Data_Sheet_2_The impact of Traditional Chinese Medicine on mouse gut microbiota abundances and interactions based on Granger causality and pathway analysis.PDF

ObjectivesThe intestinal microbiota is essential in absorbing nutrients and defending against pathogens and is associated with various diseases, including obesity, type 2 diabetes, and hypertension. As an alternative medicine, Traditional Chinese Medicine (TCM) has long been used in disease treatment and healthcare, partly because it may mediate gut microbiota. However, the specific effects of TCM on the abundance and interactions of microbiota remain unknown. Moreover, using TCM ingredients and data detailing changes in the abundance of gut microorganisms, we developed bioinformatic methods that decipher the impact of TCM on microorganism interactions.
MethodsThe dynamics of gut microorganisms affected by TCM treatments is explored using a mouse model, which provided the abundance of 70 microorganisms over time. The Granger causality analysis was used to measure microorganism interactions. Novel “serial connection” and “diverging connection” models were used to identify molecular mechanisms underlying the impact of TCM on gut microorganism interactions, based on microorganism proteins, TCM chemical ingredients, and KEGG reaction equations.
ResultsCodonopsis pilosula (Dangshen), Cassia twig (Gui Zhi), Radices saussureae (Mu Xiang), and Sijunzi Decoction did not cause an increase in the abundance of harmful microorganisms. Most TCMs decreased the abundance of Bifidobacterium pseudolongum, suggesting a Bifidobacterium pseudolongum supplement should be used during TCM treatment. The Granger causality analysis indicated that TCM treatment changes more than half the interactions between the 70 microorganisms, and “serial connection” and “diverging connection” models suggested that changes in interactions may be related to the reaction number connecting species proteins and TCM ingredients. From a species diversity perspective, a TCM decoction is better than a single herb for healthcare. The Sijunzi Decoction only significantly increased the abundance of Bifidobacterium pseudolongum and did not cause a decrease in the abundance of other species but was found to improve the alpha diversity with the lowest replacement rate.
ConclusionsBecause most of the nine TCMs are medicinal and edible plants, we expect the methods and results presented can be used to optimize and integrate microbiota and TCMs into healthcare processes. Moreover, as a control study, these results can be combined with future disease mouse models to link variations in species abundance with particular diseases.

【研究目的】肠道菌群（intestinal microbiota）在营养吸收、抵御病原体过程中发挥关键作用，且与肥胖、2型糖尿病、高血压等多种疾病密切相关。作为替代疗法，传统中医药（Traditional Chinese Medicine, TCM）长期应用于疾病治疗与健康保健，其部分作用机制可能与调控肠道菌群相关。然而，中医药对菌群丰度及菌群间相互作用的具体影响仍不明确。本研究基于中药成分与肠道微生物丰度变化数据，开发了生物信息学方法，用以解析中医药对微生物相互作用的调控效应。【研究方法】本研究利用小鼠模型探究中医药干预下肠道微生物的动态变化，获取了70种微生物随时间变化的丰度数据。采用格兰杰因果分析（Granger causality analysis）量化微生物间的相互作用。基于微生物蛋白、中药化学成分与KEGG反应方程式（KEGG reaction equation），本研究构建了新型“串联连接”与“发散连接”模型，以揭示中医药调控肠道微生物相互作用的分子机制。【研究结果】党参（Codonopsis pilosula, Dangshen）、桂枝（Cassia twig, Gui Zhi）、木香（Radices saussureae, Mu Xiang）与四君子汤（Sijunzi Decoction）均未引起有害微生物丰度升高。多数中医药可降低假长双歧杆菌（Bifidobacterium pseudolongum）的丰度，提示在中医药治疗期间可配合补充假长双歧杆菌。格兰杰因果分析结果显示，中医药干预可改变70种微生物中超过半数的相互作用关系；“串联连接”与“发散连接”模型表明，微生物相互作用的变化可能与连接物种蛋白与中药成分的反应数目相关。从物种多样性角度来看，中药复方相较于单味草药更适用于健康保健。四君子汤仅可显著提升假长双歧杆菌的丰度，未引起其他物种丰度下降，且可在最低替换率下提升样本的α多样性。【研究结论】本研究涉及的9种中医药大多属于药食同源植物，我们期望本研究开发的方法与所得结果可用于优化菌群与中医药的整合方案，并将其应用于健康保健流程。此外，作为一项对照研究，本研究结果可与未来的疾病小鼠模型相结合，以将物种丰度变化与特定疾病建立关联。