Table_6_The impact of Traditional Chinese Medicine on mouse gut microbiota abundances and interactions based on Granger causality and pathway analysis.doc

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）在营养吸收与抵御病原体方面发挥不可或缺的作用，且与肥胖、2型糖尿病、高血压等多种疾病密切相关。作为替代医学，传统中医药（Traditional Chinese Medicine, TCM）长期应用于疾病治疗与健康养护，其部分作用机制可能与调控肠道菌群有关。然而，中医药对菌群丰度及菌群间相互作用的具体调控效应仍不明确。此外，本研究结合中药成分与肠道微生物丰度变化数据，开发了可解析中医药对微生物相互作用影响的生物信息学方法。

研究方法：本研究通过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）衡量微生物间的相互作用。基于微生物蛋白、中药化学成分与京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）反应方程式，本研究运用全新的“串联连接”与“发散连接”模型，解析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）以及四君子汤（Sijunzi Decoction）均未引起有害微生物丰度的升高。多数中医药干预会降低假长双歧杆菌（Bifidobacterium pseudolongum）的丰度，提示在中医药治疗过程中可配合补充假长双歧杆菌制剂。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；而“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方程式数量相关。从物种多样性角度来看，中药复方汤剂用于健康养护的效果优于单味中药。四君子汤仅能显著提升假长双歧杆菌的丰度，未造成其他物种丰度的降低，且可提升α多样性，同时具有最低的物种替换率。

研究结论：由于本研究涉及的9种中医药大多属于药食同源植物，本研究开发的方法与所得结果有望用于优化肠道菌群与中医药的整合方案，并将其应用于健康养护流程。此外，本研究作为对照研究，其结果可与未来的疾病小鼠模型相结合，以阐明物种丰度变化与特定疾病之间的关联。