Elucidating Fibroblast Growth Factor-Induced Kinome Dynamics Using Targeted Mass Spectrometry and Dynamic Modeling.
Elucidating Fibroblast Growth Factor-Induced Kinome Dynamics Using Targeted Mass Spectrometry and Dynamic Modeling.
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DOI：
10.1016/j.mcpro.2023.100594
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发表时间：
2023-08
影响因子：
7
通讯作者：
Altelaar, Maarten
中科院分区：
文献类型：
--
作者：
Veth, Tim S.;Francavilla, Chiara;Heck, Albert J. R.;Altelaar, Maarten
成纤维细胞生长因子 (FGF) 是旁分泌或内分泌信号蛋白，由其配体激活，引发广泛的健康和疾病相关过程，例如细胞增殖和上皮间质转化。协调这些反应的详细分子途径动力学仍有待确定。为了阐明这些，我们用 FGF2、FGF3、FGF4、FGF10 或 FGF19 刺激 MCF-7 乳腺癌细胞。受体激活后，我们使用靶向质谱分析对 44 种激酶的激酶活性动态进行了定量。我们的全系统激酶活性数据，辅以（磷酸）蛋白质组学数据，揭示了配体依赖性的独特通路动力学，阐明了早期未报道的激酶（例如 MARK）的参与，并修正了一些通路对生物学结果的影响。此外，基于逻辑的激酶组动力学动态建模进一步验证了预测模型的生物学拟合优度，并揭示了 FGF2 处理后 BRAF 驱动的激活和 FGF4 处理后 ARAF 驱动的激活。使用不同的 FGF 治疗可激活癌细胞中不同的信号通路。靶向激酶组活性测定可以量化激酶组动力学。不同的 FGF 刺激会产生不同的激酶组动力学。基于逻辑的动态建模提供生物途径验证。 FGF2 治疗诱导 BRAF 激活，FGF4 导致 ARAF 激活。我们通过定制的靶向激酶组活性测定，对乳腺癌细胞中 FGF 调节的激酶组信号转导进行了广泛的概述。我们应用基于逻辑的动态建模来验证文献衍生的生物途径。 FGF2、FGF3、FGF4、FGF10 和 FGF19 信号传导之间的深入比较揭示了迄今为止在 FGF 背景下尚未描述的激酶的差异反应和参与。此外，我们扩展了现有的 FGF 信号传导知识，例如，分别揭示了 ARAF 和 BRAF 对 FGF4 和 FGF2 的差异激活。
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. Treatment with different FGFs activate distinct signaling pathways in cancer cells. A targeted kinome activity assay enables the quantification of kinome dynamics. Different FGF stimulations generate disparate kinome dynamics. Logic-based dynamic modeling provides biological pathway validation. FGF2 treatment induces BRAF activation and FGF4 results in ARAF activation. We provide an extensive overview of FGF-regulated kinome signaling in breast cancer cells, enabled via a custom-made targeted kinome activity assay. We apply logic-based dynamic modeling to verify literature-derived biological pathways. This in-depth comparison between FGF2, FGF3, FGF4, FGF10, and FGF19 signaling revealed differential response and involvement of kinases hitherto undescribed in the FGF context. Moreover, we expanded on the existing FGF-signaling knowledge, for example, by revealing differential activation of ARAF and BRAF for FGF4 and FGF2, respectively.
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影响因子：
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