Chemo-structural diversity of anti-obesity compound database.

Nature plays a major role in the development of new drugs which helps in preventing and treating human diseases. Anti-obesity compound database (AOCD) contains comprehensive information on all published small molecules from natural sources with anti-obesity potential targeting pancreatic lipase (PL), appetite suppressant (AS) and adipogenesis (AD). Presently the database contains 349 compounds isolated from 307 plants, 26 marine and 16 microbial sources. Users can query the AOCD database (https://aocd.swmd.co.in/) in several ways. The database was divided into three datasets (PL, AS and AD) to perform chemoinformatic analysis using Platform for Unified Molecular Analysis (PUMA), which were analyzed based on molecular descriptors, scaffold diversity and structural fingerprint diversity. Chemoinformatics study inferred the PL dataset has the highest diversity of compounds based on the Euclidean distance on molecular properties, scaffold diversity and pairwise similarity on fingerprint diversity. This study would hasten the process of anti-obesity drug discovery.

Meta-analysis of lean and obese RNA-seq datasets to identify genes targeting obesity.

Obesity is a global crisis leading to several metabolic disorders. Modernization and technology innovation has been easier for next generation sequencing using open-source online software galaxy, which allows the users to share their data and workflow mapping in an effortless manner. This study is to identify candidate genes for obesity by performing differential expression of genes. RNA-Seq analysis was performed for six different datasets retrieved from GEO database. 258 datasets from obese patients and 55 datasets from lean patients were analysed for differentially expressed genes (DEGs). DEGs analysis showed 1971 upregulated genes and 615 downregulated genes with log2FC count ≥ 2.5 and p-value < 0.05. The Gene enrichment analysis performed using Gene Ontology resource highlighted pathways associated to obesity such as cholesterol metabolism, Fat digestion and absorption and glycerolipid metabolism. Using string database protein-protein interactions network was built and the network clusters were visualized using Cytoscape software. The protein-protein interactions of the upregulated and downregulated genes were mapped to form a network, wherein PNLIP (Pancreatic lipase) and FTO (Fat mass and obesity associated protein) gene clusters were visualized as densely connected clusters in MCODE. PNLIP and FTO with its associated genes were identified as candidate genes for targeting obesity.

Benefits of merging paired-end reads before pre-processing environmental metagenomics data.

BACKGROUND: High throughput sequencing of environmental DNA has applications in biodiversity monitoring, taxa abundance estimation, understanding the dynamics of community ecology, and marine species studies and conservation. Environmental DNA, especially, marine eDNA, has a fast degradation rate. Aside from the good quality reads, the data could have a significant number of reads that fall slightly below the default PHRED quality threshold of 30 on sequencing. For quality control, trimming methods are employed, which generally precede the merging of the read pairs. However, in the case of eDNA, a significant percentage of reads within the acceptable quality score range are also dropped. METHODS: To infer the ideal merge tool that is sensitive to eDNA, two Hiseq paired-end eDNA datasets were utilized to study the merging by the tools - FLASH (Fast Length Adjustment of SHort reads), PANDAseq, COPE, BBMerge, and VSEARCH without preprocessing. We assessed these tools on the following parameters: Time taken to process, the quality, and the number of merged reads. Trimmomatic, a widely-used preprocessing tool, was also assessed by preprocessing the datasets at different parameters for the two approaches of preprocessing: Sliding Window and Maximum Information. The preprocessed read pairs were then merged using the ideal merge tool identified earlier. RESULTS: FLASH is the most efficient merge tool balancing data conservation, quality of reads, and processing time. We compared Trimmomatic's two quality trimming options with increasing strictness with FLASH's direct merge. The raw reads processed with Trimmomatic then merged, yielded a significant drop in reads compared to the direct merge. An average of 29% of reads was dropped when directly merged with FLASH. Maximum Information option resulted in 30.7% to 68.05% read loss with lowest and highest stringency parameters, respectively. The Sliding Window approach conserves approximately 10% more reads at a PHRED score of 25 set as the threshold for a window of size 4. The lowered PHRED cut off conserves about 50% of the reads that could potentially be informative. We noted no significant reduction of data while optimizing the number of reads read in a window with the ideal quality (Q) score. CONCLUSIONS: Losing reads can negatively impact the downstream processing of the environmental data, especially for sequence alignment studies. The quality trim-first-merge-later approach can significantly decrease the number of reads conserved. However, direct merging of pair-end reads using FLASH conserved more than 60% of the reads. Therefore, direct merging of the paired-end reads can prevent potential removal of informative reads that do not comply by the trimming tool's strict checks. FLASH to be an efficient tool in conserving reads while carrying out quality trimming in moderation. Overall, our results show that merging paired-end reads of eDNA data before trimming can conserve more reads.

Computational study of potential inhibitors for fat mass and obesity-associated protein from seaweed and plant compounds.

Background: Over the past three decades, with substantial changes in lifestyle, the tendency to gain weight has increased, which is resulting in significant consequences affecting an individual's well-being. The fat mass and obesity-associated (FTO) gene is involved in food intake and energy expenditure and plays a crucial role in regulating homeostasis and controlling energy expenditure by hindering signals that generate from the brain. Edible seaweeds have been shown to enhance satiety owing to their health benefits. Methods: Extensive screening of plant-derived anti-obesity compounds and seaweed compounds was conducted and validated for ADME properties and toxicity prediction. Further, the top ranked compounds were docked against the FTO protein to identify potential inhibitors and were subjected to molecular dynamic simulation studies to understand the binding stability of ligand protein complex. Finally, MM/PBSA studies were performed to calculate the binding free energy of the protein-ligand complexes. Results: Through the virtual screening of 1,210 compounds, 443 compounds showed good docking scores less than -7.00 kcal/mol. Drug likeness screenings of 443 compounds showed that only 369 compounds were in accordance with these properties. Further toxicity prediction resulted in 30 non-toxic compounds. Molecular docking studies revealed four top ranked marine compounds. Finally, RL074 (2-hydroxyluzofuranone B) and RL442 (10-acetoxyangasiol) from marine red alga Laurencia sp showed good stability from molecular dynamic simulation studies. MM/PBSA results revealed that BT012 (24ε-hydroperoxy-6ß-hydroxy-24-ethylcholesta-4,-28(29)-dien-3-one), an oxygenated fucosterol from brown alga Turbinaria conoides, possessed higher binding energy. Hence, with all the data obtained it could be concluded that three seaweed compounds, BT012, RL074 and RL442, may act as a potential anti-obesity lead compound in targeting FTO.

GenPop-An Online Tool to Analyze Human Population Genetic Data.

GenPop is a web based online cross platform tool developed to help Geneticist and Epidemiologist to deal with association studies in analyzing human population genetic data. The tool features include descriptive analysis such as Hardy-Weinberg equilibrium test, chi-square p-value and analysis of single nucleotide polymorphisms (SNPs) with multiple inheritance models such as dominant, recessive, allelic, genotype, odd's ratio and relative risk at 95% confidence interval and analysis of multiple SNPs including haplotype frequencies and linkage disequilibrium for a pair of biallelic markers. This is a user-driven human population genetic data analysis tool that is easily scalable and acceptable with multiple implementations of different algorithms. GenPop has been developed using PHP, JavaScript and with PHPExcel library to analyse the genetic data for case control studies.

Radioprotective effect of DL-alpha-lipoic acid on mice skin fibroblasts.

During the course of cancer radiation treatment, normal skin invariably suffers from the cytotoxic effects of gamma-radiation and reactive oxygen species (ROS), which are generated from the interaction between radiation and the water molecules in cells. The present study was designed to investigate the radioprotective role of alpha-lipoic acid (LA), an antioxidant on murine skin fibroblasts exposed to a single dose of 2, 4, 6, or 8Gy gamma-radiation. Irradiation of fibroblasts significantly increased ROS, nitric oxide, and lipid peroxidation (P < 0.001); all of these factors substantially decreased with 100 microM LA treatment. Hydroxyl radical (OH(.)) production from 8Gy irradiated fibroblasts was measured directly by electron spin resonance using spin-trapping techniques. LA was found to inhibit OH(.) production at 100-microM concentrations. Dose-dependent depletion of antioxidants, such as catalase and glutathione reductase, was observed in irradiated fibroblasts (P < 0.001), along with increased superoxide dismutase (P < 0.001). LA treatment restored antioxidant levels. Concentration of the pro-inflammatory cytokine IL-1beta was significantly reduced in irradiated fibroblasts when treated with LA. MTT and lactate dehydrogenase assays demonstrated that LA treatment reduced cell injury and protected cells against irradiation-induced cytotoxicity. Thus, we conclude that results are encouraging and need further experiments to demonstrate a possible benefit in cancer patients and the reduction of harmful effects of radiation therapy.

Melatonin prevents amyloid protofibrillar induced oxidative imbalance and biogenic amine catabolism.

Oxidative stress is one of the hypothesized pathogenic mechanisms for neurodegenerative diseases, including Alzheimer's disease (AD); numerous studies suggest that Abeta is toxic to neurons by free radical mediated mechanism. A constant feature in AD brain is selective neuronal loss, accompanied by dysfunction of several neurotransmitter systems, such as cholinergic, serotoninergic and noradrenergic systems. In the present study, we studied the neuroprotective role of melatonin against amyloid protofibrils and the toxicity of protofibrils on serotoninergic and noradrenergic systems. Mice were divided into four groups (n=8 each), control, Scrambles Abeta(35-25) treated, Abeta(25-35) injected, and melatonin treated. A single dose of Abeta(25-35) (25 microg) was administered to mice via intraperitoneal injection. Melatonin (50 mg/kg body weight) was administered intraperitoneally for 3 days to the Abeta(25-35) injected mice. Control mice received only physiological saline and Scrambles receives Abeta(35-25) single intraperitoneal injection of 25 microg of Abeta(35-25). Our study showed that melatonin significantly reduces reactive oxygen species (ROS) production in the astrocytes, lymphocytes and hepatocytes of Abeta injected mice by increasing the levels of scavenging enzymes, SOD, catalase and GSH when compared to the untreated group. Immunohistochemistry study reveals that melatonin prevents the activation of GFAP in neocortex and transcription factor NF-kappaB in liver and neocortex of Abeta injected mice. It also prevents the elevation of dopamine depletion and its degradation products. Thus, while melatonin may be a potential therapeutic agent in the prevention of oxidative stress associated with Abeta and AD, it can also prevent dopamine turnover induced by Abeta.

The protective role of DL-alpha-lipoic acid in biogenic amines catabolism triggered by Abeta amyloid vaccination in mice.

The major pathological consequence of Alzheimer disease (AD) is accumulation of beta-amyloid (Abeta) peptide fibrillar plaque in the brain and subsequent inflammatory reaction associated with the surrounding cells due to the presence of these aggregates. Inflammation is the major complication associated with Abeta peptide vaccination. Abeta peptide activated T-helper cells are shown to enhance the existing-inflammatory conditions in the brain and other organs of AD patients. Hence systematic studies on potential approaches that will prevent inflammation during the vaccination are highly desired. DL-alpha-lipoic acid (LA), an antioxidant with known function as cofactor in mitochondrial dehydrogenase reactions, will be a good candidate to annul the oxidative damage due to vaccination triggered inflammation. For the first time, levels of principal neurotransmitters and their major metabolites in hippocampus and neocortex regions of brain are quantified to find out the level of inflammation. We have used high performance liquid chromatography with electro chemical detection (HPLC-EC) for monitoring neurotransmitter levels. We have shown a significant (p<0.05) reduction of 5-hydroxytryptamine (5-HT), dopamine (DA) and norepinephrine (NE) in the systemic inflammation induced (SI), vaccinated (VA) and inflammation induced vaccinated (IV) mice. Nevertheless their metabolites such as 5-hydroxyindole acetic acid (5-HIAA) and homovanillic acid (HVA) are significantly (p<0.05) increased when compared with control. Interestingly, antioxidant LA treated mice with systemic inflammation (IL), vaccinated (VL) and inflammation induced vaccinated (IVL) mice exhibited enhanced level of 5-HT, DA and NE and the concentration of 5-HIAA and HVA gradually returned to normal. These results suggest a possible new way for monitoring and modifying the inflammation and thereby preventing Abeta vaccination mediated tissue damage.

QSAR based docking studies of marine algal anticancer compounds as inhibitors of protein kinase B (PKBß).

Marine algae are prolific source of bioactive secondary metabolites and are found to be active against different cancer cell lines. QSAR studies will explicate the significance of a particular class of descriptor in eliciting anticancer activity against a cancer type. Marine algal compounds showing anticancer activity against six different cancer cell lines namely MCF-7, A431, HeLa, HT-29, P388 and A549 taken from Seaweed metabolite database were subjected to comprehensive QSAR modeling studies. A hybrid-GA (genetic algorithm) optimization technique for descriptor space reduction and multiple linear regression analysis (MLR) approach was used as fitness functions. Cell lines HeLa and MCF-7 showed good statistical quality (R(2)∼0.75, Q(2)∼0.65) followed by A431, HT29 and P388 cell lines with reasonable statistical values (R(2)∼0.70, Q(2)∼0.60). The models developed were interpretable, with good statistical and predictive significance. Molecular descriptor analyses revealed that Baumann's alignment-independent topological descriptors had a major role in variation of activity along with other descriptors. Incidentally, earlier QSAR analysis on a variety of chemically diverse PKBα inhibitors revealed Baumann's alignment-independent topological descriptors that differentiated the molecules binding to Protein kinase B (PKBα) kinase or PH domain, hence a docking study of two crystal structures of PKBß was performed for identification of novel ATP-competitive inhibitors of PKBß. Five compounds had a good docking score and Callophycin A showed better ligand efficiency than other PKBß inhibitors. Furthermore in silico pharmacokinetic and toxicity studies also showed that Callophycin A had a high drug score (0.85) compared to the other inhibitors. These results encourages discovering novel inhibitors for cancer therapeutic targets by screening metabolites from marine algae.

Seaweed metabolite database (SWMD): A database of natural compounds from marine algae.

UNLABELLED: The cataloguing of marine chemicals is a fundamental aspect for bioprospecting. This has applications in the development of drugs from marine sources. A publicly accessible database that provides comprehensive information about these compounds is therefore helpful. The Seaweed Metabolite Database (SWMD) is designed to provide information about the known compounds and their biological activity described in the literature. Geographical origin of the seaweed, extraction method and the chemical descriptors of each the compounds are recorded to enable effective chemo-informatics analysis. Crosslinks to other databases are also introduced to facilitate the access of information about 3D Structure by X-ray and NMR activity, drug properties and related literature for each compound. This database currently contains entries for 517 compounds encompassing 25 descriptive fields mostly from the Red algae of the genus Laurencia (Ceramiales, Rhodomelaceae). The customized search engine of this database will enable wildcard querying, which includes Accession Number, Compound type, Seaweed Binomial name, IUPAC name, SMILES notation or InChI. AVAILABILITY: The database is available for free at http://www.swmd.co.in.