Role of network topology based methods in discovering novel gene-phenotype associations

The cell is governed by the complex interactions among various types of biomolecules. Coupled with environmental factors, variations in DNA can cause alterations in normal gene function and lead to a disease condition. Often, such disease phenotypes involve coordinated dysregulation of multiple genes that implicate inter-connected pathways. Towards a better understanding and characterization of mechanisms underlying human diseases, here, I present GUILD, a network-based disease-gene prioritization framework. GUILD associates genes with diseases using the global topology of the protein-protein interaction network and an initial set of genes known to be implicated in the disease. Furthermore, I investigate the mechanistic relationships between disease-genes and explain the robustness emerging from these relationships. I also introduce GUILDify, an online and user-friendly tool which prioritizes genes for their association to any user-provided phenotype. Finally, I describe current state-of-the-art systems-biology approaches where network modeling has helped extending our view on diseases such as cancer.La cèl•lula es regeix per interaccions complexes entre diferents tipus de biomolècules. Juntament amb factors ambientals, variacions en el DNA poden causar alteracions en la funció normal dels gens i provocar malalties. Sovint, aquests fenotips de malaltia involucren una desregulació coordinada de múltiples gens implicats en vies interconnectades. Per tal de comprendre i caracteritzar millor els mecanismes subjacents en malalties humanes, en aquesta tesis presento el programa GUILD, una plataforma que prioritza gens relacionats amb una malaltia en concret fent us de la topologia de xarxe. A partir d’un conjunt conegut de gens implicats en una malaltia, GUILD associa altres gens amb la malaltia mitjancant la topologia global de la xarxa d’interaccions de proteïnes. A més a més, analitzo les relacions mecanístiques entre gens associats a malalties i explico la robustesa es desprèn d’aquesta anàlisi. També presento GUILDify, un servidor web de fácil ús per la priorització de gens i la seva associació a un determinat fenotip. Finalment, descric els mètodes més recents en què el model•latge de xarxes ha ajudat extendre el coneixement sobre malalties complexes, com per exemple a càncer

Exploiting protein-protein interaction networks for genome-wide disease-gene prioritization

Complex genetic disorders often involve products of multiple genes acting cooperatively. Hence, the pathophenotype is the outcome of the perturbations in the underlying pathways, where gene products cooperate through various mechanisms such as protein-protein interactions. Pinpointing the decisive elements of such disease pathways is still challenging. Over the last years, computational approaches exploiting interaction network topology have been successfully applied to prioritize individual genes involved in diseases. Although linkage intervals provide a list of disease-gene candidates, recent genome-wide studies demonstrate that genes not associated with any known linkage interval may also contribute to the disease phenotype. Network based prioritization methods help highlighting such associations. Still, there is a need for robust methods that capture the interplay among disease-associated genes mediated by the topology of the network. Here, we propose a genome-wide network-based prioritization framework named GUILD. This framework implements four network-based disease-gene prioritization algorithms. We analyze the performance of these algorithms in dozens of disease phenotypes. The algorithms in GUILD are compared to state-of-the-art network topology based algorithms for prioritization of genes. As a proof of principle, we investigate top-ranking genes in Alzheimer's disease (AD), diabetes and AIDS using disease-gene associations from various sources. We show that GUILD is able to significantly highlight disease-gene associations that are not used a priori. Our findings suggest that GUILD helps to identify genes implicated in the pathology of human disorders independent of the loci associated with the disorders.Departament d’Educació i Universitats de la Generalitat de Catalunya i del Fons Social Europeu (Department of Education and Universities of the Generalitat of Catalonia and the European Social Fons). Spanish Ministry of Science and Innovation (MICINN), FEDER (Fonds Européen de Développement Régional) BIO2008-0205, BIO2011-22568, PSE-0100000-2007, and PSE-0100000-2009; and by EU grant EraSysbio+(SHIPREC) Euroinvestigación (EUI2009-04018

Analysis of the robustness of network-based disease-gene prioritization methods reveals redundancy in the human interactome and functional diversity of disease-genes

Complex biological systems usually pose a trade-off between robustness and fragility where a small number of perturbations can substantially disrupt the system. Although biological systems are robust against changes in many external and internal conditions, even a single mutation can perturb the system substantially, giving rise to a pathophenotype. Recent advances in identifying and analyzing the sequential variations beneath human disorders help to comprehend a systemic view of the mechanisms underlying various disease phenotypes. Network-based disease-gene prioritization methods rank the relevance of genes in a disease under the hypothesis that genes whose proteins interact with each other tend to exhibit similar phenotypes. In this study, we have tested the robustness of several network-based disease-gene prioritization methods with respect to the perturbations of the system using various disease phenotypes from the Online Mendelian Inheritance in Man database. These perturbations have been introduced either in the protein-protein interaction network or in the set of known disease-gene associations. As the network-based disease-gene prioritization methods are based on the connectivity between known disease-gene associations, we have further used these methods to categorize the pathophenotypes with respect to the recoverability of hidden disease-genes. Our results have suggested that, in general, disease-genes are connected through multiple paths in the human interactome. Moreover, even when these paths are disturbed, network-based prioritization can reveal hidden disease-gene associations in some pathophenotypes such as breast cancer, cardiomyopathy, diabetes, leukemia, parkinson disease and obesity to a greater extend compared to the rest of the pathophenotypes tested in this study. Gene Ontology (GO) analysis highlighted the role of functional diversity for such diseases.Spanish Ministry of Science and Innovation (MICINN) FEDER BIO2011-22568 (http://www.mineco.gob.es/portal/site/mineco/); and by EU grantEraSysbio+(SHIPREC http://www.erasysbio.net/index.php?index=279) Euroinvestigación (EUI2009-04018

Integrating structure to protein-protein interaction networks that drive metastasis to brain and lung in breast cancer

Blocking specific protein interactions can lead to human diseases. Accordingly, protein interactions and the structural knowledge on interacting surfaces of proteins (interfaces) have an important role in predicting the genotype-phenotype relationship. We have built the phenotype specific sub-networks of protein-protein interactions (PPIs) involving the relevant genes responsible for lung and brain metastasis from primary tumor in breast cancer. First, we selected the PPIs most relevant to metastasis causing genes (seed genes), by using the “guilt-by-association” principle. Then, we modeled structures of the interactions whose complex forms are not available in Protein Databank (PDB). Finally, we mapped mutations to interface structures (real and modeled), in order to spot the interactions that might be manipulated by these mutations. Functional analyses performed on these sub-networks revealed the potential relationship between immune system-infectious diseases and lung metastasis progression, but this connection was not observed significantly in the brain metastasis. Besides, structural analyses showed that some PPI interfaces in both metastasis sub-networks are originating from microbial proteins, which in turn were mostly related with cell adhesion. Cell adhesion is a key mechanism in metastasis, therefore these PPIs may be involved in similar molecular pathways that are shared by infectious disease and metastasis. Finally, by mapping the mutations and amino acid variations on the interface regions of the proteins in the metastasis sub-networks we found evidence for some mutations to be involved in the mechanisms differentiating the type of the metastasis.This work is supported by Spanish Government (MINECO) grant FEDER BIO2011-22568 and EUI2009-04018 (ERASysBio + SHIPREC) and The Scientific and Technological Research Council of Turkey (TUBITAK) grant 113E164

Biana: a software framework for compiling biological interactions and analyzing networks

Background: The analysis and usage of biological data is hindered by the spread of information across multiple repositories and the difficulties posed by different nomenclature systems and storage formats. In particular, there is an important need for data unification in the study and use of protein-protein interactions. Without good integration strategies, it is difficult to analyze the whole set of available data and its properties./nResults: We introduce BIANA (Biologic Interactions and Network Analysis), a tool for biological information integration and network management. BIANA is a Python framework designed to achieve two major goals: i) the integration of multiple sources of biological information, including biological entities and their relationships, and ii) the management of biological information as a network where entities are nodes and relationships are edges. Moreover, BIANA uses properties of proteins and genes to infer latent biomolecular relationships by transferring edges to entities sharing similar properties. BIANA is also provided as a plugin for Cytoscape, which allows users to visualize and interactively manage the data. A web interface to BIANA providing basic functionalities is also available. The software can be downloaded under GNU GPL license from http://sbi.imim.es/web/BIANA.php./nConclusions: BIANA's approach to data unification solves many of the nomenclature issues common to systems dealing with biological data. BIANA can easily be extended to handle new specific data repositories and new specific data types. The unification protocol allows BIANA to be a flexible tool suitable for different user requirements: non-expert users can use a suggested unification protocol while expert users can define their own specific unification rules.JGG, EG and JPI are grateful to the support from “Departament d’Educació i Universitats de la Generalitat de Catalunya i del Fons Social Europeu”. BO also acknowledges support from BSC and Mare-Nostrum facilities. This work was supported by grants from Spanish Ministry of Science and Innovation (MICINN) BIO2008-0205, PSE-0100000-2007 and PSE-0100000-2009, and from EU grant Etox (IMI 115002

Thrombin stimulates insulin secretion via protease-activated receptor-3

The disease mechanisms underlying type 2 diabetes (T2D) remain poorly defined. Here we aimed to explore the pathophysiology of T2D by analyzing gene co-expression networks in human islets. Using partial correlation networks we identified a group of co-expressed genes (‘module’) including F2RL2 that was associated with glycated hemoglobin. F2Rl2 is a G-protein-coupled receptor (GPCR) that encodes protease-activated receptor-3 (PAR3). PAR3 is cleaved by thrombin, which exposes a 6-amino acid sequence that acts as a ‘tethered ligand’ to regulate cellular signaling. We have characterized the effect of PAR3 activation on insulin secretion by static insulin secretion measurements, capacitance measurements, studies of diabetic animal models and patient samples. We demonstrate that thrombin stimulates insulin secretion, an effect that was prevented by an antibody that blocks the thrombin cleavage site of PAR3. Treatment with a peptide corresponding to the PAR3 tethered ligand stimulated islet insulin secretion and single β-cell exocytosis by a mechanism that involves activation of phospholipase C and Ca2+ release from intracellular stores. Moreover, we observed that the expression of tissue factor, which regulates thrombin generation, was increased in human islets from T2D donors and associated with enhanced β-cell exocytosis. Finally, we demonstrate that thrombin generation potential in patients with T2D was associated with increased fasting insulin and insulinogenic index. The findings provide a previously unrecognized link between hypercoagulability and hyperinsulinemia and suggest that reducing thrombin activity or blocking PAR3 cleavage could potentially counteract the exaggerated insulin secretion that drives insulin resistance and β-cell exhaustion in T2D.Supported by the NovoNordisk foundation, the Hjelt foundation and the Swedish Research Council. S.H. and R.C. acknowledge support from a Spanish MINECO grant (ref. TIN2011-22826) and S.H. and I.C. acknowledge support from the Interdisciplinary Center for Clinical Research within the faculty of Medicine at the RWTH Aachen University

Thrombin stimulates insulin secretion via protease-activated receptor-3

The disease mechanisms underlying type 2 diabetes (T2D) remain poorly defined. Here we aimed to explore the pathophysiology of T2D by analyzing gene co-expression networks in human islets. Using partial correlation networks we identified a group of co-expressed genes (‘module’) including F2RL2 that was associated with glycated hemoglobin. F2Rl2 is a G-protein-coupled receptor (GPCR) that encodes protease-activated receptor-3 (PAR3). PAR3 is cleaved by thrombin, which exposes a 6-amino acid sequence that acts as a ‘tethered ligand’ to regulate cellular signaling. We have characterized the effect of PAR3 activation on insulin secretion by static insulin secretion measurements, capacitance measurements, studies of diabetic animal models and patient samples. We demonstrate that thrombin stimulates insulin secretion, an effect that was prevented by an antibody that blocks the thrombin cleavage site of PAR3. Treatment with a peptide corresponding to the PAR3 tethered ligand stimulated islet insulin secretion and single β-cell exocytosis by a mechanism that involves activation of phospholipase C and Ca2+ release from intracellular stores. Moreover, we observed that the expression of tissue factor, which regulates thrombin generation, was increased in human islets from T2D donors and associated with enhanced β-cell exocytosis. Finally, we demonstrate that thrombin generation potential in patients with T2D was associated with increased fasting insulin and insulinogenic index. The findings provide a previously unrecognized link between hypercoagulability and hyperinsulinemia and suggest that reducing thrombin activity or blocking PAR3 cleavage could potentially counteract the exaggerated insulin secretion that drives insulin resistance and β-cell exhaustion in T2D.Supported by the NovoNordisk foundation, the Hjelt foundation and the Swedish Research Council. S.H. and R.C. acknowledge support from a Spanish MINECO grant (ref. TIN2011-22826) and S.H. and I.C. acknowledge support from the Interdisciplinary Center for Clinical Research within the faculty of Medicine at the RWTH Aachen University

FN14 and GRP94 expression are prognostic/predictive biomarkers of brain metastasis outcome that open up new therapeutic strategies

Brain metastasis is a devastating problem in patients with breast, lung and melanoma tumors. GRP94 and FN14 are predictive biomarkers over-expressed in primary breast carcinomas that metastasized in brain. To further validate these brain metastasis biomarkers, we performed a multicenter study including 318 patients with breast carcinomas. Among these patients, there were 138 patients with metastasis, of whom 84 had brain metastasis. The likelihood of developing brain metastasis increased by 5.24-fold (95%CI 2.83-9.71) and 2.55- (95%CI 1.52-4.3) in the presence of FN14 and GRP94, respectively. Moreover, FN14 was more sensitive than ErbB2 (38.27 vs. 24.68) with similar specificity (89.43 vs. 89.55) to predict brain metastasis and had identical prognostic value than triple negative patients (p < 0.0001). Furthermore, we used GRP94 and FN14 pathways and GUILD, a network-based disease-gene prioritization program, to pinpoint the genes likely to be therapeutic targets, which resulted in FN14 as the main modulator and thalidomide as the best scored drug. The treatment of mice with brain metastasis improves survival decreasing reactive astrocytes and angiogenesis, and down-regulate FN14 and its ligand TWEAK. In conclusion our results indicate that FN14 and GRP94 are prediction/prognosis markers which open up new possibilities for preventing/treating brain metastasis.This study was supported by grants from the Spanish Ministry of Health and Consumer Affairs FIS-PI10/00057 and FIS-PI14/00336, both from the I+D+I National Plan with the financial support from ISCIII-Subdirección General de Evaluación and the Fondo Europeo de Desarrollo Regional (FEDER), and by grants from MIIN FIS2008–00114 and BIO2014-57518-R, 2014 SGR 530 and 2009-SGR-159 from the Generalitat de Catalunya, Fundació Privada Cellex Barcelona and the AECC (Spanish Association Against Cancer) Scientific Foundatio