Showing results 121 to 140 on 7303 in total
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- Équipe Éléments transposables, Évolution, Populations - Pôle informatique (présentation des travaux réalisés avec Sonia Kéfi (ISEM) et des collaborateurs chiliens/états-uniens, publiés dans le numéro d'août de PLoS Biology. Il s'agit de la première étude d'un réseau écologique "multiplexe", i.e. qui recense toutes les relations connues (trophiques et non-trophiques [facilitation, compétition]) entre les espèces d'une communauté écologique. Notre jeu de données concerne toutes les espèces présentes sur la côte du centre du Chili).
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Merci de me confirmer votre présence par mail à mariethe.chaumeil@chu-lyon.fr
The "Peer Community in" project is a non-profit scientific organization aimed at creating specific communities of researchers reviewing and recommending papers in their field. These specific communities are entitled Peer Community in X, e.g. Peer Community in Evolutionary Biology, Peer Community in Microbiology.The motivation behind this project is the establishment of a high-quality, free, public system for identifying high-quality papers by a specific recommendation that would be recognized within and subsequently beyond the community, including by funding and research agencies.This project should lead to a new scientific publication system, in which papers are deposited in open and free archives, and if appropriate, reviewed and awarded a recommendation publicly guaranteeing their scientific quality. This recommendation could replace the current evaluation and editing process of scientific journals, which is very costly for research institutions.
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The lack of ecological realism in current prospective environmental risk assessment (ERA) is widely recognised as a limitation in this field. As organisms are living in a multistressed environment, involving both chemical and environmental stressors, it is worth understanding how these combined stressors will affect the organisms and subsequently the populations. A way forward to include more ecological relevance in ERAs is the use of environmental scenarios that will represent key differences in environmental factors such as the food availability, the temperature variability, the predation, etc. and in exposure factors. All these factors will influence the capability of an organism to grow and reproduce as well as its resilience to additional stressors. As growth and reproduction are driven by an organisms' energy balance, Dynamic Energy Budget models are particularly well suited to integrate toxicant and environmental stressors. Indeed, the DEB theory analyses the fluxes of energy within an organism, how stressors can impact these fluxes, and how this will affect the organism's life history traits. This mechanistic description of an organism can then be used as a building block of a population model. It is therefore of interest to analyse the effect of a mixture of ecological and chemical stressors on the bioenergetic fluxes of organisms. The outcome of such an integrated analysis will lead to complex and multi-scaled results that can be challenging to graphically depict. A potential solution that is simple enough to understand, yet incorporates sufficient detail to make informed decisions is the use of prevalence plots. Improving the ecological relevance of ERAs via the use of prevalence plots provides a risk-based approach that combines risk assessment and risk management in a meaningful way. This framework presents a truly mechanistic alternative to the threshold approach currently employed in chemical risk assessment
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Infectious diseases account for one fourth of human deaths worldwide. With pathogen collections of ever-increasing sizes, it becomes possible in theory to reconstruct past epidemic events on continental or worldwide scales and to gain actionable insights into the driving forces of pathogen dispersal and evolution. Current population genetics methods excel at this task, however their computational cost hampers their application on massive (n > 1,000) genomic datasets. Here we introduce a novel approach, ancestral state interpolation (AncSI), to reconstruct epidemics through space and time in a computationally efficient fashion. AncSI infers past information (including location, resistance or transmission success) relative to all isolates in the study population in a given time period. By computing series of fine-grained time period, AncSI allows for the visualization of epidemic dispersal in the form of video files. We reconstruct the epidemic progression across Eurasia and Africa of two deadly bacterial pathogens, namely the Mycobacterium tuberculosis Beijing family (n = 4,000 isolates with an evolution on the millenial scale) and the Salmonella Typhi H58 clone (n = 2,000 isolates with an evolution on the decade scale). In both cases, AncSI-inferred epidemic dynamics exhibited a near-perfect match with the conclusions of previous studies based on hypothesis-driven population genetics analyses. Furthermore, AncSI results highlighted previously unreported features of the epidemics such as a Korean (rather than Chinese) emergence of M.tuberculosis Beijing. Our results indicate that an accurate reconstruction of past epidemics can be obtained efficiently from genomic datasets, potentially leading to novel discoveries by leveraging the fast growing collections of pathogen genomes.
The recent West African Ebola outbreak has been a terrible reminder for the need to gain timely situation awareness, in order to inform and guide public health intervention and maximise the chances of mitigating disease outbreaks. Unfortunately, many tools are still lacking for addressing the challenges, both statistical and technical, posed by the analysis of outbreak data. This presentation will introduce the R Epidemics Consortium (RECON), an initiative bringing together public health officers, statisticians, modellers and software developers to develop a new generation of tools for outbreak response using the R software. We will argue that R is a platform of choice for the development of cutting-edge methodology which can further our understanding of disease dynamics. This point will be illustrated using outbreaker2, a new R package for reconstructing disease outbreaks using various kinds of epidemiological and genetic data. We will also show how R can be used for addressing some of the more technical challenges inherent to the outbreak response context, taking the packages incidence and epicontacts as examples. We will conclude by reflecting on how the typical life-cycle of methodological development is altered during emergency outbreak response, and on what novel practices may be considered to address some of these issues.http://repidemicsconsortium.org/ Keywords: methods, R, statistics, software, RECON, tools
Le vieillissement est un phénomène complexe intervenant à toutes les échelles de l'organisation biologique, du niveau moléculaire jusqu'au niveau des performances de l'organisme. La locomotion est une fonction neurophysiologique hautement intégrée illustrant un tel processus multi-échelle. Le déclin des performances de locomotion avec l'âge, comme la vitesse maximale, a été observé pour de nombreuses espèces, aussi bien en captivité qu'en milieu naturel. Cependant, ces descriptions restent souvent succinctes, sans précision sur la progression de ces performances au cours du vieillissement. Dans ces travaux, nous utilisons une équation bi-phasique pour décrire la relation entre performance de locomotion et âge sur l'ensemble de la durée de la vie pour Caenorhabditis elegans, Mus domesticus, Canis familiaris, Equus caballus et Homo sapiens. Les performances maximales de locomotion se révèlent être des bio-marqueurs robustes pour suivre la progression des performances sur l'ensemble de la durée de vie des animaux, permettant ainsi d'estimer le pic physiologique et le début du déclin des performances. De plus, dans tous les cas, nous remarquons que la forme de progression des performances maximales selon l'âge est similaire et conservée d'une espèce à l'autre ; seule varie la pente dans le temps, dépendant de l'espèce et la performance mesurée. Nous avons ensuite étudié le développement et l'expansion de cette dynamique au cours du siècle dernier pour les performances athlétiques maximales d'Homo sapiens. Cette étude révèle que la forme s'est progressivement précisée au cours du temps en s'étendant à tous les âges et suivant homothétiquement la progression des records du monde. Néanmoins, la progression semble ralentir au cours des dernières décennies, laissant présager l'atteinte possible des limites biologiques d'Homo sapiens. Ces travaux offrent de nouvelles perspectives sur l'utilité des approches comparatives et l'utilisation d'un bio-marqueur comme les performances de locomotion pour suivre les dynamiques sur l'ensemble de la durée de vie à différentes échelles. Elles apportent aussi un regard novateur sur la progression des performances avec l'âge, en intégrant à la fois les processus de développement et de vieillissement, permettant ainsi de préciser les pics physiologiques et la forme des progressions des performances sur toute la durée de la vie. Institut de Recherche bio-Médicale et d'Epidémiologie du Sport (IRMES), EA 7329, Institut National du Sport, de l'Expertise et de la Performance (INSEP) and Université Paris Descartes, Sorbonne Paris Cité, Paris, France / (2) Laboratoire Matière et Systèmes Complexes, UMR 7057, Université Paris Diderot and CNRS, Sorbonne Paris Cité, Paris, France.
Functional prediction is a cornerstone in molecular biology. In this seminar, I will talk about current phylogenomic approaches to functional annotation and gene family characterization. In particular, I will focus on the use of fine-grained orthology detection for functionally annotating novel genomes, phylostratigraphic methods to date the emergence of particular gene functions, and current challenges in the discovery of novel gene functions out massive metagenomic datasets.
Animal bodies house trillions of bacteria, which can influence host behavior in ways that have far-reaching implications for host ecology and evolution. Recent studies have revealed surprising roles for bacteria in shaping behavior across many animal taxa. But questions remain and recent perspective papers have emphasized the need of studying the interactions between microbiota and host behavior. Although it has long been posited that microbiota can influence host behaviour by affecting chemical cues that animals use to communicate, this purported relationship has only recently received renewed attention owing to the advent of new sequencing technologies. My work takes place into this framework, and I will present various results that link microbiota and odor cues in birds and mammals.Odeurs et microbiote chez les vertébrésDepuis quelques années, le rôle des bactéries dans l'écologie et l'évolution des animaux suscite un grand intérêt théorique. Néanmoins, bien que les bactéries semblent jouer un rôle fondamental dans la valeur adaptative des individus, la communauté scientifique vient tout juste de découvrir l'ampleur de leur diversité et fonctions. En particulier, il a été suggéré que le microbiote pouvait être, en partie, responsable de la production des odeurs corporelles utilisées par les animaux pour communiquer avec leurs conspécifiques. Etant donné l'importance de la communication chimique à travers le règne animal, l'altération des odeurs par le microbiote pourrait être une force qui façonne de nombreux comportements. Cependant, malgré l'intérêt que ce sujet suscite, très peu d'études se sont, jusqu'à présent, penchées sur cette question. Mes travaux de recherches se placent dans ce contexte théorique, et je présenterai différents résultats sur les signaux odeurs et le lien avec le microbiote chez les oiseaux et les mammifères.
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The turn from the 20th to the 21st Century was marked by a drastic change in the scale at which biologists study regulatory networks. In the 1990, a PhD student could spend years analysing the regulation of one particular gene by one or a few transcription factors. Microarray technologies enabled monitoring the expression of all the genes of an organism in a single experiment (transcriptome arrays), and to lead genome-wide location analysis to report supposedly exhaustive lists of transcription factor binding sites. Next Generation Sequencing amplified the movement, and many labs are now combining ChIP-seq and RNA-seq experiments to get a wide view on transcription factor binding locations, histone modifications, and transcriptional responses to a multitude of conditions, cell types, developmental stages, etc. In the first part of the talk, I will present some of the bioinformatics approaches and tools that we developed to analyse regulatory motifs from various types of high-throughput data (e.g. co-expression clusters, ChIP-seq peaks, replication origins).At the light of the evolution of the domain, I would also like to address a more general question about the insights gained from high-throughput approaches on fundamental mechanisms of regulation. Indeed, it implicitly became standard to consider that a typical high-throughput experiments should return thousands of significant features (differentially expressed genes, TF binding sites, active enhancers). This however does not fit with our classical models, were transcription factors would turn on or off specific sets of target genes ("regulatory switches"), thereby forming regulatory networks whose behaviour was understandably determined by feedback loops. How can we conciliate the undeniable robustness of regulatory networks with the apparent noisiness of binding and transcription profiles?