Primordia Vita. Deconvolution from Modern Sequences.

Evolution of the triplet code is reconstructed on the basis of consensus temporal order of appearance of amino acids. Several important predictions are confirmed by computational sequence analyses. The earliest amino acids, alanine and glycine, have been encoded by GCC and GGC codons, as today. They were succeeded, respectively, by A- and G-series of amino acids, encoded by pyrimidine-central and purine-central codons. The length of the earliest proteins is estimated to be 6–7 residues. The earliest mRNAs were short G+C-rich molecules. These short sequences could have formed hairpins. This is confirmed by analysis of modern prokaryotic mRNA sequences. Predominant size of detected ancient hairpins also corresponds to 6–7 amino acids, as above. Vestiges of last common ancestor can be found in extant proteins in form of entirely conserved short sequences of size six to nine residues present in all or almost all sequenced prokaryotic proteomes (omnipresent motifs). The functions of the topmost conserved octamers are not involved in the basic elementary syntheses. This suggests an initial abiotic supply of amino acids, bases and sugars. Presented at: National Workshop on Astrobiology: Search for Life in the Solar System, Capri, Italy, 26 to 28 October, 2005.     

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.