Guanylate Cyclase-Activating Protein-2 Undergoes Structural Changes upon Binding to Detergent Micelles and Bicelles

GCAPs are neuronal Ca^2 +-sensors playing a central role in light adaptation. GCAPs are N-terminally myristoylated membrane-associated proteins. Although, the myristoylation of GCAPs plays an important role in light adaptation its structural and physiological roles are not yet clearly understood. The crystal-structure of GCAP-1 shows the myristoyl moiety inside the hydrophobic core of the protein, stabilizing the protein structure; but ²H-solid-state NMR investigations on the deuterated myristoyl moiety of GCAP-2 in the presence of liposomes showed that it is inserted into the lipid bilayer. In this study, we address the question of the localization of the myristoyl group of Ca^2 +-bound GCAP-2, and the influence of CHAPS-, DPC-micelles and DMPC/DHPC-bicelles on the structure, and on the localization of the myristoyl group, of GCAP-2 by solution-state NMR. We also carried out the backbone assignment. Characteristic chemical shift differences have been observed between the myristoylated and the non-myristoylated forms of the protein. Our results support the view that in the absence of membrane forming substances the myristoyl moiety is buried inside a hydrophobic pocket of GCAP-2 similar to the crystal structure of GCAP-1. Addition of CHAPS-micelles and DMPC/DHPC-bicelles cause specific structural changes localized in and around the myristoyl binding pocket. We interpret these changes as an indication for the extrusion of the myristoyl moiety from its binding pocket and its insertion into the hydrophobic interior of the membrane mimic. On the basis of the backbone chemical shifts, we propose a structural model of myristoylated GCAP-2 in the presence of Ca^2 + and membrane mimetics.     

Upscaling leaf area index in an Arctic landscape through multiscale observations

Monitoring and understanding global change requires a detailed focus on upscaling, the process for extrapolating from the site‐specific scale to the smallest scale resolved in regional or global models or earth observing systems. Leaf area index (LAI) is one of the most sensitive determinants of plant production and can vary by an order of magnitude over short distances. The landscape distribution of LAI is generally determined by remote sensing of surface reflectance (e.g. normalized difference vegetation index, NDVI) but the mismatch in scales between ground and satellite measurements complicates LAI upscaling. Here, we describe a series of measurements to quantify the spatial distribution of LAI in a sub‐Arctic landscape and then describe the upscaling process and its associated errors. Working from a fine‐scale harvest LAI–NDVI relationship, we collected NDVI data over a 500 m × 500 m catchment in the Swedish Arctic, at resolutions from 0.2 to 9.0 m in a nested sampling design. NDVI scaled linearly, so that NDVI at any scale was a simple average of multiple NDVI measurements taken at finer scales. The LAI–NDVI relationship was scale invariant from 1.5 to 9.0 m resolution. Thus, a single exponential LAI–NDVI relationship was valid at all these scales, with similar prediction errors. Vegetation patches were of a scale of ～0.5 m and at measurement scales coarser than this, there was a sharp drop in LAI variance. Landsat NDVI data for the study catchment correlated significantly, but poorly, with ground‐based measurements. A variety of techniques were used to construct LAI maps, including interpolation by inverse distance weighting, ordinary Kriging, External Drift Kriging using Landsat data, and direct estimation from a Landsat NDVI–LAI calibration. All methods produced similar LAI estimates and overall errors. However, Kriging approaches also generated maps of LAI estimation error based on semivariograms. The spatial variability of this Arctic landscape was such that local measurements assimilated by Kriging approaches had a limited spatial influence. Over scales >50 m, interpolation error was of similar magnitude to the error in the Landsat NDVI calibration. The characterisation of LAI spatial error in this study is a key step towards developing spatio‐temporal data assimilation systems for assessing C cycling in terrestrial ecosystems by combining models with field and remotely sensed data.     

Phenology model from surface meteorology does not capture satellite-based greenup estimations

Seasonal temperature change in temperate forests is known to trigger the start of spring growth, and both interannual and spatial variations in spring onset have been tied to climatic variability. Satellite dates are increasingly being used in phenology studies, but to date that has been little effort to link remotely sensed phenology to surface climate records. In this research, we use a two‐parameter spring warming phenology model to explore the relationship between climate and satellite‐based phenology. We employ daily air temperature records between 2000 and 2005 for 171 National Oceanographic and Atmospheric Administration weather stations located throughout New England to construct spring warming models predicting the onset of spring, as defined by the date of half‐maximum greenness (D₅₀) in deciduous forests as detected from Moderate Resolution Imaging Spectrometer. The best spring warming model starts accumulating temperatures after March 20th and when average daily temperatures exceed 5°C. The accumulated heat sums [heating degree day (HDD)] required to reach D₅₀ range from 150 to 300 degree days over New England, with the highest requirements to the south and in coastal regions. We test the ability of the spring warming model to predict phenology against a null photoperiod model (average date of onset). The spring warming model offers little improvement on the null model when predicting D₅₀. Differences between the efficacies of the two models are expressed as the ‘climate sensitivity ratio’ (CSR), which displays coherent spatial patterns. Our results suggest that northern (beech‐maple‐birch) and central (oak‐hickory) hardwood forests respond to climate differently, particularly with disparate requirements for the minimum temperature necessary to begin spring growth (3 and 6°C, respectively). We conclude that spatial location and species composition are critical factors for predicting the phenological response to climate change: satellite observations cannot be linked directly to temperature variability if species or community compositions are unknown.