History of antibiotic adaptation influences microbial evolutionary dynamics during subsequent treatment.
ABSTRACT: Antibiotic regimens often include the sequential changing of drugs to limit the development and evolution of resistance of bacterial pathogens. It remains unclear how history of adaptation to one antibiotic can influence the resistance profiles when bacteria subsequently adapt to a different antibiotic. Here, we experimentally evolved Pseudomonas aeruginosa to six 2-drug sequences. We observed drug order-specific effects, whereby adaptation to the first drug can limit the rate of subsequent adaptation to the second drug, adaptation to the second drug can restore susceptibility to the first drug, or final resistance levels depend on the order of the 2-drug sequence. These findings demonstrate how resistance not only depends on the current drug regimen but also the history of past regimens. These order-specific effects may allow for rational forecasting of the evolutionary dynamics of bacteria given knowledge of past adaptations and provide support for the need to consider the history of past drug exposure when designing strategies to mitigate resistance and combat bacterial infections.
Project description:Resistance arises quickly during chemotherapeutic selection and is particularly problematic during long-term treatment regimens such as those for tuberculosis, HIV infections, or cancer. Although drug combination therapy reduces the evolution of drug resistance, drug pairs vary in their ability to do so. Thus, predictive models are needed to rationally design resistance-limiting therapeutic regimens. Using adaptive evolution, we studied the resistance response of the common pathogen Escherichia coli to 5 different single antibiotics and all 10 different antibiotic drug pairs. By analyzing the genomes of all evolved E. coli lineages, we identified the mutational events that drive the differences in drug resistance levels and found that the degree of resistance development against drug combinations can be understood in terms of collateral sensitivity and resistance that occurred during adaptation to the component drugs. Then, using engineered E. coli strains, we confirmed that drug resistance mutations that imposed collateral sensitivity were suppressed in a drug pair growth environment. These results provide a framework for rationally selecting drug combinations that limit resistance evolution.
Project description:Antibiotic-resistant bacteria are an increasingly serious public health concern, as strains emerge that demonstrate resistance to almost all available treatments. One factor that contributes to the crisis is the adaptive ability of bacteria, which exhibit remarkable phenotypic and gene expression heterogeneity in order to gain a survival advantage in damaging environments. This high degree of variability in gene expression across biological populations makes it a challenging task to identify key regulators of bacterial adaptation. Here, we research the regulation of adaptive resistance by investigating transcriptome profiles of Escherichia coli upon adaptation to disparate toxins, including antibiotics and biofuels. We locate potential target genes via conventional gene expression analysis as well as using a new analysis technique examining differential gene expression variability. By investigating trends across the diverse adaptation conditions, we identify a focused set of genes with conserved behavior, including those involved in cell motility, metabolism, membrane structure, and transport, and several genes of unknown function. To validate the biological relevance of the observed changes, we synthetically perturb gene expression using clustered regularly interspaced short palindromic repeat (CRISPR)-dCas9. Manipulation of select genes in combination with antibiotic treatment promotes adaptive resistance as demonstrated by an increased degree of antibiotic tolerance and heterogeneity in MICs. We study the mechanisms by which identified genes influence adaptation and find that select differentially variable genes have the potential to impact metabolic rates, mutation rates, and motility. Overall, this work provides evidence for a complex nongenetic response, encompassing shifts in gene expression and gene expression variability, which underlies adaptive resistance. IMPORTANCE Even initially sensitive bacteria can rapidly thwart antibiotic treatment through stress response processes known as adaptive resistance. Adaptive resistance fosters transient tolerance increases and the emergence of mutations conferring heritable drug resistance. In order to extend the applicable lifetime of new antibiotics, we must seek to hinder the occurrence of bacterial adaptive resistance; however, the regulation of adaptation is difficult to identify due to immense heterogeneity emerging during evolution. This study specifically seeks to generate heterogeneity by adapting bacteria to different stresses and then examines gene expression trends across the disparate populations in order to pinpoint key genes and pathways associated with adaptive resistance. The targets identified here may eventually inform strategies for impeding adaptive resistance and prolonging the effectiveness of antibiotic treatment.
Project description:Bacteria gain antibiotic resistance genes by horizontal acquisition of mobile genetic elements (MGE) from other lineages. Newly acquired MGEs are often poorly adapted causing intragenomic conflicts, resolved by compensatory adaptation of the chromosome, the MGE or reciprocal coadaptation. The footprints of such intragenomic coevolution are present in bacterial genomes, suggesting an important role promoting genomic integration of horizontally acquired genes, but direct experimental evidence of the process is limited. Here we show adaptive modulation of tetracycline resistance via intragenomic coevolution between Escherichia coli and the multi-drug resistant (MDR) plasmid RK2. Tetracycline treatments, including monotherapy or combination therapies with ampicillin, favoured de novo chromosomal resistance mutations coupled with mutations on RK2 impairing the plasmid-encoded tetracycline efflux-pump. These mutations together provided increased tetracycline resistance at reduced cost. Additionally, the chromosomal resistance mutations conferred cross-resistance to chloramphenicol. Reciprocal coadaptation was not observed under ampicillin-only or no antibiotic selection. Intragenomic coevolution can create genomes comprised of multiple replicons that together provide high-level, low-cost resistance, but the resulting co-dependence may limit the spread of coadapted MGEs to other lineages.
Project description:Despite the administration of multiple drugs that are highly effective in vitro, tuberculosis (TB) treatment requires prolonged drug administration and is confounded by the emergence of drug-resistant strains. To understand the mechanisms that limit antibiotic efficacy, we performed a comprehensive genetic study to identify Mycobacterium tuberculosis genes that alter the rate of bacterial clearance in drug-treated mice. Several functionally distinct bacterial genes were found to alter bacterial clearance, and prominent among these was the glpK gene that encodes the glycerol-3-kinase enzyme that is necessary for glycerol catabolism. Growth on glycerol generally increased the sensitivity of M. tuberculosis to antibiotics in vitro, and glpK-deficient bacteria persisted during antibiotic treatment in vivo, particularly during exposure to pyrazinamide-containing regimens. Frameshift mutations in a hypervariable homopolymeric region of the glpK gene were found to be a specific marker of multidrug resistance in clinical M. tuberculosis isolates, and these loss-of-function alleles were also enriched in extensively drug-resistant clones. These data indicate that frequently observed variation in the glpK coding sequence produces a drug-tolerant phenotype that can reduce antibiotic efficacy and may contribute to the evolution of resistance.IMPORTANCE TB control is limited in part by the length of antibiotic treatment needed to prevent recurrent disease. To probe mechanisms underlying survival under antibiotic pressure, we performed a genetic screen for M. tuberculosis mutants with altered susceptibility to treatment using the mouse model of TB. We identified multiple genes involved in a range of functions which alter sensitivity to antibiotics. In particular, we found glycerol catabolism mutants were less susceptible to treatment and that common variation in a homopolymeric region in the glpK gene was associated with drug resistance in clinical isolates. These studies indicate that reversible high-frequency variation in carbon metabolic pathways can produce phenotypically drug-tolerant clones and have a role in the development of resistance.
Project description:The worldwide increase in the prevalence of multi-antibiotic-resistant bacteria has threatened the physician's ability to provide appropriate therapy for infections. The relationship between antimicrobial drug concentration and infecting pathogen population reduction is of primary interest. Using data derived from mice infected with the bacterium Pseudomonas aeruginosa and treated with a fluoroquinolone antibiotic, a mathematical model was developed that described relationships between antimicrobial drug exposures and changes in drug-susceptible and -resistant bacterial subpopulations at an infection site. Dosing regimens and consequent drug exposures that amplify or suppress the emergence of resistant bacterial subpopulations were identified and prospectively validated. Resistant clones selected in vivo by suboptimal regimens were characterized. No mutations were identified in the quinolone resistance-determining regions of gyrA/B or parC/E. However, all resistant clones demonstrated efflux pump overexpression. At base line, MexAB-OprM, MexCD-OprJ, and MexEF-OprN were represented in the drug-resistant population. After 28 hours of therapy, MexCD-OprJ became the predominant pump expressed in the resistant clones. The likelihood of achieving resistance-suppression exposure in humans with a clinically prescribed antibiotic dose was determined. The methods developed in this study provide insight regarding how mathematical models can be used to identify rational dosing regimens that suppress the amplification of the resistant mutant population.
Project description:Genetic constraints can block many mutational pathways to optimal genotypes in real fitness landscapes, yet the extent to which this can limit evolution remains to be determined. Interestingly, mutator bacteria elevate only specific types of mutations, and therefore could be very sensitive to genetic constraints. Testing this possibility is not only clinically relevant, but can also inform about the general impact of genetic constraints in adaptation. Here, we evolved 576 populations of two mutator and one wild-type Escherichia coli to doubling concentrations of the antibiotic cefotaxime. All strains carried TEM-1, a ?-lactamase enzyme well known by its low availability of mutational pathways. Crucially, one of the mutators does not elevate any of the relevant first-step mutations known to improve cefatoximase activity. Despite this, both mutators displayed a similar ability to evolve more than 1000-fold resistance. Initial adaptation proceeded in parallel through general multi-drug resistance mechanisms. High-level resistance, in contrast, was achieved through divergent paths; with the a priori inferior mutator exploiting alternative mutational pathways in PBP3, the target of the antibiotic. These results have implications for mutator management in clinical infections and, more generally, illustrate that limits to natural selection in real organisms are alleviated by the existence of multiple loci contributing to fitness.
Project description:Abstract In order to mitigate antibiotic resistance, a new strategy to increase antibiotic potency and reverse drug resistance is needed. Herein, the translocation mechanism of an antimicrobial guanidinium?functionalized polycarbonate is leveraged in combination with traditional antibiotics to afford a potent treatment for drug?resistant bacteria. Particularly, this polymer–antibiotic combination approach reverses rifampicin resistance phenotype in Acinetobacter baumannii demonstrating a 2.5 × 105?fold reduction in minimum inhibitory concentration (MIC) and a 4096?fold reduction in minimum bactericidal concentration (MBC). This approach also enables the repurposing of auranofin as an antibiotic against multidrug?resistant (MDR) Gram?negative bacteria with a 512?fold MIC and 128?fold MBC reduction, respectively. Finally, the in vivo efficacy of polymer–rifampicin combination is demonstrated in a MDR bacteremia mouse model. This combination approach lays foundational ground rules for a new class of antibiotic adjuvants capable of reversing drug resistance phenotype and repurposing drugs against MDR Gram?negative bacteria. Guanidinium?functionalized polycarbonates reverse antibiotic resistance phenotype and improve antibiotic potency via intracellular protein precipitation, gene binding, and subsequent reactive oxygen species generation. Importantly, the polymer allows for the effective repositioning of rifampicin and auranofin meant for Gram?positive bacteria and arthritis, respectively, to be highly effective against multidrug?resistant Gram?negative bacteria both in vitro and in vivo.
Project description:Antibiotic resistance is prevalent among the bacterial pathogens causing urinary tract infections. However, antimicrobial treatment is often prescribed 'empirically', in the absence of antibiotic susceptibility testing, risking mismatched and therefore ineffective treatment. Here, linking a 10-year longitudinal data set of over 700,000 community-acquired urinary tract infections with over 5,000,000 individually resolved records of antibiotic purchases, we identify strong associations of antibiotic resistance with the demographics, records of past urine cultures and history of drug purchases of the patients. When combined together, these associations allow for machine-learning-based personalized drug-specific predictions of antibiotic resistance, thereby enabling drug-prescribing algorithms that match an antibiotic treatment recommendation to the expected resistance of each sample. Applying these algorithms retrospectively, over a 1-year test period, we find that they greatly reduce the risk of mismatched treatment compared with the current standard of care. The clinical application of such algorithms may help improve the effectiveness of antimicrobial treatments.
Project description: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.
Project description:The global incidence of drug-resistant Gram-negative bacillary infections has been increasing, and there is a dire need to develop novel strategies to overcome this problem. Intrinsic resistance in Gram-negative bacteria, such as their protective outer membrane and constitutively overexpressed efflux pumps, is a major survival weapon that renders them refractory to current antibiotics. Several potential avenues to overcome this problem have been at the heart of antibiotic drug discovery in the past few decades. We review some of these strategies, with emphasis on antibiotic hybrids either as stand-alone antibacterial agents or as adjuvants that potentiate a primary antibiotic in Gram-negative bacteria. Antibiotic hybrid is defined in this review as a synthetic construct of two or more pharmacophores belonging to an established agent known to elicit a desired antimicrobial effect. The concepts, advances, and challenges of antibiotic hybrids are elaborated in this article. Moreover, we discuss several antibiotic hybrids that were or are in clinical evaluation. Mechanistic insights into how tobramycin-based antibiotic hybrids are able to potentiate legacy antibiotics in multidrug-resistant Gram-negative bacilli are also highlighted. Antibiotic hybrids indeed have a promising future as a therapeutic strategy to overcome drug resistance in Gram-negative pathogens and/or expand the usefulness of our current antibiotic arsenal.