Regulatory mechanism of circular RNAs in neurodegenerative diseases

肌萎缩侧索硬化 生物 神经退行性变 疾病 机制(生物学) 蛋白酶体 小RNA 神经科学 细胞生物学 医学 病理 遗传学 基因 哲学 认识论
作者
Feng Xiao,Zhi He,Siqi Wang,Jiamei Li,Xiaolan Fan,Taiming Yan,Mingyao Yang,Deying Yang
出处
期刊:CNS Neuroscience & Therapeutics [Wiley]
被引量:1
标识
DOI:10.1111/cns.14499
摘要

Neurodegenerative disease is a collective term for a category of diseases that are caused by neuronal dysfunction, such as Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS). Circular RNAs (circRNAs) are a class of non-coding RNAs without the 3' cap and 5' poly(A) and are linked by covalent bonds. CircRNAs are highly expressed in brain neurons and can regulate the pathological process of neurodegenerative diseases by affecting the levels of various deposition proteins.This review is aiming to suggest that the majority of circRNAs influence neurodegenerative pathologies mainly by affecting the abnormal deposition of proteins in neurodegenerative diseases.We systematically summarized the pathological features of neurodegenerative diseases and the regulatory mechanisms of circRNAs in various types of neurodegenerative diseases.Neurodegenerative disease main features include intercellular ubiquitin-proteasome system abnormalities, changes in cytoskeletal proteins, and the continuous deposition of insoluble protein fragments and inclusion bodies in the cytoplasm or nucleus, resulting in impairment of the normal physiological processes of the neuronal system. CircRNAs have multiple mechanisms, such as acting as microRNA sponges, binding to proteins, and regulating transcription. CircRNAs, which are highly stable molecules, are expected to be potential biomarkers for the pathological detection of neurodegenerative diseases such as AD and PD.In this review, we describe the regulatory roles and mechanisms of circRNAs in neurodegenerative diseases and aim to employ circRNAs as biomarkers for the diagnosis and treatment of neurodegenerative diseases.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
Dean应助cdsd采纳,获得150
1秒前
1秒前
申申完成签到,获得积分10
1秒前
彭于晏应助阿萨卡先生采纳,获得10
1秒前
2秒前
刘惠兴完成签到,获得积分20
2秒前
chemstation完成签到,获得积分10
2秒前
深情安青应助花生采纳,获得10
4秒前
乐乐应助wendy.lv采纳,获得10
4秒前
申申发布了新的文献求助10
5秒前
彦祖完成签到 ,获得积分10
5秒前
5秒前
阿伟发布了新的文献求助10
7秒前
7秒前
7秒前
8秒前
8秒前
cdsd完成签到,获得积分10
8秒前
牧心SKar完成签到 ,获得积分10
8秒前
9秒前
9秒前
10秒前
科研通AI6.3应助qqqqqqqw采纳,获得30
10秒前
明理的天蓝完成签到,获得积分20
11秒前
团子呀发布了新的文献求助10
12秒前
望月白发布了新的文献求助10
13秒前
13秒前
14秒前
yy发布了新的文献求助10
14秒前
栗荔完成签到 ,获得积分10
15秒前
数据女工应助阿伟采纳,获得10
15秒前
ranrai完成签到,获得积分20
16秒前
16秒前
17秒前
18秒前
FashionBoy应助笑点低夜春采纳,获得10
20秒前
次我完成签到,获得积分10
20秒前
ashley关注了科研通微信公众号
20秒前
望月白完成签到,获得积分10
21秒前
ZsJJkk发布了新的文献求助10
21秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
PowerCascade: A Synthetic Dataset for Cascading Failure Analysis in Power Systems 2000
The Composition and Relative Chronology of Dynasties 16 and 17 in Egypt 1500
Picture this! Including first nations fiction picture books in school library collections 1500
Signals, Systems, and Signal Processing 610
Unlocking Chemical Thinking: Reimagining Chemistry Teaching and Learning 555
Scientific Writing and Communication: Papers, Proposals, and Presentations 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6370293
求助须知:如何正确求助?哪些是违规求助? 8184235
关于积分的说明 17266401
捐赠科研通 5424858
什么是DOI,文献DOI怎么找? 2870073
邀请新用户注册赠送积分活动 1847049
关于科研通互助平台的介绍 1693826