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

Yi Zhang; Dahan Zhang; Xiaogang Bai; Yang Chen; Qinwei Qiu; Xiaoxiao Shang; Yusheng Deng; Hongyan Yang; Xiaodong Fang; Zhimin Yang; Lijuan Han

doi:10.3389/fmicb.2022.980082

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

Front Microbiol. 2022 Nov 11:13:980082. doi: 10.3389/fmicb.2022.980082. eCollection 2022.

Authors

Yi Zhang^{1

2}, Dahan Zhang^{1

3}, Xiaogang Bai^{1

2}, Yang Chen⁴, Qinwei Qiu⁴, Xiaoxiao Shang⁴, Yusheng Deng⁴, Hongyan Yang⁴, Xiaodong Fang^{1

4}, Zhimin Yang⁴, Lijuan Han^{1

4}

Affiliations

¹ Department of Scientific Research, Kangmeihuada GeneTech Co., Ltd., (KMHD), Shenzhen, China.
² Department of Mathematics of Science College/Hebei Province Key Laboratory of Molecular Chemistry for Drug, Hebei University of Science and Technology, Shijiazhuang, China.
³ Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China.
⁴ State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.

Abstract

Objectives: The 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.

Methods: The 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.

Results: Codonopsis 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.

Conclusions: Because 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.

Keywords: Granger causality; Traditional Chinese Medicine; bioinformatics; intestinal microbiology; molecular mechanisms.