FOLLOWUS
- Share with wechat friends or circle of friends
基于加权基因共表达网络分析阿尔茨海默病相关核心靶点
- Affiliations：
1. 1Pharmacy School, Guangxi University of Chinese Medicine, Nanning 530200, China
2. 2Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Pharmacy School, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
- Author bio：
* Tel: 86-13317712838; E-mail: gxtcmuzzg@163.com.
- Funds：National Natural Science Foundation of China;国家自然科学基金(81460598);National Natural Science Foundation of China;国家自然科学基金(81660644);the Natural Science Foundation of Jiangsu Province;江苏省自然科学基金青年基金项目(BK20170267);Guangxi Special Fund for the First-Class Discipline Construction Project;广西一流学科专项经费项目(05019038)
DOI：10.24920/003695
中图分类号：收稿日期：2019-12-09，
录用日期：2020-5-6，
网络出版日期：2020-12-31，
纸质出版日期：2020-12-31
- Accepted：
Scan QR Code
张帆, 钟斯然, 杨斯漫, 等. 基于加权基因共表达网络分析阿尔茨海默病相关核心靶点[J]. 中国医学科学杂志（英文版）, 2020,35(4):330-341.
Zhang Fan, Zhong Siran, Yang Siman, et al. Identification of Potential Therapeutic Targets of Alzheimer’s Disease By Weighted Gene Co-Expression Network Analysis[J]. Chinese medical sciences journal, 2020, 35(4): 330-341.
张帆, 钟斯然, 杨斯漫, 等. 基于加权基因共表达网络分析阿尔茨海默病相关核心靶点[J]. 中国医学科学杂志（英文版）, 2020,35(4):330-341. DOI： 10.24920/003695.
Zhang Fan, Zhong Siran, Yang Siman, et al. Identification of Potential Therapeutic Targets of Alzheimer’s Disease By Weighted Gene Co-Expression Network Analysis[J]. Chinese medical sciences journal, 2020, 35(4): 330-341. DOI： 10.24920/003695.
摘要
目的
阿尔茨海默病 (Alzheimer‘s disease
AD）是痴呆症最常见的病因
但其发病机制尚不明确。本研究拟通过加权基因共表达网络探究AD发病相关的核心靶点。
方法
GSE36980数据集下载于高通量基因表达数据库（Gene Expression Omnibus
GEO）。首先进行数据标准化
质控和过滤
以及计算软阈值
然后根据基因表达的相关性
聚类划分为不同的模块
通过计算各模块与临床特征的相关系数
确定关键基因模块。我们通过基因富集分析（Gene Ontology
GO）和通路富集分析（Kyoto Encyclopedia of Genes and Genomes
KEGG）探究关键基因模块中的基因功能。随后通过STRING数据库构建蛋白-蛋白相互作用网络
并使用Cytoscape软件MCODE插件进行网络拓扑分析筛选核心调控基因
最后使用GEO外部数据集GSE1297和GSE28146对核心基因进行验证。
结果
共表达基因共聚类27个模块
其中6个模块与AD发病显著相关
以此作为关键模块用于下游分析。通过基因功能富集分析发现关键模块与神经递质传递（GO：0007268）、三羧酸循环和电子传递链有关（R-HSA-1428517）。结合共表达网络和蛋白-蛋白相互作用网络的分析结果发现
基因
WDR47
OXCT1
C3orf14
ATP6V1A
SLC25A14
NAPB
可能与AD发病相关。外部数据集进一步证实了其差异表达。
结论
通过加权基因共表达网络分析和蛋白互作用网络分析
我们发现AD的核心基因
WDR47
、
OXCT1
、
C3orf14
、
ATP6V1A
、
SLC25A14
和
NAPB
其中
ATP6V1A
SLC25A14
OXCT1
可能通过影响三羧酸循环而影响AD的发病。
Abstract
Objective
Alzheimer’s disease (AD) is the most common cause of dementia. The pathophysiology of the disease mostly remains unearthed
thereby challenging drug development for AD. This study aims to screen high throughput gene expression data using weighted co-expression network analysis (WGCNA) to explore the potential therapeutic targets.
Methods
The dataset of GSE36980 was obtained from the Gene Expression Omnibus (GEO) database. Normalization
quality control
filtration
and soft-threshold calculation were carried out before clustering the co-expressed genes into different modules. Furthermore
the correlation coefficients between the modules and clinical traits were computed to identify the key modules. Gene ontology and pathway enrichment analyses were performed on the key module genes. The STRING database was used to construct the protein-protein interaction (PPI) networks
which were further analyzed by Cytoscape app (MCODE). Finally
validation of hub genes was conducted by external GEO datasets of GSE 1297 and GSE 28146.
Results
Co-expressed genes were clustered into 27 modules
among which 6 modules were identified as the key module relating to AD occurrence. These key modules are primarily involved in chemical synaptic transmission (GO:0007268)
the tricarboxylic acid (TCA) cycle and respiratory electron transport (R-HSA-1428517).
WDR47
OXCT1
C3orf14
ATP6V1A
SLC25A14
NAPB
were found as the hub genes and their expression were validated by external datasets.
Conclusions
Through modules co-expression network analyses and PPI network analyses
we identified the hub genes of AD
including
WDR47
OXCT1
C3orf14
ATP6V1A
SLC25A14
and
NAPB
. Among them
three hub genes (
ATP6V1A
SLC25A14
OXCT1
) might contribute to AD pathogenesis through pathway of TCA cycle.
关键词
Keywords
references
Hardy J , Selkoe DJ . The amyloid hypothesis of Alzheimer’s disease: progress and problems on the road to therapeutics . Science 2002 ; 297 ( 5580 ): 353 - 6 . doi: 10.1126/science.1072994 10.1126/science.1072994 . DOI: 10.1126/science.1072994 http://doi.org/10.1126/science.1072994 https://www.ncbi.nlm.nih.gov/pubmed/12130773 https://www.ncbi.nlm.nih.gov/pubmed/12130773
Gouras GK , Tsai J , Naslund J , et al . Intraneuronal A-beta 42 accumulation in human brain . Am J Pathol 2000 ; 156 ( 1 ): 15 - 20 . doi: 10.1016/s0002-9440(10)64700-1 10.1016/s0002-9440(10)64700-1 . https://www.ncbi.nlm.nih.gov/pubmed/10623648 https://www.ncbi.nlm.nih.gov/pubmed/10623648
Williamson J , Goldman J , Marder KS . Genetic aspects of Alzheimer disease . Neurologist 2016 ; 15 ( 2 ): 80 . doi: 10.1097/NRL.0b013e318187e76b 10.1097/NRL.0b013e318187e76b .
Langfelder P , Horvath S . WGCNA: an R package for weighted correlation network analysis . BMC bioinforma-tics 2008 ; 9 ( 1 ): 559 . doi: 10.1186/1471-2105-9-559 10.1186/1471-2105-9-559 .
Ritchie ME , Phipson B , Wu D , et al . Limma powers differential expression analyses for RNA-sequen-cing and microarray studies . Nucleic Acids Res 2015 ; 43 ( 7 ): e47 . doi: 10.1093/nar/gkv007 10.1093/nar/gkv007 .
Zhang B , Horvath S . A general framework for weighted gene co-expression newtork analysis . Sta Appl Genet Mol Biol 2005 ; 4:Article 17. doi: 10.2202/1544-6115.1128 10.2202/1544-6115.1128 .
Zheng CH , Yuan L , Sha W , et al . Gene differential coexpression analysis based on biweight correlation and maximum clique . BMC bioinformatics 2014 ; 15 ( Suppl 15 ): S3 . doi: 10.1186/1471-2105-15-S15-S3 10.1186/1471-2105-15-S15-S3 .
Horvath S , Dong J , Miyano S . Geometric interpretation of gene coexpression network analysis . PLoS comput biol 2008 ; 4 ( 8 ): e1000117 . doi: 10.1371/journal.pcbi.1000117 10.1371/journal.pcbi.1000117 . https://www.ncbi.nlm.nih.gov/pubmed/18704157 https://www.ncbi.nlm.nih.gov/pubmed/18704157
Zhou Y , Zhou B , Pache L , et al . Metascape provides a biologist-oriented resource for the analysis of systems-level datasets . Nat Commun 2019 ; 10 ( 1 ): 1523 . doi: 10.1038/s41467-019-09234-6 10.1038/s41467-019-09234-6 .
Shannon P . Cytoscape: a software environment for integrated models of biomolecular interaction networks . Genome Res 2003 ; 13 ( 11 ): 2498 . doi: 10.1101/gr.1239303 10.1101/gr.1239303 . https://www.ncbi.nlm.nih.gov/pubmed/14597658 https://www.ncbi.nlm.nih.gov/pubmed/14597658
Szklarczyk D , Franceschini A , Kuhn M , et al . The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored . Nucleic Acids Res 2011 ; 39 ( database issue ): D561 - 8 . doi: 10.1093/nar/gkq973 10.1093/nar/gkq973 .
Liu ZP , Wang Y , Zhang XS , et al . Network-based ana-lysis of complex diseases . IET Syst Biol 2012 ; 6 ( 1 ): 22 - 33 . doi: 10.3390/ iet-syb.2010.0052 10.3390/ iet-syb.2010.0052 . https://www.ncbi.nlm.nih.gov/pubmed/22360268 https://www.ncbi.nlm.nih.gov/pubmed/22360268
Liu ZP , Wang Y , Zhang XS , et al . Detecting and analyzing differentially activated pathways in brain regions of Alzheimer’s disease patients . Mol Bio Syst 2011 ; 7 ( 5 ): 1441 - 52 . doi: 10.1039/c0mb00325e 10.1039/c0mb00325e .
Liu ZP , Wang Y , Zhang XS , et al . Identifying dysfunctional crosstalk of pathways in various regions of Alzheimer’s disease brains . BMC Syst Biol 2010 ; 4 ( Suppl 2 ): S11 . doi: 10.1186/1752-0509-4-S2-S11 10.1186/1752-0509-4-S2-S11 .
Ficz G . New insights into mechanisms that regulate DNA methylation patterning . J Exp Biol 2015 ; 218 ( Pt 1 ): 14 - 20 . doi: 10.1242/jeb.107961 10.1242/jeb.107961 .
Yu NK , Beak SH , Kaang BK . DNA methylation-mediated control of learning and memory . Mol Brain 2011 ; 4 : 5 . doi: 10.1186/1756-6606-4-5 10.1186/1756-6606-4-5 .
Lando M , Fjeldbo CS , Wilting SM , et al . Interplay between promoter methylation and chromosomal loss in gene silencing at 3p11-p14 in cervical cancer . Epigenetics 2015 ; 10 ( 10 ): 970 - 80 . doi: 10.1080/15592294.2015.1085140 10.1080/15592294.2015.1085140 . DOI: 10.1080/15592294.2015.1085140 http://doi.org/10.1080/15592294.2015.1085140 https://www.ncbi.nlm.nih.gov/pubmed/26291246 https://www.ncbi.nlm.nih.gov/pubmed/26291246
Van GM , Baranger K , Benech P , et al . Metabolic changes and inflammation in cultured astrocytes from the 5xFAD mouse model of Alzheimer’s disease: Alleviation by pantethine . PloS One 2018 ; 12 ( 4 ): e0175369 . doi: 10.1371/journal.pone.0175369 10.1371/journal.pone.0175369 .
Akihiro Y , Takagi H , Kimata D , et al . Deficiency in protein L-iso-aspartyl methyltaransferase results in a fatal progressive epilepsy . J Neurosci 1998 ; 18 ( 6 ): 2063 - 74 . doi: 10.1016/S0165-5728(97)00232-4 10.1016/S0165-5728(97)00232-4 . https://www.ncbi.nlm.nih.gov/pubmed/9482793 https://www.ncbi.nlm.nih.gov/pubmed/9482793
Shimizu T , Watanabe A , Ogawara M , et al . Isoaspartate formation and neurodegeneration in Alzheimer’s disease . Arch Biochem Biophys 2000 ; 381 ( 2 ): 225 - 34 . doi: 10.1006/abbi.2000.1955 10.1006/abbi.2000.1955 . https://www.ncbi.nlm.nih.gov/pubmed/11032409 https://www.ncbi.nlm.nih.gov/pubmed/11032409
Pawlosky RJ , Kemper MF , Kashiwaya Y , et al . Effects of a dietary ketone ester on hippocampal glycolytic and tricarboxylic acid cycle intermediates and amino acids in a 3xTgAD mouse model of Alzheimer’s disease . J Neurochem 2017 ; 141 ( 2 ): 195 - 207 . doi: 10.1111/jnc.13958 10.1111/jnc.13958 . DOI: 10.1111/jnc.2017.141.issue-2 http://doi.org/10.1111/jnc.2017.141.issue-2 http://doi.wiley.com/10.1111/jnc.2017.141.issue-2 http://doi.wiley.com/10.1111/jnc.2017.141.issue-2
Russell H . Swerdlow. Mitochondria and mitochondrial cascades in Alzheimer’s disease . J Alzheimers Dis 2017 ; 62 ( 3 ): 1403 - 16 . doi: 10.3233/JAD-170585 10.3233/JAD-170585 .
Han Y , Chu X , Cui L , et al . Neuronal mitochondria-targeted therapy for Alzheimer’s disease by systemic delivery of resveratrol using dual-modified novel biomimetic nanosystems . Drug Deliv 2020 ; 27 ( 1 ): 502 - 18 . doi: 10.1080/10717544.2020.1745328 10.1080/10717544.2020.1745328 . DOI: 10.1080/10717544.2020.1745328 http://doi.org/10.1080/10717544.2020.1745328 https://www.ncbi.nlm.nih.gov/pubmed/32228100 https://www.ncbi.nlm.nih.gov/pubmed/32228100
Gao C , Wang Y , Sun J , et al . Neuronal mitochondria-targeted delivery of curcumin by biomimetic engineered nanosystems in Alzheimer’s disease mice . Acta Biomater 2020 ; 108 : 285 - 99 . doi: 10.1016/j.actbio.2020.03.029 10.1016/j.actbio.2020.03.029 .
Khosravi S , Harner ME . The MICOS complex, a structural element of mitochondria with versatile functions . Biol Chem 2020 ; 401 ( 6-7 ): 765 - 78 . doi: 10.1515/hsz-2020-0103 10.1515/hsz-2020-0103 . DOI: 10.1515/hsz-2020-0103 http://doi.org/10.1515/hsz-2020-0103 https://www.ncbi.nlm.nih.gov/pubmed/32229686 https://www.ncbi.nlm.nih.gov/pubmed/32229686
van Gijsel-Bonnello M , Baranger K , Benech P , et al . Metabolic changes and inflammation in cultured astrocytes from the 5xFAD mouse model of Alzheimer’s disease: Alleviation by pantethine . PLoS One 2017 ; 12 ( 4 ): e0175369 . doi: 10.1371/journal.pone.0175369 10.1371/journal.pone.0175369. Erratum in PLoS One 2018 . Erratum in PLoS One 2018; 13(3):e0194586. DOI: 10.1371/journal.pone.0175369 http://doi.org/10.1371/journal.pone.0175369 https://www.ncbi.nlm.nih.gov/pubmed/28410378 https://www.ncbi.nlm.nih.gov/pubmed/28410378
Dong Y , Brewer GJ . Global metabolic shifts in age and Alzheimer’s disease mouse brains pivot at NAD+/NADH redox sites . J Alzheimers Dis 2019 ; 71 ( 1 ): 119 - 40 . doi: 10.3233/JAD-190408 10.3233/JAD-190408 . DOI: 10.3233/JAD-190408 http://doi.org/10.3233/JAD-190408 https://www.ncbi.nlm.nih.gov/pubmed/31356210 https://www.ncbi.nlm.nih.gov/pubmed/31356210
Fassio A , Esposito A , Kato M , et al . De novo mutations of the ATP6V 1 A gene cause developmental encephalopathy with epilepsy . Brain 2018 ; 141 ( 6 ): 1703 - 18 . doi: 10.1093/brain/awy092 10.1093/brain/awy092 . DOI: 10.1093/brain/awy092 http://doi.org/10.1093/brain/awy092 https://www.ncbi.nlm.nih.gov/pubmed/29668857 https://www.ncbi.nlm.nih.gov/pubmed/29668857
Palmieri F . The mitochondrial transporter family SLC25: identification, properties and physiopathology . Mol Aspects Med 2013 ; 34 ( 2-3 ): 465 - 84 . doi: 10.1016/j.mam.2012.05.005 10.1016/j.mam.2012.05.005 . DOI: 10.1016/j.mam.2012.05.005 http://doi.org/10.1016/j.mam.2012.05.005 http://dx.doi.org/10.1016/j.mam.2012.05.005 http://dx.doi.org/10.1016/j.mam.2012.05.005
Anitha A , Nakamura K , Thanseem I , et al . Brain region-specific altered expression and association of mitochondria-related genes in autism . Mol Autism 2012 ; 3 ( 1 ): 12 . doi: 10.1186/2040-2392-3-12 10.1186/2040-2392-3-12 . DOI: 10.1186/2040-2392-3-12 http://doi.org/10.1186/2040-2392-3-12 https://www.ncbi.nlm.nih.gov/pubmed/23116158 https://www.ncbi.nlm.nih.gov/pubmed/23116158
Shafqat N , Kavanagh KL , Sass JO , et al . A structural mapping of mutations causing succinyl-CoA:3-ketoacid CoA transferase (SCOT) deficiency . J Inherit Metab Dis 2013 ; 36 ( 6 ): 983 - 7 . doi: 10.1007/s10545-013-9589-z 10.1007/s10545-013-9589-z . DOI: 10.1007/s10545-013-9589-z http://doi.org/10.1007/s10545-013-9589-z https://www.ncbi.nlm.nih.gov/pubmed/23420214 https://www.ncbi.nlm.nih.gov/pubmed/23420214
关联资源
相关文章
相关作者
相关机构
- © 2024 中国医学科学院 京ICP备09064830号-19
京公网安备11010802024621 - 本系统建议在Chrome、 IE9+ 以上版本浏览器阅读本站内容，360浏览器请切换至极速模式
- Cookies帮助我们提供服务并提供个性化体验。使用本网站，即表示您同意我们使用Cookies