Adrenergic receptor stimulation of the mitogen-activated protein kinase cascade and cardiac hypertrophy.

Phenylephrine and noradrenaline (alpha-adrenergic agonism) or isoprenaline (beta-adrenergic agonism) stimulated protein synthesis rates, increased the activity of the atrial natriuretic factor gene promoter and activated mitogen-activated protein kinase (MAPK). The EC50 for MAPK activation by noradrenaline was 2-4 microM and that for isoprenaline was 0.2-0.3 microM. Maximal activation of MAPK by isoprenaline was inhibited by the beta-adrenergic antagonist, propranolol, whereas the activation by noradrenaline was inhibited by the alpha1-adrenergic antagonist, prazosin. FPLC on a Mono-Q column separated two peaks of MAPK (p42MAPK and p44MAPK) and two peaks of MAPK-activating activity (MEK) activated by isoprenaline or noradrenaline. Prolonged phorbol ester exposure partially down-regulated the activation of MAPK by noradrenaline but not by isoprenaline. This implies a role for protein kinase C in MAPK activation by noradrenaline but not isoprenaline. A role for cyclic AMP in activation of the MAPK pathway was eliminated when other agonists that elevate cyclic AMP in the cardiac myocyte did not activate MAPK. In contrast, MAPK was activated by exposure to ionomycin, Bay K8644 or thapsigargin that elevate intracellular Ca2+. Furthermore, depletion of extracellular Ca2+ concentrations with bis-(o-aminophenoxy)ethane-NNN'N'-tetra-acetic acid (BAPTA) or blocking of the L-type Ca2+ channel with nifepidine or verapamil inhibited the response to isoprenaline without inhibiting the responses to noradrenaline. We conclude that alpha- and beta-adrenergic agonists can activate the MEK/MAPK pathway in the heart by different signalling pathways. Elevation of intracellular Ca2+ rather than cyclic AMP appears important in the activation of MAPK by isoprenaline in the cardiac myocyte.

The Hugh Sinclair Unit of Human Nutrition – 20 years of research 1995–2015

The Hugh Sinclair Unit of Human Nutrition (HSUHN) at the University of Reading was founded in October 1995 with the appointment of Christine Williams OBE as the first Hugh Sinclair Chair in Human Nutrition. This was made possible by the competitively won funds from the estate and legacy of the late Professor Hugh Macdonald Sinclair (1910–1990). The vision for the newly established HSUHN was to ‘strengthen the evidence base for dietary recommendations for prevention of degenerative chronic diseases’. This has remained the research focus of the HSUHN under the leadership of Professors Christine Williams (1995–2005), Ian Rowland (2006–2013) and Julie Lovegrove (2014-present). Our mission is to improve population health and evaluate mechanisms of action for the effects of dietary components on health, which reflects Hugh Sinclair’s life ambition within nutritional science. Over the past 20 years, the HSUHN has developed an international reputation within the nutrition science community, and in recognition of the 20th anniversary, this paper highlights Hugh Sinclair’s contributions to the field of nutrition and key research achievements by members of the Unit.

Bayesian analysis of uncertainty in the GlobCover 2009 land cover product at climate model grid scale

Land cover data derived from satellites are commonly used to prescribe inputs to models of the land surface. Since such data inevitably contains errors, quantifying how uncertainties in the data affect a model’s output is important. To do so, a spatial distribution of possible land cover values is required to propagate through the model’s simulation. However, at large scales, such as those required for climate models, such spatial modelling can be difficult. Also, computer models often require land cover proportions at sites larger than the original map scale as inputs, and it is the uncertainty in these proportions that this article discusses. This paper describes a Monte Carlo sampling scheme that generates realisations of land cover proportions from the posterior distribution as implied by a Bayesian analysis that combines spatial information in the land cover map and its associated confusion matrix. The technique is computationally simple and has been applied previously to the Land Cover Map 2000 for the region of England and Wales. This article demonstrates the ability of the technique to scale up to large (global) satellite derived land cover maps and reports its application to the GlobCover 2009 data product. The results show that, in general, the GlobCover data possesses only small biases, with the largest belonging to non–vegetated surfaces. In vegetated surfaces, the most prominent area of uncertainty is Southern Africa, which represents a complex heterogeneous landscape. It is also clear from this study that greater resources need to be devoted to the construction of comprehensive confusion matrices.