Short-term TCM intervention alters the composition and functions of the gut microbiota
To investigate the impact of short-term TCMs with either Cold or Hot properties on the composition and functions of the gut microbiome, C57BL/6J mice were given TCM extracts or vehicle once a day for 7 days. The 16S rDNA sequencing results for fecal bacteria showed an increased richness and evenness of the gut microbiome after a Hot TCM (RG, GJ and FZ) intervention, but not Cold TCM (Fig. 1a). However, the PCoA at the operational taxonomic units (OTU) level showed that Hot TCM treated mice were closer to the Control group than Cold TCM treated mice, suggesting Cold TCM (DH, HQ and HL) exerted more impacts on the structural composition of the gut microbiota than Hot TCM (Fig. 1b, c). Among the Hot TCM treated groups, the relative abundance of Proteobacteria was significantly increased in FZ group, while Desulfobacterota was reduced in both the FZ and GJ groups (Fig. 1d). In contrast, decreased relative abundance of Desulfobacterota and increased Proteobacteria were observed in the 3 groups of Cold TCM, while Firmicutes was only significantly increased in the HQ group (Fig. 1e). Additionally, the Firmicutes-to-Bacteroidetes ratio (F/B ratio) was universally reduced by Hot TCM, but not by Cold TCM. To compare the common or unique characteristics that were altered by Hot or Cold TCM short-term intervention, Venn analysis was performed based on the changed microbiota at the genus level. First, the regulated genus in RG, GJ and FZ were used in the Venn analysis, and 16 genera which were regulated by two and three water extracts simultaneously were considered as Hot TCM regulated genus. Then, 15 genera were observed in Cold TCM in the same way. Next, the above 16 Hot TCM regulated genus and 15 Cold TCM regulated genus were involved for further Venn analysis and 7 genera were found to be significantly altered by both Hot and Cold TCM, while the remaining 9 and 8 genera were uniquely altered by Hot TCM and Cold TCM respectively (Fig. 1f, Additional file 1: Fig. S1A). In the 7 shared genus, only g__Allobaculum was reverse regulated, which was increased by Hot_ST and decreased by Cold_ST, the remaining genus was decreased by both natures. Hot TCM intervention specifically increased the abundance of g__Tuzzerella, g__Eubacterium_oxidoreducens_group, g__unclassified_c__Clostridia, and g__Colidextribacter, and decreased the abundance of g__UBA1819, g__unclassified_p__Firmicutes, g__Enterorhabdus, g__Lactobacillus and g__Faecalibaculum. The Cold TCM intervention increased the abundance of g__Bacteroides and g__Bacteroides, and decreased the abundance of g__unclassified_f__Erysipelotrichaceae, g__Clostridium_sensu_stricto_1, g__DNF00809, g__Muribaculum, g__Candidatus_Saccharimonas and g__Odoribacter.
To reveal the impact on microbial functions after a TCM intervention, PICRUSt2 analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway was performed. At KEGG pathway level 3, Venn analysis between Hot and Cold TCM showed that more changes were induced by Hot TCM on microbial functions (Fig. 1g), 11 pathways were uniquely changed by Hot TCM, and most of them belonged to Metabolism at level 1, including the adipocytokine signaling pathway, tyrosine metabolism, d-arginine and d-ornithine metabolism. While 8 pathways were only changed by Cold TCM and most belonged to environmental information processing at level 1 including the cAMP signaling pathway (Fig. 1g, Additional file 1: Fig. S1B). In addition, the capability of the gut microbiome on aromatic compound degradation and drug metabolism were regulated by both Hot and Cold TCM. In summary, short-term intervention with Hot TCM significantly increased microbiota diversity, while Hot and Cold TCM made significant but different changes to the gut microbiota and its functions.
The compositional and functional changes of gut microbiota after long-term TCM intervention
Given that the TCM treatment period is highly variable in the clinic, we intended to explore the impact of long-term TCM intervention on mice during a 35-day treatment period. In contrast to the short-term intervention, only HL treated mice showed a decreased value in the Shannon index (Fig. 2a), suggesting reduced α diversity of the gut microbiota. In line with the short-term intervention (Fig. 1b), PCoA at the OTU level showed that the long-term Cold_TCM group was clearly separated with the Con group alongside PC1, but not the Hot_TCM group (Fig. 2b, c). Among the three cold herbs, DH and HQ share similar structures and separated with Con alongside PC1. HL ran the farthest distance, with Con among the three cold herbs and was separated with Con alongside PC2. According to the most significant changes in the microbiome structure in the HL group, we can claim that the clear separation of the long-term Cold TCM group from the control group is mainly contributed by HL. As shown in Fig. 2, long-term intervention of Hot TCM had only a minor impact on the relative abundance of gut microbiota at the phylum level, except for the reduced Desulfobacterota in GJ and FZ treated mice, while HL treatment resulted in increases in Verrucomicrobiota and Campilobacterota, and depletion of Deferribacterota. At the genus level, the relative abundance of g__Muribaculum was increased, and g__Dubosiella was decreased by both Hot_LT and Cold_LT. Four genera were specifically regulated by Hot_LT including g_parabacterodies and g_desulfovibrio, and 13 genera were significantly changed by Cold_LT including g_faecalibaculum and g_alloprevotella (Fig. 2f, Additional file 2: Fig. S2A).
We next analyzed the impact on functions of the gut microbiota using PICRUSt2 analysis. In contrast to the functional changes induced by the short-term TCM intervention, there were 4 and 65 pathways associated with the long-term Hot or Cold TCM intervention respectively, but no shared pathway (Additional file 2: Fig. S2B). Most of the changed pathways belonged to metabolism and human disease (Fig. 2g, Additional file 2: Fig. S2C). The results suggested that long-term TCM intervention with Hot nature exerted relatively minor impacts on the composition and functions of the gut microbiota, but Cold TCM substantially influenced the composition of the gut microbiota, especially in terms of microbiome functions.
The divergent compositional and functional changes of the gut microbiome after short-term or long-term TCM intervention
To compare the influence on the composition of gut microbiota of short-term or long-term TCM intervention, conjoint analysis based on 16S rDNA sequencing data was adopted. The variations of the major 6 phylum under short-term or long-term TCM interventions are shown in Fig. 3a, including Actinobacteriota, Firmicutes, Verrucomicrobiota, Bacterodiota, Desulfobacterota and Proteobacteria. The long-term Cold TCM intervention resulted in a universally increased abundance of Actinobacteriota and Desulfobacterota, but reduced Bacterodiota and Proteobacteria. Interestingly, the impact of the long-term FZ intervention on Actinobacteriota, Bacterodiota and Proteobacteria was similar with Cold TCM. This may due to the high alkaloid components in FZ, which is widely associated with cold drugs. In addition, long-term HQ and HL treatment induced the same trend of changes in Firmicutes and Verrucomicrobiota. In general, the number of altered genera was reduced after long-term TCM intervention compared to the short-term group interventions in the Hot TCM and HL group, but was increased in the DH and HQ group (Fig. 3b).
In order to compare the impact on gut microbial functions, the number of altered pathways after short- or long-term TCM interventions were analyzed based on 16S rDNA gene sequencing data (Fig. 3c). The altered pathways were 7, 33 and 52 in the short-term RG, GJ and FZ groups, and 28, 28 and 52 in the DH, HQ and HL groups, respectively. The majority of altered pathways were downregulated by the short-term TCM intervention, except for FZ and HL. In contrast, long-term Hot TCM treatment resulted in 4, 26 and 17 altered pathways by RG, GJ and FZ, and most of them were upregulated. However, long-term Cold TCM treatment induced a dramatic increase in the number of altered pathways, and most of them were upregulated. In summary, the impact of TCM interventions on microbial functions were TCM nature- and time-related. The influence on gut microbial functions were enhanced by long-term treatment with Cold TCM than with a short-term intervention. Although the number of altered pathways was decreased in long-term Hot TCM treated mice compared to their short-term partners, the alteration trend was opposite in Cold TCM between the two time points.
Short-term TCM intervention altered the serum metabolic profile
Given the link between the gut microbiota and host metabolism [33], we further compared the impact of short-term TCM intervention with either Hot or Cold nature on the serum metabolic profile using untargeted metabolomics. With the criteria of variable importance in projection (VIP) > 1, the short-term intervention of RG, GJ and FZ induced changes of 70, 67, and 73 in serum metabolites, while DH, HQ and HL induced 70, 66 and 67 metabolites change respectively (Fig. 4a, Additional file 4: Fig. S4A). The short-term intervention of RG regulated carnitine metabolites specifically, and the phenyl propanoic acids class in the GJ group. Venn analysis was performed to explore further the differences between Hot and Cold TCM (Fig. 4b). Hot TCM induced 67 changed metabolites, while 58 metabolites were regulated by Cold TCM, most of them being amino acids, organic acid and carbohydrates. The fourth metabolite category was lipids in Hot TCM with a proportion of 10.45%, and fatty acids in Cold TCM with a proportion of 12.07% (Additional file 4: Fig. S4B). Thirty-three metabolites were commonly altered in both groups.
Pathway enrichment analysis was employed to explore changes in biological functions. Hot TCM regulated 2 metabolic pathways specifically while 5 pathways were regulated by Cold TCM according to the Venn analysis; most pathways were regulated by both of them (Fig. 4c, d, Additional file 4: Fig. S4C, D). It was noticed that Hot_ST had a more extensive disturbance effect on synthesis and decomposition of amino acids, while Cold_ST regulated glucose and lipid metabolism, like glycolysis/gluconeogenesis, linoleic acid metabolism and d-glutamate metabolism. These results suggested that a short-term TCM intervention with two properties exerted relatively major impacts on serum metabolites, but minor influences on the function of metabolites, especially for Hot_ST.
Serum metabolic profile after a long-term TCM intervention
Next, to explore the long-term impact of the TCM intervention on host metabolism, mice received 35 days TCM treatment with a Hot or Cold nature. After the long-term intervention, the number of screen metabolites was reduced in 6 groups meeting the condition of VIP > 1 compared to their short-term partners (Figs. 4a, 5a). The serum concentration of indole metabolites was increased by FZ_LT, while serum concentrations of benzoic acid and phenyl propanoic acid metabolites were increased, while pyridines metabolites were decreased by HQ_LT. Based on the Venn analysis, most of the changed metabolites in Hot_LT or Cold_LT were included in amino acids, but the second metabolite class was fatty acids (proportion of metabolites, 17.07%) in Hot_LT and organic acid (18.42%) in Cold_LT (Fig. 5b, Additional file 5: Fig. S5B).
Corresponding to pathway enrichment analysis of the short-term intervention, most pathways were regulated by both Hot_LT and Cold_LT (Fig. 5c, Additional file 5: Fig. S5C, D). Also, the linoleic acid metabolism pathway was specific reduced by Hot_LT, and Cold_LT specifically regulated the glycine, serine and threonine metabolism pathways, the aminoacyl-tRNA biosynthesis pathway, and the cysteine and methionine metabolism pathways. Together, serum metabonomic variety by TCM was property specific and narrowed with a prolonged intervention.
The regulation of serum metabolomic by TCM is nature-specific and time-related
To evaluate further the impact of TCM on host metabolism, time-cross analysis based on serum metabolomic data was employed. The number of changed metabolites and pathways were found to be reduced along with extension of the intervention time in two properties (Fig. 6a–e). Hot TCM and Cold TCM had different impacts on metabolites class with an increase in the intervention time (Fig. 6b, c). HQ specifically increased the metabolite numbers of the benzoic acid and phenyl propanoic acid classes, and RG specific decreases in the metabolite numbers of the carnitines class. The metabolite numbers of amino acids, fatty acids and inorganic oxide classes were found to be regulated inversely by two properties. Based on KEGG pathway enrichment and Venn analysis, Hot_ST, Cold_ST, Hot_LT and Cold_LT regulated 2, 5, 1 and 3 pathways respectively, and the metabolites included in each pathway are shown in Additional file 6: Fig. S6, which were suspected to play a constructive role in distinguishing Hot TCM from Cold TCM. In the short-term intervention (Additional file 6: Fig. S6A), the 3 Hot TCM groups had reduced the concentration of l-tyrosine and increased the concentration of l-phenylalanine, which included the phenylalanine, tyrosine and tryptophan biosynthesis pathways, and the phenylalanine metabolism pathway. DH and HL significantly reduced the relative abundance of lactic acid involved in the glycolysis/gluconeogenesis pathway (Fig. 6d). In the long-term intervention (Additional file 6: Fig. S6B, Fig. 6e), linoleic acid was associated with the linoleic acid metabolism pathway of Hot_LT and 12 metabolites were including in 3 pathways of Cold_LT. Tryptophan concentrations were significantly increased by DH, proline was decreased by HQ, and threonine was decreased by HL. In addition, the presence or absence of carnitine, benzoic acid and phenyl propanoic acid classes may be used to classify Hot and Cold TCM.
Correlation analysis between altered gut microbiota and serum metabolites
Summarizing the above changes, the number of specific genera, bacteria pathways, metabolites and metabolite pathways was decreased in Hot_LT compared with Hot_ST (Fig. 7a–d). These numbers showed a different tendency in Cold_LT when compared to Cold_ST. In the short-term intervention, specific genus and bacteria pathway numbers were similar during Cold TCM compared to Hot TCM, but these numbers were surprisingly increased during Cold_LT (Fig. 7a, b). Despite Cold TCM maintaining the same change tendency of specific metabolite and metabolites pathway numbers with Hot TCM, the numbers in Cold TCM were much bigger than for Hot TCM at two time points (Fig. 7c, d), suggesting a wider body metabolism effect after the Cold TCM intervention.
Given the strong association of serum metabolites with the microbiome, a correlation analysis based on a changed gut microbiota (specific differential genus with P < 0.05) and changed serum metabolites (specific differential metabolites with VIP > 1) was carried out with the condition of the correlation P < 0.05. After the short-term intervention, 8 genera were significantly associated with 15 serum metabolites in Hot TCM (Fig. 7e). Pathway enrichment analysis of these 15 metabolites revealed two pathways which were the same as Hot_ST specific pathways that are represented in Fig. 4e, and the metabolites phenylalanine and tyrosine were associated with these two pathways (Fig. 7f). The correlation of phenylalanine with g__Enterorhabdus, g__Eubacterium_oxidoreducens_group, g__Faecalibaculum, g__Lactobacillus and g__Tuzzerella was negative, with g__UBA1819, g__unclassified_c__Clostridia, g__unclassified_p__Firmicutes was positive, while tyrosine was negatively associated with g__unclassified_p__Firmicutes, but positively associated with g__Tuzzerella, g__unclassified_c__Clostridia, g__Lactobacillus, and g__UBA1819. These results suggested that the changes in the gut microbiome may partly induce the specific alterations in serum metabolites and functions. Our conjecture was verified in subsequent correlation analyses.
In Cold TCM, there were 7 genera associated with 14 metabolites, and these metabolites were evaluated in pathway enrichment analysis. Two of 8 pathways obtained by enrichment (impact > 0) were coincident with the Cold_ST specific pathway (Figs. 4e, 7g, h). Glutamic acid and oxo glutaric acid were included in the d-glutamine and d-glutamate metabolism pathway. Oxo glutaric acid was positively correlated with g_norank_f_Ruminoccoccaceae, which was negatively correlated with glutamic acid. Also, oxo glutaric acid was negatively correlated with g__Bacterodiess and g__Clostridium_sensu_stricto_1. Linoleic acid which was clearly involved in the linoleic acid metabolism pathway was negatively associated with g__Bacteroides and g__Clostridium_sensu_stricto_1, and positively associated with g_norank_f_Ruminoccoccaceae.
In Hot_LT, only the g__Eubacterium_ventriosum_group was negatively associated with isoleucine, leucine and valine. This finding may be attributed to the significant reduction in genus numbers (Fig. 8a), shown by the slight effect of Hot_LT on host metabolism. In Cold_LT, 8 genera were significantly associated with 4 metabolites (Fig. 8b), and KEGG pathway enrichment indicated that Cold_LT specifically regulated the pathways involved in glycine, serine and threonine metabolism (impact > 0, Fig. 8c), which also involved the metabolite threonine. Threonine was negatively associated with g__Gordonibacter and g__A2, and positively associated with g__Monoglobus and g__norank_f__Eggerthellaceae. These results partly verified our conjecture, and also revealed different changes in the microbiome and in metabolism caused by Hot TCM and Cold TCM.