Epistasis between antibiotic resistance mutations and genetic background shape the fitness effect of resistance across species of Pseudomonas

The distribution of beneficial mutational effects between two sister yeast species poorly explains natural outcomes of vineyard adaptation

Domesticated strains of Saccharomyces cerevisiae have adapted to resist copper and sulfite, two chemical stressors commonly used in winemaking. S. paradoxus, has not adapted to these chemicals despite being consistently present in sympatry with S. cerevisiae in vineyards. This contrast represents a case of apparent evolutionary constraints favoring greater adaptive capacity in S. cerevisiae. In this study, we used a comparative mutagenesis approach to test whether S. paradoxus is mutationally constrained with respect to acquiring greater copper and sulfite resistance. For both species, we assayed the rate, effect size, and pleiotropic costs of resistance mutations and sequenced a subset of 150 mutants isolated from our screen. We found that the distributions of mutational effects displayed by the two species were very similar and poorly explained the natural pattern. We also found that chromosome VIII aneuploidy and loss of function mutations in PMA1 confer copper resistance in both species, whereas loss of function mutations in REG1 were only a viable route to copper resistance in S. cerevisiae. We also observed a single de novo duplication of the CUP1 gene in S. paradoxus but none in S. cerevisiae. For sulfite, loss of function mutations in RTS1 and KSP1 confer resistance in both species, but mutations in RTS1 have larger average effects in S. paradoxus. Our results show that even when the distributions of mutational effects are largely similar, species can differ in the adaptive paths available to them. They also demonstrate that assays of the distribution of mutational effects may lack predictive insight concerning adaptive outcomes.

Unpredictability of the Fitness Effects of Antimicrobial Resistance Mutations Across Environments in Escherichia coli

The evolution of antimicrobial resistance (AMR) in bacteria is a major public health concern, and antibiotic restriction is often implemented to reduce the spread of resistance. These measures rely on the existence of deleterious fitness effects (i.e. costs) imposed by AMR mutations during growth in the absence of antibiotics. According to this assumption, resistant strains will be outcompeted by susceptible strains that do not pay the cost during the period of restriction. The fitness effects of AMR mutations are generally studied in laboratory reference strains grown in standard growth environments; however, the genetic and environmental context can influence the magnitude and direction of a mutation's fitness effects. In this study, we measure how three sources of variation impact the fitness effects of Escherichia coli AMR mutations: the type of resistance mutation, the genetic background of the host, and the growth environment. We demonstrate that while AMR mutations are generally costly in antibiotic-free environments, their fitness effects vary widely and depend on complex interactions between the mutation, genetic background, and environment. We test the ability of the Rough Mount Fuji fitness landscape model to reproduce the empirical data in simulation. We identify model parameters that reasonably capture the variation in fitness effects due to genetic variation. However, the model fails to accommodate the observed variation when considering multiple growth environments. Overall, this study reveals a wealth of variation in the fitness effects of resistance mutations owing to genetic background and environmental conditions, which will ultimately impact their persistence in natural populations.

Global epistasis in plasmid-mediated antimicrobial resistance

Antimicrobial resistance (AMR) in bacteria is a major public health threat and conjugative plasmids play a key role in the dissemination of AMR genes among bacterial pathogens. Interestingly, the association between AMR plasmids and pathogens is not random and certain associations spread successfully at a global scale. The burst of genome sequencing has increased the resolution of epidemiological programs, broadening our understanding of plasmid distribution in bacterial populations. Despite the immense value of these studies, our ability to predict future plasmid-bacteria associations remains limited. Numerous empirical studies have recently reported systematic patterns in genetic interactions that enable predictability, in a phenomenon known as global epistasis. In this perspective, we argue that global epistasis patterns hold the potential to predict interactions between plasmids and bacterial genomes, thereby facilitating the prediction of future successful associations. To assess the validity of this idea, we use previously published data to identify global epistasis patterns in clinically relevant plasmid-bacteria associations. Furthermore, using simple mechanistic models of antibiotic resistance, we illustrate how global epistasis patterns may allow us to generate new hypotheses on the mechanisms associated with successful plasmid-bacteria associations. Collectively, we aim at illustrating the relevance of exploring global epistasis in the context of plasmid biology.

Machine learning and feature extraction for rapid antimicrobial resistance prediction of Acinetobacter baumannii from whole-genome sequencing data

Background

Whole-genome sequencing (WGS) has contributed significantly to advancements in machine learning methods for predicting antimicrobial resistance (AMR). However, the comparisons of different methods for AMR prediction without requiring prior knowledge of resistance remains to be conducted.

Methods

We aimed to predict the minimum inhibitory concentrations (MICs) of 13 antimicrobial agents against Acinetobacter baumannii using three machine learning algorithms (random forest, support vector machine, and XGBoost) combined with k-mer features extracted from WGS data.

Results

A cohort of 339 isolates was used for model construction. The average essential agreement and category agreement of the best models exceeded 90.90% (95%CI, 89.03-92.77%) and 95.29% (95%CI, 94.91-95.67%), respectively; the exceptions being levofloxacin, minocycline and imipenem. The very major error rates ranged from 0.0 to 5.71%. We applied feature selection pipelines to extract the top-ranked 11-mers to optimise training time and computing resources. This approach slightly improved the prediction performance and enabled us to obtain prediction results within 10 min. Notably, when employing these top-ranked 11-mers in an independent test dataset (120 isolates), we achieved an average accuracy of 0.96.

Conclusion

Our study is the first to demonstrate that AMR prediction for A. baumannii using machine learning methods based on k-mer features has competitive performance over traditional workflows; hence, sequence-based AMR prediction and its application could be further promoted. The k-mer-based workflow developed in this study demonstrated high recall/sensitivity and specificity, making it a dependable tool for MIC prediction in clinical settings.

The Pseudomonas aeruginosa Resistome: Permanent and Transient Antibiotic Resistance, an Overview

One of the most concerning characteristics of Pseudomonas aeruginosa is its low susceptibility to several antibiotics of common use in clinics, as well as its facility to acquire increased resistance levels. Consequently, the study of the antibiotic resistance mechanisms of this bacterium is of relevance for human health. For such a study, different types of resistance should be distinguished. The intrinsic resistome is composed of a set of genes, present in the core genome of P. aeruginosa, which contributes to its characteristic, species-specific, phenotype of susceptibility to antibiotics. Acquired resistance refers to those genetic events, such as the acquisition of mutations or antibiotic resistance genes that reduce antibiotic susceptibility. Finally, antibiotic resistance can be transiently acquired in the presence of specific compounds or under some growing conditions. The current article provides information on methods currently used to analyze intrinsic, mutation-driven, and transient antibiotic resistance in P. aeruginosa.

Chance Favors the Prepared Genomes: Horizontal Transfer Shapes the Emergence of Antibiotic Resistance Mutations in Core Genes

Bacterial lineages acquire novel traits at diverse rates in part because the genetic background impacts the successful acquisition of novel genes by horizontal transfer. Yet, how horizontal transfer affects the subsequent evolution of core genes remains poorly understood. Here, we studied the evolution of resistance to quinolones in Escherichia coli accounting for population structure. We found 60 groups of genes whose gain or loss induced an increase in the probability of subsequently becoming resistant to quinolones by point mutations in the gyrase and topoisomerase genes. These groups include functions known to be associated with direct mitigation of the effect of quinolones, with metal uptake, cell growth inhibition, biofilm formation, and sugar metabolism. Many of them are encoded in phages or plasmids. Although some of the chronologies may reflect epidemiological trends, many of these groups encoded functions providing latent phenotypes of antibiotic low-level resistance, tolerance, or persistence under quinolone treatment. The mutations providing resistance were frequent and accumulated very quickly. Their emergence was found to increase the rate of acquisition of other antibiotic resistances setting the path for multidrug resistance. Hence, our findings show that horizontal gene transfer shapes the subsequent emergence of adaptive mutations in core genes. In turn, these mutations further affect the subsequent evolution of resistance by horizontal gene transfer. Given the substantial gene flow within bacterial genomes, interactions between horizontal transfer and point mutations in core genes may be a key to the success of adaptation processes.

Translating eco-evolutionary biology into therapy to tackle antibiotic resistance

Antibiotic resistance is currently one of the most important public health problems. The golden age of antibiotic discovery ended decades ago, and new approaches are urgently needed. Therefore, preserving the efficacy of the antibiotics currently in use and developing compounds and strategies that specifically target antibiotic-resistant pathogens is critical. The identification of robust trends of antibiotic resistance evolution and of its associated trade-offs, such as collateral sensitivity or fitness costs, is invaluable for the design of rational evolution-based, ecology-based treatment approaches. In this Review, we discuss these evolutionary trade-offs and how such knowledge can aid in informing combination or alternating antibiotic therapies against bacterial infections. In addition, we discuss how targeting bacterial metabolism can enhance drug activity and impair antibiotic resistance evolution. Finally, we explore how an improved understanding of the original physiological function of antibiotic resistance determinants, which have evolved to reach clinical resistance after a process of historical contingency, may help to tackle antibiotic resistance.

The balance between antibiotic resistance and fitness/virulence in Pseudomonas aeruginosa: an update on basic knowledge and fundamental research

The interplay between antibiotic resistance and bacterial fitness/virulence has attracted the interest of researchers for decades because of its therapeutic implications, since it is classically assumed that resistance usually entails certain biological costs. Reviews on this topic revise the published data from a general point of view, including studies based on clinical strains or in vitro-evolved mutants in which the resistance phenotype is seen as a final outcome, i.e., a combination of mechanisms. However, a review analyzing the resistance/fitness balance from the basic research perspective, compiling studies in which the different resistance pathways and respective biological costs are individually approached, was missing. Here we cover this gap, specifically focusing on Pseudomonas aeruginosa, a pathogen that stands out because of its extraordinary capacity for resistance development and for which a considerable number of recent and particular data on the interplay with fitness/virulence have been released. The revised information, split into horizontally-acquired vs. mutation-driven resistance, suggests a great complexity and even controversy in the resistance-fitness/virulence balance in the acute infection context, with results ranging from high costs linked to certain pathways to others that are seemingly cost-free or even cases of resistance mechanisms contributing to increased pathogenic capacities. The elusive mechanistic basis for some enigmatic data, knowledge gaps, and possibilities for therapeutic exploitation are discussed. The information gathered suggests that resistance-fitness/virulence interplay may be a source of potential antipseudomonal targets and thus, this review poses the elementary first step for the future development of these strategies harnessing certain resistance-associated biological burdens.

Epistasis decreases with increasing antibiotic pressure but not temperature

Predicting mutational effects is essential for the control of antibiotic resistance (ABR). Predictions are difficult when there are strong genotype-by-environment (G × E), gene-by-gene (G × G or epistatic) or gene-by-gene-by-environment (G × G × E) interactions. We quantified G × G × E effects in Escherichia coli across environmental gradients. We created intergenic fitness landscapes using gene knock-outs and single-nucleotide ABR mutations previously identified to vary in the extent of G × E effects in our environments of interest. Then, we measured competitive fitness across a complete combinatorial set of temperature and antibiotic dosage gradients. In this way, we assessed the predictability of 15 fitness landscapes across 12 different but related environments. We found G × G interactions and rugged fitness landscapes in the absence of antibiotic, but as antibiotic concentration increased, the fitness effects of ABR genotypes quickly overshadowed those of gene knock-outs, and the landscapes became smoother. Our work reiterates that some single mutants, like those conferring resistance or susceptibility to antibiotics, have consistent effects across genetic backgrounds in stressful environments. Thus, although epistasis may reduce the predictability of evolution in benign environments, evolution may be more predictable in adverse environments. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.

The relative transmission fitness of multidrug-resistant Mycobacterium tuberculosis in a drug resistance hotspot

Multidrug-resistant tuberculosis (MDR-TB) is among the most frequent causes of death due to antimicrobial resistance. Although only 3% of global TB cases are MDR, geographical hotspots with up to 40% of MDR-TB have been observed in countries of the former Soviet Union. While the quality of TB control and patient-related factors are known contributors to such hotspots, the role of the pathogen remains unclear. Here we show that in the country of Georgia, a known hotspot of MDR-TB, MDR Mycobacterium tuberculosis strains of lineage 4 (L4) transmit less than their drug-susceptible counterparts, whereas most MDR strains of L2 suffer no such defect. Our findings further indicate that the high transmission fitness of these L2 strains results from epistatic interactions between the rifampicin resistance-conferring mutation RpoB S450L, compensatory mutations in the RNA polymerase, and other pre-existing genetic features of L2/Beijing clones that circulate in Georgia. We conclude that the transmission fitness of MDR M. tuberculosis strains is heterogeneous, but can be as high as drug-susceptible forms, and that such highly drug-resistant and transmissible strains contribute to the emergence and maintenance of hotspots of MDR-TB. As these strains successfully overcome the metabolic burden of drug resistance, and given the ongoing rollout of new treatment regimens against MDR-TB, proper surveillance should be implemented to prevent these strains from acquiring resistance to the additional drugs.

Tackling antibiotic resistance by inducing transient and robust collateral sensitivity

Collateral sensitivity (CS) is an evolutionary trade-off traditionally linked to the mutational acquisition of antibiotic resistance (AR). However, AR can be temporally induced, and the possibility that this causes transient, non-inherited CS, has not been addressed. Mutational acquisition of ciprofloxacin resistance leads to robust CS to tobramycin in pre-existing antibiotic-resistant mutants of Pseudomonas aeruginosa. Further, the strength of this phenotype is higher when nfxB mutants, over-producing the efflux pump MexCD-OprJ, are selected. Here, we induce transient nfxB-mediated ciprofloxacin resistance by using the antiseptic dequalinium chloride. Notably, non-inherited induction of AR renders transient tobramycin CS in the analyzed antibiotic-resistant mutants and clinical isolates, including tobramycin-resistant isolates. Further, by combining tobramycin with dequalinium chloride we drive these strains to extinction. Our results support that transient CS could allow the design of new evolutionary strategies to tackle antibiotic-resistant infections, avoiding the acquisition of AR mutations on which inherited CS depends.

Rapid Phenotypic Convergence towards Collateral Sensitivity in Clinical Isolates of Pseudomonas aeruginosa Presenting Different Genomic Backgrounds

Collateral sensitivity (CS) is an evolutionary trade-off by which acquisition of resistance to an antibiotic leads to increased susceptibility to another. This Achilles' heel of antibiotic resistance could be exploited to design evolution-based strategies for treating bacterial infections. To date, most studies in the field have focused on the identification of CS patterns in model strains. However, one of the main requirements for the clinical application of this trade-off is that it must be robust and has to emerge in different genomic backgrounds, including preexisting drug-resistant isolates, since infections are frequently caused by pathogens already resistant to antibiotics. Here, we report the first analysis of CS robustness in clinical strains of Pseudomonas aeruginosa presenting different ab initio mutational resistomes. We identified a robust CS pattern associated with short-term evolution in the presence of ciprofloxacin of clinical P. aeruginosa isolates, including representatives of high-risk epidemic clones belonging to sequence type (ST) 111, ST175, and ST244. We observed the acquisition of different ciprofloxacin resistance mutations in strains presenting varied STs and different preexisting mutational resistomes. Importantly, despite these genetic differences, the use of ciprofloxacin led to a robust CS to aztreonam and tobramycin. In addition, we describe the possible application of this evolutionary trade-off to drive P. aeruginosa infections to extinction by using the combination of ciprofloxacin-tobramycin or ciprofloxacin-aztreonam. Our results support the notion that the identification of robust patterns of CS may establish the basis for developing evolution-informed treatment strategies to tackle bacterial infections, including those due to antibiotic-resistant pathogens. IMPORTANCE Collateral sensitivity (CS) is a trade-off of antibiotic resistance evolution that could be exploited to design strategies for treating bacterial infections. Clinical application of CS requires it to robustly emerge in different genomic backgrounds. In this study, we performed an analysis to identify robust patterns of CS associated with the use of ciprofloxacin in clinical isolates of P. aeruginosa presenting different mutational resistomes and including high-risk epidemic clones (ST111, ST175, and ST244). We demonstrate the robustness of CS to tobramycin and aztreonam and the potential application of this evolutionary observation to drive P. aeruginosa infections to extinction. Our results support the notion that the identification of robust CS patterns may establish the basis for developing evolutionary strategies to tackle bacterial infections, including those due to antibiotic-resistant pathogens.

Pre-existing chromosomal polymorphisms in pathogenic E. coli potentiate the evolution of resistance to a last-resort antibiotic

Bacterial pathogens show high levels of chromosomal genetic diversity, but the influence of this diversity on the evolution of antibiotic resistance by plasmid acquisition remains unclear. Here, we address this problem in the context of colistin, a 'last line of defence' antibiotic. Using experimental evolution, we show that a plasmid carrying the MCR-1 colistin resistance gene dramatically increases the ability of Escherichia coli to evolve high-level colistin resistance by acquiring mutations in lpxC, an essential chromosomal gene involved in lipopolysaccharide biosynthesis. Crucially, lpxC mutations increase colistin resistance in the presence of the MCR-1 gene, but decrease the resistance of wild-type cells, revealing positive sign epistasis for antibiotic resistance between the chromosomal mutations and a mobile resistance gene. Analysis of public genomic datasets shows that lpxC polymorphisms are common in pathogenic E. coli, including those carrying MCR-1, highlighting the clinical relevance of this interaction. Importantly, lpxC diversity is high in pathogenic E. coli from regions with no history of MCR-1 acquisition, suggesting that pre-existing lpxC polymorphisms potentiated the evolution of high-level colistin resistance by MCR-1 acquisition. More broadly, these findings highlight the importance of standing genetic variation and plasmid/chromosomal interactions in the evolutionary dynamics of antibiotic resistance.

Idiosyncratic Fitness Costs of Ampicillin-Resistant Mutants Derived from a Long-Term Experiment with Escherichia coli

Antibiotic resistance is a growing concern that has prompted a renewed focus on drug discovery, stewardship, and evolutionary studies of the patterns and processes that underlie this phenomenon. A resistant strain’s competitive fitness relative to its sensitive counterparts in the absence of drug can impact its spread and persistence in both clinical and community settings. In a prior study, we examined the fitness of tetracycline-resistant clones that evolved from five different Escherichia coli genotypes, which had diverged during a long-term evolution experiment. In this study, we build on that work to examine whether ampicillin-resistant mutants are also less fit in the absence of the drug than their sensitive parents, and whether the cost of resistance is constant or variable among independently derived lines. Like the tetracycline-resistant lines, the ampicillin-resistant mutants were often less fit than their sensitive parents, with significant variation in the fitness costs among the mutants. This variation was not associated with the level of resistance conferred by the mutations, nor did it vary across the different parental backgrounds. In our earlier study, some of the variation in fitness costs associated with tetracycline resistance was explained by the effects of different mutations affecting the same cellular pathway and even the same gene. In contrast, the variance among the ampicillin-resistant mutants was associated with different sets of target genes. About half of the resistant clones suffered large fitness deficits, and their mutations impacted major outer-membrane proteins or subunits of RNA polymerases. The other mutants experienced little or no fitness costs and with, one exception, they had mutations affecting other genes and functions. Our findings underscore the importance of comparative studies on the evolution of antibiotic resistance, and they highlight the nuanced processes that shape these phenotypes.

Evolutionary Processes Driving the Rise and Fall of Staphylococcus aureus ST239, a Dominant Hybrid Pathogen

Selection plays a key role in the spread of antibiotic resistance, but the evolutionary drivers of clinically important resistant strains remain poorly understood. Here, we use genomic analyses and competition experiments to study Staphylococcus aureus ST239, a prominent MRSA strain that is thought to have been formed by large-scale recombination between ST8 and ST30. Genomic analyses allowed us to refine the hybrid model for the origin of ST239 and to date the origin of ST239 to 1920 to 1945, which predates the clinical introduction of methicillin in 1959. Although purifying selection has dominated the evolution of ST239, parallel evolution has occurred in genes involved in antibiotic resistance and virulence, suggesting that ST239 has evolved toward an increasingly pathogenic lifestyle. Crucially, ST239 isolates have low competitive fitness relative to both ST8 and ST30 isolates, supporting the idea that fitness costs have driven the demise of this once-dominant pathogen strain.

Expression Dysregulation as a Mediator of Fitness Costs in Antibiotic Resistance

Antimicrobial resistance (AMR) poses a threat to global health and the economy. Rifampicin-resistant Mycobacterium tuberculosis accounts for a third of the global AMR burden. Gaining the upper hand on AMR requires a deeper understanding of the physiology of resistance. AMR often results in a fitness cost in the absence of drug. Identifying the molecular mechanisms underpinning this cost could help strengthen future treatment regimens. Here, we used a collection of M. tuberculosis strains that provide an evolutionary and phylogenetic snapshot of rifampicin resistance and subjected them to genome-wide transcriptomic and proteomic profiling to identify key perturbations of normal physiology. We found that the clinically most common rifampicin resistance-conferring mutation, RpoB Ser450Leu, imparts considerable gene expression changes, many of which are mitigated by the compensatory mutation in RpoC Leu516Pro. However, our data also provide evidence for pervasive epistasis—the same resistance mutation imposed a different fitness cost and functionally distinct changes to gene expression in genetically unrelated clinical strains. Finally, we report a likely posttranscriptional modulation of gene expression that is shared in most of the tested strains carrying RpoB Ser450Leu, resulting in an increased abundance of proteins involved in central carbon metabolism. These changes contribute to a more general trend in which the disruption of the composition of the proteome correlates with the fitness cost of the RpoB Ser450Leu mutation in different strains.

Learning epistatic gene interactions from perturbation screens

The treatment of complex diseases often relies on combinatorial therapy, a strategy where drugs are used to target multiple genes simultaneously. Promising candidate genes for combinatorial perturbation often constitute epistatic genes, i.e., genes which contribute to a phenotype in a non-linear fashion. Experimental identification of the full landscape of genetic interactions by perturbing all gene combinations is prohibitive due to the exponential growth of testable hypotheses. Here we present a model for the inference of pairwise epistatic, including synthetic lethal, gene interactions from siRNA-based perturbation screens. The model exploits the combinatorial nature of siRNA-based screens resulting from the high numbers of sequence-dependent off-target effects, where each siRNA apart from its intended target knocks down hundreds of additional genes. We show that conditional and marginal epistasis can be estimated as interaction coefficients of regression models on perturbation data. We compare two methods, namely glinternet and xyz, for selecting non-zero effects in high dimensions as components of the model, and make recommendations for the appropriate use of each. For data simulated from real RNAi screening libraries, we show that glinternet successfully identifies epistatic gene pairs with high accuracy across a wide range of relevant parameters for the signal-to-noise ratio of observed phenotypes, the effect size of epistasis and the number of observations per double knockdown. xyz is also able to identify interactions from lower dimensional data sets (fewer genes), but is less accurate for many dimensions. Higher accuracy of glinternet, however, comes at the cost of longer running time compared to xyz. The general model is widely applicable and allows mining the wealth of publicly available RNAi screening data for the estimation of epistatic interactions between genes. As a proof of concept, we apply the model to search for interactions, and potential targets for treatment, among previously published sets of siRNA perturbation screens on various pathogens. The identified interactions include both known epistatic interactions as well as novel findings.

Idiosyncratic variation in the fitness costs of tetracycline-resistance mutations in Escherichia coli

A bacterium's fitness relative to its competitors, both in the presence and absence of antibiotics, plays a key role in its ecological success and clinical impact. In this study, we examine whether tetracycline-resistant mutants are less fit in the absence of the drug than their sensitive parents, and whether the fitness cost of resistance is constant or variable across independently derived lines. Tetracycline-resistant lines suffered, on average, a reduction in fitness of almost 8%. There was substantial among-line variation in the fitness cost. This variation was not associated with the level of resistance conferred by the mutations, nor did it vary significantly across several genetic backgrounds. The two resistant lines with the most extreme fitness costs involved functionally unrelated mutations on different genetic backgrounds. However, there was also significant variation in the fitness costs for mutations affecting the same pathway and even different alleles of the same gene. Our findings demonstrate that the fitness costs of antibiotic resistance do not always correlate with the phenotypic level of resistance or the underlying genetic changes. Instead, these costs reflect the idiosyncratic effects of particular resistance mutations and the genetic backgrounds in which they occur.

Relationships between community composition, productivity and invasion resistance in semi-natural bacterial microcosms

Common garden experiments that inoculate a standardised growth medium with synthetic microbial communities (i.e. constructed from individual isolates or using dilution cultures) suggest that the ability of the community to resist invasions by additional microbial taxa can be predicted by the overall community productivity (broadly defined as cumulative cell density and/or growth rate). However, to the best of our knowledge, no common garden study has yet investigated the relationship between microbial community composition and invasion resistance in microcosms whose compositional differences reflect natural, rather than laboratory-designed, variation. We conducted experimental invasions of two bacterial strains (Pseudomonas fluorescens and Pseudomonas putida) into laboratory microcosms inoculated with 680 different mixtures of bacteria derived from naturally occurring microbial communities collected in the field. Using 16S rRNA gene amplicon sequencing to characterise microcosm starting composition, and high-throughput assays of community phenotypes including productivity and invader survival, we determined that productivity is a key predictor of invasion resistance in natural microbial communities, substantially mediating the effect of composition on invasion resistance. The results suggest that similar general principles govern invasion in artificial and natural communities, and that factors affecting resident community productivity should be a focal point for future microbial invasion experiments.

Amino Acid k-mer Feature Extraction for Quantitative Antimicrobial Resistance (AMR) Prediction by Machine Learning and Model Interpretation for Biological Insights

Machine learning algorithms can learn mechanisms of antimicrobial resistance from the data of DNA sequence without any a priori information. Interpreting a trained machine learning algorithm can be exploited for validating the model and obtaining new information about resistance mechanisms. Different feature extraction methods, such as SNP calling and counting nucleotide k-mers have been proposed for presenting DNA sequences to the model. However, there are trade-offs between interpretability, computational complexity and accuracy for different feature extraction methods. In this study, we have proposed a new feature extraction method, counting amino acid k-mers or oligopeptides, which provides easier model interpretation compared to counting nucleotide k-mers and reaches the same or even better accuracy in comparison with different methods. Additionally, we have trained machine learning algorithms using different feature extraction methods and compared the results in terms of accuracy, model interpretability and computational complexity. We have built a new feature selection pipeline for extraction of important features so that new AMR determinants can be discovered by analyzing these features. This pipeline allows the construction of models that only use a small number of features and can predict resistance accurately.

Prediction of rifampicin resistance beyond the RRDR using structure-based machine learning approaches

Rifampicin resistance is a major therapeutic challenge, particularly in tuberculosis, leprosy, P. aeruginosa and S. aureus infections, where it develops via missense mutations in gene rpoB. Previously we have highlighted that these mutations reduce protein affinities within the RNA polymerase complex, subsequently reducing nucleic acid affinity. Here, we have used these insights to develop a computational rifampicin resistance predictor capable of identifying resistant mutations even outside the well-defined rifampicin resistance determining region (RRDR), using clinical M. tuberculosis sequencing information. Our tool successfully identified up to 90.9% of M. tuberculosis rpoB variants correctly, with sensitivity of 92.2%, specificity of 83.6% and MCC of 0.69, outperforming the current gold-standard GeneXpert-MTB/RIF. We show our model can be translated to other clinically relevant organisms: M. leprae, P. aeruginosa and S. aureus, despite weak sequence identity. Our method was implemented as an interactive tool, SUSPECT-RIF (StrUctural Susceptibility PrEdiCTion for RIFampicin), freely available at https://biosig.unimelb.edu.au/suspect_rif/ .

Piperacillin/tazobactam resistance in a clinical isolate of Escherichia coli due to IS26-mediated amplification of blaTEM-1B

A phenotype of Escherichia coli and Klebsiella pneumoniae, resistant to piperacillin/tazobactam (TZP) but susceptible to carbapenems and 3rd generation cephalosporins, has emerged. The resistance mechanism associated with this phenotype has been identified as hyperproduction of the β-lactamase TEM. However, the mechanism of hyperproduction due to gene amplification is not well understood. Here, we report a mechanism of gene amplification due to a translocatable unit (TU) excising from an IS26-flanked pseudo-compound transposon, PTn6762, which harbours blaTEM-1B. The TU re-inserts into the chromosome adjacent to IS26 and forms a tandem array of TUs, which increases the copy number of blaTEM-1B, leading to TEM-1B hyperproduction and TZP resistance. Despite a significant increase in blaTEM-1B copy number, the TZP-resistant isolate does not incur a fitness cost compared to the TZP-susceptible ancestor. This mechanism of amplification of blaTEM-1B is an important consideration when using genomic data to predict susceptibility to TZP.

HIV Coinfection Is Associated with Low-Fitness rpoB Variants in Rifampicin-Resistant Mycobacterium tuberculosis

We analyzed 312 drug-resistant genomes of Mycobacterium tuberculosis isolates collected from HIV-coinfected and HIV-negative TB patients from nine countries with a high tuberculosis burden. We found that rifampicin-resistant M. tuberculosis strains isolated from HIV-coinfected patients carried disproportionally more resistance-conferring mutations in rpoB that are associated with a low fitness in the absence of the drug, suggesting these low-fitness rpoB variants can thrive in the context of reduced host immunity.

Exploiting Homoplasy in Genome-Wide Association Studies to Enhance Identification of Antibiotic-Resistance Mutations in Bacterial Genomes

Many antibacterial drugs have multiple mechanisms of resistance, which are often represented simultaneously by a mixture of resistance mutations (some more frequent than others) in a clinical population. This presents a challenge for Genome-Wide Association Studies (GWAS) methods, making it difficult to detect less prevalent resistance mechanisms purely through (weak) statistical associations. Homoplasy, or the occurrence of multiple independent mutations at the same site, is often observed with drug resistance mutations and can be a strong indicator of positive selection. However, traditional GWAS methods, such as those based on allele counting or linear regression, are not designed to take homoplasy into account. In this article, we present a new method, called ECAT (for Evolutionary Cluster-based Association Test), that extends traditional regression-based GWAS methods with the ability to take advantage of homoplasy. This is achieved through a preprocessing step which identifies hypervariable regions in the genome exhibiting statistically significant clusters of distinct evolutionary changes, to which association testing by a linear mixed model (LMM) is applied using GEMMA (a well-established LMM-based GWAS tool). Thus, the approach can be viewed as extending GEMMA from the usual site- or gene-level analysis to focusing on clustered regions of mutations. This approach was evaluated on a large collection of more than 600 clinical isolates of multidrug-resistant (MDR) Mycobacterium tuberculosis from Lima, Peru. We show that ECAT does a better job of detecting known resistance mutations for several antitubercular drugs (including less prevalent mutations with weaker associations), compared with (site- or gene-based) GEMMA, as representative of existing GWAS methods. The power of the multiphase approach in ECAT comes from focusing association testing on the hypervariable regions of the genome, which reduces complexity in the model and increases statistical power.

Rapid and robust evolution of collateral sensitivity in Pseudomonas aeruginosa antibiotic-resistant mutants

The analysis of trade-offs, as collateral sensitivity, associated with the acquisition of antibiotic resistance, is mainly based on the use of model strains. However, the possibility of exploiting these trade-offs for fighting already resistant isolates has not been addressed in depth, despite the fact that bacterial pathogens are frequently antibiotic-resistant, forming either homogeneous or heterogeneous populations. Using a set of Pseudomonas aeruginosa-resistant mutants, we found that ceftazidime selects pyomelanogenic tobramycin-hypersusceptible mutants presenting chromosomal deletions in the analyzed genetic backgrounds. Since pyomelanogenic resistant mutants frequently coexist with other morphotypes in patients with cystic fibrosis, we analyzed the exploitation of this trade-off to drive extinction of heterogeneous resistant populations by using tobramycin/ceftazidime alternation. Our work shows that this approach is feasible because phenotypic trade-offs associated with the use of ceftazidime are robust. The identification of conserved collateral sensitivity networks may guide the rational design of evolution-based antibiotic therapies in P. aeruginosa infections.

Highly parallel lab evolution reveals that epistasis can curb the evolution of antibiotic resistance

Genetic perturbations that affect bacterial resistance to antibiotics have been characterized genome-wide, but how do such perturbations interact with subsequent evolutionary adaptation to the drug? Here, we show that strong epistasis between resistance mutations and systematically identified genes can be exploited to control spontaneous resistance evolution. We evolved hundreds of Escherichia coli K-12 mutant populations in parallel, using a robotic platform that tightly controls population size and selection pressure. We find a global diminishing-returns epistasis pattern: strains that are initially more sensitive generally undergo larger resistance gains. However, some gene deletion strains deviate from this general trend and curtail the evolvability of resistance, including deletions of genes for membrane transport, LPS biosynthesis, and chaperones. Deletions of efflux pump genes force evolution on inferior mutational paths, not explored in the wild type, and some of these essentially block resistance evolution. This effect is due to strong negative epistasis with resistance mutations. The identified genes and cellular functions provide potential targets for development of adjuvants that may block spontaneous resistance evolution when combined with antibiotics.

The antibiotic resistance crisis calls for new ways of restricting the ability of bacteria to evolve resistance. Here, Lukačišinová et al. perform highly controlled evolution experiments in E. coli strains to identify genetic perturbations that strongly limit the evolution of antibiotic resistance through epistasis.

Parallel Evolution of Tobramycin Resistance across Species and Environments

The rise of antimicrobial resistance is a leading medical threat, motivating efforts to forecast both its evolutionary dynamics and its genetic causes. Aminoglycosides are a major class of antibiotics that disrupt translation, but resistance may occur by a number of mechanisms. Here, we show the repeated evolution of resistance to the aminoglycoside tobramycin in both P. aeruginosa and A. baumannii via mutations in fusA1, encoding elongation factor G, and ptsP, encoding the nitrogen-specific phosphotransferase system. Laboratory evolution and whole-population genome sequencing were used to identify these targets, but mutations at identical amino acid positions were also found in published genomes of diverse bacterial species and clinical isolates. We also identified other resistance mechanisms associated with growth in biofilms that likely interfere with drug binding or uptake. Characterizing the evolution of multiple species in the presence of antibiotics can identify new, repeatable causes of resistance that may be predicted and counteracted by alternative treatment.

Different species exposed to a common stress may adapt by mutations in shared pathways or in unique systems, depending on how past environments have molded their genomes. Understanding how diverse bacterial pathogens evolve in response to an antimicrobial treatment is a pressing example of this problem, where discovery of molecular parallelism could lead to clinically useful predictions. Evolution experiments with pathogens in environments containing antibiotics, combined with periodic whole-population genome sequencing, can be used to identify many contending routes to antimicrobial resistance. We separately propagated two clinically relevant Gram-negative pathogens, Pseudomonas aeruginosa and Acinetobacter baumannii, in increasing concentrations of tobramycin in two different environments each: planktonic and biofilm. Independently of the pathogen, the populations adapted to tobramycin selection by parallel evolution of mutations in fusA1, encoding elongation factor G, and ptsP, encoding phosphoenolpyruvate phosphotransferase. As neither gene is a direct target of this aminoglycoside, mutations to either are unexpected and underreported causes of resistance. Additionally, both species acquired antibiotic resistance-associated mutations that were more prevalent in the biofilm lifestyle than in the planktonic lifestyle; these mutations were in electron transport chain components in A. baumannii and lipopolysaccharide biosynthesis enzymes in P. aeruginosa populations. Using existing databases, we discovered site-specific parallelism of fusA1 mutations that extends across bacterial phyla and clinical isolates. This study suggests that strong selective pressures, such as antibiotic treatment, may result in high levels of predictability in molecular targets of evolution, despite differences between organisms’ genetic backgrounds and environments.

The Genetic Background Modulates the Evolution of Fluoroquinolone-Resistance in Mycobacterium tuberculosis

Fluoroquinolones (FQ) form the backbone in experimental treatment regimens against drug-susceptible tuberculosis. However, little is known on whether the genetic variation present in natural populations of Mycobacterium tuberculosis (Mtb) affects the evolution of FQ-resistance (FQ-R). To investigate this question, we used nine genetically distinct drug-susceptible clinical isolates of Mtb and measured their frequency of resistance to the FQ ofloxacin (OFX) in vitro. We found that the Mtb genetic background led to differences in the frequency of OFX-resistance (OFX-R) that spanned two orders of magnitude and substantially modulated the observed mutational profiles for OFX-R. Further, in vitro assays showed that the genetic background also influenced the minimum inhibitory concentration and the fitness effect conferred by a given OFX-R mutation. To test the clinical relevance of our in vitro work, we surveyed the mutational profile for FQ-R in publicly available genomic sequences from clinical Mtb isolates, and found substantial Mtb lineage-dependent variability. Comparison of the clinical and the in vitro mutational profiles for FQ-R showed that 51% and 39% of the variability in the clinical frequency of FQ-R gyrA mutation events in Lineage 2 and Lineage 4 strains, respectively, can be attributed to how Mtb evolves FQ-R in vitro. As the Mtb genetic background strongly influenced the evolution of FQ-R in vitro, we conclude that the genetic background of Mtb also impacts the evolution of FQ-R in the clinic.

Antibiotic Resistance Evolution Is Contingent on the Quorum-Sensing Response in Pseudomonas aeruginosa

Different works have explored independently the evolution toward antibiotic resistance and the role of eco-adaptive mutations in the adaptation to a new habitat (as the infected host) of bacterial pathogens. However, knowledge about the connection between both processes is still limited. We address this issue by comparing the evolutionary trajectories toward antibiotic resistance of a Pseudomonas aeruginosa lasR defective mutant and its parental wild-type strain, when growing in presence of two ribosome-targeting antibiotics. Quorum-sensing lasR defective mutants are selected in P. aeruginosa populations causing chronic infections. Further, we observed they are also selected in vitro as a first adaptation for growing in culture medium. By using experimental evolution and whole-genome sequencing, we found that the evolutionary trajectories of P. aeruginosa in presence of these antibiotics are different in lasR defective and in wild-type backgrounds, both at the phenotypic and the genotypic levels. Recreation of a set of mutants in both genomic backgrounds (either wild type or lasR defective) allowed us to determine the existence of negative epistatic interactions between lasR and antibiotic resistance determinants. These epistatic interactions could lead to mutual contingency in the evolution of antibiotic resistance when P. aeruginosa colonizes a new habitat in presence of antibiotics. If lasR mutants are selected first, this would constraint antibiotic resistance evolution. Conversely, when resistance mutations (at least those studied in the present work) are selected, lasR mutants may not be selected in presence of antibiotics. These results underlie the importance of contingency and epistatic interactions in modulating antibiotic resistance evolution.

A large-scale whole-genome comparison shows that experimental evolution in response to antibiotics predicts changes in naturally evolved clinical Pseudomonas aeruginosa

Pseudomonas aeruginosa is an opportunistic pathogen that causes a wide range of acute and chronic infections. An increasing number of isolates have mutations that make them antibiotic resistant, making treatment difficult. To identify resistance-associated mutations we experimentally evolved the antibiotic sensitive strain P. aeruginosa PAO1 to become resistant to three widely used anti-pseudomonal antibiotics, ciprofloxacin, meropenem and tobramycin. Mutants could tolerate up to 2048-fold higher concentrations of antibiotic than strain PAO1. Genome sequences were determined for thirteen mutants for each antibiotic. Each mutant had between 2 and 8 mutations. For each antibiotic at least 8 genes were mutated in multiple mutants, demonstrating the genetic complexity of resistance. For all three antibiotics mutations arose in genes known to be associated with resistance, but also in genes not previously associated with resistance. To determine the clinical relevance of mutations uncovered in this study we analysed the corresponding genes in 558 isolates of P. aeruginosa from patients with chronic lung disease and in 172 isolates from the general environment. Many genes identified through experimental evolution had predicted function-altering changes in clinical isolates but not in environmental isolates, showing that mutated genes in experimentally evolved bacteria can predict those that undergo mutation during infection. Additionally, large deletions of up to 479kb arose in experimentally evolved meropenem resistant mutants and large deletions were present in 87 of the clinical isolates. These findings significantly advance understanding of antibiotic resistance in P. aeruginosa and demonstrate the validity of experimental evolution in identifying clinically-relevant resistance-associated mutations.

Rifampin resistance and its fitness cost in Riemerella anatipestifer

Background

Riemerella anatipestifer (R. anatipestifer) is one of the most important poultry pathogens worldwide, with associated infections causing significant economic losses. Rifampin Resistance is an important mechanism of drug resistance. However, there is no information about rpoB mutations conferring rifampin resistance and its fitness cost in Riemerella anatipestifer.

Results

Comparative analysis of 18 R.anatipestifer rpoB sequences and the determination of rifampin minimum inhibitory concentrations showed that five point mutations, V382I, H491N, G502K, R494K and S539Y, were related to rifampin resistance. Five overexpression strains were constructed using site-directed mutagenesis to validate these sites. To investigate the origin and fitness costs of the rpoB mutations, 15 types of rpoB mutations were isolated from R. anatipestifer ATCC 11845 by using spontaneous mutation in which R494K was identical to the type of mutation detected in the isolates. The mutation frequency of the rpoB gene was calculated to be 10^− 8. A total of 98.8% (247/250) of the obtained mutants were located in cluster I of the rifampin resistance-determining region of the rpoB gene. With the exception of D481Y, I537N and S539F, the rifampin minimum inhibitory concentrations of the remaining mutants were at least 64 μg/mL. The growth performance and competitive experiments of the mutant strains in vitro showed that H491D and 485::TAA exhibit growth delay and severely impaired fitness. Finally, the colonization abilities and sensitivities of the R494K and H491D mutants were investigated. The sensitivity of the two mutants to hydrogen peroxide (H₂O₂₎ and sodium nitroprusside (SNP) increased compared to the parental strain. The number of live colonies colonized by the two mutants in the duckling brain and trachea were lower than that of the parental strain within 24 h.

Conclusions

Mutations of rpoB gene in R. anatipestifer mediate rifampin resistance and result in fitness costs. And different single mutations confer different levels of fitness costs. Our study provides, to our knowledge, the first estimates of the fitness cost associated with the R. anatipestifer rifampin resistance in vitro and in vivo.

Electronic supplementary material

The online version of this article (10.1186/s12866-019-1478-7) contains supplementary material, which is available to authorized users.

Microbial evolutionary medicine: from theory to clinical practice

Medicine and clinical microbiology have traditionally attempted to identify and eliminate the agents that cause disease. However, this traditional approach is becoming inadequate for dealing with a changing disease landscape. Major challenges to human health are non-communicable chronic diseases, often driven by altered immunity and inflammation, and communicable infections from agents which harbour antibiotic resistance. This Review focuses on the so-called evolutionary medicine framework, to study how microbial communities influence human health. The evolutionary medicine framework aims to predict and manipulate microbial effects on human health by integrating ecology, evolutionary biology, microbiology, bioinformatics, and clinical expertise. We focus on the potential of evolutionary medicine to address three key challenges: detecting microbial transmission, predicting antimicrobial resistance, and understanding microbe-microbe and human-microbe interactions in health and disease, in the context of the microbiome.

Proteostasis Environment Shapes Higher-Order Epistasis Operating on Antibiotic Resistance

Recent studies have affirmed that higher-order epistasis is ubiquitous and can have large effects on complex traits. Yet, we lack frameworks for understanding how epistatic interactions are influenced by central features of cell physiology. In this study, we assess how protein quality control machinery-a critical component of cell physiology-affects epistasis for different traits related to bacterial resistance to antibiotics. Specifically, we disentangle the interactions between different protein quality control genetic backgrounds and two sets of mutations: (i) SNPs associated with resistance to antibiotics in an essential bacterial enzyme (dihydrofolate reductase, or DHFR) and (ii) differing DHFR bacterial species-specific amino acid background sequences (Escherichia coli, Listeria grayi, and Chlamydia muridarum). In doing so, we improve on generic observations that epistasis is widespread by discussing how patterns of epistasis can be partly explained by specific interactions between mutations in an essential enzyme and genes associated with the proteostasis environment. These findings speak to the role of environmental and genotypic context in modulating higher-order epistasis, with direct implications for evolutionary theory, genetic modification technology, and efforts to manage antimicrobial resistance.

Uncoupled Quorum Sensing Modulates the Interplay of Virulence and Resistance in a Multidrug-Resistant Clinical Pseudomonas aeruginosa Isolate Belonging to the MLST550 Clonal Complex

Pseudomonas aeruginosa is a prevalent and pernicious pathogen equipped with extraordinary capabilities both to infect the host and to develop antimicrobial resistance (AMR). Monitoring the emergence of AMR high-risk clones and understanding the interplay of their pathogenicity and antibiotic resistance is of paramount importance to avoid resistance dissemination and to control P. aeruginosa infections. In this study, we report the identification of a multidrug-resistant (MDR) P. aeruginosa strain PA154197 isolated from a blood stream infection in Hong Kong. PA154197 belongs to a distinctive MLST550 clonal complex shared by two other international P. aeruginosa isolates VW0289 and AUS544. Comparative genome and transcriptome analysis of PA154197 with the reference strain PAO1 led to the identification of a variety of genetic variations in antibiotic resistance genes and the hyperexpression of three multidrug efflux pumps MexAB-OprM, MexEF-OprN, and MexGHI-OpmD in PA154197. Unexpectedly, the strain does not display a metabolic cost and a compromised virulence compared to PAO1. Characterizing its various physiological and virulence traits demonstrated that PA154197 produces a substantially higher level of the P. aeruginosa major virulence factor pyocyanin (PYO) than PAO1, but it produces a decreased level of pyoverdine and displays decreased biofilm formation compared with PAO1. Further analysis revealed that the secondary quorum-sensing (QS) system Pqs that primarily controls the PYO production is hyperactive in PA154197 independent of the master QS systems Las and Rhl. Together, these investigations disclose a unique, uncoupled QS mediated pathoadaptation mechanism in clinical P. aeruginosa which may account for the high pathogenic potentials and antibiotic resistance in the MDR isolate PA154197.

Effects of Beneficial Mutations in pykF Gene Vary over Time and across Replicate Populations in a Long-Term Experiment with Bacteria

The fitness effects of mutations can depend on the genetic backgrounds in which they occur and thereby influence future opportunities for evolving populations. In particular, mutations that fix in a population might change the selective benefit of subsequent mutations, giving rise to historical contingency. We examine these effects by focusing on mutations in a key metabolic gene, pykF, that arose independently early in the history of 12 Escherichia coli populations during a long-term evolution experiment. Eight different evolved nonsynonymous mutations conferred similar fitness benefits of ∼10% when transferred into the ancestor, and these benefits were greater than the one conferred by a deletion mutation. In contrast, the same mutations had highly variable fitness effects, ranging from ∼0% to 25%, in evolved clones isolated from the populations at 20,000 generations. Two mutations that were moved into these evolved clones conferred similar fitness effects in a given clone, but different effects between the clones, indicating epistatic interactions between the evolved pykF alleles and the other mutations that had accumulated in each evolved clone. We also measured the fitness effects of six evolved pykF alleles in the same populations in which they had fixed, but at seven time points between 0 and 50,000 generations. Variation in fitness effects was high at intermediate time points, and declined to a low level at 50,000 generations, when the mean fitness effect was lowest. Our results demonstrate the importance of genetic context in determining the fitness effects of different beneficial mutations even within the same gene.

Genome-Based Prediction of Bacterial Antibiotic Resistance

Clinical microbiology has long relied on growing bacteria in culture to determine antimicrobial susceptibility profiles, but the use of whole-genome sequencing for antibiotic susceptibility testing (WGS-AST) is now a powerful alternative. This review discusses the technologies that made this possible and presents results from recent studies to predict resistance based on genome sequences.

Clinical microbiology has long relied on growing bacteria in culture to determine antimicrobial susceptibility profiles, but the use of whole-genome sequencing for antibiotic susceptibility testing (WGS-AST) is now a powerful alternative. This review discusses the technologies that made this possible and presents results from recent studies to predict resistance based on genome sequences. We examine differences between calling antibiotic resistance profiles by the simple presence or absence of previously known genes and single-nucleotide polymorphisms (SNPs) against approaches that deploy machine learning and statistical models. Often, the limitations to genome-based prediction arise from limitations of accuracy of culture-based AST in addition to an incomplete knowledge of the genetic basis of resistance. However, we need to maintain phenotypic testing even as genome-based prediction becomes more widespread to ensure that the results do not diverge over time. We argue that standardization of WGS-AST by challenge with consistently phenotyped strain sets of defined genetic diversity is necessary to compare the efficacy of methods of prediction of antibiotic resistance based on genome sequences.

Resistance Gene Carriage Predicts Growth of Natural and Clinical Escherichia coli Isolates in the Absence of Antibiotics

Bacterial pathogens that carry antibiotic resistance alleles sometimes pay a cost in the form of impaired growth in antibiotic-free conditions. This cost of resistance is expected to be a key parameter for understanding how resistance spreads and persists in pathogen populations. Analysis of individual resistance alleles from laboratory evolution and natural isolates has shown they are typically costly, but these costs are highly variable and influenced by genetic variation at other loci. It therefore remains unclear how strongly resistance is linked to impaired antibiotic-free growth in bacteria from natural and clinical scenarios, where resistance alleles are likely to coincide with other types of genetic variation. To investigate this, we measured the growth of 92 natural and clinical Escherichia coli isolates across three antibiotic-free environments. We then tested whether variation of antibiotic-free growth among isolates was predicted by their resistance to 10 antibiotics, while accounting for the phylogenetic structure of the data. We found that isolates with similar resistance profiles had similar antibiotic-free growth profiles, but it was not simply that higher average resistance was associated with impaired growth. Next, we used whole-genome sequences to identify antibiotic resistance genes and found that isolates carrying a greater number of resistance gene types grew relatively poorly in antibiotic-free conditions, even when the resistance genes they carried were different. This suggests that the resistance of bacterial pathogens is linked to growth costs in nature, but it is the total genetic burden and multivariate resistance phenotype that predict these costs, rather than individual alleles or mean resistance across antibiotics.IMPORTANCE Managing the spread of antibiotic resistance in bacterial pathogens is a major challenge for global public health. Central to this challenge is understanding whether resistance is linked to impaired bacterial growth in the absence of antibiotics, because this determines whether resistance declines when bacteria are no longer exposed to antibiotics. We studied 92 isolates of the key bacterial pathogen Escherichia coli; these isolates varied in both their antibiotic resistance genes and other parts of the genome. Taking this approach, rather than focusing on individual genetic changes associated with resistance as in much previous work, revealed that growth without antibiotics was linked to the number of specialized resistance genes carried and the combination of antibiotics to which isolates were resistant but was not linked to average antibiotic resistance. This approach provides new insights into the genetic factors driving the long-term persistence of antibiotic-resistant bacteria, which is important for future efforts to predict and manage resistance.

Mycoplasmas under experimental antimicrobial selection: The unpredicted contribution of horizontal chromosomal transfer

Horizontal Gene Transfer was long thought to be marginal in Mycoplasma a large group of wall-less bacteria often portrayed as minimal cells because of their reduced genomes (ca. 0.5 to 2.0 Mb) and their limited metabolic pathways. This view was recently challenged by the discovery of conjugative exchanges of large chromosomal fragments that equally affected all parts of the chromosome via an unconventional mechanism, so that the whole mycoplasma genome is potentially mobile. By combining next generation sequencing to classical mating and evolutionary experiments, the current study further explored the contribution and impact of this phenomenon on mycoplasma evolution and adaptation using the fluoroquinolone enrofloxacin (Enro), for selective pressure and the ruminant pathogen Mycoplasma agalactiae, as a model organism. For this purpose, we generated isogenic lineages that displayed different combination of spontaneous mutations in Enro target genes (gyrA, gyrB, parC and parE) in association to gradual level of resistance to Enro. We then tested whether these mutations can be acquired by a susceptible population via conjugative chromosomal transfer knowing that, in our model organism, the 4 target genes are scattered in three distinct and distant loci. Our data show that under antibiotic selective pressure, the time scale of the mutational pathway leading to high-level of Enro resistance can be readily compressed into a single conjugative step, in which several EnroR alleles were transferred from resistant to susceptible mycoplasma cells. In addition to acting as an accelerator for antimicrobial dissemination, mycoplasma chromosomal transfer reshuffled genomes beyond expectations and created a mosaic of resistant sub-populations with unpredicted and unrelated features. Our findings provide insights into the process that may drive evolution and adaptability of several pathogenic Mycoplasma spp. via an unconventional conjugative mechanism.

Environmental pleiotropy and demographic history direct adaptation under antibiotic selection

Evolutionary rescue following environmental change requires mutations permitting population growth in the new environment. If change is severe enough to prevent most of the population reproducing, rescue becomes reliant on mutations already present. If change is sustained, the fitness effects in both environments, and how they are associated—termed ‘environmental pleiotropy’—may determine which alleles are ultimately favoured. A population’s demographic history—its size over time—influences the variation present. Although demographic history is known to affect the probability of evolutionary rescue, how it interacts with environmental pleiotropy during severe and sustained environmental change remains unexplored. Here, we demonstrate how these factors interact during antibiotic resistance evolution, a key example of evolutionary rescue fuelled by pre-existing mutations with pleiotropic fitness effects. We combine published data with novel simulations to characterise environmental pleiotropy and its effects on resistance evolution under different demographic histories. Comparisons among resistance alleles typically revealed no correlation for fitness—i.e., neutral pleiotropy—above and below the sensitive strain’s minimum inhibitory concentration. Resistance allele frequency following experimental evolution showed opposing correlations with their fitness effects in the presence and absence of antibiotic. Simulations demonstrated that effects of environmental pleiotropy on allele frequencies depended on demographic history. At the population level, the major influence of environmental pleiotropy was on mean fitness, rather than the probability of evolutionary rescue or diversity. Our work suggests that determining both environmental pleiotropy and demographic history is critical for predicting resistance evolution, and we discuss the practicalities of this during in vivo evolution.

Epistatic control of intrinsic resistance by virulence genes in Listeria

Elucidating the relationships between antimicrobial resistance and virulence is key to understanding the evolution and population dynamics of resistant pathogens. Here, we show that the susceptibility of the gram-positive bacterium Listeria monocytogenes to the antibiotic fosfomycin is a complex trait involving interactions between resistance and virulence genes and the environment. We found that a FosX enzyme encoded in the listerial core genome confers intrinsic fosfomycin resistance to both pathogenic and non-pathogenic Listeria spp. However, in the genomic context of the pathogenic L. monocytogenes, FosX-mediated resistance is epistatically suppressed by two members of the PrfA virulence regulon, hpt and prfA, which upon activation by host signals induce increased fosfomycin influx into the bacterial cell. Consequently, in infection conditions, most L. monocytogenes isolates become susceptible to fosfomycin despite possessing a gene that confers high-level resistance to the drug. Our study establishes the molecular basis of an epistatic interaction between virulence and resistance genes controlling bacterial susceptibility to an antibiotic. The reported findings provide the rationale for the introduction of fosfomycin in the treatment of Listeria infections even though these bacteria are intrinsically resistant to the antibiotic in vitro.

Experimental Design, Population Dynamics, and Diversity in Microbial Experimental Evolution

In experimental evolution, laboratory-controlled conditions select for the adaptation of species, which can be monitored in real time. Despite the current popularity of such experiments, nature's most pervasive biological force was long believed to be observable only on time scales that transcend a researcher's life-span, and studying evolution by natural selection was therefore carried out solely by comparative means. Eventually, microorganisms' propensity for fast evolutionary changes proved us wrong, displaying strong evolutionary adaptations over a limited time, nowadays massively exploited in laboratory evolution experiments. Here, we formulate a guide to experimental evolution with microorganisms, explaining experimental design and discussing evolutionary dynamics and outcomes and how it is used to assess ecoevolutionary theories, improve industrially important traits, and untangle complex phenotypes. Specifically, we give a comprehensive overview of the setups used in experimental evolution. Additionally, we address population dynamics and genetic or phenotypic diversity during evolution experiments and expand upon contributing factors, such as epistasis and the consequences of (a)sexual reproduction. Dynamics and outcomes of evolution are most profoundly affected by the spatiotemporal nature of the selective environment, where changing environments might lead to generalists and structured environments could foster diversity, aided by, for example, clonal interference and negative frequency-dependent selection. We conclude with future perspectives, with an emphasis on possibilities offered by fast-paced technological progress. This work is meant to serve as an introduction to those new to the field of experimental evolution, as a guide to the budding experimentalist, and as a reference work to the seasoned expert.

Directed evolution of multiple genomic loci allows the prediction of antibiotic resistance

Antibiotic development is frequently plagued by the rapid emergence of drug resistance. However, assessing the risk of resistance development in the preclinical stage is difficult. Standard laboratory evolution approaches explore only a small fraction of the sequence space and fail to identify exceedingly rare resistance mutations and combinations thereof. Therefore, new rapid and exhaustive methods are needed to accurately assess the potential of resistance evolution and uncover the underlying mutational mechanisms. Here, we introduce directed evolution with random genomic mutations (DIvERGE), a method that allows an up to million-fold increase in mutation rate along the full lengths of multiple predefined loci in a range of bacterial species. In a single day, DIvERGE generated specific mutation combinations, yielding clinically significant resistance against trimethoprim and ciprofloxacin. Many of these mutations have remained previously undetected or provide resistance in a species-specific manner. These results indicate pathogen-specific resistance mechanisms and the necessity of future narrow-spectrum antibacterial treatments. In contrast to prior claims, we detected the rapid emergence of resistance against gepotidacin, a novel antibiotic currently in clinical trials. Based on these properties, DIvERGE could be applicable to identify less resistance-prone antibiotics at an early stage of drug development. Finally, we discuss potential future applications of DIvERGE in synthetic and evolutionary biology.

Predictable Phenotypes of Antibiotic Resistance Mutations

Antibiotic-resistant bacteria represent a major threat to our ability to treat bacterial infections. Two factors that determine the evolutionary success of antibiotic resistance mutations are their impact on resistance level and the fitness cost. Recent studies suggest that resistance mutations commonly show epistatic interactions, which would complicate predictions of their stability in bacterial populations. We analyzed 13 different chromosomal resistance mutations and 10 host strains of Salmonella enterica and Escherichia coli to address two main questions. (i) Are there epistatic interactions between different chromosomal resistance mutations? (ii) How does the strain background and genetic distance influence the effect of chromosomal resistance mutations on resistance and fitness? Our results show that the effects of combined resistance mutations on resistance and fitness are largely predictable and that epistasis remains rare even when up to four mutations were combined. Furthermore, a majority of the mutations, especially target alteration mutations, demonstrate strain-independent phenotypes across different species. This study extends our understanding of epistasis among resistance mutations and shows that interactions between different resistance mutations are often predictable from the characteristics of the individual mutations.

The spread of antibiotic-resistant bacteria imposes an urgent threat to public health. The ability to forecast the evolutionary success of resistant mutants would help to combat dissemination of antibiotic resistance. Previous studies have shown that the phenotypic effects (fitness and resistance level) of resistance mutations can vary substantially depending on the genetic context in which they occur. We conducted a broad screen using many different resistance mutations and host strains to identify potential epistatic interactions between various types of resistance mutations and to determine the effect of strain background on resistance phenotypes. Combinations of several different mutations showed a large amount of phenotypic predictability, and the majority of the mutations displayed strain-independent phenotypes. However, we also identified a few outliers from these patterns, illustrating that the choice of host organism can be critically important when studying antibiotic resistance mutations.

Identifying and exploiting genes that potentiate the evolution of antibiotic resistance

There is an urgent need to develop novel approaches for predicting and preventing the evolution of antibiotic resistance. Here we show that the ability to evolve de novo resistance to a clinically important β-lactam antibiotic, ceftazidime, varies drastically across the genus Pseudomonas. This variation arises because strains possessing the ampR global transcriptional regulator evolve resistance at a high rate. This does not arise because of mutations in ampR. Instead, this regulator potentiates evolution by allowing mutations in conserved peptidoglycan biosynthesis genes to induce high levels of β-lactamase expression. Crucially, blocking this evolutionary pathway by co-administering ceftazidime with the β-lactamase inhibitor avibactam can be used to eliminate pathogenic P. aeruginosa populations before they can evolve resistance. In summary, our study shows that identifying potentiator genes that act as evolutionary catalysts can be used to both predict and prevent the evolution of antibiotic resistance.

Interaction between rpsL and gyrA mutations affects the fitness and dual resistance of Mycobacterium tuberculosis clinical isolates against streptomycin and fluoroquinolones

Background

The interaction between different drug-resistant mutations is important to the development of drug resistance and its evolution. In this study, we aimed to reveal the potential relationships between mutations conferring resistance to two important antituberculosis drugs streptomycin (STR) and fluoroquinolones (FLQ).

Materials and methods

We used an in vitro competitive fitness assay to reveal the interactions between different mutations of rpsL and gyrA in drug-resistant Mycobacterium smegmatis, followed by the analysis of the frequency of rpsL and gyrA mutation combinations in 213 STR-FLQ dual-resistant clinical Mycobacterium tuberculosis isolates from Sichuan region, which was also investigated by the whole genome data from 3,056 global clinical M. tuberculosis isolates.

Results

The strains with K43R and K88R mutation in rpsL showed no difference in relative fitness compared with their susceptible ancestor, while K43N, K43M, K43T, and K88E exhibited a significantly lower relative fitness (P<0.05). For the FLQ-resistant mutants, all mutation types showed no difference in their relative fitness. Among STR-FLQ dual-resistant M. smegmatis strains, a lower fitness was detected in those with K43N/M/T and K88E instead of K43R and K88R mutations in rpsL. Among M. tuberculosis isolates harboring rpsL and gyrA dual mutations, the most two frequent combinatorial mutation types were K43R/D94G (n=37) and K43R/A90V (n=24), with the former being the most frequent one by both in vitro tests and clinical survey.

Conclusion

Our results suggest that the interaction between rpsL and gyrA mutations affects the fitness cost in STR-FLQ dual-resistant M. smegmatis and also the predilection of mutation combinations in clinical M. tuberculosis isolates.

Slow and temperature-mediated pathogen adaptation to a nonspecific fungicide in agricultural ecosystem

The spread of antimicrobial resistance and global change in air temperature represent two major phenomena that are exerting a disastrous impact on natural and social issues but investigation of the interaction between these phenomena in an evolutionary context is limited. In this study, a statistical genetic approach was used to investigate the evolution of antimicrobial resistance in agricultural ecosystem and its association with local air temperature, precipitation, and UV radiation. We found no resistance to mancozeb, a nonspecific fungicide widely used in agriculture for more than half a century, in 215 Alternaria alternata isolates sampled from geographic locations along a climatic gradient and cropping system representing diverse ecotypes in China, consistent with low resistance risk in many nonspecific fungicides. Genetic variance accounts for ~35% of phenotypic variation, while genotype-environment interaction is negligible, suggesting that heritability plays a more important role in the evolution of resistance to mancozeb in plant pathogens than phenotypic plasticity. We also found that tolerance to mancozeb in agricultural ecosystem is under constraining selection and significantly associated with local air temperature, possibly resulting from a pleiotropic effect of resistance with thermal and other ecological adaptations. The implication of these results for fungicide and other antimicrobial management in the context of global warming is discussed.

Environmental and genetic modulation of the phenotypic expression of antibiotic resistance

Antibiotic resistance can be acquired by mutation or horizontal transfer of a resistance gene, and generally an acquired mechanism results in a predictable increase in phenotypic resistance. However, recent findings suggest that the environment and/or the genetic context can modify the phenotypic expression of specific resistance genes/mutations. An important implication from these findings is that a given genotype does not always result in the expected phenotype. This dissociation of genotype and phenotype has important consequences for clinical bacteriology and for our ability to predict resistance phenotypes from genetics and DNA sequences. A related problem concerns the degree to which the genes/mutations currently identified in vitro can fully explain the in vivo resistance phenotype, or whether there is a significant additional amount of presently unknown mutations/genes (genetic ‘dark matter’) that could contribute to resistance in clinical isolates. Finally, a very important question is whether/how we can identify the genetic features that contribute to making a successful pathogen, and predict why some resistant clones are very successful and spread globally? In this review, we describe different environmental and genetic factors that influence phenotypic expression of antibiotic resistance genes/mutations and how this information is needed to understand why particular resistant clones spread worldwide and to what extent we can use DNA sequences to predict evolutionary success.

Molecular and cellular bases of adaptation to a changing environment in microorganisms

Environmental heterogeneity constitutes an evolutionary challenge for organisms. While evolutionary dynamics under variable conditions has been explored for decades, we still know relatively little about the cellular and molecular mechanisms involved. It is of paramount importance to examine these molecular bases because they may play an important role in shaping the course of evolution. In this review, we examine the diversity of adaptive mechanisms in the face of environmental changes. We exploit the recent literature on microbial systems because those have benefited the most from the recent emergence of genetic engineering and experimental evolution followed by genome sequencing. We identify four emerging trends: (i) an adaptive molecular change in a pathway often results in fitness trade-off in alternative environments but the effects are dependent on a mutation's genetic background; (ii) adaptive changes often modify transcriptional and signalling pathways; (iii) several adaptive changes may occur within the same molecular pathway but be associated with pleiotropy of different signs across environments; (iv) because of their large associated costs, macromolecular changes such as gene amplification and aneuploidy may be a rapid mechanism of adaptation in the short-term only. The course of adaptation in a variable environment, therefore, depends on the complexity of the environment but also on the molecular relationships among the genes involved and between the genes and the phenotypes under selection.

Evolutionary consequences of multidriver environmental change in an aquatic primary producer

Climate change is altering aquatic environments in a complex way, and simultaneous shifts in many properties will drive evolutionary responses in primary producers at the base of both freshwater and marine ecosystems. So far, evolutionary studies have shown how changes in environmental drivers, either alone or in pairs, affect the evolution of growth and other traits in primary producers. Here, we evolve a primary producer in 96 unique environments with different combinations of between one and eight environmental drivers to understand how evolutionary responses to environmental change depend on the identity and number of drivers. Even in multidriver environments, only a few dominant drivers explain most of the evolutionary changes in population growth rates. Most populations converge on the same growth rate by the end of the evolution experiment. However, populations adapt more when these dominant drivers occur in the presence of other drivers. This is due to an increase in the intensity of selection in environments with more drivers, which are more likely to include dominant drivers. Concurrently, many of the trait changes that occur during the initial short-term response to both single and multidriver environmental change revert after about 450 generations of evolution. In future aquatic environments, populations will encounter differing combinations of drivers and intensities of selection, which will alter the adaptive potential of primary producers. Accurately gauging the intensity of selection on key primary producers will help in predicting population size and trait evolution at the base of aquatic food webs.