Transforming Growth Factor-β1 Decreases β2-Agonist–induced Relaxation in Human Airway Smooth Muscle

Helper T effector cytokines implicated in asthma modulate the contractility of human airway smooth muscle (HASM) cells. We have reported recently that a profibrotic cytokine, transforming growth factor (TGF)-β1, induces HASM cell shortening and airway hyperresponsiveness. Here, we assessed whether TGF-β1 affects the ability of HASM cells to relax in response to β2-agonists, a mainstay treatment for airway hyperresponsiveness in asthma. Overnight TGF-β1 treatment significantly impaired isoproterenol (ISO)-induced relaxation of carbachol-stimulated, isolated HASM cells. This single-cell mechanical hyporesponsiveness to ISO was corroborated by sustained increases in myosin light chain phosphorylation. In TGF-β1–treated HASM cells, ISO evoked markedly lower levels of intracellular cAMP. These attenuated cAMP levels were, in turn, restored with pharmacological and siRNA inhibition of phosphodiesterase 4 and Smad3, respectively. Most strikingly, TGF-β1 selectively induced phosphodiesterase 4D gene expression in HASM cells in a Smad2/3-dependent manner. Together, these data suggest that TGF-β1 decreases HASM cell β2-agonist relaxation responses by modulating intracellular cAMP levels via a Smad2/3-dependent mechanism. Our findings further define the mechanisms underlying β2-agonist hyporesponsiveness in asthma, and suggest TGF-β1 as a potential therapeutic target to decrease asthma exacerbations in severe and treatment-resistant asthma

Accelerating Live Single-Cell Signalling Studies.

The dynamics of signalling networks that couple environmental conditions with cellular behaviour can now be characterised in exquisite detail using live single-cell imaging experiments. Recent improvements in our abilities to introduce fluorescent sensors into cells, coupled with advances in pipelines for quantifying and extracting single-cell data, mean that high-throughput systematic analyses of signalling dynamics are becoming possible. In this review, we consider current technologies that are driving progress in the scale and range of such studies. Moreover, we discuss novel approaches that are allowing us to explore how pathways respond to changes in inputs and even predict the fate of a cell based upon its signalling history and state