Toxicogenomics-based discrimination of toxic mechanism in HepG2 human hepatoma cells
Toxicogenomics-based discrimination of toxic mechanism in HepG2 human hepatoma cells
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DOI：
10.1093/toxsci/58.2.399
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发表时间：
2000-12-01
影响因子：
3.8
通讯作者：
Johnson, MD
中科院分区：
文献类型：
--
作者：
Burczynski, ME;McMillian, M;Johnson, MD
人类基因组计划中序列信息的快速发现，使生物医学实验中可检索的数据量呈指数级增长。基因表达谱，通过使用微阵列技术，是迅速有助于提高全球的理解，协调细胞事件在各种范式。在毒理学领域，毒理基因组学的潜在应用表明未知化合物的毒性已被提出，但至今仍在很大程度上未经证实。毒理基因组学的一个主要假设是个体mRNA表达的整体变化（即，细胞对毒物的转录反应）将是足够不同的、稳健的和可再现的，以允许区分来自不同类别的毒物。这种特异性的“遗传指纹”仍然缺乏证据，而非特异性的一般应激反应可能无法区分化合物，因此不适合作为毒性机制的探针。目前的研究表明，毒理基因组学的一般应用，区分两个机械无关类毒物（细胞毒性抗炎药和DNA损伤剂）的基础上，单独的聚类类型的基因差异诱导或抑制在培养细胞中暴露于这些化合物的分析。使用DNA微阵列上所有相似的250个基因（类似于250万个数据点）对100种有毒化合物的表达模式进行初步比较，未能区分有毒物质类别。在这些研究中遇到的一个主要障碍是缺乏可重复的基因反应，可能是由于生物变异性和技术限制。因此，对原型DNA损伤剂顺铂和非甾体抗炎药（NSAID）二氟尼柳和氟芬那酸进行了多次重复观察，并选择产生可重现诱导/抑制的基因子集进行比较。在这些研究中鉴定的许多“指纹基因”与文献中报道的先前观察结果一致（例如，顺铂对p53调节的转录物如p21（waf 1/cip 1）和PCNA [增殖细胞核抗原]的充分表征的诱导）。这些基因子集不仅区分了学习集中的三种化合物，而且对数据库的其余部分（类似于各种毒性机制的100种化合物）也具有预测价值。进一步完善的聚类策略，使用基于计算机的优化算法，产生了更好的结果，并证明，最终最好区分DNA损伤和NSAID的基因参与了DNA修复，异生物质代谢，转录激活，结构维持，细胞周期控制，信号转导和细胞凋亡等不同的过程。确定基因的反应适当分组和解离抗炎剂与DNA损伤剂提供了一个初始的范例，在此基础上建立未来的，更高的通量为基础的鉴定毒性化合物单独使用基因表达模式。
The rapid discovery of sequence information from the Human Genome Project has exponentially increased the amount of data that can be retrieved from biomedical experiments. Gene expression profiling, through the use of microarray technology, is rapidly contributing to an improved understanding of global, coordinated cellular events in a variety of paradigms. In the field of toxicology, the potential application of toxicogenomics to indicate the toxicity of unknown compounds has been suggested but remains largely unsubstantiated to date. A major supposition of toxicogenomics is that global changes in the expression of individual mRNAs (i.e., the transcriptional responses of cells to toxicants) will be sufficiently distinct, robust, and reproducible to allow discrimination of toxicants from different classes. Definitive demonstration is still lacking for such specific "genetic fingerprints," as opposed to nonspecific general stress responses that may be indistinguishable between compounds and therefore not suitable as probes of toxic mechanisms. The present studies demonstrate a general application of toxicogenomics that distinguishes two mechanistically unrelated classes of toxicants (cytotoxic anti-inflammatory drugs and DNA-damaging agents) based solely upon a cluster-type analysis of genes differentially induced or repressed in cultured cells during exposure to these compounds. Initial comparisons of the expression patterns for 100 toxic compounds, using all similar to 250 genes on a DNA microarray(similar to 2.5 million data points), failed to discriminate between toxicant classes. A major obstacle encountered in these studies was the lack of reproducible gene responses, presumably due to biological variability and technological limitations. Thus multiple replicate observations for the prototypical DNA damaging agent, cisplatin, and the non-steroidal anti-inflammatory drugs (NSAIDs) diflunisal and flufenamic acid were made, and a subset of genes yielding reproducible inductions/repressions was selected for comparison. Many of the "fingerprint genes" identified in these studies were consistent with previous observations reported in the literature (e.g., the well-characterized induction by cisplatin of p53-regulated transcripts such as p21(waf1/cip1) and PCNA [proliferating cell nuclear antigen]). These gene subsets not only discriminated among the three compounds in the learning set but also showed predictive value for the rest of the database (similar to 100 compounds of various toxic mechanisms). Further refinement of the clustering strategy, using a computer-based optimization algorithm, yielded even better results and demonstrated that genes that ultimately best discriminated between DNA damage and NSAIDs were involved in such diverse processes as DNA repair, xenobiotic metabolism, transcriptional activation, structural maintenance, cell cycle control, signal transduction, and apoptosis. The determination of genes whose responses appropriately group and dissociate anti-inflammatory versus DNA-damaging agents provides an initial paradigm upon which to build for future, higher throughput-based identification of toxic compounds using gene expression patterns alone.