抑制HDAC9-MEF2相互作用可通过上调cGMP依赖性激酶Ⅱ表达抑制缺血性脑卒中后的神经元凋亡
Chinese Journal of Pharmacology and Toxicology(2023)
Abstract
目的 全基因组关联研究发现组蛋白去乙酰化酶9(HDAC9)与缺血性脑卒中相关.因此需要研究HDAC9在缺血性脑卒中中的作用与机制.方法 在野生型(WT)小鼠和神经元缺失HDAC9(HDAC9 cKO)的小鼠上,利用脑中动脉栓塞再灌注(tMCAO)模型模拟缺血性脑卒中脑损伤.通过TTC检测脑损伤,利用免疫荧光组化、Western印迹法、蛋白免疫共沉淀、染色质免疫共沉淀和荧光定量PCR检测HDAC9在缺血性脑卒中的作用和机制.结果 HDAC9 cKO小鼠与WT小鼠相比,半暗带中神经元凋亡及脑梗死体积显著降低的同时cGMP依赖性激酶Ⅱ(cGKⅡ)表达上调,抑制cGKⅡ表达后能够逆转上述现象.进一步研究发现,HDAC9通过与MEF2相互作用抑制tMCAO后cGKⅡ表达.利用BML210抑制HDAC9与MEF2的相互作用能够显著降低脑梗死体积.结论 抑制MEF2与HDAC9相互作用能够上调cGKⅡ表达,进而抑制缺血性脑卒中后神经元凋亡,降低脑损伤.
More上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined