Genome-Wide Analyses Suggest Mechanisms Involving Early B-Cell Development in Canine IgA Deficiency.

Immunoglobulin A deficiency (IgAD) is the most common primary immune deficiency disorder in both humans and dogs, characterized by recurrent mucosal tract infections and a predisposition for allergic and other immune mediated diseases. In several dog breeds, low IgA levels have been observed at a high frequency and with a clinical resemblance to human IgAD. In this study, we used genome-wide association studies (GWAS) to identify genomic regions associated with low IgA levels in dogs as a comparative model for human IgAD. We used a novel percentile groups-approach to establish breed-specific cut-offs and to perform analyses in a close to continuous manner. GWAS performed in four breeds prone to low IgA levels (German shepherd, Golden retriever, Labrador retriever and Shar-Pei) identified 35 genomic loci suggestively associated (p <0.0005) to IgA levels. In German shepherd, three genomic regions (candidate genes include KIRREL3 and SERPINA9) were genome-wide significantly associated (p <0.0002) with IgA levels. A ~20kb long haplotype on CFA28, significantly associated (p = 0.0005) to IgA levels in Shar-Pei, was positioned within the first intron of the gene SLIT1. Both KIRREL3 and SLIT1 are highly expressed in the central nervous system and in bone marrow and are potentially important during B-cell development. SERPINA9 expression is restricted to B-cells and peaks at the time-point when B-cells proliferate into antibody-producing plasma cells. The suggestively associated regions were enriched for genes in Gene Ontology gene sets involving inflammation and early immune cell development.

Ms. Ff. Mus. 1587 - [Arien und Duette]

[Georg Philipp Telemann]

A probabilistic model estimating oil spill clean-up costs - a case study for the Gulf of Finland

Existing models estimating oil spill costs at sea are based on data from the past, and they usually lack a systematic approach. This make them passive, and limits their ability to forecast the effect of the changes in the oil combating fleet or location of a spill on the oil spill costs. In this paper we make an attempt towards the development of a probabilistic and systematic model estimating the costs of clean-up operations for the Gulf of Finland. For this purpose we utilize expert knowledge along with the available data and information from literature. Then, the obtained information is combined into a framework with the use of a Bayesian Belief Networks. Due to lack of data, we validate the model by comparing its results with existing models, with which we found good agreement. We anticipate that the presented model can contribute to the cost-effective oil-combating fleet optimization for the Gulf of Finland. It can also facilitate the accident consequences estimation in the framework of formal safety assessment (FSA).