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.