Diagnostic biomarkers for active tuberculosis: progress and challenges

Non-sputum-based samples and biomarkers for detection of Mycobacterium tuberculosis: the hope to improve childhood and HIV-associated tuberculosis diagnosis

In 2014, the World Health Organisation (WHO) published target product profiles (TPP) for development of novel tuberculosis (TB) diagnostics. One of the key highlights is the need for point-of-care non-sputum-based tests capable of detecting all forms of TB through identification of characteristic biomarkers or biosignatures. Compared to the limitations associated with sputum-based TB tests, non-sputum samples are easy to collect, non-invasive, with potential to improve TB diagnosis among children and among people living with HIV/AIDS (PLHIV). This review gives an overview of the existing evidence on TB diagnostic studies of non-sputum-based samples collected non-invasively from or through the oral-gastrointestinal tract (GI) and nasal pharynx regions of humans and the biomarkers detected. We further summarized evidence of these biomarkers and sample types from research done in paediatric and PLHIV. The review identified; saliva, cough aerosols, oral swabs, oral wash, dental plaque, tongue swabs, face mask sampling, exhaled breath, and stool, as the non-sputum samples investigated. These biomarkers can be categorized into Deoxyribose Nucleic Acid (DNA), Ribonucleic Acid (RNA), inflammatory, antigen-antibody, volatile and non-volatile compounds, microbiome and microbiota. The biomarkers identified were derived both from the host and pathogen. Similar biomarkers were identified in the general population, children and among PLHIV. These biomarkers have been detected by either already approved simple point of care or sophisticated devices. Differences in methodology and sample types investigated, small sample size of children and PLHIV populations, bias due to confounding factors, were some of the identified challenges in these studies. There is need to conduct larger and standardized multi centre studies to evaluate non-sputum-based biomarker-based tests in children and PLHIV.

Clinical diagnostic value of CRISPR-Cas13a-based molecular technology for tuberculosis

OBJECTIVE

To address the clinical diagnostic value of CRISPR-Cas13a-based molecular technology for tuberculosis (TB).

METHODS

The 189 suspected TB patients were simultaneously sent for acid-fast staining smear of bronchoalveolar lavage fluid, MGIT 960 cultures, Xpert MTB/RIF assay, and CRISPR-Cas13a assay. Using the final clinical diagnosis as the gold standard, the TB and non-TB groups were determined, and the diagnostic values of the four assays and the combined test in TB were compared. Using MGIT 960 culture as the gold standard, the diagnostic value of CRISPR-Cas13a assay was explored in TB, and the concordance between the CRISPR-Cas13a assay and MGIT 960 culture was compared.

RESULTS

The 189 preliminary diagnosed patients with suspected TB were diagnosed, with 147 in the TB group and 42 in the non-TB group. Taking the final clinical diagnosis as the gold standard, the sensitivity, negative predictive value, and accuracy of CRISPR-Cas13a assay, MGIT 960 culture, and XpertMTB/RIF assay were higher than those of acid-fast staining smear; by comparing the area under the ROC curve, the diagnostic value of the CRISPR-Cas13a assay, MGIT 960 culture, and XpertMTB/RIF assay was superior to that of acid-fast staining smear (all P < 0.05). Using the MGIT 960 culture results as the gold standard, there was a moderate concordance between the CRISPR-Cas13a assay and the MGIT 960 culture (kappa = 0.666).

CONCLUSION

Bronchoalveolar lavage fluid CRISPR-Cas13a assay has high application value in the clinical diagnosis of TB and can be recommended for the initial screening of patients with suspected TB.

Host long noncoding RNAs in bacterial infections

Bacterial infections remain a significant global health concern, necessitating a comprehensive understanding of the intricate host-pathogen interactions that play a critical role in the outcome of infectious diseases. Recent investigations have revealed that noncoding RNAs (ncRNAs) are key regulators of these complex interactions. Among them, long noncoding RNAs (lncRNAs) have gained significant attention because of their diverse regulatory roles in gene expression, cellular processes and the production of cytokines and chemokines in response to bacterial infections. The host utilizes lncRNAs as a defense mechanism to limit microbial pathogen invasion and replication. On the other hand, some host lncRNAs contribute to the establishment and maintenance of bacterial pathogen reservoirs within the host by promoting bacterial pathogen survival, replication, and dissemination. However, our understanding of host lncRNAs in the context of bacterial infections remains limited. This review focuses on the impact of host lncRNAs in shaping host-pathogen interactions, shedding light on their multifaceted functions in both host defense and bacterial survival, and paving the way for future research aimed at harnessing their regulatory potential for clinical applications.

Integrating pathogen- and host-derived blood biomarkers for enhanced tuberculosis diagnosis: a comprehensive review

This review explores the evolving landscape of blood biomarkers in the diagnosis of tuberculosis (TB), focusing on biomarkers derived both from the pathogen and the host. These biomarkers provide critical insights that can improve diagnostic accuracy and timeliness, essential for effective TB management. The document highlights recent advancements in molecular techniques that have enhanced the detection and characterization of specific biomarkers. It also discusses the integration of these biomarkers into clinical practice, emphasizing their potential to revolutionize TB diagnostics by enabling more precise detection and monitoring of the disease progression. Challenges such as variability in biomarker expression and the need for standardized validation processes are addressed to ensure reliability across different populations and settings. The review calls for further research to refine these biomarkers and fully harness their potential in the fight against TB, suggesting a multidisciplinary approach to overcome existing barriers and optimize diagnostic strategies. This comprehensive analysis underscores the significance of blood biomarkers as invaluable tools in the global effort to control and eliminate TB.

Tuberculosis susceptibility in genetically diverse mice reveals functional diversity of neutrophils

Tuberculosis (TB) is a heterogenous disease in humans with individuals exhibiting a wide range of susceptibility. This heterogeneity is not captured by standard laboratory mouse lines. We used a new collection of 19 wild-derived inbred mouse lines collected from diverse geographic sites to identify novel phenotypes during Mycobacterium tuberculosis ( Mtb ) infection. Wild derived mice have heterogenous immune responses to infection that result in differential ability to control disease at early timepoints. Correlation analysis with multiple parameters including sex, weight, and cellular immune responses in the lungs revealed that enhanced control of infection is associated with increased numbers of CD4 T cells, CD8 T cells and B cells. Surprisingly, we did not observe strong correlations between IFN-γ production and control of infection. Although in most lines high neutrophils were associated with susceptibility, we identified a mouse line that harbors high neutrophils numbers yet controls infection. Using single-cell RNA sequencing, we identified a novel neutrophil signature associated with failure to control infection.

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.

Exosomal microRNAs associated with tuberculosis among people living with human immunodeficiency virus

Objective

To investigate the diagnostic value of selected exosomal miRNAs for Tuberculosis (TB) among people living with human immunodeficiency virus (PLHIV).

Methods

A total of 43 adult HIV patients, including 20 diagnosed with TB and 23 controls, were enrolled. The levels of six exosomal miRNAs (miR-20a, miR-20b, miR-26a, miR-106a, miR-191, and miR-486) were measured using qRT-PCR.

Results

The levels of these six exosomal miRNAs (miR-20a, miR-20b, miR-26a, miR-106a, miR-191, and miR-486) were significantly higher in the plasma of TB patients compared to controls among PLHIV. The Receiver Operating Characteristic (ROC) curve of these six miRNAs showed a fair performance in distinguishing TB patients from controls, with Area Under Curve (AUC) values of 0.78 (95 %CI 0.63-0.93), 0.81 (95 %CI 0.67-0.95), 0.77 (95 %CI 0.61-0.93), 0.84 (95 %CI 0.70-0.98), 0.82 (95 %CI 0.68-0.95) and 0.79 (95 %CI 0.65-0.93), respectively. These miRNAs showed higher AUC values for extrapulmonary tuberculosis compared to pulmonary tuberculosis. An analysis of subgroups was performed based on CD4 + T cell count (＜200 and ≥ 200 cells·µL-1). In the high CD4 count group, all these six exosomal miRNAs appeared to have higher AUC values compared to the low CD4 count group.

Conclusions

These six exosomal miRNAs could serve as potential biomarkers for diagnosing TB among PLHIV.

Discovery of urinary biosignatures for tuberculosis and nontuberculous mycobacteria classification using metabolomics and machine learning

Nontuberculous mycobacteria (NTM) infection diagnosis remains a challenge due to its overlapping clinical symptoms with tuberculosis (TB), leading to inappropriate treatment. Herein, we employed noninvasive metabolic phenotyping coupled with comprehensive statistical modeling to discover potential biomarkers for the differential diagnosis of NTM infection versus TB. Urine samples from 19 NTM and 35 TB patients were collected, and untargeted metabolomics was performed using rapid liquid chromatography-mass spectrometry. The urine metabolome was analyzed using a combination of univariate and multivariate statistical approaches, incorporating machine learning. Univariate analysis revealed significant alterations in amino acids, especially tryptophan metabolism, in NTM infection compared to TB. Specifically, NTM infection was associated with upregulated levels of methionine but downregulated levels of glutarate, valine, 3-hydroxyanthranilate, and tryptophan. Five machine learning models were used to classify NTM and TB. Notably, the random forest model demonstrated excellent performance [area under the receiver operating characteristic (ROC) curve greater than 0.8] in distinguishing NTM from TB. Six potential biomarkers for NTM infection diagnosis, including methionine, valine, glutarate, 3-hydroxyanthranilate, corticosterone, and indole-3-carboxyaldehyde, were revealed from univariate ROC analysis and machine learning models. Altogether, our study suggested new noninvasive biomarkers and laid a foundation for applying machine learning to NTM differential diagnosis.

Improving the diagnosis of tuberculosis: old and new laboratory tools

INTRODUCTION

Despite recent advances in diagnostic technologies and new drugs becoming available, tuberculosis (TB) remains a major global health burden. If detected early, screened for drug resistance, and fully treated, TB could be easily controlled.

AREAS COVERED

Here the authors discuss M. tuberculosis culture methods which are considered the definitive confirmation of M. tuberculosis infection, and limited advances made to build on these core elements of TB laboratory diagnosis. Literature searches showed that molecular techniques provide enhanced speed of turnaround, sensitivity, and richness of data. Sequencing of the whole genome, is becoming well established for identification and inference of drug resistance. PubMed® literature searches were conducted (November 2022-March 2024).

EXPERT OPINION

This section highlights future advances in diagnosis and infection control. Prevention of prolonged hospital admissions and rapid TAT are of the most benefit to the overall patient experience. Host transcriptional blood markers have been used in treatment monitoring studies and, with appropriate evaluation, could be rolled out in a diagnostic setting. Additionally, the MBLA is being incorporated into latest clinical trial designs. Whole genome sequencing has enhanced epidemiological evidence. Artificial intelligence, along with machine learning, have the ability to revolutionize TB diagnosis and susceptibility testing within the next decade.

Expression profiling & functional characterization of candidate miRNAs in serum exosomes among Indians with & without HIV-tuberculosis coinfection

Background & objectives Despite the evidence of population differences in miRNA expression, limited information is available about the expression profile of miRNAs in Indian tuberculosis (TB) patients. The present study aimed to investigate the expression profile of candidate serum exosomal microRNAs in Indian patients with and without HIV-TB coinfection. Methods The pool samples of serum exosomes of study participants (HIV-TB coinfection, extra-pulmonary TB, HIV mono-infection, pulmonary TB) and healthy humans were processed for the isolation of total RNA followed by miRNA analysis using miRCURY LNA human focus PCR panel by real-time PCR. The significantly altered miRNAs were identified using differential expression analysis. The target genes prediction and potential functional analysis of exclusively differentially expressed miRNAs were performed using bioinformatics tools. Results The expression profile of 57, 58, 49 and 11 miRNAs was significantly altered in exosome samples of HIV-TB coinfected, extra-pulmonary TB, HIV mono-infected and pulmonary TB patients compared to healthy controls, respectively. The set of three (hsa-let-7i-5p, hsa-miR-24-3p, hsa-miR-92a-3p), three (hsa-miR-20a-5p, hsa-let-7e-5p, hsa-miR-26a-5p) and four (hsa-miR-21-5p, hsa-miR-19a-3p, hsa-miR-19b-3p, hsa-miR-146a-5p) miRNAs were exclusively significantly differentially expressed in study participants with HIV-TB coinfection, extra-pulmonary TB and pulmonary TB, respectively. Most of the target genes of exclusively differentially expressed miRNAs were enriched in pathways in cancer, MAPK signalling pathway and Ras signalling pathway. Interpretation & conclusions The present study demonstrates a distinct expression profile of miRNAs in serum exosomes of the study participants and identified crucial miRNAs which may have a significant impact on the biomarker analysis and pathogenesis of TB in Indian patients.

Individualized lipid profile in urine-derived extracellular vesicles from clinical patients with Mycobacterium tuberculosis infections

Background

Lipids are a key nutrient source for the growth and reproduction of Mycobacterium tuberculosis (Mtb). Urine-derived extracellular vesicles (EVs), because of its non-invasive sampling, lipid enrichment, and specific sorting character, have been recognized as a promising research target for biomarker discovery and pathogenesis elucidation in tuberculosis (TB). We aim to profile lipidome of Mtb-infected individuals, offer novel lipid signatures for the development of urine-based TB testing, and provide new insights into the lipid metabolism after Mtb infection.

Methods

Urine-derived extracellular vesicles from 41 participants (including healthy, pulmonary tuberculosis, latent tuberculosis patients, and other lung disease groups) were isolated and individually detected using targeted lipidomics and proteomics technology platforms. Biomarkers were screened by multivariate and univariate statistical analysis and evaluated by SPSS software. Correlation analyses were performed on lipids and proteins using the R Hmisc package.

Results

Overall, we identified 226 lipids belonging to 14 classes. Of these, 7 potential lipid biomarkers for TB and 6 for latent TB infection (LTBI) were identified, all of which were classified into diacylglycerol (DAG), monoacylglycerol (MAG), free fatty acid (FFA), and cholesteryl ester (CE). Among them, FFA (20:1) was the most promising biomarker target in diagnosing TB/LTBI from other compared groups and also have great diagnostic performance in distinguishing TB from LTBI with AUC of 0.952. In addition, enhanced lipolysis happened as early as individuals got latent Mtb infection, and ratio of raft lipids was gradually elevated along TB progression.

Conclusion

This study demonstrated individualized lipid profile of urinary EVs in patients with Mtb infection, revealed novel potential lipid biomarkers for TB/LTBI diagnosis, and explored mechanisms by which EV lipid raft-dependent bio-processes might affect pathogenesis. It lays a solid foundation for the subsequent diagnosis and therapeutic intervention of TB.

Therapeutic developments for tuberculosis and nontuberculous mycobacterial lung disease

Tuberculosis (TB) drug discovery and development has undergone nothing short of a revolution over the past 20 years. Successful public-private partnerships and sustained funding have delivered a much-improved understanding of mycobacterial disease biology and pharmacology and a healthy pipeline that can tolerate inevitable attrition. Preclinical and clinical development has evolved from decade-old concepts to adaptive designs that permit rapid evaluation of regimens that might greatly shorten treatment duration over the next decade. But the past 20 years also saw the rise of a fatal and difficult-to-cure lung disease caused by nontuberculous mycobacteria (NTM), for which the drug development pipeline is nearly empty. Here, we discuss the similarities and differences between TB and NTM lung diseases, compare the preclinical and clinical advances, and identify major knowledge gaps and areas of cross-fertilization. We argue that applying paradigms and networks that have proved successful for TB, from basic research to clinical trials, will help to populate the pipeline and accelerate curative regimen development for NTM disease.

Exploring TSPAN4 promoter methylation as a diagnostic biomarker for tuberculosis

Background

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), is a persistent infectious disease threatening human health. The existing diagnostic methods still have significant shortcomings, including a low positivity rate in pathogen-based diagnoses and the inability of immunological diagnostics to detect active TB. Hence, it is urgent to develop new techniques to detect TB more accurate and earlier. This research aims to scrutinize and authenticate DNA methylation markers suitable for tuberculosis diagnosis. Concurrently, Providing a new approach for tuberculosis diagnosis.

Methods

Blood samples from patients with newly diagnosed tuberculosis and healthy controls (HC) were utilized in this study. Examining methylation microarray data from 40 whole blood samples (22TB + 18HC), we employed two procedures: signature gene methylated position analysis and signature region methylated position analysis to pinpoint distinctive methylated positions. Based on the screening results, diagnostic classifiers are constructed through machine learning, and validation was conducted through pyrosequencing in a separate queue (22TB + 18HC). Culminating in the development of a new tuberculosis diagnostic method via quantitative real-time methylation specific PCR (qMSP).

Results

The combination of the two procedures revealed a total of 10 methylated positions, all of which were located in the promoter region. These 10 signature methylated positions facilitated the construction of a diagnostic classifier, exhibiting robust diagnostic accuracy in both cross-validation and external test sets. The LDA model demonstrated the best classification performance, achieving an AUC of 0.83, specificity of 0.8, and sensitivity of 0.86 on the external test set. Furthermore, the validation of signature methylated positions through pyrosequencing demonstrated high agreement with screening outcomes. Additionally, qMSP detection of 2 potential hypomethylated positions (cg04552852 and cg12464638) exhibited promising results, yielding an AUC of 0.794, specificity of 0.720, and sensitivity of 0.816.

Conclusion

Our study demonstrates that the validated signature methylated positions through pyrosequencing emerge as plausible biomarkers for tuberculosis diagnosis. The specific methylation markers in the TSPAN4 gene, identified in whole blood samples, hold promise for improving tuberculosis diagnosis. This approach could significantly enhance diagnostic accuracy and speed, offering a new avenue for early detection and treatment.

Diagnostic markers reflecting dysregulation of the host response in the transition to tuberculosis disease

Sustained control of Mycobacterium tuberculosis infection without evidence of disease is based on a finely tuned balance between pro- and anti-inflammatory responses. Loss of this balance leads to tuberculosis (TB) disease, in which exacerbated myeloid and neutrophil activation is common. Proteomic and transcriptomic assessment of the host response can detect increasing immune activation associated with TB disease progression several months before clinical disease. Future diagnostic methods based on measuring host response biomarkers that are able to detect this dysregulation could therefore be valuable in the early detection of TB disease progression.

Integrated plasma proteomics identifies tuberculosis-specific diagnostic biomarkers

BACKGROUNDNovel biomarkers to identify infectious patients transmitting Mycobacterium tuberculosis are urgently needed to control the global tuberculosis (TB) pandemic. We hypothesized that proteins released into the plasma in active pulmonary TB are clinically useful biomarkers to distinguish TB cases from healthy individuals and patients with other respiratory infections.METHODSWe applied a highly sensitive non-depletion tandem mass spectrometry discovery approach to investigate plasma protein expression in pulmonary TB cases compared to healthy controls in South African and Peruvian cohorts. Bioinformatic analysis using linear modeling and network correlation analyses identified 118 differentially expressed proteins, significant through 3 complementary analytical pipelines. Candidate biomarkers were subsequently analyzed in 2 validation cohorts of differing ethnicity using antibody-based proximity extension assays.RESULTSTB-specific host biomarkers were confirmed. A 6-protein diagnostic panel, comprising FETUB, FCGR3B, LRG1, SELL, CD14, and ADA2, differentiated patients with pulmonary TB from healthy controls and patients with other respiratory infections with high sensitivity and specificity in both cohorts.CONCLUSIONThis biomarker panel exceeds the World Health Organization Target Product Profile specificity criteria for a triage test for TB. The new biomarkers have potential for further development as near-patient TB screening assays, thereby helping to close the case-detection gap that fuels the global pandemic.FUNDINGMedical Research Council (MRC) (MR/R001065/1, MR/S024220/1, MR/P023754/1, and MR/W025728/1); the MRC and the UK Foreign Commonwealth and Development Office; the UK National Institute for Health Research (NIHR); the Wellcome Trust (094000, 203135, and CC2112); Starter Grant for Clinical Lecturers (Academy of Medical Sciences UK); the British Infection Association; the Program for Advanced Research Capacities for AIDS in Peru at Universidad Peruana Cayetano Heredia (D43TW00976301) from the Fogarty International Center at the US NIH; the UK Technology Strategy Board/Innovate UK (101556); the Francis Crick Institute, which receives funding from UKRI-MRC (CC2112); Cancer Research UK (CC2112); and the NIHR Biomedical Research Centre of Imperial College NHS.

Neutrophil lncRNA ZNF100-6:2 is a potential diagnostic marker for active pulmonary tuberculosis

Active pulmonary tuberculosis (PTB) poses challenges in rapid diagnosis within complex clinical conditions. Given the close association between neutrophils and tuberculosis, we explored differentially expressed long non-coding RNAs (lncRNAs) in neutrophils as potential molecular markers for diagnosing active PTB. We employed a gene microarray to screen for lncRNA alterations in neutrophil samples from three patients with active PTB and three healthy controls. The results revealed differential expression of 1457 lncRNAs between the two groups, with 916 lncRNAs upregulated and 541 lncRNAs down-regulated in tuberculosis patients. Subsequent validation tests demonstrated down-regulation of lncRNA ZNF100-6:2 in patients with active PTB, which was restored following anti-tuberculosis treatment. Our findings further indicated a high diagnostic potential for lncRNA ZNF100-6:2, as evidenced by an area under the receiver operating characteristic (ROC) curve of 0.9796 (95% confidence interval: 0.9479 to 1.000; P < 0.0001). This study proposes lncRNA ZNF100-6:2 as a promising and novel diagnostic biomarker for active PTB.

Advancements in Tuberculosis Diagnostics: A Comprehensive Review of the Critical Role and Future Prospects of Xpert MTB/RIF Ultra Technology

Tuberculosis remains a persistent global health challenge, demanding swift and accurate diagnostic methods for effective treatment. The emergence of the Xpert MTB/RIF Ultra system marks a significant milestone in combating tuberculosis, streamlining the identification of Mycobacterium tuberculosis, and advancing our pursuit of eradicating the disease. Delving into the therapeutic landscape of tuberculosis and rifampicin resistance, this scientific narrative review offers a comprehensive exploration. It begins by delving into the historical backdrop and the hurdles encountered with traditional tuberculosis diagnostics. From there, it traces the journey of the Xpert MTB/RIF technology, underscoring its molecular underpinnings. In this narrative review, the performance of the Xpert MTB/RIF Ultra system undergoes thorough scrutiny, encompassing investigations into sensitivity, specificity, and comparisons with alternative diagnostic methods. The spotlight shines on its clinical applications across diverse scenarios, from diagnosing pulmonary and extrapulmonary tuberculosis to its pivotal role in identifying rifampicin resistance. The study also evaluates its clinical efficacy in enhancing patient outcomes and supporting global tuberculosis control initiatives. However, the review does not shy away from discussing the challenges and limitations associated with the Xpert MTB/RIF Ultra system. It meticulously addresses concerns regarding cost, infrastructure requirements, and potential diagnostic inaccuracies. Offering a panoramic view, the review assesses the system's impact in resource-constrained settings and its potential to bolster tuberculosis elimination endeavors worldwide. Peering into the future, it explores ongoing research avenues and potential enhancements in Xpert MTB/RIF Ultra technology, envisioning a landscape of improved performance, broader applications, and emerging diagnostic innovations in the realm of tuberculosis diagnostics.

Evaluation of Systemic Inflammation Before and After Standard Anti-tuberculosis Treatment in Patients With Active Pulmonary Tuberculosis and Diabetes Mellitus

Background Diabetes mellitus (DM) is a common comorbidity of active pulmonary tuberculosis (APTB) that increases the risk of treatment failure during anti-tuberculosis chemotherapy. Evaluating systemic inflammatory response could help determine differences in response to treatment between APTB patients and those with APTB and DM. Methodology To explore changes in systemic inflammation, measured by a set of inflammatory mediators in subjects with APTB and TBDM before and after six months of anti-tuberculosis chemotherapy, 30 APTB and nine TBDM subjects underwent cytokine testing, including interleukin (IL)-6, IL-8, IL-10, interferon-gamma (IFN-γ), tumor necrosis factor-alpha (TNF-α), and transforming growth factor-beta 1 (TGF-β1) by enzyme-linked immunosorbent assay, C-reactive protein by nephelometry, and sialic acid by colorimetric assay at baseline and following six months of standard anti-tuberculosis treatment. Sputum smear microscopy or molecular biology (Xpert MTB/RIF) was used for diagnosis, and sputum smear microscopy was performed monthly during the treatment of the patient with pulmonary tuberculosis to evaluate his evolution. Principal component analysis examined changes in the inflammatory status. Results Both groups showed negative sputum smear microscopy in the sixth month after starting anti-tuberculosis chemotherapy. TGF-β1 was found to be significantly higher in subjects with TBDM before treatment compared to APTB patients (p<0.001), and systemic inflammation continued only in TBDM subjects after treatment (accumulation and persistence of inflammatory mediators like IL-6, IL-8, IL-10, IFN-γ, TNF-α, TGF-β1, C-reactive protein, and sialic acid in blood). On the other hand, the mediators IFN-γ, C-reactive protein, and total sialic acid were found to be most influential in distinguishing pre- and post-treatment inflammatory response in subjects with APTB without DM. Conclusions Inflammatory mediators analyzed in combination, including IFN-γ, CRP, and total sialic acid, may be useful in evaluating the systemic inflammatory response in subjects with APTB and TBDM before and after anti-tuberculosis treatment. Determining these mediators revealed persistent systemic inflammation in TBDM subjects after six months of standard tuberculosis treatment, despite negative sputum smear microscopy results and good glycemic control. This suggests a need for inflammation-modulating therapies during tuberculosis control. Finally, monitoring sputum smear microscopy results alongside the determination of proposed inflammatory mediators (IFN-γ, CRP, and total sialic acid) are effective in evaluating the response to anti-tuberculosis treatment in APTB subjects without DM, warranting further investigation.

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

Introduction

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

Methods

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

Results

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

Conclusion

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

Mycobacterium tuberculosis in a Trap: The Role of Neutrophil Extracellular Traps in Tuberculosis

Mycobacterium tuberculosis complex causes tuberculosis (TB), a disease that causes pulmonary inflammation but can also affect other tissues. Despite macrophages having a defined role in TB immunopathogenesis, other innate immune cells, such as neutrophils, are involved in this process. These cells have high phagocytic ability and a microbial-killing machine comprised of enzymes, antimicrobial peptides, and reactive oxygen species. In the last two decades, a new neutrophil immune response, the neutrophil extracellular traps (NETs), has been intensely researched. NETs comprise DNA associated with histones, enzymes, and antimicrobial peptides. These structures are related to antimicrobial immune response and some immuno-pathogenesis mechanisms. This mini review highlights the role of NETs in tuberculosis and how they can be helpful as a diagnostic tool and/or therapeutic target.

Predicting the Progress of Tuberculosis by Inflammatory Response-Related Genes Based on Multiple Machine Learning Comprehensive Analysis

Background

Tuberculosis (TB), caused by the bacterium Mycobacterium tuberculosis, affects approximately one-quarter of the global population and is considered one of the most lethal infectious diseases worldwide. The prevention of latent tuberculosis infection (LTBI) from progressing into active tuberculosis (ATB) is crucial for controlling and eradicating TB. Unfortunately, currently available biomarkers have limited effectiveness in identifying subpopulations that are at risk of developing ATB. Hence, it is imperative to develop advanced molecular tools for TB risk stratification.

Methods

The TB datasets were downloaded from the GEO database. Three machine learning models, namely LASSO, RF, and SVM-RFE, were used to identify the key characteristic genes related to inflammation during the progression of LTBI to ATB. The expression and diagnostic accuracy of these characteristic genes were subsequently verified. These genes were then used to develop diagnostic nomograms. In addition, single-cell expression clustering analysis, immune cell expression clustering analysis, GSVA analysis, immune cell correlation, and immune checkpoint correlation of characteristic genes were conducted. Furthermore, the upstream shared miRNA was predicted, and a miRNA-genes network was constructed. Candidate drugs were also analyzed and predicted.

Results

In comparison to LTBI, a total of 96 upregulated and 26 downregulated genes related to the inflammatory response were identified in ATB. These characteristic genes have demonstrated excellent diagnostic performance and significant correlation with many immune cells and immune sites. The results of the miRNA-genes network analysis suggested a potential role of hsa-miR-3163 in the molecular mechanism of LTBI progressing into ATB. Moreover, retinoic acid may offer a potential avenue for the prevention of LTBI progression to ATB and for the treatment of ATB.

Conclusion

Our research has identified key inflammatory response-related genes that are characteristic of LTBI progression to ATB and hsa-miR-3163 as a significant node in the molecular mechanism of this progression. Our analyses have demonstrated the excellent diagnostic performance of these characteristic genes and their significant correlation with many immune cells and immune checkpoints. The CD274 immune checkpoint presents a promising target for the prevention and treatment of ATB. Furthermore, our findings suggest that retinoic acid may have a role in preventing LTBI from progressing to ATB and in treating ATB. This study provides a new perspective for differential diagnosis of LTBI and ATB and may uncover potential inflammatory immune mechanisms, biomarkers, therapeutic targets, and effective drugs in the progression of LTBI into ATB.

Machine learning approaches in diagnosing tuberculosis through biomarkers - A systematic review

Biomarker-based tests may facilitate Tuberculosis (TB) diagnosis, accelerate treatment initiation, and thus improve outcomes. This review synthesizes the literature on biomarker-based detection for TB diagnosis using machine learning. The systematic review approach follows the PRISMA guideline. Articles were sought using relevant keywords from Web of Science, PubMed, and Scopus, resulting in 19 eligible studies after a meticulous screening. All the studies were found to have focused on the supervised learning approach, with Support Vector Machine (SVM) and Random Forest emerging as the top two algorithms, with the highest accuracy, sensitivity and specificity reported to be 97.0%, 99.2%, and 98.0%, respectively. Further, protein-based biomarkers were widely explored, followed by gene-based such as RNA sequence and, Spoligotypes. Publicly available datasets were observed to be popularly used by the studies reviewed whilst studies targeting specific cohorts such as HIV patients or children gathering their own data from healthcare facilities, leading to smaller datasets. Of these, most studies used the leave one out cross validation technique to mitigate overfitting. The review shows that machine learning is increasingly assessed in research to improve TB diagnosis through biomarkers, as promising results were shown in terms of model's detection performance. This provides insights on the possible application of machine learning approaches to diagnose TB using biomarkers as opposed to the traditional methods that can be time consuming. Low-middle income settings, where access to basic biomarkers could be provided as compared to sputum-based tests that are not always available, could be a major application of such models.

A united model for diagnosing pulmonary tuberculosis with random forest and artificial neural network

Background: Pulmonary tuberculosis (PTB) is a chronic infectious disease and is the most common type of TB. Although the sputum smear test is a gold standard for diagnosing PTB, the method has numerous limitations, including low sensitivity, low specificity, and insufficient samples.

Methods: The present study aimed to identify specific biomarkers of PTB and construct a model for diagnosing PTB by combining random forest (RF) and artificial neural network (ANN) algorithms. Two publicly available cohorts of TB, namely, the GSE83456 (training) and GSE42834 (validation) cohorts, were retrieved from the Gene Expression Omnibus (GEO) database. A total of 45 and 61 differentially expressed genes (DEGs) were identified between the PTB and control samples, respectively, by screening the GSE83456 cohort. An RF classifier was used for identifying specific biomarkers, following which an ANN-based classification model was constructed for identifying PTB samples. The accuracy of the ANN model was validated using the receiver operating characteristic (ROC) curve. The proportion of 22 types of immunocytes in the PTB samples was measured using the CIBERSORT algorithm, and the correlations between the immunocytes were determined.

Results: Differential analysis revealed that 11 and 22 DEGs were upregulated and downregulated, respectively, and 11 biomarkers specific to PTB were identified by the RF classifier. The weights of these biomarkers were determined and an ANN-based classification model was subsequently constructed. The model exhibited outstanding performance, as revealed by the area under the curve (AUC), which was 1.000 for the training cohort. The AUC of the validation cohort was 0.946, which further confirmed the accuracy of the model.

Conclusion: Altogether, the present study successfully identified specific genetic biomarkers of PTB and constructed a highly accurate model for the diagnosis of PTB based on blood samples. The model developed herein can serve as a reliable reference for the early detection of PTB and provide novel perspectives into the pathogenesis of PTB.

Diagnosis and biomarkers for ocular tuberculosis: From the present into the future

Tuberculosis is an airborne disease caused by Mycobacterium tuberculosis (Mtb) and can manifest both pulmonary and extrapulmonary disease, including ocular tuberculosis (OTB). Accurate diagnosis and swift optimal treatment initiation for OTB is faced by many challenges combined with the lack of standardized treatment regimens this results in uncertain OTB outcomes. The purpose of this study is to summarize existing diagnostic approaches and recently discovered biomarkers that may contribute to establishing OTB diagnosis, choice of anti-tubercular therapy (ATT) regimen, and treatment monitoring. The keywords ocular tuberculosis, tuberculosis, Mycobacterium, biomarkers, molecular diagnosis, multi-omics, proteomics, genomics, transcriptomics, metabolomics, T-lymphocytes profiling were searched on PubMed and MEDLINE databases. Articles and books published with at least one of the keywords were included and screened for relevance. There was no time limit for study inclusion. More emphasis was placed on recent publications that contributed new information about the pathogenesis, diagnosis, or treatment of OTB. We excluded abstracts and articles that were not written in the English language. References cited within the identified articles were used to further supplement the search. We found 10 studies evaluating the sensitivity and specificity of interferon-gamma release assay (IGRA), and 6 studies evaluating that of tuberculin skin test (TST) in OTB patients. IGRA (Sp = 71-100%, Se = 36-100%) achieves overall better sensitivity and specificity than TST (Sp = 51.1-85.7%; Se = 70.9-98.5%). For nuclear acid amplification tests (NAAT), we found 7 studies on uniplex polymerase chain reaction (PCR) with different Mtb targets, 7 studies on DNA-based multiplex PCR, 1 study on mRNA-based multiplex PCR, 4 studies on loop-mediated isothermal amplification (LAMP) assay with different Mtb targets, 3 studies on GeneXpert assay, 1 study on GeneXpert Ultra assay and 1 study for MTBDRplus assay for OTB. Specificity is overall improved but sensitivity is highly variable for NAATs (excluding uniplex PCR, Sp = 50-100%; Se = 10.5-98%) as compared to IGRA. We also found 3 transcriptomic studies, 6 proteomic studies, 2 studies on stimulation assays, 1 study on intraocular protein analysis and 1 study on T-lymphocyte profiling in OTB patients. All except 1 study evaluated novel, previously undiscovered biomarkers. Only 1 study has been externally validated by a large independent cohort. Future theranostic marker discovery by a multi-omics approach is essential to deepen pathophysiological understanding of OTB. Combined these might result in swift, optimal and personalized treatment regimens to modulate the heterogeneous mechanisms of OTB. Eventually, these studies could improve the current cumbersome diagnosis and management of OTB.

Diagnostic biomarkers for active tuberculosis: progress and challenges

Tuberculosis (TB) is a leading cause of morbidity and mortality from a single infectious agent, despite being preventable and curable. Early and accurate diagnosis of active TB is critical to both enhance patient care, improve patient outcomes, and break Mycobacterium tuberculosis (Mtb) transmission cycles. In 2020 an estimated 9.9 million people fell ill from Mtb, but only a little over half (5.8 million) received an active TB diagnosis and treatment. The World Health Organization has proposed target product profiles for biomarker- or biosignature-based diagnostics using point-of-care tests from easily accessible specimens such as urine or blood. Here we review and summarize progress made in the development of pathogen- and host-based biomarkers for active TB diagnosis. We describe several unique patient populations that have posed challenges to development of a universal diagnostic TB biomarker, such as people living with HIV, extrapulmonary TB, and children. We also review additional limitations to widespread validation and utilization of published biomarkers. We conclude with proposed solutions to enhance TB diagnostic biomarker validation and uptake.