Use of the frc gene as a molecular marker to characterize oxalate-oxidizing bacterial abundance and diversity structure in soil.

Oxalate catabolism, which can have both medical and environmental implications, is performed by phylogenetically diverse bacteria. The formyl-CoA-transferase gene was chosen as a molecular marker of the oxalotrophic function. Degenerated primers were deduced from an alignment of frc gene sequences available in databases. The specificity of primers was tested on a variety of frc-containing and frc-lacking bacteria. The frc-primers were then used to develop PCR-DGGE and real-time SybrGreen PCR assays in soils containing various amounts of oxalate. Some PCR products from pure cultures and from soil samples were cloned and sequenced. Data were used to generate a phylogenetic tree showing that environmental PCR products belonged to the target physiological group. The extent of diversity visualised on DGGE pattern was higher for soil samples containing carbonate resulting from oxalate catabolism. Moreover, the amount of frc gene copies in the investigated soils was detected in the range of 1.64x10(7) to 1.75x10(8)/g of dry soil under oxalogenic tree (representing 0.5 to 1.2% of total 16S rRNA gene copies), whereas the number of frc gene copies in the reference soil was 6.4x10(6) (or 0.2% of 16S rRNA gene copies). This indicates that oxalotrophic bacteria are numerous and widespread in soils and that a relationship exists between the presence of the oxalogenic trees Milicia excelsa and Afzelia africana and the relative abundance of oxalotrophic guilds in the total bacterial communities. This is obviously related to the accomplishment of the oxalate-carbonate pathway, which explains the alkalinization and calcium carbonate accumulation occurring below these trees in an otherwise acidic soil. The molecular tools developed in this study will allow in-depth understanding of the functional implication of these bacteria on carbonate accumulation as a way of atmospheric CO(2) sequestration.

Landscape structure affects dispersal in the greater white-toothed shrew: inference between genetic and simulated ecological distances

Animal dispersal in a fragmented landscape depends on the complex interaction between landscape structure and animal behavior. To better understand how individuals disperse, it is important to explicitly represent the properties of organisms and the landscape in which they move. A common approach to modelling dispersal includes representing the landscape as a grid of equal sized cells and then simulating individual movement as a correlated random walk. This approach uses a priori scale of resolution, which limits the representation of all landscape features and how different dispersal abilities are modelled. We develop a vector-based landscape model coupled with an object-oriented model for animal dispersal. In this spatially explicit dispersal model, landscape features are defined based on their geographic and thematic properties and dispersal is modelled through consideration of an organism's behavior, movement rules and searching strategies (such as visual cues). We present the model's underlying concepts, its ability to adequately represent landscape features and provide simulation of dispersal according to different dispersal abilities. We demonstrate the potential of the model by simulating two virtual species in a real Swiss landscape. This illustrates the model's ability to simulate complex dispersal processes and provides information about dispersal such as colonization probability and spatial distribution of the organism's path

Inferring epidemic contact structure from phylogenetic trees.

Contact structure is believed to have a large impact on epidemic spreading and consequently using networks to model such contact structure continues to gain interest in epidemiology. However, detailed knowledge of the exact contact structure underlying real epidemics is limited. Here we address the question whether the structure of the contact network leaves a detectable genetic fingerprint in the pathogen population. To this end we compare phylogenies generated by disease outbreaks in simulated populations with different types of contact networks. We find that the shape of these phylogenies strongly depends on contact structure. In particular, measures of tree imbalance allow us to quantify to what extent the contact structure underlying an epidemic deviates from a null model contact network and illustrate this in the case of random mixing. Using a phylogeny from the Swiss HIV epidemic, we show that this epidemic has a significantly more unbalanced tree than would be expected from random mixing.