Deep Learning-Enabled Morphometric Analysis for Toxicity Screening Using Zebrafish Larvae

Model-driven high-throughput zebrafish embryo assay for evaluating whole effluent toxicity variation across 100 full-scale wastewater treatment plants

The zebrafish embryo is a valuable model for evaluating whole effluent toxicity (WET). However, the widely recognized acute toxicity indicator, based on International Organization of Standardization (ISO) methods, requires large numbers of embryos and is often time-consuming due to its complex experimental procedures. In this study, we propose an alternative to the conventional reliance on ISO standards by developing a model-driven high-throughput assay that utilizes actual wastewater, enabling rapid LC10 (the lethal concentration at which 10 % of the test organisms are affected) prediction through machine learning techniques and multidimensional indicators derived from streamlined experimental procedures. We compared three streamlined toxicity assays-developmental toxicity, behavioral toxicity, and vascular toxicity-along with five different models. Among these, the Lasso model based on behavioral toxicity emerged as the most effective, achieving an R2 value of 0.893 while reducing experimental time by 5- to 8-fold. Furthermore, fivefold cross-validation confirmed its robust predictive accuracy. The application of this model-driven high-throughput assay across 100 wastewater treatment plants in China highlights the crucial role of biological treatment, particularly aerobic processes and secondary sedimentation, in reducing toxicity, thereby providing valuable insights into their functions. This high-throughput assay not only surpasses the ISO standard method in efficiency but also substantially decreases embryo usage, facilitating rapid WET assessments of actual wastewater with larger sample sizes.

Revealing the synergistic impacts of ZIF-8 and copper co-exposure on zebrafish behavior, tissue damage, and intestine microbial community

The application of metal-organic frameworks (MOFs) has garnered significant attention in contemporary research. However, the impacts of MOFs on aquatic environments remain largely unclear. This study revealed that the water stability of Zeolitic Imidazolate Framework-8 (ZIF-8) is influenced by its concentration, with lower concentrations resulting in higher percentages of Zn2+ release. At 10 mg/L, ZIF-8 significantly reduced zebrafish locomotor activity, with total swimming distance decreasing by approximately 40.5%. Oxidative stress and neurotoxicity markers, reactive oxygen species (ROS), and acetylcholinesterase (AChE) levels increased by 2.18-2.24-fold and 1.92-2.24-fold, respectively. Zebrafish ingestion of ZIF-8 was observed, with further analysis showing severe vacuolization and necrosis in tissues, as well as a significant increase in the relative abundance of Proteobacteria in the gut microbiota. Additionally, the study examined the toxicity of co-exposure to ZIF-8 and other pollutants. When ZIF-8 and copper (Cu) were simultaneously introduced, zebrafish survival rates dropped significantly to 79.2%. Co-exposure caused more severe behavioral impairments, with swimming distance reduced by approximately 53.0%, compared to individual exposures to ZIF-8 or Cu. ROS and AChE levels rose by 2.68-3.37-fold and 2.93-3.77-fold, respectively, while tissue vacuolization and necrosis became more pronounced. The relative abundance of Proteobacteria increased to 92.2%. This study provides critical insights into the environmental and ecological impacts of MOFs, emphasizing the necessity of considering these effects for their sustainable application.

Advancements in the Developmental Zebrafish Model for Predictive Human Toxicology

The rapid assessment of chemical hazards to human health, with reduced reliance on mammalian testing, is essential in the 21st century. Early life stage zebrafish have emerged as a leading model in the field due to their amenability to high throughput developmental toxicity testing while retaining the benefits of using a whole vertebrate organism with high homology with humans. Zebrafish are particularly well suited for a variety of study areas that are more challenging in other vertebrate model systems including microbiome work, transgenerational studies, gene-environment interactions, molecular responses, and mechanisms of action. The high volume of data generated from zebrafish screening studies is highly valuable for QSAR modeling and dose modeling for use in predictive hazard assessment. Recent advancements and challenges in using early life stage zebrafish for predictive human toxicology are reviewed.

Common issues of data science on the eco-environmental risks of emerging contaminants

Data-driven approaches (e.g., machine learning) are increasingly used to replace or assist laboratory studies in the study of emerging contaminants (ECs). In the past ten years, an increasing number of models or approaches have been applied to ECs, and the datasets used are continuously enriched. However, there are large knowledge gaps between what we have found and the natural eco-environmental meaning. For most published reviews, the contents are organized by the types of ECs, but the common issues of data science, regardless of the type of pollutant, are not sufficiently addressed. To close or narrow the knowledge gaps, we highlight the following issues ignored in the field of data-driven EC research. Complicated biological and ecological data and ensemble models revealing mechanisms and spatiotemporal trends with strong causal relationships and without data leakage deserve more attention in the future. In addition, the matrix influence, trace concentration, and complex scenario have often been ignored in previous works. Therefore, an integrated research framework related to natural fields, ecological systems, and large-scale environmental problems, rather than relying solely on laboratory data-related analysis, is urgently needed. Beyond the current prediction purposes, data science can inspire the discovery of scientific questions, and mutual inspiration among data science, process and mechanism models, and laboratory and field research is a critical direction. Focusing on the above urgent and common issues related to data, frameworks, and purposes, regardless of the type of pollutant, data science is expected to achieve great advancements in addressing the eco-environmental risks of ECs.

Cross-Shaped Heat Tensor Network for Morphometric Analysis Using Zebrafish Larvae Feature Keypoints

Deep learning-based morphometric analysis of zebrafish is widely utilized for non-destructively identifying abnormalities and diagnosing diseases. However, obtaining discriminative and continuous organ category decision boundaries poses a significant challenge by directly observing zebrafish larvae from the outside. To address this issue, this study simplifies the organ areas to polygons and focuses solely on the endpoint positioning. Specifically, we introduce a deep learning-based feature endpoint detection method for quantitatively determining zebrafish larvae's essential phenotype and organ features. We propose the cross-shaped heat tensor network (CSHT-Net), a feature point detection framework consisting of a novel keypoint training method named cross-shaped heat tensor and a feature extractor called combinatorial convolutional block. Our model alleviates the problem of the heatmap-based method that restricts attention to local regions around key points while enhancing the model's ability to learn continuous, strip-like features. Moreover, we compiled a dataset of 4389 bright-field micrographs of zebrafish larvae at 120 h post-fertilization for the model training and algorithm evaluation of zebrafish phenotypic traits. The proposed framework achieves an average precision (AP) of 83.2% and an average recall (AR) of 85.8%, outperforming multiple widely adopted keypoint detection techniques. This approach enables robust phenotype extraction and reliable morphometric analysis for zebrafish larvae, fostering efficient hazard identification for chemicals and medical products.

Let the two sides of the same coin meet-Environmental health and safety-oriented development of functional nanomaterials for environmental remediations

Nanotechnology and engineered nanomaterials have been at the forefront of technological breakthroughs of the 21st century. With the challenges of increasingly complex and emergent environmental pollution, nanotechnology offers exciting complementary approaches to achieve high efficiencies with low or green energy input. However, unknown and unintended hazardous effects and health risks associated with nanotechnology hinder its full-scale implementation. Therefore, the development of safer nanomaterials lies in the critical balance between the applications and implications of nanomaterials. To facilitate constructive dialogue between the two sides (i.e., applications and implications) of the same coin, this review sets forth to summarize the current progress of the environmental applications of nanomaterials and establish the structure-property-functionality relationship. A systematic analysis of the structure-property-toxicity relationship is also provided to advocate the Safe and Sustainable-by-Design strategy for nanomaterials. Lastly, the review also discusses the future of artificial intelligence-assisted environmental health and safety-oriented development of nanomaterials.

Cadmium impacts on calcium mineralization of zebrafish skeletal development and behavioral impairment

Cadmium (Cd) poses significant risks to aquatic organisms due to its toxicity and ability to disrupt the cellular processes. Given the similar atomic radius of Cd and calcium (Ca), Cd may potentially affect the Ca homeostasis, which can lead to impaired mineralization of skeletal structures and behavioral abnormalities. The formation of the spinal skeleton involves Ca transport and mineralization. In this study, we conducted an in-depth investigation on the effects of Cd at environmental concentrations on zebrafish (Danio rerio) skeletal development and the underlying molecular mechanisms. As the concentration of Cd increased, the accumulation of Cd in zebrafish larvae also rose, while the Ca content decreased significantly by 3.0 %-57.3 %, and vertebral deformities were observed. Transcriptomics analysis revealed that sixteen genes involved in metal absorption were affected. Exposure to 2 µg/L Cd significantly upregulated the expression of these genes, whereas exposure to 10 µg/L resulted in their downregulation. Consequently, exposure of zebrafish larvae to 10 µg/L of Cd inhibited the body segmentation growth and skeletal mineralization development by 29.1 %-56.7 %. This inhibition was evidenced by the downregulation of mineral absorption genes and decreased Ca accumulation. The findings of this study suggested that the inhibition of skeletal mineralization was likely attributed to the disruption of mineral absorption, thus providing novel insights into the mechanisms by which metal pollutants inhibit the skeletal development of fish.

Highly sensitive response to the toxicity of environmental chemicals in transparent casper zebrafish

The response sensitivity to toxic substances is the most concerned performance of animal model in chemical risk assessment. Casper (mitfaw2/w2;mpv17a9/a9), a transparent zebrafish mutant, is a useful in vivo model for toxicological assessment. However, the ability of casper to respond to the toxicity of exogenous chemicals is unknown. In this study, zebrafish embryos were exposed to five environmental chemicals, chlorpyrifos, lindane, α-endosulfan, bisphenol A, tetrabromobisphenol A (TBBPA), and an antiepileptic drug valproic acid. The half-lethal concentration (LC50) values of these chemicals in casper embryos were 62-87 % of that in the wild-type. After TBBPA exposure, the occurrence of developmental defects in the posterior blood island of casper embryos was increased by 67-77 % in relative to the wild-type, and the half-maximal effective concentration (EC50) in casper was 73 % of that in the wild-type. Moreover, the casper genetic background significantly increased the hyperlocomotion caused by chlorpyrifos and lindane exposure compared with the wild-type. These results demonstrated that casper had greater susceptibility to toxicity than wild-type zebrafish in acute toxicity, developmental toxicity and neurobehavioral toxicity assessments. Our data will inform future toxicological studies in casper and accelerate the development of efficient approaches and strategies for toxicity assessment via the use of casper.

Bringing Artificial Intelligence (AI) into Environmental Toxicology Studies: A Perspective of AI-Enabled Zebrafish High-Throughput Screening

The booming development of artificial intelligence (AI) has brought excitement to many research fields that could benefit from its big data analysis capability for causative relationship establishment and knowledge generation. In toxicology studies using zebrafish, the microscopic images and videos that illustrate the developmental stages, phenotypic morphologies, and animal behaviors possess great potential to facilitate rapid hazard assessment and dissection of the toxicity mechanism of environmental pollutants. However, the traditional manual observation approach is both labor-intensive and time-consuming. In this Perspective, we aim to summarize the current AI-enabled image and video analysis tools to realize the full potential of AI. For image analysis, AI-based tools allow fast and objective determination of morphological features and extraction of quantitative information from images of various sorts. The advantages of providing accurate and reproducible results while avoiding human intervention play a critical role in speeding up the screening process. For video analysis, AI-based tools enable the tracking of dynamic changes in both microscopic cellular events and macroscopic animal behaviors. The subtle changes revealed by video analysis could serve as sensitive indicators of adverse outcomes. With AI-based toxicity analysis in its infancy, exciting developments and applications are expected to appear in the years to come.

Deep learning in water protection of resources, environment, and ecology: achievement and challenges

The breathtaking economic development put a heavy toll on ecology, especially on water pollution. Efficient water resource management has a long-term influence on the sustainable development of the economy and society. Economic development and ecology preservation are tangled together, and the growth of one is not possible without the other. Deep learning (DL) is ubiquitous in autonomous driving, medical imaging, speech recognition, etc. The spectacular success of deep learning comes from its power of richer representation of data. In view of the bright prospects of DL, this review comprehensively focuses on the development of DL applications in water resources management, water environment protection, and water ecology. First, the concept and modeling steps of DL are briefly introduced, including data preparation, algorithm selection, and model evaluation. Finally, the advantages and disadvantages of commonly used algorithms are analyzed according to their structures and mechanisms, and recommendations on the selection of DL algorithms for different studies, as well as prospects for the application and development of DL in water science are proposed. This review provides references for solving a wider range of water-related problems and brings further insights into the intelligent development of water science.

Developmental toxicity and estrogenicity of glyphosate in zebrafish in vivo and in silico studies

As the most heavily used herbicide globally, glyphosate (GLY) has been detected in a variety of environments and has raised concerns about its ecological and health effects. There is debate as to whether GLY may disrupt the endocrine system. Here, we investigated the developmental toxicity of GLY in zebrafish based on deep learning-enabled morphometric analysis (DLMA). In addition, the estrogenic activity of GLY was assessed by endocrine disruption prediction, docking study and in vivo experiments. Results showed that exposure to environmental concentrations of GLY negatively impacted zebrafish development, causing yolk edema and pericardial edema. Endocrine disruption prediction suggested that GLY may target estrogen receptors (ER). Molecular docking analysis revealed binding of GLY to three zebrafish ER. In vivo zebrafish experiment, GLY enhanced the protein levels of ERα and the mRNA levels of cyp19a, HSD17b1, vtg1, vtg2, esr1, esr2a and esr2b. These results suggest that GLY may act as an endocrine disruptor by targeting ER, which warrants further attention for its potential toxicity to aquatic animals.