A team of scientists at the Northeastern University, Boston have found a way to connect diseases based on their shared molecular interactions. Published in the journal Science (link is external), the researchers created a mathematical tool to analyse the human interactome and found that overlapping disease modules—neighbourhoods of disease-associated proteins—result in sometimes unexpected relationships between diseases.
Their results constitute a remarkable step in understanding human diseases. “It is increasingly obvious that human diseases can be interpreted only in the context of the intricate molecular network between the cell’s components,” said Albert-László Barabási, Robert Gray Dodge Professor of Network Science and University Distinguished Professor and director of Northeastern’s Center for Complex Network Research.
Within this paper the team of researchers used only physical protein-protein interactions with experimental support, excluding interactions from gene expression data or evolutionary considerations to build an interactome providing valuable information about the molecular origins of disease-disease relationships.
The team analysed 229 diseases that has at least 20 associated genes and found that all but three of those examined had their own specific ‘neighbourhood’ within the interactome. They also discovered that diseases that were far away from each other within the interactome had very little in common in terms of molecular functions or symptoms, while ones in the same “neighbourhood” were more similar.
Shared genes offer only limited information about the relationship between two diseases. By applying their network science tools to analyse the interactome, Barabasi and his team found that two seemingly unrelated diseases can actually be connected based on the network distance between the disease modules.
They found that asthma, a respiratory disease, and celiac disease, an autoimmune disease of the small intestine, are localized in overlapping neighbourhoods suggesting shared molecular roots despite their rather different pathobiologies. Three genes were identified as being shared between the two diseases- HLA-DQA1, IL18R1 and IL1RL1.
Similarly, the diseases of multiple sclerosis and rheumatoid arthritis were shown to possess overlapping disease modules, suggesting phenotypic similarities and high comorbidity, whilst non-overlapping diseases, such as multiple sclerosis and peroxisomal disorders lack any detectable clinical relationships.
The power of the interactome map could be drawn upon in this way to develop better remedies for disease. Doctors typically diagnose patients based on self-described symptoms, but using the network map it may be possible to find out what is happening intracelllularly in terms of protein interactions to cause any particular ailment.
Despite impressive advances in interactome mapping and disease gene identification, both the interactome and our knowledge of disease-associated genes are still woefully incomplete. Yet, this study shows that the currently available map has reached sufficient coverage to allow the investigation of the molecular mechanisms underlying many diseases and the exploration of the pathobiological relationships between diseases on a molecular level.