The evolution and spread of antibiotic resistance in bacterial pathogens is a grow- ing threat to public health. The frequency of antibiotic resistance in many bacterial pathogens is increasing around the world, and the resulting failures of antibiotic ther- apy cause hundreds of thousands of deaths annually1. The hope of addressing this crisis by developing new antibiotics is diminished both by the low rate of novel antibiotic dis- covery and by the likelihood that pathogens will evolve resistance to novel antibiotics just as they have to existing antibiotics. The long- term threat, therefore, is just as much the process of evolution as the microbial patho- gens themselves. Although the use of anti- biotics inevitably promotes resistance, the rate of evolution depends on the genomic background and treatment strategies. Thus, understanding the genomics and evolution- ary biology of antibiotic resistance could inform therapeutic strategies that are both effective and mitigate the future potential to evolve resistance. Antibiotic resistance can be acquired either by mutation or by the horizontal transfer of resistance-conferring genes, often in mobile genetic cassettes. The rela- tive contribution of these factors depends on the class of antibiotic and on the genetic plasticity of the bacterial species. For exam- ple, Mycobacterium tuberculosis primarily acquires antibiotic resistance through nucleo- tide changes, whereas hospital-acquired Enterobacteriaceae infections often pos- sess multidrug resistance cassettes and may also acquire nucleotide changes that confer resistance to drugs that are not often resisted by mobile elements, such as quinolones2. Progress in DNA sequencing and other genotyping technologies means that the genotypes of pathogens will soon be widely available in clinical as well as research settings. Genotype-based antibiotic resist- ance profiling is already faster and more economical than phenotypic profiling in select cases (for example, rifampicin resist- ance in M. tuberculosis caused by nucleotide substitutions, and methicillin resistance in Staphylococcus aureus caused by a resistance cassette), and over time therapeutic and infection control strategies will more heavily rely on information derived from genome sequencing of the infecting agents3. Importantly, genotypes can inform not only on the current drug susceptibility of a pathogen but also on its future potential to evolve resistance and spread. For example, sequencing could determine whether a drug- susceptible strain carries precursors to resist- ance genes (which are termed proto-resistance genes4), such as drug-degrading enzymes or efflux pumps, that might be mutated to increase expression or to strengthen activity. Sequencing could also determine whether resistance cassettes may be only one mutation away from increased potency or from the capacity to resist other drugs related to the originally resi ...

1. The evolution and spread of antibiotic
resistance in bacterial pathogens is a grow-
ing threat to public health. The frequency
of antibiotic resistance in many bacterial
pathogens is increasing around the world,
and the resulting failures of antibiotic ther-
apy cause hundreds of thousands of deaths
annually1. The hope of addressing this crisis
by developing new antibiotics is diminished
both by the low rate of novel antibiotic dis-
covery and by the likelihood that pathogens
will evolve resistance to novel antibiotics just
as they have to existing antibiotics. The long-
term threat, therefore, is just as much the
process of evolution as the microbial patho-
gens themselves. Although the use of anti-
biotics inevitably promotes resistance, the
rate of evolution depends on the genomic
background and treatment strategies. Thus,
understanding the genomics and evolution-
ary biology of antibiotic resistance could
inform therapeutic strategies that are both
effective and mitigate the future potential to
evolve resistance.
Antibiotic resistance can be acquired
either by mutation or by the horizontal
transfer of resistance-conferring genes,
often in mobile genetic cassettes. The rela-
tive contribution of these factors depends
on the class of antibiotic and on the genetic

2. plasticity of the bacterial species. For exam-
ple, Mycobacterium tuberculosis primarily
acquires antibiotic resistance through nucleo-
tide changes, whereas hospital-acquired
Enterobacteriaceae infections often pos-
sess multidrug resistance cassettes and may
also acquire nucleotide changes that confer
resistance to drugs that are not often resisted
by mobile elements, such as quinolones2.
Progress in DNA sequencing and
other genotyping technologies means that
the genotypes of pathogens will soon be
widely available in clinical as well as research
settings. Genotype-based antibiotic resist-
ance profiling is already faster and more
economical than phenotypic profiling in
select cases (for example, rifampicin resist-
ance in M. tuberculosis caused by nucleotide
substitutions, and methicillin resistance in
Staphylococcus aureus caused by a resistance
cassette), and over time therapeutic and
infection control strategies will more heavily
rely on information derived from genome
sequencing of the infecting agents3.
Importantly, genotypes can inform not
only on the current drug susceptibility of a
pathogen but also on its future potential to
evolve resistance and spread. For example,
sequencing could determine whether a drug-
susceptible strain carries precursors to resist-
ance genes (which are termed proto-resistance
genes4), such as drug-degrading enzymes
or efflux pumps, that might be mutated to

3. increase expression or to strengthen activity.
Sequencing could also determine whether
resistance cassettes may be only one mutation
away from increased potency or from the
capacity to resist other drugs related to
the originally resisted drug4,5. Even a mod-
est predictive power might improve thera-
peutic outcomes by informing the selection
of drugs, the preference between monotherapy
or combination therapy and the temporal
dosing regimen to select genotype-based
treatments that are most resilient to evolu-
tion of resistance. To realize such a potential
will require new tools to explore how differ-
ent treatment regimes affect the genotypic
and phenotypic evolutionary paths to anti-
biotic resistance in the laboratory and in the
clinic. Here we discuss new tools to select
for drug resistance, strategies for identifying
and characterizing adaptive mutations in the
evolved genotypes, and approaches to study
the genetic constraints on the evolution of
resistance.
Selection for drug resistance
Drug resistance in laboratory experiments.
Laboratory evolution6 can investigate how
the rate and genotypic path to resistance
varies across different controlled drug treat-
ment regimens. In a traditional selection
experiment, bacteria are exposed to fixed
drug doses that permit only the growth of
resistant mutants. Typically, this approach
identifies only a single adaptive step and
does not reveal how multiple mutations can
accrue sequentially to confer strong resist-

4. ance (FIG. 1a). Technological innovations
now facilitate rapid multistep experimental
evolution, revealing long-term evolutionary
paths. Recurrent evolutionary patterns, such
as the appearance of mutations in a preferred
order, provide some level of predictability
to a seemingly stochastic evolutionary
process7. Devices for establishing spatial or
temporal gradients of drug concentration
allow evolving populations to be continu-
ously challenged by effectively increasing the
drug dosage to maintain selective pressure
Understanding, predicting and
manipulating the genotypic evolution
of antibiotic resistance
Adam C. Palmer and Roy Kishony
Abstract | 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

5. resistance in bacterial infections.
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© 2013 Macmillan Publishers Limited. All rights reserved
as stronger antibiotic resistance evolves.
Continuous culture devices (for example,
turbidostats) can be modified to increase
drug dose steadily over time8, to imple-
ment automated feedback control of drug
dosage in response to increasing levels of
resistance7 or to mimic the antibiotic dos-
ing regime experienced within a patient
(FIG. 1b). Multistep experimental evolution
can also be carried out in spatial drug gra-
dients, as was demonstrated by a microfluidic
device of connected chambers implementing
a spatial drug gradient, allowing bacteria
to expand throughout the device only as
they evolve increasing levels of antibiotic
resistance9 (FIG. 1c). These experiments
have revealed that although the evolution
of resistance can follow similar phenotypic
paths in replicate experiments, the underly-
ing genotypic process can be variable for
some drugs (for example, chloramphenicol
and doxycycline) but reproducible for other
drugs (for example, trimethoprim and cip-
rofloxacin)7,9. Substantial variability in rate

6. is also observed: resistance to some drugs
increases 1,000-fold over 20 days, whereas
resistance to other drugs might increase only
tenfold over the same period7. Therefore,
for any specific genotype there could be vast
differences between drugs in the propensity
for resistance and the mechanisms by which
resistance is acquired; these factors are cru-
cial to the design of combination treatments
that inhibit the evolution of resistance.
Combination therapy has the potential to
slow the evolution of resistance, as a bacterial
subpopulation with a mutation that renders
it resistant to one drug may still be inhibited
by a second drug, preventing the growth of a
large drug-resistant population (that might
subsequently evolve multidrug resistance)10.
However, the choice of an optimal combina-
tion to slow evolution can crucially depend
on the details of the treatment regimen,
drug interactions and cross-resistance10–13.
Experimental evolution has facilitated the
systematic analysis of evolution under differ-
ent combination therapies and is revealing
the principles behind their ability to slow
down and possibly even to reverse the evolu-
tion of resistance12–15 (reviewed in REF. 16).
Several approaches have been used to select
for drug resistance in multidrug environ-
ments: mutants can be selected from a grid
of drug concentrations across multiple agar
dishes12 or in a microtitre plate13. Multiple
mutations that confer strong multidrug
resistance can be selected by serial passaging
across such gradients13 or through the use of

7. drug combinations in the continuous culture
devices described above7.
Many questions about the evolution of
multidrug resistance remain, including: to
what extent is resistance acquired by a series
of drug-specific mutations versus mutations
that each confer resistance to multiple drugs
(that is, positive cross-resistance); in which
cases can resistance to one drug lead to
sensitivity to another (that is, negative cross-
resistance); and, even when resistance to one
drug does not immediately confer positive or
negative cross-resistance to a second drug,
can it affect the future evolution of resistance
to the second drug? These and other ques-
tions about the evolution of resistance to
single or multiple drug treatments are being
addressed by the systematic selection meth-
odologies outlined above. Although these
methods are often first applied to model
organisms, they can and should be more
widely applied to study pathogens isolated
from human infections.
Drug resistance in clinical isolates. The
increasing capacity to sequence whole bacte-
rial genomes has allowed detailed analyses of
large collections of clinical isolates. Various
sampling approaches are available to view
the evolution and spread of antibiotic-
resistant bacteria over different scales (FIG. 2).
Isolates collected from individual patients
over the course of acute and chronic infec-
tions have revealed the within-patient evo-

8. lution of antibiotic resistance, instances of
cross-resistance between antibiotics, the evo-
lution of compensatory mutations that alle-
viate the fitness costs of resistance, and the
transmission of specific antibiotic-resistant
clones between organs17–19. Sampling dur-
ing the spread of an epidemic has been
used to identify the likely patient-to-patient
transmission of antibiotic-sensitive or
antibiotic-resistant bacteria and may reveal
trade-offs between infectivity and antibiotic
resistance18. At the largest scale, worldwide
sampling of endemic infections over decades
has been used to determine long-term trends
in the evolution of antibiotic resistance and
pathogenicity and to determine transmission
patterns across continents20,21.
Finding the genotypic basis
Identifying adaptive mutations. Comparing
the genomes of ancestral and evolved strains
identifies the precise genetic changes that
underlie adaptive evolution. However,
separating adaptive mutations from neutral
or passenger mutations is challenging, par-
ticularly for clinical strains that may have
been evolving antibiotic resistance over
decades. In the context of contemporary
bacterial evolution, tests for adaptive evolu-
tion based on rates of nonsynonymous and
synonymous substitutions (dN/dS) cannot be
applied on a per-gene basis as there are typi-
cally too few mutations for statistical power;
also, they should not be applied to a whole
Figure 1 | Selection of antibiotic-resistant bac-

9. teria from experimental evolution. Gradients
of drug concentration over time or space facili-
tate multistep experimental evolution. a | In a
classical selection for antibiotic resistance,
a uniform drug concentration selects for only a
single mutation. b | A continuous culture device
can select for multiple resistance-conferring
mutations by dynamically increasing drug con-
centration in response to increasing drug resist-
ance. c | If bacteria can migrate over a spatial
gradient of drug concentration then they can
explore larger regions of space only as they
evolve increasing levels of drug resistance.
Time
Time
Time
Sp
ac
e
Concentration of antibiotic
a
b
c
High Medium None
D

10. ru
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si
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an
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D
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Nature Reviews | Genetics

11. PROGRESS
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© 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

12. 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

13. 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

14. 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.

15. 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
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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

16. 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

17. 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

18. 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

19. 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,

20. 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

21. Outgroup
Isolate Y
Isolate X
Isolate Z
a b Mutation occurrencesc
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© 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.
Towards therapies informed by evolution
The short- to medium-term challenge is
to develop novel antibiotics to kill today’s

22. antibiotic-resistant bacteria, but the ‘arms
race’ between antibiotic development and
the evolution of resistance in pathogens is
growing increasingly difficult. Addressing
the long-term challenge posed by antibiotic-
resistant pathogens will require therapeutic
strategies10 and compound development46,47
that consider ways to manipulate and to slow
the evolution of resistance.
The capacity for genome sequencing is
approaching the point at which endemic
pathogens can be extensively sampled and
sequenced around the world, and evolu-
tion during bacterial outbreaks can be
tracked in real time48–50. The ability to trace
routes of bacterial transmission precisely
is clearly of benefit to infection control
efforts, which are especially important for
highly drug-resistant infections for which
few therapeutic options remain. Yet the
value of genome sequencing extends beyond
epidemic control: the genome of an organ-
ism defines its current antibiotic resistance
and, to a large extent, its potential to evolve
further resistance. The application of the
methods reviewed here to genotype and to
phenotype drug-resistant pathogens could
identify resistance (or proto-resistance)
genes and the ways that they might mutate
to increase antibiotic resistance. Whole-
genome sequencing of pathogens thus has
the potential to provide a catalogue of vari-
ous pathways to resistance under different
treatment regimens.

23. To treat a drug-resistant infection, an
understanding of interactions between drugs
and resistance mutations can guide the selec-
tion of second-line therapies or combination
therapies. Such therapies should have the
weakest possible cross-resistance — nega-
tive cross-resistance if possible — and they
should be chosen such that the genetic inter-
actions between drug-specific resistance
mutations lead to an accumulation of fitness
Figure 4 | Constrained evolutionary pathways to antibiotic
resistance. The properties of evo-
lutionary processes can be illustrated by the concept of the
‘fitness landscape’. In this demonstration
of several experimentally observed behaviours, height
represents drug resistance. Different starting
genotypes (A and B) may have a different propensity to evolve
resistance owing to their proximities to
drug-resistance peaks of varying height. The first genotypic step
towards resistance can sometimes
define the final genotype and the level of resistance (arrows
from B). The pathways to resistance can
at times be constrained and predictable (dark arrows), but
evolutionary pathways can diverge (light
arrows) to distinct peaks separated by negative genetic
interactions.
Nature Reviews | Genetics
A
B
Glossary
β-lactamase

24. An enzyme that can confer resistance to β-lactam
antibiotics by catalysing their degradation.
Commensal microbes
Microbes living on or in a host without causing disease,
although they typically include opportunistic pathogens.
Cross-resistance
The propensity of a genetic change that confers
resistance to one drug also to affect resistance to a different
drug (by either increasing or decreasing resistance).
dN/dS
The ratio of mutation rates at nonsynonymous (N) and
synonymous (S) sites. dN/dS is increased by selection for
amino acid changes (a signature of adaptive selection)
and decreased by selection against amino acid changes
(purifying selection).
Horizontal gene transfer
The acquisition of a gene by a means other than direct
inheritance from a parent cell (vertical transfer). Common
in many bacteria and archaea, mechanisms of horizontal
gene transfer include transformation, conjugation and
transduction.
Maximum likelihood and Bayesian approaches
This definition applies to the context of phylogenetics.
Phylogenetic trees can be constructed by maximum
parsimony, maximum likelihood and Bayesian inference.
Maximum parsimony methods select from all possible
trees the one containing the fewest mutations. Trees
chosen by maximum likelihood and other Bayesian
methods may contain more mutations, as they weigh the
relative probabilities of different mutations according to
various models.

25. Microfluidic device
Customized, microscopic chambers in which fluid flows
can be precisely controlled. Applied to microbiology, these
allow the study of bacterial behaviour in spatially and
temporally controllable environments.
Monotherapy
Chemical therapy by a single drug.
Parallel evolution
When the same mutations (or a range of mutations in the
same gene) repeatedly occur in independent lineages; this
provides an indication that these mutations may have been
fixed by positive selection rather than by chance.
Proto-resistance genes
Evolutionary precursors to drug-resistance genes that do
not yet contribute to drug resistance but may do so on
mutation and selection by drug stress.
Resistance cassettes
A genetic element containing one or more drug resistance
genes, often carried in transposable elements or plasmids
that facilitate horizontal gene transfer.
Transposon mutagenesis
The insertion of transposons at random locations
throughout a genome to generate a library of different
gene disruptions. Transposons can be constructed with
outward-facing promoters also to introduce gene
overexpression into the library.
Turbidostats
Devices that maintain constant cell density (turbidity) in
a continuously growing microbial culture by routinely

26. removing a small volume of culture and replacing it with
fresh sterile media.
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© 2013 Macmillan Publishers Limited. All rights reserved
costs rather than compensation. Finally,
the specific genetic basis of resistance to a
first-line drug may, through genetic interac-
tions, alter the expected capacity to evolve
resistance to second-line drugs. Such cases
might identify sets of drugs that, when used
in sequence or in combinations, minimize
the risk that strong resistance will evolve.
The widespread application of the new
methods reviewed in this article might thus
facilitate an evolutionary medicine paradigm
in which pathogen genotyping coupled with
evolutionary genetics guides the optimal
insights from choice of temporal and combi-
natorial drug treatment.
Adam C. Palmer is at the Department of Systems
Biology, Harvard Medical School, Boston,
Massachusetts, USA.
Roy Kishony is at the Department of Systems Biology,
Harvard Medical School, Boston, Massachusetts, USA;

27. the School of Engineering and Applied Sciences,
Harvard University, Cambridge, Massachussetts, USA;
and the Faculty of Biology, Technion — Israel Institute
of Technology, Haifa, Israel.
Correspondence to R.K.
e-mail: [email protected]
doi:10.1038/nrg3351
Published online 19 February 2013
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Acknowledgements
We thank T. Lieberman for discussions on phylogeny and com-
ments on the manuscript. This work was supported in part
by US National Institutes of Health grants R01GM081617
and US National Institute of General Medical Science Center
grant P50GM068763, and the Novartis Institutes for
BioMedical Research.
Competing interests statement
The authors declare no competing financial interests.
PROGRESS
248 | APRIL 2013 | VOLUME 14
www.nature.com/reviews/genetics
© 2013 Macmillan Publishers Limited. All rights reserved
322 Week #2 response: Need a peer response to each post
(Discussion). There are 2 post total
Specific Rules for Discussions
No Title page

34. Peer responses - Must be a minimum of 100 - 200 words and a
maximum of 300 words for peer responses.
Must provide a minimum of at least one (1) reference in your
discussion andpeer responses.
· Urusha Honesty posted Feb 8, 2016 11:43 PM
Subscribed Subscribe
1. a.
There is a lack of consistency in the current reimbursement and
payment methodologies. There is a large gap in the interests of
private and public provider’s, which makes a difference in
maximizing efficiency and effectiveness versus increasing
expenditures in private care. Medicaid, the primary source for
the majority of patients, does not allow much cost shifting,
resulting in a lack of options in who to choose as a provider.
(Vraciu, R.A., 1979)(nithinmohantk, 2010)
Many people are unsure of what medical treatment entails, as a
result, they are unaware of the financial impact of unneeded
treatments. (Prof. J. Bozek)
High costs leave those who are not covered by employer
insurance, covered by a government program or who can not
afford to pay for private services or out of pocket insurance
with a lack of access to healthcare. High deductibles also limit
access in some cases. (nithinmohantk, 2010)
b. Strategies I would implement in response to 1a.
Creating a level of control and consistency to narrow the gap
between efficiency, effectiveness and expenditures by
generating a mixture of patients using different payment sources
while establishing a level of high-quality care for all patients.
Offering other financing options such as discounted financing,
low-interest financing and educating patients on the benefits of
HSA’s when they may be available for a solution to high
deductibles. Ensuring prevention education is would be a go to
solution to lower costs would be a first priority. Having
personnel that is aware of financing options to discuss with
patients is vital.
2 .a.

35. A financial budget is required to present investors and
stakeholders a picture of how the operation will cover fixed
costs and how it will generate a profit. The stability of the
organization depends on a well planned and properly executed
budget. (Vraciu, R.A.,1979).
2 b.What are the key components of a financial budget of a
health care organization, and what is their purpose in
accomplishing the goals you identified in part 2a.
I would say preparation and planning a budget that is based on
historical costs as well as forecasting based on the strategic
plan is most important to create a picture of how the operation
will stay afloat.
Control would be essential to maintaining the budget. Analysis
of operations, analysis of financial performance and other
utilization reviews would need to take place often. Assessment
performance should also be done timely. I would also
utilize both capitation and fee for payment services to be
competitive to generate revenue.
nithinmohantk (2010). US health care system overview 1.
LinkedIn Slide Share. Retrieved from
http://www.slideshare.net/nithinmohantk/us-health-care-system-
overview-1
Professor Jerome Bozek, UMUC. Week 5 Lecture. Retrieved
from
https://learn.umuc.edu/d2l/le/content/174191/viewContent/5554
449/View
Vraciu, R.A. (1979). Programming, budgeting, and control in
health care organization: the state of the art. Health Services
Research. 14(2): 126–149· Example of a response:
Actions for reply by James Booker at February 9 at 10:48 PM
Urusha,
This was a good post. I wanted to respond to you saying you
would utilize the fee for service option. I would disagree. I feel
it is not entirely too bad of an option but then the waters get
murky. In healthcare , organizations are ordering extra and
unnecessary tests to make more money. So with saying that,

36. physicians would use that. The Government is already cracking
down hard on violations like that. Its called the Stark Law.
Resond to ursha here:
· Discussion 5
Miguel Wolfe posted Feb 10, 2016 6:35 PM
1. As a healthcare manager:
a. Identify three major challenges healthcare organizations face
with current reimbursement and payment methodologies.
i.e.(consider reimbursement models under Medicare, Medicaid,
Plans under Health Insurance Exchanges (Affordable Care Act),
military health plans, and commercial health plans based on
your readings.
Based on my readings; challenges that the Affordable Care Act
face include lower reimbursement to physicians and hospitals;
as well as mounting patient debt caused by high-deductible
plans. A Challenge that occur in medicaid is controlling cost of
the budgets during economic downturns as enrollment increases,
due to loss of jobs and health benefits.
Dichiara Jacqueline( March,2015) Affordable Care Act Means
Lower Reimbursement for Physicians
http://revcycleintelligence.com/news/affordable-care-act-means-
lower-reimbursement-for-physicians
KaiserHealthNews (July,2015) 5 Challenges Facing Medicaid at
50 http://www.usnews.com/news/articles/2015/07/30/5-
challenges-facing-medicaid-at-50
b. What actions/strategies will you implement in response to
what you have identified?
In response to the challenges faced by the Affordable Care Act
I would advised giving the physicians incentives that would
help balance out the lowered reimbursement. These incentives
may come in the form of discounts on medical. As for the
mounting patient debt caused by high deductible, I would
suggest a limit to how high patient debt is allowed to grow. As

37. well as advise the patients on what services they may actually
need to insure that their money is not wasted. Controlling cost
for medicaid could come from limiting services to more chronic
health conditions.
2 .As a healthcare manager:
. a. Why do a financial budget? What goals does it hope to
accomplish?
Financial budgets are important as they help plan and monitor
healthcare organization budgets and will help identify
wasteful expenditures, adapt quickly as financial situation
changes, and achieve financial goals.
b.What are the key components of a financial budget of a
healthcare organization, and what is their purpose in
accomplishing the goals you identified in part 2a.
Key components of a financial budget of a healthcare
organization include a cash flow analysis which will help
health management determine where is most of the money going
in the organization. Having a long term strategic plan is also
important, as it gives the organization order and will give the
staff a goal to accomplish.
Respond to Miguel here:
HMGT 320 Week #4 Response. Need a response to each post
(Discussion). There are 3 post total. The postings that require
my peer response are listed first.. These post are very lengthy, I
apologize. You may put your response on a different sheet of
paper but please ensure you put the “name” of the person’s
discussions you are responding to. Just give one general overall
response, not a response to each article and please provide one
contributing factor to the discussion. A response should be
about 5-10 sentences. Don’t over do it. No Title page!!!!!
· Brittany Brown posted Feb 10, 2016 10:54 AM
Good Morning,

38. How important is budgeting in running a health care company?
How often should budgets be prepared?
Budgeting is required for any company and especially for a
heath care company. Healthcare is a good that has no physical
substance. Unlike a grocery store that can look and see how
many apple have been sold. I have seen many small town
medical clinic go underwater because they did track and budget
their expenses.
A health care company has so many moving parts. The billing to
health insurance then to patient and wait for payment from the
patients. In my current job I have six different budgets that need
managed. I do a review of my budgets once a month and then
anytime we have an expense or a bill is paid.
These types of budgets have so many complex parts to them that
it can be easy to make a mistake. If a mistake no one want to be
“That Guy” that made a mulita million dollar mistake. A lot of
times I triple check myself to avoid this.
V/r
Brittany Brown
Respond to Brittany Here:
· Erica Kaltenbach posted Feb 10, 2016 4:24 PM
Subscribed Subscribe
3) REVISED: What factors indicate a company is not doing
well. Based on the link below, identify a company who is
having trouble in one of these areas and describe their financial
situation based on the indicators discussed.
http://quickbooks.intuit.com/r/financial-management/7-signs-
your-company-has-poor-financial-health
The factors that indicate if a company is not doing well are:
1. Debt levels – the debt-to-quality ratio and the debt- to-
assets ratio
2. Review accounts receivable
3. Check your current capital
4. Review your company’s current cash ratio
5. Take stock of your sales pipeline

39. 6. Keep an eye on your profit margin
7. Look forward, not just backward
One company who is having trouble with their debt levels is
Sears. According to CNBC, the CEO is selling any assets that
are available because their revenue is minimal at best. The
company plans to sell their auto centers which are worth
approximately $2.5 billion but unfortunately the company’s debt
is estimated to be approximately $2.8 billion, leaving no
financial gain for the owner.
Reference:
Berr, J. (2013, December 27). Five Companies That May Not
Survive Past 2014. Retrieved February 9. 2016, from
http://www.cnbc.com/2013/12/27/five-companies-that-may-not-
survive-past-2014.html
Respond to Erica here:
· Frederina Grant posted Feb 14, 2016 10:11 AM
Subscribed Subscribe
What factors indicate a company is not doing well.
One of the major indicators is not taking stock of sale pipeline.
Knowing how many leads the business currently have in their
pipeline as well as the status of these leads is a great indicator
of a business' poor or good financial health.
A post in the Baltimore City Paper stated "a 45 year-old
network of medical clinics that treats tens of thousands of poor
people in Baltimore, is in financial trouble". This company has
10 locations through out the Baltimore City metropolitan area.
Because of financial difficulty some of these locations has been
reduced in staffing and services with a possibility of closing.
There has numerous expansions, including the application for
JACHO Accreditation into the PCMH Program.
Personally I think upper management has over extended the
financial range of the corporation. because of personal
involvement with this organization I have included the link
where the information can be view at will.
www.citypaper.com/blogs/the-news-hole/bcpnews-trouble-at-

40. total-health-care-treatnment-center-for-thousands-of-poor-
20160212-story.html.
7 Signs Your Company Has Poor Financial Health
Freddie
Respond to Frederina here: