A cDNA microarray approach to decipher sunflower (Helianthus annuus) responses to the necrotrophic fungus Phoma macdonaldii

To identify the genes involved in the partial resistance of sunflower (Helianthus annuus) to the necrotrophic fungus Phoma macdonaldii, we developed a 1000‐element cDNA microarray containing carefully chosen genes putatively involved in primary metabolic pathways, signal transduction and biotic stress responses. A two‐pass general linear model was used to normalize the data and then to detect differentially expressed genes. This method allowed us to identify 38 genes differentially expressed among genotypes, treatments and times, mainly belonging to plant defense, signaling pathways and amino acid metabolism. Based on a set of genes whose differential expression was highly significant, we propose a model in which negative regulation of a dual‐specificity MAPK phosphatase could be implicated in sunflower defense mechanisms against the pathogen. The resulting activation of the MAP kinase cascade could subsequently trigger defense responses (e.g. thaumatin biosynthesis and phenylalanine ammonia lyase activation), under the control of transcription factors belonging to MYB and WRKY families. Concurrently, the activation of protein phosphatase 2A (PP2A), which is implicated in cell death inhibition, could limit pathogen development. The results reported here provide a valuable first step towards the understanding and analysis of the P. macdonaldii–sunflower interaction

Recherche des partenaires protéiques et étude fonctionnelle de la région cytoplasmique des protéines BP-80 chez arabidopsis thaliana et helianthus annus

TOULOUSE3-BU Sciences (315552104) / SudocSudocFranceF

Characterization of the Interaction Between the Bacterial Wilt Pathogen Ralstonia solanacearum and the Model Legume Plant Medicago truncatula

The soilborne pathogen Ralstonia solanacearum is the causal agent of bacterial wilt and attacks more than 200 plant species, including some legumes and the model legume plant Medicago truncatula. We have demonstrated that M. truncatula accessions Jemalong A17 and F83005.5 are sus- ceptible to R. solanacearum and, by screening 28 R. solana- cearum strains on the two M. truncatula lines, differential interactions were identified. R. solanacearum GMI1000 infected Jemalong A17 line, and disease symptoms were dependent upon functional hrp genes. An in vitro root in- oculation method was employed to demonstrate that R. solanacearum colonized M. truncatula via the xylem and intercellular spaces. R. solanacearum multiplication was restricted by a factor greater than 1 × 105 in the resistant line F83005.5 compared with susceptible Jemalong A17. Genetic analysis of recombinant inbred lines from a cross between Jemalong A17 and F83005.5 revealed the presence of major quantitative trait loci for bacterial wilt resistance located on chromosome 5. The results indicate that the root pathosystem for M. truncatula will provide useful traits for molecular analyses of disease and resistance in this model plant species

Modélisation de l'expression des gènes à partir de données de séquence ADN

International audienceGene expression is tightly controlled to ensure a wide variety of cell types and functions. The development of diseases, particularly cancers, is invariably related to deregulations of these controls. Our objective is to model the link between gene expression and nucleotide composition of different regulatory regions in the genome. We propose to address this problem in a regression framework using a Lasso approach coupled to a regression tree. We use exclusively sequence data and we fit a different model for each cell type. We show that (i) different regulatory regions provide particular and complementary information and that (ii) the only information contained in the nucleotide compositions allows predicting gene expression with an error comparable to that obtained using experimental data. Moreover, the fitted linear model is not as powerful for all genes, but better fit certain groups of genes with particular nucleotides compositions.L'expression des gènes est étroitement contrôlée pour assurer une grande variété de fonctions et de types cellulaires. Le développement des maladies, en particulier les cancers, est invariablement lié à la dérégulation de ces contrôles. Notre objectif est de modéliser le lien entre l'expression des gènes et la composition nucléotidique des différentes régions régulatrices du génome. Nous proposons d'aborder ce problème dans un cadre de régression avec une approche Lasso couplée à un arbre de régression. Nous utilisons exclusivement des données de séquences et nous apprenons un modèle différent pour chaque type cellulaire. Nous montrons (i) que les différentes régions régulatrices apportent des informations diffé-rentes et complémentaires et (ii) que la seule information de leur composition nucléotidique permet de prédire l'expression des gènes avec une erreur comparable à celle obtenue en utilisant des données expérimentales. En outre, le modèle linéaire appris n'est pas aussi performant pour tous les gènes, mais modélise mieux certaines classes de gènes avec des compositions nucléotidiques particulières

Modélisation de l'expression des gènes à partir de données de séquence ADN

International audienceGene expression is tightly controlled to ensure a wide variety of cell types and functions. The development of diseases, particularly cancers, is invariably related to deregulations of these controls. Our objective is to model the link between gene expression and nucleotide composition of different regulatory regions in the genome. We propose to address this problem in a regression framework using a Lasso approach coupled to a regression tree. We use exclusively sequence data and we fit a different model for each cell type. We show that (i) different regulatory regions provide particular and complementary information and that (ii) the only information contained in the nucleotide compositions allows predicting gene expression with an error comparable to that obtained using experimental data. Moreover, the fitted linear model is not as powerful for all genes, but better fit certain groups of genes with particular nucleotides compositions.L'expression des gènes est étroitement contrôlée pour assurer une grande variété de fonctions et de types cellulaires. Le développement des maladies, en particulier les cancers, est invariablement lié à la dérégulation de ces contrôles. Notre objectif est de modéliser le lien entre l'expression des gènes et la composition nucléotidique des différentes régions régulatrices du génome. Nous proposons d'aborder ce problème dans un cadre de régression avec une approche Lasso couplée à un arbre de régression. Nous utilisons exclusivement des données de séquences et nous apprenons un modèle différent pour chaque type cellulaire. Nous montrons (i) que les différentes régions régulatrices apportent des informations diffé-rentes et complémentaires et (ii) que la seule information de leur composition nucléotidique permet de prédire l'expression des gènes avec une erreur comparable à celle obtenue en utilisant des données expérimentales. En outre, le modèle linéaire appris n'est pas aussi performant pour tous les gènes, mais modélise mieux certaines classes de gènes avec des compositions nucléotidiques particulières

Modélisation de l'expression des gènes à partir de données de séquence ADN

International audienceGene expression is tightly controlled to ensure a wide variety of cell types and functions. The development of diseases, particularly cancers, is invariably related to deregulations of these controls. Our objective is to model the link between gene expression and nucleotide composition of different regulatory regions in the genome. We propose to address this problem in a regression framework using a Lasso approach coupled to a regression tree. We use exclusively sequence data and we fit a different model for each cell type. We show that (i) different regulatory regions provide particular and complementary information and that (ii) the only information contained in the nucleotide compositions allows predicting gene expression with an error comparable to that obtained using experimental data. Moreover, the fitted linear model is not as powerful for all genes, but better fit certain groups of genes with particular nucleotides compositions.L'expression des gènes est étroitement contrôlée pour assurer une grande variété de fonctions et de types cellulaires. Le développement des maladies, en particulier les cancers, est invariablement lié à la dérégulation de ces contrôles. Notre objectif est de modéliser le lien entre l'expression des gènes et la composition nucléotidique des différentes régions régulatrices du génome. Nous proposons d'aborder ce problème dans un cadre de régression avec une approche Lasso couplée à un arbre de régression. Nous utilisons exclusivement des données de séquences et nous apprenons un modèle différent pour chaque type cellulaire. Nous montrons (i) que les différentes régions régulatrices apportent des informations diffé-rentes et complémentaires et (ii) que la seule information de leur composition nucléotidique permet de prédire l'expression des gènes avec une erreur comparable à celle obtenue en utilisant des données expérimentales. En outre, le modèle linéaire appris n'est pas aussi performant pour tous les gènes, mais modélise mieux certaines classes de gènes avec des compositions nucléotidiques particulières

XIVe édition des journées du cinéma muet de Pordenone (13-21 octobre 1995)

Bourget Jean-Loup, Amy de la Bretèque François, Glachant Isabelle, Lefebvre Thierry, Le Plongeon Nathalie, Marie Michel, Petitprez Véronique, Taillibert Christel. XIVe édition des journées du cinéma muet de Pordenone (13-21 octobre 1995). In: 1895, revue d'histoire du cinéma, n°19, 1995. pp. 50-72

XIVe édition des journées du cinéma muet de Pordenone (13-21 octobre 1995)

Bourget Jean-Loup, Amy de la Bretèque François, Glachant Isabelle, Lefebvre Thierry, Le Plongeon Nathalie, Marie Michel, Petitprez Véronique, Taillibert Christel. XIVe édition des journées du cinéma muet de Pordenone (13-21 octobre 1995). In: 1895, revue d'histoire du cinéma, n°19, 1995. pp. 50-72

Predictors and Consequences of Sac Shrinkage after Endovascular Infrarenal Aortic Aneurysm Repair

Background: Aneurysm shrinkage has been proposed as a marker of successful endovascular aneurysm repair (EVAR). We evaluated the impact of sac shrinkage on secondary interventions, on survival and its association with endoleaks, and on compliance with instructions for use (IFU). Methods: This observational retrospective study was conducted on all consecutive patients receiving EVAR for an infrarenal abdominal aortic aneurysm (AAA) using exclusively Endurant II/IIs endograft from 2014 to 2018. Sixty patients were entered in the study. Aneurysm sac shrinkage was defined as decrease ≥5 mm of the maximum aortic diameter. Univariate methods and Kaplan–Meier plots assessed the potential impact of shrinkage. Results: Twenty-six patients (43.3%) experienced shrinkage at one year, and thirty-four (56.7%) had no shrinkage. Shrinkage was not significantly associated with any demographics or morbidity, except hypertension (p = 0.01). No aneurysm characteristics were associated with shrinkage. Non-compliance with instructions for use (IFU) in 13 patients (21.6%) was not associated with shrinkage. Three years after EVAR, freedom from secondary intervention was 85 ± 2% for the entire series, 92.3 ± 5.0% for the shrinkage group and 83.3 ± 9% for the no-shrinkage group (Logrank: p = 0.49). Survival at 3 years was not significantly different between the two groups (85.9 ± 7.0% vs. 79.0 ± 9.0%, Logrank; p = 0.59). Strict compliance with IFU was associated with less reinterventions at 3 years (92.1 ± 5.9% vs. 73.8 ± 15%, Logrank: p = 0.03). Similarly, survival at 3 years did not significantly differ between strict compliance with IFU and non-compliance (81.8 ± 7.0% vs. 78.6 ± 13.0%, Logrank; p = 0.32). Conclusion: This study suggests that shrinkage ≥5 mm at 1-year is not significantly associated with a better survival rate or a lower risk of secondary intervention than no-shrinkage. In this series, the risk of secondary intervention regardless of shrinkage seems to be linked more to non-compliance with IFU. Considering the small number of patients, these results must be confirmed by extensive prospective studies