Evaluation of potential flavonoid inhibitors of glyoxalase-I based on virtual screening and in vitro studies

Glyoxalase-I (GLO-I) is a component of the ubiquitous detoxification system involved in the conversion of methylglyoxal (MG) to d-lactate in the glycolytic pathway. MG toxicity arises from its ability to form advanced glycation end products. GLO-I has been reported to be frequently overexpressed in various types of cancer cells. In this study, we performed structure-based virtual screening of focused flavonoids commercial library to identify potential and specific inhibitors of GLO-I. The compounds were ranked based on Glide extra precision docking score and five hits (curcumin, quercetin, morin, naringin and silibinin) were selected on the basis of their interaction with active site amino acid residues of GLO-I. Mixed mode QM/MM calculation was performed on the top-scoring hit to ascertain the role of zinc ion in ligand binding. In addition, the identified hits were subjected to MM/GBSA binding energy prediction, ADME prediction and similarity studies. The hits were tested in vitro for cell viability, and GLO-I inhibition. Naringin (ST072162) was found to be most potent inhibitor of GLO-I among the identified hits with highest glide XP dock score of ?14.906. These findings suggest that naringin could be a new scaffold for designing inhibitors against GLO-I with potential application as anticancer agents.     

Fructose Mediated Non-Alcoholic Fatty Liver Is Attenuated by HO-1-SIRT1 Module in Murine Hepatocytes and Mice Fed a High Fructose Diet

Background

Oxidative stress underlies the etiopathogenesis of nonalcoholic fatty liver disease (NAFLD), obesity and cardiovascular disease (CVD). Heme Oxygenase-1 (HO-1) is a potent endogenous antioxidant gene that plays a key role in decreasing oxidative stress. Sirtuin1 (SIRT1) belongs to the family of NAD-dependent de-acyetylases and is modulated by cellular redox.

Hypothesis

We hypothesize that fructose-induced obesity creates an inflammatory and oxidative environment conducive to the development of NAFLD and metabolic syndrome. The aim of this study is to determine whether HO-1 acts through SIRT1 to form a functional module within hepatocytes to attenuate steatohepatitis, hepatic fibrosis and cardiovascular dysfunction.

Methods and Results

We examined the effect of fructose, on hepatocyte lipid accumulation and fibrosis in murine hepatocytes and in mice fed a high fructose diet in the presence and absence of CoPP, an inducer of HO-1, and SnMP, an inhibitor of HO activity. Fructose increased oxidative stress markers and decreased HO-1 and SIRT1 levels in hepatocytes (p<0.05). Further fructose supplementation increased FAS, PPARα, pAMPK and triglycerides levels; CoPP negated this increase. Concurrent treatment with CoPP and SIRT1 siRNA in hepatocytes increased FAS, PPARα, pAMPK and triglycerides levels suggesting that HO-1 is upstream of SIRT1 and suppression of SIRT1 attenuates the beneficial effects of HO-1. A high fructose diet increased insulin resistance, blood pressure, markers of oxidative stress and lipogenesis along with fibrotic markers in mice (p<0.05). Increased levels of HO-1 increased SIRT1 levels and ameliorated fructose-mediated lipid accumulation and fibrosis in liver along with decreasing vascular dysfunction (p<0.05 vs. fructose). These beneficial effects of CoPP were reversed by SnMP.

Conclusion

Taken together, our study demonstrates, for the first time, that HO-1 induction attenuates fructose-induced hepatic lipid deposition, prevents the development of hepatic fibrosis and abates NAFLD-associated vascular dysfunction; effects that are mediated by activation of SIRT1 gene expression.     

Design and synthesis of (4E)-4-(4-substitutedbenzylideneamino)-3-substituted-2,3-dihydro-2-thioxothiazole-5-carbonitrile as novel A2A receptor antagonists

Novel 2-thioxothiazole derivatives (6–19) as potential adenosine A_2A receptor (A_2AR) antagonists were synthesized. The strong interaction of the compounds (6–19) with A_2AR in docking study was confirmed by high binding affinity with human A_2AR expressed in HEK293T cells using radioligand-binding assay. The compound 19 demonstrated very high selectivity for A_2AR as compared to standard A_2AR antagonist SCH58261. Decrease in A_2AR-coupled release of endogenous cAMP in treated HEK293T cells demonstrated in vitro A_2AR antagonist potential of the compound 19. Attenuation in haloperidol-induced impairment (catalepsy) in Swiss albino male mice pre-treated with compound 19 is evocative to explore its prospective in therapy of PD.     

Comparative genome analysis of Solanum lycopersicum and Solanum tuberosum

Solanum lycopersicum and Solanum tuberosum are agriculturally important crop species as they are rich sources of starch, protein, antioxidants, lycopene, beta-carotene, vitamin C, and fiber. The genomes of S. lycopersicum and S. tuberosum are currently available. However the linear strings of nucleotides that together comprise a genome sequence are of limited significance by themselves. Computational and bioinformatics approaches can be used to exploit the genomes for fundamental research for improving their varieties. The comparative genome analysis, Pfam analysis of predicted reviewed paralogous proteins was performed. It was found that S. lycopersicum proteins belong to more families, domains and clans in comparison with S. tuberosum. It was also found that mostly intergenic regions are conserved in two genomes followed by exons, intron and UTR. This can be exploited to predict regions between genomes that are similar to each other and to study the evolutionary relationship between two genomes, leading towards the development of disease resistance, stress tolerance and improved varieties of tomato.     

Longitudinal Metabolomic Profiling of Amino Acids and Lipids across Healthy Pregnancy

Pregnancy is characterized by a complexity of metabolic processes that may impact fetal development and ultimately, infant health outcomes. However, our understanding of whole body maternal and fetal metabolism during this critical life stage remains incomplete. The objective of this study is to utilize metabolomics to profile longitudinal patterns of fasting maternal metabolites among a cohort of non-diabetic, healthy pregnant women in order to advance our understanding of changes in protein and lipid concentrations across gestation, the biochemical pathways by which they are metabolized and to describe variation in maternal metabolites between ethnic groups. Among 160 pregnant women, amino acids, tricarboxylic acid (TCA) cycle intermediates, keto-bodies and non-esterified fatty acids were detected by liquid chromatography coupled with mass spectrometry, while polar lipids were detected through flow-injected mass spectrometry. The maternal plasma concentration of several essential and non-essential amino acids, long-chain polyunsaturated fatty acids, free carnitine, acetylcarnitine, phosphatidylcholines and sphingomyelins significantly decreased across pregnancy. Concentrations of several TCA intermediates increase as pregnancy progresses, as well as the keto-body β-hydroxybutyrate. Ratios of specific acylcarnitines used as indicators of metabolic pathways suggest a decreased beta-oxidation rate and increased carnitine palmitoyltransferase-1 enzyme activity with advancing gestation. Decreasing amino acid concentrations likely reflects placental uptake and tissue biosynthesis. The absence of any increase in plasma non-esterified fatty acids is unexpected in the catabolic phase of later pregnancy and may reflect enhanced placental fatty acid uptake and utilization for fetal tissue growth. While it appears that energy production through the TCA cycle increases as pregnancy progresses, decreasing patterns of free carnitine and acetylcarnitine as well as increased carnitine palmitoyltransferase-1 rate and β-hydroxybutyrate levels suggest a concomitant upregulation of ketogenesis to ensure sufficient energy supply in the fasting state. Several differences in metabolomic profiles between Hispanic and non-Hispanic women demonstrate phenotypic variations in prenatal metabolism which should be considered in future studies.     

Residue-level prediction of DNA-binding sites and its application on DNA-binding protein predictions

Protein-DNA interactions are crucial to many cellular activities such as expression-control and DNA-repair. These interactions between amino acids and nucleotides are highly specific and any aberrance at the binding site can render the interaction completely incompetent. In this study, we have three aims focusing on DNA-binding residues on the protein surface: to develop an automated approach for fast and reliable recognition of DNA-binding sites; to improve the prediction by distance-dependent refinement; use these predictions to identify DNA-binding proteins. We use a support vector machines (SVM)-based approach to harness the features of the DNA-binding residues to distinguish them from non-binding residues. Features used for distinction include the residue's identity, charge, solvent accessibility, average potential, the secondary structure it is embedded in, neighboring residues, and location in a cationic patch. These features collected from 50 proteins are used to train SVM. Testing is then performed on another set of 37 proteins, much larger than any testing set used in previous studies. The testing set has no more than 20% sequence identity not only among its pairs, but also with the proteins in the training set, thus removing any undesired redundancy due to homology. This set also has proteins with an unseen DNA-binding structural class not present in the training set. With the above features, an accuracy of 66% with balanced sensitivity and specificity is achieved without relying on homology or evolutionary information. We then develop a post-processing scheme to improve the prediction using the relative location of the predicted residues. Balanced success is then achieved with average sensitivity, specificity and accuracy pegged at 71.3%, 69.3% and 70.5%, respectively. Average net prediction is also around 70%. Finally, we show that the number of predicted DNA-binding residues can be used to differentiate DNA-binding proteins from non-DNA-binding proteins with an accuracy of 78%. Results presented here demonstrate that machine-learning can be applied to automated identification of DNA-binding residues and that the success rate can be ameliorated as more features are added. Such functional site prediction protocols can be useful in guiding consequent works such as site-directed mutagenesis and macromolecular docking.