Using immune clusters for classifying Mycobacterium tuberculosis infection

被引：0

作者：

Wang, Xiaochen ^{[1
]}

Tang, Guoxing ^{[1
]}

Huang, Yi ^{[1
]}

Song, Huijuan ^{[1
]}

Zhou, Siyu ^{[1
]}

Mao, Liyan ^{[1
]}

Sun, Ziyong ^{[1
]}

Xiong, Zhigang ^{[1
,2
]}

Wu, Shiji ^{[1
,2
]}

Hou, Hongyan ^{[1
,2
]}

Wang, Feng ^{[1
,2
]}

机构：

[1] Huazhong Univ Sci & Technol, Tongji Hosp, Tongji Med Coll, Dept Lab Med, Wuhan, Peoples R China

[2] Huazhong Univ Sci & Technol, Tongji Hosp, Tongji Med Coll, Dept Lab Med, Jiefang Ave 1095, Wuhan, Hubei, Peoples R China

来源：

INTERNATIONAL IMMUNOPHARMACOLOGY | 2024年 / 128卷

基金：

中国国家自然科学基金;

关键词：

ATB; LTBI; Subtypes; Prediction model; GAMMA RELEASE ASSAYS; T-CELL EXHAUSTION; LATENT TUBERCULOSIS; PULMONARY TUBERCULOSIS; ACTIVE TUBERCULOSIS; TBAG/PHA RATIO; DIAGNOSIS; DISEASE; CYTOKINE; CHILDREN;

D O I：

10.1016/j.intimp.2024.111572

中图分类号：

R392 [医学免疫学]; Q939.91 [免疫学];

学科分类号：

100102 ;

摘要：

Background: The differential diagnosis between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) is still a challenge worldwide. Methods: Immune indicators involved in innate, humoral, and cellular immune cells, as well as antigen-specific cells were simultaneously assessed in patients with ATB and LTBI. Results: Of 54 immune indicators, no indicator could distinguish ATB from LTBI, likely due to an obvious heterogeneity of immune indicators noticed in ATB patients. Cluster analysis of ATB patients identified three immune clusters with different severity. Cluster 1 (42.1 %) was a "Treg/Th1/Tfh unbalance type" cluster, whereas cluster 2 (42.1 %) was an "effector type" cluster, and cluster 3 was a "inhibition type" cluster (15.8 %) which showed the highest severity. A prediction model based on immune indicators was established and showed potential in classifying Mycobacterium tuberculosis infection. Conclusions: We depicted the immune landscape of patients with ATB and LTBI. Three immune subtypes were identified in ATB patients with different severity.

引用

页数：8

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