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 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.

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 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.

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 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.

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 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.

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, 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.

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, 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.