Showing papers by "Rainer Spang published in 2016"

Early dissemination seeds metastasis in breast cancer

Abstract: Accumulating data suggest that metastatic dissemination often occurs early during tumour formation, but the mechanisms of early metastatic spread have not yet been addressed. Here, by studying metastasis in a HER2-driven mouse breast cancer model, we show that progesterone-induced signalling triggers migration of cancer cells from early lesions shortly after HER2 activation, but promotes proliferation in advanced primary tumour cells. The switch from migration to proliferation was regulated by increased HER2 expression and tumour-cell density involving microRNA-mediated progesterone receptor downregulation, and was reversible. Cells from early, low-density lesions displayed more stemness features, migrated more and founded more metastases than cells from dense, advanced tumours. Notably, we found that at least 80% of metastases were derived from early disseminated cancer cells. Karyotypic and phenotypic analysis of human disseminated cancer cells and primary tumours corroborated the relevance of these findings for human metastatic dissemination.

Adjusting microbiome profiles for differences in microbial load by spike-in bacteria

Abstract: Next-generation 16S ribosomal RNA gene sequencing is widely used to determine the relative composition of the mammalian gut microbiomes. However, in the absence of a reference, this does not reveal alterations in absolute abundance of specific operational taxonomic units if microbial loads vary across specimens. Here we suggest the spiking of exogenous bacteria into crude specimens to quantify ratios of absolute bacterial abundances. We use the 16S rDNA read counts of the spike-in bacteria to adjust the read counts of endogenous bacteria for changes in total microbial loads. Using a series of dilutions of pooled faecal samples from mice containing defined amounts of the spike-in bacteria Salinibacter ruber, Rhizobium radiobacter and Alicyclobacillus acidiphilus, we demonstrate that spike-in-based calibration to microbial loads allows accurate estimation of ratios of absolute endogenous bacteria abundances. Applied to stool specimens of patients undergoing allogeneic stem cell transplantation, we were able to determine changes in both relative and absolute abundances of various phyla, especially the genus Enterococcus, in response to antibiotic treatment and radio-chemotherapeutic conditioning. Exogenous spike-in bacteria in gut microbiome studies enable estimation of ratios of absolute OTU abundances, providing novel insights into the structure and the dynamics of intestinal microbiomes.

Analyzing synergistic and non-synergistic interactions in signalling pathways using Boolean Nested Effect Models.

Abstract: MOTIVATION Understanding the structure and interplay of cellular signalling pathways is one of the great challenges in molecular biology. Boolean Networks can infer signalling networks from observations of protein activation. In situations where it is difficult to assess protein activation directly, Nested Effect Models are an alternative. They derive the network structure indirectly from downstream effects of pathway perturbations. To date, Nested Effect Models cannot resolve signalling details like the formation of signalling complexes or the activation of proteins by multiple alternative input signals. Here we introduce Boolean Nested Effect Models (B-NEM). B-NEMs combine the use of downstream effects with the higher resolution of signalling pathway structures in Boolean Networks. RESULTS We show that B-NEMs accurately reconstruct signal flows in simulated data. Using B-NEM we then resolve BCR signalling via PI3K and TAK1 kinases in BL2 lymphoma cell lines. AVAILABILITY AND IMPLEMENTATION R code is available at https://github.com/MartinFXP/B-NEM (github). The BCR signalling dataset is available at the GEO database (http://www.ncbi.nlm.nih.gov/geo/) through accession number GSE68761. CONTACT martin-franz-xaver.pirkl@ukr.de, Rainer.Spang@ukr.de SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Identification of a new gene regulatory circuit involving B cell receptor activated signaling using a combined analysis of experimental, clinical and global gene expression data

Abstract: // Alexandra Schrader 1, 2, 10, 11 , Katharina Meyer 3, 6 , Neele Walther 1 , Ailine Stolz 4 , Maren Feist 1, 7 , Elisabeth Hand 1, 6 , Frederike von Bonin 1 , Maurits Evers 3, 6, 13 , Christian Kohler 3, 6 , Katayoon Shirneshan 1 , Martina Vockerodt 5, 8, 10, 12 , Wolfram Klapper 5, 6, 7, 9 , Monika Szczepanowski 5, 6, 7, 9 , Paul G. Murray 8 , Holger Bastians 4 , Lorenz Trumper 1, 2, 5, 7 , Rainer Spang 3, 5, 6, 7 , Dieter Kube 1, 2, 5, 6, 7 1 Department of Haematology and Medical Oncology, University Medical Centre of the Georg-August University Gottingen, Gottingen, Germany 2 GRK1034 of the Deutsche Forschungsgemeinschaft, Georg-August University Gottingen, Gottingen, Germany 3 Department of Statistical Bioinformatics, Institute for Functional Genomics, University of Regensburg, Regensburg, Germany 4 Goettingen Center for Molecular Biosciences (GZMB) and University Medical Center, Institute of Molecular Oncology, Section for Cellular Oncology, Gottingen, Germany 5 Network Molecular Mechanism of Malignant Lymphoma (MMML) of the Deutsche Krebshilfe, Germany 6 BMBF-Network HamatoSys, Germany 7 BMBF-Network Myc-Sys, Germany 8 School of Cancer Sciences, University of Birmingham, Birmingham, UK 9 University-Hospital Schleswig-Holstein, Hematopathology Section and Lymph Node Registry Kiel, Kiel, Germany 10 Department of Anatomy, University Medical Centre of the Georg-August University Gottingen, Gottingen, Germany 11 Present address: Laboratory of Lymphocyte Signaling and Oncoproteome, Department I of Internal Medicine, University Hospital Cologne, Center for Integrated Oncology (CIO) Koln-Bonn, Cologne, Germany 12 Present address: Department of Anatomy, University Medical Centre of the Georg-August University Gottingen, Gottingen, Germany 13 Current address: The John Curtin School of Medical Research the Australian National University Canberra, Australia Correspondence to: Dieter Kube, e-mail: dieter.kube@med.uni-goettingen.de Keywords: lymphoma, B cell receptor signaling, guided clustering, cell cycle delay, chromosomal aberrations Received: September 23, 2015 Accepted: March 31, 2016 Published: May 7, 2016 ABSTRACT To discover new regulatory pathways in B lymphoma cells, we performed a combined analysis of experimental, clinical and global gene expression data. We identified a specific cluster of genes that was coherently expressed in primary lymphoma samples and suppressed by activation of the B cell receptor (BCR) through αIgM treatment of lymphoma cells in vitro . This gene cluster, which we called BCR.1, includes numerous cell cycle regulators. A reduced expression of BCR.1 genes after BCR activation was observed in different cell lines and also in CD10 + germinal center B cells. We found that BCR activation led to a delayed entry to and progression of mitosis and defects in metaphase. Cytogenetic changes were detected upon long-term αIgM treatment. Furthermore, an inverse correlation of BCR.1 genes with c-Myc co-regulated genes in distinct groups of lymphoma patients was observed. Finally, we showed that the BCR.1 index discriminates activated B cell-like and germinal centre B cell-like diffuse large B cell lymphoma supporting the functional relevance of this new regulatory circuit and the power of guided clustering for biomarker discovery.

Refining pathways: A model comparison approach

Abstract: Cellular signalling pathways consolidate multiple molecular interactions into working models of signal propagation, amplification, and modulation. They are described and visualized as networks. Adjusting network topologies to experimental data is a key goal of systems biology. While network reconstruction algorithms like nested effects models are well established tools of computational biology, their data requirements can be prohibitive for their practical use. In this paper we suggest focussing on well defined aspects of a pathway and develop the computational tools to do so. We adapt the framework of nested effect models to focus on a specific aspect of activated Wnt signalling in HCT116 colon cancer cells: Does the activation of Wnt target genes depend on the secretion of Wnt ligands or do mutations in the signalling molecule β-catenin make this activation independent from them? We framed this question into two competing classes of models: Models that depend on Wnt ligands secretion versus those that do not. The model classes translate into restrictions of the pathways in the network topology. Wnt dependent models are more flexible than Wnt independent models. Bayes factors are the standard Bayesian tool to compare different models fairly on the data evidence. In our analysis, the Bayes factors depend on the number of potential Wnt signalling target genes included in the models. Stability analysis with respect to this number showed that the data strongly favours Wnt ligands dependent models for all realistic numbers of target genes.

Validation Study: Response-Predictive Gene Expression Profiling of Glioma Progenitor Cells In Vitro.

Abstract: Background In a previous publication we introduced a novel approach to identify genes that hold predictive information about treatment outcome. A linear regression model was fitted by using the least angle regression algorithm (LARS) with the expression profiles of a construction set of 18 glioma progenitor cells enhanced for brain tumor initiating cells (BTIC) before and after in vitro treatment with the tyrosine kinase inhibitor Sunitinib. Profiles from treated progenitor cells allowed predicting therapy-induced impairment of proliferation in vitro. Prediction performance was validated in leave one out cross validation. Methods In this study, we used an additional validation set of 18 serum-free short-term treated in vitro cell cultures to test the predictive properties of the signature in an independent cohort. We assessed proliferation rates together with transcriptome-wide expression profiles after Sunitinib treatment of each individual cell culture, following the methods of the previous publication. Results We confirmed treatment-induced expression changes in our validation set, but our signature failed to predict proliferation inhibition. Neither re-calculation of the combined dataset with all 36 BTIC cultures nor separation of samples into TCGA subclasses did generate a proliferation prediction. Conclusion Although the gene signature published from our construction set exhibited good prediction accuracy in cross validation, we were not able to validate the signature in an independent validation data set. Reasons could be regression to the mean, the moderate numbers of samples, or too low differences in the response to proliferation inhibition in the validation set. At this stage and based on the presented results, we conclude that the signature does not warrant further developmental steps towards clinical application.