TNFRp75-dependent immune regulation of alveolar macrophages and neutrophils during early Mycobacterium tuberculosis and Mycobacterium bovis BCG infection

Identification of hub genes and establishment of a diagnostic model in tuberculosis infection

Tuberculosis (TB) poses significant challenges due to its high transmissibility within populations and intrinsic resistance to treatment, rendering it a formidable respiratory disease with a substantial susceptibility burden. This study was designed to identify new potential therapeutic targets for TB and establish a diagnostic model. mRNA expression data for TB were from GEO database, followed by conducting differential expression analysis. The top 50 genes with differential expression were subjected to GO and KEGG enrichment analyses. To establish a PPI network, the STRING database was utilized, and hub genes were identified utilizing five algorithms (EPC, MCC, MNC, Radiality, and Stress) within the cytoHubba plugin of Cytoscape software. Furthermore, a hub gene co-expression network was constructed using the GeneMANIA database. Consistency clustering was performed on hub genes, and ssGSEA was utilized to analyze the extent of immune infiltration in different subgroups. LASSO analysis was employed to construct a diagnostic model, and ROC curves were used for validation. Through the analysis of GEO data, a total of 159 genes were identified as differentially expressed. Further, GO and KEGG enrichment analyses revealed that these genes were mainly enriched in viral defense, symbiotic defense, and innate immune response-related pathways. Hub genes, including DDX58, IFIT2, IFIH1, RSAD2, IFI44L, OAS2, OAS1, OASL, IFIT1, IFIT3, MX1, STAT1, and ISG15, were identified using cytoHubba analysis of the PPI network. The GeneMANIA analysis unmasked that the co-expression rate of hub genes was 81.55%, and the physical interaction rate was 12.27%. Consistency clustering divided TB patients into two subgroups, and ssGSEA revealed different degrees of immune infiltration in different subgroups. LASSO analysis identified IFIT1, IFIT2, IFIT3, IFIH1, RSAD2, OAS1, OAS2, and STAT1 as eight immune-related key genes, and a diagnostic model was constructed. The ROC curve demonstrated that the model exhibited excellent diagnostic performance. DDX58, IFIT2, IFIH1, RSAD2, IFI44L, OAS2, OAS1, OASL, IFIT1, IFIT3, MX1, STAT1, and ISG15 were hub genes in TB, and the diagnostic model based on eight immune-related key genes exhibited good diagnostic performance.

Virulence Factors of Mycobacterium tuberculosis as Modulators of Cell Death Mechanisms

Mycobacterium tuberculosis (Mtb) modulates diverse cell death pathways to escape the host immune responses and favor its dissemination, a complex process of interest in pathogenesis-related studies. The main virulence factors of Mtb that alter cell death pathways are classified according to their origin as either non-protein (for instance, lipomannan) or protein (such as the PE family and ESX secretion system). The 38 kDa lipoprotein, ESAT-6 (early antigen-secreted protein 6 kDa), and another secreted protein, tuberculosis necrotizing toxin (TNT), induces necroptosis, thereby allowing mycobacteria to survive inside the cell. The inhibition of pyroptosis by blocking inflammasome activation by Zmp1 and PknF is another pathway that aids the intracellular replication of Mtb. Autophagy inhibition is another mechanism that allows Mtb to escape the immune response. The enhanced intracellular survival (Eis) protein, other proteins, such as ESX-1, SecA2, SapM, PE6, and certain microRNAs, also facilitate Mtb host immune escape process. In summary, Mtb affects the microenvironment of cell death to avoid an effective immune response and facilitate its spread. A thorough study of these pathways would help identify therapeutic targets to prevent the survival of mycobacteria in the host.

Allele-dependent interaction of LRRK2 and NOD2 in leprosy

Leprosy, caused by Mycobacterium leprae, rarely affects children younger than 5 years. Here, we studied a multiplex leprosy family that included monozygotic twins aged 22 months suffering from paucibacillary leprosy. Whole genome sequencing identified three amino acid mutations previously associated with Crohn’s disease and Parkinson’s disease as candidate variants for early onset leprosy: LRRK2 N551K, R1398H and NOD2 R702W. In genome-edited macrophages, we demonstrated that cells expressing the LRRK2 mutations displayed reduced apoptosis activity following mycobacterial challenge independently of NOD2. However, employing co-immunoprecipitation and confocal microscopy we showed that LRRK2 and NOD2 proteins interacted in RAW cells and monocyte-derived macrophages, and that this interaction was substantially reduced for the NOD2 R702W mutation. Moreover, we observed a joint effect of LRRK2 and NOD2 variants on Bacillus Calmette-Guérin (BCG)-induced respiratory burst, NF-κB activation and cytokine/chemokine secretion with a strong impact for the genotypes found in the twins consistent with a role of the identified mutations in the development of early onset leprosy.

Transmembrane TNF and Its Receptors TNFR1 and TNFR2 in Mycobacterial Infections

Tumor necrosis factor (TNF) is one of the main cytokines regulating a pro-inflammatory environment. It has been related to several cell functions, for instance, phagocytosis, apoptosis, proliferation, mitochondrial dynamic. Moreover, during mycobacterial infections, TNF plays an essential role to maintain granuloma formation. Several effector mechanisms have been implicated according to the interactions of the two active forms, soluble TNF (solTNF) and transmembrane TNF (tmTNF), with their receptors TNFR1 and TNFR2. We review the impact of these interactions in the context of mycobacterial infections. TNF is tightly regulated by binding to receptors, however, during mycobacterial infections, upstream activation signalling pathways may be influenced by key regulatory factors either at the membrane or cytosol level. Detailing the structure and activation pathways used by TNF and its receptors, such as its interaction with solTNF/TNFRs versus tmTNF/TNFRs, may bring a better understanding of the molecular mechanisms involved in activation pathways which can be helpful for the development of new therapies aimed at being more efficient against mycobacterial infections.