Bioelectrical Impedance Vector Pattern and Biomarkers of Physical Functioning of Prostate Cancer Survivors in Rehabilitation.

BACKGROUND: Knowledge of clinically established factors of physical function such as body composition, bioelectrical phase angle (PhA) and handgrip strength (HGS) with mortality predictive and health-related relevance is limited in prostate cancer survivors (PCS). Therefore, the aim of this study was to characterise and compare body composition data of PCS with extensive reference data as well as to analyse PhA and HGS and the prevalence of critical prognostic values at an early stage of cancer survivorship. METHODS: One hundred and forty-eight PCS were examined at the start (T1) and end (T2) of a 3-week hospitalised urooncological rehabilitation, which began median 28 days after acute cancer therapy. Examinations included a bioimpedance analysis and HGS test. Comparison of body composition between PCS and reference data was performed using bioimpedance vector analysis (BIVA). RESULTS: BIVA of the whole PCS group showed abnormal physiology with a cachectic state and a state of overhydration/oedema, without significant changes between T1 and T2. The age- and BMI-stratified subgroup analysis showed that PCS aged 60 years and older had this abnormal pattern compared to the reference population. HGS (T1: 38.7 ± 8.9 vs T2: 40.8 ± 9.4, kg), but not PhA (T1/T2: 5.2 ± 0.7, °), changed significantly between T1 and T2. Values below a critical threshold reflecting a potentially higher risk of mortality and impaired function were found for PhA in 20% (T1) and 22% (T2) of PCS and in 41% (T1) and 29% (T2) for HGS. CONCLUSIONS: BIVA pattern and the prevalence of critically low HGS and PhA values illustrate the necessity for intensive continuation of rehabilitation and survivorship care especially in these 'at risk' cases. The routine assessment of body composition, PhA and HGS offer the opportunity to conduct a risk stratification for PCS and could help personalising and optimising treatment in rehabilitation and ongoing survivorship care.

Ontologizer 2.0--a multifunctional tool for GO term enrichment analysis and data exploration.

UNLABELLED: The Ontologizer is a Java application that can be used to perform statistical analysis for overrepresentation of Gene Ontology (GO) terms in sets of genes or proteins derived from an experiment. The Ontologizer implements the standard approach to statistical analysis based on the one-sided Fisher's exact test, the novel parent-child method, as well as topology-based algorithms. A number of multiple-testing correction procedures are provided. The Ontologizer allows users to visualize data as a graph including all significantly overrepresented GO terms and to explore the data by linking GO terms to all genes/proteins annotated to the term and by linking individual terms to child terms. AVAILABILITY: The Ontologizer application is available under the terms of the GNU GPL. It can be started as a WebStart application from the project homepage, where source code is also provided: http://compbio.charite.de/ontologizer. REQUIREMENTS: Ontologizer requires a Java SE 5.0 compliant Java runtime engine and GraphViz for the optional graph visualization tool.

Rap1A-deficient T and B cells show impaired integrin-mediated cell adhesion.

Studies in tissue culture cells have demonstrated a role for the Ras-like GTPase Rap1 in the regulation of integrin-mediated cell-matrix and cadherin-mediated cell-cell contacts. To analyze the function of Rap1 in vivo, we have disrupted the Rap1A gene by homologous recombination. Mice homozygous for the deletion allele are viable and fertile. However, primary hematopoietic cells isolated from spleen or thymus have a diminished adhesive capacity on ICAM and fibronectin substrates. In addition, polarization of T cells from Rap1-/- cells after CD3 stimulation was impaired compared to that of wild-type cells. Despite this, these defects did not result in hematopoietic or cell homing abnormalities. Although it is possible that the relatively mild phenotype is a consequence of functional complementation by the Rap1B gene, our genetic studies confirm a role for Rap1A in the regulation of integrins.

Simultaneous alignment and annotation of cis-regulatory regions.

MOTIVATION: Current methods that annotate conserved transcription factor binding sites in an alignment of two regulatory regions perform the alignment and annotation step separately and combine the results in the end. If the site descriptions are weak or the sequence similarity is low, the local gap structure of the alignment poses a problem in detecting the conserved sites. It is therefore desirable to have an approach that is able to simultaneously consider the alignment as well as possibly matching site locations. RESULTS: With SimAnn we have developed a tool that serves exactly this purpose. By combining the annotation step and the alignment of the two sequences into one algorithm, it detects conserved sites more clearly. It has the additional advantage that all parameters are calculated based on statistical considerations. This allows for its successful application with any binding site model of interest. We present the algorithm and the approach for parameter selection and compare its performance with that of other, non-simultaneous methods on both simulated and real data. AVAILABILITY: A command-line based C++ implementation of SimAnn is available from the authors upon request. In addition, we provide Perl scripts for calculating the input parameters based on statistical considerations.

Improved detection of overrepresentation of Gene-Ontology annotations with parent child analysis.

MOTIVATION: High-throughput experiments such as microarray hybridizations often yield long lists of genes found to share a certain characteristic such as differential expression. Exploring Gene Ontology (GO) annotations for such lists of genes has become a widespread practice to get first insights into the potential biological meaning of the experiment. The standard statistical approach to measuring overrepresentation of GO terms cannot cope with the dependencies resulting from the structure of GO because they analyze each term in isolation. Especially the fact that annotations are inherited from more specific descendant terms can result in certain types of false-positive results with potentially misleading biological interpretation, a phenomenon which we term the inheritance problem. RESULTS: We present here a novel approach to analysis of GO term overrepresentation that determines overrepresentation of terms in the context of annotations to the term's parents. This approach reduces the dependencies between the individual term's measurements, and thereby avoids producing false-positive results owing to the inheritance problem. ROC analysis using study sets with overrepresented GO terms showed a clear advantage for our approach over the standard algorithm with respect to the inheritance problem. Although there can be no gold standard for exploratory methods such as analysis of GO term overrepresentation, analysis of biological datasets suggests that our algorithm tends to identify the core GO terms that are most characteristic of the dataset being analyzed.

T-Reg Comparator: an analysis tool for the comparison of position weight matrices.

T-Reg Comparator is a novel software tool designed to support research into transcriptional regulation. Sequence motifs representing transcription factor binding sites are usually encoded as position weight matrices. The user inputs a set of such weight matrices or binding site sequences and our program matches them against the T-Reg database, which is presently built on data from the Transfac [E. Wingender (2004) In Silico Biol., 4, 55-61] and Jaspar [A. Sandelin, W. Alkema, P. Engstrom, W. W. Wasserman and B. Lenhard (2004) Nucleic Acids Res., 32, D91-D94]. Our tool delivers a detailed report on similarities between user-supplied motifs and motifs in the database. Apart from simple one-to-one relationships, T-Reg Comparator is also able to detect similarities between submatrices. In addition, we provide a user interface to a program for sequence scanning with weight matrices. Typical areas of application for T-Reg Comparator are motif and regulatory module finding and annotation of regulatory genomic regions. T-Reg Comparator is available at http://treg.molgen.mpg.de.

A new statistical model to select target sequences bound by transcription factors.

Transcription factors (TFs) play a key role in gene regulation by binding to target sequences. In silico prediction of potential binding to a sequence is a main task in computational biology. Although many methods have been proposed to tackle this problem, the statistical significance of the prediction is still not solved. We propose an approach to give a good approximation for the potential of a sequence to be bound by a TF. Instead of assessing distinct binding sites, we motivate to focus on the number of binding sites. Based on a suitable statistical model, probabilities for scoring are approximated for a TF to bind to a sequence. Two examples show the necessity of such a model as well as the superiority of the proposed method compared to standard approaches.

Comparative promoter region analysis powered by CORG.

BACKGROUND: Promoters are key players in gene regulation. They receive signals from various sources (e.g. cell surface receptors) and control the level of transcription initiation, which largely determines gene expression. In vertebrates, transcription start sites and surrounding regulatory elements are often poorly defined. To support promoter analysis, we present CORG http://corg.molgen.mpg.de, a framework for studying upstream regions including untranslated exons (5' UTR). DESCRIPTION: The automated annotation of promoter regions integrates information of two kinds. First, statistically significant cross-species conservation within upstream regions of orthologous genes is detected. Pairwise as well as multiple sequence comparisons are computed. Second, binding site descriptions (position-weight matrices) are employed to predict conserved regulatory elements with a novel approach. Assembled EST sequences and verified transcription start sites are incorporated to distinguish exonic from other sequences. As of now, we have included 5 species in our analysis pipeline (man, mouse, rat, fugu and zebrafish). We characterized promoter regions of 16,127 groups of orthologous genes. All data are presented in an intuitive way via our web site. Users are free to export data for single genes or access larger data sets via our DAS server http://tomcat.molgen.mpg.de:8080/das. The benefits of our framework are exemplarily shown in the context of phylogenetic profiling of transcription factor binding sites and detection of microRNAs close to transcription start sites of our gene set. CONCLUSION: The CORG platform is a versatile tool to support analyses of gene regulation in vertebrate promoter regions. Applications for CORG cover a broad range from studying evolution of DNA binding sites and promoter constitution to the discovery of new regulatory sequence elements (e.g. microRNAs and binding sites).