Download Resistance to Fluoroquinolones in Escherichia coli

Digital Comprehensive Summaries of Uppsala Dissertations
from the Faculty of Science and Technology 302
Resistance to Fluoroquinolones in
Escherichia coli: Prevention,
Genetics and Fitness Costs
LINDA L. MARCUSSON
ACTA
UNIVERSITATIS
UPSALIENSIS
UPPSALA
2007
ISSN 1651-6214
ISBN 978-91-554-6884-2
urn:nbn:se:uu:diva-7851
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List of papers
This thesis is based on following papers, which will be referred to in the text
by their Roman numerals:
I)
Marcusson L.L, Olofsson S.K, Komp Lindgren P, Cars O, Hughes D.
Mutant prevention concentrations of ciprofloxacin for urinary tract infection isolates of
Escherichia coli.
Journal of Antimicrobial Chemotherapy 2005; 55: 938-943
II)
Olofsson S.K, Marcusson L.L, Komp Lindgren P, Hughes D, Cars O.
Selection of ciprofloxacin resistance in Escherichia coli in an in vitro kinetic model: relation between drug exposure and mutant prevention concentration.
Journal of Antimicrobial Chemotherapy 2006; 57: 1116-1121
III)
Olofsson S.K, Marcusson L.L, Strömbäck A, Hughes D, Cars O.
Dose-related selection of fluoroquinolone-resistant Escherichia coli.
Submitted
IV)
Komp Lindgren P, Marcusson L.L, Sandvang D, Frimodt-Møller N, Hughes D
Biological cost of single and multiple norfloxacin resistance mutations in Escherichia
coli implicated in urinary tract infections.
Antimicrobial Agents and Chemotherapy 2005; 49:6 2343-2351
V)
Marcusson L.L, Hughes D.
Increased bacterial fitness selected with antimicrobial drug resistance.
Submitted
VI)
Marcusson L.L, Hughes D
Fitness compensation in fluoroquinolone resistant Escherichia coli by mutant RNA
polymerase.
Manuscript
Reprints were made with permission from the publishers.
Contents
INTRODUCTION ........................................................................................13
The story of microorganisms and the discovery of antibiotics.................13
Antibiotics and their targets .....................................................................14
Antibiotic resistance development ...........................................................15
Mechanisms of resistance.........................................................................18
Spread of antibiotics in society and the increasing resistance problem ...20
Escherichia coli and Urinary Tract Infections .........................................21
Fluoroquinolones and their targets ...........................................................22
Fluoroquinolone resistance.......................................................................25
Persistant bacteria.....................................................................................28
The biological cost of being resistant.......................................................29
Fitness compensation ...............................................................................30
Susceptibility versus resistance ................................................................31
Dosing regimens: pharmacokinetics and pharmacodynamics..................31
Taking on the resistance problem - Restricting development of mutants 34
Mutant prevention concentration..............................................................34
Mutant selective window .........................................................................35
PRESENT INVESTIGATIONS ...................................................................39
Prevention of resistance development and alternative dosing regimens.......40
The genetics of resistance and bacterial fitness ............................................47
Is there a solution of the antibiotic resistance problem and perspectives for
the future .......................................................................................................57
Swedish Summary ........................................................................................59
Acknowledgments.........................................................................................61
References.....................................................................................................64
Abbreviations
ATP
AUC
Cfu
Cmax
DDD
dTMP
DNA
EARSS
FQ
HGT
MIC
MPC
MSW
CLSI
OMP
PCR
PMF
PD
PK
RNA
RNAP
SRGA
T1/2
T>MIC
T>MPC
QRDR
Adenosine triphosphate
Area under the serum concentration-time curve
Colony forming unit
Maximal serum concentration
Defined daily doses
Deoxythymidine 5'-phosphate
Deoxyribonucleic acid
European Antimicrobial Resistance Surveillance System
Fluoroquinolones
Horizontal gene transfer
Minimum inhibitory concentration
Mutant prevention concentration
Mutant selective window
Clinical Laboratory Standards Institute (previously NCCLS)
Outer membrane porin
Polymerase chain reaction
Proton motive force
Pharmacodynamics
Pharmacokinetics
Ribonucleic acid
Ribonucleic acid polymerase
Swedish reference group for antibiotics
Elimination half-life
Time over MIC
Time over MPC
Quinolone resistance determining region
INTRODUCTION
The story of microorganisms and the discovery of
antibiotics
As its name indicates, contagion is an infection that passes from one thing to another. The infection is precisely similar in both the carrier and the receiver of the contagion; we say the contagion has occurred when a certain similar taint has affected them both.
So, when a person die of drinking poison, we say perhaps that they were infected, but not
that they suffered contagion; and in the case of things that naturally go bad when exposed to the
air, such as milk, meat, etc., we say that they have become corrupt, but not that they have suffered contagion, unless indeed the air itself has also become corrupt in a precisely similar way.
Girolamo Fracastoro, 1546
These thoughts and observations might have been the starting point of the
understanding of infectious bacteria (the contagion) and their relation to
disease. The observation of small ‘animalcules’ by Anton van Leeuwenhoek
almost 200 years later also gave a glimpse into the invisible world of micro
organisms and encouraged the development of microbiology. The pioneers
who followed, during the golden age of microbiology, included among others, Pasteur and Koch. Their work was incredibly important in leading to our
understanding of microbiology and its relationship to infections. Pasteur’s
proposal of the germ theory of disease provided a satisfying explanation for
the origin of many human illnesses, and Koch’s postulates provided a rigorous analytical framework for future investigations into the causes of infectious disease.
In 1909 a new era began with the discovery in Paul Ehrlich’s lab of the
sulfa drug. This was probably one of the most important medical discoveries
of the period and encouraged the search for other anti-infective agents paving the way for the discovery of the chemotherapeutic agents and antibiotics
we use today. The discovery made by Alexander Fleming two decades later
that Penicillium notatum had an inhibitory effect on staphylococci would
come to save the lives of many millions of people, but not until given impetus by the outbreak of World War II. The success of penicillin influenced
many other scientists and pharmaceutical companies to discover and develop
new classes of antibiotics over the following years (Brock, 1999).
13
Antibiotics and their targets
Antibiotics can be divided into different classes according to their mechanism of action (Figure 1). Antibiotics target vital mechanisms within the
bacterial cell including cell wall formation, folic acid metabolism, DNA
replication, RNA transcription, and protein synthesis.
ȕ-lactam antibiotics are by volume of use the single most important group
and inhibit cell wall formation by blocking the cross-linking of carbohydrates in the peptidoglycan layer (Waxman and Strominger, 1983). The
trimethoprims and sulfonamides were the first antimicrobials drugs developed and they target the folic acid metabolism pathway which is involved in
production of the essential coenzyme tetrahydrofolate and the synthesis of
dTMP and purines. DNA replication is targeted by the fluoroquinolones, a
group of synthetic antimicrobial drugs which interfere with DNA gyrase and
topoisomerase IV. These enzymes are required for the unwinding of DNA
during chromosome replication and transcription by the introduction of
negative supercoils (Marians and Hiasa, 1997). RNA polymerase which
transcribes RNA from DNA is inhibited by the binding of rifampicin to the
polymerase (Hartmann et al., 1967). After transcription, messenger RNA’s
are translated into proteins by the translation machinery which includes the
ribosome and its many interacting factors. A great many antibiotics target
the protein synthesis machinery. Among these, the macrolides, like erythromycin, binds to the peptide exit tunnel of the 50S ribosomal subunit, inhibiting further protein synthesis. Aminoglycosides have their mode of action by
irreversibly attaching to the 30S subunit of the ribosome, thereby inhibiting
protein synthesis (Waksman, 1965). Tetracyclines also interfere with the
translation machinery by preventing the attachment of aminoacyl-tRNA to
the ribosomal acceptor site in the 30S subunit of the ribosome.
Figure 1. Some commonly used antibiotics and their mode of action.
14
Antibiotic resistance development
Evolution of resistance
Resistance towards antibiotics is not a new phenomenon; it has existed in
nature a long time before the word was coined as a problematic concept in
our era. Antibiotics are natural compounds and microorganisms in nature
produce by themselves almost all of the different antibiotic classes known
(Datta and Hughes, 1983). Even though the resistance phenomenon towards
these “natural” antibiotics is not new per se, the increased prevalence of
resistance among infectious bacteria towards antibiotics used in human
medicine during the last couple of decades is alarming. Analysis of the
“Murray collection” of bacterial strains supports the conclusion that resistance development has increased drastically because of antibiotic use and
misuse over the last sixty years. Thus, clinical strains collected between
1917 and 1952 did not have any significant resistance towards antibiotics
used today (Datta and Hughes, 1983). The forces that drive and select resistance evolution can be divided into four main categories; (i) spontaneous
mutations and mutator strains within the bacterial population that generate
new variants, (ii) horizontal gene transfer between different strains that
spread resistance genes, (iii) phenotypic tolerance of some bacteria to antibiotics that prevents eradication, and (iv) antibiotic selective pressure that
drives the selection of resistant variants in the population.
Spontaneous mutations and mutator bacteria
Mutations play an important role in the evolution process because they generate new variants of existing genes. Mutations are also a potential threat to
the organism’s viability because they may reduce fitness. All organisms are
under periodic environmental pressure to change and adapt, and new mutations can provide solutions to these pressures. But making the wrong
changes can be lethal and to maintain genetic stability there are many repair
and accuracy systems to limit the accumulation of mutations. There is a fine
balance between the accumulation of adaptive changes within the genome
and the risk of loss of fitness associated with new mutations. The average
spontaneous mutation rate in bacteria is 10-9-10-10 mutations per base pair per
generation (Andersson and Hughes, 1996; Drake et al., 1998).
Mutator strains are genetic variants within the population that exhibit a
higher mutation rate than normal cells, usually because they lack one or
more of the systems that support the high fidelity of DNA replication, or the
repair of mismatch of mutations in the double helix. Mutator bacteria are
always present at a low frequency in bacterial populations due to spontaneous mutagenesis affecting repair and fidelity genes. Mutators can vary in
their mutation rate dependent on their mutator genotype (Baquero et al.,
15
2004; Matic et al., 1997; Örlen and Hughes, 2006). One of the strongest
mutator phenotypes involves knockout mutations affecting the methyldirected mismatch repair system. Mutations within the mutHLS, uvrD and
dam genes that are a part of this system result in up to a 103-fold increase in
mutation rate compared to the wild-type (Komp Lindgren et al., 2003;
LeClerc et al., 1996; Matic et al., 1997; Modrich, 1989, 1991). When bacteria are exposed to different environments e.g. antibiotic selective conditions,
a strain with a mutator phenotype can have a selective advantage over nonmutator strains and become enriched during selection (Mao et al., 1997;
Miller et al., 2004). Since mutators are more likely to generate adaptive mutations that will be beneficial in a selective environment. They will reshape
the population and hitchhike with the fitter phenotype which outcompetes
the pre-existing population. As expected, mutators accelerate antibiotic resistance development in vitro (Schaaff et al., 2002), and this was also seen in
vivo when patients were treated with high levels of antibiotics (Gustafsson et
al., 2003b). In a recent study when a collection of fluoroquinolone-resistant
Escherichia.coli (E.coli) urinary tract infection (UTI) strains were investigated, a positive correlation was found between the number of mutations
conferring resistance to fluoroquinolones and the relative increase in mutation rate (Komp Lindgren et al., 2003). It has also been showed that even
weak mutators can drive the evolution of fluoroquinolone resistance in E.coli
(Örlen and Hughes, 2006).
Extra chromosomal transfer of resistance
Horizontal gene transfer (HGT) plays an important role in the evolution of
bacteria. It has been shown that bacterial genomes are genetic mosaics due to
the acquisition of exogenous DNA and its incorporation into the chromosome (Blattner et al., 1997). Resistance mechanisms can be transferred by
HGT between microorganisms by transformation or via vectors such as
plasmids, transposons and bacteriophages. Plasmids provide a versatile and
efficient means for HGT of resistance genes and many plasmids carry systems that promote their transmission to other bacterial cells. Plasmids can
replicate and maintain themselves in a host cell, they can integrate into the
bacterial chromosome, and sometimes when they excise they take chromosomal genes with them (Bryan, 1988). Plasmids often carry integrons which
are assemblies of multiple resistance genes which are transferred between
cells together with the plasmid, but can also be inserted into the chromosome
by recombination (Recchia and Hall, 1997). Transposons can also contain a
variety of resistance genes, but in contrast to integrons they are responsible
for their own transfer and integration into the bacterial chromosome or plasmid genome.
16
Antibiotic tolerance
There are genetic factors within the bacterial genome that by themselves do
not increase resistance to antibiotics but can ensure that a certain proportion
of the bacterial population is resistant. For example, the tolerance phenomenon enables a small fraction of cells to survive despite the presence of antibiotics, e.g. the cell wall alterations facilitating ȕ-lactam resistance (Novak
and Tuomanen, 1999; Tuomanen et al., 1991). Accumulating evidence suggests that antibiotic tolerance, or ‘persistence’, results from heterogeneity in
bacterial populations and the ability of some bacteria to enter into a dormant
(non-dividing) state (Dhar and McKinney, 2007; Lewis, 2007).
Antibiotic selective pressure
When antibiotics are used in treatment of pathogenic strains there will be a
selection pressure for antibiotic resistance both in the bacterial population
within the person being treated and in the bigger human population consuming the antibiotic. Quantitative models and correlations between antibiotic
usage and resistance support the conclusion that the volume of drug use in a
society is an important factor in resistance development (Austin et al., 1999).
Other factors include the inappropriate use of antibiotics, for example, antibiotic dosing at concentrations that select for the growth of resistant bacteria.
In addition, the co-selection of different resistance determinants due to genetic linkage or cross-resistance will be factors driving resistance development. Under gradual antibiotic pressure bacteria will adapt by genetic
changes that enable its survival in specific environments. For example, when
antibiotics are used in therapy, concentrations can vary in different body
compartments and may not eradicate the complete bacterial population; concentration variations like this will rather create an environment suitable for
the selection of a stepwise increase in resistance (Austin et al., 1998;
Baquero et al., 1993, 1997). Low-level resistant bacteria may then survive
and continue to evolve and become high-level resistant.
17
Mechanisms of resistance
Bacteria become resistant to antibiotic drugs by decreasing the effective drug
concentration at the drug target. This can be achieved in three different
ways: by reducing the interaction between the drug and the drug target (by
target modification or target protection); by reducing the concentration of
drug in the cell (by decreased influx or increased efflux); or by modification
or inactivation of the drug (Figure 2).
Reduced interaction by target modification and protection
Antibiotics are usually developed to bind to and inhibit a specific target. This
target can become directly altered by mutations that reduce the affinity of the
antibiotic to its binding site. The consequence is thereby a decrease in the
effective concentration of the drug at the target. An alternative mechanism to
reduce the interaction between the drug and the target is the production by
the bacteria of a protein product that protects the target from the antibiotic.
Recently a plasmid-encoded resistance protein QnrA, was shown to protect
DNA gyrase and topoisomerase IV against fluoroquinolone binding (Tran
and Jacoby, 2002; Tran et al., 2005). Resistance associated with chromosomal mutations is usually inherited vertically whereas plasmid-borne (or
transposon-associated) resistance determinants such as QnrA can more easily be transmitted horizontally between bacteria.
Reduced accumulation of antibiotics by decreased influx or increased efflux
Bacterial outer membrane proteins (OMPs) play a major role in controlling
the diffusion of ions and different small molecules into and out of the bacterial cell (Benz and Bauer, 1988). Mutations or deletion of OMPs could create an obstacle for the entrance of certain antibiotics. It has been shown that
some porin–deficient strains are more resistant towards tetracyclines,
chloramphenicol and certain ȕ-lactam antibiotics than porin-sufficient strains
(Harder et al., 1981).
Antibiotics can be exported from the cell by efflux pumps which are driven
either by ATP hydrolysis or by the proton motive force (PMF). There are
five different classes of bacterial efflux pumps, The first class, called the
ATP-binding cassette (ABC) superfamily, includes both specific and multidrug-transporters (MDR). The remaining four classes are included in the
PMF dependent family. These are the Small Multidrug Resistance Family
(SMR), the Major Facilitator Superfamily (MFS), the Multidrug and Toxic
Compound Extrusion Family (MATE), and the Resistance/Nodulation/Cell
Division Family (RND). One of the best characterized efflux pumps in E.coli
belongs to the RND family and is made of the multiprotein transmembrane
complex AcrAB-TolC. The AcrAB-TolC efflux pump is active in effluxing
18
many different drugs from E. coli, including fluoroquinolones, tetracyclines
and chloramphenicol.
Modification and inactivation of the drug
Chemical modification of an antibiotic by enzyme activity can result in its
inactivation. Antibiotic resistance by enzymatic modification is usually specific for a single drug class, e.g. ȕ-lactamases act on ß-lactam antibiotics,
chloramphenicol acetyltransferases act on chloramphenicol, and aminoglycoside acetyltransferase act on aminoglycosides. The ȕ-lactamases work by
opening the ȕ-lactam ring thereby inactivating the antibiotic before it reaches
its target (Livermore, 1995). An enzyme that modifies fluoroquinolones has
recently been identified in gram-negative bacteria (Robicsek et al., 2006).
This is a variant of an aminoglycoside-modifying enzyme (aac (6’)-Ib) that
has acquired the ability to acetylate and reduce the activity of certain fluoroquinolones.
Figure 2. Different resistance mechanisms of the bacterial cell.
19
Spread of antibiotics in society and the increasing
resistance problem
Antimicrobial drug resistance presents an ever-increasing global public
health threat that involves all major microbial pathogens and antimicrobial
drugs. This is, at least in part, a consequence of the widespread misuse of
antibiotics within the community over the last couple of decades. It has been
estimated that at least 40% of all antibiotics prescribed in the industrialized
countries of the West seem to have an incorrect basis; being, for example,
prescriptions for viral infections (Wise et al., 1998). The usage of antibiotics
in many countries without any prescription is also a problem that increases
the rate of resistance development (Cars and Nordberg, 2004).
Even countries with very strict controls in place have a problem with increased antibiotic selective pressure. For example, in the five year period
between 2000-2005, the use of antibiotics increased by 13% measured in
defined daily doses (DDD)/1000 inhabitants within hospitals in Sweden. The
most common antibiotics used were chephalosporins, penicillins, tetracyclines and fluoroquinolones (Cars and Olsson Liljequist, 2005). In Europe as
a whole, the number of countries with a frequency of fluoroquinolone resistant E.coli above 25% doubled from 2004-2005, and now comprises six
countries. Only four countries among the 28 surveyed maintained fluoroquinolone resistance levels at or below 5% (EARSS).
20
Escherichia coli and Urinary Tract Infections
The work of this thesis has been performed with Escherichia coli as the
model organism (Figure 3). The gram-negative rod-shaped bacteria E. coli
belongs to the Enterobacteriaceae family. In addition to being the causative
agent of several diseases, E. coli is also one of the best characterized microorgansims.
E. coli is a member of our intestinal commensal flora. However, some strains
of E.coli are pathogenic and cause diarrhea, gastroenteritis, sepsis and (UTI)
(Kaper et al., 2004). E.coli is responsible for 80% or more of uncomplicated
UTI. UTI are usually classified according to their syndromes: (i) asymptomatic bacteriuria; where there is growth of bacteria in the urine but without
any general symptoms, (ii) cystitis; where the infection is restricted to the
bladder, (iii) acute pyelonephritis; refers to the inflammation of the renal
pelvis and the kidneys. Pathogenic strains that are able to colonize the small
intestine and urethra contain specific adherence factors that are very important for their virulence, e.g. type 1 fimbriae that are especially important in
the colonization of the bladder (Johnson, 1991). Several toxins, such as
haemolysin, cytotoxic necrotizing factors and proteases are also produced in
different amounts varying between different uropathogenic E.coli (Johnson
and Stell, 2000).
Figure 3. Electron scanning micrograph of Escherichia coli.
(Kindly provided by Leif Ljung)
21
Fluoroquinolones and their targets
Fluoroquinolones
This group of antibiotics are analogs of nalidixic acid which was initially
isolated as a bi-product of anti-malarial research (Lesher et al., 1962; Neu,
1987). The chemical development of the fluoroquinolones (FQ) has been
very active, with both second (e.g. norfloxacin, ciprofloxacin) and third (e.g.
levofloxacin, gatifloxacin and moxifloxacin) generations of the drugs being
used clinically (Figure 4). These antimicrobials have broad spectrum activity
and very good pharmacokinetic properties such as relatively long half-lives
due to slow elimination, good distribution into body compartments and fluids as well as good penetration into human cells.
Figure 4. Chemical structures for three fluoroquinolones
Gyrase and topoisomerase
DNA gyrase and topoisomerase IV are bacterial type II topoisomerases.
Type II topoisomerases alter DNA supercoiling in a reaction that involves
making a double-strand break in the DNA, in contrast to type I topoisomerases (e.g. topoisomerase I) that make single-stranded cuts in DNA. DNA
gyrase and topoisomerase IV are essential enzymes in the bacterial cell.
DNA gyrase introduces negative supercoils in DNA using the energy of ATP
hydrolysis, whereas topoisomerase IV counters this activity by relaxing
negative supercoils. Two other activities of topisomerase IV are unknotting
DNA and decatenation, for example at the end of chromosome replication
(Figure 5). DNA gyrase is a tetramer composed of two GyrA and two GyrB
subunits encoded by gyrA and gyrB, respectively. GyrA is involved in the
double-strand breakage and reunion reaction, whereas GyrB is responsible
for the ATPase activity of the enzyme. The introduction of negative supercoiling by DNA gyrase is important for the binding of DnaA initiator protein
to the replication origin of E. coli, and also for facilitating the bi-directional
progression of the replication forks around the chromosome (Postow et al.,
2001). Topoisomerase IV has about 40% amino acid homology with DNA
22
gyrase and is also a tetramer with two ParC and two ParE subunits. Both
enzymes have also been reported to function in the initiation and progression
of RNA polymerase by modulating the superhelicity of the DNA and relieving the positive tension that builds up in front of the transcription machinery
(Figure 5) (Liu and Wang, 1987; Nöllmann et al., 2007; Peter et al., 2004;
Wu et al., 1988).
Figure 5. Illustration of DNA gyrase and topoisomerase IV involvement in DNA
replication and transcription.
Drug and target interaction
Fluoroquinolones act by making a ternary complex of the drug, topoisomerase and DNA, that remains bound on the cut DNA, thereby inhibiting further replication and bacterial growth. The cleavage reaction is initiated by
gyrase first wrapping the DNA around itself and then making a double cut of
the two strands. The DNA binds at two recognition helixes of the GyrA
dimer whereupon the drug then gets bound, one drug molecule at each recognition helix, since there are probably two quinolones per complex created.
It has been suggested that the gyrase, quinolone, and DNA complex forms
before cleavage of the DNA occurs, and that it is stabilized even more after
cleavage (Kampranis and Maxwell, 1998). In gram-negative bacteria the
preferred target for nalidixic acid and ciprofloxacin binding is the DNA gyrase before topoisomerase IV. In gram-positives e.g. Staphylococcus aureus
and Streptococcus pneumoniae the primary target has been shown to be topoisomerase IV (Munoz and De La Campa, 1996; Ng et al., 1996; Pan et al.,
1996; Pan and Fisher, 1996, 1999) In contrast, when sparfloxacin was tested
against S. pneumoniae, mutations in gyrA appeared as first-step mutations
23
while second-step mutations appeared in the grlA gene (parC) (Pan and
Fisher, 1997). This suggests that structural differences between fluoroquinolones may be responsible for the preferred target binding of either DNA
gyrase or topoisomerase IV.
Effects on both DNA replication and RNA transcription
DNA synthesis is inhibited within minutes after formation of the
DNA:gyrase:drug complex (Fournier et al., 2000; Snyder and Drlica, 1979),
since the complex forms immediately in front of the replication fork (Drlica
et al., 1980). Ternary complex formation is a reversible event (Goss et al.,
1965), whereas the release of broken DNA from the complex is irreversible
(Krasin and Hutchinson, 1977). Bacteriostatic concentrations of oxolinic
acid trap gyrase and block DNA synthesis without releasing broken DNA
from gyrase-DNA complexes. Release of DNA occurs only at higher bactericidal oxolinic acid concentrations (Chen et al., 1996; Snyder and Drlica,
1979).
RNA transcription is also effected by formation of the DNA:gyrase:drug
complex. Purified gyrase in complex with ciprofloxacin on DNA has been
shown to block the movement of RNA polymerase, but at a slower rate than
inhibition of DNA synthesis (Goss et al., 1965; Willmott et al., 1994) Termination of transcription seems to happen 20 bp upstream of the complex in
E. coli (Willmott et al., 1994). Quinolone-mediated inhibiton of transcription
seems to be bacteriostatic (Drlica and Malik, 2003).
There is a clear distinction between the bacteriostatic and lethal effects of the
fluoroquinolones since the formation of the DNA:gyrase:drug complex only
inhibits the growth of bacteria whereas it is the release of DNA breaks from
the complex that results in cell death (Chen et al., 1996). Different fluoroquinolones seems to have different bacteriostatic or bactericidal effects.
Compounds that contains a C8-methoxyl group has been shown to be particularly lethal, and incubation of wild-type cultures on agar containing C8methoxyl fluoroquinolones produced no resistant mutants (Zhao et al.,
1997). Even though bacterial strains might give similar MIC values, different compounds could be different in their lethal ability (Goldman et al.,
1996; Morrissey and George, 2000). Some compounds will trap singlestrand breaks or cause release of pairs of single-strand breaks easier than
others (Chen et al., 1996; Krasin and Hutchinson, 1977; Snyder and Drlica,
1979).
24
Fluoroquinolone resistance
Resistance toward fluoroquinolones in gram-negative bacteria is the result of
a multistep selection event. Lowered susceptibility towards fluoroquinolones
is associated with (i) alterations within the topoisomerases, (ii) protection of
the target from drug attack, (iii) changes in proteins responsible for intracellular drug concentration, and (iv) drug modification.
Target mutations
Resistance towards fluoroquinolones seems to primarily involve alterations
within the genes of the drug targets, DNA gyrase and topoisomearse IV,
being gyrA, gyrB and parC, parE respectively. It has been shown that DNA
gyrase is the primary target for fluoroquinolones in E.coli and that mutations
within its genes reduce the susceptibility towards the drug (Drlica et al.,
1980; Heaton et al., 2000). GyrA resistance mutations map in a region called
the quinolone resistance-determining region (QRDR) which encodes amino
acids 51 through 106 (Friedman et al., 2001; Yoshida et al., 1990). Mutations in parC and parE have no effect on resistance in E. coli by themselves
but rather in combination with mutations in gyrA and gyrB (Breines et al.,
1997; Khodursky et al., 1995) and (Paper V).
Target protection
Until recently fluoroquinolone resistance was thought only to be acquired by
chromosomal alterations. Plasmid mediated resistance towards nalidixic acid
in Shigella dysenteriae had been reported, but not confirmed (Munshi et al.,
1987). However, in 1998, Martinez-Martinez and co-workers found a multiresistance plasmid, pMG252, in a clinical isolate from Klebsiella pnemoniae which could be transferred between isolates and which when transferred
to E.coli conferred a low level of resistance and led to the generation of
high-level fluoroquinolone resistant mutants at 100 times the frequency of a
plasmid-free strain. The gene responsible for the increase in resistance towards fluoroquinolones was designated qnr (Martinez-Martinez et al., 1998).
Qnr is a member of the pentapeptide repeat family and protects DNA gyrase
and topoisomerase IV from fluoroquinolone binding (Tran and Jacoby,
2002; Tran et al., 2005). Jacoby et al. recently found a new qnr gene (qnrB)
that seems to have the same protective action, but has less than 40% amino
acid identity to the original qnrA gene. Another qnr gene, qnrS, has also
recently been found in a plasmid from a Shigella flexneri isolate (Hata et al.,
2005). Yet another member of this pentapeptide repeat family has been
shown to have a DNA-like structure in Mycobacterium tuberculosis and it
apparently mimics DNA as a substrate for DNA gyrase (Hegde et al., 2005).
25
Influx and Efflux mediated resistance
The mar (multiple antibiotic resistance) locus (Figure 6) is part of a regulon
that has been shown to be involved in controlling the level of fluoroquinolone resistance because when it’s repressors are mutated resistance levels
to fluoroquinolones and many other drugs increase (Cohen et al., 1988a;
Cohen et al., 1988b; Cohen et al., 1989; Okusu et al., 1996; Piddock et al.,
1991). The mar regulon also controls genes that are involved in cellular metabolism, physiology and virulence (Barbosa and Levy, 2000; Pomposiello et
al., 2001).
The marRAB locus consists of an operator, from which two different transcripts can be made, namely; marC and marRAB (Figure 6). MarR act as a
negative regulator of the operon, whereas MarA is a positive transcriptional
activator, and when overexpressed it will result in multiple antibiotic resistance involving the expression of at least 60 different genes in E.coli
(Barbosa and Levy, 2000; Pomposiello et al., 2001). This upregulation includes the transcription of acrA and tolC, which are components of the
AcrAB-TolC efflux pump. MarA is also involved in the downregulation of
the gene for the outer membrane protein, OmpF, through which hydrophilic
substances enter the cell (Cohen et al., 1988b; Cohen et al., 1989).
Fluoroquinolone resistant clinical isolates have often been associated with a
multiple antibiotic resistance phenotype. This is due to the decreased intracellular drug concentrations achieved by the combined effects of decreased
expression of the OmpF porin and overexpression of the AcrAB-TolC efflux
pump (Cohen et al., 1988b; Okusu et al., 1996). The overexpression of this
efflux pump is also associated with organic solvent tolerance (OST) in E.coli
(Wang et al., 2001). Data has shown that highly fluoroquinolone resistant
E.coli exhibit OST up to 30% more than sensitive E.coli strains (Kern et al.,
2000; Oethinger et al., 1998). There was a good correlation between the
OST phenotype and the presence of mutations within the marOR and acrR
genes in a set of clinical UTI E.coli that were fluoroquinolone resistant
(Komp Lindgren et al., 2003).
26
Figure 6. Regulation of influx and efflux mediated by the mar regulon.
Drug modification
Enzymes that degrade fluoroquinolones have been found only in fungal systems (Martens et al., 1996; Wetzstein et al., 1997; Wetzstein et al., 1999).
However, recently it has been shown that a variant of the aminoglycoside
acetyltransferase; (aac (6’)-Ib) called aac (6’)-Ib-cr modifies not only aminoglycosides but also fluoroquinolones, resulting in reduced fluoroquinolone
activity (Robicsek et al., 2006).
27
Persistant bacteria
When a bacterial population is growing exponentially and is then exposed to
bactericidal concentrations of antibiotic, the expectation is that the population should decrease until all bacteria are dead. However, it has been shown
that a small fraction of the population often remains alive. This phenomenon
has been called phenotypic tolerance (Balaban et al., 2004; Miller et al.,
2004; Wiuff et al., 2005). The phenomenon shows that a bacterial population
that is genetically homogeneous can be physiologically heterogeneous in
relation to its antibiotic susceptibility. Persister cells of Staphylococcus
aureus were isolated already in the 1940’s when exposed to penicillin
(Bigger, 1944). When E.coli was exposed to increasing levels of ciprofloxacin over time a surviving population of 10-7-10-8 was isolated and the persisters were not altered in their susceptibility to the drug (Marcusson et al.,
2005; Olofsson et al., 2006). There are many different suggestions about
what underlies this tolerance phenotypes. An interesting observation is that
there is cell-to-cell variation in growth rate within a genetically homogenous
population, and that the cells that have the slower growth rate have an increased resistance towards ampicillin exposure (Balaban et al., 2004). Induction of the SOS-response has also been shown to be involved in the disturbance of cell wall synthesis and growth, thereby protecting E.coli from ampicillin (Miller et al., 2004). Mutations within the hipA and hipB genes have
been associated with the enhanced probability of survival of E.coli in the
presence of ȕ-lactams and fluoroquinolones (Moyed and Bertrand, 1983;
Scherrer and Moyed, 1988; Wolfson et al., 1990). The toxin-anti toxin systems including HipAB and RelE can be upregulated and overexpressed giving a high toxin concentration in cell, leading to the shutdown of multiple
drug targets, thereby preventing the drug from inactivating the target molecules (Keren et al., 2004). A majority of infections are associated with bacterial biofilms where the survival of persisters has been shown to play a significant roll (Lewis, 2005). The importance of persisters in clinical infections
has been mathematically simulated in relation to dosing regimens, showing
that phenotypic tolerance could prevent clearance of the infection by the
antibiotic (Wiuff et al., 2005).
28
The biological cost of being resistant
Antibiotic resistance is frequently associated with a biological fitness cost
associated with the resistance mutations acquired. Since antibiotics usually
target essential genes and functions within the cell, mutations within these
genes, will most often negatively affect bacterial metabolism. Biological
costs reduce the growth fitness of bacteria, a parameter that can be measured
by competition experiments between clonally or isogenic related susceptible
and resistant strains in vitro or in vivo.
There are many examples of antibiotic resistance mutations reducing the
fitness of bacteria. Fusidic-acid resistant Staphylococcus areus containing
mutations in EF-G which is involved in translation have reduced fitness both
in vitro and in vivo (Nagaev et al., 2001). This is also true for fusidic acid
resistant and streptomycin resistant Salmonella typhimurium measured in
vitro and in vivo (Björkman et al., 2000). Similarly, rpoB mutations conferring resistance to rifampicin in E.coli, and rpsL mutations conferring resistance to streptomycin in E.coli and S. typhimurium reduce bacterial fitness
both in vitro and in vivo (Björkman et al., 2000; Levin et al., 2000; Reynolds, 2000). In a competition experiment made on human skin, fusidic acid
resistant Staphylococcus epidermis with fusA mutations had reduced fitness
relative to an isogenic antibiotic-susceptible strain (Gustafsson et al., 2003a).
However, there are also some antibiotic resistance mutations that seem to
have relatively low fitness costs for the bacteria. For example, certain mutations in rpsL in E.coli, S. typhimurium and Mycobacterium tuberculosis,
some gyrA and parC mutations in Streptococcus pneumoniae, and some
mutations within katG in M. tuberculosis have each been shown to cause no
measurable reduction in growth rate (Björkman et al., 1998; Björkman et al.,
1999; Gillespie et al., 2002; Pym et al., 2002; Sander et al., 2002).
There are very few experimental studies concerning the biological fitness
effects on E.coli of fluoroquinolone resistance. However, some recent studies showed that the accumulation of fluoroquinolone resistance mutations in
E.coli was associated with a large reduction in biological fitness both in vitro
and in vivo (Gualco et al., 2007; Komp Lindgren et al., 2005).
29
Fitness compensation
An antibiotic-resistant mutant has a beneficial advantage in an antibioticcontaining environment, and the mutant genotype will prevail and increase
in frequency in the population, even though it might have a decreased fitness
compared to the antibiotic-susceptible parent in an antibiotic-free environment. However, if the antibiotic selection is removed from the environment
this mutant genotype would be at a disadvantage in competition with the
susceptible wild-type strain, and might be driven to extinction.
There are two evolutionary pathways that could lead to the survival of the
resistant strain: (i) reversion to its ancestral genotype, i.e. reverting to antibiotic susceptibility (ii) the acquisition of compensatory mutations that would
ameliorate the fitness loss associated with resistance while maintaining the
resistance phenotype (Björkman et al., 1998; Reynolds, 2000). The first
pathway has a low probability, since a reversion would usually require a
single specific nucleotide substitution to return to its original antibioticsensitive state. The second evolutionary route is more likely in most cases
since the genetic target for a compensatory mutation that would give an increased fitness is likely to be larger. How frequent and restorable the fitness
compensation might be depends on the environmental factors.
Compensatory evolution has been observed both in vitro (Björkman et al.,
1998; Björkman et al., 2000; Johanson et al., 1996; Johnsen et al., 2002;
Levin et al., 2000; Maisnier-Patin et al., 2002; Nagaev et al., 2001;
Reynolds, 2000; Schrag et al., 1997), in vivo (Björkman et al., 2000;
Johnsen et al., 2002; Nagaev et al., 2001) and in clinical situations
(Björkholm et al., 2001; Nagaev et al., 2001). Streptomycin resistant rpsL
mutants in S. typhimurium were shown to be compensated by different mutations in ribosomal proteins S4, S5 and L19 (Björkman et al., 1999; MaisnierPatin et al., 2002). Studies both in S. typhimurium and S.aureus showed that
compensated mutants became both partly and fully restored in their fitness
without effecting their high level of resistance (Björkman et al., 1998;
Björkman et al., 2000; Nagaev et al., 2001). The same compensatory pattern
was seen with rifampicin resistant E.coli and in clinical isoniazid resistant
M.tuberculosis (Reynolds, 2000; Sherman et al., 1996). Compensatory evolution will tend to stabilize antibiotic resistance in a bacterial population
(Björkman et al., 2000; Levin et al., 2000).
30
Susceptibility versus resistance
Antibiotic susceptibility is defined and usually measured by the antibiotic
concentration that it required to have an effect on the specific organism. The
bacterium is considered susceptible if the agent restricts bacterial growth. In
contrast, if the agent does not restrict bacterial growth, the bacteria can be
considered to be resistant. Bacteria may be inherently resistant to certain
antibiotics, for example because the drug fails to enter the bacterial cell, or
they may acquire resistance, for example by mutation or HGT of resistance
genes.
Dosing regimens: pharmacokinetics and
pharmacodynamics
Pharmacological parameters of antibiotics are most important to consider in
respect to their in vivo activity and their administration to clear infections
(Figure 7). Dosing regimens are usually based on the pharmacokinetics and
the pharmacodynamics of the antibiotic. Pharmacokinetics (PK) involves the
absorption, distribution and elimination of the drug. Pharmacodynamics
(PD) shows the relationship between serum concentrations of the drug and
the pharmacological and toxicological effects of the drug. After a drug has
been administrated it will initially reach a maximum serum concentration
(Cmax) within the body. This concentration gradually falls as the drug is metabolised or excreted. The area under the serum concentration-time curve
(AUC) represents the total amount of drug exposure in the body for a
particular dosing regimen.
The Minimum inhibitory concentration - MIC
The aim of antibiotic therapy is to eradicate or inhibit growth of the infecting
bacteria. The concentration of drug needed to inhibit the growth of bacteria
in vitro is called the Minimum Inhibitory Concentration (MIC). The definition is set as the lowest concentration needed to inhibit visible growth in
liquid media or on solid media under standardized conditions. These conditions are set by national or international organizations such as the CLSI in
the USA, and the SRGA in Sweden. Another parameter that can correlate
with success in antibiotic treatment is the amount of time the antibiotic concentration remains over the MIC so as to eradicate the infection (T>MIC). It
is important to maintain antibiotic serum concentrations above the MIC
value to eliminate re-growth of the bacteria (Craig, 1998).
31
Mutant prevention concentration – MPC
Mutant Prevention Concentration (MPC) dosing is a relatively new concept
that has been introduced to prevent the selection or enrichment of drug resistant mutants during therapy. The MPC is the lowest drug concentration that
inhibits growth of the least-susceptible single-step mutant in a large (1010
cfu) bacterial population.
AUC/MIC
Fluoroquinolones exhibit concentration-dependent killing kinetics and the
AUC/MIC ratio is a PK/PD parameter that shows a good correlation with
bactericidal efficacy (Craig, 1998). The relationship between the AUC/MIC
and the eradication of the infection has been calculated for different antibiotics. Studies have shown that an AUC/MIC value of t125 for intravenous
ciprofloxacin was associated with a satisfactory outcome for seriously ill
patients (Forrest et al., 1993). In contrast, levofloxacin seemed more effective requiring a lower AUC/MIC value of ~100 to clear the infection (Craig,
1998).
AUC/MPC
The mutant prevention concentration has also been included in these formulas since it is important to restrict the growth of resistant mutants during
therapy. There have been efforts made to measure the AUC/MPC and the
time within the so called Mutant Selective Window (MSW) which is the
concentration range where mutants are selected (Blondeau et al., 2001; Dong
et al., 1999; Drlica, 2003; Firsov et al., 2003; Zinner et al., 2003). An in
vitro study showed that an AUC/MPC of • 22 was needed to restrict mutant
outgrowth of wild-type Escherichia coli using ciprofloxacin (Olofsson et al.,
2006). Similar values were found in an in vivo study where rabbits were
treated with levofloxacin against a Staphylococcus aures infection, where an
AUC/MPC > 25 was needed to restrict the acquisition of resistance (Cui et
al., 2006).
Breakpoints
Breakpoints are used as a tool to translate the actual MIC values of bacteria
in relation to specific drugs into a simple categorization as susceptible, intermediate or resistant. Breakpoints are defined based on the pharmacokinetic data on the relationship between the drug and specific species, as well
as on the basis of a population analysis of the range of drug susceptibility
values in the natural bacterial population. Breakpoints are used clinically as
a warning system in relation to the suitability of a particular drug treatment,
and to follow the evolution of resistance patterns in the population.
32
Figure 7. Pharmacodynamic and pharmacokinetic parameters important for effective bacterial elimation and mutant prevention.
33
Taking on the resistance problem - Restricting
development of mutants
Increasing resistance towards fluoroquinolones
Fluoroquinolones are a very important class of antibiotics with 18% of total
market share (Kresse et al., 2007). According to the EARSS database 25 of
28 European countries showed a significant increase in resistance towards
fluoroquinolones between the years 2001 and 2005. The northern countries
still remain at a low level of resistance whereas the southern countries, including Bulgaria, Cyprus, Spain, Italy, Malta and Portugal, have doubled
their resistance levels between 2004 and 2005 and each country report
fluoroquinolone resistant E.coli levels above 25% (EARSS).
How do we prevent the resistance development?
As fluoroquinolone resistance develops in a step-wise manner it can be hard
to distinguish when bacterial strains has passed the point of no return. Dealing effectively with the resistance problem will probably require a series of
different approaches including (i) the development of new antimicrobial
agents, (ii) efforts to reduce selection pressure for resistance by reducing the
consumption of antibiotics and (iii) the optimisation of dosing regimens so
as to minimize the selection of resistant mutants. The following section will
discuss the importance of an anti-mutant dosing strategy.
Mutant prevention concentration
The mutant prevention concentration is defined as the drug concentration
that will prevent the least susceptible, single-step mutant in a large bacterial
population (>1010) from growing (Baquero, 1990; Baquero and Negri,
1997b; Dong et al., 1999; Drlica, 2003). Thus, the bacteria would need to
acquire two or more resistance mutations to be able to grow at the MPC.
This is unlikely to happen since the mutation frequency for resistance is
~10-7, and would thus require a bacterial population of ~1014 for a double
mutant, which is not common in most infections (Low, 2001).
The MPC concept was coined after the discovery made in studies with Mycobacterium where the recovery of resistant mutants showed two distinct
drops in response to treatment with increasing concentrations of drug. Thus,
when cells were treated with increasing concentrations of fluoroquinolones,
the number of cfu first dropped at a concentration corresponding to the level
that inhibited the susceptible cells within the population, corresponding to
the MIC of the population. The plateau of surviving cfu at higher drug concentrations above MIC was due to the presence of resistant mutant popula34
tions that were present at low frequencies (10-7). As the antibiotic concentration was increased further there was an additional drop in cfu due to the inhibition of the least susceptible, single-step mutants in the population. This
higher drug concentration was referred to as the MPC (Dong et al., 1999;
Sindelar et al., 2000; Zhou et al., 2000).
The method used to determine the MPC level is by applying >1010 cells onto
a series of drug containing agar plates and incubating for a minimum of 48
hours. The MPC is the lowest drug concentration at which no resistant colonies grow (Dong et al., 1999). There has been several studies that have
measured MPC for a range of different pathogens and a variety of different
antibiotic compounds (Hansen and Blondeau, 2005; Hansen et al., 2006;
Hermsen et al., 2005; Li et al., 2004; Linde and Lehn, 2004; Marcusson et
al., 2005; Metzler et al., 2004; Randall et al., 2004; Rodriguez et al., 2004a;
Rodriguez et al., 2004b; Rodriguez et al., 2005; Smith et al., 2004;
Wetzstein, 2005).
Mutant selective window
When antibiotics are used to treat bacterial infections, antibiotic gradients
are formed within the human body (Baquero and Negri, 1997a; Negri et al.,
1994). Baquero introduced the notion that there existed a dangerous drug
concentration range were drug-resistant mutants were selected (Baquero and
Negri, 1997b). The mutants were said to “hill climb” by achieving additional
mutations to be able to survive at higher drug concentrations (Figure 8).
These concentration ranges will be achieved within the human body in a
series of compartments dependent on diffusion rates as well as elimination
rates. These antibiotic concentration ranges will be an optimal environment
for the selection of bacterial populations harbouring antibiotic resistance
determinants, while at the same time suppressing or slowing growth of the
susceptible population (Baquero, 1990; Baquero and Negri, 1997b).
The MSW hypothesis suggests that mutants are most likely to be selected
within the drug concentration window between the MIC and the MPC (Zhao
and Drlica, 2001, 2002). Below MIC there is no drug selection effect. Within
the MSW susceptible cells are inhibited while resistant cells are selected and
grow. Above the MSW (above MPC) even resistant cells are inhibited. It has
been shown that mutants having a low level of resistance towards a drug will
in a stepwise way be selected for, and will become even less susceptible
towards the drug. (Dong et al., 1999; Zhao and Drlica, 2001, 2002; Zhou et
al., 2000). Concentration-dependent selection towards ceftotaximine, giving
low-level antibiotic-resistant genetic variants, has been demonstrated both in
vitro and in vivo (Negri et al., 2000). In a study with S. aureus exposed to
35
fluoroquinolone concentrations that were below, within, and above the
MSW, mutant enrichment was only observed when the concentration range
fell within the MSW, for at least 20% of the time (Firsov et al., 2003). These
results suggest that the drug concentration in therapy should never be within
the MSW under any circumstances. The aim should be to develop dosing
regimens that would restrict the selection of resistance; by maintaining drug
concentrations above the MPC throughout therapy (Drlica and Malik, 2003).
Figure 8. Mutants will “Hill climb” within the Mutant Selective Window.
Combination-therapy
By using only one antibiotic in therapy there will always be a risk of selecting mutants since there is only one target for the antibiotic. This risk is
greater if the antibiotic concentrations during therapy are not kept above the
MPC. In cases where MPC treatment is not desirable (e.g. for reasons of
toxicity) then combination therapy, using two antibiotic simultaneously,
would probably restrict resistance development. To be able to use dual compounds in therapy requires that the antibiotics target different intracellular
mechanisms and may also require that they have similar pharmacokinetic
properties. It is important that neither of the drug concentrations fall below
its MIC during the period of therapy, as that would be equivalent to monotherapy for part of the treatment period (Drlica, 2001; Zhao and Drlica,
2001).
36
37
38
PRESENT INVESTIGATIONS
Understanding how antibiotic resistance develops is fundamental knowledge
required in the battle to reduce the emergence and spread of resistance. The
aims of this research investigation were the following:
x To measure for a set of clinical UTI E. coli isolates in vitro their
MPC values for ciprofloxacin and ask whether these could be predicted from their MIC values.
x To apply an in vitro kinetic model to investigate the MPC-related
pharmacodynamic indices of ciprofloxacin that predict the prevention of resistance.
x To investigate the relationship between different approved fluoroquinolone dosing regimens and the selection of pre-existing
resistant mutants.
x To make a step-wise selection for fluoroquinolone resistance and
measure the associated fitness costs both in vitro and in vivo.
x To investigate different combinations of mutations associated with
fluoroquinolone resistance and measure their effects on resistance
and bacterial fitness.
x To identify fitness-compensating mutations in highly fluoroquinolone-resistant E. coli.
39
Prevention of resistance development and
alternative dosing regimens
Study I) Mutant prevention concentrations of ciprofloxacin for urinary
tract infection isolates of Escherichia coli.
The mutant prevention concentration (MPC) is defined as the drug concentration needed to prevent the least susceptible resistant mutant in a large
bacterial population from growing. The fluoroquinolones are an important
group of antimicrobials used to treat various different infections, in particular those caused by Gram-negative pathogens (Kresse et al., 2007).
The study included to measure the MPC level of ciprofloxacin for a set of
UTI E. coli isolates with different levels of susceptibility and determine
whether MPC could be predicted from MIC. In our test panel we had isolates
that had MIC values ranging from completely susceptible up to those that
exceeded the CLSI (NCCLS) breakpoint for ciprofloxacin (4 µg/ml). These
latter strains had MPCs greater than 32 µg/ml, and was not further evaluated
since they would need very high dosing (•32 µg/ml) during monotherapy
which would not be possible with approved dosing procedures (Boy et al.,
2004; Talan et al., 2004). 22 of the isolates that had MICs below the breakpoint for ciprofloxacin susceptibility were shown to have MPC values that
ranged between 0.1 µg/ml and 5 µg/ml (Figure 9). MIC was not a good predictor of MPC, and correlated badly with a linear regression value of R2 =
0.58. For most isolates the ratio MPC/MIC was ~16. The MSW however
varied greatly between strains. Four strains with low MICs (0.016 – 0.023
µg/ml) had unusually high MPCs of 1 – 2.5 µg/ml, giving an MPC/MIC
>100. This shows that the MPC cannot be accurately predicted from MIC.
There are presumably other genetic factors causing the high MPC in these
strains which are not reflected in the MIC. For most of the susceptible strains
it should be possible to dose above MPC using monotherapy with currently
approved dosing procedures (Boy et al., 2004; Talan et al., 2004), thus reducing the chance of selecting resistance.
40
Figure 9. 22 clinical strains and their MIC and MPC values.
One concentration step below the MPC level of each strain tested, resistant
mutants were selected and tested for their MIC values. These mutants frequently had MICs equal to, or greater than, the MPC for that strain. A potential consequence of sub-optimal therapy that selects for resistance is illustrated by these mutants. This implies that they are not the product of singlestep mutations. A logical explanation for this is that within the MSW some
mutants that survive grow poorly, and are subject to selection for secondstep mutations that enhance growth associated with increasing the level of
resistance (see also Paper V). Multi-step selection for colony growth has
been well documented in the phenomenon of adaptive mutation (Foster,
2004; Rosenberg and Hastings, 2004; Roth and Andersson, 2004). At MPC,
in contrast, the growth of first-step mutants is completely inhibited and a
multi-step evolution process is prevented.
At the end of the MPC experiment, i.e after 96 h, the MPC plates were
screened for potential survivors. Surviving cfu was isolated after 96 h incubation at MPC at a frequency of ~10-8 from the strains C47, C50 and Nu14,
and at ~10-7 for C97. A strain with a heritable enhanced survival frequency (
C97-1) relative to its parent C97, was also isolated. Survival at MPC was not
associated with an increase in MIC, nor was it associated with mutations in
the hipA or hipB genes where a mutation that enhances survival in the presence of ß-lactams and fluoroquinolones has previously been identified
(Moyed and Bertrand, 1983; Scherrer and Moyed, 1988; Wolfson et al.,
1990). These results rule out mutations in hipA or hipB as the cause of enhanced survival at MPC in these isolates. Thus, although some cells survive
41
prolonged exposure to MPC, there were no resistant mutants among the survivors, showing that the MPC concept works as expected. Whether persisters
are important for re-infection of UTI are not known and needs futher investigation.
This study has shown that variable genetic factors in clinical isolates determine the drug concentration needed to prevent accumulation of mutants. The
MPC cannot be accurately predicted by determining the MIC for the strain.
In the in vitro assay of MPC described here, constant antibiotic concentrations were used throughout the experiments. As has been pointed out many
times (Allen et al., 2003; Blondeau et al., 2001; Dong et al., 1999; Drlica
and Malik, 2003; Firsov et al., 2003; Firsov et al., 2004; Zinner et al., 2003)
to be clinically useful, the MPC concept should be tested with consideration
for the drugs pharmacokinetic properties, i.e. the critical magnitude of the
drug exposure required to prevent emergence of resistant mutants in relation
to the MPC needs to be determined.
42
Study II) Selection of ciprofloxacin resistance in Escherichia coli in an in
vitro kinetic model: relation between drug exposure and mutant
prevention concentration.
To be able to evaluate the significance and reliability of MPC for clinical
application it is important to measure this under conditions that closely
mimic the clinical environment. Previous studies done with fluoroquinolones
integrating PK/PD implied that the area under the concentration-time curve
AUC/MIC and the maximum concentration of the drug in serum Cmax/MIC
are the most important pharmacodynimics indicies that predict the efficacy
of bacterial killing (Blondeau et al., 2004; Zinner and Firsov, 1999; Zinner et
al., 2004).
In this study the aim was to investigate and evaluate the MPC of ciprofloxacin for two susceptible wild-type strains (Nu14, CCUG17620) and one
strain carrying a single resistance mutation in the gyrA gene (Nu118) by
using an in vitro kinetic model. MPC was determined by using a constant
infusion of ciprofloxacin and then related to the PK/PD of the drug. The
latter was assessed by the prevention of growth of resistant mutants in relation to different dosing profiles.
The study was started by measuring the effects of static ciprofloxacin concentrations for the three strains (Nu14, CCUG17620 and Nu118), by exposing them to the drug for 24 h. For both the susceptible strains an MPC of
0.128 mg/L ciprofloxacin was needed to prevent any mutant formation. This
corresponds to a MPC/MIC of ~16, which is in full agreement with the previous in vitro study on agar (Paper I). The MPC level determined for the
gyrA mutant gave a lower MPC/MIC index of ~4, giving no mutants at 0.188
mg/L. All strains gave resistant mutants under the MPC i.e. within the
MSW, as expected.
Next, the T > MPCPR was measured in a pharmacokinetic model. In the first
round of experiments Nu14 was exposed to a static concentration at its MPC
(128 mg/L) for 2-18 h before initiation of the dilution phase with a T1/2 of 4
h. The results showed that a T > MPC of 18 h was sufficient to prevent mutant growth (Figure 10, left panel). In the second trial the Cmax was varied for
each strains drug dilution with a T1/2 of 4 h that was begun at time zero. It
was found that for both susceptible strains resistance development was prevented when a Cmax of 64xMIC, corresponding to a T>MPC of 8 h (33% of
the dosing interval) was achieved (Figure 10, right panel). For the mutant
Nu118 a Cmax of only 8xMIC prevented resistance development, corresponding to a T>MPC of 4 h (17% of the dosing interval). Results in this study
43
suggest that these parameters by themselves do not predict the prevention of
resistance development.
Figure 10. Ciprofloxacin concentration and time needed to get the mutant prevention concentration for wild-type Nu14. Solid lines indicates concentrations that
selected resistant mutants; dashed lines indicates the concentration that prevented
mutant formation.
As in the previous study (Paper I) there was a surviving population at the
end of the experiments. These persistering cells were unchanged in their
MIC compared to their original MIC values. Persistance of bacterial subpopulations in the presence of antibiotics is not a new phenomenon (Lewis,
2005). The number of persisters found at the highest ciprofloxacin concentrations tested here were: for Nu14, 104 cfu/mL (1024 x MIC); CCUG17620,
106 cfu/mL (128 x MIC); and Nu118, 107 cfu/mL (32 x MIC).
The PK/PD parameters were measured and suggest that the index that correlated best with the prevention of mutant development was AUC/MPC. For
both the susceptible strains the AUC/MPC index gave a value of • 22 that
was associated with the prevention of resistance development. AUC/MIC is
the parameter that has been associated with efficient bacterial killing and is
used for determining current dosing regimens (Craig, 1998). The results in
this study suggest that AUC/MPC might be a more predictive parameter to
consider in dosing for the prevention of mutant development.
44
Study III) Dose-related selection of fluoroquinolone-resistant
Escherichia coli.
Fluoroquinolone doses used in treatment vary according to the suspected
pathogen and the localization and severity of the infection. By using an in
vitro kinetic model the aim was to study the effectiveness of six different
approved dosing regimens of commonly used fluoroquinolones and to evaluate their potential to select pre-existing mutants.
The plan was to investigate the effects of clinically used doses of norfloxacin, ciprofloxacin and moxifloxacin on survival and selection in E. coli
populations with pre-existing fluoroquinolone-resistant mutants. Cultures of
susceptible wild-type (araBǻ) and isogenic single- (gyrA S83L) or double
(gyrA S83L, marRǻ) fluoroquinolone-resistant mutants were mixed, and
then exposed to fluoroquinolones for 24 h in an in vitro kinetic model. The
antibiotic concentrations used corresponded to approved clinical doses of
norfloxacin, 200 mg administered twice daily; ciprofloxacin, 100 – 750 mg
administered twice daily; and moxifloxacin, 400 mg administered once
daily. Resistance development was monitored together with MIC determinations of survivors and sequencing of known resistance genes.
All tested doses were sufficient to eradicate the susceptible wild-type strain.
Norfloxacin 200 mg administered twice daily selected for both the single and
double resistant mutants. Dosing with ciprofloxacin 250 mg administered
twice daily eradicated the gyrAS83L single mutant, but 750 mg administered
twice daily was required to eradicate the double-mutant population.
Moxifloxacin administrated as 400 mg once daily was sufficient to eliminate
the wild-type and the single mutant but did not completely remove the double mutant.
Five mutants that had an increased MIC were isolated at the end point of the
ciprofloxacin experiments and the genes which are most associated with
fluoroquinolone resistance were sequenced. In one double- mutant exposed
to a ciprofloxacin dose of 500 mg administered twice daily, a novel parC
mutation was identified: parC G171C. The mutant had a ciprofloxacin MIC
of 1.5 µg/ml, corresponding to a two-step increase on the Etest scale compared to the parental strain. The remaining four mutant strains did not reveal
any new mutations within gyrA, gyrB, parC, parE, marOR, or acrR. This is
consistent with published findings that many mutations causing small increases in fluoroquinolone resistance are not found in the classical resistance-associated genes (Paper IV).
45
The pharmacodynamic parameter AUC/MIC is known to correlate with efficacy of the fluoroquinolones. Clinical and experimental studies have shown
that an AUC/MIC value of about 100-125 is sufficient to predict clinical and
microbiological success (Forrest et al., 1993; Odenholt and Cars, 2006;
Wright et al., 2000). For the wild-type population, AUC/MIC values were
above 100 for all the tested fluoroquinolone doses, which was sufficient for
full eradication in all cases. In the present study an AUC/MPCwild-type value
of 35 prevented selection of the single mutant when present initially as 1%
of the total population. These results suggest that AUC/MPC is a useful predictor of outcome. Among antibiotic-resistant mutants isolated from patients
there is a great variation in both MIC and MPC (Komp Lindgren et al.,
2003) and (Paper I) and pharmacodynamic indices such as AUC/MIC and
AUC/MPC could vary significantly depending on these values.
This study shows that several of the currently recommended doses of norfloxacin, ciprofloxacin and moxifloxacin are not sufficiently effective to
prevent the selection of fluoroquinolone resistant mutants in E. coli when
serum concentrations are simulated. The pharmacodynamic exposure needed
to prevent selection of resistance found in this study may well be obtained in
the urine where the drug is concentrated. However, some bacteria may reside
in the bladder epithelium (Kau et al., 2005) and selection may also take
place in the commensal flora. Ciprofloxacin 750 mg administered twice
daily was the only tested clinical dose that eradicated all E. coli bacteria,
including single and double mutants. Thus, focusing on the target pathogen
in a simplified system, ciprofloxacin 750 mg administered twice daily would
be the preferred choice for treatment. Moxifloxacin 400 mg administered
once daily also showed high efficacy. It is of the highest importance that,
during drug development, antibiotics as well as their dosing regimes should
be carefully selected based on the pharmacokinetic and pharmacodynamic
properties that support prevention of antibiotic resistance.
46
The genetics of resistance and bacterial fitness
IV) Biological cost of single and multiple norfloxacin resistance
mutations in Escherichia coli implicated in urinary tract infections.
Resistance to fluoroquinolones in UTI E. coli is associated with multiple
mutations, typically altering DNA gyrase, DNA topoisomerase IV, and regulation of AcrAB-TolC-mediated efflux. This investigation asked whether
there was a fitness cost associated with the accumulation of multiple resistance-associated mutations.
The susceptible E. coli UTI isolate Nu14 were used to select resistant mutants through 3-5 successive steps with norfloxacin. Each selection step was
made at twice the MIC of the selected strain. After each selection the MIC of
the selected mutants were measured and regions of gyrA, gyrB, parC, and
parE, previously associated with resistance mutation, and all of marOR and
acrR were sequenced. The first selection step yielded mutations in gyrA,
gyrB, and marOR. 2 mutants of each class were used as the starting point for
subsequent selection steps which yielded mutations in gyrA, parE, and marOR, but not in gyrB or parC or acrR. Resistance-associated mutations were
identified among these six genes in almost all isolates after selection steps 1
and 2, but in less than 50% of isolates after subsequent selection steps.
Selected strains were competed in vitro, in urine, and in a mouse UTI infection model, against the starting strain, Nu14. First step mutations were not
associated with significant fitness costs, having fitness values very close to
those of the parental Nu14 strain. Among the nine second-step mutants recovered, six were fit within ±3% of the Nu14 value, close to the standard
error of these measurements, while three were significantly less fit, with
growth rates 13 – 16% slower than Nu14. The accumulation of three or more
resistance-associated mutations was strongly associated with a large reduction in biological fitness with generation times increased by between 6% and
55% with a mean increase in generation time of 31%. The reduced fitness
was observed both in vitro and in vivo. Two fifth-step mutants tested had
generation times increased by 23% and 43% respectively, relative to Nu14.
47
Interestingly, in some lineages a partial restoration of fitness was associated
with the accumulation of additional mutations in late selection steps. In one
of the lineages specific resistance-associated mutations were identified for
each of the selection steps. Each of the strains from two different lineages
(2a and 2b) was competed with Nu14 to measure their relative fitness both in
vitro and in vivo in a mouse model (Figure 11). In lineage 2a the in vitro
results showed a progressive decline in fitness with each of the four selection
steps increasing the resistance. In contrast, the in vivo results for the same
lineage showed a similar pattern with decreased fitness in the first three selection steps, but increased fitness associated with acquisition of the fourth
mutation. In the second lineage (2b) the in vitro and in vivo results followed
each other. A small fitness loss was associated with the first mutation, followed by a greater loss associated with the second mutation, and finally a
partial restoration of fitness associated with the third mutation.
Figure 11. The fitness cost in vitro and in vivo for two different pathways of accumulating resistance mutations.
This study show that the accumulation of resistance-associated mutations is
associated with a loss of bacterial fitness, but also suggest that particular
combinations of these resistance mutations can partially compensate for
these fitness losses.
The reversal of fitness costs associated with the accumulation of mutations is
interesting in terms of the clinical development of fluoroquinolone resistance. Thus, if the multiple mutations required for resistance impose a severe
fitness cost, resistant bacteria will be under selection pressure to avoid,
minimize, or compensate for these costs. There could also be a parallel accumulation of fitness-compensating mutations either within or outside of the
genes associated with resistance. In the clinical setting these fitnesscompensating mutations might be acquired in the time-interval between an48
tibiotic treatment (selection) which has shown to be the case in vitro in the
absence of antibiotics (Björkman et al., 1998; Nagaev et al., 2001). The suggestion made is that the relative biological costs of multiple mutations may
influence the evolution of E. coli strains developing resistance to fluoroquinolones.
49
V) Increased bacterial fitness selected with antimicrobial drug
resistance
It is known that fluoroquinolone resistance mutations accumulate in a stepwise manner and that this accumulation of mutations is associated with
growth rate reduction. This study investigated in a systematic way the consequences of specific combinations of mutations on resistance and fitness. A
set of isogenic strains carrying combinations of resistance mutations found
commonly in clinical isolates were constructed and their associated levels of
resistance and fitness were measured.
A typical highly resistant E. coli UTI isolate, C1186, was the basis for the
mutations used in these constructions. The clinical strain is highly-resistant
to fluoroquinolones with a MIC for ciprofloxacin of 32 µg/ml. It carries resistance mutations altering topoisomerases (gyrA S83L, D87N; parC S80I),
and up-regulating drug efflux (marOR small deletion, and amino acid substitution; acrR IS1 insertion). C1186 has a growth advantage of 3% per generation in competition against a laboratory wild-type. A total of 28 isogenic
derivatives of wild-type MG1655 were constructed each mimicking in part
the complex resistance genotype of C1186.
MIC for ciprofloxacin was measured for each of the 28 strains. Single mutations in gyrA, S83L and D87N, increased MIC 24-fold and 15-fold respectively. Knockout mutations in marR and acrR each increased MIC only 2 3-fold, while the substitution S80I in parC had no effect on MIC. Double
mutation combinations had MIC’s 8 – 63-fold wild-type level, with the
combination 'marR + 'acrR having the smallest increase. Triple mutation
combinations had MIC’s in the range 0.38 – 32 Pg/ml, with 5/9 above the
breakpoint that defines clinical resistance in Europe (1 Pg/ml) (EUCAST,
2006), and 2/9 above the breakpoint currently used in the USA (4 Pg/ml).
The 28 strains were also tested in growth competitions against wild-type to
measure their fitness. Some single mutations gyrA S83L, gyrA D87N and
parC S80I were near neutral (having a fitness index of 1.0), whereas 'marR
and 'acrR reduced fitness significantly having fitness values of 0.83 and
0.92, respectively. The mean fitness of single resistance mutations was 0.95,
ranging from 0.83 – 1.01. With two mutations the fitness decreased to a
mean value of 0.89, ranging between 0.79 – 0.99, whilst with three mutations it was reduced even further to 0.80 ranging between 0.54 – 0.98. However, the addition of a fourth resistance mutation reversed this downward
trend and increased mean fitness to 0.87 with a range of 0.81 – 0.92. The
ultimate strain, LM705 which contained all five mutations mimicking the
resistance genotype of C1186, had a fitness of 0.90.
50
Six strains were of special interest since they showed that an additional resistance mutation increased both resistance and fitness at the same time. In five
of the six strains the additional mutation that increasing resistance was gyrA
S83L. In four cases this mutation increased the resistance level to 32 µg/ml.
When analyzing the fitness of these strains the average increase in fitness
associated with the acquisition of an extra mutation was 10% ranging between 5-24%. From the data it can be concluded that for certain mutation
combinations, fluoroquinolone resistance correlates positively with fitness.
An important clinical implication of this correlation between resistance mutations and fitness is that once a strain has acquired the first few resistance
mutations (and still lies well under the breakpoint defining clinical resistance) it can continue to evolve high-level fluoroquinolone resistance, even
in the absence of further antibiotic selective pressure.
Figure 12. Illustration of increasing resistance to ciprofloxacin due to accumulation
of mutations (top panel). Loss in fitness by accumulation of up to three resistance
mutations but an increase in fitness by the addition of the two later resistance mutations (bottom panel).
51
A novel evolutionary strategy that reduces the fitness costs for bacteria of
mutations conferring antimicrobial drug resistance has been identified. This
study shows how bacteria can reduce mutational fitness costs while simultaneously increasing their level of drug resistance. The details of how the evolution of fluoroquinolone resistance plays out in nature will depend on the
particular selective environment. Bacteria that progress down an evolutionary path with reduced fitness relative to a competing population may continue to extinction, or may, given the opportunity by mutation, acquire a
change that increases their relative fitness thus enhancing their chances of
survival (Figure 13).
Figure 13. Illustration of different possible evolutionary pathways dependent on
bacterial fitness and resistance accumulation. (A) Resistance mutations can be accumulated that have low or no fitness burden on the bacterial population. (B) Resistance mutations that have a fitness cost can become fitness compensated without
changing the resistance level. (C) Resistance mutations that lower the fitness of a
bacterial population can lead to extinction. (D) Fitness can be compensated and
simultaneously co-select for increased resistance.
52
VI) Fitness compensation in fluoroquinolone resistant Escherichia coli
by mutant RNA polymerase
Knowledge from much published previous work is that the evolution of susceptible bacteria to drug resistance, whether by mutation or by horizontal
gene acquisition usually is associated with a loss of bacterial fitness expressed as reduced growth rate in vitro and reduced virulence in vivo. E. coli
lineages were previously evolved (Paper IV) in the laboratory to high-level
fluoroquinolone resistance which resulted in reduced fitness compared to
their parental strain. There are several strategies bacteria can exploit to reduce or avoid these resistance-associated fitness costs while still maintaining
drug resistance, (i) they could accumulate only cost-free or low-cost resistance mutations, (ii) but it is also possible to acquire so-called fitness compensating mutations that ameliorate the fitness costs of resistance without
losing drug resistance.
Antibiotic-susceptible E. coli were evolved step-wise to high level resistance
to the fluoroquinolone ciprofloxacin. This resulted in lineages that were
shown by DNA sequencing to contain multiple mutations altering genes
coding for DNA gyrase, DNA topoisomerase IV, and genes controlling the
regulation of drug efflux. Two highly-resistant mutants were named LM597
and LM642, respectively. The growth rates of these mutants were measured
and showed that they grew at less than half the wild-type rate. These strains
were then cycled in drug-free rich medium to select for bacteria with new
mutations ameliorating the fitness costs of resistance. Two fast-growing
clones with improved growth rates were chosen for further analysis. LM668
was derived from LM597, while LM683 was derived from LM642. The MIC
of ciprofloxacin in the fast-growing mutants was reduced relative to the parental strains but in each case remained well over the breakpoint for clinical
resistance.
To identify compensatory mutations responsible for the improved growth
rate in the two strains a transposome, <R6KJori/KAN-2->, was introduced
separately into cultures of LM668 and LM683, with selection for kanamycin
resistance at 25 µg/ml. This resulted in populations of cells with the transposome inserted into the chromosome at random positions. Kanamycinresistant transformants of each strain were pooled separately and bacteriophage P1 was grown on each pool. The P1 pool for each of the fast growing
strains was then used to transduce its slow growing parental strain selecting
for the transposome and screening for fast-growing colonies. Fast growing
transposome-linked transductants were retained from both the slow growing
strain LM597 and LM642. Chromosomal DNA was prepared from each of
the candidate strains, cut and ligated into a circular plasmid form and trans53
formed by electroporation in a pir+ E. coli selecting for kanamycin resistance. Plasmid DNA prepared from the transformants was sequenced to
identify the site of transposome insertion into the chromosome. Sequencing
revealed that the transposome insertions linked to fast growth in the derivative of LM668 were inserted at two different sites; within frwD and in between ppc and argE. Both of these insertion sites are in the same region of
the chromosome (Figure 14a), and the mutation responsible for fast growth
in LM668 was provisionally designated fast-1. The mutation that compensated growth in LM683 was linked to a transposome inserted within gspD
(Figure 14b) in its derivative, and the mutation responsible for fast growth in
this strain was provisionally designated fast-2.
To identify the mutations fast-1 and fast-2 three-point mapping was performed. The transposome-linked fast-1 strain was subjected to transduction
with btuB::Tn10. This gave the gene order fast-1 – Tn5 – btuB::Tn10., We
also used spontaneous rifampicin-resistant rpoB mutations in a second mapping experiment of the same region and fortuitously discovered that these
rpoB mutations conferred the fast-growth phenotype. These mutations in
rpoB were designated fast-3 and fast-4 (Figure 14a). A similar mapping
experiment was made for a derivative of the fast-2 strain that had been transduced with a strain containing zhd-126::Tn10, rpsE100. Fast-2 maps on the
distal side of rpsE in a region carrying many ribosomal protein genes and the
RNA polymerase D-subunit gene rpoA (Figure 14b).
Figure 14. Location of the compensatory mutations, fast-1, fast-3, fast-4 and fast-2,
in the E. coli genome.
54
DNA sequence analysis identified fast-2 as the mutation Arg195Pro in rpoA.
The mutations fast-3 and fast-4 are His526Tyr and Leu533Pro in rpoB. The
fast-1 mutation has yet to be identified. These are the first mutations shown
to compensate the fitness defects associated with fluoroquinolone resistance.
This study has shown that several individual mutations altering RNA polymerase (RNAP) can substantially improve the growth fitness of multiplymutant fluoroquinolone resistant strains. Mutations were identified in the Dand the ß-subunits of RNA polymerase.. It seems that mutations that alter
RNA polymerase can significantly improve the growth rate of fluoroquinolone resistant strains with reduced fitness.
The suggested model to explain the data is the following: (i) Normal growth
rate is strongly dependent on the rate of gene transcription; (ii) The rate of
transcription is dependent on type II topoisomerase activity to modulate the
superhelicity of DNA; (iii) Fluoroquinolone-resistant mutant topoisomerases
are less efficient at modulating superhelicity and this reduces the rate of
transcription and thus growth rate; and (iv) Mutant RNA polymerase is less
sensitive to local DNA topology and thus less dependent on topoisomerase
activity for transcription. These phenotypes could be mediated at the level of
transcription initiation or possibly at the level of Rho-dependent transcription termination.
One of the implications of this model is that some fluoroquinolone resistant
clinical isolates may be expected to carry mutations in RNAP or its associated regulatory factors, that can promote transcription efficiently, independent of the actions of topoisomerases in re-modelling the structure of DNA.
55
56
Is there a solution of the antibiotic resistance
problem and perspectives for the future
So we are facing a real problem with the increasing resistance development
towards the antibiotics used to treat for small infections as well as life threatening diseases. Because there has been a misuse of antibiotics since their
introduction we now see many antibiotic classes lose their activity towards
important bacterial pathogens.
The work presented in this thesis has involved studies to increase our understanding of the genetic basis for the development of resistance to fluoroquinolones, and also how we can use new concepts in dosing that may help
prevent the selection of mutants during treatment.
Fluoroquinolone resistance involves accumulating multiple mutations in the
target genes for the drug and in genes regulating drug efflux. Resistance is
associated with a loss in growth rate both in vitro and in vivo as shown in
one of the studies. By constructing different combinations containing common fluoroquinolone resistance mutations it was possible to analyse how
resistance evolution can occur more in detail. The data presented here shows
that some resistant strains that had acquired resistance mutations and decreased fitness, could, by acquiring an additional resistance mutation increase their fitness as well as their resistance level. By these results a novel
evolutionary strategy has been discovered: that reducing the fitness costs of
resistance for bacteria can correlate with increased antimicrobial drug resistance. There have been discussions about whether it would be possible to
prevent resistance development by decreasing the use of antibiotics. In this
context the findings in these studies are very disturbing since they imply that
bacteria could, without being subject to antibiotic selection, increase their
resistance level by selection for increased growth fitness. The last study investigated the nature of fitness-compensatory mutations and the discovery
made was that fitness loss in highly fluoroquinolone resistant E. coli, is most
likely due a reduced efficiency of transcription, and that this can be compensated by mutations altering RNA polymerase. It will be interesting to determine whether fitness-compensatory mutations within RNA polymerase can
explain the high fitness found in resistant clinical strains.
57
The mutant prevention concentration measured for a set of clinical UTI
strains suggested that they had complex genetic backgrounds resulting in a
great variety in their MPC values. There was a low correlation between the
MPC values and the corresponding MIC values. These results suggests that
MPC should be included among the PK/PD parameters used to determine the
optimal dosing regimens for fluoroquinolones since this concentration and
not MIC will minimize the selection of mutants. To be able to use the MPC
concept in clinical therapy one needs to investigate it in relation to other
PK/PD parameters. The results from Paper II showed that T>MPC and
AUC/MPC were the single PK/PD parameters that best predicted the prevention of resistance development. Efforts were made to test the ability of different approved fluoroquinolone doses that are used to treat various infections to prevent the selection of resistant mutants. It was found that only
some approved dosing regimens eradicated or prevented the selection of
resistant mutants, while other approved doses were less effective and selected pre-existing resistant mutants.
As we face a serious global resistance problem we need to adopt a series of
measures to address the problem. These include (i) decreasing antibiotic use
to reduce selection pressure for resistance, (ii) improving our knowledge of
how bacteria become resistant and how they ameliorate the resulting fitness
costs, (iii) improving dosing regimens with the aim of reducing the emergence of resistance and preventing the selection of resistant mutants.
58
Swedish Summary
Antibiotikaresistens är ett stort problem i dagens samhälle. Efter att antibiotika införts för behandling av infektionssjukdomar efter andra världskriget
har resistensutvecklingen skett mycket snabbt. Detta är resultatet av att man
har användt antibiotika på fel sett och inte varit mer restriktiv med behandling. För att kunna förstå hur bakterier utvecklar resistens och motverka deras spridning måste man använda molekylärbiologiska metoder för att undersöka hur resistens uppkommer i bakterien, vilka evolutionära vägar som är
fördelaktigt för bakterien samt försöka förbättra de behandlingsmetoder som
finns.
I de studier som ingår i denna avhandling undersöks grundläggande resistensmekanismer mot en av de vanligaste typerna av antibiotika, fluorokinoloner. Fluorokinoloner hämmar replikationen i cellen genom att binda till
DNA-gyras (kodas från generna gyrA och gyrB) och topoisomeras IV (kodas
från generna parC och parE). Bakterien som har användts i dessa studier är
Escherichia coli som normalt finns i människans tarmflora men som också
kan orsaka allvarliga infektioner. Fluorokinolonresisistens innebär vanligen
att flera mutationer uppkommit i flera gener, såväl i målgenerna gyrA, gyrB,
parC och parE som i de reglerande generna marR och acrR, vilkas produkter
upprätthåller antibiotikakoncentrationen i cellen genom att styra dess transport över cellmembranet. Mutationer har vanligen en kostnad för cellen. Ju
fler mutationer cellen drar på sig för att överleva under antibiotikatryck desto
sämre växer den. Trots detta växer kliniska stammar långsammare.
Efter att ha selekterat ökad fluorokinolonresistens i en vildtypsstam konstaterades en ökad resistens på grund av flertalet uppkomna mutationer. I de flesta fall erhölls även en minskad tillväxthastighet både in vitro och in vivo. I
vissa fall sågs även en tillväxtökning tillsammans med ökad selekterad resistens men några mutationer kunde inte identifiera vare sig i gyrA, gyrB,
parC, parE, marR eller acrR som var förknippade med denna tillväxtökning.
En av studierna syftade till att undersöka olika evolutionära vägar som kan
leda fram till höggradig resistens mot fluorokinoloner, därför konstruerades
stammar innehållande olika kombinationer av mutationer som vanligen finns
i kliniskt högresistanta stammar och sedan mätte tillväxthastigheten samt
resistensnivån hos dessa stammar. En stark korrelation mellan antal mutationer och reducerad tillväxthastighet, men vissa kombinationer av mutationer
59
resulterade i bättre tillväxt än andra. Det som var mest intressant var att en
enda mutation hos vissa stammar kunde resultera i ökad resistens och samtidigt ökad tillväxt. Dessa data indikerar en helt ny resistensmekanism där en
mutation som leder till ökad antibiotikaresistens samtidigt leder till ökad
tillväxt.
En av studierna visar hur så kallade kompenserande mutationer hos högresistanta bakterier kan återställa tillväxthastigheten. Sekvenseringsresultat visade en sådan mutation i rpoA, som kodar för en subenhet i RNA-polymeras.
Resistens mot fluorokinoloner påverkar replikationen, men även transkriptionen, genom att topoisomerasaktiviteten blir mindre effektiv. Hypotesen
som framkommit av detta arbete är att en mutation i RNA-polymeras kan
överkomma transkriptionsproblemet och därmed öka tillväxten.
Dagens antibiotikadoser baseras på den minsta hämmande koncentrationen
(”minimal inhibitory concentration”, MIC) som hämmar tillväxten av bakterien. Ett nytt koncept innebär att man i stället använder högre koncentrationer av antibiotika, så kallad mutationshindrande koncentration (”mutation
prevention concentration”, MPC). Syftet med en så hög koncentration är att
man därmed skulle kunna motverka att mutanter selekteras under behandling. I en av studierna undersöktes hur hög denna MPC var i kliniska urinvägspatogena stammar av E. coli, slutsatsen var att koncentrationen som
behövdes för att hindra uppkomst av ytterligare resistensmutationer var olika
i olika stammar. Detta gällde även för stammar som man tror är väldigt känsliga för antibiotika. MPC-värdet kunde inte förutsägas ur MIC-värdet.
I en av studierna testades även detta i en farmakodynamisk modell som visade att T>MPC (tiden då antibiotikakoncentrationen i serum är på MPC-nivå)
samt AUC/MPC (AUC = ”area under curve”, d v s den totala antibiotikamängden i serum under den tidsperiod under vilken mätningar gjorts) var
viktiga parametrar för att motverka selektion av resistanta mutanter. Några
vanligt förekommande fluorokinolondoser som används vid klinisk behandling av olika infektioner testades även och resultaten visade att vissa var
bättre än andra på att motverka resistensutveckling beroende på ursprungspopulationen av bakterier. Fler studier behövs dock för att säkert veta om det
är i urinvägarna eller någon annan stans i kroppen selektionen sker och vilken dos som skulle vara bäst för att eliminera bakterier på ett specifikt infektionsställe utan att selektera mutanter.
Det finns ett stort behov av att utveckla nya antibiotika eftersom bakterier
snabbt utvecklar multiresistens mot tillgängliga antibiotika. För att kunna
göra det på rätt sätt måste man förstå hur bakterierna anpassas för att överleva antibiotikabehandling samt ligga ett steg före dess evolution.
60
Acknowledgments
There are many people that have influenced and coloured my path through life in so many
ways. The pathway that leads to the work and writing of this thesis was lined with fantastic
people that are very important for me. In particular I would give my greatest gratitude and
appreciation to:
My supervisor and mentor Diarmaid that has been the best of supervisors that one ever could
have. I’m completely grateful and humbled for all the time you always have for science and
for your extensive knowledge and great mind! Thanks for believing in me and always wanting
me to do my best. Also for making it possible to attend conferences that has contributed to so
many valuable scientific contacts.
Otto Cars, for the great collaborations of our groups and for the discussions about that we
need to act to prevent the resistance problem. It has been a pleasure to work with you.
Thank you to All seniors at the ICM department, thank you Santanu for all valuable conversations and for always lending me P1 stock, also for helping out with improving my text.
Karin, for all the help with Swedish words and expressions!!
Bindu, my guru in so many ways! Thanks for coming into my life enriching it endlessly, I
think there was a purpose for us to meet and spend the time we got together. MU and Seattle,
we had the greatest time! Thanks for teaching me about essence of life and science! I miss
you!
Sara, thank you for all the great work we have done together, it was really so much fun! For
being such a great friend and being the “partypingla” you are! We have had the greatest conference trips ever, Washington-New York and jeans hunting, Atlanta, enjoying the pool and
shopping liters of schampoo, San Fransisco, working out with overweight suitcases containing Kitchen aid blenders etc.! For continuous great work, collaborations and friendship!
Niels and Dorthe, thanks for good collaboration and for the Danish julefest!
People at the CSHL course that truly showed me how incredibly interesting and fun science
can be. These weeks really made me to become in love with science.
Mirjana, thanks for being stylish and for all the chats we had and all good advice you gave!
Patricia, thanks for being the first person to guide me in the lab and all the collaboration we
have done together. I enjoyed the time we had down in Copenhagen! Good luck ahead! Tobias, I appreciated the time we have had as grad students together. The “enormous” drink,
boa, clubbing, drummer at the jazz club, and great hotel in New Orleans, walking down dark
alleys in Atlanta! I have enjoyed your crazy sense of humour, it has been a blast! Marie, good
luck with the competitions, there will be other experiment, I promise!! Thanks for all laughs
we have had and for baking the hardest “lusse bullar” ever with me! Disa, I’m so happy that
you got to experience CSHL, keep that good feeling of science close to your heart! The three
of us had a great road trip through the desert down to SD, how many times did we cross that
silver strip?? Jonas, it’s great to have a postdoc. in the group! Project workers; I had quite
few exam students through the years which were great to teach and also great students; I
would acknowledge some students that showed a great mind for science: Kattis ”Katten”,
you’re a great, cool gal! I enjoyed having you in the lab, you were a great student! Hanna, for
61
being so sweet and nice, I always treasured your way of working very clean and for being so
structured in the lab. Julia, you really had a touch for science, working very hard being away
from home. I hope that you had a good experience. Kaire, you have been really great of
taking on the aim and organize your study (endless amounts of plates, I know what you are
going trough). You are a good spirit to have in the lab.
Ema, my dear, dear friend of soul and mind. What could I have done without you? Paper no.
1 together and now only three days apart for our big day. I wish you all the best in life and to
start living the life that you have been waiting for so long. Fredrik, thanks for being so nice, I
have enjoyed teaching with you. Good luck with everything. Mattias B, thanks for all the
“scones”, hope you are enjoying the south and the Spanish heat! Shiying, always smiling,
good luck with your work! Jaydip, good luck with future plans. Ulrika, thanks for always
answering all questions about everything, and for being such a sweet person. We had a great
time in Las Vegas and overlooking the sunset in Grand Canyon. Solan, thank you for all the
knowledge around the lab, Akiko, always helping out with all the trouble I have had with the
computer, a big thanks!! Klas, we have had such a great time through these years, even
though you and T picked on me all the time…it was fun!! All the very best to you and your
family!! Helene, thanks for always and then I mean always being so nice, happy and enthusiastic. You go girl!! Magnus, thnx for that great taco party we arranged back then. Miss you
in the lab! Cia, good luck with your lab work and all that skiing! Erik, I’m happy that you got
the Ȝ-red to work, good luck!! Johan, I enjoyed our discussion about possible evolutionary
pathways! you are soon there! Janne, for help with centrifuges’ etc. in the early days in the
old lab. Sonchita and Bupi, good luck with everything in life and in science! Pernilla,
Emma, Hanna S all the best to you and good luck with everything. Daniel, thank you so
much for all the help and time you put in for the cover picture! Per, I enjoyed our conversations about everything, see you in San Diego!! Pontus, I really digged your “Magnum” lookalike! Niclas, it was fun “guiding” you to places in the US! Thanks for sharing pictures! All
the immunology girls: Maike, Jenny, Maria, Sofia and Kajsa, thanks for letting me in to
you corridor, I’m truly happy you did! Mattias E, sorry that I had to beat you in poker every
time, good luck in Sthlm! Maria Ave, an angel , thanks for saving me in the last minute and
for being so sweet! The Administrators for helping out with everything you do so well! Eva,
thanks for everything, you are such a great person to know. I truly appreciate all our talks
about life and everything around it. I’m waiting for that coffee invitation at your new apartment. Christer, you are the best, always helping out with everything that needs to be fixed!
Majsan, thank you for letting strangers stay for the night and for that dinner that started our
lovely friendship! Silvia, it was so great that we ended up in the same class at last. Also those
other paths that winded up to end in our friendship. I truly treasure our conversations! Eva,
we have had many great times through the Uppsala time. You have been so much fun and
hilarious with your puns and hanging out with “lillKerstin å Berit” on “tuvor”! Camilla,
thanks for letting me to move in back in the days! We have had so much fun with “Top Gun”
scenarios! Maria, you are missed in Uppsala! Thank you for “almost” being my roommate
sleeping on the couch so many times after late nights out and for being a great friend! Sofia, it
is so great that you moved back, I missed you when you were gone! Memories from skiing
trips, and summer adventures! Thank you for being the lovely friend you are! Malin, for
being one of the four “musketeers” since gymnasium. We have had so many fun times trough
the years. For making wonderful memories to come back every time I hear “Die mauer”!
Linda, probably the first to receive a written letter from me, turtle drawings and silly things!
Continuously note writing throughout gymnasium being my bench companion, and now here
at BMC, good luck with everything, you’re soon done! Tina, thank you for being a true friend
which whom I always can count on! For being my room mate for sometime and for all the fun
we have had through all the years I have known you! Anna, my lost and found friend! My
lucky star that we found each other again! Everything that we have experienced and lived
trough all these last years! Road trips in the country and “over there”, so many tears, laughs
and memories!
62
Mamma & Pappa, you are the reason for what I have accomplished. Thank you for always
believing in me and for your endless love and support, everything is possible! I couldn’t have
done it without you! I love you so much! Peter, thanks for being there, helping out anytime
being the older brother! I appreciate our conversations when I lived in Seattle! Love you!
David, what a blast we had “down under”, the road trip with “sugar and garbage”!! Thanks
for making me to remember to focus and that I could do it, no problem! You have always
been the one to look up to, I love you bro!
Mattias, my love and friend of the heart! Thanks for climbing the top of the mountain looking
at the sparkling stars and diving to the bottom of the ocean experiencing the beautiful colours
and complete harmony. Thanks for making me calm, to slow down, making me laugh, taking
our moments and walking this pathway with me!!
/L
Linda
63
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Acta Universitatis Upsaliensis
Digital Comprehensive Summaries of Uppsala Dissertations
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