A metabolomics investigation of non-genotoxic carcinogenicity in the rat.
A metabolomics investigation of non-genotoxic carcinogenicity in the rat.
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DOI：
10.1021/pr4007766
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发表时间：
2013-12-06
影响因子：
4.4
通讯作者：
Griffin JL
中科院分区：
文献类型：
--
作者：
Ament Z;Waterman CL;West JA;Waterfield C;Currie RA;Wright J;Griffin JL
非遗传毒性致癌物（NGC）通过改变基因表达促进肿瘤生长，最终导致癌症，而不直接引起DNA序列的变化。因此，在诱变试验中未检测到NGC。虽然提出了致癌潜力的生物标志物，但非遗传毒性致癌物的确定性鉴定仍取决于大鼠和小鼠的长期生物测定。这种检测方法昂贵、耗时，需要大量的动物，而且它们与人类健康风险评估的相关性值得商榷。代谢组学和脂质组学与病理学和临床化学相结合，用于分析10种化合物产生的扰动，这些化合物代表了一系列大鼠非遗传毒性肝癌（NGC）、非遗传毒性非肝癌（non-NGC）和遗传毒性肝癌。将每种化合物以其最大耐受剂量水平给予雄性Fisher 344大鼠7、28和91天。肝脏代谢物浓度的变化在不同时间点区分了给药组。最显著的差异是由药理学作用模式驱动的，特别是过氧化物酶体增殖物激活受体α（PPAR-α）激动剂。尽管有这些主要影响，但在区分NGC和非NGC时可以做出良好的预测。在给药28天后，通过留一交叉验证测量的预测能力分别为NGC和非NGC的87%和77%。在歧视性代谢物中，我们鉴定了游离脂肪酸、磷脂、三酰甘油以及类花生酸的前体和与炎症、增殖和氧化应激过程相关的活性氧物质的产物。因此，代谢谱能够识别由于外源性物质的药理学作用模式引起的变化，并有助于早期筛查非遗传毒性潜力。
Non-genotoxic carcinogens (NGCs) promote tumour growth by altering gene expression which ultimately leads to cancer without directly causing a change in DNA sequence. As a result NGCs are not detected in mutagenesis assays. Whilst there are proposed biomarkers of carcinogenic potential, the definitive identification of non-genotoxic carcinogens still rests with the rat and mouse long term bioassay. Such assays are expensive, time consuming, require a large number of animals and their relevance to human health risk assessments is debatable. Metabolomics and lipidomics in combination with pathology and clinical chemistry were used to profile perturbations produced by 10 compounds which represented a range of rat non-genotoxic hepatocarcinogens (NGC), non-genotoxic non-hepatocarcinogens (non-NGC) and a genotoxic hepatocarcinogen. Each compound was administered at its maximum tolerated dose level for 7, 28 and 91 days to male Fisher 344 rats. Changes in liver metabolite concentration differentiated the treated groups across different time points. The most significant differences were driven by pharmacological mode of action, specifically by the peroxisome proliferator activated receptor alpha (PPAR-α) agonists. Despite these dominant effects, good predictions could be made when differentiating NGCs from non-NGCs. Predictive ability measured by leave one out cross validation was 87% and 77% after 28 days of dosing for NGCs and non-NGCs, respectively. Amongst the discriminatory metabolites we identified free fatty acids, phospholipids, triacylglycerols, as well as precursors of eicosanoid and the products of reactive oxygen species linked to processes of inflammation, proliferation and oxidative stress. Thus, metabolic profiling is able to identify changes due to the pharmacological mode of action of xenobiotics and contribute to early screening for non-genotoxic potential.
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通讯作者：
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