Ferran Sanz

DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes

DisGeNET is a comprehensive discovery platform designed to address a variety of questions concerning the genetic underpinning of human diseases. DisGeNET contains over 380 000 associations between >16 000 genes and 13 000 diseases, which makes it one of the largest repositories currently available of its kind. DisGeNET integrates expert-curated databases with text-mined data, covers information on Mendelian and complex diseases, and includes data from animal disease models. It features a score based on the supporting evidence to prioritize gene-disease associations. It is an open access resource available through a web interface, a Cytoscape plugin and as a Semantic Web resource. The web interface supports user-friendly data exploration and navigation. DisGeNET data can also be analysed via the DisGeNET Cytoscape plugin, and enriched with the annotations of other plugins of this popular network analysis software suite. Finally, the information contained in DisGeNET can be expanded and complemented using Semantic Web technologies and linked to a variety of resources already present in the Linked Data cloud. Hence, DisGeNET offers one of the most comprehensive collections of human gene-disease associations and a valuable set of tools for investigating the molecular mechanisms underlying diseases of genetic origin, designed to fulfill the needs of different user profiles, including bioinformaticians, biologists and health-care practitioners. Database URL: http://www.disgenet.org/

Pathway databases and tools for their exploitation: benefits, current limitations and challenges

In past years, comprehensive representations of cell signalling pathways have been developed by manual curation from literature, which requires huge effort and would benefit from information stored in databases and from automatic retrieval and integration methods. Once a reconstruction of the network of interactions is achieved, analysis of its structural features and its dynamic behaviour can take place. Mathematical modelling techniques are used to simulate the complex behaviour of cell signalling networks, which ultimately sheds light on the mechanisms leading to complex diseases or helps in the identification of drug targets. A variety of databases containing information on cell signalling pathways have been developed in conjunction with methodologies to access and analyse the data. In principle, the scenario is prepared to make the most of this information for the analysis of the dynamics of signalling pathways. However, are the knowledge repositories of signalling pathways ready to realize the systems biology promise? In this article we aim to initiate this discussion and to provide some insights on this issue.

DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants

The information about the genetic basis of human diseases lies at the heart of precision medicine and drug discovery. However, to realize its full potential to support these goals, several problems, such as fragmentation, heterogeneity, availability and different conceptualization of the data must be overcome. To provide the community with a resource free of these hurdles, we have developed DisGeNET (http://www.disgenet.org), one of the largest available collections of genes and variants involved in human diseases. DisGeNET integrates data from expert curated repositories, GWAS catalogues, animal models and the scientific literature. DisGeNET data are homogeneously annotated with controlled vocabularies and community-driven ontologies. Additionally, several original metrics are provided to assist the prioritization of genotype–phenotype relationships. The information is accessible through a web interface, a Cytoscape App, an RDF SPARQL endpoint, scripts in several programming languages and an R package. DisGeNET is a versatile platform that can be used for different research purposes including the investigation of the molecular underpinnings of specific human diseases and their comorbidities, the analysis of the properties of disease genes, the generation of hypothesis on drug therapeutic action and drug adverse effects, the validation of computationally predicted disease genes and the evaluation of text-mining methods performance.

Gene-Disease Network Analysis Reveals Functional Modules in Mendelian, Complex and Environmental Diseases

Background Scientists have been trying to understand the molecular mechanisms of diseases to design preventive and therapeutic strategies for a long time. For some diseases, it has become evident that it is not enough to obtain a catalogue of the disease-related genes but to uncover how disruptions of molecular networks in the cell give rise to disease phenotypes. Moreover, with the unprecedented wealth of information available, even obtaining such catalogue is extremely difficult. Principal Findings We developed a comprehensive gene-disease association database by integrating associations from several sources that cover different biomedical aspects of diseases. In particular, we focus on the current knowledge of human genetic diseases including mendelian, complex and environmental diseases. To assess the concept of modularity of human diseases, we performed a systematic study of the emergent properties of human gene-disease networks by means of network topology and functional annotation analysis. The results indicate a highly shared genetic origin of human diseases and show that for most diseases, including mendelian, complex and environmental diseases, functional modules exist. Moreover, a core set of biological pathways is found to be associated with most human diseases. We obtained similar results when studying clusters of diseases, suggesting that related diseases might arise due to dysfunction of common biological processes in the cell. Conclusions For the first time, we include mendelian, complex and environmental diseases in an integrated gene-disease association database and show that the concept of modularity applies for all of them. We furthermore provide a functional analysis of disease-related modules providing important new biological insights, which might not be discovered when considering each of the gene-disease association repositories independently. Hence, we present a suitable framework for the study of how genetic and environmental factors, such as drugs, contribute to diseases. Availability The gene-disease networks used in this study and part of the analysis are available at http://ibi.imim.es/DisGeNET/DisGeNETweb.html#Download.

Role of UEV-1, an Inactive Variant of the E2 Ubiquitin- Conjugating Enzymes, in In Vitro Differentiation and Cell Cycle Behavior of HT-29-M6 Intestinal Mucosecretory Cells

ABSTRACT By means of differential RNA display, we have isolated a cDNA corresponding to transcripts that are down-regulated upon differentiation of the goblet cell-like HT-29-M6 human colon carcinoma cell line. These transcripts encode proteins originally identified as CROC-1 on the basis of their capacity to activate transcription of c-fos. We show that these proteins are similar in sequence, and in predicted secondary and tertiary structure, to the ubiquitin-conjugating enzymes, also known as E2. Despite the similarities, these proteins lack a critical cysteine residue essential for the catalytic activity of E2 enzymes and, in vitro, they do not conjugate or transfer ubiquitin to protein substrates. These proteins constitute a distinct subfamily within the E2 protein family and are highly conserved in phylogeny from yeasts to mammals. Therefore, we have designated them UEV (ubiquitin-conjugating E2 enzyme variant) proteins, defined as proteins similar in sequence and structure to the E2 ubiquitin-conjugating enzymes but lacking their enzymatic activity (HW/GDB-approved gene symbol, UBE2V). At least two human genes code for UEV proteins, and one of them, located on chromosome 20q13.2, is expressed as at least four isoforms, generated by alternative splicing. All human cell types analyzed expressed at least one of these isoforms. Constitutive expression of exogenous human UEV in HT-29-M6 cells inhibited their capacity to differentiate upon confluence and caused both the entry of a larger proportion of cells in the division cycle and an accumulation in G2-M. This was accompanied with a profound inhibition of the mitotic kinase, cdk1. These results suggest that UEV proteins are involved in the control of differentiation and could exert their effects by altering cell cycle distribution.

DisGeNET: a Cytoscape plugin to visualize, integrate, search and analyze gene-disease networks

UNLABELLED DisGeNET is a plugin for Cytoscape to query and analyze human gene-disease networks. DisGeNET allows user-friendly access to a new gene-disease database that we have developed by integrating data from several public sources. DisGeNET permits queries restricted to (i) the original data source, (ii) the association type, (iii) the disease class or (iv) specific gene(s)/disease(s). It represents gene-disease associations in terms of bipartite graphs and provides gene centric and disease centric views of the data. It assists the user in the interpretation and exploration of the genetic basis of human diseases by a variety of built-in functions. Moreover, DisGeNET permits multicolouring of nodes (genes/diseases) according to standard disease classification for expedient visualization. AVAILABILITY DisGeNET is compatible with Cytoscape 2.6.3 and 2.7.0, please visit http://ibi.imim.es/DisGeNET/DisGeNETweb.html for installation guide, user tutorial and download.

A multiscale simulation system for the prediction of drug-induced cardiotoxicity.

The preclinical assessment of drug-induced ventricular arrhythmia, a major concern for regulators, is typically based on experimental or computational models focused on the potassium channel hERG (human ether-a-go-go-related gene, K(v)11.1). Even if the role of this ion channel in the ventricular repolarization is of critical importance, the complexity of the events involved make the cardiac safety assessment based only on hERG has a high risk of producing either false positive or negative results. We introduce a multiscale simulation system aiming to produce a better cardiotoxicity assessment. At the molecular scale, the proposed system uses a combination of docking simulations on two potassium channels, hERG and KCNQ1, plus three-dimensional quantitative structure-activity relationship modeling for predicting how the tested compound will block the potassium currents IK(r) and IK(s). The obtained results have been introduced in electrophysiological models of the cardiomyocytes and the ventricular tissue, allowing the direct prediction of the drug effects on electrocardiogram simulations. The usefulness of the whole method is illustrated by predicting the cardiotoxic effect of several compounds, including some examples in which classic hERG-based models produce false positive or negative results, yielding correct predictions for all of them. These results can be considered a proof of concept, suggesting that multiscale prediction systems can be suitable for being used for preliminary screening in lead discovery, before the compound is physically available, or in early preclinical development when they can be fed with experimentally obtained data.

Induced Effects of Sodium Ions on Dopaminergic G-Protein Coupled Receptors

G-protein coupled receptors, the largest family of proteins in the human genome, are involved in many complex signal transduction pathways, typically activated by orthosteric ligand binding and subject to allosteric modulation. Dopaminergic receptors, belonging to the class A family of G-protein coupled receptors, are known to be modulated by sodium ions from an allosteric binding site, although the details of sodium effects on the receptor have not yet been described. In an effort to understand these effects, we performed microsecond scale all-atom molecular dynamics simulations on the dopaminergic D2 receptor, finding that sodium ions enter the receptor from the extracellular side and bind at a deep allosteric site (Asp2.50). Remarkably, the presence of a sodium ion at this allosteric site induces a conformational change of the rotamer toggle switch Trp6.48 which locks in a conformation identical to the one found in the partially inactive state of the crystallized human β2 adrenergic receptor. This study provides detailed quantitative information about binding of sodium ions in the D2 receptor and reports a possibly important sodium-induced conformational change for modulation of D2 receptor function.

3D-QSAR methods on the basis of ligand-receptor complexes. Application of COMBINE and GRID/GOLPE methodologies to a series of CYP1A2 ligands.

Many heterocyclic amines (HCA) present in cooked food exert a genotoxic activity when they are metabolised (N-oxidated) by the human cytochrome P450 1A2 (CYP1A2h). In order to rationalize the observed differences in activity of this enzyme on a series of 12 HCA, 3D-QSAR methods were applied on the basis of models of HCA–CYP1A2h complexes. The CYP1A2h enzyme model has been previously reported and was built by homology modeling based on cytochrome P450 BM3. The complexes were automatically generated applying the AUTODOCK software and refined using AMBER. A COMBINE analysis on the complexes identified the most important enzyme–ligand interactions that account for the differences in activity within the series. A GRID/GOLPE analysis was then performed on just the ligands, in the conformations and orientations found in the modeled complexes. The results from both methods were concordant and confirmed the advantages of incorporating structural information from series of ligand–receptor complexes into 3D-QSAR methodologies.

Automatic search for maximum similarity between molecular electrostatic potential distributions

SummaryA new computer program has been developed to automatically obtain the relative position of two molecules in which the similarity between molecular electrostatic-potential distributions is greatest. These distributions are considered in a volume around the molecules, and the similarity is measured by the Spearman rank coefficient. The program has been tested using several pairs of molecules: water vs. water; phenylethylamine and phenylpropylamine vs. benzylamine; and methotrexate vs. dihydrofolic acid.