Validation of Differentially Expressed Immune Biomarkers in Latent and Active Tuberculosis by Real-Time PCR

Sex-specific blood-derived RNA biomarkers for childhood tuberculosis

Confirmatory diagnosis of childhood tuberculosis (TB) remains a challenge mainly due to its dependence on sputum samples and the paucibacillary nature of the disease. Thus, only ~ 30% of suspected cases in children are diagnosed and the need for minimally invasive, non-sputum-based biomarkers remains unmet. Understanding host molecular changes by measuring blood-based transcriptomic markers has shown promise as a diagnostic tool for TB. However, the implication of sex contributing to disease heterogeneity and therefore diagnosis remains to be understood. Using publicly available gene expression data (GSE39939, GSE39940; n = 370), we report a sex-specific RNA biomarker signature that could improve the diagnosis of TB disease in children. We found four gene biomarker signatures for male (SLAMF8, GBP2, WARS, and FCGR1C) and female pediatric patients (GBP6, CELSR3, ALDH1A1, and GBP4) from Kenya, South Africa, and Malawi. Both signatures achieved a sensitivity of 85% and a specificity of 70%, which approaches the WHO-recommended target product profile for a triage test. Our gene signatures outperform most other gene signatures reported previously for childhood TB diagnosis.

The pseudogene GBP1P1 suppresses influenza A virus replication by acting as a protein decoy for DHX9

Recently, substantial evidence has demonstrated that pseudogene-derived long noncoding RNAs (lncRNAs) as regulatory RNAs have been implicated in basic physiological processes and disease development through multiple modes of functional interaction with DNA, RNA, and proteins. Here, we report an important role for GBP1P1, the pseudogene of guanylate-binding protein 1, in regulating influenza A virus (IAV) replication in A549 cells. GBP1P1 was dramatically upregulated after IAV infection, which is controlled by JAK/STAT signaling. Functionally, ectopic expression of GBP1P1 in A549 cells resulted in significant suppression of IAV replication. Conversely, silencing GBP1P1 facilitated IAV replication and virus production, suggesting that GBP1P1 is one of the interferon-inducible antiviral effectors. Mechanistically, GBP1P1 is localized in the cytoplasm and functions as a sponge to trap DHX9 (DExH-box helicase 9), which subsequently restricts IAV replication. Together, these studies demonstrate that GBP1P1 plays an important role in antagonizing IAV replication.IMPORTANCELong noncoding RNAs (lncRNAs) are extensively expressed in mammalian cells and play a crucial role as regulators in various biological processes. A growing body of evidence suggests that host-encoded lncRNAs are important regulators involved in host-virus interactions. Here, we define a novel function of GBP1P1 as a decoy to compete with viral mRNAs for DHX9 binding. We demonstrate that GBP1P1 induction by IAV is mediated by JAK/STAT activation. In addition, GBP1P1 has the ability to inhibit IAV replication. Importantly, we reveal that GBP1P1 acts as a decoy to bind and titrate DHX9 away from viral mRNAs, thereby attenuating virus production. This study provides new insight into the role of a previously uncharacterized GBP1P1, a pseudogene-derived lncRNA, in the host antiviral process and a further understanding of the complex GBP network.

Early detection of tuberculosis: a systematic review

Tuberculosis remains a significant global health challenge. Tuberculosis affects millions of individuals worldwide. Early detection of tuberculosis plays a relevant role in the management of treatment of tuberculosis. This systematic review will analyze the findings of several published studies on the topic of the early detection of tuberculosis. This systematic review highlights their methodologies and limitations as well as their contributions to our understanding of this pressing issue. Early detection of tuberculosis can be achieved through tuberculosis screening for contacts. Comprehensive health education for household contacts can be used as early detection. The in-house deep learning models can be used in the X-ray used for automatic detection of tuberculosis. Interferon gamma release assay, routine passive and active case detection, portable X-ray and nucleic acid amplification testing, and highly sensitive enzyme-linked immunosorbent assay tests play critical roles in improving tuberculosis detection.

Construction of Immune-Related Diagnostic Model for Latent Tuberculosis Infection and Active Tuberculosis

Background

Tuberculosis (TB) is one of the most infectious diseases caused by Mycobacterium tuberculosis (M. tb), and the diagnosis of active tuberculosis (TB) and latent TB infection (LTBI) remains challenging.

Methods

Gene expression files were downloaded from the GEO database to identify the differentially expressed genes (DEGs). The ssGSEA algorithm was applied to assess the immunological characteristics of patients with LTBI and TB. Weighted gene co-expression network analysis, protein-protein interaction network, and the cytoHubba plug-in of Cytoscape were used to identify the real hub genes. Finally, a diagnostic model was constructed using real hub genes and validated using a validation set.

Results

Macrophages and natural killer cells were identified as important immune cells strongly associated with TB. In total, 726 mRNAs were identified as DEGs. MX1, STAT1, IFIH1, DDX58, and IRF7 were identified as real hub immune-related genes. The diagnostic model generated by the five real hub genes could distinguish active TB from healthy controls or patients with LTBI.

Conclusion

Our study may provide implications for the diagnosis and drug development of M. tb infections.

Whole blood RNA signatures in tuberculosis patients receiving H56:IC31 vaccine as adjunctive therapy

Introduction

Therapeutic vaccination in tuberculosis (TB) represents a Host Directed Therapy strategy which enhances immune responses in order to improve clinical outcomes and shorten TB treatment. Previously, we have shown that the subunit H56:IC31 vaccine induced both humoral and cellular immune responses when administered to TB patients adjunctive to standard TB treatment (TBCOX2 study, NCT02503839). Here we present the longitudinal whole blood gene expression patterns in H56:IC31 vaccinated TB patients compared to controls receiving standard TB treatment only.

Methods

The H56:IC31 group (N=11) and Control group (N=7) underwent first-line TB treatment for 182 days. The H56:IC31 group received 5 micrograms of the H56:IC31 vaccine (Statens Serum Institut; SSI, Valneva Austria GmbH) intramuscularly at day 84 and day 140. Total RNA was extracted from whole blood samples collected in PAXgene tubes on days 0, 84, 98, 140, 154, 182 and 238. The expression level of 183 immune-related genes was measured by high-throughput microfluidic qPCR (Biomark HD system, Standard BioTools).

Results

The targeted gene expression profiling unveiled the upregulation of modules such as interferon (IFN) signalling genes, pattern recognition receptors and small nucleotide guanosine triphosphate (GTP)-ases in the vaccinated group compared to controls two weeks after administration of the first H56:IC31 vaccine. Additionally, the longitudinal analysis of the Adolescent Cohort Study-Correlation of Risk (ACS-COR) signature showed a progressive downregulation in both study arms towards the end of TB treatment, in congruence with reported treatment responses and clinical improvements. Still, two months after the end of TB treatment, vaccinated patients, and especially those developing both cellular and humoral vaccine responses, showed a lower expression of the ACS-COR genes compared to controls.

Discussion

Our data report gene expression patterns following H56:IC31 vaccination which might be interpreted as a lower risk of relapse in therapeutically vaccinated patients. Further studies are needed to conclude if these gene expression patterns could be used as prognostic biosignatures for therapeutic TB vaccine responses.

Cutoff estimation and construction of their confidence intervals for continuous biomarkers under ternary umbrella and tree stochastic ordering settings

Tuberculosis (TB) studies often involve four different states under consideration, namely, "healthy," "latent infection," "pulmonary active disease," and "extra-pulmonary active disease." While highly accurate clinical diagnosis tests do exist, they are expensive and generally not accessible in regions where they are most needed; thus, there is an interest in assessing the accuracy of new and easily obtainable biomarkers. For some such biomarkers, the typical stochastic ordering assumption might not be justified for all disease classes under study, and usual ROC methodologies that involve ROC surfaces and hypersurfaces are inadequate. Different types of orderings may be appropriate depending on the setting, and these may involve a number of ambiguously ordered groups that stochastically exhibit larger (or lower) marker scores than the remaining groups. Recently, there has been scientific interest on ROC methods that can accommodate these so-called "tree" or "umbrella" orderings. However, there is limited work discussing the estimation of cutoffs in such settings. In this article, we discuss the estimation and inference around optimized cutoffs when accounting for such configurations. We explore different cutoff alternatives and provide parametric, flexible parametric, and non-parametric kernel-based approaches for estimation and inference. We evaluate our approaches using simulations and illustrate them through a real data set that involves TB patients.

Identification of diagnostic biomarkers correlate with immune infiltration in extra-pulmonary tuberculosis by integrating bioinformatics and machine learning

The diagnosis of tuberculosis depends on detecting Mycobacterium tuberculosis (Mtb). Unfortunately, recognizing patients with extrapulmonary tuberculosis (EPTB) remains challenging due to the insidious clinical presentation and poor performance of diagnostic tests. To identify biomarkers for EPTB, the GSE83456 dataset was screened for differentially expressed genes (DEGs), followed by a gene enrichment analysis. One hundred and ten DEGs were obtained, mainly enriched in inflammation and immune -related pathways. Weighted gene co-expression network analysis (WGCNA) was used to identify 10 co-expression modules. The turquoise module, correlating the most highly with EPTB, contained 96 DEGs. Further screening with the least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) narrowed down the 96 DEGs to five central genes. All five key genes were validated in the GSE144127 dataset. CARD17 and GBP5 had high diagnostic capacity, with AUC values were 0.763 (95% CI: 0.717-0.805) and 0.833 (95% CI: 0.793-0.869) respectively. Using single sample gene enrichment analysis (ssGSEA), we evaluated the infiltration of 28 immune cells in EPTB and explored their relationships with key genes. The results showed 17 immune cell subtypes with significant infiltrations in EPTB. CARD17, GBP5, HOOK1, LOC730167, and HIST1H4C were significantly associated with 16, 14, 12, 6, and 4 immune cell subtypes, respectively. The RT-qPCR results confirmed that the expression levels of GBP5 and CARD17 were higher in EPTB compared to control. In conclusion, CARD17 and GBP5 have high diagnostic efficiency for EPTB and are closely related to immune cell infiltration.

Reanalysis and validation of the transcriptional pleural fluid signature in pleural tuberculosis

Introduction

Pleural tuberculosis (PlTB), the most common site of extrapulmonary TB, is characterized by a paucibacillary nature and a compartmentalized inflammatory response in the pleural cavity, both of which make diagnosis and management extremely challenging. Although transcriptional signatures for pulmonary TB have already been described, data obtained by using this approach for extrapulmonary tuberculosis and, specifically, for pleural tuberculosis are scarce and heterogeneous. In the present study, a set of candidate genes previously described in pulmonary TB was evaluated to identify and validate a transcriptional signature in clinical samples from a Brazilian cohort of PlTB patients and those with other exudative causes of pleural effusion.

Methods

As a first step, target genes were selected by a random forest algorithm with recursive feature elimination (RFE) from public microarray datasets. Then, peripheral blood (PB) and pleural fluid (PF) samples from recruited patients presenting exudative pleural effusion were collected during the thoracentesis procedure. Transcriptional analysis of the selected top 10 genes was performed by quantitative RT-PCR (RT-qPCR).

Results

Reanalysis of the public datasets identified a set of candidate genes (CARD17, BHLHE40, FCGR1A, BATF2, STAT1, BTN3A1, ANKRD22, C1QB, GBP2, and SEPTIN4) that demonstrated a global accuracy of 89.5% in discriminating pulmonary TB cases from other respiratory diseases. Our validation cohort consisted of PlTB (n = 35) patients and non-TB (n = 34) ones. The gene expressions of CARD17, GBP2, and C1QB in PF at diagnosis were significantly different between the two (PlTB and non-TB) groups (p < 0.0001). It was observed that the gene expressions of CARD17 and GBP2 were higher in PlTB PF than in non-TB patients. C1QB showed the opposite behavior, being higher in the non-TB PF. After anti-TB therapy, however, GBP2 gene expression was significantly reduced in PlTB patients (p < 0.001). Finally, the accuracy of the three above-cited highlighted genes in the PF was analyzed, showing AUCs of 91%, 90%, and 85%, respectively. GBP2 was above 80% (sensitivity = 0.89/specificity = 0.81), and CARD17 showed significant specificity (Se = 0.69/Sp = 0.95) in its capacity to discriminate the groups.

Conclusion

CARD17, GBP2, and C1QB showed promise in discriminating PlTB from other causes of exudative pleural effusion by providing accurate diagnoses, thus accelerating the initiation of anti-TB therapy.

Discovering common pathogenetic processes between COVID-19 and tuberculosis by bioinformatics and system biology approach

Introduction

The coronavirus disease 2019 (COVID-19) pandemic, stemming from the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has persistently threatened the global health system. Meanwhile, tuberculosis (TB) caused by Mycobacterium tuberculosis (M. tuberculosis) still continues to be endemic in various regions of the world. There is a certain degree of similarity between the clinical features of COVID-19 and TB, but the underlying common pathogenetic processes between COVID-19 and TB are not well understood.

Methods

To elucidate the common pathogenetic processes between COVID-19 and TB, we implemented bioinformatics and systematic research to obtain shared pathways and molecular biomarkers. Here, the RNA-seq datasets (GSE196822 and GSE126614) are used to extract shared differentially expressed genes (DEGs) of COVID-19 and TB. The common DEGs were used to identify common pathways, hub genes, transcriptional regulatory networks, and potential drugs.

Results

A total of 96 common DEGs were selected for subsequent analyses. Functional enrichment analyses showed that viral genome replication and immune-related pathways collectively contributed to the development and progression of TB and COVID-19. Based on the protein-protein interaction (PPI) network analysis, we identified 10 hub genes, including IFI44L, ISG15, MX1, IFI44, OASL, RSAD2, GBP1, OAS1, IFI6, and HERC5. Subsequently, the transcription factor (TF)-gene interaction and microRNA (miRNA)-gene coregulatory network identified 61 TFs and 29 miRNAs. Notably, we identified 10 potential drugs to treat TB and COVID-19, namely suloctidil, prenylamine, acetohexamide, terfenadine, prochlorperazine, 3'-azido-3'-deoxythymidine, chlorophyllin, etoposide, clioquinol, and propofol.

Conclusion

This research provides novel strategies and valuable references for the treatment of tuberculosis and COVID-19.

Advancing personalized medicine for tuberculosis through the application of immune profiling

While early and precise diagnosis is the key to eliminating tuberculosis (TB), conventional methods using culture conversion or sputum smear microscopy have failed to meet demand. This is especially true in high-epidemic developing countries and during pandemic-associated social restrictions. Suboptimal biomarkers have restricted the improvement of TB management and eradication strategies. Therefore, the research and development of new affordable and accessible methods are required. Following the emergence of many high-throughput quantification TB studies, immunomics has the advantages of directly targeting responsive immune molecules and significantly simplifying workloads. In particular, immune profiling has been demonstrated to be a versatile tool that potentially unlocks many options for application in TB management. Herein, we review the current approaches for TB control with regard to the potentials and limitations of immunomics. Multiple directions are also proposed to hopefully unleash immunomics' potential in TB research, not least in revealing representative immune biomarkers to correctly diagnose TB. The immune profiles of patients can be valuable covariates for model-informed precision dosing-based treatment monitoring, prediction of outcome, and the optimal dose prediction of anti-TB drugs.

The emerging roles of IFIT3 in antiviral innate immunity and cellular biology

The interferon-inducible protein with tetrapeptide repeats 3 (IFIT3) is one of the most important members in both the IFIT family and interferon-stimulated genes family. IFIT3 has typical features of the IFIT family in terms of gene and protein structures, and is able to be activated through the classical PRRs-IFN-JAK/STAT pathway. A variety of viruses can induce the expression of IFIT3, which in turn inhibits the replication of viruses, with the underlying mechanism showing its crucial role in antiviral innate immunity. Emerging studies have also identified that IFIT3 is involved in cellular biology changes, including cell proliferation, apoptosis, differentiation, and cancer development. In this review, we summarize the characteristics of IFIT3 with respect to molecular structure and regulatory pathways, highlighting the role of IFIT3 in antiviral innate immunity, as well as its diverse biological roles. We also discuss the potential of IFIT3 as a biomarker in disease diagnosis and therapy.

Candidate genes and sequence variants for susceptibility to mycobacterial infection identified by whole-exome sequencing

Inborn errors of immunity are known to influence susceptibility to mycobacterial infections. The aim of this study was to characterize the genetic profile of nine patients with mycobacterial infections (eight with BCGitis and one with disseminated tuberculosis) from the Republic of Moldova using whole-exome sequencing. In total, 12 variants in eight genes known to be associated with Mendelian Susceptibility to Mycobacterial Disease (MSMD) were detected in six out of nine patients examined. In particular, a novel splice site mutation c.373–2A>C in STAT1 gene was found and functionally confirmed in a patient with disseminated tuberculosis. Trio analysis was possible for seven out of nine patients, and resulted in 23 candidate variants in 15 novel genes. Four of these genes - GBP2, HEATR3, PPP1R9B and KDM6A were further prioritized, considering their elevated expression in immune-related tissues. Compound heterozygosity was found in GBP2 in a single patient, comprising a maternally inherited missense variant c.412G>A/p.(Ala138Thr) predicted to be deleterious and a paternally inherited intronic mutation c.1149+14T>C. Functional studies demonstrated that the intronic mutation affects splicing and the level of transcript. Finally, we analyzed pathogenicity of variant combinations in gene pairs and identified five patients with putative oligogenic inheritance. In summary, our study expands the spectrum of genetic variation contributing to susceptibility to mycobacterial infections in children and provides insight into the complex/oligogenic disease-causing mode.

Evaluation of Host Protein Biomarkers by ELISA From Whole Lysed Peripheral Blood for Development of Diagnostic Tests for Active Tuberculosis

Tuberculosis (TB) remains a significant global health crisis and the number one cause of death for an infectious disease. The health consequences in high-burden countries are significant. Barriers to TB control and eradication are in part caused by difficulties in diagnosis. Improvements in diagnosis are required for organisations like the World Health Organisation (WHO) to meet their ambitious target of reducing the incidence of TB by 50% by the year 2025, which has become hard to reach due to the COVID-19 pandemic. Development of new tests for TB are key priorities of the WHO, as defined in their 2014 report for target product profiles (TPPs). Rapid triage and biomarker-based confirmatory tests would greatly enhance the diagnostic capability for identifying and diagnosing TB-infected individuals. Protein-based test methods e.g. lateral flow devices (LFDs) have a significant advantage over other technologies with regard to assay turnaround time (minutes as opposed to hours) field-ability, ease of use by relatively untrained staff and without the need for supporting laboratory infrastructure. Here we evaluate the diagnostic performance of nine biomarkers from our previously published biomarker qPCR validation study; CALCOCO2, CD274, CD52, GBP1, IFIT3, IFITM3, SAMD9L, SNX10 and TMEM49, as protein targets assayed by ELISA. This preliminary evaluation study was conducted to quantify the level of biomarker protein expression across latent, extra-pulmonary or pulmonary TB groups and negative controls, collected across the UK and India, in whole lysed blood samples (WLB). We also investigated associative correlations between the biomarkers and assessed their suitability for ongoing diagnostic test development, using receiver operating characteristic/area under the curve (ROC) analyses, singly and in panel combinations. The top performing single biomarkers for pulmonary TB versus controls were CALCOCO2, SAMD9L, GBP1, IFITM3, IFIT3 and SNX10. TMEM49 was also significantly differentially expressed but downregulated in TB groups. CD52 expression was not highly differentially expressed across most of the groups but may provide additional patient stratification information and some limited use for incipient latent TB infection. These show therefore great potential for diagnostic test development either in minimal configuration panels for rapid triage or more complex formulations to capture the diversity of disease presentations.

Transcriptomic Analysis of Mycobacterium leprae-Stimulated Response in Peripheral Blood Mononuclear Cells Reveal Potential Biomarkers for Early Diagnosis of Leprosy

We aimed to identify an unique host transcriptional signature in peripheral blood mononuclear cells (PBMCs) in response to Mycobacterium leprae antigens to distinguish between patients with leprosy and non-leprosy controls for early diagnosis of the disease. Sixteen individuals were enrolled in the discovery cohort [eight patients with leprosy, comprising four multibacillary (MB) and four paucibacillary (PB); and eight non-leprosy controls, comprising four healthy house contacts (HHCs) and four endemic controls (ECs)]. The differences in the transcriptome response of PBMCs to M. leprae sonicate antigen were evaluated between leprosy patients and non-leprosy controls, and 12 differentially expressed genes (CCL2/MCP-1, IL-8, JAKM, ATP, ND1, SERP, FLJ10489, LINC00659, LOC34487, LOC101928143, MIR22, and NCF1C) were identified. The accuracy of the 12 differentially expressed genes was further validated for the diagnosis of leprosy using real-time quantitative PCR in 82 individuals (13 MB, 10 PB, 37 HHCs, and 22 ECs) in the validation cohort. We found that a 5 gene signature set IL-8, CCL2/MCP-1, SERP, LINC00659 and FLJ10489 had a suitable performance in discriminating leprosy from ECs. In addition, elevated expression of IL-8, CCL2/MCP-1, SERP and LINC00659 was associated with MB diagnosis compared with ECs, whereas increased expression of IL-8, CCL2/MCP-1, SERP and FLJ10489 was found to be useful biomarkers for PB diagnosis from ECs. Moreover, we found decreased expression of NCF1C among leprosy patients could distinguish leprosy from HHCs, whereas higher expression of CCL2 among MB than PB could distinguish different leprosy patients. In conclusion, among the 12 candidate host genes identified, a three gene signature IL-8, CCL2/MCP-1, and SERP showed the best performance in distinguishing leprosy patients from healthy controls. These findings may have implications for developing a rapid blood-based test for early diagnosis of leprosy.

Revisiting tuberculosis screening: An insight to complementary diagnosis and prospective molecular approaches for the recognition of the dormant TB infection in human and cattle hosts

Tuberculosis (TB) is defined as a chronic infection in both human and cattle hosts and many subclinical cases remain undetected. After the pathogen is inhaled by a host, phagocyted bacilli can persist inside macrophages surviving intracellularly. Hosts develop granulomatous lesions in the lungs or lymph nodes, limiting infection. However, bacilli become persister cells. Immunological diagnosis of TB is performed basically by routine tuberculin skin test (TST), and in some cases, by ancillary interferon-gamma release assay (IGRA). The concept of human latent TB infection (LTBI) by M. tuberculosis is recognized in cohorts without symptoms by routine clinical diagnostic tests, and nowadays IGRA tests are used to confirm LTBI with either active or latent specific antigens of M. tuberculosis. On the other hand, dormant infection in cattle by M. bovis has not been described by TST or IGRA testing as complications occur by cross-reactive immune responses to homolog antigens of environmental mycobacteria or a false-negative test by anergic states of a wained bovine immunity, evidencing the need for deciphering more specific biomarkers by new-generation platforms of analysis for detection of M. bovis dormant infection. The study and description of bovine latent TB infection (boLTBI) would permit the recognition of hidden animal infection with an increase in the sensitivity of routine tests for an accurate estimation of infected dairy cattle. Evidence of immunological and experimental analysis of LTBI should be taken into account to improve the study and the description of the still neglected boLTBI.

Activation Phenotype of Mycobacterium tuberculosis-Specific CD4+ T Cells Promoting the Discrimination Between Active Tuberculosis and Latent Tuberculosis Infection

Background

Rapid and effective discrimination between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) remains a challenge. There is an urgent need for developing practical and affordable approaches targeting this issue.

Methods

Participants with ATB and LTBI were recruited at Tongji Hospital (Qiaokou cohort) and Sino-French New City Hospital (Caidian cohort) based on positive T-SPOT results from June 2020 to January 2021. The expression of activation markers including HLA-DR, CD38, CD69, and CD25 was examined on Mycobacterium tuberculosis (MTB)-specific CD4⁺ T cells defined by IFN-γ, TNF-α, and IL-2 expression upon MTB antigen stimulation.

Results

A total of 90 (40 ATB and 50 LTBI) and another 64 (29 ATB and 35 LTBI) subjects were recruited from the Qiaokou cohort and Caidian cohort, respectively. The expression patterns of Th1 cytokines including IFN-γ, TNF-α, and IL-2 upon MTB antigen stimulation could not differentiate ATB patients from LTBI individuals well. However, both HLA-DR and CD38 on MTB-specific cells showed discriminatory value in distinguishing between ATB patients and LTBI individuals. As for developing a single candidate biomarker, HLA-DR had the advantage over CD38. Moreover, HLA-DR on TNF-α⁺ or IL-2⁺ cells had superiority over that on IFN-γ⁺ cells in differentiating ATB patients from LTBI individuals. Besides, HLA-DR on MTB-specific cells defined by multiple cytokine co-expression had a higher ability to discriminate patients with ATB from LTBI individuals than that of MTB-specific cells defined by one kind of cytokine expression. Specially, HLA-DR on TNF-α⁺IL-2⁺ cells produced an AUC of 0.901 (95% CI, 0.833–0.969), with a sensitivity of 93.75% (95% CI, 79.85–98.27%) and specificity of 72.97% (95% CI, 57.02–84.60%) as a threshold of 44% was used. Furthermore, the performance of HLA-DR on TNF-α⁺IL-2⁺ cells for differential diagnosis was obtained with validation cohort data: 90.91% (95% CI, 72.19–97.47%) sensitivity and 68.97% (95% CI, 50.77–82.73%) specificity.

Conclusions

We demonstrated that HLA-DR on MTB-specific cells was a potentially useful biomarker for accurate discrimination between ATB and LTBI.

Microarray-based selection of a serum biomarker panel that can discriminate between latent and active pulmonary TB

Bacterial culture of M. tuberculosis (MTB), the causative agent of tuberculosis (TB), from clinical specimens is the gold standard for laboratory diagnosis of TB, but is slow and culture-negative TB cases are common. Alternative immune-based and molecular approaches have been developed, but cannot discriminate between active TB (ATB) and latent TB (LTBI). Here, to identify biomarkers that can discriminate between ATB and LTBI/healthy individuals (HC), we profiled 116 serum samples (HC, LTBI and ATB) using a protein microarray containing 257 MTB secreted proteins, identifying 23 antibodies against MTB antigens that were present at significantly higher levels in patients with ATB than in those with LTBI and HC (Fold change > 1.2; p < 0.05). A 4-protein biomarker panel (Rv0934, Rv3881c, Rv1860 and Rv1827), optimized using SAM and ROC analysis, had a sensitivity of 67.3% and specificity of 91.2% for distinguishing ATB from LTBI, and 71.2% sensitivity and 96.3% specificity for distinguishing ATB from HC. Validation of the four candidate biomarkers in ELISA assays using 440 serum samples gave consistent results. The promising sensitivity and specificity of this biomarker panel suggest it merits further investigation for its potential as a diagnostic for discriminating between latent and active TB.