McMillian Michael, Nie Alex Y, Parker J Brandon, Leone Angelique, Bryant Stewart, Kemmerer Michael, Herlich Judy, Liu Yanhong, Yieh Lynn, Bittner Anton, Liu Xuejun, Wan Jackson, Johnson Mark D
Johnson and Johnson Pharmaceutical Research and Development, LLC, Raritan, NJ, USA.
Biochem Pharmacol. 2004 Dec 1;68(11):2249-61. doi: 10.1016/j.bcp.2004.08.003.
Formation of free radicals and other reactive molecules is responsible for the adverse effects produced by a number of hepatotoxic compounds. cDNA microarray technology was used to compare transcriptional profiles elicited by training and testing sets of 15 oxidant stressors/reactive metabolite treatments to those produced by approximately 85 other paradigm compounds (mostly hepatotoxicants) to determine a shared signature profile for oxidant stress-associated hepatotoxicity. Initially, 100 genes were chosen that responded significantly different to oxidant stressors/reactive metabolites (OS/RM) compared to other samples in the database, then a 25-gene subset was selected by multivariate analysis. Many of the selected genes (e.g., aflatoxin aldehyde reductase, diaphorase, epoxide hydrolase, heme oxgenase and several glutathione transferases) are well-characterized oxidant stress/Nrf-2-responsive genes. Less than 10 other compounds co-cluster with our training and testing set compounds and these are known to generate OS/RMs as part of their mechanisms of toxicity. Using OS/RM signature gene sets, compounds previously associated with macrophage activation formed a distinct cluster separate from OS/RM and other compounds. A 69-gene set was chosen to maximally separate compounds in control, macrophage activator, peroxisome proliferator and OS/RM classes. The ease with which these 'oxidative stressor' classes can be separated indicates a role for microarray technology in early prediction and classification of hepatotoxicants. The ability to rapidly screen the oxidant stress potential of compounds may aid in avoidance of some idiosyncratic drug reactions as well as overtly toxic compounds.
自由基和其他活性分子的形成是多种肝毒性化合物产生不良反应的原因。利用cDNA微阵列技术,将15种氧化应激源/活性代谢物处理的训练集和测试集所引发的转录谱与约85种其他典型化合物(主要是肝毒性物质)所产生的转录谱进行比较,以确定氧化应激相关肝毒性的共同特征谱。最初,选择了100个基因,这些基因对氧化应激源/活性代谢物(OS/RM)的反应与数据库中的其他样本有显著差异,然后通过多变量分析选择了一个25个基因的子集。许多选定的基因(如黄曲霉毒素醛还原酶、黄递酶、环氧水解酶、血红素加氧酶和几种谷胱甘肽转移酶)是已被充分表征的氧化应激/Nrf-2反应基因。与我们的训练集和测试集化合物共聚类的其他化合物不到10种,并且已知这些化合物在其毒性机制中会产生OS/RM。使用OS/RM特征基因集,先前与巨噬细胞激活相关的化合物形成了一个与OS/RM和其他化合物不同的独特聚类。选择了一个69个基因的集合,以最大限度地分离对照组、巨噬细胞激活剂、过氧化物酶体增殖剂和OS/RM类别的化合物。这些“氧化应激源”类别易于分离,表明微阵列技术在肝毒性物质的早期预测和分类中具有作用。快速筛选化合物氧化应激潜力的能力可能有助于避免一些特异质药物反应以及明显有毒的化合物。