93 research outputs found

    Oncogenic Gq/11 signaling acutely drives and chronically sustains metabolic reprogramming in uveal melanoma

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    Metabolic reprogramming has been shown to occur in uveal melanoma (UM), the most common intraocular tumor in adults. Mechanisms driving metabolic reprogramming in UM are poorly understood. Elucidation of these mechanisms could inform development of new therapeutic strategies for metastatic UM, which has poor prognosis because existing therapies are ineffective. Here, we determined whether metabolic reprogramming is driven by constitutively active mutant α-subunits of the heterotrimeric G proteins Gq or G11 (Gq/11), the oncogenic drivers in ∼90% of UM patients. Using PET-computed tomography imaging, microphysiometry, and GC/MS, we found that inhibition of oncogenic Gq/11 with the small molecule FR900359 (FR) attenuated glucose uptake by UM cells in vivo and in vitro, blunted glycolysis and mitochondrial respiration in UM cell lines and tumor cells isolated from patients, and reduced levels of several glycolytic and tricarboxylic acid cycle intermediates. FR acutely inhibited glycolysis and respiration and chronically attenuated expression of genes in both metabolic processes. UM therefore differs from other melanomas that exhibit a classic Warburg effect. Metabolic reprogramming in UM cell lines and patient samples involved protein kinase C and extracellular signal-regulated protein kinase 1/2 signaling downstream of oncogenic Gq/11. Chronic administration of FR upregulated expression of genes involved in metabolite scavenging and redox homeostasis, potentially as an adaptive mechanism explaining why FR does not efficiently kill UM tumor cells or regress UM tumor xenografts. These results establish that oncogenic Gq/11 signaling is a crucial driver of metabolic reprogramming in UM and lay a foundation for studies aimed at targeting metabolic reprogramming for therapeutic development

    Uveal melanoma cells use ameboid and mesenchymal mechanisms of cell motility crossing the endothelium

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    Uveal melanomas (UMs) are malignant cancers arising from the pigmented layers of the eye. UM cells spread through the bloodstream, and circulating UM cells are detectable in patients before metastases appear. Extravasation of UM cells is necessary for formation of metastases, and transendothelial migration (TEM) is a key step in extravasation. UM cells execute TEM via a stepwise process involving the actin-based processes of ameboid blebbing and mesenchymal lamellipodial protrusion. UM cancers are driven by oncogenic mutations that activate Gαq/11, and this activates TRIO, a guanine nucleotide exchange factor for RhoA and Rac1. We found that pharmacologic inhibition of Gαq/11 in UM cells reduced TEM. Inhibition of the RhoA pathway blocked amoeboid motility but led to enhanced TEM; in contrast, inhibition of the Rac1 pathway decreased mesenchymal motility and reduced TEM. Inhibition of Arp2/3 complex allowed cells to transmigrate without intercalation, a direct mechanism similar to the one often displayed by immune cells. BAP1-deficient (+/-) UM subclones displayed motility behavior and increased levels of TEM, similar to the effects of RhoA inhibitors. We conclude that RhoA and Rac1 signaling pathways, downstream of oncogenic Gαq/11, combine with pathways regulated by BAP1 to control the motility and transmigration of UM cells

    Simulation of spontaneous G protein activation reveals a new intermediate driving GDP unbinding

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    Activation of heterotrimeric G proteins is a key step in many signaling cascades. However, a complete mechanism for this process, which requires allosteric communication between binding sites that are ~30 Å apart, remains elusive. We construct an atomically detailed model of G protein activation by combining three powerful computational methods: metadynamics, Markov state models (MSMs), and CARDS analysis of correlated motions. We uncover a mechanism that is consistent with a wide variety of structural and biochemical data. Surprisingly, the rate-limiting step for GDP release correlates with tilting rather than translation of the GPCR-binding helix 5. β-Strands 1 - 3 and helix 1 emerge as hubs in the allosteric network that links conformational changes in the GPCR-binding site to disordering of the distal nucleotide-binding site and consequent GDP release. Our approach and insights provide foundations for understanding disease-implicated G protein mutants, illuminating slow events in allosteric networks, and examining unbinding processes with slow off-rates

    Ways of Working in the Interpretive Tradition

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    The kinds of specialised questions that tend to be generated in educational contexts are intimately connected to professional practices, with the aim of understanding the complexities of social, discursive and textual practices within those contexts. This chapter presents an analysis of a range of difficult-to-categorise qualitative work that spans grounded theory, through post-structural analysis to structural linguistics, and which the authors in this section have used to address such complex and contextualised questions. What draws the work together is the notion of interpretation. The social, linguistic and psychological phenomena which form the heart of these questions raises challenges for researchers as they develop interpretive analyses that honours agency, multiplicity and difference. This chapter showcases and analyses the approaches of nine researchers as they undertake this kind of interpretive work. In the process, it also highlights the evolution of research methods, as new ‘emerging’ and continuously expanding forms of educational research driven by an ever-increasing range of educational problems, contexts, and interpretive tools to understand them

    Renal DCE-MRI Model Selection Using Bayesian Probability Theory

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    The goal of this work was to demonstrate the utility of Bayesian probability theory-based model selection for choosing the optimal mathematical model from among 4 competing models of renal dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data. DCE-MRI data were collected on 21 mice with high (n = 7), low (n = 7), or normal (n = 7) renal blood flow (RBF). Model parameters and posterior probabilities of 4 renal DCE-MRI models were estimated using Bayesian-based methods. Models investigated included (1) an empirical model that contained a monoexponential decay (washout) term and a constant offset, (2) an empirical model with a biexponential decay term (empirical/biexponential model), (3) the Patlak–Rutland model, and (4) the 2-compartment kidney model. Joint Bayesian model selection/parameter estimation demonstrated that the empirical/biexponential model was strongly favored for all 3 cohorts, the modeled DCE signals that characterized each of the 3 cohorts were distinctly different, and individual empirical/biexponential model parameter values clearly distinguished cohorts of low and high RBF from one another. The Bayesian methods can be readily extended to a variety of model analyses, making it a versatile and valuable tool for model selection and parameter estimation