Assessment of the Cepheid 3-gene Host Response Fingerstick Blood Test (MTB-HR) on rapid diagnosis of tuberculosis

Identification of serum tRNA-derived small RNAs biosignature for diagnosis of tuberculosis

The tRNA-derived small RNAs (tsRNAs) are a new class of non coding RNAs, which are stable in body fluids and can be used as potential biomarkers for disease diagnosis. However, the exact value of tsRNAs in the diagnosis of tuberculosis (TB) is still unclear. The objective of the present study was to evaluate the performance of the serum tsRNAs biosignature to distinguish between active TB, healthy controls, latent TB infection, and other respiratory diseases. The differential expression profiles of tsRNAs in serum from active TB patients and healthy controls were analyzed by high-throughput sequencing. A total of 905 subjects were prospectively recruited for our study from three different cohorts. Levels of tsRNA-Gly-CCC-2, tsRNA-Gly-GCC-1, and tsRNA-Lys-CTT-2-M2 were significantly elevated in the serum of TB patients compared to non-TB individuals, showing a correlation with lung injury severity and acid-fast bacilli grades in TB patients. The accuracy of the three-tsRNA biosignature for TB diagnosis was evaluated in the training (n = 289), test (n = 124), and prediction (n = 292) groups. By utilizing cross-validation with a random forest algorithm approach, the training cohort achieved a sensitivity of 100% and specificity of 100%. The test cohort exhibited a sensitivity of 75.8% and a specificity of 91.2%. Within the prediction group, the sensitivity and specificity were 73.1% and 92.5%, respectively. The three-tsRNA biosignature generally decreased within 3 months of treatment and then remained stable. In conclusion, the three-tsRNA biosignature might serve as biomarker to diagnose TB and to monitor the effectiveness of treatment in a high-burden TB clinical setting.

Clustering Mycobacterium tuberculosis-specific CD154+CD4+ T cells for distinguishing tuberculosis disease from infection based on single-cell RNA-seq analysis

BACKGROUND

Distinguishing between active tuberculosis disease (TBD) and latent tuberculosis infection (TBI) is crucial for TB control but remains challenging.

METHODS

Single-cell RNA sequencing was conducted on purified Mycobacterium tuberculosis (MTB)-specific CD154+CD4+ T cells.

RESULTS

We observe a superior role of CD154 in detecting MTB infection, whereas its ability in distinguishing TBD from TBI is still limited due to patient heterogeneity. Single-cell RNA sequencing of MTB-specific CD154+CD4+ T cells identifies 10 distinct clusters, including Treg, T_act, Th1_pex, Th1_eff, Tfh, T_na, Th17_ex, Th2, NKT, and Th1_cyt. Notably, effector and apoptotic Th1 cells are predominant in CD154+CD4+ T cells of TBD. However, Tfh cells are the primary component in TBI. Most Th1_pex cells are positioned at the end of the developmental trajectory and are regulated by key genes associated with apoptosis and early exhaustion, such as GADD45B, FOS, and EZH2. Oxidative stress-induced metabolic disorder, marked by increased metabolism of nitrogen, cysteine, and glutathione, also contributes to the apoptosis of Th1_pex cells. Using seven features including NA, CM, EM, EMRA, CXCR3+ Th1, IFN-γ+ Th1, and Tfh of CD154+CD4+ T cells, both TBD and TBI can be classified into different subtypes, and a further established random forest model can accurately differentiate TBD from TBI. Additionally, the key checkpoints of exhausted MTB-specific Th1 cells are identified and blocking ADORA2A efficiently restores their function.

CONCLUSIONS

We depict the cellular compositions, transcriptional characteristics, and developmental trajectories of MTB-specific CD154+CD4+ T cells from TBI to TBD, putting forward a new direction in the diagnosis and prognosis of disease.

A Comprehensive Review of the Diagnostics for Pediatric Tuberculosis Based on Assay Time, Ease of Operation, and Performance

Pediatric tuberculosis (TB) is still challenged by several diagnostic bottlenecks, imposing a high TB burden in low- and middle-income countries (LMICs). Diagnostic turnaround time (TAT) and ease of operation to suit resource-limited settings are critical aspects that determine early treatment and influence morbidity and mortality. Based on TAT and ease of operation, this article reviews the evolving landscape of TB diagnostics, from traditional methods like microscopy and culture to cutting-edge molecular techniques and biomarker-based approaches. We examined the benefits of efficient rapid results against potential trade-offs in accuracy and clinical utility. The review highlights emerging molecular methods and artificial intelligence-based detection methods, which offer promising improvements in both speed and sensitivity. The review also addresses the challenges of implementing these technologies in resource-limited settings, where most pediatric TB cases occur. Gaps in the existing diagnostic methods, algorithms, and operational costs were also reviewed. Developing optimal diagnostic strategies that balance speed, performance, cost, and feasibility in diverse healthcare settings can provide valuable insights for clinicians, researchers, and policymakers.

Molecular Indicator for Distinguishing Multi-drug-Resistant Tuberculosis from Drug Sensitivity Tuberculosis and Potential Medications for Treatment

The issue of multi-drug-resistant tuberculosis (MDR-TB) presents a substantial challenge to global public health. Regrettably, the diagnosis of drug-resistant tuberculosis (DR-TB) frequently necessitates an extended period or more extensive laboratory resources. The swift identification of MDR-TB poses a particularly challenging endeavor. To identify the biomarkers indicative of multi-drug resistance, we conducted a screening of the GSE147689 dataset for differentially expressed genes (DEGs) and subsequently conducted a gene enrichment analysis. Our analysis identified a total of 117 DEGs, concentrated in pathways related to the immune response. Three machine learning methods, namely random forest, decision tree, and support vector machine recursive feature elimination (SVM-RFE), were implemented to identify the top 10 genes according to their feature importance scores. A4GALT and S1PR1, which were identified as common genes among the three methods, were selected as potential molecular markers for distinguishing between MDR-TB and drug-susceptible tuberculosis (DS-TB). These markers were subsequently validated using the GSE147690 dataset. The findings suggested that A4GALT exhibited area under the curve (AUC) values of 0.8571 and 0.7121 in the training and test datasets, respectively, for distinguishing between MDR-TB and DS-TB. S1PR1 demonstrated AUC values of 0.8163 and 0.5404 in the training and test datasets, respectively. When A4GALT and S1PR1 were combined, the AUC values in the training and test datasets were 0.881 and 0.7551, respectively. The relationship between hub genes and 28 immune cells infiltrating MDR-TB was investigated using single sample gene enrichment analysis (ssGSEA). The findings indicated that MDR-TB samples exhibited a higher proportion of type 1 T helper cells and a lower proportion of activated dendritic cells in contrast to DS-TB samples. A negative correlation was observed between A4GALT and type 1 T helper cells, whereas a positive correlation was found with activated dendritic cells. S1PR1 exhibited a positive correlation with type 1 T helper cells and a negative correlation with activated dendritic cells. Furthermore, our study utilized connectivity map analysis to identify nine potential medications, including verapamil, for treating MDR-TB. In conclusion, our research identified two molecular indicators for the differentiation between MDR-TB and DS-TB and identified a total of nine potential medications for MDR-TB.

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.

Active versus latent pulmonary tuberculosis: which one is the appropriate distinguishing biomarker?

This study tried to assess the possibility of using the estimated levels of plasma expression of microRNAs (miR-) for distinguishing healthy subjects with latent pulmonary tuberculosis (LTB) from healthy controls (HC) and patients with active tuberculosis (ATB). Study participants included 30 newly diagnosed ATB patients, 30 of the households of ATB patients who were free of clinical manifestations, had normal chest radiography but had positive results on the whole-blood QuantiFERON tuberculosis (TB) Gold In-Tube (QFT-GIT) test (LTB patients), and 30 HC who were free of clinical symptoms and showed normal chest X-rays and negative QFT-GIT tests. All participants gave blood samples for quantitation of the plasma expression levels of miR- using the reverse transcription-quantitative polymerase chain reaction. Plasma levels of miR-150-5p were significantly downregulated in ATB samples than in other samples. However, miR-155-5p and miR-378-5p were significantly overexpressed in patients' samples compared to HC's samples and in ATB samples compared to LTB samples. On the contrary, plasma miR-4523-5p showed significant upregulation in LTB samples compared to ATB and HC samples, indicating insignificant in-between differences. The receiver operating characteristic curve analysis showed the ability of the estimated levels of the four miR- to differentiate TB patients from HC. Multivariate regression analysis defined expression levels of miR-155-5p and miR-378-5p as the significant biomarkers for distinguishing TB patients and levels of miR-378-5p and miR-4523-5p for identification of LTB patients. Pulmonary TB induces deregulated expression of miR-, according to the infection severity. An estimation of the expression levels of miR-378-5p and miR-4523-5p might be a reliable combination for identifying LTB patients.

Identification of important modules and biomarkers in tuberculosis based on WGCNA

Background

Tuberculosis (TB) is a significant public health concern, particularly in China. Long noncoding RNAs (lncRNAs) can provide abundant pathological information regarding etiology and could include candidate biomarkers for diagnosis of TB. However, data regarding lncRNA expression profiles and specific lncRNAs associated with TB are limited.

Methods

We performed ceRNA-microarray analysis to determine the expression profile of lncRNAs in peripheral blood mononuclear cells (PBMCs). Weighted gene co-expression network analysis (WGCNA) was then conducted to identify the critical module and genes associated with TB. Other bioinformatics analyses, including Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and co-expression networks, were conducted to explore the function of the critical module. Finally, real-time quantitative polymerase chain reaction (qPCR) was used to validate the candidate biomarkers, and receiver operating characteristic analysis was used to assess the diagnostic performance of the candidate biomarkers.

Results

Based on 8 TB patients and 9 healthy controls (HCs), a total of 1,372 differentially expressed lncRNAs were identified, including 738 upregulated lncRNAs and 634 downregulated lncRNAs. Among all lncRNAs and mRNAs in the microarray, the top 25% lncRNAs (3729) and top 25% mRNAs (2824), which exhibited higher median expression values, were incorporated into the WGCNA. The analysis generated 16 co-expression modules, among which the blue module was highly correlated with TB. GO and KEGG analyses showed that the blue module was significantly enriched in infection and immunity. Subsequently, considering module membership values (>0.85), gene significance values (>0.90) and fold-change value (>2 or < 0.5) as selection criteria, the top 10 upregulated lncRNAs and top 10 downregulated lncRNAs in the blue module were considered as potential biomarkers. The candidates were then validated in an independent validation sample set (31 TB patients and 32 HCs). The expression levels of 8 candidates differed significantly between TB patients and HCs. The lncRNAs ABHD17B (area under the curve [AUC] = 1.000) and ENST00000607464.1 (AUC = 1.000) were the best lncRNAs in distinguishing TB patients from HCs.

Conclusion

This study characterized the lncRNA profiles of TB patients and identified a significant module associated with TB as well as novel potential biomarkers for TB diagnosis.