Dataset Information

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 when activated by their ligands, can elicit a wide range of healthy and disease-related processes such as cell proliferation and the epithelial-to-mesenchymal transition (EMT). The detailed molecular pathway dynamics that coordinate these responses have remained unclear. To elucidate and monitor these dynamics, we stimulated MCF-7 breast cancer cells with either FGF2, 3, 4, 10, or 19. 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 (phopsho)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 tight regulation of the RAF kinase family.

ORGANISM(S): Homo Sapiens

SUBMITTER: Tim Veth

PROVIDER: PXD039005 | panorama | Sun Jul 02 00:00:00 BST 2023

REPOSITORIES: PanoramaPublic

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Publications

Elucidating Fibroblast Growth Factor-Induced Kinome Dynamics Using Targeted Mass Spectrometry and Dynamic Modeling.

Veth Tim S TS Francavilla Chiara C Heck Albert J R AJR Altelaar Maarten M

Molecular & cellular proteomics : MCP 20230615 8

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 q ...[more]

PMID: 37328066

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Project description:Gene transcription mediated by RNA polymerase II (pol-II) is a key step in gene expression. The dynamics of pol-II moving along the transcribed region influences the rate and timing of gene expression. In this work we present a probabilistic model of transcription dynamics which is fitted to pol-II occupancy time course data measured using ChIP-Seq. The model can be used to estimate transcription speed and to infer the temporal pol-II activity profile at the gene promoter. Model parameters are determined using either maximum likelihood estimation or via Bayesian inference using Markov chain Monte Carlo sampling. The Bayesian approach provides confidence intervals for parameter estimates and allows the use of priors that capture domain knowledge, e.g. the expected range of transcription speeds, based on previous experiments. The model describes the movement of pol-II down the gene body and can be used to identify the time of induction for transcriptionally engaged genes. By clustering the inferred promoter activity time profiles, we are able to determine which genes respond quickly to stimuli and group genes that share activity profiles and may therefore be co-regulated. We apply our methodology to biological data obtained using ChIP-seq to measure pol-II occupancy genome-wide when MCF-7 human breast cancer cells are treated with estradiol (E2). The transcription speeds we obtain agree with those obtained previously for smaller numbers of genes with the advantage that our approach can be applied genome- wide. We validate the biological significance of the pol-II promoter activity clusters by investigating cluster-specific transcription factor binding patterns and determining canonical pathway enrichment. MCF-7 cells were mock treated or with 10nM 17b-E2 to seven time points (5', 10', 20', 40', 80', 160', 320') Genome-wide identification of RNA polymerase II (RNAPII) occupancy at different time points following E2 induction of MCF-7 cells

Dataset Information

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

Publications

Elucidating Fibroblast Growth Factor-Induced Kinome Dynamics Using Targeted Mass Spectrometry and Dynamic Modeling.

Similar Datasets

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