Ribosome-binding antibiotics increase bacterial longevity and growth efficiency

Antibiotics, by definition, reduce bacterial growth rates in optimal culture conditions; however, the real-world environments bacteria inhabit see rapid growth punctuated by periods of low nutrient availability. How antibiotics mediate population decline during these periods is poorly understood. Bacteria cannot optimize for all environmental conditions because a growth-longevity tradeoff predicts faster growth results in faster population decline, and since bacteriostatic antibiotics slow growth, they should also mediate longevity. We quantify how antibiotics, their targets, and resistance mechanisms influence longevity using populations of Escherichia coli and, as the tradeoff predicts, populations are maintained for longer if they encounter ribosome-binding antibiotics doxycycline and erythromycin, a finding that is not observed using antibiotics with alternative cellular targets. This tradeoff also predicts resistance mechanisms that increase growth rates during antibiotic treatment could be detrimental during nutrient stresses, and indeed, we find resistance by ribosomal protection removes benefits to longevity provided by doxycycline. We therefore liken ribosomal protection to a "Trojan horse" because it provides protection from an antibiotic but, during nutrient stresses, it promotes the demise of the bacteria. Seeking mechanisms to support these observations, we show doxycycline promotes efficient metabolism and reduces the concentration of reactive oxygen species. Seeking generality, we sought another mechanism that affects longevity and we found the number of doxycycline targets, namely, the ribosomal RNA operons, mediates growth and longevity even without antibiotics. We conclude that slow growth, as observed during antibiotic treatment, can help bacteria overcome later periods of nutrient stress.

Canonical host-pathogen tradeoffs 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.

The Antibiotic Dosage of Fastest Resistance Evolution: gene amplifications underpinning the inverted-U

To determine the dosage at which antibiotic resistance evolution is most rapid, we treated Escherichia coli in vitro, deploying the antibiotic erythromycin at dosages ranging from zero to high. Adaptation was fastest just below erythromycin’s minimal inhibitory concentration (MIC) and genotype-phenotype correlations determined from whole genome sequencing revealed the molecular basis: simultaneous selection for copy number variation in three resistance mechanisms which exhibited an “inverted-U” pattern of dose-dependence, as did several insertion sequences and an integron. Many genes did not conform to this pattern, however, reflecting changes in selection as dose increased: putative media adaptation polymorphisms at zero antibiotic dosage gave way to drug target (ribosomal RNA operon) amplification at mid dosages whereas prophage-mediated drug efflux amplifications dominated at the highest dosages. All treatments exhibited E. coli increases in the copy number of efflux operons acrAB and emrE at rates that correlated with increases in population density. For strains where the inverted-U was no longer observed following the genetic manipulation of acrAB, it could be recovered by prolonging the antibiotic treatment at subMIC dosages.

Mre11 exonuclease activity removes the chain-terminating nucleoside analog gemcitabine from the nascent strand during DNA replication

The Mre11 nuclease is involved in early responses to DNA damage, often mediated by its role in DNA end processing. MRE11 mutations and aberrant expression are associated with carcinogenesis and cancer treatment outcomes. While, in recent years, progress has been made in understanding the role of Mre11 nuclease activities in DNA double-strand break repair, their role during replication has remained elusive. The nucleoside analog gemcitabine, widely used in cancer therapy, acts as a replication chain terminator; for a cell to survive treatment, gemcitabine needs to be removed from replicating DNA. Activities responsible for this removal have, so far, not been identified. We show that Mre11 3' to 5' exonuclease activity removes gemcitabine from nascent DNA during replication. This contributes to replication progression and gemcitabine resistance. We thus uncovered a replication-supporting role for Mre11 exonuclease activity, which is distinct from its previously reported detrimental role in uncontrolled resection in recombination-deficient cells.

Alternative Evolutionary Paths to Bacterial Antibiotic Resistance Cause Distinct Collateral Effects

When bacteria evolve resistance against a particular antibiotic, they may simultaneously gain increased sensitivity against a second one. Such collateral sensitivity may be exploited to develop novel, sustainable antibiotic treatment strategies aimed at containing the current, dramatic spread of drug resistance. To date, the presence and molecular basis of collateral sensitivity has only been studied in few bacterial species and is unknown for opportunistic human pathogens such as Pseudomonas aeruginosa. In the present study, we assessed patterns of collateral effects by experimentally evolving 160 independent populations of P. aeruginosa to high levels of resistance against eight commonly used antibiotics. The bacteria evolved resistance rapidly and expressed both collateral sensitivity and cross-resistance. The pattern of such collateral effects differed to those previously reported for other bacterial species, suggesting interspecific differences in the underlying evolutionary trade-offs. Intriguingly, we also identified contrasting patterns of collateral sensitivity and cross-resistance among the replicate populations adapted to the same drug. Whole-genome sequencing of 81 independently evolved populations revealed distinct evolutionary paths of resistance to the selective drug, which determined whether bacteria became cross-resistant or collaterally sensitive towards others. Based on genomic and functional genetic analysis, we demonstrate that collateral sensitivity can result from resistance mutations in regulatory genes such as nalC or mexZ, which mediate aminoglycoside sensitivity in β-lactam-adapted populations, or the two-component regulatory system gene pmrB, which enhances penicillin sensitivity in gentamicin-resistant populations. Our findings highlight substantial variation in the evolved collateral effects among replicates, which in turn determine their potential in antibiotic therapy.

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.

Testing the optimality properties of a dual antibiotic treatment in a two-locus, two-allele model

Mathematically speaking, it is self-evident that the optimal control of complex, dynamical systems with many interacting components cannot be achieved with 'non-responsive' control strategies that are constant through time. Although there are notable exceptions, this is usually how we design treatments with antimicrobial drugs when we give the same dose and the same antibiotic combination each day. Here, we use a frequency- and density-dependent pharmacogenetics mathematical model based on a standard, two-locus, two-allele representation of how bacteria resist antibiotics to probe the question of whether optimal antibiotic treatments might, in fact, be constant through time. The model describes the ecological and evolutionary dynamics of different sub-populations of the bacterium Escherichia coli that compete for a single limiting resource in a two-drug environment. We use in vitro evolutionary experiments to calibrate and test the model and show that antibiotic environments can support dynamically changing and heterogeneous population structures. We then demonstrate, theoretically and empirically, that the best treatment strategies should adapt through time and constant strategies are not optimal.

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.

The optimal deployment of synergistic antibiotics: a control-theoretic approach

Medical and pharmacological communities have long searched for antimicrobial drugs that increase their effect when used in combination, an interaction known as synergism. These drug combinations, however, impose selective pressures in favour of multi-drug resistance and as a result, the benefit of synergy may be lost after only a few bacterial generations. Furthermore, there is experimental evidence that antibiotic treatment can disrupt colonization resistance by shifting the balance between enteropathogenic and commensal bacteria in favour of the pathogens, with the potential to increase the risk of infections. So, we ask, what is the best way of using synergistic drugs? We pose an evolutionary model of commensal and pathogenic bacteria competing in a continuous culture device for a single limiting carbon source under the effect of two bacteriostatic and synergistic antibiotics. This model allows us to evaluate the efficacy of different drug deployment strategies and, using ideas from optimal control theory, to understand whether there are circumstances in which other types of therapy might be favoured over those based on fixed-dose multi-drug combinations. Our main result can be stated thus: the optimal deployment of synergistic antibiotics to remove a pathogen in the presence of commensal bacteria in our model system occurs not in combination, but by deploying them sequentially.

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.

Modelling cognitive decline in the Hypertension in the Very Elderly Trial [HYVET] and proposed risk tables for population use

Introduction

Although, on average, cognition declines with age, cognition in older adults is a dynamic process. Hypertension is associated with greater decline in cognition with age, but whether treatment of hypertension affects this is uncertain. Here, we modelled dynamics of cognition in relation to the treatment of hypertension, to see if treatment effects might better be discerned by a model that included baseline measures of cognition and consequent mortality

Methodology/Principal Findings

This is a secondary analysis of the Hypertension in the Very Elderly Trial (HYVET), a double blind, placebo controlled trial of indapamide, with or without perindopril, in people aged 80+ years at enrollment. Cognitive states were defined in relation to errors on the Mini-Mental State Examination, with more errors signifying worse cognition. Change in cognitive state was evaluated using a dynamic model of cognitive transition. In the model, the probabilities of transitions between cognitive states is represented by a Poisson distribution, with the Poisson mean dependent on the baseline cognitive state.

The dynamic model of cognitive transition was good (R² = 0.74) both for those on placebo and (0.86) for those on active treatment. The probability of maintaining cognitive function, based on baseline function, was slightly higher in the actively treated group (e.g., for those with the fewest baseline errors, the chance of staying in that state was 63% for those on treatment, compared with 60% for those on placebo). Outcomes at two and four years could be predicted based on the initial state and treatment.

Conclusions/Significance

A dynamic model of cognition that allows all outcomes (cognitive worsening, stability improvement or death) to be categorized simultaneously detected small but consistent differences between treatment and control groups (in favour of treatment) amongst very elderly people treated for hypertension. The model showed good fit, and suggests that most change in cognition in very elderly people is small, and depends on their baseline state and on treatment. Additional work is needed to understand whether this modelling approach is well suited to the valuation of small effects, especially in the face of mortality differences between treatment groups.

Trial Registration

ClinicalTrials.gov NCT0012281