Evolutionary paths to antibiotic resistance under dynamically sustained drug selection

Fitness and proteome changes accompanying the development of erythromycin resistance in a population of Escherichia coli grown in continuous culture

We studied the impact of a sublethal concentration of erythromycin on the fitness and proteome of a continuously cultivated population of Escherichia coli. The development of resistance to erythromycin in the population was followed over time by the gradient plate method and minimum inhibitory concentration (MIC) measurements. We measured the growth rate, standardized efficiency of synthesis of radiolabeled proteins, and translation accuracy of the system. The proteome changes were followed over time in two parallel experiments that differed in the presence or absence of erythromycin. A comparison of the proteomes at each time point (43, 68, and 103 h) revealed a group of unique proteins differing in expression. From all 35 proteins differing throughout the cultivation, only three were common to more than one time point. In the final population, a significant proportion of upregulated proteins was localized to the outer or inner cytoplasmic membranes or to the periplasmic space. In a population growing for more than 100 generations in the presence of antibiotic, erythromycin-resistant bacterial clones with improved fitness in comparison to early resistant culture predominated. This phenomenon was accompanied by distinct changes in protein expression during a stepwise, population-based development of erythromycin resistance.

Adaptation of Enterococcus faecalis to daptomycin reveals an ordered progression to resistance

With increasing numbers of hospital-acquired antibiotic resistant infections each year and staggering health care costs, there is a clear need for new antimicrobial agents, as well as novel strategies to extend their clinical efficacy. While genomic studies have provided a wealth of information about the alleles associated with adaptation to antibiotics, they do not provide essential information about the relative importance of genomic changes, their order of appearance, or potential epistatic relationships between adaptive changes. Here we used quantitative experimental evolution of a single polymorphic population in continuous culture with whole-genome sequencing and allelic frequency measurements to study daptomycin (DAP) resistance in the vancomycin-resistant clinical pathogen Enterococcus faecalis S613. Importantly, we sustained both planktonic and nonplanktonic (i.e., biofilm) populations in coculture as the concentration of antibiotic was raised, facilitating the development of more ecological complexity than is typically observed in laboratory evolution. Quantitative experimental evolution revealed a clear order and hierarchy of genetic changes leading to resistance, the signaling and metabolic pathways responsible, and the relative importance of these mutations to the evolution of DAP resistance. Despite the relative simplicity of this ex vivo approach compared to the ecological complexity of the human body, we showed that experimental evolution allows for rapid identification of clinically relevant adaptive molecular pathways and new targets for drug design in pathogens.

Bacterial cheating drives the population dynamics of cooperative antibiotic resistance plasmids

Inactivation of β-lactam antibiotics by resistant bacteria is a 'cooperative' behavior that may allow sensitive bacteria to survive antibiotic treatment. However, the factors that determine the fraction of resistant cells in the bacterial population remain unclear, indicating a fundamental gap in our understanding of how antibiotic resistance evolves. Here, we experimentally track the spread of a plasmid that encodes a β-lactamase enzyme through the bacterial population. We find that independent of the initial fraction of resistant cells, the population settles to an equilibrium fraction proportional to the antibiotic concentration divided by the cell density. A simple model explains this behavior, successfully predicting a data collapse over two orders of magnitude in antibiotic concentration. This model also successfully predicts that adding a commonly used β-lactamase inhibitor will lead to the spread of resistance, highlighting the need to incorporate social dynamics into the study of antibiotic resistance.

Antimicrobial resistance in commensal Escherichia coli isolated from animals at slaughter

Monitoring of antimicrobial resistance in commensal Escherichia coli (N = 3430) isolated from slaughtered broilers, laying hens, turkeys, swine, and cattle in Poland has been run between 2009 and 2012. Based on minimal inhibitory concentration (MIC) microbiological resistance to each of 14 tested antimicrobials was found reaching the highest values for tetracycline (43.3%), ampicillin (42.3%), and ciprofloxacin (39.0%) whereas the lowest for colistin (0.9%), cephalosporins (3.6 ÷ 3.8%), and florfenicol (3.8%). The highest prevalence of resistance was noted in broiler and turkey isolates, whereas it was rare in cattle. That finding along with resistance patterns specific to isolation source might reflect antimicrobial consumption, usage preferences or management practices in specific animals. Regression analysis has identified changes in prevalence of microbiological resistance and shifts of MIC values. Critically important fluoroquinolone resistance was worrisome in poultry isolates, but did not change over the study period. The difference (4.7%) between resistance to ciprofloxacin and nalidixic acid indicated the scale of plasmid-mediated quinolone resistance. Cephalosporin resistance were found in less than 3.8% of the isolates but an increasing trends were observed in poultry and MIC shift in the ones from cattle. Gentamycin resistance was also increasing in E. coli of turkey and cattle origin although prevalence of streptomycin resistance in laying hens decreased considerably. Simultaneously, decreasing MIC for phenicols observed in cattle and layers isolates as well as tetracycline values in E. coli from laying hens prove that antimicrobial resistance is multivariable phenomenon not only directly related to antimicrobial usage. Further studies should elucidate the scope of commensal E. coli as reservoirs of resistance genes, their spread and possible threats for human and animal health.

Functional profiling of the gut microbiome in disease-associated inflammation

The microbial residents of the human gut are a major factor in the development and lifelong maintenance of health. The gut microbiota differs to a large degree from person to person and has an important influence on health and disease due to its interaction with the human immune system. Its overall composition and microbial ecology have been implicated in many autoimmune diseases, and it represents a particularly important area for translational research as a new target for diagnostics and therapeutics in complex inflammatory conditions. Determining the biomolecular mechanisms by which altered microbial communities contribute to human disease will be an important outcome of current functional studies of the human microbiome. In this review, we discuss functional profiling of the human microbiome using metagenomic and metatranscriptomic approaches, focusing on the implications for inflammatory conditions such as inflammatory bowel disease and rheumatoid arthritis. Common themes in gut microbial ecology have emerged among these diverse diseases, but they have not yet been linked to targetable mechanisms such as microbial gene and genome composition, pathway and transcript activity, and metabolism. Combining these microbial activities with host gene, transcript and metabolic information will be necessary to understand how and why these complex interacting systems are altered in disease-associated inflammation.

Microbial evolution in vivo and in silico: methods and applications

Microbial evolution has been extensively studied in the past fifty years, which has lead to seminal discoveries that have shaped our understanding of evolutionary forces and dynamics. It is only recently however, that transformative technologies and computational advances have enabled a larger in-scale and in-depth investigation of the genetic basis and mechanistic underpinnings of evolutionary adaptation. In this review we focus on the strengths and limitations of in vivo and in silico techniques for studying microbial evolution in the laboratory, and we discuss how these complementary approaches can be integrated in a unifying framework for elucidating microbial evolution.

Insights into mechanisms and proteomic characterisation of Pseudomonas aeruginosa adaptation to a novel antimicrobial substance

Antibiotic resistance has been reported since the introduction of synthetic antibiotics. Bacteria, such as one of the most common nosocomial pathogens P. aeruginosa, adapt quickly to changing environmental conditions, due to their short generation time. Thus microevolutional changes can be monitored in situ. In this study, the microevolutional process of Pseudomonas aeruginosa PAO1 resistance against a recently developed novel antibacterial zinc Schiff-base (ZSB) was investigated at the proteome level. After extended exposure to ZSB the passaged strain differed in tolerance against ZSB, with the adapted P. aeruginosa PAO1 exhibiting 1.6 times higher minimal inhibitory concentration. Using Two-dimensional Difference Gel Electrophoresis, the changes in the proteome of ZSB adapted P. aeruginosa PAO1 were examined by comparison with the non-adapted P. aeruginosa PAO1. The proteome of the adapted P. aeruginosa PAO1 strain differed significantly from the non-adapted in the abundance of two proteins when both strains were grown under stressing conditions. One protein could be identified as the outer membrane protein D that plays a role in uptake of basic amino acids as well as in carbapeneme resistance. The second protein has been identified as alkyl peroxide reductase subunit F. Our data indicated a slight increase in abundance of alkyl peroxide reductase F (AhpF) in the case of ZSB passaged P. aeruginosa PAO1. Higher abundance of Ahp has been discussed in the literature as a promoter of accelerated detoxification of benzene derivatives. The observed up-regulated AhpF thus appears to be connected to an increased tolerance against ZSB. Changes in the abundance of proteins connected to oxidative stress were also found after short-time exposure of P. aeruginosa PAO1 to the ZSB. Furthermore, adapted P. aeruginosa PAO1 showed increased tolerance against hydrogen peroxide and, in addition, showed accelerated degradation of ZSB, as determined by HPLC measurements.

Back to the future: evolving bacteriophages to increase their effectiveness against the pathogen Pseudomonas aeruginosa PAO1

Antibiotic resistance is becoming increasingly problematic for the treatment of infectious disease in both humans and livestock. The bacterium Pseudomonas aeruginosa is often found to be resistant to multiple antibiotics and causes high patient mortality in hospitals. Bacteriophages represent a potential option to combat pathogenic bacteria through their application in phage therapy. Here, we capitalize on previous studies showing how evolution may increase phage infection capacity relative to ancestral genotypes. We passaged four different phage isolates (podoviridae, myoviridae) through six serial transfers on the ancestral strain of Pseudomonas aeruginosa PAO1. We first demonstrate that repeated serial passage on ancestral bacteria increases infection capacity of bacteriophage on ancestral hosts and on those evolved for one transfer. This result is confirmed when examining the ability of evolved phage to reduce ancestral host population sizes. Second, through interaction with a single bacteriophage for 24 h, P. aeruginosa can evolve resistance to the ancestor of that bacteriophage; this also provides these evolved bacteria with cross-resistance to the other three bacteriophages. We discuss how the evolutionary training of phages could be employed as effective means of combatting bacterial infections or disinfecting surfaces in hospital settings, with reduced risk of bacterial resistance compared with conventional methods.

Evolutionary systems biology: what it is and why it matters

Evolutionary systems biology (ESB) is a rapidly growing integrative approach that has the core aim of generating mechanistic and evolutionary understanding of genotype-phenotype relationships at multiple levels. ESB's more specific objectives include extending knowledge gained from model organisms to non-model organisms, predicting the effects of mutations, and defining the core network structures and dynamics that have evolved to cause particular intracellular and intercellular responses. By combining mathematical, molecular, and cellular approaches to evolution, ESB adds new insights and methods to the modern evolutionary synthesis, and offers ways in which to enhance its explanatory and predictive capacities. This combination of prediction and explanation marks ESB out as a research manifesto that goes further than its two contributing fields. Here, we summarize ESB via an analysis of characteristic research examples and exploratory questions, while also making a case for why these integrative efforts are worth pursuing.

Experimental interrogation of the path dependence and stochasticity of protein evolution using phage-assisted continuous evolution

To what extent are evolutionary outcomes determined by a population's recent environment, and to what extent do they depend on historical contingency and random chance? Here we apply a unique experimental system to investigate evolutionary reproducibility and path dependence at the protein level. We combined phage-assisted continuous evolution with high-throughput sequencing to analyze evolving protein populations as they adapted to divergent and then convergent selection pressures over hundreds of generations. Independent populations of T7 RNA polymerase genes were subjected to one of two selection histories ("pathways") demanding recognition of distinct intermediate promoters followed by a common final promoter. We observed distinct classes of solutions with unequal phenotypic activity and evolutionary potential evolve from the two pathways, as well as from replicate populations exposed to identical selection conditions. Mutational analysis revealed specific epistatic interactions that explained the observed path dependence and irreproducibility. Our results reveal in molecular detail how protein adaptation to different environments, as well as stochasticity among populations evolved in the same environment, can both generate evolutionary outcomes that preclude subsequent convergence.

The intrinsic resistome of bacterial pathogens

Intrinsically resistant bacteria have emerged as a relevant health problem in the last years. Those bacterial species, several of them with an environmental origin, present naturally low-level susceptibility to several drugs. It has been proposed that intrinsic resistance is mainly the consequence of the impermeability of cellular envelopes, the activity of multidrug efflux pumps or the lack of appropriate targets for a given family of drugs. However, recently published articles indicate that the characteristic phenotype of susceptibility to antibiotics of a given bacterial species depends on the concerted activity of several elements, what has been named as intrinsic resistome. These determinants comprise not just classical resistance genes. Other elements, several of them involved in basic bacterial metabolic processes, are of relevance for the intrinsic resistance of bacterial pathogens. In the present review we analyze recent publications on the intrinsic resistomes of Escherichia coli and Pseudomonas aeruginosa. We present as well information on the role that global regulators of bacterial metabolism, as Crc from P. aeruginosa, may have on modulating bacterial susceptibility to antibiotics. Finally, we discuss the possibility of searching inhibitors of the intrinsic resistome in the aim of improving the activity of drugs currently in use for clinical practice.

Evolution after introduction of a novel metabolic pathway consistently leads to restoration of wild-type physiology

Organisms cope with physiological stressors through acclimatizing mechanisms in the short-term and adaptive mechanisms over evolutionary timescales. During adaptation to an environmental or genetic perturbation, beneficial mutations can generate numerous physiological changes: some will be novel with respect to prior physiological states, while others might either restore acclimatizing responses to a wild-type state, reinforce them further, or leave them unchanged. We examined the interplay of acclimatizing and adaptive responses at the level of global gene expression in Methylobacterium extorquens AM1 engineered with a novel central metabolism. Replacing central metabolism with a distinct, foreign pathway resulted in much slower growth than wild-type. After 600 generations of adaptation, however, eight replicate populations founded from this engineered ancestor had improved up to 2.5-fold. A comparison of global gene expression in wild-type, engineered, and all eight evolved strains revealed that the vast majority of changes during physiological adaptation effectively restored acclimatizing processes to wild-type expression states. On average, 93% of expression perturbations from the engineered strain were restored, with 70% of these occurring in perfect parallel across all eight replicate populations. Novel changes were common but typically restricted to one or a few lineages, and reinforcing changes were quite rare. Despite this, cases in which expression was novel or reinforced in parallel were enriched for loci harboring beneficial mutations. One case of parallel, reinforced changes was the pntAB transhydrogenase that uses NADH to reduce NADP⁺ to NADPH. We show that PntAB activity was highly correlated with the restoration of NAD(H) and NADP(H) pools perturbed in the engineered strain to wild-type levels, and with improved growth. These results suggest that much of the evolved response to genetic perturbation was a consequence rather than a cause of adaptation and that physiology avoided “reinventing the wheel” by restoring acclimatizing processes to the pre-stressed state.

Acclimatizing and adaptive (evolutionary) processes allow organisms to thrive despite cellular and environmental perturbations. Our work examined whether adaptation restores stress responses towards wild-type (pre-stressed) versus novel physiological states during adaptation by studying a bacterium (Methylobacterium extorquens AM1) that was experimentally engineered and evolved with a novel central metabolism. The engineered strain was much slower and less fit than wild-type, but eight replicate populations evolved for six hundred generations showed substantial improvements. We found that changes in gene expression during adaptation consistently restored acclimatizing processes to the wild-type state, often in 8/8 evolved lines. Novel changes were common and largely restricted to one lineage; however, highly parallel novel changes revealed loci harboring beneficial mutations. Even rarer were reinforced changes, such as pntAB transhydrogenase, which increased beyond immediate acclimation during evolution to restore NAD(P)(H) metabolism and improve growth. Overall, a few novel or reinforcing changes drove the mass-restoration of physiology back to wild-type.

The Loci of repeated evolution: a catalog of genetic hotspots of phenotypic variation

What is the nature of the genetic changes underlying phenotypic evolution? We have catalogued 1008 alleles described in the literature that cause phenotypic differences among animals, plants, and yeasts. Surprisingly, evolution of similar traits in distinct lineages often involves mutations in the same gene ("gene reuse"). This compilation yields three important qualitative implications about repeated evolution. First, the apparent evolution of similar traits by gene reuse can be traced back to two alternatives, either several independent causative mutations or a single original mutational event followed by sorting processes. Second, hotspots of evolution-defined as the repeated occurrence of de novo mutations at orthologous loci and causing similar phenotypic variation-are omnipresent in the literature with more than 100 examples covering various levels of analysis, including numerous gain-of-function events. Finally, several alleles of large effect have been shown to result from the aggregation of multiple small-effect mutations at the same hotspot locus, thus reconciling micromutationist theories of adaptation with the empirical observation of large-effect variants. Although data heterogeneity and experimental biases prevented us from extracting quantitative trends, our synthesis highlights the existence of genetic paths of least resistance leading to viable evolutionary change.

Building a morbidostat: an automated continuous-culture device for studying bacterial drug resistance under dynamically sustained drug inhibition

We present a protocol for building and operating an automated fluidic system for continuous culture that we call the 'morbidostat'. The morbidostat is used to follow the evolution of microbial drug resistance in real time. Instead of exposing bacteria to predetermined drug environments, the morbidostat constantly measures the growth rates of evolving microbial populations and dynamically adjusts drug concentrations inside culture vials in order to maintain a constant drug-induced inhibition. The growth rate measurements are done using an optical detection system that is based on measuring the intensity of back-scattered light from bacterial cells suspended in the liquid culture. The morbidostat can additionally be used as a chemostat or a turbidostat. The whole system can be built from readily available components within 2-3 weeks by biologists with some electronics experience or engineers familiar with basic microbiology.

Understanding, predicting and manipulating the genotypic evolution of antibiotic resistance

The evolution of antibiotic resistance can now be rapidly tracked with high-throughput technologies for bacterial genotyping and phenotyping. Combined with new approaches to evolve resistance in the laboratory and to characterize clinically evolved resistant pathogens, these methods are revealing the molecular basis and rate of evolution of antibiotic resistance under treatment regimens of single drugs or drug combinations. In this Progress article, we review these new tools for studying the evolution of antibiotic resistance and discuss how the genomic and evolutionary insights they provide could transform the diagnosis, treatment and predictability of antibiotic resistance in bacterial infections.

The evolutionary history of amino acid variations mediating increased resistance of S. aureus identifies reversion mutations in metabolic regulators

The evolution of resistance in Staphylococcus aureus occurs rapidly, and in response to all known antimicrobial treatments. Numerous studies of model species describe compensatory roles of mutations in mediating competitive fitness, and there is growing evidence that these mutation types also drive adaptation of S. aureus strains. However, few studies have tracked amino acid changes during the complete evolutionary trajectory of antibiotic adaptation or been able to predict their functional relevance. Here, we have assessed the efficacy of computational methods to predict biological resistance of a collection of clinically known Resistance Associated Mutations (RAMs). We have found that >90% of known RAMs are incorrectly predicted to be functionally neutral by at least one of the prediction methods used. By tracing the evolutionary histories of all of the false negative RAMs, we have discovered that a significant number are reversion mutations to ancestral alleles also carried in the MSSA476 methicillin-sensitive isolate. These genetic reversions are most prevalent in strains following daptomycin treatment and show a tendency to accumulate in biological pathway reactions that are distinct from those accumulating non-reversion mutations. Our studies therefore show that in addition to non-reversion mutations, reversion mutations arise in isolates exposed to new antibiotic treatments. It is possible that acquisition of reversion mutations in the genome may prevent substantial fitness costs during the progression of resistance. Our findings pose an interesting question to be addressed by further clinical studies regarding whether or not these reversion mutations lead to a renewed vulnerability of a vancomycin or daptomycin resistant strain to antibiotics administered at an earlier stage of infection.

Phenotypic plasticity in evolutionary rescue experiments

Population persistence in a new and stressful environment can be influenced by the plastic phenotypic responses of individuals to this environment, and by the genetic evolution of plasticity itself. This process has recently been investigated theoretically, but testing the quantitative predictions in the wild is challenging because (i) there are usually not enough population replicates to deal with the stochasticity of the evolutionary process, (ii) environmental conditions are not controlled, and (iii) measuring selection and the inheritance of traits affecting fitness is difficult in natural populations. As an alternative, predictions from theory can be tested in the laboratory with controlled experiments. To illustrate the feasibility of this approach, we briefly review the literature on the experimental evolution of plasticity, and on evolutionary rescue in the laboratory, paying particular attention to differences and similarities between microbes and multicellular eukaryotes. We then highlight a set of questions that could be addressed using this framework, which would enable testing the robustness of theoretical predictions, and provide new insights into areas that have received little theoretical attention to date.

Residue mutations and their impact on protein structure and function: detecting beneficial and pathogenic changes

The present review focuses on the evolution of proteins and the impact of amino acid mutations on function from a structural perspective. Proteins evolve under the law of natural selection and undergo alternating periods of conservative evolution and of relatively rapid change. The likelihood of mutations being fixed in the genome depends on various factors, such as the fitness of the phenotype or the position of the residues in the three-dimensional structure. For example, co-evolution of residues located close together in three-dimensional space can occur to preserve global stability. Whereas point mutations can fine-tune the protein function, residue insertions and deletions (‘decorations’ at the structural level) can sometimes modify functional sites and protein interactions more dramatically. We discuss recent developments and tools to identify such episodic mutations, and examine their applications in medical research. Such tools have been tested on simulated data and applied to real data such as viruses or animal sequences. Traditionally, there has been little if any cross-talk between the fields of protein biophysics, protein structure–function and molecular evolution. However, the last several years have seen some exciting developments in combining these approaches to obtain an in-depth understanding of how proteins evolve. For example, a better understanding of how structural constraints affect protein evolution will greatly help us to optimize our models of sequence evolution. The present review explores this new synthesis of perspectives.

Genetic diversity in Mycobacterium tuberculosis

Recent years have witnessed an increased appreciation of the extent and relevance of strain-to-strain variation in Mycobacterium tuberculosis. This paradigm shift can largely be attributed to an improved understanding of the global population structure of this organism, and to the realisation that the various members of the M. tuberculosis complex (MTBC) harbour more genetic diversity than previously realised. Moreover, many studies using experimental models of infection have demonstrated that MTBC diversity translates into significant differences in immunogenecity and virulence . However, linking these experimental phenotypes to relevant clinical phenotypes has been difficult, and to date, largely unsuccessful. Nevertheless, emerging high-throughput technologies, in particular next-generation sequencing , offer new opportunities, and have already lead to important new insights. Given the complexity of the host-pathogen interaction in tuberculosis, systems approaches will be key to define the role of MTBC diversity in the fight against one of humankind's most important pathogens.

Predictability of evolution depends nonmonotonically on population size

To gauge the relative importance of contingency and determinism in evolution is a fundamental problem that continues to motivate much theoretical and empirical research. In recent evolution experiments with microbes, this question has been explored by monitoring the repeatability of adaptive changes in replicate populations. Here, we present the results of an extensive computational study of evolutionary predictability based on an experimentally measured eight-locus fitness landscape for the filamentous fungus Aspergillus niger. To quantify predictability, we define entropy measures on observed mutational trajectories and endpoints. In contrast to the common expectation of increasingly deterministic evolution in large populations, we find that these entropies display an initial decrease and a subsequent increase with population size N, governed, respectively, by the scales Nμ and Nμ(2), corresponding to the supply rates of single and double mutations, where μ denotes the mutation rate. The amplitude of this pattern is determined by μ. We show that these observations are generic by comparing our findings for the experimental fitness landscape to simulations on simple model landscapes.

The heterogeneous evolution of multidrug-resistant Mycobacterium tuberculosis

Recent surveillance data of multidrug-resistant tuberculosis (MDR-TB) reported the highest rates of resistance ever documented. As further amplification of resistance in MDR strains of Mycobacterium tuberculosis occurs, extensively drug-resistant (XDR) and totally drug-resistant (TDR) TB are beginning to emerge. Although for the most part, the epidemiological factors involved in the spread of MDR-TB are understood, insights into the bacterial drivers of MDR-TB have been gained only recently, largely owing to novel technologies and research in other organisms. Herein, we review recent findings on how bacterial factors, such as persistence, hypermutation, the complex interrelation between drug resistance and fitness, compensatory evolution, and epistasis affect the evolution of multidrug resistance in M. tuberculosis. Improved knowledge of these factors will help better predict the future trajectory of MDR-TB, and contribute to the development of new tools and strategies to combat this growing public health threat.

Putative compensatory mutations in the rpoC gene of rifampin-resistant Mycobacterium tuberculosis are associated with ongoing transmission

Rifampin resistance in clinical isolates of Mycobacterium tuberculosis arises primarily through the selection of bacterial variants harboring mutations in the 81-bp rifampin resistance-determining region of the rpoB gene. While these mutations were shown to infer a fitness cost in the absence of antibiotic pressure, compensatory mutations in rpoA and rpoC were identified which restore the fitness of rifampin-resistant bacteria carrying mutations in rpoB. To investigate the epidemiological relevance of these compensatory mutations, we analyzed 286 drug-resistant and 54 drug-susceptible clinical M. tuberculosis isolates from the Western Cape, South Africa, a high-incidence setting of multidrug-resistant tuberculosis. Sequencing of a portion of the RpoA-RpoC interaction region of the rpoC gene revealed that 23.5% of all rifampin-resistant isolates tested carried a nonsynonymous mutation in this region. These putative compensatory mutations in rpoC were associated with transmission, as 30.8% of all rifampin-resistant isolates with an IS6110 restriction fragment length polymorphism (RFLP) pattern belonging to a recognized RFLP cluster harbored putative rpoC mutations. Such mutations were present in only 9.4% of rifampin-resistant isolates with unique RFLP patterns (P < 0.01). Moreover, these putative compensatory mutations were associated with specific strain genotypes and the rpoB S531L rifampin resistance mutation. Among isolates harboring this rpoB mutation, 44.1% also harbored rpoC mutations, while only 4.1% of the isolates with other rpoB mutations exhibited mutations in rpoC (P < 0.001). Our study supports a role for rpoC mutations in the transmission of multidrug-resistant tuberculosis and illustrates how epistatic interactions between drug resistance-conferring mutations, compensatory mutations, and different strain genetic backgrounds might influence compensatory evolution in drug-resistant M. tuberculosis.

Chromosomal duplication is a transient evolutionary solution to stress

Aneuploidy, an abnormal number of chromosomes, is a widespread phenomenon found in unicellulars such as yeast, as well as in plants and in mammalians, especially in cancer. Aneuploidy is a genome-scale aberration that imposes a severe burden on the cell, yet under stressful conditions specific aneuploidies confer a selective advantage. This dual nature of aneuploidy raises the question of whether it can serve as a stable and sustainable evolutionary adaptation. To clarify this, we conducted a set of laboratory evolution experiments in yeast and followed the long-term dynamics of aneuploidy under diverse conditions. Here we show that chromosomal duplications are first acquired as a crude solution to stress, yet only as transient solutions that are eliminated and replaced by more efficient solutions obtained at the individual gene level. These transient dynamics of aneuploidy were repeatedly observed in our laboratory evolution experiments; chromosomal duplications gained under stress were eliminated not only when the stress was relieved, but even if it persisted. Furthermore, when stress was applied gradually rather than abruptly, alternative solutions appear to have emerged, but not aneuploidy. Our findings indicate that chromosomal duplication is a first evolutionary line of defense, that retains survivability under strong and abrupt selective pressures, yet it merely serves as a "quick fix," whereas more refined and sustainable solutions take over. Thus, in the perspective of genome evolution trajectory, aneuploidy is a useful yet short-lived intermediate that facilitates further adaptation.

Replaying the tape of life: quantification of the predictability of evolution

The question whether adaptation follows a deterministic route largely prescribed by the environment or can proceed along a large number of alternative trajectories has engaged extensive research over the recent years. Experimental evolution studies enabled by advances in high throughput techniques for genome sequencing and manipulation, along with increasingly detailed mathematical modeling of fitness landscapes, are beginning to allow quantitative exploration of the repeatability of evolutionary trajectories. It is becoming clear that evolutionary trajectories in static correlated fitness landscapes are substantially non-random but the relative contributions of determinism and stochasticity in the evolution of specific phenotypes strongly depend on the specific conditions, particularly the magnitude of the selective pressure and the number of available beneficial mutations.

Developing insights into the mechanisms of evolution of bacterial pathogens from whole-genome sequences

Evolution of bacterial pathogen populations has been detected in a variety of ways including phenotypic tests, such as metabolic activity, reaction to antisera and drug resistance and genotypic tests that measure variation in chromosome structure, repetitive loci and individual gene sequences. While informative, these methods only capture a small subset of the total variation and, therefore, have limited resolution. Advances in sequencing technologies have made it feasible to capture whole-genome sequence variation for each sample under study, providing the potential to detect all changes at all positions in the genome from single nucleotide changes to large-scale insertions and deletions. In this review, we focus on recent work that has applied this powerful new approach and summarize some of the advances that this has brought in our understanding of the details of how bacterial pathogens evolve.

The effect of bacterial recombination on adaptation on fitness landscapes with limited peak accessibility

There is ample empirical evidence revealing that fitness landscapes are often complex: the fitness effect of a newly arisen mutation can depend strongly on the allelic state at other loci. However, little is known about the effects of recombination on adaptation on such fitness landscapes. Here, we investigate how recombination influences the rate of adaptation on a special type of complex fitness landscapes. On these landscapes, the mutational trajectories from the least to the most fit genotype are interrupted by genotypes with low relative fitness. We study the dynamics of adapting populations on landscapes with different compositions and numbers of low fitness genotypes, with and without recombination. Our results of the deterministic model (assuming an infinite population size) show that recombination generally decelerates adaptation on these landscapes. However, in finite populations, this deceleration is outweighed by the accelerating Fisher-Muller effect under certain conditions. We conclude that recombination has complex effects on adaptation that are highly dependent on the particular fitness landscape, population size and recombination rate.

The emergence and persistence of recombination is a long-standing open question in evolutionary biology. Most previous theoretical studies assumed relatively simple fitness landscapes, i.e., simple relationships between allelic states at different loci and fitness. By contrast, empirically determined bacterial and viral fitness landscapes reveal pervasive complex interactions between alleles at different loci. In this study, we explore the effect of recombination on adaptation on fitness landscapes where some trajectories leading to a global fitness peak are interrupted by genotypes of very low fitness. We find that in infinitely large populations, recombination generally reduces the rate of adaptation. However, in finite populations and under certain conditions, recombination can substantially speed up adaptation. Our study provides insights into the effect of recombination on more realistic fitness landscapes. Moreover, it helps gain a better understanding of the dynamics of the spread of adaptive genes in recombining bacterial populations during niche expansion and colonization of new habitats.

Systems mapping of HIV-1 infection

Mathematical models of viral dynamics in vivo provide incredible insights into the mechanisms for the nonlinear interaction between virus and host cell populations, the dynamics of viral drug resistance, and the way to eliminate virus infection from individual patients by drug treatment. The integration of these mathematical models with high-throughput genetic and genomic data within a statistical framework will raise a hope for effective treatment of infections with HIV virus through developing potent antiviral drugs based on individual patients’ genetic makeup. In this opinion article, we will show a conceptual model for mapping and dictating a comprehensive picture of genetic control mechanisms for viral dynamics through incorporating a group of differential equations that quantify the emergent properties of a system.

A mutation of the RNA polymerase β' subunit (rpoC) confers cephalosporin resistance in Bacillus subtilis

In bacteria, mutations affecting the major catalytic subunits of RNA polymerase (encoded by rpoB and rpoC) emerge in response to a variety of selective pressures. Here we isolated a Bacillus subtilis strain with high-level resistance to cefuroxime (CEF). Whole-genome resequencing revealed only one missense mutation affecting an invariant residue in close proximity to the C-terminal DNA-binding domain of RpoC (G1122D). Genetic reconstruction experiments demonstrate that this substitution is sufficient to confer CEF resistance. The G1122D mutation leads to elevated expression of stress-responsive regulons, including those of extracytoplasmic function (ECF) σ factors (σ(M), σ(W), and σ(X)) and the general stress σ factor (σ(B)). The increased CEF resistance of the rpoC(G1122D) strain is lost in the sigM rpoC(G1122D) double mutant, consistent with a major role for σ(M) in CEF resistance. However, a sigM mutant is very sensitive to CEF, and this sensitivity is still reduced by the G1122D mutation, suggesting that other regulatory effects are also important. Indeed, the ability of the G1122D mutation to increase CEF resistance is further reduced in a triple mutant strain lacking three ECF σ factors (σ(M), σ(W), and σ(X)), which are known from prior studies to control overlapping sets of genes. Collectively, our findings highlight the ability of mutations in RNA polymerase to confer antibiotic resistance by affecting the activity of alternative σ factors that control cell envelope stress-responsive regulons.

LoFreq: a sequence-quality aware, ultra-sensitive variant caller for uncovering cell-population heterogeneity from high-throughput sequencing datasets

The study of cell-population heterogeneity in a range of biological systems, from viruses to bacterial isolates to tumor samples, has been transformed by recent advances in sequencing throughput. While the high-coverage afforded can be used, in principle, to identify very rare variants in a population, existing ad hoc approaches frequently fail to distinguish true variants from sequencing errors. We report a method (LoFreq) that models sequencing run-specific error rates to accurately call variants occurring in <0.05% of a population. Using simulated and real datasets (viral, bacterial and human), we show that LoFreq has near-perfect specificity, with significantly improved sensitivity compared with existing methods and can efficiently analyze deep Illumina sequencing datasets without resorting to approximations or heuristics. We also present experimental validation for LoFreq on two different platforms (Fluidigm and Sequenom) and its application to call rare somatic variants from exome sequencing datasets for gastric cancer. Source code and executables for LoFreq are freely available at http://sourceforge.net/projects/lofreq/.

Mutational pathway determines whether drug gradients accelerate evolution of drug-resistant cells

Drug gradients are believed to play an important role in the evolution of bacteria resistant to antibiotics and tumors resistant to anticancer drugs. We use a statistical physics model to study the evolution of a population of malignant cells exposed to drug gradients, where drug resistance emerges via a mutational pathway involving multiple mutations. We show that a nonuniform drug distribution has the potential to accelerate the emergence of resistance when the mutational pathway involves a long sequence of mutants with increasing resistance, but if the pathway is short or crosses a fitness valley, the evolution of resistance may actually be slowed down by drug gradients. These predictions can be verified experimentally, and may help to improve strategies for combating the emergence of resistance.

Oxidative DNA damage is important to the evolution of antibiotic resistance: evidence of mutation bias and its medicinal implications

Several (1) studies have revealed that the reactive oxygen species (ROS) induced by antibacterial stimulation accelerates the evolution of antibiotic resistance, which uncovered new links between oxygen rise and evolution and inspired new strategies to prevent antibiotic resistance. Considering many other mechanisms cause DNA mutations aside from ROS damage, evaluating the significance of oxidative DNA damage in the development of antibiotic resistance is of great interest. In this study, we examined the ratio of G:C > T:A transversion to G:C > A:T transition in drug-resistant Escherichia coli and Mycobacterium tuberculosis and found that it is significantly higher than the background values. This finding strongly suggests that ROS damage plays a critical role in the development of antibacterial resistance. Considering the long-term co-evolution between host organisms and pathogenic bacteria, we speculate that the hosts may have evolved strategies for combating antibiotic resistance by controlling DNA damage in bacteria. Analysis of the global transcriptional profiles of Staphylococcus aureus treated with berberine (derived from Berberis, a traditional antibacterial medicine) revealed that the transcription of DNA repair enzymes was markedly upregulated, whereas the antioxidant enzymes were significantly downregulated. Thus, we propose that consolidating the DNA repair systems of bacteria may be a viable strategy for preventing antibiotic resistance. (1)These authors contributed equally to this work.

Harnessing recombination to speed adaptive evolution in Escherichia coli

Evolutionary engineering typically involves asexual propagation of a strain to improve a desired phenotype. However, asexual populations suffer from extensive clonal interference, a phenomenon where distinct lineages of beneficial clones compete and are often lost from the population given sufficient time. Improved adaptive mutants can likely be generated by genetic exchange between lineages, thereby reducing clonal interference. We present a system that allows continuous in situ recombination by using an Esherichia coli F-based conjugation system lacking surface exclusion. Evolution experiments revealed that Hfr-mediated recombination significantly speeds adaptation in certain circumstances. These results show that our system is stable, effective, and suitable for use in evolutionary engineering applications.

Quantifying the adaptive potential of an antibiotic resistance enzyme

For a quantitative understanding of the process of adaptation, we need to understand its "raw material," that is, the frequency and fitness effects of beneficial mutations. At present, most empirical evidence suggests an exponential distribution of fitness effects of beneficial mutations, as predicted for Gumbel-domain distributions by extreme value theory. Here, we study the distribution of mutation effects on cefotaxime (Ctx) resistance and fitness of 48 unique beneficial mutations in the bacterial enzyme TEM-1 β-lactamase, which were obtained by screening the products of random mutagenesis for increased Ctx resistance. Our contributions are threefold. First, based on the frequency of unique mutations among more than 300 sequenced isolates and correcting for mutation bias, we conservatively estimate that the total number of first-step mutations that increase Ctx resistance in this enzyme is 87 [95% CI 75-189], or 3.4% of all 2,583 possible base-pair substitutions. Of the 48 mutations, 10 are synonymous and the majority of the 38 non-synonymous mutations occur in the pocket surrounding the catalytic site. Second, we estimate the effects of the mutations on Ctx resistance by determining survival at various Ctx concentrations, and we derive their fitness effects by modeling reproduction and survival as a branching process. Third, we find that the distribution of both measures follows a Fréchet-type distribution characterized by a broad tail of a few exceptionally fit mutants. Such distributions have fundamental evolutionary implications, including an increased predictability of evolution, and may provide a partial explanation for recent observations of striking parallel evolution of antibiotic resistance.

Parallel genetic changes and nonparallel gene-environment interactions characterize the evolution of drug resistance in yeast

Beneficial mutations are required for adaptation to novel environments, yet the range of mutational pathways that are available to a population has been poorly characterized, particularly in eukaryotes. We assessed the genetic changes of the first mutations acquired during adaptation to a novel environment (exposure to the fungicide, nystatin) in 35 haploid lines of Saccharomyces cerevisiae. Through whole-genome resequencing we found that the genomic scope for adaptation was narrow; all adapted lines acquired a mutation in one of four late-acting genes in the ergosterol biosynthesis pathway, with very few other mutations found. Lines that acquired different ergosterol mutations in the same gene exhibited very similar tolerance to nystatin. All lines were found to have a cost relative to wild type in an unstressful environment; the level of this cost was also strongly correlated with the ergosterol gene bearing the mutation. Interestingly, we uncovered both positive and negative effects on tolerance to other harsh environments for mutations in the different ergosterol genes, indicating that these beneficial mutations have effects that differ in sign among environmental challenges. These results demonstrate that although the genomic target was narrow, different adaptive mutations can lead populations down different evolutionary pathways, with respect to their ability to tolerate (or succumb to) other environmental challenges.

Synthetic approaches to understanding biological constraints

Microbes can be readily cultured and their genomes can be easily manipulated. For these reasons, laboratory systems of unicellular organisms are increasingly used to develop and test theories about biological constraints, which manifest themselves at different levels of biological organization, from optimal gene-expression levels to complex individual and social behaviors. The quantitative description of biological constraints has recently advanced in several areas, such as the formulation of global laws governing the entire economy of a cell, the direct experimental measurement of the trade-offs leading to optimal gene expression, the description of naturally occurring fitness landscapes, and the appreciation of the requirements for a stable bacterial ecosystem.

Visualizing evolution in real-time method for strain engineering

The adaptive landscape for an industrially relevant phenotype is determined by the effects of the genetic determinants on the fitness of the microbial system. Identifying the underlying adaptive landscape for a particular phenotype of interest will greatly enhance our abilities to engineer more robust microbial strains. Visualizing evolution in real-time (VERT) is a recently developed method based on in vitro adaptive evolution that facilitates the identification of fitter mutants throughout the course of evolution. Combined with high-throughput genomic tools, VERT can greatly enhance the mapping of adaptive landscapes of industrially relevant phenotypes in microbial systems, thereby expanding our knowledge on the parameters that can be used for strain engineering.

Assessing the probability of detection of horizontal gene transfer events in bacterial populations

Experimental approaches to identify horizontal gene transfer (HGT) events of non-mobile DNA in bacteria have typically relied on detection of the initial transformants or their immediate offspring. However, rare HGT events occurring in large and structured populations are unlikely to be detected in a short time frame. Population genetic modeling of the growth dynamics of bacterial genotypes is therefore necessary to account for natural selection and genetic drift during the time lag and to predict realistic time frames for detection with a given sampling design. Here we draw on statistical approaches to population genetic theory to construct a cohesive probabilistic framework for investigation of HGT of exogenous DNA into bacteria. In particular, the stochastic timing of rare HGT events is accounted for. Integrating over all possible event timings, we provide an equation for the probability of detection, given that HGT actually occurred. Furthermore, we identify the key variables determining the probability of detecting HGT events in four different case scenarios that are representative of bacterial populations in various environments. Our theoretical analysis provides insight into the temporal aspects of dissemination of genetic material, such as antibiotic resistance genes or transgenes present in genetically modified organisms. Due to the long time scales involved and the exponential growth of bacteria with differing fitness, quantitative analyses incorporating bacterial generation time, and levels of selection, such as the one presented here, will be a necessary component of any future experimental design and analysis of HGT as it occurs in natural settings.

Third-generation sequencing techniques and applications to drug discovery

INTRODUCTION

There is an immediate need for functional and molecular studies to decipher differences between disease and 'normal' settings to identify large quantities of validated targets with the highest therapeutic utilities. Furthermore, drug mechanism of action and biomarkers to predict drug efficacy and safety need to be identified for effective design of clinical trials, decreasing attrition rates, regulatory agency approval process and drug repositioning. By expanding the power of genetics and pharmacogenetics studies, next-generation nucleic acid sequencing technologies have started to play an important role in all stages of drug discovery.

AREAS COVERED

This article reviews the first- and second-generation sequencing technologies (SGSTs) and challenges they pose to biomedicine. The article then focuses on the emerging third-generation sequencing technologies (TGSTs), their technological foundations and potential contributions to drug discovery.

EXPERT OPINION

Despite the scientific and commercial success of SGSTs, the goal of rapid, comprehensive and unbiased sequencing of nucleic acids has not been achieved. TGSTs promise to increase sequencing throughput and read lengths, decrease costs, run times and error rates, eliminate biases inherent in SGSTs and offer capabilities beyond nucleic acid sequencing. Such changes will have positive impact on all sequencing applications to drug discovery.

Whack-an-E. coli with the morbidostat

Using a device termed the 'morbidostat', a recent study sheds new light on the determinism of genetic and phenotypic trajectories leading to high antibiotic resistance.

Following evolution of bacterial antibiotic resistance in real time

A new study reports the development of the 'morbidostat', a device that allows for continuous culture of bacteria under a constant drug selection pressure using computer feedback control of antibiotic concentration. This device, together with bacterial whole-genome sequencing, allowed the authors to follow the evolution of resistance-conferring mutations in Escherichia coli populations in real time, providing support for deterministic evolution of resistance in some situations.