| Literature DB >> 36033571 |
Xinyu Wang1, Lei Ye1, Wanru Sun1, Liya Li1, Chaoyun Wang1, Xiaoyan Xu1, Zhaohai Pan1, Jianwei Gong1.
Abstract
Dihuang Yinzi, as a classical Chinese medicine prescription, plays an important role for the treatment of ischemic stroke. Gut microbiota play a functional role for the expression of proinflammatory cytokines and anti-inflammatory cytokines, which further affect central nervous system and change brain function. Our research confirmed that Dihuang Yinzi can exert brain protection by inhibiting inflammatory reaction. Dihuang Yinzi can significantly decrease the contents of interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), and interleukin-17 (IL-17) in brain, serum, and colon tissues and increase the contents of transforming growth factor-β (TGF-β) and interleukin-10 (IL-10) in cerebral ischemia-reperfusion model rats. The results of 16s rRNA high-throughput sequencing showed that Dihuang Yinzi had a significant effect on microbiome in rats. The firmicutes, bacteroidetes, and proteobacteria were dominant in Dihuang Yinzi group. The content of firmicutes increased with the increase of dosage of Dihuang Yinzi. Especially, the content of actinomycetes in the high-dose group was higher than other groups. At the genus level, the number of bacteroides in the antibiotic groups was significantly higher than that in the other treatment groups. The results suggest that Dihuang Yinzi may play important roles in treatment of ischemic stroke by regulating the gut microbiota and the inflammatory reaction in the colon tissues, serum, and brain of the model rats, to verify the scientific nature of this prescription in relieving brain inflammatory reaction and brain injury by this way and to reveal the brain-gut related mechanism of Dihuang Yinzi in treating ischemic stroke.Entities:
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Year: 2022 PMID: 36033571 PMCID: PMC9402306 DOI: 10.1155/2022/3768880
Source DB: PubMed Journal: Biomed Res Int Impact factor: 3.246
Figure 1An overview of the study design and timeline. One mouse from each cage was treated according to experiment condition (N = 42, 6 mice per group).
Effects of Dihuang Yinzi on levels of proinflammatory factors Il-6, TNF-α, and Il-17 in the cerebral cortex of rats.
| Group | IL-6 (pg/g) | TNF- | IL-17 (pg/g) |
|---|---|---|---|
| Sham-operated group | 733.74 ± 9.26 | 1837.90 ± 21.00 | 93.57 ± 15.11 |
| Model group | 1915.10 ± 41.80## | 3887.68 ± 61.57## | 527.04 ± 37.52## |
| Low-dose group | 1713.59 ± 26.52∗ | 3242.96 ± 43.84∗∗ | 430.16 ± 21.67∗∗ |
| Intermediate-dose group | 1396.17 ± 15.95∗∗ | 2749.47 ± 29.54∗∗ | 320.34 ± 13.35∗∗ |
| High-dose group | 1187.95 ± 26.22∗∗ | 2347.12 ± 24.70∗∗ | 280.35 ± 11.54∗∗ |
| Antibiotics group | 1132.88 ± 24.75∗∗ | 2157.77 ± 28.47∗∗ | 181.59 ± 6.13∗∗ |
## P < 0.01 compared with sham-operated group; ∗P < 0.05, ∗∗P < 0.01 compared with model group.
Effect of Dihuangyinzi on the contents of anti-inflammatory factors TGF-β and IL-10 in rat cerebral cortex.
| Group | TGF- | IL-10 (pg/g) |
|---|---|---|
| Sham-operated group | 2637.56 ± 42.76 | 934.68 ± 24.68 |
| Model group | 646.28 ± 30.35## | 543.32 ± 39.92## |
| Low-dose group | 1105.80 ± 24.74∗∗ | 629.47 ± 23.56∗∗ |
| Intermediate-dose group | 1438.53 ± 37.76∗∗ | 677.57 ± 19.33∗∗ |
| High-dose group | 1895.97 ± 43.35∗∗ | 769.12 ± 32.32∗∗ |
| Antibiotics group | 2137.29 ± 32.09∗∗ | 846.06 ± 31.02∗∗ |
## P < 0.01 compared with sham-operated group; ∗∗P < 0.01 compared with model group.
Effects of Dihuang Yinzi on serum levels of proinflammatory factors Il-6, TNF-α, and Il-17 in rats.
| Group | IL-6 (pg/ml) | TNF- | IL-17 (pg/ml) |
|---|---|---|---|
| Sham-operated group | 88.42 ± 2.22 | 153.33 ± 2.38 | 10.53 ± 0.64 |
| Model group | 221.05 ± 7.86## | 414.32 ± 10.91## | 58.57 ± 2.47## |
| Low-dose group | 186.60 ± 4.23∗∗ | 351.01 ± 6.40∗∗ | 46.21 ± 2.56∗∗ |
| Intermediate-dose group | 171.70 ± 2.53∗∗ | 300.87 ± 8.09∗∗ | 36.55 ± 1.10∗∗ |
| High-dose group | 140.46 ± 2.85∗∗ | 262.73 ± 5.53∗∗ | 31.71 ± 1.91∗∗ |
| Antibiotics group | 119.64 ± 4.89∗∗ | 218.36 ± 11.66∗∗ | 19.70 ± 0.67∗∗ |
## P < 0.01 compared with sham-operated group; ∗∗P < 0.01 compared with model group.
Effect of Dihuang Yinzi on serum levels of anti-inflammatory factors TGF-β and IL-10 in rats.
| Group | TGF- | IL-10 (pg/ml) |
|---|---|---|
| Sham-operated group | 282.32 ± 4.25 | 93.52 ± 1.81 |
| Model group | 87.48 ± 2.29## | 52.19 ± 3.22## |
| Low-dose group | 120.34 ± 2.17∗∗ | 60.94 ± 2.69∗∗ |
| Intermediate-dose group | 155.97 ± 7.42∗∗ | 73.80 ± 1.22∗∗ |
| High-dose group | 194.43 ± 5.07∗∗ | 81.13 ± 3.26∗∗ |
| Antibiotics group | 242.30 ± 7.51∗∗ | 90.77 ± 4.30∗∗ |
## P < 0.01 compared with sham-operated group; ∗∗P < 0.01 compared with model group.
Effects of Dihuang Yinzi decoction on the contents of proinflammatory factors Il-6, TNF-α, and Il-17 in colon tissues of rats.
| Group | IL-6 (pg/g) | TNF- | IL-17 (pg/g) |
|---|---|---|---|
| Sham-operated group | 753.36 ± 33.90 | 1536.40 ± 26.25 | 156.88 ± 1.87 |
| Model group | 1914.23 ± 23.49## | 3317.93 ± 52.33## | 585.88 ± 13.06## |
| Low-dose group | 1594.68 ± 27.84∗∗ | 3203.75 ± 48.85∗ | 444.11 ± 15.23∗∗ |
| Intermediate-dose group | 1369.23 ± 38.96∗∗ | 2759.59 ± 54.46∗∗ | 344.76 ± 9.04∗∗ |
| High-dose group | 1233.13 ± 16.20∗∗ | 2356.35 ± 47.34∗∗ | 324.67 ± 10.86∗∗ |
| Antibiotics group | 847.22 ± 21.69∗∗ | 2241.24 ± 9.13∗∗ | 247.39 ± 3.98∗∗ |
## P < 0.01 compared with sham-operated group; ∗P < 0.05, ∗∗P < 0.01 compared with model group.
Effect of Dihuang Yinzi on contents of anti-inflammatory factors TGF-β and Il-10 in colon tissues of rats.
| Group | TGF- | IL-10 (pg/g) |
|---|---|---|
| Sham-operated group | 2800.04 ± 45.29 | 910.33 ± 18.25 |
| Model group | 946.28 ± 27.49## | 421.83 ± 7.30## |
| Low-dose group | 1255.37 ± 51.88∗∗ | 571.00 ± 11.78∗∗ |
| Intermediate-dose group | 1494.92 ± 62.90∗∗ | 642.28 ± 15.82∗∗ |
| High-dose group | 1828.07 ± 38.31∗∗ | 727.52 ± 6.92∗∗ |
| Antibiotics group | 2277.89 ± 15.58∗∗ | 772.17 ± 13.46∗∗ |
## P < 0.01 compared with sham-operated group; ∗∗P < 0.01 compared with model group.
Figure 2Diversity estimation of the 16S rRNA gene. (a) ACE indice; (b) Chao indice; (c) Shannon indice.
Figure 3PCoA based on unweighted UniFrac matrix showed that the overall faecal microbiota composition was different in different group.
Figure 4Relative abundances of the gut microbiota at phylum level (a) and genus level (b).
Figure 5Taxonomic differences in 7 group by LEfSe and LDA. (a) LEfSe results for the bacterial communities. (b) Cladogram using the LDA model results for the bacterial hierarchy.
Figure 6Functional analyses of predicted metagenomes. (a) Differentially abundant KEGG pathways across A and C groups. (b) Differentially abundant KEGG pathways across C and G groups.