Abstract
The molecular complexity of genetic diseases requires novel approaches to break it down into coherent biological modules. For this purpose, many disease network models have been created and analyzed. We highlight two of them, “the human diseases networks” (HDN) and “the orphan disease networks” (ODN). However, in these models, each single node represents one disease or an ambiguous group of diseases. In these cases, the notion of diseases as unique entities reduces the usefulness of network-based methods. We hypothesize that using the clinical features (pathophenotypes) to define pathophenotypic connections between disease-causing genes improve our understanding of the molecular events originated by genetic disturbances. For this, we have built a pathophenotypic similarity gene network (PSGN) and compared it with the unipartite projections (based on gene-to-gene edges) similar to those used in previous network models (HDN and ODN). Unlike these disease network models, the PSGN uses semantic similarities. This pathophenotypic similarity has been calculated by comparing pathophenotypic annotations of genes (human abnormalities of HPO terms) in the “Human Phenotype Ontology”. The resulting network contains 1075 genes (nodes) and 26197 significant pathophenotypic similarities (edges). A global analysis of this network reveals: unnoticed pairs of genes showing significant pathophenotypic similarity, a biological meaningful re-arrangement of the pathological relationships between genes, correlations of biochemical interactions with higher similarity scores and functional biases in metabolic and essential genes toward the pathophenotypic specificity and the pleiotropy, respectively. Additionally, pathophenotypic similarities and metabolic interactions of genes associated with maple syrup urine disease (MSUD) have been used to merge into a coherent pathological module.Our results indicate that pathophenotypes contribute to identify underlying co-dependencies among disease-causing genes that are useful to describe disease modularity.
Highlights
Phenotypes are the result of the expression of specific genetic backgrounds submitted to the influence of changing environmental conditions [1]
Each gene dataset was subdivided in four different classes (Tables S3 and S4 for human diseases networks’’ (HDN) and orphan disease networks’’ (ODN) respectively) according to our proposed criteria (Figure 1C): two monotropic classes (MD-MG and polygenic diseases associated with monotropic genes (PD-MG)) and two pleiotropic classes (MD-PG and polygenic diseases associated with pleiotropic genes (PD-PG))
Monotropic subsets are exclusive because their relationship with the disease is unique so genes take part in only one subset and they represent 72% and 69% of the total genes in HDN and ODN, respectively
Summary
Phenotypes are the result of the expression of specific genetic backgrounds submitted to the influence of changing environmental conditions [1]. ‘‘the human disease network’’ and ‘‘the orphan disease networks’’) [10,11] These diseasomes open the possibility to work on different types of network projections, treating networks as graphs, which can be used to detect emergent information. Several different biomolecular interactomes based on physical, metabolic or functional interactions have been used to capture some frames of the biological complexity associated with pathologies [12,13,14,15,16,17] In this case, one of the most direct applications of network medicine approaches lies in the systematic exploration of the molecular mechanism shared by ‘‘apparently’’ distinct diseases [7]. All these challenges take part in a wider emergent discipline known as Systems Medicine [18]
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