BACTOME-a reference database to explore the sequence- and gene expression-variation landscape of Pseudomonas aeruginosa clinical isolates.

Extensive use of next-generation sequencing (NGS) for pathogen profiling has the potential to transform our understanding of how genomic plasticity contributes to phenotypic versatility. However, the storage of large amounts of NGS data and visualization tools need to evolve to offer the scientific community fast and convenient access to these data. We introduce BACTOME as a database system that links aligned DNA- and RNA-sequencing reads of clinical Pseudomonas aeruginosa isolates with clinically relevant pathogen phenotypes.

Diagnostics and Resistance Profiling of Bacterial Pathogens.

Worldwide infectious disease is one of the leading causes of death. Despite improvements in technology and healthcare services, morbidity and mortality due to infections have remained unchanged over the past few decades. The high and increasing rate of antibiotic resistance is further aggravating the situation.

The Pseudomonas aeruginosa Transcriptional Landscape Is Shaped by Environmental Heterogeneity and Genetic Variation.

Unlabelled: Phenotypic variability among bacteria depends on gene expression in response to different environments, and it also reflects differences in genomic structure. In this study, we analyzed transcriptome sequencing (RNA-seq) profiles of 151 Pseudomonas aeruginosa clinical isolates under standard laboratory conditions and of one P. aeruginosa type strain under 14 different environmental conditions.

Elucidation of sigma factor-associated networks in Pseudomonas aeruginosa reveals a modular architecture with limited and function-specific crosstalk.

Sigma factors are essential global regulators of transcription initiation in bacteria which confer promoter recognition specificity to the RNA polymerase core enzyme. They provide effective mechanisms for simultaneously regulating expression of large numbers of genes in response to challenging conditions, and their presence has been linked to bacterial virulence and pathogenicity. In this study, we constructed nine his-tagged sigma factor expressing and/or deletion mutant strains in the opportunistic pathogen Pseudomonas aeruginosa.

Identification of key mechanisms controlling gene expression in Leishmania infected macrophages using genome-wide promoter analysis.

The present study describes the in silico prediction of the regulatory network of Leishmania infected human macrophages. The construction of the gene regulatory network requires the identification of Transcription Factor Binding Sites (TFBSs) in the regulatory regions (promoters, enhancers) of genes that are regulated upon Leishmania infection. The promoters of human, mouse, rat, dog and chimpanzee genes were first identified in the whole genomes using available experimental data on full length cDNA sequences or deep CAGE tag data (DBTSS, FANTOM3, FANTOM4), mRNA models (ENSEMBL), or using hand annotated data (EPD, TRANSFAC).

Activation of TLX3 and NKX2-5 in t(5;14)(q35;q32) T-cell acute lymphoblastic leukemia by remote 3'-BCL11B enhancers and coregulation by PU.1 and HMGA1.

In T-cell acute lymphoblastic leukemia, alternative t(5;14)(q35;q32.2) forms effect dysregulation of either TLX3 or NKX2-5 homeobox genes at 5q35 by juxtaposition with 14q32.2 breakpoints dispersed across the BCL11B downstream genomic desert.

TiProD: the Tissue-specific Promoter Database.

TiProD is a database of human promoter sequences for which some functional features are known. It allows a user to query individual promoters and the expression pattern they mediate, gene expression signatures of individual tissues, and to retrieve sets of promoters according to their tissue-specific activity or according to individual Gene Ontology terms the corresponding genes are assigned to. We have defined a measure for tissue-specificity that allows the user to discriminate between ubiquitously and specifically expressed genes.

Composition-sensitive analysis of the human genome for regulatory signals.

Known transcription regulatory signals which generally act as transcription factor binding sites (TFs) differ significantly in their base composition. Therefore, their occurrence in a genome largely depends on the local base composition. In an attempt to initiate an all human genome analysis for the occurrence of potential TFs, we systematically analyzed the GC-content of distinct functional regions (e.