Abstract
Signaling pathways are a cornerstone of systems biology. Several databases store high-quality representations of these pathways that are amenable for automated analyses. Despite painstaking and manual curation, these databases remain incomplete. We present PATHLINKER, a new computational method to reconstruct the interactions in a signaling pathway of interest. PATHLINKER efficiently computes multiple short paths from the receptors to transcriptional regulators (TRs) in a pathway within a background protein interaction network. We use PATHLINKER to accurately reconstruct a comprehensive set of signaling pathways from the NetPath and KEGG databases. We show that PATHLINKER has higher precision and recall than several state-of-the-art algorithms, while also ensuring that the resulting network connects receptor proteins to TRs. PATHLINKER’s reconstruction of the Wnt pathway identified CFTR, an ABC class chloride ion channel transporter, as a novel intermediary that facilitates the signaling of Ryk to Dab2, which are known components of Wnt/β-catenin signaling. In HEK293 cells, we show that the Ryk–CFTR–Dab2 path is a novel amplifier of β-catenin signaling specifically in response to Wnt 1, 2, 3, and 3a of the 11 Wnts tested. PATHLINKER captures the structure of signaling pathways as represented in pathway databases better than existing methods. PATHLINKER’s success in reconstructing pathways from NetPath and KEGG databases point to its applicability for complementing manual curation of these databases. PATHLINKER may serve as a promising approach for prioritizing proteins and interactions for experimental study, as illustrated by its discovery of a novel pathway in Wnt/β-catenin signaling. Our supplementary website at http://bioinformatics.cs.vt.edu/~murali/supplements/2016-sys-bio-applications-pathlinker/ provides links to the PATHLINKER software, input datasets, PATHLINKER reconstructions of NetPath pathways, and links to interactive visualizations of these reconstructions on GraphSpace.
Highlights
A major focus in systems biology is the identification of the networks of reactions that guide the propagation of cellular signals from receptors to downstream transcriptional regulators (TRs)
One of the highest scoring paths computed by PATHLINKER in the Wnt pathway reconstruction suggests that cystic fibrosis transmembrane conductance regulator (CFTR) and its interactions with receptor-like tyrosine kinase (Ryk) and Dab, mitogen-responsive phosphoprotein, homolog 2 (Dab2), both of which are known members of the Wnt pathway, comprise a novel signaling mechanism from Wnts to β-catenin
We first evaluated the ability of PATHLINKER and other algorithms to reconstruct a diverse collection of 15 signaling pathways in the NetPath database (Supplementary Section S1)
Summary
A major focus in systems biology is the identification of the networks of reactions that guide the propagation of cellular signals from receptors to downstream transcriptional regulators (TRs). Over the past two decades, databases have been developed to store the interactions present in signaling pathways,[1,2,3,4,5] facilitating their retrieval for computational analyses. While these databases have been iteratively improved over the years, they are still largely built through extensive and time-consuming manual curation. The proteins and interactions within the same signaling pathway may vary considerably from one database to another Inspired by these challenges, we sought to develop a computational approach to automatically reconstruct signaling pathways from a background network of molecular interactions (the interactome). Compared with several other approaches,[15,16,17,18,19,20] we show that PATHLINKER is the only
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