Grid Added Value to Address Malaria

Through this paper, we call for a distributed, internet-based collaboration to address one of the worst plagues of our present world, malaria. The spirit is a non-proprietary peer-production of information-embedding goods. And we propose to use the grid technology to enable such a world wide "open source" like collaboration. The first step towards this vision has been achieved during the summer on the EGEE grid infrastructure where 46 million ligands were docked for a total amount of 80 CPU years in 6 weeks in the quest for new drugs.Comment: 7 pages, 1 figure, 6th IEEE International Symposium on Cluster Computing and the Grid, Singapore, 16-19 may 2006, to appear in the proceeding

Identification of gene pathways implicated in Alzheimer's disease using longitudinal imaging phenotypes with sparse regression

We present a new method for the detection of gene pathways associated with a multivariate quantitative trait, and use it to identify causal pathways associated with an imaging endophenotype characteristic of longitudinal structural change in the brains of patients with Alzheimer's disease (AD). Our method, known as pathways sparse reduced-rank regression (PsRRR), uses group lasso penalised regression to jointly model the effects of genome-wide single nucleotide polymorphisms (SNPs), grouped into functional pathways using prior knowledge of gene-gene interactions. Pathways are ranked in order of importance using a resampling strategy that exploits finite sample variability. Our application study uses whole genome scans and MR images from 464 subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. 66,182 SNPs are mapped to 185 gene pathways from the KEGG pathways database. Voxel-wise imaging signatures characteristic of AD are obtained by analysing 3D patterns of structural change at 6, 12 and 24 months relative to baseline. High-ranking, AD endophenotype-associated pathways in our study include those describing chemokine, Jak-stat and insulin signalling pathways, and tight junction interactions. All of these have been previously implicated in AD biology. In a secondary analysis, we investigate SNPs and genes that may be driving pathway selection, and identify a number of previously validated AD genes including CR1, APOE and TOMM40

XML in Motion from Genome to Drug

Information technology (IT) has emerged as a central to the solution of contemporary genomics and drug discovery problems. Researchers involved in genomics, proteomics, transcriptional profiling, high throughput structure determination, and in other sub-disciplines of bioinformatics have direct impact on this IT revolution. As the full genome sequences of many species, data from structural genomics, micro-arrays, and proteomics became available, integration of these data to a common platform require sophisticated bioinformatics tools. Organizing these data into knowledgeable databases and developing appropriate software tools for analyzing the same are going to be major challenges. XML (eXtensible Markup Language) forms the backbone of biological data representation and exchange over the internet, enabling researchers to aggregate data from various heterogeneous data resources. The present article covers a comprehensive idea of the integration of XML on particular type of biological databases mainly dealing with sequence-structure-function relationship and its application towards drug discovery. This e-medical science approach should be applied to other scientific domains and the latest trend in semantic web applications is also highlighted

Metabolomic systems biology of trypanosomes

Metabolomics analysis, which aims at the systematic identification and quantification of all metabolites in biological systems, is emerging as a powerful new tool to identify biomarkers of disease, report on cellular responses to environmental perturbation, and to identify the targets of drugs. Here we discuss recent developments in metabolomic analysis, from the perspective of trypanosome research, highlighting remaining challenges and the most promising areas for future research

Integration and mining of malaria molecular, functional and pharmacological data: how far are we from a chemogenomic knowledge space?

The organization and mining of malaria genomic and post-genomic data is highly motivated by the necessity to predict and characterize new biological targets and new drugs. Biological targets are sought in a biological space designed from the genomic data from Plasmodium falciparum, but using also the millions of genomic data from other species. Drug candidates are sought in a chemical space containing the millions of small molecules stored in public and private chemolibraries. Data management should therefore be as reliable and versatile as possible. In this context, we examined five aspects of the organization and mining of malaria genomic and post-genomic data: 1) the comparison of protein sequences including compositionally atypical malaria sequences, 2) the high throughput reconstruction of molecular phylogenies, 3) the representation of biological processes particularly metabolic pathways, 4) the versatile methods to integrate genomic data, biological representations and functional profiling obtained from X-omic experiments after drug treatments and 5) the determination and prediction of protein structures and their molecular docking with drug candidate structures. Progresses toward a grid-enabled chemogenomic knowledge space are discussed.Comment: 43 pages, 4 figures, to appear in Malaria Journa

Phytochemical characterization of Tabernanthe iboga root bark and its effects on dysfunctional metabolism and cognitive performance in high-fat-fed C57BL/6J mice

Preparations of the root bark of Tabernanthe iboga have long been used in Central and West African traditional medicine to combat fatigue, as a neuro-stimulant in rituals, and for treatment of diabetes. The principal alkaloid of T. iboga, ibogaine, has attracted attention in many countries around the world for providing relief for opioid craving in drug addicts. Using a plant metabolomics approach, we detected five phenolic compounds, including 3- O-caffeoylquinic acid, and 30 alkaloids, seven of which were previously reported from T. iboga root bark. Following a report that iboga extracts contain insulinotropic agents, we aimed to determine the potential alleviating effects of the water extract of iboga root bark on high-fat diet (HFD)-induced hyperglycemia as well as its effects on cognitive function in male C57BL/6J mice. Feeding a HFD to mice for 10 weeks produced manifestations of metabolic syndrome such as increased body weight and increased plasma levels of glucose, triacylglycerols, total cholesterol, LDL-cholesterol, insulin, leptin, and pro-inflammatory mediators (IL-6, MCP-1, ICAM-1), as compared to mice fed a low-fat diet (LFD). Supplementation of HFD with iboga extract at ibogaine doses of 0.83 (low) and 2.07 (high) mg/kg/day did not improve these HFD-induced metabolic effects except for a reduction of plasma MCP-1 in the low dose group, indicative of an anti-inflammatory effect. When the HFD mice were tested in the water maze, the high-dose iboga extract caused hippocampus-dependent impairments in spatial learning and memory, as compared to mice receiving only a HFD.Peer reviewedFinal Published versio

Hepatoprotective mechanism of Silybum marianum on nonalcoholic fatty liver disease based on network pharmacology and experimental verification

The study aimed to identify the key active components in Silybum marianum (S. marianum) and determine how they protect against nonalcoholic fatty liver disease (NAFLD). TCMSP, DisGeNET, UniProt databases, and Venny 2.1 software were used to identify 11 primary active components, 92 candidate gene targets, and 30 core hepatoprotective gene targets in this investigation, respectively. The PPI network was built using a string database and Cytoscape 3.7.2. The KEGG pathway and GO biological process enrichment, biological annotation, as well as the identified hepatoprotective core gene targets were analyzed using the Metascape database. The effect of silymarin on NAFLD was determined using H&E on pathological alterations in liver tissues. The levels of liver function were assessed using biochemical tests. Western blot experiments were used to observe the proteins that were expressed in the associated signaling pathways on the hepatoprotective effect, which the previous network pharmacology predicted. According to the KEGG enrichment study, there are 35 hepatoprotective signaling pathways. GO enrichment analysis revealed that 61 biological processes related to the hepatoprotective effect of S. marianum were identified, which mainly involved in response to regulation of biological process and immune system process. Silymarin was the major ingredient derived from S. marianum, which exhibited the hepatoprotective effect by reducing the levels of ALT, AST, TC, TG, HDL-C, LDL-C, decreasing protein expressions of IL-6, MAPK1, Caspase 3, p53, VEGFA, increasing protein expression of AKT1. The present study provided new sights and a possible explanation for the molecular mechanisms of S. marianum against NAFLD

Systematic elucidation of the traditional Chinese medicine prescription Danxiong particles via network pharmacology and molecular docking

Purpose: To investigate the pharmacological effect of the traditional Chinese medicine (TCM) prescription Danxiong particles (TDX105) and its mechanism of action.Methods: The active compound and targets of TDX105 were investigated via network pharmacology. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were enriched, and protein-protein interaction network (PPI) was constructed. A network of ‘components-targets-pathways’ was developed with Cytoscape 3.8.0 software, while the formation of molecular docking analysis was conducted using Autodock vina software.Results: There were 304 compounds and 482 targets identified in total. Genes with degree ≥ mean node values were selected as the crucial targets, and string database was to be combined to 64 targets identified with cytoscape so as to draw a protein interaction map. A total of 137 pathways were enriched from 64 targets involving mainly 10 pathways, for example, PI3K-Akt signaling pathway, pathways in cancer, human cytomegalovirus infection and focal adhesion. Then, compound-target and compoundtarget- pathways were constructed using cytoscape (3.8.0). Finally, the five most active compounds, viz, quercetin, myricetin, luteolin, ellagic acid and kaempferol, and the top ten targets AKT1, GAPDH, TP53, ALB, EGFR, MAPK3, JUN, MAPK1, SRC and ESR1 were selected for molecular docking. These targets and compounds had strong interactions through a combination of hydrogen bonds and hydrophobic forces.Conclusion: The mechanism of action of TDX105 has been successfully explained using the combination of network pharmacology and molecular docking. This may offer a solid foundation to the clinical use of TDX105, and further strengthen the prospects of its development for clinical use