Schwartz Ashley V, Sant Karilyn E, George Uduak Z
Computational Science Research Center, San Diego State University.
School of Public Health, San Diego State University.
Toxicol Sci. 2025 May 15. doi: 10.1093/toxsci/kfaf069.
Zebrafish (Danio rerio) are a popular vertebrate model for high-throughput toxicity testing, serving as a model for embryonic development and disease etiology. However, standardized protocols using zebrafish tend to explore pathologies and behaviors at the organism level, rather than at the organ-specific level. This study investigates the effects of chemical exposures on pancreatic function in whole-embryo zebrafish by integrating network analysis and machine learning, leveraging widely-available datasets to probe an organ-specific effect. We compiled transcriptomics data for zebrafish exposed to 53 exposures from 25 unique chemicals, including halogenated organic compounds, pesticides/herbicides, endocrine-disrupting chemicals, pharmaceuticals, parabens, and solvents. All raw sequencing data were processed through a uniform bioinformatics pipeline for re-analysis and quality control, identifying differentially expressed genes and altered pathways related to pancreatic function and development. Clustering analysis revealed five distinct clusters of chemical exposures with similar impacts on pancreatic pathways with gene co-expression network analysis identifying key driver genes within these clusters, providing insights into potential biomarkers of chemical-induced pancreatic toxicity. Machine learning was utilized to identify chemical properties that influence pancreatic pathway response, including average mass, biodegradation half-life. The random forest model achieved robust performance (4-fold cross-validation accuracy: 74%) over eXtreme Gradient Boosting, support vector machine, and multiclass logistic regression. This integrative approach enhances our understanding of the relationships between chemical properties and biological responses in a target organ, supporting the use of zebrafish whole-embryos as a high-throughput vertebrate model. This computational workflow can be leveraged to investigate the complex effects of other exposures on organ-specific development.
斑马鱼（Danio rerio）是用于高通量毒性测试的一种流行的脊椎动物模型，可作为胚胎发育和疾病病因学的模型。然而，使用斑马鱼的标准化方案倾向于在生物体水平而非器官特异性水平上探索病理和行为。本研究通过整合网络分析和机器学习，利用广泛可用的数据集来探究器官特异性效应，从而研究化学物质暴露对斑马鱼全胚胎胰腺功能的影响。我们汇编了暴露于25种独特化学物质的53种暴露条件下的斑马鱼转录组学数据，这些化学物质包括卤代有机化合物、农药/除草剂、内分泌干扰化学物质、药物、对羟基苯甲酸酯和溶剂。所有原始测序数据均通过统一的生物信息学流程进行处理，以进行重新分析和质量控制，识别与胰腺功能和发育相关的差异表达基因和改变的通路。聚类分析揭示了对胰腺通路具有相似影响的五个不同化学物质暴露簇，基因共表达网络分析确定了这些簇内的关键驱动基因，为化学诱导的胰腺毒性潜在生物标志物提供了见解。利用机器学习来识别影响胰腺通路反应的化学性质，包括平均质量、生物降解半衰期。随机森林模型在极端梯度提升、支持向量机和多类逻辑回归方面表现出稳健的性能（4折交叉验证准确率：74%）。这种综合方法增强了我们对目标器官中化学性质与生物学反应之间关系的理解，支持将斑马鱼全胚胎用作高通量脊椎动物模型。这种计算工作流程可用于研究其他暴露对器官特异性发育的复杂影响。
