Evaluating the diversity of circulating natural killer cells between active tuberculosis and latent tuberculosis infection

Zhou, Yu; Lan, Haiping; Shi, Hanlu; Wu, Peihao; Zhou, Yonglie

doi:10.1016/j.tube.2022.102221

Cited by 7 publications

(5 citation statements)

References 37 publications

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“…Furthermore, the ratio of CD56 dim NK cells was higher in individuals with LTBI, and the amount of IFN-γ produced in vitro was higher in individuals with LTBI than in those with ATB [ 50 ]. Similarly, other study has found that the number of CD56 dim CD16 + and CD56 dim CD27 + NK cells was significantly higher in individuals with LTBI than in ATB patients [ 54 ]. These findings suggest that different states of TB infection are associated with the diversity of NK cells, and these newly discovered associations will further highlight the potential biomarkers for differential diagnosis of LTBI and ATB.…”

Section: Immunological Mechanisms Of Ltbimentioning

confidence: 64%

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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Section: Immunological Mechanisms Of Ltbimentioning

confidence: 64%

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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“…Conversely, another study did not report statistically significant differences in the frequencies of circulating NK cells between patients with active TB disease, LTBI subjects and HD [15]. In addition, there are also contrasting results on the percentages of the CD56 bright subset of NK cells in active TB patients, which were reported reduced in one study [12] but increased in another [15]. Single-cell sequencing technology offers an unprecedented opportunity to deepen our understanding of the transcriptomic, genomic, proteomic, epigenomic and metabolomic information of single cells.…”

Section: Introductionmentioning

confidence: 87%

“…Some studies have reported reduced percentages of circulating total NK cells [11,12], or their subsets [12,13], in patients with active TB disease-as compared with LTBI subjects-and HD, which normalize after successful therapy, suggesting a role in the control of Mtb infection [14]. Conversely, another study did not report statistically significant differences in the frequencies of circulating NK cells between patients with active TB disease, LTBI subjects and HD [15]. In addition, there are also contrasting results on the percentages of the CD56 bright subset of NK cells in active TB patients, which were reported reduced in one study [12] but increased in another [15].…”

Section: Introductionmentioning

confidence: 98%

Integrated Analysis of Single-Cell and Bulk RNA Sequencing Data Reveals Memory-like NK Cell Subset Associated with Mycobacterium tuberculosis Latency

Shekarkar Azgomi,

Badami,

Lo Pizzo

et al. 2024

Cells

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Natural killer (NK) cells are innate-like lymphocytes that belong to the family of type-1 innate lymphoid cells and rapidly respond to virus-infected and tumor cells. In this study, we have combined scRNA-seq data and bulk RNA-seq data to define the phenotypic and molecular characteristics of peripheral blood NK cells. While the role of NK cells in immune surveillance against virus infections and tumors has been well established, their contribution to protective responses to other intracellular microorganisms, such as Mycobacterium tuberculosis (Mtb), is still poorly understood. In this study, we have combined scRNA-seq data and bulk RNA-seq data to illuminate the molecular characteristics of circulating NK cells in patients with active tuberculosis (TB) disease and subjects with latent Mtb infection (LTBI) and compared these characteristics with those of healthy donors (HDs) and patients with non-TB other pulmonary infectious diseases (ODs). We show here that the NK cell cluster was significantly increased in LTBI subjects, as compared to patients with active TB or other non-TB pulmonary diseases and HD, and this was mostly attributable to the expansion of an NK cell population expressing KLRC2, CD52, CCL5 and HLA-DRB1, which most likely corresponds to memory-like NK2.1 cells. These data were validated by flow cytometry analysis in a small cohort of samples, showing that LTBI subjects have a significant expansion of NK cells characterized by the prevalence of memory-like CD52+ NKG2C+ NK cells. Altogether, our results provide some new information on the role of NK cells in protective immune responses to Mtb.

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“…In the case of TB, although there have been conflicting reports from human studies indicating decreased levels of NK cells with disease ( 178 – 181 ) or no significant differences ( 182 , 183 ), a loss of NK cells and its activation markers is shown to correlate with active disease ( 184 ). A recent study shown reduced degranulation and IFNg in TB patients pre-treatment when compared to post-treatment ( 185 ).…”

Section: Major Immune Cells In Innate Host Defense Against Tbmentioning

confidence: 99%

Early innate cell interactions with Mycobacterium tuberculosis in protection and pathology of tuberculosis

Sankar,

Mishra

2023

Front. Immunol.

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Tuberculosis (TB) remains a significant global health challenge, claiming the lives of up to 1.5 million individuals annually. TB is caused by the human pathogen Mycobacterium tuberculosis (Mtb), which primarily infects innate immune cells in the lungs. These immune cells play a critical role in the host defense against Mtb infection, influencing the inflammatory environment in the lungs, and facilitating the development of adaptive immunity. However, Mtb exploits and manipulates innate immune cells, using them as favorable niche for replication. Unfortunately, our understanding of the early interactions between Mtb and innate effector cells remains limited. This review underscores the interactions between Mtb and various innate immune cells, such as macrophages, dendritic cells, granulocytes, NK cells, innate lymphocytes-iNKT and ILCs. In addition, the contribution of alveolar epithelial cell and endothelial cells that constitutes the mucosal barrier in TB immunity will be discussed. Gaining insights into the early cellular basis of immune reactions to Mtb infection is crucial for our understanding of Mtb resistance and disease tolerance mechanisms. We argue that a better understanding of the early host-pathogen interactions could inform on future vaccination approaches and devise intervention strategies.

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Evaluating the diversity of circulating natural killer cells between active tuberculosis and latent tuberculosis infection

Cited by 7 publications

References 37 publications

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

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

Integrated Analysis of Single-Cell and Bulk RNA Sequencing Data Reveals Memory-like NK Cell Subset Associated with Mycobacterium tuberculosis Latency

Early innate cell interactions with Mycobacterium tuberculosis in protection and pathology of tuberculosis

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