Multiform antimicrobial resistance from a metabolic mutation

Design, synthesis, and biological activity study of 6,7-dimethoxy-1,2,3,4-tetrahydroisoquinoline derivatives against multidrug resistance in Eca109/VCR cells

The advent of multidrug resistance (MDR) in tumors markedly diminishes the effectiveness of anticancer therapies. P-glycoprotein (P-gp) plays a crucial role in tumor MDR by mediating the efflux of drugs and cytotoxic agents. Presently, small molecule agents targeting P-gp are among the promising therapeutic approaches to counteract MDR. In previous research, our team identified a novel class of P-gp inhibitors featuring a 6,7-dimethoxy-1,2,3,4-tetrahydroisoquinoline scaffold. To further delineate the structure-activity relationship, this study conducted an extensive structural optimization, synthesizing 42 novel compounds. Evaluation on the drug-resistant cell line Eca109/VCR indicated that the majority of these compounds exhibited remarkable MDR-reversing activity. Notably, the optimized compound 41 demonstrated an outstanding ability to reverse MDR, with a reversal fold of up to 467.7, surpassing the efficacy of the standard third-generation P-gp inhibitor TQ, as evidenced by plate cloning assay and flow cytometry analysis. Subsequent mechanism validation experiments-including western blotting, chemosensitization tests, and fluorescent substrate accumulation assays-complemented by molecular docking studies, confirmed that compound 41 exerts its MDR-reversing effects through P-gp inhibition. This research offers new perspectives for the development of drug sensitizers targeting resistant tumors based on the tetrahydroisoquinoline scaffold.

Rational design of peptides to overcome drug resistance by metabolic regulation

Chemotherapy is widely used clinically, however, its efficacy is often compromised by the development of drug resistance, which arises from prolonged administration of drugs or other stimuli. One of the driven causes of drug resistance in tumors or bacterial infections is metabolic reprogramming, which alters mitochondrial metabolism, disrupts metabolic pathways and causes ion imbalance. Bioactive peptide materials, due to their biocompatibility, diverse bioactivities, customizable sequences, and ease of modification, have shown promise in overcoming drug resistance. This review provides an in-depth analysis of metabolic reprogramming and associated microenvironmental changes that contribute to drug resistance in common tumors and bacterial infections, suggesting potential therapeutic targets. Additionally, we explore peptide-based materials for regulating metabolism and their potential synergic effect with other therapies, highlighting the mechanisms by which these peptides reverse drug resistance. Finally, we discuss future perspectives and the clinical challenges in peptide-based treatments, aiming to offer insights for overcoming drug-resistant diseases.

Metabolic mutations reduce antibiotic susceptibility of E. coli by pathway-specific bottlenecks

Metabolic variation across pathogenic bacterial strains can impact their susceptibility to antibiotics and promote the evolution of antimicrobial resistance (AMR). However, little is known about how metabolic mutations influence metabolism and which pathways contribute to antibiotic susceptibility. Here, we measured the antibiotic susceptibility of 15,120 Escherichia coli mutants, each with a single amino acid change in one of 346 essential proteins. Across all mutants, we observed modest increases of the minimal inhibitory concentration (twofold to tenfold) without any cases of major resistance. Most mutants that showed reduced susceptibility to either of the two tested antibiotics carried mutations in metabolic genes. The effect of metabolic mutations on antibiotic susceptibility was antibiotic- and pathway-specific: mutations that reduced susceptibility against the β-lactam antibiotic carbenicillin converged on purine nucleotide biosynthesis, those against the aminoglycoside gentamicin converged on the respiratory chain. In addition, metabolic mutations conferred tolerance to carbenicillin by reducing growth rates. These results, along with evidence that metabolic bottlenecks are common among clinical E. coli isolates, highlight the contribution of metabolic mutations for AMR.

Differential producibility analysis reveals drug-associated carbon and nitrogen metabolite expressions in Mycobacterium tuberculosis

Mycobacterium tuberculosis (Mtb) is one of the world's successful pathogens that flexibly adapts its metabolic nature during infection of the host, and in response to drugs. Here we used genome scale metabolic modelling coupled with differential producibility analysis (DPA) to translate RNA-seq datasets into metabolite signals and identified drug-associated metabolic response profiles. We tested four tuberculosis (TB) drugs bedaquiline (BDQ), isoniazid (INH), rifampicin (RIF), and clarithromycin (CLA); conducted RNA-seq experiments of Mtb exposed to the individual drugs at subinhibitory concentrations, followed by DPA of gene expression data to map up and downregulated metabolites. Here we highlight those metabolic pathways that were flexibly used by Mtb to tolerate stress generated upon drug exposure. BDQ and INH upregulated maximum number of central carbon metabolites in glycolysis, pentose phosphate pathway and tri-carboxylic acid cycle with concomitant downregulation of lipid and amino acid metabolite classes. Oxaloacetate was significantly upregulated in all four drug-treated Mtb cells highlighting it as an important metabolite in Mtb's metabolism. Amino acid metabolism was selectively induced by different drugs. We have enhanced our knowledge on Mtb's carbon and nitrogen metabolic adaptations in the presence of drugs and identify metabolic nodes for therapeutic development against TB. Our work also provides DPA omics platform to interrogate RNA-seq datasets of any organism that can be reconstructed as a genome scale metabolic network.

Multidrug tolerance conferred by loss-of-function mutations in anti-sigma factor RshA of Mycobacterium abscessus

Low-level drug resistance in noncanonical pathways can constitute steppingstones toward acquisition of high-level on-target resistance mutations in the clinic. To capture these intermediate steps in Mycobacterium abscessus (Mab), we performed classic mutant selection experiments with moxifloxacin at twofold its minimum inhibitory concentration (MIC) on solid medium. We found that low-level resistance emerged reproducibly as loss-of-function mutations in RshA (MAB_3542c), an anti-sigma factor that negatively regulates activity of SigH, which orchestrates a response to oxidative stress in mycobacteria. Since oxidative stress is generated in response to many antibiotics, we went on to show that deletion of rshA confers low to moderate resistance-by measure of MIC-to a dozen agents recommended or evaluated for the treatment of Mab pulmonary infections. Interestingly, this moderate resistance was associated with a wide range of drug tolerance, up to 1,000-fold increased survival of a ΔrshA Mab mutant upon exposure to several β-lactams and DNA gyrase inhibitors. Consistent with the putative involvement of the SigH regulon, we showed that addition of the transcription inhibitor rifabutin (RBT) abrogated the high-tolerance phenotype of ΔrshA to representatives of the β-lactam and DNA gyrase inhibitor classes. In a survey of 10,000 whole Mab genome sequences, we identified several loss-of-function mutations in rshA as well as non-synonymous polymorphisms in two cysteine residues critical for interactions with SigH. Thus, the multidrug multiform resistance phenotype we have uncovered may not only constitute a step toward canonical resistance acquisition during treatment but also contribute directly to treatment failure.

Trehalose catalytic shift is an intrinsic factor in Mycobacterium tuberculosis that enhances phenotypic heterogeneity and multidrug resistance

Drug-resistance (DR) in many bacterial pathogens often arises from the repetitive formation of drug-tolerant bacilli, known as persisters. However, it is unclear whether Mycobacterium tuberculosis (Mtb), the bacterium that causes tuberculosis (TB), undergoes a similar phenotypic transition. Recent metabolomics studies have identified that a change in trehalose metabolism is necessary for Mtb to develop persisters and plays a crucial role in metabolic networks of DR-TB strains. The present study used Mtb mutants lacking the trehalose catalytic shift and showed that the mutants exhibited a significantly lower frequency of the emergence of DR mutants compared to wildtype, due to reduced persister formation. The trehalose catalytic shift enables Mtb persisters to survive under bactericidal antibiotics by increasing metabolic heterogeneity and drug tolerance, ultimately leading to development of DR. Intriguingly, rifampicin (RIF)-resistant bacilli exhibit cross-resistance to a second antibiotic, due to a high trehalose catalytic shift activity. This phenomenon explains how the development of multidrug resistance (MDR) is facilitated by the acquisition of RIF resistance. In this context, the heightened risk of MDR-TB in the lineage 4 HN878 W-Beijing strain can be attributed to its greater trehalose catalytic shift. Genetic and pharmacological inactivation of the trehalose catalytic shift significantly reduced persister formation, subsequently decreasing the incidence of MDR-TB in HN878 W-Beijing strain. Collectively, the trehalose catalytic shift serves as an intrinsic factor of Mtb responsible for persister formation, cross-resistance to multiple antibiotics, and the emergence of MDR-TB. This study aids in the discovery of new TB therapeutics by targeting the trehalose catalytic shift of Mtb.

Restriction of arginine induces antibiotic tolerance in Staphylococcus aureus

Staphylococcus aureus is responsible for a substantial number of invasive infections globally each year. These infections are problematic because they are frequently recalcitrant to antibiotic treatment. Antibiotic tolerance, the ability of bacteria to persist despite normally lethal doses of antibiotics, contributes to antibiotic treatment failure in S. aureus infections. To understand how antibiotic tolerance is induced, S. aureus biofilms exposed to multiple anti-staphylococcal antibiotics are examined using both quantitative proteomics and transposon sequencing. These screens indicate that arginine metabolism is involved in antibiotic tolerance within a biofilm and support the hypothesis that depletion of arginine within S. aureus communities can induce antibiotic tolerance. Consistent with this hypothesis, inactivation of argH, the final gene in the arginine synthesis pathway, induces antibiotic tolerance. Arginine restriction induces antibiotic tolerance via inhibition of protein synthesis. In murine skin and bone infection models, an argH mutant has enhanced ability to survive antibiotic treatment with vancomycin, highlighting the relationship between arginine metabolism and antibiotic tolerance during S. aureus infection. Uncovering this link between arginine metabolism and antibiotic tolerance has the potential to open new therapeutic avenues targeting previously recalcitrant S. aureus infections.

AI-guided few-shot inverse design of HDP-mimicking polymers against drug-resistant bacteria

Host defense peptide (HDP)-mimicking polymers are promising therapeutic alternatives to antibiotics and have large-scale untapped potential. Artificial intelligence (AI) exhibits promising performance on large-scale chemical-content design, however, existing AI methods face difficulties on scarcity data in each family of HDP-mimicking polymers (<102), much smaller than public polymer datasets (>105), and multi-constraints on properties and structures when exploring high-dimensional polymer space. Herein, we develop a universal AI-guided few-shot inverse design framework by designing multi-modal representations to enrich polymer information for predictions and creating a graph grammar distillation for chemical space restriction to improve the efficiency of multi-constrained polymer generation with reinforcement learning. Exampled with HDP-mimicking β-amino acid polymers, we successfully simulate predictions of over 105 polymers and identify 83 optimal polymers. Furthermore, we synthesize an optimal polymer DM0.8iPen0.2 and find that this polymer exhibits broad-spectrum and potent antibacterial activity against multiple clinically isolated antibiotic-resistant pathogens, validating the effectiveness of AI-guided design strategy.

Altered serine metabolism promotes drug tolerance in Mycobacterium abscessus via a WhiB7-mediated adaptive stress response

Mycobacterium abscessus is an emerging opportunistic pathogen responsible for chronic lung diseases, especially in patients with cystic fibrosis. Treatment failure of M. abscessus infections is primarily associated with intrinsic or acquired antibiotic resistance. However, there is growing evidence that antibiotic tolerance, i.e., the ability of bacteria to transiently survive exposure to bactericidal antibiotics through physiological adaptations, contributes to the relapse of chronic infections and the emergence of acquired drug resistance. Yet, our understanding of the molecular mechanisms that underlie antibiotic tolerance in M. abscessus remains limited. In the present work, a mutant with increased cross-tolerance to the first- and second-line antibiotics cefoxitin and moxifloxacin, respectively, has been isolated by experimental evolution. This mutant harbors a mutation in serB2, a gene involved in L-serine biosynthesis. Metabolic changes caused by this mutation alter the intracellular redox balance to a more reduced state that induces overexpression of the transcriptional regulator WhiB7 during the stationary phase, promoting tolerance through activation of a WhiB7-dependant adaptive stress response. These findings suggest that alteration of amino acid metabolism and, more generally, conditions that trigger whiB7 overexpression, makes M. abscessus more tolerant to antibiotic treatment.

Beyond antibiotic resistance: The whiB7 transcription factor coordinates an adaptive response to alanine starvation in mycobacteria

Pathogenic mycobacteria are a significant cause of morbidity and mortality worldwide. The conserved whiB7 stress response reduces the effectiveness of antibiotic therapy by activating several intrinsic antibiotic resistance mechanisms. Despite our comprehensive biochemical understanding of WhiB7, the complex set of signals that induce whiB7 expression remain less clear. We employed a reporter-based, genome-wide CRISPRi epistasis screen to identify a diverse set of 150 mycobacterial genes whose inhibition results in constitutive whiB7 expression. We show that whiB7 expression is determined by the amino acid composition of the 5' regulatory uORF, thereby allowing whiB7 to sense amino acid starvation. Although deprivation of many amino acids can induce whiB7, whiB7 specifically coordinates an adaptive response to alanine starvation by engaging in a feedback loop with the alanine biosynthetic enzyme, aspC. These findings describe a metabolic function for whiB7 and help explain its evolutionary conservation across mycobacterial species occupying diverse ecological niches.

Mutation-driven resistance development in wastewater E. coli upon low-level cephalosporins: Pharmacophore contribution and novel mechanism

Cephalosporins have been widely applied in clinical and veterinary settings and detected at increasing concentrations in water environments. They potentially induce high-level antibiotic resistance at environmental concentrations. This study characterized how typical wastewater bacteria developed heritable antibiotic resistance under exposure to different cephalosporins, including pharmacophore-resistance correlation, resistance mechanism, and occurrence of resistance-relevant mutations in different water environments. Wastewater-isolated E. coli JX1 was exposed to eight cephalosporins individually at 25 µg/L for 60 days. Multidrug resistance developed and diverse mutations arose in selected mutants, where a single mutation in ATP phosphoribosyltransferase encoding gene (hisG) resulted in up to 128-fold increase in resistance to meropenem. Molprint2D pharma RQSAR analysis revealed that hydrogen-bond acceptors and hydrophobic groups in the R1 and R2 substituents of cephalosporins contributed positively to antibiotic resistance. Some of these pharmacophores may persist during bio- or photo-degradation in the environment. hisG mutation confers a novel resistance mechanism by inhibiting fatty acid degradation, and its variants were more abundant in water-related E. coli (especially in the effluent of wastewater treatment plants) compared with those in non-water environments. These results suggest that specific degradation of particular pharmacophores in cephalosporins could be useful for controlling resistance development, and mutations in previously unreported resistance genes (e.g., hisG) can lead to overlooked antibiotic resistance risks in water environments.

Lipid Metabolites as Potential Regulators of the Antibiotic Resistome in Tetramorium caespitum

Antibiotic resistance genes (ARGs) are ancient but have become a modern critical threat to health. Gut microbiota, a dynamic reservoir for ARGs, transfer resistance between individuals. Surveillance of the antibiotic resistome in the gut during different host growth phases is critical to understanding the dynamics of the resistome in this ecosystem. Herein, we disentangled the ARG profiles and the dynamic mechanism of ARGs in the egg and adult phases of Tetramorium caespitum. Experimental results showed a remarkable difference in both gut microbiota and gut resistome with the development of T. caespitum. Meta-based metagenomic results of gut microbiota indicated the generalizability of gut antibiotic resistome dynamics during host development. By using Raman spectroscopy and metabolomics, the metabolic phenotype and metabolites indicated that the biotic phase significantly changed lipid metabolism as T. caespitum aged. Lipid metabolites were demonstrated as the main factor driving the enrichment of ARGs in T. caespitum. Cuminaldehyde, the antibacterial lipid metabolite that displayed a remarkable increase in the adult phase, was demonstrated to strongly induce ARG abundance. Our findings show that the gut resistome is host developmental stage-dependent and likely modulated by metabolites, offering novel insights into possible steps to reduce ARG dissemination in the soil food chain.

A reconstructed genome-scale metabolic model of Helicobacter pylori for predicting putative drug targets in clarithromycin and rifampicin resistance conditions

BACKGROUND

Helicobacter pylori is considered a true human pathogen for which rising drug resistance constitutes a drastic concern globally. The present study aimed to reconstruct a genome-scale metabolic model (GSMM) to decipher the metabolic capability of H. pylori strains in response to clarithromycin and rifampicin along with identification of novel drug targets.

MATERIALS AND METHODS

The iIT341 model of H. pylori was updated based on genome annotation data, and biochemical knowledge from literature and databases. Context-specific models were generated by integrating the transcriptomic data of clarithromycin and rifampicin resistance into the model. Flux balance analysis was employed for identifying essential genes in each strain, which were further prioritized upon being nonhomologs to humans, virulence factor analysis, druggability, and broad-spectrum analysis. Additionally, metabolic differences between sensitive and resistant strains were also investigated based on flux variability analysis and pathway enrichment analysis of transcriptomic data.

RESULTS

The reconstructed GSMM was named as HpM485 model. Pathway enrichment and flux variability analyses demonstrated reduced activity in the ribosomal pathway in both clarithromycin- and rifampicin-resistant strains. Also, a significant decrease was detected in the activity of metabolic pathways of clarithromycin-resistant strain. Moreover, 23 and 16 essential genes were exclusively detected in clarithromycin- and rifampicin-resistant strains, respectively. Based on prioritization analysis, cyclopropane fatty acid synthase and phosphoenolpyruvate synthase were identified as putative drug targets in clarithromycin- and rifampicin-resistant strains, respectively.

CONCLUSIONS

We present a robust and reliable metabolic model of H. pylori. This model can predict novel drug targets to combat drug resistance and explore the metabolic capability of H. pylori in various conditions.

Machine learning and phylogenetic analysis allow for predicting antibiotic resistance in M. tuberculosis

BACKGROUND

Antimicrobial resistance (AMR) poses a significant global health threat, and an accurate prediction of bacterial resistance patterns is critical for effective treatment and control strategies. In recent years, machine learning (ML) approaches have emerged as powerful tools for analyzing large-scale bacterial AMR data. However, ML methods often ignore evolutionary relationships among bacterial strains, which can greatly impact performance of the ML methods, especially if resistance-associated features are attempted to be detected. Genome-wide association studies (GWAS) methods like linear mixed models accounts for the evolutionary relationships in bacteria, but they uncover only highly significant variants which have already been reported in literature.

RESULTS

In this work, we introduce a novel phylogeny-related parallelism score (PRPS), which measures whether a certain feature is correlated with the population structure of a set of samples. We demonstrate that PRPS can be used, in combination with SVM- and random forest-based models, to reduce the number of features in the analysis, while simultaneously increasing models' performance. We applied our pipeline to publicly available AMR data from PATRIC database for Mycobacterium tuberculosis against six common antibiotics.

CONCLUSIONS

Using our pipeline, we re-discovered known resistance-associated mutations as well as new candidate mutations which can be related to resistance and not previously reported in the literature. We demonstrated that taking into account phylogenetic relationships not only improves the model performance, but also yields more biologically relevant predicted most contributing resistance markers.

In Vitro Synergistic Inhibitory Effects of Plant Extract Combinations on Bacterial Growth of Methicillin-Resistant Staphylococcus aureus

Methicillin-resistant Staphylococcus aureus (MRSA) is one of the most common pathogens of healthcare-associated infections. Medicinal plants have long been used in the traditional treatment of diseases or syndromes worldwide. Combined use of plant extracts could improve the effectiveness of pharmacological action by obtaining synergism, acting on multiple targets simultaneously, reducing the doses of individual components, and minimizing side effects. We aimed to investigate the synergistic inhibitory effects of selected medicinal plants (Caesalpinia sappan L. (CS), Glycyrrhiza uralensis Fisch. (GU), Sanguisorba officinalis L. (SO), and Uncaria gambir Roxb. (UG)) on the bacterial growth of MRSA and its clinical isolates. SO and UG extracts generated the best synergistic interaction as adjudged by checkerboard synergy assays. MICs of the individual extracts decreased 4-fold from 250 to 62.5 μg/mL, respectively. The SO + UG combination was further evaluated for its effects on bacterial growth inhibition, minimum bactericidal/inhibitory concentration (MBC/MIC) ratio, and time-kill kinetics. The results indicate that the SO + UG combination synergistically inhibited the bacterial growth of MRSA strains with bactericidal effects. SO + UG combination also exhibited more potent effects against clinical isolates. In multistep resistance selection experiments, both standard and isolates of MRSA showed no resistance to the SO + UG combination even after repeated exposure over fourteen passages. Our data suggest that using plant extract combinations could be a potential strategy to treat MRSA infections.

MtrA modulates Mycobacterium tuberculosis cell division in host microenvironments to mediate intrinsic resistance and drug tolerance

The success of Mycobacterium tuberculosis (Mtb) is largely attributed to its ability to physiologically adapt and withstand diverse localized stresses within host microenvironments. Here, we present a data-driven model (EGRIN 2.0) that captures the dynamic interplay of environmental cues and genome-encoded regulatory programs in Mtb. Analysis of EGRIN 2.0 shows how modulation of the MtrAB two-component signaling system tunes Mtb growth in response to related host microenvironmental cues. Disruption of MtrAB by tunable CRISPR interference confirms that the signaling system regulates multiple peptidoglycan hydrolases, among other targets, that are important for cell division. Further, MtrA decreases the effectiveness of antibiotics by mechanisms of both intrinsic resistance and drug tolerance. Together, the model-enabled dissection of complex MtrA regulation highlights its importance as a drug target and illustrates how EGRIN 2.0 facilitates discovery and mechanistic characterization of Mtb adaptation to specific host microenvironments within the host.

Beyond antibiotic resistance: the whiB7 transcription factor coordinates an adaptive response to alanine starvation in mycobacteria

Pathogenic mycobacteria are a significant cause of morbidity and mortality worldwide. These bacteria are highly intrinsically drug resistant, making infections challenging to treat. The conserved whiB7 stress response is a key contributor to mycobacterial intrinsic drug resistance. Although we have a comprehensive structural and biochemical understanding of WhiB7, the complex set of signals that activate whiB7 expression remain less clear. It is believed that whiB7 expression is triggered by translational stalling in an upstream open reading frame (uORF) within the whiB7 5' leader, leading to antitermination and transcription into the downstream whiB7 ORF. To define the signals that activate whiB7, we employed a genome-wide CRISPRi epistasis screen and identified a diverse set of 150 mycobacterial genes whose inhibition results in constitutive whiB7 activation. Many of these genes encode amino acid biosynthetic enzymes, tRNAs, and tRNA synthetases, consistent with the proposed mechanism for whiB7 activation by translational stalling in the uORF. We show that the ability of the whiB7 5' regulatory region to sense amino acid starvation is determined by the coding sequence of the uORF. The uORF shows considerable sequence variation among different mycobacterial species, but it is universally and specifically enriched for alanine. Providing a potential rationalization for this enrichment, we find that while deprivation of many amino acids can activate whiB7 expression, whiB7 specifically coordinates an adaptive response to alanine starvation by engaging in a feedback loop with the alanine biosynthetic enzyme, aspC. Our results provide a holistic understanding of the biological pathways that influence whiB7 activation and reveal an extended role for the whiB7 pathway in mycobacterial physiology, beyond its canonical function in antibiotic resistance. These results have important implications for the design of combination drug treatments to avoid whiB7 activation, as well as help explain the conservation of this stress response across a wide range of pathogenic and environmental mycobacteria.

Reframing antimicrobial resistance as a continuous spectrum of manifestations

To fight antimicrobial resistance (AMR), we must recognize and target all its manifestations. In this review, we briefly summarize the history that led to recognition of the various manifestations of AMR in bacterial pathogens and the ways in which they interrelate. We emphasize the importance of distinguishing between AMR arising from genetic alterations versus induction of endogenous machinery in response to environmental triggers, including - paradoxically - stresses from host immunity and antimicrobial therapy. We present an integrated view of AMR by reframing it as a spectrum of phenotypes within a continuous three-dimensional space defined by the growth rate, prevalence, and kill rate of cells displaying AMR. Finally, we reflect on strategies that may help stem the emergence of AMR.

Sirtuin-dependent metabolic and epigenetic regulation of macrophages during tuberculosis

Macrophages are the preeminent phagocytic cells which control multiple infections. Tuberculosis a leading cause of death in mankind and the causative organism Mycobacterium tuberculosis (MTB) infects and persists in macrophages. Macrophages use reactive oxygen and nitrogen species (ROS/RNS) and autophagy to kill and degrade microbes including MTB. Glucose metabolism regulates the macrophage-mediated antimicrobial mechanisms. Whereas glucose is essential for the growth of cells in immune cells, glucose metabolism and its downsteam metabolic pathways generate key mediators which are essential co-substrates for post-translational modifications of histone proteins, which in turn, epigenetically regulate gene expression. Herein, we describe the role of sirtuins which are NAD+-dependent histone histone/protein deacetylases during the epigenetic regulation of autophagy, the production of ROS/RNS, acetyl-CoA, NAD+, and S-adenosine methionine (SAM), and illustrate the cross-talk between immunometabolism and epigenetics on macrophage activation. We highlight sirtuins as emerging therapeutic targets for modifying immunometabolism to alter macrophage phenotype and antimicrobial function.

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.

Advances in the design of combination therapies for the treatment of tuberculosis

INTRODUCTION

Tuberculosis requires lengthy multi-drug therapy. Mycobacterium tuberculosis occupies different tissue compartments during infection, making drug access and susceptibility patterns variable. Antibiotic combinations are needed to ensure each compartment of infection is reached with effective drug treatment. Despite drug combinations' role in treating tuberculosis, the design of such combinations has been tackled relatively late in the drug development process, limiting the number of drug combinations tested. In recent years, there has been significant progress using in vitro, in vivo, and computational methodologies to interrogate combination drug effects.

AREAS COVERED

This review discusses the advances in these methodologies and how they may be used in conjunction with new successful clinical trials of novel drug combinations to design optimized combination therapies for tuberculosis. Literature searches for approaches and experimental models used to evaluate drug combination effects were undertaken.

EXPERT OPINION

We are entering an era richer in combination drug effect and pharmacokinetic/pharmacodynamic data, genetic tools, and outcome measurement types. Application of computational modeling approaches that integrate these data and produce predictive models of clinical outcomes may enable the field to generate novel, effective multidrug therapies using existing and new drug combination backbones.

Unraveling the mechanisms of intrinsic drug resistance in Mycobacterium tuberculosis

Tuberculosis (TB) is among the most difficult infections to treat, requiring several months of multidrug therapy to produce a durable cure. The reasons necessitating long treatment times are complex and multifactorial. However, one major difficulty of treating TB is the resistance of the infecting bacterium, Mycobacterium tuberculosis (Mtb), to many distinct classes of antimicrobials. This review will focus on the major gaps in our understanding of intrinsic drug resistance in Mtb and how functional and chemical-genetics can help close those gaps. A better understanding of intrinsic drug resistance will help lay the foundation for strategies to disarm and circumvent these mechanisms to develop more potent antitubercular therapies.

Transcriptional regulation and drug resistance in Mycobacterium tuberculosis

Bacterial drug resistance is one of the major challenges to present and future human health, as the continuous selection of multidrug resistant bacteria poses at serious risk the possibility to treat infectious diseases in the near future. One of the infection at higher risk to become incurable is tuberculosis, due to the few drugs available in the market against Mycobacterium tuberculosis. Drug resistance in this species is usually due to point mutations in the drug target or in proteins required to activate prodrugs. However, another interesting and underexplored aspect of bacterial physiology with important impact on drug susceptibility is represented by the changes in transcriptional regulation following drug exposure. The main regulators involved in this phenomenon in M. tuberculosis are the sigma factors, and regulators belonging to the WhiB, GntR, XRE, Mar and TetR families. Better understanding the impact of these regulators in survival to drug treatment might contribute to identify new drug targets and/or to design new strategies of intervention.

Central carbon metabolism remodeling as a mechanism to develop drug tolerance and drug resistance in Mycobacterium tuberculosis

Suboptimal efficacy of the current antibiotic regimens and frequent emergence of antibiotic-resistant Mycobacterium tuberculosis (Mtb), an etiological agent of tuberculosis (TB), render TB the world’s deadliest infectious disease before the COVID-19 outbreak. Our outdated TB treatment method is designed to eradicate actively replicating populations of Mtb. Unfortunately, accumulating evidence suggests that a small population of Mtb can survive antimycobacterial pressure of antibiotics by entering a “persister” state (slowly replicating or non-replicating and lacking a stably heritable antibiotic resistance, termed drug tolerance). The formation of drug-tolerant Mtb persisters is associated with TB treatment failure and is thought to be an adaptive strategy for eventual development of permanent genetic mutation-mediated drug resistance. Thus, the molecular mechanisms behind persister formation and drug tolerance acquisition are a source of new antibiotic targets to eradicate both Mtb persisters and drug-resistant Mtb. As Mtb persisters are genetically identical to antibiotic susceptible populations, metabolomics has emerged as a vital biochemical tool to differentiate these populations by determining phenotypic shifts and metabolic reprogramming. Metabolomics, which provides detailed insights into the molecular basis of drug tolerance and resistance in Mtb, has unique advantages over other techniques by its ability to identify specific metabolic differences between the two genetically identical populations. This review summarizes the recent advances in our understanding of the metabolic adaptations used by Mtb persisters to achieve intrinsic drug tolerance and facilitate the emergence of drug resistance. These findings present metabolomics as a powerful tool to identify previously unexplored antibiotic targets and improved combinations of drug regimens against drug-resistant TB infection.

Effect of Zanthoxylum bungeanum essential oil on rumen enzyme activity, microbiome, and metabolites in lambs

Antibiotics were once used in animal production to improve productivity and resistance to pathogenic microbiota. However, due to its negative effects, the search for a new class of substances that can replace its efficacy has become one of the urgent problems to be solved. Plant essential oils (EOs) as a natural feed additive can maintain microbiota homeostasis and improve animal performance. However, its specific mechanism of action needs to be further investigated. Therefore, we added different doses of essential oil of Zanthoxylum bungeanum (EOZB) to the diets of Small Tail Han Sheep hybrid male lambs (STH lambs) to evaluate the effect of EOZB on rumen enzyme activity, rumen microbiology, and its metabolites in STH lambs. Twenty STH lambs were randomly divided into four groups (n = 5/group) and provided with the same diet. The dietary treatments were as follows: basal diet (BD) group; BD+EOZB 5 ml/kg group; BD+EOZB 10 ml/kg group; BD+EOZB 15 ml/kg group. We found that EOZB 10 ml/kg helped to increase rumen pectinase (P<0.05) and lipase (P<0.05) activities. Microbial 16S rRNA gene analysis showed that EOZB significantly altered the abundance of rumen microbiota (P<0.05). LC/GC-MS metabolomic analysis showed that the addition of EOZB produced a total of 1073 differential metabolites, with 58 differential metabolites remaining after raising the screening criteria. These differential metabolites were mainly enriched in glycerophospholipid metabolism, choline metabolism in cancer, retrograde endocannabinoid signaling, benzoxazinoid biosynthesis, and protein digestion and absorption. Correlation analysis showed that some rumen microbiota were significantly correlated with differential metabolite and enzyme activities.

Development of 4-[4-(Anilinomethyl)-3-phenyl-pyrazol-1-yl] Benzoic Acid Derivatives as Potent Anti-Staphylococci and Anti-Enterococci Agents

From a library of compounds, 11 hit antibacterial agents have been identified as potent anti-Gram-positive bacterial agents. These pyrazole derivatives are active against two groups of pathogens, staphylococci and enterococci, with minimum inhibitory concentration (MIC) values as low as 0.78 μg/mL. These potent compounds showed bactericidal action, and some were effective at inhibiting and eradicating Staphylococcus aureus and Enterococcus faecalis biofilms. Real-time biofilm inhibition by the potent compounds was studied, by using Bioscreen C. These lead compounds were also very potent against S. aureus persisters as compared to controls, gentamycin and vancomycin. In multiple passage studies, bacteria developed little resistance to these compounds (no more than 2 × MIC). The plausible mode of action of the lead compounds is the permeabilization of the cell membrane determined by flow cytometry and protein leakage assays. With the detailed antimicrobial studies, both in planktonic and biofilm contexts, some of these potent compounds have the potential for further antimicrobial drug development.

CRISPRi chemical genetics and comparative genomics identify genes mediating drug potency in Mycobacterium tuberculosis

Mycobacterium tuberculosis (Mtb) infection is notoriously difficult to treat. Treatment efficacy is limited by Mtb's intrinsic drug resistance, as well as its ability to evolve acquired resistance to all antituberculars in clinical use. A deeper understanding of the bacterial pathways that influence drug efficacy could facilitate the development of more effective therapies, identify new mechanisms of acquired resistance, and reveal overlooked therapeutic opportunities. Here we developed a CRISPR interference chemical-genetics platform to titrate the expression of Mtb genes and quantify bacterial fitness in the presence of different drugs. We discovered diverse mechanisms of intrinsic drug resistance, unveiling hundreds of potential targets for synergistic drug combinations. Combining chemical genetics with comparative genomics of Mtb clinical isolates, we further identified several previously unknown mechanisms of acquired drug resistance, one of which is associated with a multidrug-resistant tuberculosis outbreak in South America. Lastly, we found that the intrinsic resistance factor whiB7 was inactivated in an entire Mtb sublineage endemic to Southeast Asia, presenting an opportunity to potentially repurpose the macrolide antibiotic clarithromycin to treat tuberculosis. This chemical-genetic map provides a rich resource to understand drug efficacy in Mtb and guide future tuberculosis drug development and treatment.

Expanding the search for small-molecule antibacterials by multidimensional profiling

New techniques for systematic profiling of small-molecule effects can enhance traditional growth inhibition screens for antibiotic discovery and change how we search for new antibacterial agents. Computational models that integrate physicochemical compound properties with their phenotypic and molecular downstream effects can not only predict efficacy of molecules yet to be tested, but also reveal unprecedented insights on compound modes of action (MoAs). The unbiased characterization of compounds that themselves are not growth inhibitory but exhibit diverse MoAs, can expand antibacterial strategies beyond direct inhibition of core essential functions. Early and systematic functional annotation of compound libraries thus paves the way to new models in the selection of lead antimicrobial compounds. In this Review, we discuss how multidimensional small-molecule profiling and the ever-increasing computing power are accelerating the discovery of unconventional antibacterials capable of bypassing resistance and exploiting synergies with established antibacterial treatments and with protective host mechanisms.

Modulating the evolutionary trajectory of tolerance using antibiotics with different metabolic dependencies

Antibiotic tolerance, or the ability of bacteria to survive antibiotic treatment in the absence of genetic resistance, has been linked to chronic and recurrent infections. Tolerant cells are often characterized by a low metabolic state, against which most clinically used antibiotics are ineffective. Here, we show that tolerance readily evolves against antibiotics that are strongly dependent on bacterial metabolism, but does not arise against antibiotics whose efficacy is only minimally affected by metabolic state. We identify a mechanism of tolerance evolution in E. coli involving deletion of the sodium-proton antiporter gene nhaA, which results in downregulated metabolism and upregulated stress responses. Additionally, we find that cycling of antibiotics with different metabolic dependencies interrupts evolution of tolerance in vitro, increasing the lifetime of treatment efficacy. Our work highlights the potential for limiting the occurrence and extent of tolerance by accounting for antibiotic dependencies on bacterial metabolism.