Reconstructing phosphorylation signalling networks from quantitative phosphoproteomic data

Invergo, Brandon M.; Beltrão, Pedro

doi:10.1042/ebc20180019

Cited by 39 publications

(58 citation statements)

References 59 publications

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“…Kinase-kinase and other kinase-substrate relationships have also been annotated in databases such as PhosphoSitePlus and Phospho.ELM (Dinkel et al, 2011;Hornbeck et al, 2015). However, bias in the study of kinase regulatory relationships has left the majority of the kinase-kinase interaction space largely unexplored (Invergo and Beltrao, 2018). Similar biases have been reported for protein-protein interaction databases (Gillis, Ballouz, and Pavlidis, 2014) .…”

Section: Introductionmentioning

confidence: 71%

“…Because we set out to overcome the study bias inherent to signaling pathway annotations, we checked for relationships between kinase connectivity on the high-confidence network and kinase publication counts (Figure 4f; kinase publication counts were retrieved from Invergo and Beltrao (2018)). Interactions between kinases in the top three publicationcount deciles (more than 95 publications) accounted for only 31% of the network.…”

Section: Predicting a Global Network Of Kinase Regulatory Relationshipsmentioning

confidence: 99%

“…Existing methods for data-driven reconstruction of signaling networks are generally designed for data that has been produced for the study of a specific pathway (e.g. via perturbation experiments) and typically benefit from the incorporation of prior knowledge about that pathway into the model (see, e.g., Hill et al (2016) and Invergo and Beltrao (2018)). Given the study bias inherent to this approach, these methods are less likely to provide insight into broader patterns of protein kinase regulation, especially of understudied kinases or cross-module signaling.…”

Section: Introductionmentioning

confidence: 99%

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Proteome-wide inference of protein kinase regulatory circuits

Invergo

Petursson

Bradley

et al. 2019

Preprint

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Complex networks of regulatory relationships between protein kinases comprise a major component of intracellular signaling. Although many kinase-kinase regulatory relationships have been described in detail, these are biased towards well-studied kinases while the majority of possible relationships remains unexplored. Here, we implement data-driven, unbiased methods to predict human kinase-kinase regulatory relationships and whether they have activating or inhibiting effects. We incorporate high-throughput data, kinase specificity profiles, and structural information to produce our predictions. The results successfully recapitulate previously annotated regulatory relationships and can reconstruct known signaling pathways from the ground up. The full network of predictions is relatively sparse, with the vast majority of relationships assigned low probabilities. However, it nevertheless suggests denser modes of inter-kinase regulation than normally considered in intracellular signaling research.

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Section: Introductionmentioning

confidence: 71%

Section: Predicting a Global Network Of Kinase Regulatory Relationshipsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Proteome-wide inference of protein kinase regulatory circuits

Invergo

Petursson

Bradley

et al. 2019

Preprint

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“…Depending on the cancer type, several kinases may act either as an oncogene or a tumor suppressor (An & Brognard, 2019), demonstrating the intricacies of kinase regulation and their role in defining cell phenotypes. Despite kinases being excellent targets for drug intervention (Bhullar et al, 2018), the majority of them, including the Fes kinase, are understudied (Invergo & Beltrao, 2018), thus making it difficult to link their function with cell phenotype. Here we have applied an unbiased phosphoproteomic and data-driven strategy to shed light on the contrasting oncogenic and tumor suppressor function of the understudied Fes kinase.…”

Section: Discussionmentioning

confidence: 99%

Decoding the functional Fes kinase signaling network topology in a lymphocyte model

Helbig

Kofler

Gish

et al. 2017

Preprint

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SummaryThe c-Fes protein tyrosine kinase is a proto-oncogene that can also act as a tumor suppressor. We implemented a novel phosphoproteomics-based analysis that establishes cognate kinase-substrate associations, and revealed that c-Fes directly phosphorylates Dok1, Ptpn18 and Sts1, facilitating recruitment of the Src inhibitory kinase Csk to these substrates. These interactions resulted in modulation of Src signaling following B-cell receptor (BCR) stimulation and subsequent alteration of the protein levels of CD19, a membrane-localized BCR co-receptor and emerging key protein affecting the development of B-and plasma cell-lymphoma. Strikingly, manipulating c-Fes expression levels drove opposing biological outcomes. Low-level exogenous c-Fes expression led to a strong increase in CD19 protein levels while high c-Fes expression abolished CD19 protein levels. Thus, we propose that a balance of c-Fes and Src signaling can regulate maintenance of CD19, which may influence cellular outcome of accelerated hematopoietic tumorigenesis or tumor suppression.

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“…We point out that the analysis of the SIGNOR PPI network already found biological applications reported by Sacco et al, 2016;Lun.X-K. et al, 2017;Kanhaiya et al, 2017;Dimitrakopoulos et al, 2018. The detailed review of various applications of the PPI signaling networks is given by Invergo and Beltrao, 2018. However, the Google matrix analysis has not been used in these studies.…”

Section: Introductionmentioning

confidence: 99%

Google matrix analysis of bi-functional SIGNOR network of protein-protein interactions

Frahm

Shepelyansky

2019

Preprint

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Motivation: Directed protein networks with only a few thousand of nodes are rather complex and do not allow to extract easily the effective influence of one protein to another taking into account all indirect pathways via the global network. Furthermore, the different types of activation and inhibition actions between proteins provide a considerable challenge in the frame work of network analysis. At the same time these protein interactions are of crucial importance and at the heart of cellular functioning. Results: We develop the Google matrix analysis of the protein-protein network from the open public database SIGNOR. The developed approach takes into account the bi-functional activation or inhibition nature of interactions between each pair of proteins describing it in the frame work of Ising-spin matrix transitions. We also apply a recently developed linear response theory for the Google matrix which highlights a pathway of proteins whose PageRank probabilities are most sensitive with respect to two proteins selected for the analysis. This group of proteins is analyzed by the reduced Google matrix algorithm which allows to determine the effective interactions between them due to direct and indirect pathways in the global network. We show that the dominating activation or inhibition function of each protein can be characterized by its magnetization. The results of this Google matrix analysis are presented for three examples of selected pairs of proteins. The developed methods work rapidly and efficiently even for networks with several million of nodes and can be applied to various biological networks. Availability: The Google matrix data and executive code of described algorithms are available at

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Reconstructing phosphorylation signalling networks from quantitative phosphoproteomic data

Cited by 39 publications

References 59 publications

Proteome-wide inference of protein kinase regulatory circuits

Proteome-wide inference of protein kinase regulatory circuits

Decoding the functional Fes kinase signaling network topology in a lymphocyte model

Google matrix analysis of bi-functional SIGNOR network of protein-protein interactions

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