Edgetic perturbation models of human inherited disorders

Abstract

Cellular functions are mediated through complex systems of macromolecules and metabolites linked through biochemical and physical interactions, represented in interactome models as 'nodes' and 'edges', respectively. Better understanding of genotype-to-phenotype relationships in human disease will require modeling of how disease-causing mutations affect systems or interactome properties. Here we investigate how perturbations of interactome networks may differ between complete loss of gene products ('node removal') and interaction-specific or edge-specific ('edgetic') alterations. Global computational analyses of B50 000 known causative mutations in human Mendelian disorders revealed clear separations of mutations probably corresponding to those of node removal versus edgetic perturbations. Experimental characterization of mutant alleles in various disorders identified diverse edgetic interaction profiles of mutant proteins, which correlated with distinct structural properties of disease proteins and disease mechanisms. Edgetic perturbations seem to confer distinct functional consequences from node removal because a large fraction of cases in which a single gene is linked to multiple disorders can be modeled by distinguishing edgetic network perturbations. Edgetic network perturbation models might improve both the understanding of dissemination of disease alleles in human populations and the development of molecular therapeutic strategies.

MSX1 mutations and associated disease phenotypes: genotype-phenotype relations

“…We found that MSX1 in-frame mutations are more frequent than truncating mutations (18 vs13) ( Figure 1a) which is consistent with many other Mendelian genes. 44 We analysed the phenotypes in terms of the domains where the variants are located and identified a clear segregation. The average number of missing teeth associated with in-frame mutations is lower than with truncating mutations leading to more severe phenotypes ( Figure 3).…”

“…48 Recently, network modeling has been introduced in order to explain how specific mutations may lead to distinct phenotypes. 44,46,[49][50][51] In a specific network model called the edgetic perturbation model, a mutation is considered to alter molecular interactions either due to edgetic perturbations or due to node removal ( Figure 4). Edgetic perturbation leads to the removal or addition of specific interactions while all other interactions (or edges) remain equal.…”

Navigating cancer network attractors for tumor-specific therapy

“…In line with their importance, network-attacking mutations have attracted more attention in recent years [43][44][45][46][47][48] . Moreover, information has been accumulating steadily about how specificity in signaling networks and modular protein domains emerges [49][50][51] , leading to the definition of determinants of specificity in protein domains 52,53 .…”

Edge biomarkers for classification and prediction of phenotypes

“…As mentioned in the Introduction, perturbations are of two types; loss of gene products ('node removal', or removal of a node from the interactome network), and loss of gene interactions ('edge removal', or removal of an edge from the interactome network). Interaction (edge) removals, rather than gene (node) removals, are responsible for the so-called 'edgetic' diseases [5]. Thus, differentially expressed interactions or networks can provide more details about human pathogenic states and realize better molecular therapeutic strategies than conventional differentially expressed genes.…”

“…Perturbations fall into two categories; loss of gene products ('node removal', the removal of a node from the interaction network), and loss of protein or gene interactions ('edge removal', the removal of an edge from the interaction network). Recently, 'edgetic' disease has been identified as the result of an interaction removal (edge removal) rather than a gene removal (node removal) [5]. On the other hand, our recent EdgeMarker analysis [6] demonstrated that non-differentially expressed genes, which are traditionally ignored, can be as informative as differentially expressed genes for classifying biological conditions and sample phenotypes.…”