メタボリックシンドロームジャーナル

Since the descovery of D-serine as an endogenous metabolite, chiral metabolomics has become more and more prevalent, exploring the role of chirality in development and progression of different diseases. In biological systems, the most notable class of chiral metabolites is represented by amino acids. It is well known that in mammals the L- enantiomer of amino acids (AA) is naturally occurring. Nevertheless, D-AAs are present in very low concentrations (low nM to low µM range) compared to L-AAs (low µM to mM range), impeding their detection and quantification. Even though many analytical approaches have been developed, most of them are difficult to implement since they require special infrastructure (2D-LC-MS) or expensive chiral selectors. The objective of our study was to develop a fast UHPLC-MS method capable to offer baseline enantiomeric resolution (Rs>2) for all proteinogenic amino acids, using the indirect enantioseparation approach. The starting point were some several observations made in the past [1,2], where a dependance of chiral resolution on mobile phase pH was documented. Therefore, several qualitative variables were identified and included in the study, such as: chiral derivatization reagent (CDA), stationary phase, aqueous phase pH and the nature of organic modifier. Two CDAs were selected: (+)-1-(9-Fluorenyl)ethyl chloroformate ((+)-FLEC) and N-(4-Nitrophenoxycarbonyl)-Lphenylalanine 2-methoxyethyl ester ((S)-NIFE), together with five stationary phases (C18, Polar-C19, PS-C18, Phenyl and F5), two organic modifiers (acetonitrile and methanol) and a pH range between 2 and 8. After a comprehensive screening of these variables, it was observed that (S)-NIFE and acetonitrile have a positive impact on enantioseparation, as compared to (+)-FLEC and methanol, respectively. A further optimization using design of experiments was carried out for finding the best gradient and for fine tuning the pH of the aqueous phase. In optimal conditions, baseline chiral separation of all proteinogenic D- and L-amino acids was achieved in less than 20 minutes.

The history of the study of allelopathy in aquatic ecosystems goes back over 100 years. However, the importance of this phenomenon for understanding the structural and functional organization of aquatic ecosystems is beginning to become clear only now. The known information on the low molecular weight metabolome (LMWM) of various macrophytes demonstrates that over 1500 different LMWOCs (low molecular weight organic compounds) can be found in its composition. Moreover, the number of LMWOCs in certain plant species growing in certain habitats can exceed 200 compounds. It is shown that there are patterns of formation and changes in LMWM macrophytes, both depending on the geographical place of plant growth, and the impact of various biotic and abiotic factors. Particular attention is drawn to the issues related to the study of the allelopathy of macrophytes in freshwater ecosystems. For macrophytes of various ecological groups, their inhibiting allelochemicals are described, as well as ecological targets - algae, and cyanobacteria. The direction of studying the potential biological activities of major LMWOCs of aquatic macrophytes using the QSAR method is of great importance. The questions of the chemical protection of aquatic plants against consumers, pests, and pathogens are of theoretical and practical importance. It is shown that it is realistic to create new generation algicides based on natural allelochemicals to prevent and suppress the "bloom" of water bodies. The allelopathy (as a natural phenomenon) can be used for the development of a nature-like convergent technology to control the “bloom” in aquatic ecosystems. Metabolomics is the scientific study of chemical processes involving metabolites, the small molecule substrates, intermediates and products of cell metabolism. The metabolome represents the complete set of metabolites in a biological cell, tissue, organ or organism, which are the end products of cellular processes. Messenger RNA (mRNA), gene expression data and proteomic analyses reveal the set of gene products being produced in the cell, data that represents one aspect of cellular function.

Psoriasis is an inflammatory and multifaceted disease of the epidermis based on an immunological mechanism involving Langerhans cells and T lymphocytes that produce proinflammatory cytokines. Genetic factors, environmental factors, and improper nutrition are considered triggers of the disease. Numerous studies have reported that in a high number of patients, psoriasis is associated with obesity. Excess adipose tissue, typical of obesity, causes a systemic inflammatory status coming from the inflammatory active adipose tissue; therefore, weight reduction is a strategy to fight this pro-inflammatory state. We performed a NMR metabolomic study in order to evaluate how a nutritional regimen based on a ketogenic diet, characterized by a reduction in carbohydrates and a relative increase in protein and fat, influences the clinical parameters, metabolic profile, and inflammatory state of psoriasis patients. Thirty (30) psoriasis patients were subjected to a ketogenic nutritional regimen and monitored for 4 weeks by evaluating the clinical data, biochemical and clinical parameters, NMR metabolomic profile, and IL-2, IL-1β, TNF-α, IFN-γ, and IL-4 concentrations before and after the nutritional regimen. Metabolomic profiles of psoriasis patients compared to those of healthy controls before and after a 4 week ketogenic diet provided preliminary indications to identify candidate biomarkers useful in the theranostic control of psoriasis. Results of the metabolic pathway analysis reveal the therapeutic potential of a dietary regimen and provide new insights into the etiopathogenesis of psoriasis. Manuela Grimaldi is a Researcher at the Department of Pharmacy at University of Salerno, involved in the realization of a project entitled "NMR metabolomic characterization of cancerous tissues and biological fluids." In 2007 she graduated in Pharmaceutical Chemistry and Technologies and in the following years she obtained the Specialization in Hospital Pharmacy and the PhD in Pharmaceutical Sciences with a thesis entitled “NMR study of protein-ligand interaction”.

Since the descovery of D-serine as an endogenous metabolite, chiral metabolomics has become more and more prevalent, exploring the role of chirality in development and progression of different diseases. In biological systems, the most notable class of chiral metabolites is represented by amino acids. It is well known that in mammals the L- enantiomer of amino acids (AA) is naturally occurring. Nevertheless, D-AAs are present in very low concentrations (low nM to low µM range) compared to L-AAs (low µM to mM range), impeding their detection and quantification. Even though many analytical approaches have been developed, most of them are difficult to implement since they require special infrastructure (2D-LC-MS) or expensive chiral selectors. The objective of our study was to develop a fast UHPLC-MS method capable to offer baseline enantiomeric resolution (Rs>2) for all proteinogenic amino acids, using the indirect enantioseparation approach. The starting point were some several observations made in the past [1,2], where a dependance of chiral resolution on mobile phase pH was documented. Therefore, several qualitative variables were identified and included in the study, such as: chiral derivatization reagent (CDA), stationary phase, aqueous phase pH and the nature of organic modifier. Two CDAs were selected: (+)-1-(9-Fluorenyl)ethyl chloroformate ((+)-FLEC) and N-(4-Nitrophenoxycarbonyl)-Lphenylalanine 2-methoxyethyl ester ((S)-NIFE), together with five stationary phases (C18, Polar-C19, PS-C18, Phenyl and F5), two organic modifiers (acetonitrile and methanol) and a pH range between 2 and 8. After a comprehensive screening of these variables, it was observed that (S)-NIFE and acetonitrile have a positive impact on enantioseparation, as compared to (+)-FLEC and methanol, respectively. A further optimization using design of experiments was carried out for finding the best gradient and for fine tuning the pH of the aqueous phase. In optimal conditions, baseline chiral separation of all proteinogenic D- and L-amino acids was achieved in less than 20 minutes.

The determination of the lipidome profile is often called lipidomics. The lipidome represents all the small molecules metabolomes whit mass lower than 1500 in system. In recent years, the crucial role of lipidomes in the pathogenesis and therapy of deseases has become increasingly apparent. For example, ischemia-reperfusion (IR) injury can initiate oxidative stress that leads to harmful changes in membrane lipids, with an unwanted accumulation of fatty acids that leads to lipotoxicity. Lipid analysis provides additional insight into the pathogenesis of IR disorders and reveals new targets for drug action. A therapeutic approach to reperfusion lipotoxicity involves attenuation of fatty acids overload, i.e., their transport to adipose tissue and/or inhibition of the adverse effects of fatty acids on cell damage and death The framework of this lecture is analysis of the lipid metabolites and other products that can affect the metabolites of lipids. We analyzed lipid profile of heart, lung, brain kidney liver. The carbohydrate profile was also examined. Based on these results we tried to correlate it with various metabolic processes. In order to obtained even better conclusions the chemometrics was employed. The obtained results showed similarities between hearts and lung, whereas liver and brain exhibits specific behavior. Modern mass spectrometric technologies provide quantitative readouts for a wide variety of lipid specimens. However, many studies do not report absolute lipid concentrations and differ vastly in methodologies, workflows and data presentation. Therefore, we encourage researchers to engage with the Lipidomics Standards Initiative to develop common standards for minimum acceptable data quality and reporting for lipidomics data, to take lipidomics research to the next level Lipidomics has evolved rapidly over the past decade because it offers new opportunities for studying the roles of lipids in cellular biology as well as in health and disease. The lipidomes of eukaryotic cells comprise hundreds of individual lipid species that structurally and chemically regulate cell membrane dynamics, store energy and/or serve as precursors of bioactive metabolites. Membranes of cells and organelles have unique lipid compositions that are intimately linked to their biological functions. The biophysical properties of membranes are also affected by seemingly minor structural differences among individual lipid species, such as the number, position and geometry of double bonds in acyl chains. These characteristics drive membrane budding and fission events and may regulate protein function. Lipid species in membranes act not as single molecules but as a collective, and must be analysed quantitatively and comprehensively to understand their biological function.