Biomedical subjects

Jörg Rahnenführer

Publications and source records attributed to Jörg Rahnenführer.

17 recordsLinked to original sources

PubMed2007

Hyperdiploidy defines a distinct cytogenetic entity of meningiomas.

BACKGROUND: The most common chromosomal aberration found in meningiomas is monosomy 22. Progression and recurrence of meningiomas are usually associated with additional chromosome losses. Rarely, however, meningiomas have strongly hyperdiploid karyotypes with over 50 chromosomes; the objective of this study was to explore the cytogenetic and histopathologic patterns as well as the clinical significance of hyperdiploidy in meningiomas. METHODS: Within a series of 677 consecutive meningiomas, we identified a subgroup comprising 16 cases that display a strikingly uniform pattern of hyperdiploidy mostly without structural chromosome rearrangements, as shown by banding techniques and, in the single structurally aberrant case, spectral karyotyping. RESULTS: These meningiomas each have between 50 and 56 chromosomes, with trisomy 12 (14/16 cases), trisomy 20 (13/16 cases), trisomy 5 (12/16 cases), and trisomy 17 (10/16 cases). Histomorphologically, hyperdiploid meningiomas feature a heterogeneous phenotype. However, they are associated with a higher histological grade, and decreased expression of alkaline phosphatase as compared to meningiomas with typical karyotype. In two patients, recurrences were documented and three patients died of disease during the period of observation, indicating a worse prognosis of hyperdiploid than of cytogenetically typical meningiomas. CONCLUSION: We conclude that hyperdiploidy constitutes a small but clinically relevant entity of biologically aggressive meningiomas, which are cytogenetically distinguishable from the majority of common-type meningiomas.

Adult↗

PubMed2006

Model selection for mixtures of mutagenetic trees.

The evolution of drug resistance in HIV is characterized by the accumulation of resistance-associated mutations in the HIV genome. Mutagenetic trees, a family of restricted Bayesian tree models, have been applied to infer the order and rate of occurrence of these mutations. Understanding and predicting this evolutionary process is an important prerequisite for the rational design of antiretroviral therapies. In practice, mixtures models of K mutagenetic trees provide more flexibility and are often more appropriate for modelling observed mutational patterns. Here, we investigate the model selection problem for K-mutagenetic trees mixture models. We evaluate several classical model selection criteria including cross-validation, the Bayesian Information Criterion (BIC), and the Akaike Information Criterion. We also use the empirical Bayes method by constructing a prior probability distribution for the parameters of a mutagenetic trees mixture model and deriving the posterior probability of the model. In addition to the model dimension, we consider the redundancy of a mixture model, which is measured by comparing the topologies of trees within a mixture model. Based on the redundancy, we propose a new model selection criterion, which is a modification of the BIC. Experimental results on simulated and on real HIV data show that the classical criteria tend to select models with far too many tree components. Only cross-validation and the modified BIC recover the correct number of trees and the tree topologies most of the time. At the same optimal performance, the runtime of the new BIC modification is about one order of magnitude lower. Thus, this model selection criterion can also be used for large data sets for which cross-validation becomes computationally infeasible.

Bayes Theorem↗

PubMed2006

A new measure for functional similarity of gene products based on Gene Ontology.

BACKGROUND: Gene Ontology (GO) is a standard vocabulary of functional terms and allows for coherent annotation of gene products. These annotations provide a basis for new methods that compare gene products regarding their molecular function and biological role. RESULTS: We present a new method for comparing sets of GO terms and for assessing the functional similarity of gene products. The method relies on two semantic similarity measures; simRel and funSim. One measure (simRel) is applied in the comparison of the biological processes found in different groups of organisms. The other measure (funSim) is used to find functionally related gene products within the same or between different genomes. Results indicate that the method, in addition to being in good agreement with established sequence similarity approaches, also provides a means for the identification of functionally related proteins independent of evolutionary relationships. The method is also applied to estimating functional similarity between all proteins in Saccharomyces cerevisiae and to visualizing the molecular function space of yeast in a map of the functional space. A similar approach is used to visualize the functional relationships between protein families. CONCLUSION: The approach enables the comparison of the underlying molecular biology of different taxonomic groups and provides a new comparative genomics tool identifying functionally related gene products independent of homology. The proposed map of the functional space provides a new global view on the functional relationships between gene products or protein families.

Algorithms↗

PubMed2006

Improved scoring of functional groups from gene expression data by decorrelating GO graph structure.

MOTIVATION: The result of a typical microarray experiment is a long list of genes with corresponding expression measurements. This list is only the starting point for a meaningful biological interpretation. Modern methods identify relevant biological processes or functions from gene expression data by scoring the statistical significance of predefined functional gene groups, e.g. based on Gene Ontology (GO). We develop methods that increase the explanatory power of this approach by integrating knowledge about relationships between the GO terms into the calculation of the statistical significance. RESULTS: We present two novel algorithms that improve GO group scoring using the underlying GO graph topology. The algorithms are evaluated on real and simulated gene expression data. We show that both methods eliminate local dependencies between GO terms and point to relevant areas in the GO graph that remain undetected with state-of-the-art algorithms for scoring functional terms. A simulation study demonstrates that the new methods exhibit a higher level of detecting relevant biological terms than competing methods.

Algorithms↗

PubMed2005

Computational methods for the design of effective therapies against drug resistant HIV strains.

The development of drug resistance is a major obstacle to successful treatment of HIV infection. The extraordinary replication dynamics of HIV facilitates its escape from selective pressure exerted by the human immune system and by combination drug therapy. We have developed several computational methods whose combined use can support the design of optimal antiretroviral therapies based on viral genomic data.

Database Management Systems↗

PubMed2005

Confirmation of human protein interaction data by human expression data.

BACKGROUND: With microarray technology the expression of thousands of genes can be measured simultaneously. It is well known that the expression levels of genes of interacting proteins are correlated significantly more strongly in Saccharomyces cerevisiae than those of proteins that are not interacting. The objective of this work is to investigate whether this observation extends to the human genome. RESULTS: We investigated the quantitative relationship between expression levels of genes encoding interacting proteins and genes encoding random protein pairs. Therefore we studied 1369 interacting human protein pairs and human gene expression levels of 155 arrays. We were able to establish a statistically significantly higher correlation between the expression levels of genes whose proteins interact compared to random protein pairs. Additionally we were able to provide evidence that genes encoding proteins belonging to the same GO-class show correlated expression levels. CONCLUSION: This finding is concurrent with the naive hypothesis that the scales of production of interacting proteins are linked because an efficient interaction demands that involved proteins are available to some degree. The goal of further research in this field will be to understand the biological mechanisms behind this observation.

Cluster Analysis↗

PubMed2005

Estimating cancer survival and clinical outcome based on genetic tumor progression scores.

MOTIVATION: In cancer research, prediction of time to death or relapse is important for a meaningful tumor classification and selecting appropriate therapies. Survival prognosis is typically based on clinical and histological parameters. There is increasing interest in identifying genetic markers that better capture the status of a tumor in order to improve on existing predictions. The accumulation of genetic alterations during tumor progression can be used for the assessment of the genetic status of the tumor. For modeling dependences between the genetic events, evolutionary tree models have been applied. RESULTS: Mixture models of oncogenetic trees provide a probabilistic framework for the estimation of typical pathogenetic routes. From these models we derive a genetic progression score (GPS) that estimates the genetic status of a tumor. GPS is calculated for glioblastoma patients from loss of heterozygosity measurements and for prostate cancer patients from comparative genomic hybridization measurements. Cox proportional hazard models are then fitted to observed survival times of glioblastoma patients and to times until PSA relapse following radical prostatectomy of prostate cancer patients. It turns out that the genetically defined GPS is predictive even after adjustment for classical clinical markers and thus can be considered a medically relevant prognostic factor. AVAILABILITY: Mtreemix, a software package for estimating tree mixture models, is freely available for non-commercial users at http://mtreemix.bioinf.mpi-sb.mpg.de. The raw cancer datasets and R code for the analysis with Cox models are available upon request from the corresponding author.

Biomarkers, Tumor↗

PubMed2005

Mtreemix: a software package for learning and using mixture models of mutagenetic trees.

SUMMARY: Mixture models of mutagenetic trees constitute a class of probabilistic models for describing evolutionary processes that are characterized by the accumulation of permanent genetic changes. They have been applied to model the accumulation of chromosomal gains and losses in tumor development and the development of drug resistance-associated mutations in the HIV genome.Mtreemix is a software package for estimating mutagenetic trees mixture models from observed cross-sectional data and for using these models for predictions. We provide programs for model fitting, model selection, simulation, likelihood computation and waiting time estimation. AVAILABILITY: Mtreemix, including source code, documentation, sample data files and precompiled Solaris and Linux binaries, is freely available for non-commercial users at http://mtreemix.bioinf.mpi-sb.mpg.de/

Algorithms↗

PubMed2005

Retinal properties and potential of the adult mammalian ciliary epithelium stem cells.

The ciliary epithelium (CE) in the adult mammalian eye harbors a mitotic quiescent population of neural stem cells. Here we have compared the cellular and molecular properties of CE stem cells and populations of retinal progenitors that define the early and late stages of histogenesis. The CE stem cells and retinal progenitors proliferate in the presence of mitogens and share the expression of universal neural and retinal progenitor markers. However, the expression of the majority of retinal progenitor markers (e.g., Chx10) is transient in the former when compared to the latter, in vitro. They are similar to early than late retinal progenitors in their proliferative response to FGF2 and/or EGF. Analysis of the differentiation potential of CE stem cells shows that they are capable of generating both early (e.g., retinal ganglion cells) and late (e.g., rod photoreceptors) born retinal neurons. However, under identical differentiation conditions, i.e., in the presence of 1% FBS, they generate more early-born retinal neurons than late-born retinal neurons showing a preference for generating early retinal neurons. Transcription profiling of these cells and retinal progenitors demonstrate that they share approximately 80% of the expressed genes. The CE stem cells have more unique genes in common with early retinal progenitors than late retinal progenitors. Both proliferative/differential potential and transcription profiles suggest that CE stem cells may be a residual population of stem cells of optic neuroepithelium, representing a stage antecedent to retinal progenitors.

Animals↗

PubMed2005

Patients with high-grade gliomas harboring deletions of chromosomes 9p and 10q benefit from temozolomide treatment.

Surgical cure of glioblastomas is virtually impossible and their clinical course is mainly determined by the biologic behavior of the tumor cells and their response to radiation and chemotherapy. We investigated whether response to temozolomide (TMZ) chemotherapy differs in subsets of malignant glioblastomas defined by genetic lesions. Eighty patients with newly diagnosed glioblastoma were analyzed with comparative genomic hybridization and loss of heterozygosity. All patients underwent radical resection. Fifty patients received TMZ after radiotherapy (TMZ group) and 30 patients received radiotherapy alone (RT group). The most common aberrations detected were gains of parts of chromosome 7 and losses of 10q, 9p, or 13q. The spectrum of genetic aberrations did not differ between the TMZ and RT groups. Patients treated with TMZ showed significantly better survival than patients treated with radiotherapy alone (19.5 vs 9.3 months). Genomic deletions on chromosomes 9 and 10 are typical for glioblastoma and associated with poor prognosis. However, patients with these aberrations benefited significantly from TMZ in univariate analysis. In multivariate analysis, this effect was pronounced for 9p deletion and for elderly patients with 10q deletions, respectively. This study demonstrates that molecular genetic and cytogenetic analyses potentially predict responses to chemotherapy in patients with newly diagnosed glioblastomas.

Adult↗

PubMed2004

Identification of c-Kit receptor as a regulator of adult neural stem cells in the mammalian eye: interactions with Notch signaling.

Neural stem cells are present in specific regions of the adult central nervous system (CNS). Recent evidence suggests that the ciliary epithelium (CE), a CNS derivative, in the adult mammalian eye, harbors a quiescent population of neural stem cells. Here, we report the identification of c-Kit signaling as one of the regulators of adult CE neural stem cells in vitro. c-Kit receptors are expressed in proliferating adult CE neural stem cells and colocalized with neural progenitor markers. Perturbation of c-Kit signaling influences the self-renewal and differentiation of CE neural stem cells, thus demonstrating the role of c-Kit signaling in the maintenance of these cells. In addition, we observed an influence of c-Kit-mediated signaling on the expression of Notch1, another critical regulator of neural stem cells. Our observations suggest that, given the importance of preservation of a stem cell pool for generating different cell types at different times, multiple signaling pathways act in concert for the maintenance of neural stem cells.

Animals↗

PubMed2004

Automated clustering of ensembles of alternative models in protein structure databases.

Experimentally determined protein structures have been classified in different public databases according to their structural and evolutionary relationships. Frequently, alternative structural models, determined using X-ray crystallography or NMR spectroscopy, are available for a protein. These models can present significant structural dissimilarity. Currently there is no classification available for these alternative structures. In order to classify them, we developed STRuster, an automated method for clustering ensembles of structural models according to their backbone structure. The method is based on the calculation of carbon alpha (Calpha) distance matrices. Two filters are applied in the calculation of the dissimilarity measure in order to identify both large and small (but significant) backbone conformational changes. The resulting dissimilarity value is used for hierarchical clustering and partitioning around medoids (PAM). Hierarchical clustering reflects the hierarchy of similarities between all pairs of models, while PAM groups the models into the 'optimal' number of clusters. The method has been applied to cluster the structures in each SCOP species level and can be easily applied to any other sets of conformers. The results are available at: http://bioinf.mpi-sb.mpg.de/projects/struster/.

Aldehyde-Lyases↗

PubMed2004

Calculating the statistical significance of changes in pathway activity from gene expression data.

We present a statistical approach to scoring changes in activity of metabolic pathways from gene expression data. The method identifies the biologically relevant pathways with corresponding statistical significance. Based on gene expression data alone, only local structures of genetic networks can be recovered. Instead of inferring such a network, we propose a hypothesis-based approach. We use given knowledge about biological networks to improve sensitivity and interpretability of findings from microarray experiments. Recently introduced methods test if members of predefined gene sets are enriched in a list of top-ranked genes in a microarray study. We improve this approach by defining scores that depend on all members of the gene set and that also take pairwise co-regulation of these genes into account. We calculate the significance of co-regulation of gene sets with a nonparametric permutation test. On two data sets the method is validated and its biological relevance is discussed. It turns out that useful measures for co-regulation of genes in a pathway can be identified adaptively. We refine our method in two aspects specific to pathways. First, to overcome the ambiguity of enzyme-to-gene mappings for a fixed pathway, we introduce algorithms for selecting the best fitting gene for a specific enzyme in a specific condition. In selected cases, functional assignment of genes to pathways is feasible. Second, the sensitivity of detecting relevant pathways is improved by integrating information about pathway topology. The distance of two enzymes is measured by the number of reactions needed to connect them, and enzyme pairs with a smaller distance receive a higher weight in the score calculation.

Journal Article↗

PubMed2004

Hybrid clustering for microarray image analysis combining intensity and shape features.

BACKGROUND: Image analysis is the first crucial step to obtain reliable results from microarray experiments. First, areas in the image belonging to single spots have to be identified. Then, those target areas have to be partitioned into foreground and background. Finally, two scalar values for the intensities have to be extracted. These goals have been tackled either by spot shape methods or intensity histogram methods, but it would be desirable to have hybrid algorithms which combine the advantages of both approaches. RESULTS: A new robust and adaptive histogram type method is pixel clustering, which has been successfully applied for detecting and quantifying microarray spots. This paper demonstrates how the spot shape can be effectively integrated in this approach. Based on the clustering results, a bivalence mask is constructed. It estimates the expected spot shape and is used to filter the data, improving the results of the cluster algorithm. The quality measure 'stability' is defined and evaluated on a real data set. The improved clustering method is compared with the established Spot software on a data set with replicates. CONCLUSION: The new method presents a successful hybrid microarray image analysis solution. It incorporates both shape and histogram features and is specifically adapted to deal with typical microarray image characteristics. As a consequence of the filtering step pixels are divided into three groups, namely foreground, background and deletions. This allows a separate treatment of artifacts and their elimination from the further analysis.

Algorithms↗

PubMed2004

Cellular and molecular characterization of early and late retinal stem cells/progenitors: differential regulation of proliferation and context dependent role of Notch signaling.

Retinal stem cells/progenitors that define the evolutionarily conserved early and late stages of retinal histogenesis are known to have distinct competence to give rise to stage-specific retinal cell types. However, the information regarding their innate proliferative behavior and phenotypic potential in terms of generating neurons and glia is lacking. Here we demonstrate that, like their counterparts in other central nervous system (CNS) regions during early and late stages of embryonic development, the early and late retinal stem cells/progenitors display different proliferative response to fibroblast growth factor 2 (FGF2) and epidermal growth factor (EGF) and bias towards generating neurons or glia. Although the former predominantly generate neurons, the latter are partial towards giving rise to glia. Transcription profiling identified classes of genes that are differentially expressed in early and late retinal stem cells/progenitors in proliferating conditions and suggested that the distinct proliferative response to FGF2 and EGF is likely due to differential expression of FGF receptor 1 (FGFR1) and EGF receptor (EGFR). However, the proliferative maintenance of retinal stem cells/progenitors is likely to include other signaling pathways such as those mediated by insulin-like growth factors (IGFs) and stem cell factor (SCF). Transcription profiling of early and late retinal stem cells/progenitors in proliferating and differentiating conditions suggested a context dependent role for Notch signaling, which may constitute one of the mechanisms underlying the stage-dependent phenotypic potential of retinal stem cells/progenitors.

Age Factors↗

PubMed2002

Unsupervised technique for robust target separation and analysis of DNA microarray spots through adaptive pixel clustering.

MOTIVATION: Microarray images challenge existing analytical methods in many ways given that gene spots are often comprised of characteristic imperfections. Irregular contours, donut shapes, artifacts, and low or heterogeneous expression impair corresponding values for red and green intensities as well as their ratio R/G. New approaches are needed to ensure accurate data extraction from these images. RESULTS: Herein we introduce a novel method for intensity assessment of gene spots. The technique is based on clustering pixels of a target area into foreground and background. For this purpose we implemented two clustering algorithms derived from k-means and Partitioning Around Medoids (PAM), respectively. Results from the analysis of real gene spots indicate that our approach performs superior to other existing analytical methods. This is particularly true for spots generally considered as problematic due to imperfections or almost absent expression. Both PX(PAM) and PX(KMEANS) prove to be highly robust against various types of artifacts through adaptive partitioning, which more correctly assesses expression intensity values. AVAILABILITY: The implementation of this method is a combination of two complementary tools Extractiff (Java) and Pixclust (free statistical language R), which are available upon request from the authors.

Algorithms↗

PubMedDate not supplied

Learning multiple evolutionary pathways from cross-sectional data.

We introduce a mixture model of trees to describe evolutionary processes that are characterized by the ordered accumulation of permanent genetic changes. The basic building block of the model is a directed weighted tree that generates a probability distribution on the set of all patterns of genetic events. We present an EM-like algorithm for learning a mixture model of K trees and show how to determine K with a maximum likelihood approach. As a case study, we consider the accumulation of mutations in the HIV-1 reverse transcriptase that are associated with drug resistance. The fitted model is statistically validated as a density estimator, and the stability of the model topology is analyzed. We obtain a generative probabilistic model for the development of drug resistance in HIV that agrees with biological knowledge. Further applications and extensions of the model are discussed.

Algorithms↗