Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular and intercellular network that links tissue and organ systems. The emerging tools of network medicine offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships among apparently distinct (patho)phenotypes. Advances in this direction are essential for identifying new disease genes, for uncovering the biological significance of disease-associated mutations identified by genome-wide association studies and full-genome sequencing, and for identifying drug targets and biomarkers for complex diseases.

Network Medicine: A Network-Based Approach to Human Disease

The Biological General Repository for Interaction Datasets (BioGRID: http://thebiogrid.org) is an open access database that houses genetic and protein interactions curated from the primary biomedical literature for all major model organism species and humans. As of September 2014, the BioGRID contains 749 912 interactions as drawn from 43 149 publications that represent 30 model organisms. This interaction count represents a 50% increase compared to our previous 2013 BioGRID update. BioGRID data are freely distributed through partner model organism databases and meta-databases and are directly downloadable in a variety of formats. In addition to general curation of the published literature for the major model species, BioGRID undertakes themed curation projects in areas of particular relevance for biomedical sciences, such as the ubiquitin-proteasome system and various human disease-associated interaction networks. BioGRID curation is coordinated through an Interaction Management System (IMS) that facilitates the compilation interaction records through structured evidence codes, phenotype ontologies, and gene annotation. The BioGRID architecture has been improved in order to support a broader range of interaction and post-translational modification types, to allow the representation of more complex multi-gene/protein interactions, to account for cellular phenotypes through structured ontologies, to expedite curation through semi-automated text-mining approaches, and to enhance curation quality control.

/pdf/the-biogrid-interaction-database-2015-update-nqn27vvq8w.pdf

The BioGRID interaction database: 2015 update

Though difficult, the study of gene-environment interactions in multifactorial diseases is crucial for interpreting the relevance of non-heritable factors and prevents from overlooking genetic associations with small but measurable effects. We propose a "candidate interactome" (i.e. a group of genes whose products are known to physically interact with environmental factors that may be relevant for disease pathogenesis) analysis of genome-wide association data in multiple sclerosis. We looked for statistical enrichment of associations among interactomes that, at the current state of knowledge, may be representative of gene-environment interactions of potential, uncertain or unlikely relevance for multiple sclerosis pathogenesis: Epstein-Barr virus, human immunodeficiency virus, hepatitis B virus, hepatitis C virus, cytomegalovirus, HHV8-Kaposi sarcoma, H1N1-influenza, JC virus, human innate immunity interactome for type I interferon, autoimmune regulator, vitamin D receptor, aryl hydrocarbon receptor and a panel of proteins targeted by 70 innate immune-modulating viral open reading frames from 30 viral species. Interactomes were either obtained from the literature or were manually curated. The P values of all single nucleotide polymorphism mapping to a given interactome were obtained from the last genome-wide association study of the International Multiple Sclerosis Genetics Consortium & the Wellcome Trust Case Control Consortium, 2. The interaction between genotype and Epstein Barr virus emerges as relevant for multiple sclerosis etiology. However, in line with recent data on the coexistence of common and unique strategies used by viruses to perturb the human molecular system, also other viruses have a similar potential, though probably less relevant in epidemiological terms.

https://discovery.ucl.ac.uk/1394522/1/journal.pone.0063300.pdf

A “candidate-interactome” aggregate analysis of genome-wide association data in multiple sclerosis

In the post-genomic era, the elucidation of the relationship between the molecular origins of diseases and their resulting phenotypes is a crucial task for medical research. Here, we use a large-scale biomedical literature database to construct a symptom-based human disease network and investigate the connection between clinical manifestations of diseases and their underlying molecular interactions. We find that the symptom-based similarity of two diseases correlates strongly with the number of shared genetic associations and the extent to which their associated proteins interact. Moreover, the diversity of the clinical manifestations of a disease can be related to the connectivity patterns of the underlying protein interaction network. The comprehensive, high-quality map of disease-symptom relations can further be used as a resource helping to address important questions in the field of systems medicine, for example, the identification of unexpected associations between diseases, disease etiology research or drug design.

Human symptoms–disease network

Genotypic differences greatly influence susceptibility and resistance to disease. Understanding genotype-phenotype relationships requires that phenotypes be viewed as manifestations of network properties, rather than simply as the result of individual genomic variations. Genome sequencing efforts have identified numerous germline mutations, and large numbers of somatic genomic alterations, associated with a predisposition to cancer. However, it remains difficult to distinguish background, or 'passenger', cancer mutations from causal, or 'driver', mutations in these data sets. Human viruses intrinsically depend on their host cell during the course of infection and can elicit pathological phenotypes similar to those arising from mutations. Here we test the hypothesis that genomic variations and tumour viruses may cause cancer through related mechanisms, by systematically examining host interactome and transcriptome network perturbations caused by DNA tumour virus proteins. The resulting integrated viral perturbation data reflects rewiring of the host cell networks, and highlights pathways, such as Notch signalling and apoptosis, that go awry in cancer. We show that systematic analyses of host targets of viral proteins can identify cancer genes with a success rate on a par with their identification through functional genomics and large-scale cataloguing of tumour mutations. Together, these complementary approaches increase the specificity of cancer gene identification. Combining systems-level studies of pathogen-encoded gene products with genomic approaches will facilitate the prioritization of cancer-causing driver genes to advance the understanding of the genetic basis of human cancer.

/pdf/interpreting-cancer-genomes-using-systematic-host-network-1o9dno5573.pdf

Interpreting cancer genomes using systematic host network perturbations by tumour virus proteins

Many human diseases, arising from mutations of disease susceptibility genes (genetic diseases), are also associated with viral infections (virally implicated diseases), either in a directly causal manner or by indirect associations. Here we examine whether viral perturbations of host interactome may underlie such virally implicated disease relationships. Using as models two different human viruses, Epstein-Barr virus (EBV) and human papillomavirus (HPV), we find that host targets of viral proteins reside in network proximity to products of disease susceptibility genes. Expression changes in virally implicated disease tissues and comorbidity patterns cluster significantly in the network vicinity of viral targets. The topological proximity found between cellular targets of viral proteins and disease genes was exploited to uncover a novel pathway linking HPV to Fanconi anemia.

/pdf/viral-perturbations-of-host-networks-reflect-disease-50pfi996nc.pdf

Viral Perturbations of Host Networks Reflect Disease Etiology

article i nfo 10 11 Available online xxxx 12 15 Keywords: 16 cDNA microarrays 17 Brain inflammation 18 Cerebral stroke 19 Alzheimer's disease 20 Gene expression profiling 21 The brain responds to injury and infection by activating innate defense and tissue repair mechanisms. Working 22 upon the hypothesis that the brain defense response involves common genes and pathways across diverse pa- 23 thologies, we analysed globalgene expression inbrainfrommousemodels representing threemajor centralner- 24 vous system disorders, cerebral stroke, multiple sclerosis and Alzheimer's disease compared to normal brain

http://cdn.elsevier.com/promis_misc/gdata_data_in_brief.pdf

2 Gene expression in mouse models for multiple sclerosis, Alzheimer's disease and stroke

4128 Advanced ovarian cancer (stages III and IV) accounts for more than 75% of the approximately 23,000 new cases of epithelial ovarian cancer per year in the United States. Over 16,000 deaths per year occur, making this cancer the most lethal gynecologic malignancy. Although ovarian cancer is notable for its initial chemotherapy sensitivity using combination platinum and taxane chemotherapy following debulking surgery (greater than 75% response rates), the vast majority of these women (75%-80%) relapse within 12 to 24 months after diagnosis and die of a progressively chemotherapy-resistant tumor. Despite significant investment, no major improvements in survival have been achieved in more than 10 years.  Recently, we embarked on a large-scale project to develop a detailed understanding of the molecular events associated with chemotherapy resistance in ovarian cancer. Using more than 250 well-annotated formalin-fixed, paraffin embedded (FFPE) samples, we have generated expression profiles for 12,000 mRNA and 735 miRNAs using the Illumina DASL® platform. The resulting de-identified data have been collected with the relevant clinical information in a web-accessible relational database and subjected to rigorous statistical and computational analysis to identify expression profiles predictive of outcome. Categorical functional analysis of the resulting gene lists has identified a number of novel mechanisms associated with chemoresistance. Application of a recently-developed seeded Bayesian Network algorithm has allowed construction of predictive models that provide testable hypotheses regarding mechanism and potential therapeutic intervention. Results are being validated using qRT-PCR and tissue microarray analysis and network models are being refined using an iterative strategy using RNA interference, expression profiling, and computational analysis.  We will present an overview of the laboratory and analytical methodologies we have developed and describe our findings relevant to overcoming chemoresistance.

Renee Rubio

Papers

Viral Perturbations of Host Networks Reflect Disease Etiology

2 Gene expression in mouse models for multiple sclerosis, Alzheimer's disease and stroke

Molecular fingerprints and mechanisms of chemoresistance in ovarian cancer