Biology

© 2013 Macmillan Publishers Limited. All rights reserved

genome because different subsets of genes may have undergone adaptive, neutral or purifying selection. Furthermore, such tests cannot determine whether an individual mutation is adaptive or neutral, and they neglect the possible role of adaptive non-coding or regulatory mutations.

Fortunately, with increasing capacity to sequence many evolved strains, this challenge can be overcome by looking for parallel evolution. Parallel evolution provides a tool to distinguish adaptive mutations from neutral or deleterious mutations, as non-advantageous mutations should not independently arise and fix at the same loci as frequently as adaptive mutations. Additionally, the iden- tification of adaptive mutations by parallel evolution is not biased against synonymous or regulatory mutations, even though the set of adaptive mutations will probably be enriched for nonsynonymous substitutions. In a recent study of a bacterial epidemic in which parallel evolution occurred within multiple patients, the bacterial genome as a whole showed no statistical sign of adaptive evolution (the ratio of nonsynonymous to synonymous substitutions, dN/dS, was as expected under drift), but examining dN/dS identified adaptive evolution against a background signal of purifying selection when genes were classified by whether they mutated only once or whether repeatedly across the cohort. Genes that mutated only once showed signs of purifying selection (that is, unexpectedly few nonsynonymous substitutions), and genes that repeatedly mutated showed a strong signature of adap- tive evolution (that is, an unexpectedly high rate of nonsynonymous substitutions)18. An important caveat applies to the identification of parallel evolution in clinical isolates: the repeated observation of a mutation could be a result of shared ancestry and does not necessarily imply that the same mutation arose repeatedly; a phylogenetic tree must be constructed to estimate the number of inde- pendent mutational events at each locus in the strains’ histories (FIG. 3).

Phylogenetic trees describe the evolu- tionary history of related strains, providing crucial contributions to understanding mutation, selection and transmission in the evolution of antibiotic resistance (see REF. 22 for a tutorial). A phylogenetic tree provides a view of transmission events across as large or as fine a scale as is represented by clinical isolates, from intercontinental transmission to transfer between the organs of a single patient. Specific genetic changes throughout the evolutionary history of bacterial strains can be correlated with the appearance of

novel phenotypes, including antibiotic resist- ance, changes in pathogenicity or fitness or propensity for transmission. Whereas recombination among related bacterial strains can complicate the construction of a phylogenetic tree, maximum likelihood and Bayesian approaches can identify clusters of mutations that are more likely to be shared by recombination than point mutation23. Phylogenetic reconstruction can then be carried out only on vertically transmitted point mutations, as demonstrated in a recent study of worldwide isolates of the highly recombinogenic Streptococcus pneumoniae21.

Measuring the phenotypic effects of muta- tions. Even when adaptive mutations are identified, it is not necessarily straightfor- ward to determine their specific phenotypic effects: that is, whether they increase antibiotic resistance, compensate for the fitness costs of antibiotic resistance or confer adaptation to a host environment. Thus, the lessons of high-throughput geno- typing are limited unless combined with high-throughput phenotyping.

Fitness costs are a common feature of mutations that confer antibiotic resistance, which epidemiological models predict to substantially affect the spread of drug- resistant pathogens24,25. The relative fitness of evolved versus ancestral strains can be measured by competition experiments in drug-free or antibiotic-containing environ- ments. Throughput and precision were previously limited by the labour of count- ing colonies, but these experiments can now be automated using fluorescent labels for counting by flow cytometry or DNA barcodes for counting by next-generation sequencing14,15,26. Similar methods can also be applied to genetically intractable clinical isolates by deep-sequencing of mutated loci to measure allele frequencies27. Improvements in the precision of fitness measurements will probably be of benefit to epidemiological modelling25. Another high-throughput phenotyping tool, which is applicable to model organisms and clinical isolates alike, is automated imaging arrays built from flatbed scanners. These cheap custom systems acquire time-lapse videos of colony growth on large numbers of agar plates that can be arranged to span ranges of antibi- otic concentration or multiple antibiotics12,28. Studies using genetic complementation have also benefited from technological progress: the relative contributions to fitness of each mutated locus in an evolved strain can be simultaneously determined by a competition experiment between a mixture of strains, each

transduced with a different fragment of the evolved genome29. Repeating such an experi- ment in the presence and absence of antibi- otic could reveal both the degree of antibiotic resistance conferred by each mutation and the fitness cost during drug-free growth.

Figure 2 | Selection of antibiotic-resistant bacteria from clinical isolates. The evolution and transmission of antibiotic-resistant bacteria can be studied over scales ranging from conti- nents to organs by different approaches from clinical sampling. Worldwide sampling of iso- lates reveals intercontinental transmission, sam- pling within a localized epidemic reveals patient-to-patient transmission networks, and sampling within a single patient can reveal transfer between sites of the body and possibly organ-specific evolution.

Nature Reviews | Genetics

PROGRESS

NATURE REVIEWS | GENETICS VOLUME 14 | APRIL 2013 | 245

© 2013 Macmillan Publishers Limited. All rights reserved

Evolutionary potential and constraints The approaches described above are based on natural selection methodologies to iden- tify adaptive mutations that spontaneously appear under drug treatments. Such evolution-based approaches are powerful for determining the rate of adaptation and for revealing its most likely genotypic paths, but they do not explicitly elucidate unlikely or ‘forbidden’ steps that can have the effect of directing evolution repeatedly along the few permitted paths. To systematically explore the effects of defined genetic changes or combinations thereof, whether advantageous or deleterious, a reverse-genetics approach can be used. Here we review recent crea- tive uses of reverse genetics to explore how systematic genetic perturbations, mutation combinations and horizontal gene transfer can enhance or constrain evolutionary potential.

Systematic genetic perturbations. The genetic determinants of antibiotic resistance can be explored with pre-constructed libraries of mutant strains. For example, known resist- ance genes can be mutagenized to explore their adaptive potential and to measure the distribution of mutational effects. Applying this method to the most common β-lactamase gene in Gram-negative bacteria, TEM-1, has identified a long-tailed distribution with a few highly beneficial mutations30, potentially explaining the high degree of reproducibility often observed in the evolution of antibiotic resistance7,9,31. A genome-wide view can be taken with gene deletion libraries and open reading frame expression libraries; although these were first constructed only for model organisms, advances in transposon mutagenesis are making possible the rapid construction of comparable libraries for clinically rel- evant pathogens32. These libraries can be screened in pools by using next-generation

sequencing to count the abundance of each strain in a mixture following drug selec- tion32. Screening mutant strain libraries under antibiotic treatment identifies genes for which deletion or overexpression alters drug susceptibility, revealing the genetic basis of intrinsic antibiotic susceptibility or identifying paths to stronger antibiotic resistance33. Future studies could also use these mutant strain libraries as starting material for pooled evolution experiments, thereby identifying not only the immediate effects of the genetic perturbations but also their effect on the potential to evolve yet higher levels of resistance.

Combinatorial genetic libraries. The effects of a mutation depend on the genetic back- ground on which it arises. Genetic interac- tions between alleles impose constraints on the evolutionary pathways to antibiotic resistance, as a mutation may be beneficial only in the presence or absence of certain other mutations. The synthetic construc- tion of different combinations of mutations that have previously been identified from the clinic or experimental evolution can reveal genetic constraints that would not be observed from studying only those muta- tion combinations favoured in nature. This method has been applied to genes found in resistance cassettes as well as drug target genes, both being cases in which resistance can be increased by repeated mutation of the same gene. These studies have consist- ently observed strong constraints that can be responsible for the repeatability, and hence predictability, of evolutionary pathways34. Genetic interactions have been observed to limit the possible pathways to a few select sequences of mutations35,36 (FIG. 4) and to limit to the reversibility of evolution when switching between different drugs37. This

approach has shown that, in certain combi- nations, resistance mutations can also act as compensatory mutations that alleviate one another’s fitness costs, producing strongly drug-resistant or multidrug-resistant strains without substantial fitness costs38–40. Evolutionary experiments can also be car- ried out starting from different pre-built genotypes to investigate genetic influences on the reproducibility of evolution: one such study has revealed that different initial muta- tions in the TEM-1 β-lactamase gene can define the subsequent evolutionary path- ways31 (FIG. 4). These approaches could iden- tify those genotypes with a greater or lesser potential to evolve resistance to particular drugs, which could be valuable in selecting genotype-specific treatments that avoid the most harmful evolutionary outcomes.

Horizontal transfer of environmental genes. The acquisition of resistance by horizontal gene transfer (HGT) provides evolutionary potential that cannot be predicted from the original (pre-transfer) genome of an organ- ism. Instead, the potential for resistance by HGT can be investigated by sampling the extensive and ancient ability of genes in environmental or commensal microbes to resist a drug41–43. This approach has been implemented by extracting and cloning microbial DNA from soil samples or from human gut samples into a laboratory strain and plating on inhibitory concentrations of a range of antibiotics to identify novel micro- bial drug resistance genes that might in the future transfer into pathogens42,44. Although this approach is limited to identifying genes that can be successfully expressed in the laboratory strain, a more recent study demonstrated an expression-independent approach that directly assessed the capacity of environmental microbes to degrade the

Figure 3 | Phylogenetic inference identifies parallel evolution. a | A collection of related isolates will possess many shared mutations relative to a more distant strain (an outgroup), but this does not neces- sarily imply that any of these mutations repeatedly occurred. b | Phylogenetic inference estimates the likely evolutionary history that connects the isolates and identifies when each mutation occurred. Note that many other mutations would need to have occurred for

accurate phylogenetic inference; in this example, only three mutations are shown to illustrate the principle. c | From the phylogenetic tree, the number of times that a gene independently mutated in separate line- ages can be counted to distinguish mutations that are shared merely by common ancestry (red and blue) from mutations that are shared by par- allel evolution (green), strongly indicating adaptive evolution. SNP, single-nucleotide polymorphism.

Nature Reviews | Genetics

Ancestor of isolates

SN P

1

SN P

2

SN P

3

SNP 1

SNP 2

SNP 3

Isolate X

Isolate Y

Isolate Z

Outgroup

Outgroup

Isolate Y

Isolate X

Isolate Z

a b Mutation occurrencesc

PROGRESS

246 | APRIL 2013 | VOLUME 14 www.nature.com/reviews/genetics

© 2013 Macmillan Publishers Limited. All rights reserved

new and rarely clinically resisted antibiotic daptomycin45. A collection of environmental actinomycetes was screened for daptomycin resistance, and the supernatants of resistant cultures were analysed by mass spectrometry to view the structures of daptomycin and its inactivation products. By precisely viewing the drug degradation products, the molecu- lar mechanisms of resistance by degradation were inferred. This level of understanding has the potential to suggest structural vari- ants of drugs that could resist environmental mechanisms of degradation.

Order now and get 10% discount on all orders above $50 now!!The professional are ready and willing handle your assignment.

ORDER NOW »»