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. 2023 Jun 30;45(7):5558-5574.
doi: 10.3390/cimb45070351.

Microbiomic and Metabolomic Analyses Unveil the Protective Effect of Saffron in a Mouse Colitis Model

Affiliations

Microbiomic and Metabolomic Analyses Unveil the Protective Effect of Saffron in a Mouse Colitis Model

Gulshan Singh et al. Curr Issues Mol Biol. .

Abstract

Despite the existence of effective drugs used to treat inflammatory bowel disease (IBD), many patients fail to respond or lose response over time. Further, many drugs can carry serious adverse effects, including increased risk of infections and malignancies. Saffron (Crocus sativus) has been reported to have anti-inflammatory properties. Its protective role in IBD and how the microbiome and metabolome play a role has not been explored extensively. We aimed to establish whether saffron treatment modulates the host microbiome and metabolic profile in experimental colitis. Colitis was induced in C57BL/6 mice with 3% DSS and treated with either saffron in a dose of 20 mg/kg body weight or vehicle through daily gavage. On day 10, stool pellets from mice were collected and analyzed to assess saffron's effect on fecal microbiota and metabolites through 16S rRNA sequencing and untargeted primary metabolite analysis. Saffron treatment maintained gut microbiota homeostasis by counter-selecting pro-inflammatory bacteria and maintained Firmicutes/Bacteroides ratio, which was otherwise disturbed by DSS treatment. Several metabolites (uric acid, cholesterol, 2 hydroxyglutaric acid, allantoic acid, 2 hydroxyhexanoic acid) were altered significantly with saffron treatment in DSS-treated mice, and this might play a role in mediating saffron's colitis-mitigating effects. These data demonstrate saffron's therapeutic potential, and its protective role is modulated by gut microbiota, potentially acting through changes in metabolites.

Keywords: colitis; inflammation; metabolites; microbiome; saffron.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Schematic diagram representing methodology of the experiment of DSS colitis model and collection of stool samples for metabolomics and microbiomics.
Figure 2
Figure 2
Representation of microbiome relative abundance at two time points (day 0 and day 10) between DSS + vehicle and DSS + SFE20 mg groups. (A) Relative percent bacterial abundance of individual subject at phylum level, (B) relative percent bacterial abundance of treatment week at phylum level, (C) hierarchical clustering and heatmap showing saffron treatment changes DSS-induced microbiome profile.
Figure 2
Figure 2
Representation of microbiome relative abundance at two time points (day 0 and day 10) between DSS + vehicle and DSS + SFE20 mg groups. (A) Relative percent bacterial abundance of individual subject at phylum level, (B) relative percent bacterial abundance of treatment week at phylum level, (C) hierarchical clustering and heatmap showing saffron treatment changes DSS-induced microbiome profile.
Figure 3
Figure 3
Microbial diversity comparison between two timepoints (day 0 and day 10) between DSS + vehicle and DSS + SFE20 mg groups. The OTU number representing the bacterial species richness of the microbiota was estimated for alpha diversity using the following: (A) Simpson (p = 0.018), (B) Shannon index (p = 0.007), (C) Chao1 (p = 0.172), (D) Fischer (p = 0.126). (E) β-diversity using PCOA (Bray–Curtis, [PERMANOVA] F-value: 11.132; R-squared: 0.736; p-value < 0.001, (F) Jaccard distance PERMANOVA] F-value: 6.4871; R-squared: 0.618; p-value < 0.001.
Figure 4
Figure 4
Saffron increases the abundance of gut microbial taxa associated with maintenance of microbiome homeostasis (A). Firmicutes/Bacteroidetes ratio in DSS (day 0, 579 and day 10, 0.974) and DSS-SFE (day 0, 0.548, day 10, 0.501) show maintenance of microbiome hemostasis in saffron-treated animals (B). Relative abundance of bacterial order in the colonic mucosa (C). Bars represent mice group. (D) Labels indicate families with average relative abundances >1% in at least one treatment group (E). Relative abundance of mucosa-associated bacteria at genus was significantly altered in saffron-treated animals. Data are presented as means ± SEM. * p < 0.05 DSS vs. DSS + SFE gavaged mice.
Figure 4
Figure 4
Saffron increases the abundance of gut microbial taxa associated with maintenance of microbiome homeostasis (A). Firmicutes/Bacteroidetes ratio in DSS (day 0, 579 and day 10, 0.974) and DSS-SFE (day 0, 0.548, day 10, 0.501) show maintenance of microbiome hemostasis in saffron-treated animals (B). Relative abundance of bacterial order in the colonic mucosa (C). Bars represent mice group. (D) Labels indicate families with average relative abundances >1% in at least one treatment group (E). Relative abundance of mucosa-associated bacteria at genus was significantly altered in saffron-treated animals. Data are presented as means ± SEM. * p < 0.05 DSS vs. DSS + SFE gavaged mice.
Figure 4
Figure 4
Saffron increases the abundance of gut microbial taxa associated with maintenance of microbiome homeostasis (A). Firmicutes/Bacteroidetes ratio in DSS (day 0, 579 and day 10, 0.974) and DSS-SFE (day 0, 0.548, day 10, 0.501) show maintenance of microbiome hemostasis in saffron-treated animals (B). Relative abundance of bacterial order in the colonic mucosa (C). Bars represent mice group. (D) Labels indicate families with average relative abundances >1% in at least one treatment group (E). Relative abundance of mucosa-associated bacteria at genus was significantly altered in saffron-treated animals. Data are presented as means ± SEM. * p < 0.05 DSS vs. DSS + SFE gavaged mice.
Figure 5
Figure 5
Untargeted primary metabolite analysis of stool samples collected from mice treated with DSS colitis at day 10. (A) Bar graph representing abundance of metabolites. (B) Hierarchy clustering of samples based on differential metabolites.
Figure 5
Figure 5
Untargeted primary metabolite analysis of stool samples collected from mice treated with DSS colitis at day 10. (A) Bar graph representing abundance of metabolites. (B) Hierarchy clustering of samples based on differential metabolites.

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