Bottleneck size and selection level reproducibly impact evolution of antibiotic resistance.

During antibiotic treatment, the evolution of bacterial pathogens is fundamentally affected by bottlenecks and varying selection levels imposed by the drugs. Bottlenecks-that is, reductions in bacterial population size-lead to an increased influence of random effects (genetic drift) during bacterial evolution, and varying antibiotic concentrations during treatment may favour distinct resistance variants. Both aspects influence the process of bacterial evolution during antibiotic therapy and thereby treatment outcome. Surprisingly, the joint influence of these interconnected factors on the evolution of antibiotic resistance remains largely unexplored. Here we combine evolution experiments with genomic and genetic analyses to demonstrate that bottleneck size and antibiotic-induced selection reproducibly impact the evolutionary path to resistance in pathogenic Pseudomonas aeruginosa, one of the most problematic opportunistic human pathogens. Resistance is favoured-expectedly-under high antibiotic selection and weak bottlenecks, but-unexpectedly-also under low antibiotic selection and severe bottlenecks. The latter is likely to result from a reduced probability of losing favourable variants through drift under weak selection. Moreover, the absence of high resistance under low selection and weak bottlenecks is caused by the spread of low-resistance variants with high competitive fitness under these conditions. We conclude that bottlenecks, in combination with drug-induced selection, are currently neglected key determinants of pathogen evolution and outcome of antibiotic treatment.

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.

The Genomic Basis of Rapid Adaptation to Antibiotic Combination Therapy in Pseudomonas aeruginosa.

Combination therapy is a common antibiotic treatment strategy that aims at minimizing the risk of resistance evolution in several infectious diseases. Nonetheless, evidence supporting its efficacy against the nosocomial opportunistic pathogen Pseudomonas aeruginosa remains elusive. Identification of the possible evolutionary paths to resistance in multidrug environments can help to explain treatment outcome. For this purpose, we here performed whole-genome sequencing of 127 previously evolved populations of P. aeruginosa adapted to sublethal doses of distinct antibiotic combinations and corresponding single-drug treatments, and experimentally characterized several of the identified variants. We found that alterations in the regulation of efflux pumps are the most favored mechanism of resistance, regardless of the environment. Unexpectedly, we repeatedly identified intergenic variants in the adapted populations, often with no additional mutations and usually associated with genes involved in efflux pump expression, possibly indicating a regulatory function of the intergenic regions. The experimental analysis of these variants demonstrated that the intergenic changes caused similar increases in resistance against single and multidrug treatments as those seen for efflux regulatory gene mutants. Surprisingly, we could find no substantial fitness costs for a majority of these variants, most likely enhancing their competitiveness toward sensitive cells, even in antibiotic-free environments. We conclude that the regulation of efflux is a central target of antibiotic-mediated selection in P. aeruginosa and that, importantly, changes in intergenic regions may represent a usually neglected alternative process underlying bacterial resistance evolution, which clearly deserves further attention in the future.

Antibiotic combination efficacy (ACE) networks for a Pseudomonas aeruginosa model.

The spread of antibiotic resistance is always a consequence of evolutionary processes. The consideration of evolution is thus key to the development of sustainable therapy. Two main factors were recently proposed to enhance long-term effectiveness of drug combinations: evolved collateral sensitivities between the drugs in a pair and antagonistic drug interactions. We systematically assessed these factors by performing over 1,600 evolution experiments with the opportunistic nosocomial pathogen Pseudomonas aeruginosa in single- and multidrug environments. Based on the growth dynamics during these experiments, we reconstructed antibiotic combination efficacy (ACE) networks as a new tool for characterizing the ability of the tested drug combinations to constrain bacterial survival as well as drug resistance evolution across time. Subsequent statistical analysis of the influence of the factors on ACE network characteristics revealed that (i) synergistic drug interactions increased the likelihood of bacterial population extinction-irrespective of whether combinations were compared at the same level of inhibition or not-while (ii) the potential for evolved collateral sensitivities between 2 drugs accounted for a reduction in bacterial adaptation rates. In sum, our systematic experimental analysis allowed us to pinpoint 2 complementary determinants of combination efficacy and to identify specific drug pairs with high ACE scores. Our findings can guide attempts to further improve the sustainability of antibiotic therapy by simultaneously reducing pathogen load and resistance evolution.

Comprehensive benchmarking of SNV callers for highly admixed tumor data.

Precision medicine attempts to individualize cancer therapy by matching tumor-specific genetic changes with effective targeted therapies. A crucial first step in this process is the reliable identification of cancer-relevant variants, which is considerably complicated by the impurity and heterogeneity of clinical tumor samples. We compared the impact of admixture of non-cancerous cells and low somatic allele frequencies on the sensitivity and precision of 19 state-of-the-art SNV callers. We studied both whole exome and targeted gene panel data and up to 13 distinct parameter configurations for each tool. We found vast differences among callers. Based on our comprehensive analyses we recommend joint tumor-normal calling with MuTect, EBCall or Strelka for whole exome somatic variant calling, and HaplotypeCaller or FreeBayes for whole exome germline calling. For targeted gene panel data on a single tumor sample, LoFreqStar performed best. We further found that tumor impurity and admixture had a negative impact on precision, and in particular, sensitivity in whole exome experiments. At admixture levels of 60% to 90% sometimes seen in pathological biopsies, sensitivity dropped significantly, even when variants were originally present in the tumor at 100% allele frequency. Sensitivity to low-frequency SNVs improved with targeted panel data, but whole exome data allowed more efficient identification of germline variants. Effective somatic variant calling requires high-quality pathological samples with minimal admixture, a consciously selected sequencing strategy, and the appropriate variant calling tool with settings optimized for the chosen type of data.

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 ß-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.

Reliability of algorithmic somatic copy number alteration detection from targeted capture data.

MOTIVATION: Whole exome and gene panel sequencing are increasingly used for oncological diagnostics. To investigate the accuracy of SCNA detection algorithms on simulated and clinical tumor samples, the precision and sensitivity of four SCNA callers were measured using 50 simulated whole exome and 50 simulated targeted gene panel datasets, and using 119 TCGA tumor samples for which SNP array data were available. RESULTS: On synthetic exome and panel data, VarScan2 mostly called false positives, whereas Control-FREEC was precise (>90% correct calls) at the cost of low sensitivity (<40% detected). ONCOCNV was slightly less precise on gene panel data, with similarly low sensitivity. This could be explained by low sensitivity for amplifications and high precision for deletions. Surprisingly, these results were not strongly affected by moderate tumor impurities; only contaminations with more than 60% non-cancerous cells resulted in strongly declining precision and sensitivity. On the 119 clinical samples, both Control-FREEC and CNVkit called 71.8% and 94%, respectively, of the SCNAs found by the SNP arrays, but with a considerable amount of false positives (precision 29% and 4.9%). DISCUSSION: Whole exome and targeted gene panel methods by design limit the precision of SCNA callers, making them prone to false positives. SCNA calls cannot easily be integrated in clinical pipelines that use data from targeted capture-based sequencing. If used at all, they need to be cross-validated using orthogonal methods. AVAILABILITY AND IMPLEMENTATION: Scripts are provided as supplementary information. CONTACT: gunther.jansen@molecularhealth.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Association between clinical antibiotic resistance and susceptibility of Pseudomonas in the cystic fibrosis lung.

BACKGROUND AND OBJECTIVES: Cystic fibrosis patients suffer from chronic lung infections that require long-term antibiotic therapy. Pseudomonas readily evolve resistance, rendering antibiotics ineffective. In vitro experiments suggest that resistant bacteria may be treated by exploiting their collateral sensitivity to other antibiotics. Here, we investigate correlations of sensitivity and resistance profiles of Pseudomonas aeruginosa that naturally adapted to antibiotics in the cystic fibrosis lung. METHODOLOGY: Resistance profiles for 13 antibiotics were obtained using broth dilution, E-test and VITEK mass spectroscopy. Genetic variants were determined from whole-genome sequences and interrelationships among isolates were analyzed using 13 MLST loci. RESULT: Our study focused on 45 isolates from 13 patients under documented treatment with antibiotics. Forty percent of these were clinically resistant and 15% multi-drug resistant. Colistin resistance was found once, despite continuous colistin treatment and even though colistin resistance can readily evolve experimentally in the laboratory. Patients typically harbored multiple genetically and phenotypically distinct clones. However, genetically similar clones often had dissimilar resistance profiles. Isolates showed mutations in genes encoding cell wall synthesis, alginate production, efflux pumps and antibiotic modifying enzymes. Cross-resistance was commonly observed within antibiotic classes and between aminoglycosides and ß-lactam antibiotics. No evidence was found for consistent phenotypic resistance to one antibiotic and sensitivity to another within one genotype. CONCLUSIONS AND IMPLICATIONS: Evidence supporting potential collateral sensitivity in clinical P. aeruginosa isolates remains equivocal. However, cross-resistance within antibiotic classes is common. Colistin therapy is promising since resistance to it was rare despite its intensive use in the studied patients.

Temporal variation in antibiotic environments slows down resistance evolution in pathogenic Pseudomonas aeruginosa.

Antibiotic resistance is a growing concern to public health. New treatment strategies may alleviate the situation by slowing down the evolution of resistance. Here, we evaluated sequential treatment protocols using two fully independent laboratory-controlled evolution experiments with the human pathogen Pseudomonas aeruginosa PA14 and two pairs of clinically relevant antibiotics (doripenem/ciprofloxacin and cefsulodin/gentamicin). Our results consistently show that the sequential application of two antibiotics decelerates resistance evolution relative to monotherapy. Sequential treatment enhanced population extinction although we applied antibiotics at sublethal dosage. In both experiments, we identified an order effect of the antibiotics used in the sequential protocol, leading to significant variation in the long-term efficacy of the tested protocols. These variations appear to be caused by asymmetric evolutionary constraints, whereby adaptation to one drug slowed down adaptation to the other drug, but not vice versa. An understanding of such asymmetric constraints may help future development of evolutionary robust treatments against infectious disease.

Evolutionary Transition from Pathogenicity to Commensalism: Global Regulator Mutations Mediate Fitness Gains through Virulence Attenuation.

Symbiotic interactions are indispensable for metazoan function, but their origin and evolution remain elusive. We use a controlled evolution experiment to demonstrate the emergence of novel commensal interactions between Pseudomonas aeruginosa, an initially pathogenic bacterium, and a metazoan host, Caenorhabditis elegans. We show that commensalism evolves through loss of virulence, because it provides bacteria with a double fitness advantage: Increased within-host fitness and a larger host population to infect. Commensalism arises irrespective of host immune status, as the adaptive path in immunocompromised C. elegans knockouts does not differ from that in wild type. Dissection of temporal dynamics of genomic adaptation for 125 bacterial populations reveals highly parallel evolution of incipient commensalism across independent biological replicates. Adaptation is mainly achieved through frame shift mutations in the global regulator lasR and nonsynonymous point mutations in the polymerase gene rpoB that arise early in evolution. Genetic knockouts of lasR not only corroborate its role in virulence attenuation but also show that further mutations are necessary for the fully commensal phenotype. The evolutionary transition from pathogenicity to commensalism as we observe here is facilitated by mutations in global regulators such as lasR, because few genetic changes cause pleiotropic effects across the genome with large phenotypic effects. Finally, we found that nucleotide diversity increased more quickly in bacteria adapting to immunocompromised hosts than in those adapting to immunocompetent hosts. Nevertheless, the outcome of evolution was comparable across host types. Commensalism can thus evolve independently of host immune state solely as a side-effect of bacterial adaptation to novel hosts.

When experimental selection for virulence leads to loss of virulence.

Information on virulence evolution is critical for understanding disease dynamics. Theory predicts that under certain evolutionary conditions virulence should increase; for example, during host-parasite coevolution. Although these theoretical predictions are supported by natural observations, tests of these hypotheses using experimental evolution have yielded confounding and contradictory results, with discrepancies often being seen among experiments. Here we provide a critical overview of experimental tests of hypotheses regarding virulence evolution and provide potential explanations for the contradictory results. We emphasise the key role of parasite transmission mechanisms that can explain many of the observed discrepancies among evolution experiments. Finally, we make suggestions for how evolution experiments could be conducted in the future to avoid potentially confounding factors.

Cancer across the tree of life: cooperation and cheating in multicellularity.

Multicellularity is characterized by cooperation among cells for the development, maintenance and reproduction of the multicellular organism. Cancer can be viewed as cheating within this cooperative multicellular system. Complex multicellularity, and the cooperation underlying it, has evolved independently multiple times. We review the existing literature on cancer and cancer-like phenomena across life, not only focusing on complex multicellularity but also reviewing cancer-like phenomena across the tree of life more broadly. We find that cancer is characterized by a breakdown of the central features of cooperation that characterize multicellularity, including cheating in proliferation inhibition, cell death, division of labour, resource allocation and extracellular environment maintenance (which we term the five foundations of multicellularity). Cheating on division of labour, exhibited by a lack of differentiation and disorganized cell masses, has been observed in all forms of multicellularity. This suggests that deregulation of differentiation is a fundamental and universal aspect of carcinogenesis that may be underappreciated in cancer biology. Understanding cancer as a breakdown of multicellular cooperation provides novel insights into cancer hallmarks and suggests a set of assays and biomarkers that can be applied across species and characterize the fundamental requirements for generating a cancer.

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.

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.

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.

Sex differences in host defence interfere with parasite-mediated selection for outcrossing during host-parasite coevolution.

The Red Queen hypothesis proposes that coevolving parasites select for outcrossing in the host. Outcrossing relies on males, which often show lower immune investment due to, for example, sexual selection. Here, we demonstrate that such sex differences in immunity interfere with parasite-mediated selection for outcrossing. Two independent coevolution experiments with Caenorhabditis elegans and its microparasite Bacillus thuringiensis produced decreased yet stable frequencies of outcrossing male hosts. A subsequent systematic analysis verified that male C. elegans suffered from a direct selective disadvantage under parasite pressure (i.e. lower resistance, decreased sexual activity, increased escape behaviour), which can reduce outcrossing and thus male frequencies. At the same time, males offered an indirect selective benefit, because male-mediated outcrossing increased offspring resistance, thus favouring male persistence in the evolving populations. As sex differences in immunity are widespread, such interference of opposing selective constraints is likely of central importance during host adaptation to a coevolving parasite.

Experimental evolution as an efficient tool to dissect adaptive paths to antibiotic resistance.

Antibiotic treatments increasingly fail due to rapid dissemination of drug resistance. Comparative genomics of clinical isolates highlights the role of de novo adaptive mutations and horizontal gene transfer (HGT) in the acquisition of resistance. Yet it cannot fully describe the selective pressures and evolutionary trajectories that yielded today's problematic strains. Experimental evolution offers a compelling addition to such studies because the combination of replicated experiments under tightly controlled conditions with genomics of intermediate time points allows real-time reconstruction of evolutionary trajectories. Recent studies thus established causal links between antibiotic deployment therapies and the course and timing of mutations, the cost of resistance and the likelihood of compensating mutations. They particularly underscored the importance of long-term effects. Similar investigations incorporating horizontal gene transfer (HGT) are wanting, likely because of difficulties associated with its integration into experiments. In this review, we describe current advances in experimental evolution of antibiotic resistance and reflect on ways to incorporate horizontal gene transfer into the approach. We contend it provides a powerful tool for systematic and highly controlled dissection of evolutionary paths to antibiotic resistance that needs to be taken into account for the development of sustainable anti-bacterial treatment strategies.

Phylogeny, divergence-time estimation, biogeography and social parasite-host relationships of the Holarctic ant genus Myrmica (Hymenoptera: Formicidae).

We reconstructed a molecular phylogeny of the ant genus Myrmica, tested reciprocal monophyly of the Nearctic and Palearctic representatives, and inferred social parasite-host relationships for five workerless inquilines and four temporary parasites. We sequenced six gene fragments of 106 specimens (17 not identified to species), analysed the data with Bayesian phylogenetic inference and maximum likelihood, and estimated divergence times using penalized likelihood. Our well resolved phylogeny supported most morphologically defined species groups. The Nearctic and Palearctic species were not reciprocally monophyletic, which suggested repeated species interchange across the Beringian land bridge. Parasitism evolved several times in Myrmica. Three inquilines and one temporary parasite were closest relatives of their host, two inquiline species and one temporary parasite clustered basally to their host(s), and two temporary parasites more distantly. Myrmica probably diversified following drastic climatic cooling at the Eocene-Oligocene boundary ca. 34 Ma, the oldest species groups being rugosa and ritae in central and southeastern Asia. The oldest inquiline, Myrmica karavajevi, was estimated at 17 Ma, the youngest species M. hirsuta at 0.8 Ma, whereas the microgyne of M.rubra is an intraspecific inquiline.