Augmenting Signaling Pathway Reconstructions

Signaling pathways drive cellular response, and understanding such pathways is fundamental to molecular systems biology. A mounting volume of experimental protein interaction data has motivated the development of algorithms to computationally reconstruct signaling pathways. However, existing methods suffer from low recall in recovering protein interactions in ground truth pathways, limiting our confidence in any new predictions for experimental validation. We present the Pathway Reconstruction AUGmenter (PRAUG), a higher-order function for producing high-quality pathway reconstruction algorithms. PRAUG modifies any existing pathway reconstruction method, resulting in augmented algorithms that outperform their un-augmented counterparts for six different algorithms across twenty-nine diverse signaling pathways. The algorithms produced by PRAUG collectively reveal potential new proteins and interactions involved in the Wnt and Notch signaling pathways. PRAUG offers a valuable framework for signaling pathway prediction and discovery.

[1]  D. Karger,et al.  Bridging high-throughput genetic and transcriptional data reveals cellular responses to alpha-synuclein toxicity , 2009, Nature Genetics.

[2]  Martin H. Schaefer,et al.  HIPPIE v2.0: enhancing meaningfulness and reliability of protein–protein interaction networks , 2016, Nucleic Acids Res..

[3]  The UniProt Consortium,et al.  UniProt: a worldwide hub of protein knowledge , 2018, Nucleic Acids Res..

[4]  Olga G. Troyanskaya,et al.  GIANT 2.0: genome-scale integrated analysis of gene networks in tissues , 2018, Nucleic Acids Res..

[5]  Gary D Bader,et al.  NetPath: a public resource of curated signal transduction pathways , 2010, Genome Biology.

[6]  Ryan Miller,et al.  WikiPathways: capturing the full diversity of pathway knowledge , 2015, Nucleic Acids Res..

[7]  R. Hendriks,et al.  Notch Signaling in T Helper Cell Subsets: Instructor or Unbiased Amplifier? , 2017, Front. Immunol..

[8]  Deborah Chasman,et al.  Network inference reveals novel connections in pathways regulating growth and defense in the yeast salt response , 2017, bioRxiv.

[9]  Damian Szklarczyk,et al.  The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible , 2016, Nucleic Acids Res..

[10]  Ernest Fraenkel,et al.  Network-Based Interpretation of Diverse High-Throughput Datasets through the Omics Integrator Software Package , 2016, PLoS Comput. Biol..

[11]  Chris Sander,et al.  Causal interactions from proteomic profiles: molecular data meets pathway knowledge , 2018, bioRxiv.

[12]  R. Nusse,et al.  The Wnt signaling pathway in development and disease. , 2004, Annual review of cell and developmental biology.

[13]  Chris Sander,et al.  Pathway Commons 2019 Update: integration, analysis and exploration of pathway data , 2019, Nucleic Acids Res..

[14]  T. M. Murali,et al.  Xtalk: a path-based approach for identifying crosstalk between signaling pathways , 2015, Bioinform..

[15]  A. Chédotal,et al.  Targeting NCK-Mediated Endothelial Cell Front-Rear Polarity Inhibits Neovascularization , 2016, Circulation.

[16]  Henning Hermjakob,et al.  The Reactome pathway knowledgebase , 2013, Nucleic Acids Res..

[17]  Benjamin J. Raphael,et al.  Network propagation: a universal amplifier of genetic associations , 2017, Nature Reviews Genetics.

[18]  Andreas Zell,et al.  BowTieBuilder: modeling signal transduction pathways , 2009, BMC Systems Biology.

[19]  R. Sharan,et al.  Elucidation of Signaling Pathways from Large-Scale Phosphoproteomic Data Using Protein Interaction Networks. , 2016, Cell systems.

[20]  Esti Yeger Lotem,et al.  The TissueNet v.2 database: A quantitative view of protein-protein interactions across human tissues , 2016, Nucleic Acids Res..

[21]  Susumu Goto,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 2000, Nucleic Acids Res..

[22]  Anna Ritz,et al.  Pathways on demand: automated reconstruction of human signaling networks , 2016, npj Systems Biology and Applications.

[23]  Hiroyuki Ogata,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 1999, Nucleic Acids Res..

[24]  Miguel A. Andrade-Navarro,et al.  The latent geometry of the human protein interaction network , 2017, bioRxiv.

[25]  A. McKenna,et al.  Synthesizing Signaling Pathways from Temporal Phosphoproteomic Data , 2017, bioRxiv.

[26]  David Haussler,et al.  Discovering causal pathways linking genomic events to transcriptional states using Tied Diffusion Through Interacting Events (TieDIE) , 2013, Bioinform..

[27]  Ilan Y. Smoly,et al.  MyProteinNet: build up-to-date protein interaction networks for organisms, tissues and user-defined contexts , 2015, Nucleic Acids Res..

[28]  T M Murali,et al.  Reconstructing signaling pathways using regular language constrained paths , 2019, Bioinform..

[29]  Christian Borgs,et al.  Simultaneous Reconstruction of Multiple Signaling Pathways via the Prize-Collecting Steiner Forest Problem , 2012, J. Comput. Biol..

[30]  Raphael Kopan,et al.  The Canonical Notch Signaling Pathway: Unfolding the Activation Mechanism , 2009, Cell.

[31]  Yanchun Liang,et al.  A dynamic programing approach to integrate gene expression data and network information for pathway model generation , 2020, Bioinform..

[32]  Esti Yeger Lotem,et al.  ResponseNet v.3: revealing signaling and regulatory pathways connecting your proteins and genes across human tissues , 2019, Nucleic Acids Res..

[33]  Gregorio Alanis-Lobato,et al.  Mining protein interactomes to improve their reliability and support the advancement of network medicine , 2015, Front. Genet..

[34]  Jeffrey N. Law,et al.  Integrating protein localization with automated signaling pathway reconstruction , 2018, BMC Bioinformatics.

[35]  Christopher S Magnano,et al.  Automating parameter selection to avoid implausible biological pathway models , 2019, npj Systems Biology and Applications.