A Plasma 5-Marker Host Biosignature Identifies Tuberculosis in High and Low Endemic Countries

C1q and HBHA-specific IL-13 levels as surrogate plasma biomarkers for monitoring tuberculosis treatment efficacy: a cross-sectional cohort study in Paraguay

Introduction

New diagnostic tools are needed to rapidly assess the efficacy of pulmonary tuberculosis (PTB) treatment. The aim of this study was to evaluate several immune biomarkers in an observational and cross-sectional cohort study conducted in Paraguay.

Methods

Thirty-two patients with clinically and microbiologically confirmed PTB were evaluated before starting treatment (T0), after 2 months of treatment (T1) and at the end of treatment (T2). At each timepoint plasma levels of IFN-y, 17 pro- and anti-inflammatory cytokines/chemokines and complement factors C1q, C3 and C4 were assessed in unstimulated and Mtb-specific stimulated whole blood samples using QuantiFERON-TB gold plus and recombinant Mycobacterium smegmatis heparin binding hemagglutinin (rmsHBHA) as stimulation antigen. Complete blood counts and liver enzyme assays were also evaluated and correlated with biomarker levels in plasma.

Results

In unstimulated plasma, C1q (P<0.001), C4 (P<0.001), hemoglobin (P<0.001), lymphocyte proportion (P<0.001) and absolute white blood cell count (P=0.01) were significantly higher in PTB patients at baseline than in cured patients. C1q and C4 levels were found to be related to Mycobacterium tuberculosis load in sputum. Finally, a combinatorial analysis identified a plasma host signature comprising the detection of C1q and IL-13 levels in response to rmsHBHA as a tool differentiating PTB patients from cured TB profiles, with an AUC of 0.92 (sensitivity 94% and specificity 79%).

Conclusion

This observational study provides new insights on host immune responses throughout anti-TB treatment and emphasizes the role of host C1q and HBHA-specific IL-13 response as surrogate plasma biomarkers for monitoring TB treatment efficacy.

From simple to complex: Protein-based biomarker discovery in tuberculosis

Tuberculosis (TB) is a deadly infectious disease that affects millions of people globally. TB proteomics signature discovery has been a rapidly growing area of research that aims to identify protein biomarkers for the early detection, diagnosis, and treatment monitoring of TB. In this review, we have highlighted recent advances in this field and how it is moving from the study of single proteins to high-throughput profiling and from only using proteomics to include additional types of data in multi-omics studies. We have further covered the different sample types and experimental technologies used in TB proteomics signature discovery, focusing on studies of HIV-negative adults. The published signatures were defined as either coming from hypothesis-based protein targeting or from unbiased discovery approaches. The methodological approaches influenced the type of proteins identified and were associated with the circulating protein abundance. However, both approaches largely identified proteins involved in similar biological pathways, including acute-phase responses and T-helper type 1 and type 17 responses. By analysing the frequency of proteins in the different signatures, we could also highlight potential robust biomarker candidates. Finally, we discuss the potential value of integration of multi-omics data and the importance of control cohorts and signature validation.

Diagnostic performance of host protein signatures as a triage test for active pulmonary TB

The current four-symptom screen recommended by the World Health Organization (WHO) is widely used as screen to initiate diagnostic testing for active pulmonary tuberculosis (TB), yet the performance is poor especially when TB prevalence is low. In contrast, more sensitive molecular tests are less suitable for placement at primary care level in low-resource settings. In order to meet the WHO End TB targets, new diagnostic approaches are urgently needed to find the missing undiagnosed cases. Proteomics-derived blood host biomarkers have been explored because protein detection technologies are suitable for the point-of-care setting and could meet cost targets. This study aimed to find a biomarker signature that fulfills WHO's target product profile (TPP) for a TB screening. Twelve blood-based protein biomarkers from three sample populations (Vietnam, Peru, and South Africa) were analyzed individually and in combinations via advanced statistical methods and machine learning algorithms. The combination of I-309, SYWC and kallistatin showed the most promising results to discern active TB throughout the data sets meeting the TPP for a triage test in adults from two countries (Peru and South Africa). The top-performing individual markers identified at the global level (I-309 and SYWC) were also among the best-performing markers at country level in South Africa and Vietnam. This analysis clearly shows that a host protein biomarker assay is feasible in adults for certain geographical regions based on one or two biomarkers with a performance that meets minimal WHO TPP criteria.

Utility of a three-gene transcriptomic signature in the diagnosis of tuberculosis in a low-endemic hospital setting

BACKGROUND

Host transcriptomic blood signatures have demonstrated diagnostic potential for tuberculosis (TB), requiring further validation across different geographical settings. Discriminating TB from other diseases with similar clinical manifestations is crucial for the development of an accurate immunodiagnostic tool. In this exploratory cohort study, we evaluated the performance of potential blood-based transcriptomic signatures in distinguishing TB disease from non-TB lower respiratory tract infections in hospitalised patients in a TB low-endemic country.

METHOD

Quantitative real-time polymerase chain reaction qPCR) was used to evaluate 26 previously published genes in blood from 31 patients (14 TB and 17 lower respiratory tract infection cases) admitted to Oslo University Hospital in Norway. The diagnostic accuracies of differentially expressed genes were determined by receiver operating characteristic curves.

RESULTS

A significant difference (p < .01) in the age distribution was observed between patients with TB (mean age, 40 ± 15 years) and lower respiratory tract infection (mean age 59 ± 12 years). Following adjustment for age, ETV7, GBP1, GBP5, P2RY14 and BLK were significantly differentially expressed between patients with TB and those with LRI. A general discriminant analysis generated a three-gene signature (BAFT2, ETV7 and CD1C), which diagnosed TB with an area under the receiver operating characteristic curve (AUC) of 0.86 (95% CI, 0.69 - 1.00), sensitivity of 69.23% (95% CI, 38.57%-90.91%) and specificity of 94.12% (95% CI, 71.31%-99.85%).

CONCLUSION

The three-genes signature may have potential to improve diagnosis of TB in a hospitalised low-burden setting. However, the influence of confounding variables or covariates such as age requires further evaluation in larger studies.

Plasma host protein signatures correlating with Mycobacterium tuberculosis activity prior to and during antituberculosis treatment

There is a need for rapid non-sputum-based tests to identify and treat patients infected with Mycobacterium tuberculosis (Mtb). The overall objective of this study was to measure and compare the expression of a selected panel of human plasma proteins in patients with active pulmonary tuberculosis (ATB) throughout anti-TB treatment (from baseline to the end of treatment), in Mtb-infected individuals (TBI) and healthy donors (HD) to identify a putative host-protein signature useful for both TB diagnosis and treatment monitoring. A panel of seven human host proteins CLEC3B, SELL, IGFBP3, IP10, CD14, ECM1 and C1Q were measured in the plasma isolated from an HIV-negative prospective cohort of 37 ATB, 24 TBI and 23 HD. The protein signatures were assessed using a Luminex xMAP® to quantify the plasmatic levels in unstimulated blood of the different clinical group as well as the protein levels at baseline and at three timepoints during the 6-months ATB treatment, to compare the plasma protein levels between culture slow and fast converters that may contribute to monitor the TB treatment outcome. Protein signatures were defined using the CombiROC algorithm and multivariate models. The studied plasma host proteins showed different levels between the clinical groups and during the TB treatment. Six of the plasma proteins (CLEC3B, SELL, IGFBP3, IP10, CD14 and C1Q) showed significant differences in normalised median fluorescence intensities when comparing ATB vs HD or TBI groups while ECM1 revealed a significant difference between fast and slow sputum culture converters after 2 months following treatment (p = 0.006). The expression of a four-host protein markers (CLEC3B-ECM1-IP10-SELL) was significantly different between ATB from HD or TBI groups (respectively, p < 0.05). The expression of the same signature was significantly different between the slow vs the fast sputum culture converters after 2 months of treatment (p < 0.05). The results suggest a promising 4 host-plasma marker signature that would be associated with both TB diagnostic and treatment monitoring.

Metabolic biological markers for diagnosing and monitoring the course of tuberculosis

The international biomedical community has been currently facing a need to find a simple and most accessible type of analysis that helps to diagnose tuberculosis (TB) with the maximum reliability even before the onset of clinical manifestations. Tuberculosis results in more deaths than any other pathogen, second only to pneumonia caused by the SARS-CoV-2 virus, but the majority of infected people remain asymptomatic. In addition, it is important to develop methods to distinguish various forms of tuberculosis infection course at early stages and to reliably stratify patients into appropriate groups (persons with a rapidly progressing infection, chronic course, latent infection carriers). Immunometabolism investigates a relationship between bioenergetic pathways and specific functions of immune cells that has recently become increasingly important in scientific research. The host anti-mycobacteria immune response in tuberculosis is regulated by a number of metabolic networks that can interact both cooperatively and antagonistically, influencing an outcome of the disease. The balance between inflammatory and immune reactions limits the spread of mycobacteria in vivo and protects from developing tuberculosis. Cytokines are essential for host defense, but if uncontrolled, some mediators may contribute to developing disease and pathology. Differences in plasma levels of metabolites between individuals with advanced infection, LTBI and healthy individuals can be detected long before the onset of the major related clinical signs. Changes in amino acid and cortisol level may be detected as early as 12 months before the onset of the disease and become more prominent at verifying clinical diagnosis. Assessing serum level of certain amino acids and their ratios may be used as additional diagnostic markers of active pulmonary TB. Metabolites, including serum fatty acids, amino acids and lipids may contribute to detecting active TB. Metabolic profiles indicate about increased indolamine 2.3-dioxygenase 1 (IDO1) activity, decreased phospholipase activity, increased adenosine metabolite level, and fibrous lesions in active vs. latent infection. TB treatment can be adjusted based on individual patient metabolism and biomarker profiles. Thus, exploring immunometabolism in tuberculosis is necessary for development of new therapeutic strategies.

Differential activation of innate and adaptive lymphocytes during latent or active infection with Mycobacterium tuberculosis

Most individuals infected with Mycobacterium tuberculosis (Mtb) have latent tuberculosis (TB), which can be diagnosed with tests (like the QuantiFERON test, QFT) that detect the production of IFN-γ by memory T cells in response to the Mtb-specific antigens ESAT-6, CFP-10 and TB7.7. However, the immunological mechanisms that determine if an individual will develop latent or active TB remain incompletely understood. Here we compared the response of innate and adaptive peripheral blood lymphocytes from healthy individuals without Mtb infection (QFT-negative) and from individuals with latent (QFT-positive) or active TB infection, in order to determine the characteristics of these cells that correlate with each condition. In active TB patients, the levels of IFN-γ that were produced in response to Mtb-specific antigens had high positive correlations with IL-1β, TNF-α, MCP-1, IL-6, IL-12p70 and IL-23, while the pro-inflammatory cytokines had high positive correlations between themselves and with IL-12p70 and IL-23. These correlations were not observed in QFT-negative or QFT-positive healthy volunteers. Activation with Mtb soluble extract (a mixture of Mtb antigens and pathogen-associated molecular patterns [PAMPs]) increased the percentage of IFN-γ/IL-17-producing NK cells and of IL-17-producing ILC3 in the peripheral blood of active TB patients, but not of QFT-negative or QFT-positive healthy volunteers. Thus, active TB patients have both adaptive and innate lymphocyte subsets that produce characteristic cytokine profiles in response to Mtb-specific antigens or PAMPs. These profiles are not observed in uninfected individuals or in individuals with latent TB, suggesting that they are a response to active TB infection. This article is protected by copyright. All rights reserved.

Validation of host cerebrospinal fluid protein biomarkers for early diagnosis of tuberculous meningitis in children: a replication and new biosignature discovery study

The diagnosis of tuberculous meningitis (TBM) in children is often delayed due to diagnostic difficulties. New tools are urgently needed to improve the diagnosis of the disease in this vulnerable group. The present study aimed to validate the accuracy of recently identified host cerebrospinal (CSF) biomarkers as candidates for the diagnosis of TBM in children. We collected CSF samples from 87 children aged 3 months to 13 years, that were consecutively admitted at a tertiary hospital in Cape Town, South Africa, on suspicion of having TBM. We evaluated the concentrations of 67 selected host protein biomarkers using a multiplex platform. Previously identified 3-marker (VEGF-A + IFN-γ + MPO) and 4-marker (IFN-γ + MPO + ICAM-1 + IL-8) signatures diagnosed TBM with AUCs of 0.89 (95% CI, 0.81-0.97) and 0.87 (95% CI, 0.79-0.95) respectively; sensitivities of 80.6% (95% CI, 62.5-92.5%) and 81.6% (95% CI, 65.7-92.3%), and specificities of 86.8% (71.9-95.6%) and 83.7% (70.4-92.7%) respectively. Furthermore, a new combination between the analytes (CC4b + CC4 + procalcitonin + CCL1) showed promise, with an AUC of 0.98 (95% CI, 0.94-1.00). We have shown that the accuracies of previously identified candidate CSF biomarkers for childhood TBM was reproducible. Our findings augur well for the future development of a simple bedside test for the rapid diagnosis of TBM in children.

Machine learning based on routine laboratory indicators promoting the discrimination between active tuberculosis and latent tuberculosis infection

BACKGROUND

Discriminating active tuberculosis (ATB) from latent tuberculosis infection (LTBI) remains challenging. The present study aims to evaluate the performance of diagnostic models established using machine learning based on routine laboratory indicators in differentiating ATB from LTBI.

METHODS

Participants were respectively enrolled at Tongji Hospital (discovery cohort) and Sino-French New City Hospital (validation cohort). Diagnostic models were established based on routine laboratory indicators using machine learning.

RESULTS

A total of 2619 participants (1025 ATB and 1594 LTBI) were enrolled in discovery cohort and another 942 subjects (388 ATB and 554 LTBI) were recruited in validation cohort. ATB patients had significantly higher levels of tuberculosis-specific antigen/phytohemagglutinin ratio and coefficient variation of red blood cell volume distribution width, and lower levels of albumin and lymphocyte count than those of LTBI individuals. Six models were built and the optimal performance was obtained from GBM model. GBM model derived from training set (n = 1965) differentiated ATB from LTBI in the test set (n = 654) with a sensitivity of 84.38% (95% CI, 79.42%-88.31%) and a specificity of 92.71% (95% CI, 89.73%-94.88%). Further validation by an independent cohort confirmed its encouraging value with a sensitivity of 87.63% (95% CI, 83.98%-90.54%) and specificity of 91.34% (95% CI, 88.70%-93.40%), respectively.

CONCLUSIONS

We successfully developed a model with promising diagnostic value based on machine learning for the first time. Our study proposed that GBM model may be of great benefit served as a tool for the accurate identification of ATB.

Improving Assignments for Therapeutic and Prophylactic Treatment Within TB Households. A Potential for Immuno-Diagnosis?

Delays in diagnosis and treatment of pulmonary tuberculosis (TB) can lead to more severe disease and increased transmission. Contact investigation among household contacts (HHCs) of TB patients is crucial to ensure optimal outcomes. In the context of a prospective cohort study in Palamaner, Southern India, this study attempted to assess the potential of 27 different soluble immune markers to accurately assign HHCs for appropriate treatment. A multiplex bead assay was applied on QuantiFERON (QFT)-nil supernatants collected from 89 HHCs grouped by longitudinal QFT status; M. tuberculosis (Mtb) infected (QFT positive at baseline and follow-up, n = 30), recent QFT converters (QFT-negative at baseline, n = 27) and converted to QFT-positivity within 6 months of exposure (at follow-up, n = 24) and QFT consistent negatives (n = 32). The 29 TB index cases represented Active TB. Active TB cases and HHCs with Mtb infection produced significantly different levels of both pro-inflammatory (IFNγ, IL17, IL8, IP10, MIP-1α, MIP1β, and VEGF) and anti-inflammatory (IL9 and IL1RA) cytokines. We identified a 4-protein signature (bFGF, IFNγ, IL9, and IP10) that correctly classified HHCs with Mtb infection vs. Active TB with a specificity of 92.6%, suggesting that this 4-protein signature has the potential to assign HHCs for either full-length TB treatment or preventive TB treatment. We further identified a 4-protein signature (bFGF, GCSF, IFNγ, and IL1RA) that differentiated HHCs with Mtb infection from QFT consistent negatives with a specificity of 62.5%, but not satisfactory to safely assign HHCs to no preventive TB treatment. QFT conversion, reflecting new Mtb infection, induced an elevated median concentration in nearly two-thirds (19/27) of the analyzed soluble markers compared to the levels measured at baseline. Validation in other studies is warranted in order to establish the potential of the immune biosignatures for optimized TB case detection and assignment to therapeutic and preventive treatment of Mtb infected individuals.

Concurrent evaluation of cytokines improves the accuracy of antibodies against Mycobacterium tuberculosis antigens in the diagnosis of active tuberculosis

BACKGROUND

Antibodies against mycobacterial proteins are highly specific, but lack sensitivity, whereas cytokines have been shown to be sensitive but not very specific in the diagnosis of tuberculosis (TB). We assessed combinations between antibodies and cytokines for diagnosing TB.

METHODS

Immuoglubulin (Ig) A and IgM antibody titres against selected mycobacterial antigens including Apa, NarL, Rv3019c, PstS1, LAM, "Kit 1" (MTP64 and Tpx)", and "Kit 2" (MPT64, Tpx and 19 kDa) were evaluated by ELISA in plasma samples obtained from individuals under clinical suspicion for TB. Combinations between the antibody titres and previously published cytokine responses in the same participants were assessed for diagnosing active TB.

RESULTS

Antibody responses were more promising when used in combination (AUC of 0.80), when all seven antibodies were combined. When anti-"Kit 1"-IgA levels were combined with five host cytokine biomarkers, the AUC increased to 97% (92-100%) with a sensitivity of 95% (95% CI, 73-100%), and specificity of 88.5% (95% CI, 68.7-97%) achieved after leave-one-out cross validation.

CONCLUSION

When used in combination, IgA titres measured with ELISA against multiple Mycobacterium tuberculosis antigens may be useful in the diagnosis of TB. However, diagnostic accuracy may be improved if the antibodies are used in combination with cytokines.

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.

Candidate Biomarkers to Distinguish Spinal Tuberculosis From Mechanical Back Pain in a Tuberculosis Endemic Setting

Background

Spinal tuberculosis (TB) may have a variable, non-specific presentation including back pain with- or without- constitutional symptoms. Further tools are needed to aid early diagnosis of this potentially severe form of TB and immunological biomarkers may show potential in this regard. The aim of this study was to investigate the utility of host serum biomarkers to distinguish spinal TB from mechanical back pain.

Methods

Patients with suspected spinal TB or suspected mechanical back pain were recruited from a tertiary hospital in the Western Cape, South Africa, and provided a blood sample for biomarker analysis. Diagnosis was subsequently confirmed using bacteriological testing, advanced imaging and/or clinical evaluation, as appropriate. The concentrations of 19 host biomarkers were evaluated in serum samples using the Luminex platform. Receiver Operating Characteristic (ROC) curves and General Discriminant Analysis were used to identify biomarkers with the potential to distinguish spinal TB from mechanical back pain.

Results

Twenty-six patients with spinal TB and 17 with mechanical back pain were recruited. Seven out of 19 biomarkers were significantly different between groups, of which Fibrinogen, CRP, IFN-γ and NCAM were the individual markers with the highest discrimination utility (Area Under Curve ROC plot 0.88-0.99). A five-marker biosignature (CRP, NCAM, Ferritin, CXCL8 and GDF-15) correctly classified all study participants after leave-one-out cross-validation.

Conclusion

This study identified host serum biomarkers with the potential to diagnose spinal TB, including a five-marker biosignature. These preliminary findings require validation in larger studies.

CCL1 and IL-2Ra differentiate Tuberculosis disease from latent infection Irrespective of HIV infection in low TB burden countries

OBJECTIVES

To evaluate the performance of selected host immunological biomarkers in differentiating tuberculosis (TB) disease from latent TB infection (LTBI) in HIV uninfected and infected individuals enrolled in TB low-burden countries.

DESIGN

Participants with TB disease (N = 85) and LTBI (N = 150) were recruited from prospective cohorts at hospitals in Norway and Denmark. Plasma concentrations of 54 host markers were assessed by Luminex multiplex immunoassays. Using receiver operator characteristic curves and general discriminant analysis, we determined the abilities of individual and combined biomarkers to discriminate between TB disease and LTBI including when patients were stratified according to HIV infection status.

RESULTS

Regardless of the groups compared, CCL1 and IL-2Ra were the most accurate single biomarkers in differentiating TB disease from LTBI. Regardless of HIV status, a 4-marker signature (CCL1+RANTES+CRP+MIP-1α) derived from a training set (n = 155) differentiated TB disease from LTBI in the test set (n = 67) with a sensitivity of 56.0% (95% CI, 34.9-75.6) and a specificity of 85.7% (95% CI, 71.5-94.6). A 5-marker signature derived from the HIV uninfected group (CCL1+RANTES+MIP-1α+procalcitonin+IP-10) performed in HIV-infected individuals with a sensitivity of 75.0% and a specificity of 96.7% after leave-one-out cross validation. A 2-marker signature (CCL1+TNF-α) identified in HIV-infected persons performed in HIV-uninfected with a sensitivity and specificity of 66.7% and 100% respectively in the test set.

CONCLUSIONS

Plasma CCL1 and IL-2Ra have potential as biomarkers for differentiating TB disease from LTBI in low TB burden settings unaffected by HIV infection. Combinations between these and other biomarkers in bio-signatures for global use warrant further exploration.

Prediction of Treatment Outcome with Inflammatory Biomarkers after 2 Months of Therapy in Pulmonary Tuberculosis Patients: Preliminary Results

Pro-inflammatory mediators play an important role in the pathogenesis of pulmonary tuberculosis. Consecutively, 26 pulmonary tuberculosis patients were enrolled in our study based on the exclusion criteria. We have used Spearman’s correlation analysis, hierarchical clustering and regression modelling to evaluate the association of 11 biomarkers with culture status after antituberculosis treatment. The results of our study demonstrated that six inflammatory biomarkers of 11, C-reactive protein (CRP), white blood cells (WBC), neutrophils, interferon gamma inducible protein 10, C-reactive protein (CRP) to albumin ratio (CAR) and neutrophil to albumin ratio (NAR), were significantly associated with culture negativity. The predictive ability of a composite model of seven biomarkers was superior to that of any single biomarker based on area under the receiver operating characteristic curve (AUC) analysis, indicating an excellent prediction efficacy (AUC:0.892; 95% CI:0.732-1.0). We also found that the highest significant trends and lower levels of CRP and IP-10 were observed in the two-month treated tuberculosis (TB) patients. We believe that our study may be valuable in providing preliminary results for an additional strategy in monitoring and management of the clinical outcome of pulmonary tuberculosis. Using a panel of predictors added a superior value in predicting culture status after anti-TB therapy.