Hierarchical modelling of species sensitivity distribution: development and application to the case of diatoms exposed to several herbicides

The Species Sensitivity Distribution (SSD) is a key tool to assess the ecotoxicological threat of contaminant to biodiversity. It predicts safe concentrations for a contaminant in a community. Widely used, this approach suffers from several drawbacks: i)summarizing the sensitivity of each species by a single value entails a loss of valuable information about the other parameters characterizing the concentration-effect curves; ii)it does not propagate the uncertainty on the critical effect concentration into the SSD; iii)the hazardous concentration estimated with SSD only indicates the threat to biodiversity, without any insight about a global response of the community related to the measured endpoint. We revisited the current SSD approach to account for all the sources of variability and uncertainty into the prediction and to assess a global response for the community. For this purpose, we built a global hierarchical model including the concentration-response model together with the distribution law for the SSD. Working within a Bayesian framework, we were able to compute an SSD taking into account all the uncertainty from the original raw data. From model simulations, it is also possible to extract a quantitative indicator of a global response of the community to the contaminant. We applied this methodology to study the toxicity of 6 herbicides to benthic diatoms from Lake Geneva, measured from biomass reduction

Multiple morbidities in companion dogs: a novel model for investigating age-related disease

The proportion of men and women surviving over 65 years has been steadily increasing over the last century. In their later years, many of these individuals are afflicted with multiple chronic conditions, placing increasing pressure on healthcare systems. The accumulation of multiple health problems with advanced age is well documented, yet the causes are poorly understood. Animal models have long been employed in attempts to elucidate these complex mechanisms with limited success. Recently, the domestic dog has been proposed as a promising model of human aging for several reasons. Mean lifespan shows twofold variation across dog breeds. In addition, dogs closely share the environments of their owners, and substantial veterinary resources are dedicated to comprehensive diagnosis of conditions in dogs. However, while dogs are therefore useful for studying multimorbidity, little is known about how aging influences the accumulation of multiple concurrent disease conditions across dog breeds. The current study examines how age, body weight, and breed contribute to variation in multimorbidity in over 2,000 companion dogs visiting private veterinary clinics in England. In common with humans, we find that the number of diagnoses increases significantly with age in dogs. However, we find no significant weight or breed effects on morbidity number. This surprising result reveals that while breeds may vary in their average longevity and causes of death, their age-related trajectories of morbidities differ little, suggesting that age of onset of disease may be the source of variation in lifespan across breeds. Future studies with increased sample sizes and longitudinal monitoring may help us discern more breed-specific patterns in morbidity. Overall, the large increase in multimorbidity seen with age in dogs mirrors that seen in humans and lends even more credence to the value of companion dogs as models for human morbidity and mortality

myTAI: evolutionary transcriptomics with R.

MOTIVATION: Next Generation Sequencing (NGS) technologies generate a large amount of high quality transcriptome datasets enabling the investigation of molecular processes on a genomic and metagenomic scale. These transcriptomics studies aim to quantify and compare the molecular phenotypes of the biological processes at hand. Despite the vast increase of available transcriptome datasets, little is known about the evolutionary conservation of those characterized transcriptomes. RESULTS: The myTAI package implements exploratory analysis functions to infer transcriptome conservation patterns in any transcriptome dataset. Comprehensive documentation of myTAI functions and tutorial vignettes provide step-by-step instructions on how to use the package in an exploratory and computationally reproducible manner. AVAILABILITY AND IMPLEMENTATION: The open source myTAI package is available at https://github.com/HajkD/myTAI and https://cran.r-project.org/web/packages/myTAI/index.html. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

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

Deep dive into the chronic toxicity of tyre particle mixtures and their leachates.

This is the final version. Available from Elsevier via the DOI in this record. Data Availability: Data will be made available on request.Particles from the tread of vehicle tyres are a global pollutant, which are emitted into the environment at an approximate rate of 1.4 kg.year-1 for an average passenger-car. In this study, popular tyre brands were used to generate a tyre tread microparticle mixture. The chronic toxicity of both particles and chemical leachates were compared on a planktonic test species (Daphnia magna). Over 21 days of exposure, pristine tyre tread microparticles were more toxic (LC50 60 mg.L-1) than chemical lechates alone (LC50 542 mg.L-1). Microparticles and leachates showed distinct effects on reproduction and morphological development at environmentally relevant concentrations, with dose-dependent uptake of particles visible in the digestive tract. Chemical characterization of leachates revealed a metal predominance of zinc, titanium, and strontium. Of the numerous organic chemicals present, at least 54 were shared across all 5 tyre brands, with many classified to be very toxic. Our results provide a critically needed information on the toxicity of tyre tread particles and the associated chemicals that leach from them to inform future mitigation measures. We conclude that tyre particles are hazardous pollutants of particular concern that are close to or possibly above chronic environmental safety limits in some locations.Natural Environment Research Council (NERC

Evaluation using latent class models of the diagnostic performances of three ELISA tests commercialized for the serological diagnosis of <i>Coxiella burnetii</i> infection in domestic ruminants

International audienceELISA methods are the diagnostic tools recommended for the serological diagnosis of Coxiella burnetii infection in ruminants but their respective diagnostic performances are difficult to assess because of the absence of a gold standard. This study focused on three commercial ELISA tests with the following objectives (1) assess their sensitivity and specificity in sheep, goats and cattle, (2) assess the between-and within-herd seroprevalence distribution in these species, accounting for diagnostic errors, and (3) estimate optimal sample sizes considering sensitivity and specificity at herd level. We comparatively tested 1413 cattle, 1474 goat and 1432 sheep serum samples collected in France. We analyzed the cross-classified test results with a hierarchical zero-inflated beta-binomial latent class model considering each herd as a population and conditional dependence as a fixed effect. Potential biases and coverage probabilities of the model were assessed by simulation. Conditional dependence for truly seropositive animals was high in all species for two of the three ELISA methods. Specificity estimates were high, ranging from 94.8% [92.1; 97.8] to 99.2% [98.5; 99.7], whereas sensitivity estimates were generally low, ranging from 39.3 [30.7; 47.0] to 90.5% [83.3; 93.8]. Betweenand within-herd seroprevalence estimates varied greatly among geographic areas and herds. Overall, goats showed higher within-herd seroprevalence levels than sheep and cattle. The optimal sample size maximizing both herd sensitivity and herd specificity varied from 3 to at least 20 animals depending on the test and ruminant species. This study provides better interpretation of three widely used commercial ELISA tests and will make it possible to optimize their implementation in future studies. The methodology developed may likewise be applied to other human or animal diseases