Molecular Mechanism for Human Sperm Chemotaxis Mediated by Progesterone

Sperm chemotaxis is a chemical guiding mechanism that may orient spermatozoa to the egg surface. A picomolar concentration gradient of Progesterone (P), the main steroidal component secreted by the cumulus cells that surround the egg, attracts human spermatozoa. In order to elucidate the molecular mechanism of sperm chemotaxis mediated by P, we combine the application of different strategies: pharmacological inhibition of signaling molecules, measurements of the concentrations of second messengers and activation of the chemotactic signaling. Our data implicate a number of classic signal transduction pathways in the response and provide a model for the sequence of events, where the tmAC-cAMP-PKA pathway is activated first, followed by protein tyrosine phosphorylation (equatorial band and flagellum) and calcium mobilization (through IP₃R and SOC channels), whereas the sGC-cGMP-PKG cascade, is activated later. These events lead to sperm orientation towards the source of the chemoattractant. The finding proposes a molecular mechanism which contributes to the understanding of the signal transduction pathway that takes place in a physiological process as chemotaxis.     

A LIN28-dependent structural change in pre-let-7g directly inhibits dicer processing

Several recent studies have provided evidence that LIN28, a cytoplasmic RNA-binding protein, inhibits the biogenesis of members of the let-7 microRNA family at the Dicer step in both mammals and Caenorhabditis elegans. However, the precise mechanism of inhibition is still poorly understood. Here we report on an in vitro study, which combined RNase footprinting, gel shift binding assays, and processing assays, to investigate the molecular basis and function of the interaction between the native let-7g precursor (pre-let-7g) and LIN28. We have mapped the structure of pre-let-7g and identified some regions of the terminal loop of pre-let-7g that physically interact with LIN28. We have also identified a conformational change upon LIN28 binding that results in the unwinding of an otherwise double-stranded region at the Dicer processing site of pre-let-7g. Furthermore, we showed that a mutant pre-let-7g that displays an open upper stem inhibited pre-let-7g Dicer processing to the same extent as LIN28. The data support a mechanism by which LIN28 can directly inhibit let-7g biogenesis at the Dicer processing step.     

Climate change and the future distributions of aquatic macrophytes across boreal catchments

Aim Aquatic–terrestrial ecotones are vulnerable to climate change, and degradation of the emergent aquatic macrophyte zone would have severe ecological consequences for freshwater, wetland and terrestrial ecosystems. Our aim was to uncover future changes in boreal emergent aquatic macrophyte zones by modelling the occurrence and percentage cover of emergent aquatic vegetation under different climate scenarios in Finland by the 2050s. Location Finland, northern Europe. Methods Data derived from different GIS sources were used to estimate future emergent aquatic macrophyte distributions in all catchments in Finland (848 in total). We used generalized additive models (GAM) with a full stepwise selection algorithm and Akaike information criterion to explore the main environmental determinates (climate and geomorphology) of emergent aquatic macrophyte distributions, which were derived from the national subclass of CORINE land‐cover classification. The accuracy of the distribution models (GAMs) was cross‐validated, using percentage of explained deviance and the area under the curve derived from the receiver‐operating characteristic plots. Results Our results indicated that emergent aquatic macrophytes will expand their distributions northwards from the current catchments and percentage cover will increase in all of the catchments in all climate scenarios. Growing degree‐days was the primary determinant affecting distributions of emergent aquatic macrophytes. Inclusion of geomorphological variables clearly improved model performance in both model exercises compared with pure climate variables. Main conclusions Emergent aquatic macrophyte distributions will expand due to climate change. Many emergent aquatic plant species have already expanded their distributions during the past decades, and this process will continue in the years 2051–80. Emergent aquatic macrophytes pose an increasing overgrowth risk for sensitive macrophyte species in boreal freshwater ecosystems, which should be acknowledged in management and conservation actions. We conclude that predictions based on GIS data can provide useful ‘first‐filter’ estimates of changes in aquatic–terrestrial ecotones.     

Stream diatom assemblages as predictors of climate

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Geomorphological disturbance is necessary for predicting fine‐scale species distributions

Disturbances related to geomorphological processes are frequent, widespread and often intense at high latitudes and altitudes, affecting the fine‐scale distribution of many plant species. While the inclusion of physical disturbances into models of species geographic ranges is widely recommended, no studies have yet tested the utility of field‐quantified geomorphological disturbances for terrestrial species distribution modelling. Here we apply generalized additive models and boosted regression trees to examine if the explicit inclusion of terrestrial and fluvial geomorphological variables alters species distribution models for 154 vascular plant, bryophyte and lichen species in north European mountain tundra. The inclusion of these disturbances significantly improved both the explanatory and predictive power of distribution models, with consistent results for all three species groups. Spatial distribution predictions changed considerably for some species after the inclusion of disturbance variables, with fluvial disturbances generating strongly linear features for species influenced by erosion or sediment deposition. As a consequence, models incorporating geomorphological variables produced markedly more refined distribution maps than simpler models. Predictions of species distributions will thus benefit strongly from the inclusion of fine‐scale geomorphological variables, particularly in areas of active earth surface processes, enabling more accurate forecasting of future species ranges under changing conditions.     

Does the interpolation accuracy of species distribution models come at the expense of transferability?

Model transferability (extrapolative accuracy) is one important feature in species distribution models, required in several ecological and conservation biological applications. This study uses 10 modelling techniques and nationwide data on both (1) species distribution of birds, butterflies, and plants and (2) climate and land cover in Finland to investigate whether good interpolative prediction accuracy for models comes at the expense of transferability – i.e. markedly worse performance in new areas. Models’ interpolation and extrapolation performance was primarily assessed using AUC (the area under the curve of a receiver characteristic plot) and Kappa statistics, with supplementary comparisons examining model sensitivity and specificity values. Our AUC and Kappa results show that extrapolation to new areas is a greater challenge for all included modelling techniques than simple filling of gaps in a well‐sampled area, but there are also differences among the techniques in the degree of transferability. Among the machine‐learning modelling techniques, MAXENT, generalized boosting methods (GBM), and artificial neural networks (ANN) showed good transferability while the performance of GARP and random forest (RF) decreased notably in extrapolation. Among the regression‐based methods, generalized additive models (GAM) and generalized linear models (GLM) showed good transferability. A desirable combination of good prediction accuracy and good transferability was evident for three modelling techniques: MAXENT, GBM, and GAM. However, examination of model sensitivity and specificity revealed that model types may differ in their tendencies to either increased over‐prediction of presences or absences in extrapolation, and some of the methods show contrasting changes in sensitivity vs specificity (e.g. ANN and GARP). Among the three species groups, the best transferability was seen with birds, followed closely by butterflies, whereas reliable extrapolation for plant species distribution models appears to be a major challenge at least at this scale. Overall, detailed knowledge of the behaviour of different techniques in various study settings and with different species groups is of utmost importance in predictive modelling.     

Iron and iron/manganese ratio in forage from Icelandic sheep farms: relation to scrapie

This study was undertaken in order to examine whether any connection existed between the amounts of iron in forage and the sporadic occurrence of scrapie observed in certain parts of Iceland. As iron and manganese are considered antagonistic in plants, calculation of the Fe/Mn ratios was also included by using results from Mn determination earlier performed in the same samples. Forage samples (n = 170) from the summer harvests of 2001–2003, were collected from 47 farms for iron and manganese analysis. The farms were divided into four categories: 1. Scrapie-free farms in scrapie-free areas (n = 9); 2. Scrapie-free farms in scrapie-afflicted areas (n = 17); 3. Scrapie-prone farms (earlier scrapie-afflicted, restocked farms) (n = 12); 4. Scrapie-afflicted farms (n = 9). Farms in categories 1 and 2 are collectively referred to as scrapie-free farms. The mean iron concentration in forage samples from scrapie-afflicted farms was significantly higher than in forage samples from farms in the other scrapie categories (P = 0.001). The mean Fe/Mn ratio in forage from scrapie-afflicted farms was significantly higher than in forage from scrapie-free and scrapie-prone farms (P < 0.001). The results indicated relative dominance of iron over manganese in forage from scrapie-afflicted farms as compared to farms in the other categories. Thus thorough knowledge of iron, along with manganese, in soil and vegetation on sheep farms could be a pivot in studies on sporadic scrapie.