Subclinical tuberculosis (TB) is the presence of TB disease among people who are either asymptomatic or have minimal symptoms.

Currently, there are no accurate diagnostic tools and clear treatment approaches for subclinical TB. In this study, a comprehensive literature search was conducted across major databases. This review aimed to uncover the latest advancements in diagnostic approaches, explore their clinical implications, and outline potential future perspectives. While innovative technologies are in development to enable sputum-free TB tests, there remains a critical need for precise diagnostic tools tailored to the unique characteristics of subclinical TB. Given the complexity of subclinical TB, a multidisciplinary approach involving clinicians, microbiologists, epidemiologists, and public health experts is essential. Further research is needed to establish standardized diagnostic criteria and treatment guidelines specifically tailored for subclinical TB, acknowledging the unique challenges posed by this elusive stage of the disease.

Efforts are needed for the detection, diagnosis, and treatment of subclinical TB. In this review, we describe the importance of subclinical TB, both from a clinical and public health perspective and highlight the diagnostic and treatment gaps of this stage.

Tuberculosis is a serious life-threatening disease among the top global health challenges and rapid and effective diagnostic biomarkers are vital for early diagnosis especially given the increasing prevalence of multidrug resistance.

Two human whole blood microarray datasets, GSE42826 and GSE42830 were retrieved from publicly available gene expression omnibus (GEO) database. Deregulated genes (DEGs) were identified using GEO2R online tool and Gene Ontology (GO), protein-protein interaction (PPI) network analysis was performed using Metascape and STRING databases. Significant genes (n = 8) were identified using T-test/ANOVA and Molecular Complex Detection (MCODE) score ≥10, which was validated in GSE34608 dataset. The diagnostic potential of three biomarkers was assessed using Area Under Curve (AUC) of Receiver Operating Characteristic (ROC) plot. The transcriptional levels of these genes were also examined in a separate dataset GSE31348, to monitor the patterns of variation during tuberculosis treatment.

A total of 62 common DEGs (57 upregulated, 7 downregulated genes) were identified in two discovery datasets. GO functions and pathway enrichment analysis shed light on the functional roles of these DEGs in immune response and type-II interferon signaling. The genes in Module-1 (n = 18) were linked to innate immune response, interferon-gamma signaling. The common genes (n = 8) were validated in GSE34608 dataset, that corroborates the results obtained from discovery sets. The gene expression levels demonstrated responsiveness to Mtb infection during anti-TB therapy in GSE31348 dataset. In GSE34608 dataset, the expression levels of three specific genes, GBP5, IFITM3, and EPSTI1, emerged as potential diagnostic makers. In combination, these genes scored remarkable diagnostic performance with 100% sensitivity and 89% specificity, resulting in an impressive Area Under Curve (AUC) of 0.958. However, GBP5 alone showed the highest AUC of 0.986 with 100% sensitivity and 89% specificity.

The study presents valuable insights into the critical gene network perturbed during tuberculosis. These genes are determinants for assessing the effectiveness of an anti-TB response and distinguishing between active TB and healthy individuals. GBP5, IFITM3 and EPSTI1 emerged as candidate core genes in TB and holds potential as novel molecular targets for the development of interventions in the treatment of TB.

In recent years, infectious disease diagnosis has increasingly turned to host-centered approaches as a complement to pathogen-directed ones. The former, however, typically requires the interpretation of complex multiple biomarker datasets to arrive at an informative diagnostic outcome. This report describes a machine learning (ML)-based classification workflow that is intended as a template for researchers seeking to apply ML approaches for developing host-based infectious disease biomarker classifiers. As an example, we built a classification model that could accurately distinguish between three disease etiology classes: bacterial, viral, and normal in human sera using host protein biomarkers of known diagnostic utility. After collecting protein data from known disease samples, we trained a series of increasingly complex Auto-ML models until arriving at an optimized classifier that could differentiate viral, bacterial, and non-disease samples. Even when limited to a relatively small training set size, the model had robust diagnostic characteristics and performed well when faced with a blinded sample set. We present here a flexible approach for applying an Auto-ML-based workflow for the identification of host biomarker classifiers with diagnostic utility for infectious disease, and which can readily be adapted for multiple biomarker classes and disease states.

The high morbidity and mortality rate of individuals with human immunodeficiency virus (HIV) / acquired immunodeficiency syndrome (AIDS) coinfected with Mycobacterium tuberculosis (MTB) is a tough challenge for current global tuberculosis prevention and control efforts. HIV/MTB coinfection is more complex than a single infection, and the interaction between the two diseases aggravates the deterioration caused by the disease, resulting in increased hospitalizations and deaths. Rapid screening and early diagnosis facilitate the timely initiation of anti-tuberculosis treatment in HIV/MTB coinfected individuals, thereby reducing transmission and the incidence of adverse prognoses. To date, pathogenic detection has remained the gold standard for diagnosing tuberculosis, but its sensitivity and specificity are greatly affected by the body's immune status, which limits its application in the diagnosis of HIV/MTB coinfection. Recently, immunology and molecular detection technology has developed rapidly. New detection technologies, such as interferon-γ release assays, interferon-gamma inducible protein 10, and GeneXpert MTB/RIF assay have overcome the limitations of traditional detection methods, significantly improved the sensitivity and specificity of tuberculosis diagnosis, and brought new hope to the detection of HIV/MTB coinfection. In this article, the principle, scope of application, and latest research progress of relevant detection methods are reviewed to provide a reference for the early diagnosis of HIV/MTB coinfection.

Tuberculosis-diabetes mellitus (TB-DM) is linked to a distinct inflammatory profile, which can be assessed using multi-omics analyses. Here, a machine learning algorithm was applied to multi-platform data, including cytokines and gene expression in peripheral blood and eicosanoids in urine, in a Brazilian multi-center TB cohort. There were four clinical groups: TB-DM(n = 24), TB only(n = 28), DM(HbA1c ≥ 6.5%) only(n = 11), and a control group of close TB contacts who did not have TB or DM(n = 13). After cross-validation, baseline expression or abundance of MMP-28, LTE-4, 11-dTxB2, PGDM, FBXO6, SECTM1, and LINCO2009 differentiated the four patient groups. A distinct multi-omic-derived, dimensionally reduced, signature was associated with TB, regardless of glycemic status. SECTM1 and FBXO6 mRNA levels were positively correlated with sputum acid-fast bacilli grade in TB-DM. Values of the biomarkers decreased during the course of anti-TB therapy. Our study identified several markers associated with the pathophysiology of TB-DM that could be evaluated in future mechanistic investigations.

Inhalation of Mycobacterium tuberculosis (Mtb) bacilli can lead to a range of TB categories including early clearance (EC), latent TB infection (LTBI) and active TB (ATB). There are few biomarkers available to differentiate among these TB categories: effective new biomarkers are badly needed. Here, we analyzed the serum proteins from 26 ATB cases, 20 LTBI cases, 34 EC cases and 38 healthy controls (HC) using label-free LC-MS/MS. The results were analyzed using MaxQuant software and matched to three different bacterial proteomics databases, including Mtb, Mycobacterium spp. and normal lung flora. PCA of protein candidates using the three proteomics databases revealed 44.5% differentiation power to differentiate among four TB categories. There were 289 proteins that showed potential for distinguishing between each pair of groups among TB categories. There were 50 candidate protein markers specifically found in ATB and LTBI but not in HC and EC groups. Decision trees using the top five candidate biomarkers (A0A1A2RWZ9, A0A1A3FMY8, A0A1A3KIY2, A0A5C7MJH5 and A0A1X0XYR3) had 92.31% accuracy to differentiate among TB categories and the accuracy was increased to 100% when using 10 candidate biomarkers. Our study shows that proteins expressed from Mycobacterium spp. have the potential to be used to differentiate among TB categories.

Osteoarticular tuberculosis is one of the extrapulmonary tuberculosis (EPTB) diseases, which is mainly caused by infection of Mycobacterium tuberculosis (MTB) in bone and joints. The limitation of current clinical test methods is leading to a high misdiagnosis rate and affecting the treatment and prognosis. This study aims to search serum biomarkers that can assist in the diagnosis of osteoarticular tuberculosis.

Proteomics can serve as an important method in the discovery of disease biomarkers. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to analyze proteins in 90 serum samples, which were collected from June 2020 to December 2021, then evaluated by statistical analysis to screen potential biomarkers. After that, potential biomarkers were validated by ELISA and diagnostic models were also established for observation of multi-index diagnostic efficacy.

118 differential expressed proteins (DEPs) were obtained in serum after statistical analysis. After the diagnostic efficacy evaluation and clinical verification, inter-alpha-trypsin inhibitor heavy chain H2 (ITIH2), complement factor H-related protein 2 (CFHR2), complement factor H-related protein 3 (CFHR3) and complement factor H-related protein 5 (CFHR5) were found as potential biomarkers, with 0.7167 (95%CI: 0.5846-0.8487), 0.8600 (95%CI: 0.7701-0.9499), 0.8150 (95%CI: 0.6998-0.9302), and 0.9978 (95%CI: 0.9918-1.0040) AUC value, respectively. The remaining DEPs except CFHR5 were constructed as diagnostic models, the diagnostic model contained CFHR2 and CFHR3 had good diagnostic efficacy with 0.942 (95%CI: 0.872-0.980) AUC value compared to other models.

This study provides a reference for the discovery of serum protein markers for osteoarticular tuberculosis diagnosis, and the screened DEPs can also provide directions for subsequent pathogenesis research.

To determine whether leucine-rich alpha-2 glycoprotein 1 (LRG1) is a potential prognostic and severity biomarker in patients with aneurysmal subarachnoid hemorrhage (aSAH).

This observational and prospective study included 44 patients with aSAH from Nanjing Drum Tower Hospital from June to December 2020. Concentrations of LRG1 in the cerebrospinal fluid (CSF) were determined by enzyme-linked immunosorbent assay within 24 hours after aSAH. We further determined the relationship of CSF LRG1 levels with disease severity and prognosis 3 months after aSAH.

Higher CSF LRG1 levels were associated with a higher Hunt-Hess grade (P < 0.05). Using univariate analysis, poor outcomes at 3 months were associated with higher World Federation of Neurological Surgeons scale grade, higher Hunt-Hess grade, higher CSF LRG1 levels, and higher Fisher grade. Logistic regression analysis revealed a significant impact of LRG1 on poor outcomes as well as after adjustment for confounding factors.

These findings suggest an increase in CSF LRG1 levels in patients with aSAH, which may serve as a potential biomarker of unfavorable prognosis and disease severity.

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

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.

Tuberculosis (TB) continues to threaten many peoples' health worldwide, regardless of their country of residence or age. The current diagnosis of TB still uses mainly traditional, time-consuming, and/or culture-based techniques. Efforts have focused on discovering new biomarkers with higher efficiency and accuracy for TB diagnosis. Proteomics-the systematic study of protein diversity-is being applied to the discovery of novel protein biomarkers for different types of diseases. Mass spectrometry (MS) technology plays a revolutionary role in proteomics, and its applicability benefits from the development of other technologies, such as matrix-based and immune-based methods. MS and derivative strategies continuously contribute to disease-related discoveries, and some promising proteomic biomarkers for efficient TB diagnosis have been identified, but challenges still exist. For example, there are discrepancies in the biomarkers identified among different reports and the diagnostic accuracy of clinically applied proteomic biomarkers. The present review summarizes the current status and future perspectives of proteomics in the field of TB biomarker discovery and aims to elicit more promising findings for rapid and accurate TB diagnosis.

A growing amount of information about the different types, functions, and roles played by non-coding RNAs (ncRNAs) is becoming available, as more and more research is done. ncRNAs have been identified as potential therapeutic targets in the treatment of tuberculosis (TB), because they may be essential regulators of the gene network. ncRNA profiling and sequencing has recently revealed significant dysregulation in tuberculosis, primarily due to aberrant processes of ncRNA synthesis, including amplification, deletion, improper epigenetic regulation, or abnormal transcription. Despite the fact that ncRNAs may have a role in TB characteristics, the detailed mechanisms behind these occurrences are still unknown. The dark matter of the genome can only be explored through the development of cutting-edge bioinformatics and molecular technologies. In this review, ncRNAs' synthesis and functions are discussed in detail, with an emphasis on the potential role of ncRNAs in tuberculosis. We also focus on current platforms, experimental strategies, and computational analyses to explore ncRNAs in TB. Finally, a viewpoint is presented on the key challenges and novel techniques for the future and for a wide-ranging therapeutic application of ncRNAs.

The secreted glycoprotein leucine-rich α-2 glycoprotein 1 (LRG1) was first described as a key player in pathogenic ocular neovascularization almost a decade ago. Since then, an increasing number of publications have reported the involvement of LRG1 in multiple human conditions including cancer, diabetes, cardiovascular disease, neurological disease, and inflammatory disorders. The purpose of this review is to provide, for the first time, a comprehensive overview of the LRG1 literature considering its role in health and disease. Although LRG1 is constitutively expressed by hepatocytes and neutrophils, Lrg1-/- mice show no overt phenotypic abnormality suggesting that LRG1 is essentially redundant in development and homeostasis. However, emerging data are challenging this view by suggesting a novel role for LRG1 in innate immunity and preservation of tissue integrity. While our understanding of beneficial LRG1 functions in physiology remains limited, a consistent body of evidence shows that, in response to various inflammatory stimuli, LRG1 expression is induced and directly contributes to disease pathogenesis. Its potential role as a biomarker for the diagnosis, prognosis and monitoring of multiple conditions is widely discussed while dissecting the mechanisms underlying LRG1 pathogenic functions. Emphasis is given to the role that LRG1 plays as a vasculopathic factor where it disrupts the cellular interactions normally required for the formation and maintenance of mature vessels, thereby indirectly contributing to the establishment of a highly hypoxic and immunosuppressive microenvironment. In addition, LRG1 has also been reported to affect other cell types (including epithelial, immune, mesenchymal and cancer cells) mostly by modulating the TGFβ signalling pathway in a context-dependent manner. Crucially, animal studies have shown that LRG1 inhibition, through gene deletion or a function-blocking antibody, is sufficient to attenuate disease progression. In view of this, and taking into consideration its role as an upstream modifier of TGFβ signalling, LRG1 is suggested as a potentially important therapeutic target. While further investigations are needed to fill gaps in our current understanding of LRG1 function, the studies reviewed here confirm LRG1 as a pleiotropic and pathogenic signalling molecule providing a strong rationale for its use in the clinic as a biomarker and therapeutic target.