Current Metabolomics - Current Issue

The establishment of metabolomics has benefited from recent developments in analytical platforms and bioinformatics, which allow for comprehensive analysis of volatile metabolites in a biosystem. Consequently, metabolomics has become one of the most dynamic of all the omics over the last decade. Mass spectrometry based metabolomics of fungal and plant volatile metabolites has been a recent focus of intense study. Volatile metabolites are important information carriers providing insight into the health of organisms throughout their lifecycle while elucidating the physiological effects of varied environmental perturbations and disease pathogenesis. Here, we review recent literature including methods and applications of metabolomics analysis. We begin with an introduction of metabolomics, then focus on identification and quantification of the volatile metabolites produced from fungus and plants. Detailed discussion on sampling methods, pre-treatment of gas chromatography/mass spectrometry data, and chemometric analysis of volatile compounds follow. The review also provides examples of volatile metabolomic applications in a number of research fields. The aim of this review is to provide a basic understanding of and an outline of the required workflow for a metabolomics study of the volatile organic compounds produced by plants and fungi.

The use of genome-scale metabolic modelling in system strategies for the production of platform chemical such as succinic acid in Escherichia coli has received renewed attention in recent years. The advances in high-throughput technologies and the development of E. coli genome-scale metabolic models (GEMs) have been considered indispensable in the current emerging strategies for strain design and biobased succinic acid production. Thus, much interest has developed in understanding the metabolic fluxes at genome-scale level that increase succinic acid production in E. coli. A range of proof-ofprinciple strains were previously constructed by single gene knockouts guided by GEMs in E. coli as case studies. This review pinpoints recent advances in genome-scale metabolic modelling for increasing succinic acid production by deploying different versions of E. coli GEMs and probably beyond succinic acid.

Increasingly sophisticated instrumentation for chemical separations and identification has facilitated rapid advancements in our understanding of the metabolome. Since many analyses are performed using either mass spectroscopy (MS) or nuclear magnetic resonance (NMR) spectroscopy, the spin ½ stable 13C isotope is now widely used as a metabolic tracer. There is a strong interest in quantitative analysis of metabolic flux through pathways in vivo, particularly in human patients. Although instrumentation advances and scientific interests in metabolism are increasing in parallel, a practical and rational design of a 13C tracer study can be challenging. Prior to planning the details of a tracer experiment, is it important to consider whether the analytical results will be sensitive to flux through the pathways of interest. Here, we briefly summarize the various approaches that have been used to design carbon tracer experiments, outline the sources of complexity, and illustrate the use of a software tool, tcaSIM, to aid in the experimental design of both MS and NMR data in complex systems including patients.

Background: Natural product research is the most enormous field of research in terms of the amount of data and importance of information. Natural products discovery and metabolomics deals with a crucial mode of representation of the profile of biologically active metabolites. In this regard, the profiling of the chemical makeup of complex natural plant extracts necessarily requires employing sophisticated and advanced analytical methods like RP-HPLC–ESI-Q-TOF-MS as well as data mining and processing methods. The genus Garcinia (Clusiaceae) contains phenolic, flavonoids, xanthones, triterpenes, and benzophenones which have been reported for their significant biological properties. Methods: Due to its high content of secondary metabolites and its large domestic usage, we have developed a simple, rapid and precise method to characterize all the secondary metabolites using Reverse- Phase Ultra Performance Liquid Chromatography coupled to Electrospray Ionization Quadruple Time-of-Flight Mass Spectrometry (RP-HPLC–ESI-Q-TOF-MS) for the hydro-methanolic extract. A total of about 3443 secondary metabolites from the fruit and 3757 secondary metabolites from the seed were identified by the Progenesis-QI data analysis. Among these a total of 74 compounds from fruits and 86 polar compounds from seeds were manually identified using the mass error limit of <+5ppm including the pSigma score less than 40. The unexplored bioactives belonging to the class of glycosides, flavones, xanthones, organic acids and other phenolic derivatives. Results and Conclusion: Garcinia xanthochymus was found to contain significant number of diverse phytochemical components. These results indicate the profile of molecules present in Garcinia xanthochymus and will be helpful for industries and researchers involved in isolation of their molecules of interest.

Background: Succinic acid is an important platform and/or commodity or specialty chemical with a broad range of applications. The metabolic role of pyruvate formate lyase A (pflA) in relation to succinate production in Escherichia coli under anaerobic conditions from glycerol substrate remained largely unspecified. Methods: Herein, we identified pflA gene for the first time, as a novel gene knockout target for increasing succinate production in E. coli. Guided by E. coli reconstruction iJO1366, we engineered the E. coli host metabolism by deleting the pflA, thereby causing the up-regulation of glyceraldehyde-3-phosphate dehydrogenase (GAPDH), which hypothetically increases the generation of NADH and the pool of phosphoenolpyruvate (PEP) in the central carbon metabolism, required for succinate production. This strategy produced succinic acid that is 32 fold (1.53 g l-1 in 7 days) from glycerol substrate. Results and Conclusion: This work elucidates that pflA is a novel gene deletion target for increasing succinic acid production from glycerol in E. coli under anaerobic conditions. In addition, these results highlight the power of metabolic model in identifying novel gene deletion target and ultimately driving novel biological discovery.

Background: Chinese hamster ovary (CHO) cells are extremely important host cells for recombinant DNA technology with their utility requiring optimization of growth. The ability to test conditions using in silico models of growing CHO cells can help advance the optimization of bioreactor conditions towards higher viable cell concentration and increased mAb production. Methods: A new kinetic model of CHO cell metabolism is presented, tested and provided in this publication. RNASeq data from CHO cells was used to guide the selection of major metabolic pathways that were included in the kinetic model. The kinetic model includes 37 completely described reactions processing 45 species (metabolites). This model is based on previously published kinetic characteristics for this system and was evaluated against metabolomics data for mAb producing CHO cells grown under two different feeding regimes. Results and Conclusion: This work provides a kinetic model for energy metabolism of CHO cells. Application of the model offers insights into possible causes of the different performance of two feeding strategies, thus suggesting possible problems and future optimization routes.

Introduction: Prostate Cancer (PCa) is the second most common neoplasia in men. Because it is often diagnosed at a late stage, mortality rates remain high. Studying cancer metabolome, which reflects early changes that occur in cells, has gained relevance and may contribute to the identification of early diagnostic biomarkers and understanding tumor biology. Methods: Fourier-transform infrared (FTIR) spectroscopy is a metabolomics technique that probes the biochemical composition of the analyzed samples and allows to discriminate samples with distinct metabolic profiles, allowing the discrimination between cancerous and non-cancerous samples. In this study, FTIR spectra were acquired from PCa and normal prostate cell lines and analyzed by Principal Component Analysis (PCA). Results & Conclusion: Our results indicate a clear discrimination between the different cell lines, meaning at they exhibit distinct metabolic profiles. This discrimination can be attributed to an altered lipid metabolism (3000-2800 cm-1, 1800-1700 cm-1 and 1500-1400 cm-1) and changes in protein conformation (1700-1600 cm-1). These results suggest that studying cancer metabolome with FTIR spectroscopy not only allows the understanding of tumor metabolic behavior and may be useful to the development of new therapeutic targets.