心力衰竭相关新基因的筛选和分析Screening and analysis of the novel genes related to heart failure
陈淼然,郭可欣,贺忠梅,宁俊娅,封启龙,高丽娟,曹济民
摘要(Abstract):
目的 分析心力衰竭潜在的相关基因以及相关机制。方法 从基因表达综合数据库中下载数据集GSE18703,使用R软件中“limma”包分析差异表达基因(DEGs)。对DEGs进行基因本体论(GO)和京都基因和基因组百科全书(KEGG)通路富集分析,以探索心力衰竭相关的生物学功能和信号通路。使用Cytoscape软件构建蛋白质相互作用(PPI)网络,识别心力衰竭发病机制相关的关键基因,用荧光定量聚合酶链反应(qPCR)法对血管紧张素Ⅱ(AngⅡ)诱导的大鼠H9C2心肌细胞模型进行转录水平的验证。通过h TFtarget数据库分析关键基因的转录因子网络,以探究心力衰竭的发生机制。结果 共筛选出292个DEGs,其中上调基因140个,下调基因152个。GO分析显示,DEGs主要富集于细胞外区、细胞外空间、血液微粒等细胞成分以及嗅觉转导、血管平滑肌收缩、代谢途径等通路。KEGG通路分析发现,DEGs主要富集于环磷酸鸟苷(cGMP)-蛋白激酶G(PKG)信号通路、肥厚型心肌病和扩张型心肌病等信号通路。通过PPI网络,筛选出6个关键基因,分别是Gprc6a、C3、Anxa1、Oxt、Gpr4和Avpr1b,并发现有528个转录因子对这6个关键基因进行转录调节。qPCR结果显示,心力衰竭情况下Anxa1和Gpr4的mRNA表达差异有统计学意义,进一步验证了以上筛选结果。结论 通过生物信息学分析方法发现了6个与心力衰竭相关的关键基因,有助于更好地了解心力衰竭的发生机制和探索新的治疗靶点。
关键词(KeyWords): 心力衰竭;差异表达基因;生物信息学
基金项目(Foundation): 国家自然科学基金(82170523);; 山西省基础研究计划青年科学研究项目(202103021223238);; 山西省自然科学研究面上项目(202303021211110)~~
作者(Author): 陈淼然,郭可欣,贺忠梅,宁俊娅,封启龙,高丽娟,曹济民
参考文献(References):
- [1] Mosterd A, Hoes AW. Clinical epidemiology of heart failure[J].Heart,2007, 93(9):1137-1146. DOI:10.1136/hrt.2003.025270.
- [2] Groenewegen A, Rutten FH, Mosterd A, et al. Epidemiology of heart failure[J]. Eur J Heart Fail,2020,22(8):1342-1356. DOI:10.1002/ejhf.1858.
- [3] Mahmood A, Ray M, Dobalian A, et al. Insomnia symptoms and incident heart failure:a population-based cohort study[J]. Eur Heart J,2021,42(40):4169-4176.DOI:10.1093/eurheartj/e-hab500.
- [4] Billingsley HE, Hummel SL, Carbone S. The role of diet and nutrition in heart failure:a state-of-the-art narrative review[J].Prog Cardiovasc Dis,2020,63(5):538-551. DOI:10.1016/j.pcad.2020.08.004.
- [5] Travers JG, Kamal FA, Robbins J, et al. Cardiac fibrosis:the fibroblast awakens[J]. Circ Res, 2016, 118(6):1021-1040.DOI:10.1161/circresaha.115.306565.
- [6] Porter KE, Turner NA. Cardiac fibroblasts:at the heart of myocardial remodeling[J]. Pharmacol Ther,2009, 123(2):255-278. DOI:10.1016/j.pharmthera.2009.05.002.
- [7] Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources[J]. Nat Protoc,2009,4(1):44-57. DOI:10.1038/nprot.2008.211.
- [8] Bu D, Luo H, Huo P, et al. KOBAS-i:intelligent prioritization and exploratory visualization of biological functions for gene enrichment analysis[J]. Nucleic Acids Res,2021,49(W1):W317-W325. DOI:10.1093/nar/gkab447.
- [9] Stelzl U, Worm U, Lalowski M, et al. A human protein-protein interaction network:a resource for annotating the proteome[J].Cell, 2005, 122(6):957-968. DOI:10.1016/j.cell.2005.08.029.
- [10] Shannon P, Markiel A, Ozier O, et al. Cytoscape:a software environment for integrated models of biomolecular interaction networks[J]. Genome Res,2003,13(11):2498-2504. DOI:10.1101/gr.1239303.
- [11] Bader GD, Hogue CW. An automated method for finding molecular complexes in large protein interaction networks[J].BMC Bioinformatics,2003,4:2. DOI:10.1186/1471-2105-4-2.
- [12] Zhang Q, Liu W, Zhang HM,et al. hTFtarget:a comprehensive database for regulations of human transcription factors and their targets[J]. Genomics Proteomics Bioinformatics,2020,18(2):120-128. DOI:10.1016/j.gpb.2019.09.006.
- [13] Liu X, Xu S, Li Y, et al. Identification of CALU and PALLD as potential biomarkers associated with immune infiltration in heart failure[J]. Front Cardiovasc Med, 2021, 8:774755. DOI:10.3389/fcvm.2021.774755.
- [14] Shrivastava A, Haase T, Zeller T, et al. Biomarkers for heart failure prognosis:proteins, genetic scores and non-coding RNAs[J]. Front Cardiovasc Med,2020,7:601364. DOI:10.3389/fcvm.2020.601364.
- [15] Tonry C, McDonald K, Ledwidge M, et al. Multiplexed measurement of candidate blood protein biomarkers of heart failure[J]. ESC Heart Fail,2021,8(3):2248-2258. DOI:10.1002/ehf2.13320.
- [16] Silva N, Patrício E, Bettencourt P, et al. Evaluation of Innate immunity biomarkers on admission and at discharge from an acute heart failure episode[J]. J Clin Lab Anal, 2016, 30(6):1183-1190. DOI:10.1002/jcla.22001.
- [17] Cui G, Tian M, Hu S, et al. Identifying functional non-coding vari ants in APOA5/A4/C3/A1 gene cluster associated with coronary heart disease[J]. J Mol Cell Cardiol, 2020, 144:54-62.DOI:10.1016/j.yjmcc.2020.05.003.
- [18] Jiang H, Guo M, Dong L, et al. Levels of acylation stimulating protein and the complement component 3 precursor are associated with the occurrence and development of coronary heart disease[J]. Exp Ther Med,2014, 8(6):1861-1866. DOI:10.3892/etm.2014.2018.
- [19] Ren J, Tsilafakis K, Chen L, et al. Crosstalk between coagulation and complement activation promotes cardiac dysfunction in arrhythmogenic right ventricular cardiomyopathy[J]. Theranostics,2021,11(12):5939-5954. DOI:10.7150/thno.58160.
- [20] Ferraro B, Leoni G, Hinkel R, et al. Pro-angiogenic macrophage phenotype to promote myocardial repair[J]. J Am Coll Cardiol,2019,73(23):2990-3002. DOI:10.1016/j.jacc.2019.03.503.
- [21] Adel FW, Rikhi A, Wan SH, et al. Annexin A1 is a potential novel biomarker of congestion in acute heart failure[J]. J Card Fail,2020,26(8):727-732. DOI:10.1016/j.cardfail.2020.05.012.
- [22] Wang SC, Wang YF. Cardiovascular protective properties of oxytocin against COVID-19[J]. Life Sci,2021,270:119130. DOI:10.1016/j.lfs.2021.119130.
- [23] Dyavanapalli J, Rodriguez J, Rocha Dos Santos C, et al. Activation of oxytocin neurons improves cardiac function in a pressureoverload model of heart failure[J]. JACC Basic Transl Sci,2020, 5(5):484-497. DOI:10.1016/j.jacbts.2020.03.007.
- [24] Torán JL, López JA, Gomes-Alves P, et al. Definition of a cell surface signature for human cardiac progenitor cells after comprehensive comparative transcriptomic and proteomic characterization[J]. Sci Rep, 2019, 9(1):4647. DOI:10.1038/s41598-019-39571-x.
- [25] Douglas SA, Ao Z, Johns DG, et al. Quantitative analysis of orphan G protein-coupled receptor mRNAs by TaqMan real-time PCR:G2A and GPR4 lysophospholipid receptor expression in leukocytes and in a rat myocardial infarction-heart failure model[J]. Methods Mol Biol,2005,306:27-49. DOI:10.1385/1-59259-927-3:027.
- [26] Tian C, Yang Y, Ke Y, et al. Integrative analyses of genes associated with right ventricular cardiomyopathy induced by tricuspid regurgitation[J]. Front Genet,2021,12:708275. DOI:10.3389/fgene.2021.708275.
- [27] Rainer PP, Kass DA. Old dog, new tricks:novel cardiac targets and stress regulation by protein kinase G[J]. Cardiovasc Res,2016, 111(2):154-162. DOI:10.1093/cvr/cvw107.
- [28] Maron BJ, Rowin EJ, Udelson JE, et al. Clinical spectrum and management of heart failure in hypertrophic cardiomyopathy[J].JACC Heart Fail, 2018, 6(5):353-363. DOI:10.1016/j.jchf.2017.09.011.
- [29] Evangelista I, Nuti R, Picchioni T, et al. Molecular dysfunction and phenotypic derangement in diabetic cardiomyopathy[J]. Int J Mol Sci, 2019, 20(13):3264. DOI:10.3390/ijms20133264.