Tuberculosis最新文献

Background/objectives: Tuberculous pleuritis (TP), a common manifestation of Mycobacterium tuberculosis infection, poses challenges in differentiating microbiologically positive (PEMP-MT) from negative (PEMN-MT) pleural effusions due to the limited sensitivity of traditional diagnostic methods.

Methods: Proteomics analysis using iTRAQ, non-targeted metabolomics, parallel reaction monitoring (PRM), and machine learning were employed to diagnose PEMN-MT or PEMP-MT. A validation cohort of 63 PEMN-MT and 28 PEMP-MT patients underwent ELISA experiments. Receiver operating characteristic (ROC) curves evaluated the predictive value of LDH and LV218 individually and in combination.

Results: Differentially expressed proteins (DEPs) and metabolites (DEMs) were identified using bioinformatics tools and pathway enrichment analyses. A machine learning model utilizing six biomarkers (LV218, F13A, RET4, LV321, TBA1C, and LDH) demonstrated excellent diagnostic performance with an AUROC of 0.987 and an AUPR of 0.974, distinguishing PEMP-MT from PEMN-MT. ROC curve analysis showed that both LDH and LV218, alone and in combination, provided strong predictive value for distinguishing the two groups.

Conclusion: LDH and LV218 are promising biomarkers for differentiating microbiologically positive and negative pleural effusions in tuberculous pleuritis. These biomarkers, particularly when combined, could improve diagnostic accuracy and clinical management.

Tuberculosis (TB) remains the world's deadliest infectious disease, with treatment increasingly complicated by the emergence of multidrug-resistant strains (MDR-TB). This study conducted structure-based drug screening targeting Mycobacterium tuberculosis protein kinase B (MtPknB), a serine/threonine kinase essential for M. tuberculosis survival and proliferation, to identify novel anti-TB drug candidates. From the ChemBridge library, a hierarchical screening pipeline integrating docking and molecular dynamics simulations identified candidate compounds. Among these, a quinoline-pyridine hybrid chemical demonstrated antibacterial activity against Mycobacterium smegmatis (IC₅₀ = 31.8 μM) without toxicity to Escherichia coli or mammalian cells. MM-PBSA and ab initio fragment molecular orbital (FMO) analyses revealed LEU17, VAL25, and MET155 as key stabilizing residues in the MtPknB active site. ProLIF interaction fingerprinting confirmed stable hydrophobic and van der Waals interactions formed by the quinoline-pyridine hybrid chemical. SwissADME and ProTox-3.0 predictions indicated favorable drug-like properties for the quinoline-pyridine hybrid chemical, despite potential toxicity risks. Structure-activity relationship analysis of the quinoline-pyridine hybrid chemical analogs demonstrated that subtle variations in hydrophobic interactions and substituent positioning significantly influence antibacterial potency. These findings position these chemicals as promising lead compounds for MtPknB-targeted anti-TB drug development.

Direct sputum whole genome sequencing (dsWGS) can revolutionize Mycobacterium tuberculosis (Mtb) diagnosis by enabling rapid detection clinically relevant resistance mutations and strain diversity without the biohazard of culture. We searched PubMed, Web of Science, and Google Scholar, identifying 8 studies meeting inclusion criteria for testing protocols for dsWGS. Utilising meta-regression, we identified factors positively associated with dsWGS success, including higher Mtb bacillary load, mechanical disruption, enzymatic/chemical lysis and sequencing volume. Decontamination with sodium hydroxide (NaOH) was negatively associated with dsWGS success (OR = 0.00032, 95 % CI: 1.33 × 10ˆ-6-0.077; p = 0.004), likely due to its harsh effects on Mtb cells. Mechanical lysis (OR = 6120, 95 % CI: 7.23-5.18 × 10ˆ6; p = 0.011) and enzymatic/chemical lysis (OR = 131, 95 % CI: 1.68-1.03 × 10ˆ4; p = 0.028) were positively associated with sequencing success, as was heat inactivation (OR = 4.66, 95 % CI: 1.24-17.5; p = 0.023). Total sequencing volume was also strongly associated with dsWGS success (OR = 10.35, 95 % CI: 4.43-24.2; p = 6.53 × 10ˆ-8). In addition to these effects, we also observed high variability in pre-processing approaches, highlighting the need for standardized practices and identified pre-processing steps including decontamination and DNA extraction as priorities for further optimization. Considering the strong association between Mtb load and successful dsWGS, protocols for optimal sputum sample collection, handling, and storage could also further enhance the success rate of dsWGS.

Tuberculosis, primarily caused by Mycobacterium tuberculosis (Mtb), remains a leading global health issue. We investigate the interplay between Mtb infection and various programmed cell death (PCD) in active pulmonary tuberculosis (ATB). Using GSE19491 and GSE107994 datasets, we identified 1306 overlapping differentially expressed genes (DEGs) in peripheral blood from ATB patients and healthy controls. Gene set variation analysis revealed that, except for cuproptosis, the PCD pathways: necroptosis, apoptosis, pyroptosis, and ferroptosis were significantly elevated in ATB patients. Weighted Gene Co-expression Network Analysis further identified 392 PCD-associated hub genes. KEGG and GO analyses highlighted key functional enrichments in immune responses, cellular stress, and PCD pathways. Moreover, we found a positive correlation between PCD types and specific immune cell populations. Additionally, by integrating DEGs of peripheral blood samples and lung granuloma tissues with PCD-associated hub genes, we identified 30 PCD-related genes in ATB patients. RT-qPCR results demonstrated significantly elevated GCLC, RBCK1, ZEB1, and EIF2AK2 levels, alongside lowered PLA2G4C and CAMK2G levels in patients' peripheral blood. These findings underscore the critical role of PCD pathways in modulating the immune response during Mtb infection. Future mechanistic studies are required to definitively establish the causal roles of these pathways in regulating cell death and bacterial control.