Seed weight increases with altitude in the Swiss Alps between related species but not among populations of individual species

Seed weight is a crucial plant life history trait, determining establishment success and dispersal ability. Especially in stressful environments, larger seeds may be selected at the expense of seed number, because larger seeds have a better chance of giving rise to an established offspring. We tested the hypotheses that between related species-pairs and among populations of single species a similar trend for increasing seed weight with increasing altitude should be present. Firstly, we measured seed weights from 29 species-pairs, with one species occurring in lowland areas and a congeneric species from high altitudes. Seeds of the alpine species were 28+/-8% larger than seeds from lowland species (P > 0.01). Compared to the related lowland species, 55% of the alpine species had heavier seeds, 3% (one species) had lighter, and 41% had seeds of approximately equal weight. Secondly, we compared seed weights among populations of four species from different habitats and with different life histories. Seeds from between 11 and 34 populations per species were sampled along altitudinal gradients of 800-1,500 m (ca. 800 m in Scabiosa lucida, ca. 1,000 m in Saxifraga oppositifolia, ca. 1,000 m in Epilobium fleischeri, and ca. 1,500 m in Carex flacca). In all the four species, we found no indication for heavier seeds at higher altitudes. Our results indicate a selection pressure for species with heavier seeds at higher altitude, but the trend does not seem to operate across all cases. Phylogenetic constraints may limit the correlation among altitude and seed weight, operating particularly against selection for larger seed size, the closer populations and species are related to each other

Molecular Phylogeny, Classification and Evolution of Conopeptides

Conopeptides are toxins expressed in the venom duct of cone snails (Conoidea, Conus). These are mostly well-structured peptides and mini-proteins with high potency and selectivity for a broad range of cellular targets. In view of these properties, they are widely used as pharmacological tools and many are candidates for innovative drugs. The conopeptides are primarily classified into superfamilies according to their peptide signal sequence, a classification that is thought to reflect the evolution of the multigenic system. However, this hypothesis has never been thoroughly tested. Here we present a phylogenetic analysis of 1,364 conopeptide signal sequences extracted from GenBank. The results validate the current conopeptide superfamily classification, but also reveal several important new features. The so-called "cysteine-poor” conopeptides are revealed to be closely related to "cysteine-rich” conopeptides; with some of them sharing very similar signal sequences, suggesting that a distinction based on cysteine content and configuration is not phylogenetically relevant and does not reflect the evolutionary history of conopeptides. A given cysteine pattern or pharmacological activity can be found across different superfamilies. Furthermore, a few conopeptides from GenBank do not cluster in any of the known superfamilies, and could represent yet-undefined superfamilies. A clear phylogenetically based classification should help to disentangle the diversity of conopeptides, and could also serve as a rationale to understand the evolution of the toxins in the numerous other species of conoideans and venomous animals at larg

Predicting function from sequence in a large multifunctional toxin family

Venoms contain active substances with highly specific physiological effects and are increasingly being used as sources of novel diagnostic, research and treatment tools for human disease. Experimental characterisation of individual toxin activities is a severe rate-limiting step in the discovery process, and in-silico tools which allow function to be predicted from sequence information are essential. Toxins are typically members of large multifunctional families of structurally similar proteins that can have different biological activities, and minor sequence divergence can have significant consequences. Thus, existing predictive tools tend to have low accuracy. We investigated a classification model based on physico-chemical attributes that can easily be calculated from amino-acid sequences, using over 250 (mostly novel) viperid phospholipase A2 toxins. We also clustered proteins by sequence profiles, and carried out in-vitro tests for four major activities on a selection of isolated novel toxins, or crude venoms known to contain them. The majority of detected activities were consistent with predictions, in contrast to poor performance of a number of tested existing predictive methods. Our results provide a framework for comparison of active sites among different functional sub-groups of toxins that will allow a more targeted approach for identification of potential drug leads in the future

Isolation and antiviral activity of the gymnemic acids

Aus den Blättern von Gymnema sylvestre wurden 4 Gymneasäuren (A, B, C und D) isoliert. Die Antivirusaktivität der Säuren A und B wurde geprüft.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/42450/1/18_2005_Article_BF02152834.pd

Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya

The Mugling–Narayanghat road section falls within the Lesser Himalaya and Siwalik zones of Central Nepal Himalaya and is highly deformed by the presence of numerous faults and folds. Over the years, this road section and its surrounding area have experienced repeated landslide activities. For that reason, landslide susceptibility zonation is essential for roadside slope disaster management and for planning further development activities. The main goal of this study was to investigate the application of the frequency ratio (FR), statistical index (SI), and weights-of-evidence (WoE) approaches for landslide susceptibility mapping of this road section and its surrounding area. For this purpose, the input layers of the landslide conditioning factors were prepared in the first stage. A landslide inventory map was prepared using earlier reports, aerial photographs interpretation, and multiple field surveys. A total of 438 landslide locations were detected. Out these, 295 (67 %) landslides were randomly selected as training data for the modeling using FR, SI, and WoE models and the remaining 143 (33 %) were used for the validation purposes. The landslide conditioning factors considered for the study area are slope gradient, slope aspect, plan curvature, altitude, stream power index, topographic wetness index, lithology, land use, distance from faults, distance from rivers, and distance from highway. The results were validated using area under the curve (AUC) analysis. From the analysis, it is seen that the FR model with a success rate of 76.8 % and predictive accuracy of 75.4 % performs better than WoE (success rate, 75.6 %; predictive accuracy, 74.9 %) and SI (success rate, 75.5 %; predictive accuracy, 74.6 %) models. Overall, all the models showed almost similar results. The resultant susceptibility maps can be useful for general land use planning