The divergent impacts on gut microbiome and host metabolism by Traditional Chinese Medicine with Cold or Hot property in mice

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Herbs within TCM usually are classified into different properties ranging from cold, cool, warm to hot, which are simplified as Cold and Hot properties. TCMs with either Cold or Hot property are differently used in formulae for the purpose of overcoming the imbalance of patients. Emerging evidence has highlighted that altered gut microbiota or host metabolism are critically involved in affecting TCM properties. However, the exact influences and crosstalk on gut microbiota and host metabolism remain poorly understood. Methods In current study, we investigated the divergent impacts of six TCMs with either Cold or Hot properties on gut microbiome and host metabolism during short- or long-term intervention in mice. Six typical TCMs with Hot or Cold properties including Cinnamomum cassia (L.)J.Presl (rougui, RG), Zingiber officinale Rosc. (ganjiang, GJ), Aconitum carmichaeli Debx. (fuzi, FZ), Rheum palmatum L. (dahuang, DH), Scutellaria baicalensis Georgi (huangqin, HQ), and Coptis chinensis Franch. (huanglian, HL) were selected and orally administrated on male C57BL/6J mice for a short- or long-term (7 or 35 days). At the end of experiments, serum and cecal contents were collected for metabolomic and gut microbiome analysis with GC-MS or 16S rDNA sequencing. Results The results showed that gut microbiome experienced divergent changes both in composition and function by short-term intervention of TCM with either Cold or Hot properties, whereas long-term intervention of Hot TCM reduced the number of changed genus and functional pathways of bacteria, but were increased by Cold TCM, especially in HL group. The serum metabolic profile showed the Glycolipid metabolism related pathways were specifically regulated by Cold TCM, and the influence of TCM on host metabolism minimized with time. Further correlation analysis indicated the altered gut microbiota was closely correlated with serum metabolites as well. Conclusion Our results indicated that the TCM properties could be, at least partially, characterized by the alteration of gut microbiota and metabolic profile, implying that the divergent responses of gut microbiome and host metabolism are involved in different natures of TCM. Traditional Chinese medicine Hot-Cold theory Host metabolism 16S rDNA Serum metabonomic Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Traditional Chinese Medicine (TCM) has been practiced and developed over thousands of years from ancient China. In past decades, there is increasing passion on the clinical application of TCM, as well as the research on the scientific basis of TCM theory in the worldwide [ 1 , 2 ]. However, the complexity of TCM theories and numerous chemical components within each herb is still the great challenge for the modernization of TCM. The TCM nature (Yao Xing in Chinese) including Cold, Cool, Hot and Warm is a type of basic TCM characters that was recorded in "Shen Nong’s Materia Medica (Shen Nong Ben Cao Jing)" about 2000 years ago. Although we still know little about the scientific basis of TCM nature, different herbs with either Hot or Cold property were selectively applied in TCM formulae for treating diseases with opposite Syndrome (Zheng in Chinese) by TCM physicians in clinic, that is “Treating the Hot Syndrome with Cold nature medicine and treating Cold Syndrome with Hot nature medicine” [ 2 – 5 ]. In recent years, a lot of studies have been performed to explore the biological basis of TCM nature by testing the different impacts of Hot or Cold TCM on body temperature regulation [ 6 – 8 ], host energy metabolism [ 9 – 11 ], including Na + /K + ATP enzyme activity [ 12 , 13 ], et al. Previously, we compared the divergent influences of Hot or Cold TCM on physiology of mice by orally given Hot or Cold TCM for 7 or 35 days, including RG, GJ, FZ, DH, HQ, and HL. Our results indicated that short-term Hot or Cold TCM intervention exerted more significant impacts on lipids metabolism in liver or adipose tissue than long-term intervention [ 14 ], suggesting the host adaptation to long-term TCM intervention. In addition to the direct influence on host physiology, gut microbiome modulation is the intrinsic way for TCM actions [ 15 , 16 ]. recently investigated the effects of 12 TCM with different degree of Hot or Cold nature on gut microbiota in mice, and demonstrated that the influences on gut microbiota was in the order of Cold > Cool > Warm > Hot TCM [ 17 ]. It is reasonable that Cold TCM usually contained much more alkaloids than Hot TCM, which possess anti-bacteria activity [ 18 ]. Meanwhile, the interaction between TCM and gut microbiota may give rise to the production of microbial metabolites, as well as regulation on host metabolism simultaneously [ 19 , 20 ]. Unfortunately, there are few studies on the TCM nature by incorporating the changes in gut microbiota and host metabolism at the same time. In current study, we investigated the divergent impacts of Hot or Cold TCM on gut microbiome and host metabolism in mice by orally giving aqueous extraction of six TCM with typical Hot or Cold nature for a short-term (7 days) and a long-term (35 days) interventions based on 16S rDNA and un-targeted metabolomics approaches. Our results revealed that TCM with Hot or Cold nature exerted unique influences on gut microbiome and host metabolism under either short-term or long-term intervention. The correlation analysis suggested the interplay between altered gut microbiome and metabolic pathways that were modulated by Hot or Cold TCM specifically. Materials And Methods Plant extract materials Six TCM of RG (200702), GJ (200415), FZ (200706), DH (200717), HQ (200701), and HL (200820), were purchased from Shanghai KangQiao Chinese Medicine Co. Ltd. (Shanghai, China), and were authenticated by Professor Lihong Wu of the Institute of Chinese Medicine, Shanghai University of Traditional Chinese Medicine. A voucher specimen of the individual TCMs was deposited at the School of Pharmacy, Shanghai University of Traditional Chinese Medicine. For GJ, HL, and HQ, boiling violently for 15 minutes, keep slightly boiling for 45 minutes. Reflux extraction device is used in the whole process. For RG and DH, boiling violently for 25 minutes, keep slightly boiling for 65 minutes. For FZ, boiling violently for 10 minutes, keep slightly boiling for 20 minutes. Each TCM was extract twice, combine the filtrate, and concentrated at 60 ◦C under reduced pressure. Approximately 100 g raw material of each TCM was soaked in ultrapure water for 4 hours before extraction. Shorter extraction time was selected based on the content of volatile components in RG and anthraquinone in DH. And longer extraction time was aim to reduce the toxic component in FZ. These extracts have been analyzed for compositional. Compositional analysis of the aqueous extract The Ultrahigh performance liquid chromatography coupled with quadrupole time flight mass spectrometry (UPLC-QTOF-MS/MS) analysis was performed on Agilent 1290 UPLC system coupled to Agilent 6545 Q-TOF instrument (Agilent, Santa Clara, CA, USA). Spectrometer was operated in full-scan TOF-MS at m/z 50-1500 and information-dependent acquisition (IDA) MS/MS modes. Specific conditions as follows: gas temperature (320◦C), Drying Gas (11L/min), Nebulizer (35psi), Shealth Gas Temp (350◦C), Shealth Gas Flow (11L/min), and Collision Energy (40). Chromatographic separation was achieved using Agilent ZORBAX RRHD Eclipse Plus C18 (3.0× 100 mm, 1.8 μm) analytical column. The mobile phase consisted of 0.1% formic acid (v/v) in water and acetonitrile. The temperature(◦C), detection wavelength (nm), and velocity of flow (mL/min) was 30, 260, 0.5 for RG, 40, 215, 0.3 for GJ, 30, 190-400, 0.3 for FZ, 25, 260, 0.3 for DH, 30, 280, 0.3 for HQ. For HL, HPLC analysis was obtained for stable baseline and coupled to Agilent ZORBAX Extend-C18(4.6 mm × 250 mm, 5 µm), which was employed at 25◦C with sample ingection volume of 2µL. The mobile phase consisted of acetonitrile and 0.1% trifluoroacetic acid solution. Each TCM was analysis by specific gradient elution procedure. Animal experiments Male C57BL/6J mice of 5 weeks were provided by Laboratory Animal Center of Shanghai University of Traditional Chinese Medicine (Shanghai, China). Mice were orally administrated with vehicle or respective test extracts once daily for 7 days or 35 days. The mouse dose is conversed from the maximum clinical dose specified in the Pharmacopoeia of the People’s Republic of China (2015 edition) according to body surface area, that is 0.65g/kg for RG, 1.3g/kg for GJ, 1.95g/kg for FZ, 1.95g/kg for DH, 1.3g/kg for HQ, and 0.65g/kg for HL. All mice were housed in a 12-hour light (7 AM to 7 PM) and 12-hour dark (7 PM to 7 AM) cycle, with free access to water and diet. The experiments were conducted under the Guidelines for Animal Experiment of Shanghai University of Traditional Chinese Medicine and the protocol was approved by the institutional Animal Ethics Committee. 16S rDNA Sequencing and Untargeted metabolomics Fecal DNA was isolated for 16S rDNA sequencing and deproteinized serum was collected for untargeted metabolomics. For 16S rDNA sequencing, fecal DNA was isolated using the Qiagen QIAamp DNA Stool Mini Kit (Qiagen, Dusseldorf, Germany), Illumina sequencing was done based on published methods[21, 22]. For untargeted metabolomics, ion peaks were determined and denoising baseline was corrected in XploreMET, then metabolite peaks were resolved by GC-TOFMS. All the steps were according to previous literature [23, 24]. Sequence data associated with this project have been deposited in the NCBI Short Read Archive (PRJNA832599, PRJNA832616) database. Statistical analysis Data shown in this study are expressed as mean ± Standard Error of Mean (±SEM) unless otherwise noted. Differences between groups in microbiota and function were calculated by Mann-Whitney U test. Other comparisons between two groups were calculated by two-tailed Student’s t-test. A p value < 0.05 was considered statistically significant. Results And Discussion Short-term TCM intervention alters the composition and function of gut microbiota To investigate the impacts of short-term TCM intervention with either Cold or Hot properties on composition and function of gut microbiome, C57BL/6J mice were given with corresponding TCM extracts or vehicle once daily for 7 days. The 16S rDNA sequencing of fecal bacteria showed increased richness and evenness after Hot TCM (RG, GJ, and FZ) intervention, but not Cold TCM (Fig 1A), however, the PCoA at OTU level showed mice treated by Hot TCM were closer to Control group than group of Cold TCM, suggesting Cold TCM (DH, HQ, and HL) exerted more impacts on structural composition of gut microbiota than Hot TCM (Fig1B-C). Among the Hot TCM treated groups, the relative abundance of Proteobacteria was significantly increased in FZ group, while Desulfobacterota was reduced in both FZ and GJ groups (Fig 1D). In contrast, reduced relative abundance of Desulfobacterota and increased Proteobacteria were observed in the three groups of Cold TCM, while Firmicutes was only significantly increased in HQ group (Fig 1E). Additionally, the Firmicutes-to-Bacteroidetes ratio (F/B ratio) was universally reduced by Hot TCM, but not Cold TCM. To compare the common or unique characters that were altered by Hot or Cold TCM short-term intervention, Venn analysis was performed based on the changed microbiota at genus level. Firstly, the regulated genus in RG, GJ, and FZ were involved for Venn analysis, and 16 genera which was 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, seven genera were found to be significantly altered by both Hot and Cold TCM, while the remaining nine and eight genera were uniquely altered by Hot TCM and Cold TCM respectively (Fig 1F, Fig S1A). To reveal the impacts on microbial functions after TCM intervention, PICRUSt2 analysis based on 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 unique changed after Hot TCM intervention, and most of them were belong to Metabolism at level 1 including adipocytokine signaling pathway, tyrosine metabolism, d-arginine and d-ornithine metabolism. While 8 pathways were only changed by Cold TCM and most were belonging to environmental information processing at level 1, including cAMP signaling pathway (Fig1G and FigS1B). In addition, the capability of gut microbiome on aromatic compounds 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 on gut microbiota and functions. The compositional and functional changes of gut microbiota by long-term TCM intervention Given the TCM treatment period is highly variable in clinic, we intended to explore the impacts of long-term TCM intervention on mice by a 35-day of treatment. In contrast to short-term intervention, only HL treated mice showed decreased value in Shannon index (Fig 2A), suggesting reduced α diversity of gut microbiota. In line with the short-term intervention (Fig 1B), PCoA at OTU level showed long-term Cold_TCM group was clearly separated with Con group alongside PC1, but not in Hot_TCM group (Fig 2B-C). As shown in Fig2, long-term intervention of Hot TCM did minor impacts on relative abundance of gut microbiota at phylum level, except for the reduced Desulfobacterota in GJ and FZ treated mice, while HL treatment resulted in increasing of the relative abundance of Verrucomicrobiota and Campilobacterota, and depletion of Deferribacterota. At genus level, the relative abundance of g__Muribaculum was increased, and of 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 , 13 genus was significantly changed by Cold_LT including g_faecalibaculum and g_alloprevotella in the meantime (Fig 2F, Fig S2A). We next analyzed the impacts on function of gut microbiota by PICRUSt2 analysis. In contrast to the functional changes by short-term TCM intervention, there were 4 and 65 pathways that were associated with the long-term Hot or Cold TCM intervention respectively, but no shared pathway (Fig S2B). Most of the changed pathways belong to metabolism and human disease (Fig2G, Fig S2C). These results suggested that long-term TCM intervention with either Hot or Cold nature exerted relatively minor impacts on composition of gut microbiota, but substantially influenced the functions of gut microbiota, especially in Cold TCM treated mice. The divergently compositional and functional changes of gut microbiome under short-term or long-term TCM intervention To compare the influences on the composition of gut microbiota by 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 were shown in Fig 3A, including Actinobacteriota, Firmicutes, Verrucomicrobiota, Bacterodiota, Desulfobacterota and Proteobacteria. Long-term Cold TCM intervention resulted in universally increased abundance of Actinobacteriota and Desulfobacterota, but reduced Bacterodiota and Proteobacteria. Interestingly, the impact of long-term FZ intervention on Actinobacteriota, Bacterodiota and Proteobacteria was similar with Cold TCM. In addition, long-term HQ and HL treatment induced same trend of changes in Firmicutes and Verrucomicrobiota. In general, the number of altered genera was reduced in long-term TCM intervention compared to short-term groups in Hot TCM and HL group, but increased in DH and HQ group (Fig 3B). In order to compare the impact on gut microbial functions, the number of altered pathways by short- or long-term TCM intervention were analyzed based on 16S rRNA gene sequencing data. The altered pathways were 7, 33 and 52 in short-term RG, GJ and FZ groups, while 28, 28 and 52 in DH, HQ and HL groups, respectively. The majority of altered pathways were down-regulated by 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 up-regulated. However, long-term Cold TCM treatment induced dramatic increase in number of altered pathways, and most of them were up-regulated. In summary, the impacts of TCM intervention on microbial functions were TCM nature- and time-related. The influences on gut microbial functions were enlarged by long-term treatment of Cold TCM than their short-term intervention. Although the number of altered pathways were 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 alters serum metabolic profile Given the interlink of gut microbiota and host metabolism[25], we further compared the impact of short-term intervention of TCM with either Hot or Cold nature on serum metabolic profile by un-targeted metabolomics. With the criteria of VIP>1 (Variable Importance in Projection), the short-term intervention of RG, GJ, and FZ induced changes of 70, 67, and 73 serum metabolites, while DH, HQ, and HL induced 70, 66, and 67 serum metabolites respectively (Fig 4A, Fig S4A). The short-term intervention of RG regulated carnitines metabolites specifically, and phenyl propanoic acids class in GJ group. Venn analysis was performed to further explore the differences between Hot and Cold TCM (Fig 4B). Hot TCM induced 67 changed metabolites, while 58 metabolites were regulated in Cold TCM, most of them belong to amino acids, organic acid, and carbohydrates. The fourth metabolite category is lipids in Hot TCM with proportion 10.45%, and is fatty acids in Cold TCM with proportion 12.07% (Fig.S4B). Thirty-three metabolites were commonly altered at both groups. Pathway enrichment analysis was employed to explore the changes of biological function. Hot TCM regulated two metabolic pathways specifically while five pathways were regulated by Cold TCM according to Venn analysis, also most pathways were regulated by both of them (Fig 4C-D, Fig S4C-D). We noticed that Hot_ST has 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 short-term TCM intervention with two properties exerted relatively major impacts on serum metabolites, but minor specifically influenced the function of metabolites, especially in Hot_ST. Serum metabolic profile after long-term TCM intervention Next, to explore the long-term impact of TCM intervention on host metabolism, mice were given 35 days TCM with Hot or Cold nature. After long-term intervention, the number of screen metabolites was reduced in six groups meet the condition of VIP>1 compared to their short-term partners (Fig4A, Fig5A). Serum level of indoles metabolites was increased by FZ_LT, while serum level of benzoic acids metabolites, phenyl propanoic acids metabolites were increased and pyridines metabolites were decreased by HQ_LT. Based on the Venn analysis, most of the metabolites in Hot_LT or Cold_LT were included in Amino acids, but the second metabolites class is fatty acid (17.07%) in Hot_LT and organic acid (18.42%) in Cold_LT (Fig5B, Fig S5B). Correspond to pathway enrichment analysis of short-term intervention, most pathways were regulated by both Hot_LT and Cold_LT (Fig 5C, Fig S5C-D), Also, linoleic acid metabolism pathway was specific reduced by Hot_LT, and Cold_LT specifically regulated glycine, serine and threonine metabolism pathway, aminoacyl-tRNA biosynthesis pathway, and cysteine and methionine metabolism pathway. Together, serum metabonomic variety by TCM was properties specific and narrowed with prolonged intervention. The regulation of serum metabolomic by TCM is nature-specific and time-related To further evaluate the TCM impacts on host metabolism, time-cross analysis based on serum metabolomic data were adopted, and the number of changed metabolites and pathways were found to be reduced along with the extension of intervention time in two properties (Fig6A-E). Hot TCM and Cold TCM has made different impact on metabolites class with the increase of intervention time (Fig6B, C), which HQ specific increased the metabolites number of benzoic acid class and phenyl propanoic acid class, RG specific decreased the metabolites number of carnitines class. And the metabolites number of amino acids class, fatty acids class, and inorganic oxide class was regulated inversely by two properties. Based on KEGG pathway enrichment and Venn analysis, Hot_ST, Cold_ST, Hot_LT, and Cold_LT has regulated two, five, one, and three pathways respectively, and metabolites included in each pathway were displayed in FigS6, which were suspected to play a constructive role in distinguishing Hot TCM from Cold TCM. In short-term intervention (FigS6A), three Hot TCM groups had reduced the relative abundance of l-tyrosine and increased the relative abundance of l-phenylalanine which included in phenylalanine, tyrosine and tryptophan biosynthesis pathway, and phenylalanine metabolism pathway. DH and HL significantly reduced the relative abundance of lactic acid which contained in glycolysis/gluconeogenesis pathway (Fig6D). In long-term intervention (FigS6B, Fig6E), linoleic acid was containing in linoleic acid metabolism pathway of Hot_LT, 12 metabolites were including in three pathways of Cold_LT. The relative abundance of tryptophan was significantly increased by DH, proline was decreased by HQ, and threonine was decreased by HL. In addition, the presence or absence of carnitines class, benzoic acid class, and phenyl propanoic acid class 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, specific bacteria pathway, specific metabolite, and specific metabolites pathway was decreased in Hot_LT compared with Hot_ST (Fig7A-D). In the meanwhile, these number shown different tendency in Cold_LT when compared with Cold_ST. In short-term intervention, specific genus number and specific bacteria pathway number were nearly in Cold TCM with Hot TCM, but these number were surprisingly increased in Cold_LT (Fig7A, B). Despite Cold TCM kept same change tendency of specific metabolite number and specific metabolites pathway number with Hot TCM, the numbers in Cold TCM were huger than Hot TCM at two time points (Fig7C, D), suggest the wider body metabolism effect after Cold TCM intervention. Given the strong association of serum metabolites with microbiome, correlation analysis based on changed gut microbiota (specific differential genus with p1) was determined with the condition of correlation p<0.05. At short-term intervention, eight genera were significant associated with 15 serum metabolites in Hot TCM (Fig7E). Pathway enrichment analysis on these 15 metabolites obtained two pathways which are same with Hot_ST specific pathways that were represented in Fig 4E, and metabolites phenylalanine and tyrosine were contained in these two pathways (Fig7F). The correlation of phenylalanine with g__Enterorhabdus , g__Eubacterium_oxidoreducens_group , g__Faecalibaculum , g__Lactobacillus , 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 of gut microbiome may partly induced the specific changes on serum metabolites and functions. Our conjecture was verified in subsequent correlation analysis. In Cold TCM, there were seven genera associated with 14 metabolites, these metabolites were conducted in pathway enrichment analysis. Two of eight pathways obtained by enrichment (impact>0) were coincidence with Cold_ST specific pathway, which were displayed in Fig 4E (Fig7G, H). Glutamic acid and oxo glutaric acid were included in d-glutamine and d-glutamate metabolism pathway, oxo glutaric acid was positive correlated with g_norank_f_Ruminoccoccaceae, which was negative correlated with glutamic acid, also oxo glutaric acid was negative correlated with g__Bacterodiess and g__Clostridium_sensu_stricto_1 . Linoleic acid which contained in linoleic acid metabolism pathway was negative associated with g__Bacteroides and g__Clostridium_sensu_stricto_1 , and positive associated with g_ norank_f_Ruminoccoccaceae . In Hot_LT, only g__Eubacterium_ventriosum_group was negative associated with isoleucine, leucine, and valine, these may be attributed to the significant reduction genus number (Fig8A), shown the slight effect of Hot_LT on host metabolism. In Cold_LT, eight genera were significantly associated with four metabolites (Fig8B), and KEGG pathway enrichment leads to Cold_LT specifically regulated pathway glycine, serine and threonine metabolism (impact>0, Fig8C), which contained metabolite threonine. Threonine was negative associated with g__Gordonibacter and g__A2, and positive associated with g__Monoglobus and g__norank_f__Eggerthellaceae . These results partly verified our conjecture, and also shown different changes of microbiome and metabolism caused by Hot TCM and Cold TCM. Discussion In the current study, we investigated the divergent impacts of TCM with either Cold or Hot property on gut microbiome and host metabolism in the context of a short- (7 days) or long-term (35 days) intervention in mice, as well as the long-term. Our results revealed that either Hot or Cold TCM intervention exerted obvious influences on gut microbiome and serum metabolic profile, and the microbial or metabolic changes by Hot or Cold TCM were narrowed with time. The theory of medicinal properties of TCM is one of the core theories of TCM, which is the important basis for guiding the clinical application of TCM [ 26 ]. It is usually recognized that TCM with different properties divergently influence the function of central nervous system, sympathetic nerves and endocrine system, basal metabolic rate and secretion of cytokines [ 27 ]. The Hot TCM, like RG, GJ, and FZ, has the capacity of increasing body temperature [ 28 ], which are usually used for treatment of nervous system diseases [ 29 ], and depression [ 30 ], while Cold TCM such as DH, HQ, and HL has opposite effect on body temperature [ 31 ], which are used for improving depressive-like behavior [ 32 ] with the capacity of regulating energy metabolism and improving mitochondrial function [ 33 ]. In general, given the holistic effect of most TCM on host, those single parameter cannot reflect the systemic impacts of TCM on host metabolism. Therefore, omics approaches, especially metabolomics have been increasingly applied in research on TCM action, and TCM property as well. Meanwhile, the modulation on gut microbiome of TCM has attracted more attentions due to the fact that the complex components within orally administered TCM extracts usually have profound impacts on gut microbiome [ 34 ]. Therefore, in our current study, we adopted combined gut microbiome and serum metabolomics to investigate the impacts of TCM with either Cold or Hot property on mice in the context of short- or long-term intervention. In the short-term intervention, Hot or Cold TCM made different impacts on gut microbiome structure and function, despite the comparable number of changed genera or pathways between the two classes. Previous publications suggest that the components within Hot or Cold TCM regulate lipid metabolism [ 35 – 37 ] and cAMP signaling [ 38 – 40 ] respectively, which were consistent with our findings that adipocytokine signaling pathway was increased by Hot TCM while cAMP signaling pathway was increased by Cold TCM according to PICRUSt2 analysis. Similarly, Zhang et al. studied the impacts of TCM with Hot, Warm, Cool, or Cold nature on gut microbiota of C57BL/6J mice by four weeks intervention, in which dramatic changes of gut microbiota were observed in Cold TCM, but not Hot TCM [ 17 ], suggesting Cold TCM has greater impacts on gut microbiome than Hot TCM, consistent with our findings. Although extremely complicated and poorly understood, the diversified actions of TCM are due to the differences in chemical components. Similarly, the nature of Hot or Cold TCM is associated with their chemical components. In Cold TCM, the main active element of DH was gallic acid, which could be absorbed in intestinal tract producing extensive effects on host metabolism [ 41 ]. However, baicalin and berberine, the main ingredients of HQ and HL, has many biological functions but with low absorption [ 42 , 43 ]. The growing evidence suggest the role of the gut microbiota as targets for the multifunctional role of baicalin and berberine [ 44 , 45 ]. These results echoed our current data, that is, the influence of Cold TCM on gut microbiome was more obvious than Hot TCM, and increased over long-term intervention, but reduced in Hot TCM. In addition to the analysis of gut microbiome and serum metabolite profile individually, correlation analysis between altered gut microbiota and serum metabolites were performed. Our results indicated that changed genera were closely related with serum metabolites. Also, the pathway enrichment analysis based on metabolites which was significantly connected with genus revealed that the changes of phenylalanine, tyrosine and tryptophan biosynthesis pathway and phenylalanine metabolism pathway in Hot_ST, and the changes of d-glutamine and d-glutamate metabolism pathway and linoleic acid metabolism pathway in Cold_ST, and the changes of glycine, serine and threonine metabolism pathway in Cold_LT were related with the regulation of TCM on gut microbiota. In addition, minor impacts on host metabolism were observed in Hot_LT group based on enrichment analysis. Therefore, we speculated that the differential regulation of Hot or Cold TCM on serum metabolic pathways was associated with the modulation on gut microbiota. Conclusion In conclusion, our current study indicated that TCM with Hot or Cold property exerted profound impacts on host metabolism or gut microbiome in composition and function. These influences on host metabolism or gut microbiome were changed with the intervention period, in which long-term intervention with Cold TCM may result in greater impacts than Hot TCM, but reduced in Hot TCM, suggesting the different adaptation of host metabolism or gut microbiome towards TCM intervention. The results highlighted the potential of characterizing TCM natures by using gut microbiome and metabolomics. However, further investigations are warranted to reveal the common or unique characters that are intricately correlated with Hot or Cold properties of TCM by adopting more medicines and applied in disease models or TCM formulae. Abbreviations 16S rRNA: 16S ribosomal RNA DNA: Deoxyribonucleic Acid GC-MS/MS: Gas Chromatography-tandem Mass Spectrometry GC-TOF/MS: Fast Gas Chromatography-Time of Flight Mass Spectrometry KEGG: Kyoto Encyclopedia of Genes and Genomes OPLS-DA: Orthogonal Partial Least Squares Discriminant Analysis OTU: Operational Taxonomic Units RNA: Ribonucleic Acid TCM: Traditional Chinese Medicine UPLC-QTOF-MS/MS: Ultrahigh Performance Liquid Chromatography Coupled with Quadrupole Time Flight Mass Spectrometry VIP: Variable Importance in Projection RG: Rougui for Chinese, Cinnamomum cassia (L.) J.Presl for Latin name GJ: Ganjiang for Chinese, Zingiber officinale Rosc. For Latin name FZ: Fuzi for Chinese, Aconitum carmichaeli Debx. For Latin name DH: Dahuang for Chinese, Rheum palmatum L for Latin name HQ: Huangqin for Chinese, Scutellaria baicalensis Georgi for Latin name HL: Huanglian for Chinese, Coptis chinensis Franch for Latin name Declarations Ethics approval and consent to participate The animal experiments containing in this research were conducted under the Guidelines for Animal Experiment of Shanghai University of Traditional Chinese Medicine and the protocol was approved by the institutional Animal Ethics Committee. Consent for publication Not applicable. Availability of data and materials Sequence data associated with this project have been deposited in the NCBI Short Read Archive (PRJNA832599, PRJNA832616) database. Competing interests The authors declare that they have no competing interests Funding This work was funded by Three-year Action Plan for Shanghai TCM Development and Inheritance Program [ZY (2021-2023)-0401]. Authors' contributions Xin Tao and Bingbing Li performed animal experiments. Bingbing Li performed untargeted metabolomics and 16S rDNA data analysis, and manuscript writing. Yan Li and Ningning Zheng helped in animal experiment. Lili Sheng helped in project management and results discussion. Houkai Li supervised the project and revised the manuscript. All the authors approved its final version of the manuscript. Acknowledgements Not applicable. Conflicts of Interest We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome. References Huang K, Zhang P, Zhang Z, Youn JY, Wang C, Zhang H, et al. Traditional Chinese Medicine (TCM) in the treatment of COVID-19 and other viral infections: Efficacies and mechanisms. Pharmacol Ther 2021; 225 107843. Li X, Wu D, Niu J, Sun Y, Wang Q, Yang B, et al. Intestinal Flora: A Pivotal Role in Investigation of Traditional Chinese Medicine. Am J Chin Med 2021; 49 (2): 237-268. 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Cinnamon extract improves the body composition and attenuates lipogenic processes in the liver and adipose tissue of rats. Food Funct 2015; 6 (10): 3257-3265. Miyamoto M, Matsuzaki K, Katakura M, Hara T, Tanabe Y, Shido O. Oral intake of encapsulated dried ginger root powder hardly affects human thermoregulatory function, but appears to facilitate fat utilization. Int J Biometeorol 2015; 59 (10): 1461-1474. Yang JY, Shi ZH, Ma W, Tao DQ, Liu S, Chen L, et al. Effect of Fuzi Lizhong decoction in reducing liver injury of rats with non-alcoholic fatty liver via activating AMPK and suppressing NF-kappaBp65 pathway. Zhongguo Zhong Yao Za Zhi 2018; 43 (15): 3176-3183. Zeng P, Shi Y, Wang XM, Lin L, Du YJ, Tang N, et al. Emodin Rescued Hyperhomocysteinemia-Induced Dementia and Alzheimer's Disease-Like Features in Rats. Int J Neuropsychopharmacol 2019; 22 (1): 57-70. Lin YL, Dai ZK, Lin RJ, Chu KS, Chen IJ, Wu JR, et al. Baicalin, a flavonoid from Scutellaria baicalensis Georgi, activates large-conductance Ca2+-activated K+ channels via cyclic nucleotide-dependent protein kinases in mesenteric artery. Phytomedicine 2010; 17 (10): 760-770. Yang Y, Lu R, Gao F, Zhang J, Liu F. Berberine induces lipolysis in porcine adipocytes by activating the AMPactivated protein kinase pathway. Mol Med Rep 2020; 21 (6): 2603-2614. Manach C, Williamson G, Morand C, Scalbert A, Remesy C. Bioavailability and bioefficacy of polyphenols in humans. I. Review of 97 bioavailability studies. Am J Clin Nutr 2005; 81 (1 Suppl): 230S-242S. Huang T, Liu Y, Zhang C. Pharmacokinetics and Bioavailability Enhancement of Baicalin: A Review. Eur J Drug Metab Pharmacokinet 2019; 44 (2): 159-168. Wang K, Feng X, Chai L, Cao S, Qiu F. The metabolism of berberine and its contribution to the pharmacological effects. Drug Metab Rev 2017; 49 (2): 139-157. Wang J, Ishfaq M, Li J. Baicalin ameliorates Mycoplasma gallisepticum-induced inflammatory injury in the chicken lung through regulating the intestinal microbiota and phenylalanine metabolism. Food Funct 2021; 12 (9): 4092-4104. Habtemariam S. Berberine pharmacology and the gut microbiota: A hidden therapeutic link. Pharmacol Res 2020; 155 104722. Table 1 Table 1 Main active constituents of the six TCM. Herbs Nature Main constituents’ type Constituents Peak number Proportion of peak area (%) Cinnamomi Cortex (RG) Hot Organic acids Coumarin 1 10.27 Cinnamic alcohol 2 6.24 Cinnamic acid 3 33.87 Cinnamaldehyde 4 9.12 Zingiberis Rhizoma (GJ) Hot Phenolic Hexahydrocurcumin 1 5.8 6-Gingerol 2 43.87 6-Shogaol 3 13.87 Aconiti Lateralis Radix Praeparata (FZ) Hot Alkaloid Benzoylmesaconine 1 16.56 Benzoylaconitine 2 3.07 Benzoylhypacoitine 3 7.79 Rhei Radix et Rhizoma (DH) Cold Organic acids Gallic acid 1 10.31 Rhein-8-glucoside 2 2.87 Sennoside A 3 0.55 Torachrysone 8-O-glucoside 4 2.17 Emodin-8-glucoside 5 4.36 Aloe-emodin 6 2.17 Rhein 7 9.06 Emodin 8 2.39 Chrysophanol 9 2.04 Physcion 10 0.53 Scutellariae Radix (HQ) Cold Glycosides Chrysin 6-C-arabinoside-8-C-glucoside 1 2.14 Chrysin 6-C-glucoside-8-C-arabinoside 2 1.48 Baicalin 3 64.13 Norwogonin 7-O-β-D-glucuronide 4 3.01 Oroxylin A 7-O-glucuronide 5 7.03 Wogonoside 6 15.8 Baicalein 7 0.15 Wogonin 8 0.39 Copitdis Rhizoma (HL) Cold Alkaloid Jatrorrhizine 1 8.09 Coptisine 2 13.55 Palmatine 3 13.14 Berberine 4 48.16 Supplementary Files Graphicabstract.pptx Male C57BL/6J mice (6-week-old) were given Hot TCM (RG_ST, GJ_ST, FZ_ST) or Cold TCM (DH_ST, HQ_ST, HL_ST) by oral gavage once daily for seven days. Mice in control group were orally administrated with water (Con_ST). () Shannon index. () Principal coordinate analysis based on the Binary-Pearson distance algorithm on OTU level. () Fecal microbiota at phylum level by 16S rDNA sequencing under Mann–Whitney U test. () Venn diagram between Hot TCM and Cold TCM based on shared genus of each nature. () Venn diagram shown the overlap between Hot TCM shared pathway and Cold TCM shared pathway by PICRUSt2 analysis on KEGG pathway level 3 under Mann–Whitney U test. N = 5 each group, *p < 0.05, **p < 0.01. FigureS1.pptx Figure S1 (A)Heatmap shown the specific genus and shared genus between two natures. (B) Heatmap shown 11 Hot_ST specific pathways, eight Cold_ST specific pathways, and 16 shared pathways at KEGG pathway level 3. FigureS2.pptx Figure S2 (A) Venn diagram between Hot_LT and Cold_LT based on shared genus in each nature. (B) Venn diagram shows the overlap between Hot TCM shared pathway and Cold TCM shared pathway by PICRUSt2 analysis on KEGG pathway level 3 under Mann–Whitney U test. (C) Heatmap shown the four Hot TCM specific pathways and 65 Cold TCM specific pathways at KEGG level 3 by PICRUSt2. FigureS3.pptx Figure S3 (A) Volcano map shows the distribution of metabolites in each group. Red spots presented down-regulated metabolites, while blue spots mean up-regulated metabolites under the condition VIP>1. (B) The number of changed metabolites in each class in Hot_ST and Cold_ST. (C) Under enrichment analysis, the pathways satisfying the conditional impact> 0 in each group. FigureS4.pptx Figure S4 (A) Volcano map shows the distribution of metabolites in each group. Red spots presented down-regulated metabolites, while blue spots mean up-regulated metabolites under the condition VIP>1. (B) Venn diagram between Hot_LT and Cold_LT based on shared metabolites of each nature. (C) Venn diagram between Hot_LT and Cold_LT based on shared pathways by metabolic pathway enrichment analysis on KEGG pathway level 3 with condition of pathway impact >0. (D) Under enrichment analysis, the pathways satisfying the conditional impact> 0 in each group. FigureS5.pptx Figure S5 (A-B) The metabolites involved in specific changed pathways in Hot_ and Cold_TCM. N=5 each group, Students’ t-test *p < 0.05, **p < 0.01. FigureS6.pptx Figure S6 The main peaks of six extracts. Copitdis Rhizoma was determined by UV chromatograph, others were determined by mass chromatograph. The baseline was instable under TOF-MS/MS, thus HPLC was chosen for composition determination of Copitdis Rhizoma . The names and proportions of peaks were shown in table 1. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2045539","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":135839552,"identity":"f67a3ecd-1774-477a-9f35-ead145dd19b6","order_by":0,"name":"Bingbing Li","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Bingbing","middleName":"","lastName":"Li","suffix":""},{"id":135839553,"identity":"2bee4838-422c-477d-afd6-cdedeeade9ae","order_by":1,"name":"Xin Tao","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Tao","suffix":""},{"id":135839554,"identity":"6d77e90a-f064-46c9-b69c-6829441dd1e7","order_by":2,"name":"Lili Sheng","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Lili","middleName":"","lastName":"Sheng","suffix":""},{"id":135839555,"identity":"fb67d304-f73d-439d-829e-648ffc445cd2","order_by":3,"name":"Yan Li","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Li","suffix":""},{"id":135839556,"identity":"66e35095-a3ca-4f12-bd30-541aa595727c","order_by":4,"name":"Ningning Zheng","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ningning","middleName":"","lastName":"Zheng","suffix":""},{"id":135839557,"identity":"e8ac14ea-f5e6-4e25-9665-ceae96204691","order_by":5,"name":"Li Houkai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYBACAwYGZiApIWd/vPnAgQ8VxGuxMGY4cyzx4IwzRGthqEhsuOFjfJi3hQgt5uw9xgY/CiQSG2fwfDjA28Agzy92AL8Wy54zxok9BhLGzdK9Gw5I7mAwnDk7gYDDbuQYH+AxkJBtkzm74YDhGYYEg9tEaDn4x0CCsUci58GBxDYitSQDbVGcIZHDcOAgUVrOHCs2lgH6xYDnmMHBhjMSRPjlePNmyTd/6uQM2Jsff/5TYSPPL01ACzqQIE35KBgFo2AUjALsAAC2pEjaLT9+xQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-2846-7895","institution":"Shanghai University of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Li","middleName":"","lastName":"Houkai","suffix":""}],"badges":[],"createdAt":"2022-09-08 13:36:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2045539/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2045539/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13020-022-00697-2","type":"published","date":"2022-12-26T18:07:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":26470118,"identity":"02504c0c-39b9-4d8b-a22f-a245809ea67a","added_by":"auto","created_at":"2022-09-14 20:50:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":109511,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe effect of TCM short-term intervention on gut microbiota composition and function.\u003c/strong\u003e Male C57BL/6J mice (6-week-old) were given Hot TCM (RG_ST, GJ_ST, FZ_ST) or Cold TCM (DH_ST, HQ_ST, HL_ST) by oral gavage once daily for seven days. Mice in control group were orally administrated with water (Con_ST). (\u003cstrong\u003eA\u003c/strong\u003e) Shannon index. (\u003cstrong\u003eB, C\u003c/strong\u003e) Principal coordinate analysis based on the Binary-Pearson distance algorithm on OTU level. (\u003cstrong\u003eD, E\u003c/strong\u003e) Fecal microbiota at phylum level by 16S rDNA sequencing under Mann–Whitney U test. (\u003cstrong\u003eF\u003c/strong\u003e) Venn diagram between Hot TCM and Cold TCM based on shared genus of each nature. (\u003cstrong\u003eG\u003c/strong\u003e) Venn diagram shown the overlap between Hot TCM shared pathway and Cold TCM shared pathway by PICRUSt2 analysis on KEGG pathway level 3 under Mann–Whitney U test. N=5 each group, *p \u0026lt; 0.05, **p \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"Slide1.png","url":"https://assets-eu.researchsquare.com/files/rs-2045539/v1/887a90b4d53d27738a06248d.png"},{"id":26470120,"identity":"904a52b3-077f-4925-8127-05b74639905a","added_by":"auto","created_at":"2022-09-14 20:50:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":145910,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe distinct regulation on gut microbiome by TCM after 35 days intervention. \u003c/strong\u003eMale C57BL/6J mice (6-week-old) were given Hot TCM (RG_LT, GJ_LT, FZ_LT) or Cold TCM (DH_LT, HQ_LT, HL_LT) by oral gavage once daily for 35 days. Mice in Control group were orally administrated with water (Con_LT). (\u003cstrong\u003eA\u003c/strong\u003e) Shannon index. (\u003cstrong\u003eB, C\u003c/strong\u003e) Principal coordinate analysis based on the Binary-Pearson distance algorithm on OTU level. (\u003cstrong\u003eD, E\u003c/strong\u003e) Fecal microbiota at phylum level by 16S rDNA sequencing under Mann–Whitney U test. (\u003cstrong\u003eF\u003c/strong\u003e) Heatmap shown the specific genus of Hot_LT and Cold_LT. (\u003cstrong\u003eG\u003c/strong\u003e) The number of regulated pathways in two TCM natures at KEGG pathway level 3. N=5 each group, *p \u0026lt; 0.05, **p \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-2045539/v1/41874669242cbcc1d0b91692.png"},{"id":26470126,"identity":"59f3045a-9d8f-4733-b65b-5a0d584eb5cb","added_by":"auto","created_at":"2022-09-14 20:50:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":105490,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe nature-specific and time-depend regulation of TCM on gut microbiome. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) The expression and variation of top six phylum in each group. The brown column shows the abundance in long-term experiment minus the abundance in short-term experiment. (\u003cstrong\u003eB-C\u003c/strong\u003e) The regulated genus, and pathways in each group under two timepoints. N=5 each group, Students’ t-test *p \u0026lt; 0.05, **p \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-2045539/v1/4bb39945bad6c36a2e1e5216.png"},{"id":26471597,"identity":"73ae3ae6-7b23-4d8f-93b4-898e317f6112","added_by":"auto","created_at":"2022-09-14 20:55:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":98996,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe impact of TCM short-term intervention on serum metabonomic.\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) The classification of metabolites with VIP\u0026gt;1 in each group. (\u003cstrong\u003eB\u003c/strong\u003e) Venn diagram between Hot_ST and Cold_ST based on shared metabolites of each nature. (\u003cstrong\u003eC\u003c/strong\u003e) Venn diagram between Hot_ST and Cold_ST based on shared pathways by metabolic pathway enrichment analysis on KEGG pathway level 3 with pathway impact \u0026gt;0. (\u003cstrong\u003eD\u003c/strong\u003e) The pathway impact of specific pathways in Hot_ST and Cold_ST. Class 1 is alcohols, 2 is alkylamines, 3 is amino acids, 4 is benzoic acids, 5 is carbohydrates, 6 is carnitines, 7 is fatty acids, 8 is indoles, 9 is inorganic Oxide, 10 is lipids, 11 is nucleotides,12 is organic acids, 13 is organooxygen compound, 14 is peptides, 15 is phenols, 16 is phenyl propanoic acids, 17 is pyridines, 18 is reaction ratio, and 19 is vitamins.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-2045539/v1/292170e5e0b508e232bcd0de.png"},{"id":26470121,"identity":"5a18558e-7fd8-4787-a47a-b03d35f81455","added_by":"auto","created_at":"2022-09-14 20:50:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":100869,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe impact of TCM long-term intervention on serum metabonomic. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) The classification of metabolites with VIP\u0026gt;1 in each group. (\u003cstrong\u003eB\u003c/strong\u003e) Venn diagram between Hot_LT and Cold_LT based on shared metabolites of each nature, and the number of changed metabolites in each class. (\u003cstrong\u003eC\u003c/strong\u003e) Venn diagram based on Hot_LT shared pathway and Cold_LT shared pathway by metabolic pathway enrichment analysis on KEGG pathway level 3 with pathway impact \u0026gt;0, and bar chart shows the pathway impact of specific pathways. Metabolite classes as figure 4.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-2045539/v1/69635f35652a59d028f2c239.png"},{"id":26470123,"identity":"c5866626-e1e9-4423-b3ff-1e458d58c8e2","added_by":"auto","created_at":"2022-09-14 20:50:54","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":160262,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe different changes of serum metabolites over time in Hot- and Cold-TCM treated group. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) The number of regulated metabolites with condition of VIP\u0026gt;1 and pathways with condition of impact\u0026gt;0 in each group under two timepoints. (\u003cstrong\u003eB-C\u003c/strong\u003e) The metabolites number of each class in each group under two timepoints. Triangles on the left indicates specific changed class in Hot_ or Cold_TCM. (\u003cstrong\u003eD-E\u003c/strong\u003e) The significantly changed metabolites which involved in specific changed pathways in Cold_ST and Cold_LT. N=5 each group, Students’ t-test *p \u0026lt; 0.05, **p \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-2045539/v1/23f9eae8f9d123620a466e15.png"},{"id":26471602,"identity":"eebf7ab0-ca1d-4866-a422-da4ef3067c15","added_by":"auto","created_at":"2022-09-14 20:55:55","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":350272,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe relevance analysis between specific changed genus and specific changed metabolites in each property at short-term intervention. \u003c/strong\u003e(\u003cstrong\u003eA-D\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003eThe number of specific genera, specific bacteria pathways, specific metabolites, and specific metabolic pathways in Hot_ and Cold_TCM under two timepoints. (\u003cstrong\u003eE, G\u003c/strong\u003e) The relevance analysis between specific changed genera and specific changed metabolites in each property at short-term intervention under spearman analysis. Only significantly association would be presented in the figure. (\u003cstrong\u003eF, H\u003c/strong\u003e) KEGG pathway enrichment analysis based on metabolites in (E) and (G) which are significantly correlated with genus. N=5 each group.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-2045539/v1/26a173f15c06eaefa18eefb6.png"},{"id":26471601,"identity":"f575ccc8-139c-4489-9f56-068e8e28aa88","added_by":"auto","created_at":"2022-09-14 20:55:55","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":117975,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe relevance analysis between specific changed genus and specific changed metabolites in each property at long-term intervention. \u003c/strong\u003e(\u003cstrong\u003eA-B\u003c/strong\u003e) The relevance analysis between specific changed genera and specific changed metabolites in each property at long-term intervention under spearman analysis. Only significantly association would be presented in the figure. (\u003cstrong\u003eC\u003c/strong\u003e) KEGG pathway enrichment analysis based on metabolites in (B) which are significantly correlated with genus. N=5 each group.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-2045539/v1/293fb3d3b5405b7f8b586232.png"},{"id":44714908,"identity":"90f92068-10dc-4793-b01a-0f8aa5d8a10e","added_by":"auto","created_at":"2023-10-16 18:10:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1522073,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2045539/v1/85512bd6-6097-4725-8c7a-304153603ff5.pdf"},{"id":26470115,"identity":"fbeb8614-d7f2-4a8c-afbc-b7c7bf1f1aa9","added_by":"auto","created_at":"2022-09-14 20:50:54","extension":"pptx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1938031,"visible":true,"origin":"","legend":" Male C57BL/6J mice (6-week-old) were given Hot TCM (RG_ST, GJ_ST, FZ_ST) or Cold TCM (DH_ST, HQ_ST, HL_ST) by oral gavage once daily for seven days. Mice in control group were orally administrated with water (Con_ST). () Shannon index. () Principal coordinate analysis based on the Binary-Pearson distance algorithm on OTU level. () Fecal microbiota at phylum level by 16S rDNA sequencing under Mann\u0026ndash;Whitney U test. () Venn diagram between Hot TCM and Cold TCM based on shared genus of each nature. () Venn diagram shown the overlap between Hot TCM shared pathway and Cold TCM shared pathway by PICRUSt2 analysis on KEGG pathway level 3 under Mann\u0026ndash;Whitney U test. N\u0026thinsp;=\u0026thinsp;5 each group, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01.","description":"","filename":"Graphicabstract.pptx","url":"https://assets-eu.researchsquare.com/files/rs-2045539/v1/814fb09341cbd11a730397bd.pptx"},{"id":26471600,"identity":"cb21ea8a-0fab-44a0-ae84-e4ec6e5718d4","added_by":"auto","created_at":"2022-09-14 20:55:55","extension":"pptx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":4102544,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S1\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e)Heatmap shown the specific genus and shared genus between two natures. (\u003cstrong\u003eB\u003c/strong\u003e) Heatmap shown 11 Hot_ST specific pathways, eight Cold_ST specific pathways, and 16 shared pathways at KEGG pathway level 3.\u003c/p\u003e","description":"","filename":"FigureS1.pptx","url":"https://assets-eu.researchsquare.com/files/rs-2045539/v1/b2c0c015a3e2e98884df9136.pptx"},{"id":26471603,"identity":"90b1b6eb-ec8b-4462-ae82-2ceb95a43464","added_by":"auto","created_at":"2022-09-14 20:55:55","extension":"pptx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":4361184,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S2\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) Venn diagram between Hot_LT and Cold_LT based on shared genus in each nature. (\u003cstrong\u003eB\u003c/strong\u003e) Venn diagram shows the overlap between Hot TCM shared pathway and Cold TCM shared pathway by PICRUSt2 analysis on KEGG pathway level 3 under Mann–Whitney U test. (\u003cstrong\u003eC\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003eHeatmap shown the four Hot TCM specific pathways and 65 Cold TCM specific pathways at KEGG level 3 by PICRUSt2.\u003c/p\u003e","description":"","filename":"FigureS2.pptx","url":"https://assets-eu.researchsquare.com/files/rs-2045539/v1/ea9475452206c1fd72fa0eb2.pptx"},{"id":26470127,"identity":"e118deac-0d15-4670-95f8-ece1ebae73d5","added_by":"auto","created_at":"2022-09-14 20:50:54","extension":"pptx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":5600691,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S3\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003eVolcano map shows the distribution of metabolites in each group. Red spots presented down-regulated metabolites, while blue spots mean up-regulated metabolites under the condition VIP\u0026gt;1. (\u003cstrong\u003eB\u003c/strong\u003e) The number of changed metabolites in each class in Hot_ST and Cold_ST. (\u003cstrong\u003eC\u003c/strong\u003e) Under enrichment analysis, the pathways satisfying the conditional impact\u0026gt; 0 in each group.\u003c/p\u003e","description":"","filename":"FigureS3.pptx","url":"https://assets-eu.researchsquare.com/files/rs-2045539/v1/1374a96966efb7be3ef45355.pptx"},{"id":26471598,"identity":"7c0fb78d-c846-4683-8b29-b191d84e068f","added_by":"auto","created_at":"2022-09-14 20:55:54","extension":"pptx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":305043,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S4\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003eVolcano map shows the distribution of metabolites in each group. Red spots presented down-regulated metabolites, while blue spots mean up-regulated metabolites under the condition VIP\u0026gt;1. (\u003cstrong\u003eB\u003c/strong\u003e) Venn diagram between Hot_LT and Cold_LT based on shared metabolites of each nature. (\u003cstrong\u003eC\u003c/strong\u003e) Venn diagram between Hot_LT and Cold_LT based on shared pathways by metabolic pathway enrichment analysis on KEGG pathway level 3 with condition of pathway impact \u0026gt;0. (\u003cstrong\u003eD\u003c/strong\u003e) Under enrichment analysis, the pathways satisfying the conditional impact\u0026gt; 0 in each group.\u003c/p\u003e","description":"","filename":"FigureS4.pptx","url":"https://assets-eu.researchsquare.com/files/rs-2045539/v1/2854984c674ac37488796ee0.pptx"},{"id":26470130,"identity":"6154b5a8-79c0-4528-b1c8-6690af4fce0b","added_by":"auto","created_at":"2022-09-14 20:50:54","extension":"pptx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":164607,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S5\u003c/strong\u003e (\u003cstrong\u003eA-B\u003c/strong\u003e) The metabolites involved in specific changed pathways in Hot_ and Cold_TCM. N=5 each group, Students’ t-test *p \u0026lt; 0.05, **p \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"FigureS5.pptx","url":"https://assets-eu.researchsquare.com/files/rs-2045539/v1/6332e81bef52c92872ffdb7e.pptx"},{"id":26471604,"identity":"b36f50cd-91ea-4538-919e-2052e6cedcb1","added_by":"auto","created_at":"2022-09-14 20:55:55","extension":"pptx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":1213616,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S6 \u003c/strong\u003eThe main peaks of six extracts.\u003cstrong\u003e \u003c/strong\u003e\u003cem\u003eCopitdis Rhizoma \u003c/em\u003ewas determined by UV chromatograph, others were determined by mass chromatograph. The baseline was instable under TOF-MS/MS, thus HPLC was chosen for composition determination of \u003cem\u003eCopitdis Rhizoma\u003c/em\u003e. The names and proportions of peaks were shown in table 1.\u003c/p\u003e","description":"","filename":"FigureS6.pptx","url":"https://assets-eu.researchsquare.com/files/rs-2045539/v1/4e29462ccc5c2c19470964e1.pptx"}],"financialInterests":"","formattedTitle":"The divergent impacts on gut microbiome and host metabolism by Traditional Chinese Medicine with Cold or Hot property in mice","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTraditional Chinese Medicine (TCM) has been practiced and developed over thousands of years from ancient China. In past decades, there is increasing passion on the clinical application of TCM, as well as the research on the scientific basis of TCM theory in the worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, the complexity of TCM theories and numerous chemical components within each herb is still the great challenge for the modernization of TCM. The TCM nature (Yao Xing in Chinese) including Cold, Cool, Hot and Warm is a type of basic TCM characters that was recorded in \"Shen Nong\u0026rsquo;s Materia Medica (Shen Nong Ben Cao Jing)\" about 2000 years ago. Although we still know little about the scientific basis of TCM nature, different herbs with either Hot or Cold property were selectively applied in TCM formulae for treating diseases with opposite Syndrome (Zheng in Chinese) by TCM physicians in clinic, that is \u0026ldquo;Treating the Hot Syndrome with Cold nature medicine and treating Cold Syndrome with Hot nature medicine\u0026rdquo; [\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, a lot of studies have been performed to explore the biological basis of TCM nature by testing the different impacts of Hot or Cold TCM on body temperature regulation [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], host energy metabolism [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], including Na\u003csup\u003e+\u003c/sup\u003e/K\u003csup\u003e+\u003c/sup\u003e ATP enzyme activity [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], et al. Previously, we compared the divergent influences of Hot or Cold TCM on physiology of mice by orally given Hot or Cold TCM for 7 or 35 days, including RG, GJ, FZ, DH, HQ, and HL. Our results indicated that short-term Hot or Cold TCM intervention exerted more significant impacts on lipids metabolism in liver or adipose tissue than long-term intervention [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], suggesting the host adaptation to long-term TCM intervention. In addition to the direct influence on host physiology, gut microbiome modulation is the intrinsic way for TCM actions [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. recently investigated the effects of 12 TCM with different degree of Hot or Cold nature on gut microbiota in mice, and demonstrated that the influences on gut microbiota was in the order of Cold\u0026thinsp;\u0026gt;\u0026thinsp;Cool\u0026thinsp;\u0026gt;\u0026thinsp;Warm\u0026thinsp;\u0026gt;\u0026thinsp;Hot TCM [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. It is reasonable that Cold TCM usually contained much more alkaloids than Hot TCM, which possess anti-bacteria activity [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Meanwhile, the interaction between TCM and gut microbiota may give rise to the production of microbial metabolites, as well as regulation on host metabolism simultaneously [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Unfortunately, there are few studies on the TCM nature by incorporating the changes in gut microbiota and host metabolism at the same time.\u003c/p\u003e \u003cp\u003eIn current study, we investigated the divergent impacts of Hot or Cold TCM on gut microbiome and host metabolism in mice by orally giving aqueous extraction of six TCM with typical Hot or Cold nature for a short-term (7 days) and a long-term (35 days) interventions based on 16S rDNA and un-targeted metabolomics approaches. Our results revealed that TCM with Hot or Cold nature exerted unique influences on gut microbiome and host metabolism under either short-term or long-term intervention. The correlation analysis suggested the interplay between altered gut microbiome and metabolic pathways that were modulated by Hot or Cold TCM specifically.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003ePlant extract materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSix TCM of RG (200702), GJ (200415), FZ (200706), DH (200717), HQ (200701), and HL (200820), were purchased from Shanghai KangQiao Chinese Medicine Co. Ltd. (Shanghai, China), and were authenticated by Professor Lihong Wu of the Institute of Chinese Medicine, Shanghai University of Traditional Chinese Medicine. A voucher specimen of the individual TCMs was deposited at the School of Pharmacy, Shanghai University of Traditional Chinese Medicine. For GJ, HL, and HQ, boiling violently for 15 minutes, keep slightly boiling for 45 minutes. Reflux extraction device is used in the whole process. For RG and DH, boiling violently for 25 minutes, keep slightly boiling for 65 minutes. For FZ, boiling violently for 10 minutes, keep slightly boiling for 20 minutes. Each TCM was extract twice, combine the filtrate, and concentrated at 60 ◦C under reduced pressure. Approximately 100 g raw material of each TCM was soaked in ultrapure water for 4 hours before extraction. Shorter extraction time was selected based on the content of volatile components in RG and anthraquinone in DH. And longer extraction time was aim to reduce the toxic component in FZ. These extracts have been analyzed for compositional.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompositional analysis of the aqueous extract\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Ultrahigh performance liquid chromatography coupled with quadrupole time flight mass spectrometry (UPLC-QTOF-MS/MS) analysis was performed on Agilent 1290 UPLC system coupled to Agilent 6545 Q-TOF instrument (Agilent, Santa Clara, CA, USA). Spectrometer was operated in full-scan TOF-MS at m/z 50-1500 and information-dependent acquisition (IDA) MS/MS modes. Specific conditions as follows: gas temperature (320◦C), Drying Gas (11L/min), Nebulizer (35psi), Shealth Gas Temp (350◦C), Shealth Gas Flow (11L/min), and Collision Energy (40). Chromatographic separation was achieved using Agilent ZORBAX RRHD Eclipse Plus C18 (3.0\u0026times; 100 mm, 1.8 \u0026mu;m) analytical column. The mobile phase consisted of 0.1% formic acid (v/v) in water and acetonitrile. The temperature(◦C), detection wavelength (nm), and velocity of flow (mL/min) was 30, 260, 0.5 for RG, 40, 215, 0.3 for GJ, 30, 190-400, 0.3 for FZ, 25, 260, 0.3 for DH, 30, 280, 0.3 for HQ. For HL, HPLC analysis was obtained for stable baseline and coupled to Agilent ZORBAX Extend-C18(4.6 mm \u0026times; 250 mm, 5 \u0026micro;m), which was employed at 25◦C with sample ingection volume of 2\u0026micro;L. The mobile phase consisted of acetonitrile and 0.1% trifluoroacetic acid solution. Each TCM was analysis by specific gradient elution procedure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnimal experiments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMale C57BL/6J mice of 5 weeks were provided by Laboratory Animal Center of Shanghai University of Traditional Chinese Medicine (Shanghai, China). Mice were orally administrated with vehicle or respective test extracts once daily for 7 days or 35 days. The mouse dose is conversed from the maximum clinical dose specified in the Pharmacopoeia of the People\u0026rsquo;s Republic of China (2015 edition) according to body surface area, that is 0.65g/kg for RG, 1.3g/kg for GJ, 1.95g/kg for FZ, 1.95g/kg for DH, 1.3g/kg for HQ, and 0.65g/kg for HL. All mice were housed in a 12-hour light (7 AM to 7 PM) and 12-hour dark (7 PM to 7 AM) cycle, with free access to water and diet. The experiments were conducted under the Guidelines for Animal Experiment of Shanghai University of Traditional Chinese Medicine and the protocol was approved by the institutional Animal Ethics Committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e16S rDNA Sequencing and Untargeted metabolomics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFecal DNA was isolated for 16S rDNA sequencing and deproteinized serum was collected for untargeted metabolomics. For 16S rDNA sequencing, fecal DNA was isolated using the Qiagen QIAamp DNA Stool Mini Kit (Qiagen, Dusseldorf, Germany), Illumina sequencing was done based on published methods[21, 22]. For untargeted metabolomics, ion peaks were determined and denoising baseline was corrected in XploreMET, then metabolite peaks were resolved by GC-TOFMS. All the steps were according to previous literature\u0026nbsp;[23, 24]. Sequence data associated with this project have been deposited in the NCBI Short Read Archive (PRJNA832599, PRJNA832616) database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData shown in this study are expressed as mean \u0026plusmn; Standard Error of Mean (\u0026plusmn;SEM) unless otherwise noted. Differences between groups in microbiota and function were calculated by Mann-Whitney U test. Other comparisons between two groups were calculated by two-tailed Student\u0026rsquo;s t-test. A \u003cem\u003ep\u003c/em\u003e value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results And Discussion","content":"\u003cp\u003e\u003cstrong\u003eShort-term TCM intervention alters the composition and function of gut microbiota\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the impacts of short-term TCM intervention with either Cold or Hot properties on composition and function of gut microbiome, C57BL/6J mice were given with corresponding TCM extracts or vehicle once daily for 7 days. The 16S rDNA sequencing of fecal bacteria showed increased richness and evenness after Hot TCM (RG, GJ, and FZ) intervention, but not Cold TCM (Fig 1A), however, the PCoA at OTU level showed mice treated by Hot TCM were closer to Control group than group of Cold TCM, suggesting Cold TCM (DH, HQ, and HL) exerted more impacts on structural composition of gut microbiota than Hot TCM (Fig1B-C). Among the Hot TCM treated groups, the relative abundance of Proteobacteria was significantly increased in FZ group, while Desulfobacterota was reduced in both FZ and GJ groups (Fig 1D). In contrast, reduced relative abundance of Desulfobacterota and increased Proteobacteria were observed in the three groups of Cold TCM, while Firmicutes was only significantly increased in HQ group (Fig 1E). Additionally, the Firmicutes-to-Bacteroidetes ratio (F/B ratio) was universally reduced by Hot TCM, but not Cold TCM. To compare the common or unique characters that were altered by Hot or Cold TCM short-term intervention, Venn analysis was performed based on the changed microbiota at genus level. Firstly, the regulated genus in RG, GJ, and FZ were involved for Venn analysis, and 16 genera which was 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, seven genera were found to be significantly altered by both Hot and Cold TCM, while the remaining nine and eight genera were uniquely altered by Hot TCM and Cold TCM respectively (Fig 1F, Fig S1A).\u003c/p\u003e\n\u003cp\u003eTo reveal the impacts on microbial functions after TCM intervention, PICRUSt2 analysis based on 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 unique changed after Hot TCM intervention, and most of them were belong to Metabolism at level 1 including adipocytokine signaling pathway, tyrosine metabolism, d-arginine and d-ornithine metabolism. While 8 pathways were only changed by Cold TCM and most were belonging to environmental information processing at level 1, including cAMP signaling pathway (Fig1G and FigS1B). In addition, the capability of gut microbiome on aromatic compounds 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 on gut microbiota and functions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe compositional and functional changes of gut microbiota by long-term TCM intervention\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven the TCM treatment period is highly variable in clinic, we intended to explore the impacts of long-term TCM intervention on mice by a 35-day of treatment. In contrast to short-term intervention, only HL treated mice showed decreased value in Shannon index (Fig 2A), suggesting reduced \u0026alpha; diversity of gut microbiota. In line with the short-term intervention (Fig 1B), PCoA at OTU level showed long-term Cold_TCM group was clearly separated with Con group alongside PC1, but not in Hot_TCM group (Fig 2B-C). As shown in Fig2, long-term intervention of Hot TCM did minor impacts on relative abundance of gut microbiota at phylum level, except for the reduced Desulfobacterota in GJ and FZ treated mice, while HL treatment resulted in increasing of the relative abundance of Verrucomicrobiota and Campilobacterota, and depletion of Deferribacterota. At genus level, the relative abundance of \u003cem\u003eg__Muribaculum\u003c/em\u003e was increased, and of \u003cem\u003eg__Dubosiella\u0026nbsp;\u003c/em\u003ewas decreased by both Hot_LT and Cold_LT. Four genera were specifically regulated by Hot_LT including \u003cem\u003eg_parabacterodies\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;g_desulfovibrio\u003c/em\u003e, 13 genus was significantly changed by Cold_LT including \u003cem\u003eg_faecalibaculum\u003c/em\u003e and \u003cem\u003eg_alloprevotella\u0026nbsp;\u003c/em\u003ein the meantime (Fig 2F, Fig S2A).\u003c/p\u003e\n\u003cp\u003eWe next analyzed the impacts on function of gut microbiota by PICRUSt2 analysis. In contrast to the functional changes by short-term TCM intervention, there were 4 and 65 pathways that were associated with the long-term Hot or Cold TCM intervention respectively, but no shared pathway (Fig S2B). Most of the changed pathways belong to metabolism and human disease (Fig2G, Fig S2C). These results suggested that long-term TCM intervention with either Hot or Cold nature exerted relatively minor impacts on composition of gut microbiota, but substantially influenced the functions of gut microbiota, especially in Cold TCM treated mice.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe divergently compositional and functional changes of gut microbiome under short-term or long-term TCM intervention\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo compare the influences on the composition of gut microbiota by 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 were shown in Fig 3A, including Actinobacteriota, Firmicutes, Verrucomicrobiota, Bacterodiota, Desulfobacterota and Proteobacteria. Long-term Cold TCM intervention resulted in universally increased abundance of Actinobacteriota and Desulfobacterota, but reduced Bacterodiota and Proteobacteria. Interestingly, the impact of long-term FZ intervention on Actinobacteriota, Bacterodiota and Proteobacteria was similar with Cold TCM. In addition, long-term HQ and HL treatment induced same trend of changes in Firmicutes and Verrucomicrobiota. In general, the number of altered genera was reduced in long-term TCM intervention compared to short-term groups in Hot TCM and HL group, but increased in DH and HQ group (Fig 3B).\u003c/p\u003e\n\u003cp\u003eIn order to compare the impact on gut microbial functions, the number of altered pathways by short- or long-term TCM intervention were analyzed based on 16S rRNA gene sequencing data. The altered pathways were 7, 33 and 52 in short-term RG, GJ and FZ groups, while 28, 28 and 52 in DH, HQ and HL groups, respectively. The majority of altered pathways were down-regulated by 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 up-regulated. However, long-term Cold TCM treatment induced dramatic increase in number of altered pathways, and most of them were up-regulated. In summary, the impacts of TCM intervention on microbial functions were TCM nature- and time-related. The influences on gut microbial functions were enlarged by long-term treatment of Cold TCM than their short-term intervention. Although the number of altered pathways were 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eShort-term TCM intervention alters serum metabolic profile\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven the interlink of gut microbiota and host metabolism[25], we further compared the impact of short-term intervention of TCM with either Hot or Cold nature on serum metabolic profile by un-targeted metabolomics. With the criteria of VIP\u0026gt;1 (Variable Importance in Projection), the short-term intervention of RG, GJ, and FZ induced changes of 70, 67, and 73 serum metabolites, while DH, HQ, and HL induced 70, 66, and 67 serum metabolites respectively (Fig 4A, Fig S4A). The short-term intervention of RG regulated carnitines metabolites specifically, and phenyl propanoic acids class in GJ group. Venn analysis was performed to further explore the differences between Hot and Cold TCM (Fig 4B). Hot TCM induced 67 changed metabolites, while 58 metabolites were regulated in Cold TCM, most of them belong to amino acids, organic acid, and carbohydrates. The fourth metabolite category is lipids in Hot TCM with proportion 10.45%, and is fatty acids in Cold TCM with proportion 12.07% (Fig.S4B). Thirty-three metabolites were commonly altered at both groups.\u003c/p\u003e\n\u003cp\u003ePathway enrichment analysis was employed to explore the changes of biological function. Hot TCM regulated two metabolic pathways specifically while five pathways were regulated by Cold TCM according to Venn analysis, also most pathways were regulated by both of them (Fig 4C-D, Fig S4C-D). We noticed that Hot_ST has 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 short-term TCM intervention with two properties exerted relatively major impacts on serum metabolites, but minor specifically influenced the function of metabolites, especially in Hot_ST.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSerum metabolic profile after long-term TCM intervention\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNext, to explore the long-term impact of TCM intervention on host metabolism, mice were given 35 days TCM with Hot or Cold nature. After long-term intervention, the number of screen metabolites was reduced in six groups meet the condition of VIP\u0026gt;1 compared to their short-term partners (Fig4A, Fig5A). Serum level of indoles metabolites was increased by FZ_LT, while serum level of benzoic acids metabolites, phenyl propanoic acids metabolites were increased and pyridines metabolites were decreased by HQ_LT. Based on the Venn analysis, most of the metabolites in Hot_LT or Cold_LT were included in Amino acids, but the second metabolites class is fatty acid (17.07%) in Hot_LT and organic acid (18.42%) in Cold_LT (Fig5B, Fig S5B).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCorrespond to pathway enrichment analysis of short-term intervention, most pathways were regulated by both Hot_LT and Cold_LT (Fig 5C, Fig S5C-D), Also, linoleic acid metabolism pathway was specific reduced by Hot_LT, and Cold_LT specifically regulated glycine, serine and threonine metabolism pathway, aminoacyl-tRNA biosynthesis pathway, and cysteine and methionine metabolism pathway. Together, serum metabonomic variety by TCM was properties specific and narrowed with prolonged intervention.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe regulation of serum metabolomic by TCM is nature-specific and time-related\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further evaluate the TCM impacts on host metabolism, time-cross analysis based on serum metabolomic data were adopted, and the number of changed metabolites and pathways were found to be reduced along with the extension of intervention time in two properties (Fig6A-E). Hot TCM and Cold TCM has made different impact on metabolites class with the increase of intervention time (Fig6B, C), which HQ specific increased the metabolites number of benzoic acid class and phenyl propanoic acid class, RG specific decreased the metabolites number of carnitines class. And the metabolites number of amino acids class, fatty acids class, and inorganic oxide class was regulated inversely by two properties. Based on KEGG pathway enrichment and Venn analysis, Hot_ST, Cold_ST, Hot_LT, and Cold_LT has regulated two, five, one, and three pathways respectively, and metabolites included in each pathway were displayed in FigS6, which were suspected to play a constructive role in distinguishing Hot TCM from Cold TCM. In short-term intervention (FigS6A), three Hot TCM groups had reduced the relative abundance of l-tyrosine and increased the relative abundance of l-phenylalanine which included in phenylalanine, tyrosine and tryptophan biosynthesis pathway, and phenylalanine metabolism pathway. DH and HL significantly reduced the relative abundance of lactic acid which contained in glycolysis/gluconeogenesis pathway (Fig6D). In long-term intervention (FigS6B, Fig6E), linoleic acid was containing in linoleic acid metabolism pathway of Hot_LT, 12 metabolites were including in three pathways of Cold_LT. The relative abundance of tryptophan was significantly increased by DH, proline was decreased by HQ, and threonine was decreased by HL. In addition, the presence or absence of carnitines class, benzoic acid class, and phenyl propanoic acid class may be used to classify Hot and Cold TCM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation analysis between altered gut microbiota and serum metabolites\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSummarizing the above changes, the number of specific genera, specific bacteria pathway, specific metabolite, and specific metabolites pathway was decreased in Hot_LT compared with Hot_ST (Fig7A-D). In the meanwhile, these number shown different tendency in Cold_LT when compared with Cold_ST. In short-term intervention, specific genus number and specific bacteria pathway number were nearly in Cold TCM with Hot TCM, but these number were surprisingly increased in Cold_LT (Fig7A, B). Despite Cold TCM kept same change tendency of specific metabolite number and specific metabolites pathway number with Hot TCM, the numbers in Cold TCM were huger than Hot TCM at two time points (Fig7C, D), suggest the wider body metabolism effect after Cold TCM intervention.\u003c/p\u003e\n\u003cp\u003eGiven the strong association of serum metabolites with microbiome, correlation analysis based on changed gut microbiota (specific differential genus with p\u0026lt;0.05) and changed serum metabolites (specific differential metabolites with VIP\u0026gt;1) was determined with the condition of correlation p\u0026lt;0.05. At short-term intervention, eight genera were significant associated with 15 serum metabolites in Hot TCM (Fig7E). Pathway enrichment analysis on these 15 metabolites obtained two pathways which are same with Hot_ST specific pathways that were represented in Fig 4E, and metabolites phenylalanine and tyrosine were contained in these two pathways (Fig7F). The correlation of phenylalanine with \u003cem\u003eg__Enterorhabdus\u003c/em\u003e, \u003cem\u003eg__Eubacterium_oxidoreducens_group\u003c/em\u003e, \u003cem\u003eg__Faecalibaculum\u003c/em\u003e, \u003cem\u003eg__Lactobacillus\u003c/em\u003e, \u003cem\u003eg__Tuzzerella\u003c/em\u003e was negative, with \u003cem\u003eg__UBA1819\u003c/em\u003e, \u003cem\u003eg__unclassified_c__Clostridia\u003c/em\u003e, \u003cem\u003eg__unclassified_p__Firmicutes\u003c/em\u003e was positive, while tyrosine was negatively associated with \u003cem\u003eg__unclassified_p__Firmicutes\u003c/em\u003e, but positively associated with \u003cem\u003eg__Tuzzerella\u003c/em\u003e, \u003cem\u003eg__unclassified_c__Clostridia\u003c/em\u003e, \u003cem\u003eg__Lactobacillus\u003c/em\u003e, and \u003cem\u003eg__UBA1819\u003c/em\u003e. These results suggested that the changes of gut microbiome may partly induced the specific changes on serum metabolites and functions. Our conjecture was verified in subsequent correlation analysis.\u003c/p\u003e\n\u003cp\u003eIn Cold TCM, there were seven genera associated with 14 metabolites, these metabolites were conducted in pathway enrichment analysis. Two of eight pathways obtained by enrichment (impact\u0026gt;0) were coincidence with Cold_ST specific pathway, which were displayed in Fig 4E (Fig7G, H). Glutamic acid and oxo glutaric acid were included in d-glutamine and d-glutamate metabolism pathway, oxo glutaric acid was positive correlated with \u003cem\u003eg_norank_f_Ruminoccoccaceae,\u003c/em\u003e which was negative correlated with\u003cem\u003e\u0026nbsp;\u003c/em\u003eglutamic acid, also oxo glutaric acid was negative correlated with \u003cem\u003eg__Bacterodiess\u003c/em\u003e and \u003cem\u003eg__Clostridium_sensu_stricto_1\u003c/em\u003e. Linoleic acid which contained in linoleic acid metabolism pathway was negative associated with \u003cem\u003eg__Bacteroides\u003c/em\u003e and \u003cem\u003eg__Clostridium_sensu_stricto_1\u003c/em\u003e, and positive associated with \u003cem\u003eg_\u003c/em\u003e\u003cem\u003enorank_f_Ruminoccoccaceae\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn Hot_LT, only \u003cem\u003eg__Eubacterium_ventriosum_group\u003c/em\u003e was negative associated with isoleucine, leucine, and valine, these may be attributed to the significant reduction genus number (Fig8A), shown the slight effect of Hot_LT on host metabolism. In Cold_LT, eight genera were significantly associated with four metabolites (Fig8B), and KEGG pathway enrichment leads to Cold_LT specifically regulated pathway glycine, serine and threonine metabolism (impact\u0026gt;0, Fig8C), which contained metabolite threonine. Threonine was negative associated with \u003cem\u003eg__Gordonibacter\u003c/em\u003e and \u003cem\u003eg__A2,\u003c/em\u003e and positive associated with \u003cem\u003eg__Monoglobus\u003c/em\u003e and \u003cem\u003eg__norank_f__Eggerthellaceae\u003c/em\u003e. These results partly verified our conjecture, and also shown different changes of microbiome and metabolism caused by Hot TCM and Cold TCM.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the current study, we investigated the divergent impacts of TCM with either Cold or Hot property on gut microbiome and host metabolism in the context of a short- (7 days) or long-term (35 days) intervention in mice, as well as the long-term. Our results revealed that either Hot or Cold TCM intervention exerted obvious influences on gut microbiome and serum metabolic profile, and the microbial or metabolic changes by Hot or Cold TCM were narrowed with time.\u003c/p\u003e \u003cp\u003eThe theory of medicinal properties of TCM is one of the core theories of TCM, which is the important basis for guiding the clinical application of TCM [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. It is usually recognized that TCM with different properties divergently influence the function of central nervous system, sympathetic nerves and endocrine system, basal metabolic rate and secretion of cytokines [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The Hot TCM, like RG, GJ, and FZ, has the capacity of increasing body temperature [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], which are usually used for treatment of nervous system diseases [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], and depression [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], while Cold TCM such as DH, HQ, and HL has opposite effect on body temperature [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], which are used for improving depressive-like behavior [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] with the capacity of regulating energy metabolism and improving mitochondrial function [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In general, given the holistic effect of most TCM on host, those single parameter cannot reflect the systemic impacts of TCM on host metabolism. Therefore, omics approaches, especially metabolomics have been increasingly applied in research on TCM action, and TCM property as well. Meanwhile, the modulation on gut microbiome of TCM has attracted more attentions due to the fact that the complex components within orally administered TCM extracts usually have profound impacts on gut microbiome [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Therefore, in our current study, we adopted combined gut microbiome and serum metabolomics to investigate the impacts of TCM with either Cold or Hot property on mice in the context of short- or long-term intervention.\u003c/p\u003e \u003cp\u003eIn the short-term intervention, Hot or Cold TCM made different impacts on gut microbiome structure and function, despite the comparable number of changed genera or pathways between the two classes. Previous publications suggest that the components within Hot or Cold TCM regulate lipid metabolism [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] and cAMP signaling [\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] respectively, which were consistent with our findings that adipocytokine signaling pathway was increased by Hot TCM while cAMP signaling pathway was increased by Cold TCM according to PICRUSt2 analysis. Similarly, Zhang et al. studied the impacts of TCM with Hot, Warm, Cool, or Cold nature on gut microbiota of C57BL/6J mice by four weeks intervention, in which dramatic changes of gut microbiota were observed in Cold TCM, but not Hot TCM [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], suggesting Cold TCM has greater impacts on gut microbiome than Hot TCM, consistent with our findings. Although extremely complicated and poorly understood, the diversified actions of TCM are due to the differences in chemical components. Similarly, the nature of Hot or Cold TCM is associated with their chemical components. In Cold TCM, the main active element of DH was gallic acid, which could be absorbed in intestinal tract producing extensive effects on host metabolism [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. However, baicalin and berberine, the main ingredients of HQ and HL, has many biological functions but with low absorption [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The growing evidence suggest the role of the gut microbiota as targets for the multifunctional role of baicalin and berberine [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. These results echoed our current data, that is, the influence of Cold TCM on gut microbiome was more obvious than Hot TCM, and increased over long-term intervention, but reduced in Hot TCM.\u003c/p\u003e \u003cp\u003eIn addition to the analysis of gut microbiome and serum metabolite profile individually, correlation analysis between altered gut microbiota and serum metabolites were performed. Our results indicated that changed genera were closely related with serum metabolites. Also, the pathway enrichment analysis based on metabolites which was significantly connected with genus revealed that the changes of phenylalanine, tyrosine and tryptophan biosynthesis pathway and phenylalanine metabolism pathway in Hot_ST, and the changes of d-glutamine and d-glutamate metabolism pathway and linoleic acid metabolism pathway in Cold_ST, and the changes of glycine, serine and threonine metabolism pathway in Cold_LT were related with the regulation of TCM on gut microbiota. In addition, minor impacts on host metabolism were observed in Hot_LT group based on enrichment analysis. Therefore, we speculated that the differential regulation of Hot or Cold TCM on serum metabolic pathways was associated with the modulation on gut microbiota.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our current study indicated that TCM with Hot or Cold property exerted profound impacts on host metabolism or gut microbiome in composition and function. These influences on host metabolism or gut microbiome were changed with the intervention period, in which long-term intervention with Cold TCM may result in greater impacts than Hot TCM, but reduced in Hot TCM, suggesting the different adaptation of host metabolism or gut microbiome towards TCM intervention. The results highlighted the potential of characterizing TCM natures by using gut microbiome and metabolomics. However, further investigations are warranted to reveal the common or unique characters that are intricately correlated with Hot or Cold properties of TCM by adopting more medicines and applied in disease models or TCM formulae.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003e16S rRNA:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e16S ribosomal RNA\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDNA:\u003c/em\u003e\u003c/strong\u003e Deoxyribonucleic Acid\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGC-MS/MS:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eGas Chromatography-tandem Mass Spectrometry\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGC-TOF/MS:\u003c/em\u003e\u003c/strong\u003e Fast Gas Chromatography-Time of Flight Mass Spectrometry\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eKEGG:\u003c/em\u003e\u003c/strong\u003e Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eOPLS-DA:\u003c/em\u003e\u003c/strong\u003e Orthogonal Partial Least Squares Discriminant Analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eOTU:\u003c/em\u003e\u003c/strong\u003e Operational Taxonomic Units\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRNA:\u003c/em\u003e\u003c/strong\u003e Ribonucleic Acid\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTCM:\u003c/em\u003e\u003c/strong\u003e Traditional Chinese Medicine\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eUPLC-QTOF-MS/MS:\u003c/em\u003e\u003c/strong\u003e Ultrahigh Performance Liquid Chromatography Coupled with Quadrupole Time Flight Mass Spectrometry\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eVIP:\u003c/em\u003e\u003c/strong\u003e Variable Importance in Projection\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRG:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eRougui for Chinese, \u003cem\u003eCinnamomum cassia\u003c/em\u003e (L.) J.Presl for Latin name\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGJ:\u003c/em\u003e\u003c/strong\u003e Ganjiang for Chinese, \u003cem\u003eZingiber officinale\u003c/em\u003e Rosc. For Latin name\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFZ:\u003c/em\u003e\u003c/strong\u003e Fuzi for Chinese, \u003cem\u003eAconitum carmichaeli\u003c/em\u003e Debx. For Latin name\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDH:\u003c/em\u003e\u003c/strong\u003e Dahuang for Chinese, \u003cem\u003eRheum palmatum\u003c/em\u003e L for Latin name\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHQ:\u003c/em\u003e\u003c/strong\u003e Huangqin for Chinese, \u003cem\u003eScutellaria baicalensis\u003c/em\u003e Georgi for Latin name\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHL:\u003c/em\u003e\u003c/strong\u003e Huanglian for Chinese, \u003cem\u003eCoptis chinensis\u003c/em\u003e Franch for Latin name\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe animal experiments containing in this research were conducted under the Guidelines for Animal Experiment of Shanghai University of Traditional Chinese Medicine and the protocol was approved by the institutional Animal Ethics Committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSequence data associated with this project have been deposited in the NCBI Short Read Archive (PRJNA832599, PRJNA832616) database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by Three-year Action Plan for Shanghai TCM Development and Inheritance Program [ZY (2021-2023)-0401].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXin Tao and Bingbing Li performed animal experiments. Bingbing Li performed untargeted metabolomics and 16S rDNA data analysis, and manuscript writing. Yan Li and Ningning Zheng helped in animal experiment. Lili Sheng helped in project management and results discussion. Houkai Li supervised the project and revised the manuscript. All the authors approved its final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHuang K, Zhang P, Zhang Z, Youn JY, Wang C, Zhang H, et al. 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Berberine pharmacology and the gut microbiota: A hidden therapeutic link. Pharmacol Res 2020; 155 104722.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003eTable 1\u003c/span\u003e\u003c/p\u003e\n\u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003eMain active constituents of the six TCM.\u003c/span\u003e\u003c/p\u003e\n\u003ctable style=\"border: none;width:524.95pt;border-collapse:collapse;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94.3pt;border-top: 1pt solid windowtext;border-left: none;border-bottom: 1pt solid windowtext;border-right: none;padding: 0in 5.4pt;height: 17.5pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003eHerbs\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46.3pt;border-top: 1pt solid windowtext;border-left: none;border-bottom: 1pt solid windowtext;border-right: none;padding: 0in 5.4pt;height: 17.5pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003eNature\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91.5pt;border-top: 1pt solid windowtext;border-left: none;border-bottom: 1pt solid windowtext;border-right: none;padding: 0in 5.4pt;height: 17.5pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003eMain constituents\u0026rsquo; type\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170.85pt;border-top: 1pt solid windowtext;border-left: none;border-bottom: 1pt solid windowtext;border-right: none;padding: 0in 5.4pt;height: 17.5pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:left;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003eConstituents\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border-top: 1pt solid windowtext;border-left: none;border-bottom: 1pt solid windowtext;border-right: none;padding: 0in 5.4pt;height: 17.5pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003ePeak number\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71.3pt;border-top: 1pt solid windowtext;border-left: none;border-bottom: 1pt solid windowtext;border-right: none;padding: 0in 5.4pt;height: 17.5pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003eProportion of peak area (%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 94.3pt;border: none;background: rgb(231, 230, 230);padding: 0in 5.4pt;height: 0.2in;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cem\u003e\u003cspan style='font-family: \"Arial\",sans-serif;'\u003eCinnamomi Cortex (RG)\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 46.3pt;border: none;background: rgb(231, 230, 230);padding: 0in 5.4pt;height: 0.2in;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003eHot\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" style=\"width: 91.5pt;border: none;background: rgb(231, 230, 230);padding: 0in 5.4pt;height: 0.2in;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003e\u0026nbsp;Organic acids\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170.85pt;border: none;background: rgb(231, 230, 230);padding: 0in 5.4pt;height: 0.2in;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003eCoumarin\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;border: none;background: rgb(231, 230, 230);padding: 0in 5.4pt;height: 0.2in;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003e1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71.3pt;border: none;background: rgb(231, 230, 230);padding: 0in 5.4pt;height: 0.2in;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003e10.27\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170.85pt;background: rgb(231, 230, 230);padding: 0in 5.4pt;height: 0.2in;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003eCinnamic alcohol\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;background: rgb(231, 230, 230);padding: 0in 5.4pt;height: 0.2in;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003e2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71.3pt;background: rgb(231, 230, 230);padding: 0in 5.4pt;height: 0.2in;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003e6.24\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170.85pt;background: rgb(231, 230, 230);padding: 0in 5.4pt;height: 0.2in;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003eCinnamic acid\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;background: rgb(231, 230, 230);padding: 0in 5.4pt;height: 0.2in;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003e3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71.3pt;background: rgb(231, 230, 230);padding: 0in 5.4pt;height: 0.2in;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan 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style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003e9.12\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 94.3pt;padding: 0in 5.4pt;height: 0.2in;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cem\u003e\u003cspan style='font-family: \"Arial\",sans-serif;'\u003eZingiberis Rhizoma (GJ)\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 46.3pt;padding: 0in 5.4pt;height: 0.2in;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003eHot\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" 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5.4pt;height:14.85pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;color:black;'\u003e10\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:71.3pt;padding:0in 5.4pt 0in 5.4pt;height:14.85pt;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;color:black;'\u003e0.53\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"8\" style=\"width: 94.3pt;background: rgb(231, 230, 230);padding: 0in 5.4pt;height: 0.2in;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cem\u003e\u003cspan style='font-family: \"Arial\",sans-serif;'\u003eScutellariae Radix 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style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003eChrysin 6-C-arabinoside-8-C-glucoside\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;background: rgb(231, 230, 230);padding: 0in 5.4pt;height: 0.2in;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003e1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71.3pt;background: rgb(231, 230, 230);padding: 0in 5.4pt;height: 0.2in;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003e2.14\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170.85pt;background: rgb(231, 230, 230);padding: 0in 5.4pt;height: 0.2in;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003eChrysin 6-C-glucoside-8-C-arabinoside\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;background: rgb(231, 230, 230);padding: 0in 5.4pt;height: 0.2in;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003e2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71.3pt;background: rgb(231, 230, 230);padding: 0in 5.4pt;height: 0.2in;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003e1.48\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170.85pt;background: rgb(231, 230, 230);padding: 0in 5.4pt;height: 0.2in;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003eBaicalin\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;background: rgb(231, 230, 230);padding: 0in 5.4pt;height: 0.2in;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003e3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71.3pt;background: rgb(231, 230, 230);padding: 0in 5.4pt;height: 0.2in;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003e64.13\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170.85pt;background: rgb(231, 230, 230);padding: 0in 5.4pt;height: 0.2in;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003eNorwogonin 7-O-\u0026beta;-D-glucuronide\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;background: rgb(231, 230, 230);padding: 0in 5.4pt;height: 0.2in;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003e4\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n 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50.7pt;background: rgb(231, 230, 230);padding: 0in 5.4pt;height: 0.2in;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003e6\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71.3pt;background: rgb(231, 230, 230);padding: 0in 5.4pt;height: 0.2in;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003e15.8\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170.85pt;background: rgb(231, 230, 230);padding: 0in 5.4pt;height: 0.2in;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003eBaicalein\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;background: rgb(231, 230, 230);padding: 0in 5.4pt;height: 0.2in;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003e7\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71.3pt;background: rgb(231, 230, 230);padding: 0in 5.4pt;height: 0.2in;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003e0.15\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170.85pt;background: rgb(231, 230, 230);padding: 0in 5.4pt;height: 0.2in;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003eWogonin\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50.7pt;background: rgb(231, 230, 230);padding: 0in 5.4pt;height: 0.2in;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003e8\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71.3pt;background: rgb(231, 230, 230);padding: 0in 5.4pt;height: 0.2in;vertical-align: top;\"\u003e\n \u003cp style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003e0.39\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 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style=\"margin:0in;margin-bottom:.0001pt;text-align:justify;font-size:14px;font-family:DengXian;\"\u003e\u003cspan style='font-family:\"Arial\",sans-serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"chinese-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cmed","sideBox":"Learn more about [Chinese Medicine](http://cmjournal.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/cmed/default.aspx","title":"Chinese Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Traditional Chinese medicine, Hot-Cold theory, Host metabolism, 16S rDNA, Serum metabonomic","lastPublishedDoi":"10.21203/rs.3.rs-2045539/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2045539/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTraditional Chinese Medicine (TCM) has been practiced and developed in China over thousands of years under the guidance of a series of complicated traditional theories. Herbs within TCM usually are classified into different properties ranging from cold, cool, warm to hot, which are simplified as Cold and Hot properties. TCMs with either Cold or Hot property are differently used in formulae for the purpose of overcoming the imbalance of patients. Emerging evidence has highlighted that altered gut microbiota or host metabolism are critically involved in affecting TCM properties. However, the exact influences and crosstalk on gut microbiota and host metabolism remain poorly understood.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn current study, we investigated the divergent impacts of six TCMs with either Cold or Hot properties on gut microbiome and host metabolism during short- or long-term intervention in mice. Six typical TCMs with Hot or Cold properties including \u003cem\u003eCinnamomum cassia\u003c/em\u003e (L.)J.Presl (rougui, RG), \u003cem\u003eZingiber officinale\u003c/em\u003e Rosc. (ganjiang, GJ), \u003cem\u003eAconitum carmichaeli\u003c/em\u003e Debx. (fuzi, FZ), \u003cem\u003eRheum palmatum\u003c/em\u003e L. (dahuang, DH), \u003cem\u003eScutellaria baicalensis\u003c/em\u003e Georgi (huangqin, HQ), and \u003cem\u003eCoptis chinensis\u003c/em\u003e Franch. (huanglian, HL) were selected and orally administrated on male C57BL/6J mice for a short- or long-term (7 or 35 days). At the end of experiments, serum and cecal contents were collected for metabolomic and gut microbiome analysis with GC-MS or 16S rDNA sequencing.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe results showed that gut microbiome experienced divergent changes both in composition and function by short-term intervention of TCM with either Cold or Hot properties, whereas long-term intervention of Hot TCM reduced the number of changed genus and functional pathways of bacteria, but were increased by Cold TCM, especially in HL group. The serum metabolic profile showed the Glycolipid metabolism related pathways were specifically regulated by Cold TCM, and the influence of TCM on host metabolism minimized with time. Further correlation analysis indicated the altered gut microbiota was closely correlated with serum metabolites as well.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur results indicated that the TCM properties could be, at least partially, characterized by the alteration of gut microbiota and metabolic profile, implying that the divergent responses of gut microbiome and host metabolism are involved in different natures of TCM.\u003c/p\u003e","manuscriptTitle":"The divergent impacts on gut microbiome and host metabolism by Traditional Chinese Medicine with Cold or Hot property in mice","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-09-14 20:50:52","doi":"10.21203/rs.3.rs-2045539/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2022-10-10T11:19:02+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2022-09-12T12:05:13+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-09-12T03:10:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-09-10T04:05:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Chinese Medicine","date":"2022-09-08T09:34:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"chinese-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cmed","sideBox":"Learn more about [Chinese Medicine](http://cmjournal.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/cmed/default.aspx","title":"Chinese Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"babb40af-9aa4-4f88-94fb-3480262b085e","owner":[],"postedDate":"September 14th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T18:08:18+00:00","versionOfRecord":{"articleIdentity":"rs-2045539","link":"https://doi.org/10.1186/s13020-022-00697-2","journal":{"identity":"chinese-medicine","isVorOnly":false,"title":"Chinese Medicine"},"publishedOn":"2022-12-26 18:07:00","publishedOnDateReadable":"December 26th, 2022"},"versionCreatedAt":"2022-09-14 20:50:52","video":"","vorDoi":"10.1186/s13020-022-00697-2","vorDoiUrl":"https://doi.org/10.1186/s13020-022-00697-2","workflowStages":[]},"version":"v1","identity":"rs-2045539","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2045539","identity":"rs-2045539","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}