Diosmetin suppressed oxidative stress in vitro and in vivo
As shown in Fig. 5A-H, compared with DSS (the concentration of 3% and 5%) only groups, MDA levels in colon tissues were significantly decreased, and the levels of SOD, GSH and GSH-Px were reversely increased by diosmetin. As shown in Fig. 5I, J, the intracellular ROS level of caco2 and IEC-6 cells in LPS group were remarkably increased compared to control group. However, diosmetin significantly decreased ROS level in caco2 and IEC-6 cells compared with LPS group. Next, we evaluated the mitochondrial ROS production in Fig. 5K, L, compare with LPS group, diosmetin markedly decreased mitochondrial ROS level of caco2 and IEC-6 cells.
Effects ofdiosmetin and DSS on gut microbiota
Different Alpha diversity indices, including Shannon-Wiener curve, Simpson index, PD_whole_tree index, Good_coverage index, Chao1 index, observed_species index and Rank Abundance curve of each sample are near flat (Fig. S1), which indicates that the bacterial diversity and sequencing depth and results were sufficient. Alpha diversity index box of difference between groups were shown in Fig. 6A-F. The diversity of DSS group observably decreased (p<0.05) compared to the control group, however the diversity of DSS with diosmetin group was markedly increased compared to the DSS only group. With 97% sequence similarity, the USearch61 clustering method in QIIME software was used to group the sequence into multiple Operation Taxonomic Unit (OTUs). As shown in Fig. 6G, there were 1629 unique (OTU) found in the control group, 536 in the DSS only group, 397 in the DSS with diosmetin group. Beta diversity analysis, principal component analysis (PCA) was displayed in Fig. 6H, and UniFrac heatmap analysis was displayed in Fig. S2. UniFrac analysis is divided into two measurement methods: weighted unifrac and unweighted unifrac. Unweighted unifrac can detect the existence of variation between samples, while weighted unifrac can further quantitatively detect the variation that occurs on different lineages between samples. PCA and UniFrac heatmap analysis showed that DSS significantly changed the gut microbiota, and on this basis, diosmetin could regulate the gut microbiota. As shown as in Fig. 6 I, J, DSS group decreased the relative abundance of Bacteroidetes and cyanobacteria and increased the relative abundance of Firmicutes compared with the control group in phylum level of bacterial composition, however, DSS with diosmetin group increased the relative abundance of Bacteroidetes and cyanobacteria and decreased the relative abundance of Firmicutes compared with the DSS group.
The changes of the main microbiota at Class, Order, family and Genus level are shown in Fig. S3 and Fig. S4. After DSS treatment, the relative abundancens ofLachnospiraceae and Ruminococcaceae markedly decreased and the relative abundances of Bacteroidaceae, Clostridiaceae, Lactobacillaceae and Turicibacteraceae significantly increased compared to the control group. The whole sample microbial community structure was analyzed by Species abundance clustering heatmap at the phylum level shown in Fig. S5.
In order to find maker bacteria with statistical differences among the groups. LEfSe analysis, or LDA Effect Size analysis were used to discover high-dimensional biomarkersand reveal genomic features. The non-parametric factorial Kruskal-Wallis (KW) sum-rank test is used to detect features with significant abundance differences and find Taxa with significant differences from abundance (Puri et al., 2018). As results, 42 dominant OTUs from the 3 groups are displayed. Herein, a total of 3 distinct OTUs were observably reversed by diosmetin intervention (Fig. S6A). The results are also shown in Evolutionary branch graph of LEfSe analysis (Fig. S6B). AS shown as in Fig. S7, diosmetin treatment and markedly decreased bacteria abundance ofEggerthella,Flavobacterium andClostridiumand significantly increase bacteria abundance of Odoribacteraceae, prevotella, Rikenellaceae, Ruminococcus, Coprococcus, Roseburia, Oscillospira, Anaeroplasma and Synergistales compared with DSS group.
Furthermore, the PICRUSt was used to predicted the discrepancy of functional profiles between different groups (Fig. S8). Compared with DSS group, 14 (8 enriched, 6depleted) functional modules were significantly altered (p < 0.05) by treatments with diosmetin. Diosmetin mainly caused changes in some metabolism pathways, such as arginine, ornithine and proline interconversion, methanogenesis from acetate and super-pathway of tetrahydrofolate biosynthesis. In conclusion, these data illustrated that diosmetin contributed to the functional difference of gut microbiota.
Diosmetin inhibitedNF-κB acetylation and promoted the Nrf2 pathway by activating the circSirt1/Sirt1 axis
To verify the mechanism of diosmetin in relieving colitis, we first detected the expression of circ-Sirt1 by qRT-PCR. As shown in Fig. 7A-7D, in vitro and in vivo, diosmetin can dose-dependently alleviate the inhibition of circSirt1 by DSS or LPS. Because there is no mouse circ-Sirt1 sequence in any database, we used the end-to-end sequence of mouse Sirt1 mRNA exons 2 to 7 to design forward and reverse primers. As shown in Fig. 7E, the RT-PCR product was purified and sequenced to confirm the circ-Sirt1 connection sequence and confirm the presence of circSirt1 in mouse colon tissue. As shown in Fig. 8A-8D, diosmetin significantly increased the expression of Sirt1, Nrf2, HO-1 and nucleus Nrf2, and decreased the ratio of acetyl NF-κB and NF-κB in vivo and in vitro. These data suggested that diosmetin inhibited inflammation and oxidative stress by activating the circSirt1/Sirt1 axis.