Toll-like receptors blocking restores in vitro microbicidal activity in latent tuberculosis-infected subjects

Silva, F. D. Coelho da; Covre, Luciana Polaco; Stringari, Lorenzzo Lyrio; Palaci, Moisés; Dietze, Reynaldo; Gomes, Daniel Cláudio Oliveira; Ribeiro‐Rodrigues, Rodrigo

doi:10.5588/ijtld.18.0392

Cited by 5 publications

(4 citation statements)

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“…There is evidence to suggest that TLR9 promotes the maturation of CD8 + T cells and their recognition of MTB antigens by inducing the secretion of type I IFN [ 121 ]. On the other hand, a cohort study has shown that blocking TLR9 and TLR4 can significantly reduce the number of Tregs and the expression of IL-10 in LTBI individuals, thereby enhancing host killing of MTB, indicating that MTB infection may activate the TLR4 and TLR9 pathways to suppress immune responses in the LTBI population [ 122 ].…”

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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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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“…Conversely, another study has demonstrated the activation of TLR9 in the pathogenesis of Mtb in vitro by a significant increase in the Treg and IL-10 expression, which is responsible for dampening the immune response and facilitating the reactivation and dissemination of Mtb. On that note, the blockage of TLR9 resulted in a reduction in Treg and IL-10, with increased IFN-γ production, cumulatively providing a protective effect against Mtb [123]. Intriguingly, TLR9 activation leads to a dichotomous dose-dependent effect, with low-level activation leading to a primarily Th1 response, while increased activation suppresses the immune response [103].…”

Section: Toll-like Receptor 7 and 9 And Mtbmentioning

confidence: 99%

A Role of Intracellular Toll-Like Receptors (3, 7, and 9) in Response to Mycobacterium tuberculosis and Co-Infection with HIV

Nguyen

Gazy

Venketaraman

2020

IJMS

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Mycobacterium tuberculosis (Mtb) is a highly infectious acid-fast bacillus and is known to cause tuberculosis (TB) in humans. It is a leading cause of death from a sole infectious agent, with an estimated 1.5 million deaths yearly worldwide, and up to one third of the world’s population has been infected with TB. The virulence and susceptibility of Mtb are further amplified in the presence of Human Immunodeficiency Virus (HIV). Coinfection with Mtb and HIV forms a lethal combination. Previous studies had demonstrated the synergistic effects of Mtb and HIV, with one disease accelerating the disease progression of the other through multiple mechanisms, including the modulation of the immune response to these two pathogens. The response of the endosomal pattern recognition receptors to these two pathogens, specifically toll-like receptors (TLR)-3, -7, and -9, has not been elucidated, with some studies producing mixed results. This article seeks to review the roles of TLR-3, -7, and -9 in response to Mtb infection, as well as Mtb-HIV-coinfection via Toll-interleukin 1 receptor (TIR) domain-containing adaptor inducing INF-β (TRIF)-dependent and myeloid differentiation factor 88 (MyD88)-dependent pathways.

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“…HIV hinders the production of type I IFN, a potent antiviral, by impairing dendritic cells while increasing IL10, anti-inflammatory cytokine, which dampens protection [ 16 , 20 , 21 ]. Likewise, TLR9-dependent increase in production of IL10 by regulatory T cell hindered immunity against M.tb [ 22 ]. Blocking TLR9 averted IL10 production and resulted a surge in type I IFN production.…”

Section: Introductionmentioning

confidence: 99%

Protein Expression of TLR2, TLR4, and TLR9 on Monocytes in TB, HIV, and TB/HIV

Tamene,

Wassie,

Marconi

et al. 2024

Journal of Immunology Research

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Toll-like receptors (TLRs) have a critical role in recognizing pathogenic patterns and initiating immune responses against TB and HIV. Previously, studies described the gene expression of TLRs in patients with TB and HIV. Here, we demonstrated TLRs protein expressions and their association with clinical status and plasma markers in TB, HIV, and TB/HIV coinfection. The phenotyping of TLR2, TLR4, and TLR9 on CD14+ monocytes and their subsets were determined by multicolor flow cytometry. Host plasma biomarkers and microbial indices were measured using Luminex Multiplex assay and standard of care tools, respectively. TLR2 expression significantly enhanced in TB, slightly increased in HIV but slightly reduced in TB/HIV coinfection compared to apparently health controls (HC). On the other hand, TLR4 expression was significantly increased in TB, HIV, and TB/HIV compared to HC. Expression of TLR4 was equally enhanced on classical and intermediate monocytes while higher TLR2 expression on intermediate than classical monocytes. TLR4 had a positive correlation pattern with plasma biomarkers while TLR2 had an inverse correlation pattern. TLR4 is associated with disease severity while TLR2 is with the immune-competent status of patients. Our findings demonstrated that the pattern of TLR expression is disease as well as monocyte subset specific and distinct factors drive these differences.

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Toll-like receptors blocking restores in vitro microbicidal activity in latent tuberculosis-infected subjects

Cited by 5 publications

References 0 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

A Role of Intracellular Toll-Like Receptors (3, 7, and 9) in Response to Mycobacterium tuberculosis and Co-Infection with HIV

Protein Expression of TLR2, TLR4, and TLR9 on Monocytes in TB, HIV, and TB/HIV

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