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Brockschmidt F.F.,University of Bonn | Heilmann S.,University of Bonn | Ellis J.A.,University of Melbourne | Ellis J.A.,Murdoch Childrens Research Institute | And 22 more authors.
British Journal of Dermatology | Year: 2011

Background Male-pattern baldness (androgenetic alopecia, AGA) is the most common form of hair loss among humans. Research has shown that it is caused by genetic factors. Numerous studies have unequivocally identified two major genetic risk loci for AGA: the X-chromosomal AR/EDA2R locus, and the PAX1/FOXA2 locus on chromosome 20. Objectives To identify further candidate genes for AGA, and thus gain further insights into this phenotype. Methods A German sample of 581 severely affected cases and 617 controls was used to perform a genome-wide association study. The identified associated locus was further analysed by fine-mapping, and then independently replicated in an Australian sample. Expression and pathway analyses were performed to characterize the susceptibility gene identified. Results The most significant association signal was obtained for rs756853 (P = 1·64 × 10 -7), which is located intronically in the histone deacetylase 9 (HDAC9) gene. Fine-mapping and a family-based analysis revealed that rs756853 and the 6-kb distal rs2249817 were the most highly associated single nucleotide polymorphisms. The association finding was replicated in an independent Australian sample, when the analysis was restricted to severely affected cases and unaffected controls (P = 0·026). Analysis of rs2249817 in a combined sample of severely affected German and Australian cases and unaffected controls revealed a strong association signal (P = 9·09 × 10 -8). Tissue expression studies demonstrated HDAC9 expression in various tissues, including tissues of relevance to AGA. No strong genotypic effects were observed in genotype-specific expression or splice studies. Pathway analyses supported the hypothesis that HDAC9 plays a functional role in AGA via interaction with the AR gene. Conclusions The present study suggests that HDAC9 is the third AGA susceptibility gene. © 2011 British Association of Dermatologists. Source

Holtick U.,University of Cologne | Frenzel L.P.,University of Cologne | Shimabukuro-Vornhagen A.,University of Cologne | Theurich S.,University of Cologne | And 7 more authors.
Annals of Hematology | Year: 2015

The recovery of the host immune system after allogeneic hematopoietic stem cell transplantation is pivotal to prevent infections, relapse, and secondary malignancies. In particular, numerical CD4+ T cells reconstitution is delayed and CD4 helper cell function is considered impaired as a consequence of the transplant procedure and concomitant immunosuppressive medication. From HIV/AIDS patients, it is known that numerical and functional CD4 defects increase the risk of opportunistic infections. However, and in contrast to patients with HIV, anti-infective prophylaxis after allogeneic transplantation is usually given for 6 months depending on immunosuppressive medication and existing graft-versus-host disease but independently of absolute CD4+ T cells counts. We hypothesized that a qualitative T cell defect is existing after allogeneic transplantation, especially in patients with delayed immune-reconstitution. Applying transcriptional as well as functional approaches, we show that CD4+ T cells with delayed recovery have a distinct transcriptional profile and cluster differently from T cells originated from patients with completed immune recovery. Moreover, inhibitory signatures are substantially enriched within the transcriptional profile of these T cells translating to functional defects and impaired interleukin 2 production. In addition to time after transplant, CD4+ T cells numbers should be considered for the decision to stop or maintain antimicrobial prophylaxis in patients after allogeneic stem cell transplantation. © 2014, Springer-Verlag Berlin Heidelberg. Source

Samal S.S.,Bonn Aachen International Center for | Grigoriev D.,French National Center for Scientific Research | Frohlich H.,Bonn Aachen International Center for | Radulescu O.,Montpellier University
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2015

We discuss a novel analysis method for reaction network systems with polynomial or rational rate functions. This method is based on computing tropical equilibrations defined by the equality of at least two dominant monomials of opposite signs in the differential equations of each dynamic variable. In algebraic geometry, the tropical equilibration problem is tantamount to finding tropical prevarieties, that are finite intersections of tropical hypersurfaces. Tropical equilibrations with the same set of dominant monomials define a branch or equivalence class. Minimal branches are particularly interesting as they describe the simplest states of the reaction network. We provide a method to compute the number of minimal branches and to find representative tropical equilibrations for each branch. © 2015 Springer International Publishing Switzerland. Source

Bender C.,German Cancer Research Center | Henjes F.,German Cancer Research Center | Frohlich H.,Bonn Aachen International Center for | Wiemann S.,German Cancer Research Center | And 2 more authors.
Bioinformatics | Year: 2010

Motivation: Network modelling in systems biology has become an important tool to study molecular interactions in cancer research, because understanding the interplay of proteins is necessary for developing novel drugs and therapies. De novo reconstruction of signalling pathways from data allows to unravel interactions between proteins and make qualitative statements on possible aberrations of the cellular regulatory program. We present a new method for reconstructing signalling networks from time course experiments after external perturbation and show an application of the method to data measuring abundance of phosphorylated proteins in a human breast cancer cell line, generated on reverse phase protein arrays.Results: Signalling dynamics is modelled using active and passive states for each protein at each timepoint. A fixed signal propagation scheme generates a set of possible state transitions on a discrete timescale for a given network hypothesis, reducing the number of theoretically reachable states. A likelihood score is proposed, describing the probability of measurements given the states of the proteins over time. The optimal sequence of state transitions is found via a hidden Markov model and network structure search is performed using a genetic algorithm that optimizes the overall likelihood of a population of candidate networks. Our method shows increased performance compared with two different dynamical Bayesian network approaches. For our real data, we were able to find several known signalling cascades from the ERBB signalling pathway. © The Author(s) 2010. Published by Oxford University Press. Source

Johannes M.,German Cancer Research Center | Brase J.C.,German Cancer Research Center | Frohlich H.,Bonn Aachen International Center for | Gade S.,German Cancer Research Center | And 4 more authors.
Bioinformatics | Year: 2010

Motivation: One of the main goals of high-throughput gene-expression studies in cancer research is to identify prognostic gene signatures, which have the potential to predict the clinical outcome. It is common practice to investigate these questions using classification methods. However, standard methods merely rely on gene-expression data and assume the genes to be independent. Including pathway knowledge a priori into the classification process has recently been indicated as a promising way to increase classification accuracy as well as the interpretability and reproducibility of prognostic gene signatures. Results: We propose a new method called Reweighted Recursive Feature Elimination. It is based on the hypothesis that a gene with a low fold-change should have an increased influence on the classifier if it is connected to differentially expressed genes. We used a modified version of Google's PageRank algorithm to alter the ranking criterion of the SVM-RFE algorithm. Evaluations of our method on an integrated breast cancer dataset comprising 788 samples showed an improvement of the area under the receiver operator characteristic curve as well as in the reproducibility and interpretability of selected genes. © The Author 2010. Published by Oxford University Press. Source

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