BBP (No. 20210601, Fujian Guizhentang Pharmaceutical Co., Ltd., China), STZ (No. WXBD7077V, Sigma, United States), sodium citrate buffer (No. 20210615, Beijing Solebao Technology Co., Ltd., China), metformin (No. ABZ8820, Sino-US Shanghai Squibb Pharmaceuticals Co., Ltd., China), 4% paraformaldehyde (No. 21348860, Beijing Lanjie Technology Co., Ltd., China), trifluoroacetic acid (Shanghai Macklin Biochemical Co., Ltd., China), methanol and acetonitrile (Merck, Germany).

CDCA (No. Z01011LA14), UDCA (No. Y16J7C17898), sodium taurochenodeoxycholate (TCDCA-Na, No. Y09S8K43540), and sodium tauroursodeoxycholate (TUDCA-Na, No. B25J9K64146) were purchased from Shanghai Yuanye Biotechnology Co., Ltd., with a purity of more than 98%. Acetic acid (AA, No. H1808008), propionic acid (PA, No. A1903124), isobutyric acid (IBA, No. K1822187), butyric acid (BA, No. D1917157), isovaleric acid (isovaleric acid, IVA, No. C1919091), and valeric acid (valeric acid, VA, No. G1818016) were purchased from Shanghai Aladdin Biochemical Technology Co., Ltd., with purity greater than 99.5%.

A high-sugar and high-fat feed (formula: basic feed 43.5%, lard 17.5%, casein 13%, sucrose 12%, whole milk powder 10%, calcium hydrogen phosphate 2%, experimental animal premix 2%) was purchased from Jiangsu Synergy Pharmaceutical and Biological Engineering Co., Ltd.

The contents of CDCA, UDCA, TCDCA, and TUDCA in BBP were determined by HPLC-ELSD (Waters, USA; Alltech, USA). The determination method is provided in Supplementary Method S1.

SPF male SD rats (body weight 170–200 g) were purchased from Beijing Charles River Experimental Animal Technology Co., Ltd., Beijing, China (SCXK (Jing) 2021-0011). The animals were raised in the barrier environment of the Experimental Animal Center of Nanjing University of Traditional Chinese Medicine, the temperature was (22 ± 2)°C, the relative humidity was (55 ± 10)%, the light was alternated for 12 h, and the rats were free to drink and eat. This experiment was examined and approved by the Animal Ethics Committee of Nanjing University of Traditional Chinese Medicine, and the ethics application number is 202202A046.

After 1 week of adaptive feeding, the rats were randomly divided into a control group (Con, n = 10) and the T2DM group (n = 48) according to body weight by stratified grouping method. The control group was fed a regular diet, and the T2DM model group was fed a high-sugar and -fat diet. 4 weeks later, the rats fasted for 12 h, and the T2DM model group was intraperitoneally injected with 1% STZ citric acid buffer for 2 consecutive days (0.1 mmol hands, pH 4.5°C and 4°C) at a dose of 30 mg/kg, while rats in the control group were intraperitoneally injected with the same amount of sodium citrate buffer. 72 h later (fasting 12 h in advance), fasting blood glucose was measured by tail vein blood sampling. FBG ≥11.1 mmol/L was considered to be successful. 2 rats failed to make the model, and all of them died after continuing to make the model, and a total of 3 rats died after the end of the model. The 45 rats with the successfully established model were randomly divided into 4 groups according to blood glucose by stratified grouping method, which were model group (MOD, n = 12), metformin group (MET, 180 mg/kg, n = 11), low-dose BBP group (BBP-L, 41.67 mg/kg, n = 11), and high-dose BBP group (BBP-H, 83.33 mg/kg, n = 11). The rats in the treatment group were given corresponding drugs, while those in the control group and model group were given distilled water by gastric perfusion with a volume of 10 ml/kg once a day for 30 days. Finally, 10 rats survived in the control group, 7 in the model group, 9 in the metformin group, 8 in the low-dose BBP group, and 8 in the high-dose BBP group, with a mortality rate of 32.5%.

The weight of the rats was measured once a week since they were given a high-sugar and high-fat diet. Before administration and 1, 2, 3, and 4 weeks after administration, after fasting for 12 h, fasting blood glucose (FBG) was measured.

The whole blood of rats was kept at room temperature for 2 h and then centrifuged (3,000 rpm for 4 min) to obtain serum. Determination of triglyceride (TG), total cholesterol (T-CHO), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), aspartate aminotransferase (AST), and alanine aminotransferase (ALT) according to test box instructions (Nanjing Jiancheng Bioengineering Institute, Nanjing, China). Interleukin 6 (IL-6), Interleukin 10 (IL-10), tumor necrosis factor-alpha (TNF-α), and fasting insulin (FINS) were quantified by ELISA kits (Nanjing Lapuda Biotechnology Co., Ltd., Nanjing, China). The insulin resistance index (HOMA-IR) was calculated by a steady-state model.

The liver, kidney, spleen, and pancreas were fixed with 4% paraformaldehyde, then dehydrated, embedded, sectioned, and stained with Histopathological Examination. The microscopic images of the sections were observed under the Panorama DESK/MIDI/250/1000 scanner (3DHISTECH, recording).

100 µl plasma samples were mixed with 300 μl cold acetonitrile, while 200 μl urine samples were mixed with 200 μl cold acetonitrile. The mixture was vortexed for the 60s, placed at 4°C for 30 min, and centrifuged at 13,000 rpm at 4°C for 15 min. After the supernatant was concentrated to dryness by vacuum centrifugation, the plasma was dissolved in 165 μl 50% cold acetonitrile and the urine was dissolved in 190 μl 50% cold acetonitrile. After the solution was vortexed and centrifuged again, 2 μl of the supernatant was injected into UPLC-Q-TOF/MS (Waters, USA) for metabolic analysis. Furthermore, equal aliquots of the processed supernatants from each sample as the quality control (QC) sample.

Samples were analyzed by an ACQUITY UPLC BEH C₁₈ column (100 mm × 2.1 mm, 1.7 μm, Waters, USA), which was maintained at 35°C. The mobile phase was 0.1% formic acid solution (mobile phase A) and acetonitrile (mobile phase B). The injection volume was 2 μL and the flow rate was 0.4 ml/min. The gradient elution conditions for plasma analysis were as follows: 0.0–3.0 min, 95%–45% A; 3.0–13.0 min, 45%–5% A; 13.0–14.0 min, 5%–95% A; 14.0–15.0 min, 95% A. The gradient elution conditions for urine analysis were as follows: 0.0–8.0 min, 95%–70% A; 8.0–11.0 min, 70%–30% A; 11.0–13.0 min, 30%–5% A; 13.0–14.0 min, 5%–95% A; 14.0–15.0 min, 95% A. The positive and negative ionization modes of electrospray ionization (ESI) mass spectra were adopted, the mass scanning range was 100–1,000 m/z, the capillary voltage was 3.0 kV, the cone voltage was 30 V, the extraction voltage was 2.0 V, the collision energy was 20∼50 eV, the desolvent temperature and ion source temperature were 350°C and 120°C, the desolvent gas flow rate and cone hole gas flow rate were 800 L/h and 50 L/h, the scanning time was 0.3s, and the interval scanning time was 0.02s. Leucine-enkephalin (ESI⁺: 556.2771 m/z, ESI⁻: 554.2615 m/z) solution was the locked mass solution.

Import the original data into ProgensisQI (Waters, USA) software for denoising, peak alignment, peak picking, standardization, and normalization. The data were analyzed by partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) using SIMCA software (version 14.0, Sweden). Variables with S-plot ≥.05, S-plot ≤−.05 and VIP >1 were selected as potential differential metabolites. T-test and non-parametric tests were used to analyze whether there was a significant difference in the relative peak area of biomarkers between the two groups, and the difference metabolites were screened by p <.05. Human Metabolic Database (HMDB, http://www.hmdb.ca/) and MetaboAnalyst database (http://www.metaboanalyst.ca/) were used to identify differential metabolism and analyze metabolic pathways. Origin was used to analyze the correlation between biochemical indexes and metabolites by spearman.

According to the E.Z.N.A. ^®Soil DNA kit (Omega Bio-Tek, Norcross, GA, United States), the total genomic DNA of the microbial community was extracted from the cecal contents of rats. 1% agarose gel electrophoresis was used to detect the quality of extracted genomic DNA, and Nano Drop 2000 (Thermo Scientific Inc., United States) was used to determine the concentration and purity of DNA. The V3-V4 hypervariable region of the bacterial 16s rRNA gene was amplified by PCR (Gene Amp 9700, ABI, United States) with primer 338F (5′-ACT​CCT​ACG​GGA​GGC​AGC​AG-3′) and 806 R (5′-GGACTACHVGGGTWTCTAAT-3′). The PCR amplification procedure was as follows Pre-denatured 3 min at 95°C, 27 cycles (denatured at 95°C for the 30s, annealed at 55°C for 30s, extended at 72°C for 30s), and single extension at 72°C for 10 min. The PCR products were recovered by 2% agarose gel, purified by AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, United States), quantified by Quantus gel Fluorometer (Promega, United States), and sequenced using Illumina’s MiSeq PE300 platform.

Using UPARSE software (http://drive5.com/uparse/version 7.1), the sequence after quality control splicing was operated by Operational taxonomic unit (OTU) clustering, and chimerism was eliminated according to 97% similarity. The RDP classifier (http://rdp.cme.msu.edu/, version 2.11) ratio was used to annotate the OTU taxonomy of the Silva 16s rRNA gene database with a 70% confidence threshold. Mothur software (http://www.mothur.org/wiki/Calculators) was used to calculate Alpha diversity. Using R language tools to draw principal coordinates analysis (PCA) and principal coordinates analysis (PCoA) diagrams to test the similarity of microbial community structure among samples. Linear discriminant analysis (LDA) was performed on samples of different groups to identify the species that had significant differences in sample classification using linear discriminate analysis effect size (LEfSe) software. The Spearman correlation coefficient between biochemical indexes and IM and between metabolites and IM were evaluated using the R package. The 16s function was predicted and analyzed by using PICRUSt2 (version 2.2.0) software.

The contents of AA, PA, IBA, BA, IVA, and VA were determined by GC-FID (Clarus® 680, PerkinElmer, United States). Detailed experimental steps are provided in Supplementary Method S2.

GraphPad Prism 8 software was used for statistical analysis, and One-way ANOVA analysis and the Kruskal-Wallis test were used for comparison between groups. The value of p <.05 was statistically significant.

Four main chemical components of BBP were detected by HPLC-ELSD (Figure 1). The contents of CDCA, UDCA, TCDCA, and TUDCA were 1.95%, 0.59%, 45.78%, and 38.49%, respectively (Supplementary Tables S1, S2; Supplementary Figure S1).

As shown in Figure 2, compared with the control group, the body weight of rats in the model group decreased significantly and the level of FBG increased significantly. Compared with the model group, the body weight of the treatment group had no significant change, and the FBG tended to be adjusted to the control group. It is suggested that BBP can reduce blood glucose in T2DM rats, and the efficacy is enhanced with the increase in dose.

As shown in Figure 3, compared with the control group, the levels of FINS, HOMA-IR, T-CHO, TG, LDL-C, AST, ALT, IL-6, and TNF-α in the model group were significantly increased, while the levels of HDL-C and IL-10 were significantly decreased. Compared with the model group, the level of FINS and HOMA-IR in each treatment group decreased significantly, indicating that BBP can reduce IR. After administration, the levels of AST and ALT had a downward trend, and the effect of the BBP-H group was better, with a significant difference, suggesting that BBP can improve the liver injury of T2D rats. After administration, the levels of T-CHO, TG, and LDL-C all had a downward trend, among which the effect of the BBP-H group was better, and the difference was significant, while the level of HDL-C was up-regulated, and the effect of the BBP-L group was better, which suggested that T2D rats had the symptom of lipid metabolism disorder, and BBP could improve the blood lipid metabolism of T2D rats. After administration, the levels of IL-6 and TNF-α decreased significantly, while the level of IL-10 tended to increase, and the effect of the BBP-H group was better, with a significant difference, indicating that BBP can improve the inflammatory response of T2D rats.

The result of Histopathological Examination staining of the liver, kidney, spleen, and pancreas tissue is shown in Figure 4. Compared with the control group, the arrangement of liver tissue in the diabetic group is more disordered, there are more vascular congestion, hepatocyte necrosis, structure disappearance, and more granulocytes around focal infiltration. After administration, the liver tissue cells of rats were arranged neatly, and the inflammation was improved. Compared with the control group, the spleen tissue of diabetic rats occasionally showed purulent foci, more granulocytes showed focal infiltration, and few necrotic cell fragments and cavity-like structures were seen. After administration, the pathological changes of the spleen in all groups were improved to varying degrees. Compared with the control group, there was more vascular congestion, obvious dilatation of renal tubules, necrosis of renal tubular epithelial cells, deep staining, and fragmentation of nuclei in the diabetic group. After administration, the dilatation of renal tubules and the necrosis of renal tubular epithelial cells were significantly alleviated. Compared with the control group, the number of islets in the diabetic group was less, the size was small, the shape was irregular, few acinar cells infiltrated into the islets, more islet cells were necrotic, and the nucleus pyknosis was deeply stained. After administration, the tissue structure and cell necrosis of rat islets were improved. Therefore, BBP can improve the tissue damage of the liver, spleen, kidney, and pancreas in T2D rats.

The results of the PLS-DA analysis are shown in Figures 5A1–A4. The plasma and urine samples of the control group and the model group can be separated obviously in both positive and negative ion modes. The results showed that the levels of metabolites in the serum and urine of T2D rats changed significantly, showing metabolic disorders. From the results of the OPLS-DA (Figures 5B1–B4) analysis, S-plot (Figures 5C1–C4) can be obtained. The farther the point on S-plot is from zero, the greater the contribution to the difference between groups. A total of 55 metabolites were identified by introducing the differential metabolites into the HMDB database, including 28 metabolites from plasma and 27 metabolites from urine. The mass spectrometric data and the changing trend of the model group compared with the control group are shown in Supplementary Table S3. As shown in Figure 6, 33 of these metabolites can be recalled by BBP after administration, which are considered to be potential biometabolites for the hypoglycemic effect of BBP.

The identified differential metabolites were introduced into the MetaboAnalyst database for metabolic pathway analysis (Figures 5D, E). The key metabolic pathways were screened according to the impact value >0.1. The results show that the occurrence of T2DM may be related to many metabolic pathways, including phenylalanine, tyrosine and tryptophan biosynthesis, tryptophan metabolism, retinol metabolism, tyrosine metabolism, purine metabolism, pentose and glucuronate interconversions, starch and sucrose metabolism, and glycerophospholipid metabolism. The metabolic pathways of the hypoglycemic effect of BBP are tryptophan metabolism, pentose and glucuronate interconversions, starch and sucrose metabolism, and glycerophospholipid metabolism. It is suggested that BBP administration can improve the metabolic disorder of T2DM by changing the metabolic pathway and related biomarkers.

The correlation analysis between biochemical indexes and metabolites is shown in Figure 9B. LysoPC (22:6 (4Z, 7Z, 10Z, 13Z, 16Z, 19Z)/0:0), 5-HT, and NAS were positively correlated with FINS, dyslipidemia, inflammatory factors, and liver injury. PC (22:5(7Z, 10Z, 13Z, 16Z, 19Z)/14:0) and other Lysophosphatidylcholines (lysoPCs) were negatively correlated with these biochemical indexes. The correlation analysis between biochemical indexes and IM is shown in Figure 9C. UCG-005, Ruminococcus, Christensenellaceae_R-7_group, Romboutsia, and norank_f__Eggerthellaceae were negatively correlated with FINS, dyslipidemia, inflammation, and liver injury. Dubosiella, Bifidobacterium, and Anaerostipes were positively correlated with these biochemical indexes. The correlation analysis between metabolites and IM is shown in Figure 9D. UCG-005, Ruminococcus, Christensenellaceae_R-7_group, Romboutsia, and norank_f__Eggerthellaceae were negatively correlated with LysoPC (22:6 (4Z, 7Z, 10Z, 13Z, 16Z, 19Z)/0:0), 5-HT, and NAS, and positively correlated with PC and other lysoPCs. Dubosiella, Bifidobacterium, and Anaerostipes were positively correlated with LysoPC (22:6 (4Z, 7Z, 10Z, 13Z, 16Z, 19Z)/0:0), 5-HT and NAS, and negatively correlated with PC and the rest of lysoPCs.

The contents of six SCFAs in the cecum of T2DM rats were determined by GC-FID (Supplementary Tables S4, S5; Supplementary Figure S2). The contents of six SCFAs in cecal contents are shown in Figure 10. Compared with the control group, the level of AA, PA, IBA, BA, IVA, and VA in the model group decreased significantly. After administration, the above concentrations tended to increase, and their concentrations increased with the increase in drug dose. It is suggested that BBP can restore the decrease of SCFAs in the feces of T2DM rats.