The evolving biology of Mycobacterium tuberculosis drug resistance

Prediction of Mycobacterium tuberculosis cell wall permeability using machine learning methods

Tuberculosis (TB) caused by the bacteria Mycobacterium tuberculosis (M. tb), continues to pose a significant worldwide health threat. The advent of drug-resistant strains of the disease highlights the critical need for novel treatments. The unique cell wall of M. tb provides an extra layer of protection for the bacteria and hence only compounds that can penetrate this barrier can reach their targets within the bacterial cell wall. The creation of a reliable machine learning (ML) model to predict the mycobacterial cell wall permeability of small molecules is presented in this work and four ML algorithms, including Random Forest, Support Vector Machines (SVM), k-nearest Neighbour (k-NN) and Logistic Regression were trained on a dataset of 5368 compounds. RDKit and Mordred toolkits were used to calculate features. To determine the most effective model, various performance metrics were used such as accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve. The best-performing model was further refined with hyperparameter tuning and tenfold cross-validation. The SVM model with filtering outperformed the other machine learning models and demonstrated 80.26% and 81.13% accuracy on the test and validation datasets, respectively. The study also provided insights into the molecular descriptors that play the most important role in predicting the ability of a molecule to pass the M. tb cell wall, which could guide future compound design. The model is available at https://github.com/PGlab-NIPER/MTB_Permeability .

Cystatin F Depletion in Mycobacterium tuberculosis-Infected Macrophages Improves Cathepsin C/Granzyme B-Driven Cytotoxic Effects on HIV-Infected Cells during Coinfection

Cystatin F (CstF) is a protease inhibitor of cysteine cathepsins, including those involved in activating the perforin/granzyme cytotoxic pathways. It is targeted at the endolysosomal pathway but can also be secreted to the extracellular milieu or endocytosed by bystander cells. CstF was shown to be significantly increased in tuberculous pleurisy, and during HIV coinfection, pleural fluids display high viral loads. In human macrophages, our previous results revealed a strong upregulation of CstF in phagocytes activated by interferon γ or after infection with Mycobacterium tuberculosis (Mtb). CstF manipulation using RNA silencing led to increased proteolytic activity of lysosomal cathepsins, improving Mtb intracellular killing. In the present work, we investigate the impact of CstF depletion in macrophages during the coinfection of Mtb-infected phagocytes with lymphocytes infected with HIV. The results indicate that decreasing the CstF released by phagocytes increases the major pro-granzyme convertase cathepsin C of cytotoxic immune cells from peripheral blood-derived lymphocytes. Consequently, an observed augmentation of the granzyme B cytolytic activity leads to a significant reduction in viral replication in HIV-infected CD4+ T-lymphocytes. Ultimately, this knowledge can be crucial for developing new therapeutic approaches to control both pathogens based on manipulating CstF.

Decoding drug resistance in Mycobacterium tuberculosis complex: genetic insights and future challenges

INTRODUCTION

Tuberculosis (TB), particularly its drug-resistant forms (MDR-TB and XDR-TB), continues to pose a significant global health challenge. Despite advances in treatment and diagnosis, the evolving nature of drug resistance in Mycobacterium tuberculosis (MTB) complicates TB eradication efforts. This review delves into the complexities of anti-TB drug resistance, its mechanisms, and implications on healthcare strategies globally.

AREAS COVERED

We explore the genetic underpinnings of resistance to both first-line and second-line anti-TB drugs, highlighting the role of mutations in key genes. The discussion extends to advanced diagnostic techniques, such as Whole-Genome Sequencing (WGS), CRISPR-based diagnostics and their impact on identifying and managing drug-resistant TB. Additionally, we discuss artificial intelligence applications, current treatment strategies, challenges in managing MDR-TB and XDR-TB, and the global disparities in TB treatment and control, translating to different therapeutic outcomes and have the potential to revolutionize our understanding and management of drug-resistant tuberculosis.

EXPERT OPINION

The current landscape of anti-TB drug resistance demands an integrated approach combining advanced diagnostics, novel therapeutic strategies, and global collaborative efforts. Future research should focus on understanding polygenic resistance and developing personalized medicine approaches. Policymakers must prioritize equitable access to diagnosis and treatment, enhancing TB control strategies, and support ongoing research and augmented government funding to address this critical public health issue effectively.

Persistence and evidence for accelerated biodegradation of streptomycin in agricultural soils

Some antibiotics are used for the treatment of various bacterial crop diseases, and there is a concern that this practice may represent a selection pressure that increases the reservoir of antibiotic resistance carried by bacteria in crop production systems. Since the 1950s the aminoglycoside antibiotic streptomycin has been widely used for the treatment of some bacterial crop diseases such as fire blight in apples and pears. Following application, the time that bacteria will be exposed to the antibiotic, and therefore the pressure for selection of resistance, will vary according to the environmental persistence of the antibiotic. In the present study, the dissipation of streptomycin was examined in soils supplemented with 5 mg streptomycin/kg soil and incubated for 21 days under laboratory conditions. The impact of two key rate-controlling variables, soil texture (sandy loam, loam, clay loam) and temperature (4, 20, 30 °C) on streptomycin persistence were explored. -Robust methods for streptomycin extraction and analysis by LC-MS/MS were developed. Streptomycin dissipation followed first order kinetics, with the time to dissipate 50 % of the parent compound (DT50) in soils of varying texture incubated at 20 °C ranging from about seven to 15 days. In contrast, the DT50 of streptomycin in autoclaved loam soil incubated at 20 °C was about 111 days. At 4 °C the DT50 ranged from 49 to 137 days. Under no incubation conditions were any extractable transformation products obtained. Streptomycin was dissipated significantly more rapidly in field soil that had a prior history of exposure to the antibiotic than in soil that did not. Taken together, these results indicate that streptomycin is amenable to biodegradation in agricultural soils with DT50s of several days when temperature is permissive.

Tackling Drug-Resistant Tuberculosis: New Challenges from the Old Pathogen Mycobacterium tuberculosis

Antibiotics have played a crucial role in the reduction in the incidence of TB globally as evidenced by the fact that before the mid-20th century, the mortality rate within five years of the onset of the disease was 50%. The use of antibiotics has eliminated TB as a devastating disease, but the challenge of resistance to anti-TB drugs, which had already been described at the time of the introduction of streptomycin, has become a major global issue in disease management. Mismanagement of multidrug-resistant tuberculosis (MDR-TB) cases, resulting from intermittent drug use, prescription errors, and non-compliance of patients, has been identified as a critical risk factor for the development of extensively drug-resistant tuberculosis (XDR-TB). Antimicrobial resistance (AMR) in TB is a multi-factorial, complex problem of microbes evolving to escape antibiotics, the gradual decline in antibiotic development, and different economic and social conditions. In this review, we summarize recent advances in our understanding of how Mycobacterium tuberculosis evolves drug resistance. We also highlight the importance of developing shorter regimens that rapidly reach bacteria in diverse host environments, eradicating all mycobacterial populations and preventing the evolution of drug resistance. Lastly, we also emphasize that the current burden of this ancient disease is driven by a combination of complex interactions between mycobacterial and host factors, and that only a holistic approach that effectively addresses all the critical issues associated with drug resistance will limit the further spread of drug-resistant strains throughout the community.

Clinical implications of molecular drug resistance testing for Mycobacterium tuberculosis: a 2023 TBnet/RESIST-TB consensus statement

Drug-resistant tuberculosis is a substantial health-care concern worldwide. Despite culture-based methods being considered the gold standard for drug susceptibility testing, molecular methods provide rapid information about the Mycobacterium tuberculosis mutations associated with resistance to anti-tuberculosis drugs. This consensus document was developed on the basis of a comprehensive literature search, by the TBnet and RESIST-TB networks, about reporting standards for the clinical use of molecular drug susceptibility testing. Review and the search for evidence included hand-searching journals and searching electronic databases. The panel identified studies that linked mutations in genomic regions of M tuberculosis with treatment outcome data. Implementation of molecular testing for the prediction of drug resistance in M tuberculosis is key. Detection of mutations in clinical isolates has implications for the clinical management of patients with multidrug-resistant or rifampicin-resistant tuberculosis, especially in situations when phenotypic drug susceptibility testing is not available. A multidisciplinary team including clinicians, microbiologists, and laboratory scientists reached a consensus on key questions relevant to molecular prediction of drug susceptibility or resistance to M tuberculosis, and their implications for clinical practice. This consensus document should help clinicians in the management of patients with tuberculosis, providing guidance for the design of treatment regimens and optimising outcomes.

The Mycobacterium bovis BCG GroEL1 Contributes to Isoniazid Tolerance in a Dormant-Like State Model

Due to the Mycobacterium tuberculosis complex, including M. tuberculosis and M. bovis, tuberculosis still causes 1.6 million deaths per year. Therefore, efforts to improve tuberculosis treatment are necessary. We previously showed that the GroEL1 protein is involved in antibiotic intrinsic resistance. Indeed, the M. bovis BCG cpn60.1 gene (encoding GroEL1)-disrupted strain (Δcpn60.1) exhibits higher rifampicin and vancomycin susceptibility due to defective cell wall integrity. Here, we show that during hypoxia-triggered growth stasis, in the Wayne dormancy model, the mutant exhibited comparable rifampicin and ethionamide susceptibility but higher isoniazid susceptibility compared to the wild-type strain. Although the Δcpn60.1 strain showed compromised induction of the DosR regulon, growth stasis was achieved, but an ATP burst and a higher reactive oxygen species (ROS) production were observed in the isoniazid-treated Δcpn60.1 strain. GroEL1 could contribute to INH tolerance by reducing ROS.

Tools to develop antibiotic combinations that target drug tolerance in Mycobacterium tuberculosis

Combination therapy is necessary to treat tuberculosis to decrease the rate of disease relapse and prevent the acquisition of drug resistance, and shorter regimens are urgently needed. The adaptation of Mycobacterium tuberculosis to various lesion microenvironments in infection induces various states of slow replication and non-replication and subsequent antibiotic tolerance. This non-heritable tolerance to treatment necessitates lengthy combination therapy. Therefore, it is critical to develop combination therapies that specifically target the different types of drug-tolerant cells in infection. As new tools to study drug combinations earlier in the drug development pipeline are being actively developed, we must consider how to best model the drug-tolerant cells to use these tools to design the best antibiotic combinations that target those cells and shorten tuberculosis therapy. In this review, we discuss the factors underlying types of drug tolerance, how combination therapy targets these populations of bacteria, and how drug tolerance is currently modeled for the development of tuberculosis multidrug therapy. We highlight areas for future studies to develop new tools that better model drug tolerance in tuberculosis infection specifically for combination therapy testing to bring the best drug regimens forward to the clinic.