RESUMO
Kinase and phosphatase overexpression drives tumorigenesis and drug resistance. We previously developed a mass-cytometry-based single-cell proteomics approach that enables quantitative assessment of overexpression effects on cell signaling. Here, we applied this approach in a human kinome- and phosphatome-wide study to assess how 649 individually overexpressed proteins modulated cancer-related signaling in HEK293T cells in an abundance-dependent manner. Based on these data, we expanded the functional classification of human kinases and phosphatases and showed that the overexpression effects include non-catalytic roles. We detected 208 previously unreported signaling relationships. The signaling dynamics analysis indicated that the overexpression of ERK-specific phosphatases sustains proliferative signaling. This suggests a phosphatase-driven mechanism of cancer progression. Moreover, our analysis revealed a drug-resistant mechanism through which overexpression of tyrosine kinases, including SRC, FES, YES1, and BLK, induced MEK-independent ERK activation in melanoma A375 cells. These proteins could predict drug sensitivity to BRAF-MEK concurrent inhibition in cells carrying BRAF mutations.
Assuntos
Carcinogênese/genética , Melanoma/genética , Monoéster Fosfórico Hidrolases/genética , Fosfotransferases/genética , Proteínas Proto-Oncogênicas B-raf/genética , Proliferação de Células/genética , Resistencia a Medicamentos Antineoplásicos/genética , Regulação Neoplásica da Expressão Gênica/efeitos dos fármacos , Células HEK293 , Humanos , Melanoma/enzimologia , Melanoma/patologia , Mutação , Fosforilação/genética , Inibidores de Proteínas Quinases/farmacologia , Proteômica , Transdução de Sinais/efeitos dos fármacosRESUMO
Carvedilol is among the most effective ß-blockers for improving survival after myocardial infarction. Yet the mechanisms by which carvedilol achieves this superior clinical profile are still unclear. Beyond blockade of ß1-adrenoceptors, arrestin-biased signalling via ß2-adrenoceptors is a molecular mechanism proposed to explain the survival benefits. Here, we offer an alternative mechanism to rationalize carvedilol's cellular signalling. Using primary and immortalized cells genome-edited by CRISPR/Cas9 to lack either G proteins or arrestins; and combining biological, biochemical, and signalling assays with molecular dynamics simulations, we demonstrate that G proteins drive all detectable carvedilol signalling through ß2ARs. Because a clear understanding of how drugs act is imperative to data interpretation in basic and clinical research, to the stratification of clinical trials or to the monitoring of drug effects on the target pathway, the mechanistic insight gained here provides a foundation for the rational development of signalling prototypes that target the ß-adrenoceptor system.
Assuntos
Antagonistas Adrenérgicos beta , Infarto do Miocárdio , Humanos , Carvedilol/farmacologia , Antagonistas Adrenérgicos beta/farmacologia , Receptores Adrenérgicos beta 2/genética , Infarto do Miocárdio/tratamento farmacológicoRESUMO
Signaling networks are key regulators of cellular function. Although the concentrations of signaling proteins are perturbed in disease states, such as cancer, and are modulated by drug therapies, our understanding of how such changes shape the properties of signaling networks is limited. Here we couple mass-cytometry-based single-cell analysis with overexpression of tagged signaling proteins to study the dependence of signaling relationships and dynamics on protein node abundance. Focusing on the epidermal growth factor receptor (EGFR) signaling network in HEK293T cells, we analyze 20 signaling proteins during a 1-h EGF stimulation time course using a panel of 35 antibodies. Data analysis with BP-R2, a measure that quantifies complex signaling relationships, reveals abundance-dependent network states and identifies novel signaling relationships. Further, we show that upstream signaling proteins have abundance-dependent effects on downstream signaling dynamics. Our approach elucidates the influence of node abundance on signal transduction networks and will further our understanding of signaling in health and disease.