整合网络分析和机器学习来阐明化学诱导的斑马鱼胚胎胰腺毒性。

IF 3.4 3区 医学 Q2 TOXICOLOGY
Ashley V Schwartz, Karilyn E Sant, Uduak Z George
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引用次数: 0

摘要

斑马鱼(Danio rerio)是一种流行的脊椎动物模型,用于高通量毒性测试,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上探索病理和行为,而不是在器官特异性水平上。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索器官特异性效应,研究化学暴露对全胚胎斑马鱼胰腺功能的影响。我们收集了斑马鱼的转录组学数据,这些斑马鱼暴露于25种不同化学物质的53次暴露中,包括卤化有机化合物、杀虫剂/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管道进行处理,以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通路。聚类分析揭示了五个不同的化学暴露簇,对胰腺通路有相似的影响,基因共表达网络分析确定了这些簇中的关键驱动基因,为化学诱导胰腺毒性的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。随机森林模型比极端梯度增强、支持向量机和多类逻辑回归具有鲁棒性(4倍交叉验证精度:74%)。这种综合方法增强了我们对靶器官中化学特性和生物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以用来研究其他暴露对器官特异性发育的复杂影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrating Network Analysis and Machine Learning to Elucidate Chemical-Induced Pancreatic Toxicity in Zebrafish Embryos.

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.

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来源期刊
Toxicological Sciences
Toxicological Sciences 医学-毒理学
CiteScore
7.70
自引率
7.90%
发文量
118
审稿时长
1.5 months
期刊介绍: The mission of Toxicological Sciences, the official journal of the Society of Toxicology, is to publish a broad spectrum of impactful research in the field of toxicology. The primary focus of Toxicological Sciences is on original research articles. The journal also provides expert insight via contemporary and systematic reviews, as well as forum articles and editorial content that addresses important topics in the field. The scope of Toxicological Sciences is focused on a broad spectrum of impactful toxicological research that will advance the multidisciplinary field of toxicology ranging from basic research to model development and application, and decision making. Submissions will include diverse technologies and approaches including, but not limited to: bioinformatics and computational biology, biochemistry, exposure science, histopathology, mass spectrometry, molecular biology, population-based sciences, tissue and cell-based systems, and whole-animal studies. Integrative approaches that combine realistic exposure scenarios with impactful analyses that move the field forward are encouraged.
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