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利用网络扰动特征对草药对复杂疾病的作用进行系统映射。

Systems-Mapping of Herbal Effects on Complex Diseases Using the Network-Perturbation Signatures.

作者信息

Chen Xuetong, Zheng Chunli, Wang Chun, Guo Zihu, Gao Shuo, Ning Zhangchi, Huang Chao, Lu Cheng, Fu Yingxue, Guan Daogang, Lu Aiping, Wang Yonghua

机构信息

Center of Bioinformatics, College of Life Science, Northwest A & F University, Yangling, China.

School of Chinese Medicine, Institute of Integrated Bioinformedicine and Translational Science, Hong Kong Baptist University, Hong Kong, Hong Kong.

出版信息

Front Pharmacol. 2018 Oct 18;9:1174. doi: 10.3389/fphar.2018.01174. eCollection 2018.

DOI:10.3389/fphar.2018.01174
PMID:30405409
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6201628/
Abstract

The herbs have proven to hold great potential to improve people's health and wellness during clinical practice over the past millennia. However, herbal medicine for the personalized treatment of disease is still under investigation owing to the complex multi-component interactions in herbs. To reveal the valuable insights for herbal synergistic therapy, we have chosen Traditional Chinese Medicine (TCM) as a case to illustrate the art and science behind the complicated multi-molecular, multi-genes interaction systems, and how the good practices of herbal combination therapy are applicable to personalized treatment. Here, we design system-wide interaction map strategy to provide a generic solution to establish the links between diseases and herbs based on comprehensive testing of molecular signatures in herb-disease pairs. Firstly, we integrated gene expression profiles from 189 diseases to characterize the disease-pathological feature. Then, we generated the perturbation signatures from the huge chemical informatics data and pharmacological data for each herb, which were represented the targets affected by the ingredients in the herb. So that we could assess the effects of herbs on the individual. Finally, we integrated the data of 189 diseases and 502 herbs, yielding the optimal herbal combinations for the diseases based on the strategy, and verifying the reliability of the strategy through the permutation testing and literature verification. Furthermore, we propose a novel formula as a candidate therapeutic drugs of rheumatoid arthritis and demonstrate its therapeutic mechanism through the systematic analysis of the influencing targets and biological processes. Overall, this computational method provides a systematic approach, which blended herbal medicine and omics data sets, allowing for the development of novel drug combinations for complex human diseases.

摘要

在过去的几千年临床实践中,草药已被证明在改善人们的健康状况方面具有巨大潜力。然而,由于草药中复杂的多成分相互作用,用于疾病个性化治疗的草药医学仍在研究中。为了揭示草药协同治疗的宝贵见解,我们选择了中医作为案例,来说明复杂的多分子、多基因相互作用系统背后的艺术和科学,以及草药联合治疗的良好实践如何应用于个性化治疗。在此,我们设计了全系统相互作用图谱策略,以提供一种通用解决方案,基于对草药-疾病对中分子特征的全面测试,建立疾病与草药之间的联系。首先,我们整合了来自189种疾病的基因表达谱,以表征疾病的病理特征。然后,我们从庞大的化学信息学数据和每种草药的药理数据中生成扰动特征,这些特征代表了草药中成分所影响的靶点。这样我们就可以评估草药对个体的影响。最后,我们整合了189种疾病和502种草药的数据,根据该策略得出针对这些疾病的最佳草药组合,并通过置换检验和文献验证来验证该策略的可靠性。此外,我们提出了一种新的配方作为类风湿性关节炎的候选治疗药物,并通过对影响靶点和生物学过程的系统分析来证明其治疗机制。总体而言,这种计算方法提供了一种系统方法,将草药医学和组学数据集相结合,有助于开发针对复杂人类疾病的新型药物组合。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2270/6201628/443f05d09a14/fphar-09-01174-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2270/6201628/e2814841463d/fphar-09-01174-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2270/6201628/f0b614ce3023/fphar-09-01174-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2270/6201628/22205cd1d3b5/fphar-09-01174-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2270/6201628/bb8eb82d45da/fphar-09-01174-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2270/6201628/e086e3cc82c3/fphar-09-01174-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2270/6201628/443f05d09a14/fphar-09-01174-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2270/6201628/e2814841463d/fphar-09-01174-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2270/6201628/f0b614ce3023/fphar-09-01174-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2270/6201628/22205cd1d3b5/fphar-09-01174-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2270/6201628/bb8eb82d45da/fphar-09-01174-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2270/6201628/e086e3cc82c3/fphar-09-01174-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2270/6201628/443f05d09a14/fphar-09-01174-g0006.jpg

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