上海交通大学学报（医学版）

上海交通大学学报（医学版） >

2021 , Vol. 41 >Issue 6: 724 - 731

DOI: https://doi.org/10.3969/j.issn.1674-8115.2021.06.004

论著 · 基础研究

基于加权基因共表达网络分析识别抑郁症的差异表达基因模块

  • 耿瑞杰 ,
  • 姚琳 ,
  • 黄欣欣 ,
  • 禹顺英 ,
  • 苑成梅 ,
  • 洪武 ,
  • 吕钦谕 ,
  • 王庆中 ,
  • 易正辉 ,
  • 方贻儒
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  • 1.复旦大学附属中山医院心理医学科，上海 200032
    2.上海交通大学医学院附属精神卫生中心精神科，上海 200030
    3.上海市浦东新区南汇精神卫生中心精神科，上海 201399
    4.上海中医药大学中药研究所神经药理实验室，上海 201203
    5.中国科学院脑科学与智能技术卓越创新中心，上海 200031
    6.上海市重性精神病重点实验室，上海 201108
耿瑞杰(1992—)，女，住院医师，硕士；电子信箱：geng.ruijie@zs-hospital.sh.cn。

网络出版日期: 2021-06-29

基金资助

国家重点研发计划(2016YFC1307100);国家自然科学基金(81930033);上海交通大学遗传发育与精神神经疾病教育部重点实验室开放课题(2019GDND02);上海市浦东新区卫生系统重点专科建设资助项目(PWZzk2017-27);上海市卫生和计划生育委员会青年项目(20164Y0209)

收起

Identification of differentially expressed gene modules in major depressive disorder based on weighted gene co-expression network analysis

  • Rui-jie GENG ,
  • Lin YAO ,
  • Xin-xin HUANG ,
  • Shun-ying YU ,
  • Cheng-mei YUAN ,
  • Wu HONG ,
  • Qin-yu Lü ,
  • Qing-zhong WANG ,
  • Zheng-hui YI ,
  • Yi-ru FANG
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  • 1.Department of Psychological Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China
    2.Department of Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
    3.Department of Psychiatry, Nanhui Mental Health Center, Pudong New Area, Shanghai 201399, China
    4.Neuropharmacology Laboratory, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
    5.CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai 200031, China
    6.Shanghai Key Laboratory of Psychotic Disorders, Shanghai 201108, China

Online published: 2021-06-29

Supported by

National Key R&D Program of China(2016YFC1307100);National Natural Science Foundation of China(81930033);Open Project of Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University(2019GDND02);Key Specialty Construction Project of Pudong Health and Family Planning Commission of Shanghai(PWZzk2017-27);Youth Program of Shanghai Health and Family Planning Commission(20164Y0209)

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摘要

目的·采用加权基因共表达网络分析（weighted gene co-expression network analysis，WGCNA）探索抑郁症相关的差异基因模块及其枢纽基因，并对差异基因模块进行生物功能注释。方法·在之前对8例抑郁症患者及8名健康对照者（对照组）的外周血mRNA微阵列分析实验的基础上，应用t检验筛选抑郁症患者与对照组的差异表达基因，通过R软件WGCNA包进行分析；当关联系数阈值设定为0.9时，参数β=14，以此构建基因数据集的共表达网络。应用混合动态树切割方法切割网络产生基因模块。采用Pearson相关性检验评估基因模块和抑郁症之间的相关性，分别选取与抑郁症正相关性和负相关性最强的基因模块，并选择模块内连接性最强的前3个基因作为枢纽基因。最后利用GO功能富集分析和KEGG通路分析对2个模块进行功能注释。结果·从16个样品中获得4 125个差异表达基因，从中识别出9个基因模块，选择蓝色（blue）模块（R=－0.91，P=0.000）和青色（cyan）模块（R=0.76，P=0.000）进行后续研究。Blue模块的枢纽基因为JAM2（junctional adhesion molecule 2）、SCRN2（secernin 2）和IGHV7-81（immunoglobulin heavy variable 7-81）；cyan模块的枢纽基因为SCFD2（Sec1 family domain containing 2）、NR5A2（nuclear receptor subfamily 5 group A member 2）和KCNMA1（potassium calcium-activated channel subfamily M alpha 1）。生物功能注释发现，cyan模块的基因主要富集在胚胎发育、细胞生长、免疫及炎症等生物学过程，blue模块基因则主要在物质加工转运及感染等方面富集。结论·得到2个外周血mRNA基因模块和6个枢纽基因（JAM2、SCRN2、IGHV7-81、SCFD2、NR5A2和KCNMA1），可能与抑郁症显著相关；这2个基因模块可能在胚胎发育、免疫和炎症反应、物质加工转运等方面发挥作用。

关键词： 抑郁症; 加权基因共表达网络分析; 差异表达基因; 枢纽基因

本文引用格式

耿瑞杰 , 姚琳 , 黄欣欣 , 禹顺英 , 苑成梅 , 洪武 , 吕钦谕 , 王庆中 , 易正辉 , 方贻儒 . 基于加权基因共表达网络分析识别抑郁症的差异表达基因模块[J]. 上海交通大学学报（医学版）, 2021 , 41(6) : 724 -731 . DOI: 10.3969/j.issn.1674-8115.2021.06.004

Abstract

Objective

·To explore the differential gene modules and hub genes of major depressive disorder (MDD) by weighted gene co-expression network analysis (WGCNA), and annotate the biological functions of the differential gene modules.

Methods

·Based on the previous experiment of peripheral blood mRNA microarray analysis of 8 MDD patients and 8 healthy controls (control group), t-test statistical method was used to screen the differentially expressed genes between MDD and the control group. WGCNA package in R software was used to analyze the weighted gene co-expression network. The correlation coefficient threshold was set to 0.9 with β =14 to construct the co-expression network of the gene dataset. The hybrid dynamic tree cutting method was used to cut the network to generate gene modules. Pearson correlation test was used to evaluate the correlation between gene modules and MDD. The gene modules with the strongest positive correlation and the strongest negative correlation with depression were selected respectively, and the top three genes with the highest connectivity in the modules were defined as hub genes. Finally, GO functional enrichment analysis and KEGG pathway analysis were used to annotate the functions of the two modules.

Results

·A total of 4 125 differentially expressed genes were obtained from 16 samples, and 9 gene modules were identified. The blue module (R=-0.91, P=0.000) and the cyan module (R=0.76, P=0.000) were selected for further study. The hub genes of blue module were JAM2 (junctional adhesion molecule 2), SCRN2 (secernin 2) and IGHV7-81 (immunoglobulin heavy variable 7-81). The hub genes of cyan module were SCFD2 (Sec1 family domain containing 2), NR5A2 (nuclear receptor subfamily 5 group A member 2) and KCNMA1 (potassium calcium-activated channel subfamily M alpha 1). It was found in the annotation of biological function that the genes of the cyan module were mainly enriched in the embryo development, cell growth and immune and inflammatory response, while the genes of the blue module were mainly enriched in material processing and transport and infection.

Conclusion

·Two peripheral blood mRNA gene modules and six hub genes (JAM2, SCRN2, IGHV7-81, SCFD2, NR5A2 and KCNMA1) may be significantly associated with MDD. These two gene modules may play a role in the embryonic development, immune and inflammatory response, and material processing and transportation.

Key words： major depressive disorder (MDD); weighted gene co-expression network analysis (WGCNA); differentially expressed gene (DEG); hub gene

参考文献

1 Kessler RC, Berglund P, Demler O, et al. The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R)[J]. JAMA, 2003, 289(23): 3095-3105.
2 Labonté B, Engmann O, Purushothaman I, et al. Sex-specific transcriptional signatures in human depression[J]. Nat Med, 2017, 23(9): 1102-1111.
3 Mandelli L, Serretti A. Gene environment interaction studies in depression and suicidal behavior: an update[J]. Neurosci Biobehav Rev, 2013, 37(10Pt 1): 2375-2397.
4 Ferrari AJ, Charlson FJ, Norman RE, et al. Burden of depressive disorders by country, sex, age, and year: findings from the global burden of disease study 2010[J]. PLoS Med, 2013, 10(11): e1001547.
5 Hing B, Sathyaputri L, Potash JB. A comprehensive review of genetic and epigenetic mechanisms that regulate BDNF expression and function with relevance to major depressive disorder[J]. Am J Med Genet B Neuropsychiatr Genet, 2018, 177(2): 143-167.
6 Hoffmann A, Sportelli V, Ziller M, et al. Epigenomics of major depressive disorders and schizophrenia: early life decides[J]. Int J Mol Sci, 2017, 18(8): E1711.
7 de Sousa RT, Loch AA, Carvalho AF, et al. Genetic studies on the tripartite glutamate synapse in the pathophysiology and therapeutics of mood disorders[J]. Neuropsychopharmacology, 2017, 42(4): 787-800.
8 Klengel T, Binder EB. Gene-environment interactions in major depressive disorder[J]. Can J Psychiatry, 2013, 58(2): 76-83.
9 Sullivan PF, Neale MC, Kendler KS. Genetic epidemiology of major depression: review and meta-analysis[J]. Am J Psychiatry, 2000, 157(10): 1552-1562.
10 Nishino J, Ochi H, Kochi Y, et al. Sample size for successful genome-wide association study of major depressive disorder[J]. Front Genet, 2018, 9: 227.
11 Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium. A mega-analysis of genome-wide association studies for major depressive disorder[J]. Mol Psychiatry, 2013, 18(4): 497-511.
12 Hyde CL, Nagle MW, Tian C, et al. Identification of 15 genetic loci associated with risk of major depression in individuals of European descent[J]. Nat Genet, 2016, 48(9): 1031-1036.
13 Menke A, Arloth J, Pütz B, et al. Dexamethasone stimulated gene expression in peripheral blood is a sensitive marker for glucocorticoid receptor resistance in depressed patients[J]. Neuropsychopharmacology, 2012, 37(6): 1455-1464.
14 Guilloux JP, Bassi S, Ding Y, et al. Testing the predictive value of peripheral gene expression for nonremission following citalopram treatment for major depression[J]. Neuropsychopharmacology, 2015, 40(3): 701-710.
15 Jansen R, Penninx BW, Madar V, et al. Gene expression in major depressive disorder[J]. Mol Psychiatry, 2016, 21(3): 339-347.
16 Mostafavi S, Battle A, Zhu X, et al. Type I interferon signaling genes in recurrent major depression: increased expression detected by whole-blood RNA sequencing[J]. Mol Psychiatry, 2014, 19(12): 1267-1274.
17 Lin E, Tsai SJ. Genome-wide microarray analysis of gene expression profiling in major depression and antidepressant therapy[J]. Prog Neuropsychopharmacol Biol Psychiatry, 2016, 64: 334-340.
18 Yi ZH, Li ZZ, Yu SY, et al. Blood-based gene expression profiles models for classification of subsyndromal symptomatic depression and major depressive disorder[J]. PLoS One, 2012, 7(2): e31283.
19 Smyth GK. Linear models and empirical Bayes methods for assessing differential expression in microarray experiments[J]. Stat Appl Genet Mol Biol, 2004, 3: Article3.
20 Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis[J]. BMC Bioinformatics, 2008, 9: 559.
21 Smith RS. The macrophage theory of depression[J]. Med Hypotheses, 1991, 35(4): 298-306.
22 Maes M. Evidence for an immune response in major depression: a review and hypothesis[J]. Prog Neuropsychopharmacol Biol Psychiatry, 1995, 19(1): 11-38.
23 Dantzer R, O′Connor JC, Freund GG, et al. From inflammation to sickness and depression: when the immune system subjugates the brain[J]. Nat Rev Neurosci, 2008, 9(1): 46-56.
24 Felger JC. Imaging the role of inflammation in mood and anxiety-related disorders[J]. Curr Neuropharmacol, 2018, 16(5): 533-558.
25 Patel A. Review: the role of inflammation in depression[J]. Psychiatr Danub, 2013, 25(): S216-S223.
26 Young JJ, Bruno D, Pomara N. A review of the relationship between proinflammatory cytokines and major depressive disorder[J]. J Affect Disord, 2014, 169: 15-20.
27 Noto C, Rizzo LB, Mansur RB, et al. Targeting the inflammatory pathway as a therapeutic tool for major depression[J]. Neuroimmunomodulation, 2014, 21(2/3): 131-139.
28 de Melo LGP, Nunes SOV, Anderson G, et al. Shared metabolic and immune-inflammatory, oxidative and nitrosative stress pathways in the metabolic syndrome and mood disorders[J]. Prog Neuropsychopharmacol Biol Psychiatry, 2017, 78: 34-50.
29 Wang QZ, Roy B, Turecki G, et al. Role of complex epigenetic switching in tumor necrosis factor-α upregulation in the prefrontal cortex of suicide subjects[J]. Am J Psychiatry, 2018, 175(3): 262-274.
30 Miller AH, Raison CL. The role of inflammation in depression: from evolutionary imperative to modern treatment target[J]. Nat Rev Immunol, 2016, 16(1): 22-34.
31 Kappelmann N, Lewis G, Dantzer R, et al. Antidepressant activity of anti-cytokine treatment: a systematic review and meta-analysis of clinical trials of chronic inflammatory conditions[J]. Mol Psychiatry, 2018, 23(2): 335-343.
32 Du W, Bautista JF, Yang HH, et al. Calcium-sensitive potassium channelopathy in human epilepsy and paroxysmal movement disorder[J]. Nat Genet, 2005, 37(7): 733-738.
33 Baudewijns L, Ronsse E, Verstraete V, et al. Problem behaviours and major depressive disorder in adults with intellectual disability and autism[J]. Psychiatry Res, 2018, 270: 769-774.
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