Analyse und Visualisierung von Effekten in genomweiten Expressionsdaten

Einleitung: Modell-basierte Vorhersagen für molekulare Netzwerke und zelluläre Interaktionen können durch zwei verschiedene Strategien der Systembiologie getroffen werden, die top-down und bottom-up Strategien. Die bottom-up Strategie beginnt bei a priori Wissen über einzelne Grundelemente und fügt diese zu größeren Einheiten wie Signalwegen oder ganzen Systemen zusammen. Top-down Strategien setzen bei Datensätzen eines Systems an und versuchen Netzwerke, Interaktionen oder Komponenten zu identifizieren, die für das Systemverhalten (z.B. Phänotyp) verantwortlich sind. Im Folgenden werden beide Strategien auf unterschiedliche Transkriptionsdaten angewendet und die Ergebnisse visualisiert. Beide Strategien können auf linearen Regressionsmodellen basieren. In dieser Arbeit werden lineare Regressionsmodelle höherer Ordnung mittels eines neuen visuellen Hilfsmittels, des Eruptionsdiagramms, verglichen. Methodik: Eruptionsdiagramme werden durch die Überlagerung zweier Vulkandiagramme erstellt. Beide Vulkandiagramme werden von derselben Datengrundlage generiert, stammen jedoch von zwei verschiedenen Modellen. Jedes Gen wird von einem Pfeil repräsentiert, welcher bei dem Punkt des Vulkandiagramms von Modell 1 startet und bei dem Punkt des Vulkandiagramms aus Modell 2 endet. Im Rahmen der Modellselektion können Eruptionsdiagramme als visuelles Hilfsmittel verwendet werden, um (ir)relevante Kovariaten, Störfaktoren und Effektmodifikation aufzudecken. Ergebnisse: Es werden zwei verschiedene Transkriptionsdatensätze analysiert: ein Maus-Infektionsdatensatz und ein humaner Asthmadatensatz. Für die Analyse des Infektionsdatensatzes werden verschiedene lineare Regressionsmodelle miteinander verglichen. Durch eine rückwärts-gewandte Modellselektionsstrategie wird gezeigt, dass durch die Infektionskovariaten erster Ordnung zusätzliche erklärende Kraft gewonnen wird. Durch das Eruptionsdiagramm werden Effekte zweiter Ordnung aufgedeckt. Ein Modellvergleich identifiziert die Kovariaten dritter Ordnung als Störfaktoren. Das Modell zweiter Ordnung, welches am besten zu den Daten passt, wird für die weiterführende Analyse verwendet. Die Ergebnisse der Interaktionskovariate werden in aggravating und alleviating Effekte unterteilt. Ein Interaktionseffekt ist alleviating (aggravating, neutral), falls der Effekt der kombinierten Kovariaten schwächer (stärker, identisch) als die Summe der individuellen Effekte dieser Kovariaten ist. Bei der bottom-up Analyse des Asthmadatensatzes werden die Daten nicht auf Einzelgenebene sondern auf Gengruppenebene analysiert. Zunächst wird das passende Regressionsmodell mit Hilfe des Eruptionsdiagramms aufgestellt. Der Einfluss der einzelnen Gene auf das globale Testergebnis der Gengruppen wird in diagnostischen Balkendiagrammen genauer untersucht. Eine Signalweganalyse der Gengruppen zeigt neue Biomarker und Signalwege für die Charakterisierung von allergischem und nicht-allergischem Asthma auf. Diskussion: Die Ergebnisse der Transkriptionsanalyse werden durch Anreicherungsanalysen auf ihre funktionelle Relevanz hin untersucht. Die Ergebnisse zeigten unterschiedliche funktionelle Eigenschaften der aggravating und alleviating Gene auf. Die Anreicherungsanalyse des Asthmadatensatzes der Gene, die von Störfaktoren beeinflusst werden und durch Effektmodifikation gekennzeichnet sind, weisen jedoch keine funktionellen Unterschiede auf.Introduction: Model-based prediction of molecular networks and cellular interactions can be identified by two different strategies of systems biology, top-down and bottom-up strategies. The bottom-up strategy starts at a priori knowledge about single elements and merges into more complex units like signalling pathways or whole systems. Top-down strategies explore datasets of a system and try to identify networks, interactions or components responsible for the system behaviour (i.e. phenotype). In this thesis both strategies are applied to different transcription datasets and the corresponding results are visualized. The strategies can be based on linear regression models. In this work higher order regression models are compared using a new visual tool, the eruption plot. Methods: Eruption plots are generated by overlapping two volcano plots. Both volcano plots are based on the same data, but originate from two different models. Each gene is symbolized by an arrow, which connects the point from the volcano plot of the first model to the point from the volcano plot of the second model. The eruption plot is a visual supporting tool in model selection revealing (ir)relevant covariates, confounding factors and effect modification. Results: Two different transcription datasets are analysed in this work, a mouse-infection dataset and a human asthma dataset. For the analysis of the infection dataset two different linear regression models are being compared. As part of a backward driven model selection strategy the infection covariates provide additional explanatory power. The eruption plot highlights second order effects. A model comparison reveals third order covariates as confounding factors. The second order model that matches the data best is applied in the subsequent analysis. The results of the interaction covariates are divided into aggravating and alleviating effects. An interaction effect is alleviating (aggravating, neutral) if the effect of both covariates is lower (higher, identical) than the sum of both individual effects of these covariates. Within the bottom-up analysis of the asthma dataset the genes are not analysed on the single gene level but on the level of gene groups. By application of an eruption plot the adequate regression model is selected. Diagnostic bar plots help to further investigate the influence of the single gene on the global test result of the gene group. A pathway analysis of the gene groups shows new biomarkers and signalling pathways to characterize allergic and non-allergic asthma. Discussion: The functional relevance of both types of analysis is explored in detail through enrichment analysis. The results of the aggravating and alleviating genes show distinct functional properties. Genes of the asthma dataset are divided into genes influenced by confounding factors and effect modification. The enrichment analysis of these two groups, however, reveals no functional differences

Deciphering the modulation of gene expression by type I and II interferons combining 4sU-tagging, translational arrest and in silico promoter analysis

Interferons (IFN) play a pivotal role in innate immunity, orchestrating a cell-intrinsic anti-pathogenic state and stimulating adaptive immune responses. The complex interplay between the primary response to IFNs and its modulation by positive and negative feedback loops is incompletely understood. Here, we implement the combination of high-resolution gene-expression profiling of nascent RNA with translational inhibition of secondary feedback by cycloheximide. Unexpectedly, this approach revealed a prominent role of negative feedback mechanisms during the immediate (≤60 min) IFNα response. In contrast, a more complex picture involving both negative and positive feedback loops was observed on IFNγ treatment. IFNγ-induced repression of genes associated with regulation of gene expression, cellular development, apoptosis and cell growth resulted from cycloheximide-resistant primary IFNγ signalling. In silico promoter analysis revealed significant overrepresentation of SP1/SP3-binding sites and/or GC-rich stretches. Although signal transducer and activator of transcription 1 (STAT1)-binding sites were not overrepresented, repression was lost in absence of STAT1. Interestingly, basal expression of the majority of these IFNγ-repressed genes was dependent on STAT1 in IFN-naïve fibroblasts. Finally, IFNγ-mediated repression was also found to be evident in primary murine macrophages. IFN-repressed genes include negative regulators of innate and stress response, and their decrease may thus aid the establishment of a signalling perceptive milieu.Fil: Trilling, Mirko. Universitat Duisburg - Essen; AlemaniaFil: Bellora, Nicolás. Parque de Investigación Biomédica de Barcelona; España. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Patagonia Norte. Instituto de Investigación en Biodiversidad y Medioambiente; ArgentinaFil: Rutkowski, Andrzej J.. University of Cambridge; Reino UnidoFil: de Graaf, Miranda. University of Cambridge; Reino UnidoFil: Dickinson, Paul. University Of Edinburgh; Reino UnidoFil: Robertson, Kevin. University Of Edinburgh; Reino UnidoFil: Da Costa, Olivia Prazeres. Universitat Technical Zu Munich; AlemaniaFil: Ghazal, Peter. University Of Edinburgh; Reino UnidoFil: Friedel, Caroline C.. Ludwig-Maximilians-University Munich; AlemaniaFil: Albà, M. Mar. Institució Catalana de Recerca I Estudis Avancats; España. Parque de Investigación Biomédica de Barcelona; EspañaFil: Dölken, Lars. University of Cambridge; Reino Unid

Autonomous role of medullary thymic epithelial cells in central CD4+ T cell tolerance

International audienceMedullary thymic epithelial cells (mTECs) serve an essential function in central tolerance through expressing peripheral tissue-antigens. These antigens may be transferred to and presented by dendritic cells. Therefore, it is unclear whether mTECs, besides being an 'antigen-reservoir', also serve a mandatory function as antigen presenting cells. Here, we reduced MHC class II on mTECs through transgenic expression of a C2TA-specific 'designer miRNA'. This resulted in an enlarged polyclonal CD4 single-positive compartment and, among thymocytes specific for model-antigens expressed in mTECs, enhanced selection of regulatory T cells (Treg) at the expense of deletion. Our data document an autonomous contribution of mTECs to dominant and recessive mechanisms of CD4+ T cell tolerance and support an avidity model of Treg development versus deletion

Autonomous role of medullary thymic epithelial cells in central CD4+ T cell tolerance

International audienceMedullary thymic epithelial cells (mTECs) serve an essential function in central tolerance through expressing peripheral tissue-antigens. These antigens may be transferred to and presented by dendritic cells. Therefore, it is unclear whether mTECs, besides being an 'antigen-reservoir', also serve a mandatory function as antigen presenting cells. Here, we reduced MHC class II on mTECs through transgenic expression of a C2TA-specific 'designer miRNA'. This resulted in an enlarged polyclonal CD4 single-positive compartment and, among thymocytes specific for model-antigens expressed in mTECs, enhanced selection of regulatory T cells (Treg) at the expense of deletion. Our data document an autonomous contribution of mTECs to dominant and recessive mechanisms of CD4+ T cell tolerance and support an avidity model of Treg development versus deletion

Selection of higher order regression models in the analysis of multi-factorial transcription data

Introduction: Many studies examine gene expression data that has been obtained under the influence of multiple factors, such as genetic background, environmental conditions, or exposure to diseases. The interplay of multiple factors may lead to effect modification and confounding. Higher order linear regression models can account for these effects. We present a new methodology for linear model selection and apply it to microarray data of bone marrow-derived macrophages. This experiment investigates the influence of three variable factors: the genetic background of the mice from which the macrophages were obtained, Yersinia enterocolitica infection (two strains, and a mock control), and treatment/non-treatment with interferon-γ. Results: We set up four different linear regression models in a hierarchical order. We introduce the eruption plot as a new practical tool for model selection complementary to global testing. It visually compares the size and significance of effect estimates between two nested models. Using this methodology we were able to select the most appropriate model by keeping only relevant factors showing additional explanatory power. Application to experimental data allowed us to qualify the interaction of factors as either neutral (no interaction), alleviating (co-occurring effects are weaker than expected from the single effects), or aggravating (stronger than expected). We find a biologically meaningful gene cluster of putative C2TA target genes that appear to be co-regulated with MHC class II genes. Conclusions: We introduced the eruption plot as a tool for visual model comparison to identify relevant higher order interactions in the analysis of expression data obtained under the influence of multiple factors. We conclude that model selection in higher order linear regression models should generally be performed for the analysis of multi-factorial microarray data

In vivo hematopoietic Myc activation directs a transcriptional signature in endothelial cells within the bone marrow microenvironment

Cancer pathogenesis involves tumor-intrinsic genomic aberrations and tumor-cell extrinsic mechanisms such as failure of immunosurveillance and structural and functional changes in the microenvironment. Using Myc as a model oncogene we established a conditional mouse bone marrow transduction/transplantation model where the conditional activation of the oncoprotein Myc expressed in the hematopoietic system could be assessed for influencing the host microenvironment. Constitutive ectopic expression of Myc resulted in rapid onset of a lethal myeloproliferative disorder with a median survival of 21 days. In contrast, brief 4-day Myc activation by means of the estrogen receptor (ER) agonist tamoxifen did not result in gross changes in the percentage/frequency of hematopoietic lineages or hematopoietic stem/progenitor cell (HSPC) subsets, nor did Myc activation significantly change the composition of the non-hematopoietic microenvironment defined by phenotyping for CD31, ALCAM, and Sca-1 expression. Transcriptome analysis of endothelial CD45-Ter119-cells from tamoxifen-treated MycER bone marrow graft recipients revealed a gene expression signature characterized by specific changes in the Rho subfamily pathway members, in the transcription-translation-machinery and in angiogenesis. In conclusion, intra-hematopoietic Myc activation results in significant transcriptome alterations that can be attributed to oncogene-induced signals from hematopoietic cells towards the microenvironment, e. g. endothelial cells, supporting the idea that even pre-leukemic HSPC highjack components of the niche which then could protect and support the cancer-initiating population

Experimental setup.

<p>Experimental setup.</p

Interaction effects calculated by multiple linear regression.

<p>This schematic visualization of second order linear regression models interaction effects. The diagram of the linear regression model includes two main covariates (strain <i>H</i> and stimulation with <i>Γ</i>) and their interaction covariate <i>H∶Γ</i>. The main covariates can assume two values (<i>H</i>: C57BL/6 or BALB/c; <i>Γ</i>: IFN-γ stimulation or no stimulation). The arrows indicate the estimated effects β. The pink and turquoise arrows reflect the aggravating or alleviating interaction effects as deviations from the additive model. A second order linear model can dissect the effects arising from two perturbations and their interaction by looking at the magnitude and significance of its regression covariates. Most importantly, the interaction covariate can indicate either an alleviating (weaker than expected from the single intervention effects) or aggravating (stronger than expected) interaction. The linear model includes two main covariates <i>H</i> and <i>Γ</i> and their interaction covariate <i>Η∶Γ</i>.</p