Tryptophan catabolism via the kynurenine pathway regulates infection and inflammation: from mechanisms to biomarkers and therapies.

BACKGROUND: L-Tryptophan (L-Trp), an essential amino acid, is the only amino acid whose level is regulated specifically by immune signals. Most proportions of Trp are catabolized via the kynurenine (Kyn) pathway (KP) which has evolved to align the food availability and environmental stimulation with the host pathophysiology and behavior. Especially, the KP plays an indispensable role in balancing the immune activation and tolerance in response to pathogens. SCOPE OF REVIEW: In this review, we elucidate the underlying immunological regulatory network of Trp and its KP-dependent catabolites in the pathophysiological conditions by participating in multiple signaling pathways. Furthermore, the KP-based regulatory roles, biomarkers, and therapeutic strategies in pathologically immune disorders are summarized covering from acute to chronic infection and inflammation. MAJOR CONCLUSIONS: The immunosuppressive effects dominate the functions of KP induced-Trp depletion and KP-produced metabolites during infection and inflammation. However, the extending minor branches from the KP are not confined to the immune tolerance, instead they go forward to various functions according to the specific condition. Nevertheless, persistent efforts should be made before the clinical use of KP-based strategies to monitor and cure infectious and inflammatory diseases.

Atorvastatin-induced intracerebral hemorrhage is inhibited by berberine in zebrafish.

Intracerebral hemorrhage (ICH), for which there are currently no effective preventive or treatment methods, has a very high fatality rate. Statins, such as atorvastatin (ATV), are the first-line drugs for regulating blood lipids and treating hyperlipidemia-related cardiovascular diseases. However, ATV-associated ICH has been reported, although its incidence is rare. In this study, we aimed to investigate the protective action and mechanisms of berberine (BBR) against ATV-induced brain hemorrhage. We established an ICH model in zebrafish induced by ATV (2 µM) and demonstrated the effects of BBR (10, 50, and 100 µM) on ICH via protecting the vascular network using hemocyte staining and three transgenic zebrafish. BBR was found to reduce brain inflammation and locomotion injury in ICH-zebrafish. Mechanism research showed that ATV increased the levels of VE-cadherin and occludin proteins but disturbed their localization at the cell membrane by abnormal phosphorylation, which decreased the number of intercellular junctions between vascular endothelial cells (VECs), disrupting the integrity of vascular walls. BBR reversed the effects of ATV by promoting autophagic degradation of phosphorylated VE-cadherin and occludin in ATV-induced VECs examined by co-immunoprecipitation (co-IP). These findings provide crucial insights into understanding the BBR mechanisms involved in the maintenance of vascular integrity and in mitigating adverse reactions to ATV.

Prediction of lncRNA functions using deep neural networks based on multiple networks.

BACKGROUND: More and more studies show that lncRNA is widely involved in various physiological processes of the organism. However, the functions of the vast majority of them continue to be unknown. In addition, data related to lncRNAs in biological databases are constantly increasing. Therefore, it is quite urgent to develop a computing method to make the utmost of these data. RESULTS: In this paper, we propose a new computational method based on global heterogeneous networks to predict the functions of lncRNAs, called DNGRGO. DNGRGO first calculates the similarities among proteins, miRNAs, and lncRNAs, and annotates the functions of lncRNAs according to its similar protein-coding genes, which have been labeled with gene ontology (GO). To evaluate the performance of DNGRGO, we manually annotated GO terms to lncRNAs and implemented our method on these data. Compared with the existing methods, the results of DNGRGO show superior predictive performance of maximum F-measure and coverage. CONCLUSIONS: DNGRGO is able to annotate lncRNAs through capturing the low-dimensional features of the heterogeneous network. Moreover, the experimental results show that integrating miRNA data can help to improve the predictive performance of DNGRGO.

The comparative analysis of gastrointestinal toxicity of azithromycin and 3'-decladinosyl azithromycin on zebrafish larvae.

The most commonly reported side effect of azithromycin is gastrointestinal (GI) disorders, and the main acid degradation product is 3'-Decladinosyl azithromycin (impurity J). We aimed to compare the GI toxicity of azithromycin and impurity J on zebrafish larvae and investigate the mechanism causing the differential GI toxicity. Results of our study showed that the GI toxicity induced by impurity J was higher than that of azithromycin in zebrafish larvae, and the effects of impurity J on transcription in the digestive system of zebrafish larvae were significantly stronger than those of azithromycin. Additionally, impurity J exerts stronger cytotoxic effects on GES-1 cells than azithromycin. Simultaneously, impurity J significantly increased ghsrb levels in the zebrafish intestinal tract and ghsr levels in human GES-1 cells compared to azithromycin, and ghsr overexpression significantly reduced cell viability, indicating that GI toxicity induced by azithromycin and impurity J may be correlated with ghsr overexpression induced by the two compounds. Meanwhile, molecular docking analysis showed that the highest -CDOCKER interaction energy scores with the zebrafish GHSRb or human GHSR protein might reflect the effect of azithromycin and impurity J on the expression of zebrafish ghsrb or human ghsr. Thus, our results suggest that impurity J has higher GI toxicity than azithromycin due to its greater ability to elevate ghsrb expression in zebrafish intestinal tract.

ABLNCPP: Attention Mechanism-Based Bidirectional Long Short-Term Memory for Noncoding RNA Coding Potential Prediction.

With the continuous development of ribosome profiling, sequencing technology, and proteomics, evidence is mounting that noncoding RNA (ncRNA) may be a novel source of peptides or proteins. These peptides and proteins play crucial roles in inhibiting tumor progression and interfering with cancer metabolism and other essential physiological processes. Therefore, identifying ncRNAs with coding potential is vital to ncRNA functional research. However, existing studies perform well in classifying ncRNAs and mRNAs, and no research has been explicitly raised to distinguish whether ncRNA transcripts have coding potential. For this reason, we propose an attention mechanism-based bidirectional LSTM network called ABLNCPP to assess the coding possibility of ncRNA sequences. Considering the sequential information loss in previous methods, we introduce a novel nonoverlapping trinucleotide embedding (NOLTE) method for ncRNAs to obtain embeddings containing sequential features. The extensive evaluations show that ABLNCPP outperforms other state-of-the-art models. In general, ABLNCPP overcomes the bottleneck of ncRNA coding potential prediction and is expected to provide valuable contributions to cancer discovery and treatment in the future. The source code and data sets are freely available at https://github.com/YinggggJ/ABLNCPP.

A Combination of In Silico ADMET Prediction, In Vivo Toxicity Evaluation, and Potential Mechanism Exploration of Brucine and Brucine N-oxide-A Comparative Study.

Brucine (BRU) and brucine N-oxide (BNO) are prominent, bioactive, and toxic alkaloids in crude and processed Semen Strychni. Studies have demonstrated that BRU and BNO possess comprehensive pharmacological activities, such as anti-inflammatory and analgesic. In this context, a comparative study of BRU and BNO was performed by combination analysis of in silico ADMET prediction, in vivo toxicity evaluation, and potential action mechanism exploration. ADMET prediction showed that BRU and BNO might induce liver injury, and BRU may have a stronger hepatoxic effect. The prediction was experimentally verified using the zebrafish model. The BRU-induced hepatotoxicity of zebrafish larvae had a dose-response relationship. The mechanism of BRU-induced hepatotoxicity might relate to phosphorylation, kinase activity, and signal transduction. By comparison, signal transduction and gap junctions might involve BNO-induced hepatotoxicity. Our results provided a better understanding of BRU- and BNO-induced hepatotoxicity. We also built a foundation to elucidate the material base of the hepatotoxicity of traditional Chinese medicine Semen Strychni.

Predicting circRNA-drug sensitivity associations via graph attention auto-encoder.

BACKGROUND: Circular RNAs (circRNAs) play essential roles in cancer development and therapy resistance. Many studies have shown that circRNA is closely related to human health. The expression of circRNAs also affects the sensitivity of cells to drugs, thereby significantly affecting the efficacy of drugs. However, traditional biological experiments are time-consuming and expensive to validate drug-related circRNAs. Therefore, it is an important and urgent task to develop an effective computational method for predicting unknown circRNA-drug associations. RESULTS: In this work, we propose a computational framework (GATECDA) based on graph attention auto-encoder to predict circRNA-drug sensitivity associations. In GATECDA, we leverage multiple databases, containing the sequences of host genes of circRNAs, the structure of drugs, and circRNA-drug sensitivity associations. Based on the data, GATECDA employs Graph attention auto-encoder (GATE) to extract the low-dimensional representation of circRNA/drug, effectively retaining critical information in sparse high-dimensional features and realizing the effective fusion of nodes' neighborhood information. Experimental results indicate that GATECDA achieves an average AUC of 89.18% under 10-fold cross-validation. Case studies further show the excellent performance of GATECDA. CONCLUSIONS: Many experimental results and case studies show that our proposed GATECDA method can effectively predict the circRNA-drug sensitivity associations.

Isarubrolone C Promotes Autophagic Degradation of Virus Proteins via Activating ATG10S in HepG2 Cells.

Isarubrolone C is a bioactive polycyclic tropoloalkaloid from Streptomyces. Our previous study showed that isarubrolone C could trigger autophagy. Here, we report isarubrolone C potential in broad-spectrum antiviral effect and its antiviral mechanism in vitro. Our results show that isarubrolone C activated autophagy and reduced levels of viral proteins in the cells harboring HCV-CORE/NS5B, HBx, ZIKV-NS5, and HIV-RT, respectively. The role of isarubrolone C in suppression of the viral proteins was via an autophagic degradation pathway rather than a proteasome pathway. Co-immunoprecipitation assays revealed that isarubrolone C promoted both autophagy flux opening and the viral proteins being enwrapped in autolysosomes. PCR assays showed that isarubrolone C elevated the transcription levels of ATG10/ATG10S and IL28A. Further, ATG10S high expression could efficiently enhance IL28A expression and the ability of isarubrolone C to degrade the viral proteins by promoting the colocalization of viral proteins with autolysosomes. Additionally, knockdown of endogenous IL28A caused both losses of the isarubrolone C antiviral effect and autolysosome formation. These results indicate that the role of isarubrolone C antiviruses is achieved by triggering the autophagic mechanism, which is mediated by endogenous ATG10S and IL28A activation. This is the first report about isarubrolone C potential of in vitro broad-spectrum antiviruses.

Hazard assessment of beta-lactams: Integrating in silico and QSTR approaches with in vivo zebrafish embryo toxicity testing.

Antibiotics have emerged as a well-known representative of pharmaceuticals and personal care products (PPCPs) by causing public health and environmental problems due to their potential toxicity. ß-lactams are the most commonly used antibiotics in the world. This study used zebrafish embryos to evaluate the toxicity of ß-lactams. The results showed that 23 ß-lactam compounds induced malformation and death in a concentration-response manner. Moreover, this study established and validated quantitative structure-toxicity relationship (QSTR) models for the toxicity of ß-lactams in zebrafish. These models performed well and fast in the prediction of the acute toxicity of ß-lactams. Structural interpretation indicated that the ß-lactam ring, the thiazolidine/dihydrothiazine rings, the side chains, and spatial configuration are the main factors responsible for the toxicity of ß-lactams. The results from our previous studies and this study also revealed that the potential biological risks caused by ß-lactams and their degradation products could not be ignored. This study provided important data for further environmental risk assessment of ß-lactams and regulatory purposes.

Systems assessment of statins hazard: Integrating in silico prediction, developmental toxicity profile and transcriptomics in zebrafish.

Statins are prescribed widely as lipid-lowering agents. However, statins are associated with an increased harmful risk on public health and the ecosystem. Little is known about statins' toxicity on biological development and the underlying molecular mechanisms. We exposed zebrafish embryos to a series of statins to evaluate their development toxicity. Statins-induced embryonic developmental defects in a concentration-dependent manner. 72 h LC50 values for lovastatin, simvastatin, fluvastatin, atorvastatin, rosuvastatin, and pravastatin were 0.01 µM, 0.04 µM, 1.93 µM, 37.28 µM, 79.29 µM, and 2170 µM, respectively. Moreover, the expression of genes involved in heart contraction, calcium ion binding, transcription factors, nucleus, and G protein-coupled receptor signaling pathway was altered by statins. The early growth response gene (egr4) and transcription factor genes (fosab and fosb) were screened as potential toxicity targets due to their significant upregulation based on protein-protein interaction (PPI) and drug-gene interaction network analysis. Finally, the ecotoxicity profile of statins was predicted by in silico method, and statins were high or moderate risk to aquatic organisms. We provide a systems toxicology strategy to explore the toxicity of statins and illustrate the potential mechanisms of action.

In silico drug repositioning based on the integration of chemical, genomic and pharmacological spaces.

BACKGROUND: Drug repositioning refers to the identification of new indications for existing drugs. Drug-based inference methods for drug repositioning apply some unique features of drugs for new indication prediction. Complementary information is provided by these different features. It is therefore necessary to integrate these features for more accurate in silico drug repositioning. RESULTS: In this study, we collect 3 different types of drug features (i.e., chemical, genomic and pharmacological spaces) from public databases. Similarities between drugs are separately calculated based on each of the features. We further develop a fusion method to combine the 3 similarity measurements. We test the inference abilities of the 4 similarity datasets in drug repositioning under the guilt-by-association principle. Leave-one-out cross-validations show the integrated similarity measurement IntegratedSim receives the best prediction performance, with the highest AUC value of 0.8451 and the highest AUPR value of 0.2201. Case studies demonstrate IntegratedSim produces the largest numbers of confirmed predictions in most cases. Moreover, we compare our integration method with 3 other similarity-fusion methods using the datasets in our study. Cross-validation results suggest our method improves the prediction accuracy in terms of AUC and AUPR values. CONCLUSIONS: Our study suggests that the 3 drug features used in our manuscript are valuable information for drug repositioning. The comparative results indicate that integration of the 3 drug features would improve drug-disease association prediction. Our study provides a strategy for the fusion of different drug features for in silico drug repositioning.

MSCFS: inferring circRNA functional similarity based on multiple data sources.

BACKGROUND: More and more evidence shows that circRNA plays an important role in various biological processes and human health. Therefore, inferring the circRNA's potential functions and obtaining circRNA functional similarity has become more and more significant. However, there is no effective approach to explore the functional similarity of circRNAs. METHODS: In this paper, we propose a new approach, called MSCFS, to calculate the functional similarity of circRNA by integrating multiple data sources. We combine circRNA-disease association, circRNA-gene-Gene Ontology association, and circRNA sequence information to explore the functional similarity of circRNA. Firstly, we employ different learning representation methods from three data sources to establish three circRNA functional similarity networks. Then we integrate the three networks to obtain the final circRNA functional similarity. RESULTS: We utilize circRNA-miRNA association similarity and circRNA co-expression similarity to evaluate the performance of MSCFS. The results show a positive correlation with miRNA association ([Formula: see text]) and circRNA co-expression similarity ([Formula: see text]). Finally, we construct a circRNA functional similarity network and perform case analysis. The result shows our method can be applied to infer new potential functions of circRNA and other associations. CONCLUSIONS: MSCFS combines multiple data sources related to circRNA functions. Correlation analysis and case analyses prove that MSCFS is a useful method to explore circRNA functional similarity.

Investigation of combining-efficiency loss induced by a diffractive optical element in a single-aperture coherent beam combining system.

Filled-aperture geometries can be obtained using a diffractive optical element (DOE) in the coherent beam combining (CBC) architecture. Minimizing the beam deviation is crucial to maintain single-aperture output and reduce the combining-efficiency losses. In this study, we developed a theoretical model for investigating the combining-efficiency losses with beam deviation in a DOE-based CBC architecture. The beam deviations induced by the DOE-mount-tilt error, emitter-incident angular error, and DOE-groove-tilt error are discussed theoretically in detail and verified experimentally. The combining-efficiency losses caused by the three error sources are calculated. Meanwhile, the combining-efficiency losses affected by the beam size and the DOE period are analyzed. For an 11-channel CBC architecture with a DOE period of 50 µm and a beam size of 30 mm, the maximum combining-efficiency losses caused by the three error sources were 3.2%, 1.87%, and 36.41%, respectively, whereas those in case of a DOE period of 20 µm and a beam size of 10 mm were 14.34%, 8.58%, and 25.29%, respectively. We found that the combining-efficiency loss is most sensitive to the DOE-groove-tilt error.

DeepciRGO: functional prediction of circular RNAs through hierarchical deep neural networks using heterogeneous network features.

BACKGROUND: Circular RNAs (circRNAs) are special noncoding RNA molecules with closed loop structures. Compared with the traditional linear RNA, circRNA is more stable and not easily degraded. Many studies have shown that circRNAs are involved in the regulation of various diseases and cancers. Determining the functions of circRNAs in mammalian cells is of great significance for revealing their mechanism of action in physiological and pathological processes, diagnosis and treatment of diseases. However, determining the functions of circRNAs on a large scale is a challenging task because of the high experimental costs. RESULTS: In this paper, we present a hierarchical deep learning model, DeepciRGO, which can effectively predict gene ontology functions of circRNAs. We build a heterogeneous network containing circRNA co-expressions, protein-protein interactions and protein-circRNA interactions. The topology features of proteins and circRNAs are calculated using a novel representation learning approach HIN2Vec across the heterogeneous network. Then, a deep multi-label hierarchical classification model is trained with the topology features to predict the biological process function in the gene ontology for each circRNA. In particular, we manually curated a benchmark dataset containing 185 GO annotations for 62 circRNAs, namely, circRNA2GO-62. The DeepciRGO achieves promising performance on the circRNA2GO-62 dataset with a maximum F-measure of 0.412, a recall score of 0.400, and an accuracy of 0.425, which are significantly better than other state-of-the-art RNA function prediction methods. In addition, we demonstrate the considerable potential of integrating multiple interactions and association networks. CONCLUSIONS: DeepciRGO will be a useful tool for accurately annotating circRNAs. The experimental results show that integrating multi-source data can help to improve the predictive performance of DeepciRGO. Moreover, The model also can combine RNA structure and sequence information to further optimize predictive performance.

Ontological function annotation of long non-coding RNAs through hierarchical multi-label classification.

Motivation: Long non-coding RNAs (lncRNAs) are an enormous collection of functional non-coding RNAs. Over the past decades, a large number of novel lncRNA genes have been identified. However, most of the lncRNAs remain function uncharacterized at present. Computational approaches provide a new insight to understand the potential functional implications of lncRNAs. Results: Considering that each lncRNA may have multiple functions and a function may be further specialized into sub-functions, here we describe NeuraNetL2GO, a computational ontological function prediction approach for lncRNAs using hierarchical multi-label classification strategy based on multiple neural networks. The neural networks are incrementally trained level by level, each performing the prediction of gene ontology (GO) terms belonging to a given level. In NeuraNetL2GO, we use topological features of the lncRNA similarity network as the input of the neural networks and employ the output results to annotate the lncRNAs. We show that NeuraNetL2GO achieves the best performance and the overall advantage in maximum F-measure and coverage on the manually annotated lncRNA2GO-55 dataset compared to other state-of-the-art methods. Availability and implementation: The source code and data are available at http://denglab.org/NeuraNetL2GO/. Contact: leideng@csu.edu.cn. Supplementary information: Supplementary data are available at Bioinformatics online.

Isarubrolones Containing a Pyridooxazinium Unit from Streptomyces as Autophagy Activators.

Isarubrolones are bioactive polycyclic tropoloalkaloids from Streptomyces. Three new isarubrolones (2-4), together with the known isarubrolone C (1) and isatropolones A (5) and C (6, 3( R)-hydroxyisatropolone A), were identified from Streptomyces sp. CPCC 204095. The structures of these compounds were determined using a combination of mass spectrometry, 1D and 2D NMR spectroscopy, and ECD. Compounds 3 and 4 feature a pyridooxazinium unit, which is rarely seen in natural products. Compound 6 could conjugate with amino acids or amines to expand the structural diversity of isarubrolones with a pentacyclic or hexacyclic core. Importantly, 1 and 3-6 were found to induce complete autophagy.

MultiSourcDSim: an integrated approach for exploring disease similarity.

BACKGROUND: A collection of disease-associated data contributes to study the association between diseases. Discovering closely related diseases plays a crucial role in revealing their common pathogenic mechanisms. This might further imply treatment that can be appropriated from one disease to another. During the past decades, a number of approaches for calculating disease similarity have been developed. However, most of them are designed to take advantage of single or few data sources, which results in their low accuracy. METHODS: In this paper, we propose a novel method, called MultiSourcDSim, to calculate disease similarity by integrating multiple data sources, namely, gene-disease associations, GO biological process-disease associations and symptom-disease associations. Firstly, we establish three disease similarity networks according to the three disease-related data sources respectively. Secondly, the representation of each node is obtained by integrating the three small disease similarity networks. In the end, the learned representations are applied to calculate the similarity between diseases. RESULTS: Our approach shows the best performance compared to the other three popular methods. Besides, the similarity network built by MultiSourcDSim suggests that our method can also uncover the latent relationships between diseases. CONCLUSIONS: MultiSourcDSim is an efficient approach to predict similarity between diseases.

DeepMiR2GO: Inferring Functions of Human MicroRNAs Using a Deep Multi-Label Classification Model.

MicroRNAs (miRNAs) are a highly abundant collection of functional non-coding RNAs involved in cellular regulation and various complex human diseases. Although a large number of miRNAs have been identified, most of their physiological functions remain unknown. Computational methods play a vital role in exploring the potential functions of miRNAs. Here, we present DeepMiR2GO, a tool for integrating miRNAs, proteins and diseases, to predict the gene ontology (GO) functions based on multiple deep neuro-symbolic models. DeepMiR2GO starts by integrating the miRNA co-expression network, protein-protein interaction (PPI) network, disease phenotype similarity network, and interactions or associations among them into a global heterogeneous network. Then, it employs an efficient graph embedding strategy to learn potential network representations of the global heterogeneous network as the topological features. Finally, a deep multi-label classification network based on multiple neuro-symbolic models is built and used to annotate the GO terms of miRNAs. The predicted results demonstrate that DeepMiR2GO performs significantly better than other state-of-the-art approaches in terms of precision, recall, and maximum F-measure.

Up-regulation of autophagy-related gene 5 (ATG5) protects dopaminergic neurons in a zebrafish model of Parkinson's disease.

Parkinson's disease (PD) is one of the most epidemic neurodegenerative diseases and is characterized by movement disorders arising from loss of midbrain dopaminergic (DA) neurons. Recently, the relationship between PD and autophagy has received considerable attention, but information about the mechanisms involved is lacking. Here, we report that autophagy-related gene 5 (ATG5) is potentially important in protecting dopaminergic neurons in a 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced PD model in zebrafish. Using analyses of zebrafish swimming behavior, in situ hybridization, immunofluorescence, and expressions of genes and proteins related to PD and autophagy, we found that the ATG5 expression level was decreased and autophagy flux was blocked in this model. The ATG5 down-regulation led to the upgrade of PD-associated proteins, such as ß-synuclein, Parkin, and PINK1, aggravation of MPTP-induced PD-mimicking pathological locomotor behavior, DA neuron loss labeled by tyrosine hydroxylase (TH) or dopamine transporter (DAT), and blocked autophagy flux in the zebrafish model. ATG5 overexpression alleviated or reversed these PD pathological features, rescued DA neuron cells as indicated by elevated TH/DAT levels, and restored autophagy flux. The role of ATG5 in protecting DA neurons was confirmed by expression of the human atg5 gene in the zebrafish model. Our findings reveal that ATG5 has a role in neuroprotection, and up-regulation of ATG5 may serve as a goal in the development of drugs for PD prevention and management.

Cardiac safety evaluation in zebrafish and in silico ADME prediction of cephalosporins with an aminothiazoyl ring at the C-7 position.

Systems toxicology approaches have been used as important tools in the drug discovery and medicine quality control processes. The aim of this study was to assess the pharmacokinetic and toxicity properties of cephalosporins with an aminothiazoyl ring at the C-7 position (CATRs). Cardiac toxicity of the compounds was assessed in zebrafish embryos, and it was determined that CATRs disturbed the formation and development of the heart in a dose-dependent manner. Differentially expressed genes (DEGs) related to the heart were also identified by transcriptome analysis, and co-DEGs were obtained in the protein-protein interaction (PPI) network. Several Gene Ontology (GO) terms and pathways that were enriched by DEGs were identified, and the most significantly enriched pathways were adrenergic signaling in cardiomyocytes, cardiac muscle contraction, and vascular smooth muscle contraction. Combined molecular docking results elucidated that cardiac toxicity mainly depends on the mother nucleus structure 7-aminocephalosporanic acid (7-ACA). The predicted absorption, distribution, metabolism and excretion (ADME) profile suggests that there is a modification at the C-3 side chain of 7-ACA that could change the compound distribution in vivo. The 7-ACA mother nucleus is responsible for the CATRs induced cardiac toxicity, and the three DEGs (nppa, adra2c, and tnni1c) may potentially be utilized as novel biomarkers for CATRs. Our results show that zebrafish embryos may be used to reveal the pathways of cardiac toxicity and they play a vital role in drug safety assessments.