Karen Lemmens

A large-scale multicentre study of patient skin doses in interventional cardiology: dose–area product action levels and dose reference levels

For 318 patients in 8 different Belgian hospitals, the entire skin-dose distribution was mapped using a grid of 70 thermoluminescence dosimeters per patient, allowing an accurate determination of the maximum skin dose (MSD). Dose-area product (DAP) values, exposure parameters and geometry, together with procedure, patient and cardiologist characteristics, were also registered. Procedures were divided into two groups: diagnostic procedures (coronary angiography) and therapeutic procedures (dilatation, stent, combined procedures (e.g. coronary angiography + dilatation + stent)). The mean value of the MSD was 0.310 Gy for diagnostic and 0.699 Gy for therapeutic procedures. The most critical projection for receiving the MSD is the LAO90 (left anterior oblique) geometry. In 3% of cases, the MSD exceeded the 2 Gy dose threshold for deterministic effects. Action levels in terms of DAP values as the basis for a strategy for follow-up of patients for deterministic radiation skin effects were derived from measured MSD and cumulative DAP values. Two DAP action levels are proposed. A first DAP action level of 125 Gy cm(2) corresponding to the dose threshold of 2 Gy would imply an optional radiopathological follow-up depending on the cardiologists decision. A second DAP action level of 250 Gy cm(2) corresponding to the 3 Gy skin dose would imply a systematic follow-up. Dose reference levels - 71.3 Gy cm(2) for diagnostic and 106.0 Gy cm(2) for therapeutic procedures - were derived from the 75 percentile of the DAP distributions. As a conclusion, we propose that total DAP is registered in patients record file, as it can serve to improve the follow-up of patients for radiation-induced skin injuries.

In silico identification and experimental validation of PmrAB targets in Salmonella typhimurium by regulatory motif detection

BackgroundThe PmrAB (BasSR) two-component regulatory system is required for Salmonella typhimurium virulence. PmrAB-controlled modifications of the lipopolysaccharide (LPS) layer confer resistance to cationic antibiotic polypeptides, which may allow bacteria to survive within macrophages. The PmrAB system also confers resistance to Fe3+-mediated killing. New targets of the system have recently been discovered that seem not to have a role in the well-described functions of PmrAB, suggesting that the PmrAB-dependent regulon might contain additional, unidentified targets.ResultsWe performed an in silico analysis of possible targets of the PmrAB system. Using a motif model of the PmrA binding site in DNA, genome-wide screening was carried out to detect PmrAB target genes. To increase confidence in the predictions, all putative targets were subjected to a cross-species comparison (phylogenetic footprinting) using a Gibbs sampling-based motif-detection procedure. As well as the known targets, we detected additional targets with unknown functions. Four of these were experimentally validated (yibD, aroQ, mig-13 and sseJ). Site-directed mutagenesis of the PmrA-binding site (PmrA box) in yibD revealed specific sequence requirements.ConclusionsWe demonstrated the efficiency of our procedure by recovering most of the known PmrAB-dependent targets and by identifying unknown targets that we were able to validate experimentally. We also pinpointed directions for further research that could help elucidate the S. typhimurium virulence pathway.

DISTILLER: a data integration framework to reveal condition dependency of complex regulons in Escherichia coli

We present DISTILLER, a data integration framework for the inference of transcriptional module networks. Experimental validation of predicted targets for the well-studied fumarate nitrate reductase regulator showed the effectiveness of our approach in Escherichia coli. In addition, the condition dependency and modularity of the inferred transcriptional network was studied. Surprisingly, the level of regulatory complexity seemed lower than that which would be expected from RegulonDB, indicating that complex regulatory programs tend to decrease the degree of modularity.

Inferring the transcriptional network of Bacillus subtilis

The adaptation of bacteria to the vigorous environmental changes they undergo is crucial to their survival. They achieve this adaptation partly via intricate regulation of the transcription of their genes. In this study, we infer the transcriptional network of the Gram-positive model organism, Bacillus subtilis. We use a data integration workflow, exploiting both motif and expression data, towards the generation of condition-dependent transcriptional modules. In building the motif data, we rely on both known and predicted information. Known motifs were derived from DBTBS, while predicted motifs were generated by a de novo motif detection method that utilizes comparative genomics. The expression data consists of a compendium of microarrays across different platforms. Our results indicate that a considerable part of the B. subtilis network is yet undiscovered; we could predict 417 new regulatory interactions for known regulators and 453 interactions for yet uncharacterized regulators. The regulators in our network showed a preference for regulating modules in certain environmental conditions. Also, substantial condition-dependent intra-operonic regulation seems to take place. Global regulators seem to require functional flexibility to attain their roles by acting as both activators and repressors.

Query-driven module discovery in microarray data

MOTIVATION Existing (bi)clustering methods for microarray data analysis often do not answer the specific questions of interest to a biologist. Such specific questions could be derived from other information sources, including expert prior knowledge. More specifically, given a set of seed genes which are believed to have a common function, we would like to recruit genes with similar expression profiles as the seed genes in a significant subset of experimental conditions. RESULTS We introduce QDB, a novel Bayesian query-driven biclustering framework in which the prior distributions allow introducing knowledge from a set of seed genes (query) to guide the pattern search. In two well-known yeast compendia, we grow highly functionally enriched biclusters from small sets of seed genes using a resolution sweep approach. In addition, relevant conditions are identified and modularity of the biclusters is demonstrated, including the discovery of overlapping modules. Finally, our method deals with missing values naturally, performs well on artificial data from a recent biclustering benchmark study and has a number of conceptual advantages when compared to existing approaches for focused module search.

Inferring Transcriptional Networks by Mining 'Omics' Data

Inferring comprehensive regulatory networks from high-throughput data is one of the foremost challenges of modern computational biology. As high-throughput expression profiling experiments have gained common ground in many laboratories, different techniques have been proposed to infer transcriptional regulatory networks from them. Furthermore, with the advent of diverse types of high-throughput data, the research in network inference has received a new impulse. The use of diverse types of data, together with the increasing tendency of building the inference on biologically plausible simplifications, allows a more reliable and more complete description of networks. Here, we discuss how the research focus in the field of network inference is increasingly shifting from methods trying to reconstruct networks from a single data type towards integrative approaches dealing with several data sources simultaneously to infer regulatory modules.

COLOMBOS: Access Port for Cross-Platform Bacterial Expression Compendia

Background Microarrays are the main technology for large-scale transcriptional gene expression profiling, but the large bodies of data available in public databases are not useful due to the large heterogeneity. There are several initiatives that attempt to bundle these data into expression compendia, but such resources for bacterial organisms are scarce and limited to integration of experiments from the same platform or to indirect integration of per experiment analysis results. Methodology/Principal Findings We have constructed comprehensive organism-specific cross-platform expression compendia for three bacterial model organisms (Escherichia coli, Bacillus subtilis, and Salmonella enterica serovar Typhimurium) together with an access portal, dubbed COLOMBOS, that not only provides easy access to the compendia, but also includes a suite of tools for exploring, analyzing, and visualizing the data within these compendia. It is freely available at http://bioi.biw.kuleuven.be/colombos. The compendia are unique in directly combining expression information from different microarray platforms and experiments, and we illustrate the potential benefits of this direct integration with a case study: extending the known regulon of the Fur transcription factor of E. coli. The compendia also incorporate extensive annotations for both genes and experimental conditions; these heterogeneous data are functionally integrated in the COLOMBOS analysis tools to interactively browse and query the compendia not only for specific genes or experiments, but also metabolic pathways, transcriptional regulation mechanisms, experimental conditions, biological processes, etc. Conclusions/Significance We have created cross-platform expression compendia for several bacterial organisms and developed a complementary access port COLOMBOS, that also serves as a convenient expression analysis tool to extract useful biological information. This work is relevant to a large community of microbiologists by facilitating the use of publicly available microarray experiments to support their research.

ModuleDigger: an itemset mining framework for the detection of cis-regulatory modules

BackgroundThe detection of cis-regulatory modules (CRMs) that mediate transcriptional responses in eukaryotes remains a key challenge in the postgenomic era. A CRM is characterized by a set of co-occurring transcription factor binding sites (TFBS). In silico methods have been developed to search for CRMs by determining the combination of TFBS that are statistically overrepresented in a certain geneset. Most of these methods solve this combinatorial problem by relying on computational intensive optimization methods. As a result their usage is limited to finding CRMs in small datasets (containing a few genes only) and using binding sites for a restricted number of transcription factors (TFs) out of which the optimal module will be selected.ResultsWe present an itemset mining based strategy for computationally detecting cis-regulatory modules (CRMs) in a set of genes. We tested our method by applying it on a large benchmark data set, derived from a ChIP-Chip analysis and compared its performance with other well known cis-regulatory module detection tools.ConclusionWe show that by exploiting the computational efficiency of an itemset mining approach and combining it with a well-designed statistical scoring scheme, we were able to prioritize the biologically valid CRMs in a large set of coregulated genes using binding sites for a large number of potential TFs as input.

Evolutionary trails of plant steroid genes

Plant steroids - brassinosteroids (BRs) and their precursors, phytosterols - play a major role in plant growth, development, stress tolerance, and have high potential for agricultural applications. Currently, this prospect is limited by a lack of information about their evolution and expression dynamics (spatial and temporal) across plant species. The increasing number of sequenced genomes offers an opportunity for evolutionary studies that might help to prioritize functional analyses with the aim to improve crop yield and stress tolerance. In this review we provide a glimpse of the origin, evolution, and functional conservation of phytosterol and BR genes in the green plant lineage using comparative sequence and expression analyses of publicly available datasets.

The condition-dependent transcriptional network in Escherichia coli

Thanks to the availability of high‐throughput omics data, bioinformatics approaches are able to hypothesize thus‐far undocumented genetic interactions. However, due to the amount of noise in these data, inferences based on a single data source are often unreliable. A popular approach to overcome this problem is to integrate different data sources. In this study, we describe DISTILLER, a novel framework for data integration that simultaneously analyzes microarray and motif information to find modules that consist of genes that are co‐expressed in a subset of conditions, and their corresponding regulators. By applying our method on publicly available data, we evaluated the condition‐specific transcriptional network of Escherichia coli. DISTILLER confirmed 62% of 736 interactions described in RegulonDB, and 278 novel interactions were predicted.