Antimicrobial resistance level and conjugation permissiveness shape plasmid distribution in clinical enterobacteria.

Conjugative plasmids play a key role in the dissemination of antimicrobial resistance (AMR) genes across bacterial pathogens. AMR plasmids are widespread in clinical settings, but their distribution is not random, and certain associations between plasmids and bacterial clones are particularly successful. For example, the globally spread carbapenem resistance plasmid pOXA-48 can use a wide range of enterobacterial species as hosts, but it is usually associated with a small number of specific Klebsiella pneumoniae clones. These successful associations represent an important threat for hospitalized patients. However, knowledge remains limited about the factors determining AMR plasmid distribution in clinically relevant bacteria. Here, we combined in vitro and in vivo experimental approaches to analyze pOXA-48-associated AMR levels and conjugation dynamics in a collection of wild-type enterobacterial strains isolated from hospitalized patients. Our results revealed significant variability in these traits across different bacterial hosts, with Klebsiella spp. strains showing higher pOXA-48-mediated AMR and conjugation frequencies than Escherichia coli strains. Using experimentally determined parameters, we developed a simple mathematical model to interrogate the contribution of AMR levels and conjugation permissiveness to plasmid distribution in bacterial communities. The simulations revealed that a small subset of clones, combining high AMR levels and conjugation permissiveness, play a critical role in stabilizing the plasmid in different polyclonal microbial communities. These results help to explain the preferential association of plasmid pOXA-48 with K. pneumoniae clones in clinical settings. More generally, our study reveals that species- and strain-specific variability in plasmid-associated phenotypes shape AMR evolution in clinically relevant bacterial communities.

Evolutionary History and Strength of Selection Determine the Rate of Antibiotic Resistance Adaptation.

Bacterial adaptation to stressful environments often produces evolutionary constraints whereby increases in resistance are associated with reduced fitness in a different environment. The exploitation of this resistance-cost trade-off has been proposed as the basis of rational antimicrobial treatment strategies designed to limit the evolution of drug resistance in bacterial pathogens. Recent theoretical, laboratory, and clinical studies have shown that fluctuating selection can maintain drug efficacy and even restore drug susceptibility, but can also increase the rate of adaptation and promote cross-resistance to other antibiotics. In this paper, we combine mathematical modeling, experimental evolution, and whole-genome sequencing to follow evolutionary trajectories towards ß-lactam resistance under fluctuating selective conditions. Our experimental model system consists of eight populations of Escherichia coli K12 evolving in parallel to a serial dilution protocol designed to dynamically control the strength of selection for resistance. We implemented adaptive ramps with mild and strong selection, resulting in evolved populations with similar levels of resistance, but with different evolutionary dynamics and diverging genotypic profiles. We found that mutations that emerged under strong selection are unstable in the absence of selection, in contrast to resistance mutations previously selected in the mild selection regime that were stably maintained in drug-free environments and positively selected for when antibiotics were reintroduced. Altogether, our population dynamics model and the phenotypic and genomic analysis of the evolved populations show that the rate of resistance adaptation is contingent upon the strength of selection, but also on evolutionary constraints imposed by prior drug exposures.

Canonical Host-Pathogen Trade-Offs Subverted by Mutations with Dual Benefits.

AbstractHost-parasite coevolution is expected to drive the evolution of genetic diversity because the traits used in arms races-namely, host range and parasite resistance-are hypothesized to trade off with traits used in resource competition. We therefore tested data for several trade-offs among 93 isolates of bacteriophage λ and 51 Escherichia coli genotypes that coevolved during a laboratory experiment. Surprisingly, we found multiple trade-ups (positive trait correlations) but little evidence of several canonical trade-offs. For example, some bacterial genotypes evaded a trade-off between phage resistance and absolute fitness, instead evolving simultaneous improvements in both traits. This was surprising because our experimental design was predicted to expose resistance-fitness trade-offs by culturing E. coli in a medium where the phage receptor, LamB, is also used for nutrient acquisition. On reflection, LamB mediates not one but many trade-offs, allowing for more complex trait interactions than just pairwise trade-offs. Here, we report that mathematical reasoning and laboratory data highlight how trade-ups should exist whenever an evolutionary system exhibits multiple interacting trade-offs. Does this mean that coevolution should not promote genetic diversity? No, quite the contrary. We deduce that whenever positive trait correlations are observed in multidimensional traits, other traits may trade off and so provide the right circumstances for diversity maintenance. Overall, this study reveals that there are predictive limits when data account only for pairwise trait correlations, and it argues that a wider range of circumstances than previously anticipated can promote genetic and species diversity.

Genetic dominance governs the evolution and spread of mobile genetic elements in bacteria.

Mobile genetic elements (MGEs), such as plasmids, promote bacterial evolution through horizontal gene transfer (HGT). However, the rules governing the repertoire of traits encoded on MGEs remain unclear. In this study, we uncovered the central role of genetic dominance shaping genetic cargo in MGEs, using antibiotic resistance as a model system. MGEs are typically present in more than one copy per host bacterium, and as a consequence, genetic dominance favors the fixation of dominant mutations over recessive ones. In addition, genetic dominance also determines the phenotypic effects of horizontally acquired MGE-encoded genes, silencing recessive alleles if the recipient bacterium already carries a wild-type copy of the gene. The combination of these two effects governs the catalog of genes encoded on MGEs. Our results help to understand how MGEs evolve and spread, uncovering the neglected influence of genetic dominance on bacterial evolution. Moreover, our findings offer a framework to forecast the spread and evolvability of MGE-encoded genes, which encode traits of key human interest, such as virulence or antibiotic resistance.

Identification of the Flavobacterium johnsoniae cysteate-fatty acyl transferase required for capnine synthesis and for efficient gliding motility.

Sulfonolipids (SLs) are bacterial lipids that are structurally related to sphingolipids. Synthesis of this group of lipids seems to be mainly restricted to Flavobacterium, Cytophaga and other members of the phylum Bacteroidetes. These lipids have a wide range of biological activities: they can induce multicellularity in choanoflagellates, act as von Willebrand factor receptor antagonists, inhibit DNA polymerase, or function as tumour suppressing agents. In Flavobacterium johnsoniae, their presence seems to be required for efficient gliding motility. Until now, no genes/enzymes involved in SL synthesis have been identified, which has been limiting for the study of some of the biological effects these lipids have. Here, we describe the identification of the cysteate-fatty acyl transferase Fjoh_2419 required for synthesis of the SL precursor capnine in F. johnsoniae. This enzyme belongs to the α-oxoamine synthase family similar to serine palmitoyl transferases, 2-amino-3-oxobutyrate coenzyme A ligase and 8-amino-7-oxononanoate synthases. Expression of the gene fjoh_2419 in Escherichia coli caused the formation of a capnine-derived molecule. Flavobacterium johnsoniae mutants deficient in fjoh_2419 lacked SLs and were more sensitive to many antibiotics. Mutant growth was not affected in liquid medium but the cells exhibited defects in gliding motility.

Antibiotic Cycling and Antibiotic Mixing: Which One Best Mitigates Antibiotic Resistance?

Can we exploit our burgeoning understanding of molecular evolution to slow the progress of drug resistance? One role of an infection clinician is exactly that: to foresee trajectories to resistance during antibiotic treatment and to hinder that evolutionary course. But can this be done at a hospital-wide scale? Clinicians and theoreticians tried to when they proposed two conflicting behavioral strategies that are expected to curb resistance evolution in the clinic, these are known as "antibiotic cycling" and "antibiotic mixing." However, the accumulated data from clinical trials, now approaching 4 million patient days of treatment, is too variable for cycling or mixing to be deemed successful. The former implements the restriction and prioritization of different antibiotics at different times in hospitals in a manner said to "cycle" between them. In antibiotic mixing, appropriate antibiotics are allocated to patients but randomly. Mixing results in no correlation, in time or across patients, in the drugs used for treatment which is why theorists saw this as an optimal behavioral strategy. So while cycling and mixing were proposed as ways of controlling evolution, we show there is good reason why clinical datasets cannot choose between them: by re-examining the theoretical literature we show prior support for the theoretical optimality of mixing was misplaced. Our analysis is consistent with a pattern emerging in data: neither cycling or mixing is a priori better than the other at mitigating selection for antibiotic resistance in the clinic. Key words: : antibiotic cycling, antibiotic mixing, optimal control, stochastic models.

Using a sequential regimen to eliminate bacteria at sublethal antibiotic dosages.

We need to find ways of enhancing the potency of existing antibiotics, and, with this in mind, we begin with an unusual question: how low can antibiotic dosages be and yet bacterial clearance still be observed? Seeking to optimise the simultaneous use of two antibiotics, we use the minimal dose at which clearance is observed in an in vitro experimental model of antibiotic treatment as a criterion to distinguish the best and worst treatments of a bacterium, Escherichia coli. Our aim is to compare a combination treatment consisting of two synergistic antibiotics to so-called sequential treatments in which the choice of antibiotic to administer can change with each round of treatment. Using mathematical predictions validated by the E. coli treatment model, we show that clearance of the bacterium can be achieved using sequential treatments at antibiotic dosages so low that the equivalent two-drug combination treatments are ineffective. Seeking to treat the bacterium in testing circumstances, we purposefully study an E. coli strain that has a multidrug pump encoded in its chromosome that effluxes both antibiotics. Genomic amplifications that increase the number of pumps expressed per cell can cause the failure of high-dose combination treatments, yet, as we show, sequentially treated populations can still collapse. However, dual resistance due to the pump means that the antibiotics must be carefully deployed and not all sublethal sequential treatments succeed. A screen of 136 96-h-long sequential treatments determined five of these that could clear the bacterium at sublethal dosages in all replicate populations, even though none had done so by 24 h. These successes can be attributed to a collateral sensitivity whereby cross-resistance due to the duplicated pump proves insufficient to stop a reduction in E. coli growth rate following drug exchanges, a reduction that proves large enough for appropriately chosen drug switches to clear the bacterium.

Bistable expression of virulence genes in salmonella leads to the formation of an antibiotic-tolerant subpopulation.

Phenotypic heterogeneity can confer clonal groups of organisms with new functionality. A paradigmatic example is the bistable expression of virulence genes in Salmonella typhimurium, which leads to phenotypically virulent and phenotypically avirulent subpopulations. The two subpopulations have been shown to divide labor during S. typhimurium infections. Here, we show that heterogeneous virulence gene expression in this organism also promotes survival against exposure to antibiotics through a bet-hedging mechanism. Using microfluidic devices in combination with fluorescence time-lapse microscopy and quantitative image analysis, we analyzed the expression of virulence genes at the single cell level and related it to survival when exposed to antibiotics. We found that, across different types of antibiotics and under concentrations that are clinically relevant, the subpopulation of bacterial cells that express virulence genes shows increased survival after exposure to antibiotics. Intriguingly, there is an interplay between the two consequences of phenotypic heterogeneity. The bet-hedging effect that arises through heterogeneity in virulence gene expression can protect clonal populations against avirulent mutants that exploit and subvert the division of labor within these populations. We conclude that bet-hedging and the division of labor can arise through variation in a single trait and interact with each other. This reveals a new degree of functional complexity of phenotypic heterogeneity. In addition, our results suggest a general principle of how pathogens can evade antibiotics: Expression of virulence factors often entails metabolic costs and the resulting growth retardation could generally increase tolerance against antibiotics and thus compromise treatment.

When the most potent combination of antibiotics selects for the greatest bacterial load: the smile-frown transition.

Conventional wisdom holds that the best way to treat infection with antibiotics is to 'hit early and hit hard'. A favoured strategy is to deploy two antibiotics that produce a stronger effect in combination than if either drug were used alone. But are such synergistic combinations necessarily optimal? We combine mathematical modelling, evolution experiments, whole genome sequencing and genetic manipulation of a resistance mechanism to demonstrate that deploying synergistic antibiotics can, in practice, be the worst strategy if bacterial clearance is not achieved after the first treatment phase. As treatment proceeds, it is only to be expected that the strength of antibiotic synergy will diminish as the frequency of drug-resistant bacteria increases. Indeed, antibiotic efficacy decays exponentially in our five-day evolution experiments. However, as the theory of competitive release predicts, drug-resistant bacteria replicate fastest when their drug-susceptible competitors are eliminated by overly-aggressive treatment. Here, synergy exerts such strong selection for resistance that an antagonism consistently emerges by day 1 and the initially most aggressive treatment produces the greatest bacterial load, a fortiori greater than if just one drug were given. Whole genome sequencing reveals that such rapid evolution is the result of the amplification of a genomic region containing four drug-resistance mechanisms, including the acrAB efflux operon. When this operon is deleted in genetically manipulated mutants and the evolution experiment repeated, antagonism fails to emerge in five days and antibiotic synergy is maintained for longer. We therefore conclude that unless super-inhibitory doses are achieved and maintained until the pathogen is successfully cleared, synergistic antibiotics can have the opposite effect to that intended by helping to increase pathogen load where, and when, the drugs are found at sub-inhibitory concentrations.

Plasmid-mediated phenotypic noise leads to transient antibiotic resistance in bacteria.

The rise of antibiotic resistance is a critical public health concern, requiring an understanding of mechanisms that enable bacteria to tolerate antimicrobial agents. Bacteria use diverse strategies, including the amplification of drug-resistance genes. In this paper, we showed that multicopy plasmids, often carrying antibiotic resistance genes in clinical bacteria, can rapidly amplify genes, leading to plasmid-mediated phenotypic noise and transient antibiotic resistance. By combining stochastic simulations of a computational model with high-throughput single-cell measurements of blaTEM-1 expression in Escherichia coli MG1655, we showed that plasmid copy number variability stably maintains populations composed of cells with both low and high plasmid copy numbers. This diversity in plasmid copy number enhances the probability of bacterial survival in the presence of antibiotics, while also rapidly reducing the burden of carrying multiple plasmids in drug-free environments. Our results further support the tenet that multicopy plasmids not only act as vehicles for the horizontal transfer of genetic information between cells but also as drivers of bacterial adaptation, enabling rapid modulation of gene copy numbers. Understanding the role of multicopy plasmids in antibiotic resistance is critical, and our study provides insights into how bacteria can transiently survive lethal concentrations of antibiotics.

Selecting against antibiotic-resistant pathogens: optimal treatments in the presence of commensal bacteria.

Using optimal control theory as the basic theoretical tool, we investigate the efficacy of different antibiotic treatment protocols in the most exacting of circumstances, described as follows. Viewing a continuous culture device as a proxy for a much more complex host organism, we first inoculate the device with a single bacterial species and deem this the 'commensal' bacterium of our host. We then force the commensal to compete for a single carbon source with a rapidly evolving and fitter 'pathogenic bacterium', the latter so-named because we wish to use a bacteriostatic antibiotic to drive the pathogen toward low population densities. Constructing a mathematical model to mimic the biology, we do so in such a way that the commensal would be eventually excluded by the pathogen if no antibiotic treatment were given to the host or if the antibiotic were over-deployed. Indeed, in our model, all fixed-dose antibiotic treatment regimens will lead to the eventual loss of the commensal from the host proxy. Despite the obvious gravity of the situation for the commensal bacterium, we show by example that it is possible to design drug deployment protocols that support the commensal and reduce the pathogen load. This may be achieved by appropriately fluctuating the concentration of drug in the environment; a result that is to be anticipated from the theory optimal control where bang-bang solutions may be interpreted as intermittent periods of either maximal and minimal drug deployment. While such 'antibiotic pulsing' is near-optimal for a wide range of treatment objectives, we also use this model to evaluate the efficacy of different antibiotic usage strategies to show that dynamically changing antimicrobial therapies may be effective in clearing a bacterial infection even when every 'static monotherapy' fails.

Segregational instability of multicopy plasmids: A population genetics approach.

Plasmids are extra-chromosomal genetic elements that encode a wide variety of phenotypes and can be maintained in bacterial populations through vertical and horizontal transmission, thus increasing bacterial adaptation to hostile environmental conditions like those imposed by antimicrobial substances. To circumvent the segregational instability resulting from randomly distributing plasmids between daughter cells upon division, nontransmissible plasmids tend to be carried in multiple copies per cell, with the added benefit of exhibiting increased gene dosage and resistance levels. But carrying multiple copies also results in a high metabolic burden to the bacterial host, therefore reducing the overall fitness of the population. This trade-off poses an existential question for plasmids: What is the optimal plasmid copy number? In this manuscript, we address this question by postulating and analyzing a population genetics model to evaluate the interaction between selective pressure, the number of plasmid copies carried by each cell, and the metabolic burden associated with plasmid bearing in the absence of selection for plasmid-encoded traits. Parameter values of the model were estimated experimentally using Escherichia coli K12 carrying a multicopy plasmid encoding for a fluorescent protein and bla TEM-1, a gene conferring resistance to ß-lactam antibiotics. By numerically determining the optimal plasmid copy number for constant and fluctuating selection regimes, we show that plasmid copy number is a highly optimized evolutionary trait that depends on the rate of environmental fluctuation and balances the benefit between increased stability in the absence of selection with the burden associated with carrying multiple copies of the plasmid.

Single-cell time-lapse analysis of depletion of the universally conserved essential protein YgjD.

BACKGROUND: The essential Escherichia coli gene ygjD belongs to a universally conserved group of genes whose function has been the focus of a number of recent studies. Here, we put ygjD under control of an inducible promoter, and used time-lapse microscopy and single cell analysis to investigate the phenotypic consequences of the depletion of YgjD protein from growing cells. RESULTS: We show that loss of YgjD leads to a marked decrease in cell size and termination of cell division. The transition towards smaller size occurs in a controlled manner: cell elongation and cell division remain coupled, but cell size at division decreases. We also find evidence that depletion of YgjD leads to the synthesis of the intracellular signaling molecule (p)ppGpp, inducing a cellular reaction resembling the stringent response. Concomitant deletion of the relA and spoT genes - leading to a strain that is uncapable of synthesizing (p)ppGpp - abrogates the decrease in cell size, but does not prevent termination of cell division upon YgjD depletion. CONCLUSIONS: Depletion of YgjD protein from growing cells leads to a decrease in cell size that is contingent on (p)ppGpp, and to a termination of cell division. The combination of single-cell timelapse microscopy and statistical analysis can give detailed insights into the phenotypic consequences of the loss of essential genes, and can thus serve as a new tool to study the function of essential genes.

Mathematical Models of Plasmid Population Dynamics.

With plasmid-mediated antibiotic resistance thriving and threatening to become a serious public health problem, it is paramount to increase our understanding of the forces that enable the spread and maintenance of drug resistance genes encoded in mobile genetic elements. The relevance of plasmids as vehicles for the dissemination of antibiotic resistance genes, in addition to the extensive use of plasmid-derived vectors for biotechnological and industrial purposes, has promoted the in-depth study of the molecular mechanisms controlling multiple aspects of a plasmids' life cycle. This body of experimental work has been paralleled by the development of a wealth of mathematical models aimed at understanding the interplay between transmission, replication, and segregation, as well as their consequences in the ecological and evolutionary dynamics of plasmid-bearing bacterial populations. In this review, we discuss theoretical models of plasmid dynamics that span from the molecular mechanisms of plasmid partition and copy-number control occurring at a cellular level, to their consequences in the population dynamics of complex microbial communities. We conclude by discussing future directions for this exciting research topic.

Variability of plasmid fitness effects contributes to plasmid persistence in bacterial communities.

Plasmid persistence in bacterial populations is strongly influenced by the fitness effects associated with plasmid carriage. However, plasmid fitness effects in wild-type bacterial hosts remain largely unexplored. In this study, we determined the fitness effects of the major antibiotic resistance plasmid pOXA-48_K8 in wild-type, ecologically compatible enterobacterial isolates from the human gut microbiota. Our results show that although pOXA-48_K8 produced an overall reduction in bacterial fitness, it produced small effects in most bacterial hosts, and even beneficial effects in several isolates. Moreover, genomic results showed a link between pOXA-48_K8 fitness effects and bacterial phylogeny, helping to explain plasmid epidemiology. Incorporating our fitness results into a simple population dynamics model revealed a new set of conditions for plasmid stability in bacterial communities, with plasmid persistence increasing with bacterial diversity and becoming less dependent on conjugation. These results help to explain the high prevalence of plasmids in the greatly diverse natural microbial communities.

Diffusion-driven enhancement of the antibiotic resistance selection window.

The current crisis of antimicrobial resistance in clinically relevant pathogens has highlighted our limited understanding of the ecological and evolutionary forces that drive drug resistance adaptation. For instance, although human tissues are highly heterogeneous, most of our mechanistic understanding about antibiotic resistance evolution is based on constant and well-mixed environmental conditions. A consequence of considering spatial heterogeneity is that, even if antibiotics are prescribed at high dosages, the penetration of drug molecules through tissues inevitably produces antibiotic gradients, exposing bacterial populations to a range of selective pressures and generating a dynamic fitness landscape that changes in space and time. In this paper, we will use a combination of mathematical modelling and computer simulations to study the population dynamics of susceptible and resistant strains competing for resources in a network of micro-environments with varying degrees of connectivity. Our main result is that highly connected environments increase diffusion of drug molecules, enabling resistant phenotypes to colonize a larger number of spatial locations. We validated this theoretical result by culturing fluorescently labelled Escherichia coli in 3D-printed devices that allow us to control the rate of diffusion of antibiotics between neighbouring compartments and quantify the spatio-temporal distribution of resistant and susceptible bacterial cells.

Multicopy plasmids allow bacteria to escape from fitness trade-offs during evolutionary innovation.

Understanding the mechanisms governing innovation is a central element of evolutionary theory. Novel traits usually arise through mutations in existing genes, but trade-offs between new and ancestral protein functions are pervasive and constrain the evolution of innovation. Classical models posit that evolutionary innovation circumvents the constraints imposed by trade-offs through genetic amplifications, which provide functional redundancy. Bacterial multicopy plasmids provide a paradigmatic example of genetic amplification, yet their role in evolutionary innovation remains largely unexplored. Here, we reconstructed the evolution of a new trait encoded in a multicopy plasmid using TEM-1 ß-lactamase as a model system. Through a combination of theory and experimentation, we show that multicopy plasmids promote the coexistence of ancestral and novel traits for dozens of generations, allowing bacteria to escape the evolutionary constraints imposed by trade-offs. Our results suggest that multicopy plasmids are excellent platforms for evolutionary innovation, contributing to explain their extreme abundance in bacteria.

Evaluating the effect of horizontal transmission on the stability of plasmids under different selection regimes.

In theory, plasmids can only be maintained in a population when the rate of horizontal gene transfer is larger than the combined effect of segregational loss and the decrease of fitness associated with plasmid carriage. Recent advances in genome sequencing have shown, however, that a large fraction of plasmids do not carry the genes necessary for conjugation or mobilization. So, how are so-called non-transmissible plasmids able to persist? In order to address this question, we examined a previously published evolutionary model based on the interaction between P. aeruginosa and the non-transmissible plasmid pNUK73. Both our in silico and in vitro results demonstrated that, although compensatory adaptation can decrease the rate of plasmid decay, the conditions for the maintenance of a non-transmissible plasmid are very stringent if the genes it carries are not beneficial to the bacterial host. This result suggests that apparently non-transmissible plasmids may still experience episodes of horizontal gene transfer occurring at very low frequencies, and that these scattered transmission events are sufficient to stabilize these plasmids. We conclude by discussing different genomic and microbiological approaches that could allow for the detection of these rare transmission events and thus to obtain a reliable estimate of the rate of horizontal gene transfer.

Genomics of rapid adaptation to antibiotics: convergent evolution and scalable sequence amplification.

Evolutionary adaptation can be extremely fast, especially in response to high selection intensities. A prime example is the surge of antibiotic resistance in bacteria. The genomic underpinnings of such rapid changes may provide information on the genetic processes that enhance fast responses and the particular trait functions under selection. Here, we use experimentally evolved Escherichia coli for a detailed dissection of the genomics of rapid antibiotic resistance evolution. Our new analyses demonstrate that amplification of a sequence region containing several known antibiotic resistance genes represents a fast genomic response mechanism under high antibiotic stress, here exerted by drug combination. In particular, higher dosage of such antibiotic combinations coincided with higher copy number of the sequence region. The amplification appears to be evolutionarily costly, because amplification levels rapidly dropped after removal of the drugs. Our results suggest that amplification is a scalable process, as copy number rapidly changes in response to the selective pressure encountered. Moreover, repeated patterns of convergent evolution were found across the experimentally evolved bacterial populations, including those with lower antibiotic selection intensities. Intriguingly, convergent evolution was identified on different organizational levels, ranging from the above sequence amplification, high variant frequencies in specific genes, prevalence of individual nonsynonymous mutations to the unusual repeated occurrence of a particular synonymous mutation in Glycine codons. We conclude that constrained evolutionary trajectories underlie rapid adaptation to antibiotics. Of the identified genomic changes, sequence amplification seems to represent the most potent, albeit costly genomic response mechanism to high antibiotic stress.