Uncovering the genetic basis of adaptive change: on the intersection of landscape genomics and theoretical population genetics

A workshop recently held at the Ecole Polytechnique Federale de Lausanne (EPFL, Switzerland) was dedicated to understanding the genetic basis of adaptive change, taking stock of the different approaches developed in theoretical population genetics and landscape genomics and bringing together knowledge accumulated in both research fields. Indeed, an important challenge in theoretical population genetics is to incorporate effects of demographic history and population structure. But important design problems (e.g. focus on populations as units, focus on hard selective sweeps, no hypothesis-based framework in the design of the statistical tests) reduce their capability of detecting adaptive genetic variation. In parallel, landscape genomics offers a solution to several of these problems and provides a number of advantages (e.g. fast computation, landscape heterogeneity integration). But the approach makes several implicit assumptions that should be carefully considered (e.g. selection has had enough time to create a functional relationship between the allele distribution and the environmental variable, or this functional relationship is assumed to be constant). To address the respective strengths and weaknesses mentioned above, the workshop brought together a panel of experts from both disciplines to present their work and discuss the relevance of combining these approaches, possibly resulting in a joint software solution in the future.

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.

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.

Schizophrenia: glutathione deficit as a new vulnerability factor for disconnectivity syndrome

(from the journal abstract) Schizophrenia, a major psychiatric disease, affects individuals in the centre of their personality. Its aetiology is not clearly established. In this review, we will present evidence that patients suffering of schizophrenia present a brain deficit in glutathione, a major endogenous redox regulator and antioxidant. We will also show that, in experimental models, a decrease in glutathione, particularly during development, induces morphological, electrophysiological and behavioural anomalies consistent with those observed in the disease. In the cerebrospinal fluid of drug-naive schizophrenics, glutathione level was decreased by 27% and its direct metabolite of glutathione by 16%. Glutathione level in prefrontal cortex of patients, measured by magnetic resonance spectroscopy, was 52% lower than in controls. Patients' fibroblasts reveal a decrease in mRNA levels of the two glutathione synthesising enzymes, glutamatecysteine ligase modulatory subunit (GCLM) and glutathione synthetase. GCLM expression level in fibroblasts correlates negatively with symptoms severity. Glutathione is an important endogenous redox regulator and neuroactive substance. It is protecting cells from damage by reactive oxygen species generated, among others, by dopamine metabolism. A glutathione deficit-induced oxidative stress would lead to lipid peroxidation and micro-lesions at the level of dendritic spines, a synaptic damage responsible for abnormal nervous connections or structural disconnectivity. On the other hand, a glutathione deficit could also lead to a functional disconnectivity by depressing NMDA neurotransmission, in analogy to phencyclidine effects. Present experimental data are consistent with the proposed hypothesis: decreasing pharmacologically glutathione level in experimental models, with or without blocking dopamine (DA) uptake (GBR12909), induces morphological, electrophysiological and behavioural changes similar to those observed in patients. In summary, a deficit of glutathione and/or glutathione-related enzymes during early development would lead to both a functional and a structural disconnectivity, which could be at the basis of some perceptive, cognitive and behavioural troubles of the disease. It could constitute a major vulnerability factor for schizophrenia. Attempts to restore physiological glutathione functions could open new therapeutic avenues. This translational research, made possible by a close interaction between clinicians and neuroscientists, should also pave the way to the identification of biological markers for schizophrenia. In turn, they should allow early diagnostic and hopefully preventive intervention to this devastating disease. (PsycINFO Database Record (c) 2005 APA, all rights reserved)

Differential gene expression in the pathogenic dermatophyte Arthroderma benhamiae in vitro versus during infection.

Although dermatophytes are the most common agents of superficial mycoses in humans and animals, the molecular basis of the pathogenicity of these fungi is largely unknown. In vitro digestion of keratin by dermatophytes is associated with the secretion of multiple proteases, which are assumed to be responsible for their particular specialization to colonize and degrade keratinized host structures during infection. To investigate the role of individual secreted proteases in dermatophytosis, a guinea pig infection model was established for the zoophilic dermatophyte Arthroderma benhamiae, which causes highly inflammatory cutaneous infections in humans and rodents. By use of a cDNA microarray covering approximately 20-25 % of the A. benhamiae genome and containing sequences of at least 23 protease genes, we revealed a distinct in vivo protease gene expression profile in the fungal cells, which was surprisingly different from the pattern elicited during in vitro growth on keratin. Instead of the major in vitro -expressed proteases, others were activated specifically during infection. These enzymes are therefore suggested to fulfil important functions that are not exclusively associated with the degradation of keratin. Most notably, the gene encoding the serine protease subtilisin 6, which is a known major allergen in the related dermatophyte Trichophyton rubrum and putatively linked to host inflammation, was found to be the most strongly upregulated gene during infection. In addition, our approach identified other candidate pathogenicity-related factors in A. benhamiae, such as genes encoding key enzymes of the glyoxylate cycle and an opsin-related protein. Our work provides what we believe to be the first broad-scale gene expression profile in human pathogenic dermatophytes during infection, and points to putative virulence-associated mechanisms that make these micro-organisms the most successful aetiological agents of superficial mycoses.