Compound ingredient analysis
UPLC-MS/MS analysis detected a total of 1472 compounds in the herbal compound, which were classified into 11 classes. The contents of lipids, organic acids, and alkaloids in SKZ were higher than those in YQL, while that of flavonoids in YQL was higher than that in SKZ (Fig. 1a). Among the 11 classes, amino acids and derivatives ranked the first in terms of compound types, accounting for the highest proportion (22.83%). Phenolic acids had the second highest types, accounting for 20.11% (Fig. 1b).
UPLC-MS/MS analysis of the berbal compound. (a) Quantitative results of the first-level classification of YQL and SKZ. (b) Compound distribution of the eleven classes.
Herbal compound of YQL and SKZ enhanced intestinal recovery and reduced inflammation in Salmonella-infected mice
To investigate the effect of the YQL-SKZ compound on mice infected with Salmonella, histological sections of intestinal tissues from different groups of mice were examined (Supplementary Fig. 1). Measurements of villus height and crypt depth showed a significant decrease in the positive control group compared with the blank control group (P < 0.05) (Fig. 2), indicating successful construction of the mice model. The three treatment groups all showed significantly higher ratios than the positive control group (Fig. 2), indicating a potential reparative effects of the compound extract on intestinal mucosal damage, and a possible promotion of intestinal absorptive function in mice infected with Salmonella.
Pathological analysis of mouse intestinal tissues. Hematoxylin and eosin (H&E) staining was used to visualize the intestinal tissues, and measurements were taken for villus height, crypt depth, and the villus-to-crypt ratio. Control represents the blank control group, Positive represents the positive control group, High represents the high-concentration treatment group, Medium represents the medium-concentration treatment group, and Low represents the low-concentration treatment group.
Further analysis involved measuring the levels of inflammatory cytokines in the serum of mice treated with various concentrations of the compound. We found that compound administration significantly reduced the levels of inflammatory cytokines IL-6, IL-8, sigA, and TNF-α in mouse serum (P < 0.05), with the most notable reductions observed in the high- and low-concentration groups (Fig. 3A-D). This indicated that compound treatment can effectively ameliorate the intestinal inflammation condition in Salmonella-infected mice, with the high and low concentration groups showing the most pronounced effects. To further analyze the reparative effects of the compound, we examined the expression of genes that play crucial roles in mucosal repair processes: proliferating cell nuclear antigen (PCNA), transforming growth factor-beta 1 (TGF-β1), and epidermal growth factor receptor (EGFR). The results showed that in all the three treatment groups, the expression levels of PCNA, TGF, and EGFR were significantly increased (P < 0.05) (Fig. 3e-g). This may suggest that compound treatment can induce the expression of genes involved in the repair of intestinal mucosa, with the medium concentration group showing the greatest change.
Levels of intestinal inflammatory factors and genes involved in mucosal repair. (a–d) Levels of inflammatory factors IL-6, IL-8, sigA, and TNF-α in different treatment groups; (e–g) Relative expressive of PCNA, TGF-β1, and EGFR. * denotes statistically significant difference compared with the blank control group (p < 0.05), # indicates significant difference compared with the positive control group (p < 0.05), while ** and ## represent highly significant differences (p < 0.01).
Compound treatment altered intestinal microbiota diversity in Salmonella-infected mice
To delve deeper into the effects of the compound on improving the intestinal condition in mice with diarrhea, we conducted 16 S rRNA sequencing on the intestines of mice treated differently. Correlation analysis between samples revealed correlation coefficients exceeding 0.95 for all three replicates of each treatment, indicating strong consistency between replicates and suggesting good reproducibility of the samples (Fig. 4a). PCoA was able to separate the five groups clearly, indicating pronounced differences in microbial community composition between the groups. Through diversity analyses, our results demonstrated significant differences in microbial community composition between treatment groups (Fig. 4b-c). All samples had a Good’s coverage greater than 0.965, indicating that the sequencing depth was reasonable. Based on the α diversity indices, it was observed that the microbial richness and evenness treated with the compound were significantly lower compared with those in the positive control group. Moreover, the high-concentration group showed the highest levels, closely followed by the medium-concentration group, with the low-concentration group showing the lowest levels (Fig. 4c). This may suggest that compound treatment can lead to changes in the richness and diversity of the intestinal microbiota in Salmonella-Infected mice.
Sample correlation and diversity analysis. (a) Correlation analysis between samples; (b) PCoA analysis of intestinal microbiota; (c) Diversity analysis of the mice intestinal microbiota. * denotes statistically significant difference compared with the blank control group (p < 0.05), # indicates significant difference compared with the positive control group (p < 0.05), while ** and ## represent highly significant differences (p < 0.01).
Compound treatment induced concentration-dependent shifts in intestinal microbiota composition of Salmonella-infested mice
To explore the impact of different treatments on the microbial community composition, we visualized the relative abundances of microbial taxa at phylum and genus levels using stacked bar charts. At the phylum level, the abundances of Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria accounted for over 90% of the microbial composition across all groups. In the blank control group, Firmicutes were the predominant phylum, followed by Bacteroidetes. Conversely, in the positive control group and in mice treated with high and medium concentrations of the compound, Bacteroidetes were the most abundant, followed by Firmicutes. Additionally, all the three treatment groups exhibited higher levels of Proteobacteria compared with the blank control group. The low-concentration group displayed a significant difference in microbial abundance from other treatments, showing the highest abundance of Proteobacteria, followed by Firmicutes and Bacteroidetes (Fig. 5a). At the genus level, Muribaculum was the most abundant across all groups. Compared with the positive control group, compound treatment reduced the abundance of Muribaculum, with the most significant change observed in the low-concentration group. Helicobacter exhibited the most significant abundance change in the low-concentration group, followed by Salmonella. Both the bacterial genera showed a notable increase in abundance in response to the low-concentration treatment. The abundances of Prevotella and Alistipes were higher in the compound treatment groups compared with the positive control group, while Bacteroides and Alloprevotella were lower (Fig. 5b).
Analysis of intestinal microbial species composition. (a) Phylum-level composition of intestinal microbiota; (b) Genus-level composition of intestinal microbiota; (c) Heatmap of phylum-level intestinal microbial species abundance. (d) Heatmap of genus-level intestinal microbial species abundance.
Further analysis revealed that the abundance of Actinobacteria (Fig. 5a), Muribaculum, Prevotella, Odoribacter, Desulfovibrio, Flavonifractor, and Eisenbergiella (Fig. 5b) decreased with decreasing concentration. At the phylum level, the abundance of Firmicutes surpassed that of other groups in the blank control group. In the high-concentration group, an upregulation in the levels of Actinobacteria, Tenericutes, and Verrucomicrobia was observed, whereas in the low-concentration group, the levels of Proteobacteria and Deferribacteres notably exceeded those of other groups. (Fig. 5c). In contrast, genera such as Helicobacter, Salmonella, Pseudobutyrivibrio, Klebsiella, Sutterella, Vibrio, and Cronobacter had higher abundances in the low-concentration treatment group. In contrast, genera such as Muribaculum, Marvinbryantia, Enterococcus, Blautia, Erysipelatoclostridium, Bacteroides, Ihubacter, Alloprevotella, and Escherichia had lower abundances in compound treatment, significantly lower than those in the positive control group (Fig. 5d). These results indicated that compound treatment may induce changes in the intestinal microbial community structure of mice. Furthermore, the abundances of Actinobacteria, Muribaculum, Prevotella, Odoribacter, Desulfovibrio, Flavonifractor, and Eisenbergiella, among others, increased with higher compound concentrations, suggesting a concentration-dependent response to the compound treatment. Significant differences in the abundances of genera such as Muribaculum, Helicobacter, Salmonella, Prevotella, Alistipes, Bacteroides, and Alloprevotella were observed between compound-treated groups and control groups, suggesting that these microorganisms may play a crucial role in maintaining normal intestinal function in mice.
Differential microbial and functional annotation revealed the impact of compound treatment on intestinal microbiota in Salmonella-infected mice
To gain deeper insights into the effect of compound treatment on the composition of the intestinal microbiome, pairwise differential comparisons were conducted between the three compound treatment groups and both blank and positive control groups. Significant microorganisms were selected based on |logFC| ≥ 1 and FDR < 0.05, and the results were visualized in a scatter plot. We identified 792, 905, 664, and 721 significantly differential microorganisms when the blank control group was compared with the positive control group, high-concentration group, medium-concentration group, and low-concentration group, respectively. Meanwhile, 717, 581, and 509 significantly differential microorganisms were detected in pairwise comparisons between the positive control group and the high-, medium-, and low-concentration groups, respectively (Fig. 6a). To understand the impact of compound treatment on the functional potential of the mouse intestinal microbiome, normalized pathway abundance was analyzed using metagenomeSeq. The results showed that the low-concentration group had 18 significantly altered pathways compared with the positive control group, ranking the first among other groups. Pathways such as steroid biosynthesis, toluene degradation, toxoplasmosis, and xylene degradation were downregulated, while pathways included cyanoamino acid metabolism, flavonoid biosynthesis, D-Arginine and D-ornithine metabolism, betalain biosynthesis, chloroalkane and chloroalkene degradation were upregulated. The medium-concentration group resulted in nine significantly differential pathways, all of which were upregulated. These pathways included atrazine degradation, D-Arginine and D-ornithine metabolism, shigellosis, bacterial invasion of epithelial cells, hypertrophic cardiomyopathy, and penicillin and cephalosporin biosynthesis, among others. In contrast, the high-concentration group exhibited five significantly differential pathways, with betalain biosynthesis, bacterial invasion of epithelial cells, hypertrophic cardiomyopathy among those upregulated, while Parkinson’s disease and shigellosis were downregulated (Fig. 6b).
Differential microbial analysis and functional analysis of intestinal of intestinal microbiota. (a) Differential analysis of microorganisms between groups. (b) Pathway enrichment analysis of mice intestinal microorganisms.
Additionally, two pathways were common across three comparison pairs, with shigellosis and hypertrophic cardiomyopathy pathways upregulated in both low and medium-concentration groups. However, in the high-concentration group, hypertrophic cardiomyopathy was upregulated, while shigellosis was downregulated. Nine unique pathways were identified in the low-concentration group, including flavonoid biosynthesis, African trypanosomiasis, xylene degradation, chloroalkane and chloroalkene degradation, cyanoamino acid metabolism, limonene and pinene degradation, non-homologous end-joining, and toluene degradation, among others (Fig. 6b).
Comparing pathways across comparison pairs revealed that African trypanosomiasis, carotenoid biosynthesis, and Staphylococcus aureus infection pathways were common among them. Additionally, chlorocyclohexane and chlorobenzene degradation, calcium signaling pathway, and D-Arginine and D-ornithine metabolism were enriched between compound treatment groups and the blank control group. Penicillin and cephalosporin biosynthesis, as well as the shigellosis pathways, were unique to the comparison between the positive control group and the blank control group. Moreover, unique metabolic pathways were identified in the high- and low-concentration groups compared with the blank control group, including cyanoamino acid metabolism, xylene degradation, beta-lactam resistance, photosynthesis, photosynthesis-antenna proteins, and toluene degradation (Fig. 6b).
These findings demonstrated that compound treatments significantly influenced the diversity and composition of the intestinal microbiome in mice. Specifically, the high-concentration group showed the most significant difference in microbial abundances and the low-concentration group exhibited the highest number of differentially abundant KEGG metabolic pathways when compared with both the blank and positive control groups.
Comparative analysis revealed differences in intestinal microbial composition across groups
To further understand the differences in intestinal microbial composition among groups, Venn and LEfSe analyses were performed. The results of Venn analysis showed that the numbers of microbial species uniquely expressed in the blank control, positive control, high-concentration, medium-concentration, and low-concentration groups were 2743, 2716, 2896, 1926, and 1315, respectively, with 55 microbial species commonly expressed across all groups. Observation of the number of shared microbial species between groups revealed the highest number shared between the positive control and high-concentration groups, totaling 516 species. This was followed by 346 species shared between the blank control and positive control groups, and 301 species shared between the medium and low-concentration groups. The lowest numbers were shared when the low-concentration group was compared with both the positive and blank control groups, with 26 and 13 species detected, respectively (Fig. 7a). This may indicate a greater similarity in species composition between the positive control and high-concentration groups, the blank control and positive control groups, and the medium and low-concentration groups, with the lowest similarity observed between the low-concentration group and the positive and blank control groups.
Group-specific taxonomic signature analysis of mouse intestinal microbiota. (a) Venn analysis of intestinal microbial composition across groups; (b) LEfSe analysis of intestinal microbial composition across groups.
The results of LEfSe analysis identified 3 phyla, 6 classes, 9 orders, 15 families, and 23 genera among the five groups. In the blank control group, genera such as Anaerotruncus, Kineothrix, Lactobacillus, and Staphylococcus were identified. The positive control group comprised genera including Alloprevotella, Bacteroides, Escherichia, Ihubacter, and Muribaculum. In the high-concentration group, genera such as Desulfovibrio, Flavonifractor, and Prevotella were identified. The medium-concentration group identified genera including Absiella, Alistipes, Eisenbergiella, Odoribacter, Parabacteroides, and Ruminococcus. In the low-concentration group, genera such as Cronobacter, Enterobacter, Helicobacter, Salmonella, and Sutterella were identified (Fig. 7b).
Compound treatment altered SCFA composition in diarrheic mice
To reveal the impact of compound treatment on the intestinal environment of mice, UPLC-MS/MS analysis was conducted on the feces from mice, and SCFAs were relatively quantified. The results indicated that hexanoic acid had a higher level in the blank control group and was significantly higher than in other groups. Acetic acid, butyric acid, isovaleric acid, valeric acid, propionic acid, and isobutyric acid were significantly higher in the high-concentration group compared with other groups. Acetic acid, butyric acid, isovaleric acid, propionic acid, and isobutyric acid also exhibited higher levels in the low-concentration group (Fig. 8a). Correlation analysis of seven SCFAs demonstrated significant positive correlations. Specifically, valeric acid showed a significant positive correlation with both butyric and propionic acids. Acetic acid exhibited significant positive correlations with butyric, propionic, isovaleric, and isobutyric acids. Butyric acid displayed significant positive correlations with propionic and isobutyric acids, while propionic acid showed significant positive correlations with isovaleric and isobutyric acids. Lastly, isovaleric acid exhibited a significant positive correlation with isobutyric acid (P < 0.01) (Fig. 8b).
Metabolomic analysis of short-chain fatty acids (SCFAs). (a) Heatmap displaying the levels of SCFAs in samples; (b) Correlation analysis of SCFAs; (c) Number of significantly differential SCFAs; (d) Differential profiling of SCFAs.
To understand the differential changes in fatty acids caused by compound treatment, further analysis was conducted by comparing each treatment group with the blank and positive control groups, selecting significant SCFAs based on VIP > 1 and FDR < 0.05. Compared with the blank control group, the positive control, high, medium, and low-concentration groups had 5, 6, 5, and 4 differential SCFAs, respectively. In contrast, compared with the positive control group, the high, medium, and low-concentration groups each exhibited seven differential SCFAs (Fig. 8c). The levels of hexanoic acid, valeric acid, butyric acid, and acetic acid were higher in the positive control group than those in the blank control group, while the level of isovaleric acid showed the opposite trend. The medium-concentration group displayed a decrease in hexanoic acid, valeric acid, and isovaleric acid levels, coupled with an increase in acetic and butyric acid levels. The levels of differential SCFAs in both the high and low-concentration groups increased. Compared to the positive control group, only the medium-concentration group showed a decrease in isovaleric and isobutyric acids, with an increase in hexanoic acid, valeric acid, acetic acid, and butyric acid, while the high- and low-concentration groups saw an increase in all seven SCFAs. Additionally, acetic and butyric acids showed differential expression across all groups (Fig. 8d).
SCFA changes and KEGG pathway enrichment analysis revealed differential impacts on mouse physiological processes across groups
To uncover the changes of physiological and biochemical processes in Salmonella-infected mice, we conducted KEGG pathway enrichment analysis of the seven SCFAs. The analysis revealed that certain SCFAs were involved in multiple metabolic pathways. Pathways exclusively involving acetate synthesis included cholinergic synapse, glycolysis/gluconeogenesis, glycosaminoglycan biosynthesis – heparan sulfate/heparin, glyoxylate and dicarboxylate metabolism, phosphonate and phosphinate metabolism, pyruvate metabolism, sulfur metabolism, and taurine and hypotaurine metabolism. Pathways solely involving propionate synthesis were nicotinate and nicotinamide metabolism, and those exclusively involving butyrate were butanoate metabolism. Additionally, multiple SCFAs participated in the same metabolic processes. Specifically, acetate, propionate, and butyrate jointly contributed to the carbohydrate digestion and absorption pathway. Acetate and propionate together joined in propanoate metabolism, while acetate, propionate, butyrate, isobutyrate, and isovalerate collectively engaged in the protein digestion and absorption pathway (Fig. 9).
Enrichment analysis of SCFAs.
From this analysis, we discovered that compared with the blank control group, the positive control group was primarily enriched in pathways like protein digestion and absorption, carbohydrate digestion and absorption, propanoate metabolism, glycosaminoglycan biosynthesis – heparan sulfate/heparin, and cholinergic synapse. The high-concentration group was mainly enriched in protein digestion and absorption, carbohydrate digestion and absorption, propanoate metabolism, and butanoate metabolism pathways. The medium-concentration group were primarily enriched in taurine and hypotaurine metabolism, propanoate metabolism, carbohydrate digestion and absorption, and protein digestion and absorption pathways. The low-concentration group was primarily enriched in protein digestion and absorption, carbohydrate digestion and absorption, propanoate metabolism, and butanoate metabolism pathways. Compared with the positive control group, the high, medium, and low-concentration groups predominantly enriched in protein digestion and absorption, carbohydrate digestion and absorption, and propanoate metabolism, etc.
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- Source: https://www.nature.com/articles/s41598-024-73034-2