ZOOLOGY

Background: Oxidative stress is common in type 2 diabetes. It is characterised by increased levels of reactive oxygen species (ROS), of which hypochlorous acid (HOCl) is an important component. Type 2 diabetes is characterised not only by hyperglycemia, but also by dyslipidemia. It probably underlies both the development of diabetes and also resulting complications, like cardiovascular disease. We applied a novel fluoroprobe RHQ to estimate endogenous HOCl and myeloperoxidase (MPO) activity in diabetes and dyslipidemia. Methods: Our newly designed probe, RHQ (rhodamine-quinoline based chemodosimeter) is capable of estimating endogenous HOCl selectively out of the ROS components. Isolated leukocytes from study subjects were treated with DCFDA and monocytes and neutrophils with RHQ for estimating endogenous ROS and HOCl respectively. Plasma AOPP, an indicator of HOCl was also measured. We attempted to find out the key reasons of higher HOCl content in diabetic dyslipidemic subjects by quantitating endogenous hydrogen peroxide (H 2 O 2) and myeloperoxidase (MPO) activity. Results: Isolated PBMCs from diabetic dyslipidemic subjects indicated enhanced ROS and HOCl generation followed by diabetic subjects without dyslipidemia and healthy controls. We explored increased production of H 2 O 2 and enhanced enzymatic activity of myeloperoxidase (MPO) among diabetic dyslipidemic subjects (pb 0.0001) resulting in higher HOCl content. Conclusion: The hyperglycemic and hyperlipidemic challenges together enhance the production of HOCl and the fluoroprobe RHQ may be used as a novel diagnostic marker to evaluate the extent of this toxicity.

Dielectric properties of a living biological membrane play crucial role indicating the status of the cell in pathogenic or healthy condition. A distinct variation in membrane capacitance and impedance was observed for peripheral blood mononuclear cell (PBMC) suspensions for diabetic and diabetic-dyslipidemic subjects compared to healthy control. Low frequency region were explicitly considered in electrical analysis to address complex membrane dielectric factors that alter the system capacitance of a PBMC suspension. Such variation was marked in size, morphology and membrane function of PBMCs for control and diseased cases. Scanning electron microscopy (SEM) and atomic force microscopy (AFM) studies reveal significant alteration in surface morphology of PBMCs in diseased condition. Side scatter of flow cytometry reveals complexity of PBMCs in diseased condition. Changes in size between groups were not found by SEM and forward scatter. Functional alteration in PBMCs was manifested by significant changes in cell membrane properties like Na + , K + ATPase and Ca 2+ , Mg 2+ ATPase activity, reduced plasma membrane fluidity and changes in intracellular Ca 2+ content, which bear significant correlation in diabetic and diabetic dyslipidemic subjects. Therefore, dielectric parameters of PBMCs in diabetic-dyslipidemic challenges may led to interesting correlation opening the possibility of identifying crucial signature biomarkers.

PathVisio is a commonly used pathway editor, visualization and analysis software. Biological pathways have been used by biologists for many years to describe the detailed steps in biological processes. Those powerful, visual representations help researchers to better understand, share and discuss knowledge. Since the first publication of PathVisio in 2008, the original paper was cited more than 170 times and PathVisio was used in many different biological studies. As an online editor PathVisio is also integrated in the community curated pathway database WikiPathways. Here we present the third version of PathVisio with the newest additions and improvements of the application. The core features of PathVisio are pathway drawing, advanced data visualization and pathway statistics. Additionally, PathVisio 3 introduces a new powerful extension systems that allows other developers to contribute additional functionality in form of plugins without changing the core application.

A solvent system that extracts a maximum number of metabolites belonging to diverse chemical classes from complex biofluids, such as plasma, may offer useful inputs to understand the metabolic and physiological state of an individual. The present study compared seven solvent systems for extraction of metabolites from plasma. The extracts were analyzed by mass spectrometry (MS) and MS/MS (MS2) using a quadrupole time-of-flight liquid chromatography/MS system in positive and negative modes of ionization. Metabolites with molecular mass below 400 were identified using Human Metabolome Database MS2 and MS search interfaces. The acetone/isopropanol (2:1) system yielded promising results in positive ionization mode, as the maximum number of MS and MS2 features was detected in the extract. It was found to be superior in extraction of various classes of metabolites, especially organic acids, nucleosides and nucleoside derivatives, and heterocyclic molecules. Glycerophosphocholines in the mass range of 400-700 were found to be efficiently extracted by the methanol/chloroform/water (8:1:1) system. In negative mode as well, the maximum number of MS2 features was detected in methanol/chloroform/water and acetone/isopropanol extracts. The fingerprints of molecular features obtained in the negative and positive modes differed from each other to a significant extent.

EuroDISH (www.eurodish.eu) is a 3 year FP7 programme with 15 partners, including the worldwide Nutrigenomics Organisation NuGO, focused on identifying existing food and health research infrastructures, their integration, and the need of new developments. EuroDISH uses the nutritional instance of the dbXP infrastructure, dbNP, to run an illustrative case study focused on the metabolic

The fluxes for the reactions between 1,3BisphosphoDglycerate > 3Phosphoglycerate and 3Phosphoglycerate > 2 Phosphoglycerate were defined in the opposite (upward) direction which becomes clear from the visualization. Those would probably be adjusted in the next modeling stage.

Biological pathways provide intuitive frameworks to co-analyze system-wide transcriptomics, proteomics, and metabolomics measurements. Using our open-source pathway analysis platform, PathVisio, we bridge pathway analysis to the wealth of quantitative approaches already in development for metabolic network modelling, such as flux balance analysis and dynamic simulation. Our focus is on the integrated visualization of the modelled and experimental results, which will be critical for understanding how simulated models correlate with experimental measurements.

Biological pathways provide intuitive frameworks to integrate and co-analyze different kinds of biological data, such as system-wide transcriptomic, proteomic, and metabolomic measurements. While insightful, pathway analysis is generally limited to qualitative conclusions, and the analyses can only be as powerful as the curated annotations can enable. Using our open-source pathway analysis platform, PathVisio, we bridge pathway analysis to the wealth of quantitative approaches already in development for metabolic network modeling, such as flux balance analysis and dynamic simulation. Our focus is on the visualization of the modeling results, which will be critical for understanding how simulated models correlate with experimental measurements. The same biological processes that are visualized in pathways are also described by quantitative models. For example, the arrows that connect entities within metabolic pathways actually represent metabolite fluxes. The integration of large sca...

PathVisio (pathvisio.org) is an open source pathway visualization and analysis tool. It allows users to not only draw and edit biological pathways, but also to visualize and analyze their data. Biological pathways provide intuitive frameworks to integrate and co-analyze different kinds of biological data, such as system-wide transcriptomic, proteomic, and metabolomic measurements. We developed a plugin for PathVisio that allows the visualization of experimental or modeled data on the lines in the pathway, i.e. interactions, reactions, etc.

Metabolome refers to the complete set of small-molecule metabolites (such as metabolic intermediates, hormones, signaling molecules and secondary metabolites) to be found within a biological sample, such as a single organism. Metabolomics is the scientific study of the metabolome. Thus, while mRNA gene expression data and proteomic analyses do not tell the whole story of what might be happening in a cell, metabolic profiling can give an instantaneous snapshot of the physiology of that cell. One of the challenges of systems biology and functional genomics is to integrate proteomic, transcriptomic, and metabolomic information to give a more complete picture of living organisms. The investigation presented in this thesis describes the analysis of blood plasma samples extracted using seven different extraction solvents to perform metabolomic analysis of human blood plasma by liquid chromatography/mass spectrometry (LC/MS). Five of the seven extraction solvents used for the current study...

Background: Biological pathways are descriptive diagrams of biological processes widely used for functional analysis of differentially expressed genes or proteins. Primary data analysis, such as quality control, normalisation, and statistical analysis, is often performed in scripting languages like R, Perl, and Python. Subsequent pathway analysis is usually performed using dedicated external applications. Workflows involving manual use of multiple environments are time consuming and error prone. Therefore, tools are needed that enable pathway analysis directly within the same scripting languages used for primary data analyses. Existing tools have limited capability in terms of available pathway content, pathway editing and visualisation options, and export file formats. Consequently, making the full-fledged pathway analysis tool PathVisio available from various scripting languages will benefit researchers. Results: We developed PathVisioRPC, an XMLRPC interface for the pathway analysis software PathVisio. PathVisioRPC enables creating and editing biological pathways, visualising data on pathways, performing pathway statistics, and exporting results in several image formats in multiple programming environments. We demonstrate PathVisioRPC functionalities using examples in Python. Subsequently, we analyse a publicly available NCBI GEO gene expression dataset studying tumour bearing mice treated with cyclophosphamide in R. The R scripts demonstrate how calls to existing R packages for data processing and calls to PathVisioRPC can directly work together. To further support R users, we have created RPathVisio simplifying the use of PathVisioRPC in this environment. We have also created a pathway module for the microarray data analysis portal ArrayAnalysis.org that calls the PathVisioRPC interface to perform pathway analysis. This module allows users to use PathVisio functionality online without having to download and install the software and exemplifies how the PathVisioRPC interface can be used by data analysis pipelines for functional analysis of processed genomics data. Conclusions: PathVisioRPC enables data visualisation and pathway analysis directly from within various analytical environments used for preliminary analyses. It supports the use of existing pathways from WikiPathways or pathways created using the RPC itself. It also enables automation of tasks performed using PathVisio, making it useful to PathVisio users performing repeated visualisation and analysis tasks. PathVisioRPC is freely available for academic and commercial use at http://projects.bigcat.unimaas.nl/pathvisiorpc.

WikiPathways (http://www.wikipathways.org) is an open, collaborative platform for capturing and disseminating models of biological pathways for data visualization and analysis. Since our last NAR update, 4 years ago, WikiPathways has experienced massive growth in content, which continues to be contributed by hundreds of individuals each year. New aspects of the diversity and depth of the collected pathways are described from the perspective of researchers interested in using pathway information in their studies. We provide updates on extensions and services to support pathway analysis and visualization via popular standalone tools, i.e. PathVisio and Cytoscape, web applications and common programming environments. We introduce the Quick Edit feature for pathway authors and curators, in addition to new means of publishing pathways and maintaining custom pathway collections to serve specific research topics and communities. In addition to the latest milestones in our pathway collection and curation effort, we also highlight the latest means to access the content as publishable figures, as standard data files, and as linked data, including bulk and programmatic access.

specific to biological context (Fig. 1c). The notion of a causal link is fundamentally distinct from a correlational link (Fig. 1d). Causal network inference is profoundly challenging 15,16 , and many methods for inferring regulatory networks connect correlated, or mutually dependent, nodes that might not have any causal relationship. Some approaches (e.g., directed acyclic graphs 17-19) can in principle be used to infer causal relationships, but their success can be guaranteed only under strong assumptions 15,20 that are almost certainly violated in biological settings. This is due to many limitations-some possibly fundamentalin our ability to observe and perturb biological systems. These observations imply that it is essential to undertake careful empirical assessment in order to learn whether computational methods can provide causal insights in specific biological settings. Network inference methods are often assessed using data simulated from a known causal network structure (a so-called gold-standard network 5,17). Such studies (and their synthetic biology counterparts 21) are convenient and useful, but at the same time they are limited because it is difficult to truly mimic specific biological systems of interest. Inferred networks are often compared to the literature, but for the purpose of learning novel, potentially context-specific, regulatory relationships, this is an inherently limited approach, and experimental validation of network inference methods has remained limited 9,10,19,22. With the support of the Heritage Provider Network (HPN), we developed the HPN-DREAM challenge to assess the ability to learn causal networks and predict molecular time-course data. The Dialogue for Reverse Engineering Assessment and Methods (DREAM) project 23 (http://dreamchallenges.org) has run several challenges focused on network inference 22,24-27. Here we focused on causal signaling networks in human cancer cell lines. Protein assays were carried out using reverse-phase protein lysate arrays 28,29 (RPPAs) that included functional phosphorylated proteins. The HPN-DREAM challenge comprised three sub-challenges. Sub-challenge 1 was to infer causal signaling networks using protein time-course data. To focus on networks specific to genetic and epigenetic background, the task spanned 32 different contexts, each defined by a combination of cell line and stimulus, and each with its own training and test data. The test data were used to assess the causal validity of inferred networks, as described below. A companion in silico data task also focused on causal networks but by design did not allow the use of known biology. Sub-challenge 2 was to predict phosphoprotein time-course data under perturbation. This sub-challenge comprised both an experimental data task and npg

Genetic mapping and genome-wide studies provide evidence for the association of several genetic polymorphisms with malaria, a complex pathological disease with multiple severity degrees. We have previously described Berr1and Berr2 as candidate genes identified in the WLA/Pas inbreed mouse strain predisposing to resistance to cerebral malaria (CM) induced by P. berghei ANKA. We report in this study the phenotypic and functional characteristics of a congenic strain we have derived for Berr2 WLA allele on the C57BL/6JR (B6) background. B6.WLA-Berr2 was found highly resistant to CM compared to C57BL/6JR susceptible mice. The mechanisms associated with CM resistance were analyzed by combining genotype, transcriptomic and immune response studies. We found that B6.WLA-Berr2 mice showed a reduced parasite sequestration and blood-brain barrier disruption with low CXCR3 + T cell infiltration in the brain along with altered glial cell response upon P. berghei ANKA infection compared to B6. In addition, we have identified the CD300f, belonging to a family of Ig-like encoding genes, as a potential candidate associated with CM resistance. Microglia cells isolated from the brain of infected B6.WLA-Berr2 mice significantly expressed higher level of CD300f compared to CM S mice and were associated with inhibition of inflammatory response.