Biology subjects

Charles, S.

Publications and source records attributed to Charles, S..

At least 19 recordsLinked to original sources

bioRxiv2026

New perspectives in assessing environmental risks for birds: a simple TKTD framework to link growth and reproduction energy budget to chemical stress

Assessing the risk of pesticides to birds requires models that can extrapolate laboratory data to realistic exposure scenarios. In this work, we propose a new modeling framework BIRDkiss (Bird - Impact on Reproduction via Diet, keep it simple and suitable). This framework integrates a simplified Dynamic Energy Budget (DEBkiss) of organisms and the toxicokinetic-toxicodynamic (TKTD) of chemical substances according to a trait-based approach, thereby reducing the number of parameters to identify and strengthening the statistical robustness of the critical endpoints. The BIRDkiss model describes how food intake and toxicant exposure affect growth and egg production in birds over time. The model is fully embedded within an R package, including routines for calibration, validation and prediction under single-compound scenarios performed via Bayesian inference using standard data from the OECD avian reproduction tests. The BIRDkiss model also allows the simulations of scenarios under both varying food availability and multi-compound exposures based on the two classical mixture-toxicity paradigms: Concentration Addition (CA) and Independent Action (IA). The results of calibration for single compounds show good results matching with observed weights and egg counts. From these calibrations, predictions for new exposure scenarios can be readily generated. For mixtures, the IA algorithm is simpler and does not require to scale variables as in CA. Simulations indicate that high food levels do not further increase egg production (saturation), whereas substantial food reductions markedly decrease reproduction because energy is reallocated to maintenance. Exposure to chemicals combined to low food availability amplify the decline in reproductive output. The ready-to-use mechanistic, open-source BIRDkiss tool enables predicting the impact of pesticides on avian reproduction under realistic dietary exposure profiles. The implementation of CA and IA models is a first step toward mechanistic assessment of chemical mixtures, although validation still requires empirical mixture data. The model highlights the importance of food availability and shows that chemical stress can exacerbate the negative effects of nutritional stress. Integrating such models into regulatory frameworks could improve the ecological relevance of risk assessments.

systems biology↗

bioRxiv2026

Exploring the Influence of Chemical Exposures in Breast Cancer Disparities: High-Throughput Transcriptomic Analysis in Normal Breast Cells from Diverse Donors

Racial disparities in the incidence of, and mortality from, aggressive breast cancers are a pressing public health issue. Many factors have been investigated in these inequities; however, the role of toxicant exposures is not well characterized. We and others have identified substantial inequities in chemical biomarker concentrations by race. The goal of this study was to test the hypothesis that exposure to these chemicals is linked to biological changes relevant to aggressive breast cancers, such as dysregulation of the Hallmarks of Cancer. We used high throughput transcriptomic profiling of normal primary human breast epithelial cells from diverse donors (n=6) to test effects of 8 chemicals (cadmium, lead, arsenic, copper, PFNA, BPA, BPS, p,p-DDE) with documented exposure disparities by race/ethnicity across 3 concentrations (100nM, 1{micro}M, 10{micro}M). Across chemicals, we identified that pathways related to cell cycle regulation and protein secretion were commonly affected. Through bioinformatic estimation of cell type proportions, we found that metals like lead and cadmium induced cell-type shifts, consistent with the dysregulated cellular plasticity cancer hallmark. Lead and arsenic response genes were enriched for genes associated with poor breast cancer survival in the Cancer Genome Atlas. Integrating concentration-response modeling and chemical biomonitoring data, BPA, p,p-DDE, copper, and lead elicited expression changes at concentrations relevant to the US population. Finally, we identified substantial interindividual heterogeneity in response to organic compounds, but less so in metals. These findings highlight the value of high-throughput transcriptomics as a New Approach Methodology (NAM) in quantifying how common exposures may impact aggressive breast cancer associated biological processes.

pharmacology and toxicology↗

bioRxiv2026

Biases introduced by Ficoll-based isolation in acute myeloid leukemia sample analyses support the use of hemolysis

Acute myeloid leukemia (AML) is a heterogeneous malignancy whose characterization relies on immunophenotyping and molecular profiling. While hemolysis is recommended for leukocyte isolation in clinical diagnostics, Ficoll-based density gradient centrifugation is widely used in research and biobanking. Here, we evaluated the impact of Ficoll isolation on commonly performed analyses of AML samples. Ficoll altered flow cytometry-based characterization by systematically enriching lymphocytes and AML blasts while depleting granulocytes. The increased T-cell content impaired AML engraftment in NSG mice, as T cells mediated terminal graft-versus-host disease. Although Ficoll had minimal impact on ex vivo AML blast expansion or chemotherapy response, RNA sequencing identified 1,136 differentially expressed genes compared with hemolysis, with Ficoll-processed samples notably leading to an overestimation of leukemic stem cell gene set expression. Immunogenomic deconvolution highlighted that Ficoll leads to an overestimation of CD8+ T-cell and monocyte abundances in sequenced samples. Mutation calling from RNA-seq data revealed substantial discrepancies between methods, including failure to detect a clinically relevant DNMT3A R882 mutation in a Ficoll-processed sample. Together, these findings support the systematic use of hemolysis to preserve cellular diversity and avoid unpredictable biases introduced by Ficoll-based isolation.

cell biology↗

bioRxiv2026

Structural determinants of broadly neutralizing human antibodies binding to morphological dengue virus variants.

Dengue virus (DENV) is a global health threat, with four antigenically distinct serotypes impeding vaccine and therapeutic development. We describe here, structural features of patient-derived, heavy chain-driven E-dimer-recognizing (HEDR) antibodies that achieve potent, pan-serotype activity by binding a conserved quaternary epitope on DENVs envelope (E) glycoprotein dimer. Single-particle cryo-EM structures of three HEDR broadly neutralizing antibodies (bnAbs) complexed to DENV delineate epitope engagement via E-glycoproteins fusion loop region and conserved glycans. We also identify capsule-shaped DENV, with helical cryo-EM reconstructions of Fab-bound tubular DENV from different strains revealing that the E-glycoprotein arrangement is determined by virus strain, not bnAb identity. Our findings elucidate key binding features of HEDR bnAbs and signify the need for intervention strategies capable of neutralizing diverse DENV morphologies.

biophysics↗

bioRxiv2025

When good guides go bad: empirical evaluation of all unique Cas9 targets in E. coli reveal widespread functionality and rules for gRNA biological design

The Cas9 nuclease has become central to modern methods and technologies in synthetic biology, largely due to the ease in which it can be targeted to specific DNA loci via guide RNAs (gRNAs). Reports vary widely on the actual specificity of this targeting, with some studies observing 60% of gRNAs possessing no activity against the genome, yet an assumption that inactive gRNAs are rare persists in the E. coli community. To resolve these contradictions, we evaluated the activity of nearly 500,000 unique gRNAs in the E. coli K12 MG1655 genome. We show that the overwhelming majority (at least 93%) of unique gRNAs are functional while only 0.3% are nonfunctional.These nonfunctional gRNAs exhibit strong spacer self-interaction, which can be either excluded using a simple design rule or "repaired" during library design. Finally, this work provides the greater microbial synthetic biology community both a set of nearly half a million E. coli gRNAs that have been empirically evaluated in vivo as well as a thoroughly evaluated experimental procedure, complete with appropriate controls for Cas9 activity, for conducting Cas9 assays in E. coli specifically and bacteria more generally. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=125 SRC="FIGDIR/small/651106v3_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@1038ddcorg.highwire.dtl.DTLVardef@566e37org.highwire.dtl.DTLVardef@5df6b2org.highwire.dtl.DTLVardef@ef453c_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2024

Non-Invasive Quality Control of Organoid Cultures Using Mesofluidic CSTR Bioreactors and High-Content Imaging

Human brain organoids produce anatomically relevant cellular structures and recapitulate key aspects of in vivo brain function, which holds great potential to model neurological diseases and screen therapeutics. However, the long growth time of 3D systems complicates the culturing of brain organoids and results in heterogeneity across samples hampering their applications. We developed an integrated platform to enable robust and long-term culturing of 3D brain organoids. We designed a mesofluidic bioreactor device based on a reaction-diffusion scaling theory, which achieves robust media exchange for sufficient nutrient delivery in long-term culture. We integrated this device with longitudinal tracking and machine learning-based classification tools to enable non-invasive quality control of live organoids. This integrated platform allows for sample pre-selection for downstream molecular analysis. Transcriptome analyses of organoids revealed that our mesofluidic bioreactor promoted organoid development while reducing cell death. Our platform thus offers a generalizable tool to establish reproducible culture standards for 3D cellular systems for a variety of applications beyond brain organoids.

bioengineering↗

bioRxiv2024

Deep Geometric Framework to Predict Antibody-Antigen Binding Affinity

In drug development, the efficacy of an antibody depends on how the antibody interacts with the target antigen. The strength of these interactions gives an indication of how successful an antibody is in neutralizing an antigen. Therefore, the strength, measured by "binding affinity", is a critical aspect of antibody engineering. In theory, the higher the binding affinity, the higher the chances are that the antibody is successful against the target antigen. Currently, techniques such as molecular docking and molecular dynamics are utilized in quantifying the binding affinity. However, owing to the computational complexity of the aforementioned techniques, running simulations for large antibodies/antigens remains a daunting task. Despite the commendable improvements in deep learning-based binding affinity prediction, such approaches are highly dependent on the quality of the antibody-antigen structures and they tend to overlook the importance of capturing the evolutionary details of proteins upon mutation. Further, most of the existing datasets for the task only include antibody-antigen pairs related to one antigen variant and, thus, are not suitable for developing comprehensive data-driven approaches. To circumvent the said complexities, we first curate the largest and most generalized datasets for antibody-antigen binding affinity prediction, consisting of both protein sequences and structures. Subsequently, we propose a deep geometric neural network comprising a structure-based model and a sequence-based model that considers both atomistic and evolutionary details when predicting the binding affinity. The proposed framework exhibited a 10% improvement in mean absolute error compared to the state-of-the-art models while showing a strong correlation between the predictions and target values. We release the datasets and code publicly (https://drug-discovery-entc.github.io/p2pxml/) to support the development of antibody-antigen binding affinity prediction frameworks for the benefit of science and society.

bioinformatics↗

bioRxiv2023

A thin-film optogenetic visual prosthesis

Retinitis pigmentosa and macular degeneration lead to photoreceptor death and loss of visual perception. Despite recent progress, restorative technologies for photoreceptor degeneration remain largely unavailable. Here, we describe a novel optogenetic visual prosthesis (FlexLED) based on a combination of a thin-film retinal display and optogenetic activation of retinal ganglion cells (RGCs). The FlexLED implant is a 30 {micro}m thin, flexible, wireless {micro}LED display with 8,192 pixels, each with an emission area of 66 {micro}m2. The display is affixed to the retinal surface, and the electronics package is mounted under the conjunctiva in the form factor of a conventional glaucoma drainage implant. In a rabbit model of photoreceptor degeneration, optical stimulation of the retina using the FlexLED elicits activity in visual cortex. This technology is readily scalable to hundreds of thousands of pixels, providing a route towards an implantable optogenetic visual prosthesis capable of generating vision by stimulating RGCs at near-cellular resolution.

neuroscience↗

bioRxiv2023

hb or not hb - when and why accounting for background mortality in toxicological survival models matters?

Decisions in Environmental Risk Assessment (ERA) about impacts of chemical compounds on different species are based on critical effect indicators such as the 50% lethal concentration (LC50). Regulatory documents recommend concentration-response (or concentration-effect) model fitting on standard toxicity test data to get LC50 values. However, toxicokinetic-toxicodynamic (TKTD) models proved their efficiency to better exploit toxicity test data, at Tier-2 but also at Tier-1, delivering time-independent indicators. In particular, LC50 values can be obtained from the reduced General Unified Threshold model of Survival (GUTS-RED) with both variants, Stochastic Death and Individual Tolerance, that include parameter hb, the background mortality. Estimating hb during the fitting process or not depends on studies and fitting habits, while it may strongly influence the other GUTS-RED parameters, and consequently the LC50 estimate. We hypothesized that estimating hb from all data in all replicates over time should provide more precise LC50 estimates. We then explored how estimating hb impacted: (i) GUTS-RED model parameters; (ii) goodness-of-fit criteria (fitting plot, posterior predictive check, parameter correlation); (iii) LC50 accuracy and precision. We finally show that estimating hb does not impact the LC50 precision while providing more accurate and precise GUTS parameter estimates. Hence, estimating hb would lead to a more protective ERA. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=197 HEIGHT=200 SRC="FIGDIR/small/525496v2_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@3ee285org.highwire.dtl.DTLVardef@89931eorg.highwire.dtl.DTLVardef@19c70forg.highwire.dtl.DTLVardef@5bb72c_HPS_FORMAT_FIGEXP M_FIG C_FIG Specifications table O_TBL View this table: org.highwire.dtl.DTLVardef@156b31eorg.highwire.dtl.DTLVardef@109991borg.highwire.dtl.DTLVardef@122f597org.highwire.dtl.DTLVardef@29a3a7org.highwire.dtl.DTLVardef@1b75315_HPS_FORMAT_FIGEXP M_TBL C_TBL

pharmacology and toxicology↗

bioRxiv2022

Ongoing emergence of M1UK lineage among invasive group A streptococcus isolates in 2020 and use of allele-specific PCR

BackgroundAn increasing burden of invasive group A streptococcal infections is reported in multiple countries, notably England, where scarlet fever cases are also abundant. In England, increased scarlet fever and invasive infections have been associated with emergence of a sublineage of emm1 Streptococcus pyogenes that expresses increased SpeA scarlet fever erythrogenic toxin. Wider surveillance for toxigenic Streptococcus pyogenes lineage M1UK is much needed however, to date, lineage assignment has required genome sequencing limiting surveillance to those centres with access to such facilities. MethodsTo circumvent the requirement for genome sequencing, an allele-specific PCR was developed to distinguish M1UK from other emm1 strains. Additional PCR assays were developed to distinguish M1UK from two intermediate lineages that were detected previously. The assay was evaluated using DNA from genome-sequenced upper respiratory tract emm1 S. pyogenes strains and a further set of 16 genome-sequenced invasive S. pyogenes isolates that included the two intermediate lineages. The assay was then applied to DNA from all 305 invasive emm1 isolates that had been submitted to the reference laboratory in the one pear period Jan 1-Dec 31 2020, in order to assign lineage. ResultsThe allele specific PCR was 100% accurate when compared with genome sequencing, correctly identifying M1UK, two intermediate sublineages, and other emm1 strains. The assay demonstrated the M1UK lineage to be dominant among emm1 invasive isolates in England, representing 278/305 (91%) of invasive emm1 isolates by end of 2020. ImplicationsEmm1 S. pyogenes have a prominent role in invasive infections; any emm1 lineage that demonstrates enhanced fitness within the population is of public health concern. The allele specific PCR provides a readily available method to subtype emm1 isolates and does not require access to complex sequencing facilities. The data confirm that the M1UK lineage has persisted and further expanded in England underlining the importance of wider global surveillance.

microbiology↗

bioRxiv2022

Drivers of mangrove vulnerability and resilience to tropical cyclones in the North Atlantic Basin

The North Atlantic Basin (NAB) is seeing a significant increase in the frequency and intensity of tropical cyclones since the 1980s, with record-breaking seasons such as 2017 and 2020. However, little is known about how coastal ecosystems, particularly mangroves in the Gulf of Mexico and the Caribbean, are responding to these new "climate normals" at regional and subregional scales. Wind speed, rainfall, pre-cyclone forest structure, and hydro-geomorphology are known to influence mangrove damage and recovery following cyclones in the NAB. However, these studies have focused on site-specific responses and individual cyclonic events. Here, we analyze 25 years (1996-2020) of mangrove vulnerability (damage after a cyclone) and short-term resilience (recovery after damage) for the entire NAB and its subregions, using multi-annual, remote sensing-derived databases. We applied machine learning to characterize the influence of 22 potential drivers that include previously researched variables and new ones such as human development and long-term climate trends. The characteristics of the cyclones mainly drive vulnerability at the regional level, while resilience is largely driven by site-specific conditions. These include long-term climate conditions, such as air temperature and drought trends, pre-cyclone habitat conditions, such as canopy cover and height and soil organic carbon stock, and human interventions on the land. Rates and drivers of mangrove vulnerability and resilience vary across subregions in the NAB, and hotspots for restoration and conservation actions are highlighted within subregions. The impacts of increasing cyclone activity need to be framed in the context of climate change compound effects and heavy human influences in the region. There is an urgent need to value the restoration and conservation of mangroves as fundamental Nature-based Solutions against cyclone impacts in the NAB.

ecology↗

bioRxiv2022

Multi-scale observations of mangrove blue carbon fluxes; the NASA Carbon Monitoring System BlueFlux field campaign

The BlueFlux field campaign is supported by NASAs Carbon Monitoring System (CMS) and will develop prototype blue carbon products to inform coastal carbon management. Blue carbon is included in carbon-dioxide removal actions proposed to reduce atmospheric CO2 concentrations to mitigate climate change. Due to their high productivity and carbon storage, combined with historic losses and a wide-range of beneficial ecosystem services, the restoration and conservation of mangrove ecosystems features prominently in blue-carbon planning. The goal of BlueFlux is to carry out multi-scale measurements of CO2 and CH4 fluxes using chambers, flux towers, and aircraft and scale these to gridded products using space-based observations of forest structure and surface reflectance. The measurements cover gradients in disturbance, mainly from the history of hurricanes in the region that drive the dieback of mangroves and the formation of ghost forests. The fluxes of CH4 emissions will be contrasted with CO2 uptake to provide a more complete budget of radiative forcing and to understand the net climate benefits of blue carbon. BlueFlux demonstrates that quantifying the removals of CO2 and emissions of CH4 using a multi-scale approach can provide increased confidence in regional greenhouse-gas accounting, contribute to process-understanding, and help inform restoration and conservation efforts in the context of climate mitigation.

ecology↗

bioRxiv2022

Generic solving of physiologically-based kinetic models in support of next generation risk assessment due to chemicals

Increasing the confidence in using in vitro and in silico model-based data to aid the chemical risk assessment process is one, if not the most, important challenge currently facing regulatory authorities. A particularly crucial concern is to fully take advantage of scientifically valid Physiologically-Based Kinetic (PBK) models. Nevertheless, risk assessors remain still unwilling in employing PBK models within their daily work. Indeed, PBK models are not often included in current official guidance documents. In addition, most users have limited experience in using modelling in general. So, the complexity of PBK models, together with a lack to evaluation methods of their performances, certainly contributes to their under-use in practical risk assessment. This paper proposes an innovative and unified modelling framework, in both the writing of PBK equations as matrix ordinary differential equations (ODE), and in its exact solving simply expressed with matrix products. This generic PBK solution allows to consider as many as state-variables as needed to quantify chemical absorption, distribution, metabolism and excretion processes within living organisms when exposed to chemical substances. This generic PBK model makes possible any compartmentalisation to be considered, as well as all appropriate inter-connections between compartments and with the external medium. We first introduce our PBK modelling framework, with all intermediate steps from the matrix ODE to the exact solution. Then we apply this framework to bioaccumulation testing, before illustrating its concrete use through complementary case studies in terms of species, compounds and model complexity. O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

pharmacology and toxicology↗

bioRxiv2022

Improvements in estimating bioaccumulation metrics in the light of toxicokinetics models and Bayesian inference

The surveillance of chemical substances in the scope of Environmental Risk Assessment (ERA) is classically performed through bio-assays from which data are collected and then analysed and/or modelled. Some analysis are based on the fitting of toxicokinetic (TK) models to assess the bioaccumulation capacity of chemical substances via the estimation of bioaccumulation metrics as required by regulatory documents. Given that bio-assays are particularly expensive and time consuming, it is of crucial importance to deeply benefit from all information contained in the data. By revisiting the calculation of bioaccumulation metrics under a Bayesian framework, this paper suggests changes in the way of characterising the bioaccumulation capacity of chemical substances. For this purpose, a meta-analysis of a data-rich TK database was performed, considering uncertainties around bioaccumulation metrics. Our results were statistically robust enough to suggest an additional criterion to the single median estimate of bioaccumulation metrics to assign a chemical substance to a given bioaccumulation capacity. Our proposal is to use the 75th percentile of the uncertainty interval of the bioaccumulation metrics, which revealed an appropriate complement for the classification of chemical substances (e.g., PBT (persistent, bioaccumulative and toxic) and vPvB (very persistent and very bioaccumulative) under the EU chemicals legislation). The 75% quantile proved its efficiency, similarly classifying 90% of the chemical substances as the conventional method.

pharmacology and toxicology↗

bioRxiv2022

Emergence of Heartbeat Frailty in Advanced Age: Perspectives from Life-Long EKG Recordings in Mice

SAN failure, aka sick-sinus syndrome, which features sinus bradycardia, SAN impulse pauses, and irregularity of RR interval rhythms are manifestations of SAN cell dysfunction that increases exponentially with advanced age, i.e., SAN frailty. Abnormalities in intrinsic RR interval variability may be the earliest signatures of SAN cell dysfunction leading to SAN frailty in late life. We measured RR interval variability within EKG timeseries prior to and during double autonomic blockade in long-lived C57/BL6 mice at 3 month intervals from 6 months of age until the end of life. Long-lived mice (those that achieved the median cohort lifespan of 24 months and beyond) displayed relatively minor changes in intrinsic RR interval variability prior to 21 months of age. Between 21 and 30 months of age, marked changes in intrinsic RR interval variability signatures in time, frequency, non-linear, and fragmentation domains result in a marked increase in the mean intrinsic RR interval. The effects of autonomic input partially compensated for the prolongation of the mean RR interval by impacting the age-associated deterioration in the RR interval variability signatures toward a youthful pattern. Cross-sectional analyses of other subsets of mice at ages at or beyond the median life span of our longitudinal cohort demonstrated increased non-cardiac, constitutional, whole body frailty, a decrease in energetic efficiency, and an increase in respiratory exchange ratio. In this context, we interpret the progressive increase in intrinsic RR interval variability beyond 21 months of age to be an indication of heartbeat frailty.

physiology↗

bioRxiv2021

morse: an R-package in support of Environmental Risk Assessment

Package morse is devoted to the analysis of experimental data collected from standard toxicity tests. It provides ready-to-use functions to visualize a data set and to estimate several toxicity indices to be further used in support of environmental risk assessment in full compliance with regulatory requirements. Such toxicity indices are indeed classical requested by standardized regulatory guidelines on which national agencies base their evaluation of applications for marketing authorisation of chemical active substances. Package morse can be used to get estimates of LCx (x% Lethal Concentration) or ECx (x% Effective Concentration) by fitting standard exposure-response models on toxicity test data. Risk indicator estimates as well as model parameters are provided along with the quantification of their uncertainty. Package morse can also be used to get estimates of the NEC (No Effect Concentration) by fitting a Toxicokinetic-Toxicodynamic (TKTD) model (namely GUTS models, that is General Unified Threshold models of Survival). Using GUTS models also allow to get estimates of LC(x,t) (whatever x and t) and LP(x,t), this later being defined by EFSA as the x% multiplication factor leading to an additional reduction of x% in survival at the end of the exposure profile. Above all, GUTS models can be used on data collected under time-variable exposure profiles. This paper illustrates a typical use of morse with survival data collected over time and at different increasing exposure concentrations, analysed with the reduced version of GUTS models based on the stochastic death hypothesis (namely, the GUTS-RED-SD model). This example can be followed step-by-step to analyse any new data set, as long as the data set format is respected.

ecology↗

bioRxiv2021

Taking full advantage of modelling to better assess environmental risk due to xenobiotics

In the European Union, more than 100,000 man-made chemical substances are awaiting an environmental risk assessment (ERA). Simultaneously, ERA of chemicals has now entered a new era. Indeed, recent recommendations from regulatory bodies underline a crucial need for the use of mechanistic effect models, allowing assessments that are not only ecologically relevant, but also more integrative, consistent and efficient. At the individual level, toxicokinetic-toxicodynamic (TKTD) models are particularly encouraged for the regulatory assessment of pesticide-related risks on aquatic organisms. In this paper, we first propose a brief review of classical dose-response models to put into light the on-line MOSAIC tool offering all necessary services in a turnkey web platform whatever the type of data to analyze. Then, we focus on the necessity to account for the time-dimension of the exposure by illustrating how MOSAIC can support a robust calculation of bioaccumulation factors. At last, we show how MOSAIC can be of valuable help to fully complete the EFSA workflow regarding the use of TKTD models, especially with GUTS models, providing a user-friendly interface for calibrating, validating and predicting survival over time under any time-variable exposure scenario of interest. Our conclusion proposes a few lines of thought for an even easier use of modelling in ERA. Graphical art O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=82 SRC="FIGDIR/small/436474v3_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@d98a72org.highwire.dtl.DTLVardef@1066b4org.highwire.dtl.DTLVardef@c6eef3org.highwire.dtl.DTLVardef@71c528_HPS_FORMAT_FIGEXP M_FIG C_FIG

ecology↗

bioRxiv2020

Analysis of selection signatures reveals important insights into the adaptability of high-altitude Indian sheep breed Changthangi

Changthangi is a high-altitude sheep breed of India that is adapted to cold and hypoxic climate of Himalayas. In the present study, we analysed population structure of Changthangi and contrasted it with selected Indian and European commercial sheep breeds to detect genomic regions under positive selection. The studied domesticated sheep breeds included Changthangi, Indian Garole, Deccani, Tibetan, Rambouillet and Australian Merino. While the PCA results revealed Changthangi clustered with Tibetan sheep; TREEMIX and ADMIXTURE results also detected the introgression of lowland Indian sheep inheritance in Changthangi. Cross-population comparisons of Changthangi using XP-EHH showed multiple functional regions present on OAR 7, 15 and 16, to be under selection in Changthangi sheep. These regions are related to adaptation to climatic and hypoxic stressors, nervous system and mitochondrial functioning. The genes present in these regions are suitable candidates for future studies on the genetic mechanisms underlying high-altitude adaptation.

genomics↗