RiteeshGrandhi/sciclaimeval-shared-task huggingface dataset:
Conclusion
Whether SNP can affect the gas volume, metabolite profile, and compositions of gut microbiota during in-vitro fecal fermentation of T2DM patients that can measure gas production, SCFA production, metabolites and 16s rRNA of microbiota responding to SNP addition was investigated. SNP was found to produce larger amounts of CO 2 and CH 4 , and the amount of CO 2 produced was highly correlated with Bifidobacterium . SNP also yielded greater amounts of SCFAs, such as acetic acid, propionic acid, butyric acid, isobutyric acid, and valeric acid. Furthermore, SNP played a positive role by modulating structures of gut microbiota and ameliorating metabolic pathways related to dysbiosis of amino acids. The aforementioned results suggest that SNP has potential when seeking to develop active natural functional components for managing diabetes. Integration of quantitative and qualitative analyses of intestinal gases, metabolite and microbiota offers opportunities for improving the understanding of activities and functions of chemical compositions of the gut, physical stimuli and intraluminal bacteria. It also allows elucidation of the significance of fermentation of dietary polysaccharide substrates in diseased and healthy cases.

Several limitations associated with the present study warrant mention: (1) In-vitro fermentation models are regarded as a practical alternative for in-vivo studies for several reasons, such as their being cheaper and quicker. The present study identifies an association between SNP, gut microbiota, metabolites, and T2DM phenotypes by using in-vitro fermentation models, yet it does not establish causality. Further research should incorporate suitable in vivo models to clarify the causal relationships among SNPs, gut microbiota, and therapeutic effects. Future research should focus on validating the feasibility and effectiveness of SNP interventions targeting gut microbiota in T2DM; (2) The microbial composition was detected by 16s rRNA sequencing, which is an extensive and cost-effective technique for measuring microbial diversity, but it can only indirectly estimate microbial function. 16S rRNA short-read sequencing data have limitations in accurately distinguishing microbial species at the species level. Moreover, we were unable to assess the all metabolites by using the GC-MS. The further studies using multi-omics technologies are still needed for revealing molecular mechanisms taking part in hypoglycemic activities of SNP; (3) Polysaccharide in our study were extracted from S. ningpoensis which is natural TCM and health food source, therefore SNPs are comparably safe. However, the potential toxicity of SNP has not been thoroughly investigated. The effective dose of SNP has not yet been determined, and follow-up studies should further explore the individualized dose and safety assessment. The composition molecular weight, and bioactivity, of polysaccharide warrants further investigation. These investigations will enhance the translation of experimental findings into clinical applications.
Supplemental Information
(A) T2DM group vs . HC group; (B) HC+SNP group vs . HC group; (C) T2DM+SNP group vs . T2DM group.
Figure 1: Effects of SNP upon the diversity of fecal microflora from humans.
(A) ACE, Chao, Shannon, and Simpson indices; Comparison of bacterial compositions across all groups based on (B) PCoA and (C) PLS-DA at the OTU level. Data are presented as means ± SDs (n = 8).

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DOI: 10.7717/peerj.19374/fig-1
Figure 2: Bacterial taxonomic profiles at phylum levels.

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DOI: 10.7717/peerj.19374/fig-2
Figure 3: Bacterial taxonomic profiles at genus levels.

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DOI: 10.7717/peerj.19374/fig-3
Figure 4: Relative abundance of genera with a significant difference.
The importance of comparisons between indicated groups was evaluated by conducting Kruskal-Wallis tests on the significant difference, *P < 0.05, **P < 0.01, and ***P < 0.001.

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DOI: 10.7717/peerj.19374/fig-4
Figure 5: Significant differences of microbiota across various groups were identified using LEfSe.

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DOI: 10.7717/peerj.19374/fig-5
Figure 6: LDA scores of enriched bacterial taxa (LDA > 2.5 of LEfSe).

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Figure 7: Gas production, methane (CH4), carbon dioxide (CO2), hydrogen (H2) and hydrogen sulfide (H2S) after 24-h in-vitro fermentation with T2DM human fecal Microflora.
One-way ANOVA was used to analyze results expressed as means ± SDs, and then post-hoc Tukey’s multiple comparisons tests were performed. *P < 0.05 and **P < 0.01.

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Figure 8: Total SCFAs (acetic acid, butyric acid, propionic acid, isobutyric acid, isovaleric acid, and valeric acid) after 24-h in-vitro fermentation with T2DM human fecal microflora.
Data were expressed as means ± SDs (n = 8).

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Figure 9: Metabolite profile after 24-h in-vitro fermentation with T2DM human fecal microflora.
(A) Score plots of PCA; (B) OPLS models; differential metabolite screening in (C) T2DM group vs. HC group, (D) HC+SNP group vs. HC group, and (E) T2DM+SNP group vs. T2DM group; (F) metabolic pathway analysis.

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Figure 10: Correlation analysis of characteristic metabolites and bacteria.
Corr represents correlation. Red and blue separately denote positive and negative correlations. The darker the color, the stronger the correlation. *P < 0.05, **P < 0.01, and ***P < 0.001.

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DOI: 10.7717/peerj.19374/fig-10
Total sugar and reducing sugar contents of SNP duringin-vitrodigestion.
Species abundance in different treatment groups.
Identification of differential metabolites.