In Vitro Toxicity Testing at Massive Scale in Diverse Primary Human Cells

项目摘要-珊瑚基因组学正在结合全基因组测序和细胞内的RNA图谱- 基于功能分析，以预测人类在代谢和毒性方面遇到的化合物的变化 治疗和环境暴露。暴露前后的RNA图谱提供了高维的 表示细胞功能的变化，并使用快速、成本效益高的图谱可以实现毒性 在人体细胞的大而多样的面板中进行测试。因此，为了解决毒性测试的需要，更好地反映 人类种群的遗传多样性，珊瑚开发了创新的方法来降低成本和 提高高通量测序分析的速度。在成功的第一阶段SBIR(R43 HG010445)中，珊瑚 为样本的快速浅层排序制定了具有成本效益的样本多路传输方案 重新索引工作流，将样本汇集到单个测序运行中，并组合简化的测序 采用新的功能分析和算法的解决方案，以提高多基因风险评分(PR)的性能。 在与美国食品和药物管理局(FDA)的合作中，Coral应用了这些技术来预测 患者特异性肝细胞对乙酰氨基酚的反应谱(N=200)，发现显著的个体间差异 毒性的可变性。重要的是，研究结果表明，患者的基因型预测了很大一部分(AUC =0.85)，表明该方法对遗传多样性很敏感。初步 研究结果表明，珊瑚的方法可能是识别毒性反应差异的一种敏感手段 不同人群中的化合物。跨多个化合物的进一步开发和测试 大型、多样化的样本有可能提供可扩展、高吞吐量的平台 毒性测试更能代表人类反应的多样性。珊瑚提议直接 对NIEHS的RFA-ES-20-208做出响应的第二阶段SBIR，以在三个细胞模型和 将至少一个模型推进到全面测试，包括250个患者样本和100种已知毒性的化合物 配置文件。目的1.在三种人体细胞模型中表征RNA轮廓移位的个体间变异性(即， 免疫、肝细胞和类胚体)暴露于十种已知毒性特征的化合物。选择 里程碑：1)4,500份答复简介；2)个体内变异与个体间变异的统计意义 可变性(p&lt；0.001)；3)≥50%与暴露相关的差异表达基因 百万次读取；4)FAERS配置文件的R2&>0.3和Tox21数据集的AUC&>0.85。目标2.使用最具预测性的 细胞模型，表征对100种已知化合物的亚致死性细胞毒反应的个体间变异性 毒性曲线，以开发预测全身毒性的稳健模型。选择里程碑：1)75,000个回复 简介；2)鉴定具有高种群水平毒性变异性的≥5化合物(1-50百分位数 变异；10)；3)R2&>0.4使用化学和转录数据预测FAERS谱；5)R2预测 个人在100种化合物中的毒性分布&>0.5。

PROJECT SUMMARY—Coral Genomics is combining whole genome sequencing and RNA profiling in cell- based functional assays to predict human variation in the metabolism and toxicity of compounds encountered in therapeutic and environmental exposures. RNA profiling before and after exposure provides a high-dimensional representation of changes in cell function, and the use of rapid, cost-effective profiling could enable toxicity testing in large, diverse panels of human cells. Thus, to address the need for toxicity testing that better reflects the genetic diversity of human populations, Coral developed innovative approaches to reduce the cost and increase the speed of high-throughput sequencing assays. In a successful Phase I SBIR (R43 HG010445), Coral established protocols for cost-effective sample multiplexing for rapid shallow sequencing of samples, developed a re-indexing workflow to pool samples into a single sequencing run, and combined streamlined sequencing solutions with novel functional assays and algorithms to improve the performance of polygenic risk scores (PRS). In a collaboration with the US Food and Drug Administration (FDA), Coral applied these techniques to predict patient-specific hepatocyte response profiles to acetaminophen (N = 200) and found significant interindividual variability in toxicity. Importantly, the findings show that a patient’s genotype predicts a significant portion (AUC = 0.85) of the interindividual variation, indicating the approach is sensitive to genetic diversity. Preliminary findings indicate Coral’s approach may be a sensitive means for identifying differences in toxic responses to compounds across diverse populations. Further development and testing across multiple compounds in a large, diverse sample has the potential to provide a scalable, high-throughput platform for effective toxicity testing that is more representative of the diversity of human responses. Coral proposes a Direct to Phase II SBIR in response to NIEHS’s RFA-ES-20-208 to evaluate these methods in three cell models and advance at least one model to full-scale testing with 250 patient samples and 100 compounds with known toxicity profiles. Aim 1. Characterize the interindividual variability of RNA profile shifts in three human cell models (i.e., immune, hepatocyte, and embryoid body) exposed to ten compounds with known toxicity profiles. Select Milestones: 1) 4,500 response profiles; 2) Statistical significance of intraindividual variability vs. interindividual variability (p<0.001); 3) ≥ 50% of differentially expressed genes associated with exposure observable with < 1 million reads; 4) R2 > 0.3 for FAERS profile and AUC > 0.85 for Tox21 dataset. Aim 2. Using the most predictive cell model, characterize interindividual variability of sublethal cytotoxic responses to 100 compounds with known toxicity profiles to develop a robust model for predicting systemic toxicity. Select Milestones: 1) 75,000 response profiles; 2) Identification of ≥ 5 compounds with high population level variability in toxicity (1-50th percentile variation >10); 3) R2 > 0.4 using chemical and transcriptional data to predict FAERS profile; 5) R2 at predicting toxicity profile of an individual across 100 compounds > 0.5.