Candidate biomarkers for discrimination between infection and disease caused by Mycobacterium tuberculosis

Transcriptional profiling of human peripheral blood mononuclear cells in household contacts of pulmonary tuberculosis patients provides insights into mechanisms of Mycobacterium tuberculosis control and elimination

Household contacts (HHCs) of patients with active tuberculosis (ATB) are at higher risk of Mycobacterium tuberculosis (M. tuberculosis) infection. However, the immune factors responsible for different defense responses in HHCs are unknown. Hence, we aimed to evaluate transcriptome signatures in human peripheral blood mononuclear cells (PBMCs) of HHCs to aid risk stratification. We recruited 112 HHCs of ATB patients and followed them for 6 years. Among the HHCs, only 2 developed ATB, while the remaining HHCs were classified into three groups: (1) HHC-1 group (n = 23): HHCs with consistently positive T-SPOT.TB test, negative chest radiograph, and no clinical symptoms or evidence of ATB during the 6-year follow-up period; (2) HHC-2 group (n = 15): HHCs with an initial positive T-SPOT result that later became negative without evidence of ATB; (3) HHC-3 group (n = 14): HHCs with a consistently negative T-SPOT.TB test and no clinical or radiological evidence of ATB. HHC-2 and HHC-3 were combined as HHC-23 group for analysis. RNA sequencing (RNA-seq) in PBMCs, with and without purified protein derivative (PPD) stimulation, identified significant differences in gene signatures between HHC-1 and HHC-23. Gene ontology analysis revealed functions related to bacterial pathogens, leukocyte chemotaxis, and inflammatory and cytokine responses. Modules associated with clinical features in the HHC-23 group were linked to the IL-17 signaling pathway, ferroptosis, complement and coagulation cascades, and the TNF signaling pathway. Validation using real-time PCR confirmed key genes like ATG-7, CXCL-3, and TNFRSF1B associated with infection outcomes in HHCs. Our research enhances understanding of disease mechanisms in HHCs. HHCs with persistent latent tuberculosis infection (HHC-1) showed significantly different gene expression compared to HHCs with no M. tuberculosis infection (HHC-23). These findings can help identify HHCs at risk of developing ATB and guide targeted public health interventions.

Assessment of transcriptional markers for the differentiation of Mycobacterium mungi infection status in free-ranging banded mongoose (Mungos mungo)

There is an increasingly urgent need to improve our ability to accurately forecast and control zoonotic diseases in wildlife reservoirs. We are confronted, however, with the continued challenge of accurately determining host infection status across space and time. This dilemma is epitomized with the Mycobacterium tuberculosis Complex (MTBC) pathogens and particularly in free-ranging wildlife, a critical global challenge for both human and animal health. In humans, transcriptional markers have been increasingly identified as a robust tool for diagnosing Mycobacterium tuberculosis (MTB) infection status but have rarely been utilized for diagnosing TB in free-ranging wildlife populations. Here, we report the first use of transcriptional markers to evaluate TB infection status in a free-ranging wildlife species, banded mongoose (Mungos mungo), infected with the MTBC pathogen, Mycobacterium mungi. In this study, we found that GBP5 and DUSP3 were significantly upregulated in free-ranging banded mongoose infected with M. mungi. These results provide the first step in developing an antemortem diagnostic tool for use in free-ranging wildlife species. Our results highlight the potential of transcriptional marker-based assays to advance our ability to detect and manage TB in free-ranging wildlife, especially in field studies and other scenarios when conventional diagnostics are not feasible.

Decoding differentially expressed genes to identify potential immunity associated biomarkers in Tuberculosis: An integrative bioinformatics approach

Tuberculosis (TB) poses a significant threat to the Indian population, with India accounting for 20 % of the global TB cases. The current study aims to identify molecular biomarkers for better diagnostics by comparing the transcriptome signatures of healthy individuals against TB-affected individuals. Next-generation sequencing (NGS) tools were used to identify critical differentially expressed genes (DEGs). 302 DEGs were identified based on a logFC threshold of |3| and adjusted p-value < 0.05. STRING database was used to plot the interactions amongst the 302 DEGs. The DEGs were functionally annotated, highlighting numerous physiological functions affected due to the dysregulation of the identified hub genes. TLR4, FCGR1A, ITGAM, LTF, and CXCR2 were the hub genes identified and observed to dysregulate crucial physiological functions. TLR4 has been implicated in the progression of TB in various populations, and the findings of this study will enable researchers to improve the current landscape of diagnostics for TB.

Novel RNA biomarkers improve discrimination of children with tuberculosis disease from those with non-TB pneumonia after in vitro stimulation

The diagnosis of pediatric tuberculosis (TB) poses a challenge for clinical teams worldwide. TB-mediated changes in the expression of host genes in the peripheral blood can serve as diagnostic biomarkers and can provide better insights into the host immune mechanisms of childhood TB. Peripheral blood mononuclear cells (PBMCs) from children (n=102) with microbiologically confirmed TB disease, TB infection (TBI), pneumonia, and healthy controls (HC) were stimulated with either the Purified Protein Derivative (PPD) or the Early Secretory Antigen 6kDa-Culture Filtrate Protein 10 (ESAT6-CFP10) complex of Mycobacterium tuberculosis (Mtb). RNA was extracted and quantified using gene expression microarrays. Differential expression analysis was performed comparing microbiologically confirmed TB to the other diagnostic groups for the stimulated and unstimulated samples. Using variable selection, we identified sparse diagnostic gene signatures; one gene (PID1) was able to distinguish TB from pneumonia after ESAT6-CFP10 stimulation with an AUC of 100% in the test set, while a combination of two genes (STAT1 and IFI44) achieved an AUC of 91.7% (CI95% 75.0%-100%) in the test set after PPD stimulation. The number of significantly differentially expressed (SDE) genes was higher when contrasting TB to pneumonia or HC in stimulated samples, compared to unstimulated ones, leading to a larger pool of candidate diagnostic biomarkers. Our approach provides enlightened aspects of peripheral TB-specific responses and can form the basis for a point of care test meeting the World Health Organization (WHO) Target Product Profile (TPP) for pediatric TB.

Blood transcriptomic signatures for symptomatic tuberculosis in an African multicohort study

BACKGROUND

Multiple host blood transcriptional signatures have been developed as non-sputum triage tests for tuberculosis (TB). We aimed to compare the diagnostic performance of 20 blood transcriptomic TB signatures for differentiating between symptomatic patients who have TB versus other respiratory diseases (ORD).

METHODS

As part of a nested case-control study, individuals presenting with respiratory symptoms at primary healthcare clinics in Ethiopia, Malawi, Namibia, Uganda, South Africa and The Gambia were enrolled. TB was diagnosed based on clinical, microbiological and radiological findings. Transcriptomic signatures were measured in whole blood using microfluidic real-time quantitative PCR. Diagnostic performance was benchmarked against the World Health Organization Target Product Profile (TPP) for a non-sputum TB triage test.

RESULTS

Among 579 participants, 158 had definite, microbiologically confirmed TB, 32 had probable TB, while 389 participants had ORD. Nine signatures differentiated between ORD and TB with equivalent performance (Satproedprai7: area under the curve 0.83 (95% CI 0.79-0.87); Jacobsen3: 0.83 (95% CI 0.79-0.86); Suliman2: 0.82 (95% CI 0.78-0.86); Roe1: 0.82 (95% CI 0.78-0.86); Kaforou22: 0.82 (95% CI 0.78-0.86); Sambarey10: 0.81 (95% CI 0.77-0.85); Duffy9: 0.81 (95% CI 0.76-0.86); Gliddon3: 0.8 (95% CI 0.75-0.85); Suliman4 0.79 (95% CI 0.75-0.84)). Benchmarked against a 90% sensitivity, these signatures achieved specificities between 44% (95% CI 38-49%) and 54% (95% CI 49-59%), not meeting the TPP criteria. Signature scores significantly varied by HIV status and country. In country-specific analyses, several signatures, such as Satproedprai7 and Penn-Nicholson6, met the minimal TPP criteria for a triage test in Ethiopia, Malawi and South Africa.

CONCLUSION

No signatures met the TPP criteria in a pooled analysis of all countries, but several signatures met the minimum criteria for a non-sputum TB triage test in some countries.

Comparison of gene set scoring methods for reproducible evaluation of tuberculosis gene signatures

BACKGROUND

Blood-based transcriptional gene signatures for tuberculosis (TB) have been developed with potential use to diagnose disease. However, an unresolved issue is whether gene set enrichment analysis of the signature transcripts alone is sufficient for prediction and differentiation or whether it is necessary to use the original model created when the signature was derived. Intra-method comparison is complicated by the unavailability of original training data and missing details about the original trained model. To facilitate the utilization of these signatures in TB research, comparisons between gene set scoring methods cross-data validation of original model implementations are needed.

METHODS

We compared the performance of 19 TB gene signatures across 24 transcriptomic datasets using both rrebuilt original models and gene set scoring methods. Existing gene set scoring methods, including ssGSEA, GSVA, PLAGE, Singscore, and Zscore, were used as alternative approaches to obtain the profile scores. The area under the ROC curve (AUC) value was computed to measure performance. Correlation analysis and Wilcoxon paired tests were used to compare the performance of enrichment methods with the original models.

RESULTS

For many signatures, the predictions from gene set scoring methods were highly correlated and statistically equivalent to the results given by the original models. In some cases, PLAGE outperformed the original models when considering signatures' weighted mean AUC values and the AUC results within individual studies.

CONCLUSION

Gene set enrichment scoring of existing gene sets can distinguish patients with active TB disease from other clinical conditions with equivalent or improved accuracy compared to the original methods and models. These data justify using gene set scoring methods of published TB gene signatures for predicting TB risk and treatment outcomes, especially when original models are difficult to apply or implement.

Immunopathology in human pulmonary tuberculosis: Inflammatory changes in the plasma milieu and impaired host immune cell functions

Host immune response is key for protection in tuberculosis, but the causative agent, Mycobacterium (M.) tuberculosis, manages to survive despite immune surveillance. Key mechanisms of immune protection have been identified, but the role of immunopathology in the peripheral blood of tuberculosis patients remains unclear. Tuberculosis immunopathology in the blood is characterised by patterns of immunosuppression and hyperinflammation. These seemingly contradictory findings and the pronounced heterogeneity made it difficult to interpret the results from previous studies and to derive implications of immunopathology. However, novel approaches based on comprehensive data analyses and revitalisation of an ancient plasma milieu in vitro assay connected inflammation with immunosuppressive factors in tuberculosis. Moreover, interrelations between the aberrant plasma milieu and immune cell pathology were observed. This review provides an overview of studies on changes in plasma milieu and discusses recent findings linking plasma factors to T-cell and monocyte/macrophage pathology in pulmonary tuberculosis patients.

Effect of Protein Corona on the Specificity and Efficacy of Nanobioconjugates to Treat Intracellular Infections

Encapsulating drugs into functionalized nanoparticles (NPs) is an alternative to reach the specific therapeutic target with lower doses. However, when the NPs are in contact with physiological media, proteins adsorb on their surfaces, forming a protein corona (PC) biomolecular layer, acquiring a distinct biological identity that alters their interactions with cells. Itraconazole (ITZ), an antifungal agent, is encapsulated into PEGylated and/or functionalized NPs with high specificity for macrophages. It is evaluated how the PC impacts their cell uptake and antifungal effect. The minimum inhibitory concentration and colony-forming unit assays demonstrate that encapsulated ITZ into poly(ethylene glycol) (PEG) NPs improves the antifungal effect compared with NPs lacking PEGylation. The improvement can be related to the synergistic effect of the encapsulated ITZ and NPs composition and the reduction of PC formation in PEG NPs. Functionalized NPs with anti-F4/80 and anti-MARCO antibodies, or mannose without PEG and treated with PC, show an improved uptake but, in the presence of PEG, significantly reduce the endocytosis, dominating the stealth effect from PEG. Therefore, the PC plays a crucial role in the nanosystem uptake and antifungal effects, which suggests the need for in vivo model studies to evaluate the effect of PC in the specificity and biodistribution.

Monocyte pathology in human tuberculosis is due to plasma milieu changes and aberrant STAT signalling

Monocyte-derived macrophages contribute centrally to immune protection in Mycobacterium tuberculosis infection and changes in monocyte phenotype characterize immunopathology in tuberculosis patients. Recent studies highlighted an important role of the plasma milieu in tuberculosis immunopathology. Here, we investigated monocyte pathology in patients with acute tuberculosis and determined tuberculosis plasma milieu effects on phenotype as well as cytokine signalling of reference monocytes. Patients with tuberculosis (n = 37) and asymptomatic contacts (controls n = 35) were recruited as part of a hospital-based study in the Ashanti region of Ghana. Multiplex flow cytometry phenotyping of monocyte immunopathology was performed and effects of individual blood plasma samples on reference monocytes prior to and during treatment were characterized. Concomitantly, cell signalling pathways were analysed to elucidate underlying mechanisms of plasma effects on monocytes. Multiplex flow cytometry visualization characterized changes in monocyte subpopulations and detected higher expression of CD40, CD64 and PD-L1 in monocytes from tuberculosis patients as compared to controls. Aberrant expression normalized during anti-mycobacterial treatment and also CD33 expression decreased markedly. Notably, higher CD33, CD40 and CD64 expression was induced in reference monocytes when cultured in the presence of plasma samples from tuberculosis patients as compared to controls. STAT signalling pathways were affected by the aberrant plasma milieu and higher levels of STAT3 and STAT5 phosphorylation was found in tuberculosis plasma-treated reference monocytes. Importantly, high pSTAT3 levels were associated with high CD33 expression and pSTAT5 correlated with CD40 as well as CD64 expression. These results suggested plasma milieu effects with potential implications on monocyte phenotype and function in acute tuberculosis.

Gene signature discovery and systematic validation across diverse clinical cohorts for TB prognosis and response to treatment

While blood gene signatures have shown promise in tuberculosis (TB) diagnosis and treatment monitoring, most signatures derived from a single cohort may be insufficient to capture TB heterogeneity in populations and individuals. Here we report a new generalized approach combining a network-based meta-analysis with machine-learning modeling to leverage the power of heterogeneity among studies. The transcriptome datasets from 57 studies (37 TB and 20 viral infections) across demographics and TB disease states were used for gene signature discovery and model training and validation. The network-based meta-analysis identified a common 45-gene signature specific to active TB disease across studies. Two optimized random forest regression models, using the full or partial 45-gene signature, were then established to model the continuum from Mycobacterium tuberculosis infection to disease and treatment response. In model validation, using pooled multi-cohort datasets to mimic the real-world setting, the model provides robust predictive performance for incipient to active TB risk over a 2.5-year period with an AUROC of 0.85, 74.2% sensitivity, and 78.3% specificity, which approximates the minimum criteria (>75% sensitivity and >75% specificity) within the WHO target product profile for prediction of progression to TB. Moreover, the model strongly discriminates active TB from viral infection (AUROC 0.93, 95% CI 0.91-0.94). For treatment monitoring, the TB scores generated by the model statistically correlate with treatment responses over time and were predictive, even before treatment initiation, of standard treatment clinical outcomes. We demonstrate an end-to-end gene signature model development scheme that considers heterogeneity for TB risk estimation and treatment monitoring.

Identification of immune biomarkers in recent active pulmonary tuberculosis

Tuberculosis (TB) has remained an unsolved problem and a major public health issue, particularly in developing countries. Pakistan is one of the countries with the highest tuberculosis infection rates globally. However, methods or biomarkers to detect early signs of TB infection are limited. Here, we characterized the mRNA profiles of immune responses in unstimulated Peripheral blood mononuclear cells obtained from treatment naïve patients with early signs of active pulmonary tuberculosis without previous history of clinical TB. We identified a unique mRNA profile in active TB compared to uninfected controls, including cytokines such as IL-27, IL-15, IL-2RA, IL-24, and TGFβ, transcription factors such as STAT1 and NFATC1 and immune markers/receptors such as TLR4, IRF1, CD80, CD28, and PTGDR2 from an overall 84 different transcripts analyzed. Among 12 significant differentially expressed transcripts, we identified five gene signatures which included three upregulated IL-27, STAT1, TLR4 and two downregulated IL-24 and CD80 that best discriminate between active pulmonary TB and uninfected controls with AUC ranging from 0.9 to 1. Our data identified a molecular immune signature associated with the early stages of active pulmonary tuberculosis and it could be further investigated as a potential biomarker of pulmonary TB.

Downregulation of monocyte miRNAs: implications for immune dysfunction and disease severity in drug-resistant tuberculosis

Background

Monocyte miRNAs govern both protective and pathological responses during tuberculosis (TB) through their differential expression and emerged as potent targets for biomarker discovery and host-directed therapeutics. Thus, this study examined the miRNA profile of sorted monocytes across the TB disease spectrum [drug-resistant TB (DR-TB), drug-sensitive TB (DS-TB), and latent TB] and in healthy individuals (HC) to understand the underlying pathophysiology and their regulatory mechanism.

Methods

We sorted total monocytes including three subsets (HLA-DR⁺CD14⁺, HLA-DR⁺CD14⁺CD16⁺, and HLA-DR⁺CD16⁺cells) from peripheral blood mononuclear cells (PBMCs) of healthy and TB-infected individuals through flow cytometry and subjected them to NanoString-based miRNA profiling.

Results

The outcome was the differential expression of 107 miRNAs particularly the downregulation of miRNAs in the active TB groups (both drug-resistant and drug-sensitive). The miRNA profile revealed differential expression signatures: i) decline of miR-548m in DR-TB alone, ii) decline of miR-486-3p in active TB but significant elevation only in LTB iii) elevation of miR-132-3p only in active TB (DR-TB and DS-TB) and iv) elevation of miR-150-5p in DR-TB alone. The directionality of functions mediated by monocyte miRNAs from Gene Set Enrichment Analysis (GSEA) facilitated two phenomenal findings: i) a bidirectional response between active disease (activation profile in DR-TB and DS-TB compared to LTB and HC) and latent infection (suppression profile in LTB vs HC) and ii) hyper immune activation in the DR-TB group compared to DS-TB.

Conclusion

Thus, monocyte miRNA signatures provide pathological clues for altered monocyte function, drug resistance, and disease severity. Further studies on monocyte miRNAs may shed light on the immune regulatory mechanism for tuberculosis.

Management of Tuberculosis Infection: Current Situation, Recent Developments and Operational Challenges

Tuberculosis infection (TBI) is defined as a state of infection in which individuals host live Mycobacterium tuberculosis with or without clinical signs of active TB. It is now understood as a dynamic process covering a spectrum of responses to infection resulting from the interaction between the TB bacilli and the host immune system. The global burden of TBI is about one-quarter of the world’s population, representing a reservoir of approximately 2 billion people. On average, 5–10% of people who are infected will develop TB disease over the course of their lives, but this risk is enhanced in a series of conditions, such as co-infection with HIV. The End-TB strategy promotes the programmatic management of TBI as a crucial endeavor to achieving global targets to end the TB epidemic. The current development of new diagnostic tests capable of discriminating between simple TBI and active TB, combined with novel short-course preventive treatments, will help achieve this goal. In this paper, we present the current situation and recent developments of management of TBI and the operational challenges.

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.

Bioinformatic Analyses of Peripheral Blood Transcriptome Identify Altered Neutrophil-Related Pathway and Different Transcriptomic Profiles for Acute Pancreatitis in Patients with and without Chylomicronemia Syndrome

Acute pancreatitis (AP) is a serious inflammatory condition of the pancreas that can be associated with chylomicronemia syndrome (CS). Currently, no study has explored the differences between non-CS-associated AP and CS-associated AP in terms of gene expression. Transcriptomic profiles of blood samples from patients with AP were retrieved from GSE194331 (non-CS-associated) and GSE149607 (CS-associated). GSE31568 was used to examine the linkage between non-CS-associated AP and the expression of micro RNAs (miRNAs). Differentially expressed genes (DEGs) were identified, a gene regulatory network was constructed, and hub genes were defined. Subsequently, single-sample gene set enrichment analysis (ssGSEA) scores of hub genes were calculated to represent their regulatory-level activity. A total of 1851 shared DEGs were identified between non-CS-associated and CS-associated AP. Neutrophils were significantly enriched in both conditions. In non-CS-associated AP, miRNAs including hsa-miR-21, hsa-miR-146a, and hsa-miR-106a demonstrated a lower expression level as compared with the healthy control. Furthermore, the expression patterns and regulatory activities were largely opposite between non-CS-associated and CS-associated AP, with significantly lower estimated neutrophils in the latter case. In summary, we found that the regulation of neutrophils was altered in AP. There was a different gene expression pattern and lower estimated neutrophil infiltration in CS-associated AP. Whether these findings are clinically significant requires further investigation.

Comparison of gene set scoring methods for reproducible evaluation of multiple tuberculosis gene signatures

Rationale

Many blood-based transcriptional gene signatures for tuberculosis (TB) have been developed with potential use to diagnose disease, predict risk of progression from infection to disease, and monitor TB treatment outcomes. However, an unresolved issue is whether gene set enrichment analysis (GSEA) of the signature transcripts alone is sufficient for prediction and differentiation, or whether it is necessary to use the original statistical model created when the signature was derived. Intra-method comparison is complicated by the unavailability of original training data, missing details about the original trained model, and inadequate publicly-available software tools or source code implementing models. To facilitate these signatures' replicability and appropriate utilization in TB research, comprehensive comparisons between gene set scoring methods with cross-data validation of original model implementations are needed.

Objectives

We compared the performance of 19 TB gene signatures across 24 transcriptomic datasets using both re-rebuilt original models and gene set scoring methods to evaluate whether gene set scoring is a reasonable proxy to the performance of the original trained model. We have provided an open-access software implementation of the original models for all 19 signatures for future use.

Methods

We considered existing gene set scoring and machine learning methods, including ssGSEA, GSVA, PLAGE, Singscore, and Zscore, as alternative approaches to profile gene signature performance. The sample-size-weighted mean area under the curve (AUC) value was computed to measure each signature's performance across datasets. Correlation analysis and Wilcoxon paired tests were used to analyze the performance of enrichment methods with the original models.

Measurement and Main Results

For many signatures, the predictions from gene set scoring methods were highly correlated and statistically equivalent to the results given by the original diagnostic models. PLAGE outperformed all other gene scoring methods. In some cases, PLAGE outperformed the original models when considering signatures' weighted mean AUC values and the AUC results within individual studies.

Conclusion

Gene set enrichment scoring of existing blood-based biomarker gene sets can distinguish patients with active TB disease from latent TB infection and other clinical conditions with equivalent or improved accuracy compared to the original methods and models. These data justify using gene set scoring methods of published TB gene signatures for predicting TB risk and treatment outcomes, especially when original models are difficult to apply or implement.

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.

Taming the Devil: Antimicrobial Peptides for Safer TB Therapeutics

Tuberculosis (TB) is a highly contagious infection with extensive mortality and morbidity. The rise of TB-superbugs (drug-resistant strains) with the increase of their resistance to conventional antibiotics has prompted a further search for new anti-mycobacterial agents. It is difficult to breach the barriers around TB bacteria, including mycolic cell wall, granuloma, biofilm and mucus, by conventional antibiotics in a short span of time. Hence, there is an essential need for molecules with an unconventional mode of action and structure that can efficiently break the barriers around mycobacterium. Antimicrobial peptides (AMP) are essential components of innate immunity having cationic and amphipathic characteristics. Lines of evidence show that AMPs have good myco-bactericidal and antibiofilm activity against normal as well as antibiotic-resistant TB bacteria. These peptides have shown direct killing of bacteria by membrane lysis and indirect killing by activation of innate immune response in host cells by interacting with the component of the bacterial membrane and intracellular targets through diverse mechanisms. Despite a good anti-mycobacterial activity, some undesirable characteristics are also associated with AMP, including hemolysis, cytotoxicity, susceptibility to proteolysis and poor pharmacokinetic profile, and hence only a few clinical studies have been conducted with these biomolecules. The design of new combinatorial therapies, including AMPs and particulate drug delivery systems, could be new potential alternatives to conventional antibiotics to fight MDR- and XDRTB. This review outlined the array of AMP roles in TB therapy, possible mechanisms of actions, activities, and current advances in pragmatic strategies to improve challenges accompanying the delivery of AMP for tuberculosis therapeutics.

Development and Validation of a Parsimonious Tuberculosis Gene Signature Using the digital NanoString nCounter Platform

BACKGROUND

Blood-based biomarkers for diagnosing active tuberculosis (TB), monitoring treatment response, and predicting risk of progression to TB disease have been reported. However, validation of the biomarkers across multiple independent cohorts is scarce. A robust platform to validate TB biomarkers in different populations with clinical end points is essential to the development of a point-of-care clinical test. NanoString nCounter technology is an amplification-free digital detection platform that directly measures mRNA transcripts with high specificity. Here, we determined whether NanoString could serve as a platform for extensive validation of candidate TB biomarkers.

METHODS

The NanoString platform was used for performance evaluation of existing TB gene signatures in a cohort in which signatures were previously evaluated on an RNA-seq dataset. A NanoString codeset that probes 107 genes comprising 12 TB signatures and 6 housekeeping genes (NS-TB107) was developed and applied to total RNA derived from whole blood samples of TB patients and individuals with latent TB infection (LTBI) from South India. The TBSignatureProfiler tool was used to score samples for each signature. An ensemble of machine learning algorithms was used to derive a parsimonious biomarker.

RESULTS

Gene signatures present in NS-TB107 had statistically significant discriminative power for segregating TB from LTBI. Further analysis of the data yielded a NanoString 6-gene set (NANO6) that when tested on 10 published datasets was highly diagnostic for active TB.

CONCLUSIONS

The NanoString nCounter system provides a robust platform for validating existing TB biomarkers and deriving a parsimonious gene signature with enhanced diagnostic performance.

Differential Frequencies of Intermediate Monocyte Subsets Among Individuals Infected With Drug-Sensitive or Drug-Resistant Mycobacterium tuberculosis

The rampant increase in drug-resistant tuberculosis (TB) remains a major challenge not only for treatment management but also for diagnosis, as well as drug design and development. Drug-resistant mycobacteria affect the quality of life owing to the delayed diagnosis and require prolonged treatment with multiple and toxic drugs. The phenotypic modulations defining the immune status of an individual during tuberculosis are well established. The present study aims to explore the phenotypic changes of monocytes &amp; dendritic cells (DC) as well as their subsets across the TB disease spectrum, from latency to drug-sensitive TB (DS-TB) and drug-resistant TB (DR-TB) using traditional immunophenotypic analysis and by uniform manifold approximation and projection (UMAP) analysis. Our results demonstrate changes in frequencies of monocytes (classical, CD14++CD16-, intermediate, CD14++CD16+ and non-classical, CD14+/-CD16++) and dendritic cells (DC) (HLA-DR+CD11c+ myeloid DCs, cross-presenting HLA-DR+CD14-CD141+ myeloid DCs and HLA-DR+CD14-CD16-CD11c-CD123+ plasmacytoid DCs) together with elevated Monocyte to Lymphocyte ratios (MLR)/Neutrophil to Lymphocyte ratios (NLR) and alteration of cytokine levels between DS-TB and DR-TB groups. UMAP analysis revealed significant differential expression of CD14+, CD16+, CD86+ and CD64+ on monocytes and CD123+ on DCs by the DR-TB group. Thus, our study reveals differential monocyte and DC subset frequencies among the various TB disease groups towards modulating the immune responses and will be helpful to understand the pathogenicity driven by Mycobacterium tuberculosis.

Latent Tuberculosis Infection Diagnosis among Household Contacts in a High Tuberculosis-Burden Area: a Comparison between Transcript Signature and Interferon Gamma Release Assay

Diagnosis of latent tuberculosis infection (LTBI) using biomarkers in order to identify the risk of progressing to active TB and therefore predicting a preventive therapy has been the main bottleneck in eradication of tuberculosis. We compared two assays for the diagnosis of LTBI: transcript signatures and interferon gamma release assay (IGRA), among household contacts (HHCs) in a high tuberculosis-burden population. HHCs of active TB cases were recruited for our study; these were confirmed to be clinically negative for active TB disease. Eighty HHCs were screened by IGRA using QuantiFERON-TB Gold Plus (QFT-Plus) to identify LTBI and uninfected cohorts; further, quantitative levels of transcript for selected six genes (TNFRSF10C, ASUN, NEMF, FCGR1B, GBP1, and GBP5) were determined. Machine learning (ML) was used to construct models of different gene combinations, with a view to identify hidden but significant underlying patterns of their transcript levels. Forty-three HHCs were found to be IGRA positive (LTBI) and thirty-seven were IGRA negative (uninfected). FCGR1B, GBP1, and GBP5 transcripts differentiated LTBI from uninfected among HHCs using Livak method. ML and ROC (Receiver Operator Characteristic) analysis validated this transcript signature to have a specificity of 72.7%. In this study, we compared a quantitative transcript signature with IGRA to assess the diagnostic ability of the two, for detection of LTBI cases among HHCs of a high-TB burden population; we concluded that a three gene (FCGR1B, GBP1, and GBP5) transcript signature can be used as a biomarker for rapid screening. IMPORTANCE The study compares potential of transcript signature and IGRA to diagnose LTBI. It is first of its kind study to screen household contacts (HHCs) in high TB burden area of India. A transcript signature (FCGR1B, GBP1, & GBP5) is identified as potential biomarker for LTBI. These results can lead to development of point-of-care (POC) like device for LTBI screening in a high TB burdened area.

Contribution and Future of High-Throughput Transcriptomics in Battling Tuberculosis

While Tuberculosis (TB) infection remains a serious challenge worldwide, big data and “omic” approaches have greatly contributed to the understanding of the disease. Transcriptomics have been used to tackle a wide variety of queries including diagnosis, treatment evolution, latency and reactivation, novel target discovery, vaccine response or biomarkers of protection. Although a powerful tool, the elevated cost and difficulties in data interpretation may hinder transcriptomics complete potential. Technology evolution and collaborative efforts among multidisciplinary groups might be key in its exploitation. Here, we discuss the main fields explored in TB using transcriptomics, and identify the challenges that need to be addressed for a real implementation in TB diagnosis, prevention and therapy.

Differential Diagnosis of Latent Tuberculosis Infection and Active Tuberculosis: A Key to a Successful Tuberculosis Control Strategy

As an ancient infectious disease, tuberculosis (TB) is still the leading cause of death from a single infectious agent worldwide. Latent TB infection (LTBI) has been recognized as the largest source of new TB cases and is one of the biggest obstacles to achieving the aim of the End TB Strategy. The latest data indicate that a considerable percentage of the population with LTBI and the lack of differential diagnosis between LTBI and active TB (aTB) may be potential reasons for the high TB morbidity and mortality in countries with high TB burdens. The tuberculin skin test (TST) has been used to diagnose TB for > 100 years, but it fails to distinguish patients with LTBI from those with aTB and people who have received Bacillus Calmette–Guérin vaccination. To overcome the limitations of TST, several new skin tests and interferon-gamma release assays have been developed, such as the Diaskintest, C-Tb skin test, EC-Test, and T-cell spot of the TB assay, QuantiFERON-TB Gold In-Tube, QuantiFERON-TB Gold-Plus, LIAISON QuantiFERON-TB Gold Plus test, and LIOFeron TB/LTBI. However, these methods cannot distinguish LTBI from aTB. To investigate the reasons why all these methods cannot distinguish LTBI from aTB, we have explained the concept and definition of LTBI and expounded on the immunological mechanism of LTBI in this review. In addition, we have outlined the research status, future directions, and challenges of LTBI differential diagnosis, including novel biomarkers derived from Mycobacterium tuberculosis and hosts, new models and algorithms, omics technologies, and microbiota.

Lower IL-7 Receptor Expression of Monocytes Impairs Antimycobacterial Effector Functions in Patients with Tuberculosis

Altered monocyte differentiation and effector functions characterize immune pathogenesis of tuberculosis. IL-7 is an important factor for proliferation of T cells and impaired IL-7 sensitivity due to decreased IL-7 receptor α-chain (IL-7Rα) expression was found in patients with acute tuberculosis. Peripheral blood monocytes have moderate IL-7Rα expression and increased IL-7Rα levels were described for inflammatory diseases. In this study, we investigated a potential role of IL-7 and IL-7Rα expression for monocyte functions in tuberculosis. We analyzed the phenotype of monocytes in the blood from tuberculosis patients (n = 33), asymptomatic contacts of tuberculosis patients (contacts; n = 30), and healthy controls (n = 20) from Ghana by multicolor flow cytometry. Mycobacterial components were analyzed for their capacity to induce IL-7Rα expression in monocytes. Functional effects of monocyte to IL-7 were measured during signaling and by using an antimycobacterial in vitro kill assay. Monocytes were more frequent in peripheral blood from patients with tuberculosis and especially higher proportions of CD14⁺/CD16⁺ (M1/2) monocytes with increased PD-L1 expression characterized acute tuberculosis. IL-7Rα expression was decreased particularly on M1/2 monocytes from patients with tuberculosis and aberrant low expression IL-7Rα correlated with high PD-L1 levels. Constitutive low pSTAT5 levels of monocytes ex vivo and impaired IL-7 response confirmed functionally decreased monocyte IL-7 sensitivity of patients with tuberculosis. Mycobacteria and mycobacterial cell wall components induced IL-7 receptor expression in monocytes and IL-7 boosted mycobacterial killing by monocyte-derived macrophages in vitro. We demonstrated impaired monocyte IL-7 receptor expression as well as IL-7 sensitivity in tuberculosis with potential effects on antimycobacterial effector functions.

A Parsimonious Host Inflammatory Biomarker Signature Predicts Incident Tuberculosis and Mortality in Advanced Human Immunodeficiency Virus

BACKGROUND

People with advanced human immunodeficiency virus (HIV) (CD4 < 50) remain at high risk of tuberculosis (TB) or death despite the initiation of antiretroviral therapy (ART). We aimed to identify immunological profiles that were most predictive of incident TB disease and death.

METHODS

The REMEMBER randomized clinical trial enrolled 850 participants with HIV (CD4 < 50 cells/µL) at ART initiation to receive either empiric TB treatment or isoniazid preventive therapy (IPT). A case-cohort study (n = 257) stratified by country and treatment arm was performed. Cases were defined as incident TB or all-cause death within 48 weeks after ART initiation. Using multiplexed immunoassay panels and ELISA, 26 biomarkers were assessed in plasma.

RESULTS

In total, 52 (6.1%) of 850 participants developed TB; 47 (5.5%) died (13 of whom had antecedent TB). Biomarkers associated with incident TB overlapped with those associated with death (interleukin [IL]-1β, IL-6). Biomarker levels declined over time in individuals with incident TB while remaining persistently elevated in those who died. Dividing the cohort into development and validation sets, the final model of 6 biomarkers (CXCL10, IL-1β, IL-10, sCD14, tumor necrosis factor [TNF]-α, and TNF-β) achieved a sensitivity of 0.90 (95% confidence interval [CI]: .87-.94) and a specificity of 0.71(95% CI: .68-.75) with an area under the curve (AUC) of 0.81 (95% CI: .78-.83) for incident TB.

CONCLUSION

Among people with advanced HIV, a parsimonious inflammatory biomarker signature predicted those at highest risk for developing TB despite initiation of ART and TB preventive therapies. The signature may be a promising stratification tool to select patients who may benefit from increased monitoring and novel interventions.

CLINICAL TRIALS REGISTRATION

NCT01380080.

Antimicrobial Peptides as Potential Anti-Tubercular Leads: A Concise Review

Despite being considered a public health emergency for the last 25 years, tuberculosis (TB) is still one of the deadliest infectious diseases, responsible for over a million deaths every year. The length and toxicity of available treatments and the increasing emergence of multidrug-resistant strains of Mycobacterium tuberculosis renders standard regimens increasingly inefficient and emphasizes the urgency to develop new approaches that are not only cost- and time-effective but also less toxic. Antimicrobial peptides (AMP) are small cationic and amphipathic molecules that play a vital role in the host immune system by acting as a first barrier against invading pathogens. The broad spectrum of properties that peptides possess make them one of the best possible alternatives for a new “post-antibiotic” era. In this context, research into AMP as potential anti-tubercular agents has been driven by the increasing danger revolving around the emergence of extremely-resistant strains, the innate resistance that mycobacteria possess and the low compliance of patients towards the toxic anti-TB treatments. In this review, we will focus on AMP from various sources, such as animal, non-animal and synthetic, with reported inhibitory activity towards Mycobacterium tuberculosis.

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

Tuberculosis (TB) remains a major global threat and diagnosis of active TB ((ATB) both extra-pulmonary (EPTB), pulmonary (PTB)) and latent TB (LTBI) infection remains challenging, particularly in high-burden countries which still rely heavily on conventional methods. Although molecular diagnostic methods are available, e.g., Cepheid GeneXpert, they are not universally available in all high TB burden countries. There is intense focus on immune biomarkers for use in TB diagnosis, which could provide alternative low-cost, rapid diagnostic solutions. In our previous gene expression studies, we identified peripheral blood leukocyte (PBL) mRNA biomarkers in a non-human primate TB aerosol-challenge model. Here, we describe a study to further validate select mRNA biomarkers from this prior study in new cohorts of patients and controls, as a prerequisite for further development. Whole blood mRNA was purified from ATB patients recruited in the UK and India, LTBI and two groups of controls from the UK (i) a low TB incidence region (CNTRLA) and (ii) individuals variably-domiciled in the UK and Asia ((CNTRLB), the latter TB high incidence regions). Seventy-two mRNA biomarker gene targets were analyzed by qPCR using the Roche Lightcycler 480 qPCR platform and data analyzed using GeneSpring™ 14.9 bioinformatics software. Differential expression of fifty-three biomarkers was confirmed between MTB infected, LTBI groups and controls, seventeen of which were significant using analysis of variance (ANOVA): CALCOCO2, CD52, GBP1, GBP2, GBP5, HLA-B, IFIT3, IFITM3, IRF1, LOC400759 (GBP1P1), NCF1C, PF4V1, SAMD9L, S100A11, TAF10, TAPBP, and TRIM25. These were analyzed using receiver operating characteristic (ROC) curve analysis. Single biomarkers and biomarker combinations were further assessed using simple arithmetic algorithms. Minimal combination biomarker panels were delineated for primary diagnosis of ATB (both PTB and EPTB), LTBI and identifying LTBI individuals at high risk of progression which showed good performance characteristics. These were assessed for suitability for progression against the standards for new TB diagnostic tests delineated in the published World Health Organization (WHO) technology product profiles (TPPs).

Identification of Reduced Host Transcriptomic Signatures for Tuberculosis Disease and Digital PCR-Based Validation and Quantification

Recently, host whole blood gene expression signatures have been identified for diagnosis of tuberculosis (TB). Absolute quantification of the concentrations of signature transcripts in blood have not been reported, but would facilitate diagnostic test development. To identify minimal transcript signatures, we applied a transcript selection procedure to microarray data from African adults comprising 536 patients with TB, other diseases (OD) and latent TB (LTBI), divided into training and test sets. Signatures were further investigated using reverse transcriptase (RT)-digital PCR (dPCR). A four-transcript signature (GBP6, TMCC1, PRDM1, and ARG1) measured using RT-dPCR distinguished TB patients from those with OD (area under the curve (AUC) 93.8% (CI_95% 82.2-100%). A three-transcript signature (FCGR1A, ZNF296, and C1QB) differentiated TB from LTBI (AUC 97.3%, CI_95%: 93.3-100%), regardless of HIV. These signatures have been validated across platforms and across samples offering strong, quantitative support for their use as diagnostic biomarkers for TB.

B-Cells and Antibodies as Contributors to Effector Immune Responses in Tuberculosis

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), is still a major threat to mankind, urgently requiring improved vaccination and therapeutic strategies to reduce TB-disease burden. Most present vaccination strategies mainly aim to induce cell-mediated immunity (CMI), yet a series of independent studies has shown that B-cells and antibodies (Abs) may contribute significantly to reduce the mycobacterial burden. Although early studies using B-cell knock out animals did not support a major role for B-cells, more recent studies have provided new evidence that B-cells and Abs can contribute significantly to host defense against Mtb. B-cells and Abs exist in many different functional subsets, each equipped with unique functional properties. In this review, we will summarize current evidence on the contribution of B-cells and Abs to immunity toward Mtb, their potential utility as biomarkers, and their functional contribution to Mtb control.

Human tuberculosis and Mycobacterium tuberculosis complex: A review on genetic diversity, pathogenesis and omics approaches in host biomarkers discovery

Mycobacterium tuberculosis complex (MTBC) refers to a group of mycobacteria encompassing nine members of closely related species that causes tuberculosis in animals and humans. Among the nine members, Mycobacterium tuberculosis (M. tuberculosis) remains the main causative agent for human tuberculosis that results in high mortality and morbidity globally. In general, MTBC species are low in diversity but exhibit distinctive biological differences and phenotypes among different MTBC lineages. MTBC species are likely to have evolved from a common ancestor through insertions/deletions processes resulting in species speciation with different degrees of pathogenicity. The pathogenesis of human tuberculosis is complex and remains poorly understood. It involves multi-interactions or evolutionary co-options between host factors and bacterial determinants for survival of the MTBC. Granuloma formation as a protection or survival mechanism in hosts by MTBC remains controversial. Additionally, MTBC species are capable of modulating host immune response and have adopted several mechanisms to evade from host immune attack in order to survive in humans. On the other hand, current diagnostic tools for human tuberculosis are inadequate and have several shortcomings. Numerous studies have suggested the potential of host biomarkers in early diagnosis of tuberculosis, in disease differentiation and in treatment monitoring. "Multi-omics" approaches provide holistic views to dissect the association of MTBC species with humans and offer great advantages in host biomarkers discovery. Thus, in this review, we seek to understand how the genetic variations in MTBC lead to species speciation with different pathogenicity. Furthermore, we also discuss how the host and bacterial players contribute to the pathogenesis of human tuberculosis. Lastly, we provide an overview of the journey of "omics" approaches in host biomarkers discovery in human tuberculosis and provide some interesting insights on the challenges and directions of "omics" approaches in host biomarkers innovation and clinical implementation.

Human gene expression profiling identifies key therapeutic targets in tuberculosis infection: A systematic network meta-analysis

Tuberculosis (TB) is one of the deadliest diseases since ancient times and is still a global health problem. So, there is a need to develop new approaches for early detection of TB and understand the host-pathogen relationship. In the present study, we have analyzed microarray data sets and compared the transcriptome profiling of the healthy individual with latent infection (LTBI) and active TB (TB) patients, and identified the differentially expressed genes (DEGs). Next, we performed the systematic network meta-analysis of the DEGs, which identified the seven most influencing hub genes (IL6, IL1B, TNF, NFKB1, STAT1, JAK2, and MAPK8) as the potential therapeutic target in the tuberculosis disease. These target genes are involved in many biological processes like cell cycle control, apoptosis, complement signalling, enhanced cytokine & chemokine signalling, pro-inflammatory responses, and host immune responses. Additionally, we also identified 22 inferred genes that are mainly engaged in the induction of innate immune response, cellular response to interleukin-6, inflammatory response, apoptotic process, I-kappaB-phosphorylation, JAK-STAT signalling pathway, macrophage activation, cell growth, and cell signalling. The proper attention of these inferred genes may open up a new horizon to understand the defensive mechanisms of TB disease. The transcriptome profiling and network approach can enhance the understanding of the molecular pathogenesis of tuberculosis infection and have implications for the plan and execution of mRNA expression tools to support early diagnostics and treatment of Mycobacterium tuberculosis (M.tb).

Whole blood mRNA expression-based targets to discriminate active tuberculosis from latent infection and other pulmonary diseases

Current diagnostic tests for tuberculosis (TB) are not able to predict reactivation disease progression from latent TB infection (LTBI). The main barrier to predicting reactivation disease is the lack of our understanding of host biomarkers associated with progression from latent infection to active disease. Here, we applied an immune-based gene expression profile by NanoString platform to identify whole blood markers that can distinguish active TB from other lung diseases (OPD), and that could be further evaluated as a reactivation TB predictor. Among 23 candidate genes that differentiated patients with active TB from those with OPD, nine genes (CD274, CEACAM1, CR1, FCGR1A/B, IFITM1, IRAK3, LILRA6, MAPK14, PDCD1LG2) demonstrated sensitivity and specificity of 100%. Seven genes (C1QB, C2, CCR2, CCRL2, LILRB4, MAPK14, MSR1) distinguished TB from LTBI with sensitivity and specificity between 82 and 100%. This study identified single gene candidates that distinguished TB from OPD and LTBI with high sensitivity and specificity (both > 82%), which may be further evaluated as diagnostic for disease and as predictive markers for reactivation TB.

Differential expression of CD64 in patients with Mycobacterium tuberculosis infection: A potential biomarker for clinical diagnosis and prognosis

To evaluate the clinical utility of neutrophil (n)CD64 index to diagnose pulmonary tuberculosis (PTB) and extrapulmonary TB (ePTB) and to predict the outcome of Mycobacterium tuberculosis infection. We recruited 189 patients with active TB and 140 controls and measured the differential expression of nCD64 index using flow cytometry. The receiver operating characteristics (ROC) curve analysis was performed to estimate the diagnostic performance of the nCD64 index and T‐SPOT.TB assay for the diagnosis of TB. Furthermore, we analysed whether the nCD64 index in patients with TB was correlated with inflammatory indicators. Finally, we assessed the prognosis of patients by following the dynamic changes of the nCD64 index once a week. The nCD64 index was significantly higher in active TB group (PTB and ePTB), than in the anti‐TB and healthy controls (HC) groups. The sensitivity and specificity of nCD64 index for the differential diagnosis of PTB and pneumonia (PN) patients were 68.33% and 77.55%, respectively. The sensitivity and specificity of nCD64 index for the diagnosis of tuberculous meningitis (TBM) were 53.85% and 100%, respectively. Furthermore, there was a weak correlation between the nCD64 index and inflammatory indicators. More importantly, with the improvement in patient condition, the nCD64 index started to decline in the first week of anti‐TB therapy and significantly decreased at 4 weeks after treatment. Our study demonstrated that the CD64 assay is a rapid, non‐invasive and stable method for clinical application, and the nCD64 index can serve as a potential biomarker for the diagnosis and prognosis of TB.

Blood M2a monocyte polarization and increased formyl peptide receptor 1 expression are associated with progression from latent tuberculosis infection to active pulmonary tuberculosis disease

OBJECTIVES

This study aims to explore the role of M2a polarization and formyl peptide receptor (FPR) regulation in the reactivation of Mycobacterium tuberculosis (Mtb) infection.

METHODS

M1/M2a monocyte percentage and FPR1/2/3 protein expression of blood immune cells were measured in 38 patients with sputum culture (+) active pulmonary TB disease, 18 subjects with latent TB infection (LTBI), and 28 noninfected healthy subjects (NIHS) using flow cytometry method.

RESULTS

M1 percentage was decreased in active TB versus either NIHS or LTBI group, while M2a percentage and M2a/M1 percentage ratio were increased. FPR1 expression on M1/M2a, FPR2 expression on M1, and FPR3 expression of M1 were all decreased in active TB versus LTBI group, while FPR1 over FPR2 expression ratio on NK T cell was increased in active TB versus either NIHS or LTBI group. In 11 patients with active TB disease, M1 percentage became normal again after anti-TB treatment. In vitro Mtb-specific antigen stimulation of monocytic THP-1 cells resulted in M2a polarization in association with increased FPR2 expression on M2a.

CONCLUSIONS

Increased M2a and decreased M1 phenotypes of blood monocyte may serve as a marker for active TB disease, while decreased FPR1 on blood monocyte may indicate LTBI status.

Performance of diagnostic and predictive host blood transcriptomic signatures for Tuberculosis disease: A systematic review and meta-analysis

INTRODUCTION

Host blood transcriptomic biomarkers have potential as rapid point-of-care triage, diagnostic, and predictive tests for Tuberculosis disease. We aimed to summarise the performance of host blood transcriptomic signatures for diagnosis of and prediction of progression to Tuberculosis disease; and compare their performance to the recommended World Health Organisation target product profile.

METHODS

A systematic review and meta-analysis of the performance of host blood mRNA signatures for diagnosing and predicting progression to Tuberculosis disease in HIV-negative adults and adolescents, in studies with an independent validation cohort. Medline, Scopus, Web of Science, and EBSCO libraries were searched for articles published between January 2005 and May 2019, complemented by a search of bibliographies. Study selection, data extraction and quality assessment were done independently by two reviewers. Meta-analysis was performed for signatures that were validated in ≥3 comparable cohorts, using a bivariate random effects model.

RESULTS

Twenty studies evaluating 25 signatures for diagnosis of or prediction of progression to TB disease in a total of 68 cohorts were included. Eighteen studies evaluated 24 signatures for TB diagnosis and 17 signatures met at least one TPP minimum performance criterion. Three diagnostic signatures were validated in clinically relevant cohorts to differentiate TB from other diseases, with pooled sensitivity 84%, 87% and 90% and pooled specificity 79%, 88% and 74%, respectively. Four studies evaluated signatures for progression to TB disease and performance of one signature, assessed within six months of TB diagnosis, met the minimal TPP for a predictive test for progression to TB disease.

CONCLUSION

Host blood mRNA signatures hold promise as triage tests for TB. Further optimisation is needed if mRNA signatures are to be used as standalone diagnostic or predictive tests for therapeutic decision-making.

An RNA-seq Based Machine Learning Approach Identifies Latent Tuberculosis Patients With an Active Tuberculosis Profile

A better understanding of the response against Tuberculosis (TB) infection is required to accurately identify the individuals with an active or a latent TB infection (LTBI) and also those LTBI patients at higher risk of developing active TB. In this work, we have used the information obtained from studying the gene expression profile of active TB patients and their infected –LTBI- or uninfected –NoTBI- contacts, recruited in Spain and Mozambique, to build a class-prediction model that identifies individuals with a TB infection profile. Following this approach, we have identified several genes and metabolic pathways that provide important information of the immune mechanisms triggered against TB infection. As a novelty of our work, a combination of this class-prediction model and the direct measurement of different immunological parameters, was used to identify a subset of LTBI contacts (called TB-like) whose transcriptional and immunological profiles are suggestive of infection with a higher probability of developing active TB. Validation of this novel approach to identifying LTBI individuals with the highest risk of active TB disease merits further longitudinal studies on larger cohorts in TB endemic areas.

A blood RNA transcript signature for TB exposure in household contacts

Background

Current tools for diagnosing latent TB infection (LTBI) detect immunological memory of past exposure but are unable to determine whether exposure is recent. We sought to identify a whole-blood transcriptome signature of recent TB exposure.

Methods

We studied household contacts of TB patients; healthy volunteers without recent history of TB exposure; and patients with active TB. We performed whole-blood RNA sequencing (in all), an interferon gamma release assay (IGRA; in contacts and healthy controls) and PET/MRI lung scans (in contacts only). We evaluated differentially-expressed genes in household contacts (log2 fold change ≥1 versus healthy controls; false-discovery rate < 0.05); compared these to differentially-expressed genes seen in the active TB group; and assessed the association of a composite gene expression score to independent exposure/treatment/immunological variables.

Results

There were 186 differentially-expressed genes in household contacts (n = 26, age 22–66, 46% male) compared with healthy controls (n = 5, age 29–38, 100% male). Of these genes, 141 (76%) were also differentially expressed in active TB (n = 14, age 27–69, 71% male). The exposure signature included genes from inflammatory response, type I interferon signalling and neutrophil-mediated immunity pathways; and genes such as BATF2 and SCARF1 known to be associated with incipient TB. The composite gene-expression score was higher in IGRA-positive contacts (P = 0.04) but not related to time from exposure, isoniazid prophylaxis, or abnormalities on PET/MRI (all P > 0.19).

Conclusions

Transcriptomics can detect TB exposure and, with further development, may be an approach of value for epidemiological research and targeting public health interventions.

Expression and production of the SERPING1-encoded endogenous complement regulator C1-inhibitor in multiple cohorts of tuberculosis patients

BACKGROUND

To facilitate better discrimination between patients with active tuberculosis (TB) and latent TB infection (LTBI), whole blood transcriptomic studies have been performed to identify novel candidate host biomarkers. SERPING1, which encodes C1-inhibitor (C1-INH), the natural inhibitor of the C1-complex has emerged as candidate biomarker. Here we collated and analysed SERPING1 expression data and subsequently determined C1-INH protein levels in four cohorts of patients with TB.

METHODS

SERPING1 expression data were extracted from online deposited datasets. C1-INH protein levels were determined by ELISA in sera from individuals with active TB, LTBI as well as other disease controls in geographically diverse cohorts.

FINDINGS

SERPING1 expression was increased in patients with active TB compared to healthy controls (8/11 cohorts), LTBI (13/14 cohorts) and patients with other (non-TB) lung-diseases (7/7 cohorts). Serum levels of C1-INH were significantly increased in The Gambia and Italy in patients with active TB relative to the endemic controls but not in South Africa or Korea. In the largest cohort (n = 50), with samples collected longitudinally, normalization of C1-INH levels following successful TB treatment was observed. This cohort, also showed the most abundant increase in C1-INH, and a positive correlation between C1q and C1-INH levels. Combined presence of increased levels of both C1q and C1-INH had high specificity for active TB (96 %) but only very modest sensitivity 38 % compared to the endemic controls.

INTERPRETATION

SERPING1 transcript expression is increased in TB patients, while serum protein levels of C1-INH were increased in half of the cohorts analysed.

Probe Signal Values in mRNA Arrays Imply an Excessive Involvement of Neutrophil FCGR1 in Tuberculosis

The perturbed genes from transcriptomes are often presented in terms of relative expressions against control samples. However, the probe signal values (PSVs) of genes, implying protein abundances, are often ignored. Here, we explored the PSVs in tuberculosis (TB)-relevant signature genes. The signatures from Mycobacterium tuberculosis-infected THP-1 cells were defined as induced (TMtb-i, with a derived TMtb-iNet) and repressed (TMtb-r). The signature from human blood was defined as a pulmonary TB (PTB)-specific signature (PTBsig). The analysis showed that before infection, TMtb-i and TMtb-iNet had lower PSVs and TMtb-r genes had average PSVs. In the blood of healthy donors, PTBsig (divided into up-regulated PTBsigUp and down-regulated PTBsigDn) displayed average PSVs. This was partly due to masking by the cellular heterogeneity of blood; diverse PSVs were seen in constituent cell populations (CD4/8+ T, monocytes and neutrophils). Specifically, the PSVs of PTBsigUp in the neutrophils of healthy donors were higher (implying higher protein abundances), and much higher in the neutrophils of PTB (implying excessive protein abundances). Based on the PSV patterns of PTBsigUp in four cell populations, we identified three representative highly homologous genes (FCGR1A, FCGR1B, and the pseudogene FCGR1CP, which were often poorly distinguished), of which the summed PSVs were the highest in the neutrophils of PTB patients and healthy donors. The three genes were all up-regulated and responsive to chemotherapy in the blood of PTB, as validated in an RNA-seq-based analysis. This PSV-based study confirms the excessive involvement of neutrophil FCGR1 in PTB.

Cross-validation of existing signatures and derivation of a novel 29-gene transcriptomic signature predictive of progression to TB in a Brazilian cohort of household contacts of pulmonary TB

The goal of this study was to identify individuals at risk of progression and reactivation among household contacts (HHC) of pulmonary TB cases in Vitoria, Brazil. We first evaluated the predictive performance of six published signatures on the transcriptional dataset obtained from peripheral blood mononuclear cell samples from HHC that either progressed to TB disease or not (non-progressors) during a five-year follow-up. The area under the curve (AUC) values for the six signatures ranged from 0.670 to 0.461, and the PPVs did not reach the WHO published target product profiles (TPPs). We therefore used as training cohort the earliest time-point samples from the African cohort of adolescents (GSE79362) and applied an ensemble feature selection pipeline to derive a novel 29-gene signature (PREDICT29). PREDICT29 was tested on 16 progressors and 21 non-progressors. PREDICT29 performed better in segregating progressors from non-progressors in the Brazil cohort with the area under the curve (AUC) value of 0.911 and PPV of 20%. This proof of concept study demonstrates that PREDICT29 can predict risk of progression/reactivation to clinical TB disease in recently exposed individuals at least 5 years prior to disease development. Upon validation in larger and geographically diverse cohorts, PREDICT29 can be used to risk-stratify recently infected for targeted therapy.

Latent Tuberculosis Infection (LTBI) and Its Potential Targets: An Investigation into Dormant Phase Pathogens

One-third of the world's population harbours the latent tuberculosis infection (LTBI) with a lifetime risk of reactivation. Although, the treatment of LTBI relies significantly on the first-line therapy, identification of novel drug targets and therapies are the emerging focus for researchers across the globe. The current review provides an insight into the infection, diagnostic methods and epigrammatic explanations of potential molecular targets of dormant phase bacilli. This study also includes current preclinical and clinical aspects of tubercular infections and new approaches in antitubercular drug discovery.

Transcriptional Profiling of Human Peripheral Blood Mononuclear Cells Identifies Diagnostic Biomarkers That Distinguish Active and Latent Tuberculosis

Mycobacterium tuberculosis (M. tuberculosis) infection in humans can cause active disease or latent infection. However, the factors contributing to the maintenance of latent infection vs. disease progression are poorly understood. In this study, we used a genome-wide RNA sequencing (RNA-seq) approach to identify host factors associated with M. tuberculosis infection status and a novel gene signature that can distinguish active disease from latent infection. By RNA-seq, we characterized transcriptional differences in purified protein derivative (PPD)-stimulated peripheral blood mononuclear cells (PBMCs) among three groups: patients with active tuberculosis (ATB), individuals with latent TB infection (LTBI), and TB-uninfected controls (CON). A total of 401 differentially expressed genes enabled grouping of individuals into three clusters. A validation study by quantitative real-time PCR (qRT-PCR) confirmed the differential expression of TNFRSF10C, IFNG, PGM5, EBF3, and A2ML1 between the ATB and LTBI groups. Additional clinical validation was performed to evaluate the diagnostic performance of these five biomarkers using 130 subjects. The 3-gene signature set of TNFRSF10C, EBF3, and A2ML1 enabled correct classification of 91.5% of individuals, with a high sensitivity of 86.2% and specificity of 94.9%. Diagnostic performance of the 3-gene signature set was validated using a clinical cohort of 147 subjects with suspected ATB. The sensitivity and specificity of the 3-gene set for ATB were 82.4 and 92.4%, respectively. In conclusion, we detected distinct gene expression patterns in PBMCs stimulated by PPD depending on the status of M. tuberculosis infection. Furthermore, we identified a 3-gene signature set that could distinguish ATB from LTBI, which may facilitate rapid diagnosis and treatment for more effective disease control.

Decoding the similarities and specific differences between latent and active tuberculosis infections based on consistently differential expression networks

Although intensive efforts have been devoted to investigating latent tuberculosis (LTB) and active tuberculosis (PTB) infections, the similarities and differences in the host responses to these two closely associated stages remain elusive, probably due to the difficulty in identifying informative genes related to LTB using traditional methods. Herein, we developed a framework known as the consistently differential expression network to identify tuberculosis (TB)-related gene pairs by combining microarray profiles and protein-protein interactions. We thus obtained 774 and 693 pairs corresponding to the PTB and LTB stages, respectively. The PTB-specific genes showed higher expression values and fold-changes than the LTB-specific genes. Furthermore, the PTB-related pairs generally had higher expression correlations and would be more activated compared to their LTB-related counterparts. The module analysis implied that the detected gene pairs tended to cluster in the topological and functional modules. Functional analysis indicated that the LTB- and PTB-specific genes were enriched in different pathways and had remarkably different locations in the NF-κB signaling pathway. Finally, we showed that the identified genes and gene pairs had the potential to distinguish TB patients in different disease stages and could be considered as drug targets for the specific treatment of patients with LTB or PTB.

Mycobacterium tuberculosis-Induced Bronchoalveolar Lavage Gene Expression Signature in Latent Tuberculosis Infection Is Dominated by Pleiotropic Effects of CD4+ T Cell-Dependent IFN-γ Production despite the Presence of Polyfunctional T Cells within the Airways

Tuberculosis (TB) remains a worldwide public health threat. Development of a more effective vaccination strategy to prevent pulmonary TB, the most common and contagious form of the disease, is a research priority for international TB control. A key to reaching this goal is improved understanding of the mechanisms of local immunity to Mycobacterium tuberculosis, the causative organism of TB. In this study, we evaluated global M. tuberculosis-induced gene expression in airway immune cells obtained by bronchoalveolar lavage (BAL) of individuals with latent TB infection (LTBI) and M. tuberculosis-naive controls. In prior studies, we demonstrated that BAL cells from LTBI individuals display substantial enrichment for M. tuberculosis-responsive CD4⁺ T cells compared with matched peripheral blood samples. We therefore specifically assessed the impact of the depletion of CD4⁺ and CD8⁺ T cells on M. tuberculosis-induced BAL cell gene expression in LTBI. Our studies identified 12 canonical pathways and a 47-gene signature that was both sensitive and specific for the contribution of CD4⁺ T cells to local recall responses to M. tuberculosis In contrast, depletion of CD8⁺ cells did not identify any genes that fit our strict criteria for inclusion in this signature. Although BAL CD4⁺ T cells in LTBI displayed polyfunctionality, the observed gene signature predominantly reflected the impact of IFN-γ production on a wide range of host immune responses. These findings provide a standard for comparison of the efficacy of standard bacillus Calmette-Guérin vaccination as well as novel TB vaccines now in development at impacting the initial response to re-exposure to M. tuberculosis in the human lung.

CD27 expression of T-cells discriminates IGRA-negative TB patients from healthy contacts in Ghana

IFN-γ release assays (IGRAs) have suboptimal sensitivity for detection of Mycobacterium tuberculosis (Mtb) infection and cannot discriminate between tuberculosis (TB) patients and healthy -potentially Mtb infected- contacts (HCs). In a case-control study, we determined T-cell phenotypes of IGRAs in TB patients (n = 20) and HCs (n = 20) from Ghana. CD27 expression of T-cells was significantly lower in TB patients as compared to HCs independent from Mtb-specificity. CD27 expression discriminated both study groups - including TB patients with low or indeterminate IGRA results - effectively. We conclude that CD27 is a promising biomarker for diagnosis of TB patients with inconclusive IGRA results.

Identification of unique key genes and miRNAs in latent tuberculosis infection by network analysis

Tuberculosis (TB) is a chronic infectious disease caused by Mycobacterium tuberculosis (M.tb). New cases are now mainly caused by the progression of latent tuberculosis infection (LTBI). Thus, methods to diagnose and treat LTBI are urgently needed to prevent the development of active TB in infected individuals and the subsequent spread of the disease. In this study, a systems biology approach was utilized to obtain numerous microarray data sets for mRNAs and microRNAs (miRNAs) expressed in the peripheral blood mononuclear cells (PBMCs) of TB patients and individuals with LTBI. Within these data sets, we identified the differentially expressed mRNAs and miRNAs and further investigated which differentially expressed genes and miRNAs were uniquely expressed during LTBI. The Database for Annotation, Visualization and Integrated Discovery (DAVID) was employed to analyze the functional annotations and pathway classifications of the identified genes. To further understand the unique miRNA-gene regulatory network of LTBI, we constructed a protein-protein interaction (PPI) network for the targeted genes. The PPI network included 39 genes that were differentially and uniquely expressed in PBMCs of individuals with LTBI, and KEGG pathway enrichment analysis showed that these genes were predominantly involved in the PI3K-Akt signaling pathway, which plays an important role in chronic inflammation. DIANA TOOLs-mirPath analysis revealed that the identified miRNAs in the miRNA-gene regulatory network for LTBI were mainly associated with the Hippo signaling pathway, which functions in the development of inflammation. Quantitative real-time PCR verified the up expression of hsa-miR-212-3p and its predicted target gene -MAPK1 which had low expression and was a major component of the PPI network, and MAPK1 expression was correlated with the clinicopathological characteristics of LTBI by receiver operating characteristic (ROC) curve analysis. Therefore, MAPK1 has potential to be a new investigable marker during LTBI, which merits our further study and solution. The unique aberrant miRNA-gene regulatory network and the related PPI network identified in this study provide insight into the molecular mechanisms of the immune response to LTBI, and thus, may aid in the development of a novel treatment strategy.

Neutrophil CD64 expression levels in IGRA-positive individuals distinguish latent tuberculosis from active disease

BACKGROUND

CD64 (FcγR1) is a high-affinity receptor for monomeric IgG1 and IgG3. Circulating neutrophils express very low amounts of CD64 on their surface.

OBJECTIVES

Our primary aim was to investigate the utility of neutrophil CD64 surface expression as a biomarker of active pulmonary tuberculosis (TB). We hypothesised that elevated neutrophil CD64 expression in TB infection would be associated with interferon gamma (IFN-γ) as an inducer of CD64 expression.

METHODS

The expression level of CD64 per neutrophil (PMN CD64 index) was quantitatively measured with flow cytometry using a Leuko64 kit in samples from patients with TB and latent TB infection (LTBI) as well as healthy controls, as part of a prospective cohort study in Brazil.

FINDINGS

The PMN CD64 index in patients with TB was higher than that in healthy controls and LTBI. Receiver operating characteristic curve analyses determined that the PMN CD64 index could discriminate patients with TB from those with LTBI and healthy individuals. PMN CD64 index levels returned to baseline levels after treatment.

CONCLUSIONS

The positive regulation of CD64 expression in circulating neutrophils of patients with active TB could represent an additional biomarker for diagnosis of active TB and could be used for monitoring individuals with LTBI before progression of TB disease.

Host-response-based gene signatures for tuberculosis diagnosis: A systematic comparison of 16 signatures

BACKGROUND

The World Health Organization (WHO) and Foundation for Innovative New Diagnostics (FIND) have published target product profiles (TPPs) calling for non-sputum-based diagnostic tests for the diagnosis of active tuberculosis (ATB) disease and for predicting the progression from latent tuberculosis infection (LTBI) to ATB. A large number of host-derived blood-based gene-expression biomarkers for diagnosis of patients with ATB have been proposed to date, but none have been implemented in clinical settings. The focus of this study is to directly compare published gene signatures for diagnosis of patients with ATB across a large, diverse list of publicly available gene expression datasets, and evaluate their performance against the WHO/FIND TPPs.

METHODS AND FINDINGS

We searched PubMed, Gene Expression Omnibus (GEO), and ArrayExpress in June 2018. We included all studies irrespective of study design and enrollment criteria. We found 16 gene signatures for the diagnosis of ATB compared to other clinical conditions in PubMed. For each signature, we implemented a classification model as described in the corresponding original publication of the signature. We identified 24 datasets containing 3,083 transcriptome profiles from whole blood or peripheral blood mononuclear cell samples of healthy controls or patients with ATB, LTBI, or other diseases from 14 countries in GEO. Using these datasets, we calculated weighted mean area under the receiver operating characteristic curve (AUROC), specificity at 90% sensitivity, and negative predictive value (NPV) for each gene signature across all datasets. We also compared the diagnostic odds ratio (DOR), heterogeneity in DOR, and false positive rate (FPR) for each signature using bivariate meta-analysis. Across 9 datasets of patients with culture-confirmed diagnosis of ATB, 11 signatures had weighted mean AUROC > 0.8, and 2 signatures had weighted mean AUROC ≤ 0.6. All but 2 signatures had high NPV (>98% at 2% prevalence). Two gene signatures achieved the minimal WHO TPP for a non-sputum-based triage test. When including datasets with clinical diagnosis of ATB, there was minimal reduction in the weighted mean AUROC and specificity of all but 3 signatures compared to when using only culture-confirmed ATB data. Only 4 signatures had homogeneous DOR and lower FPR when datasets with clinical diagnosis of ATB were included; other signatures either had heterogeneous DOR or higher FPR or both. Finally, 7 of 16 gene signatures predicted progression from LTBI to ATB 6 months prior to sputum conversion with positive predictive value > 6% at 2% prevalence. Our analyses may have under- or overestimated the performance of certain ATB diagnostic signatures because our implementation may be different from the published models for those signatures. We re-implemented published models because the exact models were not publicly available.

CONCLUSIONS

We found that host-response-based diagnostics could accurately identify patients with ATB and predict individuals with high risk of progression from LTBI to ATB prior to sputum conversion. We found that a higher number of genes in a signature did not increase the accuracy of the signature. Overall, the Sweeney3 signature performed robustly across all comparisons. Our results provide strong evidence for the potential of host-response-based diagnostics in achieving the WHO goal of ending tuberculosis by 2035, and host-response-based diagnostics should be pursued for clinical implementation.

A polymeric immunoglobulin-antigen fusion protein strategy for enhancing vaccine immunogenicity

In this study, a strategy based on polymeric immunoglobulin G scaffolds (PIGS) was used to produce a vaccine candidate for Mycobacterium tuberculosis. A genetic fusion construct comprising genes encoding the mycobacterial Ag85B antigen, an immunoglobulin γ-chain fragment and the tailpiece from immunoglobulin μ chain was engineered. Expression was attempted in Chinese Hamster Ovary (CHO) cells and in Nicotiana benthamiana. The recombinant protein assembled into polymeric structures (TB-PIGS) in N. benthamiana, similar in size to polymeric IgM. These complexes were subsequently shown to bind to the complement protein C1q and FcγRs with increased affinity. Modification of the N-glycans linked to TB-PIGS by removal of xylose and fucose residues that are normally found in plant glycosylated proteins also resulted in increased affinity for low-affinity FcγRs. Immunization studies in mice indicated that TB-PIGS are highly immunogenic with and without adjuvant. However, they did not improve protective efficacy in mice against challenge with M. tuberculosis compared to conventional vaccination with BCG, suggesting that additional or alternative antigens may be needed to protect against this disease. Nevertheless, these results establish a novel platform for producing polymeric antigen-IgG γ-chain molecules with inherent functional characteristics that are desirable in vaccines.