nan Maulidiani

Metabolite Variation in Lean and Obese Streptozotocin (STZ)-Induced Diabetic Rats via 1H NMR-Based Metabolomics Approach

Diabetes mellitus (DM) is considered as a complex metabolic disease because it affects the metabolism of glucose and other metabolites. Although many diabetes studies have been conducted in animal models throughout the years, the pathogenesis of this disease, especially between lean diabetes (ND + STZ) and obese diabetes (OB + STZ), is still not fully understood. In this study, the urine from ND + STZ, OB + STZ, lean/control (ND), and OB + STZ rats were collected and compared by using 1H NMR metabolomics. The results from multivariate data analysis (MVDA) showed that the diabetic groups (ND + STZ and OB + STZ) have similarities and dissimilarities for a certain level of metabolites. Differences between ND + STZ and OB + STZ were particularly noticeable in the synthesis of ketone bodies, branched-chain amino acid (BCAA), and sensitivity towards the oral T2DM diabetes drug metformin. This finding suggests that the ND + STZ group was more similar to the T1DM model and OB + STZ to the T2DM model. In addition, we also managed to identify several pathways and metabolism aspects shared by obese (OB) and OB + STZ. The results from this study are useful in developing drug target-based research as they can increase understanding regarding the cause and effect of DM.

Application of BATMAN and BAYESIL for quantitative 1 H-NMR based metabolomics of urine: discriminant analysis of lean, obese, and obese-diabetic rats

IntroductionBATMAN and BAYESIL are software tools, which can provide a solution for automated metabolite quantifications based on the proton nuclear magnetic resonance (1H-NMR) spectral data of bio-fluids. However, their specific application for the quantitative 1H-NMR based metabolomics of urine has not been investigated.ObjectivesThe aim of this study is to evaluate the performance of BATMAN and BAYESIL in the quantitative metabolite analysis of urine based on its 1H-NMR spectra.MethodsBATMAN and BAYESIL were used for automated metabolite quantification based on the 1H-NMR spectra of the urine from the lean, obese and obese-diabetic rat groups. PLS-DA model was used to discriminate the three different groups based on the results from the quantifications.ResultsBATMAN was found to be superior to BAYESIL in identifying and quantifying the metabolites in the urine samples, owing to its flexibility that allows users to define and adjust the relevant signals of the pure standard metabolites in the database in order to fit the signals in the samples, a necessary step since variations and peak shift are natural in most 1H-NMR spectra. The results of BATMAN also agreed well with that of the manual deconvolution method, which indicated the higher accuracy in metabolite quantification, despite the need of pre-processing and longer processing time than BAYESIL. However, in the case where the problems in baseline correction and peak shift of 1H-NMR spectra are absent, the use of BAYESIL is more advantageous. Application of quantitative 1H-NMR based metabolomics of the urine showed that PLS-DA model derived from BATMAN could satisfactorily discriminate the lean, obese, and obese-diabetic rat groups.ConclusionBoth BATMAN and BAYESIL are useful for the quantitative automation of urine metabolites based on its 1H-NMR spectra. The results from BATMAN method is superior to BAYESIL but require expertise in spectroscopy and longer computer time. Both methods help in simplifying the interpretation of metabolite status in the VIP analysis.

Generalized Likelihood Uncertainty Estimation (GLUE) methodology for optimization of extraction in natural products

Optimization process is an important aspect in the natural product extractions. Herein, an alternative approach is proposed for the optimization in extraction, namely, the Generalized Likelihood Uncertainty Estimation (GLUE). The approach combines the Latin hypercube sampling, the feasible range of independent variables, the Monte Carlo simulation, and the threshold criteria of response variables. The GLUE method is tested in three different techniques including the ultrasound, the microwave, and the supercritical CO2 assisted extractions utilizing the data from previously published reports. The study found that this method can: provide more information on the combined effects of the independent variables on the response variables in the dotty plots; deal with unlimited number of independent and response variables; consider combined multiple threshold criteria, which is subjective depending on the target of the investigation for response variables; and provide a range of values with their distribution for the optimization.