and not by the host plant. We provide graphical network maps for the native plant-pathogen pairs to allow for an interactive interrogation. By exemplifying a systematic reconstruction of metabolic network pairs for five pathogen-host pairs and by outlining various theoretical approaches to study the interaction of plants and phytopathogens on a biochemical level, we demonstrate the potential of investigating pathogen-host interactions from the perspective of interacting metabolic networks that will contribute to furthering our understanding of mechanisms underlying a successful invasion and subsequent establishment of a parasite into a plant host. Metabolites 2013-01-08 3 1 Article 10.3390/metabo3010001 1 23 2218-1989 2013-01-08 doi: 10.3390/metabo3010001 Guangyou Duan Nils Christian Jens Schwachtje Dirk Walther Oliver Ebenhöh http://www.mdpi.com/2218-1989/2/4/1123 Gene targeting (GT) via homologous recombination allows precise modification of a target gene of interest. In a previous study, we successfully used GT to produce rice plants accumulating high levels of free tryptophan (Trp) in mature seeds and young leaves via targeted modification of a gene encoding anthranilate synthase—a key enzyme of Trp biosynthesis. Here, we performed metabolome analysis in the leaves and mature seeds of GT plants. Of 72 metabolites detected in both organs, a total of 13, including Trp, involved in amino acid metabolism, accumulated to levels >1.5-fold higher than controls in both leaves and mature seeds of GT plants. Surprisingly, the contents of certain metabolites valuable for both humans and livestock, such as γ-aminobutyric acid and vitamin B, were significantly increased in mature seeds of GT plants. Moreover, untargeted analysis using LC-MS revealed that secondary metabolites, including an indole alkaloid, 2-[2-hydroxy-3-β-d-glucopyranosyloxy-1-(1H-indol-3-yl)propyl] tryptophan, also accumulate to higher levels in GT plants. Some of these metabolite changes in plants produced via GT are similar to those observed in plants over expressing mutated genes, thus demonstrating that in vivo protein engineering via GT can be an effective approach to metabolic engineering in crops. Metabolites 2012-12-18 2 4 Article 10.3390/metabo2041123 1123 1138 2218-1989 2012-12-18 doi: 10.3390/metabo2041123 Hiroaki Saika Akira Oikawa Ryo Nakabayashi Fumio Matsuda Kazuki Saito Seiichi Toki http://www.mdpi.com/2218-1989/2/4/1090 The goals of metabolic engineering are well-served by the biological information provided by metabolomics: information on how the cell is currently using its biochemical resources is perhaps one of the best ways to inform strategies to engineer a cell to produce a target compound. Using the analysis of extracellular or intracellular levels of the target compound (or a few closely related molecules) to drive metabolic engineering is quite common. However, there is surprisingly little systematic use of metabolomics datasets, which simultaneously measure hundreds of metabolites rather than just a few, for that same purpose. Here, we review the most common systematic approaches to integrating metabolite data with metabolic engineering, with emphasis on existing efforts to use whole-metabolome datasets. We then review some of the most common approaches for computational modeling of cell-wide metabolism, including constraint-based models, and discuss current computational approaches that explicitly use metabolomics data. We conclude with discussion of the broader potential of computational approaches that systematically use metabolomics data to drive metabolic engineering. Metabolites 2012-12-14 2 4 Review 10.3390/metabo2041090 1090 1122 2218-1989 2012-12-14 doi: 10.3390/metabo2041090 Robert Dromms Mark Styczynski http://www.mdpi.com/2218-1989/2/4/1060 The glycosaminoglycan heparan sulphate (HS) has a heterogeneous structure; evidence shows that specific structures may be responsible for specific functions in biological processes such as blood coagulation and regulation of growth factor signalling. This review summarises the different experimental tools and methods developed to provide more rapid methods for studying the structure and functions of HS. Rapid and sensitive methods for the facile purification of HS, from tissue and cell sources are reviewed. Data sets for the structural analysis are often complex and include multiple sample sets, therefore different software and tools have been developed for the analysis of different HS data sets. These can be readily applied to chromatographic data sets for the simplification of data (e.g., charge separation using strong anion exchange chromatography and from size separation using gel filtration techniques. Finally, following the sequencing of the human genome, research has rapidly advanced with the introduction of high throughput technologies to carry out simultaneous analyses of many samples. Microarrays to study macromolecular interactions (including glycan arrays) have paved the way for bioassay technologies which utilize cell arrays to study the effects of multiple macromolecules on cells. Glycan bioassay technologies are described in which immobilisation techniques for saccharides are exploited to develop a platform to probe cell responses such as signalling pathway activation. This review aims at reviewing available techniques and tools for the purification, analysis and bioassay of HS saccharides in biological systems using “glycomics” approaches. Metabolites 2012-11-28 2 4 Review 10.3390/metabo2041060 1060 1089 2218-1989 2012-11-28 doi: 10.3390/metabo2041060 Tania Puvirajesinghe Jeremy Turnbull http://www.mdpi.com/2218-1989/2/4/1031 Specialized compounds from photosynthetic organisms serve as rich resources for drug development. From aspirin to atropine, plant-derived natural products have had a profound impact on human health. Technological advances provide new opportunities to access these natural products in a metabolic context. Here, we describe a database and platform for storing, visualizing and statistically analyzing metabolomics data from fourteen medicinal plant species. The metabolomes and associated transcriptomes (RNAseq) for each plant species, gathered from up to twenty tissue/organ samples that have experienced varied growth conditions and developmental histories, were analyzed in parallel. Three case studies illustrate different ways that the data can be integrally used to generate testable hypotheses concerning the biochemistry, phylogeny and natural product diversity of medicinal plants. Deep metabolomics analysis of Camptotheca acuminata exemplifies how such data can be used to inform metabolic understanding of natural product chemical diversity and begin to formulate hypotheses about their biogenesis. Metabolomics data from Prunella vulgaris, a species that contains a wide range of antioxidant, antiviral, tumoricidal and anti-inflammatory constituents, provide a case study of obtaining biosystematic and developmental fingerprint information from metabolite accumulation data in a little studied species. Digitalis purpurea, well known as a source of cardiac glycosides, is used to illustrate how integrating metabolomics and transcriptomics data can lead to identification of candidate genes encoding biosynthetic enzymes in the cardiac glycoside pathway. Medicinal Plant Metabolomics Resource (MPM) [1] provides a framework for generating experimentally testable hypotheses about the metabolic networks that lead to the generation of specialized compounds, identifying genes that control their biosynthesis and establishing a basis for modeling metabolism in less studied species. The database is publicly available and can be used by researchers in medicine and plant biology. Metabolites 2012-11-21 2 4 Article 10.3390/metabo2041031 1031 1059 2218-1989 2012-11-21 doi: 10.3390/metabo2041031 Eve Wurtele Joe Chappell A. Jones Mary Celiz Nick Ransom Manhoi Hur Ludmila Rizshsky Matthew Crispin Philip Dixon Jia Liu Mark P.Widrlechner Basil Nikolau http://www.mdpi.com/2218-1989/2/4/1012 Model-based analyses have become an integral part of modern metabolic engineering and systems biology in order to gain knowledge about complex and not directly observable cellular processes. For quantitative analyses, not only experimental data, but also measurement errors, play a crucial role. The total measurement error of any analytical protocol is the result of an accumulation of single errors introduced by several processing steps. Here, we present a framework for the quantification of intracellular metabolites, including error propagation during metabolome sample processing. Focusing on one specific protocol, we comprehensively investigate all currently known and accessible factors that ultimately impact the accuracy of intracellular metabolite concentration data. All intermediate steps are modeled, and their uncertainty with respect to the final concentration data is rigorously quantified. Finally, on the basis of a comprehensive metabolome dataset of Corynebacterium glutamicum, an integrated error propagation analysis for all parts of the model is conducted, and the most critical steps for intracellular metabolite quantification are detected. Metabolites 2012-11-21 2 4 Article 10.3390/metabo2041012 1012 1030 2218-1989 2012-11-21 doi: 10.3390/metabo2041012 Jana Tillack Nicole Paczia Katharina Nöh Wolfgang Wiechert Stephan Noack http://www.mdpi.com/2218-1989/2/4/1004 Glycosylation is among the most common post translation modifications of proteins in humans. Decades of research have demonstrated that aberrant glycosylation can lead to malignant degeneration. Glycoproteomic studies in the past several years have identified techniques that can successfully characterize a glycan or glycan profile associated with a high-grade dysplastic or malignant state. This review summarizes the current glycomic and glycoproteomic literature with specific reference to esophageal cancer. Esophageal adenocarcinoma represents a highly morbid and mortal cancer with a defined progression from metaplasia (Barrett's esophagus) to dysplasia to neoplasia. This disease is highlighted because (1) differences in glycan profiles between the stages of disease progression have been described in the glycoproteomic literature; (2) a glycan biomarker that identifies a given stage may be used as a predictor of disease progression and thus may have significant influence over clinical management; and (3) the differences in glycan profiles between disease and disease-free states in esophageal cancer are more dramatic than in other cancers. Metabolites 2012-11-21 2 4 Review 10.3390/metabo2041004 1004 1011 2218-1989 2012-11-21 doi: 10.3390/metabo2041004 Sanjay Mohanty Athanasios Tsiouris Zane Hammoud http://www.mdpi.com/2218-1989/2/4/983 TGFb signaling plays a major role in the reorganization of liver tissue upon injury and is an important driver of chronic liver disease. This is achieved by a deep impact on a cohort of cellular functions. To comprehensively assess the full range of affected metabolic functions, transcript changes of cultured mouse hepatocytes were analyzed with a novel method (ModeScore), which predicts the activity of metabolic functions by scoring transcript expression changes with 987 reference flux distributions, which yielded the following hypotheses. TGFb multiplies down-regulation of most metabolic functions occurring in culture stressed controls. This is especially pronounced for tyrosine degradation, urea synthesis, glucuronization capacity, and cholesterol synthesis. Ethanol degradation and creatine synthesis are down-regulated only in TGFb treated hepatocytes, but not in the control. Among the few TGFb dependently up-regulated functions, synthesis of various collagens is most pronounced. Further interesting findings include: down-regulation of glucose export is postponed by TGFb, TGFb up-regulates the synthesis capacity of ketone bodies only as an early response, TGFb suppresses the strong up-regulation of Vanin, and TGFb induces re-formation of ceramides and sphingomyelin. Metabolites 2012-11-21 2 4 Article 10.3390/metabo2040983 983 1003 2218-1989 2012-11-21 doi: 10.3390/metabo2040983 Andreas Hoppe Iryna Ilkavets Steven Dooley Hermann-Georg Holzhütter http://www.mdpi.com/2218-1989/2/4/959 Switchgrass (Panicum virgatum L., SG) is a perennial grass presently used for forage and being developed as a bioenergy crop for conversion of cell wall carbohydrates to biofuels. Up to 50% of the cell wall associated carbohydrates are xylan. SG was analyzed for xylan structural features at variable harvest maturities. Xylan from each of three maturities was isolated using classical alkaline extraction to yield fractions (Xyl A and B) with varying compositional ratios. The Xyl B fraction was observed to decrease with plant age. Xylan samples were subsequently prepared for structure analysis by digesting with pure endo-xylanase, which preserved side-groups, or a commercial carbohydrase preparation favored for biomass conversion work. Enzymatic digestion products were successfully permethylated and analyzed by reverse-phase liquid chromatography with mass spectrometric detection (RP-HPLC-MSn). This method is advantageous compared to prior work on plant biomass because it avoids isolation of individual arabinoxylan oligomers. The use of RP-HPLC- MSn differentiated 14 structural oligosaccharides (d.p. 3–9) from the monocomponent enzyme digestion and nine oligosaccharide structures (d.p. 3–9) from hydrolysis with a cellulase enzyme cocktail. The distribution of arabinoxylan oligomers varied depending upon the enzyme(s) applied but did not vary with harvest maturity. Metabolites 2012-11-19 2 4 Article 10.3390/metabo2040959 959 982 2218-1989 2012-11-19 doi: 10.3390/metabo2040959 Michael Bowman Bruce Dien Patricia O'Bryan Gautam Sarath Michael Cotta http://www.mdpi.com/2218-1989/2/4/940 Protein complexes are classified and have been charted in several large-scale screening studies in prokaryotes. These complexes are organized in a factory-like fashion to optimize protein production and metabolism. Central components are conserved between different prokaryotes; major complexes involve carbohydrate, amino acid, fatty acid and nucleotide metabolism. Metabolic adaptation changes protein complexes according to environmental conditions. Protein modification depends on specific modifying enzymes. Proteins such as trigger enzymes display condition-dependent adaptation to different functions by participating in several complexes. Several bacterial pathogens adapt rapidly to intracellular survival with concomitant changes in protein complexes in central metabolism and optimize utilization of their favorite available nutrient source. Regulation optimizes protein costs. Master regulators lead to up- and downregulation in specific subnetworks and all involved complexes. Long protein half-life and low level expression detaches protein levels from gene expression levels. However, under optimal growth conditions, metabolite fluxes through central carbohydrate pathways correlate well with gene expression. In a system-wide view, major metabolic changes lead to rapid adaptation of complexes and feedback or feedforward regulation. Finally, prokaryotic enzyme complexes are involved in crowding and substrate channeling. This depends on detailed structural interactions and is verified for specific effects by experiments and simulations. Metabolites 2012-11-16 2 4 Review 10.3390/metabo2040940 940 958 2218-1989 2012-11-16 doi: 10.3390/metabo2040940 Beate Krüger Chunguang Liang Florian Prell Astrid Fieselmann Andres Moya Stefan Schuster Uwe Völker Thomas Dandekar http://www.mdpi.com/2218-1989/2/4/913 Glycans are important partners in many biological processes, including carcinogenesis. The rapidly developing field of functional glycomics becomes one of the frontiers of biology and biomedicine. Aberrant glycosylation of proteins and lipids occurs commonly during malignant transformation and leads to the expression of specific tumor-associated glycans. The appearance of aberrant glycans on carcinoma cells is typically associated with grade, invasion, metastasis and overall poor prognosis. Cancer-associated carbohydrates are mostly located on the surface of cancer cells and are therefore potential diagnostic biomarkers. Currently, there is increasing interest in cancer-associated aberrant glycosylation, with growing numbers of characteristic cancer targets being detected every day. Breast and ovarian cancer are the most common and lethal malignancies in women, respectively, and potential glycan biomarkers hold promise for early detection and targeted therapies. However, the acceleration of research and comprehensive multi-target investigation of cancer-specific glycans could only be successfully achieved with the help of a combination of novel high-throughput glycomic approaches. Metabolites 2012-11-14 2 4 Review 10.3390/metabo2040913 913 939 2218-1989 2012-11-14 doi: 10.3390/metabo2040913 Tatiana Pochechueva Francis Jacob Andre Fedier Viola Heinzelmann-Schwarz http://www.mdpi.com/2218-1989/2/4/891 Kinetic modeling of metabolic pathways has important applications in metabolic engineering, but significant challenges still remain. The difficulties faced vary from finding best-fit parameters in a highly multidimensional search space to incomplete parameter identifiability. To meet some of these challenges, an ensemble modeling method is developed for characterizing a subset of kinetic parameters that give statistically equivalent goodness-of-fit to time series concentration data. The method is based on the incremental identification approach, where the parameter estimation is done in a step-wise manner. Numerical efficacy is achieved by reducing the dimensionality of parameter space and using efficient random parameter exploration algorithms. The shift toward using model ensembles, instead of the traditional “best-fit” models, is necessary to directly account for model uncertainty during the application of such models. The performance of the ensemble modeling approach has been demonstrated in the modeling of a generic branched pathway and the trehalose pathway in Saccharomyces cerevisiae using generalized mass action (GMA) kinetics. Metabolites 2012-11-12 2 4 Article 10.3390/metabo2040891 891 912 2218-1989 2012-11-12 doi: 10.3390/metabo2040891 Gengjie Jia Gregory Stephanopoulos Rudiyanto Gunawan http://www.mdpi.com/2218-1989/2/4/872 Quantitative knowledge of intracellular fluxes in metabolic networks is invaluable for inferring metabolic system behavior and the design principles of biological systems. However, intracellular reaction rates can not often be calculated directly but have to be estimated; for instance, via 13C-based metabolic flux analysis, a model-based interpretation of stable carbon isotope patterns in intermediates of metabolism. Existing software such as FiatFlux, OpenFLUX or 13CFLUX supports experts in this complex analysis, but requires several steps that have to be carried out manually, hence restricting the use of this software for data interpretation to a rather small number of experiments. In this paper, we present Flux-P as an approach to automate and standardize 13C-based metabolic flux analysis, using the Bio-jETI workflow framework. Exemplarily based on the FiatFlux software, it demonstrates how services can be created that carry out the different analysis steps autonomously and how these can subsequently be assembled into software workflows that perform automated, high-throughput intracellular flux analysis of high quality and reproducibility. Besides significant acceleration and standardization of the data analysis, the agile workflow-based realization supports flexible changes of the analysis workflows on the user level, making it easy to perform custom analyses. Metabolites 2012-11-12 2 4 Article 10.3390/metabo2040872 872 890 2218-1989 2012-11-12 doi: 10.3390/metabo2040872 Birgitta E. Ebert Anna-Lena Lamprecht Bernhard Steffen Lars M. Blank http://www.mdpi.com/2218-1989/2/4/844 Metabolism and signalling are tightly coupled in bacteria. Combining several theoretical approaches, a core model is presented that describes transcriptional and allosteric control of glycolysis in Escherichia coli. Experimental data based on microarrays, signalling components and extracellular metabolites are used to estimate kinetic parameters. A newly designed strain was used that adjusts the incoming glucose flux into the system and allows a kinetic analysis. Based on the results, prediction for intracelluar metabolite concentrations over a broad range of the growth rate could be performed and compared with data from literature. Metabolites 2012-11-12 2 4 Article 10.3390/metabo2040844 844 871 2218-1989 2012-11-12 doi: 10.3390/metabo2040844 Andreas Kremling Anna Goehler Knut Jahreis Markus Nees Benedikt Auerbach Wolfgang Schmidt-Heck Öznur Kökpinar Robert Geffers Ursula Rinas Katja Bettenbrock http://www.mdpi.com/2218-1989/2/4/818 Enzyme kinetics for systems biology should ideally yield information about the enzyme’s activity under in vivo conditions, including such reaction features as substrate cooperativity, reversibility and allostery, and be applicable to enzymatic reactions with multiple substrates. A large body of enzyme-kinetic data in the literature is based on the uni-substrate Michaelis–Menten equation, which makes unnatural assumptions about enzymatic reactions (e.g., irreversibility), and its application in systems biology models is therefore limited. To overcome this limitation, we have utilised NMR time-course data in a combined theoretical and experimental approach to parameterize the generic reversible Hill equation, which is capable of describing enzymatic reactions in terms of all the properties mentioned above and has fewer parameters than detailed mechanistic kinetic equations; these parameters are moreover defined operationally. Traditionally, enzyme kinetic data have been obtained from initial-rate studies, often using assays coupled to NAD(P)H-producing or NAD(P)H-consuming reactions. However, these assays are very labour-intensive, especially for detailed characterisation of multi-substrate reactions. We here present a cost-effective and relatively rapid method for obtaining enzyme-kinetic parameters from metabolite time-course data generated using NMR spectroscopy. The method requires fewer runs than traditional initial-rate studies and yields more information per experiment, as whole time-courses are analyzed and used for parameter fitting. Additionally, this approach allows real-time simultaneous quantification of all metabolites present in the assay system (including products and allosteric modifiers), which demonstrates the superiority of NMR over traditional spectrophotometric coupled enzyme assays. The methodology presented is applied to the elucidation of kinetic parameters for two coupled glycolytic enzymes from Escherichia coli (phosphoglucose isomerase and phosphofructokinase). 31P-NMR time-course data were collected by incubating cell extracts with substrates, products and modifiers at different initial concentrations. NMR kinetic data were subsequently processed using a custom software module written in the Python programming language, and globally fitted to appropriately modified Hill equations. Metabolites 2012-11-06 2 4 Article 10.3390/metabo2040818 818 843 2218-1989 2012-11-06 doi: 10.3390/metabo2040818 Johann J. Eicher Jacky L. Snoep Johann M. Rohwer http://www.mdpi.com/2218-1989/2/4/796 The suggested approach makes it feasible to screen large metabolomics data, sample sets with retained data quality or to retrieve significant metabolic information from small sample sets that can be verified over multiple studies. Hierarchical multivariate curve resolution (H-MCR), followed by orthogonal partial least squares discriminant analysis (OPLS-DA) was used for processing and classification of gas chromatography/time of flight mass spectrometry (GC/TOFMS) data characterizing human serum samples collected in a study of strenuous physical exercise. The efficiency of predictive H-MCR processing of representative sample subsets, selected by chemometric approaches, for generating high quality data was proven. Extensive model validation by means of cross-validation and external predictions verified the robustness of the extracted metabolite patterns in the data. Comparisons of extracted metabolite patterns between models emphasized the reliability of the methodology in a biological information context. Furthermore, the high predictive power in longitudinal data provided proof for the potential use in clinical diagnosis. Finally, the predictive metabolite pattern was interpreted physiologically, highlighting the biological relevance of the diagnostic pattern. Metabolites 2012-10-31 2 4 Article 10.3390/metabo2040796 796 817 2218-1989 2012-10-31 doi: 10.3390/metabo2040796 Elin Thysell Elin Chorell Michael Svensson Pär Jonsson Henrik Antti http://www.mdpi.com/2218-1989/2/4/775 Several metabolomic software programs provide methods for peak picking, retention time alignment and quantification of metabolite features in LC/MS-based metabolomics. Statistical analysis, however, is needed in order to discover those features significantly altered between samples. By comparing the retention time and MS/MS data of a model compound to that from the altered feature of interest in the research sample, metabolites can be then unequivocally identified. This paper reports on a comprehensive overview of a workflow for statistical analysis to rank relevant metabolite features that will be selected for further MS/MS experiments. We focus on univariate data analysis applied in parallel on all detected features. Characteristics and challenges of this analysis are discussed and illustrated using four different real LC/MS untargeted metabolomic datasets. We demonstrate the influence of considering or violating mathematical assumptions on which univariate statistical test rely, using high-dimensional LC/MS datasets. Issues in data analysis such as determination of sample size, analytical variation, assumption of normality and homocedasticity, or correction for multiple testing are discussed and illustrated in the context of our four untargeted LC/MS working examples. Metabolites 2012-10-18 2 4 Article 10.3390/metabo2040775 775 795 2218-1989 2012-10-18 doi: 10.3390/metabo2040775 Maria Vinaixa Sara Samino Isabel Saez Jordi Duran Joan J. Guinovart Oscar Yanes http://www.mdpi.com/2218-1989/2/4/756 Escherichia coli is a widely used microorganism in biotechnological processes. An obvious goal for current scientific and technical research in this field is the search for new tools to optimize productivity. Usually glucose is the preferred carbon source in biotechnological applications. In E. coli, glucose is taken up by the phosphoenolpyruvate-dependent glucose phosphotransferase system (PTS). The regulation of the ptsG gene for the glucose transporter is very complex and involves several regulatory proteins. Recently, a novel posttranscriptional regulation system has been identified which consists of a small regulatory RNA SgrS and a small regulatory polypeptide called SgrT. During the accumulation of glucose-6-phosphate or fructose-6-phosphate, SgrS is involved in downregulation of ptsG mRNA stability, whereas SgrT inhibits glucose transport activity by a yet unknown mechanism. The function of SgrS has been studied intensively. In contrast, the knowledge about the function of SgrT is still limited. Therefore, in this paper, we focused our interest on the regulation of glucose transport activity by SgrT. We identified the SgrT target sequence within the glucose transporter and characterized the interaction in great detail. Finally, we suggest a novel experimental approach to regulate artificially carbohydrate uptake in E. coli to minimize metabolic overflow in biotechnological applications. Metabolites 2012-10-16 2 4 Article 10.3390/metabo2040756 756 774 2218-1989 2012-10-16 doi: 10.3390/metabo2040756 Anne Kosfeld Knut Jahreis http://www.mdpi.com/2218-1989/2/4/733 Ion mobility spectrometry combined with multi-capillary columns (MCC/IMS) is a well known technology for detecting volatile organic compounds (VOCs). We may utilize MCC/IMS for scanning human exhaled air, bacterial colonies or cell lines, for example. Thereby we gain information about the human health status or infection threats. We may further study the metabolic response of living cells to external perturbations. The instrument is comparably cheap, robust and easy to use in every day practice. However, the potential of the MCC/IMS methodology depends on the successful application of computational approaches for analyzing the huge amount of emerging data sets. Here, we will review the state of the art and highlight existing challenges. First, we address methods for raw data handling, data storage and visualization. Afterwards we will introduce de-noising, peak picking and other pre-processing approaches. We will discuss statistical methods for analyzing correlations between peaks and diseases or medical treatment. Finally, we study up-to-date machine learning techniques for identifying robust biomarker molecules that allow classifying patients into healthy and diseased groups. We conclude that MCC/IMS coupled with sophisticated computational methods has the potential to successfully address a broad range of biomedical questions. While we can solve most of the data pre-processing steps satisfactorily, some computational challenges with statistical learning and model validation remain. Metabolites 2012-10-16 2 4 Review 10.3390/metabo2040733 733 755 2218-1989 2012-10-16 doi: 10.3390/metabo2040733 Anne-Christin Hauschild Till Schneider Josch Pauling Kathrin Rupp Mi Jang Jörg Baumbach Jan Baumbach http://www.mdpi.com/2218-1989/2/4/717 Metabolite profiling of E. coli W3110 and the isogenic DrelA mutant cells was used to characterize the RelA-dependent stringent control of metabolism under different growth conditions. Metabolic profiles were obtained by gas chromatography–mass spectrometry (GC-MS) analysis and revealed significant differences between E. coli strains grown at different conditions. Major differences between the two strains were assessed in the levels of amino acids and fatty acids and their precursor metabolites, especially when growing at the lower dilution rates, demonstrating differences in their metabolic behavior. Despite the fatty acid biosynthesis being the most affected due to the lack of the RelA activity, other metabolic pathways involving succinate, lactate and threonine were also affected. Overall, metabolite profiles indicate that under nutrient-limiting conditions the RelA-dependent stringent response may be elicited and promotes key changes in the E. coli metabolism. Metabolites 2012-10-12 2 4 Article 10.3390/metabo2040717 717 732 2218-1989 2012-10-12 doi: 10.3390/metabo2040717 Sónia Carneiro Silas G. Villas-Bôas Eugénio C. Ferreira Isabel Rocha http://www.mdpi.com/2218-1989/2/4/701 Hepatocellular carcinoma (HCC) accounts for most liver cancer cases worldwide. Contraction of the hepatitis C virus (HCV) is considered a major risk factor for liver cancer. In order to identify the risk of cancer, metabolic profiling of serum samples from patients with HCC (n=40) and HCV (n=22) was performed by 1H nuclear magnetic resonance spectroscopy. Multivariate statistical analysis showed a distinct separation of the two patient cohorts, indicating a distinct metabolic difference between HCC and HCV patient groups based on signals from lipids and other individual metabolites. Univariate analysis showed that three metabolites (choline, valine and creatinine) were significantly altered in HCC. A PLS-DA model based on these three metabolites showed a sensitivity of 80%, specificity of 71% and an area under the receiver operating curve of 0.83, outperforming the clinical marker alpha-fetoprotein (AFP). The robustness of the model was tested using Monte-Carlo cross validation (MCCV). This study showed that metabolite profiling could provide an alternative approach for HCC screening in HCV patients, many of whom have high risk for developing liver cancer. Metabolites 2012-10-10 2 4 Article 10.3390/metabo2040701 701 716 2218-1989 2012-10-10 doi: 10.3390/metabo2040701 Siwei Wei Yuliana Suryani G. A. Nagana Gowda Nicholas Skill Mary Maluccio Daniel Raftery http://www.mdpi.com/2218-1989/2/4/667 ATP delivery and its usage are achieved by cycling of respective intermediates through interconnected coupled reactions. At steady state, cycling between coupled reactions always occurs at zero resistance of the whole cycle without dissipation of free energy. The cross-bridge cycle can also be described by a system of coupled reactions: one energising reaction, which energises myosin heads by coupled ATP splitting, and one de-energising reaction, which transduces free energy from myosin heads to coupled actin movement. The whole cycle of myosin heads via cross-bridge formation and dissociation proceeds at zero resistance. Dissipation of free energy from coupled reactions occurs whenever the input potential overcomes the counteracting output potential. In addition, dissipation is produced by uncoupling. This is brought about by a load dependent shortening of the cross-bridge stroke to zero, which allows isometric force generation without mechanical power output. The occurrence of maximal efficiency is caused by uncoupling. Under coupled conditions, Hill’s equation (velocity as a function of load) is fulfilled. In addition, force and shortening velocity both depend on [Ca2+]. Muscular fatigue is triggered when ATP consumption overcomes ATP delivery. As a result, the substrate of the cycle, [MgATP2−], is reduced. This leads to a switch off of cycling and ATP consumption, so that a recovery of [ATP] is possible. In this way a potentially harmful, persistent low energy state of the cell can be avoided. Metabolites 2012-10-08 2 4 Article 10.3390/metabo2040667 667 700 2218-1989 2012-10-08 doi: 10.3390/metabo2040667 Frank Diederichs http://www.mdpi.com/2218-1989/2/4/648 The availability of specific exoglycosidases alongside a spectral library of O-linked oligosaccharide collision induced dissociation (CID) MS fragments, UniCarb-DB, provides a pathway to make the elucidation of O-linked oligosaccharides more efficient. Here, we advise an approach of exoglycosidase-digestion of O-linked oligosaccharide mixtures, for structures that do not provide confirmative spectra. The combination of specific exoglycosidase digestion and MS2 matching of the exoglycosidase products with structures from UniCarb-DB, allowed the assignment of unknown structures. This approach was illustrated by treating sialylated core 2 O-linked oligosaccharides, released from the human synovial glycoprotein (lubricin), with a α2–3 specific sialidase. This methodology demonstrated the exclusive 3 linked nature of the sialylation of core 2 oligosaccharides on lubricin. When specific exoglycosidases were not available, MS3 spectral matching using standards was used. This allowed the unusual 4-linked terminal GlcNAc epitope in a porcine stomach to be identified in the GlcNAc1-4Galb1–3(GlcNAcb1-6)GalNAcol structure, indicating the antibacterial epitope GlcNAca1–4. In total, 13 structures were identified using exoglycosidase and MSn, alongside UniCarb-DB fragment spectra comparison. UniCarb-DB could also be used to identify the specificity of unknown exoglycosidases in human saliva. Endogenous salivary exoglycosidase activity on mucin oligosaccharides could be monitored by comparing the generated tandem MS spectra with those present in UniCarb-DB, showing that oral exoglycosidases were dominated by sialidases with a higher activity towards 3-linked sialic acid rather than 6-linked sialic acid. Metabolites 2012-10-08 2 4 Article 10.3390/metabo2040648 648 666 2218-1989 2012-10-08 doi: 10.3390/metabo2040648 Liaqat Ali Diarmuid T. Kenny Catherine A. Hayes Niclas G. Karlsson http://www.mdpi.com/2218-1989/2/3/632 Metabolism has frequently been analyzed from a network perspective. A major question is how network properties correlate with biological features like growth rates, flux patterns and enzyme essentiality. Using methods from graph theory as well as established topological categories of metabolic systems, we analyze the essentiality of metabolic reactions depending on the growth medium and identify the topological footprint of these reactions. We find that the typical topological context of a medium-dependent essential reaction is systematically different from that of a globally essential reaction. In particular, we observe systematic differences in the distribution of medium-dependent essential reactions across three-node subgraphs (the network motif signature of medium-dependent essential reactions) compared to globally essential or globally redundant reactions. In this way, we provide evidence that the analysis of metabolic systems on the few-node subgraph scale is meaningful for explaining dynamic patterns. This topological characterization of medium-dependent essentiality provides a better understanding of the interplay between reaction deletions and environmental conditions. Metabolites 2012-09-24 2 3 Article 10.3390/metabo2030632 632 647 2218-1989 2012-09-24 doi: 10.3390/metabo2030632 Nikolaus Sonnenschein Carsten Marr Marc-Thorsten Hütt http://www.mdpi.com/2218-1989/2/3/614 Inferring decreased or increased metabolic functions from transcript profiles is at first sight a bold and speculative attempt because of the functional layers in between: proteins, enzymatic activities, and reaction fluxes. However, the growing interest in this field can easily be explained by two facts: the high quality of genome-scale metabolic network reconstructions and the highly developed technology to obtain genome-covering RNA profiles. Here, an overview of important algorithmic approaches is given by means of criteria by which published procedures can be classified. The frontiers of the methods are sketched and critical voices are being heard. Finally, an outlook for the prospects of the field is given. Metabolites 2012-09-12 2 3 Review 10.3390/metabo2030614 614 631 2218-1989 2012-09-12 doi: 10.3390/metabo2030614 Andreas Hoppe http://www.mdpi.com/2218-1989/2/3/596 STX209 is an exploratory drug comprising the single, active R-enantiomer of baclofen which is in later stage clinical trials for the treatment of fragile x syndrome (FXS) and autism spectrum disorders (ASD). New clinical data in this article on the metabolism and pharmacokinetics of the R- and S-enantiomers of baclofen presents scientific evidence for stereoselective metabolism of only S-baclofen to an abundant oxidative deamination metabolite that is sterically resolved as the S-enantiomeric configuration. This metabolite undergoes some further metabolism by glucuronide conjugation. Consequences of this metabolic difference are a lower Cmax and lower early plasma exposure of S-baclofen compared to R-baclofen and marginally lower urinary excretion of S-baclofen after racemic baclofen administration. These differences introduce compound-related exposure variances in humans in which subjects dosed with racemic baclofen are exposed to a prominent metabolite of baclofen whilst subjects dosed with STX209 are not. For potential clinical use, our findings suggest that STX209 has the advantage of being a biologically defined and active enantiomer. Metabolites 2012-09-11 2 3 Article 10.3390/metabo2030596 596 613 2218-1989 2012-09-11 doi: 10.3390/metabo2030596 Raymundo Sanchez-Ponce Li-Quan Wang Wei Lu Jana von Hehn Maryann Cherubini Roger Rush http://www.mdpi.com/2218-1989/2/3/567 A minimal cut set is a minimal set of reactions whose inactivation would guarantee a failure in a certain network function or functions. Minimal cut sets (MCSs) were initially developed from the metabolic pathway analysis method (MPA) of elementary modes (EMs); they provide a way of identifying target genes for eliminating a certain objective function from a holistic perspective that takes into account the structure of the whole metabolic network. The concept of MCSs is fairly new and still being explored and developed; the initial concept has developed into a generalized form and its similarity to other network characterizations are discussed. MCSs can be used in conjunction with other constraints-based methods to get a better understanding of the capability of metabolic networks and the interrelationship between metabolites and enzymes/genes. The concept could play an important role in systems biology by contributing to fields such as metabolic and genetic engineering where it could assist in finding ways of producing industrially relevant compounds from renewable resources, not only for economical, but also for sustainability, reasons. Metabolites 2012-09-11 2 3 Review 10.3390/metabo2030567 567 595 2218-1989 2012-09-11 doi: 10.3390/metabo2030567 Sangaalofa T. Clark Wynand S. Verwoerd http://www.mdpi.com/2218-1989/2/3/553 Due to their sessile lifestyle, plants are exposed to a large set of environmental cues. In order to cope with changes in environmental conditions a multitude of complex strategies to regulate metabolism has evolved. The complexity is mainly attributed to interlaced regulatory circuits between genes, proteins and metabolites and a high degree of cellular compartmentalization. The genetic model plant Arabidopsis thaliana was intensely studied to characterize adaptive traits to a changing environment. The availability of genetically distinct natural populations has made it an attractive system to study plant-environment interactions. The impact on metabolism caused by changing environmental conditions can be estimated by mathematical approaches and deepens the understanding of complex biological systems. In combination with experimental high-throughput technologies this provides a promising platform to develop in silico models which are not only able to reproduce but also to predict metabolic phenotypes and to allow for the interpretation of plant physiological mechanisms leading to successful adaptation to a changing environment. Here, we provide an overview of mathematical approaches to analyze plant metabolism, with experimental procedures being used to validate their output, and we discuss them in the context of establishing a comprehensive understanding of plant-environment interactions. Metabolites 2012-09-06 2 3 Review 10.3390/metabo2030553 553 566 2218-1989 2012-09-06 doi: 10.3390/metabo2030553 Thomas Nägele Wolfram Weckwerth http://www.mdpi.com/2218-1989/2/3/529 One of the challenging tasks in systems biology is to understand how molecular networks give rise to emergent functionality and whether universal design principles apply to molecular networks. To achieve this, the biophysical, evolutionary and physiological constraints that act on those networks need to be identified in addition to the characterisation of the molecular components and interactions. Then, the cellular “task” of the network—its function—should be identified. A network contributes to organismal fitness through its function. The premise is that the same functions are often implemented in different organisms by the same type of network; hence, the concept of design principles. In biology, due to the strong forces of selective pressure and natural selection, network functions can often be understood as the outcome of fitness optimisation. The hypothesis of fitness optimisation to understand the design of a network has proven to be a powerful strategy. Here, we outline the use of several optimisation principles applied to biological networks, with an emphasis on metabolic regulatory networks. We discuss the different objective functions and constraints that are considered and the kind of understanding that they provide. Metabolites 2012-08-29 2 3 Review 10.3390/metabo2030529 529 552 2218-1989 2012-08-29 doi: 10.3390/metabo2030529 Jan Berkhout Frank J. Bruggeman Bas Teusink http://www.mdpi.com/2218-1989/2/3/516 Polyamines are essential compounds for cell survival and have key roles in plant stress protection. Current evidence points to the occurrence of intricate cross-talks between polyamines, stress hormones and other metabolic pathways required for their function. In this review we integrate the polyamine metabolic pathway in the context of its immediate metabolic network which is required to understand the multiple ways by which polyamines can maintain their homeostasis and participate in plant stress responses. Metabolites 2012-08-20 2 3 Review 10.3390/metabo2030516 516 528 2218-1989 2012-08-20 doi: 10.3390/metabo2030516 Marta Bitrián Xavier Zarza Teresa Altabella Antonio F. Tiburcio Rubén Alcázar http://www.mdpi.com/2218-1989/2/3/496 Metabolomics is a new approach that is based on the systematic study of the full complement of metabolites in a biological sample. Metabolomics has the potential to fundamentally change clinical chemistry and, by extension, the fields of nutrition, toxicology, and medicine. However, it can be difficult to separate highly polar compounds. Mass spectrometry (MS), in combination with capillary electrophoresis (CE), gas chromatography (GC), or high performance liquid chromatography (HPLC) is the key analytical technique on which emerging "omics" technologies, namely, proteomics, metabolomics, and lipidomics, are based. In this review, we introduce various methods for the separation of highly polar metabolites. Metabolites 2012-08-16 2 3 Review 10.3390/metabo2030496 496 515 2218-1989 2012-08-16 doi: 10.3390/metabo2030496 Yusuke Iwasaki Takahiro Sawada Kentaro Hatayama Akihito Ohyagi Yuri Tsukuda Kyohei Namekawa Rie Ito Koichi Saito Hiroyuki Nakazawa http://www.mdpi.com/2218-1989/2/3/479 1H NMR spectroscopy and chemometric analysis were used to characterize rat urine obtained after chronic exposure to either tributyl phosphate (TBP) or triphenyl phosphate (TPP). In this study, the daily dose exposure was 1.5 mg/kg body weight for TBP, or 2.0 mg/kg body weight for TPP, administered over a 15-week period. Orthogonal signal correction (OSC) -filtered partial least square discriminant analysis (OSC-PLSDA) was used to predict and classify exposure to these organophosphates. During the development of the model, the classification error was evaluated as a function of the number of latent variables. NMR spectral regions and corresponding metabolites important for determination of exposure type were identified using variable importance in projection (VIP) coefficients obtained from the OSC-PLSDA analysis. As expected, the model for classification of chronic (1.5–2.0 mg/kg body weight daily) TBP or TPP exposure was not as strong as the previously reported model developed for identifying acute (15–20 mg/kg body weight) exposure. The set of majorly impacted metabolites identified for chronic TBP or TPP exposure was slightly different than those metabolites previously identified for acute exposure. These metabolites were then mapped to different metabolite pathways and ranked, allowing the metabolic response to chronic organophosphate exposure to be addressed. Metabolites 2012-07-24 2 3 Article 10.3390/metabo2030479 479 495 2218-1989 2012-07-24 doi: 10.3390/metabo2030479 Todd M. Alam Muniasamy Neerathilingam M. Kathleen Alam David E. Volk G. A. Shakeel Ansari Swapna Sarkar Bruce A. Luxon http://www.mdpi.com/2218-1989/2/3/458 Hypermetabolism is a significant sequela to severe trauma such as burns, as well as critical illnesses such as cancer. It persists in parallel to, or beyond, the original pathology for many months as an often-fatal comorbidity. Currently, diagnosis is based solely on clinical observations of increased energy expenditure, severe muscle wasting and progressive organ dysfunction. In order to identify the minimum number of necessary variables, and to develop a rat model of burn injury-induced hypermetabolism, we utilized data mining approaches to identify the metabolic variables that strongly correlate to the severity of injury. A clustering-based algorithm was introduced into a regression model of the extent of burn injury. As a result, a neural network model which employs VLDL and acetoacetate levels was demonstrated to predict the extent of burn injury with 88% accuracy in the rat model. The physiological importance of the identified variables in the context of hypermetabolism, and necessary steps in extension of this preliminary model to a clinically utilizable index of severity of burn injury are outlined. Metabolites 2012-07-16 2 3 Article 10.3390/metabo2030458 458 478 2218-1989 2012-07-16 doi: 10.3390/metabo2030458 Maria-Louisa Izamis Korkut Uygun Nripen S. Sharma Basak Uygun Martin L. Yarmush Francois Berthiaume http://www.mdpi.com/2218-1989/2/3/429 Formation and adaptation of metabolic networks has been a long-standing question in biology. With recent developments in biotechnology and bioinformatics, the understanding of metabolism is progressively becoming clearer from a network perspective. This review introduces the comprehensive metabolic world that has been revealed by a wide range of data analyses and theoretical studies; in particular, it illustrates the role of evolutionary events, such as gene duplication and horizontal gene transfer, and environmental factors, such as nutrient availability and growth conditions, in evolution of the metabolic network. Furthermore, the mathematical models for the formation and adaptation of metabolic networks have also been described, according to the current understanding from a perspective of metabolic networks. These recent findings are helpful in not only understanding the formation of metabolic networks and their adaptation, but also metabolic engineering. Metabolites 2012-07-12 2 3 Review 10.3390/metabo2030429 429 457 2218-1989 2012-07-12 doi: 10.3390/metabo2030429 Kazuhiro Takemoto http://www.mdpi.com/2218-1989/2/3/398 In spite of the large arsenal of methodologies developed for amino acid assessment in complex matrices, their implementation in metabolomics studies involving wide-ranging mutant screening is hampered by their lack of high-throughput, sensitivity, reproducibility, and/or wide dynamic range. In response to the challenge of developing amino acid analysis methods that satisfy the criteria required for metabolomic studies, improved reverse-phase high-performance liquid chromatography-mass spectrometry (RPHPLC-MS) methods have been recently reported for large-scale screening of metabolic phenotypes. However, these methods focus on the direct analysis of underivatized amino acids and, therefore, problems associated with insufficient retention and resolution are observed due to the hydrophilic nature of amino acids. It is well known that derivatization methods render amino acids more amenable for reverse phase chromatographic analysis by introducing highly-hydrophobic tags in their carboxylic acid or amino functional group. Therefore, an analytical platform that combines the 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate (AQC) pre-column derivatization method with ultra performance liquid chromatography-electrospray ionization-tandem mass spectrometry (UPLC-ESI-MS/MS) is presented in this article. For numerous reasons typical amino acid derivatization methods would be inadequate for large scale metabolic projects. However, AQC derivatization is a simple, rapid and reproducible way of obtaining stable amino acid adducts amenable for UPLC-ESI-MS/MS and the applicability of the method for high-throughput metabolomic analysis in Arabidopsis thaliana is demonstrated in this study. Overall, the major advantages offered by this amino acid analysis method include high-throughput, enhanced sensitivity and selectivity; characteristics that showcase its utility for the rapid screening of the preselected plant metabolites without compromising the quality of the metabolic data. The presented method enabled thirty-eight metabolites (proteinogenic amino acids and related compounds) to be analyzed within 10 min with detection limits down to 1.02 × 10−11 M (i.e., atomole level on column), which represents an improved sensitivity of 1 to 5 orders of magnitude compared to existing methods. Our UPLC-ESI-MS/MS method is one of the seven analytical platforms used by the Arabidopsis Metabolomics Consortium. The amino acid dataset obtained by analysis of Arabidopsis T-DNA mutant stocks with our platform is captured and open to the public in the web portal PlantMetabolomics.org. The analytical platform herein described could find important applications in other studies where the rapid, high-throughput and sensitive assessment of low abundance amino acids in complex biosamples is necessary. Metabolites 2012-07-06 2 3 Article 10.3390/metabo2030398 398 428 2218-1989 2012-07-06 doi: 10.3390/metabo2030398 Carolina Salazar Jenny M. Armenta Vladimir Shulaev http://www.mdpi.com/2218-1989/2/3/382 The study of biological systems at the genome scale helps us understand fundamental biological processes that govern the activity of living organisms and regulate their interactions with the environment. Genome-scale metabolic models are usually analysed using constraint-based methods, since detailed rate equations and kinetic parameters are often missing. However, constraint-based analysis is limited in capturing the dynamics of cellular processes. In this paper, we present an approach to build a genome-scale kinetic model of Mycobacterium tuberculosis metabolism using generic rate equations. M. tuberculosis causes tuberculosis which remains one of the largest killer infectious diseases. Using a genetic algorithm, we estimated kinetic parameters for a genome-scale metabolic model of M. tuberculosis based on flux distributions derived from Flux Balance Analysis. Our results show that an excellent agreement with flux values is obtained under several growth conditions, although kinetic parameters may vary in different conditions. Parameter variability analysis indicates that a high degree of redundancy remains present in model parameters, which suggests that the integration of other types of high-throughput datasets will enable the development of better constrained models accounting for a variety of in vivo phenotypes. Metabolites 2012-07-03 2 3 Article 10.3390/metabo2030382 382 397 2218-1989 2012-07-03 doi: 10.3390/metabo2030382 Delali A. Adiamah Jean-Marc Schwartz http://www.mdpi.com/2218-1989/2/2/366 The peroxisome proliferator-activated receptor-γ coactivators (PGC-1) are transcriptional coactivators with an important role in mitochondrial biogenesis and regulation of genes involved in the electron transport chain and oxidative phosphorylation in oxidative tissues including cardiac tissue. These coactivators are thought to play a key role in the development of obesity, type 2 diabetes and the metabolic syndrome. In this study we have used a combined metabolomic and lipidomic analysis of cardiac tissue from the PGC-1β null mouse to examine the effects of a high fat diet on this organ. Multivariate statistics readily separated tissue from PGC-1β null mice from their wild type controls either in gender specific models or in combined datasets. This was associated with an increase in creatine and a decrease in taurine in the null mouse, and an increase in myristic acid and a reduction in long chain polyunsaturated fatty acids for both genders. The most profound changes were detected by liquid chromatography mass spectrometry analysis of intact lipids with the tissue from the null mouse having a profound increase in a number of triglycerides. The metabolomic and lipodomic changes indicate PGC-1β has a profound influence on cardiac metabolism. Metabolites 2012-06-18 2 2 Article 10.3390/metabo2020366 366 381 2218-1989 2012-06-18 doi: 10.3390/metabo2020366 Gregor McCombie Gema Medina-Gomez Christopher J Lelliott Antonio Vidal-Puig Julian L Griffin http://www.mdpi.com/2218-1989/2/2/337 A complex structurally diverse series of eicosanoids arises from the metabolism of arachidonic acid. The metabolic profile is further complicated by the enantioselectivity of eicosanoid formation and the variety of regioisomers that arise. In order to investigate the metabolism of arachidonic acid in vitro or in vivo, targeted methods are advantageous in order to distinguish between the complex isomeric mixtures that can arise by different metabolic pathways. Over the last several years this targeted approach has become more popular, although there are still relatively few examples where chiral targeted approaches have been employed to directly analyze complex enantiomeric mixtures. To efficiently conduct targeted eicosanoid analyses, LC separations are coupled with collision induced dissociation (CID) and tandem mass spectrometry (MS/MS). Product ion profiles are often diagnostic for particular regioisomers. The highest sensitivity that can be achieved involves the use of selected reaction monitoring/mass spectrometry (SRM/MS); whereas the highest specificity is obtained with an SRM transitions between an intense parent ion, which contains the intact molecule (M) and a structurally significant product ion. This review article provides an overview of arachidonic acid metabolism and targeted chiral methods that have been utilized for the analysis of the structurally diverse eicosanoids that arise. Metabolites 2012-04-20 2 2 Review 10.3390/metabo2020337 337 365 2218-1989 2012-04-20 doi: 10.3390/metabo2020337 Clementina Mesaros Ian A. Blair http://www.mdpi.com/2218-1989/2/2/303 Historically, natural products have been used since ancient times and in folklore for the treatment of many diseases and illnesses. Classical natural product chemistry methodologies enabled a vast array of bioactive secondary metabolites from terrestrial and marine sources to be discovered. Many of these natural products have gone on to become current drug candidates. This brief review aims to highlight historically significant bioactive marine and terrestrial natural products, their use in folklore and dereplication techniques to rapidly facilitate their discovery. Furthermore a discussion of how natural product chemistry has resulted in the identification of many drug candidates; the application of advanced hyphenated spectroscopic techniques to aid in their discovery, the future of natural product chemistry and finally adopting metabolomic profiling and dereplication approaches for the comprehensive study of natural product extracts will be discussed. Metabolites 2012-04-16 2 2 Review 10.3390/metabo2020303 303 336 2218-1989 2012-04-16 doi: 10.3390/metabo2020303 Daniel A. Dias Sylvia Urban Ute Roessner http://www.mdpi.com/2218-1989/2/2/292 AICAR (5-Aminoimidazole-4-carboxamide-1-beta-D-ribofuranosyl 5'-monophosphate) is a natural metabolic intermediate of purine biosynthesis that is present in all organisms. In yeast, AICAR plays important regulatory roles under physiological conditions, notably through its direct interactions with transcription factors. In humans, AICAR accumulates in several metabolic diseases, but its contribution to the symptoms has not yet been elucidated. Further, AICAR has highly promising properties which have been recently revealed. Indeed, it enhances endurance of sedentary mice. In addition, it has antiproliferative effects notably by specifically inducing apoptosis of aneuploid cells. Some of the effects of AICAR are due to its ability to stimulate the AMP-activated protein kinase but some others are not. It is consequently clear that AICAR affects multiple targets although only few of them have been identified so far. This review proposes an overview of the field and suggests future directions. Metabolites 2012-03-23 2 2 Review 10.3390/metabo2020292 292 302 2218-1989 2012-03-23 doi: 10.3390/metabo2020292 Bertrand Daignan-Fornier Benoît Pinson http://www.mdpi.com/2218-1989/2/1/268 The liver has many complex physiological functions, including lipid, protein and carbohydrate metabolism, as well as bile and urea production. It detoxifies toxic substances and medicinal products. It also plays a key role in the onset and maintenance of abnormal metabolic patterns associated with various disease states, such as burns, infections and major traumas. Liver cells have been commonly used in in vitro experiments to elucidate the toxic effects of drugs and metabolic changes caused by aberrant metabolic conditions, and to improve the functions of existing systems, such as bioartificial liver. More recently, isolated liver perfusion systems have been increasingly used to characterize intrinsic metabolic changes in the liver caused by various perturbations, including systemic injury, hepatotoxin exposure and warm ischemia. Metabolic engineering tools have been widely applied to these systems to identify metabolic flux distributions using metabolic flux analysis or flux balance analysis and to characterize the topology of the networks using metabolic pathway analysis. In this context, hepatic metabolic models, together with experimental methodologies where hepatocytes or perfused livers are mainly investigated, are described in detail in this review. The challenges and opportunities are also discussed extensively. Metabolites 2012-03-14 2 1 Review 10.3390/metabo2010268 268 291 2218-1989 2012-03-14 doi: 10.3390/metabo2010268 Mehmet A. Orman John Mattick Ioannis P. Androulakis Francois Berthiaume Marianthi G. Ierapetritou http://www.mdpi.com/2218-1989/2/1/254 Glycerophospholipids (GP) are the building blocks of cellular membranes and play essential roles in cell compartmentation, membrane fluidity or apoptosis. In addition, GPs are sources for multifunctional second messengers. Whereas the genome and proteome of the most intensively studied eukaryotic model organism, the baker’s yeast (Saccharomyces cerevisiae), are well characterized, the analysis of its lipid composition is still at the beginning. Moreover, different yeast species can be distinguished on the DNA, RNA and protein level, but it is currently unknown if they can also be differentiated by determination of their GP pattern. Therefore, the GP compositions of five different yeast strains, grown under identical environmental conditions, were elucidated using high performance liquid chromatography coupled to negative electrospray ionization-hybrid linear ion trap-Fourier transform ion cyclotron resonance mass spectrometry in single and multistage mode. Using this approach, relative quantification of more than 100 molecular species belonging to nine GP classes was achieved. The comparative lipidomic profiling of Saccharomyces cerevisiae, Saccharomyces bayanus, Kluyveromyces thermotolerans, Pichia angusta, and Yarrowia lipolytica revealed characteristic GP profiles for each strain. However, genetically related yeast strains show similarities in their GP compositions, e.g., Saccharomyces cerevisiae and Saccharomyces bayanus. Metabolites 2012-03-02 2 1 Article 10.3390/metabo2010254 254 267 2218-1989 2012-03-02 doi: 10.3390/metabo2010254 Eva-Maria Hein Heiko Hayen http://www.mdpi.com/2218-1989/2/1/242 Metabolism is crucial to cell growth and proliferation. Deficiency or alterations in metabolic functions are known to be involved in many human diseases. Therefore, understanding the human metabolic system is important for the study and treatment of complex diseases. Current reconstructions of the global human metabolic network provide a computational platform to integrate genome-scale information on metabolism. The platform enables a systematic study of the regulation and is applicable to a wide variety of cases, wherein one could rely on in silico perturbations to predict novel targets, interpret systemic effects, and identify alterations in the metabolic states to better understand the genotype-phenotype relationships. In this review, we describe the reconstruction of the human metabolic network, introduce the constraint based modeling approach to analyze metabolic networks, and discuss systems biology applications to study human physiology and pathology. We highlight the challenges and opportunities in network reconstruction and systems modeling of the human metabolic system. Metabolites 2012-03-02 2 1 Review 10.3390/metabo2010242 242 253 2218-1989 2012-03-02 doi: 10.3390/metabo2010242 Ming Wu Christina Chan http://www.mdpi.com/2218-1989/2/1/221 Heat is one of the most fundamental and ancient environmental stresses, and response mechanisms are found in prokaryotes and shared among most eukaryotes. In the budding yeast Saccharomyces cerevisiae, the heat stress response involves coordinated changes at all biological levels, from gene expression to protein and metabolite abundances, and to temporary adjustments in physiology. Due to its integrative multi-level-multi-scale nature, heat adaptation constitutes a complex dynamic process, which has forced most experimental and modeling analyses in the past to focus on just one or a few of its aspects. Here we review the basic components of the heat stress response in yeast and outline what has been done, and what needs to be done, to merge the available information into computational structures that permit comprehensive diagnostics, interrogation, and interpretation. We illustrate the process in particular with the coordination of two metabolic responses, namely the dramatic accumulation of the protective disaccharide trehalose and the substantial change in the profile of sphingolipids, which in turn affect gene expression. The proposed methods primarily use differential equations in the canonical modeling framework of Biochemical Systems Theory (BST), which permits the relatively easy construction of coarse, initial models even in systems that are incompletely characterized. Metabolites 2012-02-27 2 1 Article 10.3390/metabo2010221 221 241 2218-1989 2012-02-27 doi: 10.3390/metabo2010221 Luis L. Fonseca Po-Wei Chen Eberhard O. Voit http://www.mdpi.com/2218-1989/2/1/214 Atlantinone A has been isolated from the psychrotolerant fungus Penicillium ribeum. The exact structure of the compound was confirmed by mass spectrometric and 1- and 2D NMR experiments. Atlantinone A was originally only produced upon chemical epigenetic manipulation of P. hirayamae, however in this study the compound was found to be produced at standard growth conditions by the following species; P. solitum, P. discolor, P. commune, P. caseifulvum, P. palitans, P. novae-zeelandiae and P. monticola. A biosynthetic pathway to atlantinone A starting from andrastin A is proposed. Metabolites 2012-02-23 2 1 Article 10.3390/metabo2010214 214 220 2218-1989 2012-02-23 doi: 10.3390/metabo2010214 Petur W. Dalsgaard Bent O. Petersen Jens Ø. Duus Christian Zidorn Jens C. Frisvad Carsten Christophersen Thomas O. Larsen http://www.mdpi.com/2218-1989/2/1/195 Shotgun lipidomics has evolved into a myriad of multi-dimensional strategies for molecular lipid characterization, including bioinformatics tools for mass spectrum interpretation and quantitative measurements to study systems-lipidomics in complex biological extracts. Taking advantage of spectral mass accuracy, scan speed and sensitivity of improved quadrupole linked time-of-flight mass analyzers, we developed a bias-free global lipid profiling acquisition technique of sequential precursor ion fragmentation called MS/MSALL. This generic information-independent tandem mass spectrometry (MS) technique consists of a Q1 stepped mass isolation window through a set mass range in small increments, fragmenting and recording all product ions and neutral losses. Through the accurate MS and MS/MS information, the molecular lipid species are resolved, including distinction of isobaric and isomeric species, and composed into more precise lipidomic outputs. The method demonstrates good reproducibility and at least 3 orders of dynamic quantification range for isomeric ceramides in human plasma. More than 400 molecular lipids in human plasma were uncovered and quantified in less than 12 min, including acquisitions in both positive and negative polarity modes. We anticipate that the performance of sequential precursor ion fragmentation both in quality and throughput will lead to the uncovering of new avenues throughout the biomedical research community, enhance biomarker discovery and provide novel information target discovery programs as it will prospectively shed new insight into affected metabolic and signaling pathways. Metabolites 2012-02-20 2 1 Article 10.3390/metabo2010195 195 213 2218-1989 2012-02-20 doi: 10.3390/metabo2010195 Brigitte Simons Dimple Kauhanen Tuulia Sylvänne Kirill Tarasov Eva Duchoslav Kim Ekroos http://www.mdpi.com/2218-1989/2/1/178 A metabolite profiling study of the antibiotic producing bacterium Streptomyces coelicolor A3(2) has been performed. The aim of this study was to monitor intracellular metabolite pool changes occurring as strains of S. coelicolor react to nutrient depletion with metabolic re-modeling, so-called metabolic switching, and transition from growth to secondary metabolite production phase. Two different culture media were applied, providing depletion of the key nutrients phosphate and L-glutamate, respectively, as the triggers for metabolic switching. Targeted GC-MS and LC-MS methods were employed to quantify important primary metabolite groups like amino acids, organic acids, sugar phosphates and other phosphorylated metabolites, and nucleotides in time-course samples withdrawn from fully-controlled batch fermentations. A general decline, starting already in the early growth phase, was observed for nucleotide pools and phosphorylated metabolite pools for both the phosphate and glutamate limited cultures. The change in amino acid and organic acid pools were more scattered, especially in the phosphate limited situation while a general decrease in amino acid and non-amino organic acid pools was observed in the L-glutamate limited situation. A phoP deletion mutant showed basically the same metabolite pool changes as the wild-type strain M145 when cultivated on phosphate limited medium. This implies that the inactivation of the phoP gene has only little effect on the detected metabolite levels in the cell. The energy charge was found to be relatively constant during growth, transition and secondary metabolite production phase. The results of this study and the employed targeted metabolite profiling methodology are directly relevant for the evaluation of precursor metabolite and energy supply for both natural and heterologous production of secondary metabolites in S. coelicolor. Metabolites 2012-02-17 2 1 Article 10.3390/metabo2010178 178 194 2218-1989 2012-02-17 doi: 10.3390/metabo2010178 Alexander Wentzel Havard Sletta Stream Consortium Trond E. Ellingsen Per Bruheim http://www.mdpi.com/2218-1989/2/1/165 Vitis vinifera c.v. Muscat de Frontignan (grape) contains various high valuable bioactive phenolic compounds with pharmaceutical properties and industrial interest which are not fully exploited. The focus of this investigation consists in testing the effects of various biological elicitors on a non-morphogenic callus suspension culture of V. vinifera. The investigated elicitors: Indanoyl-isoleucine (IN), N-linolenoyl-L-glutamine (LG), insect saliva (IS) and malonyl coenzyme A (MCoA) were aimed at mimicking the influence of environmental pathogens on plants in their natural habitats and at provoking exogenous induction of the phenylpropanoid pathway. The elicitors’ indanoyl-isoleucine (IN), N-linolenoyl-L-glutamine (LG) and insect saliva (IS), as well as malonyl coenzyme A (MCoA), were independently inoculated to stimulate the synthesis of phenylpropanoids. All of the enhancers positively increased the concentration of phenolic compounds in grape cells. The highest concentration of phenolic acids was detected after 2 h for MCoA, after 48 h for IN and after 24 h for LG and IS respectively. At the maximum production time, treated grape cells had a 3.5-fold (MCoA), 1.6-fold (IN) and 1.5-fold (IS) higher phenolic acid content compared to the corresponding control samples. The HPLC results of grape cells showed two major resveratrol derivatives: 3-O-Glucosyl-resveratrol and 4-(3,5-dihydroxyphenyl)-phenol. Their influences of the different elicitors, time of harvest and biomass concentration (p < 0.0001) were statistically significant on the synthesis of phenolic compounds. The induction with MCoA was found to demonstrate the highest statistical effect corresponding to the strongest stress response within the phenylpropanoid pathway in grape cells. Metabolites 2012-02-15 2 1 Article 10.3390/metabo2010165 165 177 2218-1989 2012-02-15 doi: 10.3390/metabo2010165 Heidi Riedel Divine N. Akumo Nay Min Min Thaw Saw Iryna Smetanska Peter Neubauer http://www.mdpi.com/2218-1989/2/1/134 Glycosphingolipids (GSLs) contain one or more sugars that are attached to a sphingolipid moiety, usually to a ceramide, but in rare cases also to a sphingoid base. A large structural heterogeneity results from differences in number, identity, linkage, and anomeric configuration of the carbohydrate residues, and also from structural differences within the hydrophobic part. GSLs form complex cell-type specific patterns, which change with the species, the cellular differentiation state, viral transformation, ontogenesis, and oncogenesis. Although GSL structures can be assigned to only a few series with a common carbohydrate core, their structural variety and the complex pattern are challenges for their elucidation and quantification by mass spectrometric techniques. We present a general overview of the application of lipidomics for GSL determination. This includes analytical procedures and instrumentation together with recent correlations of GSL molecular species with human diseases. Difficulties such as the structural complexity and the lack of standard substances for complex GSLs are discussed. Metabolites 2012-02-02 2 1 Review 10.3390/metabo2010134 134 164 2218-1989 2012-02-02 doi: 10.3390/metabo2010134 Hany Farwanah Thomas Kolter http://www.mdpi.com/2218-1989/2/1/100 Secondary metabolites are small molecules that show large structural diversity and a broad range of bioactivities. Some metabolites are attractive as drugs or pigments while others act as harmful mycotoxins. Filamentous fungi have the capacity to produce a wide array of secondary metabolites including polyketides. The majority of genes required for production of these metabolites are mostly organized in gene clusters, which often are silent or barely expressed under laboratory conditions, making discovery and analysis difficult. Fortunately, the genome sequences of several filamentous fungi are publicly available, greatly facilitating the establishment of links between genes and metabolites. This review covers the attempts being made to trigger the activation of polyketide metabolism in the fungal model organism Aspergillus nidulans. Moreover, it will provide an overview of the pathways where ten polyketide synthase genes have been coupled to polyketide products. Therefore, the proposed biosynthesis of the following metabolites will be presented; naphthopyrone, sterigmatocystin, aspyridones, emericellamides, asperthecin, asperfuranone, monodictyphenone/emodin, orsellinic acid, and the austinols. Metabolites 2012-01-30 2 1 Review 10.3390/metabo2010100 100 133 2218-1989 2012-01-30 doi: 10.3390/metabo2010100 Marie L. Klejnstrup Rasmus J. N. Frandsen Dorte K. Holm Morten T. Nielsen Uffe H. Mortensen Thomas O. Larsen Jakob B. Nielsen http://www.mdpi.com/2218-1989/2/1/77 The metabolic composition of plasma is affected by time passed since the last meal and by individual variation in metabolite clearance rates. Rat plasma in fed and fasted states was analyzed with liquid chromatography quadrupole-time-of-flight mass spectrometry (LC-QTOF) for an untargeted investigation of these metabolite patterns. The dataset was used to investigate the effect of data preprocessing on biomarker selection using three different softwares, MarkerLynxTM, MZmine, XCMS along with a customized preprocessing method that performs binning of m/z channels followed by summation through retention time. Direct comparison of selected features representing the fed or fasted state showed large differences between the softwares. Many false positive markers were obtained from custom data preprocessing compared with dedicated softwares while MarkerLynxTM provided better coverage of markers. However, marker selection was more reliable with the gap filling (or peak finding) algorithms present in MZmine and XCMS. Further identification of the putative markers revealed that many of the differences between the markers selected were due to variations in features representing adducts or daughter ions of the same metabolites or of compounds from the same chemical subclasses, e.g., lyso-phosphatidylcholines (LPCs) and lyso-phosphatidylethanolamines (LPEs). We conclude that despite considerable differences in the performance of the preprocessing tools we could extract the same biological information by any of them. Carnitine, branched-chain amino acids, LPCs and LPEs were identified by all methods as markers of the fed state whereas acetylcarnitine was abundant during fasting in rats. Metabolites 2012-01-18 2 1 Article 10.3390/metabo2010077 77 99 2218-1989 2012-01-18 doi: 10.3390/metabo2010077 Gözde Gürdeniz Mette Kristensen Thomas Skov Lars O. Dragsted http://www.mdpi.com/2218-1989/2/1/57 Lipids, such as phosphoinositides (PIPs) and diacylglycerol (DAG), are important signaling intermediates involved in cellular processes such as T cell receptor (TCR)-mediated signal transduction. Here we report identification and quantification of PIP, PIP2 and DAG from crude lipid extracts. Capitalizing on the different extraction properties of PIPs and DAGs allowed us to efficiently recover both lipid classes from one sample. Rapid analysis of endogenous signaling molecules was performed by nano-electrospray ionization tandem mass spectrometry (nano-ESI MS/MS), employing lipid class-specific neutral loss and multiple precursor ion scanning for their identification and quantification. Profiling of DAG, PIP and PIP2 molecular species in primary human T cells before and after TCR stimulation resulted in a two-fold increase in DAG levels with a shift towards 1-stearoyl-2-arachidonoyl-DAG in stimulated cells. PIP2 levels were slightly reduced, while PIP levels remained unchanged. Metabolites 2012-01-16 2 1 Article 10.3390/metabo2010057 57 76 2218-1989 2012-01-16 doi: 10.3390/metabo2010057 Mathias Haag Angelika Schmidt Timo Sachsenheimer Britta Brügger http://www.mdpi.com/2218-1989/2/1/39 Aspergillus oryzae and A. flavus are important species in industrial biotechnology and food safety and have been some of the first aspergilli to be fully genome sequenced. Bioinformatic analysis has revealed 99.5% gene homology between the two species pointing towards a large coherence in the secondary metabolite production. In this study we report on the first comparison of secondary metabolite production between the full genome sequenced strains of A. oryzae (RIB40) and A. flavus (NRRL 3357). Surprisingly, the overall chemical profiles of the two strains were mostly very different across 15 growth conditions. Contrary to previous studies we found the aflatrem precursor 13-desoxypaxilline to be a major metabolite from A. oryzae under certain growth conditions. For the first time, we additionally report A. oryzae to produce parasiticolide A and two new analogues hereof, along with four new alkaloids related to the A. flavus metabolites ditryptophenalines and miyakamides. Generally the secondary metabolite capability of A. oryzae presents several novel end products likely to result from the domestication process from A. flavus. Metabolites 2012-01-05 2 1 Article 10.3390/metabo2010039 39 56 2218-1989 2012-01-05 doi: 10.3390/metabo2010039 Christian Rank Marie Louise Klejnstrup Lene Maj Petersen Sara Kildgaard Jens Christian Frisvad Charlotte Held Gotfredsen Thomas Ostenfeld Larsen http://www.mdpi.com/2218-1989/2/1/19 One decade after the genomic and the proteomic life science revolution, new ‘omics’ fields are emerging. The metabolome encompasses the entity of small molecules—Most often end products of a catalytic process regulated by genes and proteins—with the lipidome being its fat soluble subdivision. Within recent years, lipids are more and more regarded not only as energy storage compounds but also as interactive players in various cellular regulation cycles and thus attain rising interest in the bio-medical community. The field of lipidomics is, on one hand, fuelled by analytical technology advances, particularly mass spectrometry and chromatography, but on the other hand new biological questions also drive analytical technology developments. Compared to fairly standardized genomic or proteomic high-throughput protocols, the high degree of molecular heterogeneity adds a special analytical challenge to lipidomic analysis. In this review, we will take a closer look at various mass spectrometric platforms for lipidomic analysis. We will focus on the advantages and limitations of various experimental setups like ‘shotgun lipidomics’, liquid chromatography—Mass spectrometry (LC-MS) and matrix assisted laser desorption ionization-time of flight (MALDI-TOF) based approaches. We will also examine available software packages for data analysis, which nowadays is in fact the rate limiting step for most ‘omics’ workflows. Metabolites 2012-01-05 2 1 Review 10.3390/metabo2010019 19 38 2218-1989 2012-01-05 doi: 10.3390/metabo2010019 Harald C. Köfeler Alexander Fauland Gerald N. Rechberger Martin Trötzmüller http://www.mdpi.com/2218-1989/2/1/1 The lipidome of the liver and the secreted circulating lipoproteins can now be interrogated conveniently by automated mass spectrometric methods. Multivariate analysis of the liver and serum lipid composition in various animal modes or in human patients has pointed to specific molecular species markers. The perturbations of lipid metabolism can be categorized on the basis of three basic pathological mechanisms: (1) an accelerated rate of de novo lipogenesis; (2) perturbation of the peroxisome pathway of ether-lipid and very-long-chain fatty acid biosynthesis; (3) a change in the rate of interconversion of essential omega-3 and -6 polyunsaturated fatty acids. This review provides examples to illustrate the practicalities of lipidomic studies in biomedicine. Metabolites 2012-01-04 2 1 Review 10.3390/metabo2010001 1 18 2218-1989 2012-01-04 doi: 10.3390/metabo2010001 Antonin Lamaziere Claude Wolf Peter J. Quinn http://www.mdpi.com/2218-1989/1/1/64 Cereal crops such as wheat, rice and barley underpin the staple diet for human consumption globally. A multitude of threats to stable and secure yields of these crops exist including from losses caused by pathogens, particularly fungal. Plants have evolved complex mechanisms to resist pathogens including programmed cell death responses, the release of pathogenicity-related proteins and oxidative bursts. Another such mechanism is the synthesis and release of secondary metabolites toxic to potential pathogens. Several classes of these compounds have been identified and their anti-fungal properties demonstrated. However the lack of suitable analytical techniques has hampered the progress of identifying and exploiting more of these novel metabolites. In this review, we summarise the role of the secondary metabolites in cereal crop diseases and briefly touch on the analytical techniques that hold the key to unlocking their potential in reducing yield losses. Metabolites 2011-12-15 1 1 Review 10.3390/metabo1010064 64 78 2218-1989 2011-12-15 doi: 10.3390/metabo1010064 Lauren A. Du Fall Peter S. Solomon http://www.mdpi.com/2218-1989/1/1/41 Volatile organic compounds (volatiles) comprise a chemically diverse class of low molecular weight organic compounds having an appreciable vapor pressure under ambient conditions. Volatiles produced by plants attract pollinators and seed dispersers, and provide defense against pests and pathogens. For insects, volatiles may act as pheromones directing social behavior or as cues for finding hosts or prey. For humans, volatiles are important as flavorants and as possible disease biomarkers. The marine environment is also a major source of halogenated and sulfur-containing volatiles which participate in the global cycling of these elements. While volatile analysis commonly measures a rather restricted set of analytes, the diverse and extreme physical properties of volatiles provide unique analytical challenges. Volatiles constitute only a small proportion of the total number of metabolites produced by living organisms, however, because of their roles as signaling molecules (semiochemicals) both within and between organisms, accurately measuring and determining the roles of these compounds is crucial to an integrated understanding of living systems. This review summarizes recent developments in volatile research from a metabolomics perspective with a focus on the role of recent technical innovation in developing new areas of volatile research and expanding the range of ecological interactions which may be mediated by volatile organic metabolites. Metabolites 2011-11-25 1 1 Review 10.3390/metabo1010041 41 63 2218-1989 2011-11-25 doi: 10.3390/metabo1010041 Daryl D. Rowan http://www.mdpi.com/2218-1989/1/1/21 Electrospray ionization mass spectrometry (ESI-MS) has become one of the most popular and powerful technologies to identify and quantify individual lipid species in lipidomics. Meanwhile, quantitative analysis of lipid species by ESI-MS has also become a major obstacle to meet the challenges of lipidomics. Herein, we discuss the principles, advantages, and possible limitations of different mass spectrometry-based methodologies for lipid quantification, as well as a few practical issues important for accurate quantification of individual lipid species. Accordingly, accurate quantification of individual lipid species, one of the key challenges in lipidomics, can be practically met. Metabolites 2011-11-11 1 1 Review 10.3390/metabo1010021 21 40 2218-1989 2011-11-11 doi: 10.3390/metabo1010021 Kui Yang Xianlin Han http://www.mdpi.com/2218-1989/1/1/3 Gas chromatography–mass spectrometry (GC-MS) is a widely used analytical technique in metabolomics. GC provides the highest resolution of any standard chromatographic separation method, and with modern instrumentation, retention times are very consistent between analyses. Electron impact ionization and fragmentation is generally reproducible between instruments and extensive libraries of spectra are available that enhance the identification of analytes. The major limitation is the restriction to volatile analytes, and hence the requirement to convert many metabolites to volatile derivatives through chemical derivatization. Here we compared the analytical performance of two derivatization techniques, silylation (TMS) and alkylation (MCF), used for the analysis of amino and non-amino organic acids as well as nucleotides in microbial-derived samples. The widely used TMS derivatization method showed poorer reproducibility and instability during chromatographic runs while the MCF derivatives presented better analytical performance. Therefore, alkylation (MCF) derivatization seems to be preferable for the analysis of polyfunctional amines, nucleotides and organic acids in microbial metabolomics studies. Metabolites 2011-01-17 1 1 Article 10.3390/metabo1010003 3 20 2218-1989 2011-01-17 doi: 10.3390/metabo1010003 Silas G. Villas-Bôas Kathleen F. Smart Subathira Sivakumaran Geoffrey A. Lane http://www.mdpi.com/2218-1989/1/1/1 Technological advances in analytical instrumentation and advances in data modeling are working in synergy to open up new perspectives and research agendas in metabolic research. Thanks to the legacy of the Human Genome Project and its continued impact in the post-genomic era, metabolism is now thought of in a whole-genome context, even when the focus is on a single metabolite and individual metabolic reactions. For a few model organisms we now have extensive, and in some cases complete, information about components that perform integrated metabolic functions. This promises a true paradigm shift in our understanding of the processes of metabolism, but also poses new challenges. As complex and coordinated global behaviors are observed in what were thought to be "simple" organisms, many challenges remain in the experimental domain, as well as in the integration of data generated by increasingly high-throughput analytical techniques. Indeed, in the new era of metabolic research, mathematical and computational modeling is expected to play an increasingly important role. For many complex biochemical phenomena, use of mathematical models may be the best way to build a consistent picture and generate testable hypotheses based on complex yet inevitably incomplete data sets.[...] Metabolites 2010-12-07 1 1 Editorial 10.3390/metabo1010001 1 2 2218-1989 2010-12-07 doi: 10.3390/metabo1010001 Vladimir A. Likić