[论文解读] Network medicine framework reveals generic herb-symptom effectiveness of Traditional Chinese Medicine

Xiao Gan, Zixin Shu+9 more|arXiv (Cornell University)|Jul 18, 2022

Traditional Chinese Medicine Studies被引用 6

一句话总结

本研究提出了一种网络医学框架，通过人类蛋白质相互作用组映射中医草药-症状关系。通过证明草药靶点与症状相关基因模块之间的网络接近度可预测治疗效果，该框架识别出新颖且具有生物学合理性的草药-症状配对，并在真实医院数据中得到验证。

ABSTRACT

Traditional Chinese medicine (TCM) relies on natural medical products to treat symptoms and diseases. While clinical data have demonstrated the effectiveness of selected TCM-based treatments, the mechanistic root of how TCM herbs treat diseases remains largely unknown. More importantly, current approaches focus on single herbs or prescriptions, missing the high-level general principles of TCM. To uncover the mechanistic nature of TCM on a system level, in this work we establish a generic network medicine framework for TCM from the human protein interactome. Applying our framework reveals a network pattern between symptoms (diseases) and herbs in TCM. We first observe that genes associated with a symptom are not distributed randomly in the interactome, but cluster into localized modules; furthermore, a short network distance between two symptom modules is indicative of the symptoms' co-occurrence and similarity. Next, we show that the network proximity of a herb's targets to a symptom module is predictive of the herb's effectiveness in treating the symptom. We validate our framework with real-world hospital patient data by showing that (1) shorter network distance between symptoms of inpatients correlates with higher relative risk (co-occurrence), and (2) herb-symptom network proximity is indicative of patients' symptom recovery rate after herbal treatment. Finally, we identified novel herb-symptom pairs in which the herb's effectiveness in treating the symptom is predicted by network and confirmed in hospital data, but previously unknown to the TCM community. These predictions highlight our framework's potential in creating herb discovery or repurposing opportunities. In conclusion, network medicine offers a powerful novel platform to understand the mechanism of traditional medicine and to predict novel herbal treatment against diseases.

研究动机与目标

• 揭示超越单一草药或处方研究的中医草药疗效的系统性、机制性原理。
• 解决中医草药在分子水平上治疗疾病机制理解不足的问题。
• 开发一种通用的、系统层面的框架，利用网络生物学预测草药-症状疗效。
• 利用真实住院临床数据验证预测结果，包括症状共现率与恢复率。
• 识别具有高预测潜力和临床价值的新型、此前未知的草药-症状配对。

提出的方法

• 构建人类蛋白质-蛋白质相互作用（PPI）网络，以映射与疾病和草药相关的基因。
• 通过在PPI网络中聚类与疾病相关的基因，识别与特定症状相关的基因模块。
• 将草药靶基因与症状相关基因模块之间的网络距离计算为治疗接近度的代理指标。
• 利用住院临床数据，将网络距离与症状共现率及草药治疗后的恢复率相关联。
• 应用统计和基于网络的模型，检验网络距离越短是否预测更高的症状共现率和更好的恢复率。
• 利用真实医院记录验证预测的新型草药-症状配对，确认其疗效超越传统中医知识。

实验结果

研究问题

• RQ1 中医中的症状相关基因是否在人类蛋白质相互作用组中聚集成局部模块？
• RQ2 草药靶点与症状基因模块之间的网络接近度是否与该草药治疗特定症状的有效性相关？
• RQ3 两个症状之间的网络距离越短，是否越可能在患者中共同出现？
• RQ4 草药与症状之间的基于网络的接近度是否能预测患者治疗后的实际恢复率？
• RQ5 该框架是否预测出在传统中医文献中未知但经临床数据证实的新型草药-症状配对？

主要发现

• 中医中的症状相关基因在人类蛋白质相互作用组中形成局部且具有生物学一致性的模块。
• 草药靶点与症状基因模块之间的网络距离越短，其治疗效果显著越高。
• 住院数据证实，网络距离较短的症状共现频率更高，表明其具有共享的生物学机制。
• 草药-症状的网络接近度可预测患者恢复率，接近度越高，症状缓解越快。
• 该框架成功识别出在传统中医中未被认识但经临床数据验证的新型草药-症状配对。
• 网络医学框架为传统医学中的草药发现与再利用提供了可预测的平台。