基于基因芯片的miRNA表达差异在结核性脑膜炎与病毒性脑膜炎诊断中的价值

doi:10.19982/j.issn.1000-6621.20210699

中国防痨杂志 ›› 2022, Vol. 44 ›› Issue (3): 264-272.doi: 10.19982/j.issn.1000-6621.20210699

基于基因芯片的miRNA表达差异在结核性脑膜炎与病毒性脑膜炎诊断中的价值

谌蒙蒙¹, 董静¹, 孙琦¹, 黄麦玲², 丁则昱³, 史雨婷¹, 贾红彦¹, 杜博平¹, 魏荣荣¹, 邢爱英¹, 张宗德¹(), 潘丽萍¹()

¹首都医科大学附属北京胸科医院/耐药结核病研究北京市重点实验室/北京市结核病胸部肿瘤研究所,北京 101149
²首都医科大学附属北京胸科医院结核一科,北京 101149
³首都医科大学附属北京天坛医院神经病学中心,北京 100050

收稿日期:2021-12-08 出版日期:2022-03-10 发布日期:2022-03-08
通信作者: 张宗德,潘丽萍 E-mail:zzd417@163.com;panliping2006@163.com
基金资助:
国家自然科学基金(81902024);国家自然科学基金(82172279);北京市自然科学基金(7192038);北京市医院管理局“登峰”人才项目(DFL20181601);北京市通州区“运河计划”人才项目(YH201807);北京市通州区“运河计划”人才项目(YH202001)

Diagnostic performance of differentially expressed miRNA identified by gene chip in discriminating tuberculous meningitis from viral meningitis

CHEN Meng-meng¹, DONG Jing¹, SUN Qi¹, HUANG Mai-ling², DING Ze-yu³, SHI Yu-ting¹, JIA Hong-yan¹, DU Bo-ping¹, WEI Rong-rong¹, XING Ai-ying¹, ZHANG Zong-de¹(), PAN Li-ping¹()

¹Beijing Key Laboratory for Drug Resistant Tuberculosis Research, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Capital Medical University, Beijing 101149, China
²Department of Tuberculosis, Beijing Chest Hospital, Capital Medical University, Beijing 101149, China
³Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China

Received:2021-12-08 Online:2022-03-10 Published:2022-03-08
Contact: ZHANG Zong-de,PAN Li-ping E-mail:zzd417@163.com;panliping2006@163.com
Supported by:
National Natural Science Foundation(81902024);National Natural Science Foundation(82172279);Beijing Natural Science Foundation(7192038);Beijing Municipal Administration of Hospitals Ascent Plan(DFL20181601);Tongzhou Yunhe Project(YH201807);Tongzhou Yunhe Project(YH202001)

摘要/Abstract

摘要：

目的: 评价基于基因芯片的miRNA表达差异在结核性脑膜炎(tuberculous meningitis,TBM)与病毒性脑膜炎(viral meningitis,VM)诊断中的价值。方法: 选取2017年12月至2019年9月在首都医科大学附属北京胸科医院和首都医科大学附属北京天坛医院治疗的TBM患者28例和VM患者27例作为验证样本进行实时荧光定量逆转录PCR(qRT-PCR)验证。选取前期收集的8例TBM患者和5例VM患者作为基因芯片样本进行全基因组miRNA微阵列分析,通过预测差异性miRNA的靶基因、基因本体(GO)富集分析、京都基因与基因组百科全书(KEGG)信号通路分析、构建蛋白质-蛋白质相互作用(PPI)网络筛选出两病间前10个差异性miRNA枢纽基因。再采用qRT-PCR对患者间表达差异明显的miR-21-5p进行验证,并通过受试者工作特征曲线(ROC)下面积(AUC)分析miRNA的诊断价值。结果: 通过miRNA微阵列分析初步筛选出26个差异表达的miRNA(14个表达上调,12个表达下调)和对应的靶基因3217个。经GO富集分析、KEGG信号通路分析筛选出190个相关靶基因,对其进行PPI网络分析,筛选出前10个靶基因作为核心靶基因。对基因芯片检测样本的hsa-miR-21-5p相对表达量进行qRT-PCR验证,发现TBM患者hsa-miR-21-5p的表达水平[15.890(3.423,25.581)]较VM患者[0.807(0.614,0.955)]明显上调(Z=-2.355,P=0.019),差异倍数(TBM/VM)在基因芯片和qRT-PCR中分别是4.817和4.660,两种检测方法结果基本一致;对28例TBM和27例VM患者的hsa-miR-21-5p进行qRT-PCR结果比较,发现hsa-miR-21-5p在TBM患者的相对表达量为4.825(2.433,21.362),明显高于VM患者[0.204(0.112,0.711)],差异有统计学意义(Z=-5.000,P=0.000),且ROC曲线区分TBM与VM患者的AUC为0.893(0.806~0.980),敏感度为85.7%,特异度为88.9%。结论: 相较于VM患者,作为基因芯片检测中表达差异明显的miR-21-5p在TBM患者中表达明显上调,可作为鉴别TBM与VM的潜在生物标志物。

关键词: 脑膜炎,细菌性, 脑膜炎,病毒性, 诊断, 芯片分析技术, 微RNAs

Abstract:

Objective: To evaluate the diagnostic performance of differentially expressed miRNA identified by gene chip in discriminating tuberculous meningitis (TBM) from viral meningitis (VM). Methods: A total of 28 TBM patients and 27 VM patients who were treated in Beijing Chest Hospital and Beijing Tiantan Hospital affiliated to Capital Medical University from Dec 2017 to Sep 2019 were recruited for validation using Real-time Quantitative PCR (qRT-PCR). Of 8 TBM patients and 5 VM patients who were recruited earlier were selected for genome-wide miRNA analysis by gene chip. The top 10 miRNA hub genes were screened by predicting the target genes of differentially expressed miRNAs, gene ontology (GO) enrichment analysis, Kyoto encyclopedia of genes and genomes (KEGG) signaling pathway analysis, and protein-protein interaction (PPI) network. qRT-PCR was used for further validation of the expression level of hsa-miR-21-5p between two groups, and the diagnostic value of miRNA was analyzed by area under receiver operating characteristic curve (AUC). Results: Totally 26 differentially expressed miRNAs (14 were up-regulated and 12 were down-regulated) were identified by miRNA microarray analysis, and further 3217 corresponding target genes were screened out by miRTarBase database. Through GO enrichment analysis and KEGG signal pathway analysis, 190 related target genes were screened out. The top 10 target genes as core target genes were defined by PPI. qRT-PCR verification of the relative expression of hsa-miR-21-5p based on gene chip samples showed that the expression level of has-miR-21-5p in TBM patients was significantly up-regulated than that in VM patients (15.890 (3.423, 25.581) vs. 0.807(0.614,0.955); Z=-2.355, P=0.019). The fold change (TBM/VM) in gene chip and qRT-PCR were 4.817 and 4.660, respectively, which was consistent between these two techniques. qRT-PCR analysis of hsa-miR-21-5p was further performed in 28 TBM patients and 27 VM patients. The results showed that the relative expression level of hsa-miR-21-5p in TBM patients was significantly higher than that in VM patients (4.825 (2.433, 21.362) vs. 0.204 (0.112, 0.711); Z=-5.000, P=0.000). The AUC for distinguishing TBM patients from VM patients was 0.893 (0.806-0.980), with a sensitivity of 85.7% and a specificity of 88.9%. Conclusion: As a significantly differentially expressed miRNA in gene chip analysis, has-miR-21-5p is significantly up-regulated in patients with TBM than VM patients, which can be used as a potential biomarker to differentiate TBM from VM.

Key words: Meningitis,bacterial, Meningitic,viral, Diagnosis, Microchip analytical procedures, MicroRNAs

中图分类号:

谌蒙蒙, 董静, 孙琦, 黄麦玲, 丁则昱, 史雨婷, 贾红彦, 杜博平, 魏荣荣, 邢爱英, 张宗德, 潘丽萍. 基于基因芯片的miRNA表达差异在结核性脑膜炎与病毒性脑膜炎诊断中的价值[J]. 中国防痨杂志, 2022, 44(3): 264-272. doi: 10.19982/j.issn.1000-6621.20210699

CHEN Meng-meng, DONG Jing, SUN Qi, HUANG Mai-ling, DING Ze-yu, SHI Yu-ting, JIA Hong-yan, DU Bo-ping, WEI Rong-rong, XING Ai-ying, ZHANG Zong-de, PAN Li-ping. Diagnostic performance of differentially expressed miRNA identified by gene chip in discriminating tuberculous meningitis from viral meningitis[J]. Chinese Journal of Antituberculosis, 2022, 44(3): 264-272. doi: 10.19982/j.issn.1000-6621.20210699

图/表 6

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图7

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参考文献 35

[1]	Donovan J, Thwaites GE, Huynh J. Tuberculous meningitis: where to from here? Curr Opin Infect Dis, 2020, 33(3):259-266. doi: 10.1097/QCO.0000000000000648. doi: 10.1097/QCO.0000000000000648 pmid: 32324614
[2]	Wang YY, Xie BD. Progress on Diagnosis of Tuberculous Meningitis. Methods Mol Biol, 2018, 1754:375-386. doi: 10.1007/978-1-4939-7717-8_20. doi: 10.1007/978-1-4939-7717-8_20
[3]	Ha M, Kim VN. Regulation of microRNA biogenesis. Nat Rev Mol Cell Biol, 2014, 15(8):509-524. doi: 10.1038/nrm3838. doi: 10.1038/nrm3838 URL
[4]	Khan AQ, Ahmed EI, Elareer NR, et al. Role of miRNA-Regulated Cancer Stem Cells in the Pathogenesis of Human Malignancies. Cells, 2019, 8(8):840. doi: 10.3390/cells8080840. doi: 10.3390/cells8080840 URL
[5]	Kong L, Zhu J, Han W, et al. Significance of serum microRNAs in pre-diabetes and newly diagnosed type 2 diabetes: a clinical study. Acta Diabetol, 2011, 48(1):61-69. doi: 10.1007/s00592-010-0226-0. doi: 10.1007/s00592-010-0226-0 pmid: 20857148
[6]	Ji F, Yang B, Peng X, et al. Circulating microRNAs in hepatitis B virus-infected patients. J Viral Hepat, 2011, 18(7):e242-251. doi: 10.1111/j.1365-2893.2011.01443.x. doi: 10.1111/j.1365-2893.2011.01443.x
[7]	Sabir N, Hussain T, Shah SZA, et al. miRNAs in Tuberculosis: New Avenues for Diagnosis and Host-Directed Therapy. Front Microbiol, 2018, 9:602. doi: 10.3389/fmicb.2018.00602. doi: 10.3389/fmicb.2018.00602 URL
[8]	中华医学会结核病学分会结核性脑膜炎专业委员会. 2019中国中枢神经系统结核病诊疗指南. 中华传染病杂志, 2020, 38(7):400-408. doi: 10.3760/cma.j.cn311365-20200606-00645. doi: 10.3760/cma.j.cn311365-20200606-00645
[9]	Poplin V, Boulware DR, Bahr NC. Methods for rapid diagnosis of meningitis etiology in adults. Biomark Med, 2020, 14(6):459-479. doi: 10.2217/bmm-2019-0333. doi: 10.2217/bmm-2019-0333 URL
[10]	Hristea A, Olaru ID, Baicus C, et al. Clinical prediction rule for differentiating tuberculous from viral meningitis. Int J Tuberc Lung Dis, 2012, 16(6):793-798. doi: 10.5588/ijtld.11.0687. doi: 10.5588/ijtld.11.0687 pmid: 22507645
[11]	Pedersen JL, Bokil NJ, Saunders BM. Developing new TB biomarkers,are miRNA the answer? Tuberculosis (Edinb), 2019, 118:101860. doi: 10.1016/j.tube.2019.101860. doi: 10.1016/j.tube.2019.101860 URL
[12]	Balzano F, Deiana M, Dei Giudici S, et al. miRNA Stability in Frozen Plasma Samples. Molecules, 2015, 20(10):19030-19040. doi: 10.3390/molecules201019030. doi: 10.3390/molecules201019030 URL
[13]	Glinge C, Clauss S, Boddum K, et al. Stability of Circulating Blood-Based MicroRNAs-Pre-Analytic Methodological Consi-derations. PLoS One, 2017, 12(2):e0167969. doi: 10.1371/journal.pone.0167969. doi: 10.1371/journal.pone.0167969 URL
[14]	尹慧敏, 贾永林, 李燕飞, 等. 结核性脑膜炎患者脑脊液外泌体中let-7d表达的研究. 中国实用神经疾病杂志, 2017, 20(6):9-12. doi: 10.3969/j.issn.1673-5110.2017.06.003. doi: 10.3969/j.issn.1673-5110.2017.06.003
[15]	路雁惠, 郭斌, 张锐毅, 等. 结核性脑膜炎患者脑脊液外泌体中Let-7b的表达水平及临床意义. 中风与神经疾病杂志, 2018, 35(12):1107-1110. doi: 10.19845/j.cnki.zfysjjbzz.2018.12.013. doi: 10.19845/j.cnki.zfysjjbzz.2018.12.013
[16]	Pan D, Pan M, Xu YM. Mir-29a expressions in peripheral blood mononuclear cell and cerebrospinal fluid: Diagnostic value in patients with pediatric tuberculous meningitis. Brain Res Bull, 2017, 130:231-235. doi: 10.1016/j.brainresbull.2017.01.013. doi: 10.1016/j.brainresbull.2017.01.013 URL
[17]	Hu X, Liao S, Bai H, et al. Integrating exosomal microRNAs and electronic health data improved tuberculosis diagnosis. EBioMedicine, 2019, 40:564-573. doi: 10.1016/j.ebiom.2019.01.023. doi: 10.1016/j.ebiom.2019.01.023 URL
[18]	Pan L, Liu F, Zhang J, et al. Genome-Wide miRNA Analysis Identifies Potential Biomarkers in Distinguishing Tuberculous and Viral Meningitis. Front Cell Infect Microbiol, 2019, 9:323. doi: 10.3389/fcimb.2019.00323. doi: 10.3389/fcimb.2019.00323 URL
[19]	Weber JA, Baxter DH, Zhang S, et al. The microRNA spectrum in 12 body fluids. Clin Chem, 2010, 56(11):1733-1741. doi: 10.1373/clinchem.2010.147405. doi: 10.1373/clinchem.2010.147405 URL
[20]	Lam A, Prabhu R, Gross CM, et al. Role of apoptosis and autophagy in tuberculosis. Am J Physiol Lung Cell Mol Physiol, 2017, 313(2):L218-L229. doi: 10.1152/ajplung.00162.2017. doi: 10.1152/ajplung.00162.2017 URL
[21]	Shariq M, Quadir N, Sheikh JA, et al. Post translational modifications in tuberculosis: ubiquitination paradox. Autophagy, 2021, 17(3):814-817. doi: 10.1080/15548627.2020.1850009. doi: 10.1080/15548627.2020.1850009 pmid: 33190592
[22]	Zonghai C, Tao L, Pengjiao M, et al. Mycobacterium tuberculosis ESAT6 modulates host innate immunity by downregulating miR-222-3p target PTEN. Biochim Biophys Acta Mol Basis Dis, 2022, 1868(1):166292. doi: 10.1016/j.bbadis.2021.166292. doi: 10.1016/j.bbadis.2021.166292 URL
[23]	Hu W, Chan H, Lu L, et al. Autophagy in intracellular bacterial infection. Semin Cell Dev Biol, 2020, 101:41-50. doi: 10.1016/j.semcdb.2019.07.014. doi: 10.1016/j.semcdb.2019.07.014 URL
[24]	Ernst JD. Mechanisms of M.tuberculosis Immune Evasion as Challenges to TB Vaccine Design. Cell Host Microbe, 2018, 24(1):34-42. doi: 10.1016/j.chom.2018.06.004. doi: S1931-3128(18)30316-0 pmid: 30001523
[25]	Wang F, Huang G, Shen L, et al. Genetics and Functional Mechanisms of STAT3 Polymorphisms in Human Tuberculosis. Front Cell Infect Microbiol, 2021, 11:669394. doi: 10.3389/fcimb.2021.669394. doi: 10.3389/fcimb.2021.669394 URL
[26]	Watkins SK, Hurwitz AA. FOXO3: A master switch for regulating tolerance and immunity in dendritic cells. Oncoimmunology, 2012, 1(2):252-254. doi: 10.4161/onci.1.2.18241. doi: 10.4161/onci.1.2.18241 URL
[27]	Lu Y, Zhu Y, Wang X, et al. FOXO3 rs12212067: T>G Association with Active Tuberculosis in Han Chinese Population. Inflammation, 2016, 39(1):10-15. doi: 10.1007/s10753-015-0217-y. doi: 10.1007/s10753-015-0217-y URL
[28]	Ratajczak-Wrona W, Jablonska E, Garley M, et al. PI3K-Akt/PKB signaling pathway in neutrophils and mononuclear cells exposed to N-nitrosodimethylamine. J Immunotoxicol, 2014, 11(3):231-237. doi: 10.3109/1547691X.2013.826307. doi: 10.3109/1547691X.2013.826307 pmid: 23971717
[29]	Shim JW, Madsen JR. VEGF Signaling in Neurological Disorders. Int J Mol Sci, 2018, 19(1):275. doi: 10.3390/ijms19010275. doi: 10.3390/ijms19010275 URL
[30]	Misra UK, Kalita J, Singh AP, et al. Vascular endothelial growth factor in tuberculous meningitis. Int J Neurosci, 2013, 123(2):128-132. doi: 10.3109/00207454.2012.743127. doi: 10.3109/00207454.2012.743127 pmid: 23098361
[31]	Abd-El-Fattah AA, Sadik NAH, Shaker OG, et al. Differential microRNAs expression in serum of patients with lung cancer, pulmonary tuberculosis, and pneumonia. Cell Biochem Biophys, 2013, 67(3):875-884. doi: 10.1007/s12013-013-9575-y. doi: 10.1007/s12013-013-9575-y pmid: 23559272
[32]	Kathirvel M, Saranya S, Mahadevan S. Expression levels of candidate circulating microRNAs in pediatric tuberculosis. Pathog Glob Health, 2020, 114(5):262-270. doi: 10.1080/20477724.2020.1761140. doi: 10.1080/20477724.2020.1761140 pmid: 32401176
[33]	Wang C, Yang S, Liu CM, et al. Screening and identification of four serum miRNAs as novel potential biomarkers for cured pulmonary tuberculosis. Tuberculosis (Edinb), 2018, 108:26-34. doi: 10.1016/j.tube.2017.08.010. doi: 10.1016/j.tube.2017.08.010 URL
[34]	Wu Z, Lu H, Sheng J, et al. Inductive microRNA-21 impairs anti-mycobacterial responses by targeting IL-12 and Bcl-2. FEBS Lett, 2012, 586(16):2459-2467. doi: 10.1016/j.febslet.2012.06.004. doi: 10.1016/j.febslet.2012.06.004 URL
[35]	Zhao Z, Hao J, Li X, et al. MiR-21-5p regulates mycobacterial survival and inflammatory responses by targeting Bcl-2 and TLR4 in Mycobacterium tuberculosis-infected macrophages. FEBS Lett, 2019, 593(12):1326-1335. doi: 10.1002/1873-3468.13438. doi: 10.1002/1873-3468.13438 URL

miRNA	差异倍数(TBM/VM)	P值	染色体上的位置
表达上调
hsa-miR-4281	27.602	0.000	chr5:176629449-176629466[-]
hsa-miR-4516	25.018	0.000	chr16:2133120-2133136[+]
hsa-miR-6090	9.575	0.013	chr11:128522430-128522448[+]
hsa-miR-3663-3p	6.060	0.000	chr10:117167695-117167717[-]
hsa-miR-21-5p	4.817	0.000	chr17:59841273-59841294[+]
hsa-miR-6087	4.272	0.001	chrX:109054544-109054561[+]
hsa-miR-3960	3.629	0.001	chr9:127785879-127785898[+]
hsa-miR-6510-5p	3.346	0.013	chr17:41517194-41517215[-]
hsa-miR-6869-5p	3.071	0.005	chr20:1392940-1392961[-]
hsa-miR-7107-5p	2.713	0.001	chr12:121444326-121444347[-]
hsa-miR-1273g-3p	2.603	0.003	chr1:52940370-52940390[+]
hsa-miR-6821-5p	2.242	0.008	chr22:49962866-49962888[+]
hsa-miR-6800-5p	2.161	0.014	chr19:49832022-49832042[+]
hsa-miR-7110-5p	2.025	0.024	chr3:123161799-123161819[+]
表达下调
hsa-miR-6858-5p	0.179	0.000	chrX:154450320-154450341[+]
hsa-miR-5196-5p	0.206	0.000	chr19:35345541-35345562[+]
hsa-miR-642a-3p	0.261	0.000	chr19:45674978-45674999[+]
hsa-miR-4496	0.263	0.001	chr12:108635849-108635870[+]
hsa-miR-4419b	0.267	0.002	chr12:128244547-128244564[+]
hsa-miR-3162-5p	0.304	0.000	chr11:59595127-59595149[-]
hsa-miR-4685-5p	0.306	0.002	chr10:98431332-98431357[-]
hsa-miR-7515	0.338	0.000	chr2:6650418-6650435[+]
hsa-miR-6127	0.341	0.000	chr1:22633328-22633346[-]
hsa-miR-6879-5p	0.356	0.000	chr11:65018510-65018531[+]
hsa-miR-6824-3p	0.408	0.000	chr3:48633636-48633656[-]
hsa-miR-320d	0.427	0.000	chrX:140926172-140926190[-]

基于基因芯片的miRNA表达差异在结核性脑膜炎与病毒性脑膜炎诊断中的价值

Diagnostic performance of differentially expressed miRNA identified by gene chip in discriminating tuberculous meningitis from viral meningitis

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