Rare diseases are usually caused by a single genetic defect, but searching for the cause and assessing the effects of those defects is highly complex and difficult. Researchers headed by a team at the University of Vienna have now developed a multiplex network that maps all genes and their interactions on multiple levels and improves the identification of genetic defects and the assessment of their consequences.

“The multiplex network integrates different network layers that map different levels of the biological organization of our body, from the genome to the transcriptome, proteome, and phenotype,” explained study lead Jörg Menche, PhD, adjunct principal investigator at the CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, professor at the University of Vienna, and research group leader at the Max Perutz Labs (a joint venture of the University of Vienna and the Medical University of Vienna). “By mapping protein interactions and mechanisms, we can also better characterize those proteins whose roles in diseases were previously unknown and thus track down gene defects more quickly.”

The team reported on its developments, and initial assessment of the network, in Nature Communications, in a paper titled, “Network analysis reveals rare disease signatures across multiple levels of biological organization.”

Over the past 20 years, advances in DNA sequencing technology have allowed scientists to uncover the genetic basis of over 6,000 rare diseases, the authors stated. In contrast to common diseases, which are usually characterized by a complex interaction of multiple genetic and environmental factors, rare diseases can often be traced back to a single defective gene. Targeted decoding and analysis of a gene defect and its phenotypic consequences, therefore, provide important information for understanding underlying mechanisms in the body and help in choosing targeted treatment strategies. “Rare diseases thus offer unique opportunities to mechanistically dissect the relationship between genetic aberrations and their phenotypic consequences, which can then inform targeted treatment strategies,” the team continued.

However, the search for the cause of an individual disease is usually lengthy and costly. “ … the costs and extended timelines of these individual efforts also highlight the need for novel, systematic approaches for investigating the large number of rare diseases that still remain uncharacterized,” the researchers noted. Practical and conceptual challenges also still remain. “Traditionally, rare diseases have been studied following a one-gene, one-pathway, one-disease paradigm. A systematic approach for transferring knowledge from one rare disease to another, and for investigating differences and commonalities between different diseases, is still missing.”

Menche and colleagues developed a new, systematic approach to the study of rare, uncharacterized diseases using a multiplex network. “… we introduce a multiplex network approach for integrating different network layers that represent different scales of biological organization ranging from the genome to the transcriptome and the phenome,” they stated.

Menche’s research group has for several years been dedicated to better understanding genetic interactions using molecular network analysis to improve the diagnosis and treatment of rare diseases. For their current study, first author Pisanu Buphamalai, a CeMM PhD student in Menche’s research group, built a multilayer network mapping more than 20 million gene relationships with information ranging from protein interactions to phenotypic similarities. To do this, the scientists integrated a comprehensive dataset of more than 3,700 rare diseases with a known genetic basis. “We construct a multiplex network consisting of over 20 million gene relationships that are organized into 46 network layers spanning six major biological scales between genotype and phenotype,” the investigators noted. “A comprehensive analysis of 3,771 rare diseases reveals distinct phenotypic modules within individual layers. These modules can be exploited to mechanistically dissect the impact of gene defects and accurately predict rare disease gene candidates.”

Study leader Menche further explained, “The multiplex network integrates different network layers that map different levels of the biological organization of our body, from the genome to the transcriptome, proteome, and phenotype. By mapping protein interactions and mechanisms, we can also better characterize those proteins whose roles in diseases were previously unknown and thus track down gene defects more quickly.”

Buphamalai added, “We are guided by the interactions among proteins at both physical and functional levels. This allows us to draw conclusions about the potential defective gene as well as associated effects. Our network approach increases the probability of finding the crucial gene aberration threefold compared to when these networks are considered separately.” As the team further stated, “A systematic characterization of the network signatures of all rare diseases with known genetic causes allowed us to identify the connectivity patterns that determine the importance of a particular scale of biological organization for a given rare disease.”

On the one hand, the multiplex network’s modular design makes it possible to quantify the impact of a particular rare disease on a specific level of biological organization. This means determining whether certain cells, tissue forms, organs, etc., are particularly affected by a genetic defect. On the other hand, the importance of certain molecular processes for a disease can also be measured. “It is precisely because of its complexity, the linking of molecular sequences and processes, that our multiplex network is significantly more powerful and successful than looking at each network individually, Menche noted. “It also makes it easier to make predictions about possible consequences of the genetic defect.”

The network was successfully tested for functionality in collaboration with Vanja Nagy, PhD, principal investigator at the Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases and CeMM adjunct principal investigator, using data from patients with neurological diseases whose underlying genetic defect was already known. “Our study shows how a huge dataset can be used in the context of network medicine to address several practical and conceptual challenges in rare disease research to improve diagnosis and treatment for the benefit of patients,” Menche said.

“Our results show that the disease module formalism can be applied to rare diseases and generalized beyond physical interaction networks,” the scientists concluded. “These findings open up new avenues to apply network-based tools for cross-scale data integration … Our analysis of 46 network layers containing over 20 million interactions showed that disease modules can be identified across a wide range of relevant gene relationships. We further found that the degree of modularity is indicative of the impact of disease-associated perturbations on a particular level of biological organization, and thereby determines the disease relevance of datasets from the respective level.”

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