Biomedical subjects

Dirk Repsilber

Publications and source records attributed to Dirk Repsilber.

7 recordsLinked to original sources

PubMed2006

The metabolic response of heterotrophic Arabidopsis cells to oxidative stress.

To cope with oxidative stress, the metabolic network of plant cells must be reconfigured either to bypass damaged enzymes or to support adaptive responses. To characterize the dynamics of metabolic change during oxidative stress, heterotrophic Arabidopsis (Arabidopsis thaliana) cells were treated with menadione and changes in metabolite abundance and (13)C-labeling kinetics were quantified in a time series of samples taken over a 6 h period. Oxidative stress had a profound effect on the central metabolic pathways with extensive metabolic inhibition radiating from the tricarboxylic acid cycle and including large sectors of amino acid metabolism. Sequential accumulation of metabolites in specific pathways indicated a subsequent backing up of glycolysis and a diversion of carbon into the oxidative pentose phosphate pathway. Microarray analysis revealed a coordinated transcriptomic response that represents an emergency coping strategy allowing the cell to survive the metabolic hiatus. Rather than attempt to replace inhibited enzymes, transcripts encoding these enzymes are in fact down-regulated while an antioxidant defense response is mounted. In addition, a major switch from anabolic to catabolic metabolism is signaled. Metabolism is also reconfigured to bypass damaged steps (e.g. induction of an external NADH dehydrogenase of the mitochondrial respiratory chain). The overall metabolic response of Arabidopsis cells to oxidative stress is remarkably similar to the superoxide and hydrogen peroxide stimulons of bacteria and yeast (Saccharomyces cerevisiae), suggesting that the stress regulatory and signaling pathways of plants and microbes may share common elements.

Arabidopsis↗

PubMed2006

Genome rearrangements, deletions, and amplifications in the natural population of Bartonella henselae.

Cats are the natural host for Bartonella henselae, an opportunistic human pathogen and the agent of cat scratch disease. Here, we have analyzed the natural variation in gene content and genome structure of 38 Bartonella henselae strains isolated from cats and humans by comparative genome hybridizations to microarrays and probe hybridizations to pulsed-field gel electrophoresis (PFGE) blots. The variation in gene content was modest and confined to the prophage and the genomic islands, whereas the PFGE analyses indicated extensive rearrangements across the terminus of replication with breakpoints in areas of the genomic islands. We observed no difference in gene content or structure between feline and human strains. Rather, the results suggest multiple sources of human infection from feline B. henselae strains of diverse genotypes. Additionally, the microarray hybridizations revealed DNA amplification in some strains in the so-called chromosome II-like region. The amplified segments were centered at a position corresponding to a putative phage replication initiation site and increased in size with the duration of cultivation. We hypothesize that the variable gene pool in the B. henselae population plays an important role in the establishment of long-term persistent infection in the natural host by promoting antigenic variation and escape from the host immune response.

Animals↗

PubMed2006

Detection of divergent genes in microbial aCGH experiments.

BACKGROUND: Array-based comparative genome hybridization (aCGH) is a tool for rapid comparison of genomes from different bacterial strains. The purpose of such analysis is to detect highly divergent or absent genes in a sample strain compared to an index strain. Development of methods for analyzing aCGH data has primarily focused on copy number abberations in cancer research. In microbial aCGH analyses, genes are typically ranked by log-ratios, and classification into divergent or present is done by choosing a cutoff log-ratio, either manually or by statistics calculated from the log-ratio distribution. As experimental settings vary considerably, it is not possible to develop a classical discriminant or statistical learning approach. METHODS: We introduce a more efficient method for analyzing microbial aCGH data using a finite mixture model and a data rotation scheme. Using the average posterior probabilities from the model fitted to log-ratios before and after rotation, we get a score for each gene, and demonstrate its advantages for ranking and detecting divergent genes with enlarged specificity and sensitivity. RESULTS: The procedure is tested and compared to other approaches on simulated data sets, as well as on four experimental validation data sets for aCGH analysis on fully sequenced strains of Staphylococcus aureus and Streptococcus pneumoniae. CONCLUSION: When tested on simulated data as well as on four different experimental validation data sets from experiments with only fully sequenced strains, our procedure out-competes the standard procedures of using a simple log-ratio cutoff for classification into present and divergent genes.

Computational Biology↗

PubMed2005

Ras-associated small GTPase 33A, a novel T cell factor, is down-regulated in patients with tuberculosis.

Ras-associated small GTPases (Rabs) are specific regulators of intracellular vesicle trafficking. Interference with host cell vesicular transport is a hallmark of many intracellular pathogens, including the notable example Mycobacterium tuberculosis. We performed, by quantitative polymerase chain reaction, gene-expression analyses for selected Rab molecules in peripheral-blood mononuclear cells from patients with tuberculosis (TB) and healthy control subjects, to identify candidate genes that are critically involved in the host immune response. Comparison revealed significant differences in the expression of genes for Rab13, Rab24, and Rab33A. Rab33A gene expression was down-regulated in patients with TB and was predominantly expressed in CD8+ T cells. We excluded possible influences of differences in T cell percentages between the 2 study groups, demonstrating that Rab33A gene expression changes on the single-cell level. In vitro, Rab33A RNA expression was induced in T cells on activation and by dendritic cells infected with M. tuberculosis. Our findings identify Rab33A as a T cell regulatory molecule in TB and suggest its involvement in disease processes.

Adult↗

PubMed2005

Data rotation improves genomotyping efficiency.

Unsequenced bacterial strains can be characterized by comparing their genomic DNA to a sequenced reference genome of the same species. This comparative genomic approach, also called genomotyping, is leading to an increased understanding of bacterial evolution and pathogenesis. It is efficiently accomplished by comparative genomic hybridization on custom-designed cDNA microarrays. The microarray experiment results in fluorescence intensities for reference and sample genome for each gene. The log-ratio of these intensities is usually compared to a cut-off, classifying each gene of the sample genome as a candidate for an absent or present gene with respect to the reference genome. Reducing the usually high rate of false positives in the list of candidates for absent genes is decisive for both time and costs of the experiment. We propose a novel method to improve efficiency of genomotyping experiments in this sense, by rotating the normalized intensity data before setting up the list of candidate genes. We analyze simulated genomotyping data and also re-analyze an experimental data set for comparison and illustration. We approximately halve the proportion of false positives in the list of candidate absent genes for the example comparative genomic hybridization experiment as well as for the simulation experiments.

Algorithms↗

PubMed2005

Characterization of the genome composition of Bartonella koehlerae by microarray comparative genomic hybridization profiling.

Bartonella henselae is present in a wide range of wild and domestic feline hosts and causes cat-scratch disease and bacillary angiomatosis in humans. We have estimated here the gene content of Bartonella koehlerae, a novel species isolated from cats that was recently identified as an agent of human endocarditis. The investigation was accomplished by comparative genomic hybridization (CGH) to a microarray constructed from the sequenced 1.93-Mb genome of B. henselae. Control hybridizations of labeled DNA from the human pathogen Bartonella quintana with a reduced genome of 1.58 Mb were performed to evaluate the accuracy of the array for genes with known levels of sequence divergence. Genome size estimates of B. koehlerae by pulsed-field gel electrophoresis matched that calculated by the CGH, indicating a genome of 1.7 to 1.8 Mb with few unique genes. As in B. quintana, sequences in the prophage and the genomic islands were reported absent in B. koehlerae. In addition, sequence variability was recorded in the chromosome II-like region, where B. koehlerae showed an intermediate retention pattern of both coding and noncoding sequences. Although most of the genes missing in B. koehlerae are also absent from B. quintana, its phylogenetic placement near B. henselae suggests independent deletion events, indicating that host specificity is not solely attributed to genes in the genomic islands. Rather, the results underscore the instability of the genomic islands even within bacterial populations adapted to the same host-vector system, as in the case of B. henselae and B. koehlerae.

Bartonella↗

PubMedDate not supplied

Reverse engineering of regulatory networks: simulation studies on a genetic algorithm approach for ranking hypotheses.

Reverse engineering algorithms (REAs) aim at using gene expression data to reconstruct interactions in regulatory genetic networks. This may help to understand the basis of gene regulation, the core task of functional genomics. Collecting data for a number of environmental conditions is necessary to reengineer even the smallest regulatory networks with reasonable confidence. We systematically tested the requirements for the experimental design necessary for ranking alternative hypotheses about the structure of a given regulatory network. A genetic algorithm (GA) was used to explore the parameter space of a multistage discrete genetic network model with fixed connectivity and number of states per node. Our results show that it is not necessary to determine all parameters of the genetic network in order to rank hypotheses. The ranking process is easier the more experimental environmental conditions are used for the data set. During the ranking, the number of fixed parameters increases with the number of environmental conditions, while some errors in the hypothetical network structure may pass undetected, due to a maintained dynamical behaviour.

Algorithms↗