Elucidating Fibroblast Growth Factor-induced kinome dynamics using targeted mass spectrometry and dynamic modeling

Tim S Veth; Chiara Francavilla; Albert J R Heck; Maarten Altelaar

doi:10.1016/j.mcpro.2023.100594

Elucidating Fibroblast Growth Factor-induced kinome dynamics using targeted mass spectrometry and dynamic modeling

Tim S Veth
, Chiara Francavilla
, Albert J R Heck
, Maarten Altelaar^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

Fibroblast growth factors (FGFs) are paracrine or endocrine signaling proteins that, activated by their ligands, elicit a wide range of health and disease-related processes, such as cell proliferation and the epithelial-to-mesenchymal transition. The detailed molecular pathway dynamics that coordinate these responses have remained to be determined. To elucidate these, we stimulated MCF-7 breast cancer cells with either FGF2, FGF3, FGF4, FGF10, or FGF19. Following activation of the receptor, we quantified the kinase activity dynamics of 44 kinases using a targeted mass spectrometry assay. Our system-wide kinase activity data, supplemented with (phospho)proteomics data, reveal ligand-dependent distinct pathway dynamics, elucidate the involvement of not earlier reported kinases such as MARK, and revise some of the pathway effects on biological outcomes. In addition, logic-based dynamic modeling of the kinome dynamics further verifies the biological goodness-of-fit of the predicted models and reveals BRAF-driven activation upon FGF2 treatment and ARAF-driven activation upon FGF4 treatment.

Original language	English
Article number	100594
Number of pages	18
Journal	Molecular and Cellular Proteomics
Volume	22
Issue number	8
Early online date	14 Jun 2023
DOIs	https://doi.org/10.1016/j.mcpro.2023.100594
Publication status	Published - Aug 2023

Bibliographical note

Publisher Copyright:
© 2023 THE AUTHORS.

Funding

We thank Mara Diks and Suzan Thijssen (Utrecht University) for their guidance with the qPCR experiments. We thank Jennifer Haworth and Joseph Parsons (CF team) for providing qPCR sequences. This work has been supported by EPIC-XS, project number 823839, funded by the Horizon 2020 programme of the European Union and the NWO funded Netherlands Proteomics Centre through the National Road Map for Large-scale Infrastructures program X-Omics, Project 184.034.019. Research in CF lab is supported by the Wellcome Trust (107636/Z/15/Z and 107636/Z/15/A), the Biotechnology and Biological Sciences Research Council (BB/R015864/1), and Medical Research Council (MR/ T016043/1). Acknowledgments—We thank Mara Diks and Suzan Thijs-sen (Utrecht University) for their guidance with the qPCR experiments. We thank Jennifer Haworth and Joseph Parsons (CF team) for providing qPCR sequences. This work has been supported by EPIC-XS, project number 823839, funded by the Horizon 2020 programme of the European Union and the NWO funded Netherlands Proteomics Centre through the National Road Map for Large-scale Infrastructures program X-Omics, Project 184.034.019. Research in CF lab is supported by the Wellcome Trust (107636/Z/15/Z and 107636/Z/15/A), the Biotechnology and Biological Sciences Research Council (BB/R015864/1), and Medical Research Council (MR/ T016043/1).

Funders	Funder number
Netherlands Proteomics Centre	184.034.019
Wellcome Trust	107636/Z/15/A, 107636/Z/15/Z
Horizon 2020 Framework Programme
European Proteomics Infrastructure Consortium providing access	823839
Medical Research Council	MR/ T016043/1
Biotechnology and Biological Sciences Research Council	BB/R015864/1
European Commission
Universiteit Utrecht
Nederlandse Organisatie voor Wetenschappelijk Onderzoek

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Fibroblast
Growth
Factors
Kinome
Signaling
Phosphoproteomics
Breast
Cancer
Modeling
Targeted MS
SRM
MAPK

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10.1016/j.mcpro.2023.100594Licence: CC BY-NC-ND

1-s2.0-S1535947623001056-mainFinal published version, 3.04 MBLicence: CC BY-NC-ND

Cite this

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title = "Elucidating Fibroblast Growth Factor-induced kinome dynamics using targeted mass spectrometry and dynamic modeling",

abstract = "Fibroblast growth factors (FGFs) are paracrine or endocrine signaling proteins that, activated by their ligands, elicit a wide range of health and disease-related processes, such as cell proliferation and the epithelial-to-mesenchymal transition. The detailed molecular pathway dynamics that coordinate these responses have remained to be determined. To elucidate these, we stimulated MCF-7 breast cancer cells with either FGF2, FGF3, FGF4, FGF10, or FGF19. Following activation of the receptor, we quantified the kinase activity dynamics of 44 kinases using a targeted mass spectrometry assay. Our system-wide kinase activity data, supplemented with (phospho)proteomics data, reveal ligand-dependent distinct pathway dynamics, elucidate the involvement of not earlier reported kinases such as MARK, and revise some of the pathway effects on biological outcomes. In addition, logic-based dynamic modeling of the kinome dynamics further verifies the biological goodness-of-fit of the predicted models and reveals BRAF-driven activation upon FGF2 treatment and ARAF-driven activation upon FGF4 treatment.",

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author = "Veth, \{Tim S\} and Chiara Francavilla and Heck, \{Albert J R\} and Maarten Altelaar",

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AB - Fibroblast growth factors (FGFs) are paracrine or endocrine signaling proteins that, activated by their ligands, elicit a wide range of health and disease-related processes, such as cell proliferation and the epithelial-to-mesenchymal transition. The detailed molecular pathway dynamics that coordinate these responses have remained to be determined. To elucidate these, we stimulated MCF-7 breast cancer cells with either FGF2, FGF3, FGF4, FGF10, or FGF19. Following activation of the receptor, we quantified the kinase activity dynamics of 44 kinases using a targeted mass spectrometry assay. Our system-wide kinase activity data, supplemented with (phospho)proteomics data, reveal ligand-dependent distinct pathway dynamics, elucidate the involvement of not earlier reported kinases such as MARK, and revise some of the pathway effects on biological outcomes. In addition, logic-based dynamic modeling of the kinome dynamics further verifies the biological goodness-of-fit of the predicted models and reveals BRAF-driven activation upon FGF2 treatment and ARAF-driven activation upon FGF4 treatment.

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KW - Growth

KW - Factors

KW - Kinome

KW - Signaling

KW - Phosphoproteomics

KW - Breast

KW - Cancer

KW - Modeling

KW - Targeted MS

KW - SRM

KW - MAPK

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Elucidating Fibroblast Growth Factor-induced kinome dynamics using targeted mass spectrometry and dynamic modeling

Abstract

Bibliographical note

Funding

UN SDGs

Keywords

Access to Document

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