The Human Disease Network Kwang-Il Goh, Michael...
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A network of disorders and disease genes linked by known disorder-gene associations offers a platform to explore in a single graph-theoretic framework all known phenotype and disease gene associations, indicating the common genetic origin of many diseases. Genes associated with similar disorders show both higher likelihood of physical interactions between their products and higher expression profiling similarity for their transcripts, supporting the existence of distinct disease-specific functional modules. We find that essential human genes are likely to encode hub proteins and are expressed widely in most tissues. This suggests that disease genes also would play a central role in the human interactome. In contrast, we find that the vast majority of disease genes are nonessential and show no tendency to encode hub proteins, and their expression pattern indicates that they are localized in the functional periphery of the network. A selection-based model explains the observed difference between essential and disease genes and also suggests that diseases caused by somatic mutations should not be peripheral, a prediction we confirm for cancer genes.
The global set of relationships between protein targets of all drugs and all disease-gene products in the human protein-protein interaction or 'interactome' network remains uncharacterized. We built a bipartite graph composed of US Food and Drug Administration-approved drugs and proteins linked by drug-target binary associations. The resulting network connects most drugs into a highly interlinked giant component, with strong local clustering of drugs of similar types according to Anatomical Therapeutic Chemical classification. Topological analyses of this network quantitatively showed an overabundance of 'follow-on' drugs, that is, drugs that target already targeted proteins. By including drugs currently under investigation, we identified a trend toward more functionally diverse targets improving polypharmacology. To analyze the relationships between drug targets and disease-gene products, we measured the shortest distance between both sets of proteins in current models of the human interactome network. Significant differences in distance were found between etiological and palliative drugs. A recent trend toward more rational drug design was observed.
A human disease network is a network of human disorders and diseases with reference to their genetic origins or other features. More specifically, it is the map of human disease associations referring mostly to disease genes. For example, in a human disease network, two diseases are linked if they share at least one associated gene. A typical human disease network usually derives from bipartite networks which consist of both diseases and genes information. Additionally, some human disease networks use other features such as symptoms and proteins to associate diseases.
In 2007, Goh et al. constructed a disease-gene bipartite graph using information from OMIM database and termed human disease network. In 2009, Barrenas et al. derived complex disease-gene network using GWAs (Genome Wide Association studies). In the same year, Hidalgo et al. published a novel way of building human phenotypic disease networks in which diseases were connected according to their calculated distance. In 2011, Cusick et al. summarized studies on genotype-phenotype associations in cellular context. In 2014, Zhou, et al. built a symptom-based human disease network by mining biomedical literature database.
A large-scale human disease network shows scale-free property. The degree distribution follows a power law suggesting that only a few diseases connect to a large number of diseases, whereas most diseases have few links to others. Such network also shows a clustering tendency by disease classes.
In a symptom-based disease network, disease are also clustered according to their categories. Moreover, diseases sharing the same symptom are more likely to share the same genes and protein interactions.
The first phenotypic disease network was constructed by Hidalgo et al. (2009) to help understand the origins of many diseases and the links between them. Hidalgo et al. (2009) defined diseases as specific sets of phenotypes that affect one or several physiological systems, and compiled data on pairwise comorbidity correlations for more than 10,000 diseases reconstructed from over 30 million medical records. Hidalgo et al. (2009) presented their data in the form of a network with diseases as the nodes and comorbidity correlations as the links. Intuitively, the phenotypic disease network (PDN) can be seen as a map of the phenotypic space whose structure can contribute to the understanding of disease progression.
During the last decade, several papers were published that aim at understanding the origins and interrelatedness of diseases using the analytical tools of network science. Interactions between disease-associated genes, proteins, and gene expressions have been explored. However, phenotypic information was essentially overlooked, despite the fact that there exist extensive, high-quality data on it in the form of clinical histories, until the seminal paper of Hidalgo et al. (2009) introducing the human phenotypic disease network.
Hidalgo et al. (2009) also established a connection between that the mortality associated with a given disease and its connectivity in the PDN. Diseases that are preceded by others are usually more connected than those that precede others, and they tend to be more lethal.That is to say, patients that have a disease that is more connected in the network face higher mortality rates that those patients who have less connected conditions.
Protein-protein interaction (PPI) networks serve as a powerful tool for unraveling protein functions, disease-gene and disease-disease associations. However, a direct strategy for integrating protein interaction, protein function and diseases is still absent. Moreover, the interrelated relationships among these three levels are poorly understood. Here we present a novel systematic method to integrate protein interaction, function and disease networks. We first identified topological modules in human protein interaction data using the network topological algorithm (NeTA) we previously developed. The resulting modules were then associated with functional terms using Gene Ontology to obtain functional modules. Finally, disease modules were constructed by associating the modules with OMIM and GWAS. We found that most topological modules have cohesive structure, significant pathway annotations and good modularity. Most functional modules (70.6%) fully cover corresponding topological modules and most disease modules (88.5%) are fully covered by the corresponding functional modules. Furthermore, we identified several protein modules of interest that we describe in detail, which demonstrate the power of our integrative approach. This approach allows us to link genes and pathways with their corresponding disorders, which may ultimately help us to improve the prevention, diagnosis and treatment of disease.
Network methods are powerful tools for unraveling protein functions, protein-pathway associations, disease-gene and disease-disease associations. However, these disparate types of networks are more often studied independently of each other. To date, there has been great progress in the study of protein interaction networks. Previous research on protein networks1,2,3,4,5,6,7,8,9 mainly focused on analyzing the associations between genes, functional modules and pathways. Using these approaches, usually only a fraction of detected protein modules have good mapping to biological functions or pathway annotations. Similarly, previous studies of disease networks10,11,12,13,14,15,16,17,18,19,20,21,22,23,24 mainly focused on disease classification and the prediction of disease genes. Recently, several groups have studied human disease networks25,26, to shed light on the relationship between disease genes and disease networks, as well as disease gene modules and their functional analysis. These methods start from diseasome27, which is a bipartite gene-disease network, from which we can derive two different disease networks: disease-disease networks and disease gene networks. Disease networks may help us to understand phenotype associations between proteins and diseases. Thus, a direct strategy for integrating protein interactions, protein function and disease patterns is still absent and the interrelated relationships among these three levels have been poorly investigated.
To better understand the relationships between these three network types, we present a multi-network systematic analysis method. Using our approach, protein modules are determined directly from topological modules using the network topological algorithm we previously developed (NeTA28). Traditionally, a protein module is defined as a group of proteins that carry out similar functions. These functions are associated with the same pathway and could be associated with a particular disease. Here we focus on three distinct protein modules: topological, functional and disease modules25,26. Topological modules represent a locally dense structure in protein-protein interaction (PPI) networks; function modules represent the aggregation of proteins of related function in a function network; disease modules represents a group of proteins that share a common disease phenotype within a disease network. Though the three types of modules are derived from three different types of networks, they can be closely interrelated and highly overlapping25.
Figure 1 shows a schematic of our overall approach, the framework of the integrated multi-networks mapping method, which consisted of three steps. First we determined the topological modules from a human PPI network. Next, we annotated all topological modules using Gene Ontology (GO), to obtain functional modules. Finally, we included OMIM and GWAS data to obtain disease modules. Thus, three levels of networks were constructed and modules were identified at each level, including a protein network and its topological modules, a function network and its functional modules and a disease network and its disease modules. Finally, we integrate the three types of networks and modules, to discover modules that have coherent function and disease interpretation, leading to new associations that are not evident when analyzing only a single type of network. 781b155fdc