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NEFL、NRN1在二乙酰吗啡致神经元凋亡中的作用
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作者 朱森森 苏丽萍 +3 位作者 庄梦婕 刘静宇 戴晨璐 蒲红伟 《新疆医科大学学报》 CAS 2024年第6期765-771,共7页
目的探究神经丝轻链蛋白(Neurofilament light chain,NEFL)、神经突起素(Neu-ritin,NRN1)在二乙酰吗啡致神经元凋亡中的作用。方法建立二乙酰吗啡成瘾SD大鼠模型,分为Control组、Model组,观察各组大鼠脑组织病理形态学改变、凋亡细胞数... 目的探究神经丝轻链蛋白(Neurofilament light chain,NEFL)、神经突起素(Neu-ritin,NRN1)在二乙酰吗啡致神经元凋亡中的作用。方法建立二乙酰吗啡成瘾SD大鼠模型,分为Control组、Model组,观察各组大鼠脑组织病理形态学改变、凋亡细胞数及凋亡相关因子蛋白质表达变化;Western blot检测各组大鼠脑组织中NEFL、NRN1、Bax和Bcl-2的蛋白表达;采用NEFL慢病毒转染大鼠原代小脑颗粒细胞,使用Western blot技术检测转染效率,分为NC组、Model组和sh-NEFL+M组,使用二乙酰吗啡干预Model组和sh-NEFL+M组。采用CCK-8法检测细胞活力;流式细胞术检测细胞凋亡率;Western blot检测细胞NEFL、NRN1、Bax和Bcl-2的蛋白表达。结果成瘾SD大鼠脑组织HE染色和Hoechst33342荧光染色显示,与Control组相比,Model组大鼠脑组织出现明显筛网状改变,凋亡细胞数目明显增加;Western blot结果显示,Model组中NEFL、Bax蛋白表达升高,NRN1、Bcl-2蛋白表达降低,差异具有统计学意义(P<0.05);在二乙酰吗啡干预原代小脑颗粒细胞后,与Control组相比,Model组的细胞活力明显降低,凋亡率明显升高,NEFL和Bax蛋白表达显著升高,NRN1和Bcl-2蛋白表达显著降低,差异具有统计学意义(P<0.05);与Model组相比,sh-NEFL+M组细胞活力显著升高,凋亡率显著降低,NEFL、Bax蛋白表达降低,NRN1、Bcl-2蛋白表达升高,差异具有统计学意义(P<0.05)。结论NEFL、NRN1在二乙酰吗啡致小脑颗粒细胞凋亡中发挥重要作用,降低NEFL表达可减少二乙酰吗啡致小脑颗粒细胞凋亡。 展开更多
关键词 神经元 凋亡 二乙酰吗啡 NEFL nrn1
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大鼠Nrn1基因3’UTR克隆及序列分析 被引量:2
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作者 菅辉玲 程江 +1 位作者 黄瑾 高蕊 《中风与神经疾病杂志》 CAS CSCD 北大核心 2012年第12期1066-1069,共4页
目的 neuritin(Nrn1/Cpg15)是一种神经突起生长因子,在神经修复过程中发挥着重要作用。本文旨在探讨miRNAs调控Nrn1基因表达的潜在性。方法采用PCR技术克隆大鼠Nrn1基因的3’UTR片段,同时利用MiRanda、DNAMAN软件对Nrn1基因序列及miRNA... 目的 neuritin(Nrn1/Cpg15)是一种神经突起生长因子,在神经修复过程中发挥着重要作用。本文旨在探讨miRNAs调控Nrn1基因表达的潜在性。方法采用PCR技术克隆大鼠Nrn1基因的3’UTR片段,同时利用MiRanda、DNAMAN软件对Nrn1基因序列及miRNA结合位点进行生物信息学分析。结果成功克隆了649bp的Nrn1 3’UTR核心区段;Nrn1基因在不同物种间的序列同源性呈高度保守。MiRanda靶标预测结果表明miR-204在3’UTR结合位点处高度保守,分值和能值较高,具有很强的潜在性。结论 Nrn1基因3’UTR在不同物种间序列保守性较高,miRanda软件预测miR-204结合位点处序列高度保守,进一步暗示Nrn1基因表达可能受到miR-204的调控。 展开更多
关键词 nrn1基因 3’非翻译区 序列同源性 靶标位点
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NRN1 and CAT Gene Polymorphisms, Complex Noise, and Lifestyles interactively Affect the Risk of Noise-induced Hearing Loss 被引量:1
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作者 LIU Shuang Yan SONG Wei Qin +9 位作者 XIN Jia Rui LI Zheng LEI Song CHEN Ying Qi ZHAO Tian Yu WANG Hai Yan XU Liang Wen ZHANG Mei Bian HONG Yu YANG Lei 《Biomedical and Environmental Sciences》 SCIE CAS CSCD 2021年第9期705-718,共14页
Objective The effects of interactions between genetic and environmental factors on the noise-induced hearing loss(NIHL)are still unclear.This study aimed to assess interactions among gene polymorphisms,noise metrics,a... Objective The effects of interactions between genetic and environmental factors on the noise-induced hearing loss(NIHL)are still unclear.This study aimed to assess interactions among gene polymorphisms,noise metrics,and lifestyles on the risk of NIHL.Methods A case-control study was conducted using 307 patients with NIHL and 307 matched healthy individuals from five manufacturing industries.General demographic data,lifestyle details,and noise exposure levels were recorded.The Kompetitive allele-specific polymerase chain reaction(KASP)was used to analyze the genotypes of 18 SNPs.Results GMDR model demonstrated a relevant interaction between NRN1 rs3805789 and CAT rs7943316(P=0.0107).Subjects with T allele of rs3805789 or T allele of rs7943316 had higher risks of NIHL than those with the SNP pair of rs3805789-CC and rs7943316-AA(P<0.05).There was an interaction among rs3805789,rs7943316,and kurtosis(P=0.0010).Subjects exposed to complex noise and carrying both rs3805789-CT and rs7943316-TT or rs3805789-CT/TT and rs7943316-AA had higher risks of NIHL than those exposed to steady noise and carrying both rs3805789-CC and rs7943316-AA(P<0.05).The best six-locus model involving NRN1 rs3805789,CAT rs7943316,smoking,video volume,physical exercise,and working pressure for the risk of NIHL was found to be the interaction(P=0.0010).An interaction was also found among smoking,video volume,physical exercise,working pressure,and kurtosis(P=0.0107).Conclusion Concurrence of NRN1 and CAT constitutes a genetic risk factor for NIHL.Complex noise exposure significantly increases the risk of NIHL in subjects with a high genetic risk score.Interactions between genes and lifestyles as well as noise metrics and lifestyles affect the risk of NIHL. 展开更多
关键词 Noise-induced hearing loss KURTOSIS CAT nrn1 LIFESTYLE Interaction Generalized multifactor dimensionality reduction
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