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Northeastern researchers connect diseases based on their molecular similarities

Northeastern University network scientists have found a way to connect diseases based on their shared molecular interactions. Published in the journal Science, the Northeastern team created a mathematical tool to analyze the human interactome—a map of the molecular interactions within cells—and found that overlapping disease modules—neighborhoods of disease-associated proteins—result in sometimes unexpected relationships between diseases.

The findings 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. “What was not obvious until now is whether the avail­able net­work maps offer enough cov­erage and accu­racy to help us get started in this path. In this paper we showed that they do offer that accu­racy and pro­vide valu­able infor­ma­tion about the mol­e­c­ular ori­gins of disease-disease relationships.”

The team ana­lyzed 299 dis­eases that had at least 20 asso­ci­ated genes and found that 226 of the dis­eases had their own spe­cific “neigh­bor­hood” 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 “neigh­bor­hood” were more similar.

Shared genes offer only limited information about the relationship between two diseases. By applying their network science tools to analyze 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. For example, they found that asthma, a respiratory disease, and celiac disease, an autoimmune disease of the small intestine, are localized in overlapping neighborhoods suggesting shared molecular roots despite their rather different pathobiologies.

“What we try to achieve in this paper is to lay out a more math­e­mat­ical ground­work for this intu­itive idea that you can use the inter­ac­tome as a map,” said Jörg Menche, a postodoctoral researcher and one of the authors on the paper. “It could play a major role in under­standing dis­eases on a mol­e­c­ular leveland developing better remedies.”

Menche offered an example of the power of the net­work map, explaining that doc­tors typ­i­cally diag­nose patients based on symp­toms described by the patient. But using the net­work map, he said, could make it pos­sible to find out what is hap­pening in the gene product inter­ac­tions to cause the 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. This incompleteness prompted us to systematically investigate to what extent the current data are sufficient to map out disease modules,” added Mensche.

“In conclusion,” he said “we could show that the currently available interactome data has reached sufficient coverage to systematically investigate the molecular mechanisms underlying many diseases, as well as to explore pathobiological relationships between diseases on a molecular level.”

The North­eastern researchers are based in the Center for Com­plex Net­work Research. The team comprises Barabási, Menche, postdoctoral researcher Maskim Kitsak, research assis­tant pro­fessor Amitabh Sharma, and grad­uate physics stu­dent Susan Dina Ghi­assian, PhD’15.

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