Results
The yields of crude polysaccharide and its total sugar content, protein content were determined. The yield of crude polysaccharide of 1.08 ± 0.09% was attained from water-extracted S. ningpoensis , indicating the presence of the relatively high polysaccharide content ( Tong et al., 2021 ). The total sugar and protein contents were 87.35 ± 0.13% (w/w) and 2.19 ± 0.11% (w/w), respectively.

Changes in reducing sugar and total sugar during simulated digestion of SNP in-vitro are listed in Table 1 . During the process, the total sugar content of polysaccharide remained relatively stable while the reducing sugar content exhibited a mild increment ( P > 0.05). Polymers are readily formed by polysaccharide in the aqueous solutions. In the acidic environment of the stomach, polymers may be destructed and glycosylic bonds are broken. This indicates that SNP can barely be digested or absorbed by the stomach and small intestine, which enables its arrival at the colon.

Figure 1 displays the alpha diversity, which reflects the community diversity and abundance of samples. Generally, abundance coverage estimates (ACE) and Chao indices were influenced by species abundance while Simpson and Shannon indices were under effects of community diversity ( Kong et al., 2021 ). As shown in Figure 1A , the results showed that Chao and ACE indices did not differ significantly, suggesting the absence of significant difference in community abundance among groups. Whereas, the Shannon index in T2DM+SNP group and HC+SNP group exhibited a significant decrease ( P < 0.05), suggesting indicating a lesser level of community diversity in these groups.

β diversity, also termed between-habitat diversity, is generally used for diversity comparison across ecosystems. Besides, the research also evaluated principal coordinate analysis (PCoA), which reveals diversity in the compositions of gut microbiota. Figure 1B shows that the four groups have a notable clustering of compositions of gut microbiota, with PC1 (28.61%) and PC2 (15.15%) exhibiting a total variance of 43.76%. Conforming to PCoA results, a similar trend was also shown in the PLS-DA results ( Figure 1C ). These results further showed the ability of SNP to modulate the structure and richness of intestinal flora.

Figures 2 and 3 displays evaluation results of the structure of microbial communities at phylum and genus levels. Microbes basically comprised Firmicutes , Proteobacteria , Bacteroidetes , Actinobacteri a, and Desulfobacterota at the phylum level ( Figure 2 ). Compared with the HC, the Firmicutes / Bacteroidetes (F/B) ratio of T2DM is reduced, showing similarity to results in previous research ( Fassatoui et al., 2019 ; Hamasaki-Matos et al., 2021 ). Moreover, the F/B ratio was reversed by T2DM+SNP group statistically ( vs . T2DM group, P < 0.05). The F/B relative ratio in the host serves as an effective indicator for whether intestinal homeostasis is favorable or not ( Stojanov, Berlec & Štrukelj, 2020 ). According to previous research, Bacteroidetes and Firmicutes contain intestinal bacteria in nine genera, including Phascolarctobacterium , Faecalibacterium , Blautia , and Bacteroides , and the both phyla generate SCFAs ( Li, Hu & Xiong, 2023 ). As a major phylum of gram-negative bacteria, Proteobacteria contains many pathogenic microorganisms, including Shigella , Salmonella , and Escherichia coli . These may induce mild inflammations and even chronic colitis in severe cases ( Zhang et al., 2022b ). Bacteroidetes can cause severe illness; Bacteroides fragilis involves in the majority of anaerobic infection ( Yekani et al., 2020 ). As shown in Figs. 3 , 4 , abundances of Escherichia-Shigella , Lactobacillus , Parabacteroides , Bifidobacteriumin , and Enterococcus in the T2DM group increased at the genus level, and those of Eggerthella , Phascolarctobacterium , Desulfovibrionaceae , Bilophila , Dorea , Butyricicoccus , Lachnospiraceae , Faecalibacterium , Lachnoclostridium , and Lachnospira decreased ( vs . HC group, P < 0.05). Moreover, the T2DM+SNP group showed statistical reverse in abundances of Dorea , Escherichia - Shigella , Parabacteroides , Faecalibacterium , and Lachnospira ( vs . T2DM group, P < 0.05).

Moreover, specific bacterial populations across groups were evaluated through effect size analysis (LEfSe, Figure 5 ) and linear discriminant analysis (LDA, Figure 6 ). Figure 6 shows that the HC, T2DM, HC+SNP, and T2DM+SNP groups were found to contain 26, 20, 10, and 5 significantly different OTUs (LDA score > 2.5). Phascolarctobacterium , g_unclassified_f_Lachnospiraceae Dorea , Bilophila , Eggerthella , Lachnoclostridium , Candidatus _ Soleaferrea , Desulfovibrionaceae , Holdemania , Butyricicoccus , Coprococcus , and Clostridium _ innocuum were dominant genera in the HC group. Dominant genera in the T2DM group included Escherichia - Shigella , Parabacteroides , Enterococcus , Halomonas , and Flavobacterium . Dominant genera in HC+SNP group comprised Lactococcus , Faecalibacterium , g_norank_f_Lachnospiraceae , Bacteroidales , and Lachnospira . The T2DM+SNP group was dominated by genus Bifidobacterium .

The further to assess the associations between changed gut microbiota in human fecal samples and SNP, microbiota were selected based on both up-regulation or down-regulation in the T2DM+SNP group ( vs . T2DM group) and HC+SNP group ( vs . HC group) ( Table 2 ). The results showed that Lactobacillus , Faecalibacterium , Bifidobacterium , and Lachnospira were up-regulated after treatment of SNP. While Escherichia-Shigella , Eggerthella , Phascolarctobacterium , Desulfovibrionaceae , Bilophila , Dorea , Parabacteroides , Butyricicoccus , and Lachnospiraceae were down-regulated after treatment of SNP. These results indicated that it is possible for SNP to modulate dysbiosis of gut microbiota.

Total gas volume, CO 2 , CH 4 , H 2 , and H 2 S are displayed in Figure 7 . Total gas volume, CO 2 , CH 4 , H 2 , and H 2 S decreased by 32.44%, 25.55%, 89.44%, 30.67%, and 64.54% in the T2DM group compared with those in the HC group. While total gas volume, CO 2 , CH 4 , H 2 , and H 2 S showed increases of 119.32%, 477.01%, 3210.52%, 5.51%, 9.13% in the T2DM+ SNP group compared with those in the T2DM group; there were no differences for total gas volume, CO 2 , CH 4 , H 2 , and H 2 S between T2DM+ SNP and HC+ SNP groups. The result showed that the presence of SNP could contribute to the regulation of the gas volume and composition in T2DM fecal, with a low possibility of production of H 2 and H 2 S and a high possibility of production of CO 2 and CH 4 .

Figure 8 shows that the T2DM group has lower contents of total SCFAs, acetic acid, propionic acid, butyric acid, isobutyric acid, valeric acid, and isovaleric acid, when compared with the HC group ( P < 0.05). While contents of total SCFAs, acetic acid, propionic acid, butyric acid, isobutyric acid, and valeric acid in T2DM+SNP increased by 51.07%, 20.40%, 120.82%, 67.40%, 30.88%, and 315.04% compared with the T2DM group. Moreover, the T2DM+SNP and HC+SNP groups were similar in terms of the production of total SCFAs (acetic acid, propionic acid, butyric acid, valeric acid, isobutyric acid, and isovaleric acid). This suggested the degradation of SNP by intestinal flora to produce SCFAs and the regulation of SCFA production and composition in human fecal matter.

As the investigation deepens, intestinal floral metabolites are regarded as crucial substances with involvement in metabolism of the body ( Yang et al., 2022 ). PCA and OPLS models were utilized to explore metabolic differences for the optimization of analysis. As shown in Figs. 9A , 9B , all samples had a confidence interval of 95% (Hotelling’s T -squared ellipse), these models performed an overall intergroup separation, and HC, T2DM, HC+SNP, and T2DM+SNP groups showed significant differences. Additionally, permutation tests were undertaken (200 times) for model verification. As demonstrated in Fig. S1 , R2Y had a slope greater than 0 and Q2 showed an intercept smaller than 0.05, indicative of the good predictability and the potential to use the model in subsequent analysis with no overfitting ( Liu et al., 2022b ).

Figures 9C – 9E present the screening results for the differential metabolites. In the figure, each point indicates a metabolite. Variable importance projection (VIP) values in the OPLS-DA model are represented by the size of scatter points. The final screening results are indicated by scatter colors, with red, blue, and grey separately represent significantly up-regulated, significantly down-regulated, and non-significantly different metabolites. VIP > 1 (OPLS) and P < 0.05 ( t -test) were used to identify significantly distinguishing metabolites. Table 3 displays that the levels of L-leucine, L-alanine, L-valine L-isoleucine, and xylitol in the T2DM group were remarkably higher in comparison to those in the HC group, while the T2DM group contained a significantly lower content of 5-aminovaleric acid than the HC group, which is consistent with previous research ( Dash et al., 2023 ). These metabolites were reversed by T2DM+SNP statistically ( vs . T2DM group). As shown in Figure 9F , the aforementioned differential metabolites were enriched in glutathione metabolism, alanine metabolism, glutamate metabolism, arginine and proline metabolism, urea cycle, glycine and serine metabolism, valine, leucine and isoleucine degradation, glucose-alanine cycle, propanoate metabolism, spermidine and spermine biosynthesis, ammonia recycling, methionine metabolism, and malate-aspartate shuttle, indicating SNP had the potential to regulate the gut metabolites especially amino acid dysbiosis.

Notes:

The further to explore the associations between changed metabolites in human fecal samples and SNP, up-regulated or down-regulated metabolites both in the T2DM+SNP group ( vs . T2DM group) and HC+SNP ( vs . HC group) were selected ( Table 3 ). The results showed that glycerol, lactic acid, and citric acid were up-regulated after treatment of SNP, moreover, 5-aminovaleric acid, L-proline, putrescine, and cadaverine were down-regulated after SNP treatment.

To further explore correlations of metabolites in human fecal matter and intestinal flora after SNP treatment, thermography was used to analyze contents of metabolites showing significant differences with 30 most abundant genera. Figure 10 shows the results. Bifidobacterium was positively associated with glycerol, CO 2 , 4-aminobutanoic acid, L-cysteine, and D-gluconic acid, and could be negatively correlated with DL-phenylalanine. Faecalibacterium was positively associated with pentanoic acid and 4-aminobutanoic acid, while were negatively correlated with hydracrylic acid. Lactobacillus was positively correlated with xylitol and showed a negative correlation with 5-aminovaleric acid. Dorea was positively associated with phenylalanine, butyric acid, and isovaleric acid, while showed a negative correlation with hydracrylic acid, CH 4 , myo-inositol, L-threonine, glycerol, CO 2 , L-cysteine, and D-gluconic acid. Escherichia - Shigella could be positively associated with H 2 S, L-proline, cadaverine, DL-phenylalanine, benzeneacetic acid, 1,3-propanediol, and were negatively correlated with glycerol, CO 2, 4-aminobutanoic acid, L-cysteine, D-gluconic acid, propionic acid, and L-lysine. Phascolarctobacterium was positively associated with 1,3-propanediol, L-proline, cadaverine, and DL-phenylalanine, and was negatively correlated with glycerol, CO 2 , 4-aminobutanoic acid, L-cysteine, and myo-inositol. Lachnospiraceae were positively associated with 1, 3-propanediol and L-proline, and were negatively correlated with hydracrylic acid and CH 4 . Parabacteroides had positive correlations with L-leucine, L-valine, and L-isoleucine, while negatively correlated with glycerol, CO 2 , 4-aminobutanoic acid, L-cysteine, D-gluconic acid, and propionic acid.