Using U0126 to dissect the role of the extracellular signal-regulated kinase 1/2 (ERK1/2) cascade in the regulation of gene expression by endothelin-1 in cardiac myocytes

The hypertrophic agonist endothelin-1 rapidly but transiently activates the extracellular signal-regulated kinase 1/2 (ERK1/2) cascade (and other signalling pathways) in cardiac myocytes, but the events linking this to hypertrophy are not understood. Using Affymetrix rat U34A microarrays, we identified the short-term (2-4 h) changes in gene expression induced in neonatal myocytes by endothelin-1 alone or in combination with the ERK1/2 cascade inhibitor, U0126. Expression of 15 genes was significantly changed by U0126 alone, and expression of an additional 78 genes was significantly changed by endothelin-1. Of the genes upregulated by U0126, four are classically induced through the aryl hydrocarbon receptor (AhR) by dioxins suggesting that U0126 activates the xenobiotic response element in cardiac myocytes potentially independently of effects on ERK1/2 signalling. The 78 genes showing altered expression with endothelin-1 formed five clusters: (i) three clusters showing upregulation by endothelin-1 according to time course (4 h > 2 h; 2 h > 4 h; 2 h approximately 4 h) with at least partial inhibition by U0126; (ii) a cluster of 11 genes upregulated by endothelin-1 but unaffected by U0126 suggesting regulation through signalling pathways other than ERK1/2; (iii) a cluster of six genes downregulated by endothelin-1 with attenuation by U0126. Thus, U0126 apparently activates the AhR in cardiac myocytes (which must be taken into account in protracted studies), but careful analysis allows identification of genes potentially regulated acutely via the ERK1/2 cascade. Our data suggest that the majority of changes in gene expression induced by endothelin-1 are mediated by the ERK1/2 cascade.

Seasonal surface urban energy balance and wintertime stability simulated using three land-surface models in the high-latitude city Helsinki

The performance of three urban land surface models, run in offline mode, with their default external parameters, is evaluated for two distinctly different sites in Helsinki: Torni and Kumpula. The former is a dense city centre site with 22% vegetation, while the latter is a suburban site with over 50% vegetation. At both locations the models are compared against sensible and latent heat fluxes measured using the eddy covariance technique, along with snow depth observations. The cold climate experienced by the city causes strong seasonal variations that include snow cover and stable atmospheric conditions. Most of the time the three models are able to account for the differences between the study areas as well as the seasonal and diurnal variability of the energy balance components. However, the performances are not systematic across the modelled components, season and surface type. The net all-wave radiation is well simulated, with the greatest uncertainties related to snowmelt timing, when the fraction of snow cover has a key role, particularly in determining the surface albedo. For the turbulent fluxes, more variation between the models is seen which can partly be explained by the different methods in their calculation and partly by surface parameter values. For the sensible heat flux, simulation of wintertime values was the main problem, which also leads to issues in predicting near-surface stabilities particularly at the dense city centre site. All models have the most difficulties in simulating latent heat flux. This study particularly emphasizes that improvements are needed in the parameterization of anthropogenic heat flux and thermal parameters in winter, snow cover in spring and evapotranspiration in order to improve the surface energy balance modelling in cold climate cities.

Anticipating future risk in social-ecological systems using fuzzy cognitive mapping: the case of wildfire in the Chiquitania, Bolivia

Understanding complex social-ecological systems, and anticipating how they may respond to rapid change, requires an approach that incorporates environmental, social, economic, and policy factors, usually in a context of fragmented data availability. We employed fuzzy cognitive mapping (FCM) to integrate these factors in the assessment of future wildfire risk in the Chiquitania region, Bolivia. In this region, dealing with wildfires is becoming increasingly challenging due to reinforcing feedbacks between multiple drivers. We conducted semi-structured interviews and constructed different FCMs in focus groups to understand the regional dynamics of wildfire from diverse perspectives. We used FCM modelling to evaluate possible adaptation scenarios in the context of future drier climatic conditions. Scenarios also considered possible failure to respond in time to the emergent risk. This approach proved of great potential to support decision-making for risk management. It helped identify key forcing variables and generate insights into potential risks and trade-offs of different strategies. All scenarios showed increased wildfire risk in the event of more droughts. The ‘Hands-off’ scenario resulted in amplified impacts driven by intensifying trends, affecting particularly the agricultural production. The ‘Fire management’ scenario, which adopted a bottom-up approach to improve controlled burning, showed less trade-offs between wildfire risk reduction and production compared to the ‘Fire suppression’ scenario. Findings highlighted the importance of considering strategies that involve all actors who use fire, and the need to nest these strategies for a more systemic approach to manage wildfire risk. The FCM model could be used as a decision-support tool and serve as a ‘boundary object’ to facilitate collaboration and integration of different forms of knowledge and perceptions of fire in the region. This approach has also the potential to support decisions in other dynamic frontier landscapes around the world that are facing increased risk of large wildfires.