Unveiling toxicity profile for food risk components: A manually curated toxicological databank of food-relevant chemicals.

Rigorous risk assessment of chemicals in food and feed is essential to address the growing worldwide concerns about food safety. High-quality toxicological data on food-relevant chemicals are fundamental for risk modeling and assessment in the food safety area. The organization and analysis of substantial toxicity information can positively support decision-making by providing insight into toxicity trends. However, it remains challenging to systematically obtain fragmented toxicity data, and related toxicological resources are required to meet the current demands. In this study, we collected 221,439 experimental toxicity records for 5,657 food-relevant chemicals identified from extensive databases and literature, along with their information on chemical identification, physicochemical properties, environmental fates, and biological targets. Based on the aggregated data, a freely available web-based databank, Food-Relevant Available Chemicals Toxicology Databank (FRAC-TD) is presented, which supports multiple browsing ways and search criterions. Applying FRAC-TD for data-driven analysis, we revealed the underlying toxicity profiles of food-relevant chemicals in humans, mammals, and other species in the food chain. Expectantly, FRAC-TD could positively facilitate toxicological studies, toxicity prediction, and risk assessments in the food industry.

Same sensitivity with shorter exposure: behavior as an appropriate parameter to assess metal toxicity.

The exposure of animals to toxicants may cause a depletion in the energy uptake, which compromises reproduction and growth. Although both parameters are ecologically relevant, they usually need long-term bioassays. This is a handicap for the availability of toxicological data for environmental risk assessment. Short-term bioassays conducted with environmental concentrations, and using relevant ecological parameters sensitive to short-term exposures, such as behavior, could be a good alternative. Therefore, to include this parameter in the risk assessment procedures, it is relevant the comparison of its sensitivity with that of growth and reproduction bioassays. The study aim was the assessment of differences between endpoints based on mortality, behaviour, reproduction, and growth for the toxicity of metals on aquatic animals. We used the ECOTOX database to gather data to construct chemical toxicity distribution (CTD) curves. The mean concentrations, the mean exposure time, and the ratio between the mean concentration and the exposure time were compared among endpoints. Our results showed that behavioral, growth, and reproduction bioassays presented similar sensitivity. The shortest exposure was found in behavioral and reproduction bioassays. In general, the amount of toxicant used per time was lower in growth and reproduction bioassays than in behavioral and mortality bioassays. We can conclude that, for metal toxicity, behavioral bioassays are less time-consuming than growth bioassays. As the sensitivity of behavior was similar to that of growth and reproduction, this endpoint could be a better alternative to longer bioassays.

SepPCNET: Deeping Learning on a 3D Surface Electrostatic Potential Point Cloud for Enhanced Toxicity Classification and Its Application to Suspected Environmental Estrogens.

Deep learning (DL) offers an unprecedented opportunity to revolutionize the landscape of toxicity prediction based on quantitative structure-activity relationship (QSAR) studies in the big data era. However, the structural description in the reported DL-QSAR models is still restricted to the two-dimensional level. Inspired by point clouds, a type of geometric data structure, a novel three-dimensional (3D) molecular surface point cloud with electrostatic potential (SepPC) was proposed to describe chemical structures. Each surface point of a chemical is assigned its 3D coordinate and molecular electrostatic potential. A novel DL architecture SepPCNET was then introduced to directly consume unordered SepPC data for toxicity classification. The SepPCNET model was trained on 1317 chemicals tested in a battery of 18 estrogen receptor-related assays of the ToxCast program. The obtained model recognized the active and inactive chemicals at accuracies of 82.8 and 88.9%, respectively, with a total accuracy of 88.3% on the internal test set and 92.5% on the external test set, which outperformed other up-to-date machine learning models and succeeded in recognizing the difference in the activity of isomers. Additional insights into the toxicity mechanism were also gained by visualizing critical points and extracting data-driven point features of active chemicals.

Exposure and Toxicity Characterization of Chemical Emissions and Chemicals in Products: Global Recommendations and Implementation in USEtox.

PURPOSE: Reducing chemical pressure on human and environmental health is an integral part of the global sustainability agenda. Guidelines for deriving globally applicable, life cycle based indicators are required to consistently quantify toxicity impacts from chemical emissions as well as from chemicals in consumer products. In response, we elaborate the methodological framework and present recommendations for advancing near-field/far-field exposure and toxicity characterization, and for implementing these recommendations in the scientific consensus model USEtox. METHODS: An expert taskforce was convened by the Life Cycle Initiative hosted by UN Environment to expand existing guidance for evaluating human toxicity impacts from exposure to chemical substances. This taskforce evaluated advances since the original release of USEtox. Based on these advances, the taskforce identified two major aspects that required refinement, namely integrating near-field and far-field exposure and improving human dose-response modeling. Dedicated efforts have led to a set of recommendations to address these aspects in an update of USEtox, while ensuring consistency with the boundary conditions for characterizing life cycle toxicity impacts and being aligned with recommendations from agencies that regulate chemical exposure. The proposed framework was finally tested in an illustrative rice production and consumption case study. RESULTS AND DISCUSSION: On the exposure side, a matrix system is proposed and recommended to integrate far-field exposure from environmental emissions with near-field exposure from chemicals in various consumer product types. Consumer exposure is addressed via submodels for each product type to account for product characteristics and exposure settings. Case study results illustrate that product-use related exposure dominates overall life cycle exposure. On the effect side, a probabilistic dose-response approach combined with a decision tree for identifying reliable points of departure is proposed for non-cancer effects, following recent guidance from the World Health Organization. This approach allows for explicitly considering both uncertainty and human variability in effect factors. Factors reflecting disease severity are proposed to distinguish cancer from non-cancer effects, and within the latter discriminate reproductive/developmental and other non-cancer effects. All proposed aspects have been consistently implemented into the original USEtox framework. CONCLUSIONS: The recommended methodological advancements address several key limitations in earlier approaches. Next steps are to test the new characterization framework in additional case studies and to close remaining research gaps. Our framework is applicable for evaluating chemical emissions and product-related exposure in life cycle assessment, chemical alternatives assessment and chemical substitution, consumer exposure and risk screening, and high-throughput chemical prioritization.

MicroRNA biomarkers for chemical hazard screening identified by RNA deep sequencing analysis in mouse embryonic stem cells.

We investigated the responses of microRNAs (miRNAs) using mouse embryonic stem cells (mESCs) exposed to nine chemicals (bis(2-ethylhexyl)phthalate, p-cresol, p-dichlorobenzene, phenol, pyrocatecol, chloroform, tri-n-butyl phosphate, trichloroethylene, and benzene), which are listed as "Class I Designated Chemical Substances" from the Japan Pollutant Release and Transfer Register. Using deep sequencing analysis (RNA-seq), several miRNAs were identified that show a substantial response to general chemical toxicity (i.e., to these nine chemicals considered as a group) and several miRNA biomarkers that show a substantial and specific response to benzene. The functions of the identified miRNAs were investigated in accordance with Gene Ontology terms of their predicted target genes, indicating regulation of cellular processes. We compared the results with those for the long non-coding RNAs (ncRNAs) and mRNAs reported in our previous studies in addition to previously identified miRNAs that are either up- or down-regulated in response to the benzene as stimuli. We also observed that the changes in expression of miRNAs were smaller than those for long ncRNAs and mRNAs. Taken together the current and previous results revealed that toxic chemical stimuli regulate the expression of miRNAs. We believe that the use of miRNAs, including the thus identified miRNAs, as biomarkers contribute to predicting the potential toxicity of particular chemicals or identifying human individuals that have been exposed to chemical hazards.

Age-dependent ingestion and inhalation doses due to intake of uranium and radon in water samples of Shiwalik Himalayas of Jammu and Kashmir, India.

The research work involved the ingestion and inhalation doses due to the intake of radon and uranium through water samples used by the inhabitants, measured in the villages of the Shiwalik Himalayas of Jammu and Kashmir, India. The uranium concentration in collected water samples was assessed by LED fluorimetric technique. All values of doses were found to be below the proposed limit of 100 µSv year-1 for all age categories except for infants due to the high-dose conversion factor. The annual effective doses for the various body organs due to the intake of radon was also calculated and found the maximum dose for lungs than other organs. The concentration of radon in water samples was assessed by Smart Rn Duo portable monitor and compared with RAD7. Statistical analysis was carried out and the Shapiro and Wilk (Biometrika, 52(3/4), 591-611, 1965) test has been also used for the distribution of the data. The physicochemical parameters were also measured in the collected water samples.

Omics-Based Platform for Studying Chemical Toxicity Using Stem Cells.

The new strategy for chemical toxicity testing and modeling is to use in vitro human cell-based assays in conjunction with quantitative high-throughput screening (qHTS) technology, to identify molecular mechanisms and predict in vivo responses. Stem cells are more physiologically relevant than immortalized cell lines because of their unique proliferation and differentiation potentials. We established a robust two stem cells-two lineages assay system, encompassing human mesenchymal stem cells (hMSCs) along osteogenesis and human induced pluripotent stem cells (hiPSCs) along hepatogenesis. We performed qHTS phenotypic screening of LOPAC1280 and identified 38 preliminary hits for hMSCs. This was followed by validation of a selected number of hits and determination of their IC50 values and mechanistic studies of idarubicin and cantharidin treatments using proteomics and bioinformatics. In general, hiPSCs were more sensitive than hMSCs to chemicals, and differentiated progenies were less sensitive than their progenitors. We showed that chemical toxicity depends on both stem cell types and their differentiation stages. Proteomics identified and quantified over 3000 proteins for both stem cells. Bioinformatics identified apoptosis and G2/M as the top pathways conferring idarubicin toxicity. Our Omics-based assays of stem cells provide mechanistic insights into chemical toxicity and may help prioritize chemicals for in-depth toxicological evaluations.

Analysis of the chemical toxicity effects using the enrichment of Gene Ontology terms and KEGG pathways.

BACKGROUND: Chemical toxicity is one of the major barriers for designing and detecting new chemical entities during drug discovery. Unexpected toxicity of an approved drug may lead to withdrawal from the market and significant loss of the associated costs. Better understanding of the mechanisms underlying various toxicity effects can help eliminate unqualified candidate drugs in early stages, allowing researchers to focus their attention on other more viable candidates. METHODS: In this study, we aimed to understand the mechanisms underlying several toxicity effects using Gene Ontology (GO) terms and KEGG pathways. GO term and KEGG pathway enrichment theories were adopted to encode each chemical, and the minimum redundancy maximum relevance (mRMR) was used to analyze the GO terms and the KEGG pathways. Based on the feature list obtained by the mRMR method, the most related GO terms and KEGG pathways were extracted. RESULTS: Some important GO terms and KEGG pathways were uncovered, which were concluded to be significant for determining chemical toxicity effects. CONCLUSIONS: Several GO terms and KEGG pathways are highly related to all investigated toxicity effects, while some are specific to a certain toxicity effect. GENERAL SIGNIFICANCE: The findings in this study have the potential to further our understanding of different chemical toxicity mechanisms and to assist scientists in developing new chemical toxicity prediction algorithms. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.

Chemical toxicity and radioactivity of depleted uranium: The evidence from in vivo and in vitro studies.

The main aim of this review is to summarize and discuss the current state of knowledge on chemical toxicity and radioactivity of depleted uranium (DU) and their effect on living systems and cell lines. This was done by presenting a summary of previous investigations conducted on different mammalian body systems and cell cultures in terms of potential changes caused by either chemical toxicity or radioactivity of DU. In addition, the authors aimed to point out the limitations of those studies and possible future directions. The majority of both in vitro and in vivo studies performed using animal models regarding possible effects caused by acute or chronic DU exposure has been reviewed. Furthermore, exposure time and dose, DU particle solubility, and uranium isotopes as factors affecting the extent of DU effects have been discussed. Special attention has been dedicated to chromosomal aberrations, DNA damage and DNA breaks, as well as micronuclei formation and epigenetic changes, as DU has recently been considered a possible causative factor of all these processes. Therefore, this approach might represent a novel area of study of DU-related irradiation effects on health. Since different studies offer contradictory results, the main aim of this review is to summarize and briefly discuss previously obtained results in order to identify the current opinion on DU toxicity and radioactivity effects in relation to exposure type and duration, as well as DU properties.

Comparative endpoint sensitivity of in vitro estrogen agonist assays.

Environmental and human health implications of endocrine disrupting chemicals (EDCs), particularly xenoestrogens, have received extensive study. In vitro assays are increasingly employed as diagnostic tools to comparatively evaluate chemicals, whole effluent toxicity and surface water quality, and to identify causative EDCs during toxicity identification evaluations. Recently, the U.S. Environmental Protection Agency (USEPA) initiated ToxCast under the Tox21 program to generate novel bioactivity data through high throughput screening. This information is useful for prioritizing chemicals requiring additional hazard information, including endocrine active chemicals. Though multiple in vitro and in vivo techniques have been developed to assess estrogen agonist activity, the relative endpoint sensitivity of these approaches and agreement of their conclusions remain unclear during environmental diagnostic applications. Probabilistic hazard assessment (PHA) approaches, including chemical toxicity distributions (CTD), are useful for understanding the relative sensitivity of endpoints associated with in vitro and in vivo toxicity assays by predicting the likelihood of chemicals eliciting undesirable outcomes at or above environmentally relevant concentrations. In the present study, PHAs were employed to examine the comparative endpoint sensitivity of 16 in vitro assays for estrogen agonist activity using a diverse group of compounds from the USEPA ToxCast dataset. Reporter gene assays were generally observed to possess greater endpoint sensitivity than other assay types, and the Tox21 ERa LUC BG1 Agonist assay was identified as the most sensitive in vitro endpoint for detecting an estrogenic response. When the sensitivity of this most sensitive ToxCast in vitro endpoint was compared to the human MCF-7 cell proliferation assay, a common in vitro model for biomedical and environmental monitoring applications, the ERa LUC BG1 assay was several orders of magnitude less sensitive than MCF-7. These observations highlight the importance of employing multiple assays with various molecular initiation and signaling events to inform selection, application, and interpretation of in vitro endpoint responses during future environmental diagnostic applications.

A novel multitask learning algorithm for tasks with distinct chemical space: zebrafish toxicity prediction as an example.

Data scarcity is one of the most critical issues impeding the development of prediction models for chemical effects. Multitask learning algorithms leveraging knowledge from relevant tasks showed potential for dealing with tasks with limited data. However, current multitask methods mainly focus on learning from datasets whose task labels are available for most of the training samples. Since datasets were generated for different purposes with distinct chemical spaces, the conventional multitask learning methods may not be suitable. This study presents a novel multitask learning method MTForestNet that can deal with data scarcity problems and learn from tasks with distinct chemical space. The MTForestNet consists of nodes of random forest classifiers organized in the form of a progressive network, where each node represents a random forest model learned from a specific task. To demonstrate the effectiveness of the MTForestNet, 48 zebrafish toxicity datasets were collected and utilized as an example. Among them, two tasks are very different from other tasks with only 1.3% common chemicals shared with other tasks. In an independent test, MTForestNet with a high area under the receiver operating characteristic curve (AUC) value of 0.911 provided superior performance over compared single-task and multitask methods. The overall toxicity derived from the developed models of zebrafish toxicity is well correlated with the experimentally determined overall toxicity. In addition, the outputs from the developed models of zebrafish toxicity can be utilized as features to boost the prediction of developmental toxicity. The developed models are effective for predicting zebrafish toxicity and the proposed MTForestNet is expected to be useful for tasks with distinct chemical space that can be applied in other tasks.Scieific contributionA novel multitask learning algorithm MTForestNet was proposed to address the challenges of developing models using datasets with distinct chemical space that is a common issue of cheminformatics tasks. As an example, zebrafish toxicity prediction models were developed using the proposed MTForestNet which provide superior performance over conventional single-task and multitask learning methods. In addition, the developed zebrafish toxicity prediction models can reduce animal testing.

Zinc finger 4 negatively controls the transcriptional activator Fzf1 in Saccharomyces cerevisiae.

Fzf1 is a Saccharomyces cerevisiae transcription factor containing five zinc fingers (ZFs). It regulates the expression of at least five downstream genes, including SSU1, YHB1, DDI2/3, and YNR064c, by recognizing a consensus sequence, CS2, found in these gene promoters. These gene products are involved in cellular responses to various chemical stresses. For example, SSU1 encodes a sodium sulfite efflux protein that confers sulfite resistance. However, the underlying molecular mechanism through which Fzf1 responds to chemical stress and coordinates target gene activation remains elusive. Interestingly, several mutations in the fourth ZF (ZF4) of Fzf1 have previously been reported to confer either sulfite resistance or elevated basal-level expression of YHB1, indicating that ZF4 negatively impacts Fzf1 activity. Since ZF4 is dispensable for CS2 binding in vitro, we hypothesized that ZF4 is a negative regulator of Fzf1 and that chemically induced Fzf1-regulated gene expression occurs via de-repression. All five genes examined were cross-induced by corresponding chemicals in an Fzf1-dependent manner, and all three ZF4 mutations and a ZF4 deletion conferred increased basal-level expression and SSU1-dependent sulfite resistance. A ZF4 deletion did not alter the target DNA binding, consistent with the observed codominant phenotype. These observations collectively reveal that Fzf1 remains inactive by default at the target promoters and that its activation is at least partially achieved by self-derepression through chemical modification and/or a conformational change.

The First Steps on AOPs' Concepts, Development, and Applications in Teratology.

An Adverse Outcome Pathway (AOP) is an analytical model that describes, through a graphical representation, a linear sequence of biologically connected events at different levels of biological organization, causally leading to an adverse effect on human health or the environment. In general, AOPs are constructed based on five central principles: systematic development and review, chemical-agnostic, modular, networks, and living documents. Furthermore, AOPs have the potential to be used, for example, to investigate certain molecular targets; relate the regulation of specific genes or proteins among AOPs; extrapolate biological processes, pathways, or diseases from one species to another; and even predict adverse effects in particular populations. AOPs also emerge as an alternative to animal experimentation in studies of developmental malformations. It's even possible now to develop a quantitative AOP to predict teratogenic effects for some substances. However, the construction of high-quality AOPs requires standardization in the way these models are developed and reviewed, ensuring an adequate degree of flexibility and guaranteeing efficiency. The development of AOPs should strictly be based on the guidance documents developed by the OECD. Nevertheless, an important step for those developing AOPs is the choice of an apical endpoint or an initiating molecular event in order to initiate the construction of the pathway. Another crucial step is a systematic literature review based on the random combination of the blocks of information. With these two fundamental steps completed, it only remains to follow the guidance documents on Developing and Assessing Adverse Outcome Pathways and AOP Developers' Handbook supplement provided by the OECD to organize and construct an AOP. This modern approach will bring radical changes in the field of toxicity testing, regarding the prediction of apical toxic effects using molecular-level effects.

Guided optimization of ToxPi model weights using a Semi-Automated approach.

The Toxicological Prioritization Index (ToxPi) is a visual analysis and decision support tool for dimension reduction and visualization of high throughput, multi-dimensional feature data. ToxPi was originally developed for assessing the relative toxicity of multiple chemicals or stressors by synthesizing complex toxicological data to provide a single comprehensive view of the potential health effects. It continues to be used for profiling chemicals and has since been applied to other types of "sample" entities, including geospatial (e.g. county-level Covid-19 risk and sites of historical PFAS exposure) and other profiling applications. For any set of features (data collected on a set of sample entities), ToxPi integrates the data into a set of weighted slices that provide a visual profile and a score metric for comparison. This scoring system is highly dependent on user-provided feature weights, yet users often lack knowledge of how to define these feature weights. Common methods for predicting feature weights are generally unusable due to inappropriate statistical assumptions and lack of global distributional expectation. However, users often have an inherent understanding of expected results for a small subset of samples. For example, in chemical toxicity, prior knowledge can often place subsets of chemicals into categories of low, moderate or high toxicity (reference chemicals). Ordinal regression can be used to predict weights based on these response levels that are applicable to the entire feature set, analogous to using positive and negative controls to contextualize an empirical distribution. We propose a semi-supervised method utilizing ordinal regression to predict a set of feature weights that produces the best fit for the known response ("reference") data and subsequently fine-tunes the weights via a customized genetic algorithm. We conduct a simulation study to show when this method can improve the results of ordinal regression, allowing for accurate feature weight prediction and sample ranking in scenarios with minimal response data. To ground-truth the guided weight optimization, we test this method on published data to build a ToxPi model for comparison against expert-knowledge-driven weight assignments.

Machine learning framework for predicting cytotoxicity and identifying toxicity drivers of disinfection byproducts.

Drinking water disinfection can result in the formation disinfection byproducts (DBPs, > 700 have been identified to date), many of them are reportedly cytotoxic, genotoxic, or developmentally toxic. Analyzing the toxicity levels of these contaminants experimentally is challenging, however, a predictive model could rapidly and effectively assess their toxicity. In this study, machine learning models were developed to predict DBP cytotoxicity based on their chemical information and exposure experiments. The Random Forest model achieved the best performance (coefficient of determination of 0.62 and root mean square error of 0.63) among all the algorithms screened. Also, the results of a probabilistic model demonstrated reliable model predictions. According to the model interpretation, halogen atoms are the most prominent features for DBP cytotoxicity compared to other chemical substructures. The presence of iodine and bromine is associated with increased cytotoxicity levels, while the presence of chlorine is linked to a reduction in cytotoxicity levels. Other factors including chemical substructures (CC, N, CN, and 6-member ring), cell line, and exposure duration can significantly affect the cytotoxicity of DBPs. The similarity calculation indicated that the model has a large applicability domain and can provide reliable predictions for DBPs with unknown cytotoxicity. Finally, this study showed the effectiveness of data augmentation in the scenario of data scarcity.

A quantitative weight-of-evidence method for confidence assessment of adverse outcome pathway networks: A case study on chemical-induced liver steatosis.

The field of chemical toxicity testing is undergoing a transition to overcome the limitations of in vivo experiments. This evolution involves implementing innovative non-animal approaches to improve predictability and provide a more precise understanding of toxicity mechanisms. Adverse outcome pathway (AOP) networks are pivotal in organizing existing mechanistic knowledge related to toxicological processes. However, these AOP networks are dynamic and require regular updates to incorporate the latest data. Regulatory challenges also persist due to concerns about the reliability of the information they offer. This study introduces a generic Weight-of-Evidence (WoE) scoring method, aligned with the tailored Bradford-Hill criteria, to quantitatively assess the confidence levels in key event relationships (KERs) within AOP networks. We use the previously published AOP network on chemical-induced liver steatosis, a prevalent form of human liver injury, as a case study. Initially, the existing AOP network is optimized with the latest scientific information extracted from PubMed using the free SysRev platform for artificial intelligence (AI)-based abstract inclusion and standardized data collection. The resulting optimized AOP network, constructed using Cytoscape, visually represents confidence levels through node size (key event, KE) and edge thickness (KERs). Additionally, a Shiny application is developed to facilitate user interaction with the dataset, promoting future updates. Our analysis of 173 research papers yielded 100 unique KEs and 221 KERs among which 72 KEs and 170 KERs, respectively, have not been previously documented in the prior AOP network or AOP-wiki. Notably, modifications in de novo lipogenesis, fatty acid uptake and mitochondrial beta-oxidation, leading to lipid accumulation and liver steatosis, garnered the highest KER confidence scores. In conclusion, our study delivers a generic methodology for developing and assessing AOP networks. The quantitative WoE scoring method facilitates in determining the level of support for KERs within the optimized AOP network, offering valuable insights into its utility in both scientific research and regulatory contexts. KERs supported by robust evidence represent promising candidates for inclusion in an in vitro test battery for reliably predicting chemical-induced liver steatosis within regulatory frameworks.

The first investigation on the radiotoxicity risks associated with ingesting radium in drinking water from the Oujda Region-Morocco and some bottled mineral waters.

Drinking water is essential to human life. However, it can be polluted by various factors, including radioactive substances such as radon 222Rn and radium 226Ra. Therefore, the determination of their concentrations is important for public health. The aim of this work is to measure the concentration of 226Ra in samples of tap, natural spring and well water taken from different sources in the eastern region of Morocco, as well as in a few samples of bottled mineral water. We used an AlphaGUARD detector with an AlphaKit accessory and an RTM1688-2 to carry out measurements of radon in secular equilibrium with radium. The got results show that the 226Ra activity is less than 0.104 ± 0.023 Bq/L, the Annual Effective Dose (AED)) for adults and children is less than 29.1 ± 4.7 µSv.y-1and 123.8 ± 4.7 µSv.y-1 for infants. The chemical toxicity risk evaluated using the Lifetime Average Daily Dose (LADD) was found less than 0.23 ± 0.05 µgkg-1day-1. The obtained results are reasonable in relation to international guidelines, and do not present any radiological hazard to consumers that could be attributed to the radium and radon in the analyzed water samples.

Application of CRISPR/Cas9-based genome editing in ecotoxicology.

Chemicals are widely used and released into the environment, and their degradation, accumulation, migration, and transformation processes in the environment can pose a threat to the ecosystem. The advancement in analytical methods with high-throughput screening of biomolecules has revolutionized the way toxicologists used to explore the effects of chemicals on organisms. CRISPR/Cas is a newly developed tool, widely used in the exploration of basic science and biologically engineered products given its high efficiency and low cost. For example, it can edit target genes efficiently, and save loss of the crop yield caused by environmental pollution as well as gain a better understanding of the toxicity mechanisms from various chemicals. This review briefly introduces the development history of CRISPR/Cas and summarizes the current application of CRISPR/Cas in ecotoxicology, including its application on improving crop yield and drug resistance towards agricultural pollution, antibiotic pollution and other threats. The benefits by applying the CRISPR/Cas9 system in conventional toxicity mechanism studies are fully demonstrated here together with its foreseeable expansions in other area of ecotoxicology. Finally, the prospects and disadvantages of CRISPR/Cas system in the field of ecotoxicology are also discussed.

Phytoremediation of Potentially Toxic Elements from Contaminated Saline Soils Using Salvadora persica L.: Seasonal Evaluation.

Plants in coastal ecosystems are primarily known as natural sinks of trace metals and their importance for phytoremediation is well established. Salvadora persica L., a medicinally important woody crop of marginal coasts, was evaluated for the accumulation of metal pollutants (viz. Fe, Mn, Cu, Pb, Zn, and Cr) from three coastal areas of Karachi on a seasonal basis. Korangi creek, being the most polluted site, had higher heavy metals (HM's) in soil (Fe up to 17,389, Mn: 268, Zn: 105, Cu: 23, Pb: 64.7 and Cr up to 35.9 mg kg-1) and S. persica accumulated most of the metals with >1 TF (translocation factor), yet none of them exceeded standard permissible ranges except for Pb (up to 3.1 in roots and 3.37 mg kg-1 in leaves with TF = 11.7). Seasonal data suggested that higher salinity in Clifton and Korangi creeks during pre- and post-monsoon summers resulted in lower leaf water (ΨWo) and osmotic potential at full turgor (ΨSo) and bulk elasticity (ε), higher leaf Na+ and Pb but lower extractable concentrations of other toxic metals (Cr, Cu, and Zn) in S. persica. Variation in metal accumulation may be linked to metal speciation via specific transporters and leaf water relation dynamics. Our results suggested that S. persica could be grown on Zn, Cr and Cu polluted soils but not on Pb affected soils as its leaves accumulated higher concentrations than the proposed limits.

An automatic recognition method of nematode survival rate based on bright field and dark field experimental images.

BACKGROUND: The survival rate of experimental animals is a very important index in chemical toxicity evaluation experiments. The calculation of nematode survival rate is used in many experiments. OBJECTIVE: Traditional survival rate quantification methods require manual counting. This is a time-consuming and laborious work when using 384-well plate for high-throughput chemical toxicity assessment experiments. At present, there is a great need for an automatic method to identify the survival rate of nematodes in the experiment of chemical toxicity evaluation. METHODS: We designed an automatic nematode survival rate recognition method by combining the bright field experimental image of nematodes and the dark field image of nematodes which is captured after adding Propidium Iodide dye, and used it to calculate the nematode survival rate in different chemical environments. Experiment results show that the survival rate obtained by our automatic counting method is very similar to the survival rate obtained by manual counting. RESULTS: Through several different chemical experiments, we can see that chemicals with different toxicity have different effects on the survival rate of nematodes. And the survival rate of nematodes under different chemical concentrations has an obvious gradient trend from high concentration to low concentration. In addition, our method can quantify the motility of nematodes. There are also significant differences in the motility of nematodes cultured in different chemical environments. Moreover, the nematode motility under different chemical concentrations showed an obvious gradient change trend from high concentration to low concentration. CONCLUSION: Our study provides an accurate and efficient nematode survival rate recognition method for chemical toxicology research.