结核分枝杆菌潜伏感染者外周血单个核细胞转录组学研究

doi:10.19982/j.issn.1000-6621.20240005

中国防痨杂志 ›› 2024, Vol. 46 ›› Issue (4): 449-460.doi: 10.19982/j.issn.1000-6621.20240005

结核分枝杆菌潜伏感染者外周血单个核细胞转录组学研究

尚雪恬¹, 董静¹, 黄麦玲², 孙琦¹, 贾红彦¹, 张蓝月¹, 刘秋月³, 姚明旭¹, 王颖超¹, 姬秀秀¹, 杜博平¹, 邢爱英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科/北京市结核病胸部肿瘤研究所,北京 101149
³首都医科大学附属北京胸科医院重症医学科/北京市结核病胸部肿瘤研究所,北京 101149

收稿日期:2024-01-03 出版日期:2024-04-10 发布日期:2024-04-01
通信作者: 潘丽萍 E-mail:panliping2006@163.com
基金资助:
国家自然科学基金(82172279);国家自然科学基金(82100011);北京市自然科学基金(L234055)

Transcriptome study on peripheral blood mononuclear cells of latent tuberculosis infection individuals

Shang Xuetian¹, Dong Jing¹, Huang Mailing², Sun Qi¹, Jia Hongyan¹, Zhang Lanyue¹, Liu Qiuyue³, Yao Mingxu¹, Wang Yingchao¹, Ji Xiuxiu¹, Du Boping¹, Xing Aiying¹, Pan Liping¹()

¹Beijing Key Laboratory for Drug Resistant Tuberculosis Research, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing 101149, China
²Department of Tuberculosis, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing 101149, China
³Department of Intensive Care Unit, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing 101149, China

Received:2024-01-03 Online:2024-04-10 Published:2024-04-01
Contact: Pan Liping E-mail:panliping2006@163.com
Supported by:
National Natural Science Foundation(82172279);National Natural Science Foundation(82100011);Beijing Natural Science Foundation(L234055)

摘要/Abstract

摘要：

目的：通过对比分析结核分枝杆菌潜伏感染(latent tuberculosis infection,LTBI)者和健康人的外周血单个核细胞(peripheral blood mononuclear cells,PBMC)经结核特异性抗原刺激后的转录组,寻找LTBI人群中结核抗原特异的免疫应答重要基因及可能发挥功能的信号通路,绘制LTBI基因表达谱。方法：应用微阵列芯片检测2009年12月在首都医科大学附属北京胸科医院招募的4例LTBI者(LTBI组)和4名健康人(健康对照组)经结核抗原刺激后的PBMC转录组,并对部分差异基因进行qPCR检测以验证芯片数据。应用加权基因共表达网络分析(weighted gene co-expression network analysis,WGCNA)筛选与LTBI最相关模块,并对模块中的基因分别进行基因本体论(geneontology,GO)分析、京都基因与基因组百科全书(Kyoto encylopedia of genes and genomes,KEGG)通路分析。通过基因集富集分析(gene set enrichment analysis,GSEA)从全转录组水平筛选信号通路。通过免疫浸润分析寻找与LTBI相关的免疫细胞。通过STRING数据库蛋白相互作用(protein-protein interaction,PPI)网络分析绘制两模块中差异基因相互作用网络图。结果：LTBI组与健康对照组间共发现393个差异表达基因(差异倍数>2或<0.5,P<0.05),LTBI中112个表达下调,281个表达上调。针对10个差异基因的实时荧光定量PCR(quantitative real-time PCR,qPCR)检测证实基因表达趋势与芯片数据一致,即CAMK1G、IL2、SPINK1、SUCNR1、HCAR3、IL18BP、CHI3L1、TNF均上调;RASGRP2、TPM2均下调。通过WGCNA分析确定与LTBI最相关的正相关蓝色模块(cor=0.88,P=0.004)和负相关蓝绿色模块(cor=―0.93,P=0.001)。对上述模块内基因进行KEGG富集分析,并行基于全转录组的GSEA分析,发现趋化因子信号通路(P<0.001)和凋亡信号通路(P=0.003)在两种分析中均明显富集。对两模块中的差异基因通过STRING数据库构建PPI网络图,通过内置插件计算得到每个模块中的前10个关键基因,即蓝色模块中的PTPRC、CD40、IL10、IRF8、CCR1、CD80、TLR8、CLEC7A、CD83和CD274;蓝绿色模块中的TNF、ICAM1、TNFRSF4、HAVCR2、CD276、CCL4、CD33、IL1RN、NCF1和FGR。免疫浸润分析发现,M1型巨噬细胞(P=0.029)和M2型巨噬细胞(P=0.001)等固有免疫细胞的比例在LTBI组和健康对照组间存在明显差异。结论：经结核特异性抗原刺激后的PBMC转录组在LTBI人群和健康人群间存在明显差异,这些差异基因主要聚集于凋亡信号通路和趋化因子信号通路,其中巨噬细胞介导的免疫应答在其中发挥了重要作用,揭示了固有免疫应答在LTBI中的重要作用。

关键词: 分枝杆菌, 结核, 分枝杆菌感染, 转录因子, 生物学标记, 免疫

Abstract:

Objective: The aim of the study is to explore the tuberculosis (TB) antigen specific transcriptome profile of latent TB infection (LTBI) individuals and identify the critical gene modules and pathways that may play significant roles in LTBI occurrence and development, by comparing the transcriptome results of peripheral blood mononuclear cells (PBMC) stimulated by TB-specific antigen of healthy controls (HC) and LTBI. Methods: Microarray test was used to uncover the transcriptome profile of PBMC stimulated by TB antigens in 4 cases of LTBI and 4 HC from Beijing Chest Hospital, Capital Medical University in December 2009, and further validated with qPCR to confirm the microarray data. Weighted gene co-expression network analysis (WGCNA) was used to identify the critical gene modules that were associated with LTBI. Geneontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were carried out based on the genes in the two modules significantly associated with LTBI. At the same time, GSEA-based KEGG analysis was also used in order to fully understand the transcriptome changes of PBMC after TB-specific antigen stimulation in the two groups. Immune infiltration analysis was also used to further confirm the important immune cells involving in LTBI occurrence. Protein-Protein Interaction (PPI) network analysis based on the STRING database was used to draw the differential gene interaction network diagram in the two modules. Results: A total of 393 differentially expressed genes were found between LTBI group and healthy group (fold change >2 or <0.5, P<0.05), of which 112 were down-regulated and 281 were up-regulated. qPCR analysis of 10 differential genes confirmed that the gene expression pattern was consistent with that of the microarray data, and CAMK1G, IL2, SPINK1, SUCNR1, HCAR3, IL18BP, CHI3L1 and TNF were up-regulated, while RASGRP2 and TPM2 were down-regulated. According to the analysis of WGCNA, the blue module with positive correlation (cor=0.88, P=0.004) and the turquoise module with negative correlation (cor=―0.93, P=0.001) were the modules that most significantly associated with LTBI. The genes in the two modules were then subjected to KEGG enrichment analysis, meanwhile, GSEA analysis based on complete transcriptome was also performed. The results showed that chemokine signaling pathway (P<0.001) and apoptosis signaling pathway (P=0.003) were significantly enriched by both analyses. The PPI network diagram of the differential genes in the two modules was constructed through the STRING database, and the top 10 hub genes in each module were obtained through the built-in plug-in, including PTPRC, CD40, IL10, IRF8, CCR1, CD80, TLR8, CLEC7A, CD83 and CD274 in the blue module, and TNF, ICAM1, TNFRSF4, HAVCR2, CD276, CCL4, CD33, IL1RN, NCF1 and FGR in the turquoise module. Immune infiltration analysis showed that innate immune cells, such as M1 macrophage (P=0.029) and M2 macrophage (P=0.001), were significantly concentrated in LTBI population. Conclusion: Significant differences in transcriptome of PBMC stimulated by TB-specific antigen are identified between LTBI and HC. These differential genes are mainly enriched in apoptosis signal pathway and chemokine signal pathway. Meanwhile, macrophage-mediated immune response plays an important role. These results collectively reveal the important role of innate immune response in LTBI occurrence.

Key words: Mycobacterium tuberculosis, Mycobacterium infections, Transcription factors, Biological markers, Immunity

中图分类号:

尚雪恬, 董静, 黄麦玲, 孙琦, 贾红彦, 张蓝月, 刘秋月, 姚明旭, 王颖超, 姬秀秀, 杜博平, 邢爱英, 潘丽萍. 结核分枝杆菌潜伏感染者外周血单个核细胞转录组学研究[J]. 中国防痨杂志, 2024, 46(4): 449-460. doi: 10.19982/j.issn.1000-6621.20240005

Shang Xuetian, Dong Jing, Huang Mailing, Sun Qi, Jia Hongyan, Zhang Lanyue, Liu Qiuyue, Yao Mingxu, Wang Yingchao, Ji Xiuxiu, Du Boping, Xing Aiying, Pan Liping. Transcriptome study on peripheral blood mononuclear cells of latent tuberculosis infection individuals[J]. Chinese Journal of Antituberculosis, 2024, 46(4): 449-460. doi: 10.19982/j.issn.1000-6621.20240005

图/表 10

表1

图1~3

表2

图4

图5

表3

蓝色模块和蓝绿色模块中基因GO富集分析

模块/聚类分析	条目	富集倍数	P值	基因个数
蓝色模块
BP
	炎症应答	2.46	<0.001	48
	溶酶体腔酸化	10.14	<0.001	10
	信号转导	1.67	<0.001	98
	细胞因子介导的信号通路	3.21	<0.001	23
	突触囊泡腔酸化	11.16	<0.001	8
	液泡酸化	8.73	<0.001	9
	肌动蛋白细胞骨架组织	2.68	<0.001	28
	白细胞介素-1β产生的正调节	4.33	<0.001	13
MF
	蛋白结合	1.12	<0.001	664
	质子转运ATP酶活性,旋转机制	7.68	<0.001	9
	相同蛋白结合	1.41	<0.001	115
	PDZ结构域结合	3.36	<0.001	14
	受体结合	1.81	0.001	35
	细胞因子受体活性	3.74	0.001	10
	锰离子跨膜转运体活性	12.20	0.003	4
	信号受体活性	2.01	0.003	22
CC
	质膜	1.46	<0.001	347
	溶酶体膜	2.94	<0.001	52
	细胞外外泌体	1.65	<0.001	161
	内体膜	2.94	<0.001	35
	细胞表面	2.09	<0.001	60
	溶酶体	2.62	<0.001	36
	胞液	1.27	<0.001	312
	高尔基体	1.74	<0.001	87
蓝绿色模块
BP
	转录正调控,DNA模板	1.82	<0.001	63
	RNA聚合酶Ⅱ启动子转录的正向调节	1.59	<0.001	93
	染色质组织	2.22	<0.001	34
	炎症应答	1.94	<0.001	40
	参与细胞凋亡过程的半胱氨酸型内肽酶活性的激活	3.60	<0.001	14
	RNA聚合酶Ⅱ启动子转录的调控	1.41	<0.001	116
	细胞周期	2.00	<0.001	35
	神经管闭合	3.39	<0.001	13
MF
	蛋白结合	1.15	<0.001	724
	磷脂酰肌醇结合	3.10	<0.001	17
	磷脂酰肌醇-3,4-二磷酸结合	5.35	0.001	8
	染色质结合	1.68	0.001	42
	肌动蛋白丝结合	2.07	0.001	24
	RNA聚合酶Ⅱ核心启动子近端区域序列特异性DNA结合	1.38	0.002	84
	转录辅阻遏物活性	2.07	0.003	21
	脂质结合	2.02	0.005	20
CC
	胞液	1.41	<0.001	367
	细胞质	1.30	<0.001	349
	细胞核	1.25	<0.001	353
	细胞膜	1.31	<0.001	224
	染色质	1.59	<0.001	82
	核浆	1.24	<0.001	234
	质膜的细胞质侧	2.89	0.001	15
	中心体	1.69	0.001	45

表3

表4

图6~9

图10

图11~13

参考文献 35

[1]	Getahun H, Matteelli A, Chaisson RE, et al. Latent Mycobacterium tuberculosis infection. N Engl J Med, 2015, 372(22): 2127-2135. doi:10.1056/NEJMra1405427.
[2]	中国防痨协会. 高危人群结核分枝杆菌潜伏感染检测及预防性治疗专家共识. 中国防痨杂志, 2021, 43(9): 874-878. doi:10.3969/j.issn.1000-6621.2021.09.004.
[3]	World Health Organization. Latent tuberculosis infection: updated and consolidated guidelines for programmatic management. Geneva: World Health Organization, 2018.
[4]	Bagcchi S. WHO’s Global Tuberculosis Report 2022. Lancet Microbe, 2023, 4(1): e20. doi:10.1016/S2666-5247(22)00359-7.
[5]	Zellweger JP, Sotgiu G, Corradi M, et al. The diagnosis of latent tuberculosis infection (LTBI): currently available tests, future developments, and perspectives to eliminate tuberculosis (TB). Med Lav, 2020, 111(3):170-183. doi:10.23749/mdl.v111i3.9983. pmid: 32624559
[6]	Qin H, Wang Y, Huang L, et al. Efficacy and Risk Factors of Interferon-Gamma Release Assays among HIV-Positive Individuals. Int J Environ Res Public Health, 2023, 20(5):4556. doi:10.3390/ijerph20054556.
[7]	Shen BJ, Lin HH. Time-dependent association between cancer and risk of tuberculosis: A population-based cohort study. Int J Infect Dis, 2021, 108: 340-346. doi:10.1016/j.ijid.2021.05.037.
[8]	Cadena J, Rathinavelu S, Lopez-Alvarenga JC, et al. The re-emerging association between tuberculosis and diabetes: Lessons from past centuries. Tuberculosis (Edinb), 2019, 116 S: S89-S97. doi:10.1016/j.tube.2019.04.015.
[9]	Singhania A, Verma R, Graham CM, et al. A modular trans-criptional signature identifies phenotypic heterogeneity of human tuberculosis infection. Nat Commun, 2018, 9(1):2308. doi:10.1038/s41467-018-04579-w. pmid: 29921861
[10]	Tabone O, Verma R, Singhania A, et al. Blood transcriptomics reveal the evolution and resolution of the immune response in tuberculosis. J Exp Med, 2021, 218(10): e20210915. doi:10.1084/jem.20210915.
[11]	Chen C, Wu Y, Li J, et al. TBtools-Ⅱ: A “one for all, all for one” bioinformatics platform for biological big-data mining. Mol Plant, 2023, 16(11):1733-1742. doi:10.1016/j.molp.2023.09.010.
[12]	Tang D, Chen M, Huang X, et al. SRplot: A free online platform for data visualization and graphing. PLoS One, 2023, 18(11): e294236. doi:10.1371/journal.pone.0294236.
[13]	Carlson MR, Zhang B, Fang Z, et al. Gene connectivity, function, and sequence conservation: predictions from modular yeast co-expression networks. BMC Genomics, 2006, 7: 40. doi:10.1186/1471-2164-7-40. pmid: 16515682
[14]	Verma A, Ghoshal A, Dwivedi VP, et al. Tuberculosis: The success tale of less explored dormant Mycobacterium tuberculosis. Front Cell Infect Microbiol, 2022, 12:1079569. doi:10.3389/fcimb.2022.1079569.
[15]	Khabibullina NF, Kutuzova DM, Burmistrova IA, et al. The Biological and Clinical Aspects of a Latent Tuberculosis Infection. Trop Med Infect Dis, 2022, 7(3):48. doi:10.3390/tropicalmed7030048.
[16]	Cohen GM. Caspases: the executioners of apoptosis. Biochem J, 1997, 326 (Pt 1): 1-16. doi:10.1042/bj3260001.
[17]	Blomgran R, Desvignes L, Briken V, et al. Mycobacterium tuberculosis inhibits neutrophil apoptosis, leading to delayed activation of naive CD 4 T cells. Cell Host Microbe, 2012, 11(1):81-90. doi:10.1016/j.chom.2011.11.012. pmid: 22264515
[18]	Nisa A, Kipper FC, Panigrahy D, et al. Different modalities of host cell death and their impact on Mycobacterium tuberculosis infection. Am J Physiol Cell Physiol, 2022, 323(5):C1444-C1474. doi:10.1152/ajpcell.00246.2022.
[19]	Keane J, Remold HG, Kornfeld H. Virulent Mycobacterium tuberculosis strains evade apoptosis of infected alveolar macrophages. J Immunol, 2000, 164(4):2016-2020. doi:10.4049/jimmunol.164.4.2016. pmid: 10657653
[20]	Zhang W, Ellingson L, Frascoli F, et al. An investigation of tuberculosis progression revealing the role of macrophages apoptosis via sensitivity and bifurcation analysis. J Math Biol, 2021, 83(3):31. doi:10.1007/s00285-021-01655-6. pmid: 34436682
[21]	Kim CH. Chemokine-chemokine receptor network in immune cell trafficking. Curr Drug Targets Immune Endocr Metabol Disord, 2004, 4(4):343-361. doi:10.2174/1568008043339712.
[22]	Slight SR, Khader SA. Chemokines shape the immune responses to tuberculosis. Cytokine Growth Factor Rev, 2013, 24(2):105-113. doi:10.1016/j.cytogfr.2012.10.002.
[23]	Barclay AM, Ninaber DK, van Veen S, et al. Airway epithelial cells mount an early response to mycobacterial infection. Front Cell Infect Microbiol, 2023, 13:1253037. doi:10.3389/fcimb.2023.1253037.
[24]	Jang S, Uzelac A, Salgame P. Distinct chemokine and cytokine gene expression pattern of murine dendritic cells and macrophages in response to Mycobacterium tuberculosis infection. J Leukoc Biol, 2008, 84(5):1264-1270. doi:10.1189/jlb.1107742.
[25]	Guler R, Ozturk M, Sabeel S, et al. Targeting Molecular Inflammatory Pathways in Granuloma as Host-Directed Therapies for Tuberculosis. Front Immunol, 2021, 12: 733853. doi:10.3389/fimmu.2021.733853.
[26]	Algood HM, Chan J, Flynn JL. Chemokines and tuberculosis. Cytokine Growth Factor Rev, 2003, 14(6):467-477. doi:10.1016/s1359-6101(03)00054-6.
[27]	Scott HM, Flynn JL. Mycobacterium tuberculosis in chemokine receptor 2-deficient mice: influence of dose on disease progression. Infect Immun, 2002, 70(11):5946-5954. doi:10.1128/IAI.70.11.5946-5954.2002.
[28]	Mack U, Migliori GB, Sester M, et al. LTBI: latent tuberculosis infection or lasting immune responses to M.tuberculosis? A TBNET consensus statement. Eur Respir J, 2009, 33(5):956-973. doi:10.1183/09031936.00120908. pmid: 19407047
[29]	Orme IM, Robinson RT, Cooper AM. The balance between protective and pathogenic immune responses in the TB-infected lung. Nat Immunol, 2015, 16(1):57-63. doi:10.1038/ni.3048. pmid: 25521685
[30]	Simon HU, Yousefi S, Germic N, et al. The Cellular Functions of Eosinophils: Collegium Internationale Allergologicum (CIA) Update 2020. Int Arch Allergy Immunol, 2020, 181(1):11-23. doi:10.1159/000504847.
[31]	Bohrer AC, Castro E, Hu Z, et al. Eosinophils are part of the granulocyte response in tuberculosis and promote host resis-tance in mice. J Exp Med, 2021, 218(10): e20210469. doi:10.1084/jem.20210469.
[32]	Kumar R, Singh P, Kolloli A, et al. Immunometabolism of Phagocytes During Mycobacterium tuberculosis Infection. Front Mol Biosci, 2019, 6: 105. doi:10.3389/fmolb.2019.00105.
[33]	Italiani P, Boraschi D. From Monocytes to M1/M2 Macrophages: Phenotypical vs. Functional Differentiation. Front Immunol, 2014, 5: 514. doi:10.3389/fimmu.2014.00514. pmid: 25368618
[34]	Verreck FA, de Boer T, Langenberg DM, et al. Human IL-23-producing type 1 macrophages promote but IL-10-producing type 2 macrophages subvert immunity to (myco)bacteria. Proc Natl Acad Sci U S A, 2004, 101(13):4560-4565. doi:10.1073/pnas.0400983101.
[35]	Mily A, Kalsum S, Loreti MG, et al. Polarization of M1 and M2 Human Monocyte-Derived Cells and Analysis with Flow Cytometry upon Mycobacterium tuberculosis Infection. J Vis Exp, 2020(163). doi:10.3791/61807.

分类/人口学特征	HC组	LTBI组	统计检验值	P值
微阵列芯片集
性别(名/例)			-	-
男性	0	0
女性	4	4
年龄(岁)
范围	33~44	32~42	-	-
均数±标准差	36.5±4.4	38.0±4.5	t=0.554	0.600
qPCR验证集
性别(名/例)			χ²=0.220	0.500
男性	7	6
女性	3	4
年龄(岁)
范围	21~58	26~68	-	-
均数±标准差	37.1±12.3	42.9±12.6	t=1.045	0.310

结核分枝杆菌潜伏感染者外周血单个核细胞转录组学研究

Transcriptome study on peripheral blood mononuclear cells of latent tuberculosis infection individuals

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基因	功能	芯片数据			qPCR验证
基因	功能	差异倍数	P值	表达趋势	差异倍数	t值	P值	表达趋势
CAMK1G	调节钙离子/钙调蛋白依赖性蛋白激酶活性	12.50	0.003	上调	4.36	1.296	0.212	上调
IL2	Th1型细胞因子,参与免疫反应	5.79	0.005	上调	23.46	3.539	0.002	上调
SPINK1	调节胞质钙离子浓度	5.37	0.007	上调	1.15	0.237	0.816	上调
SUCNR1	参与G蛋白偶联受体信号通路	4.56	0.001	上调	1.46	0.634	0.530	上调
HCAR3	参与G蛋白偶联受体信号通路	4.11	<0.001	上调	8.76	2.678	0.015	上调
IL18BP	参与Th1型免疫反应	4.10	0.002	上调	4.52	3.902	0.001	上调
CHI3L1	参与几丁质分解代谢	4.05	<0.001	上调	1.87	2.338	0.031	上调
TNF	促炎细胞因子	3.65	<0.001	上调	3.49	1.782	0.092	上调
SASH1	参与蛋白质多泛素化	3.56	0.003	上调
CD274	参与适应性免疫反应	3.52	0.001	上调
ABCA7	参与脂质运输	0.49	0.008	下调
C1orf186	参与红细胞生成	0.49	0.002	下调
IKZF2	参与RNA聚合酶Ⅱ启动子转录调控	0.49	0.001	下调
ACAP1	参与蛋白质运输	0.47	0.006	下调
AQP3	正向调节免疫系统过程	0.46	0.003	下调
LY9	参与细胞黏附	0.46	0.007	下调
RGS14	参与G蛋白偶联受体信号通路	0.42	0.002	下调
MYLIP	参与泛素依赖的蛋白质分解代谢过程	0.40	0.001	下调
RASGRP2	调节细胞生长	0.37	0.007	下调	0.44	3.279	0.004	下调
TPM2	调节ATP酶活性	0.37	0.002	下调	0.40	1.867	0.078	下调

模块/聚类分析	条目	富集倍数	P值	基因个数
蓝色模块
KEGG
	溶酶体	3.93	<0.001	29
	吞噬体	3.18	<0.001	27
	肺结核	2.78	<0.001	28
	类风湿性关节炎	3.65	<0.001	19
	癌症通路	1.82	<0.001	54
	肿瘤中PD-L1的表达和PD-1检查点通路	3.42	<0.001	17
	人乳头瘤病毒感染	2.05	<0.001	38
	铁死亡	4.80	<0.001	11
蓝绿色模块
KEGG
	破骨细胞分化	2.95	<0.001	21
	甲状腺激素信号通路	2.98	<0.001	19
	FcγR介导的吞噬作用	3.13	<0.001	16
	B细胞受体信号通路	3.17	0.001	14
	糖尿病并发症中的AGE-RAGE信号通路	2.85	0.001	15
	脂质与动脉粥样硬化	2.12	0.001	24
	小细胞肺癌	2.89	0.001	14
	C型凝集素受体信号通路	2.74	0.001	15

[1]	温淑芳, 魏荣荣, 李浩然, 刘毅. CD4⁺和CD8⁺T细胞在结核病免疫应答中的作用[J]. 中国防痨杂志, 2024, 46(4): 479-484.
[2]	姚阳阳, 梁长华, 韩东明, 崔俊伟, 潘犇, 王慧慧, 魏正琦, 甄思雨, 危涵羽. 基于CT影像组学结合临床特征鉴别肺结核与非结核分枝杆菌肺病的研究[J]. 中国防痨杂志, 2024, 46(3): 302-310.
[3]	张静, 付若楠, 王森路, 冯建宇, 张玲, 古丽娜·巴德尔汗, 祖力卡提阿衣·阿布都拉, 王新旗. 高负担地区肺结核密切接触者中结核分枝杆菌潜伏感染者预防性治疗接受意愿及影响因素研究[J]. 中国防痨杂志, 2024, 46(2): 165-172.
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[5]	杜毓, 张海鹏, 王鹏. 分枝杆菌噬菌体的研究现状及应用进展[J]. 中国防痨杂志, 2023, 45(9): 897-903.
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[7]	王涵飞, 赵雁林, 徐彩红. 亚临床结核病研究进展[J]. 中国防痨杂志, 2023, 45(8): 808-813.
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[10]	李艳阳, 陈华, 陈品儒. 慢生黄分枝杆菌肺病一例[J]. 中国防痨杂志, 2023, 45(7): 714-717.
[11]	中国人民解放军总医院第八医学中心结核病医学部, 《中国防痨杂志》编辑委员会, 中国医疗保健国际交流促进会结核病防治分会. 核酸基质辅助激光解吸电离飞行时间质谱技术在结核病和非结核分枝杆菌病诊断中的临床应用专家共识[J]. 中国防痨杂志, 2023, 45(6): 543-558.
[12]	董静, 史雨婷, 潘丽萍, 张宗德. 长链非编码RNA抗结核分枝杆菌感染功能的研究进展[J]. 中国防痨杂志, 2023, 45(6): 613-619.
[13]	薛人伟, 蒋慧芳, 陶叠宏, 蒋玉霞, 吴海英. 多发性骨髓瘤CAR-T细胞治疗后并发肺结核一例[J]. 中国防痨杂志, 2023, 45(6): 625-627.
[14]	史雨婷, 董静, 贾红彦, 朱传智, 杨斌, 李自慧, 孙琦, 杜博平, 邢爱英, 张宗德, 潘丽萍. miR-99a-5p在结核分枝杆菌感染宿主巨噬细胞中的免疫调控作用[J]. 中国防痨杂志, 2023, 45(5): 464-471.
[15]	赵爱华, 都伟欣, 王国治, 徐苗. 重组结核分枝杆菌变态反应原皮肤试验用制剂命名探讨[J]. 中国防痨杂志, 2023, 45(4): 333-335.