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Shenmai injection revives cardiac function in rats with hypertensive heart failure: involvement of microbial-host co-metabolism

BMC Complementary Medicine and Therapies volume 25, Article number: 24 (2025) Cite this article

Abstract

Heart failure (HF) is a complex syndrome marked by considerable expenditures and elevated mortality and morbidity rates globally. Shenmai injection (SMI), a form of Traditional Chinese Medicine-based therapy, has demonstrated effectiveness in treating HF. Recent research suggests that Traditional Chinese Medicine (TCM) may induce beneficial changes in microbial-host co-metabolism, potentially providing cardiovascular protection. This study used a rat model of hypertensive heart failure (H-HF) to explore the mechanism of SMI. The possible compounds and key targets of SMI against H-HF were investigated using network pharmacology. The pharmacodynamics of SMI were validated using the H-HF animal model, with analysis of fecal gut microbiota integrating metabolomics and 16S rRNA sequencing. Metorigin metabolite traceability analysis and the MetaboAnalyst platform were utilized to explore the action mechanism. To evaluate changes in serum TMAO levels, targeted metabolomics was performed. Finally, the study looked at the intrinsic relationships among modifications in the intestinal flora, metabolite profile changes, and the targets of SMI compounds to clarify how they might be used to treat H-HF. According to metabolomics and 16S rRNA sequencing, by reestablishing homeostasis in the gut microbiota, SMI affects vital metabolic pathways, such as energy metabolism, amino acid metabolism, and bile acid metabolism. Increased serum TMAO levels were identified to be a risk factor for H-HF, and SMI was able to downregulate the levels of TMAO-related metabolites. Network pharmacology analysis identified 13 active components of SMI targeting 46 proteins, resulting in differential expression changes in 8 metabolites and 24 gut microbes. In conclusion, this study highlights the effectiveness of SMI in alleviating H-HF and its potential to modulate microbial-host co-metabolism. Through a comprehensive discussion of the interconnected relationships among the components, targets, metabolites, and gut microbiota, it provided fresh light on the therapeutic mechanism of SMI on H-HF.

Clinical trial number

Not applicable.

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Introduction

Heart failure (HF) represents the most advanced stage of cardiac illnesses [1]. The elderly population is predominantly burdened with chronic illnesses such as heart failure, and this causes substantial economic strain on the patient’s family and society [2]. In up to 85% of cases, hypertension (HTN) is the primary modifiable risk factor for the onset of HF [3]. The current estimated population of HF in the US is 6 million [4], making hypertension-induced HF a serious public health concern.

New studies have demonstrated a link between intestinal microbial illnesses and the onset and progression of HF [5]. It has been hypothesized that HF is caused by an increase in intestinal bacteria, resulting in increased inflammation and an increased number of bacteria in the bloodstream [5]. Cui et al. used metabolomic and metagenomic analyses of feces and blood from HF patients to reveal an imbalance among intestinal microflora [6]. According to Pasini et al., the severity of HF may be correlated with the proliferation of pathogenic intestinal microflora and increased intestinal permeability [7]. The discovery of a heart-gut axis provides new approaches to the therapy of HF [8, 9]. Trimethylamine N-oxide (TMAO) is formed when trimethylamine (TMA), produced by gut microbes acting on dietary compounds, is further oxidized by liver flavin-containing mono-oxygenase (FMO) [10]. A systematic review of 19,256 subjects has previously demonstrated that raised levels of TMAO and its precursors were reported to be related to major detrimental cardiovascular complications, such as HF, and an increased risk of death from any cause [11]. An investigation using mice fed a diet high in choline or TMAO revealed heightened serum levels of TMAO and a more severe HF [12]. These findings support the notion that a better prognosis for HF patients is associated with lower TMAO level [13]. Therefore, methods for treating HF that lower the level of serum TMAO or TMAO-producing microbes are ideal.

Numerous studies have provided ample evidence of the efficacy of Traditional Chinese medicine (TCM) in HF therapy [14,15,16]. Based on recent research, TCM has been proven to regulate gut microbiota to inhibit the onset of cardiovascular diseases [15, 17]. TCM successfully maintains a healthy intestinal environment, encourages the propagation of beneficial bacteria, and balances the gut microbiota. While also inhibiting the proliferation of harmful bacteria [18]. Shenmai Injection (SMI) is a type of Traditional Chinese Medicine injection (TCMI) prepared from Ginseng Radix et Rhizoma Rubra (Panax ginseng C. A. Mey, Hongshen) and Radix Ophiopogonis (Ophiopogon japonicus (Linn. f.) Ker-Gawl, Maidong) using modern technology. Pharmacological studies established that ophiopogonin D, ginsenoside Rg1, ginsenoside Rb1, and ginsenoside Re are the compounds of SMI. It has been demonstrated that SMI protects cardiomyocytes through the regulation of the activity of enzymes related to energy metabolism, ATP production, and mitochondrial function [19, 20]. Research has demonstrated that SMI can protect against the cardiotoxicity caused by doxorubicin by maintaining mitochondrial homeostasis and the miR-30a/Beclin pathway [21, 22]. By activating Nrf2/GPX4 signaling-mediated ferroptosis, pretreatment with SMI minimized myocardial I/R damage and presented a therapeutic approach to treating and preventing ischemic heart diseases [23]. SMI has been shown in clinical trials to improve energy metabolism in HF patients [24]. Our previous research has demonstrated that SMI regulates the TGF-β 1/Smad signaling pathway, thereby preventing myocardial fibrosis and effectively improving H-HF [25], but the potential involvement of gut microbiota in these therapeutic effects remains unclear. Studies have shown that ginsenoside Rg1 alleviates acute ulcerative colitis by modulating gut microbiota and microbial tryptophan metabolism [26], while ginsenoside Rh4 inhibits colorectal cancer through the regulation of gut microbiota-mediated bile acid metabolism [27]. However, there is currently no research exploring the impact of Shenmai Injection on gut microbiota.Thus, we aim to investigate the mechanisms behind the improvement of cardiac activity in chronic heart failure by using SMI.

Materials and methods

Animals and treatment

The Institutional Animal Care and Use Committee (IACUC) at the Hunan University of Chinese Medicine (HUMC) approved the experimental protocol. Salt-sensitive rats (n = 24), aged six weeks and weighing 200–220 g, were obtained from Beijing Weitong Lihua Animal Co., Ltd., license number: SCXK(Beijing)2016-0011, animal batch number: N1100111911056755.All of the animals were housed in a standard husbandry environment.

Following the acclimation period of seven days, eight rats were allocated to three groups at random: Control (CON), H-HF Model (MOD), and H-HF Model with Shenmai injection (SM). The rat model of H-HF was created utilizing the procedures previously reported [25, 28]. For 20 weeks, the Control (CON) group had a regular diet containing 0.3% NaCl(normal diet), while the MOD and SM groups received a diet high in salt (8% NaCl). Animals were supplied with an unlimited supply of food and water. The CON and MOD groups received intraperitoneal injections of sterile water (6.0 mL/kg), whereas the SMI group received Shenmai injections (6.0 mL/kg) for a period of 15 days.

Samples

The rats were anesthetized after 15 days using urethane (1.0 g/kg, i.p.). Blood was drawn from the abdominal aorta, and euthanasia was via dislocation of the neck. Briefly, the rats were held securely by the body, with one hand gripping the back or base of the tail. The head was quickly pulled downward and backward to separate the cervical vertebrae, causing immediate loss of consciousness.

Blood was left for 3 h at room temperature and then centrifuged to separate and obtain serum. Myocardial and colonic tissues preserved with 4% paraformaldehyde underwent histopathological evaluation. After that, sections embedded in paraffin were stained with hematoxylin and eosin (HE). ELISA was conducted using commercial ELISA kits. NT-proBNP ELISA Kit(CUSABIO, CSB-E08752r). CRP ELISA Kit(CUSABIO, CSB-E07922r). IL-1β ELISA Kit(CUSABIO, CSB-E08055r). Zonulin ELISA Kit(mlbio, ml059419. LPS ELISA Kit(CUSABIO, CSB-E14247r). Samples from the colon were obtained by firmly compressing the inner contents into a clean tube, which was then frozen with liquid nitrogen and stored at -80 °C. For 16S rRNA sequencing and microbiome analysis, 6 rats were picked from each group at random.

Echocardiography and blood pressure measurement

Echocardiography was employed to assess cardiac function [28]with an ultrasound color Doppler diagnostic equipment(S2N, Shenzhen Kaili Technology Co., Ltd., China). The dimensions of the left atrium and ventricle, as well as the left ventricular ejection fraction (LVEF), were assessed using M-mode echocardiography in the parasternal long-axis view, following the American Society of Echocardiography’s M-mode technique. Three consecutive cardiac cycles were examined to calculate the mean value. The Teichholtz formula was utilized to calculate the left ventricular fractional shortening (LVFS) and LVEF. The echocardiography parameters are as follows: frame rate = 54, dynamic range/gain = 100/3, gain = 150, frequency = 8.0–12.0 MHz. Blood pressure was assessed using a Volume Pressure Recording (VPR) system (CODA; Kent Scientific). For each animal, systolic blood pressure (SBP) and diastolic blood pressure (DBP) were calculated as the average of three independent measurements.

Quality control of Shenmai injection

Shenmai injection (lot number,1909288) was manufactured by Chiatai Qingchunbao(CTQ) Pharmaceutical Co. Ltd. (Hanzhou, China) with a China FDA drug ratification number of GuoYaoZhunZi- Z33020019. It is a solution extracted from Ginseng Radix et Rhizoma Rubra (Panax ginseng C. A. Mey, Hongshen) and Radix Ophiopogonis (Ophiopogon japonicus (Linn. f.) Ker-Gawl, Maidong), as described in Table 1. Its quality meets the standard of China Food and Drug Administation (approval No: WS3-B-3428-98-2004).According to the CTQ Pharmaceutical Group Co. Ltd, the quality control standards for SMI require that the total concentration of GinsenosideRg1 (C42H72O14), GinsenosideRe (C48H82O18), and GinsenosideRb1(C54H92O23) must not be lower than 100 µg/mL and that the overall concentration of the three agents should be between 300 and 600 µg/mL [22]. To ensure the quality of the Shenmai injection, High-Performance Liquid Chromatography (HPLC) analysis was performed.Following filtration through a 0.22 μm nylon membrane, the components of SMI were analyzed using an HPLC System (U3000, ThermoFisher Scientific). Detailed amplification conditions can be found in the Supplementary Material and Methods file. The HPLC analysis demonstrated the presence of Ginsenoside Rg1, Ginsenoside Re, and GinsenosideRb1 in SMI, which agreed with the results reported previously [29]. The outputs of HPLC are summarized in Fig. S1.

Table 1 Information of raw herbs in SMI
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Network pharmacology methodology

Following the requirements of oral bioavailability (OB) of ≥ 30% and drug-likeness (DL) of ≥ 0.18, we were able to determine all of the active ingredients in red ginseng (hong shen) using the Traditional Chinese Medicine Database Analysis Platform (TCMSP, https://tcmsp-e.com/) [30]. The BATMAN-TCM database (http://bionet.ncpsb.org.cn/batman-tcm/) provided high-confidence proteins for Ophiopogon japonicus (Maidong) [31]. Disease-related targets were identified by searching for the term “heart failure” in the databases OMIM (https://www.omim.org/) and GeneCards (https://www.genecards.org/). We used Cytoscape 3.7.2 to construct the “drug component-target” network by mapping the targets of the drug component to the targets of the disease. Based on the STRING database (https://string-db.org/), a protein-protein interaction network was constructed, with a minimum interaction score of 0.7. The drug-disease intersecting genes were uploaded to the DAVID database (https://david.ncifcrf.gov/summary.jsp), with gene identifiers set to OFFICIAL_GENE_SYMBOL and the species set to Homo sapiens. DAVID 6.8 was utilized to annotate GO gene functions into three categories: Molecular Function (MF), Cellular Component (CC), and Biological Process (BP) to describe the function of active proteins in Shenmai injection therapy for heart failure.

Fecal metabolic profiling

Fecal Metabolic Profiling was carried out using the procedures described in our earlier study [28]. A QC sample was generated by combining an equivalent amount of sample supernatant (Fig. S2A, B).Analysis of the negative and positive modes identified 12,356 and 14,389 peaks, respectively, identifying 344 and 1,058 metabolites. The same software package was used for multivariate analysis, where normalized peak area data was imported into SIMCA16.0.2 [32]. Online databases such as HMDB, ChemSpider, and KEGG were searched to identify metabolites with a VIP greater than 1 and a P-value of less than 0.05 (ascertained by Student’s t-test). Metabolomics analyses were conducted by Biotree Biomedical Company (Shanghai, China).

16S rRNA sequencing

The 16S rRNA sequencing analysis was carried out using the procedures described in our earlier research [28]. In brief, PCR amplification was carried out, and the purified amplicons were pooled and sequenced using paired-end sequencing. The raw data was subsequently evaluated. Detailed sequencing analysis procedures are provided in the Supplementary Material and Methods. Biotree Biomedical Company (Shanghai, China) was responsible for sequencing and analysis.

Quantification of serum TMAO

Using UHPLC-MRM-MS/MS, the Agilent 1290 Infinity II series UHPLC System (Agilent Technologies) was employed to analyze the supernatant (80 µL). The Agilent 6460 triple quadrupole mass spectrometer, outfitted with an AJS electrospray ionization interface, was utilized to create an assay. Biotree Biomedical Company performed the analysis while Agilent MassHunter Workstation Software (B.08.00, Agilent Technologies) was utilized for the MRM data processing and capture. Detailed amplification conditions can be found in the Supplementary Material and Methods file. Metabolomics analyses were conducted by Biotree Biomedical Company (Shanghai, China).

Correlation network among “compounds-targets-metabolites- microbiota”

Correlation coefficients between the different gut microbiotas and metabolites were computed using the Spearman correlation analysis method. To identify the correlation between metabolites and targets, more SMI differential metabolites and targets were integrated into the metaboanalyst platform [33]. A “components-targets-metabolites-microbes” interaction network was created by integrating the aforementioned results to further reveal the regulatory function of SMI against H-HF. OmicShare, an online tool, was used for the visualization process.

Statistical analysis

The data analysis was carried out with SPSS 22.0 (IBM, USA). Data with equal variances and normal distribution were assessed for significance using one-way ANOVA and Tukey’s post hoc test. Otherwise, the Mann-Whitney U test was used. A significance threshold of p < 0.05 was established. Additionally, Metorigin (http://metorigin.metbioinformatics.cn/) was used to analyze the traceability of differential metabolites. Sankey network generation, origin analysis, and function analysis were all carried out utilizing the basic Metorigin analysis mode that is accessible on the official website.

Results

Pharmacodynamic study of SMI against H-HF rats

SMI improved cardiac function

We observed that the blood pressure of the MOD and SM groups increased to 190/150mmHg at 12 weeks (Fig. S3A-B). The control group was found to have significantly lower SBP and DBP readings than the MOD and SM groups. Following the treatment, no substantial changes in blood pressure were observed between the groups (Fig. S3C-D). Furthermore, no alterations in the weight of rats were observed in each group after the intervention (Fig. S3E).

To validate the H-HF rat model, we first assessed the serum level of NT-proBNP. It was observed that the MOD group had a higher NT-proBNP serum level than the CON group (Fig. 1A). Comparing the MOD groups to the CON group, the MOD groups showed lower levels of LVEF and LVFS (Fig. 1B and C), and the MOD group’s M-mode echocardiogram showed impaired cardiac performance (Fig. 1F). MOD cardiomyocytes were observed by HE staining to be enlarged, irregularly shaped, with a disordered arrangement; the interstitial space between the cells was also filled with fibrous tissue and heavily infiltrated with inflammatory cells (Fig. 1G). CRP and IL-1β are cytokines used to identify inflammation [34]. The MOD group displayed elevated serum CRP and IL-1β levels compared to controls (Fig. 1D and E). These observations corroborated the H-HF model [28], indicating that establishing the H-HF rat model had succeeded.

Administering SMI to H-HF rats reduced the elevation of NT-proBNP, CRP, and IL-1β levels. The SMI treatment restored decreased levels of LVEF and LVFS in the MOD group (Fig. 1B and C). The cardiac functions were also restored by the administration of SMI, as manifested by M-mode echocardiogram (Fig. 1F) and HE staining (Fig. 1G).

Fig. 1
figure 1

Evaluation of the cardiac and gut function and of H-HF rats (A) NT-proBNP serum level as determined by ELISA. (B) Left ventricular ejection fraction. (C) Left ventricular fractional shortening. (D) Serum level of IL-1β. (E) Serum level of CRP. (F) Representative traces of M-mode echocardiogram of each group. (G) HE staining of histological changes to heart (× 400) and (H) Serum level of LPS. (I) HE staining of colon tissues (× 100). (J) Serum level of Zonulin. CON: controls; MOD: a rat model of H-HF; SM: a rat model of H-HF treated with SM. n = 6. *p < 0.05

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SMI improved intestinal barrier function

HE staining of colonic tissue indicated a reduction in mucosal integrity and increased inflammatory cells in the MOD group (Fig. 1I). This impairment of mucosal functions was reversed with SMI treatment. The presence of Lipopolysaccharide (LPS) is indicative of damage to the intestinal mucosa [35], and Zonulin is used to assess intestinal permeability [36]. In H-HF rats, the MOD group showed noticeably higher serum concentrations of LPS and Zonulin than the control group, indicating a breakdown of the intestinal mucosal barrier and increased intestinal permeability (Fig. 1H and J). LPS and Zonulin levels were lower in the SMI group compared to the MOD group, suggesting that SMI effectively improved intestinal permeability and intestinal barrier function.

Network pharmacology analysis

Three compounds were collected from Red Ginseng (Hong Shen) and ten compounds were obtained from Ophiopogon japonicus (Mai Dong) (Table 2). Based on searches of the GeneCards and OMIM disease databases, 4158 heart failure (HF)-related disease targets and 122 overlapping targets were discovered (Fig. 2A). TNF, IL-6, IL-1β, AKT1, STAT3, NFκΒ, IFNG, IL-10, TP53, and TLR4 were identified as the primary targets by PPI protein interaction analysis (Fig. 2B). Figure 2C illustrates the findings of the “drug-component-disease-target” network. As suggested by KEGG analysis, the TNF, IL-17, and Toll-like receptor signaling pathways may be involved in the mechanism through which SMI prevents and treats HF (Fig. 2D). Apoptotic processes, inflammatory responses, response to external biotic stimuli, negative regulation of cell proliferation, negative regulation of the apoptotic process, cellular response to lipopolysaccharide, G protein-coupled receptor signaling pathway, and negative regulation of gene expression are among the main biological processes predicted by GO analysis and included 474 significantly enriched biological function entries for treating heart failure (Fig. 2E). There are 52 entries related to cellular components (CC), involving the plasma membrane, membrane, cytoplasm, extracellular space, extracellular region, extracellular exosome, cell surface, mitochondrion, endoplasmic reticulum membrane, and endoplasmic reticulum. Additionally, there are 80 entries related to molecular functions, involving protein binding, identical protein binding, enzyme binding, protein homodimerization activity, DNA binding, zinc ion binding, heme binding, signaling receptor activity, sequence-specific DNA binding, and receptor binding.

Table 2 List of active ingredients for pharmacological of SMI
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Fig. 2
figure 2

Pharmacological analysis of the SMI-HF network. (A) Venn figure. (B) PPI protein interaction. (C) Drug-component-disease-target network. (D) KEGG enrichment analysis. (E) GO enrichment analysis, including BP, CC, and MF

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SMI restored the gut microbiota of H-HF rats

Sequencing analysis of gut microbiota

The sequencing of 18 fecal samples yielded 1,440,437 raw reads, which were merged and filtered to produce 1,408,229 clean tags. On average, 67,749 clean tags were obtained. To determine whether the sequencing data adequately reflected the diversity of species in the sample, a rarefaction curve was employed. Overall consistency in the results revealed that the sequencing data was adequate (Fig. 3A). A Venn diagram depicting the OTU distributions was shown in Fig. 3B. Across the three groups, 607 OTUs were identified, with 518 being shared by all of them. Alpha diversity analysis was carried out to assess the disparities in the structural complexity of the gut microbiota. Chao 1 and Shannon indices did not uncover any significant discrepancies in diversity across the three groups (Fig. S2C, D). However, a distinct divergence of profiles was found between the CON, MOD, and SM groups according to weighted unifrac PCoA of beta diversity (Fig. 3C). ANOSIM analysis (ANOSIM: R = 0.732, p = 0.001) demonstrated that the three groups were distinctly segregated. The proximity of the CON and SM populations indicated that their gut bacteria profiles were similar. The points representing the MOD group were further away from the points of the CON and SM groups, implying that the MOD group’s colony structure was markedly different from the other two groups. The PCoA outcomes (model stress = 0.0959 < 0.2) were supported by the NMDS analysis (Fig. 3D).

Fig. 3
figure 3

Gut microbiome analysis. (A) Rarefaction curves. (B) Venn diagrams. (C) PCoA analysis. (D) NMDS analysis. (E) Species distribution at the phylum level. (F) Species distribution at the genus level

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Composition of gut microbiota and its difference analysis

The microbial community composition was evaluated at the phylum and genus level (Fig. 3E and F). Results revealed that the MOD group had a reduced abundance of Bacteroidetes, Patescibacteria, Spirochaetes, and Elusimicrobia, and an elevated proportion of Firmicutes and Proteobacteria in comparison to the controls (Fig. 4A). As shown in Fig. 4A(g), the F/B ratio of the MOD group was substantially higher in comparison to that of the other group. The MOD group was noticed to be deficient in Muribaculaceae and Lachnospiraceae_NK4A136_group in comparison to the controls while having an increased abundance of Romboutsia and Ruminococcaceae at the genus level. These findings suggest that H-HF rats had an alteration in their gut bacterial equilibrium, which SMI treatment partially reversed (Fig. 4A). The heatmaps in Fig. 4B further illustrate the variations in the gut microbiota between the three groups.

Fig. 4
figure 4

Gut microbiota profiles. (A) ANOVA analysis of gut bacteria at phylum and genus levels. (B) Heatmap of different gut microbiota in each experimental group. *p < 0.05

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Prediction of the function of the gut microbiota

By utilizing PICRUSt, a KEGG pathway analysis technique, we were able to evaluate the functional composition of the bacterial communities in the metagenome. All functional genes were shown at level III (Fig. 5). The genes associated with energy and amino acid metabolism were more abundant in the MOD group, indicating that these metabolic pathways were disturbed in H-HF. Furthermore, after receiving SMI treatment, several genes related to energy and amino acid metabolism were altered (Fig. 5).

Fig. 5
figure 5

Function prediction of the gut microbiota community at level III. (A) PICRUSt analysis between the CON and MOD groups. (B) PICRUSt analysis between the MOD and SM groups

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SMI improved disordered metabolism in H-HF

Regulation of SMI on the differential metabolites

Principal component analysis (PCA) and orthogonal partial least square discriminate analysis (OPLS-DA), which scaled and log-translated the data to reduce noise and high variance effects, were successful in differentiating between the three groups. PCA demonstrated a clear distinction between the three groups, with the SM group being closer to the CON group than the MOD group (Fig. S4 A and B). OPLS-DA analysis verified the distinctiveness among the three groups identified by PCA (Fig. S5). Metabolic analysis revealed that 17 metabolites had significantly altered levels in positive mode and other 17 metabolites in negative ion mode, while 29 biomarkers were significantly restored after SMI treatment (Table 1, Fig. S4 C-D). Our results revealed that these metabolites were linked to energy, methylamine, bile acid, and amino acid metabolisms (Table 3).

Table 3 Information of potential biomarkers
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Metorgin tracing analysis

The analysis of source-based metabolic function and metabolite traceability found 29 differential metabolites linked to SMI: 6 bacterial metabolites, 1 host-specific metabolite, and 24 bacteria-host cometabolites (Fig. 6A-B).

According to metabolites pathway enrichment analysis (MPEA), the databases for the host, bacterial, and co-metabolism metabolic pathways were paired with 1, 4, and 28 relevant metabolic pathways, respectively (Fig. 6C-D). Of these pathways, 1, 1, and 10 revealed a significant (p < 0.05) association with SMI. The origin-based functional analysis revealed that the microbial community was specific to phenylalanine metabolism and that the host was specific to primary bile acid biosynthesis. Histidine metabolism, tryptophan metabolism, valine, leucine, and isoleucine biosynthesis, beta-Alanine metabolism, butanoate metabolism, inositol phosphate metabolism, ascorbate and aldarate metabolism, nicotinate and nicotinamide metabolism, aminoacyl-tRNA biosynthesis and pentose and glucuronate interconversions were pathways of co-metabolism between microbes and hosts. The primary mechanism linked to SMI was histidine metabolism. A Bio-Sankey network based on MetOrigin analysis further visualized the biological relationships and statistical correlations between microbiota and metabolites to better depict the co-metabolic relationships between microbiota and hosts (Fig. 7A-B).

Fig. 6
figure 6

(A) Venn diagram of differential metabolites. (B) Histogram of differential metabolites. (C) Venn diagram of enrichment analysis of differential metabolites. (D) Histogram of enrichment analysis of differential metabolites

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Fig. 7
figure 7

Sanky diagram of metorigin analysis of (A) Histidine metabolism and (B) phenylalanine metabolism

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Quantification of serum TMAO

Studies have demonstrated a positive association between TMAO levels and cardiovascular conditions [37]. In comparison to the controls, the serum levels of TMAO and TMA levels in the MOD group were considerably higher (Fig. 8A–B), which supports earlier findings [12]. The drops in serum levels of TMAO and TMA after SMI treatment were not statistically significant, which may have been due to the small sample size.

Fig. 8
figure 8

Levels of serum TMAO (A) and TMA (B). (C) Network diagram of correlation analysis (Spearman’s R-value > 0.7, P < 0.05). (D) Correlation analysis of relative microbiota abundance at phylum level with TMAO and TMA. (E) Correlation of relative abundances of gut bacteria at the genus level (top 10 genera) relative to the fecal metabolite levels. Red and blue color represented the positive and negative correlations, respectively. *p < 0.05, **p < 0.01

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Correlation analysis

Correlations between the gut microbiota and fecal metabolic phenotype

The relationship between metabolites and gut genera was evaluated by the Spearman correlation coefficient. A strong correlation is indicated by a value of r greater than 0.7. Figure 8C displays the network diagram with strong correlations.

TMAO was strongly positively correlated with Elusimicrobium, _xylanophilum_group, oxidoreducens_group and negatively correlated with Catabacter, Defluviitaleaceae_UCG-011, Parvibacter, _f_Atopobiaceae, Peptococcus, Coriobacteriaceae_UCG-002, Staphylococcus, and Romboutsi. Therefore, methylamine metabolism must be impacted by the gut bacteria mentioned above. Similarly, D-glucuronic acid and D-xylitol correlated positively with the xylanophilum group, whereas creatinine correlated negatively with Coriobacteriaceae_UCG-002. These findings suggest that Coriobacteriaceae_UCG-002, Dubosiella, and the xylanophilum_group have an impact on energy metabolism. Bile acid metabolites (including chenodeoxycholic acid, cholic acid, and glycocholic acid) and amino acids (such as norvaline and L-threonine) have also been found to have a strong correlation with gut microbe composition. Our correlation data demonstrated modifications to the gut microbiome, resulting in a significantly altered metabolomic profile. Therefore, our current findings suggest that the mechanism by which SMI can improve heart function in an H-HF rat model may include effects on microbial energy, methylamine, bile acid, and amino acid metabolism in the intestine.

Correlations between TMAO and gut microbiota

Tables S1 and S2 present the results of Spearman’s correlation analysis used to evaluate the associations between the composition of the gut and the levels of TMAO metabolites. A positive correlation was uncovered between serum TMAO levels and the proportion of Actinobacteria. In contrast, a negative correlation was observed with Elusimicrobia at the phylum level (Fig. 8D). The analysis showed that serum TMAO had a direct relationship with Romboutsia, and an inverse relationship with Ruminococcaceae_UCG_014 and _f_Muribaculaceae. Furthermore, serum TMA levels had a strong negative correlation with Ruminococcaceae_UCG_014 (Fig. 8E). Based on these results, it is possible that SMI administration could alter TMAO levels by affecting the relevant microflora.

Correlations between differential metabolites and targets of SMI

Figure 9 shows the connections between the targets of SMI and differential metabolites. Additionally, the regulatory role of SMI in preventing heart failure is highlighted by the “components-targets-metabolites-microbes” interaction network depicted in Fig. 10. By controlling for 46 proteins, network integration analysis shows that the 11 potentially active components of SMI can affect the differential expression of 8 metabolites and 24 gut microbes.

Fig. 9
figure 9

The connected network of “metabolites-genes”. The Square node represented the common differential metabolite fecal. The circle represented the genes closely correlated with these metabolites through the HMDB and KEGG databases

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Fig. 10
figure 10

The interaction network of “components-targets-metabolites-microbiota” revealed the mechanism of SMI in the treatment of H-HF

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Discussion

In recent years, there has been an increasing amount of research on the connection between alterations in the gut microbiota and metabolites and the onset of heart failure [38]. However, the mechanisms by which SMI affects chronic heart failure from this perspective remain largely unknown. This study employs metabolomics, 16S rRNA high-throughput sequencing, and network pharmacology to investigate the influence of Shenmai injection on gut microbiota and metabolites in hypertensive heart failure rats. Moreover, the MetaboAnalyst platform was employed to clarify the connection between metabolites and targets, while the MetOrigin platform was used to examine the origin and function of metabolites. To establish a comprehensive analysis of the systematic relationships between the components, targets, metabolites, and gut microbiota influenced by SMI, a “component-target-metabolite-microbiota” interaction network was constructed. This provided new information about the mechanisms underlying SMI in heart failure therapy.

SMI is recognized for its effects of invigorating Qi to prevent collapse, nourishing Yin, and promoting saliva production. Both red ginseng and Radix Ophiopogonis have been shown in basic experiments and clinical studies to possess immune-regulating, blood circulation-improving, antioxidant, anti-inflammatory, and anticancer properties [39, 40]. Radix Ophiopogonis has also been shown to exhibit anti-atherosclerotic effects. Research has shown that SMI has antioxidant properties and can reduce oxidative stress [41, 42]. Based on systematic review and meta-analysis, SMI has demonstrated efficacy in treating anthracycline-induced cardiotoxicity and is consequently a possible course of therapy for this condition [43]. In this study, first, we evaluated cardiac function indicators, myocardial tissue HE staining, echocardiographic parameters (LVFS and LVEF), serum NT-proBNP levels, and inflammatory markers CRP and IL-1β to establish that SMI significantly improves cardiac function. In addition, SMI may be able to improve gut barrier function and decrease intestinal permeability based on its effects on intestinal permeability marker Zonulin and gut barrier function indicators (LPS). Indicating regulating the homeostasis of gut microbiota could be one of the primary ways that SMI enhances cardiac function.

We evaluated alterations in the composition and functionality of gut microbiota in salt-sensitive hypertensive heart failure rats to learn more about the effect of SMI on gut microbiota. The gut microbiome profiles of the CON and MOD groups differed significantly, as demonstrated by our results, suggesting that the H-HF modeling had changed the microbial structure thus offering insight into how SMI affected the gut microbiota of the H-HF model. The SMI administration successfully revived the microbiota’s structure and functions in H-HF rats. It has been observed that an augmented proportion of Proteobacteria is a potential indicator of epithelial dysfunction [44] and can also be used to diagnose gut dysbiosis and associated health risks [45]. According to our research, the proportions of Proteobacteria in H-HF rats were successfully decreased by SMI treatment, bringing the F/B ratio back to par with the CON group. This suggests that SMI has a positive effect on reestablishing the equilibrium of intestinal flora. In addition, a previous study found that the gut microbiome of patients with chronic heart failure included fewer butyrate-producing bacteria [46]. Butyrate and SCFA are known to be produced by bacteria in the Lachnospiraceae family [47]. Research has demonstrated a correlation between Muribaculaceae and propionic acid levels, an indicator of SCFA concentration [48]. Our research detected that the proportion of Lachnospiraceae_NK4A136 and _f_Muribaculaceae augmented after SMI treatment, suggesting that SMI treatment reinstates bacteria that generate SCFAs.

Assessing co-metabolic relationships between the host and gut microbiota can provide fresh perspectives on the critical function of the gut microbiome in host health [49]. Combining 16S high-throughput sequencing with metabolomics provides a powerful approach to exploring the mechanisms underlying disease development. Compared to omics approaches that employ biofluids, such as urine and serum, the fecal metabolome offers a more comprehensive view as it reflects the combined effects of genetic, environmental, and dietary factors [50]. Microbiome sequencing can therefore be used to understand the relationships between bacterial populations by using non-targeted metabolomics analysis of fecal samples. In the H-HF rat model, 34 metabolites had been significantly altered; SMI treatment restored 29 of these biomarkers. Our study demonstrates that Shenmai injection can significantly improve metabolic disorders in hypertensive heart failure rats. To determine if the host or the microbial community is the source of differential metabolites, we employed MetOrigin. Numerous identified metabolites participate in co-metabolism activities shared between the host and its resident gut microbiota. Energy metabolism, amino acid metabolism, methylamine metabolism, and bile acid metabolism are the key metabolic pathways engaged in these processes.

It is well-established that energy metabolism is disturbed in heart failure (HF), and modulating cardiac energy metabolism has been proposed as a therapeutic strategy for HF [51, 52].Recent studies have indicated that energy metabolism dysfunction plays a critical role in the pathophysiology of HF, with alterations in metabolic pathways contributing to the progression of the disease [53]. Our study further supports this notion, as the metabolites associated with energy metabolism, such as gamma-aminobutyric acid, glutaric acid, D-glucuronic acid, 2-hydroxybutyric acid, and creatinine, were significantly lower in the MOD group. These findings align with previous research showing that impaired cardiac energetics is a major contributor to HF [54]. Moreover, the 16S functional prediction analysis demonstrated that H-HF had a notable association with energy metabolism, consistent with other studies linking metabolic disturbances to HF. In particular, studies have highlighted how altered energy metabolism in HF affects mitochondrial function and cellular ATP production, contributing to cardiac dysfunction [55].

Our correlation analysis further demonstrated that metabolites like creatinine were inversely associated with Coriobacteriaceae_UCG-002, while D-glucuronic acid and D-xylitol showed significant positive correlations with the [Eubacterium]_xylanophilum_group but were negatively related to Coriobacteriaceae_UCG-002. These results suggest a close relationship between gut microbiota and energy metabolism in H-HF, which is consistent with emerging evidence on the interplay between gut microbiota and metabolic disturbances in cardiovascular diseases [56, 57]. Notably, the levels of key bacteria such as Coriobacteriaceae_UCG-002 and [Eubacterium]_xylanophilum_group returned to normal following SMI treatment, accompanied by an increase in metabolites linked to energy metabolism. This suggests that the therapeutic mechanism of SMI may involve the regulation of both microbiota and metabolites related to energy metabolism, an idea supported by similar findings in other studies on TCM and its effects on metabolic regulation.Metabolizing amino acids is indispensable to the energy supply, as it facilitates the conversion of amino acids into glucose through gluconeogenesis. Studies have shown that the administration of amino acids can be advantageous to people with HF, with improvements seen in various clinical endpoints [58]. Our study revealed a substantial decline in the metabolism of several amino acids (e.g., norvaline, ketoleucine, L-threonine, L-Valine, and N-acetylornithine) in the MOD group. Additionally, 16 S functional prediction was linked to amino acid metabolism, implying that H-HF is associated with a disruption in amino acid metabolism. The outcome of the correlation analysis indicated a close link between gut flora and metabolites related to amino acid metabolism; for example, norvaline demonstrated a strong and negative correlation with uncultured_bacterium_f_Ruminococcaceae, Adlercreutzia, Streptococcus, Faecalibaculum, [Eubacterium]_brachy_group, Dubosiella, uncultured_bacterium_f_Atopobiaceae, Coriobacteriaceae_UCG-002, and UBA1819. This study revealed that SMI treatment could adjust the relevant gut microbiota and amino acid metabolism. Bile acids have been shown in earlier studies to be integral to managing metabolism and energy expenditure [59]. Moreover, a study found that patients with HF had a higher ratio of secondary to primary bile acids in their plasma and lower levels of primary bile acids [60]. According to the correlation analysis conducted here, SMI may enhance the gut microbiota and metabolites linked to bile acid metabolism.

The interactions between differential metabolites and gut microbiota are complex and multifactorial, with each influencing the other in a dynamic manner. Our study provides evidence that gut microbiota plays a pivotal role in modulating the metabolism of key metabolites involved in energy production, amino acid metabolism, and bile acid metabolism. Moreover, the therapeutic effects of SMI seem to be partly mediated by its ability to regulate these microbiota-related metabolites, thus restoring metabolic homeostasis and improving heart function in H-HF.

Microbial homeostasis is defined as the maintenance of a balanced composition of gut microbiota in a healthy state [61]. Disruption of this equilibrium, however, can lead to the proliferation of pathogenic microorganisms, which raises serum concentrations of TMA and TMAO and increases the risk of cardiovascular diseases [62]. TMAO is considered a risk factor for cardiovascular disease as it is found in high concentrations in the blood when the intestinal wall is disrupted [63, 64]. Measuring serum TMAO levels has consequently emerged as a crucial marker of cardiovascular risk [65]. Our study revealed that the MOD group had lower fecal TMAO levels than the control group. Nevertheless, data from targeted metabolomics indicated that the MOD group had significantly higher serum TMAO and TMA concentrations than any other groups, a result that is consistent with numerous earlier studies on H-HF. A potential cause for the disparity could be the use of different sample types, such as fecal samples instead of serum samples. The reason for the decrease in TMAO in feces is thought to be related to the elevated TMAO levels in serum. According to studies by Nagatomo et al., elevated TMAO levels may cause myocardial fibrosis, LVEF reduction, multi-organ fibrosis, and an increase in BNP levels, all of which can contribute to heart failure [66]. Moreover, it has been noted that raised serum TMAO levels are linked to an increased risk of heart failure and its associated mortality [67]. A study found that TMAO combined with NT-proBNP were useful prognostic indicators for heart failure in patients [68]. This study revealed that the intervention involving SMI had a slight, albeit not statistically relevant, impact on the decrease of serum TMAO and TMA levels. Correlation analysis results indicate that SMI significantly reduces gut microbiota associated with TMAO and TMA, suggesting that SMI may influence serum TMAO levels by modulating these related microbial communities.

Thirteen of SMI’s active ingredients were screened based on the TCMSP and BATMAN-TCM database. Network integration analysis showed that by targeting 46 proteins, the 11 potentially active components of SMI can affect the differential expression of 8 metabolites and 24 gut microbes. Taken together, these investigations revealed that various SMI components work synergistically to exert their therapeutic function.

This study has some limitations. Firstly, while 13 active components of SMI were identified through database screening, their effectiveness was not experimentally validated. Future research could verify these active ingredients through pharmacokinetic experiments. Secondly, while the gut microbiota and some metabolites showed a strong correlation in this study, the associations do not always imply causation. Furthermore, the distinct levels of TMAO observed between fecal and serum samples suggest a potential tissue-specific role of TMAO. Therefore, additional experiments are warranted to investigate the role of TMAO in different tissues or organs in heart failure.

Conclusion

Our study offers a thorough investigation into the mechanisms through which SMI generates therapeutic benefits in heart failure. We observed that in hypertensive heart failure rats, SMI dramatically improves gut barrier function, cardiac function, and gut microbiota composition. By reestablishing homeostasis in the gut microbiota, SMI drives vital metabolic pathways such as energy metabolism, amino acid metabolism, and bile acid metabolism, as indicated by metabolomics and 16S rRNA sequencing analyses. Higher serum TMAO levels were found to be a risk factor for H-HF using TMAO-targeted metabolomics analysis, and SMI was able to downregulate these levels of TMAO-related metabolites. Network pharmacology analysis identified 13 active components of SMI targeting 46 proteins, resulting in differential expression changes in 8 metabolites and 24 gut microbes. This study highlights the effectiveness of SMI in alleviating H-HF and its potential to modulate microbial-host co-metabolism, underscoring the synergistic actions of multiple SMI components on various biological pathways implicated in heart failure. Future research should focus on validating these observations in clinical settings and elucidating the specific molecular mechanisms underlying SMI’s therapeutic benefits.

Data availability

The raw data for the 16S rRNA sequencing can be retrieved through the NCBI Sequence Read Archive (SRA) with the BioProject accession number PRJNA672260.

Abbreviations

DBP:

Diastolic Blood Pressure

F/B ratio:

Firmicutes to Bacteroidetes ratio

GM:

Gut Microbiome

H-HF:

Hypertensive-Heart Failure

HF:

Heart Failure

HPLC:

High-Performance Liquid Chromatography

LPS:

Lipopolysaccharide

LVEF:

Left Ventricular Ejection Fraction

LVFS:

Left Ventricular Fractional Shortening

NT-proBNP:

N-terminal ProB-type Natriuretic Peptide

OTU:

Operational Taxonomic Unit

OPLS-DA:

Orthogonal Projections to Latent Structures Discriminate Analysis

PCoA:

Principal Coordinates Analysis

PCA:

Principle Component Analysis

SCFA:

Short-Chain Fatty Acids

SBP:

Systolic Blood Pressure

SMI:

Shenmai Injection

TCMSP:

Traditional Chinese Medicine Database Analysis Platform

VIP:

Variable Importance in the Projection

TMAO:

Trimethylamine N-oxid

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Acknowledgements

We are grateful to Shanghai Biotree Biotech Co. Ltd. for their assistance with LC-MS/MS metabonomic analysis and 16Sr RNA sequencing.

Funding

The Natural Science Foundation of Changsha (grant number kq2208185), the Natural Science Foundation of Hunan (grant number 2023JJ30453), the Natural Science Foundation of China (grant number 82274412), The science and technology innovation Program of Hunan Province (2024RC3199), Additionally, the Excellent Young Scholars Research Fund Project of Hunan University of Chinese Medicine provided funding for this project (Z2023XJYQ03).

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Authors and Affiliations

  1. The Domestic First-class Discipline Construction Project of Chinese Medicine, Hunan University of Chinese Medicine, Changsha, Hunan, China

    Lin Li, Jiahao Ye, Siyuan Hu & Zhixi Hu

  2. Provincial Key Laboratory of TCM Diagnostics, Hunan University of Chinese Medicine, Changsha, Hunan, China

    Lin Li, Xiajun Xiong, Guangyu Chen & Zhixi Hu

  3. The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China

    Senjie Zhong

  4. Hunan Engineering Technology Research Center For Medicinal and Functional Food, Changsha, Hunan, China

    Guangyu Chen

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Contributions

Data curation, Xiajun Xiong and Guangyu Chen; Formal analysis, Siyuan Hu ; Methodology, Senjie Zhong and Jiahao Ye; Project administration, Zhixi Hu; Writing – original draft, Lin Li.

Corresponding author

Correspondence to Zhixi Hu.

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Ethics approval and consent to participate

The animal study was reviewed and approved by the Institutional Animal Care and Use Committee (IACUC) at the Hunan University of Chinese Medicine (NO. LL20190902402). All methods are reported in accordance with ARRIVE guidelines for the reporting of animal experiments and all methods were carried out in accordance with relevant guidelines and regulations.

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Not applicable.

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The authors declare no competing interests.

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Electronic supplementary material

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12906_2024_4737_MOESM1_ESM.pdf

Supplementary Material 1: Figure S1. (A) The chromatogram of Ginsenoside Rg1, Re, Rb1 reference solution. (B) The chromatogram of Shenmai injection test solution.

12906_2024_4737_MOESM2_ESM.pdf

Supplementary Material 2: Figure S2. (A) The PCA score of three groups and QC samples in positive-ion mode. (B) The PCA score of three groups and QC samples in negative-ion mode. (C) Chao1 index. (D) Shannon index. ##Data were represented as the mean ± SD

12906_2024_4737_MOESM3_ESM.pdf

Supplementary Material 3: Figure S3. (A) systolic blood pressure (SBP). (B) diastolic blood pressure (DBP). (C) SBP before and after treatment. (D) DBP before and after treatment. (E) Body weight prior to and after treatment. No remarkable alteration was noticed in the same group prior to and after treatment.

12906_2024_4737_MOESM4_ESM.pdf

Supplementary Material 4: Figure S4. PCA analysis of metabolites profile. CON: turquoise; MOD: red; SM: dark blue. (A) PCA score plots for positive-ion mode (R2X = 0.680). (B) PCA score plots for negative-ion mode (R2X = 0.674). (C) Heat map of the differential metabolites in positive-ion mode. (D) Heat map of the differential metabolites in negative-ion mode.

12906_2024_4737_MOESM5_ESM.pdf

Supplementary Material 5: Figure S5. OPLS-DA analysis of metabolites profile. (A) Comparison plots in positive-ion mode for CON and MOD groups (R2X=0.606, R2Y=0.998, Q2=0.974). (B) Permutation test for comparison between CON and MOD groups in positive-ion mode (n=200). (C) Comparison between CON and MOD groups in negative-ion mode (R2X=0.573, R2Y=0.999, Q2=0.974). (D) Permutation test for comparison between CON and MOD groups in positive-ion mode (n= 200). (E) Comparison between CON and MOD groups in positive-ion mode (R2X=0.513, R2Y=0.995, Q2=0.954). (F) Permutation test for comparison between CON and MOD groups in positive-ion mode (n= 200). (G) Comparison between MOD and SM group in negative-ion mode (R2X=0.532, R2Y=0.997, Q2=0.956). (H) Permutation test for comparison between MOD and SM groups in positive-ion mode (n= 200).

Supplementary Material 6: Table S1: The related dataset of spearman’s correlation at the phylum level.

Supplementary Material 7: Table S2: The related dataset of spearman’s correlation at the genus level.

Supplementary Material 8

Supplementary Material 9

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Li, L., Zhong, S., Ye, J. et al. Shenmai injection revives cardiac function in rats with hypertensive heart failure: involvement of microbial-host co-metabolism. BMC Complement Med Ther 25, 24 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12906-024-04737-2

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Keywords

BMC Complementary Medicine and Therapies

ISSN: 2662-7671

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