Analyzing time-dependent microarray data using independent component analysis derived expression modes from human macrophages infected with F. tularensis holartica.

The analysis of large-scale gene expression profiles is still a demanding and extensive task. Modern machine learning and data mining techniques developed in linear algebra, like Independent Component Analysis (ICA), become increasingly popular as appropriate tools for analyzing microarray data. We applied ICA to analyze kinetic gene expression profiles of human monocyte derived macrophages (MDM) from three different donors infected with Francisella tularensis holartica and compared them to more classical methods like hierarchical clustering. Results were compared using a pathway analysis too[, based on the Gene Ontology and the MeSH database. We could show that both methods lead to time-dependent gene regulatory patterns which fit well to known TNF alpha induced immune responses. In comparison, the nonexclusive attribute of ICA results in a more detailed view and a higher resolution in time dependent behavior of the immune response genes. Additionally, we identified NF kappa B as one of the main regulatory genes during response to F. tularensis infection

Molecular analysis of <em>Coxiella burnetii</em> in Germany reveals evolution of unique clonal clusters.

The causative agent of Q fever, Coxiella burnetii, is a query agent occurring naturally all over the world. We studied 104 German Coxiella burnetii strains/DNA samples obtained between 1969 and 2011 using a 14 microsatellite marker Multiple-locus variable-number of tandem repeat (VNTR) analysis (MLVA) technique. We were able to divide our collection into 32 different genotypes clustered into four major groups (A-D). Two of these (A and C) formed predominant clonal complexes that covered 97% of all studied samples. Group C consisted exclusively of cattle-associated isolates/DNA specimens, while group A comprised all other affected species including all sheep-derived strains/DNA samples. Within this second cluster, two major genotypes (A1, A2) were identified. Genotype A2 occurred in strains isolated from ewes in northern and central Germany, whereas genotype A1 was found in most areas of Germany. MLVA analysis of C. burnetii strains from neighbouring countries revealed a close relationship to German strains. We thus hypothesize that there is a western and central European cluster of C. burnetii. We identified predominant genotypes related to relevant host species and geographic regions which is in line with findings of the Dutch Q fever outbreak (2007-2010). Furthermore three of our analyzed German strains are closely related to the Dutch outbreak clone. These findings support the theory of predominant genotypes in the context of regional outbreaks. Our results show that a combination of 8 MLVA markers provides the highest discriminatory power for attributing C. burnetii isolates to genotypes. For future epidemiological studies we propose the use of three MLVA markers for easy and rapid classification of C. burnetii into 4 main clusters