Characterization of α-Glucosidase Inhibitors from Psychotria malayana Jack Leaves Extract Using LC-MS-Based Multivariate Data Analysis and In-Silico Molecular Docking.

Psychotria malayana Jack has traditionally been used to treat diabetes. Despite its potential, the scientific proof in relation to this plant is still lacking. Thus, the present study aimed to investigate the α-glucosidase inhibitors in P.malayana leaf extracts using a metabolomics approach and to elucidate the ligand-protein interactions through in silico techniques. The plant leaves were extracted with methanol and water at five various ratios (100, 75, 50, 25 and 0% v/v; water-methanol). Each extract was tested for α-glucosidase inhibition, followed by analysis using liquid chromatography tandem to mass spectrometry. The data were further subjected to multivariate data analysis by means of an orthogonal partial least square in order to correlate the chemical profile and the bioactivity. The loading plots revealed that the m/z signals correspond to the activity of α-glucosidase inhibitors, which led to the identification of three putative bioactive compounds, namely 5'-hydroxymethyl-1'-(1, 2, 3, 9-tetrahydro-pyrrolo (2, 1-b) quinazolin-1-yl)-heptan-1'-one (1), α-terpinyl-ß-glucoside (2), and machaeridiol-A (3). Molecular docking of the identified inhibitors was performed using Auto Dock Vina software against the crystal structure of Saccharomyces cerevisiae isomaltase (Protein Data Bank code: 3A4A). Four hydrogen bonds were detected in the docked complex, involving several residues, namely ASP352, ARG213, ARG442, GLU277, GLN279, HIE280, and GLU411. Compound 1, 2, and 3 showed binding affinity values of -8.3, -7.6, and -10.0 kcal/mol, respectively, which indicate the good binding ability of the compounds towards the enzyme when compared to that of quercetin, a known α-glucosidase inhibitor. The three identified compounds that showed potential binding affinity towards the enzymatic protein in molecular docking interactions could be the bioactive compounds associated with the traditional use of this plant.

Preliminary Phytochemical Screening, In Vitro Antidiabetic, Antioxidant Activities, and Toxicity of Leaf Extracts of Psychotria malayana Jack.

Psychotria malayana Jack belongs to the Rubiacea and is widespread in Southeast Asian countries. It is traditionally used to treat diabetes. Despite its potential medicinal use, scientific proof of this pharmacological action and the toxic effect of this plant are still lacking. Hence, this study aimed to investigate the in vitro antidiabetic and antioxidant activities, toxicity, and preliminary phytochemical screening of P. malayana leaf extracts by gas chromatography-mass spectrometry (GC-MS) after derivatization. The antidiabetic activities of different extracts of this plant were investigated through alpha-glucosidase inhibitory (AGI) and 2-NBDG glucose uptake using 3T3-L1 cell line assays, while the antioxidant activity was evaluated using DPPH and FRAP assays. Its toxicological effect was investigated using the zebrafish embryo/larvae (Danio rerio) model. The mortality, hatchability, tail-detachment, yolk size, eye size, beat per minute (BPM), and body length were taken into account to observe the teratogenicity in all zebrafish embryos exposed to methanol extract. The LC50 was determined using probit analysis. The methanol extract showed the AGI activity (IC50 = 2.71 ± 0.11 µg/mL), insulin-sensitizing activity (at a concentration of 5 µg/mL), and potent antioxidant activities (IC50 = 10.85 µg/mL and 72.53 mg AAE/g for DPPH and FRAP activity, respectively). Similarly, the water extract exhibited AGI activity (IC50 = 6.75 µg/mL), insulin-sensitizing activity at the concentration of 10 µg/mL, and antioxidant activities (IC50 = 27.12 and 33.71 µg/mL for DPPH and FRAP activity, respectively). The methanol and water extracts exhibited the LC50 value higher than their therapeutic concentration, i.e., 37.50 and 252.45 µg/mL, respectively. These results indicate that both water and methanol extracts are safe and potentially an antidiabetic agent, but the former is preferable since its therapeutic index (LC50/therapeutic concentration) is much higher than for methanol extracts. Analysis using GC-MS on derivatized methanol and water extracts of P. malayana leaves detected partial information on some constituents including palmitic acid, 1,3,5-benzenetriol, 1-monopalmitin, beta-tocopherol, 24-epicampesterol, alpha-tocopherol, and stigmast-5-ene, that could be a potential target to further investigate the antidiabetic properties of the plant. Nevertheless, isolation and identification of the bioactive compounds are required to confirm their antidiabetic activity and toxicity.

Computational prediction of changes in brain metabolic fluxes during Parkinson's disease from mRNA expression.

BACKGROUND: Parkinson's disease is a widespread neurodegenerative disorder which affects brain metabolism. Although changes in gene expression during disease are often measured, it is difficult to predict metabolic fluxes from gene expression data. Here we explore the hypothesis that changes in gene expression for enzymes tend to parallel flux changes in biochemical reaction pathways in the brain metabolic network. This hypothesis is the basis of a computational method to predict metabolic flux changes from post-mortem gene expression measurements in Parkinson's disease (PD) brain. RESULTS: We use a network model of central metabolism and optimize the correspondence between relative changes in fluxes and in gene expression. To this end we apply the Least-squares with Equalities and Inequalities algorithm integrated with Flux Balance Analysis (Lsei-FBA). We predict for PD (1) decreases in glycolytic rate and oxygen consumption and an increase in lactate production in brain cortex that correspond with measurements (2) relative flux decreases in ATP synthesis, in the malate-aspartate shuttle and midway in the TCA cycle that are substantially larger than relative changes in glucose uptake in the substantia nigra, dopaminergic neurons and most other brain regions (3) shifts in redox shuttles between cytosol and mitochondria (4) in contrast to Alzheimer's disease: little activation of the gamma-aminobutyric acid shunt pathway in compensation for decreased alpha-ketoglutarate dehydrogenase activity (5) in the globus pallidus internus, metabolic fluxes are increased, reflecting increased functional activity. CONCLUSION: Our method predicts metabolic changes from gene expression data that correspond in direction and order of magnitude with presently available experimental observations during Parkinson's disease, indicating that the hypothesis may be useful for some biochemical pathways. Lsei-FBA generates predictions of flux distributions in neurons and small brain regions for which accurate metabolic flux measurements are not yet possible.

Using bioconductor package BiGGR for metabolic flux estimation based on gene expression changes in brain.

Predicting the distribution of metabolic fluxes in biochemical networks is of major interest in systems biology. Several databases provide metabolic reconstructions for different organisms. Software to analyze flux distributions exists, among others for the proprietary MATLAB environment. Given the large user community for the R computing environment, a simple implementation of flux analysis in R appears desirable and will facilitate easy interaction with computational tools to handle gene expression data. We extended the R software package BiGGR, an implementation of metabolic flux analysis in R. BiGGR makes use of public metabolic reconstruction databases, and contains the BiGG database and the reconstruction of human metabolism Recon2 as Systems Biology Markup Language (SBML) objects. Models can be assembled by querying the databases for pathways, genes or reactions of interest. Fluxes can then be estimated by maximization or minimization of an objective function using linear inverse modeling algorithms. Furthermore, BiGGR provides functionality to quantify the uncertainty in flux estimates by sampling the constrained multidimensional flux space. As a result, ensembles of possible flux configurations are constructed that agree with measured data within precision limits. BiGGR also features automatic visualization of selected parts of metabolic networks using hypergraphs, with hyperedge widths proportional to estimated flux values. BiGGR supports import and export of models encoded in SBML and is therefore interoperable with different modeling and analysis tools. As an application example, we calculated the flux distribution in healthy human brain using a model of central carbon metabolism. We introduce a new algorithm termed Least-squares with equalities and inequalities Flux Balance Analysis (Lsei-FBA) to predict flux changes from gene expression changes, for instance during disease. Our estimates of brain metabolic flux pattern with Lsei-FBA for Alzheimer's disease agree with independent measurements of cerebral metabolism in patients. This second version of BiGGR is available from Bioconductor.

Simulating the physiology of athletes during endurance sports events: modelling human energy conversion and metabolism.

The human physiological system is stressed to its limits during endurance sports competition events. We describe a whole body computational model for energy conversion during bicycle racing. About 23 per cent of the metabolic energy is used for muscle work, the rest is converted to heat. We calculated heat transfer by conduction and blood flow inside the body, and heat transfer from the skin by radiation, convection and sweat evaporation, resulting in temperature changes in 25 body compartments. We simulated a mountain time trial to Alpe d'Huez during the Tour de France. To approach the time realized by Lance Armstrong in 2004, very high oxygen uptake must be sustained by the simulated cyclist. Temperature was predicted to reach 39°C in the brain, and 39.7°C in leg muscle. In addition to the macroscopic simulation, we analysed the buffering of bursts of high adenosine triphosphate hydrolysis by creatine kinase during cyclical muscle activity at the biochemical pathway level. To investigate the low oxygen to carbohydrate ratio for the brain, which takes up lactate during exercise, we calculated the flux distribution in cerebral energy metabolism. Computational modelling of the human body, describing heat exchange and energy metabolism, makes simulation of endurance sports events feasible.