PHONEMeS: Efficient Modeling of Signaling Networks Derived from Large-Scale Mass Spectrometry Data

Abstract

Post-translational modifications of proteins play an important role in the regulation of cellular processes. The mass spectrometry analysis of proteome modifications offers huge potential for the study of how protein inhibitors affect the phosphosignaling mechanisms inside the cells. We have recently proposed PHONEMeS, a method that uses high-content shotgun phosphoproteomic data to build logical network models of signal perturbation flow. However, in its original implementation, PHONEMeS was computationally demanding and was only used to model signaling in a perturbation context. We have reformulated PHONEMeS as an Integer Linear Program (ILP) that is orders of magnitude more efficient than the original one. We have also expanded the scenarios that can be analyzed. PHONEMeS can model data upon perturbation on not only a known target but also deregulated pathways upstream and downstream of any set of deregulated kinases. Finally, PHONEMeS can now analyze data sets with multiple time points, which helps us to obtain better insight into the dynamics of the propagation of signals. We illustrate the value of the new approach on various data sets of medical relevance, where we shed light on signaling mechanisms and drug modes of action.

Deep Phosphoproteomic Elucidation of Metformin-Signaling in Heterogenous Colorectal Cancer Cells

“…To derive mechanistic insights into MoA of metformin in individual cell lines, we contextualized the networks using PHONEMeS (Gjerga et al, 2021; Terfve et al, 2015). Three inputs were used for the analysis ( Figure 6A ), i) the top 15% of differentially expressed P-sites, ii) the top 15% of differentially activated kinases, and iii) the prior knowledge network extracted from OmniPath database consisting of protein-protein and kinase-P-site interactions (Türei et al ., 2016; Türei et al ., 2021).…”

Dynamic proteomic and phosphoproteomic atlas of corticostriatal axon neurodevelopment

“…To incorporate phosphorylation data in the context of axon development, we aim to build a signaling model that can infer upstream kinases in the context of the data, while taking into account developmental timepoint to identify functional kinase-substrate interactions. We used PHOsphorylation Networks for Mass Spectrometry (PHONEMeS) analysis tool to model our data (Gjerga et al, 2021). PHONEMeS reconstructs phosphorylation signaling networks from known kinase-substrate interaction database ( i.e ., prior knowledge network, PKN), taking into account ‘perturbed’ (differentially detected) phosphosites across time points.…”

“…For PHONEMES-ILP analysis, we adapted the code published by the Saez-Rodriguez lab github (https://github.com/saezlab/PHONEMeS-ILP) (Gjerga et al, 2021). We used OmnipathR to download the mouse kinase-substrate interactions and filtered for entries that map to Swissprot.…”

Integrating knowledge and omics to decipher mechanisms via large‐scale models of signaling networks

“…Originally released in 2015 (Terfve et al , 2015 ), and updated in 2021 (Gjerga et al , 2021 ), PHONEMES is a method to create models of signaling networks using phosphoproteomics data. It employs a directed PKN constructed from kinase‐substrate interactions from OmniPath (Türei et al , 2016 ).…”