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

doi:10.19982/j.issn.1000-6621.20240005

Abstract

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

CLC Number:

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

Figures/Tables 10

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

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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

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