Jia-Wei-Si-Miao-Yong-An decoction modulates intestinal flora and metabolites in acute coronary syndrome model

Abstract

Aims: We assessed the efficacy of the traditional Chinese medicine formulation Jia-Wei-Si-Miao-Yong-An decoction (HJ11) in the treatment of acute coronary syndrome and evaluated its impact on the intestinal microbiota and their metabolites.

Methods: An acute coronary syndrome model was established in rats, which were randomly assigned to the model, HJ11 treatment, and atorvastatin treatment groups. Rats were then administered saline solution (model and sham operation control groups) or drugs by oral gavage for 28 d. Echocardiography was performed and serum creatine kinase-MB and cardiac troponin I levels were monitored to examine the cardiac function. Inflammation was evaluated using hematoxylin and eosin staining of heart tissue, and serum interleukin-2, interleukin-6, tumor necrosis factor alpha, and high-sensitivity C-reactive protein measurements. Gut microbiota composition was analyzed via 16S rRNA gene sequencing. Metabolomics was used to determine fecal metabolites and elucidate the modes of action of HJ11 in acute coronary syndrome treatment.

Results: HJ11 improved cardiac function and attenuated inflammation in rats with acute coronary syndrome. Relative to the untreated model group, the HJ11-treated group presented normalized Firmicutes/Bacteroidetes ratio and reduced abundances of the bacterial genera norank_f__Ruminococcaceae, Desulfovibrio, Clostridium_sensu_stricto_1, Adlercreutzia, Staphylococcus, Bacteroides, Prevotella, Rikenellaceae_RC9_gut_group, unclassified_o__Bacteroidales, and Ruminococcus_gauvreauii_group. We found 23 differentially expressed intestinal metabolites, and the enriched metabolic pathways were mainly related to amino acid metabolism. We also discovered that asymmetric dimethylarginine levels were strongly associated with cardiovascular disease. Correlation analyses revealed strong associations among intestinal microflora, their metabolites, proinflammatory factors, and cardiac function. Hence, the therapeutic effects of HJ11 on acute coronary syndrome are related to specific alterations in gut microbiota and their metabolites.

Conclusion: This work demonstrated that HJ11 effectively treats acute coronary syndrome. HJ11 seems to increase the abundance of beneficial bacterial taxa (Bacteroides and Rikenellaceae_RC9_gut_group), mitigate the risk factors associated with cardiovascular disease, alter bacterial metabolites, lower asymmetric dimethylarginine levels, and effectively treat acute coronary syndrome.

Keywords: ACS; Si-Miao-Yong-An decoction; TCM; gut flora; metabolomics.

Figure 1

Workflow for predicting the therapeutic mechanism of HJ11 in ACS.

Figure 2

HPLC determination of major components in different herbs. (A) Lonicerae japonicae flos (Jinyinhua). (B) Scrophularia ningpoensis Hemsl. (Xuanshen). (C) Glycyrrhizae Radix et Rhizoma (Gancao). (D) Polygoni cuspidati Rhizoma et Radix (Huzhang). (E) Forsythiae Fructus (Lianqiao). (F) Angelicae sinensis Radix (Danggui). (G) Cinnamomum osmophloeum (Guizhi). (H) Salvia miltiorrhiza Bunge (Danshen).

Figure 3

Effects of HJ11 on ACS model rats. (A) Changes in body weight in different groups. (B) Cardiac enzyme detection in different groups. (C) Inflammatory factor detection in different groups. (D) Echocardiogram data. (E) Echocardiogram images. (F) HE staining of heart tissue. (G) Pathological scores of heart tissue. ^##P < 0.01 compared with the control group; ^*P < 0.05, ^**P < 0.01, ^***P < 0.001 compared with the model group.

Figure 4

Microbial community structures in different groups. (A) α-Diversity in different groups. (B) PLS-DA of different groups. Points with the same color and shape represent samples from the same group. Distances between points reflect differences between samples. (C) Comparison of community composition at the phylum level. Each column represents a group. (D) F/B ratio. (E) Comparison of community composition at the genus level. Each column represents a group. ^#P < 0.05, ^#P < 0.01 compared with the control group; *P < 0.05, ***P < 0.001 compared with the model group.

Figure 5

Dominant phylotypes (relative abundance > 0.05) in different groups. Connection indicates strong (Spearman's r > 0.8) and significant (P < 0.001) correlation. The co-occurrence network is colored according to the phylum or genus. The size of each node is proportional to the relative abundance of each phylotype. The thickness of each connection between two nodes (edge) is proportional to the Spearman's correlation coefficient. Purple edge indicates positive correlation between two nodes. Green edge indicates negative correlation between two nodes.

Figure 6

Differentially abundant bacterial taxa. (A) Linear discriminant analysis effect size from the phylum to the genus level (LDA score > 2.0). (B–K) Relative abundances of gut bacterial genera that were significantly reversed by HJ11 treatment. Student's t-test, two-tailed. ^#P < 0.05, ^##P < 0.01 compared with the control group; ^*P < 0.05, ^**P < 0.01, ^****P < 0.0001 compared with the model group.

Figure 7

Effects of HJ11 on metabolites in ACS model rats. (A) Distribution of metabolites in different groups. (B) Orthogonal PLS-DA of metabolomics results in different groups. (C) Volcano plot of metabolites in he model and HJ11 groups (screening conditions: log2FC ≥0 and P < 0.05). (D) Pathway enrichment based on altered metabolites.

Figure 8

Correlation analysis. (A) Correlations among gut bacteria, proinflammatory factors (TNF-α, IL-2, IL-6, and hs-CRP), and cardiac function indices (CK-MB and cTnI) (Spearman's r >0.1 or <0.1; n = 6/group). (B) Correlations among gut bacteria and metabolites (Spearman's r >0.1 or <0.1; n = 6/group). ^*P < 0.05, ^**P < 0.01, ^***P < 0.001.