Tuberculosis: Smart manipulation of a lethal host

Integrity of the Actin Cytoskeleton of Host Macrophages is Necessary for Mycobacterial Entry

Macrophages are the primary hosts for Mycobacterium tuberculosis (M. tb), an intracellular pathogen, and the causative organism of tuberculosis (TB) in humans. While M. tb has the ability to enter and survive in host macrophages, the precise mechanism of its internalization, and factors that control this essential process are poorly defined. We have previously demonstrated that perturbations in levels of cholesterol and sphingolipids in macrophages lead to significant reduction in the entry of Mycobacterium smegmatis (M. smegmatis), a surrogate model for mycobacterial internalization, signifying a role for these plasma membrane lipids in interactions at the host–pathogen interface. In this work, we investigated the role of the host actin cytoskeleton, a critical protein framework underlying the plasma membrane, in the entry of M. smegmatis into human macrophages. Our results show that cytochalasin D mediated destabilization of the actin cytoskeleton of host macrophages results in a dose-dependent reduction in the entry of mycobacteria. Notably, the internalization of Escherichia coli remained invariant upon actin destabilization of host cells, implying a specific involvement of the actin cytoskeleton in mycobacterial infection. By monitoring the F-actin content of macrophages utilizing a quantitative confocal microscopy-based technique, we observed a close correlation between the entry of mycobacteria into host macrophages with cellular F-actin content. Our results constitute the first quantitative analysis of the role of the actin cytoskeleton of human macrophages in the entry of mycobacteria, and highlight actin-mediated mycobacterial entry as a potential target for future anti-TB therapeutics.

Integrative genomics of the mammalian alveolar macrophage response to intracellular mycobacteria

Background

Bovine TB (bTB), caused by infection with Mycobacterium bovis, is a major endemic disease affecting global cattle production. The key innate immune cell that first encounters the pathogen is the alveolar macrophage, previously shown to be substantially reprogrammed during intracellular infection by the pathogen. Here we use differential expression, and correlation- and interaction-based network approaches to analyse the host response to infection with M. bovis at the transcriptome level to identify core infection response pathways and gene modules. These outputs were then integrated with genome-wide association study (GWAS) data sets to enhance detection of genomic variants for susceptibility/resistance to M. bovis infection.

Results

The host gene expression data consisted of RNA-seq data from bovine alveolar macrophages (bAM) infected with M. bovis at 24 and 48 h post-infection (hpi) compared to non-infected control bAM. These RNA-seq data were analysed using three distinct computational pipelines to produce six separate gene sets: 1) DE genes filtered using stringent fold-change and P-value thresholds (DEG-24: 378 genes, DEG-48: 390 genes); 2) genes obtained from expression correlation networks (CON-24: 460 genes, CON-48: 416 genes); and 3) genes obtained from differential expression networks (DEN-24: 339 genes, DEN-48: 495 genes). These six gene sets were integrated with three bTB breed GWAS data sets by employing a new genomics data integration tool—gwinteR. Using GWAS summary statistics, this methodology enabled detection of 36, 102 and 921 prioritised SNPs for Charolais, Limousin and Holstein-Friesian, respectively.

Conclusions

The results from the three parallel analyses showed that the three computational approaches could identify genes significantly enriched for SNPs associated with susceptibility/resistance to M. bovis infection. Results indicate distinct and significant overlap in SNP discovery, demonstrating that network-based integration of biologically relevant transcriptomics data can leverage substantial additional information from GWAS data sets. These analyses also demonstrated significant differences among breeds, with the Holstein-Friesian breed GWAS proving most useful for prioritising SNPS through data integration. Because the functional genomics data were generated using bAM from this population, this suggests that the genomic architecture of bTB resilience traits may be more breed-specific than previously assumed.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07643-w.

Mycobacterium tuberculosis Rv0580c Impedes the Intracellular Survival of Recombinant Mycobacteria, Manipulates the Cytokines, and Induces ER Stress and Apoptosis in Host Macrophages via NF-κB and p38/JNK Signaling

The Mycobacterium tuberculosis (M. tb) genome encodes a large number of hypothetical proteins, which need to investigate their role in physiology, virulence, pathogenesis, and host interaction. To explore the role of hypothetical protein Rv0580c, we constructed the recombinant Mycobacterium smegmatis (M. smegmatis) strain, which expressed the Rv0580c protein heterologously. We observed that Rv0580c expressing M. smegmatis strain (Ms_Rv0580c) altered the colony morphology and increased the cell wall permeability, leading to this recombinant strain becoming susceptible to acidic stress, oxidative stress, cell wall-perturbing stress, and multiple antibiotics. The intracellular survival of Ms_Rv0580c was reduced in THP-1 macrophages. Ms_Rv0580c up-regulated the IFN-γ expression via NF-κB and JNK signaling, and down-regulated IL-10 expression via NF-κB signaling in THP-1 macrophages as compared to control. Moreover, Ms_Rv0580c up-regulated the expression of HIF-1α and ER stress marker genes via the NF-κB/JNK axis and JNK/p38 axis, respectively, and boosted the mitochondria-independent apoptosis in macrophages, which might be lead to eliminate the intracellular bacilli. This study explores the crucial role of Rv0580c protein in the physiology and novel host-pathogen interactions of mycobacteria.

Alveolar Macrophage Chromatin Is Modified to Orchestrate Host Response to Mycobacterium bovis Infection

Bovine tuberculosis is caused by infection with Mycobacterium bovis, which can also cause disease in a range of other mammals, including humans. Alveolar macrophages are the key immune effector cells that first encounter M. bovis and how the macrophage epigenome responds to mycobacterial pathogens is currently not well understood. Here, we have used chromatin immunoprecipitation sequencing (ChIP-seq), RNA-seq and miRNA-seq to examine the effect of M. bovis infection on the bovine alveolar macrophage (bAM) epigenome. We show that H3K4me3 is more prevalent, at a genome-wide level, in chromatin from M. bovis-infected bAM compared to control non-infected bAM; this was particularly evident at the transcriptional start sites of genes that determine programmed macrophage responses to mycobacterial infection (e.g. M1/M2 macrophage polarisation). This pattern was also supported by the distribution of RNA Polymerase II (Pol II) ChIP-seq results, which highlighted significantly increased transcriptional activity at genes demarcated by permissive chromatin. Identification of these genes enabled integration of high-density genome-wide association study (GWAS) data, which revealed genomic regions associated with resilience to infection with M. bovis in cattle. Through integration of these data, we show that bAM transcriptional reprogramming occurs through differential distribution of H3K4me3 and Pol II at key immune genes. Furthermore, this subset of genes can be used to prioritise genomic variants from a relevant GWAS data set.

Efficient computational model for identification of antitubercular peptides by integrating amino acid patterns and properties

Tuberculosis (TB) is a leading killer caused by Mycobacterium tuberculosis. Recently, anti-TB peptides have provided an alternative approach to combat antibiotic tolerance. We have developed an effective computational predictor, identification of antitubercular peptides (iAntiTB), by the integration of multiple feature vectors deriving from the amino acid sequences via random forest (RF) and support vector machine (SVM) classifiers. The iAntiTB combines the RF and SVM scores via linear regression to enhance the prediction accuracy. To make a robust and accurate predictor, we prepared the two datasets with different types of negative samples. The iAntiTB achieved area under the ROC curve values of 0.896 and 0.946 on the training datasets of the first and second datasets, respectively. The iAntiTB outperformed the other existing predictors.