Efficient total variation algorithm for fetal brain MRI reconstruction.

Fetal MRI reconstruction aims at finding a high-resolution image given a small set of low-resolution images. It is usually modeled as an inverse problem where the regularization term plays a central role in the reconstruction quality. Literature has considered several regularization terms s.a. Dirichlet/Laplacian energy, Total Variation (TV)- based energies and more recently non-local means. Although TV energies are quite attractive because of their ability in edge preservation, standard explicit steepest gradient techniques have been applied to optimize fetal-based TV energies. The main contribution of this work lies in the introduction of a well-posed TV algorithm from the point of view of convex optimization. Specifically, our proposed TV optimization algorithm or fetal reconstruction is optimal w.r.t. the asymptotic and iterative convergence speeds O(1/n2) and O(1/√ε), while existing techniques are in O(1/n2) and O(1/√ε). We apply our algorithm to (1) clinical newborn data, considered as ground truth, and (2) clinical fetal acquisitions. Our algorithm compares favorably with the literature in terms of speed and accuracy.

Mental imagery for full and upper human bodies: common right hemisphere activations and distinct extrastriate activations.

The processing of human bodies is important in social life and for the recognition of another person's actions, moods, and intentions. Recent neuroimaging studies on mental imagery of human body parts suggest that the left hemisphere is dominant in body processing. However, studies on mental imagery of full human bodies reported stronger right hemisphere or bilateral activations. Here, we measured functional magnetic resonance imaging during mental imagery of bilateral partial (upper) and full bodies. Results show that, independently of whether a full or upper body is processed, the right hemisphere (temporo-parietal cortex, anterior parietal cortex, premotor cortex, bilateral superior parietal cortex) is mainly involved in mental imagery of full or partial human bodies. However, distinct activations were found in extrastriate cortex for partial bodies (right fusiform face area) and full bodies (left extrastriate body area). We propose that a common brain network, mainly on the right side, is involved in the mental imagery of human bodies, while two distinct brain areas in extrastriate cortex code for mental imagery of full and upper bodies.

Histones trigger sterile inflammation by activating the NLRP3 inflammasome.

Sterile cell death mediated inflammation is linked to several pathological disorders and involves danger recognition of intracellular molecules released by necrotic cells that activate different groups of innate pattern recognition receptors. Toll-like receptors directly interact with their extrinsic or intrinsic agonists and induce multiple proinflammatory mediators. In contrast, the NLRP3 inflammasome is rather thought to represent a downstream element integrating various indirect stimuli into proteolytic cleavage of interleukin (IL)-1β and IL-18. Here, we report that histones released from necrotic cells induce IL-1β secretion in an NLRP3-ASC-caspase-1-dependent manner. Genetic deletion of NLRP3 in mice significantly attenuated histone-induced IL-1β production and neutrophil recruitment. Furthermore, necrotic cells induced neutrophil recruitment, which was significantly reduced by histone-neutralizing antibodies or depleting extracellular histones via enzymatic degradation. These results identify cytosolic uptake of necrotic cell-derived histones as a triggering mechanism of sterile inflammation, which involves NLRP3 inflammasome activation and IL-1β secretion via oxidative stress.

Surface assembly of poly(I:C) on PEGylated microspheres to shield from adverse interactions with fibroblasts.

By expressing an array of pattern recognition receptors (PRRs), fibroblasts play an important role in stimulating and modulating the response of the innate immune system. The TLR3 ligand polyriboinosinic acid-polyribocytidylic acid, poly(I:C), a mimic of viral dsRNA, is a vaccine adjuvant candidate to activate professional antigen presenting cells (APCs). However, owing to its ligation with extracellular TLR3 on fibroblasts, subcutaneously administered poly(I:C) bears danger towards autoimmunity. It is thus in the interest of its clinical safety to deliver poly(I:C) in such a way that its activation of professional APCs is as efficacious as possible, whereas its interference with non-immune cells such as fibroblasts is controlled or even avoided. Complementary to our previous work with monocyte-derived dendritic cells (MoDCs), here we sought to control the delivery of poly(I:C) surface-assembled on microspheres to human foreskin fibroblasts (HFFs). Negatively charged polystyrene (PS) microspheres were equipped with a poly(ethylene glycol) (PEG) corona through electrostatically driven coatings with a series of polycationic poly(L-lysine)-graft-poly(ethylene glycol) copolymers, PLL-g-PEG, of varying grafting ratios g from 2.2 up to 22.7. Stable surface assembly of poly(I:C) was achieved by incubation of polymer-coated microspheres with aqueous poly(I:C) solutions. Notably, recognition of both surface-assembled and free poly(I:C) by extracellular TLR3 on HFFs halted their phagocytic activity. Ligation of surface-assembled poly(I:C) with extracellular TLR3 on HFFs could be controlled by tuning the grafting ratio g and thus the chain density of the PEG corona. When assembled on PLL-5.7-PEG-coated microspheres, poly(I:C) was blocked from triggering class I MHC molecule expression on HFFs. Secretion of interleukin (IL)-6 by HFFs after exposure to surface-assembled poly(I:C) was distinctly lower as compared to free poly(I:C). Overall, surface assembly of poly(I:C) may have potential to contribute to the clinical safety of this vaccine adjuvant candidate.

Automated identification of minimal myocardial motion for improved image quality on MR angiography at 3 T

OBJECTIVE: Imaging during a period of minimal myocardial motion is of paramount importance for coronary MR angiography (MRA). The objective of our study was to evaluate the utility of FREEZE, a custom-built automated tool for the identification of the period of minimal myocardial motion, in both a moving phantom at 1.5 T and 10 healthy adults (nine men, one woman; mean age, 24.9 years; age range, 21-32 years) at 3 T. CONCLUSION: Quantitative analysis of the moving phantom showed that dimension measurements approached those obtained in the static phantom when using FREEZE. In vitro, vessel sharpness, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were significantly improved when coronary MRA was performed during the software-prescribed period of minimal myocardial motion (p < 0.05). Consistent with these objective findings, image quality assessments by consensus review also improved significantly when using the automated prescription of the period of minimal myocardial motion. The use of FREEZE improves image quality of coronary MRA. Simultaneously, operator dependence can be minimized while the ease of use is improved.

The immunological, environmental, and phylogenetic perpetrators of metastatic leishmaniasis.

Cutaneous leishmaniases have persisted for centuries as chronically disfiguring parasitic infections affecting millions of people across the subtropics. Symptoms range from the more prevalent single, self-healing cutaneous lesion to a persistent, metastatic disease, where ulcerations and granulomatous nodules can affect multiple secondary sites of the skin and delicate facial mucosa, even sometimes diffusing throughout the cutaneous system as a papular rash. The basis for such diverse pathologies is multifactorial, ranging from parasite phylogeny to host immunocompetence and various environmental factors. Although complex, these pathologies often prey on weaknesses in the innate immune system and its pattern recognition receptors. This review explores the observed and potential associations among the multifactorial perpetrators of infectious metastasis and components of the innate immune system.

Metastatic leishmaniasis. A chronic inflammatory response to Leishmania's viral endosymbiont.

Leishmania parasites have been plaguing humankind for centuries as a range of skin diseases named the cutaneous leishmaniases (CL). Carried in a hematophagous sand fly, Leishmania usually infests the skin surrounding the bite site, causing a destructive immune response that may persist for months or even years. The various symptomatic outcomes of CL range from a benevolent self- healing reddened bump to extensive open ulcerations, resistant to treatment and resulting in life- changing disfiguration. Many of these more aggressive outcomes are geographically isolated within the habitats of certain Neotropical Leishmania species; where about 15% of cases experience metastatic complications. However, despite this correlation, genetic analysis has revealed no major differences between species causing the various disease forms. We have recently identified a cytoplasmic dsRNA virus within metastatic L. guyanensis parasites that acts as a potent innate immunogen capable of worsening lesionai inflammation and prolonging parasite survival. The dsRNA genome of Leishmania RNA virus (LRV) binds and stimulates Toll-Like-Receptor-3 (TLR3), inducing this destructive inflammation, which we speculate as a factor contributing to the development of metastatic disease. This thesis establishes the first experimental model of LRV-mediated leishmanial metastasis and investigates the role of non-TLR3 viral recognition pathways in LRV-mediated pathology. Viral dsRNA can be detected by various non-TLR3 pattern recognition receptors (PRR); two such PRR groups are the RLRs (Retinoic acid-inducible gene 1 like receptors) and the NLRs (nucleotide- binding domain, leucine-rich repeat containing receptors). The RLRs are designed to detect viral dsRNA in the cytoplasm, while the NLRs react to molecular "danger" signals of cell damage, often oligomerizing into molecular scaffolds called "inflammasomes" that activate a potent inflammatory cascade. Interestingly, we found that neither RLR signalling nor the inflammasome pathway had an effect on LRV-mediated pathology. In contrast, we found a dramatic inflammasome independent effect for the NLR family member, NLRP10, where a knockout mouse model showed little evidence of disease. This phenotype was mimicked in an NLR knockout with which NLRP10 is known to interact: NLRC2. As this pathway induces the chronic inflammatory cell lineage TH17, we investigated the role of its key chronic inflammatory cytokine, IL-17A, in human patients infected by L. guyanensis. Indeed, patients infected with LRV+ parasites had a significantly increased level of IL-17A in lesionai biopsies. Interestingly, LRV presence was also associated with a significant decrease in the correlate of protection, IFN-y. This association was repeated in our murine model, where after we were able to establish the first experimental model of LRV-dependent leishmanial metastasis, which was mediated by IL-17A in the absence of IFN-y. Finally, we tested a new inhibitor of IL-17A secretion, SR1001, and reveal its potential as a Prophylactic immunomodulator and potent parasitotoxic drug. Taken together, these findings provide a basis for anti-IL-17A as a feasible therapeutic intervention to prevent and treat the metastatic complications of cutaneous leishmaniasis. -- Les parasites Leishmania infectent l'homme depuis des siècles causant des affections cutanées, appelées leishmanioses cutanées (LC). Le parasite est transmis par la mouche des sables et réside dans le derme à l'endroit de la piqûre. Au niveau de la peau, le parasite provoque une réponse immunitaire destructrice qui peut persister pendant des mois voire des années. Les symptômes de LC vont d'une simple enflure qui guérit spontanément jusqu' à de vastes ulcérations ouvertes, résistantes aux traitements. Des manifestations plus agressives sont déterminées par les habitats géographiques de certaines espèces de Leishmania. Dans ces cas, environ 15% des patients développent des lésions métastatiques. Aucun «facteur métastatique» n'a encore été trouvé à ce jour dans ces espèces. Récemment, nous avons pu identifier un virus résidant dans certains parasites métastatiques présents en Guyane française (appelé Leishmania-virus, ou LV) et qui confère un avantage de survie à son hôte parasitaire. Ce virus active fortement la réponse inflammatoire, aggravant l'inflammation et prolongeant l'infection parasitaire. Afin de diagnostiquer, prévenir et traiter ces lésions, nous nous sommes intéressés à identifier les composants de la voie de signalisation anti-virale, responsables de la persistance de cette inflammation. Cette étude décrit le premier modèle expérimental de métastases de la leishmaniose induites par LV, et identifie plusieurs composants de la voie inflammatoire anti-virale qui facilite la pathologie métastatique. Contrairement à l'homme, les souris de laboratoire infectées par des Leishmania métastatiques (contenant LV, LV+) ne développent pas de lésions métastatiques et guérissent après quelques semaines d'infection. Après avoir analysé un groupe de patients atteints de leishmaniose en Guyane française, nous avons constaté que les personnes infectées avec les parasites métastatiques LV+ avaient des niveaux significativement plus faibles d'un composant immunitaire protecteur important, appelé l'interféron (IFN)-y. En utilisant des souris génétiquement modifiées, incapables de produire de l'IFN-y, nous avons observé de telles métastases. Après inoculation dans le coussinet plantaire de souris IFN-y7" avec des parasites LV+ ou LV-, nous avons démontré que seules les souris infectées avec des leishmanies ayant LV développent de multiples lésions secondaires sur la queue. Comme nous l'avons observé chez l'homme, ces souris sécrètent une quantité significativement élevée d'un composant inflammatoire destructeur, l'interleukine (IL)-17. IL-17 a été incriminée pour son rôle dans de nombreuses maladies inflammatoires chroniques. On a ainsi trouvé un rôle destructif similaire pour l'IL-17 dans la leishmaniose métastatique. Nous avons confirmé ce rôle en abrogeant IL-17 dans des souris IFN-y7- ce qui ralentit l'apparition des métastases. Nous pouvons donc conclure que les métastases de la leishmaniose sont induites par l'IL-17 en absence d'IFN-v. En analysant plus en détails les voies de signalisation anti-virale induites par LV, nous avons pu exclure d'autres voies d'activation de la réponse inflammatoire. Nous avons ainsi démontré que la signalisation par LV est indépendante de la signalisation inflammatoire de type « inflammasome ». En revanche, nous avons pu y lier plusieurs autres molécules, telles que NLRP10 et NLRC2, connues pour leur synergie avec les réponses inflammatoires. Cette nouvelle voie pourrait être la cible pour des médicaments inhibant l'inflammation. En effet, un nouveau médicament qui bloque la production d'IL-17 chez la souris s'est montré prometteur dans notre modèle : il a réduit le gonflement des lésions ainsi que la charge parasitaire, indiquant que la voie anti-virale /inflammatoire est une approche thérapeutique possible pour prévenir et traiter cette infection négligée.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images.

This paper presents a validation study on statistical nonsupervised brain tissue classification techniques in magnetic resonance (MR) images. Several image models assuming different hypotheses regarding the intensity distribution model, the spatial model and the number of classes are assessed. The methods are tested on simulated data for which the classification ground truth is known. Different noise and intensity nonuniformities are added to simulate real imaging conditions. No enhancement of the image quality is considered either before or during the classification process. This way, the accuracy of the methods and their robustness against image artifacts are tested. Classification is also performed on real data where a quantitative validation compares the methods' results with an estimated ground truth from manual segmentations by experts. Validity of the various classification methods in the labeling of the image as well as in the tissue volume is estimated with different local and global measures. Results demonstrate that methods relying on both intensity and spatial information are more robust to noise and field inhomogeneities. We also demonstrate that partial volume is not perfectly modeled, even though methods that account for mixture classes outperform methods that only consider pure Gaussian classes. Finally, we show that simulated data results can also be extended to real data.

Molecular characterization of the Carma1-related protein card9

ABSTRACT : Fungal infections have become a major source of diseases in immuncompromised patients, but are quite benign in healthy individuals. As fungi are eukaryotes, and share many biological processes with humans, many antifungal drugs can cause toxicity in the patients. Therefore, the characterization of signaling pathways specific to the anti-fungal immune response is relevant for the better understanding of the disease and the development of new therapeutic approaches. Dectin-1 is the major mammalian pattern recognition receptor for the fungal component zymosan. Dectin-1 is an innate non-Toll-like receptor containing immunoreceptor tyrosine-based activation motifs (ITAMs). Card9, Bc110 and Maltl are proteins that have been shown to play a key role in the Dectin-l-induced signaliñg pathway by controlling Dectin-l-mediated cell activation, cytokine production and innate anti-fungal immunity in mice. Here we investigate the role of the Card9-Bc110-Maltl complex in humans using the monocytic cell line THP-1. We show that Card9 interacts with Bc110 through a CARD-CARD interaction and that interaction of Card9 with Bc110 is required for NF-xB activation. We further demonstrate that Card9 is phosphorylated in its C-terminal part on serine residues. The phosphorylation status of Card9 can influence its ability to active NF-xB, since mutation of the phosphorylation sites increases its ability to activate NF-xB. We find that Card9 is expressed in myeloid derived cells, such as the human monocytic cell lines THP1 and U937, and in human monocyte-enriched PBLs and monocyte-derived DCs. Our findings demonstrate that Card9 is implicated in anti-fungal responses, since silencing of Card9 as well as of Bc110 and Maltl diminishes the capacity of THP1 cells to produce TNF-a in response to zymosan. Interestingly, activation of the NF-xB and MAPK pathway remained normal and levels of TNF-a mRNA produced were also not affected in THP 1 cells silenced for the expression of Card9, Bc110 or Malt1. Using a Malt1 inhibitor, we provide evidence that the proteolytic activity of Malt1 is needed for zymosan-induced TNF-a production in THP 1 cells and bone marrow-derived macrophages of mice, but further experiments are required to confirm these findings and identify the substrate(s) of Malt1. In conclusion, our results reveal an important role for Card9 in the innate immune response of human macrophages to fungi. RÉSUMÉ : Les infections fongiques sont une source majeure de maladie chez les patients immunodéprimés, alors qu'elles sont plutôt bénignes chez les individus sains. Comme les champignons sont des eucaryotes et partagent beaucoup de processus biologiques avec les humains, les médicaments antifongiques peuvent être source de toxicité chez les patients. Il est donc important de mieux caractériser les voies de signalisation intracellulaire des réponses anti-fongiques pour pouvoir développer de nouvelles approches thérapeutiques. La protéine Dectin-1 est le récepteur principal du composé fongique zymosan. Les protéines Card9, Bc110 et Maltl ont été décrites comme jouant un rôle primordial dans les signaux d'activation induits par Dectin-l, en contrôlant l'activité cellulaire, la production de cytokines et la défense anti-fongique dans les souris. Dans cette étude, nous investiguons le rôle du complexe Card9-Bc110-Maltl dans la lignée monocytaire humaine THP1. Nous montrons que Card9 interagit avec Bc110 par une interaction CARD-CARD et que cette interaction est requise pour activer le facteur de transcription NF-xB. Nous observons que Card9 est phosphorylé dans sa partie C-terminale sur des résidus serine et que l'état de phosphorylation de Card9 influence sa capacité à activer NF-xB. En effet, sa capacité à activer NF-xB est augmentée, après mutation des sites de phosphorylation. La génération d'un anticorps spécifique dirigé contre Card9 nous a permis de démontrer que Card9 est exprimé dans des cellules myéloïdes comme les lignées cellulaires monocytiques THP-1 et U-937, ainsi que dans les cellules dendritiques humaines. Nos résultats démontrent que Card9 est impliqué dans la réponse immunitaire antifongique puisque la réduction de l'expression de Card9 ainsi que de Bc110 et de Malt1 diminue la capacité des THP-1 à produire du TNF-a en réponse au zymosan. Par contre, les voies de signalisation NF-xB et MAPK ainsi que les niveaux de mRNA de TNF-a produits en réponse au zymosan ne sont pas affectés dans ces cellules. En utilisant un inhibiteur de Malt1, nous montrons que l'activité protéolytique de Malt1 est nécessaire pour la production de TNF-a induite par le zymosan dans les cellules THP-1 ainsi que dans les macrophages de souris, mais d'autres expériences seront nécessaires pour confirmer cette observation et identifier le(s) substrat(s) de Malt1 responsables de cet effet. En conclusion, nos résultats révèlent un rôle important de la protéine Card9 dans la réponse immunitaire innée antifongique dans les macrophages humains.

Targeting Toll-Like Receptors: Promising Therapeutic Strategies for the Management of Sepsis-Associated Pathology and Infectious Diseases.

Toll-like receptors (TLRs) are pattern recognition receptors playing a fundamental role in sensing microbial invasion and initiating innate and adaptive immune responses. TLRs are also triggered by danger signals released by injured or stressed cells during sepsis. Here we focus on studies developing TLR agonists and antagonists for the treatment of infectious diseases and sepsis. Positioned at the cell surface, TLR4 is essential for sensing lipopolysaccharide of Gram-negative bacteria, TLR2 is involved in the recognition of a large panel of microbial ligands, while TLR5 recognizes flagellin. Endosomal TLR3, TLR7, TLR8, TLR9 are specialized in the sensing of nucleic acids produced notably during viral infections. TLR4 and TLR2 are favorite targets for developing anti-sepsis drugs, and antagonistic compounds have shown efficient protection from septic shock in pre-clinical models. Results from clinical trials evaluating anti-TLR4 and anti-TLR2 approaches are presented, discussing the challenges of study design in sepsis and future exploitation of these agents in infectious diseases. We also report results from studies suggesting that the TLR5 agonist flagellin may protect from infections of the gastrointestinal tract and that agonists of endosomal TLRs are very promising for treating chronic viral infections. Altogether, TLR-targeted therapies have a strong potential for prevention and intervention in infectious diseases, notably sepsis.

Classifying minimally disabled multiple sclerosis patients from resting state functional connectivity.

Multiple sclerosis (MS), a variable and diffuse disease affecting white and gray matter, is known to cause functional connectivity anomalies in patients. However, related studies published to-date are post hoc; our hypothesis was that such alterations could discriminate between patients and healthy controls in a predictive setting, laying the groundwork for imaging-based prognosis. Using functional magnetic resonance imaging resting state data of 22 minimally disabled MS patients and 14 controls, we developed a predictive model of connectivity alterations in MS: a whole-brain connectivity matrix was built for each subject from the slow oscillations (<0.11Hz) of region-averaged time series, and a pattern recognition technique was used to learn a discriminant function indicating which particular functional connections are most affected by disease. Classification performance using strict cross-validation yielded a sensitivity of 82% (above chance at p<0.005) and specificity of 86% (p<0.01) to distinguish between MS patients and controls. The most discriminative connectivity changes were found in subcortical and temporal regions, and contralateral connections were more discriminative than ipsilateral connections. The pattern of decreased discriminative connections can be summarized post hoc in an index that correlates positively (ρ=0.61) with white matter lesion load, possibly indicating functional reorganisation to cope with increasing lesion load. These results are consistent with a subtle but widespread impact of lesions in white matter and in gray matter structures serving as high-level integrative hubs. These findings suggest that predictive models of resting state fMRI can reveal specific anomalies due to MS with high sensitivity and specificity, potentially leading to new non-invasive markers.

Innate Immune Sensing of Fusarium culmorum by Mouse Dendritic Cells.

Chronic inhalation of grain dust is associated with asthma and chronic bronchitis in grain worker populations. Exposure to fungal particles was postulated to be an important etiologic agent of these pathologies. Fusarium species frequently colonize grain and straw and produce a wide array of mycotoxins that impact human health, necessitating an evaluation of risk exposure by inhalation of Fusarium and its consequences on immune responses. Data showed that Fusarium culmorum is a frequent constituent of aerosols sampled during wheat harvesting in the Vaud region of Switzerland. The aim of this study was to examine cytokine/chemokine responses and innate immune sensing of F. culmorum in bone-marrow-derived dendritic cells and macrophages. Overall, dendritic cells and macrophages responded to F. culmorum spores but not to its secreted components (i.e., mycotoxins) by releasing large amounts of macrophage inflammatory protein (MIP)-1α, MIP-1β, MIP-2, monocyte chemoattractant protein (MCP)-1, RANTES, and interleukin (IL)-12p40, intermediate amounts of tumor necrosis factor (TNF), IL-6, IL-12p70, IL-33, granulocyte colony-stimulating factor (G-CSF), and interferon gamma-induced protein (IP-10), but no detectable amounts of IL-4 and IL-10, a pattern of mediators compatible with generation of Th1 or Th17 antifungal protective immune responses rather than with Th2-dependent allergic responses. The sensing of F. culmorum spores by dendritic cells required dectin-1, the main pattern recognition receptor involved in β-glucans detection, but likely not MyD88 and TRIF-dependent Toll-like receptors. Taken together, our results indicate that F. culmorum stimulates potently innate immune cells in a dectin-1-dependent manner, suggesting that inhalation of F. culmorum from grain dust may promote immune-related airway diseases in exposed worker populations.

NLRP12 is a neutrophil-specific, negative regulator of in vitro cell migration but does not modulate LPS- or infection-induced NF-κB or ERK signalling.

NOD-like receptors (NLR) are a family of cytosolic pattern recognition receptors that include many key drivers of innate immune responses. NLRP12 is an emerging member of the NLR family that is closely related to the well-known inflammasome scaffold, NLRP3. Since its discovery, various functions have been proposed for NLRP12, including the positive regulation of dendritic cell (DC) and neutrophil migration and the inhibition of NF-κB and ERK signalling in DC and macrophages. We show here that NLRP12 is poorly expressed in murine macrophages and DC, but is strongly expressed in neutrophils. Using myeloid cells from WT and Nlrp12(-/)(-) mice, we show that, contrary to previous reports, NLRP12 does not suppress LPS- or infection-induced NF-κB or ERK activation in myeloid cells, and is not required for DC migration in vitro. Surprisingly, we found that Nlrp12 deficiency caused increased rather than decreased neutrophil migration towards the chemokine CXCL1 and the neutrophil parasite Leishmania major, revealing NLRP12 as a negative regulator of directed neutrophil migration under these conditions.