Search

In recent years, gene fusions have gained significant recognition as biomarkers. They can assist treatment decisions, are seldom found in normal tissue and are detectable through Next-generation sequencing (NGS) of the transcriptome (RNA-seq). To transform the data provided by the sequencer into robust gene fusion detection several analysis steps are needed. Usually the first step is to map the sequenced transcript fragments (RNA-seq) to a reference genome. One standard application of this approach is to estimate expression and detect variants within known genes, e.g. SNPs and indels. In case of gene fusions, however, completely novel gene structures have to be detected. Here, we describe the detection of such gene fusion events based on our comprehensive transcript annotation (ElDorado). To demonstrate the utility of our approach, we extract gene fusion candidates from eight breast cancer cell lines, which we compare to experimentally verified gene fusions. We discuss several gene fusion events, like BCAS3-BCAS4 that was only detected in the breast cancer cell line MCF7. As supporting evidence we show that gene fusions occur more frequently in copy number enriched regions (CNV analysis). In addition, we present the Transcriptome Viewer (TViewer) a tool that allows to interactively visualize gene fusions. Finally, we support detected gene fusions through literature mining based annotations and network analyses. In conclusion, we present a platform that allows detecting gene fusions and supporting them through literature knowledge as well as rich visualization capabilities. This enables scientists to better understand molecular processes, biological functions and disease associations, which will ultimately lead to better biomedical knowledge for the development of biomarkers for diagnostics and therapies.

Although estrogen receptors (ERs) recognize 15-bp palindromic estrogen response elements (EREs) with maximal affinity in vitro, few near-consensus sequences have been characterized in estrogen target genes. Here we report the design of a genome-wide screen for high-affinity EREs and the identification of approximately 70000 motifs in the human and mouse genomes. EREs are enriched in regions proximal to the transcriptional start sites, and approximately 1% of elements appear conserved in the flanking regions (-10 kb to +5 kb) of orthologous human and mouse genes. Conserved and nonconserved elements were also found, often in multiple occurrences, in more than 230 estrogen-stimulated human genes previously identified from expression studies. In genes containing known EREs, we also identified additional distal elements, sometimes with higher in vitro binding affinity and/or better conservation between the species considered. Chromatin immunoprecipitation experiments in breast cancer cell lines indicate that most novel elements present in responsive genes bind ERalpha in vivo, including some EREs located up to approximately 10 kb from transcriptional start sites. Our results demonstrate that near-consensus EREs occur frequently in both genomes and that whereas chromatin structure likely modulates access to binding sites, far upstream elements can be evolutionarily conserved and bind ERs in vivo.

The fitness of sensitive and resistant Pseudomonas aeruginosa in different aquatic environments depends on genetic capacities and transcriptional regulation. Therefore, an antibiotic-sensitive isolate PA30 and a multi-resistant isolate PA49 originating from waste waters were compared via whole genome and transcriptome Illumina sequencing after exposure to municipal waste water and tap water. A number of different genomic islands (e.g. PAGIs, PAPIs) were identified in the two environmental isolates beside the highly conserved core genome. Exposure to tap water and waste water exhibited similar transcriptional impacts on several gene clusters (antibiotic and metal resistance, genetic mobile elements, efflux pumps) in both environmental P. aeruginosa isolates. The MexCD-OprJ efflux pump was overexpressed in PA49 in response to waste water. The expression of resistance genes, genetic mobile elements in PA49 was independent from the water matrix. Consistently, the antibiotic sensitive strain PA30 did not show any difference in expression of the intrinsic resistance determinants and genetic mobile elements. Thus, the exposure of both isolates to polluted waste water and oligotrophic tap water resulted in similar expression profiles of mentioned genes. However, changes in environmental milieus resulted in rather unspecific transcriptional responses than selected and stimuli-specific gene regulation.

SummaryGenome-wide association studies have revealed numerous risk loci associated with diverse diseases. However, identification of disease-causing variants within association loci remains a major challenge. Divergence in gene expression due to cis-regulatory variants in noncoding regions is central to disease susceptibility. We show that integrative computational analysis of phylogenetic conservation with a complexity assessment of co-occurring transcription factor binding sites (TFBS) can identify cis-regulatory variants and elucidate their mechanistic role in disease. Analysis of established type 2 diabetes risk loci revealed a striking clustering of distinct homeobox TFBS. We identified the PRRX1 homeobox factor as a repressor of PPARG2 expression in adipose cells and demonstrate its adverse effect on lipid metabolism and systemic insulin sensitivity, dependent on the rs4684847 risk allele that triggers PRRX1 binding. Thus, cross-species conservation analysis at the level of co-occurring TFBS provides a valuable contribution to the translation of genetic association signals to disease-related molecular mechanisms.

The abuse of anabolic substances in animal husbandry is forbidden within the EU and well controlled by detecting substance residues in different matrices. The application of newly designed drugs or substance cocktails represents big problems. Therefore developing sensitive test methods is important. The analysis of physiological changes caused by the use of anabolic agents on the molecular level, for example, by quantifying gene expression response, is a new approach to develop such screening methods. A novel technology for holistic gene expression analysis is RNA sequencing. In this study, the potential of this high-throughput method for the identification of biomarkers was evaluated. The effect of trenbolone acetate plus estradiol on gene expression in liver from Nguni heifers was analyzed with RNA sequencing. The expression of 40 selected candidate genes was verified via RT-qPCR, whereby 20 of these genes were significantly regulated. To extract the intended information from these regulated genes, biostatistical tools for pattern recognition were applied and resulted in a clear separation of the treatment groups. Those candidate genes could be verified in boars and in calves treated with anabolic substances. These results show the potential of RNA sequencing to screen for biomarker candidates to detect the abuse of anabolics. The verification of these biomarkers in boars and calves leads to the assumption that gene expression biomarkers are independent of breed or even species and that biomarkers, identified in farm animals could also act as potential biomarker candidates to detect the abuse of anabolic substances in human sports.