Global Analysis of the Human Pathophenotypic Similarity Gene Network Merges Disease Module Components

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.

Organ system heterogeneity DB: a database for the visualization of phenotypes at the organ system level

“…Numerous comparative analyses of these phenotypes have demonstrated that organismal phenotypes are a rich source of molecular and clinical information. Side effect similarity has been employed to identify new drug targets ( 1 ) and functional relations between disease genes have been found among diseases that share symptoms ( 2 , 3 ). The comparison of phenotypic information across species and perturbations has also provided novel molecular information of drugs and diseases.…”

Integrative analysis of human protein, function and disease networks

“…In recent years, a number of methods have been developed to identify functional modules 1 2 3 4 5 6 7 8 9 and disease modules 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 in PPI networks. Most methods to identify disease modules are disease protein prioritization methods.…”

“…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 networks 10 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 networks 25 26 , to shed light on the relationship between disease genes and disease networks, as well as disease gene modules and their functional analysis.…”

PhenUMA: a tool for integrating the biomedical relationships among genes and diseases

“…However, the estimated thresholds in each ROC curve were meaningful (Additional file 1 ), but they are impractical as optimal cutoffs because of the large size of the resulting networks. Therefore, we analyzed cutoff variations in the phenotypic similarity datasets using a similar approach as in one of our recent studies [ 13 ]. First, we removed all pairs of genes or diseases that had a similarity score below the 95 th percentile.…”

“…In particular, because ontologies have been beneficial in understanding diseases as a set of phenotypes rather than conceptual entities, studying correlations among distinct biological conditions affected by genetic variations would be very useful [ 13 ].…”