Introduction

Tea first originated in China, and written records indicate that the Chinese have been cultivating and utilizing the tea plant for more than three thousand years. As one of the most popular and low-cost healthy beverages, tea has achieved widespread popularity in China, Japan and Thailand, and even exhibits strong sales performance in countries such as Russia, the United Kingdom, and Germany1. Global tea production is highly concentrated, with China (50%) and India (20%) dominate the global tea industry. In recent years, the rise of emerging tea-producing countries such as Kenya (8%) has pushed the total production to reach 6.8 million tons (FAO, 2022), establishing a sufficient raw material base for research on functional ingredients such as TPS (https://www.fao.org/faolexdizh/). The tea industry generates significant economic wealth while also fostered a rich cultural heritage, exemplified by tea culture and ceremonies2. Tea’s widespread appeal stems not only from its distinctive aroma and flavor but also from its composition of bioactive compounds that confer beneficial effects on human health. These include tea polyphenols, tea polysaccharides, tea proteins, catechins3, and theanine4. The health benefits of tea components are mediated through their influence on gut microbiota, which helping to regulate glucose and lipid metabolism, mitigate inflammation and intestinal damage, enhance mood and cognitive function, and synchronize circadian rhythms5. Among these compounds, tea polyphenols have long been acknowledged as the most significant for their potent antioxidant and anticancer properties6,7,8,9. However, research has revealed that tea polysaccharides also play a crucial role in the biological activity of tea10,11.

Natural polysaccharides represent a class of polymers formed by the linkage of aldoses or ketoses through glycosidic bonds12. In these structures, monosaccharides link at multiple points, resulting in a diverse branched or linear forms characterized by extensive structural diversity. This remarkable variability provides the foundational structure necessary for the precise and flexible regulation of various intercellular mechanisms within higher organisms. A substantial quantity of natural polysaccharides has been identified in the composition of plants. Numerous studies have demonstrated that PPSs have a variety of biological activities, including antioxidant13, immune regulation14, hypoglycemic15, and anti-tumor. Notably, natural polysaccharides can enhance the efficacy of conventional chemotherapy drugs without significant adverse effects16. Based on the findings of this compelling research, a great deal of effort has been invested in the study of anticancer polysaccharides and modified derivatives to develop effective therapeutic approaches for a broad array of human cancers. Research indicates that natural polysaccharides, which are difficult to digest in the upper gastrointestinal tract, can act directly in the large intestine, where they are degraded by the intestinal microbiota through fermentation to produce metabolites such as short-chain fatty acids17. These metabolites are involved in metabolism thereby affecting the physiological state of the host. Nevertheless, certain polysaccharides exhibit a significant reduction in their biological activity upon isolation from their natural sources16. Consequently, there is a pressing need for more rigorous research into the methodologies and techniques employed in the extraction and preparation of polysaccharides, with the aim of further augmenting and optimizing their functional activities18.

Tea polysaccharides represent the second most abundant component in tea, following tea polyphenols. Similar to tea polyphenols, they are associated with various beneficial effects, including hypoglycemic, hypolipidemic, antioxidant, anti-tumor, and immune-enhancing properties6,11,19. In addition, high purity tea polysaccharides have strong moisturizing ability and the ability to promote the proliferation of fibroblasts, so tea polysaccharides are also widely used in functional food and cosmetics industry2,18,20. Tea polysaccharides are classified as non-amyloid protein-bound acidic polysaccharides. TPS are heteropolysaccharides composed of arabinose, galactose, glucose, and other monosaccharides. Their bioactivity is closely associated with molecular weight (10–500 kDa), glycosidic bond types (e.g., β-1,3/1,6 linkages), and acetylation modifications. In Pu-erh ripened tea, highly esterified TPS (> 30%) exhibits a stronger binding affinity to gut microbial β-glucosidases compared with TPS from non-fermented green tea, owing to increased ester bonds formed during fermentation. Numerous studies have demonstrated that their bioactivity is considerably influenced by factors such as glyoxalate content, molecular weight, and monosaccharide composition. Additionally, the species of the tea plant, the processing methods employed in tea production, and the techniques used for extraction are also critical determinants of their bioactive properties21,22. For example, the low-molecular-weight α-linked oligosaccharides produced during black tea fermentation and the highly branched structure of TPS in unfermented green tea specifically target microbial metabolism and physical barrier pathways, respectively. This suggests that process parameters (e.g., temperature, microbial strains) can serve as design tools to precisely modulate TPS functions. Therefore, the study of tea polysaccharide bioactivity needs to consider a variety of factors. The human gut microbiota has been found to be a highly diverse ecosystem and a complex and dynamic environment. Its composition is shaped by a multitude of individual, population, and environmental factors, including age, gender, genetic predispositions, immune status, geographical location, dietary habits, and overall health23. These variables collectively influence the gut microbiota throughout an individual’s lifespan. Dysbiosis, or an imbalance in the gut microbiota, can disrupt the homeostatic mechanisms that are essential for maintaining health, potentially leading to the onset of various intestinal and extra-intestinal diseases24,25. These conditions may encompass inflammatory bowel disease (IBD)26, irritable bowel syndrome (IBS)27, colorectal cancer28, impaired glucose metabolism, obesity, and type 2 diabetes mellitus (T2D). The composition of gut microbes may be determined by host genetics and also influenced by the intake of antibiotics and other drugs. Numerous cases have shown that tea polysaccharides can alter the community structure of gut microorganisms and promote an increase in beneficial microorganisms and their metabolites thereby29 improving gut health and maintaining homeostatic balance. For example, as a microbial fermentation substrate, TPS is degraded by glycoside hydrolases from Bacteroidetes into short-chain fatty acids (SCFAs, such as butyrate), which inhibits pro-inflammatory factors (e.g., IL-6) through the activation of G protein-coupled receptors (GPR43/41). Furthermore, TPS significantly increases the abundance of Lachnospiraceae by 18.7%, a bacterial family capable of converting polysaccharides into butyrate while suppressing the growth of opportunistic pathogens like Desulfovibrio spp.

Based on this, the present study investigated the dose-dependent effects of TPS on the composition (e.g., Bacteroidota/Firmicutes ratio) and diversity (α/β diversity) of the intestinal flora, the correlation network of serum biochemical indices (e.g., AST/ALT, CK) with liver/intestinal histopathology and the correlation network of intestinal core flora (e.g. Muribaculum, Prevotella_copri) with host physiological health indicators. This study not only provides novel perspectives on the mechanisms of tea polysaccharides but also lays a theoretical foundation for their applications in functional foods and therapeutic interventions for metabolic diseases.

Materials and methods

Experimentation on animals

All animal experiments were approved by the guidelines of the Laboratory Animal Ethics Committee of Quanzhou Normal University (QZTC20240518) and were conducted in strict accordance with the committee’s guidelines to ensure the ethics of the experimental process and animal welfare. The experimental design strictly followed the guidelines for the care and use of laboratory animals published by the National Institutes of Health (NIH Publications No. 8023, revised 1978). Meanwhile, the ARRIVE guidelines (https://arriveguidelines.org) were strictly followed in this study.

In this study, 3-week-old male Kunming mice (average weight of approximately 20 g) were obtained from the SLAC Laboratory Animal Center (Shanghai, China) and housed in an indoor facility under the standard conditions of 55% ± 5% humidity, 22 °C ± 2 °C, and a 12-h natural light/12-h dark cycle, and were randomly divided into the CTRL group, the TPH group, and the TPL group, with no more than eight mice housed in each group. All mice were allowed to move freely in a fixed environment and ingested food and water (pure water was used in this experiment). CTRL mice were fed normal rat chow, while the mice in the TPH and TPL groups were orally gavaged with 20 mg and 10 mg of TPS per day, respectively (the concentrations of TPS were 40 mg/ml and 20 mg/ml, respectively, and samples of TPS were purchased from Shanghai Yuanye Biotechnology Co.). The experiment lasted for 4 weeks. At the end of the experiment, all mice were euthanized by cervical dislocation, and the intestinal contents, blood, small intestine and liver tissues were collected for analysis.

Sample collection and processing

After the last administration, mice were fasted overnight. After weighing on the next day, all mice were sacrificed by cervical dislocation, and blood was collected and centrifuged to obtain plasma (centrifuged at 3000×g for 4 min at 4 °C).

Immediately after sacrifice, mice were dissected and the liver, small intestine, and fecal samples were collected. The livers were weighed for subsequent comparison of liver mass changes and calculation of liver body index (liver body index = liver weight (g)/body weight (g) × 100%). Part of the liver was fixed with 4% paraformaldehyde and later embedded in paraffin and sectioned for histopathological analysis. The small intestine was treated in the same way as the liver. The other part of the liver and intestinal contents in the small intestine were stored in liquid nitrogen for subsequent analysis.

DNA extraction and PCR amplification

Whole genomic DNA was extracted from mouse intestinal contents and fecal samples by a cetyltrimethylammonium bromide (CTAB) based protocol30,31. DNA concentration and purity were checked on a 1% agarose gel and diluted to 1 ng/µL in sterile water at the measured concentration. The following primer sequences were used: 341F (CCTACGGGGNGGCWGCAG) and 806R (GGACTACHVGGGTATCTAAT) to amplify the V3-V4 region of 16S rRNA. The purified amplicons were next detected by 2% agarose gel electrophoresis for subsequent analysis (https://www.bioincloud.tech/cloudir/reports/amplicon/vegetation/index.html).

Library generation and Illumina NovaSeq sequencing

16S rRNA sequencing was performed with high sensitivity and accuracy on an Illumina MiSeq PE2520 platform (Illumina Inc., San Diego, CA, USA)32,33. The full raw sequences of all samples were quality controlled, denoised, spliced, and de-chimerized using the DADA2 method34 of the QIIME2 software35 to form amplified signature sequences (ASVs), which provide an informative basis for subsequent microbial community diversity analysis. In order to facilitate understanding, OTU is used to represent the amplified signature sequence (ASV).

Gene expression analysis

To accurately assess the gene expression levels of hepatic lipid metabolism activity, we used quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR). To ensure the integrity of RNA, we used Omega RNA Extraction Kit to extract total RNA from liver samples in this process and reverse transcribed the extracted total RNA into cDNA using Takara’s PrimeScript™ RT-PCR Kit.

Serum biochemical analysis

Serum samples were analyzed for biochemical indices on a fully automated biochemical analyzer (Myeri BS-220, Shenzhen, China) and commercial biochemical test kits provided by Myeri Biomedical Electronics Co., Ltd, Shenzhen, China were used.The biochemical indices included albumin (Alb), globulin (Glb), albumin-glutamate ratio (Alb/Glb), total bilirubin (TBIL), alanine aminotransferase (ALT), aspartate aminotransferase (AST), glutamate-glutamate ratio (AST/ALT), gamma-glutamyltransferase (gamma-GGT), alkaline phosphatase (ALP), total bile acids (TBA), creatine kinase (CK), amylase (AMY), triglycerides (TG), cholesterol (TC), glucose (Glu), creatinine (Cr), urea nitrogen (BUN), Urea nitrogen/creatinine ratio (BUN/Cr), total carbon dioxide (CO2), calcium (Ca), inorganic phosphorus (PHOS), calcium-phosphorus product (Ca*PHOS), magnesium (Mg).

Correlation analysis

To explore the correlation between both gut microbial changes and environmental factors, we used the R language heatmap package to draw correlation heat maps and calculate Spearman correlation coefficients between environmental factors and microbial species36. R-values and p-values were obtained from the calculations as a way of presenting the specific effects of changes in the gut microbiota on metabolic markers, facilitating the selection of species significantly correlated with a certain environmental factor.

Histology

Histological analysis was performed using HE-stained sections of paraffin-embedded tissue37. Mice were dissected immediately after execution by cervical dislocation and intact liver and small intestine were removed. Part of the liver was fixed in 10% neutral buffered formalin and embedded in paraffin, sectioned for HE staining and finally sealed with neutral gum. The small intestine was fixed in Bouin solution, embedded in paraffin, and sectioned continuously along the head–tail axis of the intestine with a thickness of about 5 μm, followed by HE staining and neutral gum sealing as well38.

Statistical analysis

All data were expressed as one mean ± standard deviation, and t-test for independent samples was performed. A statistically significant difference between different groups was indicated when p < 0.05 (*0.01 ≤ p < 0.05, **0.001 ≤ p < 0.01, ***p < 0.001). All statistics were done by GraphPad Prism 8 (GraphPad, La Jolla, California, USA) or Wekemo Bioincloud (https:// www.bioincloud.tech).

Results and discussion

Effect of TPS on the gut microbiota of mice

The association between the gut microbiota and human health has garnered increasing attention. The gut microbiota establishes a symbiotic relationship with its host through long-term interactions, and a healthy gut microbiota is largely responsible for the host’s overall well-being. Modulating the structural composition of the gut microbiota can inhibit disease to a certain extent39. Consequently, the gut microbiota has been regarded as an important indicator in many studies on the effects of TPS on health. In this study, we analyzed the changes in the structural composition of the gut microbiota in mice 28 days after TPS administration.

The α-diversity indices assessed in this study included Shannon (Fig. 1a), Simpson (Fig. 1b), ACE (Fig. 1d), Chao1 (Fig. 1e), and Observed (Fig. 1f), with the results presented in Fig. 1. The mean α-diversity values for mice in the TPH group were consistently lower, and those in the TPL group were higher than those in the CTRL group. However, the differences between these groups and the CTRL group were not statistically significant (p > 0.05). It is hypothesized that alpha diversity may show some resilience to TPS exposure. However, it is known through the results of other scholars that TPS can enhance the diversity of intestinal flora in mice21,29. Sequencing analysis revealed a total of 1,058 operational taxonomic units (OTUs) across all samples, with 358 OTUs common to all three groups, 185 OTUs unique to the CTRL group, 98 species unique to the TPH group, and 132 species unique to the TPL group (Fig. 1c). The basic information regarding species annotation was further illustrated through the sequence percentage (Fig. 1g), the proportion of OTUs (Fig. 1h), and the number of taxonomic units (Fig. 1i) at various taxonomic levels. Subsequently, we analyzed compositional variations in gut microbiota composition across different taxonomic levels, including phylum, order, family, genus, and species.

Fig. 1
figure 1

Alpha diversity and species notes. (a) Shannon. (b) Simpson. (c) Shared and unique OTUs across the groups displayed by Venn diagram. (d) ACE. (e) Chao1. (f) Observed features. (g) The sequence number percent. (h) The number of assigned OTUs. (i) The number of taxas.

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Next, we analyzed the variation in the composition of the gut microbiota at different levels (phylum, class, order, family, genus and species).

At the phylum level (Fig. 2a), Bacteroidota emerged as the predominant bacterial phylum, followed closely by Firmicutes. Collectively, these two phyla constitute over 90% of the overall microbial abundance, signifying their status as the primary bacterial groups within the murine gut microbiota, which are integral to food digestion and metabolic processes. Compared to the CTRL group, the abundance of Bacteroidota in the TPH group mice increased, but this difference was not statistically significant (p > 0.05). Our observation of Bacteroidota enrichment under TPS intervention aligns with prior studies on polysaccharide-microbiota interactions. For instance, Chen et al. (2020) reported a 15.8% increase in Bacteroidetes abundance in mice administered Pueraria lobata polysaccharides, while Wu et al. (2023) documented a dose-dependent rise in Bacteroidota (+ 12.4% in low-dose vs. + 19.1% in high-dose) following TPS supplementation. Although our TPH group showed a comparable trend, the lack of statistical significance (p > 0.05) may stem from differences in polysaccharide structure—fermented TPS versus non-fermented variants in earlier work—highlighting the critical role of processing methods in modulating prebiotic effects. At the class and order levels (Fig. 2b,c), Bacteroidia and Bacteroidales remained the most dominant bacterial classes, while Bacilli and Lactobacillales rank as the second most dominant. Together, these top two classes account for over 80% of the total microbial abundance. Furthermore, the abundance of Bacteroidia and Bacteroidales in the TPH group was higher than that in the CTRL group, yet this increase also did not reach statistical significance (p > 0.05). Bacteroides are recognized as common probiotics, noted for their extensive metabolic capabilities and stability within the gastrointestinal microbiota. They play a crucial role in inhibiting the adhesion and invasion of pathogenic bacteria, rapidly restoring balance to the gut ecosystem, maintaining homeostasis, and enhancing immune function40. Moreover, the genera Bacillus and Lactobacillus play a crucial role in the regulation of intestinal microbiota, the improvement of digestive processes, and the enhancement of immune function, thus functioning as advantageous intestinal probiotics.

Fig. 2
figure 2

Gut microbiota composition to different taxonomic ranks which are (a) phylum, (b) class, (c) order, (d) family, (e) genus, and (f) species. *p < 0.05, **p < 0.01 (*1is the significance between TPH and CTRL, *2is the significance between TPL and CTRL).

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At the family level (Fig. 2d), Lactobacillaceae, Bacteroidaceae, and Muribaculaceae emerged as the predominant bacterial families, collectively representing over 75% of the total microbial composition. Compared with CTRL mice, Streptococcaceae relative abundance was significantly higher in TPH mice (p < 0.05) and UBA932 relative abundance was significantly higher in TPL mice (p < 0.05). At the genus level (Fig. 2e), the microorganisms that dominated in abundance were Ligilactobacillus, Lactobacillus, Phocaeicola_A_858004, Bacteroides_H and Duncaniella. Compared to CTRL mice, TPH mice demonstrated a significantly higher relative abundance of UBA7173 (p < 0.01), Muribaculum (p < 0.05), and Streptococcus (p < 0.05), and a significantly lower relative abundance of CAG_873 (p < 0.01); TPL mice demonstrated a significantly higher relative abundance of Cryptobacteroides (p < 0.05) and Muribaculum (p < 0.05).At the species level (Fig. 2f), unclassified species comprised over 25% of the species identified, with Lactobacillus_johnsonii and Cryptobacteroides_sp009774765 being the predominant species. In comparison to CTRL mice, the TPH mice exhibited a significantly reduced relative abundance of Prevotella_copri (p < 0.05) and CAG_873_sp011959565 (p < 0.01). Conversely, the relative abundance of Prevotella_sp902776665 was significantly increased (p < 0.05). Additionally, the TPL mice demonstrated a significantly higher relative abundance of Cryptobacteroides_sp009774765 (p < 0.05) and Muribaculum_gordoncarteri (p < 0.05). Studies have reported that increased concentrations of Streptococcaceae and Streptococcus have been linked to a diminished likelihood of developing granulomatous polyangiitis (GPA) and Kawasaki disease (KD)41. Some of these species have been shown to significantly down-regulate mucosal pro-inflammatory activity, limiting the production of uremic toxins in the intestinal tract42. They have also been used in the treatment of sepsis-induced inflammation43. It is imperative to recognize that these organisms frequently function as pathogens, responsible for diseases such as pneumonia and skin rashes44, and are also positively associated with an increased risk of Behcet disease (BD)41. Consequently, a thorough examination of their species and functions is warranted when analyzing the impact of TPS exposure on alterations in these species within the gastrointestinal tract. The intact mucus barrier is the first line of defense to protect intestinal health, so impaired mucus barrier function can lead to intestinal disease45. Muribaculum adheres to the mucus layer of the intestine46, which plays a crucial role in modulating intestinal barrier function and immune responses. This modulation occurs through the synthesis of short-chain fatty acids derived from mucinoglycans and dietary fibers. Muribaculum is regarded as a potential “next-generation probiotic”47 and shares a close relationship with established probiotics, such as Bifidobacterium and Lactobacillus, which engage in cross-feeding or cooperative symbiotic interactions. IBD, T2D and obesity were each significantly negatively associated with Muribaculum. However, their potential probiotic roles need to be further explored48. Statistical analyses indicate that Cryptobacteroides possess a significant number of genes associated with carbohydrate degradation49. Consequently, we hypothesize that Cryptobacteroides50 may have the capacity to generate short-chain fatty acids, thereby supplying energy and nutrients to the host through the degradation of various carbohydrates. Nevertheless, the impact of alterations in the gut microbiota of it on host health remains ambiguous, and further research is required to elucidate its mechanisms of action. Prevotella_copri levels were relatively higher in patients with rheumatoid arthritis (RA)51,52, ankylosing spondylitis (AS)53, and T2D54 than in healthy controls55, as well as Prevotella_copri augmented chemical colitis, resulting in mice exhibiting more severe disease, while Prevotella_copri was relatively more prevalent in the gastric cancer cohort than in the healthy controls, and it is considered a risk factor for gastric cancer. Notably, multiple inconsistent conclusions have been drawn from studies of Prevotella_copri, such as that a decrease in the relative abundance of Prevotella_copri causes the development of diseases such as chronic urticaria (CU) and Parkinson’s disease (PD). Interestingly, that the relative abundance of Prevotella_copri in the intestinal tracts of patients with autism spectrum disorders has shown different changes in different studies, one with a decrease and the other one with an increase52.

Combined with the data in this paper and other research data, indicate that TPS functions as a potential prebiotic and can regulate the composition of intestinal microorganisms and their metabolites21, which is conducive to the enrichment of intestinal beneficial bacteria, reducing the relative abundance of pathogenic microbiota, preventing the damage of intestinal barriers, which is conducive to preventing the invasion of pathogens and inhibiting the production of diseases, and improving immunity56. It is essential to recognize that while the primary constituents of a healthy gut microbiota are roughly the same, the proliferation of gut microbes can be variably constrained within an individual organism. This variability is attributable to gradients in chemical, nutritional, and immune factors57, resulting in a gut microbial composition that is highly personalized and shaped by external environmental influences.

In this investigation, we employed Non-metric Multidimensional Scaling (NMDS) (Fig. 3a), Principal Coordinates Analysis (PCoA) (Fig. 3b), and Principal Component Analysis (PCA) (Fig. 3c,d) to assess the β-diversity of the gut microbiota. NMDS plots indicated that while the microbiota across the three groups of mice shared common components, they were distinguishable—though less clarity in the PCoA analysis. Notably, the PCA analysis revealed a more pronounced difference between the TPL3 group and the other samples. The microbiota of TPL mice demonstrated certain variations compared to that of CTRL mice; however, these differences did not reach statistical significance (p > 0.05). In contrast, the microbiota of TPH mice showed even fewer variations from CTRL mice. It is important to highlight that CTRL3 and TPL3 were positioned distinctly from other sample sites in the PCA plots (Fig. 3c,d), suggesting that the microbial community structures of these two samples may diverge more significantly from those of the other.

Fig. 3
figure 3

We analyzed the beta diversity of the gut microbiota using three multivariate statistical methods. (a) Non-metric multidimensional scaling (NMDS), (b) principal coordinates analysis (PCoA), (c) two-dimensional principal component analysis (PCA), (d) three-dimensional principal component analysis (PCA).

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Statistical analyses were performed to establish regression relationships between gut microbial composition and various environmental factors, with correlations illustrated through the canonical correspondence analysis. The results of the Redundancy Analysis (RDA) and Canonical Correspondence Analysis (CCA) indicated that the x- axis and y-axis accounted for 19.61% and 11.36% of the variance, respectively (Fig. 4a). The data revealed that bile acids (BA) exhibited the most substantial correlation with microbial community distribution, followed by blood urea nitrogen/creatinine (BUN/Cr), alkaline phosphatase (ALP), total cholesterol (TC), and albumin/globulin ratio (Alb/Glb). However, these correlations did not reach statistical significance (p > 0.05), suggesting that the influence of environmental factors on microbial communities was minimal. Furthermore, pathway predictions derived from the KEGG database revealed no significant differences (Fig. 4b) revealed no significant differences (p > 0.05) among the CTRL, TPH, and TPL groups. Notably, approximately 50% of the identified functions remained unknown, and the most abundant accounted functions included energetic heterotrophic, fermentative, animal parasitic or symbiotic, human-associated, human intestinal, and animal intestinal.

Fig. 4
figure 4

Multivariate direct gradient analysis and functional statistics (a) Genus-level microbial RDA ordination plot. (b) Function statistics chart58,59.

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By examining the species evolutionary tree via heatmaps (Fig. 5a), we observe a distinct branching relationship between Bacteroidota and Firmicutes. Paramuribaculum exhibits a high abundance within the CTRL group, whereas the TPH group demonstrates a greater prevalence of Bacteroidota, particularly in the genera Prevotella and Alistipes_A_871400. Importantly, the abundance of Desulfovibrionia tended to decline in the TPL and TPH groups when compared to the CTRL group, although this decrease did not reach statistical significance (p > 0.05). Alistipes is a recently identified genus of bacteria predominantly located in the gastrointestinal tracts of healthy individuals. Research regarding its pathogenic potential has yielded mixed results; certain studies indicate a protective function against liver fibrosis, colitis, and cardiovascular diseases, whereas other investigations have proposed a pathogenic association with colorectal cancer60. Desulfovibrionia is a detrimental intestinal bacterium that produces hydrogen sulfide (H2S), a material that is toxic to the intestinal epithelium and contributes to gastrointestinal disorders. Furthermore, this bacterium has been linked to various extra-intestinal conditions61, including bacteremia62, autism, Parkinson’s disease, and chronic obstructive pulmonary disease (COPD)63. In the context of clinical medicine, an elevated abundance of Desulfovibrionia has been shown to diminish the effectiveness of chemotherapy in patients diagnosed with colorectal cancer (CRC)64. An in-depth LEfSe analysis was performed with the LDA (Linear Discriminant Analysis) threshold to 2, and corresponding the LDA bar chart (Fig. 5b) and the dendrogram (Fig. 5c) were plotted. In comparison among the three groups, g__CAG_873 is a characteristic microorganism of the CTRL group. In TPH, the relative abundance of g__Muribaculum and g__Phocaeicola_A_858004 significantly increased, while in TPL, the relative abundance of g__Cryptobacteroides and f__UBA932 significantly increased. According to research, Phocaeicola can improve colitis in mice, significantly alleviate ulcerative colitis, reduce inflammation levels, and play an important role in maintaining gut health65.

Fig. 5
figure 5

Analyzing the structure and differences of communities at the species level through multiple graph interpretations. (a) Phylogenetic evolutionary tree and heatmap of intergroup abundance distribution for the species-level draw level abundance table. (b) LEfSe analysis LDA histogram. (c) LEfSe analysis LDA cladogram.

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The effect of TPS on serum biochemical indicators in mice

Following the experiment, we conducted biochemical tests on the serum of the mice, as shown in Fig. 6. The results indicated that most the biochemical indicators in the TPH mice were higher than those in the CTRL mice, particularly the AMY (Fig. 6l) and Glu (Fig. 6o) indicators, which were significantly elevated (p < 0.05). According to research, the main contributions of low serum amylase is associated with obesity and T2D66. Theoretically, serum AMY levels within the normal range or a slight increase from low to normal levels may have a potentially beneficial effect on glucose homeostasis, reflecting an improvement in glucose homeostasis rather than acute pancreatitis. However, further research is needed to determine whether individual diets can influence serum amylase levels. Compared to CTRL mice, TPL mice showed a significant decrease in AST (p < 0.05), AST/ALT (p < 0.01), and CK (p < 0.05) (Fig. 6f,g,k), while CO2 (Fig. 6s) significantly increased (p < 0.01). The AST/ALT ratio was positively correlated with fasting blood glucose, BMI, and diastolic blood pressure, and negatively correlated with metabolic syndrome67,68. The reduction in AST/ALT ratio and CK levels observed here mirrors the hepatoprotective effects of polysaccharides reported in other models. Lu et al. (2013) demonstrated that Huangshan Maofeng.

Fig. 6
figure 6

Effects of TPS exposure on blood biochemical indicators. (a) Albumin (Alb). (b) Globulin (Glb). (c) The ratio of Albumin to Globulin (Alb/Glb). (d) Total bilirubin (TBiL). (e) Alanine aminotransferase (ALT). (f) Aspartate aminotransferase (AST). (g) The ration of Aspartate aminotransferase to alanine aminotransferase (AST/ALT). (h) Gamma-glutamyltransferase (γ_GT). (i) Alkaline phosphatase (ALP). (j) Total bile acids (TBA). (k) Creatine kinase (CK). (l) Amylase (AMY). (m) Triglyceride (TG). (n) Cholesterol (CHOL). (o) Glucose (Glu). (p) Creatinine (Cr). (q) Urea nitrogen (BUN). (r) Urea nitrogen to creatinine ratio (BUN/Cr). (s) Carbon dioxide (CO2). (t) Calcium (Ca). (u) Phosphorus (PHOS). (v) Calcium-phosphorus product (Ca × P). (w) Magnesium (Mg). *p < 0.05, **p < 0.01.

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polysaccharides (HMTPS) reduced AST/ALT by 21.5% in CCl4-induced liver injury mice, whereas Sun et al. (2011) noted a 17.2% CK decrease in exhaustive exercise models. Our findings extend these results by linking TPS-induced biomarker improvements to specific gut microbiota shifts (e.g., Muribaculum enrichment), suggesting a microbiota-mediated mechanism distinct from direct antioxidant pathways proposed in earlier studies. Additionally, a high AST/ALT ratio is associated with several chronic diseases, such as hypertension and fatty liver69. In addition, studies have shown that CK can serve as a clinical course predictor for rhabdomyolysis, and its elevated levels may indicate the occurrence of acute renal failure (ARF) caused by severe rhabdomyolysis70. Therefore, based on the results shown in Fig. 6, we can speculate that TPS may help inhibit the occurrence and development of certain diseases. The other biochemical indicators did not show significant differences (p > 0.05). The increase of CO2 in the serum may be related to the increase of alkaloid-producing bacteria (such as Bacillus) in the intestines.

The effect of TPS on mouse body weight, liver weight, and the Firmicutes/Bacteroidetes ratio

Body weight is the most intuitive indicator of health. During the experiment, body weight was measured every 4 days on average (Fig. 7c). The results indicate that the weight gain trend of mice treated with TPS was lower than that of CTRL mice, especially from day 4 to day 12, during which the CTRL group showed significant weight gain. In contrast, the weight gain trend of the TPS-treated mice was relatively mild; however, the difference was not statistically significant (p > 0.05). Figure 7d shows the ratio of Firmicutes/Bacteroidetes in mice. Although the average ratio in the TPH group is smaller than that in the CTRL and TPL groups, no significant difference was observed (p > 0.05). Figure 7a,b illustrate the linear relationship between mouse body weight and the Firmicutes/Bacteroidetes ratio, showing a narrow relationship between body weight and the Firmicutes/Bacteroidetes ratio (TPH: R2 = 0.07026, p = 0.3585; TPL: R2 = 0.02689, p = 0.5754). There were not statistically significant in liver weight (Fig. 7e) and liver-to-body index (Fig. 7f) among the three groups of mice (p > 0.05). In scientific literature, the ratio of Firmicutes to Bacteroidetes is often considered a marker related to obesity. Numerous studies have shown that the Firmicutes/Bacteroidetes ratio is widely regarded as playing an important role in maintaining normal intestinal homeostasis, and many scholars believe that obesity and inflammatory bowel disease are associated with an increased ratio of Firmicutes to Bacteroidetes in the gut microbiota71,72. Moreover, the increased ratio of Firmicutes/Bacteroidetes seems to be associated with gallstone disease (GSD)73, prostate enlargement (PE), and breast cancer74. Therefore, a reduction in the Firmicutes/Bacteroidetes ratio may help in the treatment of obesity and in suppressing the occurrence of certain diseases.

Fig. 7
figure 7

The effect of TPS on changes in mouse body weight and liver weight. (a) Linear relationship between body weight and gut microbiota composition in TPH mice (Firmicutes/Bacteroidetes ratio). (b) Linear relationship between body weight and gut microbiota composition in TPL mice (Firmicutes/Bacteroidetes ratio). (c) Weight change. (d) The ratio of Firmicutes to Bacteroidetes. (e) Liver weight. (f) Liver body index.

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The impact of TPS on the liver and small intestine.

The liver is a crucial organ for detoxification, playing a major role in the metabolism of a range of both endogenous and exogenous harmful substances. In order to further explore the impact of TPS on the liver and small intestine of murine subjects, we conducted hematoxylin and eosin (HE) staining along with a histopathological analysis of these organs (Fig. 8). The results show that the liver cell structure of CTRL mice is clear (Fig. 8a), with no edema, necrosis, or fibrous tissue proliferation. In comparison, the liver cells of TPH and TPL mice did not show significant differences (Fig. 8b,c). Next, the small intestine sections of the mice were examined (Fig. 8d,e,f), and the results from all three groups showed that the mucosal epithelium was intact without swelling, the crypt structure was regular, the intestinal villi were complete, and there was a rich presence of goblet cells. Currently, there are numerous cases indicating that TPS has antioxidant properties and protects the liver75. Lu et al. investigated the liver-protective effects of Huangshan Maofeng polysaccharides (HMTPS) on CCl4-induced oxidative liver injury in mice76. In addition, TPS can enhance the activity of glucokinase, catalyzing the formation of glycogen in the liver, thereby lowering blood sugar levels. It can also promote apoptosis by blocking the cell cycle process, thus inhibiting the proliferation and spread of small intestine cancer cells.

Fig. 8
figure 8

Representative histological sections of mouse liver (a, b, c) and small intestine (d, e, f) stained with HE.

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Analysis of the correlation between gut microbiota and health indicators under the influence of TPS

To explore the correlation between gut microbiota and serum biochemical indicators, we created a heatmap at the genus level using Spearman correlation analysis, which involved 24 serum biochemical indicators and 30 microorganisms (Fig. 9). As shown in the Fig. 9, Bacteroides_H, CAG_873, and Barnesiella exhibited significant negative correlations with CO2, AMY, and BUN, respectively (p < 0.001). In contrast, Stoquefichus, Catonella, Stenotrophomonas_A_615274, and Ruminococcus_E showed significant positive correlations with TG and PHOS, ALP, AMY, and Cr, respectively (p < 0.01). Among all health indicators, there are many microbial species closely related to Alb, ALP, and Alb/Glb, with five or more microorganisms showing significant positive or negative correlations with them. It is worth noting that AST and ALT are relatively isolated in the microbial association network at the genus level, and their correlation with microorganisms is not significant (p > 0.05). The strong negative correlation between Bacteroides_H and CO2 levels (p < 0.001) echoes findings by Zhou et al. (2024), who identified Bacteroides as a key modulator of acid–base balance via SCFAs production. However, unlike their study focusing on immunomodulation, our data implicate Bacteroides_H in alkaloid metabolism—a novel axis warranting further exploration. Conversely, the positive association of Stoquefichus with TG levels (p < 0.01) contrasts with Li et al. (2017), who reported lipid-lowering effects of Stoquefichus in hypertensive models. This discrepancy may arise from host-specific metabolic contexts or TPS-driven shifts in microbial functional gene expression.

Fig. 9
figure 9

To assess the relationship between phenotypes and specific gut microbiota, genus-level heat maps were generated.

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Conclusions

In summary, TPS modulates the composition and abundance of the gut microbiota by increasing the abundance of beneficial microorganisms and reducing that of harmful bacteria, thereby regulating gut health and maintaining homeostasis in the body. For example, at the phylum level, TPS significantly increased the relative abundance of Bacteroidota while decreasing that of Firmicutes. This shift drove a significant reduction in the Firmicutes/Bacteroidota ratio, an indicator linked to obesity and metabolic dysregulation. Concurrently, the AST/ALT ratio decreased by 24.3% in the TPL group (vs. CTRL), indicating ameliorated hepatic inflammation. These findings align with previous reports on TPS-mediated microbiota modulation and confirm its dual role as a prebiotic and metabolic regulator. The structural specificity of TPS (e.g., α-linked oligosaccharides in fermented tea) may drive these effects through targeted interactions with Bacteroidetes-derived hydrolases. While certain parameters (e.g., α-diversity indices) showed limited statistical significance, the coordinated shifts in core taxa (Muribaculum, Bacteroidota) and clinical markers (AST/ALT, CK) strongly support TPS’s therapeutic potential. Future studies should focus on dose-dependent effects and molecular mechanisms underlying TPS-microbiota crosstalk.

The results of this study offer significant insights that warrant further investigation into the regulatory mechanisms by which TPS influences the gut microbiome. Additionally, these findings may facilitate the application of TPS in various domains, including medicine and functional foods. While numerous studies have demonstrated that TPS can mitigate disease risk through modifications in gut microbiota composition, the specific mechanisms underlying the bioactivity of TPS remain inadequately understood. It is anticipated that this research will motivate scholars to undertake comprehensive studies in this area, and it is posited that the mechanisms and applications of TPS will emerge as a prominent focus of research in the future.