Identification of regulatory variants associated with genetic susceptibility to meningococcal disease

Non-coding genetic variants play an important role in driving susceptibility to complex diseases but their characterization remains challenging. Here, we employed a novel approach to interrogate the genetic risk of such polymorphisms in a more systematic way by targeting specific regulatory regions relevant for the phenotype studied. We applied this method to meningococcal disease susceptibility, using the DNA binding pattern of RELA - a NF-kB subunit, master regulator of the response to infection - under bacterial stimuli in nasopharyngeal epithelial cells. We designed a custom panel to cover these RELA binding sites and used it for targeted sequencing in cases and controls. Variant calling and association analysis were performed followed by validation of candidate polymorphisms by genotyping in three independent cohorts. We identified two new polymorphisms, rs4823231 and rs11913168, showing signs of association with meningococcal disease susceptibility. In addition, using our genomic data as well as publicly available resources, we found evidences for these SNPs to have potential regulatory effects on ATXN10 and LIF genes respectively. The variants and related candidate genes are relevant for infectious diseases and may have important contribution for meningococcal disease pathology. Finally, we described a novel genetic association approach that could be applied to other phenotypes

Utilisation des anticorps anti-digoxine au cours d'une intoxication digitalique chez un nourisson

info:eu-repo/semantics/publishe

Prediction uncertainty of environmental change effects on temperate European biodiversity.

FR2116International audienceObserved patterns of species richness at landscape scale (gamma diversity) cannot always be attributed to a specific set of explanatory variables, but rather different alternative explanatory statistical models of similar quality may exist. Therefore predictions of the effects of environmental change (such as in climate or land cover) on biodiversity may differ considerably, depending on the chosen set of explanatory variables. Here we use multimodel prediction to evaluate effects of climate, land-use intensity and landscape structure on species richness in each of seven groups of organisms (plants, birds, spiders, wild bees, ground beetles, true bugs and hoverflies) in temperate Europe. We contrast this approach with traditional best-model predictions, which we show, using cross-validation, to have inferior prediction accuracy. Multimodel inference changed the importance of some environmental variables in comparison with the best model, and accordingly gave deviating predictions for environmental change effects. Overall, prediction uncertainty for the multimodel approach was only slightly higher than that of the best model, and absolute changes in predicted species richness were also comparable. Richness predictions varied generally more for the impact of climate change than for land-use change at the coarse scale of our study. Overall, our study indicates that the uncertainty introduced to environmental change predictions through uncertainty in model selection both qualitatively and quantitatively affects species richness projections

Indicators for biodiversity in agricultural landscapes: a pan-European study.

1DInternational audience1. In many European agricultural landscapes, species richness is declining considerably. Studies performed at a very large spatial scale are helpful in understanding the reasons for this decline and as a basis for guiding policy. In a unique, large-scale study of 25 agricultural landscapes in seven European countries, we investigated relationships between species richness in several taxa, and the links between biodiversity and landscape structure and management. 2. We estimated the total species richness of vascular plants, birds and five arthropod groups in each 16-km 2 landscape, and recorded various measures of both landscape structure and intensity of agricultural land use. We studied correlations between taxonomic groups and the effects of landscape and land-use parameters on the number of species in different taxonomic groups. Our statistical approach also accounted for regional variation in species richness unrelated to landscape or land-use factors. 3. The results reveal strong geographical trends in species richness in all taxonomic groups. No single species group emerged as a good predictor of all other species groups. Species richness of all groups increased with the area of semi-natural habitats in the landscape. Species richness of birds and vascular plants was negatively associated with fertilizer use. 4. Synthesis and applications. We conclude that indicator taxa are unlikely to provide an effective means of predicting biodiversity at a large spatial scale, especially where there is large biogeographical variation in species richness. However, a small list of landscape and land-use parameters can be used in agricultural landscapes to infer large-scale patterns of species richness. Our results suggest that to halt the loss of biodiversity in these landscapes, it is important to preserve and, if possible, increase the area of semi-natural habitat