Showing results 1141 to 1160 on 1328 in total
When a patient contract cancer, the diagnosis and the treatment options are decided based on phenotypic factor such as the primary organ and the pathology of the tumors. However, recent discoveries have shown than the molecular pathogenesis of the tumor may be a better indicator of the prognosis and the adequate therapeutical targets, underlining the need for a better understanding of the genomic characteristics of the disease. In order to solve this issue, initiatives such as TCGA have sequenced large groups of disease samples and used the size of the cohort to distinguish between driver and passenger events. We propose to adopt the same approach to analyze liquid tumors, where widespread heterogeneity within the tumor as well as between patients adds even more noise to the measurements. We will show the software infrastructure we built in order to track and analyze the samples as well as two examples of such studies. The first cohort we will discuss is a cohort of 53 patients with acute myeloid leukemia (AML). AML is the most common type of leukemia in adult, and the is still poorly understood. All these patients achieved complete remissions after standard chemotherapy and later suffered from relapse. We performed whole exome sequencing of germline tissue, primary sample as well as relapsed sample for each of these patients and will show analysis of this data compared to the knowledge that we have of the disease. The second cohort we will discuss is a cohort of 189 adult patients with B-cell acute lymphoblastic leukemia (B-ALL). B-ALL is the most common leukemic malignancy in the pediatric population but most of the knowledge that we have from the adult form of the disease is actually deduced from the childhood form. We will present here a integrated and targeted DNA/RNA sequencing solution that allowed us to extract short events (point substitutions and short insertions and deletions) as well as copy number aberrations, rearrangements and fusion events. We will use these two cases to discuss common problems that arise when analyzing genomic data as well as potential solutions."
Mitochondrial genomes (mtDNA) are normally maternally inherited and encode for subunits of respiratory chain complexes and ATP synthase among others. The integrity of mtDNA is crucial for cellular energetic and redox homeostasis, and mtDNA mutations are associated with modifications of individual fitness and longevity. Bivalves are the only zoological group in which Doubly Uniparental Inheritance (DUI), characterized by the presence of two divergent mitochondrial genomes within different tissues of male individuals, is frequently observed. The F-genome, maternally inherited, is found in somatic tissue and female gonads whereas the M-mtDNA is found in male gonadic tissue only. The clam Arctica islandica is widely distributed throughout the North Atlantic shelf regions. Due to different environmental regimes (salinity, temperature, oxygen), the maximum lifespan of its populations varies between >500 years around Iceland and 35 years in the Baltic Sea. I will present our recent investigations that describe for the first time the existence of the DUI system in Arctica islandica. Based on 16S and cytochrome b markers, we highlight the presence of an M-genome in male gonads in individuals belonging to Baltic and North Sea populations. The two genomes show a low level of sequence divergence compared to other DUI species, around 6-8% at the nucleotide level. Whilst the analysis of mitochondrial markers generally indicated genetically homogeneity of all North Atlantic populations, they further reveal few clam individuals that carry a "divergent" mtDNA haplotype, resembling the M-genome. These individuals occurred however exclusively in the Icelandic population. Unlike the M-genome, which is confined to male gonadic tissue in DUI species, this "divergent" mtDNA occurs in somatic tissues from 20% of individuals of both sexes. In association with transcriptomic and biochemical data, we will discuss the possible impacts of this uncommon mitochondrial genome on Arctica islandica biology and cellular physiology. This study will enhance the understanding of the role of DUI and mtDNA in general for fitness, aging and adaptation of bivalves.
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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