4 resultados para 82034 G-11-82-SE

em Duke University


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Classically, 7 transmembrane receptors transduce extracellular signals by coupling to heterotrimeric G proteins, although recent in vitro studies have clearly demonstrated that they can also signal via G protein-independent mechanisms. However, the physiologic consequences of this unconventional signaling, particularly in vivo, have not been explored. In this issue of the JCI, Zhai et al. demonstrate in vivo effects of G protein-independent signaling by the angiotensin II type 1 receptor (AT1R) (see the related article beginning on page 3045). In studies of the mouse heart, they compare the physiologic and biochemical consequences of transgenic cardiac-specific overexpression of a mutant AT1R incapable of G protein coupling with those of a wild-type receptor. Their results not only provide the first glimpse of the physiologic effects of this newly appreciated mode of signaling but also provide important and previously unappreciated clues as to the underlying molecular mechanisms.

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G protein-coupled receptor activation leads to the membrane recruitment and activation of G protein-coupled receptor kinases, which phosphorylate receptors and lead to their inactivation. We have identified a novel G protein-coupled receptor kinase-interacting protein, GIT1, that is a GTPase-activating protein (GAP) for the ADP ribosylation factor (ARF) family of small GTP-binding proteins. Overexpression of GIT1 leads to reduced beta2-adrenergic receptor signaling and increased receptor phosphorylation, which result from reduced receptor internalization and resensitization. These cellular effects of GIT1 require its intact ARF GAP activity and do not reflect regulation of GRK kinase activity. These results suggest an essential role for ARF proteins in regulating beta2-adrenergic receptor endocytosis. Moreover, they provide a mechanism for integration of receptor activation and endocytosis through regulation of ARF protein activation by GRK-mediated recruitment of the GIT1 ARF GAP to the plasma membrane.

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© 2015 IEEE.Although definition of single-program benchmarks is relatively straight-forward-a benchmark is a program plus a specific input-definition of multi-program benchmarks is more complex. Each program may have a different runtime and they may have different interactions depending on how they align with each other. While prior work has focused on sampling multiprogram benchmarks, little attention has been paid to defining the benchmarks in their entirety. In this work, we propose a four-tuple that formally defines multi-program benchmarks in a well-defined way. We then examine how four different classes of benchmarks created by varying the elements of this tuple align with real-world use-cases. We evaluate the impact of these variations on real hardware, and see drastic variations in results between different benchmarks constructed from the same programs. Notable differences include significant speedups versus slowdowns (e.g., +57% vs -5% or +26% vs -18%), and large differences in magnitude even when the results are in the same direction (e.g., 67% versus 11%).