The Evaluation and Validation of Blood-Derived Novel Biomarkers for Precise and Rapid Diagnosis of Tuberculosis in Areas With High-TB Burden

Exploring the Role of Extracellular Vesicles in the Pathogenesis of Tuberculosis

Tuberculosis (TB) remains a significant global health concern, necessitating accurate diagnosis and treatment monitoring. Extracellular vesicles (EVs), including exosomes, play crucial roles in disease progression, with their associated genes serving as potential biomarkers and therapeutic targets. Leveraging publicly available RNA-Seq datasets of TB patients and healthy controls (HCs), to identify differentially expressed genes (DEGs) and their associated protein-protein interaction networks and immune cell profiles, the common EV-related DEGs were identified and validated in the GSE42830 and GSE40553 datasets. We have identified nine common EV-related DEGs (SERPINA1, TNFAIP6, MAPK14, STAT1, ITGA2B, VAMP5, CTSL, CEACAM1, and PLAUR) upregulated in TB patients. Immune cell infiltration analysis revealed significant differences between TB patients and HCs, highlighting increased proportions of various immune cells in TB patients. These DEGs are involved in crucial cellular processes and pathways related to exocytosis and immune response regulation. Notably, VAMP5 exhibited excellent diagnostic performance (AUC-0.993, sensitivity-93.8%, specificity-100%), with potential as a novel biomarker for TB. The EV-related genes can serve as novel potential biomarkers that can distinguish between TB and HCs. VAMP5, which functions in exosome biogenesis and showed significant upregulation in TB, can be targeted for therapeutic interventions and treatment outcomes.

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

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.

Host-pathogen relationship in retreated tuberculosis with major rifampicin resistance-conferring mutations

Introduction

It is assumed that host defense systems eliminating the pathogen and regulating tissue damage make a strong impact on the outcome of tuberculosis (TB) disease and that these processes are affected by rifampicin (RIF) resistance-conferring mutations of Mycobacterium tuberculosis (Mtb). However, the host responses to the pathogen harboring different mutations have not been studied comprehensively in clinical settings. We analyzed clinico-epidemiological factors and blood transcriptomic signatures associated with major rpoB mutations conferring RIF resistance in a cohort study.

Methods

Demographic data were collected from 295 active pulmonary TB patients with treatment history in Hanoi, Vietnam. When recruited, drug resistance-conferring mutations and lineage-specific variations were identified using whole-genome sequencing of clinical Mtb isolates. Before starting retreatment, total RNA was extracted from the whole blood of HIV-negative patients infected with Mtb that carried either the rpoB H445Y or rpoB S450L mutation, and the total RNA was subjected to RNA sequencing after age-gender matching. The individual RNA expression levels in the blood sample set were also measured using real-time RT-PCR. Logistic and linear regression models were used to assess possible associations.

Results

In our cohort, rpoB S450L and rpoB H445Y were major RIF resistance-conferring mutations [32/87 (36.8%) and 15/87 (17.2%), respectively]. H445Y was enriched in the ancient Beijing genotype and was associated with nonsynonymous mutations of Rv1830 that has been reported to regulate antibiotic resilience. H445Y was also more frequently observed in genetically clustered strains and in samples from patients who had received more than one TB treatment episode. According to the RNA sequencing, gene sets involved in the interferon-γ and-α pathways were downregulated in H445Y compared with S450L. The qRT-PCR analysis also confirmed the low expression levels of interferon-inducible genes, including BATF2 and SERPING1, in the H445Y group, particularly in patients with extensive lesions on chest X-ray.

Discussion

Our study results showed that rpoB mutations as well as Mtb sublineage with additional genetic variants may have significant effects on host response. These findings strengthen the rationale for investigation of host-pathogen interactions to develop countermeasures against epidemics of drug-resistant TB.

The dynamics of Mycobacterium tuberculosis phagosome and the fate of infection

Phagosomes are vesicles produced by phagocytosis of phagocytes, which are crucial in immunity against Mycobacterium tuberculosis (Mtb) infection. After the phagocyte ingests the pathogen, it activates the phagosomes to recruit a series of components and process proteins, to phagocytose, degrade and kill Mtb. Meanwhile, Mtb can resist acid and oxidative stress, block phagosome maturation, and manipulate host immune response. The interaction between Mtb and phagocytes leads to the outcome of infection. The dynamic of this process can affect the cell fate. This article mainly reviews the development and maturation of phagosomes, as well as the dynamics and modifications of Mtb effectors and phagosomes components, and new diagnostic and therapeutic markers involved in phagosomes.

A protein signature associated with active tuberculosis identified by plasma profiling and network-based analysis

Annually, approximately 10 million people are diagnosed with active tuberculosis (TB), and 1.4 million die of the disease. If left untreated, each person with active TB will infect 10-15 new individuals. The lack of non-sputum-based diagnostic tests leads to delayed diagnoses of active pulmonary TB cases, contributing to continued disease transmission. In this exploratory study, we aimed to identify biomarkers associated with active TB. We assessed the plasma levels of 92 proteins associated with inflammation in individuals with active TB (n = 20), latent TB (n = 14), or healthy controls (n = 10). Using co-expression network analysis, we identified one module of proteins with strong association with active TB. We removed proteins from the module that had low abundance or were associated with non-TB diseases in published transcriptomic datasets, resulting in a 12-protein plasma signature that was highly enriched in individuals with pulmonary and extrapulmonary TB and was further associated with disease severity.

Analysis of real-time PCR Melanocortin 3 (MC3R) gene expression to identify new biomarkers inflammation in tuberculosis

Background

Diagnosis of tuberculosis (TB) in the era of technological sophistication requires accuracy and speed to provide as much information as possible so that TB treatment can be carried out quickly and precisely. New studies have also begun to be carried out to diagnose TB, one of which is by examining genes, either by looking at polymorphisms, mutations, or expressions. Several previous studies have confirmed the association of MC3R and TB genes with polymorphisms; MC3R is a gene that participates in the regulation of the inflammatory process and is also found in macrophages; therefore, we tried to analyze gene expression in the active TB group, household contacts, and healthy controls for looked at the differences between the three groups and confirmed the correlation of MC3R with TB by seeing which group's gene expression increased the most expression of the three groups so that the results can be considered as a TB diagnostic biomarker in the future.

Methods

This study included 122 people, 49 patients with confirmed TB, 46 close relatives of patients, and 27 healthy controls. This study used a real-time PCR technique to analyze MC3R gene expression in the three groups, and all data were analyzed using Bio-Rad CFXTM software version 3.1 and one-way ANOVA using SPSS 21.0.

Results

The value of MC3R gene expression in the active TB group increased 3.6-fold in the healthy group (p = 0.143), and that of gene expression in the healthy control group increased 1.09-fold in the healthy group (p = 0.007).

Conclusion

There is a relationship between MC3R and TB based on the results of gene expression analysis that increased in the active TB group compared to the household contact group and healthy controls.

Role of ISG15 post-translational modification in immunity against Mycobacterium tuberculosis infection

ISG15 encoded by a type I interferon (IFN) inducible gene mediates an important cellular process called ISGylation. ISGylation emerges as a powerful host tactic against intracellular pathogens like Mycobacterium tuberculosis (Mtb). However, the exact role of ISGylation in immunity remains elusive. To shed light on how ISGylation, which is both interesting and complex, participates in immunity against Mtb, this manuscript summarized the current knowledge about the structural characteristics and targets of ISG15 and how ISGylation cross-talks with other host post-translational modifications to exert its effect.