Carvalho Ana Sofia, Matthiesen Rune
Computational and Experimental Biology Group, Department of Health Promotion and Chronic Diseases, National Health Institute Dr. Ricardo Jorge, INSA, I.P., Av Padre Cruz, Lisboa, 1649-016, Portugal.
Methods Mol Biol. 2016;1449:469-79. doi: 10.1007/978-1-4939-3756-1_31.
DNA-based technologies such as RNAi, chemical-genetic profiling, or gene expression profiling by DNA microarrays combined with other biochemical methods are established strategies for surveying drug mechanisms. Such approaches can provide mechanistic information on how drugs act and affect cellular pathways. By studying how cancer cells compensate for the drug treatment, novel targets used in a combined treatment can be designed. Furthermore, toxicity effects on cells not targeted can be obtained on a molecular level. For example, drug companies are particularly interested in studying the molecular side effects of drugs in the liver. In addition, experiments with the purpose of elucidating liver toxicity can be studied using samples obtained from animal models exposed to different concentrations of a drug over time. More recently considerable advances in mass spectrometry (MS) technologies and bioinformatics tools allows informative global drug profiling experiments to be performed at a cost comparable to other large-scale technologies such as DNA-based technologies. Moreover, MS-based proteomics provides an additional layer of information on the dynamic regulation of proteins translation and particularly protein degradation. MS-based proteomics approaches combined with other biochemical methods delivers information on regulatory networks, signaling cascades, and metabolic pathways upon drug treatment. Furthermore, MS-based proteomics can provide additional information on single amino acid polymorphisms, protein isoform distribution, posttranslational modifications, and subcellular localization. In this chapter, we will share our experience using MS based proteomics as a pharmacoproteomics strategy to characterize drug mechanisms of action in single drug therapy or in multidrug combination. Finally, the emergence of integrated proteogenomics analysis, such as "The Cancer Genome Atlas" program, opened interesting perspectives to extend this approach to drug target discovery and validation.
基于DNA的技术,如RNA干扰、化学遗传学分析,或通过DNA微阵列结合其他生化方法进行的基因表达分析,是研究药物作用机制的既定策略。这些方法可以提供关于药物如何作用以及影响细胞通路的机制信息。通过研究癌细胞如何补偿药物治疗,可以设计出联合治疗中使用的新靶点。此外,可以在分子水平上获得对非靶向细胞的毒性作用。例如,制药公司特别关注研究药物在肝脏中的分子副作用。此外,可以使用从长时间暴露于不同浓度药物的动物模型中获得的样本,来研究阐明肝脏毒性的实验。最近,质谱(MS)技术和生物信息学工具取得了长足进展,使得能够以与基于DNA的技术等其他大规模技术相当的成本进行信息丰富的全球药物分析实验。此外,基于MS的蛋白质组学提供了关于蛋白质翻译动态调控,特别是蛋白质降解的另一层信息。基于MS的蛋白质组学方法与其他生化方法相结合,可提供药物治疗后调控网络、信号级联和代谢途径的信息。此外,基于MS的蛋白质组学可以提供关于单氨基酸多态性、蛋白质异构体分布、翻译后修饰和亚细胞定位的额外信息。在本章中,我们将分享我们使用基于MS的蛋白质组学作为药物蛋白质组学策略来表征单一药物治疗或多药联合治疗中药物作用机制的经验。最后,综合蛋白质基因组学分析的出现,如“癌症基因组图谱”计划,为将这种方法扩展到药物靶点发现和验证开辟了有趣的前景。