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Le lupus érythémateux disseminé (LED) est une maladie chronique auto-immune caractérisée par la production d'autoanticorps dirigés contre les antigènes nucléaires. Des nombreuses études indiquent un rôle des récepteurs Toll-like (TLR). Des études antérieures de notre laboratoire ont révélé que le TLR8 murin contrôle la fonction de TLR7 dans les cellules dendritiques et est aussi impliqué dans le lupus. TLR9 contrôle également le lupus dépendant de TLR7. Mon projet de thèse avait deux objectifs dont le premier était de comprendre comment le TLR8 et TLR9 contribuent au lupus dépendant de TLR7. En outre, nous avons révélé que TLR8 contrôle l'expression de TLR7 dans les cellules dendritiques, tandis que le TLR9 contrôle la fonction de TLR7 dans les cellules B. Le deuxième objectif était d'étudier l'implication du répresseur transcriptionnel Gfi-1 dans la signalisation des TLR et le développement de lupus en utilisant des souris Genista qui portent une mutation ponctuelle dans le gène Gfi-1. Nous avons constaté que les souris Genista développent un lupus dépendant de TLR7 et que Gfi-1 agit comme un répresseur de la transcription en aval de TLR7 et contrôle l'expression d'Interféron de type I dépendante des TLR. Ainsi, le déséquilibre des interactions entre TLR ainsi que les facteurs transcriptionnels en aval de ces TLR peuvent conduire à des mécanismes d'inflammation et d'auto-immunité qu'il est important de prendre en compte dans le développement d'approches thérapeutiques nouvelles ciblant les TLRs. / Systemic lupus erythematosus is a chronic autoimmune disease characterized by production of autoantibodies against nuclear antigens. Many studies indicate a role for Toll-like receptors (TLRs) in the initiation and establishment of systemic lupus erythematosus (SLE). Previous studies in the lab revealed that murine TLR8 controls TLR7 function in dendritic cells (DCs) and is implicated in SLE. TLR9 also controls TLR7-mediated lupus. My thesis had two aims: the first was to understand how the cooperation of TLR8 and TLR9 contributes to TLR7-mediated lupus. By studying double TLR8/9-deficient mice we found that TLR8 and TLR9 have an additive effect on controlling TLR7-mediated lupus, where TLR8 controls TLR7 function on DCs, while TLR9 restrains TLR7 responses in B cells. The second aim of my thesis was to investigate the implication of Gfi-1 in lupus and TLR signaling by studying Genista mice that carry a hypomorphic mutation of Gfi-1. We found that Genista mice develop TLR7-dependent lupus and that Gfi-1 acts as a transcriptional repressor downstream of TLR7 and controls type-I IFN expression. Thus, unbalancing TLR-interactions and transcription factors downstream of TLRs can lead to inflammation and autoimmunity and these mechanisms have to be taken into account when novel therapeutic approaches are developed that target TLRs.

The fuzzy inference system has been tuned and revamped many times over and applied to numerous domains. New and improved techniques have been presented for fuzzification, implication, rule composition and defuzzification, leaving rule aggregation relatively underrepresented. Current FIS aggregation operators are relatively simple and have remained more-or-less unchanged over the years. For many problems, these simple aggregation operators produce intuitive, useful and meaningful results. However, there exists a wide class of problems for which quality aggregation requires nonditivity and exploitation of interactions between rules. Herein, the fuzzy integral, a parametric non-linear aggregation operator, is used to fill this gap. Specifically, recent advancements in extensions of the fuzzy integral to “unrestricted” fuzzy sets, i.e., subnormal and non-convex, makes this now possible. The roles of two extensions, gFI and the NDFI, are explored and demonstrate when and where to apply these aggregations, and present efficient algorithms to approximate their solutions.

O Modelo de Equações Estruturais (MEE) é habitualmente ajustado para realizar uma análise confirmatória sobre as conjecturas de um pesquisador acerca do relacionamento entre as variáveis observadas e latentes de algum estudo. Na prática, a maneira mais recorrente de avaliar a qualidade das estimativas de um MEE é a partir de medidas que buscam mensurar o quanto a usual matriz de covariâncias clássicas ou ordinárias se distancia da matriz de covariâncias do modelo ajustado, ou a magnitude do afastamento entre as funções de discrepância do modelo hipotético e do modelo saturado. Entretanto, elas podem não captar problemas no ajuste quando há muitos parâmetros a estimar ou bastantes observações. A fim de detectar irregularidades no ajustamento resultantes do impacto provocado pela presença de outliers no conjunto de dados, este trabalho contemplou alguns indicadores conhecidos na literatura, como também considerou alterações no Índice da Qualidade do Ajuste (ou GFI, de Goodness-of-Fit Index) e no Índice Corrigido da Qualidade do Ajuste (ou AGFI, de Ajusted Goodness-of-Fit Index), ambos nas expressões para estimação de parâmetros pelo método de Máxima Verossimilhança, que consistiram em substituir a tradicional matriz de covariâncias pelas matrizes de covariâncias computadas com os seguintes estimadores: Elipsoide de Volume Mínimo, Covariância de Determinante Mínimo, S, MM e Gnanadesikan-Kettenring Ortogonalizado (GKO). Através de estudos de simulação sobre perturbações de desvio de simetria e excesso de curtose, em baixa e alta frações de contaminação, em diferentes tamanhos de amostra e quantidades de variáveis observadas afetadas, foi possível constatar que as propostas de modificação do GFI e do AGFI adaptadas pelo estimador GKO foram as únicas que conseguiram ser informativas em todas essas situações, devendo-se escolher a primeira ou a segunda respectivamente quando a quantidade de parâmetros a serem estimados é baixa ou elevada. / The Structural Equation Model (SEM) is usually set to perform a confirmatory analysis on the assumptions of a researcher about the relationship between the observed variables and the latent variables of such a study. In practice, the most iterant way of evaluating the quality of the estimates of a SEM comes either from procedures of measuring how distant the usual classic or ordinary covariance matrix is from the covariance matrix of the adjusted model, or from the magnitude of the hiatus in discrepancy functions of both the hypothetical model and the saturated model. Nevertheless, they may fail to capture problems in the adjustment in the occurrence of either several parameters to estimate or several observations. This study included indicators known in the literature in order to detect irregularities in the adjustment resulting from the impact caused by the presence of outliers in the data set. This study has also considered changes in both the Goodness-of-Fit Index (GFI) and the Adjusted Goodness-of-Fit Index (AGFI) in the expressions for parameter estimation by Maximum Likelihood method, which consisted in replacing the traditional covariance matrix by the robust covariance matrices computed through the following estimators: Minimum Volume Ellipsoid, Minimum Covariance Determinant, S, MM and Orthogonalized Gnanadesikan-Kettenring (OGK). Through simulation studies on disturbances of both symmetry deviations and excess kurtosis in both low and high fractions of contamination in different sample sizes and quantities of affected observed variables it has become clear that the proposals of modification of both the GFI and the AGFI adapted by the OGK estimator were the only ones able to be informative in all these situations. It must be considered that GFI or AGFI must be used when the number of parameters to be estimated is either low or high, respectively.

O Modelo de Equações Estruturais (MEE) é habitualmente ajustado para realizar uma análise confirmatória sobre as conjecturas de um pesquisador acerca do relacionamento entre as variáveis observadas e latentes de algum estudo. Na prática, a maneira mais recorrente de avaliar a qualidade das estimativas de um MEE é a partir de medidas que buscam mensurar o quanto a usual matriz de covariâncias clássicas ou ordinárias se distancia da matriz de covariâncias do modelo ajustado, ou a magnitude do afastamento entre as funções de discrepância do modelo hipotético e do modelo saturado. Entretanto, elas podem não captar problemas no ajuste quando há muitos parâmetros a estimar ou bastantes observações. A fim de detectar irregularidades no ajustamento resultantes do impacto provocado pela presença de outliers no conjunto de dados, este trabalho contemplou alguns indicadores conhecidos na literatura, como também considerou alterações no Índice da Qualidade do Ajuste (ou GFI, de Goodness-of-Fit Index) e no Índice Corrigido da Qualidade do Ajuste (ou AGFI, de Ajusted Goodness-of-Fit Index), ambos nas expressões para estimação de parâmetros pelo método de Máxima Verossimilhança, que consistiram em substituir a tradicional matriz de covariâncias pelas matrizes de covariâncias computadas com os seguintes estimadores: Elipsoide de Volume Mínimo, Covariância de Determinante Mínimo, S, MM e Gnanadesikan-Kettenring Ortogonalizado (GKO). Através de estudos de simulação sobre perturbações de desvio de simetria e excesso de curtose, em baixa e alta frações de contaminação, em diferentes tamanhos de amostra e quantidades de variáveis observadas afetadas, foi possível constatar que as propostas de modificação do GFI e do AGFI adaptadas pelo estimador GKO foram as únicas que conseguiram ser informativas em todas essas situações, devendo-se escolher a primeira ou a segunda respectivamente quando a quantidade de parâmetros a serem estimados é baixa ou elevada. / The Structural Equation Model (SEM) is usually set to perform a confirmatory analysis on the assumptions of a researcher about the relationship between the observed variables and the latent variables of such a study. In practice, the most iterant way of evaluating the quality of the estimates of a SEM comes either from procedures of measuring how distant the usual classic or ordinary covariance matrix is from the covariance matrix of the adjusted model, or from the magnitude of the hiatus in discrepancy functions of both the hypothetical model and the saturated model. Nevertheless, they may fail to capture problems in the adjustment in the occurrence of either several parameters to estimate or several observations. This study included indicators known in the literature in order to detect irregularities in the adjustment resulting from the impact caused by the presence of outliers in the data set. This study has also considered changes in both the Goodness-of-Fit Index (GFI) and the Adjusted Goodness-of-Fit Index (AGFI) in the expressions for parameter estimation by Maximum Likelihood method, which consisted in replacing the traditional covariance matrix by the robust covariance matrices computed through the following estimators: Minimum Volume Ellipsoid, Minimum Covariance Determinant, S, MM and Orthogonalized Gnanadesikan-Kettenring (OGK). Through simulation studies on disturbances of both symmetry deviations and excess kurtosis in both low and high fractions of contamination in different sample sizes and quantities of affected observed variables it has become clear that the proposals of modification of both the GFI and the AGFI adapted by the OGK estimator were the only ones able to be informative in all these situations. It must be considered that GFI or AGFI must be used when the number of parameters to be estimated is either low or high, respectively.

Das Lipopolysaccharid Bindende Protein (LBP) ist ein überwiegend in der Leber synthetisiertes Akutphaseprotein. Es bindet den Zellwandbestandteil Lipopolysaccharid (LPS) Gram-negativer Bakterien und transportiert es zu zellulären Rezeptoren, wodurch das angeborene Immunsystem aktiviert wird. In dieser Arbeit wird die Regulation der LBP-Expression in Interleukin (IL)-1, IL-6 und Dexamethason (Dex) stimulierten humanen Hepatomzelllinien HuH-7 und HepG2 untersucht. Der wichtigste Stimulator ist dabei IL-6, dessen Wirkung über die Transkriptionsfaktoren (TF) Stat-3, C/EBP-beta und AP-1 vermittelt wird. Für alle 3 TF konnten aktive Bindungsstellen auf dem LBP-Promotor nachgewiesen werden. Für IL-1-Effekte die u. a. über den TF NF-kappaB vermittelt werden, konnten ebenfalls aktive Bindungsstellen nachgewiesen werden. Die Wirkung von Dex wird über Glucocorticoid Responsive Elements (GREs) vermittelt. Auf dem LBP-Promotor befinden, sich wie gezeigt werden konnte, mehrere aktive GREs, wobei einige verstärkend und einige hemmend wirken. Eine zu beobachtende Synergiewirkung von Dex und IL-6 wird durch die Aufregulation des IL-6-Rezeptors durch Dex verursacht. Die LBP-Expression kann durch TGF (Transforming Growth Factor)-beta gehemmt werden. Der TGF-beta-Signalweg über Smads ist in den Hepatomzellen aktiv, vermittelt aber nicht den TGF-beta-Hemmeffekt, sondern eine geringe stimulierende Wirkung, die bei alleiniger TGF-beta-Inkubation auftritt. Die inhibierende Wirkung von TGF-beta wird durch Gfi-1- und AP-1-Bindungsstellen vermittelt. Die Gfi-1-Bindungsstelle nimmt dabei, wie hier erstmals gezeigt werden konnte, eine herausragende Stellung ein. Die Aufklärung der LBP-Regulation und dabei besonders die Hemmung der LBP-Expression kann mittelfristig dazu beitragen, den klinischen Verlauf von inflammatorischen und infektiösen Erkrankungen zu beeinflussen und bietet daher Potenzial für neue Therapieansätze. / Lipopolysaccharide (LPS) binding protein (LBP) is an acute phase protein with the ability to bind and transfer LPS of Gram-negative bacteria. This soluble pattern recognition molecule represents an important defense principle of the host. Regulation of the hepatic acute phase response and its termination are important mechanisms for limiting systemic inflammatory activity of the host. Here were analyze the cooperation of Interleukin (IL)-1, IL-6, and Dexamethasone (Dex) at LBP expression in the hepatoma cell lines HuH-7 and Hep G2. The major inducer of LBP expression is IL-6. Within the LBP promoter numerously highly consensus binding sites such as AP-1, C/EBP-beta? and STAT3 are present, that confer transcriptional activity as shown by truncation and mutation experiments. Additionally, activate NF-kappaB sites activated by IL-1 were detected at the LBP promoter. By mutation experiments of the promoter furthermore were found differentially active glucocorticoid response elements (GREs). The promoter contains GREs enhancing the activity as well as inhibitory ones. The enhancing effect towards LBP expression by Dex was mediated by IL-6. Dex stimulated the expression of the IL-6 receptor and therefore upregulated the IL-6 pathway. Transforming Growth Factor (TGF)-beta is able to inhibit LBP expression in stimulated cells. An AP-1 binding site was identified mediating inhibitory TGF-beta effects towards LBP promoter activity. Furthermore it was shown that a growth factor independence (Gfi)-1 binding site localized near the AP-1 site is essential for mediating the TGF-beta inhibitory effect. The relevancy of the Gfi-1 site fore mediating TGF-beta effects indicates a novel mechanism for understanding inhibitory TGF-beta effects at the transcriptional level. In summary the complex regulation of LBP were elucidate which may help to eventually develop novel intervention strategies for acute phase, sepsis, and septic shock.