Network analysis reveals rare disease signatures across multiple levels of biological organization

Pisanu Buphamalai,Tomislav Kokotovic,Jörg Menche,Vanja Nagy

doi:10.1038/s41467-021-26674-1

Abstract

Rare genetic diseases are typically caused by a single gene defect. Despite this clear causal relationship between genotype and phenotype, identifying the pathobiological mechanisms at various levels of biological organization remains a practical and conceptual challenge. Here, we introduce a network approach for evaluating the impact of rare gene defects across biological scales. 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. 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. Our results show that the disease module formalism can be applied to rare diseases and generalized beyond physical interaction networks. These findings open up new venues to apply network-based tools for cross-scale data integration.

Highlights

Rare genetic diseases are typically caused by a single gene defect
To integrate these diverse relationships into a unifying, genecentric framework, we constructed a multiplex network comprised of several layers: The nodes in each layer represent genes, the links represent their respective relationship at a particular scale of biological organization, ranging from direct interactions between gene products at the molecular level to phenotypic similarity of associated diseases at the phenotype level (Fig. 1a, b)
We included both pan-tissue and tissue specific networks derived from RNA-seq data across 53 tissues in the Genotype-Tissue Expression (GTEx) database17. (iii) The proteome scale, where links represent physical interactions between gene products obtained from the HIPPIE database21. (iv) The pathway scale, where links represent pathway co-membership derived from the REACTOME database22. (v) The scale of biological processes and molecular functions, where links represent similar functional annotations derived from the Gene Ontology23. (vi) The phenotypic scale, where links represent similarity in annotated phenotypes derived from the Mammalian and Human Phenotype Ontologies (MPO and Human Phenotype Ontology (HPO))[24,25]

Summary

Introduction

Rare genetic diseases are typically caused by a single gene defect Despite this clear causal relationship between genotype and phenotype, identifying the pathobiological mechanisms at various levels of biological organization remains a practical and conceptual challenge. The costs and extended timelines of these individual efforts highlight the need for novel, systematic approaches for investigating the large number of rare diseases that still remain uncharacterized. To this end, several practical and conceptual challenges need to be overcome: First, rare disease phenomena cover a wide spectrum, from highly cell-type or organ-specific phenotypes to heterogeneous, syndromic diseases that affect the whole body. We explored how these systems-level insights may help contextualize individual genetic lesions, investigate the impact of disease heterogeneity, and be translated into clinically actionable tools for the genetic diagnosis of rare disease patients with unknown gene defects

Methods

Results

Conclusion