Emergence and selection of isoniazid and rifampin resistance in tuberculosis granulomas. - Related Documents




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908601.0000Emergence and selection of isoniazid and rifampin resistance in tuberculosis granulomas. Drug resistant tuberculosis is increasing world-wide. Resistance against isoniazid (INH), rifampicin (RIF), or both (multi-drug resistant TB, MDR-TB) is of particular concern, since INH and RIF form part of the standard regimen for TB disease. While it is known that suboptimal treatment can lead to resistance, it remains unclear how host immune responses and antibiotic dynamics within granulomas (sites of infection) affect emergence and selection of drug-resistant bacteria. We take a systems pharmacology approach to explore resistance dynamics within granulomas. We integrate spatio-temporal host immunity, INH and RIF dynamics, and bacterial dynamics (including fitness costs and compensatory mutations) in a computational framework. We simulate resistance emergence in the absence of treatment, as well as resistance selection during INH and/or RIF treatment. There are four main findings. First, in the absence of treatment, the percentage of granulomas containing resistant bacteria mirrors the non-monotonic bacterial dynamics within granulomas. Second, drug-resistant bacteria are less frequently found in non-replicating states in caseum, compared to drug-sensitive bacteria. Third, due to a steeper dose response curve and faster plasma clearance of INH compared to RIF, INH-resistant bacteria have a stronger influence on treatment outcomes than RIF-resistant bacteria. Finally, under combination therapy with INH and RIF, few MDR bacteria are able to significantly affect treatment outcomes. Overall, our approach allows drug-specific prediction of drug resistance emergence and selection in the complex granuloma context. Since our predictions are based on pre-clinical data, our approach can be implemented relatively early in the treatment development process, thereby enabling pro-active rather than reactive responses to emerging drug resistance for new drugs. Furthermore, this quantitative and drug-specific approach can help identify drug-specific properties that influence resistance and use this information to design treatment regimens that minimize resistance selection and expand the useful life-span of new antibiotics.201829746491
937210.9993The population genetics of collateral resistance and sensitivity. Resistance mutations against one drug can elicit collateral sensitivity against other drugs. Multi-drug treatments exploiting such trade-offs can help slow down the evolution of resistance. However, if mutations with diverse collateral effects are available, a treated population may evolve either collateral sensitivity or collateral resistance. How to design treatments robust to such uncertainty is unclear. We show that many resistance mutations in Escherichia coli against various antibiotics indeed have diverse collateral effects. We propose to characterize such diversity with a joint distribution of fitness effects (JDFE) and develop a theory for describing and predicting collateral evolution based on simple statistics of the JDFE. We show how to robustly rank drug pairs to minimize the risk of collateral resistance and how to estimate JDFEs. In addition to practical applications, these results have implications for our understanding of evolution in variable environments.202134889185
427120.9992Multi-step vs. single-step resistance evolution under different drugs, pharmacokinetics, and treatment regimens. The success of antimicrobial treatment is threatened by the evolution of drug resistance. Population genetic models are an important tool in mitigating that threat. However, most such models consider resistance emergence via a single mutational step. Here, we assembled experimental evidence that drug resistance evolution follows two patterns: (i) a single mutation, which provides a large resistance benefit, or (ii) multiple mutations, each conferring a small benefit, which combine to yield high-level resistance. Using stochastic modeling, we then investigated the consequences of these two patterns for treatment failure and population diversity under various treatments. We find that resistance evolution is substantially limited if more than two mutations are required and that the extent of this limitation depends on the combination of drug type and pharmacokinetic profile. Further, if multiple mutations are necessary, adaptive treatment, which only suppresses the bacterial population, delays treatment failure due to resistance for a longer time than aggressive treatment, which aims at eradication.202134001313
427230.9992The hidden impact of antibacterial resistance in respiratory tract infection. Steering an appropriate course: principles to guide antibiotic choice. The prevalence and degree of antibacterial resistance in common respiratory pathogens are increasing worldwide. The health impact of resistance is not yet fully understood. However, once the impact of resistance becomes measurable, it may be too late to apply interventions to reduce resistance levels and regain previous quality and cost of care. We should address resistance now, before patient care is irreversibly compromised. The association between antibiotic consumption and the prevalence of resistance is widely assumed. However, evidence suggests that there is a more complex. multifactorial relationship between antibiotic use and resistance. It is also assumed that there is an adaptive fitness cost for bacterial resistance mutations. However, in some cases, bacteria are able to acquire 'compensatory genes' negating any negative impact of resistance mutations. Mathematical modeling indicates that the timescale for the emergence of resistance is typically shorter than the decay time following a decline in antibiotic consumption. Against this background, a general principle is proposed: to maximize patient outcome whilst minimizing the potential for selection and spread of resistance. This may be achieved through the use of agents that fulfill defined pharmacodynamic and pharmacokinetic parameters and elicit rapid eradication of the bacterial population, including emerging resistant mutants, from the site of infection. The choice of agent may not be the same in all regions, as selection will depend on local resistance patterns and disease etiology; however, the application of this principle may help to preserve the benefits of antibiotic therapy.200111419671
953840.9992The Mechanism of Bacterial Resistance and Potential Bacteriostatic Strategies. Bacterial drug resistance is rapidly developing as one of the greatest threats to human health. Bacteria will adopt corresponding strategies to crack the inhibitory effect of antibiotics according to the antibacterial mechanism of antibiotics, involving the mutation of drug target, secreting hydrolase, and discharging antibiotics out of cells through an efflux pump, etc. In recent years, bacteria are found to constantly evolve new resistance mechanisms to antibiotics, including target protective protein, changes in cell morphology, and so on, endowing them with multiple defense systems against antibiotics, leading to the emergence of multi-drug resistant (MDR) bacteria and the unavailability of drugs in clinics. Correspondingly, researchers attempt to uncover the mystery of bacterial resistance to develop more convenient and effective antibacterial strategies. Although traditional antibiotics still play a significant role in the treatment of diseases caused by sensitive pathogenic bacteria, they gradually lose efficacy in the MDR bacteria. Therefore, highly effective antibacterial compounds, such as phage therapy and CRISPER-Cas precision therapy, are gaining an increasing amount of attention, and are considered to be the treatments with the moist potential with regard to resistance against MDR in the future. In this review, nine identified drug resistance mechanisms are summarized, which enhance the retention rate of bacteria under the action of antibiotics and promote the distribution of drug-resistant bacteria (DRB) in the population. Afterwards, three kinds of potential antibacterial methods are introduced, in which new antibacterial compounds exhibit broad application prospects with different action mechanisms, the phage therapy has been successfully applied to infectious diseases caused by super bacteria, and the CRISPER-Cas precision therapy as a new technology can edit drug-resistant genes in pathogenic bacteria at the gene level, with high accuracy and flexibility. These antibacterial methods will provide more options for clinical treatment, and will greatly alleviate the current drug-resistant crisis.202236139994
946950.9992Reversing bacterial resistance to antibiotics by phage-mediated delivery of dominant sensitive genes. Pathogen resistance to antibiotics is a rapidly growing problem, leading to an urgent need for novel antimicrobial agents. Unfortunately, development of new antibiotics faces numerous obstacles, and a method that resensitizes pathogens to approved antibiotics therefore holds key advantages. We present a proof of principle for a system that restores antibiotic efficiency by reversing pathogen resistance. This system uses temperate phages to introduce, by lysogenization, the genes rpsL and gyrA conferring sensitivity in a dominant fashion to two antibiotics, streptomycin and nalidixic acid, respectively. Unique selective pressure is generated to enrich for bacteria that harbor the phages carrying the sensitizing constructs. This selection pressure is based on a toxic compound, tellurite, and therefore does not forfeit any antibiotic for the sensitization procedure. We further demonstrate a possible way of reducing undesirable recombination events by synthesizing dominant sensitive genes with major barriers to homologous recombination. Such synthesis does not significantly reduce the gene's sensitization ability. Unlike conventional bacteriophage therapy, the system does not rely on the phage's ability to kill pathogens in the infected host, but instead, on its ability to deliver genetic constructs into the bacteria and thus render them sensitive to antibiotics prior to host infection. We believe that transfer of the sensitizing cassette by the constructed phage will significantly enrich for antibiotic-treatable pathogens on hospital surfaces. Broad usage of the proposed system, in contrast to antibiotics and phage therapy, will potentially change the nature of nosocomial infections toward being more susceptible to antibiotics rather than more resistant.201222113912
937460.9992Mathematical modelling of antibiotic interaction on evolution of antibiotic resistance: an analytical approach. BACKGROUND: The emergence and spread of antibiotic-resistant pathogens have led to the exploration of antibiotic combinations to enhance clinical effectiveness and counter resistance development. Synergistic and antagonistic interactions between antibiotics can intensify or diminish the combined therapy's impact. Moreover, these interactions can evolve as bacteria transition from wildtype to mutant (resistant) strains. Experimental studies have shown that the antagonistically interacting antibiotics against wildtype bacteria slow down the evolution of resistance. Interestingly, other studies have shown that antibiotics that interact antagonistically against mutants accelerate resistance. However, it is unclear if the beneficial effect of antagonism in the wildtype bacteria is more critical than the detrimental effect of antagonism in the mutants. This study aims to illuminate the importance of antibiotic interactions against wildtype bacteria and mutants on the deacceleration of antimicrobial resistance. METHODS: To address this, we developed and analyzed a mathematical model that explores the population dynamics of wildtype and mutant bacteria under the influence of interacting antibiotics. The model investigates the relationship between synergistic and antagonistic antibiotic interactions with respect to the growth rate of mutant bacteria acquiring resistance. Stability analysis was conducted for equilibrium points representing bacteria-free conditions, all-mutant scenarios, and coexistence of both types. Numerical simulations corroborated the analytical findings, illustrating the temporal dynamics of wildtype and mutant bacteria under different combination therapies. RESULTS: Our analysis provides analytical clarification and numerical validation that antibiotic interactions against wildtype bacteria exert a more significant effect on reducing the rate of resistance development than interactions against mutants. Specifically, our findings highlight the crucial role of antagonistic antibiotic interactions against wildtype bacteria in slowing the growth rate of resistant mutants. In contrast, antagonistic interactions against mutants only marginally affect resistance evolution and may even accelerate it. CONCLUSION: Our results emphasize the importance of considering the nature of antibiotic interactions against wildtype bacteria rather than mutants when aiming to slow down the acquisition of antibiotic resistance.202438426146
954070.9992Coping with antibiotic resistance: combining nanoparticles with antibiotics and other antimicrobial agents. The worldwide escalation of bacterial resistance to conventional medical antibiotics is a serious concern for modern medicine. High prevalence of multidrug-resistant bacteria among bacteria-based infections decreases effectiveness of current treatments and causes thousands of deaths. New improvements in present methods and novel strategies are urgently needed to cope with this problem. Owing to their antibacterial activities, metallic nanoparticles represent an effective solution for overcoming bacterial resistance. However, metallic nanoparticles are toxic, which causes restrictions in their use. Recent studies have shown that combining nanoparticles with antibiotics not only reduces the toxicity of both agents towards human cells by decreasing the requirement for high dosages but also enhances their bactericidal properties. Combining antibiotics with nanoparticles also restores their ability to destroy bacteria that have acquired resistance to them. Furthermore, nanoparticles tagged with antibiotics have been shown to increase the concentration of antibiotics at the site of bacterium-antibiotic interaction, and to facilitate binding of antibiotics to bacteria. Likewise, combining nanoparticles with antimicrobial peptides and essential oils generates genuine synergy against bacterial resistance. In this article, we aim to summarize recent studies on interactions between nanoparticles and antibiotics, as well as other antibacterial agents to formulate new prospects for future studies. Based on the promising data that demonstrated the synergistic effects of antimicrobial agents with nanoparticles, we believe that this combination is a potential candidate for more research into treatments for antibiotic-resistant bacteria.201122029522
937880.9992Inferring strain-level mutational drivers of phage-bacteria interaction phenotypes arising during coevolutionary dynamics. The enormous diversity of bacteriophages and their bacterial hosts presents a significant challenge to predict which phages infect a focal set of bacteria. Infection is largely determined by complementary - and largely uncharacterized - genetics of adsorption, injection, cell take-over and lysis. Here we present a machine learning approach to predict phage-bacteria interactions trained on genome sequences of and phenotypic interactions amongst 51 Escherichia coli strains and 45 phage λ strains that coevolved in laboratory conditions for 37 days. Leveraging multiple inference strategies and without a priori knowledge of driver mutations, this framework predicts both who infects whom and the quantitative levels of infections across a suite of 2,295 potential interactions. We found that the most effective approach inferred interaction phenotypes from independent contributions from phage and bacteria mutations, accurately predicting 86% of interactions while reducing the relative error in the estimated strength of the infection phenotype by 40% . Feature selection revealed key phage λ and E. coli mutations that have a significant influence on the outcome of phage-bacteria interactions, corroborating sites previously known to affect phage λ infections, as well as identifying mutations in genes of unknown function not previously shown to influence bacterial resistance. The method's success in recapitulating strain-level infection outcomes arising during coevolutionary dynamics may also help inform generalized approaches for imputing genetic drivers of interaction phenotypes in complex communities of phage and bacteria.202438260415
969390.9992Antibody-mediated crosslinking of gut bacteria hinders the spread of antibiotic resistance. The body is home to a diverse microbiota, mainly in the gut. Resistant bacteria are selected by antibiotic treatments, and once resistance becomes widespread in a population of hosts, antibiotics become useless. Here, we develop a multiscale model of the interaction between antibiotic use and resistance spread in a host population, focusing on an important aspect of within-host immunity. Antibodies secreted in the gut enchain bacteria upon division, yielding clonal clusters of bacteria. We demonstrate that immunity-driven bacteria clustering can hinder the spread of a novel resistant bacterial strain in a host population. We quantify this effect both in the case where resistance preexists and in the case where acquiring a new resistance mutation is necessary for the bacteria to spread. We further show that the reduction of spread by clustering can be countered when immune hosts are silent carriers, and are less likely to get treated, and/or have more contacts. We demonstrate the robustness of our findings to including stochastic within-host bacterial growth, a fitness cost of resistance, and its compensation. Our results highlight the importance of interactions between immunity and the spread of antibiotic resistance, and argue in the favor of vaccine-based strategies to combat antibiotic resistance.201930957218
9471100.9991Systematic analysis of putative phage-phage interactions on minimum-sized phage cocktails. The application of bacteriophages as antibacterial agents has many benefits in the "post-antibiotic age". To increase the number of successfully targeted bacterial strains, phage cocktails, instead of a single phage, are commonly formulated. Nevertheless, there is currently no consensus pipeline for phage cocktail development. Thus, although large cocktails increase the spectrum of activity, they could produce side effects such as the mobilization of virulence or antibiotic resistance genes. On the other hand, coinfection (simultaneous infection of one host cell by several phages) might reduce the potential for bacteria to evolve phage resistance, but some antagonistic interactions amongst phages might be detrimental for the outcome of phage cocktail application. With this in mind, we introduce here a new method, which considers the host range and each individual phage-host interaction, to design the phage mixtures that best suppress the target bacteria while minimizing the number of phages to restrict manufacturing costs. Additionally, putative phage-phage interactions in cocktails and phage-bacteria networks are compared as the understanding of the complex interactions amongst bacteriophages could be critical in the development of realistic phage therapy models in the future.202235165352
9441110.9991Antibiotic resistance: The challenges and some emerging strategies for tackling a global menace. BACKGROUND: Antibiotic resistance is currently the most serious global threat to the effective treatment of bacterial infections. Antibiotic resistance has been established to adversely affect both clinical and therapeutic outcomes, with consequences ranging from treatment failures and the need for expensive and safer alternative drugs to the cost of higher rates of morbidity and mortality, longer hospitalization, and high-healthcare costs. The search for new antibiotics and other antimicrobials continues to be a pressing need in humanity's battle against bacterial infections. Antibiotic resistance appears inevitable, and there is a continuous lack of interest in investing in new antibiotic research by pharmaceutical industries. This review summarized some new strategies for tackling antibiotic resistance in bacteria. METHODS: To provide an overview of the recent research, we look at some new strategies for preventing resistance and/or reviving bacteria's susceptibility to already existing antibiotics. RESULTS: Substantial pieces of evidence suggest that antimicrobials interact with host immunity, leading to potent indirect effects that improve antibacterial activities and may result in more swift and complete bactericidal effects. A new class of antibiotics referred to as immuno-antibiotics and the targeting of some biochemical resistance pathway components including inhibition of SOS response and hydrogen sulfide as biochemical underlying networks of bacteria can be considered as new emerging strategies to combat antibiotic resistance in bacteria. CONCLUSION: This review highlighted and discussed immuno-antibiotics and inhibition of SOS response and hydrogen sulfide as biochemical underlying networks of bacteria as new weapons against antibiotic resistance in bacteria.202235949048
9534120.9991Defining the Benefits of Antibiotic Resistance in Commensals and the Scope for Resistance Optimization. Antibiotic resistance is a major medical and public health challenge, characterized by global increases in the prevalence of resistant strains. The conventional view is that all antibiotic resistance is problematic, even when not in pathogens. Resistance in commensal bacteria poses risks, as resistant organisms can provide a reservoir of resistance genes that can be horizontally transferred to pathogens or may themselves cause opportunistic infections in the future. While these risks are real, we propose that commensal resistance can also generate benefits during antibiotic treatment of human infection, by promoting continued ecological suppression of pathogens. To define and illustrate this alternative conceptual perspective, we use a two-species mathematical model to identify the necessary and sufficient ecological conditions for beneficial resistance. We show that the benefits are limited to species (or strain) interactions where commensals suppress pathogen growth and are maximized when commensals compete with, rather than prey on or otherwise exploit pathogens. By identifying benefits of commensal resistance, we propose that rather than strictly minimizing all resistance, resistance management may be better viewed as an optimization problem. We discuss implications in two applied contexts: bystander (nontarget) selection within commensal microbiomes and pathogen treatment given polymicrobial infections. IMPORTANCE Antibiotic resistance is commonly viewed as universally costly, regardless of which bacterial cells express resistance. Here, we derive an opposing logic, where resistance in commensal bacteria can lead to reductions in pathogen density and improved outcomes on both the patient and public health scales. We use a mathematical model of commensal-pathogen interactions to define the necessary and sufficient conditions for beneficial resistance, highlighting the importance of reciprocal ecological inhibition to maximize the benefits of resistance. More broadly, we argue that determining the benefits as well as the costs of resistances in human microbiomes can transform resistance management from a minimization to an optimization problem. We discuss applied contexts and close with a review of key resistance optimization dimensions, including the magnitude, spectrum, and mechanism of resistance.202336475750
9087130.9991Complementary supramolecular drug associates in perfecting the multidrug therapy against multidrug resistant bacteria. The inappropriate and inconsistent use of antibiotics in combating multidrug-resistant bacteria exacerbates their drug resistance through a few distinct pathways. Firstly, these bacteria can accumulate multiple genes, each conferring resistance to a specific drug, within a single cell. This accumulation usually takes place on resistance plasmids (R). Secondly, multidrug resistance can arise from the heightened expression of genes encoding multidrug efflux pumps, which expel a broad spectrum of drugs from the bacterial cells. Additionally, bacteria can also eliminate or destroy antibiotic molecules by modifying enzymes or cell walls and removing porins. A significant limitation of traditional multidrug therapy lies in its inability to guarantee the simultaneous delivery of various drug molecules to a specific bacterial cell, thereby fostering incremental drug resistance in either of these paths. Consequently, this approach prolongs the treatment duration. Rather than using a biologically unimportant coformer in forming cocrystals, another drug molecule can be selected either for protecting another drug molecule or, can be selected for its complementary activities to kill a bacteria cell synergistically. The development of a multidrug cocrystal not only improves tabletability and plasticity but also enables the simultaneous delivery of multiple drugs to a specific bacterial cell, philosophically perfecting multidrug therapy. By adhering to the fundamental tenets of multidrug therapy, the synergistic effects of these drug molecules can effectively eradicate bacteria, even before they have the chance to develop resistance. This approach has the potential to shorten treatment periods, reduce costs, and mitigate drug resistance. Herein, four hypotheses are presented to create complementary drug cocrystals capable of simultaneously reaching bacterial cells, effectively destroying them before multidrug resistance can develop. The ongoing surge in the development of novel drugs provides another opportunity in the fight against bacteria that are constantly gaining resistance to existing treatments. This endeavour holds the potential to combat a wide array of multidrug-resistant bacteria.202438415251
8173140.9991Advancing Antibacterial Strategies: CRISPR-Phage-Mediated Gene Therapy Targeting Bacterial Resistance Genes. One of the most significant issues facing the world today is antibiotic resistance, which makes it increasingly difficult to treat bacterial infections. Regular antibiotics no longer work against many bacteria, affecting millions of people. A novel approach known as CRISPR-phage therapy may be beneficial. This technique introduces a technology called CRISPR into resistant bacteria using bacteriophages. The genes that cause bacteria to become resistant to antibiotics can be identified and cut using CRISPR. This enables antibiotics to function by inhibiting the bacteria. This approach is highly precise, unlike conventional antibiotics, so it doesn't damage our bodies' beneficial bacteria. Preliminary studies and limited clinical trials suggest that this technique can effectively target drug-resistant bacteria such as Klebsiella pneumoniae and Methicillinresistant Staphylococcus aureus (MRSA). However, challenges in phage engineering, host delivery, and the growing threat of bacterial CRISPR resistance demand urgent and strategic innovation. Our perspective underscores that without proactive resolution of these hurdles, the current hopefulness could disappear. Looking ahead, integrating next-generation Cas effectors, non-DSB editors, and resistance monitoring frameworks could transform CRISPR-phage systems from an experimental novelty into a clinical mainstay. This shift will require not only scientific ingenuity but also coordinated advances in regulatory, translational, and manufacturing efforts.202540990280
9539150.9991Materials for restoring lost Activity: Old drugs for new bugs. The escalation of bacterial resistance to conventional medical antibiotics is a serious concern worldwide. Improvements to current therapies are urgently needed to address this problem. The synergistic combination of antibiotics with other agents is a strategic solution to combat multi-drug-resistant bacteria. Although these combinations decrease the required high dosages and therefore, reduce the toxicity of both agents without compromising the bactericidal effect, they cannot stop the development of further resistance. Recent studies have shown certain elements restore the ability of antibiotics to destroy bacteria that have acquired resistance to them. Due to these synergistic activities, organic and inorganic molecules have been investigated with the goal of restoring antibiotics in new approaches that mitigate the risk of expanding resistance. Herein, we summarize recent studies that restore antibiotics once thought to be ineffective, but have returned to our armamentarium through innovative, combinatorial efforts. A special focus is placed on the mechanisms that allow the synergistic combinations to combat bacteria. The promising data that demonstrated restoration of antimicrobials, supports the notion to find more combinations that can combat antibiotic-resistant bacteria.202235461913
9546160.9991Challenge in the Discovery of New Drugs: Antimicrobial Peptides against WHO-List of Critical and High-Priority Bacteria. Bacterial resistance has intensified in recent years due to the uncontrolled use of conventional drugs, and new bacterial strains with multiple resistance have been reported. This problem may be solved by using antimicrobial peptides (AMPs), which fulfill their bactericidal activity without developing much bacterial resistance. The rapid interaction between AMPs and the bacterial cell membrane means that the bacteria cannot easily develop resistance mechanisms. In addition, various drugs for clinical use have lost their effect as a conventional treatment; however, the synergistic effect of AMPs with these drugs would help to reactivate and enhance antimicrobial activity. Their efficiency against multi-resistant and extensively resistant bacteria has positioned them as promising molecules to replace or improve conventional drugs. In this review, we examined the importance of antimicrobial peptides and their successful activity against critical and high-priority bacteria published in the WHO list.202134064302
9553170.9991A machine learning framework to predict antibiotic resistance traits and yet unknown genes underlying resistance to specific antibiotics in bacterial strains. Recently, the frequency of observing bacterial strains without known genetic components underlying phenotypic resistance to antibiotics has increased. There are several strains of bacteria lacking known resistance genes; however, they demonstrate resistance phenotype to drugs of that family. Although such strains are fewer compared to the overall population, they pose grave emerging threats to an already heavily challenged area of antimicrobial resistance (AMR), where death tolls have reached ~700 000 per year and a grim projection of ~10 million deaths per year by 2050 looms. Considering the fact that development of novel antibiotics is not keeping pace with the emergence and dissemination of resistance, there is a pressing need to decipher yet unknown genetic mechanisms of resistance, which will enable developing strategies for the best use of available interventions and show the way for the development of new drugs. In this study, we present a machine learning framework to predict novel AMR factors that are potentially responsible for resistance to specific antimicrobial drugs. The machine learning framework utilizes whole-genome sequencing AMR genetic data and antimicrobial susceptibility testing phenotypic data to predict resistance phenotypes and rank AMR genes by their importance in discriminating the resistance from the susceptible phenotypes. In summary, we present here a bioinformatics framework for training machine learning models, evaluating their performances, selecting the best performing model(s) and finally predicting the most important AMR loci for the resistance involved.202134015806
9610180.9991The evolutionary rate of antibacterial drug targets. BACKGROUND: One of the major issues in the fight against infectious diseases is the notable increase in multiple drug resistance in pathogenic species. For that reason, newly acquired high-throughput data on virulent microbial agents attract the attention of many researchers seeking potential new drug targets. Many approaches have been used to evaluate proteins from infectious pathogens, including, but not limited to, similarity analysis, reverse docking, statistical 3D structure analysis, machine learning, topological properties of interaction networks or a combination of the aforementioned methods. From a biological perspective, most essential proteins (knockout lethal for bacteria) or highly conserved proteins (broad spectrum activity) are potential drug targets. Ribosomal proteins comprise such an example. Many of them are well-known drug targets in bacteria. It is intuitive that we should learn from nature how to design good drugs. Firstly, known antibiotics are mainly originating from natural products of microorganisms targeting other microorganisms. Secondly, paleontological data suggests that antibiotics have been used by microorganisms for million years. Thus, we have hypothesized that good drug targets are evolutionary constrained and are subject of evolutionary selection. This means that mutations in such proteins are deleterious and removed by selection, which makes them less susceptible to random development of resistance. Analysis of the speed of evolution seems to be good approach to test this hypothesis. RESULTS: In this study we show that pN/pS ratio of genes coding for known drug targets is significantly lower than the genome average and also lower than that for essential genes identified by experimental methods. Similar results are observed in the case of dN/dS analysis. Both analyzes suggest that drug targets tend to evolve slowly and that the rate of evolution is a better predictor of drugability than essentiality. CONCLUSIONS: Evolutionary rate can be used to score and find potential drug targets. The results presented here may become a useful addition to a repertoire of drug target prediction methods. As a proof of concept, we analyzed GO enrichment among the slowest evolving genes. These may become the starting point in the search for antibiotics with a novel mechanism.201323374913
9613190.9991Using Selection by Nonantibiotic Stressors to Sensitize Bacteria to Antibiotics. Evolutionary adaptation of bacteria to nonantibiotic selective forces, such as osmotic stress, has been previously associated with increased antibiotic resistance, but much less is known about potentially sensitizing effects of nonantibiotic stressors. In this study, we use laboratory evolution to investigate adaptation of Enterococcus faecalis, an opportunistic bacterial pathogen, to a broad collection of environmental agents, ranging from antibiotics and biocides to extreme pH and osmotic stress. We find that nonantibiotic selection frequently leads to increased sensitivity to other conditions, including multiple antibiotics. Using population sequencing and whole-genome sequencing of single isolates from the evolved populations, we identify multiple mutations in genes previously linked with resistance to the selecting conditions, including genes corresponding to known drug targets or multidrug efflux systems previously tied to collateral sensitivity. Finally, we hypothesized based on the measured sensitivity profiles that sequential rounds of antibiotic and nonantibiotic selection may lead to hypersensitive populations by harnessing the orthogonal collateral effects of particular pairs of selective forces. To test this hypothesis, we show experimentally that populations evolved to a sequence of linezolid (an oxazolidinone antibiotic) and sodium benzoate (a common preservative) exhibit increased sensitivity to more stressors than adaptation to either condition alone. The results demonstrate how sequential adaptation to drug and nondrug environments can be used to sensitize bacteria to antibiotics and highlight new potential strategies for exploiting shared constraints governing adaptation to diverse environmental challenges.202031851309