Approche métabolomique pour l'étude de l'évolution adaptative de Pseudomonas aeruginosa au cours des infections pulmonaires chroniques dans la mucoviscidose

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.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

A metabolomics approach to study within-host adaptation of Pseudomonas aeruginosa during cystic fibrosis chronic lung infections

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

High-Resolution Magic Angle Spinning NMR-Based Metabolomics Revealing Metabolic Changes in Lung of Mice Infected with P. aeruginosa Consistent with the Degree of Disease Severity

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Carbohydrates great and small, from dietary fiber to sialic acids: How glycans influence the gut microbiome and affect human health

Gut microbiome composition depends heavily upon diet and has strong ties to human health. Dietary carbohydrates shape the gut microbiome by providing a potent nutrient source for particular microbes. This review explores how dietary carbohydrates in general, including individual monosaccharides and complex polysaccharides, influence the gut microbiome with subsequent effects on host health and disease. In particular, the effects of sialic acids, a prominent and influential class of monosaccharides, are discussed. Complex plant carbohydrates, such as dietary fiber, generally promote microbial production of compounds beneficial to the host while preventing degradation of host carbohydrates from colonic mucus. In contrast, simple and easily digestible sugars such as glucose are often associated with adverse effects on health and the microbiome. The monosaccharide class of sialic acids exerts a powerful but nuanced effect on gut microbiota. Sialic acid consumption (in monosaccharide form, or as part of human milk oligosaccharides or certain animal-based foods) drives the growth of organisms with sialic acid metabolism capabilities. Minor chemical modifications of Neu5Ac, the most common form of sialic acid, can alter these effects. All aspects of carbohydrate composition are therefore relevant to consider when designing dietary therapeutic strategies to alter the gut microbiome

synDNA—a Synthetic DNA Spike-in Method for Absolute Quantification of Shotgun Metagenomic Sequencing

Microbiome studies have the common goal of determining which microbial taxa are present, respond to specific conditions, or promote phenotypic changes in the host. Most of these studies rely on relative abundance measurements to drive conclusions. Inherent limitations of relative values are the inability to determine whether an individual taxon is more or less abundant and the magnitude of this change between the two samples. These limitations can be overcome by using absolute abundance quantifications, which can allow for a more complete understanding of community dynamics by measuring variations in total microbial loads. Obtaining absolute abundance measurements is still technically challenging. Here, we developed synthetic DNA (synDNA) spike-ins that enable precise and cost-effective absolute quantification of microbiome data by adding defined amounts of synDNAs to the samples. We designed 10 synDNAs with the following features: 2,000-bp length, variable GC content (26, 36, 46, 56, or 66% GC), and negligible identity to sequences found in the NCBI database. Dilution pools were generated by mixing the 10 synDNAs at different concentrations. Shotgun metagenomic sequencing showed that the pools of synDNAs with different percentages of GC efficiently reproduced the serial dilution, showing high correlation (r = 0.96; R2 ≥ 0.94) and significance (P < 0.01). Furthermore, we demonstrated that the synDNAs can be used as DNA spike-ins to generate linear models and predict with high accuracy the absolute number of bacterial cells in complex microbial communities. IMPORTANCE The synDNAs designed in this study enable accurate and reproducible measurements of absolute amount and fold changes of bacterial species in complex microbial communities. The method proposed here is versatile and promising as it can be applied to bacterial communities or genomic features like genes and operons, in addition to being easily adaptable by other research groups at a low cost. We also made the synDNAs' sequences and the plasmids available to encourage future application of the proposed method in the study of microbial communities

Ranking occupational contexts associated with risk of non-Hodgkin lymphoma

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Modélisation des cartes de probabilité de présence d’Aedes albopictus en Rhône-Alpes, France

International audienceUnknown outside the Asian jungles almost fourty years ago, the tiger mosquito Aedes albopictus, vector of several human pathogens, is now established across the 5 continents, including in urban areas. In France, a national plan for surveillance of dengue and chikungunya is active, and some autochthonous cases of transmission have been reported recently, underlying the present risk of transmission in areas where the mosquito is present. Probably first introduced in the Rhône-Alpes area via road transport, Ae. albopictus is since 2013 considered present and active in 5 on 8 departments. For the surveillance purposes, it is now a question of identifying areas suitable for this mosquito and how these areas could be colonized. Thus, the aim of this study is to build maps of susceptibility of Ae. albopictus presence in the Rhône-Alpes area by a modelling approach combining knowledge on both the climatic and environmental determinants for establishment.Inconnu en dehors des jungles asiatiques il y a encore quarante ans, le moustique tigre Aedes albopictus, vecteur de nombreux agents pathogènes humains, s’est désormais établi sur les cinq continents, y compris en ville. En France, il fait l’objet d’un plan national de surveillance de la dengue et du chikungunya et plusieurs cas de transmission autochtones ont été reportés récemment, soulignant ainsi la réalité du risque de transmission dans les zones où ce moustique est implanté. Probablement introduit en région Rhône-Alpes via le transport routier, Ae. albopictus est considéré comme présent et actif dans cinq des huit départements depuis 2013. Il est aujourd’hui question d’identifier les zones favorables à son implantation et comment la colonisation vers ces zones pourrait se faire. Ainsi, l’objectif de notre travail est de construire des cartes de susceptibilité de présence d’Ae. albopictus en Rhône-Alpes par modélisation combinant à la fois les connaissances sur les déterminants climatiques mais aussi environnementaux de son installation

Ranking occupational contexts associated with risk of non-Hodgkin lymphoma

Background: Risk factors associated with non-Hodgkin lymphoma (NHL) remain unknown, but certain occupational contexts (OCs) have been implicated. The objective of this study was to inventory, from the accumulated knowledge, associations between OCs and NHL risk. Methods: Literature was used to identify the NHL-associated OCs. For each context, items were ranked both by scientific interest and the association strength. Results: Three ranked lists of OCs related to NHL were constructed. We found that NHL was associated with 31 occupational activities, 91 occupational exposures, and 35 occupational activity-exposure combinations. Among them, 5 activities, 2 exposures, and 3 combinations, involving agricultural or industrial sector and solvents or pesticides, were highlighted with the highest publications number and the strongest association with NHL risk. Conclusion: These results could be useful in both providing a ranked inventory of OCs associated with NHL risk and highlighting "hot" occupational activities and exposures

Metabotypes of Pseudomonas aeruginosa Correlate with Antibiotic Resistance, Virulence and Clinical Outcome in Cystic Fibrosis Chronic Infections

Pseudomonas aeruginosa (P.a) is one of the most critical antibiotic resistant bacteria in the world and is the most prevalent pathogen in cystic fibrosis (CF), causing chronic lung infections that are considered one of the major causes of mortality in CF patients. Although several studies have contributed to understanding P.a within-host adaptive evolution at a genomic level, it is still difficult to establish direct relationships between the observed mutations, expression of clinically relevant phenotypes, and clinical outcomes. Here, we performed a comparative untargeted LC/HRMS-based metabolomics analysis of sequential isolates from chronically infected CF patients to obtain a functional view of P.a adaptation. Metabolic profiles were integrated with expression of bacterial phenotypes and clinical measurements following multiscale analysis methods. Our results highlighted significant associations between P.a "metabotypes", expression of antibiotic resistance and virulence phenotypes, and frequency of clinical exacerbations, thus identifying promising biomarkers and therapeutic targets for difficult-to-treat P.a infections

Feature-based molecular networking in the GNPS analysis environment

Molecular networking has become a key method to visualize and annotate the chemical space in non-targeted mass spectrometry data. We present feature-based molecular networking (FBMN) as an analysis method in the Global Natural Products Social Molecular Networking (GNPS) infrastructure that builds on chromatographic feature detection and alignment tools. FBMN enables quantitative analysis and resolution of isomers, including from ion mobility spectrometry