BAPS 2: enhanced possibilities for the analysis of genetic population structure

Bayesian statistical methods based on simulation techniques have recently been shown to provide powerful tools for the analysis of genetic population structure. We have previously developed a Markov chain Monte Carlo (MCMC) algorithm for characterizing genetically divergent groups based on molecular markers and geographical sampling design of the dataset. However, for large-scale datasets such algorithms may get stuck to local maxima in the parameter space. Therefore, we have modified our earlier algorithm to support multiple parallel MCMC chains, with enhanced features that enable considerably faster and more reliable estimation compared to the earlier version of the algorithm. We consider also a hierarchical tree representation, from which a Bayesian model-averaged structure estimate can be extracted. The algorithm is implemented in a computer program that features a user-friendly interface and built-in graphics. The enhanced features are illustrated by analyses of simulated data and an extensive human molecular dataset. AVAILABILITY: Freely available at http://www.rni.helsinki.fi/~jic/bapspage.html.     

Allocation of resources within mountain birch canopy after simulated winter browsing

As a response to browsing, birches are known to produce fewer but larger, more nutritious leaves, with enhanced palatability for herbivores. We simulated winter browsing in ramets of mountain birch ( Betula pubescens ssp. czerepanovii ) to find out whether it decreases subsequent foliage biomass and alters the number and type of shoots. After removal of a considerable proportion of buds (up to 35%) in late winter, the birches were able to compensate for the lost leaf biomass in the following summer; there were no differences in total leaf biomass between winter-clipped and control ramets. This indicates that foliage growth was limited by the total amount of stored resources, not by the number of buds. Depending on the position of the buds removed, different mechanisms were responsible for the compensation. After removal of apical buds, the number of leaves decreased significantly but leaves were larger than in control ramets. Removal of the same mass of basal buds – containing similar amount of carbohydrates and proteins as in the treatment removing apical buds – activated dormant buds, especially in apical locations, so that leaf number was similar as in the controls; consequently, size of individual leaves increased only slightly. Thus, while the total leaf biomass in a tree seems to be limited by resources from source organs, the distribution of resources among different canopy sections is controlled by their relative sink strengths. In terms of leaf biomass, apical parts are able to compensate for bud loss by increasing shoot number, basal parts only by increasing leaf size.     

Mammalian twinfilin sequesters ADP-G-actin and caps filament barbed ends: implications in motility

Twinfilins are conserved actin-binding proteins composed of two actin depolymerizing factor homology (ADF-H) domains. Twinfilins are involved in diverse morphological and motile processes, but their mechanism of action has not been elucidated. Here, we show that mammalian twinfilin both sequesters ADP-G-actin and caps filament barbed ends with preferential affinity for ADP-bound ends. Twinfilin replaces capping protein and promotes motility of N-WASP functionalized beads in a biomimetic motility assay, indicating that the capping activity supports twinfilin's function in motility. Consistently, in vivo twinfilin localizes to actin tails of propelling endosomes. The ADP-actin-sequestering activity cooperates with the filament capping activity of twinfilin to finely regulate motility due to processive filament assembly catalyzed by formin-functionalized beads. The isolated ADF-H domains do not cap barbed ends nor promote motility, but sequester ADP-actin, the C-terminal domain showing the highest affinity. A structural model for binding of twinfilin to barbed ends is proposed based on the similar foldings of twinfilin ADF-H domains and gelsolin segments.     

Bayesian search of functionally divergent protein subgroups and their function specific residues

MOTIVATION: The rapid increase in the amount of protein sequence data has created a need for an automated identification of evolutionarily related subgroups from large datasets. The existing methods typically require a priori specification of the number of putative groups, which defines the resolution of the classification solution. RESULTS: We introduce a Bayesian model-based approach to simultaneous identification of evolutionary groups and conserved parts of the protein sequences. The model-based approach provides an intuitive and efficient way of determining the number of groups from the sequence data, in contrast to the ad hoc methods often exploited for similar purposes. Our model recognizes the areas in the sequences that are relevant for the clustering and regards other areas as noise. We have implemented the method using a fast stochastic optimization algorithm which yields a clustering associated with the estimated maximum posterior probability. The method has been shown to have high specificity and sensitivity in simulated and real clustering tasks. With real datasets the method also highlights the residues close to the active site. AVAILABILITY: Software 'kPax' is available at http://www.rni.helsinki.fi/jic/softa.html