利用转录组学研究贝母属和百部属化合物止咳平喘机制及其新功效

目的和意义： 传统中药中有许多例如贝母、百部等具有止咳平喘功效的药
材，但是它们的作用机制往往非常复杂且尚不明确。近年来迅速发展的系统生
物学为研究上述问题提供了新的方法， 使我们可以同时对其药理机制及毒性或
其他作用做出预测。 本文中，我们将建立基于转录组的功能网络，将从贝母和
百部中纯化的天然化合物与 1309 种机制和功效已经明确的药物/化合物联系起
来，借此对天然化合物的机制和功效做出预测，从而为中药产品的质量控制和
全球化提供现代生物学依据。
研究方法： 我们首先对代表性的贝母和百部化合物与现有的镇咳药和抗哮
喘药进行收集， 其中前者为研究对象， 后者作为对照化合物。 然后用所有上述
化合物对 3 种不同细胞系进行处理， 这些细胞系分别表达涉及不同镇咳/抗哮喘
途径的不同组的关键受体基因。 通过 mRNA-seq， 我们可以获得每种化合物处理
后基因表达的变化， 即化合物的“基因特征”。 将每个化合物对应的“基因特征”
与“ Connectivity Map”数据库进行比较， 然后进行 GO 和 KEGG 富集分析，以进
一步阐明潜在的机制和途径。
结果： 通过上述方法我们已经完成并获得了 49 种化合物作用在 3 种细胞系
上的大约 400 个转录组谱。 通过对对照化合物的分析我们证明，上述方法可以
用来预测化合物可能的作用以及作用通路。 我们发现了几种具有止咳平喘潜在
功能的天然化合物，并对其可能的作用通路进行了预测。此外，我们还发现了
一些天然化合物作用在雌激素受体、组蛋白去乙酰化酶上的潜在功能，证明该
实验方法具有可行性与广大前景。
 

OBJECTIVE and SIGNIFICANCE: Traditional Chinese Medicine (TCM) with
antitussive and antiathsma effects such as genus Fritillaria and Stemona herbs often
have diverse and complex mode of actions, many of which are not clearly elucidated.
Rapid progress in systemic biology paved a way for systematic identification of
molecular targets and pathways that underly the therapeutic effects of these TCM, as
well as their possible toxicity and side effects. In this thesis, we aim to establish
transcriptome-based functional network, which links natural compounds purified
from Fritillaria and Stemona to 1309 existing drugs/compounds with known
functions in turns of their affected genes and pathways. As a result, the possible
functions and mechanisms of natural compounds can be simultaneously inferred in a
single experiment, and thus provide the modern biological evidences for the quality
control and globalization of TCM products.
METHODS: Representative Fritillaria and Stemona compounds were firstly
collected together with existing antitussive and antiasthma drugs working as
comparative controls. All the experimental compounds were applied on 3 different
cell lines that express different sets of key receptor genes involved in different
antitussive/antiasthma pathways. Through mRNA-seq, the gene expression changes
were identified in response to each compound treatment, regarded as the “gene
signatures” of the compounds. The “gene signatures” of each profile were then
compared with the so-called “connectivity map” database, followed by the GO and
KEGG enrichment analyses for further elucidation of underlying mechanism and
pathways.
RESULTS: In total, we have created around 400 transcriptome profiles for 49
compounds treated on 3 cell lines. By analysis of the control compounds we
demonstrated that the above methods can be used to predict the possible effects of
the compounds as well as the pathways of action. In consistence with clinic
practition, we have discovered several natural compounds with antitussive and
anti-asthmatic effects and predicted their possible pathways. In addition, we have
discovered the potential functions of some natural compounds on estrogen receptors
and histone deacetylases, demonstrating the feasibility and broad working spectra
prospects of this method.
 

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