L’infection pulmonaire chronique à Pseudomonas aeruginosa (P. a.) est considérée comme la principalecause de morbidité et de mortalité liée à la mucoviscidose. Au cours de cette infection persistante, labactérie s'adapte à l’environnement pulmonaire caractéristique de ces patients et évolue avec son hôtependant des décennies. Cette évolution adaptative est portée par les phénotypes, avec notamment unediminution de la virulence et une augmentation de la résistance aux antibiotiques au cours du temps. Bienque plusieurs études aient tenté d’évaluer les mécanismes génétiques de cette évolution, il demeureaujourd’hui difficile d’expliquer les relations entre les mutations accumulées dans le génome bactérien etl’expression de phénotypes cliniquement pertinents, ou encore de corréler ces mutations avec l’état desanté du patient.Nous proposons dans ce travail d’étudier les mécanismes sous-tendant cette évolution adaptative à unniveau d’observation post-génomique : la métabolomique. Dernière-née des disciplines –omiques, lamétabolomique permet la prise de vue instantanée du métabolisme, et offre une vision au plus proche duphénotype. Pour cela, nous avons constitué une banque de lignées clonales évolutives de P. a. prélevéesau cours de l’infection pulmonaire chronique chez des patients atteints de mucoviscidose. Cette banque aensuite été caractérisée aux plans clinique, phénotypique et métabolomique. L’intégration de ces différentsniveaux d’information par des méthodes statistiques multi-tableaux nous a permis de mettre en évidencedes voies métaboliques impliquées dans la patho-adaptation de P. a. à son hôte.Nos résultats permettent de faire émerger de nouvelles hypothèses pour le développement d’outilsthérapeutiques et diagnostiques visant à améliorer la prise en charge de ces infections particulièrementrésistantes aux antibiotiques. De plus, nos travaux démontrent l’intérêt de la métabolomique pour l’étudede l’évolution adaptative bactérienne en conditions naturelles. / Chronic lung infection with Pseudomonas aeruginosa (P. a.) is considered as the leading cause of cysticfibrosis (CF) morbidity and mortality. During this persistent infection, the bacterium adapts to the typical lungenvironment of these patients and evolves within its host for decades. This adaptive evolution is driven byphenotypes, including a decrease in virulence and an increase in antibiotic resistance over time. Althoughseveral studies have attempted to elucidate the genetic mechanisms of this evolution, it remains difficulttoday to explain the relationships between the accumulated genomic mutations and the expression ofclinically relevant phenotypes, or to correlate these mutations with the patient’s health status.In this work, we propose to study the mechanisms underlying this adaptive evolution at a post-genomicobservation level: metabolomics. Metabolomics, the newest of the -omics disciplines, provides an instantview of the metabolic activities, and furnishes a vision as close as possible to the phenotype. To this end,we constructed a bank of evolutive clonal P. a. lineages sampled during chronic lung infection in patientswith CF. This bank was then clinically, phenotypically and metabolomically characterized. Integration ofthese different levels of information by multi-block statistical methods has allowed us to highlight metabolicpathways involved in within-host patho-adaptation of P. a. .Our results rise new hypotheses for the development of therapeutic and diagnostic tools with the aim ofimproving the management of these infections particularly resistant to antibiotics. In addition, our workdemonstrates the interest of metabolomics to study bacterial adaptive evolution under natural conditions.

A high-order central essentially non-oscillatory (CENO) finite-volume scheme in combination with a block-based adaptive mesh refinement (AMR) algorithm is proposed for solution of hyperbolic and elliptic systems of conservation laws on body- fitted multi-block mesh. The spatial discretization of the hyperbolic (inviscid) terms is based on a hybrid solution reconstruction procedure that combines an unlimited high-order k-exact least-squares reconstruction technique following from a fixed central stencil with a monotonicity preserving limited piecewise linear reconstruction algorithm. The limited reconstruction is applied to computational cells with under-resolved solution content and the unlimited k-exact reconstruction procedure is used for cells in which the solution is fully resolved. Switching in the hybrid procedure is determined by a solution smoothness indicator. The hybrid approach avoids the complexity associated with other ENO schemes that require reconstruction on multiple stencils and therefore, would seem very well suited for extension to unstructured meshes. The high-order elliptic (viscous) fluxes are computed based on a k-order accurate average gradient derived from a (k+1)-order accurate reconstruction. A novel h-refinement criterion based on the solution smoothness indicator is used to direct the steady and unsteady refinement of the AMR mesh. The predictive capabilities of the proposed high-order AMR scheme are demonstrated for the Euler and Navier-Stokes equations governing two-dimensional compressible gaseous flows as well as for advection-diffusion problems characterized by the full range of Peclet numbers, Pe. The ability of the scheme to accurately represent solutions with smooth extrema and yet robustly handle under-resolved and/or non-smooth solution content (i.e., shocks and other discontinuities) is shown for a range of problems. Moreover, the ability to perform mesh refinement in regions of smooth but under-resolved and/or non-smooth solution content to achieve the desired resolution is also demonstrated.

A high-order central essentially non-oscillatory (CENO) finite-volume scheme in combination with a block-based adaptive mesh refinement (AMR) algorithm is proposed for solution of hyperbolic and elliptic systems of conservation laws on body- fitted multi-block mesh. The spatial discretization of the hyperbolic (inviscid) terms is based on a hybrid solution reconstruction procedure that combines an unlimited high-order k-exact least-squares reconstruction technique following from a fixed central stencil with a monotonicity preserving limited piecewise linear reconstruction algorithm. The limited reconstruction is applied to computational cells with under-resolved solution content and the unlimited k-exact reconstruction procedure is used for cells in which the solution is fully resolved. Switching in the hybrid procedure is determined by a solution smoothness indicator. The hybrid approach avoids the complexity associated with other ENO schemes that require reconstruction on multiple stencils and therefore, would seem very well suited for extension to unstructured meshes. The high-order elliptic (viscous) fluxes are computed based on a k-order accurate average gradient derived from a (k+1)-order accurate reconstruction. A novel h-refinement criterion based on the solution smoothness indicator is used to direct the steady and unsteady refinement of the AMR mesh. The predictive capabilities of the proposed high-order AMR scheme are demonstrated for the Euler and Navier-Stokes equations governing two-dimensional compressible gaseous flows as well as for advection-diffusion problems characterized by the full range of Peclet numbers, Pe. The ability of the scheme to accurately represent solutions with smooth extrema and yet robustly handle under-resolved and/or non-smooth solution content (i.e., shocks and other discontinuities) is shown for a range of problems. Moreover, the ability to perform mesh refinement in regions of smooth but under-resolved and/or non-smooth solution content to achieve the desired resolution is also demonstrated.