Systems approach reveals distinct and shared signaling networks of the four PGE2 receptors in T cells

Anna M Lone, Piero Giansanti, Marthe Jøntvedt Jørgensen, Enio Gjerga, Aurelien Dugourd, Arjen Scholten, Julio Saez-Rodriguez, Albert J R Heck, Kjetil Taskén

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Prostaglandin E2 (PGE2) promotes an immunosuppressive microenvironment in cancer, partly by signaling through four receptors (EP1, EP2, EP3, and EP4) on T cells. Here, we comprehensively characterized PGE2 signaling networks in helper, cytotoxic, and regulatory T cells using a phosphoproteomics and phosphoflow cytometry approach. We identified ~1500 PGE2-regulated phosphosites and several important EP1–4 signaling nodes, including PKC, CK2, PKA, PI3K, and Src. T cell subtypes exhibited distinct signaling pathways, with the strongest signaling in EP2-stimulated CD8+ cells. EP2 and EP4, both of which signal through Gαs, induced similar signaling outputs, but with distinct kinetics and intensity. Functional predictions from the observed phosphosite changes revealed PGE2 regulation of key cellular and immunological processes. Last, network modeling suggested signal integration between the receptors and a substantial contribution from G protein–independent signaling. This study offers a comprehensive view of the different PGE2-regulated phosphoproteomes in T cell subsets, providing a valuable resource for further research on this physiologically and pathophysiologically important signaling system.

Original languageEnglish
Article numbereabc8579
Pages (from-to)1-20
JournalScience Signaling
Volume14
Issue number703
DOIs
Publication statusPublished - 5 Oct 2021

Bibliographical note

Funding Information:
We thank J. Solheim and J. Landskron for practical assistance with and advice on the methylation studies. We further thank T. Maruyama at ONO Pharmaceutical Co. Ltd. for providing EP receptor agonists and antagonists. The work was supported by grants from the Norwegian Cancer Society (grant no. 182794 and 215850), The Regional Health Authority for South-Eastern Norway (grant no. 2017119), the Research Council of Norway (grant no. 187615 and 294916), Stiftelsen Kristian Gerhard Jebsen (grant no. SKGJ-MED-09 and SKGJ-MED-19), and the European Union Horizon 2020 program INFRAIA project Epic-XS (project 823839). E. Gjerga and A. Dugourd were supported by the European Union?s Horizon 2020 research and innovation programme (675585 Marie-Curie ITN ?SymBioSys?) and the JRC for Computational Biomedicine, which was partially funded by Bayer.

Publisher Copyright:
Copyright © 2021 The Authors, some rights reserved

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