Distinct profiles of cytotoxic granules in memory CD8 T cells correlate with functions, differentiation stage, and antigen exposure

RAPPORT DE SYNTHÈSE : Les profils des granules cytotoxiques des cellules T CD8 mémoires sont corrélés à la fonction, à leur état de différentiation et à l'exposition à l'antigène. Les lymphocytes T-CD8 cytotoxiques exercent leur fonction antivirale et antitumorale surtout par la sécrétion des granules cytotoxiques. En général, ce sont l'activité de dégranulation et les granules cytotoxiques (contenant perforine et différentes granzymes) qui définissent les lymphocytes T-CD8 cytotoxiques. Dans cette étude, nous avons investigué l'expression de granzyme K par cytométrie en flux, en comparaison avec l'expression de granzyme A, granzyme B et de perforine. L'expression des granules cytotoxiques a été déterminée dans lymphocytes T-CD8 qui étaient spécifiques pour des différents virus, en particulier spécifique pour le virus d'influenza (flu), le virus Ebstein Barr (EBV), le virus de cytomégalie (CMV) et le virus de l'immunodéficience humaine (HIV). Nous avons observé une dichotomie entre l'expression du granzyme K et de la perforine dans les lymphocytes T-CD8 qui étaient spécifiques aux virus mentionnés. Les profils des lymphocytes T-CD8 spécifiques à flu étaient positifs soit pour granzyme A et granzyme K soit pour le granzyme K seul, mais dans l'ensemble négatifs pour perforine et granzyme B. Les cellules spécifiques à CMV étaient dans la plupart positives pour perforine, granzyme B et A, mais négatives pour le granzyme K. Les cellules spécifiques à EBV et HIV étaient dans la majorité positives pour granzyme A, B et K, et dans la moitié des cas négatives pour la perforine. Nous avons également analysé, selon les marqueurs de mémoire de CD45 et CD127, les profils de différentiation cellulaire: Les cellules avec les granules cytotoxiques contenant exclusivement le granzyme K, étaient associées à un état de différentiation précoce. Au contraire, les protéines cytolytiques perforine, granzyme A et B, correspondent à une différentiation avancée. En outre, les protéines perforine et granzyme B, mais pas les granzymes A et K, sont corrélées à une activité cytotoxique. Finalement, des changements dans l'exposition d'antigène in vitro et in vivo suivant une infection primaire d' HIV ou une vaccination modulent le profil de granules cytotoxiques. Ces résultats nous permettent d'étendre la compréhension de la relation entre les différents profils de granules cytotoxiques des lymphocytes T-CD8 et leur fonction, leur état de différentiation et l'exposition à l'antigène.

Peroxisome proliferator-activated receptors: three isotypes for a multitude of functions.

The peroxisome proliferator-activated receptors (PPARs) are fatty acid and eicosanoid inducible nuclear receptors, which occur in three different isotypes. Upon activator binding, they modulate the expression of various target genes implicated in several important physiological pathways. During the past few years, the identification of both PPAR ligands, natural and synthetic, and PPAR targets and their associated functions has been one of the most important achievements in the field. It underscores the potential therapeutic application of PPAR-specific compounds on the one side, and the crucial biological roles of endogenous PPAR ligands on the other.

Ex vivo characterization of human CD8+ T subsets with distinct replicative history and partial effector functions.

After antigenic challenge, naive T lymphocytes enter a program of proliferation and differentiation during the course of which they acquire effector functions and may ultimately become memory cells. In humans, the pathways of effector and memory T-cell differentiation remain poorly defined. Here we describe the properties of 2 CD8+ T-lymphocyte subsets, RA+CCR7-27+28+ and RA+CCR7-27+28-, in human peripheral blood. These cells display phenotypic and functional features that are intermediate between naive and effector T cells. Like naive T lymphocytes, both subsets show relatively long telomeres. However, unlike the naive population, these T cells exhibit reduced levels of T-cell receptor excision circles (TRECs), indicating they have undergone additional rounds of in vivo cell division. Furthermore, we show that they also share effector-type properties. At equivalent in vivo replicative history, the 2 subsets express high levels of Fas/CD95 and CD11a, as well as increasing levels of effector mediators such as granzyme B, perforin, interferon gamma, and tumor necrosis factor alpha. Both display partial ex vivo cytolytic activity and can be found among cytomegalovirus-specific cytolytic T cells. Taken together, our data point to the presence of T cells with intermediate effector-like functions and suggest that these subsets consist of T lymphocytes that are evolving toward a more differentiated effector or effector-memory stage.

Rivascolarizzazione miocardica con arteria radiale: tecniche chirurgiche a confronto e risultati a breve termine [Myocardial revascularization and radial artery: different surgical strategies and short-term results]

BACKGROUND: The radial artery is routinely used as a graft for surgical arterial myocardial revascularization. The proximal radial artery anastomosis site remains unknown. In this study, we analyzed the short-term results and the operative risk determinants after having used four different common techniques for radial artery implantation. METHODS: From January 2000 to December 2004, 571 patients underwent coronary artery bypass grafting with radial arteries. Data were analyzed for the entire population and for subgroups following the proximal radial artery anastomosis site: 140 T-graft with the mammary artery (group A), 316 free-grafts with the proximal anastomosis to the ascending aorta (group B), 55 mammary arteries in situ elongated with the radial artery (group C) and 60 radial arteries elongated with a piece of mammary artery and anastomosed to the ascending aorta (group D). RESULTS: The mean age was 53.8 +/- 7.7 years; 55.5% of patients had a previous myocardial infarction and 73% presented with a satisfactory left ventricular function. A complete arterial myocardial revascularization was achieved in 532 cases (93.2%) and 90.2% of the procedures were performed under cardiopulmonary bypass and cardioplegic arrest. The operative mortality rate was 0.9%, a postoperative myocardial infarction was diagnosed in 19 patients (3.3%), an intra-aortic balloon pump was used in 10 patients (1.7%) and a mechanical circulatory device was implanted in 2 patients. The radial artery harvesting site remained always free from complications. The proximal radial artery anastomosis site was not a determinant of early hospital mortality. Group C showed a higher risk of postoperative myocardial infarction (p = 0.09), together with female gender (p = 0.003), hypertension (p = 0.059) and a longer cardiopulmonary bypass time. CONCLUSIONS: The radial artery and the mammary artery can guarantee multiple arterial revascularization also for patients with contraindications to double mammary artery use. The four most common techniques for proximal radial artery anastomosis are not related to a higher operative risk and they can be used alternatively to reach the best surgical results

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.

Free-breathing whole-heart coronary MRA with 3D radial SSFP and self-navigated image reconstruction.

Respiratory motion is a major source of artifacts in cardiac magnetic resonance imaging (MRI). Free-breathing techniques with pencil-beam navigators efficiently suppress respiratory motion and minimize the need for patient cooperation. However, the correlation between the measured navigator position and the actual position of the heart may be adversely affected by hysteretic effects, navigator position, and temporal delays between the navigators and the image acquisition. In addition, irregular breathing patterns during navigator-gated scanning may result in low scan efficiency and prolonged scan time. The purpose of this study was to develop and implement a self-navigated, free-breathing, whole-heart 3D coronary MRI technique that would overcome these shortcomings and improve the ease-of-use of coronary MRI. A signal synchronous with respiration was extracted directly from the echoes acquired for imaging, and the motion information was used for retrospective, rigid-body, through-plane motion correction. The images obtained from the self-navigated reconstruction were compared with the results from conventional, prospective, pencil-beam navigator tracking. Image quality was improved in phantom studies using self-navigation, while equivalent results were obtained with both techniques in preliminary in vivo studies.

Functions of the peroxisome proliferator-activated receptor (PPAR) alpha and beta in skin homeostasis, epithelial repair, and morphogenesis.

The three peroxisome proliferator-activated receptors (PPAR alpha, PPAR beta, and PPAR gamma) are ligand-activated transcription factors belonging to the nuclear hormone receptor superfamily. They are regarded as being sensors of physiological levels of fatty acids and fatty acid derivatives. In the adult mouse skin, they are found in hair follicle keratinocytes but not in interfollicular epidermis keratinocytes. Skin injury stimulates the expression of PPAR alpha and PPAR beta at the site of the wound. Here, we review the spatiotemporal program that triggers PPAR beta expression immediately after an injury, and then gradually represses it during epithelial repair. The opposing effects of the tumor necrosis factor-alpha and transforming growth factor-beta-1 signalling pathways on the activity of the PPAR beta promoter are the key elements of this regulation. We then compare the involvement of PPAR beta in the skin in response to an injury and during hair morphogenesis, and underscore the similarity of its action on cell survival in both situations.

Calcineurin as a Multifunctional Regulator: Unraveling Novel Functions in Fungal Stress Responses, Hyphal Growth, Drug Resistance, and Pathogenesis.

Calcineurin signaling plays diverse roles in fungi in regulating stress responses, morphogenesis and pathogenesis. Although calcineurin signaling is conserved among fungi, recent studies indicate important divergences in calcineurin-dependent cellular functions among different human fungal pathogens. Fungal pathogens utilize the calcineurin pathway to effectively survive the host environment and cause life-threatening infections. The immunosuppressive calcineurin inhibitors (FK506 and cyclosporine A) are active against fungi, making targeting calcineurin a promising antifungal drug development strategy. Here we summarize current knowledge on calcineurin in yeasts and filamentous fungi, and review the importance of understanding fungal-specific attributes of calcineurin to decipher fungal pathogenesis and develop novel antifungal therapeutic approaches.

Tissue-specific opposing functions of the inflammasome adaptor ASC in the regulation of epithelial skin carcinogenesis.

A chronic inflammatory microenvironment favors tumor progression through molecular mechanisms that are still incompletely defined. In inflammation-induced skin cancers, IL-1 receptor- or caspase-1-deficient mice, or mice specifically deficient for the inflammasome adaptor protein ASC (apoptosis-associated speck-like protein containing a CARD) in myeloid cells, had reduced tumor incidence, pointing to a role for IL-1 signaling and inflammasome activation in tumor development. However, mice fully deficient for ASC were not protected, and mice specifically deficient for ASC in keratinocytes developed more tumors than controls, suggesting that, in contrast to its proinflammatory role in myeloid cells, ASC acts as a tumor-suppressor in keratinocytes. Accordingly, ASC protein expression was lost in human cutaneous squamous cell carcinoma, but not in psoriatic skin lesions. Stimulation of primary mouse keratinocytes or the human keratinocyte cell line HaCaT with UVB induced an ASC-dependent phosphorylation of p53 and expression of p53 target genes. In HaCaT cells, ASC interacted with p53 at the endogenous level upon UVB irradiation. Thus, ASC in different tissues may influence tumor growth in opposite directions: it has a proinflammatory role in infiltrating cells that favors tumor development, but it also limits keratinocyte proliferation in response to noxious stimuli, possibly through p53 activation, which helps suppressing tumors.

Antagonistic functions of LMNA isoforms in energy expenditure and lifespan.

Alternative RNA processing of LMNA pre-mRNA produces three main protein isoforms, that is, lamin A, progerin, and lamin C. De novo mutations that favor the expression of progerin over lamin A lead to Hutchinson-Gilford progeria syndrome (HGPS), providing support for the involvement of LMNA processing in pathological aging. Lamin C expression is mutually exclusive with the splicing of lamin A and progerin isoforms and occurs by alternative polyadenylation. Here, we investigate the function of lamin C in aging and metabolism using mice that express only this isoform. Intriguingly, these mice live longer, have decreased energy metabolism, increased weight gain, and reduced respiration. In contrast, progerin-expressing mice show increased energy metabolism and are lipodystrophic. Increased mitochondrial biogenesis is found in adipose tissue from HGPS-like mice, whereas lamin C-only mice have fewer mitochondria. Consistently, transcriptome analyses of adipose tissues from HGPS and lamin C-only mice reveal inversely correlated expression of key regulators of energy expenditure, including Pgc1a and Sfrp5. Our results demonstrate that LMNA encodes functionally distinct isoforms that have opposing effects on energy metabolism and lifespan in mammals.

Redundant functions of TCF-1 and LEF-1 during T and NK cell development, but unique role of TCF-1 for Ly49 NK cell receptor acquisition.

Members of the TCF/LEF (T cell factor / lymphoid enhancer factor) family of DNA-binding factors play important roles during embryogenesis, the establishment and/or maintenance of self-renewing tissues such as the immune system and for malignant transformation. Specifically, it has been shown that TCF-1 is required for T cell development. A role for LEF-1 became apparent when mice harbored two hypomorphic TCF-1 alleles and consequently expressed low levels of TCF-1. Here we show that NK cell development is similarly regulated by redundant functions of TCF-1 and LEF-1, whereby TCF-1 contributes significantly more to NK cell development than LEF-1. Despite this role for NK cell development, LEF-1 is not required for the establishment of a repertoire of MHC class I-specific Ly49 receptors on NK cells. The proper formation of this repertoire depends to a large extent on TCF-1. These findings suggest common and distinct functions of TCF-1 and LEF-1 during lymphocyte development.

Adaptive changes in a radial maze task: Efficient selection of baited arms with reduced foraging in senescent hooded rats

Qualitative differences in strategy selection during foraging in a partially baited maze were assessed in young and old rats. The baited and non-baited arms were at a fixed position in space and marked by a specific olfactory cue. The senescent rats did more re-entries during the first four-trial block but were more rapid than the young rats in selecting the reinforced arms during the first visits. Dissociation between the olfactory spatial cue reference by rotating the maze revealed that only few old subjects relied on olfactory cues to select the baited arms and the remainder relied mainly on the visuo-spatial cues.