A transcriptional blood signature distinguishes early tuberculosis disease from latent tuberculosis infection and uninfected individuals in a Vietnamese cohort

Advancement in diagnostic approaches for latent tuberculosis: distinguishing recent from remote infections

Tuberculosis (TB) remains as a significant global health threat to date, with latent TB infection (LTBI) serving as a major reservoir for future active disease cases. A practical approach to an effective control and eradication of TB hence, requires an explicit identification of infected patient whom are at high risk of progressing from latent to active TB, particularly in those recently infected individuals. Current diagnostic tools however, including Tuberculin Skin Test and Interferon-Gamma Release Assays, are still lacking for their ability to critically distinguish between recent and remote infections, leading to insufficiency in optimizing targeted preventive treatment strategies. This review examines the limitations of current diagnostic tools and explores novel biomarkers to enhance distinction within the infection timeline in LTBI diagnostics. Advancement in immune profiling, dormancy antigen, along with molecular and transcriptomic approaches holds great promise to develop a diagnostic tools with better accuracy to differentiate recent from remote infections, thereby optimizing targeted interventions to improve TB control strategies. These underscores the need for further research into these emerging diagnostic tools to facilitate an effective public health strategies and contribute to the united efforts in End TB Strategy.

Clustering Mycobacterium tuberculosis-specific CD154+CD4+ T cells for distinguishing tuberculosis disease from infection based on single-cell RNA-seq analysis

BACKGROUND

Distinguishing between active tuberculosis disease (TBD) and latent tuberculosis infection (TBI) is crucial for TB control but remains challenging.

METHODS

Single-cell RNA sequencing was conducted on purified Mycobacterium tuberculosis (MTB)-specific CD154+CD4+ T cells.

RESULTS

We observe a superior role of CD154 in detecting MTB infection, whereas its ability in distinguishing TBD from TBI is still limited due to patient heterogeneity. Single-cell RNA sequencing of MTB-specific CD154+CD4+ T cells identifies 10 distinct clusters, including Treg, T_act, Th1_pex, Th1_eff, Tfh, T_na, Th17_ex, Th2, NKT, and Th1_cyt. Notably, effector and apoptotic Th1 cells are predominant in CD154+CD4+ T cells of TBD. However, Tfh cells are the primary component in TBI. Most Th1_pex cells are positioned at the end of the developmental trajectory and are regulated by key genes associated with apoptosis and early exhaustion, such as GADD45B, FOS, and EZH2. Oxidative stress-induced metabolic disorder, marked by increased metabolism of nitrogen, cysteine, and glutathione, also contributes to the apoptosis of Th1_pex cells. Using seven features including NA, CM, EM, EMRA, CXCR3+ Th1, IFN-γ+ Th1, and Tfh of CD154+CD4+ T cells, both TBD and TBI can be classified into different subtypes, and a further established random forest model can accurately differentiate TBD from TBI. Additionally, the key checkpoints of exhausted MTB-specific Th1 cells are identified and blocking ADORA2A efficiently restores their function.

CONCLUSIONS

We depict the cellular compositions, transcriptional characteristics, and developmental trajectories of MTB-specific CD154+CD4+ T cells from TBI to TBD, putting forward a new direction in the diagnosis and prognosis of disease.

Ocular Tuberculosis Diagnosis Through Biomarkers: Clinical Relevance of Serum C1q and Whole Blood Interferon Gene Signature Score

PURPOSE

To assess the clinical relevance of pathophysiology-based biomarkers, specifically serum C1q and whole blood interferon gene signature score (IGSS), in ocular tuberculosis (OTB) diagnosis by conducting an integrative analysis of clinical presentations and treatment response.

METHODS

This retrospective cohort study analysed data from 70 patients with suspected OTB at a tertiary care uveitis practice in Indonesia. Serum C1q levels and whole blood IGSS were quantified. Patients were categorized into four quadrants based on their biomarker profiles: quadrant 1 (high C1q & low IGSS), quadrant 2 (high C1q & high IGSS), quadrant 3 (low C1q & high IGSS), and quadrant 4 (low C1q & low IGSS). Characteristics of clinical presentations, work-up results, and treatment outcomes were explored according to the predefined quadrants.

RESULTS

We identified that the majority of OTB patients diagnosed with concurrent active pulmonary TB were in quadrant 1, 2, or 3 (20/23, 87.0%). Twenty-seven patients (27/47, 57.4%) with clinically undifferentiated uveitis were in quadrant 4 (p < 0.001). Among patients in quadrants 1, 2, and 3, completion of a full course of antitubercular treatment (ATT) was associated with a lower number of patients showing persistence or recurrence of ocular inflammation compared to those who were not fully treated with ATT (14.3% vs 85.7%, p = 0.001).

CONCLUSIONS

Based on the analysis of clinical features and treatment outcomes, patients with elevated levels of either or both serum C1q and whole blood IGSS may reflect active TB disease in the eye, necessitating full ATT management.

Distinguish active tuberculosis with an immune-related signature and molecule subtypes: a multi-cohort analysis

BACKGROUND

Distinguishing latent tuberculosis infection (LTBI) from active tuberculosis (ATB) is very important. This study aims to analyze cases from multiple cohorts and get the signature that can distinguish LTBI from ATB.

METHODS

Thirteen datasets were downloaded from the gene expression omnibus (GEO) database. Three datasets were selected as discovery datasets, and the hub genes were discovered through WGCNA. In the training cohort, we use machine learning to establish the signature, verify the authentication ability of the signature in the remaining datasets, and compare it with other signatures. Cluster analysis was carried out on ATB cases, immune cell infiltration analysis, GSVA analysis, and drug sensitivity analysis were carried out on different clusters.

RESULTS

In the discovery datasets, we discovered five hub genes. A signature (SLC26A8, ANKRD22, and FCGR1B) is obtained in the training cohort. In the total cohort, the three-gene signature can separate LTBI from ATB (the total area under ROC curve (AUC) is 0.801, 95% CI 0.771-0.830). Compared with other author's signatures, our signature shows good identification ability. Immunological analysis showed that SLC26A8, ANKRD22, and FCGR1B were closely related to the infiltration of immune cells. According to the expression of the three genes, ATB can be divided into two clusters, which are different in immune cell infiltration analysis, gene set variation, and drug sensitivity.

CONCLUSION

Our study produced an immune-related three-gene signature to distinguish LTBI from ATB, which may help us to manage and treat tuberculosis patients.

Development of diagnostic algorithm using machine learning for distinguishing between active tuberculosis and latent tuberculosis infection

BACKGROUND

The discrimination between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) remains challenging. The present study aims to investigate the value of diagnostic models established by machine learning based on multiple laboratory data for distinguishing Mycobacterium tuberculosis (Mtb) infection status.

METHODS

T-SPOT, lymphocyte characteristic detection, and routine laboratory tests were performed on participants. Diagnostic models were built according to various algorithms.

RESULTS

A total of 892 participants (468 ATB and 424 LTBI) and another 263 participants (125 ATB and 138 LTBI), were respectively enrolled at Tongji Hospital (discovery cohort) and Sino-French New City Hospital (validation cohort). Receiver operating characteristic (ROC) curve analysis showed that the value of individual indicator for differentiating ATB from LTBI was limited (area under the ROC curve (AUC) < 0.8). A total of 28 models were successfully established using machine learning. Among them, the AUCs of 25 models were more than 0.9 in test set. It was found that conditional random forests (cforest) model, based on the implementation of the random forest and bagging ensemble algorithms utilizing conditional inference trees as base learners, presented best discriminative power in segregating ATB from LTBI. Specially, cforest model presented an AUC of 0.978, with the sensitivity of 93.39% and the specificity of 91.18%. Mtb-specific response represented by early secreted antigenic target 6 (ESAT-6) and culture filtrate protein 10 (CFP-10) spot-forming cell (SFC) in T-SPOT assay, as well as global adaptive immunity assessed by CD4 cell IFN-γ secretion, CD8 cell IFN-γ secretion, and CD4 cell number, were found to contribute greatly to the cforest model. Superior performance obtained in the discovery cohort was further confirmed in the validation cohort. The sensitivity and specificity of cforest model in validation set were 92.80% and 89.86%, respectively.

CONCLUSIONS

Cforest model developed upon machine learning could serve as a valuable and prospective tool for identifying Mtb infection status. The present study provided a novel and viable idea for realizing the clinical diagnostic application of the combination of machine learning and laboratory findings.

Systematic review of host genomic biomarkers of invasive bacterial disease: Distinguishing bacterial from non-bacterial causes of acute febrile illness

Background

Infectious diseases play a significant role in the global burden of disease. The gold standard for the diagnosis of bacterial infection, bacterial culture, can lead to diagnostic delays and inappropriate antibiotic use. The advent of high- throughput technologies has led to the discovery of host-based genomic biomarkers of infection, capable of differentiating bacterial from other causes of infection, but few have achieved validation for use in a clinical setting.

Methods

A systematic review was performed. PubMed/Ovid Medline, Ovid Embase and Scopus databases were searched for relevant studies from inception up to 30/03/2022 with forward and backward citation searching of key references. Studies assessing the diagnostic performance of human host genomic biomarkers of bacterial infection were included. Study selection and assessment of quality were conducted by two independent reviewers. A meta-analysis was undertaken using a diagnostic random-effects model. The review was registered with PROSPERO (ID: CRD42021208462).

Findings

Seventy-two studies evaluating the performance of 116 biomarkers in 16,216 patients were included. Forty-six studies examined TB-specific biomarker performance and twenty-four studies assessed biomarker performance in a paediatric population. The results of pooled sensitivity, specificity, negative and positive likelihood ratio, and diagnostic odds ratio of genomic biomarkers of bacterial infection were 0.80 (95% CI 0.78 to 0.82), 0.86 (95% CI 0.84 to 0.88), 0.18 (95% CI 0.16 to 0.21), 5.5 (95% CI 4.9 to 6.3), 30.1 (95% CI 24 to 37), respectively. Significant between-study heterogeneity (I² 77%) was present.

Interpretation

Host derived genomic biomarkers show significant potential for clinical use as diagnostic tests of bacterial infection however, further validation and attention to test platform is warranted before clinical implementation can be achieved.

Funding

No funding received.

Combination of HLA-DR on Mycobacterium tuberculosis-Specific Cells and Tuberculosis Antigen/Phytohemagglutinin Ratio for Discriminating Active Tuberculosis From Latent Tuberculosis Infection

Background

Novel approaches for tuberculosis (TB) diagnosis, especially for distinguishing active TB (ATB) from latent TB infection (LTBI), are urgently warranted. The present study aims to determine whether the combination of HLA-DR on Mycobacterium tuberculosis (MTB)-specific cells and TB antigen/phytohemagglutinin (TBAg/PHA) ratio could facilitate MTB infection status discrimination.

Methods

Between June 2020 and June 2021, participants with ATB and LTBI were recruited from Tongji Hospital (Qiaokou cohort) and Sino-French New City Hospital (Caidian cohort), respectively. The detection of HLA-DR on MTB-specific cells upon TB antigen stimulation and T-SPOT assay were simultaneously performed on all subjects.

Results

A total of 116 (54 ATB and 62 LTBI) and another 84 (43 ATB and 41 LTBI) cases were respectively enrolled from Qiaokou cohort and Caidian cohort. Both HLA-DR on IFN-γ⁺TNF-α⁺ cells and TBAg/PHA ratio showed discriminatory value in distinguishing between ATB and LTBI. Receiver operator characteristic (ROC) curve analysis showed that HLA-DR on IFN-γ⁺TNF-α⁺ cells produced an area under the ROC curve (AUC) of 0.886. Besides, TBAg/PHA ratio yield an AUC of 0.736. Furthermore, the combination of these two indicators resulted in the accurate discrimination with an AUC of 0.937. When the threshold was set as 0.36, the diagnostic model could differentiate ATB from LTBI with a sensitivity of 92.00% and a specificity of 81.82%. The performance obtained in Qiaokou cohort was further validated in Caidian cohort.

Conclusions

The combination of HLA-DR on MTB-specific cells and TBAg/PHA ratio could serve as a robust tool to determine TB disease states.

A systematic review of potential screening biomarkers for active TB disease

INTRODUCTION

The standard TB Four Symptom Screen does not meet the World Health Organization (WHO) ideal screening criteria for having greater than 90% sensitivity to identify active TB disease, regardless of HIV status. To identify novel screening biomarkers for active TB, we performed a systematic review of any cohort or case-control study reporting associations between screening biomarkers and active TB disease.

METHODS

We searched PubMed and Embase for articles published before October 10, 2021. We included studies from high or medium tuberculosis burden countries. We excluded articles focusing on C-reactive protein and lipoarabinomannan. For all included biomarkers, we calculated sensitivity, specificity and 95% confidence intervals, and assessed study quality using a tool adapted from the QUADAS-2 risk of bias.

RESULTS

From 8,062 abstracts screened, we included 79 articles. The articles described 302 unique biomarkers, including host antibodies, host proteins, TB antigens, microRNAs, whole blood gene PCRs, and combinations of biomarkers. Of these, 23 biomarkers had sensitivity greater than 90% and specificity greater than 70%, meeting WHO criteria for an ideal screening test. Among the eleven biomarkers described in people living with HIV, only one had a sensitivity greater than 90% and specificity greater than 70% for active TB.

CONCLUSION

Further evaluation of biomarkers of active TB should be pursued to accelerate identification of TB disease.

Apolipoproteins L1-6 share key cation channel-regulating residues but have different membrane insertion and ion conductance properties

The human apolipoprotein L gene family encodes the apolipoprotein L1-6 (APOL1-6) proteins, which are effectors of the innate immune response to viruses, bacteria and protozoan parasites. Due to a high degree of similarity between APOL proteins, it is often assumed that they have similar functions to APOL1, which forms cation channels in planar lipid bilayers and membranes resulting in cytolytic activity. However, the channel properties of the remaining APOL proteins have not been reported. Here, we used transient overexpression and a planar lipid bilayer system to study the function of APOL proteins. By measuring lactate dehydrogenase release, we found that APOL1, APOL3, and APOL6 were cytolytic, whereas APOL2, APOL4, and APOL5 were not. Cells expressing APOL1 or APOL3, but not APOL6, developed a distinctive swollen morphology. In planar lipid bilayers, recombinant APOL1 and APOL2 required an acidic environment for the insertion of each protein into the membrane bilayer to form an ion conductance channel. In contrast, recombinant APOL3, APOL4, and APOL5 readily inserted into bilayers to form ion conductance at neutral pH, but required a positive voltage on the side of insertion. Despite these differences in membrane insertion properties, the ion conductances formed by APOL1-4 were similarly pH-dependent and cation-selective, consistent with conservation of the pore-lining region in each protein. Thus, despite structural conservation, the APOL proteins are functionally different. We propose that these proteins interact with different membranes and under different voltage and pH conditions within a cell to effect innate immunity to different microbial pathogens.

Lymphocyte-Related Immunological Indicators for Stratifying Mycobacterium tuberculosis Infection

Background

Easily accessible tools that reliably stratify Mycobacterium tuberculosis (MTB) infection are needed to facilitate the improvement of clinical management. The current study attempts to reveal lymphocyte-related immune characteristics of active tuberculosis (ATB) patients and establish immunodiagnostic model for discriminating ATB from latent tuberculosis infection (LTBI) and healthy controls (HC).

Methods

A total of 171 subjects consisted of 54 ATB, 57 LTBI, and 60 HC were consecutively recruited at Tongji hospital from January 2019 to January 2021. All participants were tested for lymphocyte subsets, phenotype, and function. Other examination including T-SPOT and microbiological detection for MTB were performed simultaneously.

Results

Compared with LTBI and HC, ATB patients exhibited significantly lower number and function of lymphocytes including CD4⁺ T cells, CD8⁺ T cells and NK cells, and significantly higher T cell activation represented by HLA-DR and proportion of immunosuppressive cells represented by Treg. An immunodiagnostic model based on the combination of NK cell number, HLA-DR⁺CD3⁺ T cells, Treg, CD4⁺ T cell function, and NK cell function was built using logistic regression. Based on receiver operating characteristic curve analysis, the area under the curve (AUC) of the diagnostic model was 0.920 (95% CI, 0.867-0.973) in distinguishing ATB from LTBI, while the cut-off value of 0.676 produced a sensitivity of 81.48% (95% CI, 69.16%-89.62%) and specificity of 91.23% (95% CI, 81.06%-96.20%). Meanwhile, AUC analysis between ATB and HC according to the diagnostic model was 0.911 (95% CI, 0.855-0.967), with a sensitivity of 81.48% (95% CI, 69.16%-89.62%) and a specificity of 90.00% (95% CI, 79.85%-95.34%).

Conclusions

Our study demonstrated that the immunodiagnostic model established by the combination of lymphocyte-related indicators could facilitate the status differentiation of MTB infection.

High sensitivity and specificity of a 5-analyte protein and microRNA biosignature for identification of active tuberculosis

Objectives

Non‐sputum‐based tests to accurately identify active tuberculosis (TB) disease and monitor response to therapy are urgently needed. This study examined the biomarker capacity of a panel of plasma proteins alone, and in conjunction with a previously identified miRNA signature, to identify active TB disease.

Methods

The expression of nine proteins (IP‐10, MCP‐1, sTNFR1, RANTES, VEGF, IL‐6, IL‐10, TNF and Eotaxin) was measured in the plasma of 100 control subjects and 100 TB patients, at diagnosis (treatment naïve) and over the course of treatment (1‐, 2‐ and 6‐month intervals). The diagnostic performance of the nine proteins alone, and with the miRNA, was assessed.

Results

Six proteins were significantly up‐regulated in the plasma of TB patients at diagnosis compared to controls. Receiver operator characteristic curve analysis demonstrated that IP‐10 with an AUC = 0.874, sensitivity of 75% and specificity of 87% was the best single biomarker candidate to distinguish TB patients from controls. IP‐10 and IL‐6 levels fell significantly within one month of commencing treatment and may have potential as indicators of a positive response to therapy. The combined protein and miRNA panel gave an AUC of 1.00. A smaller panel of only five analytes (IP‐10, miR‐29a, miR‐146a, miR‐99b and miR‐221) showed an AUC = 0.995, sensitivity of 96% and specificity of 97%.

Conclusions

A novel combination of miRNA and proteins significantly improves the sensitivity and specificity as a biosignature over single biomarker candidates and may be useful for the development of a non‐sputum test to aid the diagnosis of active TB disease.

Combination of prealbumin and tuberculosis-specific antigen/phytohemagglutinin ratio for discriminating active tuberculosis from latent tuberculosis infection

BACKGROUND

Given that there is no rapid and effective method for distinguishing active tuberculosis (ATB) from latent tuberculosis infection (LTBI), the discrimination between these two statuses remains challenging. This study sought to investigate the value of nutritional indexes and tuberculosis-specific antigen/phytohemagglutinin ratio (TBAg/PHA ratio) for distinguishing ATB from LTBI.

METHODS

Participants were consecutively recruited based on positive T-SPOT.TB results between January 2018 and January 2020. ATB was diagnosed by positive mycobacterial culture and/or positive GeneXpert MTB/RIF, with clinical symptoms and radiological characteristics suggestive of ATB. Individuals with positive T-SPOT.TB but without the evidence of ATB were defined as LTBI. Patients younger than 17 years and undergoing anti-TB treatment were excluded.

RESULTS

A total of 709 (312 ATB and 397 LTBI) and another 309 (120 ATB and 189 LTBI) subjects were respectively recruited from Tongji Hospital (Qiaokou cohort) and Sino-French New City Hospital (Caidian cohort). The level of prealbumin was significantly lower in ATB than in LTBI. With a cut-off value of 139 mg/L, the sensitivity and specificity of prealbumin in distinguishing ATB from LTBI were 50.96% (45.41%-56.51%) and 91.69% (88.97%-94.40%). Meanwhile, TBAg/PHA ratio was found statistically higher in ATB compared with LTBI. If using the threshold of 0.29, the sensitivity and specificity of TBAg/PHA ratio were 65.71% (60.44%-70.97%) and 90.93% (88.11%-93.76%), respectively. Moreover, the combination of prealbumin and TBAg/PHA ratio (obtaining by diagnostic model) yielded better specificity (90.18%, [87.25%-93.10%]) and sensitivity (87.18%, [83.47%-90.89%]), while the clinical utility index (CUI) positive and CUI negative were respectively 0.76 and 0.81. After anti-TB treatment, TBAg/PHA ratio was declined while the level of prealbumin was restored (Wilcoxon test, P < 0.001). Furthermore, the performance of diagnostic model obtained in Qiaokou cohort was confirmed in Caidian cohort.

CONCLUSIONS

The diagnostic model based on combination of prealbumin and TBAg/PHA ratio is a rapid and accurate tool for discriminating ATB from LTBI.

Visualizing the dynamics of tuberculosis pathology using molecular imaging

Nearly 140 years after Robert Koch discovered Mycobacterium tuberculosis, tuberculosis (TB) remains a global threat and a deadly human pathogen. M. tuberculosis is notable for complex host-pathogen interactions that lead to poorly understood disease states ranging from latent infection to active disease. Additionally, multiple pathologies with a distinct local milieu (bacterial burden, antibiotic exposure, and host response) can coexist simultaneously within the same subject and change independently over time. Current tools cannot optimally measure these distinct pathologies or the spatiotemporal changes. Next-generation molecular imaging affords unparalleled opportunities to visualize infection by providing holistic, 3D spatial characterization and noninvasive, temporal monitoring within the same subject. This rapidly evolving technology could powerfully augment TB research by advancing fundamental knowledge and accelerating the development of novel diagnostics, biomarkers, and therapeutics.