A research model combining a disease and syndrome can provide new ideas for the treatment of ischemic stroke. In the field of traditional Chinese medicine, blood stasis and toxin (BST) syndrome is considered an important syndrome seen in patients with ischemic stroke (IS). However, the biological basis of IS-BST syndrome is currently not well understood. Therefore, this study aimed to explore the biological mechanism of IS-BST syndrome. This study is divided into two parts: (1) establishment of an animal model of ischemic stroke disease and an animal model of BST syndrome in ischemic stroke; (2) use of omics methods to identify differentially expressed genes and metabolites in the models. We used middle cerebral artery occlusion (MCAO) surgery to establish the disease model, and utilized carrageenan combined with active dry yeast and MCAO surgery to construct the IS-BST syndrome model. Next, we used transcriptomics and metabolomics methods to explore the differential genes and metabolites in the disease model and IS-BST syndrome model. It is found that the IS-BST syndrome model exhibited more prominent characteristics of IS disease and syndrome features. Both the disease model and the IS-BST syndrome model share some common biological processes, such as thrombus formation, inflammatory response, purine metabolism, sphingolipid metabolism, and so on. Results of the "gene-metabolite" network revealed that the IS-BST syndrome model exhibited more pronounced features of complement-coagulation cascade reactions and amino acid metabolism disorders. Additionally, the "F2 (thrombin)-NMDAR/glutamate" pathway was coupled with the formation process of the blood stasis and toxin syndrome. This study reveals the intricate mechanism of IS-BST syndrome, offering a successful model for investigating the combination of disease and syndrome.
Exploring the "gene-metabolite" network of ischemic stroke with blood stasis and toxin syndrome by integrated transcriptomics and metabolomics strategy.
阅读:25
作者:Liu Yue, Cui Wenqiang, Liu Hongxi, Yao Mingjiang, Shen Wei, Miao Lina, Wei Jingjing, Liang Xiao, Zhang Yunling
| 期刊: | Scientific Reports | 影响因子: | 3.900 |
| 时间: | 2024 | 起止号: | 2024 May 25; 14(1):11947 |
| doi: | 10.1038/s41598-024-61633-y | ||
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