Chemical Genetic Interaction Profiling Reveals Determinants of Intrinsic Antibiotic Resistance in Mycobacterium tuberculosis

Genome-wide assessment of antimicrobial tolerance in Yersinia pseudotuberculosis under ciprofloxacin stress

Yersinia pseudotuberculosis is a Gram-negative bacterium capable of causing gastrointestinal infection and is closely related to the highly virulent plague bacillus Yersinia pestis. Infections by both species are currently treatable with antibiotics such as ciprofloxacin, a quinolone-class drug of major clinical importance in the treatment of many other infections. Our current understanding of the mechanism of action of ciprofloxacin is that it inhibits DNA replication by targeting DNA gyrase, and that resistance is primarily due to mutation of this target site, along with generic efflux and detoxification strategies. We utilized transposon-directed insertion site sequencing (TraDIS or TnSeq) to identify the non-essential chromosomal genes in Y. pseudotuberculosis that are required to tolerate sub-lethal concentrations of ciprofloxacin in vitro. As well as highlighting recognized antibiotic resistance genes, we provide evidence that multiple genes involved in regulating DNA replication and repair are central in enabling Y. pseudotuberculosis to tolerate the antibiotic, including DksA (yptb0734), a regulator of RNA polymerase, and Hda (yptb2792), an inhibitor of DNA replication initiation. We furthermore demonstrate that even at sub-lethal concentrations, ciprofloxacin causes severe cell-wall stress, requiring lipopolysaccharide lipid A, O-antigen and core biosynthesis genes to resist the sub-lethal effects of the antibiotic. It is evident that coping with the consequence(s) of antibiotic-induced stress requires the contribution of scores of genes that are not exclusively engaged in drug resistance.

Changes in Intrinsic Antibiotic Susceptibility during a Long-Term Evolution Experiment with Escherichia coli

High-level resistance often evolves when populations of bacteria are exposed to antibiotics, by either mutations or horizontally acquired genes. There is also variation in the intrinsic resistance levels of different bacterial strains and species that is not associated with any known history of exposure. In many cases, evolved resistance is costly to the bacteria, such that resistant types have lower fitness than their progenitors in the absence of antibiotics. Some longer-term studies have shown that bacteria often evolve compensatory changes that overcome these tradeoffs, but even those studies have typically lasted only a few hundred generations. In this study, we examine changes in the susceptibilities of 12 populations of Escherichia coli to 15 antibiotics after 2,000 and 50,000 generations without exposure to any antibiotic. On average, the evolved bacteria were more susceptible to most antibiotics than was their ancestor. The bacteria at 50,000 generations tended to be even more susceptible than after 2,000 generations, although most of the change occurred during the first 2,000 generations. Despite the general trend toward increased susceptibility, we saw diverse outcomes with different antibiotics. For streptomycin, which was the only drug to which the ancestral strain was highly resistant, none of the evolved lines showed any increased susceptibility. The independently evolved lineages often exhibited correlated responses to the antibiotics, with correlations usually corresponding to their modes of action. On balance, our study shows that bacteria with low levels of intrinsic resistance often evolve to become even more susceptible to antibiotics in the absence of corresponding selection.IMPORTANCE Resistance to antibiotics often evolves when bacteria encounter antibiotics. However, bacterial strains and species without any known exposure to these drugs also vary in their intrinsic susceptibility. In many cases, evolved resistance has been shown to be costly to the bacteria, such that resistant types have reduced competitiveness relative to their sensitive progenitors in the absence of antibiotics. In this study, we examined changes in the susceptibilities of 12 populations of Escherichia coli to 15 antibiotics after 2,000 and 50,000 generations without exposure to any drug. The evolved bacteria tended to become more susceptible to most antibiotics, with most of the change occurring during the first 2,000 generations, when the bacteria were undergoing rapid adaptation to their experimental conditions. On balance, our findings indicate that bacteria with low levels of intrinsic resistance can, in the absence of relevant selection, become even more susceptible to antibiotics.

A Protein Complex from Human Milk Enhances the Activity of Antibiotics and Drugs against Mycobacterium tuberculosis

Mycobacterium tuberculosis, the causative agent of human tuberculosis (TB), has surpassed HIV/AIDS as the leading cause of death from a single infectious agent. The increasing occurrence of drug-resistant strains has become a major challenge for health care systems and, in some cases, has rendered TB untreatable. However, the development of new TB drugs has been plagued with high failure rates and costs. Alternative strategies to increase the efficacy of current TB treatment regimens include host-directed therapies or agents that make M. tuberculosis more susceptible to existing TB drugs. In this study, we show that HAMLET, an α-lactalbumin-oleic acid complex derived from human milk, has bactericidal activity against M. tuberculosis HAMLET consists of a micellar oleic acid core surrounded by a shell of partially denatured α-lactalbumin molecules and unloads oleic acid into cells upon contact with lipid membranes. At sublethal concentrations, HAMLET potentiated a remarkably broad array of TB drugs and antibiotics against M. tuberculosis For example, the minimal inhibitory concentrations of rifampin, bedaquiline, delamanid, and clarithromycin were decreased by 8- to 16-fold. HAMLET also killed M. tuberculosis and enhanced the efficacy of TB drugs inside macrophages, a natural habitat of M. tuberculosis Previous studies showed that HAMLET is stable after oral delivery in mice and nontoxic in humans and that it is possible to package hydrophobic compounds in the oleic acid core of HAMLET to increase their solubility and metabolic stability. The potential of HAMLET and other liprotides as drug delivery and sensitization agents in TB chemotherapy is discussed here.

Iron Acquisition in Mycobacterium tuberculosis

The highly contagious disease tuberculosis (TB) is caused by the bacterium Mycobacterium tuberculosis (Mtb), which has been evolving drug resistance at an alarming rate. Like all human pathogens, Mtb requires iron for growth and virulence. Consequently, Mtb iron transport is an emerging drug target. However, the development of anti-TB drugs aimed at these metabolic pathways has been restricted by the dearth of information on Mtb iron acquisition. In this Review, we describe the multiple strategies utilized by Mtb to acquire ferric iron and heme iron. Mtb iron uptake is a complex process, requiring biosynthesis and subsequent export of Mtb siderophores, followed by ferric iron scavenging and ferric-siderophore import into Mtb. Additionally, Mtb possesses two possible heme uptake pathways and an Mtb-specific mechanism of heme degradation that yields iron and novel heme-degradation products. We conclude with perspectives for potential therapeutics that could directly target Mtb heme and iron uptake machineries. We also highlight how hijacking Mtb heme and iron acquisition pathways for drug import may facilitate drug transport through the notoriously impregnable Mtb cell wall.

Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance

Mycobacterium tuberculosis is a serious human pathogen threat exhibiting complex evolution of antimicrobial resistance (AMR). Accordingly, the many publicly available datasets describing its AMR characteristics demand disparate data-type analyses. Here, we develop a reference strain-agnostic computational platform that uses machine learning approaches, complemented by both genetic interaction analysis and 3D structural mutation-mapping, to identify signatures of AMR evolution to 13 antibiotics. This platform is applied to 1595 sequenced strains to yield four key results. First, a pan-genome analysis shows that M. tuberculosis is highly conserved with sequenced variation concentrated in PE/PPE/PGRS genes. Second, the platform corroborates 33 genes known to confer resistance and identifies 24 new genetic signatures of AMR. Third, 97 epistatic interactions across 10 resistance classes are revealed. Fourth, detailed structural analysis of these genes yields mechanistic bases for their selection. The platform can be used to study other human pathogens.

TnSeq of Mycobacterium tuberculosis clinical isolates reveals strain-specific antibiotic liabilities

Once considered a phenotypically monomorphic bacterium, there is a growing body of work demonstrating heterogeneity among Mycobacterium tuberculosis (Mtb) strains in clinically relevant characteristics, including virulence and response to antibiotics. However, the genetic and molecular basis for most phenotypic differences among Mtb strains remains unknown. To investigate the basis of strain variation in Mtb, we performed genome-wide transposon mutagenesis coupled with next-generation sequencing (TnSeq) for a panel of Mtb clinical isolates and the reference strain H37Rv to compare genetic requirements for in vitro growth across these strains. We developed an analytic approach to identify quantitative differences in genetic requirements between these genetically diverse strains, which vary in genomic structure and gene content. Using this methodology, we found differences between strains in their requirements for genes involved in fundamental cellular processes, including redox homeostasis and central carbon metabolism. Among the genes with differential requirements were katG, which encodes the activator of the first-line antitubercular agent isoniazid, and glcB, which encodes malate synthase, the target of a novel small-molecule inhibitor. Differences among strains in their requirement for katG and glcB predicted differences in their response to these antimicrobial agents. Importantly, these strain-specific differences in antibiotic response could not be predicted by genetic variants identified through whole genome sequencing or by gene expression analysis. Our results provide novel insight into the basis of variation among Mtb strains and demonstrate that TnSeq is a scalable method to predict clinically important phenotypic differences among Mtb strains.

Updated and standardized genome-scale reconstruction of Mycobacterium tuberculosis H37Rv, iEK1011, simulates flux states indicative of physiological conditions

BACKGROUND

The efficacy of antibiotics against M. tuberculosis has been shown to be influenced by experimental media conditions. Investigations of M. tuberculosis growth in physiological conditions have described an environment that is different from common in vitro media. Thus, elucidating the interplay between available nutrient sources and antibiotic efficacy has clear medical relevance. While genome-scale reconstructions of M. tuberculosis have enabled the ability to interrogate media differences for the past 10 years, recent reconstructions have diverged from each other without standardization. A unified reconstruction of M. tuberculosis H37Rv would elucidate the impact of different nutrient conditions on antibiotic efficacy and provide new insights for therapeutic intervention.

RESULTS

We present a new genome-scale model of M. tuberculosis H37Rv, named iEK1011, that unifies and updates previous M. tuberculosis H37Rv genome-scale reconstructions. We functionally assess iEK1011 against previous models and show that the model increases correct gene essentiality predictions on two different experimental datasets by 6% (53% to 60%) and 18% (60% to 71%), respectively. We compared simulations between in vitro and approximated in vivo media conditions to examine the predictive capabilities of iEK1011. The simulated differences recapitulated literature defined characteristics in the rewiring of TCA metabolism including succinate secretion, gluconeogenesis, and activation of both the glyoxylate shunt and the methylcitrate cycle. To assist efforts to elucidate mechanisms of antibiotic resistance development, we curated 16 metabolic genes related to antimicrobial resistance and approximated evolutionary drivers of resistance. Comparing simulations of these antibiotic resistance features between in vivo and in vitro media highlighted condition-dependent differences that may influence the efficacy of antibiotics.

CONCLUSIONS

iEK1011 provides a computational knowledge base for exploring the impact of different environmental conditions on the metabolic state of M. tuberculosis H37Rv. As more experimental data and knowledge of M. tuberculosis H37Rv become available, a unified and standardized M. tuberculosis model will prove to be a valuable resource to the research community studying the systems biology of M. tuberculosis.