Contributions to Bayesian network learning with applications to neuroscience

Neuronal morphology is a key feature in the study of brain circuits, as it is highly related to information processing and functional identification. Neuronal morphology affects the process of integration of inputs from other neurons and determines the neurons which receive the output of the neurons. Different parts of the neurons can operate semi-independently according to the spatial location of the synaptic connections. As a result, there is considerable interest in the analysis of the microanatomy of nervous cells since it constitutes an excellent tool for better understanding cortical function. However, the morphologies, molecular features and electrophysiological properties of neuronal cells are extremely variable. Except for some special cases, this variability makes it hard to find a set of features that unambiguously define a neuronal type. In addition, there are distinct types of neurons in particular regions of the brain. This morphological variability makes the analysis and modeling of neuronal morphology a challenge. Uncertainty is a key feature in many complex real-world problems. Probability theory provides a framework for modeling and reasoning with uncertainty. Probabilistic graphical models combine statistical theory and graph theory to provide a tool for managing domains with uncertainty. In particular, we focus on Bayesian networks, the most commonly used probabilistic graphical model. In this dissertation, we design new methods for learning Bayesian networks and apply them to the problem of modeling and analyzing morphological data from neurons. The morphology of a neuron can be quantified using a number of measurements, e.g., the length of the dendrites and the axon, the number of bifurcations, the direction of the dendrites and the axon, etc. These measurements can be modeled as discrete or continuous data. The continuous data can be linear (e.g., the length or the width of a dendrite) or directional (e.g., the direction of the axon). These data may follow complex probability distributions and may not fit any known parametric distribution. Modeling this kind of problems using hybrid Bayesian networks with discrete, linear and directional variables poses a number of challenges regarding learning from data, inference, etc. In this dissertation, we propose a method for modeling and simulating basal dendritic trees from pyramidal neurons using Bayesian networks to capture the interactions between the variables in the problem domain. A complete set of variables is measured from the dendrites, and a learning algorithm is applied to find the structure and estimate the parameters of the probability distributions included in the Bayesian networks. Then, a simulation algorithm is used to build the virtual dendrites by sampling values from the Bayesian networks, and a thorough evaluation is performed to show the model’s ability to generate realistic dendrites. In this first approach, the variables are discretized so that discrete Bayesian networks can be learned and simulated. Then, we address the problem of learning hybrid Bayesian networks with different kinds of variables. Mixtures of polynomials have been proposed as a way of representing probability densities in hybrid Bayesian networks. We present a method for learning mixtures of polynomials approximations of one-dimensional, multidimensional and conditional probability densities from data. The method is based on basis spline interpolation, where a density is approximated as a linear combination of basis splines. The proposed algorithms are evaluated using artificial datasets. We also use the proposed methods as a non-parametric density estimation technique in Bayesian network classifiers. Next, we address the problem of including directional data in Bayesian networks. These data have some special properties that rule out the use of classical statistics. Therefore, different distributions and statistics, such as the univariate von Mises and the multivariate von Mises–Fisher distributions, should be used to deal with this kind of information. In particular, we extend the naive Bayes classifier to the case where the conditional probability distributions of the predictive variables given the class follow either of these distributions. We consider the simple scenario, where only directional predictive variables are used, and the hybrid case, where discrete, Gaussian and directional distributions are mixed. The classifier decision functions and their decision surfaces are studied at length. Artificial examples are used to illustrate the behavior of the classifiers. The proposed classifiers are empirically evaluated over real datasets. We also study the problem of interneuron classification. An extensive group of experts is asked to classify a set of neurons according to their most prominent anatomical features. A web application is developed to retrieve the experts’ classifications. We compute agreement measures to analyze the consensus between the experts when classifying the neurons. Using Bayesian networks and clustering algorithms on the resulting data, we investigate the suitability of the anatomical terms and neuron types commonly used in the literature. Additionally, we apply supervised learning approaches to automatically classify interneurons using the values of their morphological measurements. Then, a methodology for building a model which captures the opinions of all the experts is presented. First, one Bayesian network is learned for each expert, and we propose an algorithm for clustering Bayesian networks corresponding to experts with similar behaviors. Then, a Bayesian network which represents the opinions of each group of experts is induced. Finally, a consensus Bayesian multinet which models the opinions of the whole group of experts is built. A thorough analysis of the consensus model identifies different behaviors between the experts when classifying the interneurons in the experiment. A set of characterizing morphological traits for the neuronal types can be defined by performing inference in the Bayesian multinet. These findings are used to validate the model and to gain some insights into neuron morphology. Finally, we study a classification problem where the true class label of the training instances is not known. Instead, a set of class labels is available for each instance. This is inspired by the neuron classification problem, where a group of experts is asked to individually provide a class label for each instance. We propose a novel approach for learning Bayesian networks using count vectors which represent the number of experts who selected each class label for each instance. These Bayesian networks are evaluated using artificial datasets from supervised learning problems. Resumen La morfología neuronal es una característica clave en el estudio de los circuitos cerebrales, ya que está altamente relacionada con el procesado de información y con los roles funcionales. La morfología neuronal afecta al proceso de integración de las señales de entrada y determina las neuronas que reciben las salidas de otras neuronas. Las diferentes partes de la neurona pueden operar de forma semi-independiente de acuerdo a la localización espacial de las conexiones sinápticas. Por tanto, existe un interés considerable en el análisis de la microanatomía de las células nerviosas, ya que constituye una excelente herramienta para comprender mejor el funcionamiento de la corteza cerebral. Sin embargo, las propiedades morfológicas, moleculares y electrofisiológicas de las células neuronales son extremadamente variables. Excepto en algunos casos especiales, esta variabilidad morfológica dificulta la definición de un conjunto de características que distingan claramente un tipo neuronal. Además, existen diferentes tipos de neuronas en regiones particulares del cerebro. La variabilidad neuronal hace que el análisis y el modelado de la morfología neuronal sean un importante reto científico. La incertidumbre es una propiedad clave en muchos problemas reales. La teoría de la probabilidad proporciona un marco para modelar y razonar bajo incertidumbre. Los modelos gráficos probabilísticos combinan la teoría estadística y la teoría de grafos con el objetivo de proporcionar una herramienta con la que trabajar bajo incertidumbre. En particular, nos centraremos en las redes bayesianas, el modelo más utilizado dentro de los modelos gráficos probabilísticos. En esta tesis hemos diseñado nuevos métodos para aprender redes bayesianas, inspirados por y aplicados al problema del modelado y análisis de datos morfológicos de neuronas. La morfología de una neurona puede ser cuantificada usando una serie de medidas, por ejemplo, la longitud de las dendritas y el axón, el número de bifurcaciones, la dirección de las dendritas y el axón, etc. Estas medidas pueden ser modeladas como datos continuos o discretos. A su vez, los datos continuos pueden ser lineales (por ejemplo, la longitud o la anchura de una dendrita) o direccionales (por ejemplo, la dirección del axón). Estos datos pueden llegar a seguir distribuciones de probabilidad muy complejas y pueden no ajustarse a ninguna distribución paramétrica conocida. El modelado de este tipo de problemas con redes bayesianas híbridas incluyendo variables discretas, lineales y direccionales presenta una serie de retos en relación al aprendizaje a partir de datos, la inferencia, etc. En esta tesis se propone un método para modelar y simular árboles dendríticos basales de neuronas piramidales usando redes bayesianas para capturar las interacciones entre las variables del problema. Para ello, se mide un amplio conjunto de variables de las dendritas y se aplica un algoritmo de aprendizaje con el que se aprende la estructura y se estiman los parámetros de las distribuciones de probabilidad que constituyen las redes bayesianas. Después, se usa un algoritmo de simulación para construir dendritas virtuales mediante el muestreo de valores de las redes bayesianas. Finalmente, se lleva a cabo una profunda evaluaci ón para verificar la capacidad del modelo a la hora de generar dendritas realistas. En esta primera aproximación, las variables fueron discretizadas para poder aprender y muestrear las redes bayesianas. A continuación, se aborda el problema del aprendizaje de redes bayesianas con diferentes tipos de variables. Las mixturas de polinomios constituyen un método para representar densidades de probabilidad en redes bayesianas híbridas. Presentamos un método para aprender aproximaciones de densidades unidimensionales, multidimensionales y condicionales a partir de datos utilizando mixturas de polinomios. El método se basa en interpolación con splines, que aproxima una densidad como una combinación lineal de splines. Los algoritmos propuestos se evalúan utilizando bases de datos artificiales. Además, las mixturas de polinomios son utilizadas como un método no paramétrico de estimación de densidades para clasificadores basados en redes bayesianas. Después, se estudia el problema de incluir información direccional en redes bayesianas. Este tipo de datos presenta una serie de características especiales que impiden el uso de las técnicas estadísticas clásicas. Por ello, para manejar este tipo de información se deben usar estadísticos y distribuciones de probabilidad específicos, como la distribución univariante von Mises y la distribución multivariante von Mises–Fisher. En concreto, en esta tesis extendemos el clasificador naive Bayes al caso en el que las distribuciones de probabilidad condicionada de las variables predictoras dada la clase siguen alguna de estas distribuciones. Se estudia el caso base, en el que sólo se utilizan variables direccionales, y el caso híbrido, en el que variables discretas, lineales y direccionales aparecen mezcladas. También se estudian los clasificadores desde un punto de vista teórico, derivando sus funciones de decisión y las superficies de decisión asociadas. El comportamiento de los clasificadores se ilustra utilizando bases de datos artificiales. Además, los clasificadores son evaluados empíricamente utilizando bases de datos reales. También se estudia el problema de la clasificación de interneuronas. Desarrollamos una aplicación web que permite a un grupo de expertos clasificar un conjunto de neuronas de acuerdo a sus características morfológicas más destacadas. Se utilizan medidas de concordancia para analizar el consenso entre los expertos a la hora de clasificar las neuronas. Se investiga la idoneidad de los términos anatómicos y de los tipos neuronales utilizados frecuentemente en la literatura a través del análisis de redes bayesianas y la aplicación de algoritmos de clustering. Además, se aplican técnicas de aprendizaje supervisado con el objetivo de clasificar de forma automática las interneuronas a partir de sus valores morfológicos. A continuación, se presenta una metodología para construir un modelo que captura las opiniones de todos los expertos. Primero, se genera una red bayesiana para cada experto y se propone un algoritmo para agrupar las redes bayesianas que se corresponden con expertos con comportamientos similares. Después, se induce una red bayesiana que modela la opinión de cada grupo de expertos. Por último, se construye una multired bayesiana que modela las opiniones del conjunto completo de expertos. El análisis del modelo consensuado permite identificar diferentes comportamientos entre los expertos a la hora de clasificar las neuronas. Además, permite extraer un conjunto de características morfológicas relevantes para cada uno de los tipos neuronales mediante inferencia con la multired bayesiana. Estos descubrimientos se utilizan para validar el modelo y constituyen información relevante acerca de la morfología neuronal. Por último, se estudia un problema de clasificación en el que la etiqueta de clase de los datos de entrenamiento es incierta. En cambio, disponemos de un conjunto de etiquetas para cada instancia. Este problema está inspirado en el problema de la clasificación de neuronas, en el que un grupo de expertos proporciona una etiqueta de clase para cada instancia de manera individual. Se propone un método para aprender redes bayesianas utilizando vectores de cuentas, que representan el número de expertos que seleccionan cada etiqueta de clase para cada instancia. Estas redes bayesianas se evalúan utilizando bases de datos artificiales de problemas de aprendizaje supervisado.

Identification and functional characterization of Rhizobium leguminosarum bv. viciae genetic systems involved in nickel homeostasis.

A collection of Rhizobium leguminosarum bv. viciae strains isolated from ultramafic and contaminated soils in Italy and Germany, respectively, was analyzed for resistance to nickel and cobalt ions. These assays led to the identification of strain UPM1137, which is able to grow at high concentrations of nickel and cobalt. In order to identify genetic systems involved in the homeostasis to these metals, a random mutagenesis was carried out in UPM1137 by inserting a Tn5-derivative minitransposon. As a result 4313 transconjugants were obtained, being 39 of them (0.90%) unable to grow at 1.5 mM NiCl2. The identification of the transposon insertion site in these mutants showed that the disrupted genes encode proteins belonging to different functional categories, where the secreted and membrane proteins were the most numerous. The analysis of heavy metal resistance and phenotypes in symbiotic and free –living cells will define the contribution of these genes to metal homeostasis.

Large-scale identification of gibberellin-related transcription factor defines group VII ERFs as functional of DELLA partners

DELLA proteins are the master negative regulators in gibberellin (GA) signaling acting in the nucleus as transcriptional regulators. The current view of DELLA action indicates that their activity relies on the physical interaction with transcription factors (TFs). Therefore, the identification of TFs through which DELLAs regulate GA responses is key to understanding these responses from a mechanistic point of view. Here, we have determined the TF interactome of the Arabidopsis (Arabidopsis thaliana) DELLA protein GIBBERELLIN INSENSITIVE and screened a collection of conditional TF overexpressors in search of those that alter GA sensitivity. As a result, we have found RELATED TO APETALA2.3, an ethylene-induced TF belonging to the group VII ETHYLENE RESPONSE FACTOR of the APETALA2/ethylene responsive element binding protein superfamily, as a DELLA interactor with physiological relevance in the context of apical hook development. The combination of transactivation assays and chromatin immunoprecipitation indicates that the interaction with GIBBERELLIN INSENSITIVE impairs the activity of RELATED TO APETALA2.3 on the target promoters. This mechanism represents a unique node in the cross regulation between the GA and ethylene signaling pathways controlling differential growth during apical hook development.

Optimizing Functional Network Representation of Multivariate Time Series

By combining complex network theory and data mining techniques, we provide objective criteria for optimization of the functional network representation of generic multivariate time series. In particular, we propose a method for the principled selection of the threshold value for functional network reconstruction from raw data, and for proper identification of the network's indicators that unveil the most discriminative information on the system for classification purposes. We illustrate our method by analysing networks of functional brain activity of healthy subjects, and patients suffering from Mild Cognitive Impairment, an intermediate stage between the expected cognitive decline of normal aging and the more pronounced decline of dementia. We discuss extensions of the scope of the proposed methodology to network engineering purposes, and to other data mining tasks.

Estimation of functional connectivity from electromagnetic signals and the amount of empirical data required

An increasing number of neuroimaging studies are concerned with the identification of interactions or statistical dependencies between brain areas. Dependencies between the activities of different brain regions can be quantified with functional connectivity measures such as the cross-correlation coefficient. An important factor limiting the accuracy of such measures is the amount of empirical data available. For event-related protocols, the amount of data also affects the temporal resolution of the analysis. We use analytical expressions to calculate the amount of empirical data needed to establish whether a certain level of dependency is significant when the time series are autocorrelated, as is the case for biological signals. These analytical results are then contrasted with estimates from simulations based on real data recorded with magnetoencephalography during a resting-state paradigm and during the presentation of visual stimuli. Results indicate that, for broadband signals, 50–100 s of data is required to detect a true underlying cross-correlations coefficient of 0.05. This corresponds to a resolution of a few hundred milliseconds for typical event-related recordings. The required time window increases for narrow band signals as frequency decreases. For instance, approximately 3 times as much data is necessary for signals in the alpha band. Important implications can be derived for the design and interpretation of experiments to characterize weak interactions, which are potentially important for brain processing.

Cycling Dof Factors: Molecular and functional characterization of Arabidopsis thaliana AtCDF3 and tomato (Solanum lycopersicum L.) SlCDF3 in response to abiotic stress

El tomate (Solanum lycopersicum L.) es considerado uno de los cultivos hortícolas de mayor importancia económica en el territorio Español. Sin embargo, su producción está seriamente afectada por condiciones ambientales adversas como, salinidad, sequía y temperaturas extremas. Para resolver los problemas que se presentan en condiciones de estrés, se han empleado una serie de técnicas culturales que disminuyen sus efectos negativos, siendo de gran interés el desarrollo de variedades tolerantes. En este sentido la obtención y análisis de plantas transgénicas, ha supuesto un avance tecnológico, que ha facilitado el estudio y la evaluación de genes seleccionados en relación con la tolerancia al estrés. Estudios recientes han mostrado que el uso de genes reguladores como factores de transcripción (FTs) es una gran herramienta para obtener nuevas variedades de tomate con mayor tolerancia a estreses abióticos. Las proteínas DOF (DNA binding with One Finger) son una familia de FTs específica de plantas (Yangisawa, 2002), que están involucrados en procesos fisiológicos exclusivos de plantas como: asimilación del nitrógeno y fijación del carbono fotosintético, germinación de semilla, metabolismo secundario y respuesta al fotoperiodo pero su preciso rol en la tolerancia a estrés abiótico se desconoce en gran parte. El trabajo descrito en esta tesis tiene como objetivo estudiar genes reguladores tipo DOF para incrementar la tolerancia a estrés abiotico tanto en especies modelo como en tomate. En el primer capítulo de esta tesis se muestra la caracterización funcional del gen CDF3 de Arabidopsis, así como su papel en la respuesta a estrés abiótico y otros procesos del desarrollo. La expresión del gen AtCDF3 es altamente inducido por sequía, temperaturas extremas, salinidad y tratamientos con ácido abscísico (ABA). La línea de inserción T-DNA cdf3-1 es más sensible al estrés por sequía y bajas temperaturas, mientras que líneas transgénicas de Arabidopsis 35S::AtCDF3 aumentan la tolerancia al estrés por sequía, osmótico y bajas temperaturas en comparación con plantas wild-type (WT). Además, estas plantas presentan un incremento en la tasa fotosintética y apertura estomática. El gen AtCDF3 se localiza en el núcleo y que muestran una unión específica al ADN con diferente afinidad a secuencias diana y presentan diversas capacidades de activación transcripcional en ensayos de protoplastos de Arabidopsis. El dominio C-terminal de AtCDF3 es esencial para esta localización y su capacidad activación, la delección de este dominio reduce la tolerancia a sequía en plantas transgénicas 35S::AtCDF3. Análisis por microarray revelan que el AtCDF3 regula un set de genes involucrados en el metabolismo del carbono y nitrógeno. Nuestros resultados demuestran que el gen AtCDF3 juega un doble papel en la regulación de la respuesta a estrés por sequía y bajas temperaturas y en el control del tiempo de floración. En el segundo capítulo de este trabajo se lleva a cabo la identificación de 34 genes Dof en tomate que se pueden clasificar en base a homología de secuencia en cuatro grupos A-D, similares a los descritos en Arabidopsis. Dentro del grupo D se han identificado cinco genes DOF que presentan características similares a los Cycling Dof Factors (CDFs) de Arabidopsis. Estos genes son considerados ortólogos de Arabidopsis CDF1-5, y han sido nombrados como Solanum lycopersicum CDFs o SlCDFs. Los SlCDF1-5 son proteínas nucleares que muestran una unión específica al ADN con diferente afinidad a secuencias diana y presentan diversas capacidades de activación transcripcional in vivo. Análisis de expresión de los genes SlCDF1-5 muestran diferentes patrones de expresión durante el día y son inducidos de forma diferente en respuesta a estrés osmótico, salino, y de altas y bajas temperaturas. Plantas de Arabidopsis que sobre-expresan SlCDF1 y SlCDF3 muestran un incremento de la tolerancia a la sequía y salinidad. Además, de la expresión de varios genes de respuesta estrés como AtCOR15, AtRD29A y AtERD10, son expresados de forma diferente en estas líneas. La sobre-expresión de SlCDF3 en Arabidopsis promueve un retardo en el tiempo de floración a través de la modulación de la expresión de genes que controlan la floración como CONSTANS (CO) y FLOWERING LOCUS T (FT). En general, nuestros datos demuestran que los SlCDFs están asociados a funciones aun no descritas, relacionadas con la tolerancia a estrés abiótico y el control del tiempo de floración a través de la regulación de genes específicos y a un aumento de metabolitos particulares. ABSTRACT Tomato (Solanum lycopersicum L.) is one of the horticultural crops of major economic importance in the Spanish territory. However, its production is being affected by adverse environmental conditions such as salinity, drought and extreme temperatures. To resolve the problems triggered by stress conditions, a number of agricultural techniques that reduce the negative effects of stress are being frequently applied. However, the development of stress tolerant varieties is of a great interest. In this direction, the technological progress in obtaining and analysis of transgenic plants facilitated the study and evaluation of selected genes in relation to stress tolerance. Recent studies have shown that a use of regulatory genes such as transcription factors (TFs) is a great tool to obtain new tomato varieties with greater tolerance to abiotic stresses. The DOF (DNA binding with One Finger) proteins form a family of plant-specific TFs (Yangisawa, 2002) that are involved in the regulation of particular plant processes such as nitrogen assimilation, photosynthetic carbon fixation, seed germination, secondary metabolism and flowering time bur their precise roles in abiotic stress tolerance are largely unknown. The work described in this thesis aims at the study of the DOF type regulatory genes to increase tolerance to abiotic stress in both model species and the tomato. In the first chapter of this thesis, we present molecular characterization of the Arabidopsis CDF3 gene as well as its role in the response to abiotic stress and in other developmental processes. AtCDF3 is highly induced by drought, extreme temperatures, salt and abscisic acid (ABA) treatments. The cdf3-1 T-DNA insertion mutant was more sensitive to drought and low temperature stresses, whereas the AtCDF3 overexpression enhanced the tolerance of transgenic plants to drought, cold and osmotic stress comparing to the wild-type (WT) plants. In addition, these plants exhibit increased photosynthesis rates and stomatal aperture. AtCDF3 is localized in the nuclear region, displays specific binding to the canonical DNA target sequences and has a transcriptional activation activity in Arabidopsis protoplast assays. In addition, the C-terminal domain of AtCDF3 is essential for its localization and activation capabilities and the deletion of this domain significantly reduces the tolerance to drought in transgenic 35S::AtCDF3 overexpressing plants. Microarray analysis revealed that AtCDF3 regulated a set of genes involved in nitrogen and carbon metabolism. Our results demonstrate that AtCDF3 plays dual roles in regulating plant responses to drought and low temperature stress and in control of flowering time in vegetative tissues. In the second chapter this work, we carried out to identification of 34 tomato DOF genes that were classified by sequence similarity into four groups A-D, similar to the situation in Arabidopsis. In the D group we have identified five DOF genes that show similar characteristics to the Cycling Dof Factors (CDFs) of Arabidopsis. These genes were considered orthologous to the Arabidopsis CDF1 - 5 and were named Solanum lycopersicum CDFs or SlCDFs. SlCDF1-5 are nuclear proteins that display specific binding to canonical DNA target sequences and have transcriptional activation capacities in vivo. Expression analysis of SlCDF1-5 genes showed distinct diurnal expression patterns and were differentially induced in response to osmotic, salt and low and high temperature stresses. Arabidopsis plants overexpressing SlCDF1 and SlCDF3 showed increased drought and salt tolerance. In addition, various stress-responsive genes, such as AtCOR15, AtRD29A and AtERD10, were expressed differently in these lines. The overexpression of SlCDF3 in Arabidopsis also results in the late flowering phenotype through the modulation of the expression of flowering control genes such CONSTANS (CO) and FLOWERING LOCUS T (FT). Overall, our data connet SlCDFs to undescribed functions related to abiotic stress tolerance and flowering time through the regulation of specific target genes and an increase in particular metabolites.

The TRANSPLANTA collection of Arabidopsis lines: a resource for functional analysis of transcription factors based on their conditional over-expression

Transcription factors (TFs) are key regulators of gene expression in all organisms. In eukaryotes, TFs are often represented by functionally redundant members of large gene families. Overexpression might prove a means to unveil the biological functions of redundant TFs; however, constitutive overexpression of TFs frequently causes severe developmental defects, preventing their functional characterization. Conditional overexpression strategies help to overcome this problem. Here, we report on the TRANSPLANTA collection of Arabidopsis lines, each expressing one of 949 TFs under the control of a β–estradiol-inducible promoter. Thus far, 1636 independent homozygous lines, representing an average of 2.6 lines for every TF, have been produced for the inducible expression of 634 TFs. Along with a GUS-GFP reporter, randomly selected TRANSPLANTA lines were tested and confirmed for conditional transgene expression upon β–estradiol treatment. As a proof of concept for the exploitation of this resource, β–estradiol-induced proliferation of root hairs, dark-induced senescence, anthocyanin accumulation and dwarfism were observed in lines conditionally expressing full-length cDNAs encoding RHD6, WRKY22, MYB123/TT2 and MYB26, respectively, in agreement with previously reported phenotypes conferred by these TFs. Further screening performed with other TRANSPLANTA lines allowed the identification of TFs involved in different plant biological processes, illustrating that the collection is a powerful resource for the functional characterization of TFs. For instance, ANAC058 and a TINY/AP2 TF were identified as modulators of ABA-mediated germination potential, and RAP2.10/DEAR4 was identified as a regulator of cell death in the hypocotyl–root transition zone. Seeds of TRANSPLANTA lines have been deposited at the Nottingham Arabidopsis Stock Centre for further distribution.

Functional genomics tools to decipher the pathogenicity mechanisms of the necrotrophic fungus Plectosphaerella cucumerina in Arabidopsis thaliana

The analysis of the interaction between Arabidopsis thaliana and adapted (PcBMM) and nonadapted (Pc2127) isolates of the necrotrophic fungus Plectosphaerella cucumerina has contributed to the identification of molecular mechanisms controlling plant resistance to necrotrophs.To characterize the pathogenicity bases of the virulence of necrotrophic fungi in Arabidopsis, we developed P. cucumerina functional genomics tools using Agrobacterium tumefaciens-mediated transformation.We generated PcBMM-GFP and Pc2127-GFP transformants constitutively expressing the green fluorescence protein (GFP), and a collection of random T-DNA insertional PcBMM transformants. Confocal microscopy analyses of the initial stages of PcBMM-GFP infection revealed that this pathogen, like other necrotrophic fungi, does not form an appressorium or penetrate into plant cells, but causes successive degradation of leaf cell layers