Williams Andrew, Halappanavar Sabina
Environmental Health Science and Research Bureau, Health Canada, Ottawa, Ontario, Canada K1A 0K9.
Data Brief. 2017 Oct 26;15:933-940. doi: 10.1016/j.dib.2017.10.060. eCollection 2017 Dec.
This article contains data related to the research article '' (Williams and Halappanavar, 2015) [1]. The presence of diverse types of nanomaterials (NMs) in commerce has grown significantly in the past decade and as a result, human exposure to these materials in the environment is inevitable. The traditional toxicity testing approaches that are reliant on animals are both time- and cost- intensive; employing which, it is not possible to complete the challenging task of safety assessment of NMs currently on the market in a timely manner. Thus, there is an urgent need for comprehensive understanding of the biological behavior of NMs, and efficient toxicity screening tools that will enable the development of predictive toxicology paradigms suited to rapidly assessing the human health impacts of exposure to NMs. In an effort to predict the long term health impacts of acute exposure to NMs, in Williams and Halappanavar (2015) [1], we applied bi-clustering and gene set enrichment analysis methods to derive essential features of altered lung transcriptome following exposure to NMs that are associated with lung-specific diseases. Several datasets from public microarray repositories describing pulmonary diseases in mouse models following exposure to a variety of substances were examined and functionally related bi-clusters showing similar gene expression profiles were identified. The identified bi-clusters were then used to conduct a gene set enrichment analysis on lung gene expression profiles derived from mice exposed to nano-titanium dioxide, carbon black or carbon nanotubes (nano-TiO2, CB and CNTs) to determine the disease significance of these data-driven gene sets. The results of the analysis correctly identified all NMs to be inflammogenic, and only CB and CNTs as potentially fibrogenic. Here, we elaborate on the details of the statistical methods and algorithms used to derive the disease relevant gene signatures. These details will enable other investigators to use the gene signature in future Gene Set Enrichment Analysis studies involving NMs or as features for clustering and classifying NMs of diverse properties.
本文包含与研究论文“ ”（Williams和Halappanavar，2015年）[1]相关的数据。在过去十年中，商业领域中各种类型纳米材料（NMs）的存在显著增加，因此，人类在环境中接触这些材料不可避免。依赖动物的传统毒性测试方法既耗时又成本高昂；采用这些方法，无法及时完成对目前市场上纳米材料进行安全评估这一具有挑战性的任务。因此，迫切需要全面了解纳米材料的生物学行为，以及高效的毒性筛选工具，以开发适合快速评估接触纳米材料对人类健康影响的预测毒理学范式。为了预测急性接触纳米材料的长期健康影响，在Williams和Halappanavar（2015年）[1]中，我们应用双聚类和基因集富集分析方法，以得出接触与肺部特异性疾病相关的纳米材料后肺转录组改变的基本特征。检查了来自公共微阵列储存库的几个描述小鼠模型在接触各种物质后肺部疾病的数据集，并鉴定了显示相似基因表达谱的功能相关双聚类。然后，使用所鉴定的双聚类对来自暴露于纳米二氧化钛、炭黑或碳纳米管（纳米TiO2、CB和CNTs）的小鼠的肺基因表达谱进行基因集富集分析，以确定这些数据驱动的基因集的疾病意义。分析结果正确地将所有纳米材料鉴定为具有炎症性，并且仅将CB和CNTs鉴定为潜在的纤维化性。在这里，我们详细阐述用于得出与疾病相关的基因特征的统计方法和算法的细节。这些细节将使其他研究人员能够在未来涉及纳米材料的基因集富集分析研究中使用该基因特征，或将其作为对具有不同性质的纳米材料进行聚类和分类的特征。
