The multiplex network of human diseases

Abstract

Untangling the complex interplay between phenotype and genotype is crucial to the effective characterization and subtyping of diseases. Here we build and analyze the multiplex network of 779 human diseases, which consists of a genotype-based layer and a phenotype-based layer. We show that diseases with common genetic constituents tend to share symptoms, and uncover how phenotype information helps boost genotype information. Moreover, we offer a flexible classification of diseases that considers their molecular underpinnings alongside their clinical manifestations. We detect cohesive groups of diseases that have high intra-group similarity at both the molecular and the phenotypic level. Inspecting these disease communities, we demonstrate the underlying pathways that connect diseases mechanistically. We observe monogenic disorders grouped together with complex diseases for which they increase the risk factor. We propose potentially new disease associations that arise as a unique feature of the information flow within and across the two layers.

The Human Organism as an Integrated Interaction Network: Recent Conceptual and Methodological Challenges

“…Conceptually, it considers the human organism as an evolving complex network-a radically reduced description where the full system is described by an interaction network, whose vertices represent distinct physiological subsystems and whose edges represent time-dependent, observation-derived interactions between them (see Figure 1). This reduced description has been utilized in a number of scientific disciplines, and research over the last two decades has demonstrated that the network paradigm can advance our understanding of natural and man-made complex dynamical systems (see e.g., Boccaletti et al, 2006Boccaletti et al, , 2014Arenas et al, 2008;Barthélemy, 2011;Holme and Saramäki, 2012;Bassett and Sporns, 2017;Halu et al, 2019 for an overview). Although encouraging, the data-driven network approach to the human organism faces a number of challenges.…”

Spreading of localized attacks on spatial multiplex networks with a community structure

“…Recent studies show that in different cases of spatial interdependent networks, localized attacks are significantly more damaging than random attacks [17][18][19][20]. In addition, many real networks have a modular structure [21], such as biological networks [22,23] and many infrastructure systems [24,25]. Therefore, recent studies have explored and compared the robustness of individual and interdependent modular non-spatial systems [26][27][28][29].…”

Mapping the SARS-CoV-2–Host Protein–Protein Interactome by Affinity Purification Mass Spectrometry and Proximity-Dependent Biotin Labeling: A Rational and Straightforward Route to Discover Host-Directed Anti-SARS-CoV-2 Therapeutics

“…These specific proteomics datasets here reviewed may inspire new studies aiming at better elucidating both specific and nonspecific host defense mechanisms against viral infection. An in depth interpretation of the complex SARS-CoV-2-host PPI maps based on high-throughput -omics studies here reviewed together with computational and chemo-informatics approaches as well as network medicine may provide a valid synergistic platform necessary for understanding virus–host–drug mechanisms and for predicting novel drug targets [ 62 , 104 , 105 , 106 ]. Interestingly, in this direction, a very recent study by Nadeau et al [ 107 ], starting from the computational analysis of the PPI dataset generated in HEK 293 cells by Gordon et al [ 17 ] has revealed processes theoretically affected by the virus [ 107 ].…”