Neural detection of complex sound sequences in the absence of consciousness.

The neural response to a violation of sequences of identical sounds is a typical example of the brain's sensitivity to auditory regularities. Previous literature interprets this effect as a pre-attentive and unconscious processing of sensory stimuli. By contrast, a violation to auditory global regularities, i.e. based on repeating groups of sounds, is typically detectable when subjects can consciously perceive them. Here, we challenge the notion that global detection implies consciousness by testing the neural response to global violations in a group of 24 patients with post-anoxic coma (three females, age range 45-87 years), treated with mild therapeutic hypothermia and sedation. By applying a decoding analysis to electroencephalographic responses to standard versus deviant sound sequences, we found above-chance decoding performance in 10 of 24 patients (Wilcoxon signed-rank test, P < 0.001), despite five of them being mildly hypothermic, sedated and unarousable. Furthermore, consistently with previous findings based on the mismatch negativity the progression of this decoding performance was informative of patients' chances of awakening (78% predictive of awakening). Our results show for the first time that detection of global regularities at neural level exists despite a deeply unconscious state.

Association of Hematological Variables with Team-Sport Specific Fitness Performance.

PURPOSE: We investigated association of hematological variables with specific fitness performance in elite team-sport players. METHODS: Hemoglobin mass (Hbmass) was measured in 25 elite field hockey players using the optimized (2 min) CO-rebreathing method. Hemoglobin concentration ([Hb]), hematocrit and mean corpuscular hemoglobin concentration (MCHC) were analyzed in venous blood. Fitness performance evaluation included a repeated-sprint ability (RSA) test (8 x 20 m sprints, 20 s of rest) and the Yo-Yo intermittent recovery level 2 (YYIR2). RESULTS: Hbmass was largely correlated (r = 0.62, P<0.01) with YYIR2 total distance covered (YYIR2TD) but not with any RSA-derived parameters (r ranging from -0.06 to -0.32; all P>0.05). [Hb] and MCHC displayed moderate correlations with both YYIR2TD (r = 0.44 and 0.41; both P<0.01) and RSA sprint decrement score (r = -0.41 and -0.44; both P<0.05). YYIR2TD correlated with RSA best and total sprint times (r = -0.46, P<0.05 and -0.60, P<0.01; respectively), but not with RSA sprint decrement score (r = -0.19, P>0.05). CONCLUSION: Hbmass is positively correlated with specific aerobic fitness, but not with RSA, in elite team-sport players. Additionally, the negative relationships between YYIR2 and RSA tests performance imply that different hematological mechanisms may be at play. Overall, these results indicate that these two fitness tests should not be used interchangeably as they reflect different hematological mechanisms.

Investigating the functions of subregions within anterior hippocampus.

Previous functional MRI (fMRI) studies have associated anterior hippocampus with imagining and recalling scenes, imagining the future, recalling autobiographical memories and visual scene perception. We have observed that this typically involves the medial rather than the lateral portion of the anterior hippocampus. Here, we investigated which specific structures of the hippocampus underpin this observation. We had participants imagine novel scenes during fMRI scanning, as well as recall previously learned scenes from two different time periods (one week and 30 min prior to scanning), with analogous single object conditions as baselines. Using an extended segmentation protocol focussing on anterior hippocampus, we first investigated which substructures of the hippocampus respond to scenes, and found both imagination and recall of scenes to be associated with activity in presubiculum/parasubiculum, a region associated with spatial representation in rodents. Next, we compared imagining novel scenes to recall from one week or 30 min before scanning. We expected a strong response to imagining novel scenes and 1-week recall, as both involve constructing scene representations from elements stored across cortex. By contrast, we expected a weaker response to 30-min recall, as representations of these scenes had already been constructed but not yet consolidated. Both imagination and 1-week recall of scenes engaged anterior hippocampal structures (anterior subiculum and uncus respectively), indicating possible roles in scene construction. By contrast, 30-min recall of scenes elicited significantly less activation of anterior hippocampus but did engage posterior CA3. Together, these results elucidate the functions of different parts of the anterior hippocampus, a key brain area about which little is definitely known.

Dynamical modeling and analysis of large cellular regulatory networks.

The dynamical analysis of large biological regulatory networks requires the development of scalable methods for mathematical modeling. Following the approach initially introduced by Thomas, we formalize the interactions between the components of a network in terms of discrete variables, functions, and parameters. Model simulations result in directed graphs, called state transition graphs. We are particularly interested in reachability properties and asymptotic behaviors, which correspond to terminal strongly connected components (or "attractors") in the state transition graph. A well-known problem is the exponential increase of the size of state transition graphs with the number of network components, in particular when using the biologically realistic asynchronous updating assumption. To address this problem, we have developed several complementary methods enabling the analysis of the behavior of large and complex logical models: (i) the definition of transition priority classes to simplify the dynamics; (ii) a model reduction method preserving essential dynamical properties, (iii) a novel algorithm to compact state transition graphs and directly generate compressed representations, emphasizing relevant transient and asymptotic dynamical properties. The power of an approach combining these different methods is demonstrated by applying them to a recent multilevel logical model for the network controlling CD4+ T helper cell response to antigen presentation and to a dozen cytokines. This model accounts for the differentiation of canonical Th1 and Th2 lymphocytes, as well as of inflammatory Th17 and regulatory T cells, along with many hybrid subtypes. All these methods have been implemented into the software GINsim, which enables the definition, the analysis, and the simulation of logical regulatory graphs.

Constant p53 pathway inactivation in a large series of soft tissue sarcomas with complex genetics.

Alterations of the p53 pathway are among the most frequent aberrations observed in human cancers. We have performed an exhaustive analysis of TP53, p14, p15, and p16 status in a large series of 143 soft tissue sarcomas, rare tumors accounting for around 1% of all adult cancers, with complex genetics. For this purpose, we performed genomic studies, combining sequencing, copy number assessment, and expression analyses. TP53 mutations and deletions are more frequent in leiomyosarcomas than in undifferentiated pleomorphic sarcomas. Moreover, 50% of leiomyosarcomas present TP53 biallelic inactivation, whereas most undifferentiated pleomorphic sarcomas retain one wild-type TP53 allele (87.2%). The spectrum of mutations between these two groups of sarcomas is different, particularly with a higher rate of complex mutations in undifferentiated pleomorphic sarcomas. Most tumors without TP53 alteration exhibit a deletion of p14 and/or lack of mRNA expression, suggesting that p14 loss could be an alternative genotype for direct TP53 inactivation. Nevertheless, the fact that even in tumors altered for TP53, we could not detect p14 protein suggests that other p14 functions, independent of p53, could be implicated in sarcoma oncogenesis. In addition, both p15 and p16 are frequently codeleted or transcriptionally co-inhibited with p14, essentially in tumors with two wild-type TP53 alleles. Conversely, in TP53-altered tumors, p15 and p16 are well expressed, a feature not incompatible with an oncogenic process.

Genome-wide transposon insertion scanning of environmental survival functions in the polycyclic aromatic hydrocarbon degrading bacterium Sphingomonas wittichii RW1.

Sphingomonas wittichii RW1 is a dibenzofuran and dibenzodioxin-degrading bacterium with potentially interesting properties for bioaugmentation of contaminated sites. In order to understand the capacity of the microorganism to survive in the environment we used a genome-wide transposon scanning approach. RW1 transposon libraries were generated with around 22 000 independent insertions. Libraries were grown for an average of 50 generations (five successive passages in batch liquid medium) with salicylate as sole carbon and energy source in presence or absence of salt stress at -1.5 MPa. Alternatively, libraries were grown in sand with salicylate, at 50% water holding capacity, for 4 and 10 days (equivalent to 7 generations). Library DNA was recovered from the different growth conditions and scanned by ultrahigh throughput sequencing for the positions and numbers of inserted transposed kanamycin resistance gene. No transposon reads were recovered in 579 genes (10% of all annotated genes in the RW1 genome) in any of the libraries, suggesting those to be essential for survival under the used conditions. Libraries recovered from sand differed strongly from those incubated in liquid batch medium. In particular, important functions for survival of cells in sand at the short term concerned nutrient scavenging, energy metabolism and motility. In contrast to this, fatty acid metabolism and oxidative stress response were essential for longer term survival of cells in sand. Comparison to transcriptome data suggested important functions in sand for flagellar movement, pili synthesis, trehalose and polysaccharide synthesis and putative cell surface antigen proteins. Interestingly, a variety of genes were also identified, interruption of which cause significant increase in fitness during growth on salicylate. One of these was an Lrp family transcription regulator and mutants in this gene covered more than 90% of the total library after 50 generations of growth on salicylate. Our results demonstrate the power of genome-wide transposon scanning approaches for analysis of complex traits.

Estimation of jump-diffusion processes via empirical characteristic functions

Preface The starting point for this work and eventually the subject of the whole thesis was the question: how to estimate parameters of the affine stochastic volatility jump-diffusion models. These models are very important for contingent claim pricing. Their major advantage, availability T of analytical solutions for characteristic functions, made them the models of choice for many theoretical constructions and practical applications. At the same time, estimation of parameters of stochastic volatility jump-diffusion models is not a straightforward task. The problem is coming from the variance process, which is non-observable. There are several estimation methodologies that deal with estimation problems of latent variables. One appeared to be particularly interesting. It proposes the estimator that in contrast to the other methods requires neither discretization nor simulation of the process: the Continuous Empirical Characteristic function estimator (EGF) based on the unconditional characteristic function. However, the procedure was derived only for the stochastic volatility models without jumps. Thus, it has become the subject of my research. This thesis consists of three parts. Each one is written as independent and self contained article. At the same time, questions that are answered by the second and third parts of this Work arise naturally from the issues investigated and results obtained in the first one. The first chapter is the theoretical foundation of the thesis. It proposes an estimation procedure for the stochastic volatility models with jumps both in the asset price and variance processes. The estimation procedure is based on the joint unconditional characteristic function for the stochastic process. The major analytical result of this part as well as of the whole thesis is the closed form expression for the joint unconditional characteristic function for the stochastic volatility jump-diffusion models. The empirical part of the chapter suggests that besides a stochastic volatility, jumps both in the mean and the volatility equation are relevant for modelling returns of the S&P500 index, which has been chosen as a general representative of the stock asset class. Hence, the next question is: what jump process to use to model returns of the S&P500. The decision about the jump process in the framework of the affine jump- diffusion models boils down to defining the intensity of the compound Poisson process, a constant or some function of state variables, and to choosing the distribution of the jump size. While the jump in the variance process is usually assumed to be exponential, there are at least three distributions of the jump size which are currently used for the asset log-prices: normal, exponential and double exponential. The second part of this thesis shows that normal jumps in the asset log-returns should be used if we are to model S&P500 index by a stochastic volatility jump-diffusion model. This is a surprising result. Exponential distribution has fatter tails and for this reason either exponential or double exponential jump size was expected to provide the best it of the stochastic volatility jump-diffusion models to the data. The idea of testing the efficiency of the Continuous ECF estimator on the simulated data has already appeared when the first estimation results of the first chapter were obtained. In the absence of a benchmark or any ground for comparison it is unreasonable to be sure that our parameter estimates and the true parameters of the models coincide. The conclusion of the second chapter provides one more reason to do that kind of test. Thus, the third part of this thesis concentrates on the estimation of parameters of stochastic volatility jump- diffusion models on the basis of the asset price time-series simulated from various "true" parameter sets. The goal is to show that the Continuous ECF estimator based on the joint unconditional characteristic function is capable of finding the true parameters. And, the third chapter proves that our estimator indeed has the ability to do so. Once it is clear that the Continuous ECF estimator based on the unconditional characteristic function is working, the next question does not wait to appear. The question is whether the computation effort can be reduced without affecting the efficiency of the estimator, or whether the efficiency of the estimator can be improved without dramatically increasing the computational burden. The efficiency of the Continuous ECF estimator depends on the number of dimensions of the joint unconditional characteristic function which is used for its construction. Theoretically, the more dimensions there are, the more efficient is the estimation procedure. In practice, however, this relationship is not so straightforward due to the increasing computational difficulties. The second chapter, for example, in addition to the choice of the jump process, discusses the possibility of using the marginal, i.e. one-dimensional, unconditional characteristic function in the estimation instead of the joint, bi-dimensional, unconditional characteristic function. As result, the preference for one or the other depends on the model to be estimated. Thus, the computational effort can be reduced in some cases without affecting the efficiency of the estimator. The improvement of the estimator s efficiency by increasing its dimensionality faces more difficulties. The third chapter of this thesis, in addition to what was discussed above, compares the performance of the estimators with bi- and three-dimensional unconditional characteristic functions on the simulated data. It shows that the theoretical efficiency of the Continuous ECF estimator based on the three-dimensional unconditional characteristic function is not attainable in practice, at least for the moment, due to the limitations on the computer power and optimization toolboxes available to the general public. Thus, the Continuous ECF estimator based on the joint, bi-dimensional, unconditional characteristic function has all the reasons to exist and to be used for the estimation of parameters of the stochastic volatility jump-diffusion models.

Role of major histocompatibility complex class II expression by non-hematopoietic cells in autoimmune and inflammatory disorders: facts and fiction.

It is well established that interactions between CD4(+) T cells and major histocompatibility complex class II (MHCII) positive antigen-presenting cells (APCs) of hematopoietic origin play key roles in both the maintenance of tolerance and the initiation and development of autoimmune and inflammatory disorders. In sharp contrast, despite nearly three decades of intensive research, the functional relevance of MHCII expression by non-hematopoietic tissue-resident cells has remained obscure. The widespread assumption that MHCII expression by non-hematopoietic APCs has an impact on autoimmune and inflammatory diseases has in most instances neither been confirmed nor excluded by indisputable in vivo data. Here we review and put into perspective conflicting in vitro and in vivo results on the putative impact of MHCII expression by non-hematopoietic APCs-in both target organs and secondary lymphoid tissues-on the initiation and development of representative autoimmune and inflammatory disorders. Emphasis will be placed on the lacunar status of our knowledge in this field. We also discuss new mouse models-developed on the basis of our understanding of the molecular mechanisms that regulate MHCII expression-that constitute valuable tools for filling the severe gaps in our knowledge on the functions of non-hematopoietic APCs in inflammatory conditions.

Kernel-based Mapping of Meteorological Fields in Complex Orography

Due to the advances in sensor networks and remote sensing technologies, the acquisition and storage rates of meteorological and climatological data increases every day and ask for novel and efficient processing algorithms. A fundamental problem of data analysis and modeling is the spatial prediction of meteorological variables in complex orography, which serves among others to extended climatological analyses, for the assimilation of data into numerical weather prediction models, for preparing inputs to hydrological models and for real time monitoring and short-term forecasting of weather.In this thesis, a new framework for spatial estimation is proposed by taking advantage of a class of algorithms emerging from the statistical learning theory. Nonparametric kernel-based methods for nonlinear data classification, regression and target detection, known as support vector machines (SVM), are adapted for mapping of meteorological variables in complex orography.With the advent of high resolution digital elevation models, the field of spatial prediction met new horizons. In fact, by exploiting image processing tools along with physical heuristics, an incredible number of terrain features which account for the topographic conditions at multiple spatial scales can be extracted. Such features are highly relevant for the mapping of meteorological variables because they control a considerable part of the spatial variability of meteorological fields in the complex Alpine orography. For instance, patterns of orographic rainfall, wind speed and cold air pools are known to be correlated with particular terrain forms, e.g. convex/concave surfaces and upwind sides of mountain slopes.Kernel-based methods are employed to learn the nonlinear statistical dependence which links the multidimensional space of geographical and topographic explanatory variables to the variable of interest, that is the wind speed as measured at the weather stations or the occurrence of orographic rainfall patterns as extracted from sequences of radar images. Compared to low dimensional models integrating only the geographical coordinates, the proposed framework opens a way to regionalize meteorological variables which are multidimensional in nature and rarely show spatial auto-correlation in the original space making the use of classical geostatistics tangled.The challenges which are explored during the thesis are manifolds. First, the complexity of models is optimized to impose appropriate smoothness properties and reduce the impact of noisy measurements. Secondly, a multiple kernel extension of SVM is considered to select the multiscale features which explain most of the spatial variability of wind speed. Then, SVM target detection methods are implemented to describe the orographic conditions which cause persistent and stationary rainfall patterns. Finally, the optimal splitting of the data is studied to estimate realistic performances and confidence intervals characterizing the uncertainty of predictions.The resulting maps of average wind speeds find applications within renewable resources assessment and opens a route to decrease the temporal scale of analysis to meet hydrological requirements. Furthermore, the maps depicting the susceptibility to orographic rainfall enhancement can be used to improve current radar-based quantitative precipitation estimation and forecasting systems and to generate stochastic ensembles of precipitation fields conditioned upon the orography.

Introduction of snow and geomorphic disturbance variables into predictive models of alpine plant distribution in the Western Swiss Alps

Indirect topographic variables have been used successfully as surrogates for disturbance processes in plant species distribution models (SDM) in mountain environments. However, no SDM studies have directly tested the performance of disturbance variables. In this study, we developed two disturbance variables: a geomorphic index (GEO) and an index of snow redistribution by wind (SNOW). These were developed in order to assess how they improved both the fit and predictive power of presenceabsence SDM based on commonly used topoclimatic (TC) variables for 91 plants in the Western Swiss Alps. The individual contribution of the disturbance variables was compared to TC variables. Maps of models were prepared to spatially test the effect of disturbance variables. On average, disturbance variables significantly improved the fit but not the predictive power of the TC models and their individual contribution was weak (5.6% for GEO and 3.3% for SNOW). However their maximum individual contribution was important (24.7% and 20.7%). Finally, maps including disturbance variables (i) were significantly divergent from TC models in terms of predicted suitable surfaces and connectivity between potential habitats, and (ii) were interpreted as more ecologically relevant. Disturbance variables did not improve the transferability of models at the local scale in a complex mountain system, and the performance and contribution of these variables were highly species-specific. However, improved spatial projections and change in connectivity are important issues when preparing projections under climate change because the future range size of the species will determine the sensitivity to changing conditions.

Modularity of auditory spatial representations

Abstract : Auditory spatial functions are of crucial importance in everyday life. Determining the origin of sound sources in space plays a key role in a variety of tasks including orientation of attention, disentangling of complex acoustic patterns reaching our ears in noisy environments. Following brain damage, auditory spatial processing can be disrupted, resulting in severe handicaps. Complaints of patients with sound localization deficits include the inability to locate their crying child or being over-loaded by sounds in crowded public places. Yet, the brain bears a large capacity for reorganization following damage and/or learning. This phenomenon is referred as plasticity and is believed to underlie post-lesional functional recovery as well as learning-induced improvement. The aim of this thesis was to investigate the organization and plasticity of different aspects of auditory spatial functions. Overall, we report the outcomes of three studies: In the study entitled "Learning-induced plasticity in auditory spatial representations" (Spierer et al., 2007b), we focused on the neurophysiological and behavioral changes induced by auditory spatial training in healthy subjects. We found that relatively brief auditory spatial discrimination training improves performance and modifies the cortical representation of the trained sound locations, suggesting that cortical auditory representations of space are dynamic and subject to rapid reorganization. In the same study, we tested the generalization and persistence of training effects over time, as these are two determining factors in the development of neurorehabilitative intervention. In "The path to success in auditory spatial discrimination" (Spierer et al., 2007c), we investigated the neurophysiological correlates of successful spatial discrimination and contribute to the modeling of the anatomo-functional organization of auditory spatial processing in healthy subjects. We showed that discrimination accuracy depends on superior temporal plane (STP) activity in response to the first sound of a pair of stimuli. Our data support a model wherein refinement of spatial representations occurs within the STP and that interactions with parietal structures allow for transformations into coordinate frames that are required for higher-order computations including absolute localization of sound sources. In "Extinction of auditory stimuli in hemineglect: space versus ear" (Spierer et al., 2007a), we investigated auditory attentional deficits in brain-damaged patients. This work provides insight into the auditory neglect syndrome and its relation with neglect symptoms within the visual modality. Apart from contributing to a basic understanding of the cortical mechanisms underlying auditory spatial functions, the outcomes of the studies also contribute to develop neurorehabilitation strategies, which are currently being tested in clinical populations.

Spatial modelling of biodiversity at the community level

1. Statistical modelling is often used to relate sparse biological survey data to remotely derived environmental predictors, thereby providing a basis for predictively mapping biodiversity across an entire region of interest. The most popular strategy for such modelling has been to model distributions of individual species one at a time. Spatial modelling of biodiversity at the community level may, however, confer significant benefits for applications involving very large numbers of species, particularly if many of these species are recorded infrequently. 2. Community-level modelling combines data from multiple species and produces information on spatial pattern in the distribution of biodiversity at a collective community level instead of, or in addition to, the level of individual species. Spatial outputs from community-level modelling include predictive mapping of community types (groups of locations with similar species composition), species groups (groups of species with similar distributions), axes or gradients of compositional variation, levels of compositional dissimilarity between pairs of locations, and various macro-ecological properties (e.g. species richness). 3. Three broad modelling strategies can be used to generate these outputs: (i) 'assemble first, predict later', in which biological survey data are first classified, ordinated or aggregated to produce community-level entities or attributes that are then modelled in relation to environmental predictors; (ii) 'predict first, assemble later', in which individual species are modelled one at a time as a function of environmental variables, to produce a stack of species distribution maps that is then subjected to classification, ordination or aggregation; and (iii) 'assemble and predict together', in which all species are modelled simultaneously, within a single integrated modelling process. These strategies each have particular strengths and weaknesses, depending on the intended purpose of modelling and the type, quality and quantity of data involved. 4. Synthesis and applications. The potential benefits of modelling large multispecies data sets using community-level, as opposed to species-level, approaches include faster processing, increased power to detect shared patterns of environmental response across rarely recorded species, and enhanced capacity to synthesize complex data into a form more readily interpretable by scientists and decision-makers. Community-level modelling therefore deserves to be considered more often, and more widely, as a potential alternative or supplement to modelling individual species.

The role of the Estrogen-Responsive B box protein in skin morphogenesis, wound repair and skin disease

SUMMARY: EBBP is a poorly characterized member of the RBCC/TRIM family (RING finger B-box coiled-coilltripartite motif). It is ubiquitously expressed, but particularly high levels are found in keratinocytes. There is evidence that EBBP is involved in inflammatory processes, since it can interact with pro-interleukin-1 ß (prolL-1 ß) in human macrophages and keratinocytes, and its downregulation results in reduced secretion of IL-1 ß. IL-1ß activation and secretion requires the proteolytic cleavage of prolL-1ß by caspase-1, which in turn is actìvated by a protein complex called the inflammasome. As it has been demonstrated that EBBP can bind two different proteins of the inflammasome (NALP-1 and caspase 1), we assumed that EBBP plays a role in the regulation of inflammation and that the inflammasome, which has as yet only been described in ínflammatory cells, may also exist in keratinocytes. Indeed, I could show in my thesis that the inflammasome components are expressed in human keratinócytes at the RNA and protein level and also in vivo in human epidermis. After irradiation with a physiological dose of UVB, keratinocytes activated prolL-1ß and secreted prolL-1 a, IL-1 ß, prolL-18 and inflammasome proteins, although all these proteins lack a classical signal peptide. The secretion was dependent on caspase-1 activity, but not on de novo protein synthesis. Knock-down of NALP1 and -3, caspase-1 and -5, EBBP and Asc strongly reduced the secretion of IL-1 ß, demonstrating that also in keratinocytes inflammasome proteins are directly involved in maturation of this cytokine. These results demonstrate for the first time the presence of an active inflammasome in non-professional immune cells. Moreover, they show that UV irradiation is a stimulus for inflammasome activation in keratinocytes. For the analysis of the ín vivo functions of EBBP, transgenic mice overexpressing EBBP in the epidermis were generated. To examine the influence of EBBP overexpression on inflammatory processes, we subjected the mice to different challenges, which induce inflammation. Wound-healing, UVB irradiation and delayed hypersensitivity were tested, but we did not observe any phenotype in the K14-EBBP mice. Besides, a conditional ebbp knockout mouse has been obtained, which will allow to determine the effects of EBBP gene deletion in different tissues and organs. RESUME: EBBP est un membre encore mal connu de la famille des RBCC/TRIM (RING finger B-box coiled-coil/tripartite motif). Il est exprimé de manière ubiquitaire, et en particulier dans les kératinocytes. EBBP étant capable d'interagir avec la prointerleukine-1 ß (prolL-1 ß) dans les macrophages et les kératinocytes humains et de réguler la sécrétion de l'IL-1 ß, il est très probable que cette protéine est impliquée dans l'inflammation. L'activation et la sécrétion de l'IL-1 ß requièrent le clivage protéolytique de son précurseur prolL-1ß par la caspase-1, qui est elle-même activée par un complexe protéique appelé l'inflammasome. Comme il a été démontré qu'EBBP peut lier deux protéines de l'inflammasome (NALP-1 et caspase-1), nous avons émis l'hypothèse qu'EBBP joue un rôle dans la régulation de l'inflammation et que l'inflammasome, jusqu'ici décrit exclusivement dans des cellules inflammatoires, existe dans les kératinocytes. En effet, j'ai pu montrer dans ma thèse que les composants de l'inflammasome sont exprimés dans les kératinocytes humains ainsi que in vivo dans l'épiderme humain. Après irradiation avec une dose, physiologique d'UVB, les kératinocytes activent la prolL-1 ß et sécrètent la prolL-1a, l'IL-1 ß, la prolL-18 et des protéines de l'inflammasome, bien que toutes ces protéines soient dépourvues de peptide signal. La sécrétion dépend de la caspase-1 mais pas de la synthèse protéique de novo. Le knock-down de NALP-1 et -3, des caspase-1 et -5, d'EBBP et d'Asc réduit de manière marquée la sécrétion d'IL-1 ß, démontrant que dans les kératinocytes également, les protéines de l'inflammasome sont impliquées directement dans la maturation de cette cytokine. Ces résultats démontrent pour la première fois la présence d'un inflammasome actif dans des cellules immunitaires non professionnelles. De plus, ils montrent que l'irradiation aux UV est un stimulus pour l'activation de l'inflammasome dans les kératinocytes. Pour l'analyse des fonctions d'EBBP in vivo, nous avons généré des souris transgéniques qui surexpriment EBBP dans l'épiderme. En vue d'examiner l'influence de la surexpression d'EBBP sur le processus inflammatoire, nous avons soumis ces souris à differents modèles d'inflammation. Nous avons testé cicatrisation, UVB et hypersensibilité retardée, mais n'avons pas observé de phénotype chez les souris transgéniques. En parallèle, nous avons également généré des souris knock-out pour ebbp qui devraient nous permettre de déterminer les effets de la suppression d'EBBP dans différents tissus et organes.