BOX elements modulate gene expression in Streptococcus pneumoniae: impact on the fine-tuning of competence development

More than 100 BOX elements are randomly distributed in intergenic regions of the pneumococcal genome. Here we demonstrate that these elements can affect expression of neighboring genes and present evidence that they are mobile. Together, our findings show that BOX elements enhance genetic diversity and genomic plasticity in Streptococcus pneumoniae.     

A statistical model of ChIA-PET data for accurate detection of chromatin 3D interactions

Identification of three-dimensional (3D) interactions between regulatory elements across the genome is crucial to unravel the complex regulatory machinery that orchestrates proliferation and differentiation of cells. ChIA-PET is a novel method to identify such interactions, where physical contacts between regions bound by a specific protein are quantified using next-generation sequencing. However, determining the significance of the observed interaction frequencies in such datasets is challenging, and few methods have been proposed. Despite the fact that regions that are close in linear genomic distance have a much higher tendency to interact by chance, no methods to date are capable of taking such dependency into account. Here, we propose a statistical model taking into account the genomic distance relationship, as well as the general propensity of anchors to be involved in contacts overall. Using both real and simulated data, we show that the previously proposed statistical test, based on Fisher''s exact test, leads to invalid results when data are dependent on genomic distance. We also evaluate our method on previously validated cell-line specific and constitutive 3D interactions, and show that relevant interactions are significant, while avoiding over-estimating the significance of short nearby interactions.     

Influence of substrate supply on cardiac efficiency, as measured by pressure-volume analysis in ex vivo mouse hearts

In the present study, we tested the reliability of measurements of pressure-volume area (PVA) and oxygen consumption (MVo(2)) in ex vivo mouse hearts, combining the use of a miniaturized conductance catheter and a fiber-optic oxygen sensor. Second, we tested whether we could reproduce the influence of increased myocardial fatty acid (FA) metabolism on cardiac efficiency in the isolated working mouse heart model, which has already been documented in large animal models. The hearts were perfused with crystalloid buffer containing 11 mM glucose and two different concentrations of FA bound to 3% BSA. The initial concentration was 0.3 +/- 0.1 mM, which was subsequently raised to 0.9 +/- 0.1 mM. End-systolic and end-diastolic pressure-volume relationships were assessed by temporarily occluding the preload line. Different steady-state PVA-MVo(2) relationships were obtained by changing the loading conditions (pre- and afterload) of the heart. There were no apparent changes in baseline cardiac performance or contractile efficiency (slope of the PVA-MVo(2) regression line) in response to the elevation of the perfusate FA concentration. However, all hearts (n = 8) showed an increase in the y-intercept of the PVA-MVo(2) regression line after elevation of the palmitate concentration, indicating an FA-induced increase in the unloaded MVo(2). Therefore, in the present model, unloaded MVo(2) is not independent of metabolic substrate. This is, to our knowledge, the first report of a PVA-MVo(2) relationship in ex vivo perfused murine hearts, using a pressure-volume catheter. The methodology can be an important tool for phenotypic assessment of the relationship among metabolism, contractile performance, and cardiac efficiency in various mouse models.     

Tumor classification and marker gene prediction by feature selection and fuzzy c-means clustering using microarray data

Background  

Using DNA microarrays, we have developed two novel models for tumor classification and target gene prediction. First, gene expression profiles are summarized by optimally selected Self-Organizing Maps (SOMs), followed by tumor sample classification by Fuzzy C-means clustering. Then, the prediction of marker genes is accomplished by either manual feature selection (visualizing the weighted/mean SOM component plane) or automatic feature selection (by pair-wise Fisher's linear discriminant).     

CellLineMiner: a knowledge portal for human cell lines

Experimental models of human tissues and disease phenotypes frequently rely upon immortalized cell lines, which are easily accessible and simple to use due to their infinite capability of cell division. For decades, cell lines have been used to investigate cellular mechanisms of disease and the efficacy of drugs, most prominently for human cancers. However, the large body of knowledge with respect to human cell lines exists primarily in an unstructured fashion, that is, as free text in the scientific literature. Here we present CellLineMiner, a novel text mining-based web database that provides a comprehensive view of human cell line knowledge. The application offers a simple search in all indexed cell lines, accompanied by a rapid display of all identified literature associations. The CellLineMiner is intended to serve as a knowledge resource companion to the cellular model systems used in biomedical research.

Availability

CellLineMiner is accessible at http://dev.pubgene.com/cellmine