Abstract: Glioblastoma continues to be an invariably fatal malignancy. The established approach for understanding the biology of these aggressive tumors in an effort to identify novel molecular targets has largely been genotype-based. Unfortunately, clinical gains offered by this level of understanding have been limited, largely based on the complex nature of signaling networks associated with tumorigenesis and the inability to delineate the key “functional” signaling pathways actually driving growth in an individual tumor. Metabolomics is the global quantitative assessment of endogenous metabolites within a biological system, taking into account genetic regulation, altered kinetic activity of enzymes, and changes in metabolic reactions. Thus, compared to genomics and proteomics, metabolomics reflects changes in phenotype and therefore function. In this review, we highlight some of the key advancements that have been made in applying metabolomics to understand the aggressive phenotype of glioblastoma. Collectively, these studies have provided a previously unrecognized window into the underlying biology of these tumors. Current and future efforts are designed to determine how this technology may be applied to improve diagnosis and predict the aggressiveness of glioblastoma, and more importantly, identify novel, therapeutic strategies designed to improve clinical outcomes.
Abstract: Isotope-labeling is a useful technique for understanding cellular metabolism. Recent advances in metabolomics have extended the capability of isotope-assisted studies to reveal global metabolism. For instance, isotope-assisted metabolomics technology has enabled the mapping of a global metabolic network, estimation of flux at branch points of metabolic pathways, and assignment of elemental formulas to unknown metabolites. Furthermore, some data processing tools have been developed to apply these techniques to a non-targeted approach, which plays an important role in revealing unknown or unexpected metabolism. However, data collection and integration strategies for non-targeted isotope-assisted metabolomics have not been established. Therefore, a systematic approach is proposed to elucidate metabolic dynamics without targeting pathways by means of time-resolved isotope tracking, i.e., “metabolic turnover analysis”, as well as multivariate analysis. We applied this approach to study the metabolic dynamics in amino acid perturbation of Saccharomyces cerevisiae. In metabolic turnover analysis, 69 peaks including 35 unidentified peaks were investigated. Multivariate analysis of metabolic turnover successfully detected a pathway known to be inhibited by amino acid perturbation. In addition, our strategy enabled identification of unknown peaks putatively related to the perturbation.
Abstract: In many plants, biogenic volatile organic compounds (BVOCs) are produced as specialized metabolites that contribute to the characteristics of each plant. The varieties and composition of BVOCs are chemically diverse by plant species and the circumstances in which the plants grow, and also influenced by herbivory damage and pathogen infection. Plant-produced BVOCs are receptive to many organisms, from microorganisms to human, as both airborne attractants and repellants. In addition, it is known that some BVOCs act as signals to prime a plant for the defense response in plant-to-plant communications. The compositional profiles of BVOCs can, thus, have profound influences in the physiological and ecological aspects of living organisms. Apart from that, some of them are commercially valuable as aroma/flavor compounds for human. Metabolomic technologies have recently revealed new insights in biological systems through metabolic dynamics. Here, the recent advances in metabolomics technologies focusing on plant-produced BVOC analyses are overviewed. Their application markedly improves our knowledge of the role of BVOCs in chemosystematics, ecological influences, and aroma research, as well as being useful to prove the biosynthetic mechanisms of BVOCs.
Abstract: The reconstruction of genome-scale metabolic models and their applications represent a great advantage of systems biology. Through their use as metabolic flux simulation models, production of industrially-interesting metabolites can be predicted. Due to the growing number of studies of metabolic models driven by the increasing genomic sequencing projects, it is important to conceptualize steps of reconstruction and analysis. We have focused our work in the cyanobacterium Synechococcus elongatus PCC7942, for which several analyses and insights are unveiled. A comprehensive approach has been used, which can be of interest to lead the process of manual curation and genome-scale metabolic analysis. The final model, iSyf715 includes 851 reactions and 838 metabolites. A biomass equation, which encompasses elementary building blocks to allow cell growth, is also included. The applicability of the model is finally demonstrated by simulating autotrophic growth conditions of Synechococcus elongatus PCC7942.
Abstract: Mucopolysaccharidoses (MPS) are a group of lysosomal storage disorders caused by deficiency of the lysosomal enzymes essential for catabolism of glycosaminoglycans (GAGs). Accumulation of undegraded GAGs results in dysfunction of multiple organs, resulting in distinct clinical manifestations. A range of methods have been developed to measure specific GAGs in various human samples to investigate diagnosis, prognosis, pathogenesis, GAG interaction with other molecules, and monitoring therapeutic efficacy. We established ELISA, liquid chromatography tandem mass spectrometry (LC-MS/MS), and an automated high-throughput mass spectrometry (HT-MS/MS) system (RapidFire) to identify epitopes (ELISA) or disaccharides (MS/MS) derived from different GAGs (dermatan sulfate, heparan sulfate, keratan sulfate, and/or chondroitin sulfate). These methods have a high sensitivity and specificity in GAG analysis, applicable to the analysis of blood, urine, tissues, and cells. ELISA is feasible, sensitive, and reproducible with the standard equipment. HT-MS/MS yields higher throughput than conventional LC-MS/MS-based methods while the HT-MS/MS system does not have a chromatographic step and cannot distinguish GAGs with identical molecular weights, leading to a limitation of measurements for some specific GAGs. Here we review the advantages and disadvantages of these methods for measuring GAG levels in biological specimens. We also describe an unexpected secondary elevation of keratan sulfate in patients with MPS that is an indirect consequence of disruption of catabolism of other GAGs.
Abstract: Inherited mutations in the Krebs cycle enzyme fumarate hydratase (FH) predispose to hereditary leiomyomatosis and renal cell cancer (HLRCC). Loss of FH activity in HLRCC tumours causes accumulation of the Krebs cycle intermediate fumarate to high levels, which may act as an oncometabolite through various, but not necessarily mutually exclusive, mechanisms. One such mechanism, succination, is an irreversible non-enzymatic modification of cysteine residues by fumarate, to form S-(2-succino)cysteine (2SC). Previous studies have demonstrated that succination of proteins including glyceraldehyde 3-phosphate dehydrogenase (GAPDH), kelch-like ECH-associated protein 1 (KEAP1) and mitochondrial aconitase (ACO2) can have profound effects on cellular metabolism. Furthermore, immunostaining for 2SC is a sensitive and specific biomarker for HLRCC tumours. Here, we performed a proteomic screen on an FH-mutant tumour and two HLRCC-derived cancer cell lines and identified 60 proteins where one or more cysteine residues were succinated; 10 of which were succinated at cysteine residues either predicted, or experimentally proven, to be functionally significant. Bioinformatic enrichment analyses identified most succinated targets to be involved in redox signaling. To our knowledge, this is the first proteomic-based succination screen performed in human tumours and cancer-derived cells and has identified novel 2SC targets that may be relevant to the pathogenesis of HLRCC.