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

Countdown to 2027 - maximising use of NAMs in food safety assessment: closing the gap for regulatory assessments in Europe

Safety assessments of regulated food products in the European Union (EU) largely rely on experimental animal studies. Currently, the European Commission is developing a roadmap to phase out animal testing for chemical safety assessment across all relevant pieces of legislation, including foods, while the ambition of the European Food Safety Authority (EFSA) is that by 2027, new scientific developments, i.e., new approach/non-animal methods (NAMs), will be integrated into assessments leading to "the minimisation of animal testing". However, considering recent requests that have been made to conduct new animal studies for some regulated products, significant progress is required to minimise further and ultimately replace animal testing in the food safety environment. To advance this, we review several NAMs amenable for use in food safety assessment and reflect on their presence in EU food safety regulation and sectoral guidance. For many years, proposals to incorporate NAMs into food safety assessments have been made with questionable regulatory impact. Therefore, we present several amendments which could be made to the EU food regulatory system and current strategies towards phasing out animal testing which, if taken up, could lead to a tangible difference in the extent of animal testing within the food safety environment. Recognising that research may be required for some of these NAMs to enhance regulatory uptake, we propose potential follow-up projects that complement recent research & innovation (R&I) needs published by EFSA which food safety stakeholders could coordinate or participate in.

Interactions of erythromycin and an antibiotic mixture with the gut microbiome of juvenile rainbow trout

Erythromycin (ERY) is a commonly used antibiotic found in wastewater effluents and the environment globally. Due to the bioactivity by which they kill and prevent bacterial growth, ERY and other antibiotics may have significant unwanted impacts on the gut microbiome of fishes. The overall objective of this project was to assess effects on the gut microbiome in response to exposure to ERY alone or in a mixture with other common antibiotics, which was accomplished in two experiments. The objectives of experiment 1 as a pilot study were to understand uptake and depuration of ERY in juvenile rainbow trout (RBT) over a 7-d exposure to three concentrations of ERY followed by a 7-d depuration period. Furthermore, throughout the study changes in gut microbiome were assessed. In experiment 2, an identical experimental design was used to assess the effects of a mixture of antibiotics containing, in addition to ERY, 100 μg/g each of ampicillin, metronidazole, and ciprofloxacin. In that study, three matrices were analyzed, with gut collected for 16S rRNA metabarcoding, blood plasma for non-targeted metabolomics, and brain tissue for mRNA-seq analysis. ERY was relatively quickly depurated from fish and gut microbiome dysbiosis was observed at 7 d after exposure, with a slight recovery after the 7-d depuration period. A greater number of plasma metabolites was dysregulated at 14 d compared to 7 d revealing distinct temporal dynamics compared to gut microbiome dysbiosis. Furthermore, several transformation products of antibiotics and biomarker metabolites were observed in plasma due to antibiotic exposure. The transcriptome of the brain was only slightly altered due to antibiotic exposure. Results of these studies will help inform aquaculture practitioners and risk assessors when assessing the potential impacts of antibiotics present in fish feed and the environment, with implications for host health.

BioCurve Analyzer: a web-based shiny app for analyzing biological response curves

BACKGROUND

Dose-response and time-to-event data are common in enzymology, pharmacology, and agronomy studies. Diverse biological response curves can be generated from such data. The features of these curves can be elucidated through parameters such as ED50 (the effective dose that gives 50% of the maximum response) and T50 (the time required to reach 50% of the maximum response). Properly estimating these parameters is crucial for inferring the potency of compounds or the relative timings of biological processes.

RESULTS

We present an open-source Shiny application, BioCurve Analyzer, that simplifies the process of inferring ED50 and T50 parameters from response curves exhibiting various patterns, including classic monotonic sigmoidal curves and more complicated biphasic curves. BioCurve Analyzer provides access to several packages and commonly used models for characterizing response patterns, assists users in identifying the models that best describe their data, and includes options for inferring ED50 values on both sides of biphasic curves. BioCurve Analyzer also facilitates the visualization of response patterns and allows users to customize their final graphical representation to deliver publication-quality graphs of the data.

CONCLUSION

BioCurve Analyzer integrates multiple R packages in an easy-to-use web-based interface to facilitate dose-response and time-to-event analyses.

Dose-Response Metabolomics Unveils Liver Metabolic Disruptions and Pathway Sensitivity to Alkylimidazolium Ionic Liquids: Benchmark Dose Estimation for Health Risk Assessment

Alkylimidazolium-based ionic liquids (AILs), once hailed as ″green solvents,″ have seen widespread use, but recent concerns have emerged regarding their environmental and health risks. This study integrates in vitro and in vivo dose-response metabolomics to investigate liver metabolic disturbances and pathway sensitivity to 1-octyl-3-methylimidazolium (M8OI) exposure. Important liver function indicators, including catalase, alanine aminotransferase, aspartate aminotransferase, and glycosylated serum protein, showed significant alterations (P < 0.05), indicating liver dysfunction. Metabolomics analysis revealed dose-dependent changes in energy metabolism and oxidative stress pathways in both cell and rat models, characterized by increased levels of thiamine and lipopolysaccharides, and decreased levels of nicotinamide and adenine. Key intermediates of the tricarboxylic acid cycle, such as citrate and isocitrate, exhibited significant alterations (P < 0.05). Pathway analysis identified disruptions in arginine, proline, and purine metabolism. Quantitative risk characterization based on effective concentration (EC) values identified key metabolites─adenine (EC-10 = 0.004 mg/kg), (±)12(13)-DiHOME (EC-10 = 0.024 mg/kg), and nicotinamide (EC-10 = 0.05 mg/kg) in vivo, and isocitrate (EC-10 = 0.22 μM), d-threo-isocitric acid (EC-10 = 0.23 μM), and citric acid (EC-10 = 0.40 μM) in vitro─as potential biomarkers of M8OI-induced metabolic disruption. These findings highlight hepatic metabolic disturbances induced by M8OI, with dose-response metabolomics identifying benchmark dose values based on regression models, thereby providing a basis for health risk assessment.

Modeling omics dose-response at the pathway level with DoseRider

The generation of omics data sets has become an important approach in modern pharmacological and toxicological research as it can provide mechanistic and quantitative information on a large scale. Analyses of these data frequently revealed a non-linear dose-response relationship underscoring the importance of the modeling process to infer biological exposure limits. A number of tools have been developed for dose-response modeling and various thresholds have been defined as a quantitative representation of the effect of a substance, such as effective concentrations or benchmark doses (BMD). Here we present DoseRider an easy-to-use web application and a companion R package for linear and non-linear dose-response modeling and assessment of BMD at the level of biological pathways or signatures using generalized mixed effect models. This approach allows to analyze custom or provided multi-omics data such as RNA sequencing or metabolomics data and its annotation of a collection of pathways and gene sets from various species. Moreover, we introduce the concept of the trend change doses (TCDs) as a numerical descriptor of effects derived from complex dose-response curves. The usability of DoseRider was demonstrated by analyses of RNA sequencing data of bisphenol AF (BPAF) treatment of a human breast cancer cell line (MCF-7) at 8 different concentrations using gene sets for chemical and genetic perturbations (MSigDB). The BMD for BPAF and a set of genes upregulated by estrogen in breast cancer was 0.2 µM (95 %-CI 0.1-0.5 µM) and the lowest TCD (TCD1) was 0.003 µM (95 %-CI 0.0006-0.01 µM). The comprehensive presentation of the results underlines the suitability of the system for pharmacogenomics, toxicogenomics, and applications beyond.

Bioinformatic workflows for deriving transcriptomic points of departure: current status, data gaps, and research priorities

There is a pressing need to increase the efficiency and reliability of toxicological safety assessment for protecting human health and the environment. Although conventional toxicology tests rely on measuring apical changes in vertebrate models, there is increasing interest in the use of molecular information from animal and in vitro studies to inform safety assessment. One promising and pragmatic application of molecular information involves the derivation of transcriptomic points of departure (tPODs). Transcriptomic analyses provide a snapshot of global molecular changes that reflect cellular responses to stressors and progression toward disease. A tPOD identifies the dose level below which a concerted change in gene expression is not expected in a biological system in response to a chemical. A common approach to derive such a tPOD consists of modeling the dose-response behavior for each gene independently and then aggregating the gene-level data into a single tPOD. Although different implementations of this approach are possible, as discussed in this manuscript, research strongly supports the overall idea that reference doses produced using tPODs are health protective. An advantage of this approach is that tPODs can be generated in shorter term studies (e.g. days) compared with apical endpoints from conventional tests (e.g. 90-d subchronic rodent tests). Moreover, research strongly supports the idea that reference doses produced using tPODs are health protective. Given the potential application of tPODs in regulatory toxicology testing, rigorous and reproducible wet and dry laboratory methodologies for their derivation are required. This review summarizes the current state of the science regarding the study design and bioinformatics workflows for tPOD derivation. We identify standards of practice and sources of variability in tPOD generation, data gaps, and areas of uncertainty. We provide recommendations for research to address barriers and promote adoption in regulatory decision making.

Molecular insight into reproductive toxicity and transgenerational effects of Cadmium exposure on Drosophila melanogaster

Cadmium (Cd), a widespread and serious environmental pollutant, has recently garnered increasing scientific scrutiny due to its profound adverse effects. Although the evidence for Cd-induced reproductive toxicity is well established, it remains elusive on the intricate dose-response relationship and underlying molecular mechanisms, especially for transgenerational toxicity in animals. Here, we employed fruit fly (Drosophila melanogaster) as a model organism to examine the reproductive performance across five generations by parental exposure to varying concentrations of Cd (5, 50, and 500 μM). Firstly, our observations on the number of eggs laid, pupae formed, and adult flies emerged on the directly exposed generation (F0) confirmed a dose-dependent decline in fecundity. Transcriptome analysis revealed that, Cd-induced oxidative stress and ion transport disruption in the F0 generation could underlie synaptic dysfunction and impaired follicle cell development, impacting reproductive behavior and oocyte fertility. Employing dose-response analysis, Wnt signaling pathway and mTOR signaling pathway were identified as early molecular responses to Cd-induced toxicity. Secondly, sustained detrimental effects were observed for at least two to three generations after Cd removal. At the epigenetic level, Cd could perturb fecundity across generations by modulating Dnmt2 expression, a pivotal regulator of methylation processes. Moreover, despite phenotypic recovery in F4, persistent molecular changes indicate enduring toxicity, highlighting the need for vigilance against environmental Cd contamination and its long-term effects. Collectively, our findings enhance the understanding of Cd-induced reproductive toxicity and its transgenerational effects, and highlight the need to further improve the assessment of the multigenerational consequences of environmental Cd contamination.

Transcriptomics highlights dose-dependent response of poplar to a phenanthrene contamination

Polycyclic aromatic hydrocarbon (PAH) contamination in industrial soils poses significant environmental challenges, necessitating cost-effective bioremediation approaches like tree-based phytoremediation. However, the defence mechanisms and adaptability of trees to PAH exposure remain poorly understood, while the identification of molecular markers could help in the detection of toxicity symptoms. This study explores the molecular response of Populus canadensis to a phenanthrene (PHE) contamination gradient (from 100 to 2000 mg kg-1) using RNA-seq analysis of roots and leaves after 4 weeks of exposure. Both differentially expressed genes (DEGs) and DRomics, a dose-response tool, identified transcriptomic changes, with about 50% of deregulated genes responding significantly at a benchmark dose (i.e. minimal dose that produces a significant effect) below 400 mg PHE kg-1. The highest number of DEGs was found both at a low concentration (200 and 700 mg kg-1) and at the highest concentrations (1500-2000 mg kg-1) for both roots and leaves. Ethylene signalling genes were activated via ABA-independent pathways at low concentrations and ABA-dependent pathways at high concentrations. Across the gradient, responses to oxidative stress were triggered, including reactive oxygen species scavenging and phenylpropanoid biosynthesis, specifically at 1500-2000 mg kg-1. Additionally, PHE disrupted pathways related to plant responses to biotic stress. These findings revealed unexpected dose-dependent transcriptomic shifts, demonstrating poplar's adaptive defence mechanisms against PHE toxicity.

Effects of defined voluntary running distances coupled with high-fat diet consumption on the skeletal muscle transcriptome of male mice

Exercise counters many adverse health effects of consuming a high-fat diet (HFD). However, complex molecular changes that occur in skeletal muscle in response to exercising while consuming a HFD are not yet known. We investigated the interplay between diverse exercise regimes and HFD consumption on the adaptation of skeletal muscle transcriptome. C57BL/6 male mice were randomized into five groups-one sedentary control group and four exercise groups. The exercise groups consisted of an unrestricted running group (8.3 km/day) and three groups that were restricted to 75%, 50%, or 25% of unrestricted running (6.3, 4.2, and 2.1 km/day, respectively). Total RNA was extracted from frozen gastrocnemius muscle for transcriptome analyses. DEG counts were 1347, 1823, 1103, and 1107 and there were 107, 169, 67, and 89 unique genes present in the HFD-25%, HFD-50%, HFD-75%, and HFD-U, respectively. Comparing exercise groups, we found that exercising at 50% resulted in the most differentially expressed transcripts with the MAPK and PPAR signaling pathways enriched in down- and up-regulated genes, respectively. These results demonstrate that running distance impacts the adaptation of the skeletal muscle transcriptome to exercise and suggest that middle-distance running may provide the greatest protection against high-fat diet-induced stress coupled with exercise.

Sensitivity shift of the meta-metabolome and photosynthesis to the chemical stress in periphyton between months along one year and a half period: Case study of a terbuthylazine exposure

Despite the knowledge of the effects of contaminants on periphyton, information is limited about their natural fluctuations in sensitivity to chemical stress between various months. In particular, the molecular and biochemical mechanisms associated with sensitivity of photosynthesis and its fluctuations remain poorly described. To tackle this lack of knowledge, meta-metabolomics offers a comprehensive picture of the sensitive molecular response preceding the physiological impact. This study aimed to describe changes in the sensitivity of periphyton to chemical stress at different months over one year and a half period, at both the physiological and molecular levels by measuring photosynthetic yield and meta-metabolome responses (targeted and untargeted approaches). Periphyton was colonized for four weeks and then exposed to a range of terbuthylazine concentrations (0.3-30 μg L-1) under controlled conditions for 4 h. Sensitivity was assessed by determining the benchmark doses for the meta-metabolome and photosynthesis, along with the cumulative distribution of aggregated metabolomics signals. The results showed a strong sensitivity shift in the meta-metabolome compared to a smaller shift in photosynthetic yield at different months. This study also confirmed the high sensitivity of the meta-metabolome, as most signals responded before photosynthesis. The annotation highlighted the discrepancies in the molecular response to TBA between the months in terms of metabolite classes (e.g. amino acids, alkaloids, and lipids), their sensitivity, and trends in common classes across months, and correlation to photosynthesis inhibition, notably oxylipins. Overall, this study highlights that the molecular response of the periphyton to chemical stress, and thus toxicity pathways, may differ between the months but can still lead to similar physiological responses.

Transcriptomic Point of Departure (tPOD) of androstenedione in zebrafish embryos as a potential surrogate for chronic endpoints

The transcriptomic Point of Departure (tPOD) is increasingly used in ecotoxicology to derive quantitative endpoints from RNA sequencing studies. Utilizing transcriptomic data in zebrafish embryos as a New Approach Methodology (NAM) is beneficial due to its acknowledgment as an alternative to animal testing under EU Directive 2010/63/EU. Transcriptomic profiles are available in zebrafish for various modes of action (MoA). The limited literature available suggest that tPOD values from Fish Embryo Toxicity (FET) tests align with, but are generally lower than, No Observed Effect Concentrations (NOEC) from long-term chronic fish toxicity tests. In studies with the androgenic hormone androstenedione in a Fish Sexual Development Test (FSDT), a significant shift in the sex ratio towards males was noted at all test concentrations, making it impossible to determine a NOEC (NOEC <4.34 μg/L). To avoid additional animal testing in a repetition of the FSDT and adhere to the 3Rs principle (replacement, reduction, and refinement), a modified zebrafish FET (zFET) was conducted aiming to determine a regulatory acceptable effect threshold. This involved lower concentration ranges (20 to 6105 ng/L), overlapping with the masculinization-observed concentrations in the FSDT. The tPOD analysis in zFET showed consistent results with previous FSDT findings, observing strong expression changes in androgen-dependent genes at higher concentrations but not at lower ones, demonstrating a concentration-response relationship. The tPOD values for androstenedione were determined as 24 ng/L (10th percentile), 60 ng/L (20th gene), and 69 ng/L (1st peak). The 10th percentile tPOD value in zFET was 200 times lower than the lowest concentration in the FSDT. Comparing the tPOD values to literature suggests their potential to inform on the NOEC range in FSDT tests.

Multi-omics integration analysis: Tools and applications in environmental toxicology

Nowadays, traditional single-omics study is not enough to explain the causality between molecular alterations and toxicity endpoints for environmental pollutants. With the development of high-throughput sequencing technology and high-resolution mass spectrometry technology, the integrative analysis of multi-omics has become an efficient strategy to understand holistic biological mechanisms and to uncover the regulation network in specific biological processes. This review summarized sample preparation methods, integration analysis tools and the application of multi-omics integration analyses in environmental toxicology field. Currently, omics methods have been widely applied being as the sensitivity of early biological response, especially for low-dose and long-term exposure to environmental pollutants. Integrative omics can reveal the overall changes of genes, proteins, and/or metabolites in the cells, tissues or organisms, which provide new insights into revealing the overall toxicity effects, screening the toxic targets, and exploring the underlying molecular mechanism of pollutants.

Unraveling paraquat-induced toxicity on mouse neural stem cells: Dose-response metabolomics insights and identification of sensitive biomarkers for risk assessment

Exposure to pesticide could contribute to neurodevelopmental and neurodegenerative disorders. Notably, research suggests that prenatal or early postnatal exposure to paraquat (PQ), an herbicide, might trigger neurodevelopmental toxicity in neural stem cells (NSCs) via oxidative stress. However, the molecular mechanisms of PQ-induced perturbations in NSCs, particularly at the metabolite level, are not fully understood. Using a dose-response metabolomics approach, we examined metabolic changes in murine NSCs exposed to different PQ doses (0, 10, 20, 40 μM) for 24h. At 20 μM, PQ treatment led to significant metabolic alterations, highlighting unique toxic mechanisms. Metabolic perturbations, mainly affecting amino acid metabolism pathways (e.g., phenylalanine, tyrosine, arginine, tryptophan, and pyrimidine metabolism), were associated with oxidative stress, mitochondrial dysfunction, and cell cycle dysregulation. Dose-response models were used to identify potential biomarkers (e.g., Putrescine, L-arginine, ornithine, L-histidine, N-acetyl-L-phenylalanine, thymidine) reflecting early damage from low-dose PQ exposure. These biomarkers could be used as points of departure (PoD) for characterizing PQ exposure hazard in risk assessment. Our study offers insights into mechanisms and risk assessment related to PQ-induced neurotoxicity in NSCs.

Modeling HepaRG metabolome responses to pyrrolizidine alkaloid exposure for insight into points of departure and modes of action

The new challenges in toxicology demand novel and innovative in vitro approaches for deriving points of departure (PODs) and determining the mode of action (MOA) of chemicals. Therefore, the aim of this original study was to couple in vitro studies with untargeted metabolomics to model the concentration-response of extra- and intracellular metabolome data on human HepaRG cells treated for 48 h with three pyrrolizidine alkaloids (PAs): heliotrine, retrorsine and lasiocarpine. Modeling revealed that the three PAs induced various monotonic and, importantly, biphasic curves of metabolite content. Based on unannotated metabolites, the endometabolome was more sensitive than the exometabolome in terms of metabolomic effects, and benchmark concentrations (BMCs) confirmed that lasiocarpine was the most hepatotoxic PA. Regarding its MOA, impairment of lipid metabolism was highlighted at a very low BMC (first quartile, 0.003 µM). Moreover, results confirmed that lasiocarpine targets bile acids, as well as amino acid and steroid metabolisms. Analysis of the endometabolome, based on coupling concentration-response and PODs, gave encouraging results for ranking toxins according to their hepatotoxic effects. Therefore, this novel approach is a promising tool for next-generation risk assessment, readily applicable to a broad range of compounds and toxic endpoints.

The dose disrupts the pathway: application of Paracelsus principle to mechanistic toxicology

Arguably the most famous principle of toxicology is "The dose makes the poison" formulated by Paracelsus in the 16th century. Application of the Paracelsus's principle to mechanistic toxicology may be challenging as one compound may affect many molecular pathways at different doses with different and often nonlinear dose-response relationships. As a result, many mechanistic studies of environmental and occupational compounds use high doses of xenobiotics motivated by the need to see a clear signal indicating disruption of a particular molecular pathway. This approach ignores the possibility that the same xenobiotic may affect different molecular mechanism(s) at much lower doses relevant to human exposures. To amend mechanistic toxicology with a simple and concise guiding principle, I suggest recontextualization of Paracelsus's following its letter and spirit: "The dose disrupts the pathway". Justification of this statement includes observations that many environmental and occupational xenobiotics affect a broad range of molecular cascades, that most molecular pathways are sensitive to chemical exposures, and that different molecular pathways are sensitive to different doses of a chemical compound. I suggest that this statement may become a useful guidance and educational tool in a range of toxicological applications, including experimental design, comparative analysis of mechanistic hypotheses, evaluation of the quality of toxicological studies, and risk assessment.

Genome-wide transcription response of Staphylococcus epidermidis to heat shock and medically relevant glucose levels

Skin serves as both barrier and interface between body and environment. Skin microbes are intermediaries evolved to respond, transduce, or act in response to changing environmental or physiological conditions. We quantified genome-wide changes in gene expression levels for one abundant skin commensal, Staphylococcus epidermidis, in response to an internal physiological signal, glucose levels, and an external environmental signal, temperature. We found 85 of 2,354 genes change up to ~34-fold in response to medically relevant changes in glucose concentration (0-17 mM; adj p ≤0.05). We observed carbon catabolite repression in response to a range of glucose spikes, as well as upregulation of genes involved in glucose utilization in response to persistent glucose. We observed 366 differentially expressed genes in response to a physiologically relevant change in temperature (37-45°C; adj p ≤ 0.05) and an S. epidermidis heat-shock response that mostly resembles the heat-shock response of related staphylococcal species. DNA motif analysis revealed CtsR and CIRCE operator sequences arranged in tandem upstream of dnaK and groESL operons. We identified and curated 38 glucose-responsive genes as candidate ON or OFF switches for use in controlling synthetic genetic systems. Such systems might be used to instrument the in-situ skin microbiome or help control microbes bioengineered to serve as embedded diagnostics, monitoring, or treatment platforms.

New sensitive tools to characterize meta-metabolome response to short- and long-term cobalt exposure in dynamic river biofilm communities

Untargeted metabolomics is a non-a priori analysis of biomolecules that characterizes the metabolome variations induced by short- and long-term exposures to stressors. Even if the metabolite annotation remains lacunar due to database gaps, the global metabolomic fingerprint allows for trend analyses of dose-response curves for hundreds of cellular metabolites. Analysis of dose/time-response curve trends (biphasic or monotonic) of untargeted metabolomic features would thus allow the use of all the chemical signals obtained in order to determine stress levels (defense or damage) in organisms. To develop this approach in a context of time-dependent microbial community changes, mature river biofilms were exposed for 1 month to four cobalt (Co) concentrations (from background concentration to 1 × 10-6 M) in an open system of artificial streams. The meta-metabolomic response of biofilms was compared against a multitude of biological parameters (including bioaccumulation, biomass, chlorophyll a content, composition and structure of prokaryotic and eukaryotic communities) monitored at set exposure times (from 1 h to 28 d). Cobalt exposure induced extremely rapid responses of the meta-metabolome, with time range inducing defense responses (TRIDeR) of around 10 s, and time range inducing damage responses (TRIDaR) of several hours. Even in biofilms whose structure had been altered by Co bioaccumulation (reduced biomass, chlorophyll a contents and changes in the composition and diversity of prokaryotic and eukaryotic communities), concentration range inducing defense responses (CRIDeR) with similar initiation thresholds (1.41 ± 0.77 × 10-10 M Co2+ added in the exposure medium) were set up at the meta-metabolome level at every time point. In contrast, the concentration range inducing damage responses (CRIDaR) initiation thresholds increased by 10 times in long-term Co exposed biofilms. The present study demonstrates that defense and damage responses of biofilm meta-metabolome exposed to Co are rapidly and sustainably impacted, even within tolerant and resistant microbial communities.

Meta-metabolomic responses of river biofilms to cobalt exposure and use of dose-response model trends as an indicator of effects

The response of the meta-metabolome is rarely used to characterize the effects of contaminants on a whole community. Here, the meta-metabolomic fingerprints of biofilms were examined after 1, 3 and 7 days of exposure to five concentrations of cobalt (from background concentration to 1 × 10-5 M) in aquatic microcosms. The untargeted metabolomic data were processed using the DRomics tool to build dose-response models and to calculate benchmark-doses. This approach made it possible to use 100% of the chemical signal instead of being limited to the very few annotated metabolites (7%). These benchmark-doses were further aggregated into an empirical cumulative density function. A trend analysis of the untargeted meta-metabolomic feature dose-response curves after 7 days of exposure suggested the presence of a concentration range inducing defense responses between 1.7 × 10-9 and 2.7 × 10-6 M, and of a concentration range inducing damage responses from 2.7 × 10-6 M and above. This distinction was in good agreement with changes in the other biological parameters studied (biomass and chlorophyll content). This study demonstrated that the molecular defense and damage responses can be related to contaminant concentrations and represents a promising approach for environmental risk assessment of metals.

Transcriptomic point of departure determination: a comparison of distribution-based and gene set-based approaches

A key step in assessing the potential human and environmental health risks of industrial and agricultural chemicals is to determine the toxicity point of departure (POD), which is the highest dose level that causes no adverse effect. Transcriptomic POD (tPOD) values have been suggested to accurately estimate toxicity POD values. One step in the most common approach for tPOD determination involves mapping genes to annotated gene sets, a process that might lead to substantial information loss particularly in species with poor gene annotation. Alternatively, methods that calculate tPOD values directly from the distribution of individual gene POD values omit this mapping step. Using rat transcriptome data for 79 molecules obtained from Open TG-GATEs (Toxicogenomics Project Genomics Assisted Toxicity Evaluation System), the hypothesis was tested that methods based on the distribution of all individual gene POD values will give a similar tPOD value to that obtained via the gene set-based method. Gene set-based tPOD values using four different gene set structures were compared to tPOD values from five different individual gene distribution methods. Results revealed a high tPOD concordance for all methods tested, especially for molecules with at least 300 dose-responsive probesets: for 90% of those molecules, the tPOD values from all methods were within 4-fold of each other. In addition, random gene sets based upon the structure of biological knowledge-derived gene sets produced tPOD values with a median absolute fold change of 1.3-1.4 when compared to the original biological knowledge-derived gene set counterparts, suggesting that little biological information is used in the gene set-based tPOD generation approach. These findings indicate using individual gene distributions to calculate a tPOD is a viable and parsimonious alternative to using gene sets. Importantly, individual gene distribution-based tPOD methods do not require knowledge of biological organization and can be applied to any species including those with poorly annotated gene sets.

Wildlife ecological risk assessment in the 21st century: Promising technologies to assess toxicological effects

Despite advances in toxicity testing and the development of new approach methodologies (NAMs) for hazard assessment, the ecological risk assessment (ERA) framework for terrestrial wildlife (i.e., air-breathing amphibians, reptiles, birds, and mammals) has remained unchanged for decades. While survival, growth, and reproductive endpoints derived from whole-animal toxicity tests are central to hazard assessment, nonstandard measures of biological effects at multiple levels of biological organization (e.g., molecular, cellular, tissue, organ, organism, population, community, ecosystem) have the potential to enhance the relevance of prospective and retrospective wildlife ERAs. Other factors (e.g., indirect effects of contaminants on food supplies and infectious disease processes) are influenced by toxicants at individual, population, and community levels, and need to be factored into chemically based risk assessments to enhance the "eco" component of ERAs. Regulatory and logistical challenges often relegate such nonstandard endpoints and indirect effects to postregistration evaluations of pesticides and industrial chemicals and contaminated site evaluations. While NAMs are being developed, to date, their applications in ERAs focused on wildlife have been limited. No single magic tool or model will address all uncertainties in hazard assessment. Modernizing wildlife ERAs will likely entail combinations of laboratory- and field-derived data at multiple levels of biological organization, knowledge collection solutions (e.g., systematic review, adverse outcome pathway frameworks), and inferential methods that facilitate integrations and risk estimations focused on species, populations, interspecific extrapolations, and ecosystem services modeling, with less dependence on whole-animal data and simple hazard ratios. Integr Environ Assess Manag 2024;20:725-748. © 2023 His Majesty the King in Right of Canada and The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). Reproduced with the permission of the Minister of Environment and Climate Change Canada. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

How do biomarkers dance? Specific moves of defense and damage biomarkers for biological interpretation of dose-response model trends

Omics studies are currently increasingly used in ecotoxicology to highlight the induction of known or novel biomarkers when organisms are exposed to contaminants. Although it is virtually impossible to identify all biomarkers from all organisms, biomarkers can be grouped as defense or damage biomarkers, exhibiting a limited number of response trends. Our working hypothesis is that defense and damage biomarkers follow different dose-response patterns. A meta-analysis of 156 articles and 2595 observations of dose-response curves of defense and damage biomarkers was carried out in order to characterize the response trends of these biological parameters in a large panel of living organisms (18 phyla) exposed to inorganic or organic contaminants (176 in total). Using multinomial logistic regression models, defense biomarkers were found to describe biphasic responses (bell- and U-shaped) to a greater extent (2.5 times) than damage biomarkers. In contrast, damage biomarkers varied mainly monotonically (decreasing or increasing), representing 85% of the observations. Neither the nature of the contaminant nor the type of organisms belonging to 4 kingdoms, influence these specific responses. This result suggests that cellular defense and damage mechanisms are not specific to stressors and are conserved throughout life. Trend analysis of dose-response models as a biological interpretation of biomarkers could thus be a valuable way to exploit large omics datasets.

Effects of atrazine and S-metolachlor on stream periphyton taxonomic and fatty acid compositions

Extensive pesticide use for agriculture can diffusely pollute aquatic ecosystems through leaching and runoff events and has the potential to negatively affect non-target organisms. Atrazine and S-metolachlor are two widely used herbicides often detected in high concentrations in rivers that drain nearby agricultural lands. Previous studies focused on concentration-response exposure of algal monospecific cultures, over a short exposure period, with classical descriptors such as cell density, mortality or photosynthetic efficiency as response variables. In this study, we exposed algal biofilms (periphyton) to a concentration gradient of atrazine and S-metolachlor for 14 days. We focused on fatty acid composition as the main concentration-response descriptor, and we also measured chlorophyll a fluorescence. Results showed that atrazine increased cyanobacteria and diatom chlorophyll a fluorescence. Both herbicides caused dissimilarities in fatty acid profiles between control and high exposure concentrations, but S-metolachlor had a stronger effect than atrazine on the observed increase or reduction in saturated fatty acids (SFAs) and very long-chain fatty acids (VLCFAs), respectively. Our study demonstrates that two commonly used herbicides, atrazine and S-metolachlor, can negatively affect the taxonomic composition and fatty acid profiles of stream periphyton, thereby altering the nutritional quality of this resource for primary consumers.

Exploring the effects of experimental parameters and data modeling approaches on in vitro transcriptomic point-of-departure estimates

Multiple new approach methods (NAMs) are being developed to rapidly screen large numbers of chemicals to aid in hazard evaluation and risk assessments. High-throughput transcriptomics (HTTr) in human cell lines has been proposed as a first-tier screening approach for determining the types of bioactivity a chemical can cause (activation of specific targets vs. generalized cell stress) and for calculating transcriptional points of departure (tPODs) based on changes in gene expression. In the present study, we examine a range of computational methods to calculate tPODs from HTTr data, using six data sets in which MCF7 cells cultured in two different media formulations were treated with a panel of 44 chemicals for 3 different exposure durations (6, 12, 24 hr). The tPOD calculation methods use data at the level of individual genes and gene set signatures, and compare data processed using the ToxCast Pipeline 2 (tcplfit2), BMDExpress and PLIER (Pathway Level Information ExtractoR). Methods were evaluated by comparing to in vitro PODs from a validated set of high-throughput screening (HTS) assays for a set of estrogenic compounds. Key findings include: (1) for a given chemical and set of experimental conditions, tPODs calculated by different methods can vary by several orders of magnitude; (2) tPODs are at least as sensitive to computational methods as to experimental conditions; (3) in comparison to an external reference set of PODs, some methods give generally higher values, principally PLIER and BMDExpress; and (4) the tPODs from HTTr in this one cell type are mostly higher than the overall PODs from a broad battery of targeted in vitro ToxCast assays, reflecting the need to test chemicals in multiple cell types and readout technologies for in vitro hazard screening.

Editorial trend: adverse outcome pathway (AOP) and computational strategy - towards new perspectives in ecotoxicology

The adverse outcome pathway (AOP) has been conceptualized in 2010 as an analytical construct to describe a sequential chain of causal links between key events, from a molecular initiating event leading to an adverse outcome (AO), considering several levels of biological organization. An AOP aims to identify and organize available knowledge about toxic effects of chemicals and drugs, either in ecotoxicology or toxicology, and it can be helpful in both basic and applied research and serve as a decision-making tool in support of regulatory risk assessment. The AOP concept has evolved since its introduction, and recent research in toxicology, based on integrative systems biology and artificial intelligence, gave it a new dimension. This innovative in silico strategy can help to decipher mechanisms of action and AOP and offers new perspectives in AOP development. However, to date, this strategy has not yet been applied to ecotoxicology. In this context, the main objective of this short article is to discuss the relevance and feasibility of transferring this strategy to ecotoxicology. One of the challenges to be discussed is the level of organisation that is relevant to address for the AO (population/community). This strategy also offers many advantages that could be fruitful in ecotoxicology and overcome the lack of time, such as the rapid identification of data available at a time t, or the identification of "data gaps". Finally, this article proposes a step forward with suggested priority topics in ecotoxicology that could benefit from this strategy.

Population transcriptogenomics highlights impaired metabolism and small population sizes in tree frogs living in the Chernobyl Exclusion Zone

BACKGROUND

Individual functional modifications shape the ability of wildlife populations to cope with anthropogenic environmental changes. But instead of adaptive response, human-altered environments can generate a succession of deleterious functional changes leading to the extinction of the population. To study how persistent anthropogenic changes impacted local species' population status, we characterised population structure, genetic diversity and individual response of gene expression in the tree frog Hyla orientalis along a gradient of radioactive contamination around the Chernobyl nuclear power plant.

RESULTS

We detected lower effective population size in populations most exposed to ionizing radiation in the Chernobyl Exclusion Zone that is not compensated by migrations from surrounding areas. We also highlighted a decreased body condition of frogs living in the most contaminated area, a distinctive transcriptomics signature and stop-gained mutations in genes involved in energy metabolism. While the association with dose will remain correlational until further experiments, a body of evidence suggests the direct or indirect involvement of radiation exposure in these changes.

CONCLUSIONS

Despite ongoing migration and lower total dose rates absorbed than at the time of the accident, our results demonstrate that Hyla orientalis specimens living in the Chernobyl Exclusion Zone are still undergoing deleterious changes, emphasizing the long-term impacts of the nuclear disaster.

Effects of Chemical Compounds on the Activity of the N-acetyl-β-D-Glucosaminidase of the Marine Prawn, Palaemon serratus: Screening In Vitro

N-acetyl-β-D-glucosaminidase (NAGase) is important for crustaceans because the enzyme activity is necessary for the molting process. The present study aimed to assess the sensitivity of Palaemon serratus NAGase activity to a set of compounds of diverse chemical families in the context of in vitro exposures. Compounds representing different chemical families were selected according to their abundance, impact in the environment, and relevance as disruptors of the molting process. In a first step, four solvents (dimethylsulfoxide [DMSO], methanol, acetone, and ethanol) were tested to determine their suitability to dissolve hydrophobic compounds without affecting NAGase activity. Exclusively, ethanol had no effect on enzyme activity and on the integrity of the proteins present in the enzyme extract. The 18 other compounds were tested and four of these compounds, pentoxifylline, fenoxycarb, dithiocarbamate, and RH5849, showed a specific alteration on the activity of NAGase, without affecting the protein content. However, cadmium, zinc, and glyphosate showed a nonspecific alteration, affecting both the enzyme activity and the proteins, whereas ibuprofen exclusively altered the protein content. Finally, 10 of the 22 tested compounds (including DMSO, acetone, and methanol) showed a direct alteration of NAGase activity. Environ Toxicol Chem 2023;42:846-858. © 2023 SETAC.

Multiomics Point of Departure (moPOD) Modeling Supports an Adverse Outcome Pathway Network for Ionizing Radiation

While adverse biological effects of acute high-dose ionizing radiation have been extensively investigated, knowledge on chronic low-dose effects is scarce. The aims of the present study were to identify hazards of low-dose ionizing radiation to Daphnia magna using multiomics dose-response modeling and to demonstrate the use of omics data to support an adverse outcome pathway (AOP) network development for ionizing radiation. Neonatal D. magna were exposed to γ radiation for 8 days. Transcriptomic analysis was performed after 4 and 8 days of exposure, whereas metabolomics and confirmative bioassays to support the omics analyses were conducted after 8 days of exposure. Benchmark doses (BMDs, 10% benchmark response) as points of departure (PODs) were estimated for both dose-responsive genes/metabolites and the enriched KEGG pathways. Relevant pathways derived using the BMD modeling and additional functional end points measured by the bioassays were overlaid with a previously published AOP network. The results showed that several molecular pathways were highly relevant to the known modes of action of γ radiation, including oxidative stress, DNA damage, mitochondrial dysfunction, protein degradation, and apoptosis. The functional assays showed increased oxidative stress and decreased mitochondrial membrane potential and ATP pool. Ranking of PODs at the pathway and functional levels showed that oxidative damage related functions had relatively low PODs, followed by DNA damage, energy metabolism, and apoptosis. These were supportive of causal events in the proposed AOP network. This approach yielded promising results and can potentially provide additional empirical evidence to support further AOP development for ionizing radiation.

Nitrogen to phosphorus ratio shapes the bacterial communities involved in cellulose decomposition and copper contamination alters their stoichiometric demands

All living organisms theoretically have an optimal stoichiometric nitrogen: phosphorus (N: P) ratio, below and beyond which their growth is affected, but data remain scarce for microbial decomposers. Here, we evaluated optimal N: P ratios of microbial communities involved in cellulose decomposition and assessed their stability when exposed to copper Cu(II). We hypothesized that (1) cellulose decomposition is maximized for an optimal N: P ratio; (2) copper exposure reduces cellulose decomposition and (3) increases microbial optimal N: P ratio; and (4) N: P ratio and copper modify the structure of microbial decomposer communities. We measured cellulose disc decomposition by a natural inoculum in microcosms exposed to a gradient of N: P ratios at three copper concentrations (0, 1 and 15 µM). Bacteria were most probably the main decomposers. Without copper, cellulose decomposition was maximized at an N: P molar ratio of 4.7. Contrary to expectations, at high copper concentration, the optimal N: P ratio (2.8) and the range of N: P ratios allowing decomposition were significantly reduced and accompanied by a reduction of bacterial diversity. Copper contamination led to the development of tolerant taxa probably less efficient in decomposing cellulose. Our results shed new light on the understanding of multiple stressor effects on microbial decomposition in an increasingly stoichiometrically imbalanced world.

Adverse Outcome Pathways and Linkages to Transcriptomic Effects Relevant to Ionizing Radiation Injury

BACKGROUND

In the past three decades, a large body of data on the effects of exposure to ionizing radiation and the ensuing changes in gene expression has been generated. These data have allowed for an understanding of molecular-level events and shown a level of consistency in response despite the vast formats and experimental procedures being used across institutions. However, clarity on how this information may inform strategies for health risk assessment needs to be explored. An approach to bridge this gap is the adverse outcome pathway (AOP) framework. AOPs represent an illustrative framework characterizing a stressor associated with a sequential set of causally linked key events (KEs) at different levels of biological organization, beginning with a molecular initiating event (MIE) and culminating in an adverse outcome (AO). Here, we demonstrate the interpretation of transcriptomic datasets in the context of the AOP framework within the field of ionizing radiation by using a lung cancer AOP (AOP 272: https://www.aopwiki.org/aops/272) as a case example.

METHODS

Through the mining of the literature, radiation exposure-related transcriptomic studies in line with AOP 272 related to lung cancer, DNA damage response, and repair were identified. The differentially expressed genes within relevant studies were collated and subjected to the pathway and network analysis using Reactome and GeneMANIA platforms. Identified pathways were filtered (p < 0.001, ≥ 3 genes) and categorized based on relevance to KEs in the AOP. Gene connectivities were identified and further grouped by gene expression-informed associated events (AEs). Relevant quantitative dose-response data were used to inform the directionality in the expression of the genes in the network across AEs.

RESULTS

Reactome analyses identified 7 high-level biological processes with multiple pathways and associated genes that mapped to potential KEs in AOP 272. The gene connectivities were further represented as a network of AEs with associated expression profiles that highlighted patterns of gene expression levels.

CONCLUSIONS

This study demonstrates the application of transcriptomics data in AOP development and provides information on potential data gaps. Although the approach is new and anticipated to evolve, it shows promise for improving the understanding of underlying mechanisms of disease progression with a long-term vision to be predictive of adverse outcomes.

Community metabolomics provides insights into mechanisms of pollution-induced community tolerance of periphyton

Chemical pollution is a major concern for freshwater ecosystems, but the impact and mechanisms of chemical stressors on communities are barely understood. Pollution stress beyond natural homeostatic capacities can trigger succession of tolerant species within a community, enhancing the overall community tolerance. This process was operationalized in the Pollution-Induced Community Tolerance (PICT) concept and applied in many case studies, however, the molecular mechanisms of community tolerance and implications for ecological functions remain largely unexplored. Our study aimed to demonstrate that 1) community metabolomics can unravel potential mechanisms of PICT in periphyton and 2) induced tolerance helps to maintain primary production under re-occuring pollution. To this end, we grew periphyton for 5 weeks with and without the model herbicide diuron in microcosms, quantified PICT, and determined the related metabolic fingerprint of periphyton by GC-MS-based untargeted metabolomics. Further, we explored the autotrophic community based on pigment composition and functional parameters including photosynthesis and gross primary production. Chronic diuron exposure resulted in a shift in pigment composition, higher community tolerance and an individual metabolic fingerprint in the contaminated communities. Opposing responses of selected metabolites during a short-term exposure indicated differences in diuron pre-adaptation in the different communities. Metabolites (threonic acid and two sugar acid lactones) were found to be related to tolerance development, suggesting that ascorbate metabolism was induced in contaminated communities. Despite these compensating mechanism, contaminated communities were compromised in production-to-respiration ratio and biomass. A ranking of sensitivity thresholds of different biological endpoints revealed that metabolites were less sensitive than photosynthetic parameters, which reflects the mode-of-action of the herbicide. In conclusion, we could demonstrate that community metabolomics is able to unravel complex biochemical changes and allows mechanistic insights into community tolerance. Moreover, we were able to show that induced community tolerance was insufficient to safeguard functions like primary production.

Effects and bioaccumulation of Cr(III), Cr(VI) and their mixture in the freshwater mussel Corbicula fluminea

Chromium has two main oxidation states, Cr(III) and Cr(VI), that can occur simultaneously in natural waters. Current consensus holds that Cr(VI) is of high ecotoxicological concern, but regards Cr(III) as poorly bioavailable and relatively non-toxic. In this work, the effects and bioaccumulation of Cr(III), Cr(VI) and their mixture were studied using the freshwater clam Corbicula fluminea as a model organism. Mixture exposures were carried out using solutions isotopically enriched in ⁵⁰Cr(III) or ⁵³Cr(VI), allowing to quantify the contribution of each redox form to total Cr accumulation in the clams. Following exposure to individual redox forms, Cr(III) accumulated preferentially in the digestive glands and Cr(VI) in the gills of C. fluminea. In mixture exposures, both redox forms accumulated mainly in the gills; the concentration of Cr(III) in the digestive glands being much lowered compared with individual exposures. Both oxidation states affected the expression of biomarkers related to energy reserves, cellular damage and mitochondrial functioning, as well as the expression of mRNA for detoxification genes. The observed effects differed between gills and digestive glands. The present study suggests that Cr(III) is a bioavailable and biologically active elemental species deserving more consideration by the ecotoxicological community.

Strengthening Causal Inference in Exposomics Research: Application of Genetic Data and Methods

Advances in technologies to measure a broad set of exposures have led to a range of exposome research efforts. Yet, these efforts have insufficiently integrated methods that incorporate genetic data to strengthen causal inference, despite evidence that many exposome-associated phenotypes are heritable. Objective: We demonstrate how integration of methods and study designs that incorporate genetic data can strengthen causal inference in exposomics research by helping address six challenges: reverse causation and unmeasured confounding, comprehensive examination of phenotypic effects, low efficiency, replication, multilevel data integration, and characterization of tissue-specific effects. Examples are drawn from studies of biomarkers and health behaviors, exposure domains where the causal inference methods we describe are most often applied. Discussion: Technological, computational, and statistical advances in genotyping, imputation, and analysis, combined with broad data sharing and cross-study collaborations, offer multiple opportunities to strengthen causal inference in exposomics research. Full application of these opportunities will require an expanded understanding of genetic variants that predict exposome phenotypes as well as an appreciation that the utility of genetic variants for causal inference will vary by exposure and may depend on large sample sizes. However, several of these challenges can be addressed through international scientific collaborations that prioritize data sharing. Ultimately, we anticipate that efforts to better integrate methods that incorporate genetic data will extend the reach of exposomics research by helping address the challenges of comprehensively measuring the exposome and its health effects across studies, the life course, and in varied contexts and diverse populations. https://doi.org/10.1289/EHP9098.

Metabolomics insight into the influence of environmental factors in responses of freshwater biofilms to the model herbicide diuron

Freshwater biofilms have been increasingly used during the last decade in ecotoxicology due to their ecological relevance to assess the effect(s) of environmental stress at the community level. Despite growing knowledge about the effect of various stressors on the structure and the function of these microbial communities, a strong research effort is still required to better understand their response to chemical stress and the influence of environmental stressors in this response. To tackle this challenge, untargeted metabolomics is an approach of choice because of its capacity to give an integrative picture of the exposure to multiple stress and associated effect as well as identifying the molecular pathways involved in these responses. In this context, the present study aimed to explore the use of an untargeted metabolomics approach to unravel at the molecular/biochemical level the response of the whole biofilm to chemical stress and the influence of various environmental factors in this response. To this end, archived high-resolution mass spectrometry data from previous experiments at our laboratory on the effect of the model photosynthesis inhibitor diuron on freshwater biofilm were investigated by using innovative solutions for OMICs data (e.g., DRomics) and more usual chemometric approaches (multivariate and univariate statistical analyses). The results showed a faster (1 min) and more sensitive response of the metabolome to diuron than usual functional descriptors, including photosynthesis. Also, the metabolomics response to diuron resulted from metabolites following various trends (increasing, decreasing, U/bell shape) along increasing concentration and time. This metabolomics response was influenced by the temperature, photoperiod, and flow. A focus on a plant-specific omega-3 (eicosapentaenoic acid) playing a key role in the trophic chain highlighted the potential relevance of metabolomics approach to establish the link between molecular alteration and ecosystem structure/functioning impairment but also how complex is the response and the influence of all the tested factors on this response at the metabolomics level. Altogether, our results underline that more fundamental researches are needed to decipher the metabolomics response of freshwater biofilm to chemical stress and its link with physiological, structural, and functional responses toward the unraveling of adverse outcome pathways (AOP) for key ecosystem functions (e.g., primary production).

Non-parametric synergy modeling of chemical compounds with Gaussian processes

Background

Understanding the synergetic and antagonistic effects of combinations of drugs and toxins is vital for many applications, including treatment of multifactorial diseases and ecotoxicological monitoring. Synergy is usually assessed by comparing the response of drug combinations to a predicted non-interactive response from reference (null) models. Possible choices of null models are Loewe additivity, Bliss independence and the recently rediscovered Hand model. A different approach is taken by the MuSyC model, which directly fits a generalization of the Hill model to the data. All of these models, however, fit the dose–response relationship with a parametric model.

Results

We propose the Hand-GP model, a non-parametric model based on the combination of the Hand model with Gaussian processes. We introduce a new logarithmic squared exponential kernel for the Gaussian process which captures the logarithmic dependence of response on dose. From the monotherapeutic response and the Hand principle, we construct a null reference response and synergy is assessed from the difference between this null reference and the Gaussian process fitted response. Statistical significance of the difference is assessed from the confidence intervals of the Gaussian process fits. We evaluate performance of our model on a simulated data set from Greco, two simulated data sets of our own design and two benchmark data sets from Chou and Talalay. We compare the Hand-GP model to standard synergy models and show that our model performs better on these data sets. We also compare our model to the MuSyC model as an example of a recent method on these five data sets and on two-drug combination screens: Mott et al. anti-malarial screen and O’Neil et al. anti-cancer screen. We identify cases in which the HandGP model is preferred and cases in which the MuSyC model is preferred.

Conclusion

The Hand-GP model is a flexible model to capture synergy. Its non-parametric and probabilistic nature allows it to model a wide variety of response patterns.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04508-7.

Dose-response metabolomics and pathway sensitivity to map molecular cartography of bisphenol A exposure

In the toxicological regime, the toxicological endpoint and its dose-response relationship are two of the most prominent characters in conducting a risk assessment for chemical exposure. Systems biological methods have been used to comprehensively characterize the impact of toxicants on the biochemical pathways. However, the majority of the current studies are only based on single-dose, and limited information can be extrapolated to other doses from these experiments, regardless of the sensitivity of each endpoint. This study aims to understand the dose-response metabolite dysregulation pattern and metabolite sensitivity at the system-biological level. Here, we applied bisphenol A (BPA), an endocrine-disrupting chemical (EDC), as the model chemical. We first employed the global metabolomics method to characterize the metabolome of breast cancer cells (MCF-7) upon exposure to different doses (0, 20, 50, and 100 µM) of BPA. The dysregulated features with a clear dose-response relationship were also effectively picked up with an R-package named TOXcms. Overall, most metabolites were dysregulated by showing a significant dose-dependent behaviour. The results suggested that BPA exposure greatly perturbed purine metabolism and pyrimidine metabolism. Interestingly, most metabolites within the purine metabolism were described as a biphasic dose-response relationship. With the established dose-response relationship, we were able to fully map the metabolite cartography of BPA exposure within a wide range of concentrations and observe some unique patterns. Furthermore, an effective concentration of certain fold changes (e.g., EC₊₁₀ means the dose at which metabolite is 10% upregulated) and metabolite sensitivity were defined and introduced to this dose-response omics information. The result showed that the purine metabolism pathway is the most venerable target of BPA, which can be a potential endogenous biomarker for its exposure. Overall, this study applied the dose-response metabolomics method to fully understand the biochemical pathway disruption of BPA treatment at different doses. Both dose-response omics strategy and metabolite sensitivity analysis can be further considered and emphasized in future chemical risk assessments.

Untargeted and targeted analysis of sarin poisoning biomarkers in rat urine by liquid chromatography and tandem mass spectrometry

Chemical warfare agents continue to pose a real threat to humanity, despite their prohibition under the Chemical Weapons Convention. Sarin is one of the most toxic and lethal representatives of nerve agents. The methodology for the targeted analysis of known sarin metabolites has reached great heights, but little attention has been paid to the untargeted analysis of biological samples of victims exposed to this deadly poisonous substance. At present, the development of computational and statistical methods of analysis offers great opportunities for finding new metabolites or understanding the mechanisms of action or effect of toxic substances on the organism. This study presents the targeted LC-MS/MS determination of methylphosphonic acid and isopropyl methylphosphonic acid in the urine of rats exposed to a non-lethal dose of sarin, as well as the untarget urine analysis by LC-HRMS. Targeted analysis of polar acidic sarin metabolites was performed on a mixed-mode reversed-phase anion-exchange column, and untargeted analysis on a conventional reversed-phase C18 column. Isopropyl methylphosphonic acid was detected and quantified within 5 days after subcutaneous injection of sarin at a dose of 1/4 LD₅₀. A combination of generalized additive mixed models and dose-response analysis with database searches using accurate mass of precursor ions and corresponding MS/MS spectra enabled us to propose new six potential biomarkers of biological response to exposure. The results confirm the well-known fact that sarin poisoning has a significant impact on the victims' metabolome, with inhibition of acetylcholinesterase being just the first step and trigger of the complex toxicodynamic response.

Mode of action evaluation for reduced reproduction in Daphnia pulex exposed to the insensitive munition, 1-methyl-3-nitro-1-nitroguanidine (MeNQ)

The US Department of Defense (DOD) is developing insensitive munitions (IMs) that are resistant to unintended detonation to protect warfighters. To enable material life-cycle analysis for the IM, 1-methyl-3-nitro-1-nitroguanidine (MeNQ), ecotoxicological impacts assessment was required. A previous investigation of MeNQ exposures in Daphnia pulex revealed concentration-responsive decreases in reproduction relative to controls (0 mg/L) across a 174, 346, 709, 1385, and 2286 mg/L exposure range. The present study used those exposures to conduct global transcriptomic expression analyses to establish hypothetical mode(s) of action underlying inhibited reproduction. The number of significantly affected transcripts and the magnitude of fold-change differences relative to controls tended to increase with increasing MeNQ concentration where hierarchical clustering analysis identified separation among the "low" (174 and 346 mg/L) and "high" (709, 1385, and 2286 mg/L) exposures. Vitellogenin is critical to Daphnia reproductive processes and MeNQ exposures significantly decreased transcriptional expression for vitellogenin-1 precursor at the lowest exposure level (174 mg/L) with benchmark dose (BMD) levels closely tracking concentrations that caused inhibited reproduction. Additionally, juvenile hormone-inducible protein, chorion peroxidase, and high choriolytic enzyme transcriptional expression were impacted by MeNQ exposure having potential implications for egg production / maturation and overall fecundity. In concert with these effects on specific genes involved in Daphnia reproductive physiology, MeNQ exposures caused significant enrichment of several canonical-pathways responsible for metabolism of cellular energy substrates where BMD levels for transcriptional expression were observed at ≤100 mg/L. These observations imply possible effects on whole-organism energy budgets that may also incur indirect costs on reproduction.

FastBMD: an online tool for rapid benchmark dose-response analysis of transcriptomics data

MOTIVATION

Transcriptomics dose-response analysis is a promising new approach method for toxicity testing. While international regulatory agencies have spent substantial effort establishing a standardized statistical approach, existing software that follows this approach is computationally inefficient and must be locally installed.

RESULTS

FastBMD is a web-based tool that implements standardized methods for transcriptomics benchmark dose-response analysis in R. It is >60 times faster than the current leading software, supports transcriptomics data from 13 species, and offers a comprehensive analytical pipeline that goes from processing and normalization of raw gene expression values to interactive exploration of pathway-level benchmark dose results.

AVAILABILITY AND IMPLEMENTATION

FastBMD is freely available at www.fastbmd.ca.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Intelligent cloud workflow management and scheduling method for big data applications

With the application and comprehensive development of big data technology, the need for effective research on cloud workflow management and scheduling is becoming increasingly urgent. However, there are currently suitable methods for effective analysis. To determine how to effectively manage and schedule smart cloud workflows, this article studies big data from various aspects and draws the following conclusions: Compared with the original JStorm system, the response time is shortened by a maximum of 58.26% and an average of 23.18%, CPU resource utilization is increased by a maximum of 17.96% and an average of 11.39%, and memory utilization increased by a maximum of 88.7% and an average of 71.16%. In terms of optimizing the dynamic combination of web services, the overall performance of both the MOACO and CCA algorithms is better than that of the GA algorithm, and the average performance of the MOACO algorithm is better than that of the CCA algorithm. This paper also proposes a cloud workflow scheduling strategy based on an intelligent algorithm and realizes the two-tier scheduling of cloud workflow tasks by adjusting the combination strategy for cloud service resources. We have studied three representative intelligent algorithms (ACO, PSO and GA) and improved them for scheduling optimization. It can be clearly seen that in the same scenario, the optimal values of the different algorithms vary greatly for different test cases. However, the optimal solution curve is substantially consistent with the trend of the mean curve.

A multi-omics concentration-response framework uncovers novel understanding of triclosan effects in the chlorophyte Scenedesmus vacuolatus

In aquatic ecosystems, the biocide triclosan represents a hazard for the non-target microalgae. So far, algal responses were mainly investigated at apical levels hampering the acquisition of a holistic view on primary, adaptive, and compensatory stress responses. We assessed responses of the chlorophyte Scenedesmus vacuolatus to triclosan at apical (growth, photosynthesis) and molecular (transcriptome, metabolome) levels for comparative pathway sensitivity analysis. For each responsive signal (contigs, metabolites), a concentration-response curve was modeled and effect concentrations were calculated leading to the setting of cumulative sensitivity distributions. Molecular responses showed higher sensitivity than apical observations. The functional annotation of contigs and metabolites revealed 118 metabolic pathways putatively impaired by triclosan, highlighting a wide repercussion on the algal metabolism. Metabolites involved in the lipid metabolism showed decreasing trends along the concentration gradient and a globally highest sensitivity, pointing to the primary target of triclosan. The pathways involved in xenobiotic degradation and membrane transporters were mainly regulated in the transcriptome with increasing response trends comprising compensatory responses. The suggested novel approach, combining apical and multi-omics analyses in a concentration-response framework improves mechanistic understanding and mode of action analysis on non-targeted organisms and is suggested to better implement high-throughput multi-omics data in environmental risk assessment.

Effect of interspecific competition on species sensitivity distribution models: Analysis of plant responses to chemical stress

Species Sensitivity Distributions (SSD) are widely used in environmental risk assessment to predict the concentration of a contaminant that is hazardous for 5% of species (HC₅). They are based on monospecific bioassays conducted in the laboratory and thus do not directly take into account ecological interactions. This point, among others, is accounted for in environmental risk assessment through an assessment factor (AF) that is applied to compensate for the lack of environmental representativity. In this study, we aimed to assess the effects of interspecific competition on the responses towards isoproturon of plant species representative of a vegetated filter strip community, and to assess its impact on the derived SSD and HC₅ values. To do so, we realized bioassays confronting six herbaceous species to a gradient of isoproturon exposure in presence and absence of a competitor. Several modelling approaches were applied to see how they affected the results, using different critical effect concentrations and investigating different ways to handle multiple endpoints in SSD. At the species level, there was a strong trend toward organisms being more sensitive to isoproturon in presence of a competitor than in its absence. At the community level, this trend was also observed in the SSDs and HC₅ values were always lower in presence of a competitor (1.12-11.13 times lower, depending on the modelling approach). Our discussion questions the relevance of SSD and AF as currently applied in environmental risk assessment.

Metabolome response to anthropogenic contamination on microalgae: a review

BACKGROUND

Microalgae play a key role in ecosystems and are widely used in ecological status assessment. Research focusing on such organisms is then well developed and essential. Anyway, approaches for a better comprehension of their metabolome's response towards anthropogenic stressors are only emerging.

AIM OF REVIEW

This review presents the biochemical responses of various microalgae species towards several contaminants including metals and chemicals as pesticides or industrial compounds. We aim to provide a comprehensive and up-to-date overview of analytical approaches deciphering anthropogenic contaminants impact on microalgae metabolome dynamics, in order to bring out relevant biochemical markers that could be used for risk assessment.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Studies to date on ecotoxicological metabolomics on microalgae are highly heterogeneous in both analytical techniques and resulting metabolite identification. There is a real need for studies using complementary approaches to determine biomarkers usable for ecological risk assessment.