Evolutionary paths to antibiotic resistance under dynamically sustained drug selection

Genomic Analysis and Resistance Mechanisms in Shigella flexneri 2a Strain 301

Shigella flexneri is one of the most prominent pathogenic bacteria in developing countries. In the battle against shigellosis and other bacterial diseases, antibiotic resistance has become an increasing global public health threat. Although the serious phenomenon of multidrug resistance (MDR) has been identified as one of the top three burdens on human health, resistance mechanisms are still poorly understood at the molecular level. In this study, we analyzed genomic data and the evolution of resistance in Shigella flexneri under sequential selection stress from three separate antibiotics: ciprofloxacin (CIP), ceftriaxone (CRO), and tetracycline. Through whole-genome sequencing, 82 chromosomal antibiotic resistance genes were identified. Re-sequencing of the evolved populations identified single nucleotide polymorphisms (SNPs) that contributed to MDR and SNPs that were specific to a single drug. A total of 40 SNPs in 8 genes and 3 intergenic regions, including mutations in metG (L582R) and 1538924, 1538924, and 1538924, appeared under each antibiotic. Several nonsynonymous mutations in gyrB (S464Y), ydgA (E378A), rob (R156H), and narX (K75E) were observed under selective pressure from CIP or CRO. Based on a bioinformatic analysis and previous reports, we discuss the contribution of these mutated genes to resistance. Therefore, more circumspect selection and use of antimicrobial drugs for treating shigellosis is necessary.

Rapid and Consistent Evolution of Colistin Resistance in Extensively Drug-Resistant Pseudomonas aeruginosa during Morbidostat Culture

Colistin is a last-resort antibiotic commonly used against multidrug-resistant strains of Pseudomonas aeruginosa To investigate the potential for in situ evolution of resistance against colistin and to map the molecular targets of colistin resistance, we exposed two P. aeruginosa isolates to colistin using a continuous-culture device known as a morbidostat. As a result, colistin resistance reproducibly increased 10-fold within 10 days and 100-fold within 20 days, along with highly stereotypic yet strain-specific mutation patterns. The majority of mutations hit the pmrAB two-component signaling system and genes involved in lipopolysaccharide (LPS) synthesis, including lpxC, pmrE, and migA We tracked the frequencies of all arising mutations by whole-genome deep sequencing every 3 to 4 days to obtain a detailed picture of the dynamics of resistance evolution, including competition and displacement among multiple resistant subpopulations. In 7 out of 18 cultures, we observed mutations in mutS along with a mutator phenotype that seemed to facilitate resistance evolution.

History of antibiotic adaptation influences microbial evolutionary dynamics during subsequent treatment

Antibiotic regimens often include the sequential changing of drugs to limit the development and evolution of resistance of bacterial pathogens. It remains unclear how history of adaptation to one antibiotic can influence the resistance profiles when bacteria subsequently adapt to a different antibiotic. Here, we experimentally evolved Pseudomonas aeruginosa to six 2-drug sequences. We observed drug order-specific effects, whereby adaptation to the first drug can limit the rate of subsequent adaptation to the second drug, adaptation to the second drug can restore susceptibility to the first drug, or final resistance levels depend on the order of the 2-drug sequence. These findings demonstrate how resistance not only depends on the current drug regimen but also the history of past regimens. These order-specific effects may allow for rational forecasting of the evolutionary dynamics of bacteria given knowledge of past adaptations and provide support for the need to consider the history of past drug exposure when designing strategies to mitigate resistance and combat bacterial infections.

A user-friendly, low-cost turbidostat with versatile growth rate estimation based on an extended Kalman filter

For various experimental applications, microbial cultures at defined, constant densities are highly advantageous over simple batch cultures. Due to high costs, however, devices for continuous culture at freely defined densities still experience limited use. We have developed a small-scale turbidostat for research purposes, which is manufactured from inexpensive components and 3D printed parts. A high degree of spatial system integration and a graphical user interface provide user-friendly operability. The used optical density feedback control allows for constant continuous culture at a wide range of densities and offers to vary culture volume and dilution rates without additional parametrization. Further, a recursive algorithm for on-line growth rate estimation has been implemented. The employed Kalman filtering approach based on a very general state model retains the flexibility of the used control type and can be easily adapted to other bioreactor designs. Within several minutes it can converge to robust, accurate growth rate estimates. This is particularly useful for directed evolution experiments or studies on metabolic challenges, as it allows direct monitoring of the population fitness.

Rational Design of Novel Allosteric Dihydrofolate Reductase Inhibitors Showing Antibacterial Effects on Drug-Resistant Escherichia coli Escape Variants

In drug discovery, systematic variations of substituents on a common scaffold and bioisosteric replacements are often used to generate diversity and obtain molecules with better biological effects. However, this could saturate the small-molecule diversity pool resulting in drug resistance. On the other hand, conventional drug discovery relies on targeting known pockets on protein surfaces leading to drug resistance by mutations of critical pocket residues. Here, we present a two-pronged strategy of designing novel drugs that target unique pockets on a protein's surface to overcome the above problems. Dihydrofolate reductase, DHFR, is a critical enzyme involved in thymidine and purine nucleotide biosynthesis. Several classes of compounds that are structural analogues of the substrate dihydrofolate have been explored for their antifolate activity. Here, we describe 10 novel small-molecule inhibitors of Escherichia coli DHFR, EcDHFR, belonging to the stilbenoid, deoxybenzoin, and chalcone family of compounds discovered by a combination of pocket-based virtual ligand screening and systematic scaffold hopping. These inhibitors show a unique uncompetitive or noncompetitive inhibition mechanism, distinct from those reported for all known inhibitors of DHFR, indicative of binding to a unique pocket distinct from either substrate or cofactor-binding pockets. Furthermore, we demonstrate that rescue mutants of EcDHFR, with reduced affinity to all known classes of DHFR inhibitors, are inhibited at the same concentration as the wild-type. These compounds also exhibit antibacterial activity against E. coli harboring the drug-resistant variant of DHFR. This discovery is the first report on a novel class of inhibitors targeting a unique pocket on EcDHFR.

Convection shapes the trade-off between antibiotic efficacy and the selection for resistance in spatial gradients

Since penicillin was discovered about 90 years ago, we have become used to using drugs to eradicate unwanted pathogenic cells. However, using drugs to kill bacteria, viruses or cancer cells has the serious side effect of selecting for mutant types that survive the drug attack. A crucial question therefore is how one could eradicate as many cells as possible for a given acceptable risk of drug resistance evolution. We address this general question in a model of drug resistance evolution in spatial drug gradients, which recent experiments and theories have suggested as key drivers of drug resistance. Importantly, our model takes into account the influence of convection, resulting for instance from blood flow. Using stochastic simulations, we study the fates of individual resistance mutations and quantify the trade-off between the killing of wild-type cells and the rise of resistance mutations: shallow gradients and convection into the antibiotic region promote wild-type death, at the cost of increasing the establishment probability of resistance mutations. We can explain these observed trends by modeling the adaptation process as a branching random walk. Our analysis reveals that the trade-off between death and adaptation depends on the relative length scales of the spatial drug gradient and random dispersal, and the strength of convection. Our results show that convection can have a momentous effect on the rate of establishment of new mutations, and may heavily impact the efficiency of antibiotic treatment.

Moving in the Right Direction: Protein Vibrations Steering Function

Nearly all protein functions require structural change, such as enzymes clamping onto substrates, and ion channels opening and closing. These motions are a target for possible new therapies; however, the control mechanisms are under debate. Calculations have indicated protein vibrations enable structural change. However, previous measurements found these vibrations only weakly depend on the functional state. By using the novel technique of anisotropic terahertz microscopy, we find that there is a dramatic change to the vibrational directionality with inhibitor binding to lysozyme, whereas the vibrational energy distribution, as measured by neutron inelastic scattering, is only slightly altered. The anisotropic terahertz measurements provide unique access to the directionality of the intramolecular vibrations, and immediately resolve the inconsistency between calculations and previous measurements, which were only sensitive to the energy distribution. The biological importance of the vibrational directions versus the energy distribution is revealed by our calculations comparing wild-type lysozyme with a higher catalytic rate double deletion mutant. The vibrational energy distribution is identical, but the more efficient mutant shows an obvious reorientation of motions. These results show that it is essential to characterize the directionality of motion to understand and control protein dynamics to optimize or inhibit function.

Antibiotics/antimicrobials: systemic and local administration in the therapy of mild to moderately advanced periodontitis

This review gives an update of the current scientific evidence on the efficacy of the adjunctive use of systemic and local antibiotics/antimicrobials in the treatment of periodontitis. In particular, it addresses whether their use can improve the results of nonsurgical mechanical therapy in mild-to-moderate forms of the disease. Large numbers of randomized clinical trials and systematic reviews with meta-analyses have clearly established that adjunctive systemic antibiotics, combined with mechanical debridement, offer clinical improvements additional to those obtained with scaling and root planing alone. These effects are more pronounced in aggressive periodontitis and in initially deep pockets, whereas more limited additional improvements, of 0.3 mm for additional pocket reduction and 0.2 mm for additional clinical attachment gain, have been documented for moderately deep sites (4-6 mm) in patients with chronic periodontitis. The marginal clinical benefit in patients with moderate disease has to be balanced against possible side effects. Notably, it has to be realized that an increasing number of warnings have been articulated against the unrestricted use of antibiotics in treating periodontal diseases because of the emerging global public health issue of bacterial resistance. The effects of the adjunctive local administration of antimicrobials have also been very well documented in several systematic reviews. Overall, in persistent or recurrent localized deep sites, the application of antimicrobials by sustained-delivery devices may offer a benefit of an additional 0.4 mm in pocket depth reduction and 0.3 mm in clinical attachment level gain. In conclusion, the slight additional benefits of adjunctive antimicrobials, which were shown for moderate forms of periodontitis, have to be balanced against their side effects and therefore their prescription should be limited as much as possible.

Time-programmable drug dosing allows the manipulation, suppression and reversal of antibiotic drug resistance in vitro

Multi-drug strategies have been attempted to prolong the efficacy of existing antibiotics, but with limited success. Here we show that the evolution of multi-drug-resistant Escherichia coli can be manipulated in vitro by administering pairs of antibiotics and switching between them in ON/OFF manner. Using a multiplexed cell culture system, we find that switching between certain combinations of antibiotics completely suppresses the development of resistance to one of the antibiotics. Using this data, we develop a simple deterministic model, which allows us to predict the fate of multi-drug evolution in this system. Furthermore, we are able to reverse established drug resistance based on the model prediction by modulating antibiotic selection stresses. Our results support the idea that the development of antibiotic resistance may be potentially controlled via continuous switching of drugs.

Targeted mutagenesis: A sniper-like diversity generator in microbial engineering

Mutations, serving as the raw materials of evolution, have been extensively utilized to increase the chances of engineering molecules or microbes with tailor-made functions. Global and targeted mutagenesis are two main methods of obtaining various mutations, distinguished by the range of action they can cover. While the former one stresses the mining of novel genetic loci within the whole genomic background, targeted mutagenesis performs in a more straightforward manner, bringing evolutionary escape and error catastrophe under control. In this review, we classify the existing techniques of targeted mutagenesis into two categories in terms of whether the diversity is generated in vitro or in vivo, and briefly introduce the mechanisms and applications of them separately. The inherent connections and development trends of the two classes are also discussed to provide an insight into the next generation evolution research.

Time-Resolved Tracking of Mutations Reveals Diverse Allele Dynamics during Escherichia coli Antimicrobial Adaptive Evolution to Single Drugs and Drug Pairs

Understanding the evolutionary processes that lead to antibiotic resistance can help to achieve better treatment strategies. Yet, little is known about the dynamics of the resistance alleles during adaptation. Here, we use population sequencing to monitor genetic changes in putative resistance loci at several time-points during adaptive evolution experiments involving five different antibiotic conditions. We monitor the mutational spectra in lineages evolved to be resistant to single antibiotics [amikacin (AMK), chloramphenicol (CHL), and ciprofloxacin (CIP)], as well as antibiotic combinations (AMK + CHL and CHL + CIP). We find that lineages evolved to antibiotic combinations exhibit different resistance allele dynamics compared with those of single-drug evolved lineages, especially for a drug pair with reciprocal collateral sensitivity. During adaptation, we observed interfering, superimposing and fixation allele dynamics. To further understand the selective forces driving specific allele dynamics, a subset of mutations were introduced into the ancestral wild type enabling differentiation between clonal interference and negative epistasis.

Experimental evolution and the dynamics of adaptation and genome evolution in microbial populations

Evolution is an on-going process, and it can be studied experimentally in organisms with rapid generations. My team has maintained 12 populations of Escherichia coli in a simple laboratory environment for >25 years and 60 000 generations. We have quantified the dynamics of adaptation by natural selection, seen some of the populations diverge into stably coexisting ecotypes, described changes in the bacteria's mutation rate, observed the new ability to exploit a previously untapped carbon source, characterized the dynamics of genome evolution and used parallel evolution to identify the genetic targets of selection. I discuss what the future might hold for this particular experiment, briefly highlight some other microbial evolution experiments and suggest how the fields of experimental evolution and microbial ecology might intersect going forward.

Adaptive Laboratory Evolution of Antibiotic Resistance Using Different Selection Regimes Lead to Similar Phenotypes and Genotypes

Antibiotic resistance is a global threat to human health, wherefore it is crucial to study the mechanisms of antibiotic resistance as well as its emergence and dissemination. One way to analyze the acquisition of de novo mutations conferring antibiotic resistance is adaptive laboratory evolution. However, various evolution methods exist that utilize different population sizes, selection strengths, and bottlenecks. While evolution in increasing drug gradients guarantees high-level antibiotic resistance promising to identify the most potent resistance conferring mutations, other selection regimes are simpler to implement and therefore allow higher throughput. The specific regimen of adaptive evolution may have a profound impact on the adapted cell state. Indeed, substantial effects of the selection regime on the resulting geno- and phenotypes have been reported in the literature. In this study we compare the geno- and phenotypes of Escherichia coli after evolution to Amikacin, Piperacillin, and Tetracycline under four different selection regimes. Interestingly, key mutations that confer antibiotic resistance as well as phenotypic changes like collateral sensitivity and cross-resistance emerge independently of the selection regime. Yet, lineages that underwent evolution under mild selection displayed a growth advantage independently of the acquired level of antibiotic resistance compared to lineages adapted under maximal selection in a drug gradient. Our data suggests that even though different selection regimens result in subtle genotypic and phenotypic differences key adaptations appear independently of the selection regime.

Fighting against evolution of antibiotic resistance by utilizing evolvable antimicrobial drugs

Antibiotic resistance is a worldwide public health problem (Bush et al. in Nat Rev Microbiol 9:894-896, 2011). The lack of effective therapies against resistant bacteria globally leads to prolonged treatments, increased mortality, and inflating health care costs (Oz et al. in Mol Biol Evol 31:2387-2401, 2014; Martinez in Science 321:365-367, 2008; Lipsitch et al. in Proc Natl Acad Sci USA 97:1938-1943, 2000; Taubes in Science 321:356-361, 2008; Laxminarayan et al. in Lancet, 2016; Laxminarayan et al. in Lancet Infect Dis 13:1057-1098, 2013). Current efforts towards a solution of this problem can be boiled down to two main strategies: (1) developing of new antimicrobial agents and (2) searching for smart strategies that can restore or preserve the efficacy of existing antimicrobial agents. In this short review article, we discuss the need for evolvable antimicrobial agents, focusing on a new antimicrobial technology that utilizes peptide-conjugated phosphorodiamidate morpholino oligomers to inhibit the growth of pathogenic bacteria by targeting bacterial genes.

A scalable metabolite supplementation strategy against antibiotic resistant pathogen Chromobacterium violaceum induced by NAD+/NADH+ imbalance

BACKGROUND

The leading edge of the global problem of antibiotic resistance necessitates novel therapeutic strategies. This study develops a novel systems biology driven approach for killing antibiotic resistant pathogens using benign metabolites.

RESULTS

Controlled laboratory evolutions established chloramphenicol and streptomycin resistant pathogens of Chromobacterium. These resistant pathogens showed higher growth rates and required higher lethal doses of antibiotic. Growth and viability testing identified malate, maleate, succinate, pyruvate and oxoadipate as resensitising agents for antibiotic therapy. Resistant genes were catalogued through whole genome sequencing. Intracellular metabolomic profiling identified violacein as a potential biomarker for resistance. The temporal variance of metabolites captured the linearized dynamics around the steady state and correlated to growth rate. A constraints-based flux balance model of the core metabolism was used to predict the metabolic basis of antibiotic susceptibility and resistance.

CONCLUSIONS

The model predicts electron imbalance and skewed NAD/NADH ratios as a result of antibiotics - chloramphenicol and streptomycin. The resistant pathogen rewired its metabolic networks to compensate for disruption of redox homeostasis. We foresee the utility of such scalable workflows in identifying metabolites for clinical isolates as inevitable solutions to mitigate antibiotic resistance.

Within-Host Evolution of Burkholderia pseudomallei during Chronic Infection of Seven Australasian Cystic Fibrosis Patients

Cystic fibrosis (CF) is a genetic disorder characterized by progressive lung function decline. CF patients are at an increased risk of respiratory infections, including those by the environmental bacterium Burkholderia pseudomallei, the causative agent of melioidosis. Here, we compared the genomes of B. pseudomallei isolates collected between ~4 and 55 months apart from seven chronically infected CF patients. Overall, the B. pseudomallei strains showed evolutionary patterns similar to those of other chronic infections, including emergence of antibiotic resistance, genome reduction, and deleterious mutations in genes involved in virulence, metabolism, environmental survival, and cell wall components. We documented the first reported B. pseudomallei hypermutators, which were likely caused by defective MutS. Further, our study identified both known and novel molecular mechanisms conferring resistance to three of the five clinically important antibiotics for melioidosis treatment. Our report highlights the exquisite adaptability of microorganisms to long-term persistence in their environment and the ongoing challenges of antibiotic treatment in eradicating pathogens in the CF lung. Convergent evolution with other CF pathogens hints at a degree of predictability in bacterial evolution in the CF lung and potential targeted eradication of chronic CF infections in the future.IMPORTANCEBurkholderia pseudomallei, the causative agent of melioidosis, is an environmental opportunistic bacterium that typically infects immunocompromised people and those with certain risk factors such as cystic fibrosis (CF). Patients with CF tend to develop chronic melioidosis infections, for reasons that are not well understood. This report is the first to describe B. pseudomallei evolution within the CF lung during chronic infection. We show that the pathways by which B. pseudomallei adapts to the CF lung are similar to those seen in better-studied CF pathogens such as Pseudomonas aeruginosa, Staphylococcus aureus, and Burkholderia cepacia complex species. Adaptations include the accumulation of antibiotic resistance, loss of nonessential genes, metabolic alterations, and virulence factor attenuation. Known and novel mechanisms of resistance to three of the five antibiotics used in melioidosis treatment were identified. Similar pathways of evolution in CF pathogens, including B. pseudomallei, provide exciting avenues for more-targeted treatment of chronic, recalcitrant infections.

Genome-Wide Screen Reveals sec21 Mutants of Saccharomyces cerevisiae Are Methotrexate-Resistant

Drug resistance is a consequence of how most modern medicines work. Drugs exert pressure on cells that causes death or the evolution of resistance. Indeed, highly specific drugs are rendered ineffective by a single DNA mutation. In this study, we apply the drug methotrexate, which is widely used in cancer and rheumatoid arthritis, and perform evolution experiments on Baker's yeast to ask the different ways in which cells become drug resistant. Because of the conserved nature of biological pathways between yeast and man, our results can inform how the same mechanism may operate to render human cells resistant to treatment. Exposure of cells to small molecules and drug therapies imposes a strong selective pressure. As a result, cells rapidly acquire mutations in order to survive. These include resistant variants of the drug target as well as those that modulate drug transport and detoxification. To systematically explore how cells acquire drug resistance in an unbiased manner, rapid cost-effective approaches are required. Methotrexate, as one of the first rationally designed anticancer drugs, has served as a prototypic example of such acquired resistance. Known methotrexate resistance mechanisms include mutations that increase expression of the dihydrofolate reductase (DHFR) target as well as those that maintain function yet reduce the drug's binding affinity. Recent evidence suggests that target-independent, epistatic mutations can also result in resistance to methotrexate. Currently, however, the relative contribution of such unlinked resistance mutations is not well understood. To address this issue, we took advantage of Saccharomyces cerevisiae as a model eukaryotic system that combined with whole-genome sequencing and a rapid screening methodology, allowed the identification of causative mutations that modulate resistance to methotrexate. We found a recurrent missense mutation in SEC21 (orthologous to human COPG1), which we confirmed in 10 de novo methotrexate-resistant strains. This sec21 allele (S96L) behaves as a recessive, gain-of-function allele, conferring methotrexate resistance that is abrogated by the presence of a wild-type copy of SEC21 These observations indicate that the Sec21p/COPI transport complex has previously uncharacterized roles in modulating methotrexate stress.

Characterization of trimethoprim resistant E. coli dihydrofolate reductase mutants by mass spectrometry and inhibition by propargyl-linked antifolates

Native mass spectrometry, size exclusion chromatography, and kinetic assays were employed to study trimethoprim resistance in E. coli caused by mutations P21L and W30R of dihydrofolate reductase.

Pathogenic Escherichia coli, one of the primary causes of urinary tract infections, has shown significant resistance to the most popular antibiotic, trimethoprim (TMP), which inhibits dihydrofolate reductase (DHFR). The resistance is modulated by single point mutations of DHFR. The impact of two clinically relevant mutations, P21L and W30R, on the activity of DHFR was evaluated via measurement of Michaelis–Menten and inhibitory kinetics, and structural characterization was undertaken by native mass spectrometry with ultraviolet photodissociation (UVPD). Compared to WT-DHFR, both P21L and W30R mutants produced less stable complexes with TMP in the presence of co-factor NADPH as evidenced by the relative abundances of complexes observed in ESI mass spectra. Moreover, based on variations in the fragmentation patterns obtained by UVPD mass spectrometry of binary and ternary DHFR complexes, notable structural changes were localized to the substrate binding pocket for W30R and to the M20 loop region as well as the C-terminal portion containing the essential G–H functional loop for the P21L mutant. The results suggest that the mutations confer resistance through distinctive mechanisms. A novel propargyl-linked antifolate compound 1038 was shown to be a reasonably effective inhibitor of the P21L mutant.

Functional Characterization of the DNA Gyrases in Fluoroquinolone-Resistant Mutants of Francisella novicida

Fluoroquinolone (FQ) resistance is a major health concern in the treatment of tularemia. Because DNA gyrase has been described as the main target of these compounds, our aim was to clarify the contributions of both GyrA and GyrB mutations found in Francisella novicida clones highly resistant to FQs. Wild-type and mutated GyrA and GyrB subunits were overexpressed so that the in vitro FQ sensitivity of functional reconstituted complexes could be evaluated. The data obtained were compared to the MICs of FQs against bacterial clones harboring the same mutations and were further validated through complementation experiments and structural modeling. Whole-genome sequencing of highly FQ-resistant lineages was also done. Supercoiling and DNA cleavage assays demonstrated that GyrA D87 is a hot spot FQ resistance target in F. novicida and pointed out the role of the GyrA P43H substitution in resistance acquisition. An unusual feature of FQ resistance acquisition in F. novicida is that the first-step mutation occurs in GyrB, with direct or indirect consequences for FQ sensitivity. Insertion of P466 into GyrB leads to a 50% inhibitory concentration (IC₅₀) comparable to that observed for a mutant gyrase carrying the GyrA D87Y substitution, while the D487E-ΔK488 mutation, while not active on its own, contributes to the high level of resistance that occurs following acquisition of the GyrA D87G substitution in double GyrA/GyrB mutants. The involvement of other putative targets is discussed, including that of a ParE mutation that was found to arise in the very late stage of antibiotic exposure. This study provides the first characterization of the molecular mechanisms responsible for FQ resistance in Francisella.

Relative Strengths and Regulation of Different Promoter-Associated Sequences for Various blaSHV Genes and Their Relationships to β-Lactam Resistance

AIMS

This work investigated the relative strengths of different blaSHV promoter-associated sequences and their regulation function in blaSHV expression and β-lactam resistance.

METHODS

Recombinant plasmids with the promoter-associated sequences (P-W, P-S, P-IS, and P-WPD), tac promoter, and combined fragments of promoter and blaSHV were separately constructed and transformed into Escherichia coli DH5α. The relative strengths of the promoters indicated by the intensities of green fluorescent protein and the mRNA expression levels of blaSHV were compared. The minimum inhibitory concentration and extended spectrum β-lactamase phenotypes were evaluated.

RESULTS

The relative strengths were ranked as P-tac > P-WPD > P-IS > P-S > P-W. The mRNA expression and β-lactam resistance levels of the different promoter-associated sequence groups were generally consistent with the strength rank, but the extent of gfp and blaSHV mRNA levels varied significantly in each group. The β-lactam resistance levels were inconsistent with the strength rank in certain blaSHV groups. In relation to the different promoter-associated sequences, blaSHV-ESBLs displayed significantly different change modes of β-lactam resistance compared with blaSHV-non-ESBLs.

CONCLUSION

The mRNA expression and β-lactam resistance of the blaSHV showed consistencies and inconsistencies with the strengths of the promoter-associated sequences. The mechanisms accounting for these discrepancies need further investigation.

Spatiotemporal microbial evolution on antibiotic landscapes

A key aspect of bacterial survival is the ability to evolve while migrating across spatially varying environmental challenges. Laboratory experiments, however, often study evolution in well-mixed systems. Here, we introduce an experimental device, the microbial evolution and growth arena (MEGA)-plate, in which bacteria spread and evolved on a large antibiotic landscape (120 × 60 centimeters) that allowed visual observation of mutation and selection in a migrating bacterial front. While resistance increased consistently, multiple coexisting lineages diversified both phenotypically and genotypically. Analyzing mutants at and behind the propagating front, we found that evolution is not always led by the most resistant mutants; highly resistant mutants may be trapped behind more sensitive lineages. The MEGA-plate provides a versatile platform for studying microbial adaption and directly visualizing evolutionary dynamics.

Toward a quantitative understanding of antibiotic resistance evolution

The rising prevalence of antibiotic resistant bacteria is an increasingly serious public health challenge. To address this problem, recent work ranging from clinical studies to theoretical modeling has provided valuable insights into the mechanisms of resistance, its emergence and spread, and ways to counteract it. A deeper understanding of the underlying dynamics of resistance evolution will require a combination of experimental and theoretical expertise from different disciplines and new technology for studying evolution in the laboratory. Here, we review recent advances in the quantitative understanding of the mechanisms and evolution of antibiotic resistance. We focus on key theoretical concepts and new technology that enables well-controlled experiments. We further highlight key challenges that can be met in the near future to ultimately develop effective strategies for combating resistance.

Adaptation of Escherichia coli to Long-Term Serial Passage in Complex Medium: Evidence of Parallel Evolution

With a growing body of work directed toward understanding the mechanisms of evolution using experimental systems, it is crucial to decipher what effects the experimental setup has on the outcome. If the goal of experimental laboratory evolution is to elucidate underlying evolutionary mechanisms and trends, these must be demonstrated in a variety of systems and environments. Here, we perform experimental evolution in a complex medium allowing the cells to transition through all five phases of growth, including death phase and long-term stationary phase. We show that the swiftness of selection and the specific targets of adaptive evolution are different in this system compared to others. We also observe parallel evolution where different mutations in the same genes are under positive natural selection. Together, these data show that while some outcomes of microbial evolution experiments may be generalizable, many outcomes will be environment or system specific.

Experimental evolution of bacterial populations in the laboratory has led to identification of several themes, including parallel evolution of populations adapting to carbon starvation, heat stress, and pH stress. However, most of these experiments study growth in defined and/or constant environments. We hypothesized that while there would likely continue to be parallelism in more complex and changing environments, there would also be more variation in what types of mutations would benefit the cells. In order to test our hypothesis, we serially passaged Escherichia coli in a complex medium (Luria-Bertani broth) throughout the five phases of bacterial growth. This passaging scheme allowed cells to experience a wide variety of stresses, including nutrient limitation, oxidative stress, and pH variation, and therefore allowed them to adapt to several conditions. After every ~30 generations of growth, for a total of ~300 generations, we compared both the growth phenotypes and genotypes of aged populations to the parent population. After as few as 30 generations, populations exhibit changes in growth phenotype and accumulate potentially adaptive mutations. There were many genes with mutant alleles in different populations, indicating potential parallel evolution. We examined 8 of these alleles by constructing the point mutations in the parental genetic background and competed those cells with the parent population; five of these alleles were found to be adaptive. The variety and swiftness of adaptive mutations arising in the populations indicate that the cells are adapting to a complex set of stresses, while the parallel nature of several of the mutations indicates that this behavior may be generalized to bacterial evolution.

IMPORTANCE With a growing body of work directed toward understanding the mechanisms of evolution using experimental systems, it is crucial to decipher what effects the experimental setup has on the outcome. If the goal of experimental laboratory evolution is to elucidate underlying evolutionary mechanisms and trends, these must be demonstrated in a variety of systems and environments. Here, we perform experimental evolution in a complex medium allowing the cells to transition through all five phases of growth, including death phase and long-term stationary phase. We show that the swiftness of selection and the specific targets of adaptive evolution are different in this system compared to others. We also observe parallel evolution where different mutations in the same genes are under positive natural selection. Together, these data show that while some outcomes of microbial evolution experiments may be generalizable, many outcomes will be environment or system specific.

Metabolic constraints on the evolution of antibiotic resistance

Despite our continuous improvement in understanding antibiotic resistance, the interplay between natural selection of resistance mutations and the environment remains unclear. To investigate the role of bacterial metabolism in constraining the evolution of antibiotic resistance, we evolved Escherichia coli growing on glycolytic or gluconeogenic carbon sources to the selective pressure of three different antibiotics. Profiling more than 500 intracellular and extracellular putative metabolites in 190 evolved populations revealed that carbon and energy metabolism strongly constrained the evolutionary trajectories, both in terms of speed and mode of resistance acquisition. To interpret and explore the space of metabolome changes, we developed a novel constraint‐based modeling approach using the concept of shadow prices. This analysis, together with genome resequencing of resistant populations, identified condition‐dependent compensatory mechanisms of antibiotic resistance, such as the shift from respiratory to fermentative metabolism of glucose upon overexpression of efflux pumps. Moreover, metabolome‐based predictions revealed emerging weaknesses in resistant strains, such as the hypersensitivity to fosfomycin of ampicillin‐resistant strains. Overall, resolving metabolic adaptation throughout antibiotic‐driven evolutionary trajectories opens new perspectives in the fight against emerging antibiotic resistance.

Transcriptome-Level Signatures in Gene Expression and Gene Expression Variability during Bacterial Adaptive Evolution

Antibiotic-resistant bacteria are an increasingly serious public health concern, as strains emerge that demonstrate resistance to almost all available treatments. One factor that contributes to the crisis is the adaptive ability of bacteria, which exhibit remarkable phenotypic and gene expression heterogeneity in order to gain a survival advantage in damaging environments. This high degree of variability in gene expression across biological populations makes it a challenging task to identify key regulators of bacterial adaptation. Here, we research the regulation of adaptive resistance by investigating transcriptome profiles of Escherichia coli upon adaptation to disparate toxins, including antibiotics and biofuels. We locate potential target genes via conventional gene expression analysis as well as using a new analysis technique examining differential gene expression variability. By investigating trends across the diverse adaptation conditions, we identify a focused set of genes with conserved behavior, including those involved in cell motility, metabolism, membrane structure, and transport, and several genes of unknown function. To validate the biological relevance of the observed changes, we synthetically perturb gene expression using clustered regularly interspaced short palindromic repeat (CRISPR)-dCas9. Manipulation of select genes in combination with antibiotic treatment promotes adaptive resistance as demonstrated by an increased degree of antibiotic tolerance and heterogeneity in MICs. We study the mechanisms by which identified genes influence adaptation and find that select differentially variable genes have the potential to impact metabolic rates, mutation rates, and motility. Overall, this work provides evidence for a complex nongenetic response, encompassing shifts in gene expression and gene expression variability, which underlies adaptive resistance. IMPORTANCE Even initially sensitive bacteria can rapidly thwart antibiotic treatment through stress response processes known as adaptive resistance. Adaptive resistance fosters transient tolerance increases and the emergence of mutations conferring heritable drug resistance. In order to extend the applicable lifetime of new antibiotics, we must seek to hinder the occurrence of bacterial adaptive resistance; however, the regulation of adaptation is difficult to identify due to immense heterogeneity emerging during evolution. This study specifically seeks to generate heterogeneity by adapting bacteria to different stresses and then examines gene expression trends across the disparate populations in order to pinpoint key genes and pathways associated with adaptive resistance. The targets identified here may eventually inform strategies for impeding adaptive resistance and prolonging the effectiveness of antibiotic treatment.

Antibiotic tolerance facilitates the evolution of resistance

Controlled experimental evolution during antibiotic treatment can help to explain the processes leading to antibiotic resistance in bacteria. Recently, intermittent antibiotic exposures have been shown to lead rapidly to the evolution of tolerance—that is, the ability to survive under treatment without developing resistance. However, whether tolerance delays or promotes the eventual emergence of resistance is unclear. Here we used in vitro evolution experiments to explore this question. We found that in all cases, tolerance preceded resistance. A mathematical population-genetics model showed how tolerance boosts the chances for resistance mutations to spread in the population. Thus, tolerance mutations pave the way for the rapid subsequent evolution of resistance. Preventing the evolution of tolerance may offer a new strategy for delaying the emergence of resistance.

The Complex Relationship between Virulence and Antibiotic Resistance

Antibiotic resistance, prompted by the overuse of antimicrobial agents, may arise from a variety of mechanisms, particularly horizontal gene transfer of virulence and antibiotic resistance genes, which is often facilitated by biofilm formation. The importance of phenotypic changes seen in a biofilm, which lead to genotypic alterations, cannot be overstated. Irrespective of if the biofilm is single microbe or polymicrobial, bacteria, protected within a biofilm from the external environment, communicate through signal transduction pathways (e.g., quorum sensing or two-component systems), leading to global changes in gene expression, enhancing virulence, and expediting the acquisition of antibiotic resistance. Thus, one must examine a genetic change in virulence and resistance not only in the context of the biofilm but also as inextricably linked pathologies. Observationally, it is clear that increased virulence and the advent of antibiotic resistance often arise almost simultaneously; however, their genetic connection has been relatively ignored. Although the complexities of genetic regulation in a multispecies community may obscure a causative relationship, uncovering key genetic interactions between virulence and resistance in biofilm bacteria is essential to identifying new druggable targets, ultimately providing a drug discovery and development pathway to improve treatment options for chronic and recurring infection.

Enhancement of bacterial growth with the help of immiscible oxygenated oils

Bacterial growth in an aqueous medium in the vicinity of the interface with an immiscible oxygenated fluid is a subject of this study. We tested six oxygenated liquids, including hexadecane, silicone oil, FC-40, FC-70, HFE-7200 and HFE-7500.

Expression Profiling of Antibiotic-Resistant Bacteria Obtained by Laboratory Evolution

To elucidate the mechanisms of antibiotic resistance, integrating phenotypic and genotypic features in resistant strains is important. Here, we describe the expression profiling of antibiotic-resistant Escherichia coli strains obtained by laboratory evolution, and a method for extracting a small number of genes whose expression changes can contribute to the acquisition of resistance.

Asymptotic profile in selection-mutation equations: Gauss versus Cauchy distributions

In this paper, we study the asymptotic (large time) behaviour of a selection-mutation-competition model for a population structured with respect to a phenotypic trait when the rate of mutation is very small. We assume that the reproduction is asexual, and that the mutations can be described by a linear integral operator. We are interested in the interplay between the time variable t and the rate ε of mutations. We show that depending on α > 0 , the limit ε → 0 with t = ε - α can lead to population number densities which are either Gaussian-like (when α is small) or Cauchy-like (when α is large).

Genome-wide mapping of mutations at single-nucleotide resolution for protein, metabolic and genome engineering

Improvements in DNA synthesis and sequencing have underpinned comprehensive assessment of gene function in bacteria and eukaryotes. Genome-wide analyses require high-throughput methods to generate mutations and analyze their phenotypes, but approaches to date have been unable to efficiently link the effects of mutations in coding regions or promoter elements in a highly parallel fashion. We report that CRISPR-Cas9 gene editing in combination with massively parallel oligomer synthesis can enable trackable editing on a genome-wide scale. Our method, CRISPR-enabled trackable genome engineering (CREATE), links each guide RNA to homologous repair cassettes that both edit loci and function as barcodes to track genotype-phenotype relationships. We apply CREATE to site saturation mutagenesis for protein engineering, reconstruction of adaptive laboratory evolution experiments, and identification of stress tolerance and antibiotic resistance genes in bacteria. We provide preliminary evidence that CREATE will work in yeast. We also provide a webtool to design multiplex CREATE libraries.

Beyond the Hypercube: Evolutionary Accessibility of Fitness Landscapes with Realistic Mutational Networks

Evolutionary pathways describe trajectories of biological evolution in the space of different variants of organisms (genotypes). The probability of existence and the number of evolutionary pathways that lead from a given genotype to a better-adapted genotype are important measures of accessibility of local fitness optima and the reproducibility of evolution. Both quantities have been studied in simple mathematical models where genotypes are represented as binary sequences of two types of basic units, and the network of permitted mutations between the genotypes is a hypercube graph. However, it is unclear how these results translate to the biologically relevant case in which genotypes are represented by sequences of more than two units, for example four nucleotides (DNA) or 20 amino acids (proteins), and the mutational graph is not the hypercube. Here we investigate accessibility of the best-adapted genotype in the general case of K > 2 units. Using computer generated and experimental fitness landscapes we show that accessibility of the global fitness maximum increases with K and can be much higher than for binary sequences. The increase in accessibility comes from the increase in the number of indirect trajectories exploited by evolution for higher K. As one of the consequences, the fraction of genotypes that are accessible increases by three orders of magnitude when the number of units K increases from 2 to 16 for landscapes of size N ∼ 10⁶ genotypes. This suggests that evolution can follow many different trajectories on such landscapes and the reconstruction of evolutionary pathways from experimental data might be an extremely difficult task.

Biological evolution is driven by heritable, genetic alterations that affect the fitness of organisms. However, the pool of “fitter” variants (genotypes) is often restricted and it is not at all obvious how evolution finds its way from low-fitness to high-fitness genotypes in a complex, multidimensional “fitness landscapes” with many peaks (fit organisms) and valleys (unfit ones). To address this question we investigate how likely it is for biological evolution to find a way “uphill” from a lower-fitness organism to the best adapted organism. We discover that the accessibility of the fittest organism depends on the number of types of basic “units” used to encode genotypes. These units can be, for example, the four DNA nucleotides A,T,C,G, or the ∼20 amino acids used for synthesizing proteins, and the choice of the most appropriate unit is dictated by how the genotypes and the fitnesses are related—a relationship that researchers have begun to unveil only recently. We find that increasing the number of units strongly increases the probability that there will be at least one uphill path to the best-adapted genotype, and the number of evolutionary pathways leading to it. Our findings suggest that biological evolution can follow many more pathways than previously thought.

Competition along trajectories governs adaptation rates towards antimicrobial resistance

The increasing availability of genotype-phenotype maps for different combinations of mutations has empowered evolutionary biologists with the tools to interrogate the predictability of adaptive evolution, especially in the context of the evolution of antimicrobial resistance. Large microbial populations are known to generate competing beneficial mutations, but determining how these mutations contribute to the adaptive trajectories that are most likely to be followed remains a challenge. Despite a recognition that there may also be competition between successive alleles on the same trajectory, prior studies have not fully considered how this impacts adaptation rates along, or likelihood of following, individual trajectories. Here, we develop a metric that quantifies the competition between successive alleles along adaptive trajectories and show how this competition largely governs the rate of evolution in simulations on empirical fitness landscapes for proteins involved in drug resistance in two species of malaria (Plasmodium falciparum and P. vivax). Our findings reveal that a trajectory with a larger-than-average initial fitness increase may have smaller fitness increases in later steps, which slows adaptation. In some circumstances, these trajectories may be outcompeted by alleles on faster alternative trajectories that are being explored simultaneously. The ability to predict adaptation rates along accessible trajectories has implications for efforts to manage antimicrobial resistance in real-world settings and for the broader intellectual pursuit of predictive evolution in complex adaptive fitness landscapes for a variety of application domains.

A Single Nucleotide Change in the Promoter mutp Enhances Fluoride Resistance of Streptococcus mutans

Previously, we identified a single nucleotide mutation in the promoter (mutp) of the fluoride antiporter-coding genes in a naturally fluoride-resistant Streptococcus mutans strain. Here, we studied the role of this mutation in a defined genetic background. The results confirmed that this mutation alone confers fluoride resistance on S. mutans, as shown by growth and lactic acid production assays. This resistance was explained by constitutively higher mutp promoter activity and upregulation of the fluoride antiporter-coding genes.

Phenotypic changes associated with the fitness cost in antibiotic resistant Escherichia coli strains

BACKGROUND

the acquisition of antibiotic resistance in bacterial cells is often accompanied with a reduction of fitness in the absence of antibiotics, known as the "fitness cost". The magnitude of this fitness cost is an important biological parameter that influences the degree to which antibiotic resistant strains become widespread. However, the relationship between the fitness cost and comprehensive phenotypic and genotypic changes remains unclear. Here, we quantified the fitness cost of resistant strains obtained by experimental evolution in the presence of various antibiotics, and analyzed how the cost correlated to phenotypic and genotypic changes in the resistant strains.

RESULTS

we measured the specific growth rate of the resistant strains in the presence of various concentrations of drugs or in their absence. In the absence of drugs, the resistant strains showed reductions of approximately 20% to 50% in growth rate compared with the parent strain, which corresponded to the fitness cost. We found that the decrease of the specific growth rate was correlated with overall expression changes between the parent and resistant strains, measured by the Euclid distance between expression profiles. We also found that there are a number of genes whose changes in expression levels were significantly correlated with the growth rate, which may account for the observed correlation between the fitness cost and overall expression changes.

CONCLUSIONS

our analysis provides a basis for quantitative understanding of the mechanism of the fitness cost. This understanding may provide clues on how to influence the fitness cost that accompanies resistance acquisition and consequently how to limit the spread of antibiotic resistant strains.

Population Density Modulates Drug Inhibition and Gives Rise to Potential Bistability of Treatment Outcomes for Bacterial Infections

The inoculum effect (IE) is an increase in the minimum inhibitory concentration (MIC) of an antibiotic as a function of the initial size of a microbial population. The IE has been observed in a wide range of bacteria, implying that antibiotic efficacy may depend on population density. Such density dependence could have dramatic effects on bacterial population dynamics and potential treatment strategies, but explicit measures of per capita growth as a function of density are generally not available. Instead, the IE measures MIC as a function of initial population size, and population density changes by many orders of magnitude on the timescale of the experiment. Therefore, the functional relationship between population density and antibiotic inhibition is generally not known, leaving many questions about the impact of the IE on different treatment strategies unanswered. To address these questions, here we directly measured real-time per capita growth of Enterococcus faecalis populations exposed to antibiotic at fixed population densities using multiplexed computer-automated culture devices. We show that density-dependent growth inhibition is pervasive for commonly used antibiotics, with some drugs showing increased inhibition and others decreased inhibition at high densities. For several drugs, the density dependence is mediated by changes in extracellular pH, a community-level phenomenon not previously linked with the IE. Using a simple mathematical model, we demonstrate how this density dependence can modulate population dynamics in constant drug environments. Then, we illustrate how time-dependent dosing strategies can mitigate the negative effects of density-dependence. Finally, we show that these density effects lead to bistable treatment outcomes for a wide range of antibiotic concentrations in a pharmacological model of antibiotic treatment. As a result, infections exceeding a critical density often survive otherwise effective treatments.

Using long-term experimental evolution to uncover the patterns and determinants of molecular evolution of an Escherichia coli natural isolate in the streptomycin-treated mouse gut

Although microbial ecology of the gut is now a major focus of interest, little is known about the molecular determinants of microbial adaptation in the gut. Experimental evolution coupled with whole-genome sequencing can provide insights of the adaptive process. In vitro experiments have revealed some conserved patterns: intermediate convergence, and epistatic interactions between beneficial mutations and mutations in global regulators. To test the relevance of these patterns and to identify the selective pressures acting in vivo, we have performed a long-term adaptation of an E. coli natural isolate, the streptomycin-resistant strain 536, in the digestive tract of streptomycin-treated mice. After a year of evolution, a clone from 15 replicates was sequenced. Consistently with in vitro observations, the identified mutations revealed a strong pattern of convergence at the mutation, gene, operon and functional levels. Yet, the rate of molecular evolution was lower than in in vitro, and no mutations in global regulators were recovered. More specific targets were observed: the dgo operon, involved in the galactonate pathway that improved growth on D-galactonate, and rluD and gidB, implicated in the maturation of the ribosomes, which mutations improved growth only in the presence of streptomycin. As in vitro, the nonrandom associations of mutations within the same pathways suggested a role of epistasis in shaping the adaptive landscape. Overall, we show that 'evolve and sequence' approach coupled with an analysis of convergence, when applied to a natural isolate, can be used to study adaptation in vivo and uncover the specific selective pressures of that environment.

The genomic basis of adaptation to the fitness cost of rifampicin resistance in Pseudomonas aeruginosa

Antibiotic resistance carries a fitness cost that must be overcome in order for resistance to persist over the long term. Compensatory mutations that recover the functional defects associated with resistance mutations have been argued to play a key role in overcoming the cost of resistance, but compensatory mutations are expected to be rare relative to generally beneficial mutations that increase fitness, irrespective of antibiotic resistance. Given this asymmetry, population genetics theory predicts that populations should adapt by compensatory mutations when the cost of resistance is large, whereas generally beneficial mutations should drive adaptation when the cost of resistance is small. We tested this prediction by determining the genomic mechanisms underpinning adaptation to antibiotic-free conditions in populations of the pathogenic bacterium Pseudomonas aeruginosa that carry costly antibiotic resistance mutations. Whole-genome sequencing revealed that populations founded by high-cost rifampicin-resistant mutants adapted via compensatory mutations in three genes of the RNA polymerase core enzyme, whereas populations founded by low-cost mutants adapted by generally beneficial mutations, predominantly in the quorum-sensing transcriptional regulator gene lasR. Even though the importance of compensatory evolution in maintaining resistance has been widely recognized, our study shows that the roles of general adaptation in maintaining resistance should not be underestimated and highlights the need to understand how selection at other sites in the genome influences the dynamics of resistance alleles in clinical settings.

The evolution of antimicrobial peptide resistance in Pseudomonas aeruginosa is shaped by strong epistatic interactions

Colistin is an antimicrobial peptide that has become the only remaining alternative for the treatment of multidrug-resistant Gram-negative bacterial infections, but little is known of how clinical levels of colistin resistance evolve. We use in vitro experimental evolution and whole-genome sequencing of colistin-resistant Pseudomonas aeruginosa isolates from cystic fibrosis patients to reconstruct the molecular evolutionary pathways open for high-level colistin resistance. We show that the evolution of resistance is a complex, multistep process that requires mutation in at least five independent loci that synergistically create the phenotype. Strong intergenic epistasis limits the number of possible evolutionary pathways to resistance. Mutations in transcriptional regulators are essential for resistance evolution and function as nodes that potentiate further evolution towards higher resistance by functionalizing and increasing the effect of the other mutations. These results add to our understanding of clinical antimicrobial peptide resistance and the prediction of resistance evolution.

Colistin is an antibiotic used in the treatment of Pseudomonas aeruginosa infections in cystic fibrosis patients. Here, Jochumsen et al. reconstruct the pathways for the molecular evolution of colistin resistance in P. aeruginosa and show that the number of pathways is highly constrained by interactions among genes.

Design and Use of a Low Cost, Automated Morbidostat for Adaptive Evolution of Bacteria Under Antibiotic Drug Selection

We describe a low cost, configurable morbidostat for characterizing the evolutionary pathway of antibiotic resistance. The morbidostat is a bacterial culture device that continuously monitors bacterial growth and dynamically adjusts the drug concentration to constantly challenge the bacteria as they evolve to acquire drug resistance. The device features a working volume of ~10 ml and is fully automated and equipped with optical density measurement and micro-pumps for medium and drug delivery. To validate the platform, we measured the stepwise acquisition of trimethoprim resistance in Escherichia coli MG 1655, and integrated the device with a multiplexed microfluidic platform to investigate cell morphology and antibiotic susceptibility. The approach can be up-scaled to laboratory studies of antibiotic drug resistance, and is extendible to adaptive evolution for strain improvements in metabolic engineering and other bacterial culture experiments.

Evolution of Drug Resistance in Bacteria

Resistance to antibiotics is an important and timely problem of contemporary medicine. Rapid evolution of resistant bacteria calls for new preventive measures to slow down this process, and a longer-term progress cannot be achieved without a good understanding of the mechanisms through which drug resistance is acquired and spreads in microbial populations. Here, we discuss recent experimental and theoretical advances in our knowledge how the dynamics of microbial populations affects the evolution of antibiotic resistance . We focus on the role of spatial and temporal drug gradients and show that in certain situations bacteria can evolve de novo resistance within hours. We identify factors that lead to such rapid onset of resistance and discuss their relevance for bacterial infections.

Quantitative high-throughput population dynamics in continuous-culture by automated microscopy

We present a high-throughput method to measure abundance dynamics in microbial communities sustained in continuous-culture. Our method uses custom epi-fluorescence microscopes to automatically image single cells drawn from a continuously-cultured population while precisely controlling culture conditions. For clonal populations of Escherichia coli our instrument reveals history-dependent resilience and growth rate dependent aggregation.

Development of a Comprehensive Genotype-to-Fitness Map of Adaptation-Driving Mutations in Yeast

Adaptive evolution plays a large role in generating the phenotypic diversity observed in nature, yet current methods are impractical for characterizing the molecular basis and fitness effects of large numbers of individual adaptive mutations. Here, we used a DNA barcoding approach to generate the genotype-to-fitness map for adaptation-driving mutations from a Saccharomyces cerevisiae population experimentally evolved by serial transfer under limiting glucose. We isolated and measured the fitness of thousands of independent adaptive clones and sequenced the genomes of hundreds of clones. We found only two major classes of adaptive mutations: self-diploidization and mutations in the nutrient-responsive Ras/PKA and TOR/Sch9 pathways. Our large sample size and precision of measurement allowed us to determine that there are significant differences in fitness between mutations in different genes, between different paralogs, and even between different classes of mutations within the same gene.

Antibiotic resistance: a physicist's view

The problem of antibiotic resistance poses challenges across many disciplines. One such challenge is to understand the fundamental science of how antibiotics work, and how resistance to them can emerge. This is an area where physicists can make important contributions. Here, we highlight cases where this is already happening, and suggest directions for further physics involvement in antimicrobial research.