GPS (C16H20O9, MW: 356.3, purity:≧98%) and STZ (purity≧99%) were purchased from Yuanye Bio-Technology Co. Ltd. (Shanghai, China). The high-fat feed containing 40.7% normal feed, 27% lard, 13.5% casein, 10.8% full-fat milk powder and 8% sucrose was purchased from Shanghai Shuyu Biotechnology Co., Ltd. (Shanghai, China). The nucleoprotein extraction kit was obtained from Beijing Biosynthesis Biotechnology Co., Ltd. (Nanjing, China). The insulin content determination kit was obtained from Camilo Biological (Nanjing, China). The total antioxidant capacity (T-AOC), superoxide dismutase (SOD), MDA, glutathione (GSH), aspartate aminotransferase (AST) and alanine aminotransferase (ALT) detection kit were acquired from Nanjing Jiancheng Bioengineering Institute (Nanjing, Jiangsu, China). Antibodies against nuclear factor E2 associated factor 2 (Nrf2) and heme oxygenase-1 (HO-1) were provided by Abcam (Cambridge, Cambridgeshire, UK). Antibodies to Kelch like epichlorohydrin associated protein-1 (Keap1) and NADPH quinone oxidoreductase 1 (NQO1) were purchased from Cell Signaling Technology (Danvers, MA, USA). The remaining reagents and kits for western blot were purchased from Applygen Technologies (Beijing, China).

Thirty male C57BL/6J mice, aged 7–8 weeks and weighing 24 ± 2 g, were acquired from Henan Skobes Biotechnology Co., Ltd. (Henan, China, production license: SCXK (Yu) 2020-0005). The mice were kept in a standard animal facility at North Sichuan Medical College under controlled conditions (humidity: 55 ± 10%, temperature: 23 ± 2 °C, and a 12-h light/dark cycle). All experimental protocols were strictly performed in line with the Guidelines for the Care and Use of Laboratory Animals (GB14925-2001 and MOST 2006a), and the animal experiments were approved by the Animal Ethics Committee of North Sichuan Medical College (Approval No. NSMC2024132).

A classic method involving a HFD combined with STZ was used to induce a T2DM model in C57BL/6J mice. The specific procedure was consistent with previously described methods (Wang et al., 2023). Once the model was successfully established, the diabetic mice were randomly categorized into two groups: the T2DM model group (Mod) and the GPS treatment group (50 mg/kg), which received GPS via intragastric administration. The dosage of GPS was determined based on previous studies (14, 16). The normal control (Nor) group consisted of C57BL/6J mice fed a normal diet and administered citrate buffer via intraperitoneal injection. GPS was dissolved in 0.5% carboxymethylcellulose sodium (CMC-Na) and administered by gavage for 8 weeks. Both the Nor and Mod groups received an equivalent volume of 0.5% CMC-Na. Throughout the experiment, body weight, fasting blood glucose (FBG), and postprandial random blood glucose (RBG) were measured weekly for all groups. After 8 weeks, the mice were anesthetized via intraperitoneal injection of urethane at a dose of 1 g/kg, subsequent to respiratory arrest, death was verified via cervical dislocation, in compliance with the American Veterinary Medical Association (AVMA) Guidelines. After the mice were euthanized, they were weighed, followed by blood collection via orbital sinus puncture. The livers were excised and weighed. Each liver was divided such that part was fixed in 4% neutral paraformaldehyde, and the remaining tissue was stored at −80 °C for biochemical analysis as well as protein detection. Calculation of the liver index was performed utilizing the formula shown below: liver index = liver weight (mg)/body weight (g).

The OGTT was conducted during the 7th week of drug administration. After fasting the mice for 8 h, FBG was measured as the 0-min blood glucose value. A glucose solution (2 g/kg) was then administered via oral gavage. Blood samples were collected at 30, 60, 90, and 120 min after glucose administration, and the blood glucose concentration was determined using the glucose oxidase method. A glucose tolerance curve was plotted, and the area under the curve (AUC) was calculated. The ITT was conducted during the 8th week of drug administration. After fasting the mice for 6 h, FBG was measured as the 0-min blood glucose value. This was followed by a subcutaneous injection of insulin (0.75 IU/kg). The subsequent procedures were consistent with those used in the OGTT.

After the whole blood was left to stand at room temperature for 2 h, it was centrifuged at 5000 rpm at 4 °C for 10 min to separate the supernatant. Subsequently, the serum levels of GSH, MDA, and insulin in the serum, as well as the activities of T-AOC and SOD were measured strictly according to the kit instructions.

A total of 100 mg of liver tissue from each mouse was weighed into a 1.5 mL Eppendorf (EP) tube, and 900 μL of pre-cooled physiological saline along with steel beads was added. The mixture was thoroughly homogenized using a mechanical homogenizer and then allowed to stand on ice for 40 min. It was subsequently centrifuged at 12,000 rpm for 10 min at 4 °C to obtain the supernatant. The contents of GSH, T-AOC, SOD, and MDA in the liver were determined strictly according to the instructions of the assay kits. Next, 10 μL of the supernatant was diluted tenfold, and the protein concentration was measured using the bicinchoninic acid (BCA) method. The final results of oxidative stress-related indicators in the liver were normalized to the protein concentration.

The liver tissues were fixed in 4% paraformaldehyde for 24 h, embedded in paraffin, and sectioned into 4-μm-thick slices using a microtome. Subsequently, hematoxylin and eosin (HE) staining and periodic acid-Schiff (PAS) staining were performed on the liver sections strictly following the kit instructions. Finally, pathological changes in the liver were evaluated by observation and photography under a microscope (Nikon ECLIPSE E100, Nikon, Japan).

Nuclear and cytoplasmic proteins were extracted from cultured cells or fresh tissue samples using a commercial nuclear extraction kit (Bioss, C5024). Livers from 6 randomly selected mice in each group were accurately weighed (50 mg) and placed into clean 1.5 mL EP tubes. After adding 500 μL of PBS, the mixture was homogenized with a mechanical homogenizer at 4 °C, incubated on ice for 10 min, and then centrifuged at 1000 r/min for 3 min to collect cells. Next, 200 μL of cytoplasmic protein extraction reagent was added, and the pellet was fully resuspended by vigorous vortexing, followed by incubation on ice for 10 min. After vortexing at high speed for 10 s, centrifugation was performed at 12,000 r/min at 4 °C for 10 min. The collected supernatant was cytoplasmic protein, which was stored at −70 °C for subsequent analysis. Subsequently, 100 μL of nuclear protein extraction reagent was added to the pellet, followed by incubation on ice for 10 min and centrifugation under the same conditions to extract nuclear protein. The final supernatant containing nuclear protein was aliquoted and stored at −70 °C for later use.

A total of 100 mg of liver tissue from each mouse was weighed into a 1.5 mL EP tube, and 900 μL of RIPA lysis buffer containing protease and phosphatase inhibitors was added. The mixture was then homogenized. After lysing on ice for 40 min, the samples were centrifuged at 12,000 rpm for 10 min at 4 °C. The supernatant was collected, and the protein concentration was determined using the BCA method. Subsequently, the protein concentrations were adjusted to the same level, followed by the addition of 5 × loading buffer. The samples were then boiled in boiling water for 10 min. Protein extracts from individual mouse liver samples (n = 6 per group) were analyzed separately as independent biological replicates. Subsequent experimental steps, which included sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and exposure, were implemented as previously reported (27).

During the last week of drug treatment, mice were individually placed in clean cages lined with sterile filter paper. After defecation, fecal samples were rapidly collected into sterile centrifuge tubes. Once the weight of each sample exceeded 200 mg, the tubes were immediately frozen in liquid nitrogen. The filter paper was then replaced, and feces from the next mouse were collected using the same procedure. Finally, all samples were stored on dry ice and transported to Shanghai Biotree Biological Technology Co., Ltd. (Shanghai, China) for further analysis. The 16S rDNA analysis of fecal samples was performed by the company using established techniques, following the methods previously described (28). Briefly, DNA was extracted and purified from mouse feces using the QIAamp Fast DNA Stool Mini Kit (Qiagen, CA, United States) in strict accordance with the manufacturer’s protocols. Sequencing was performed on the Illumina MiSeq (PE300) targeting the V3-V4 hypervariable regions of the 16S rRNA PCR products, and the data were analyzed in accordance with the aforementioned protocols. Amplified sequences were merged, and operational taxonomic units (OTUs) were delineated using a 97% sequence similarity cutoff. The α-diversity and β-diversity were analyzed via QIIME2 software, and the dimensionality reduction plots including principal component analysis (PCA), principal coordinate analysis (PCoA), and non-metric multidimensional scaling (NMDS) were generated using R software. Comparative analyses were performed using the Mann–Whitney U-test and Kruskal-Wallis test.

Statistical analysis and figures were performed using GraphPad Prism 6.0 software (GraphPad Software Inc.). Data were reported as mean ± standard deviation (SD), encompassed a minimum of n = 6 biological replicates per experimental group. Student’s t-test was used to analyze differences between the two groups, while one-way analysis of variance (ANOVA) was applied for comparisons among multiple groups. In addition, significant effects reported in the variance analysis were also tested using Dunnett’s or Tukey’s post hoc multiple comparison tests. Pearson’s correlation analysis was employed to assess correlations. Differences of p < 0.05 were considered as statistically significant.

During the experiment, body weight, FBG, and RBG of the mice were measured weekly to evaluate the therapeutic effect of GPS on T2DM mice. As shown in Figures 1A–D, relative to the Nor group, the levels of RBG, FBG, and insulin in the Mod group were significantly increased, while the body weight in this group was remarkably decreased. In contrast to the Mod group, the levels of RBG and FBG in the GPS group decreased markedly from the second week, and insulin levels were reduced, although GPS exerted no influence on the body weight of T2DM mice. To further evaluate the regulatory effect of GPS on blood glucose levels and islet function in T2DM mice, OGTT and ITT were carried out during the experiment. As illustrated in Figures 1E–H, following glucose loading or insulin injection, blood glucose levels at 30, 60, 90, and 120 min, as well as the AUC values, were remarkably higher in T2DM mice compared to those in the Nor group. On the contrary, GPS treatment dramatically lowered blood glucose levels at these time points and reduced the AUC values relative to the Mod group. To further explore the impact of GPS on OS in T2DM mice, the OS markers present in the serum were measured. As illustrated in Figures 1I–L, compared with the Nor group, the activities of T-AOC, SOD, and the levels of GSH in serum of the Mod group were substantially decreased, while MDA content in serum was markedly increased. Relative to the Mod group, GPS treatment significantly increased the activities of T-AOC, SOD, and levels of GSH in serum, along with a reduction in serum MDA levels. Collectively, these results indicated that GPS could alleviate hyperglycemia and reduce oxidative stress injury in T2DM mice, and may play a central role in hypoglycemic effects.

For the purpose of better comprehending the effect of GPS on liver tissue, HE staining and PAS staining were conducted on the liver tissues obtained from the mice. As shown in Figure 2A, the livers in the Nor group exhibited round and plump hepatocytes, regularly and neatly arranged hepatic plates, no obvious expansion, no protrusion of hepatic sinusoids, and normal adjacent hepatic lobule structures. However, the livers of T2DM mice in the Mod group showed extensive granular degeneration of hepatocytes, loose cytoplasm, some fatty degeneration in a subset of hepatocytes, round cytoplasmic vacuoles of varying sizes, local dilation of hepatic sinusoids, and irregular hepatocyte arrangement. Treatment with GPS partially reversed these pathological abnormalities. Furthermore, after sacrifice, the liver index was calculated, and ALT and AST activities were measured. As illustrated in Figures 2B–D, the Mod group showed significant increases in liver index and serum ALT and AST activities compared to the Nor group. GPS administration markedly reduced these elevated levels relative to the Mod group. All in all, these results indicated that GPS protects liver tissue in T2DM mice from hyperglycemia-induced damage.

To further explore the molecular mechanism by which GPS improved the abnormal pathological morphology of the liver, OS-related indicators and protein expressions were measured in the livers of T2DM mice. As presented in Figure 3, relative to the Nor group, the activities of T-AOC and SOD, levels of GSH, and protein expressions of Total-Nrf2, HO-1 and NQO1 were noticeably decreased in the Mod group. Meanwhile, MDA levels and Keap1 protein expression were markedly increased. Relative to the Mod group, GPS treatment remarkably increased the activities of T-AOC and SOD, GSH levels, and protein expressions of Nrf2, HO-1, and NQO1, while it obviously reduced MDA levels and Keap1 protein expression. To further investigated the effect of GPS on Nrf2 nuclear translocation, the expression levels of Nrf2 protein in the cytoplasm and nucleus were subsequently determined. The results showed that compared with the Nor group, the cytoplasmic Nrf2 (cyto-Nrf2) protein level in the Mod group was significantly decreased, while there was no significant change in the nuclear Nrf2 (nucl-Nrf2) protein expression. However, after GPS treatment, both the cyto-Nrf2 and nucl-Nrf2 protein expressions were remarkably increased compared with those in the Mod group. Collectively, these results suggested that the hepatoprotective effects of GPS may be related to the activation of the Nrf2/Keap1 signaling pathway, thereby alleviating oxidative stress injury.

To explore the impact of GPS on the GM in the feces of T2DM mice, the composition of the GM was analyzed using 16S rRNA high-throughput sequencing technology. As demonstrated in Figure 4, the Simpson index, Shannon index, Chao1 index, Pielou’s evenness (Pielou-e) index, and Observed_otus index in the Mod group were higher than those in the Nor group, while no significant difference was observed in the Goods_coverage index. After 8 weeks of GPS treatment, compared to the Mod group, the Chao1 and Observed_otus indices in the GPS group increased significantly, and other α-diversity indices showed a trend toward normalization. The Venn diagram showed that the Mod group had a higher number of operational taxonomic units (OTUs) than the Nor group, with 2,584, 2,914, and 1,970 OTUs detected in the Nor, Mod, and GPS groups, respectively; 648 OTUs were shared across all groups. At the β diversity level, principal coordinate analysis (PCoA) and non-metric multidimensional scaling (NMDS) showed a clear separation between the Nor and Mod groups. After 8 weeks of GPS treatment, the GM in T2DM mice feces exhibited partial restoration, trending toward the composition observed in the Nor group. Overall, these results indicated that GPS reshaped the GM in T2DM mice, suggesting its potential regulatory effect on GM in diabetes.

The impact of GPS on the composition of the GM was measured at the phylum level. As displayed in Figure 5, the GM in mouse feces mainly consisted of 10 dominant bacterial groups, including Firmicutes, Bacteroidota, Proteobacteria, Verrucomicrobiota, Actinobacteriota, Campylobacterota, Desulfobacterota, Patescibacteria, Unclassified and Cyanobacteria. Compared to the Nor group, the relative abundance of Firmicutes and the Firmicutes/Bacteroidota (F/B) ratio were significantly decreased in the Mod group, while the relative abundance of Bacteroidota, Verrucomicrobiota, Cyanobacteria and Unclassified were markedly increased. After 8 weeks of GPS treatment, these abnormal changes in the GM composition of T2DM mice were significantly reversed. Collectively, these results indicated that GPS plays an important role in maintaining GM homeostasis at the phylum level in T2DM mice.

To further ascertain the influence of GPS on the GM composition in the feces of T2DM mice, the microbiota was analyzed at the genus level. The results displayed in Figure 6, the fecal GM was mainly composed of 16 dominant bacterial genera, including Muribaculaceae_unclassified, Lactobacillus, Akkermansia, Ligilactobacillus, Desulfovibrio, Lachnospiraceae_NK4A136_group, HT002, Alistipes, Helicobacter, Muribaculum, Clostridiales_unclassified, Clostridia_UCG-014_unclassified, Alloprevotella, Escherichia-Shigella, Lachnospiraceae_unclassified and Klebsiella. Relative to the Nor group, the relative abundance of Muribaculaceae_unclassified, Akkermansia, Desulfovibrio, Muribaculum and Alloprevotella were significantly increased in the Mod group, whereas the relative abundance of Lactobacillus, HT002 and Dubosiella were noticeably decreased. After 8 weeks of GPS supplementation, the abnormal changes in GM composition at the genus level were dramatically reversed. Collectively, these data preliminarily indicated that GPS could improve the composition of GM in feces at the genus level.

In addition, LEfSe analysis was performed to identify key bacterial taxa from the phylum to genus levels and to evaluate how bacterial abundance contributed to differences between groups. As demonstrated in Figures 7A,B, using a linear discriminant analysis score threshold of >3.5, a total of 57 significantly different OTUs were identified across the three groups. The number of OTUs in the GPS group tended to approach that of the Nor group. Specifically, 9 biomarkers were found in the Nor group, mainly including genus levels (Lactobacillus, Dubosiella, Eubacterium), species levels (Lactobacillus-sp-L-YJ, Dubosiella-unclassified, uncultured-Eubacterium-sp) and several other genera; 27 biomarkers were identified in the Mod group, mainly including class levels (Bacteroidia, Verrucomicrobiae, Deltaproteobacteria), order levels (Bacteroidales, Desulfovibrionales, Verrucomicrobiales), species levels (Akkermansia-unclassified, Muribaculaceae-unclassified, Desulfovibrio-sp-ABHU1SBfatS, Alloprevotella-unclassified, Muribaculum-sp-, Alistipes-unclassified, Duncaniella-muris, Prevotellaceae-UCG-001-unclassified), family levels (Muribaculaceae, Akkermansiaceae, Desulfovibrionaceae, Prevotellaceae) and several other genera; and 21 biomarkers were present in the GPS group, primarily at family levels (Lactobacillaceae, Rikenellaceae, Clostridia-UCG-014-unclassified, Erwiniaceae, Erysipelatoclostridiaceae), genus levels (Ligilactobacillus, HT002, Alistipes, Clostridia-UCG-014-unclassified, Pantoea), species levels (Ligilactobacillus-unclassified, HT002-unclassified, Clostridia-UCG-014-unclassified, uncultured-Alistipes-sp., Pantoea-agglomerans) and several other genera. To further investigate whether the improvement of diabetic symptoms in T2DM mice by GPS is related to the GM, Spearman correlation analysis was performed between differential microbiota and biochemical indicators at the phylum and genus levels. As indicated in Figures 7C,D, at the phylum level, the abundance of Bacteroidota was positively correlated with the protein Keap1. The abundance of Firmicutes was markedly and positively correlated with T-AOC in liver, the protein Nrf2, along with markedly and negatively associated with the protein Keap1 and the serum AST activities. The abundance of unclassified bacteria showed a strong positive correlation with both Keap1 protein expression and serum ALT activity, while being significantly negatively associated with T-AOC and GSH levels in the liver. The abundance of Verrucomicrobiota was strongly and positively correlated with the RBG, the protein Keap1, FBG and the serum ALT activities, along with dramatically and negatively associated with the T-AOC, SOD and GSH in liver, the protein Nrf2, NQO1, HO-1. At the genus level, the abundance of Akkermansia was strongly positively correlated with random RBG, FBG, Keap1 protein, and serum ALT activity. Conversely, it was significantly negatively associated with liver levels of T-AOC, SOD, GSH, and the protein expressions of Nrf2, NQO1, and HO-1. The abundance of Alloprevotella was strongly and positively correlated with the RBG, FBG, insulin, the protein Keap1and the serum ALT activities, along with remarkably and negatively associated with the levels of T-AOC and GSH in liver, the protein Nrf2 and NQO1. The abundance of Desulfovibrio was strongly and positively correlated with the insulin, the protein Keap1and the MDA levels in liver, along with markedly and negatively associated with the levels of T-AOC and GSH in liver, the protein Nrf2 and HO-1. The abundance of Dubosiella was strongly and positively correlated with the protein Nrf2, NQO1, HO-1, the levels of T-AOC and GSH in liver, along with markedly and negatively associated with RBG, FBG, insulin, the MDA levels in liver, the protein Keap1, the serum ALT activities. The abundance of HT002 was strongly and positively correlated with the protein HO-1and the GSH levels in liver. The abundance of Lactobacillus was strongly and positively correlated with the protein NQO1, HO-1, along with markedly and negatively associated with RBG, FBG, the protein Keap1, the serum ALT activities. The abundance of Muribaculaceae_unclassified and Muribaculum were strongly and positively correlated with the protein Keap1. These results suggested that the improvement of diabetic symptoms in T2DM mice by GPS may be associated with the regulation of GM.

To better evaluate the effect of GPS on the GM of T2DM mice, BugBase analysis was performed to assess its impact on nine microbial communities closely related to clinical diseases. As indicated in Figure 8, relative to the Nor group, the relative abundance of Contains_Mobile_Elements bacteria, Facultatively_Anaerobic bacteria and Gram-positive bacteria in the Mod group was strongly decreased, while the relative abundance of Gram-negative bacteria increased rapidly, and other microbial communities showed varying trends. Relative to the Mod group, the GPS group showed a significant increase in the relative abundance of Aerobic bacteria, Contains_Mobile_Elements bacteria, Facultatively_Anaerobic bacteria, Gram_Positive bacteria, Stress_Tolerant bacteria, and a noticeable decrease in Anaerobic bacteria, Gram_Negative bacteria and Potentially_Pathogenic bacteria. No statistically significant differences were observed in the other microbial communities.