DRomics: A Turnkey Tool to Support the Use of the Dose–Response Framework for Omics Data in Ecological Risk Assessment

International audienceOmics approaches (e.g. transcriptomics, metabolomics) are promising for ecological risk assessment (ERA) since they provide mechanistic information and early warning signals. A crucial step in the analysis of omics data is the modelling of concentration-dependency which may have different trends including monotonic (e.g. linear, exponential) or biphasic (e.g. U shape, bell shape) forms. The diversity of responses raises challenges concerning detection and modelling of significant responses and effect concentration (EC) derivation. Furthermore, handling high-throughput datasets is time-consuming and requires effective and automated processing routines. Thus, we developed an open source tool (DRomics,available as an R-package and as a web-based service) which, after elimination of molecular responses (e.g. gene expressions from microarrays) with no concentration-dependency and/or high variability, identifies the best model for concentration-response curve description. Subsequently, an EC (e.g. a benchmark dose) is estimated from each curve and curves are classified based on their model parameters. This tool is especially dedicated to manage data obtained from an experimental design favoring a greatnumber of tested doses rather than a great number of replicates and also to handle properly monotonic and biphasic trends. The tool finally restitutes a table of results that can be directly used to perform ERA approaches

La modélisation pour comprendre les mécanismes d'effet de stress environnementaux: Mémoire d'Habilitation à Diriger des Recherches

Ecotoxicology is a science dedicated to unraveling the fate and effects of toxic substances, whether ofanthropogenic or natural origin, in ecosystems. It lies at the crossroads between the understanding offundamental mechanisms and addressing the critical societal concerns associated with environmental riskassessment. Despite being overshadowed in the realm of global change ecology, pollution ranks among theforemost causes of both present and future biodiversity loss. This HDR effectively illustrates howecotoxicological modeling can shed light on the intricate mechanisms underlying the effects of environmentalstressors across different scales of biological organization, space, and time. This knowledge is essential forpredicting how toxic compounds affect individuals, populations, communities and ecosystems under variousscenarios.L’écotoxicologie est la science qui étudie le devenir et les effets des composés toxiques, d’origine anthropiqueou naturelle, dans les écosystèmes. Elle est à la croisée entre la compréhension de mécanismes fondamentauxet de forts enjeux sociétaux d’évaluation des risques environnementaux. Elle reste le petit poucet de larecherche en écologie sur le thème du changement global, alors que la pollution est pourtant listée parmi lesprincipales causes de la perte de biodiversité actuelle et à venir. Cette HDR illustre comment la modélisationen écotoxicologie peut aider à comprendre les mécanismes d’effet de stress environnementaux, à différenteséchelles d’organisation biologique, d’espace et de temps, à partir de suivis de terrain ou de tests en conditionscontrôlées. Une compréhension indispensable pour ensuite prédire, sous divers scénarios, comment lescomposés toxiques affectent les individus, les populations, les communautés et les écosystèmes

La modélisation pour comprendre les mécanismes d'effet de stress environnementaux: Mémoire d'Habilitation à Diriger des Recherches

Ecotoxicology is a science dedicated to unraveling the fate and effects of toxic substances, whether ofanthropogenic or natural origin, in ecosystems. It lies at the crossroads between the understanding offundamental mechanisms and addressing the critical societal concerns associated with environmental riskassessment. Despite being overshadowed in the realm of global change ecology, pollution ranks among theforemost causes of both present and future biodiversity loss. This HDR effectively illustrates howecotoxicological modeling can shed light on the intricate mechanisms underlying the effects of environmentalstressors across different scales of biological organization, space, and time. This knowledge is essential forpredicting how toxic compounds affect individuals, populations, communities and ecosystems under variousscenarios.L’écotoxicologie est la science qui étudie le devenir et les effets des composés toxiques, d’origine anthropiqueou naturelle, dans les écosystèmes. Elle est à la croisée entre la compréhension de mécanismes fondamentauxet de forts enjeux sociétaux d’évaluation des risques environnementaux. Elle reste le petit poucet de larecherche en écologie sur le thème du changement global, alors que la pollution est pourtant listée parmi lesprincipales causes de la perte de biodiversité actuelle et à venir. Cette HDR illustre comment la modélisationen écotoxicologie peut aider à comprendre les mécanismes d’effet de stress environnementaux, à différenteséchelles d’organisation biologique, d’espace et de temps, à partir de suivis de terrain ou de tests en conditionscontrôlées. Une compréhension indispensable pour ensuite prédire, sous divers scénarios, comment lescomposés toxiques affectent les individus, les populations, les communautés et les écosystèmes

Time-dependent species sensitivity distributions

International audienc

Molecular and metabolic mechanisms of transgenerational effects in Daphnia exposed to radionuclides

International audienceUnderstanding how radioactive contaminants affect wildlife species at every level of biological organization is a major challenge in radioecology. Mechanistic links among observed effects are necessary to predict consequences for survival, growth and reproduction which are critical for population dynamics. With its short life cycle, the cladoceran Daphnia magna is particularly suitable to address toxicity over several generations, on a much more relevant time scale to natural biota. Multigenerational toxicity tests were conducted with depleted uranium (U), americium-241 (Am-241) and cesium-137 (Cs-137), inducing respectively a chemical toxicity, an internal alpha radiotoxicity and an external gamma radiotoxicity. Experimental results showed in all cases that toxic effects on survival, body size, fecundity increased in severity across generations, demonstrating that measured effects in one generation might not be representative of toxicity in offspring generations, and ultimately of population response.Reduction in body size and fecundity induced by depleted U, Cs-137 and Am-241 were analyzed using DEBtox models with respectively internal concentration, external gamma dose rate and internal alpha dose rate as dose metrics. For each radionuclide, a combination of several modes of action was necessary to explain observed effects. A damage compartment with hereditary damage level was introduced in order to explore possible modes of action associated with the increase in toxicity across generations. Modelling was performed using a Bayesian framework, in order to quantify uncertainty in parameter estimations and model predictions. Studies of DNA alterations, using a qPCR technique, confirmed that molecular damage was accumulated in daphnids exposed to depleted U and Cs-137 and transmitted to their progeny, as a possible underlying mechanism causing the increase in effect severity over generations. Further studies of DNA methylation recently investigated the role of epigenetic processes in the transmission of effects from parents exposed to Cs-137 to their unexposed progeny

Stochasticité et hétérogénéité spatiale en dynamique de population : implications pour l'évaluation du risque en écotoxicologie

We discuss the use of deterministic versus stochastic models (extinction risk concepts) providing population endpoints for decision-making in ecotoxicology. We developed a model depicting the dynamics of a trout metapopulation living in a theoretical river network, as chronic pollution occurs in one stretch of river. 1/ Deterministic endpoints seem to seriously underestimate the endangering effect of pollution on population level. The analysis of stochastic features such as extinction risk appears to be of broad interest to avoid this pitfall. 2/ Beside demographic stochasticity, random variations in the dispersal pattern adversely alter population dynamics with the increased extinction risk related to pollution perturbations. Increasing efforts are thus necessary to develop knowledge relative to stressor-induced spatial behaviours and to integrate such effects in the definition of environmental quality criteria

Molecular and metabolic mechanisms of transgenerational effects in Daphnia exposed to radionuclides

International audienceUnderstanding how radioactive contaminants affect wildlife species at every level of biological organization is a major challenge in radioecology. Mechanistic links among observed effects are necessary to predict consequences for survival, growth and reproduction which are critical for population dynamics. With its short life cycle, the cladoceran Daphnia magna is particularly suitable to address toxicity over several generations, on a much more relevant time scale to natural biota. Multigenerational toxicity tests were conducted with depleted uranium (U), americium-241 (Am-241) and cesium-137 (Cs-137), inducing respectively a chemical toxicity, an internal alpha radiotoxicity and an external gamma radiotoxicity. Experimental results showed in all cases that toxic effects on survival, body size, fecundity increased in severity across generations, demonstrating that measured effects in one generation might not be representative of toxicity in offspring generations, and ultimately of population response.Reduction in body size and fecundity induced by depleted U, Cs-137 and Am-241 were analyzed using DEBtox models with respectively internal concentration, external gamma dose rate and internal alpha dose rate as dose metrics. For each radionuclide, a combination of several modes of action was necessary to explain observed effects. A damage compartment with hereditary damage level was introduced in order to explore possible modes of action associated with the increase in toxicity across generations. Modelling was performed using a Bayesian framework, in order to quantify uncertainty in parameter estimations and model predictions. Studies of DNA alterations, using a qPCR technique, confirmed that molecular damage was accumulated in daphnids exposed to depleted U and Cs-137 and transmitted to their progeny, as a possible underlying mechanism causing the increase in effect severity over generations. Further studies of DNA methylation recently investigated the role of epigenetic processes in the transmission of effects from parents exposed to Cs-137 to their unexposed progeny

Sample size calculation in metabolic phenotyping studies

International audienceThe number of samples needed to identify significant effects is a key question in biomedical studies, with consequences on experimental designs, costs and potential discoveries. In metabolic phenotyping studies, sample size determination remains a complex step. This is due particularly to the multiple hypothesis-testing framework and the top-down hypothesis-free approach, with no a priori known metabolic target. Until now, there was no standard procedure available to address this purpose. In this review, we discuss sample size estimation procedures for metabolic phenotyping studies. We release an automated implementation of the Data-driven Sample size Determination (DSD) algorithm for MATLAB and GNU Octave. Original research concerning DSD was published elsewhere. DSD allows the determination of an optimized sample size in metabolic phenotyping studies. The procedure uses analytical data only from a small pilot cohort to generate an expanded data set. The statistical recoupling of variables procedure is used to identify metabolic variables, and their intensity distributions are estimated by Kernel smoothing or log-normal density fitting. Statistically significant metabolic variations are evaluated using the Benjamini–Yekutieli correction and processed for data sets of various sizes. Optimal sample size determination is achieved in a context of biomarker discovery (at least one statistically significant variation) or metabolic exploration (a maximum of statistically significant variations). DSD toolbox is encoded in MATLAB R2008A (Mathworks, Natick, MA) for Kernel and log-normal estimates, and in GNU Octave for log-normal estimates (Kernel density estimates are not robust enough in GNU octave)