Landscape of Exhausted T Cells in Tuberculosis Revealed by Single-Cell Sequencing

Pan, Jing; Zhang, Xinyue; Xu, Jianting; Chang, Zecheng; Xin, Zhuoyuan; Wang, Guoqing

doi:10.1128/spectrum.02839-22

Cited by 11 publications

(6 citation statements)

References 46 publications

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“…This subset exhibited a strong inflammatory response, energy metabolism and IFN response. However, many studies have shown ( 61 – 63 ) that T-cell exhaustion manifests as a result of inactivated T-cell proliferation, the secretion of suppressor cytokines and decreased production of IFNγ.…”

Section: Discussionmentioning

confidence: 99%

Single-cell transcriptome reveals highly complement activated microglia cells in association with pediatric tuberculous meningitis

Mo,

Shi,

Cai

et al. 2024

Front. Immunol.

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BackgroundTuberculous meningitis (TBM) is a devastating form of tuberculosis (TB) causing high mortality and disability. TBM arises due to immune dysregulation, but the underlying immune mechanisms are unclear.MethodsWe performed single-cell RNA sequencing on peripheral blood mononuclear cells (PBMCs) and cerebrospinal fluid (CSF) cells isolated from children (n=6) with TBM using 10 xGenomics platform. We used unsupervised clustering of cells and cluster visualization based on the gene expression profiles, and validated the protein and cytokines by ELISA analysis. ResultsWe revealed for the first time 33 monocyte populations across the CSF cells and PBMCs of children with TBM. Within these populations, we saw that CD4_C04 cells with Th17 and Th1 phenotypes and Macro_C01 cells with a microglia phenotype, were enriched in the CSF. Lineage tracking analysis of monocyte populations revealed myeloid cell populations, as well as subsets of CD4 and CD8 T-cell populations with distinct effector functions. Importantly, we discovered that complement-activated microglial Macro_C01 cells are associated with a neuroinflammatory response that leads to persistent meningitis. Consistently, we saw an increase in complement protein (C1Q), inflammatory markers (CRP) and inflammatory factor (TNF-α and IL-6) in CSF cells but not blood. Finally, we inferred that Macro_C01 cells recruit CD4_C04 cells through CXCL16/CXCR6. DiscussionWe proposed that the microglial Macro_C01 subset activates complement and interacts with the CD4_C04 cell subset to amplify inflammatory signals, which could potentially contribute to augment inflammatory signals, resulting in hyperinflammation and an immune response elicited by Mtb-infected tissues.

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Section: Discussionmentioning

confidence: 99%

Single-cell transcriptome reveals highly complement activated microglia cells in association with pediatric tuberculous meningitis

Mo,

Shi,

Cai

et al. 2024

Front. Immunol.

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“…Besides, the normal number of T cells was not equal to normal functions. The research found that chronic infections by viruses and bacteria led the continuous exposure to antigens, causing the overexpression of inhibitory receptors, such as PD-1 [28,29] , CTLA-4 [30] , Tim-3 [31] , and Lag-3 [32] on the surface of CD4 T cells and CD8 T cells, which could reduce the Page 13/16 release of cytokines by T cells and impair the functions of T cells [33] . The phenomenon was called T cell exhaustion, which was associated with the severity of PTB [31,32] .…”

Section: Discussionmentioning

confidence: 99%

CD8 T cell count was not associated with the severity of pulmonary tuberculosis

Xiang

Liu

et al. 2023

Preprint

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Purposes: Tuberculosis is still a global health threat, among those, severe pulmonary tuberculosis causes significant mortality. CD8 T cell plays an important role in the pathogenesis of tuberculosis. However, the association between CD8 T cell count and the severity of pulmonary TB (PTB) has not been evaluated. Methods: Patients admitted to a tertiary hospital from January 2013 to December 2017 and diagnosed as PTB with T cell subtypes tested were screened for recruitment. We compared the demographics and clinical manifestations between severe PTB patients and non-severe PTB patients, then analyzed the independent risk factors related to severe PTB after adjusting covariates. Results: There were 279 patients enrolled for analysis, 180 were severe PTB (64.5%) and 99 were non-severe PTB (35.5%). Through univariate and multivariate analysis, lung cavity (aOR 4.631, 95% CI [1.798-12.853], P= 0.002) and albumin (aOR 0.952,95% CI [0.905-1.000], P=0.05) were associated severe PTB. While CD4 T cell count (aOR 1.00,95% CI [0.999-1.001], P=0.794), CD8 T cell count (aOR 1.00,95% CI [0.999-1.001], P=0.973), and lymphocyte (aOR 0.82,95% CI [0.389-1.774], P=0.603) were not associated with severe PTB. Conclusions: Lung cavity and lower albumin level were related with an increased risk of severe PTB, while CD4, CD8 T cells count and lymphocytes were not with the severity of PTB. May be not the count, but the function of T cells plays a crucial role in the pathogenesis of tuberculosis.

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“…Similarly, active pulmonary TB patients also exhibit T cell exhaustion and dysfunction under sustained MTB antigen stimulation, characterized by reduced production of MTB-specific INF-γ, TNF-α, and IL-2 by CD4 + and CD8 + T cells, as well as increased expression of PD-1 and its ligands on T cells, monocytes, macrophages, and B cells [ 70 ]. Recently, Pan et al [ 71 ] identified 12 genes through single-cell sequencing that may be associated with exhaustion of CD4 + and CD8 + T cells following Mycobacterium infection, including RPS26 , ITM2C , GZMK , IL32 , HLA-DRB1 , TNFAIP3 , JUN , ZFP36L2 , GTF3C1 , ZFP36 , MT2A , and HOPX . Among the features of T cell exhaustion and dysfunction caused by chronic MTB infection, immune checkpoints have gained increasing attention.…”

Section: Immunological Mechanisms Of Ltbimentioning

confidence: 99%

From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning

Li,

Yang,

Zhuang

et al. 2023

Military Med Res

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Latent tuberculosis infection (LTBI) has become a major source of active tuberculosis (ATB). Although the tuberculin skin test and interferon-gamma release assay can be used to diagnose LTBI, these methods can only differentiate infected individuals from healthy ones but cannot discriminate between LTBI and ATB. Thus, the diagnosis of LTBI faces many challenges, such as the lack of effective biomarkers from Mycobacterium tuberculosis (MTB) for distinguishing LTBI, the low diagnostic efficacy of biomarkers derived from the human host, and the absence of a gold standard to differentiate between LTBI and ATB. Sputum culture, as the gold standard for diagnosing tuberculosis, is time-consuming and cannot distinguish between ATB and LTBI. In this article, we review the pathogenesis of MTB and the immune mechanisms of the host in LTBI, including the innate and adaptive immune responses, multiple immune evasion mechanisms of MTB, and epigenetic regulation. Based on this knowledge, we summarize the current status and challenges in diagnosing LTBI and present the application of machine learning (ML) in LTBI diagnosis, as well as the advantages and limitations of ML in this context. Finally, we discuss the future development directions of ML applied to LTBI diagnosis.

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Landscape of Exhausted T Cells in Tuberculosis Revealed by Single-Cell Sequencing

Cited by 11 publications

References 46 publications

Single-cell transcriptome reveals highly complement activated microglia cells in association with pediatric tuberculous meningitis

Single-cell transcriptome reveals highly complement activated microglia cells in association with pediatric tuberculous meningitis

CD8 T cell count was not associated with the severity of pulmonary tuberculosis

From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning

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