Large-scale analysis of disease pathways in the human interactome

Abstract

Discovering disease pathways, which can be defined as sets of proteins associated with a given disease, is an important problem that has the potential to provide clinically actionable insights for disease diagnosis, prognosis, and treatment. Computational methods aid the discovery by relying on protein-protein interaction (PPI) networks. They start with a few known disease-associated proteins and aim to find the rest of the pathway by exploring the PPI network around the known disease proteins. However, the success of such methods has been limited, and failure cases have not been well understood. Here we study the PPI network structure of 519 disease pathways. We find that 90% of pathways do not correspond to single well-connected components in the PPI network. Instead, proteins associated with a single disease tend to form many separate connected components/regions in the network. We then evaluate state-of-the-art disease pathway discovery methods and show that their performance is especially poor on diseases with disconnected pathways. Thus, we conclude that network connectivity structure alone may not be sufficient for disease pathway discovery. However, we show that higher-order network structures, such as small subgraphs of the pathway, provide a promising direction for the development of new methods.

Geometric characterisation of disease modules

“…In agreement with previous studies ( Agrawal et al 2018 ; Menche et al 2015 ), we found that proteins associated with a single disease (see Fig. 1 a ) form several connected components in the hPIN, instead of a unique connected entity (see Fig.…”

“…Therefore, defective or dysregulated proteins can disrupt PPIs, clog important signalling pathways and cause disease phenotypes ( Taylor and Wrana 2012 ). In fact, it has been reported that the proteins associated with a single disease agglomerate non-randomly in the same region of the hPIN, forming one or several connected components known as the disease module (DM) ( Agrawal et al 2018 ; Menche et al 2015 ). Consequently, disease-related proteins are more likely to have PPIs with each other than with random proteins.…”

Pairwise link prediction

“…interactions [30], [31]. • P-P-Pathways is a biological network of physical interactions between proteins in humans [32].…”

PANDA: Prioritization of autism‐genes using network‐based deep‐learning approach

“…Network science studies entities (as nodes) and their pairwise relationships (as edges) and can be a powerful tool to discover interaction patterns among biological components. Previous network‐based studies have demonstrated that cellular interaction networks, including protein–protein interaction networks, gene coexpression networks, and metabolic networks, can be used to understand the molecular basis of disease‐gene associations (Agrawal, Zitnik, & Leskovec, 2018; Almasi & Hu, 2019; Barabasi & Oltvai, 2004; Hu et al, 2018; Milenković & Pržulj, 2008; Palla, Derényi, Farkas, & Vicsek, 2005).…”