Acute myeloid leukemia (AML) is characterized by molecular heterogeneity. As commonly altered genomic regions point to candidate genes involved in leukemogenesis, we used microarray-based comparative genomic hybridization and single nucleotide polymorphism profiling data of 391 AML cases to further narrow down genomic regions of interest. Targeted resequencing of 1000 genes located in the critical regions was performed in a representative cohort of 50 AML samples comprising all major cytogenetic subgroups.We identified 120 missense/nonsense mutations as well as 60 insertions/deletions affecting 73 different genes (ϳ 3.6 tumorspecific aberrations/AML). While most of the newly identified alterations were nonrecurrent, we observed an enrichment of mutations affecting genes involved in epigenetic regulation including known candidates like TET2, TET1, DNMT3A, and DNMT1, as well as mutations in the histone methyltransferases NSD1, EZH2, and MLL3. Furthermore, we found mutations in the splicing factor SFPQ and in the nonclassic regulators of mRNA processing CTCF and RAD21. These splicingrelated mutations affected 10% of AML patients in a mutually exclusive manner. In conclusion, we could identify a large number of alterations in genes involved in aberrant splicing and epigenetic regulation in genomic regions commonly altered in AML, highlighting their important role in the molecular pathogenesis of AML. (Blood. 2012;120(18):e83-e92)

Differential network analysis (DiNA) denotes a recent class of network-based Bioinformatics algorithms which focus on the differences in network topologies between two states of a cell, such as healthy and disease, to identify key players in the discriminating biological processes. In contrast to conventional differential analysis, DiNA identifies changes in the interplay between molecules, rather than changes in single molecules. This ability is especially important in cases where effectors are changed, e.g. mutated, but their expression is not. A number of different DiNA approaches have been proposed, yet a comparative assessment of their performance in different settings is still lacking. In this paper, we evaluate 10 different DiNA algorithms regarding their ability to recover genetic key players from transcriptome data. We construct high-quality regulatory networks and enrich them with co-expression data from four different types of cancer. Next, we assess the results of applying DiNA algorithms on these data sets using a gold standard list (GSL). We find that local DiNA algorithms are generally superior to global algorithms, and that all DiNA algorithms outperform conventional differential expression analysis. We also assess the ability of DiNA methods to exploit additional knowledge in the underlying cellular networks. To this end, we enrich the cancer-type specific networks with known regulatory miRNAs and compare the algorithms performance in networks with and without miRNA. We find that including miRNAs consistently and considerably improves the performance of almost all tested algorithms. Our results underline the advantages of comprehensive cell models for the analysis of -omics data.

Background In 2016, an uncommon outbreak of oropharyngeal tularaemia involving six human cases occurred in Germany, caused by drinking contaminated fresh must after a grape harvest. Aim We describe the details of laboratory investigations leading to identification of the outbreak strain, its characterisation by next generation sequencing (NGS) and the finding of the possible source of contamination. Methods We incubated wine samples in different media and on agar plates. NGS was performed on DNA isolated from young wine, sweet reserve and an outbreak case’s lymph node. A draft genome of the outbreak strain was generated. Vertebrate-specific PCRs using primers targeting the mitochondrial cytochrome b gene and product analyses by blast search were used to identify the putative source of must contamination. Results No bacterial isolate could be obtained. Analysis of the draft genome sequence obtained from the sweet reserve attributed this sequence to Francisella tularensis subsp. holarctica , belonging to the B.12/B.34 phylogenetic clade (erythromycin-resistant biovar II). In addition, the DNA sequence obtained from the case’s isolate supported our hypothesis that infection was caused by drinking contaminated must. The vertebrate-specific cytochrome b sequence derived from the young wine and the sweet reserve could be assigned to Apodemus sylvaticus (wood mouse), suggesting that a wood mouse infected with F. tularensis may have contaminated the must. Conclusion The discovered source of infection and the transmission scenario of F. tularensis in this outbreak have not been observed previously and suggest the need for additional hygienic precautionary measures when processing and consuming freshly pressed must.