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Metabolites, Volume 3, Issue 2 (June 2013), Pages 204-516

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Research

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Open AccessArticle Development of a Direct Headspace Collection Method from Arabidopsis Seedlings Using HS-SPME-GC-TOF-MS Analysis
Metabolites 2013, 3(2), 223-242; doi:10.3390/metabo3020223
Received: 20 February 2013 / Revised: 21 March 2013 / Accepted: 26 March 2013 / Published: 9 April 2013
Cited by 5 | PDF Full-text (415 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
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
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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. Full article
(This article belongs to the Special Issue Metabolomics in Plant Metabolic Engineering)
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Open AccessArticle Influence of Freezing and Storage Procedure on Human Urine Samples in NMR-Based Metabolomics
Metabolites 2013, 3(2), 243-258; doi:10.3390/metabo3020243
Received: 19 February 2013 / Revised: 28 March 2013 / Accepted: 1 April 2013 / Published: 9 April 2013
Cited by 8 | PDF Full-text (3471 KB) | HTML Full-text | XML Full-text
Abstract
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
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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. Full article
(This article belongs to the Special Issue NMR-based Metabolomics and Its Application)
Open AccessArticle Peak Detection Method Evaluation for Ion Mobility Spectrometry by Using Machine Learning Approaches
Metabolites 2013, 3(2), 277-293; doi:10.3390/metabo3020277
Received: 25 January 2013 / Revised: 15 March 2013 / Accepted: 9 April 2013 / Published: 16 April 2013
Cited by 10 | PDF Full-text (424 KB) | HTML Full-text | XML Full-text
Abstract
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,
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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. Full article
(This article belongs to the Special Issue Data Processing in Metabolomics)
Open AccessArticle Applying Tandem Mass Spectral Libraries for Solving the Critical Assessment of Small Molecule Identification (CASMI) LC/MS Challenge 2012
Metabolites 2013, 3(2), 312-324; doi:10.3390/metabo3020312
Received: 1 April 2013 / Revised: 16 April 2013 / Accepted: 17 April 2013 / Published: 26 April 2013
Cited by 5 | PDF Full-text (124 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
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
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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. Full article
Open AccessArticle Metabolic and Transcriptional Reprogramming in Developing Soybean (Glycine max) Embryos
Metabolites 2013, 3(2), 347-372; doi:10.3390/metabo3020347
Received: 15 March 2013 / Revised: 16 April 2013 / Accepted: 7 May 2013 / Published: 14 May 2013
Cited by 14 | PDF Full-text (536 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
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
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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. Full article
(This article belongs to the Special Issue Metabolomics in Plant Metabolic Engineering)
Open AccessArticle CASMI—The Small Molecule Identification Process from a Birmingham Perspective
Metabolites 2013, 3(2), 397-411; doi:10.3390/metabo3020397
Received: 1 April 2013 / Revised: 8 May 2013 / Accepted: 10 May 2013 / Published: 21 May 2013
Cited by 3 | PDF Full-text (243 KB) | HTML Full-text | XML Full-text
Abstract
The Critical Assessment of Small Molecule Identification (CASMI) contest was developed to provide a systematic comparative evaluation of strategies applied for the annotation and identification of small molecules. The authors participated in eleven challenges in both category 1 (to deduce a molecular formula)
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The Critical Assessment of Small Molecule Identification (CASMI) contest was developed to provide a systematic comparative evaluation of strategies applied for the annotation and identification of small molecules. The authors participated in eleven challenges in both category 1 (to deduce a molecular formula) and category 2 (to deduce a molecular structure) related to high resolution LC-MS data. For category 1 challenges, the PUTMEDID_LCMS workflows provided the correct molecular formula in nine challenges; the two incorrect submissions were related to a larger mass error in experimental data than expected or the absence of the correct molecular formula in a reference file applied in the PUTMEDID_LCMS workflows. For category 2 challenges, MetFrag was applied to construct in silico fragmentation data and compare with experimentally-derived MS/MS data. The submissions for three challenges were correct, and for eight challenges, the submissions were not correct; some submissions showed similarity to the correct structures, while others showed no similarity. The low number of correct submissions for category 2 was a result of applying the assumption that all chemicals were derived from biological samples and highlights the importance of knowing the origin of biological or chemical samples studied and the metabolites expected to be present to define the correct chemical space to search in annotation processes. Full article
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Open AccessArticle Small Molecule Identification with MOLGEN and Mass Spectrometry
Metabolites 2013, 3(2), 440-462; doi:10.3390/metabo3020440
Received: 28 March 2013 / Revised: 16 May 2013 / Accepted: 17 May 2013 / Published: 28 May 2013
Cited by 8 | PDF Full-text (417 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
This paper details the MOLGEN entries for the 2012 CASMI contest for small molecule identification to demonstrate structure elucidation using structure generation approaches. Different MOLGEN programs were used for different categories, including MOLGEN–MS/MS for Category 1, MOLGEN 3.5 and 5.0 for Category 2
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This paper details the MOLGEN entries for the 2012 CASMI contest for small molecule identification to demonstrate structure elucidation using structure generation approaches. Different MOLGEN programs were used for different categories, including MOLGEN–MS/MS for Category 1, MOLGEN 3.5 and 5.0 for Category 2 and MOLGEN–MS for Categories 3 and 4. A greater focus is given to Categories 1 and 2, as most CASMI participants entered these categories. The settings used and the reasons behind them are described in detail, while various evaluations are used to put these results into perspective. As one author was also an organiser of CASMI, these submissions were not part of the official CASMI competition, but this paper provides an insight into how unknown identification could be performed using structure generation approaches. The approaches are semi-automated (category dependent) and benefit greatly from user experience. Thus, the results presented and discussed here may be better than those an inexperienced user could obtain with MOLGEN programs. Full article
Open AccessArticle Metabolite Identification through Machine Learning— Tackling CASMI Challenge Using FingerID
Metabolites 2013, 3(2), 484-505; doi:10.3390/metabo3020484
Received: 1 April 2013 / Revised: 24 May 2013 / Accepted: 30 May 2013 / Published: 6 June 2013
Cited by 7 | PDF Full-text (626 KB) | HTML Full-text | XML Full-text
Abstract
Metabolite identification is a major bottleneck in metabolomics due to the number and diversity of the molecules. To alleviate this bottleneck, computational methods and tools that reliably filter the set of candidates are needed for further analysis by human experts. Recent efforts in
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Metabolite identification is a major bottleneck in metabolomics due to the number and diversity of the molecules. To alleviate this bottleneck, computational methods and tools that reliably filter the set of candidates are needed for further analysis by human experts. Recent efforts in assembling large public mass spectral databases such as MassBank have opened the door for developing a new genre of metabolite identification methods that rely on machine learning as the primary vehicle for identification. In this paper we describe the machine learning approach used in FingerID, its application to the CASMI challenges and some results that were not part of our challenge submission. In short, FingerID learns to predict molecular fingerprints from a large collection of MS/MS spectra, and uses the predicted fingerprints to retrieve and rank candidate molecules from a given large molecular database. Furthermore, we introduce a web server for FingerID, which was applied for the first time to the CASMI challenges. The challenge results show that the new machine learning framework produces competitive results on those challenge molecules that were found within the relatively restricted KEGG compound database. Additional experiments on the PubChem database confirm the feasibility of the approach even on a much larger database, although room for improvement still remains. Full article
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Open AccessArticle Molecular Formula Identification with SIRIUS
Metabolites 2013, 3(2), 506-516; doi:10.3390/metabo3020506
Received: 8 April 2013 / Revised: 3 June 2013 / Accepted: 4 June 2013 / Published: 13 June 2013
Cited by 6 | PDF Full-text (236 KB) | HTML Full-text | XML Full-text
Abstract
We present results of the SIRIUS2 submission to the 2012 CASMI contest. Only results for Category 1 (molecular formula identification) were submitted. The SIRIUS method and the parameters used are briefly described, followed by detailed analysis of the results and a discussion
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We present results of the SIRIUS2 submission to the 2012 CASMI contest. Only results for Category 1 (molecular formula identification) were submitted. The SIRIUS method and the parameters used are briefly described, followed by detailed analysis of the results and a discussion of cases where SIRIUS2 was unable to come up with the correct molecular formula. SIRIUS2 returns consistently high quality results, with the exception of fragmentation pattern analysis of time-of-flight data. We then discuss possibilities for further improving SIRIUS2 in the future. Full article

Review

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Open AccessReview NMR-Based Milk Metabolomics
Metabolites 2013, 3(2), 204-222; doi:10.3390/metabo3020204
Received: 1 February 2013 / Revised: 22 March 2013 / Accepted: 25 March 2013 / Published: 2 April 2013
Cited by 18 | PDF Full-text (305 KB) | HTML Full-text | XML Full-text
Abstract
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
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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. Full article
(This article belongs to the Special Issue NMR-based Metabolomics and Its Application)
Open AccessReview Getting Your Peaks in Line: A Review of Alignment Methods for NMR Spectral Data
Metabolites 2013, 3(2), 259-276; doi:10.3390/metabo3020259
Received: 1 February 2013 / Revised: 29 March 2013 / Accepted: 2 April 2013 / Published: 15 April 2013
Cited by 10 | PDF Full-text (1122 KB) | HTML Full-text | XML Full-text
Abstract
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
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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. Full article
(This article belongs to the Special Issue NMR-based Metabolomics and Its Application)
Open AccessReview Amino Acid Biosynthesis Pathways in Diatoms
Metabolites 2013, 3(2), 294-311; doi:10.3390/metabo3020294
Received: 14 January 2013 / Revised: 4 April 2013 / Accepted: 8 April 2013 / Published: 18 April 2013
Cited by 4 | PDF Full-text (215 KB) | HTML Full-text | XML Full-text
Abstract
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,
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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. Full article
(This article belongs to the collection Metabolism in Phototrophic Prokaryotes and Algae)
Open AccessReview The Central Carbon and Energy Metabolism of Marine Diatoms
Metabolites 2013, 3(2), 325-346; doi:10.3390/metabo3020325
Received: 4 March 2013 / Revised: 25 April 2013 / Accepted: 25 April 2013 / Published: 7 May 2013
Cited by 10 | PDF Full-text (732 KB) | HTML Full-text | XML Full-text
Abstract
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
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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. Full article
(This article belongs to the collection Metabolism in Phototrophic Prokaryotes and Algae)
Open AccessReview The Future of NMR Metabolomics in Cancer Therapy: Towards Personalizing Treatment and Developing Targeted Drugs?
Metabolites 2013, 3(2), 373-396; doi:10.3390/metabo3020373
Received: 8 February 2013 / Revised: 7 May 2013 / Accepted: 10 May 2013 / Published: 17 May 2013
Cited by 8 | PDF Full-text (803 KB) | HTML Full-text | XML Full-text
Abstract
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
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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. Full article
(This article belongs to the Special Issue NMR-based Metabolomics and Its Application)
Open AccessReview CASMI: And the Winner is . . .
Metabolites 2013, 3(2), 412-439; doi:10.3390/metabo3020412
Received: 7 April 2013 / Revised: 14 May 2013 / Accepted: 16 May 2013 / Published: 24 May 2013
Cited by 15 | PDF Full-text (639 KB) | HTML Full-text | XML Full-text
Abstract
The Critical Assessment of Small Molecule Identification (CASMI) Contest was founded in 2012 to provide scientists with a common open dataset to evaluate their identification methods. In this review, we summarize the submissions, evaluate procedures and discuss the results. We received five submissions
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The Critical Assessment of Small Molecule Identification (CASMI) Contest was founded in 2012 to provide scientists with a common open dataset to evaluate their identification methods. In this review, we summarize the submissions, evaluate procedures and discuss the results. We received five submissions (three external, two internal) for LC–MS Category 1 (best molecular formula) and six submissions (three external, three internal) for LC–MS Category 2 (best molecular structure). No external submissions were received for the GC–MS Categories 3 and 4. The team of Dunn et al. from Birmingham had the most answers in the 1st place for Category 1, while Category 2 was won by H. Oberacher. Despite the low number of participants, the external and internal submissions cover a broad range of identification strategies, including expert knowledge, database searching, automated methods and structure generation. The results of Category 1 show that complementing automated strategies with (manual) expert knowledge was the most successful approach, while no automated method could compete with the power of spectral searching for Category 2—if the challenge was present in a spectral library. Every participant topped at least one challenge, showing that different approaches are still necessary for interpretation diversity. Full article
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Open AccessReview Multiple Roles of Photosynthetic and Sunscreen Pigments in Cyanobacteria Focusing on the Oxidative Stress
Metabolites 2013, 3(2), 463-483; doi:10.3390/metabo3020463
Received: 8 February 2013 / Revised: 7 May 2013 / Accepted: 22 May 2013 / Published: 30 May 2013
Cited by 11 | PDF Full-text (306 KB) | HTML Full-text | XML Full-text
Abstract
Cyanobacteria have two types of sunscreen pigments, scytonemin and mycosporine-like amino acids (MAAs). These secondary metabolites are thought to play multiple roles against several environmental stresses such as UV radiation and desiccation. Not only the large molar absorption coefficients of these sunscreen pigments,
[...] Read more.
Cyanobacteria have two types of sunscreen pigments, scytonemin and mycosporine-like amino acids (MAAs). These secondary metabolites are thought to play multiple roles against several environmental stresses such as UV radiation and desiccation. Not only the large molar absorption coefficients of these sunscreen pigments, but also their antioxidative properties may be necessary for the protection of biological molecules against the oxidative damages induced by UV radiation. The antioxidant activity and vitrification property of these pigments are thought to be requisite for the desiccation and rehydration processes in anhydrobiotes. In this review, the multiple roles of photosynthetic pigments and sunscreen pigments on stress resistance, especially from the viewpoint of their structures, biosynthetic pathway, and in vitro studies of their antioxidant activity, will be discussed. Full article
(This article belongs to the collection Metabolism in Phototrophic Prokaryotes and Algae)

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