Bacterial population kinetics in heteroresistant Mycobacterium tuberculosis harbouring rare resistance-conferring mutations in gyrA and rpoB imply an epistatic interaction of mutations in a pre-XDR-TB patient

Alterations in molecular response of Mycobacterium tuberculosis against anti-tuberculosis drugs

BACKGROUND

Tuberculosis (TB), an infectious disease caused by Mycobacterium tuberculosis, has plagued humans since the early middle-ages. More than one million deaths are recorded annually due to TB, even in present times. These deaths are primarily attributed to the constant appearance of resistant TB strains. Even with the advent of new therapeutics and diagnostics techniques, tuberculosis remains challenging to control due to resistant M. tuberculosis strains. Aided by various molecular changes, these strains adapt to stress created by anti-tuberculosis drugs.

MATERIALS AND METHODS

The review thus is an overview of ongoing research in the genome and transcriptome of antibiotic-resistant TB. It explores omics-based research to identify mutation and utilization of differential gene expression.

CONCLUSIONS

This study shows several mutations distinctive in the first- and second-line drug-resistant M. tuberculosis strains. It also explores the expressional differences of genes involved in the fundamental process of the cells and how they help in drug resistance. With the development of transcriptomics-based studies, a new insight has developed to inquire about gene expression changes in drug resistance. This information on expressional pattern changes can be utilized to design the basic platform of anti-TB treatments and therapeutic approaches. These novel insights can be instrumental in disease diagnosis and global containment of resistant TB.

The within-host evolution of antimicrobial resistance in Mycobacterium tuberculosis

Tuberculosis (TB) has been responsible for the greatest number of human deaths due to an infectious disease in general, and due to antimicrobial resistance (AMR) in particular. The etiological agents of human TB are a closely-related group of human-adapted bacteria that belong to the Mycobacterium tuberculosis complex (MTBC). Understanding how MTBC populations evolve within-host may allow for improved TB treatment and control strategies. In this review, we highlight recent works that have shed light on how AMR evolves in MTBC populations within individual patients. We discuss the role of heteroresistance in AMR evolution, and review the bacterial, patient and environmental factors that likely modulate the magnitude of heteroresistance within-host. We further highlight recent works on the dynamics of MTBC genetic diversity within-host, and discuss how spatial substructures in patients' lungs, spatiotemporal heterogeneity in antimicrobial concentrations and phenotypic drug tolerance likely modulates the dynamics of MTBC genetic diversity in patients during treatment. We note the general characteristics that are shared between how the MTBC and other bacterial pathogens evolve in humans, and highlight the characteristics unique to the MTBC.

Prediction of rifampicin resistance beyond the RRDR using structure-based machine learning approaches

Rifampicin resistance is a major therapeutic challenge, particularly in tuberculosis, leprosy, P. aeruginosa and S. aureus infections, where it develops via missense mutations in gene rpoB. Previously we have highlighted that these mutations reduce protein affinities within the RNA polymerase complex, subsequently reducing nucleic acid affinity. Here, we have used these insights to develop a computational rifampicin resistance predictor capable of identifying resistant mutations even outside the well-defined rifampicin resistance determining region (RRDR), using clinical M. tuberculosis sequencing information. Our tool successfully identified up to 90.9% of M. tuberculosis rpoB variants correctly, with sensitivity of 92.2%, specificity of 83.6% and MCC of 0.69, outperforming the current gold-standard GeneXpert-MTB/RIF. We show our model can be translated to other clinically relevant organisms: M. leprae, P. aeruginosa and S. aureus, despite weak sequence identity. Our method was implemented as an interactive tool, SUSPECT-RIF (StrUctural Susceptibility PrEdiCTion for RIFampicin), freely available at https://biosig.unimelb.edu.au/suspect_rif/ .