bacterial adaptation to novel selection pressures

Microbiology and Tumor Biology Center Karolinska Institutet and Swedish Institute for Infectious Disease Control,

Stockholm Sweden

BACTERIAL ADAPTATION TO NOVEL SELECTION

PRESSURES

ANNIKA NILSSON

STOCKHOLM 2005

All previously published papers were reproduced with permission from

the publisher.

Graphic production Johanna Nilsson

Printed by Repro print AB, Stockholm 2005.

© Annika Nilsson, 2005

ISBN 91-7140-192-X

ABSTRACT The rates and trajectories of bacterial evolution are determined both by microbial factors and

environmental parameters. In this thesis I have investigated how bacterial mutation rates and selection pressure affect the rate and extent of adaptation to novel environments. Bacterial strains with increased mutation rates have been found at high frequencies among pathogenic bacteria. We collected natural isolates of E. coli in three different European countries and determined the mutation frequency to rifampicin resistance in these strains. In this collection we found an enrichment of weak mutator strains and furthermore, weak mutators were more common among clinical isolates than among normal flora bacteria. Pathogenic bacteria experience a rapidly changing environment in its host and this has been suggested as one explanation for the high frequencies of mutator strains found among clinical isolates. We found that mutation supply rate was limiting for bacterial adaptation in a pathogenesis model where S. typhimurium was evolved in mice. Here, the rate of adaptation could be increased by either increasing population size or mutation rate. An increased mutation rate however, often comes at a cost. We could detect a decreased fitness of evolved mutator populations in second unselected environments due to accumulation of deleterious mutations.

The course of bacterial evolution is determined by how selection and genetic drift sort among genetic variation. Antibiotic resistance development is to a large degree influenced by strong selective pressures. Resistance mutations usually confer a fitness cost to the resistant bacteria in terms of reduced growth rates and /or virulence. We showed that resistance to the two antibiotics fosfomycin and actinonin generally confers heavy fitness costs on the resistant bacteria under several different growth conditions. Using mathematical modeling we demonstrated that the fitness costs associated with fosfomycin resistance significantly reduced the probability of resistance development during antibiotic therapy. The biological cost of fosfomycin resistance is therefore suggested to be a significant contributor to the low level of clinical resistance observed for this antibiotic. For resistance to develop clinically the possibility to genetically compensate for the fitness costs of antibiotic resistance is of importance. We showed that the severe fitness cost of actinonin resistance can be compensated for by acquisition of both intragenic and extragenic compensatory mutations. Among the extragenic compensatory mutations we identified tRNA gene amplifications resulting in over-production of a limiting component for bacterial growth in the resistant strains.

Evolution towards reduced bacterial genome size is associated with an intracellular life-style that is characterized by small bacterial population sizes and relaxed selection pressures. We set up an experimental system to study the process of genome shrinkage in real time where we mimicked the characteristics of an intracellular life-style. We could observe a rapid initial rate of DNA loss where large deletions removed substantial amounts of DNA in single events. The data agrees well with the observation that genome reduction in bacteria with small genomes initially was a rapid process mediated via large deletions. RecA functions have been suggested to be important for the decrease in genome size of small genomes. We could however not detect any dependence on RecA mediated functions for the deletion formation process in our experimental set-up.

LIST OF PUBLICATIONS This thesis is based on the following papers, which are referred to in the text by their roman numerals.

I. María-Rosario Baquero, Annika I. Nilsson, María del Carmen Turrientes, Dorthe Sandvang, Juan Carols Galán, José Luís Martínez, Niels Frimodt-Møller, Fernando Baquero and Dan I. Andersson Polymorphic Mutation Frequencies in Escherichia coli: Emergence of Weak Mutators in Clinical Isolates. Journal of Bacteriology. 186(16): 5538-5542. 2004

II. Annika I. Nilsson, Elisabeth Kugelberg, Otto G. Berg and Dan I. Andersson Experimental Adaptation of Salmonella typhimurium to Mice. Genetics. 168: 1119-1130. 2004

III. Annika I. Nilsson, Otto G. Berg, Olle Aspevall, Gunnar Kahlmeter and Dan I. Andersson Biological Costs and Mechanisms of Fosfomycin Resistance in Escherichia coli. Antimicrobial Agents and Chemotherapy. 47(9): 2850-2858. 2003.

IV. Annika I. Nilsson, Anna Kanth and Dan I. Andersson Reducing the Cost of Antibiotic Resistance by Selective Gene Amplifications. Manuscript.

V. Annika I. Nilsson, Sanna Koskiniemi, Sofia Eriksson, Elisabeth Kugelberg, Jay C. D. Hinton and Dan I. Andersson Experimental Evolution to Reduce Bacterial Genome Size. Manuscript.

CONTENTS INTRODUCTION .............................................................. 9

ESCHERICHIA COLI ................................................................................ 9 SALMONELLA ENTERICA......................................................................... 10

GENERATION OF GENETIC VARIATION ................................ 11

ANTI-MUTATORS AND MUTATORS ............................................................ 11 BENEFITS OF A HIGH MUTATION RATE...................................................... 13 DISADVANTAGES OF A HIGH MUTATION RATE ............................................. 14 MÜLLER’S RATCHET ............................................................................. 16 SOS-INDUCED MUTAGENESIS ................................................................. 16

ADAPTATION TO NOVEL ENVIRONMENTS ............................ 18

EXPERIMENTAL EVOLUTION.................................................................... 18 Periodic Selection .....................................................................................................18 Rate of Adaptation ...................................................................................................19 Extent of Adaptation ................................................................................................20

EVOLUTION OF ANTIBIOTIC RESISTANCE ................................................... 22

Antibiotics – Mechanisms of Action and Resistance ..........................................22 Resistance Development and stability..................................................................24

Volume of Drug Use ........................................................................................................24 Rate of Formation of Resistant Variants .....................................................................24 Biological Cost of Antibiotic Resistance .......................................................................25 Compensatory Evolution.................................................................................................27

EVOLUTION TOWARD REDUCED GENOME SIZE............................................. 30

The Eukaryotic Cell as a Growth Niche ............................................................... 31 The Process of Genome Reduction ...................................................................... 32 The Minimal Genome Concept.............................................................................. 34

PRESENT INVESTIGATION................................................ 35

RESULTS AND DISCUSSION .................................................................... 36

Weak Mutators are Enriched in Clinical Isolates (paper I) ...............................36 Mutation Supply Rate is Limiting for Adaptation of Salmonella to Mice (paper II) ...................................................................................................................38 Mutator Strains Evolved in Mice Display Fitness Trade-Offs due to Mutation Accumulation (paper II) ..........................................................................................38 Partially Different Spectra of Mutations Confer Resistance to Fosfomycin in Resistant Strains Isolated in the Laboratory and in Clinical Fosfomycin Resistant Isolates (paper III) .................................................................................39 Actinonin Resistance Mutations are Associated with High Fitness Costs (paper IV) ..................................................................................................................42 Overexpression of the Initiator tRNA via Gene Amplifications can Compensate for the Cost of Actinonin Resistance (paper IV) .........................43 Bacterial Genome is a Rapid Process under Laboratory Conditions (paper V).....................................................................................................................................45

CONCLUDING REMARKS ........................................................................ 48

ACKNOWLEDGEMENTS ................................................... 50

REFERENCES ............................................................... 52

ABBREVIATIONS 8-oxoGTP 7,8-dihydro-8-oxoguanine triphosphate bp base pair DNA deoxyribonucleic acid FMT formyl-methyl transferase kbp kilo base pair LB Luria-Bertani broth mbp mega base pair MMR methyl-directed mismatch repair ORF open reading frame PCR polymerase chain reaction PDF peptide deformylase PFGE pulsed-field-gel-electrophoresis RNA ribonucleic acid UTI urinary tract infection

BACTERIAL ADAPTATION TO NOVEL SELECTION PRESSURES

INTRODUCTION The publication of Charles Darwin’s On the Origin of Species by Means of Natural Selection in 1859 marked the beginning of evolutionary biology. Evolution is central to our understanding of biology, from the history of life on earth to current problems like the emergence of antibiotic resistance. Evolution in its simplest form relies on two facts; the presence of heritable variation among individuals and a mechanism of sorting between this variation. All populations contain individuals that differ from each other in one or several characters. Depending on the number of offspring with a specific character each individual produces, the frequency of this character within a population will vary from generation to generation. This is called descent with modification and, as a consequence, the attributes of individuals within a population will change over time. In evolutionary biology two main sorting processes operate on the variation in a population - chance and natural selection. When natural selection predominates the individuals that are best equipped to meet the demands of the existing environment will contribute most to the next generation. Evolution by means of natural selection is therefore a directed process toward increased fitness and when natural selection operates we talk of adaptive evolution. In this thesis I will focus on the mechanisms and trajectories of bacterial adaptation to novel environments and how the bacteria Escherichia coli and Salmonella enterica can be used as model systems to study adaptive evolution in the laboratory.

Escherichia coli Escherichia coli is a member of the family Enterobacteriaceae which also includes well known pathogens like Salmonella and Shigella. These bacteria are rod-shaped with a gram-negative cell wall and they are easy to cultivate due to their simple growth requirements. E. coli has been used for many years as a model system in bacterial genetics, molecular biology and biotechnology (217). Together with its close relative Salmonella, E. coli is one of the best characterized bacteria and we have detailed knowledge of its genetics, genomics and bacterial cell physiology (217). In its natural environment E. coli colonize the lower intestine of humans and other mammals and are rarely associated with disease (33, 123, 255). Specific clones of E. coli can however give rise to diseases such as enteric/diarrheal disease, urinary tract infections (UTI:s), septicemia and meningitis (123). Pathogenic E. coli are distinct from commensal E. coli in that strains causing disease express virulence factors mediating functions such as adherence, invasion, toxin production and immune evasion (123, 205). UTI:s are one of the most common infections caused by a bacterial agent and in

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more than 70% of the cases the causative agent is E. coli (120, 121). The severity of infection ranges from cystitis where the infection is confined to the bladder, to pyelonephritis where the bacteria are spread to the kidneys (63, 234). A bacterial infection of the bladder can also be asymptomatic (234). Uropathogenic E. coli are associated with virulence factors such as fimbriae and pili mediating adhesion, aerobactin for iron uptake and production of toxins, for example hemolysin (52, 63, 75, 115, 123, 234).

Figure 1. Salmonella typhimurium LT2

Salmonella enterica Unlike its close relative E. coli, Salmonella enterica is never encountered as a commensal in humans but is always associated with disease (255). Most Salmonella serovars capable of infecting humans cause gastro-intestinal disease and are spread via contaminated food and water (23, 189). The serovar Typhi is unique in that it is a strict human pathogen which gives rise to a severe systemic disease called typhoid fever (192, 193). Salmonella enterica serovar Typhimurium LT2 (which will be referred to as S. typhimurium throughout this thesis) causes a systemic disease in certain inbred mouse strains that share many similarities to typhoid fever in humans (156, 162, 186). The Salmonella infection is initiated upon ingestion of the bacteria (156, 186, 193). A small fraction of the bacteria survive the acidic environment in the stomach and establish an infection in the small intestine, where the Salmonella multiply and displace the normal flora bacteria. The bacteria then cross the intestinal epithelium by invading the M cells of the Peyer’s patches and enter the blood circulation. Systemic disease in both humans and mice is associated with the capacity of the bacteria to survive and replicate in macrophages and. in the late stages of the disease, Salmonella can be found in large numbers in the liver and spleen (156, 186, 193). The murine typhoid fever model has been used extensively to study the interactions between pathogenic bacteria and their host (156, 186).

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GENERATION OF GENETIC VARIATION Generation of genetic variation is fundamental for all evolutionary processes. In bacteria genetic variation is created via two main pathways; (i) acquisition of new functions through horizontal gene transfer or (ii) de novo by mutagenesis. DNA based microbes differ by several orders of magnitude in genome size and per nucleotide mutation rate (67). Despite the large variation, the per genome mutation rate is remarkably similar, averaging 0.0033 mutations per genome per replication cycle (67). Consequently, mutation rates seem to be shaped by strong selective pressures that are universal to many types of organisms (64, 228). The majority of all mutations are deleterious and a too high mutation rate would result in rapid disintegration of the genetic information. In E. coli, deleterious mutations are estimated to form at a rate of approximately 2x10-4 per genome per generation (125). To counteract the deleterious effects of mutagenesis bacterial cells invest much energy in high-fidelity replication and DNA repair systems. In E. coli and S. typhimurium spontaneous mutations arise with a frequency of approximately 10-10 per base pair per generation (213). The low mutation rate is the result of several mechanisms acting to protect the genetic information stored in the bacterial genome. These mechanisms can be divided into two main groups (i) maintenance of the DNA in an error-free state and (ii) high-fidelity DNA replication (105, 166, 209, 213). Like all molecules, DNA is subjected to spontaneous chemical reactions and toxic compounds that threaten to destroy the integrity of the genetic information. The bacterial cell possesses a plethora of repair system that serves to recognize damaged DNA and repair the damage in an error-free manner (209). Errors in the DNA can also be introduced during copying of the DNA chromosome. Faithful DNA replication relies on three different functions; (i) insertion of the correct base pair by DNA polymerase III, (ii) proof-reading by the polymerase and (iii) post-replicative repair (213).

ANTI-MUTATORS AND MUTATORS The spontaneous mutation rate in E. coli is not simply a reflection of the lowest possible mutation rate attainable. Strains with decreased mutation rate, anti-mutators, have been described in both E. coli and bacteriophage T4 (78, 212). In E. coli, the anti-mutator phenotype is conferred by increased accuracy of DNA polymerase III and the mutation rate is reduced 5-30 fold in these strains (78, 212). Anti-mutators are rare and the strains described have been isolated under experimental conditions designed specifically to identify mutants with decreased mutation rate. No studies have identified anti-mutators among natural isolates or in long-tem evolution experiments performed under laboratory

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conditions. The reason for the absence of anti-mutators under these conditions is not clearly understood, but several explanations have been put forward. First, the anti-mutator mutations described can confer a fitness cost in terms of decreased replication rate and they are therefore selected against (68, 212). Second, mutations conferring a general anti-mutator phenotype might be improbable due to mechanistic constraints (68, 212). An increased fidelity of one particular pathway will confer a measurable anti-mutator phenotype only if this pathway contributes to at least 50% of all mutations in the bacterial cell (212). Thus, increased efficiency of one specific repair pathway contributes very little to overall mutagenesis in the cell. Strains with a mutator phenotype, on the other hand, are easily obtained and have been identified in substantial numbers in different collections of bacteria (65, 98, 135, 141, 157, 188). The most commonly identified defect resulting in a mutator phenotype is loss of methyl-directed mismatch repair (MMR) functions (141, 188). The MMR system is responsible for correcting post-replicative errors in DNA and a functional MMR system requires gene products from the mutSLH, uvrD and dam genes (166, 171, 172). Mutations in any of these genes confer an up to 103-fold increase in general mutagenesis (105, 166, 171, 172). The MMR system is initiated when the MutS protein recognizes and binds to a mismatch in the DNA molecule (1, 116, 219, 259). MutL is then recruited to the site of the mismatch and the MutSL complex activates the endonuclease activity of MutH (19, 116, 259). Activated MutH introduces a cut 5’ to the unmethylated GATC sequence closest to the mismatch (19, 116, 259). Due to the transient unmethylation of the adenine in GATC sequences for a short time period after replication, repair can be specifically targeted to the newly synthesized strand (25, 191). After a cut is introduced in the DNA, the UvrD helicase unwinds the DNA past the mismatch, the error-containing DNA is digested and the gap filled by DNA polymerase III (58, 116, 258). Apart from post-replicative repair, the MMR system also functions to ensure the fidelity of homologous recombination with mutants defective in MutS and MutL functions displaying an increased rate of recombination between homeologous sequences (76, 116, 171, 232, 256, 257, 261). The recombination barrier observed between the close relatives E. coli and S. typhimurium, whose DNA sequences differ by approximately 15%, is a result of a functional MMR system (204, 257). The most potent mutator identified is the mutD5 mutator in E. coli. The mutD gene encodes the epsilon subunit of DNA polymerase III and the mutD5 mutant is defective in proof-reading by the polymerase (73, 138, 142). A mutD5 mutator displays an up to 105-fold increase in general mutagenesis under certain growth conditions (138). This high mutation rate is an effect of a combination of defective proof-reading by the DNA polymerase and saturation of the MMR system (57, 214, 216). Defects in specific DNA damage repair

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pathways are also known to increase mutation rates (105, 166, 209). The ung gene encodes an N-glycosylase responsible for removing uracil from DNA (70, 105, 209). Loss of function mutations in the ung gene causes a 10-15 fold increase in the rate of GC → AT transitions (70, 105). Oxidized guanines are an important contributor to DNA damage in E. coli and S. typhimurium. The bacterial cell therefore invests a lot of energy in oxidative damage repair systems (105, 166, 209). The mutT gene encodes a nucleotide triphosphatase that is important in cleansing the dGTP pool from 8-oxodGTP (81, 105, 166, 209). Mutations in mutT result in an increased rate of AT → CG transversions, due to the misincorporation of 8-oxodGTP opposite adenines during DNA replication (81, 105, 166, 209). If DNA replication takes place before MutT can perform its function, the 8-oxoG-A mismatch is recognized by the mutY encoded N-glycosylase (18, 81, 105, 166, 209). The misincorporated adenine is excised and replaced by a cytosine. The resulting 8-oxoG-C base pair can then be repaired by the MutM N-glycosylase that specifically removes the oxidized guanine (81, 105, 166, 209). Mutations in mutM and mutY both result in increased GC → TA transversions (81, 105, 166, 209). In addition, mutMY double mutations have a

multiplicative effect on the rate of GC → TA transversions (81, 105, 166, 209).

BENEFITS OF A HIGH MUTATION RATE Strains with increased mutation rate are often represented in collections of natural isolates in frequencies exceeding 1% (65, 98, 135, 141, 157, 188). Elevated mutation rates are especially common among clinical isolates. One extreme case is illustrated among Pseudomonas aeruginosa strains isolated from cystic fibrosis patients, where 20% of the strains were found to display a more than 50-fold increase in mutation rate (188). The frequency of mutators observed is several orders of magnitude higher than expected from theoretical and experimental estimations and suggest an enrichment of mutator strains due to selection (34, 244). The majority of mutator strains in natural collections are defective for mismatch repair (141, 188). As far as we know, a defective MMR system does not in itself confer any selective advantage or disadvantage to the bacterial cell (246, 247, 260). The fitness increase or decrease of a mutator strain is therefore associated with the mutations they produce (64, 228). Strains with increased mutation rate form beneficial mutations more rapidly than strains with a low mutation rate. In the absence of sexual recombination the whole genome is a linkage unit, meaning that mutations are associated with the genome in which they were formed. Therefore, when a beneficial allele arises in a mutator cell and increases in frequency in the population, so does the mutator allele. This phenomenon is called genetic hitch-hiking and, as a consequence, mutator alleles can be indirectly selected for due to their association with other mutations (49, 160).

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Adaptation to novel environments is dependent on the formation of beneficial mutations. If the formation of new mutations is the rate limiting step for adaptation, a mutator allele can confer a selective advantage due to its association with increased mutation rate (64, 228). In sufficiently large populations, cells with elevated mutation rates are always expected to be present at low numbers. Boe et. al. estimated the frequency of mutator alleles in E. coli populations not subjected to strong selective pressures to approximately 3x10-5 (34). When mutator alleles are present at such low frequencies, a beneficial mutation is more likely to appear in the majority of cells with have a low mutation rate (49, 64, 228). Therefore, if adaptation requires only one beneficial mutation, the beneficial effect of a mutator allele on the rate of adaptation is very small (64, 167, 228, 244). However, when adaptation is associated with a sequential acquisition of several mutations, the advantage of being a mutator increases significantly. If the probability of one mutation to arise in a mutator cell is 1000 fold higher than for the non-mutator cell, the probability of two consecutive mutations arising in the same cell is 106-fold higher for the mutator cell (64). Experimental evidence shows that sequential selection of beneficial mutations can rapidly increase the frequency of mutators in a population to near 100% (56, 64, 153, 167, 185, 228). The enrichment of mutator cells in a population is not only observed in experimental settings designed to select for increased mutation rate. The spontaneous ascent of mutators in populations adapting to novel environments has been observed in several experimental set ups (56, 185, 229). Lenski and co-workers evolved twelve lineages of E. coli in glucose limited minimal medium for 20,000 generations (53). After 10,000 generations, mutators had arisen and become established in three of the twelve lineages (229). Two of these mutators arose in the early phase of the experiment when the rate of adaptation was very rapid.

DISADVANTAGES OF A HIGH MUTATION RATE Mutator strains have been observed to rapidly reach frequencies near 100% in populations adapting to novel environments (56, 153, 185, 228). Even though mutator strains are frequently encountered in natural isolates, the majority of strains have a non-mutator phenotype. Bacteria with a high mutation rate have an increased rate of accumulation of deleterious mutations (64, 84, 229). The fitness cost due to such mutations becomes apparent in well adapted populations when the advantage of generating beneficial mutations no longer exists (64, 228, 244). Some experimental evidence exist that a mutator population can evolve toward a lower mutation rate once the rate of adaptation has decelerated (92, 248). In other experimental settings a high mutation rate was maintained in the bacterial populations under long periods of evolutionary stasis (145). There are two possibilities for a

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bacterium with a high mutation rate to revert to a low mutation rate. Either the functionality of a mutated repair mechanism can be regained by acquisition of a back-mutation, or the mutation rate can be reduced via suppressor mutations. As discussed earlier, loss of mismatch repair functions does not in itself confer a fitness cost. Instead the fitness costs of a mutator strain are associated with the increased rate of formation of deleterious mutations. The rate at which back mutations form is low and, furthermore, once such a mutation has arisen in the population, the probability that it will be lost due to stochastic events is high since the mutation is not maintained in the population by selection. Evolution of a decrease in mutation rate through back mutations, which rely on one specific type of mutational event, is therefore highly unlikely. The second possibility whereby mutation rates can be reduced through acquisition of suppressor mutations is a more probable scenario. Suppressor mutations do not necessarily rely on one specific mutational event, and the target for such mutations might therefore be much larger. Also, a suppressor mutation can theoretically confer a direct fitness advantage to the bacteria. Such a mutation could therefore be maintained in a population through selection long enough for the reduced rate of formation of deleterious mutations to have an effect on fitness. Reduction of mutation rate through suppressor mutations has been described to evolve under laboratory conditions in chemostat experiments (248). The genetic basis of such suppressor mutations has not yet been identified and it is therefore hard to estimate their prevalence in natural populations of bacteria. Mutator alleles are selected in bacterial populations due to their linkage with beneficial mutations. As evolution experiments in the laboratory are often conducted under asexual conditions, they do not take into account the effects of horizontal gene transfer. In natural populations of bacteria horizontal gene transfer might play an important role in disrupting the linkage between beneficial mutations and a mutator allele (66, 242). Mutator populations can acquire a low mutation rate by obtaining a gene copy that is functional for DNA repair through horizontal gene transfer. Similarly, a beneficial allele that once arose in a mutator background can be transferred to a genome with a low mutation rate.

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MÜLLER’S RATCHET Mutator populations are also more susceptible to extinction than populations with low mutation rates (64, 228). Asexual populations constantly face the threat of extinction through a phenomenon called Müller’s ratchet. Described by H. J. Müller in 1964, the operation of Müller’s ratchet is thought to be one of the driving forces behind the evolution of sexual reproduction and recombination (179). All populations will accumulate neutral and deleterious mutations due to stochastic processes. The resulting increase in mutational load will inevitably result in a ratchet-like loss of the least mutated class from the population, followed by decreasing fitness (7, 179, 254). Since the probability that a mutation will move toward fixation is directly proportional to the frequency with which it is represented in the population, small populations, populations passing through severe bottle-necks and populations experiencing high mutation rates are more susceptible to the operation of Müller’s ratchet. Once a mutation has become fixed in a population, reversion through acquisition of back-mutations is very unlikely. As the rate of back mutations is low, the probability is higher for the fixation of a second deleterious mutation than for a reversion event to occur. The result is that operation of Müller’s ratchet is virtually unidirectional and, if not counteracted, it will ultimately lead to extinction of the population. The classical way of opposing the effect of Müller’s ratchet is by reconstitution of the “mutation free” genome by means of recombination (179). Fitness can also be restored by acquisition of second-site suppressor mutations. The target for suppressor mutations is in many instances much larger than the one target for back-mutations and compensation via second site mutations constitutes a universal and rapid mechanism to increase fitness (21, 198).

SOS-INDUCED MUTAGENESIS In E. coli and S. typhimurium the SOS-system functions to respond to DNA damage and the induction of this system is associated with increased mutagenesis (108, 124, 250). The SOS-system is an intricate network of genes involved in cell-cycle control, DNA damage repair, recombination and the ability to replicate past DNA lesions. When the inducing signal, in the form of single stranded DNA, is present in the cell transcription repression of the SOS regulated genes is lifted in an orderly fashion (55, 77, 108, 250). As a first response, cell cycle progression and replication is arrested and expression of high-fidelity error repair systems up-regulated. If the DNA damage is extensive and the inducing signal persists, activity of the error-prone DNA polymerase, Pol V is activated (108, 113, 233, 250). PolV is capable of replicating past DNA lesions and has an error rate of 10-4 to 10-3 (95, 233, 238, 239). The polymerase ensures cell survival by restoring replication, but at the cost of

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increased mutagenesis. Activation of PolV does not only increase mutagenesis at the site of DNA damage, but increased mutagenesis is also observed at non-damaged sites on the bacterial chromosome (239). E. coli and S. typhimurium also harbors two additional SOS-induced polymerases, Pol II and Pol IV (95). Pol II primarily functions in the rapid replication restart observed upon SOS-induction and is associated with increased frame-shift mutagenesis (83, 182, 195, 196, 203). The role of Pol IV in SOS induced mutagenesis however is not completely known. Whereas SOS-induction of Pol IV causes extensive mutagenesis on the F’ episome, its role in increased chromosomal mutagenesis is still unclear (38, 95, 126, 127, 238, 249). The primary function of systems like the SOS-system is to respond to and repair DNA damage and thereby enhance cell survival. A mechanism to produce mutations only when necessary would combine the benefits of an increased rate of mutagenesis when adapting to new environments, with a slow rate of accumulation of deleterious mutations (36, 201). Induction of error-prone polymerases have the potential to increase mutagenesis in response to stress and a controversy exists whether such polymerases could have evolved due to their capacity of transiently increasing mutation rates, or if they have evolved simply as way to respond to DNA damage (158, 163, 200, 202, 207, 243). From an evolutionary perspective selection and evolution of a system whose sole purpose is to form mutations when necessary is problematic. A genetic variant can only be selected in a population on the basis of its present effect on fitness and not due to its possible future advantages. In other words, evolution of a system designed to transiently increase mutagenesis when needed requires a degree of foresight that is not compatible with the fundamental principles of evolution. Nevertheless, several systems have been suggested to produce transient increases in mutagenesis in response to stress (27, 80, 102, 241). The role of DNA damage in these systems is however unclear. If evolution of transient mutagenesis in response to stress is possible there should exist systems where increased mutagenesis occurs in response to stress in the absence of DNA damage. Such a system however still remains to be discovered.

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ADAPTATION TO NOVEL ENVIRONMENTS EXPERIMENTAL EVOLUTION Bacterial populations are excellent model systems for studying evolutionary processes in real time. Their short generation time makes it possible to follow evolutionary processes over many generations in a relatively short time period (74). Bacteria are small and large populations can therefore be maintained in a limited space, like a test tube. Using defined media and growth conditions, the environment can be controlled and replicated for many lineages. When an evolution experiment is completed, comparisons between the evolved and ancestral states can be performed due to the possibility to store the ancestor in a non-growing state, for example by freezing (74). Also, the availability of genetic tools makes it possible to identify the genetic basis for the evolutionary change observed. Beneficial or deleterious effects of mutations and improvement or degeneration of populations are often expressed in terms of fitness. In this context fitness is defined as the average reproductive success of a genotype or a population in a particular environment. Bacterial fitness is commonly measured in competition experiments, where the ancestral and evolved states are co-cultured for several generations (144). By introducing a genetic tag, for example an antibiotic resistance marker, the change in frequency of the two competitors relative to each other can be monitored over several generations of growth. In this way, specific clones or whole populations can be compared to a defined standard and changes in fitness estimated.

Periodic Selection The standard model for bacterial adaptation in homogenous environments is periodic selection (16, 17). One commonly used experimental set-up to study periodic selection is by evolving lineages of bacteria in chemostats, where the environment can be controlled and kept constant for the duration of the experiment (71). Under these conditions bacterial reproduction is strictly asexual and adaptation is dependent on de novo mutagenesis to create genetic variation. When a beneficial mutation arises, it will increase in frequency and spread in the population until a new genetic variant with higher fitness takes over. This sequential substitution of genetic variants with increasing fitness is a characteristic feature of bacterial adaptation (16, 17, 71, 74). Periodic selection can be observed by monitoring the frequency of a neutral allele in a population. This neutral allele will always be present in a small fraction of the population and furthermore, it will constantly increase in frequency over time due to its recurrent formation via mutagenesis. However, when beneficial mutations

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arise in the population they will most often originate in the majority of cells in the population that are not carrying the neutral allele (16, 17, 71). A selective sweep of a beneficial mutation in the population will therefore result in a drastic decrease in frequency of the neutral allele as the more fit variant displaces the original strain. In the time period following the selective sweep the frequency of the neutral allele will begin to build up again until a new beneficial mutation arises and starts spreading in the population. If the beneficial mutation by chance would arise in a cell carrying the neutral allele, this allele will increase in frequency in the population concomitantly with the beneficial mutation by means of genetic hitch-hiking (160). The selective sweep of a beneficial mutation is thought to purge the population of its genetic variation and rapidly evolving populations therefore display little genetic variation (71, 74, 99). Experimental evidence though, suggests that this might not always be the case (184). In large populations periodic selections need not be associated with one “winner” clone displacing all other variants. Rather, several different clones giving rise to one “winner” phenotype sweep through the population, thereby preserving some of the genetic variation. Smaller populations though, are more sensitive to purging of genetic variation after a selective sweep since beneficial mutations are rare enough to sweep through the population as individual clones (184). Contrary to the gradual increase of a beneficial allele in a population, populational fitness increases in a step-wise manner. The reason for this phenomenon is that a beneficial allele will not have any substantial effect on the over-all fitness of a population until it constitutes a substantial fraction of all cells (71, 74). Therefore, the selective sweep of a beneficial mutation in the population is only detected as increased fitness of the population in the last few generations of displacement. The short periods of rapid fitness increase is followed by longer periods of stasis. Under the long periods of stasis, the genetic variation that was lost in the previous selective sweep is recreated and new beneficial mutations are formed (71, 74).

Rate of Adaptation The rate of adaptation in a bacterial population is dependent on many different parameters. First, the amount of time required for a beneficial mutation to increase in frequency, from one single clone, to constituting the majority of cells in a population is dependent on the selective advantage of the beneficial mutation (71, 74). This means that big-benefit mutations reach fixation in a population more rapidly than mutations with smaller effects on fitness. Mutations conferring a large increase in fitness are however rare and beneficial mutations are more likely to have a big selective advantage in mal-adapted populations,

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where there is a large potential for adaptation (87, 190, 208). The rate of adaptation is therefore more rapid in the initial stages of adaptation to a new environment. As the population becomes more and more adapted to its environment, the potential for big-benefit mutations is decreased. Adaptation then continues at a much slower rate, by fixation of mutations with smaller effects on fitness (74, 245). This has been nicely illustrated by Lenski and co-workers. In their experimental set-up twelve lineages of an E. coli strain were evolved in glucose-limited minimal medium for 20,000 generations (54). Most of the fitness increase took place in the first 2,000 generations and then the rate of adaptation decelerated drastically (54, 145). Second, mutation supply rate of beneficial mutations can be limiting for the rate of adaptation. When beneficial mutations are rare, populations spend much time waiting for the next advantageous mutation to arise (87, 235). Limitations in mutation supply rate is a problem especially for small populations and when beneficial mutations are rare, e.g. in well adapted populations. Under these conditions, an increase in mutation rate can increase the rate of adaptation, and a limiting mutation supply rate is thought to be one reason for the enrichment of mutator strains observed among natural isolates (87, 235). Third, in small populations fewer genetic variants are present simultaneously. Which beneficial mutation that will arise first and start spreading in the population will be determined purely by chance events. As mutations with smaller effects on fitness are more common and therefore arise more often than big-benefit mutations, small populations tend to display a slower rate of adaptation (190). Fourth, adaptation rate can be decreased by clonal interference. When two or more beneficial mutations are present simultaneously, they will interfere with one another’s spread in the population. Ultimately the most fit variant will reach fixation, but the rate of its ascent is reduced compared to if the beneficial mutation had arisen singly in the population (15, 87, 190, 208).

Extent of Adaptation One fundamental question in evolutionary biology is whether or not evolution is reproducible. That is, if a set of bacterial lineages are evolved under identical conditions, will the outcome always be the same? If only one optimal genotype exists for each specific environment, different lineages of the same bacteria evolved under identical conditions would be expected to arrive at the same fitness over time. Sequential substitution of beneficial mutations with increased fitness would be predicted to move the population closer and closer toward its fitness optimum. If, on the other hand, different genotypes can move the population to diverse fitness optima, the outcome in different lineages is not expected to be the same (41, 137). Fixation of the first beneficial mutations will move the population in one direction or another and shift the population closer toward one specific

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fitness optimum. In all evolved populations adaptation will result in increased fitness, but since different fitness optima need not be equal the extent of adaptation can be different. When different fitness optima are possible, the order in which beneficial mutations arise and become fixed in a population will be important. Spontaneous mutations are formed by stochastic processes and therefore the extent and trajectories of bacterial adaptation are to a considerable degree determined by chance events. Using Lenski’s long-term evolution as an example once again, the extent of adaptation differed between the twelve evolved populations after 20,000 generations when comparing fitness of the evolved lineages (53, 145). Since the populations had experienced evolutionary stasis for the last 15,000 generations, a difference in the rate of adaptation with which the populations would ultimately reach the same fitness optimum seemed unlikely and the result supports the theory of multiple unequal fitness optima (145). Adaptation to one environment can decrease fitness in a second environment (40, 72). Such trade-off effects are produced by either of two mechanisms; (i) mutation accumulation or (ii) antagonistic pleiotrophy. The constant rate of formation of new genetic variants results in accumulation of neutral mutations in bacterial populations and these mutations can, in a second environment, prove to be deleterious. Such trade-off effects due to mutation accumulation are especially prominent in populations with increased mutation rate (64, 228, 244). Antagonistic pleiotrophy is dependent on the fact that mutations conferring increased fitness in one environment can, by themselves, decrease fitness in a second environment for mechanistic reasons. This phenomenon has been inferred as the reason for fitness trade-offs in several experimental studies (54, 72, 149). Due to fitness trade-offs there is no one genotype that is the master of all environments. Evolution in static and largely homogenous environments will select for highly specialized variants. This is often the case in experimental evolution experiments performed under laboratory conditions. In contrast, the natural environment for many bacteria is often highly variable. Such conditions tend to favor a more generalist life-style where a successful variant is not the master of any one environment but is capable of growth under many different conditions (46).

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EVOLUTION OF ANTIBIOTIC RESISTANCE The introduction of penicillins into human medicine in the 1940:s represented a revolution in treatment of infectious diseases. Infections that earlier were associated with high mortality and morbidity could now be managed with antibiotic therapy (44). Since the introduction of antibiotics in medicine, resistance has rapidly arisen and spread in bacterial populations. Treatment failure due to antibiotic resistance is an increasing problem and multi-drug resistance has rendered infections that once were easily cured very difficult to manage (44). For example, clones of Staphylococcus aureus resistant to virtually all available antibiotics is a severe problems in many hospitals and multi-drug resistant strains of Mycobacterium tuberculosis are rapidly spreading in many countries (210, 227). The usage of antibiotics in medicine can be viewed as one enormous evolution experiment and the acquisition and rapid spread of antibiotic resistance in bacterial populations is an example of the remarkable potential for bacteria to adapt to novel environments.

Antibiotics – Mechanisms of Action and Resistance Antibiotics interfere with, for the bacterium, unique and essential cellular functions. A common way of classifying antibiotics is by grouping them according to the cellular target they inhibit (table 1). The cell-wall is a unique bacterial structure important for maintenance of cell integrity. The production of cell wall components is the target for large groups of clinically important antibiotics, like penicillins and cephalosporins (148, 197). Aminoglycosides and macrolides target the bacterial ribosome, thereby interfering with protein synthesis (61, 86, 94). The ribosome is also the target for the newest group of antibiotics used clinically, the oxazolidinones, and protein synthesis continues to be an important target in the development of new antibiotics (45, 88, 109, 131, 147). Antibiotics also interfere with different aspects of nucleic aid metabolism. Examples are rifampicin that targets mRNA synthesis by inhibiting RNA polymerase, and quinolones which inhibit DNA gyrase and thereby interferes with DNA replication (69, 85). Trimethoprim and sulphonamides are examples of antibiotics that interfere with the energy status of the cell by perturbing folic acid metabolism (42). Bacteria can gain resistance to antibiotics either by acquisition of resistance determinants through horizontal gene transfer, or by mutagenesis. Genetic material can spread between bacteria through either of three different processes; transduction, transformation and conjugation (62, 161). Horizontal gene transfer of resistance determinants is an effective way of spreading resistance genes, both within and between, bacterial populations and it is an important contributor to the rapid emergence of antibiotic resistance seen clinically (62).

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Table 1. Summary of the most common classes of antibiotics and their mechanisms of action.

Mechanism of Action Antibiotic Inhibitor of cell wall biosynthesis Penicillins

Cephalosporins Fosfomycin Methicillin Vancomycin

Inhibitors of protein synthesis Aminoglycosides Macrolides Oxazolidinones

Tetracyclines Fusidic acid Chloramphenicol

Inhibitors of nucleic acid metabolism

Rifampicin Fluoroquinolones

Folic acid inhibitors Trimethoprim Sulphonamides

Mobile resistance determinants often encode enzymes that break down or modify the antibiotic, for example β-lactamases that hydrolyze the β-lactam bond in penicillins and cephalosporins (60). Resistance genes can also encode alternative versions of the target molecule of the antibiotic. This alternative version does not bind the antibiotic and can therefore substitute for the susceptible chromosomal copy. One example is the mecA gene that confers methicillin resistance (104). Methicillin inhibits cell wall biosynthesis by inactivating PBP2 and the mecA gene encodes an alternative version of PBP2 that does not bind methicillin (104). Resistance also arises through acquisition of chromosomal mutations (39, 107). The cellular concentrations of an antibiotic can be decreased due to inhibited uptake or active efflux of a drug. The antibiotic target can be altered such that the antibiotic no longer binds to it. Also, the activity of a compound can be decreased through active hydrolysis of the compound. The activity of some antibiotics relies on the conversion of a pro-drug to its active form by bacterial enzymes. Resistance can then be acquired by interfering with this process. In some instances one mutation is sufficient to confer high-level resistance to an antibiotic, whereas in other cases accumulation of several mutations is necessary (39, 107). High-level resistance to fluoroquinolones for example requires the sequential acquisition of mutations in at least two genes (135).

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Resistance Development and stability Several factors are important for the development of antibiotic resistance and its stability in bacterial populations. Among the factors directly affecting the microbe are; (i) the volume of drug used, (ii) the rate of formation of resistant variants, (iii) the biological cost of resistance and (iv) the capacity to compensate for such costs.

Volume of Drug Use The selective pressure in the form of drug use is essential for antibiotic resistance development. Antibiotic resistance determinants have existed in bacterial populations long before antibiotics were introduced into medicine and selection has since favored the spread of these genetic determinants in bacterial populations (59). Both quantitative models and correlations between antibiotic usage and resistance frequencies provide evidence that the volume of drug use is important for resistance development (20, 37). Furthermore, mathematical models suggest that the decay time of resistance after a decline in drug use is much longer than the time required for the emergence of antibiotic resistance. This means that once resistance has arisen in a bacterial population it will persist for long periods of time (20).

Rate of Formation of Resistant Variants The rate of formation of resistant variants in a population is determined by several different parameters. For horizontally transferred genetic determinants the dissemination rates of resistance genes are important, whereas for chromosomally encoded resistance the mutation rate to resistance is a central parameter. The enrichment of mutator strains among clinical isolates is considered a risk factor for antibiotic resistance development and mutator strains can speed up the rate of resistance development due to their increased rate of formation of resistant variants (50). Selection for antibiotic resistance under laboratory conditions and in experimental animals has been shown to efficiently enrich for strains with increased mutation rates (91, 153). For example, gnotobiotic mice infected with a mutator strain of E. coli developed antibiotic resistance following treatment more rapidly than mice infected with a wild-type E. coli strain (91). The importance of mutator strains in clinical resistance development is more complex. High-level resistance to fluoroquinolones requires at least two different chromosomal mutations and often three or more mutations contributing to resistance can be identified in clinical isolates (135). Komp Lindgren and co-workers compared mutation rates with the resistance level to ciprofloxacin in a set of E. coli isolates from patients with a UTI. In this strain collection a strong correlation between fluoroquinolone resistance and increased mutation rates could be detected (135). A similar

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enrichment of mutator strains among resistant isolates was, however, not observed in a collection of E. coli strains isolated in France (65). There are several possible reasons for the observed discrepancies. First, a correlation between mutation rate and resistance could be obscured if a low mutation rate has evolved after acquisition of the resistance mutations. A high mutation rate is costly due to the increased rate of formation of deleterious mutations and a decrease in mutation rate can evolve through several different pathways. Second, the enrichment of mutator strains observed in some collections might not be due to their increased mutation rate to resistance, but instead other unidentified factors are responsible for this enrichment. For pathogenic bacteria the disease pathology with tissue destruction and the immune response create a highly variable environment, and such variable environments are known to enrich for mutator strains (64, 194, 228). To circumvent the influence of disease pathology the correlation between mutation rate and resistance level to several commonly used antibiotics was recently investigated in normal flora bacteria (98). Mutator strains were observed at higher frequencies in bacteria isolated from patients receiving large amounts of antibiotics than among bacteria isolated from healthy individuals. Also, a weak correlation between mutation rate and resistance to the fluoroquinolone ciprofloxacin could be observed for E. coli strains but, for the other antibiotics investigated, there was no correlation between mutation rate and resistance (98). Whereas ciprofloxacin resistance is mainly conferred by chromosomal mutations, plasmid encoded resistance is more common for the other antibiotics included in the study. Since increased mutation rate does not significantly influence the transmission rate of resistance plasmids, this can explain the poor correlation between mutation rate and resistance.

Biological Cost of Antibiotic Resistance The fitness of a pathogenic bacteria is determined by; (i) the rate of reproduction and death within and outside a host, (ii) transmission between hosts and (iii) clearance from an infected host (8). Fitness costs of antibiotic resistance can be measured in three ways; (i) by retrospective studies where quantitative models are fitted to data of antibiotic use and relative frequencies of resistant and susceptible bacteria, (ii) by prospective studies measuring transmission rates of resistant and susceptible strains and (iii) by experimentally determining the relevant parameters (e.g. growth and death rates, transmission and clearance rates) (8). A few retrospective and prospective studies have been published, but the significant amount of evidence showing that antibiotic resistance generally confers a fitness cost comes from experimental data (Figure 2) (6, 20, 28, 30, 43, 146, 150, 206, 211, 220). The most common way of estimating biological costs in the laboratory is by performing competition experiments between isogenic susceptible and resistant strains,

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Figure 2. Most resistance mutations (designated R), but not all (R*), confer a fitness cost to the resistant bacteria. The fitness cost can be compensated for by acquisition of compensatory mutations (RC) or by reverting back to the susceptible genotype (S). If separated from the resistance mutation, the compensatory mutation by itself can confer a fitness cost.

either in laboratory media or in animal models (144). In competition experiments the relative performance of resistant and susceptible bacteria is estimated in several characteristics of bacterial growth; the length of the lag period, exponential growth rate, efficiency of resource utilization and mortality in the presence of host defenses (8). These fitness costs are highly dependent on the experimental conditions, and mutations displaying no fitness cost in laboratory media can confer high fitness costs in an animal model and vice versa (8). The cost of antibiotic resistance has been estimated for many combinations of antibiotic, resistance mutation and bacterial species (6, 28-30, 150, 181, 206, 220). One of the most extensively studied systems is that of streptomycin resistance in S. typhimurium. Streptomycin binds to the 30S subunit of the ribosome, thereby inhibiting bacterial translation. Chromosomally encoded resistance to streptomycin in S. typhimurium is then mainly conferred by point mutations in the rpsL gene, encoding for ribosomal protein S12 (32, 35). A few of the identified resistance mutations were seen not to confer any biological cost in any of several growth conditions tested, but the large majority of resistance mutations conferred decreased virulence in a mouse model and/or decreased growth rates in laboratory medium (30-32). In many cases the cost of resistance could be attributed to increased translational accuracy, resulting in reduced elongation rate of the ribosome (32).

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Similar patterns, where a majority of resistance mutations result in decreased fitness due to reduced efficiency of a cellular process, are true for many other antibiotics and resistance mechanisms (150, 206, 220). In some cases it has been possible to estimate fitness costs in clinical isolates (28, 181). When isolating antibiotic resistant strains under laboratory conditions, fitness costs can be inferred directly by comparisons with the isogenic susceptible strain. For clinical isolates an isogenic susceptible strain is rarely available. Evidence for compensatory evolution can then be inferred from the presence of resistance mutations that are known to confer a biological cost in strains that do not display the expected decrease in fitness. Compensatory Evolution Antibiotic resistant bacteria can decrease the fitness cost associated with resistance by either of two different mechanisms; (i) reversion of the resistance mutation or (ii) acquisition of second-site compensatory mutations (Figure 2). Reversion of the resistance mutation requires one specific mutational event and is always associated with loss of the resistance phenotype whereas acquisition of compensatory mutations, on the other hand, is not necessarily accompanied by a decrease in resistance level. In addition, the target for second-site mutations is larger since many different mutations can compensate for the cost of one single resistance mutation. The size and structure of bacterial populations has a large impact on whether reversion or compensatory evolution will occur (152). In small populations and in populations subjected to bottle-necks, all genetic variants are not represented in the population and compensatory evolution will be favored over reversions. Compensatory mutations need not restore fitness to the same level as for the susceptible parental strain, but their numerical superiority will favor compensation over reversion. When population sizes are large enough for all genetic variants to be present, selection will favor the most fit variant and reversions will be more common than in small populations. Compensatory evolution stabilizes antibiotic resistance in bacterial populations (29). In the absence of compensatory evolution, resistance is predicted to decline in a population as soon as the antibiotic is removed. Selection would then favor the more fit susceptible variant. If the fitness cost associated with resistance has been compensated for, the selective disadvantage of the resistant strains disappears. In many cases a compensatory mutation decreases bacterial fitness when separated from the resistance mutation. This will stabilize resistance in the bacterial population even further, since reversion of the resistance mutation in these situations results in a reduction in fitness (Figure 2).

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Compensatory mutations can improve fitness by any of several mechanisms (Figure 3) (151) . The conformation and stability of a mutated protein can be restored by intragenic compensatory mutations. Similarly the stability of a multi-subunit complex, like the ribosome, can be restored by intragenic mutations and extragenic mutations in other subunits of the complex (151). Examples of compensation through any of these mechanisms have been described for many combinations of resistance and compensatory mutations (31, 151, 181, 206, 220). The best studies example is compensation for the cost of streptomycin resistance in S. typhimurium (152). Here, resistance is conferred by a mutation in the rpsL gene, resulting in an amino acid substitution in the ribosomal protein S12 (32, 35). Many different mutations can compensate for the cost of the resistance mutation and compensatory mutations have been identified both intragenically in the rpsL gene, and extragenically in genes encoding other ribosomal proteins (152). The compensatory mutations are thought to restore a stable conformation of the ribosome, thereby increasing the translation rate (152). Compensation can also be achieved by overproducing a limiting or malfunctioning component by promoter mutations or gene amplifications. Finally, requirement of the mutated protein can be circumvented by activation of an alternative pathway to perform the same function (151). One example of compensation for the cost of antibiotic resistance via the last two mechanisms is found in isoniazid resistant Mycobacterium tuberculosis (223). Isoniazid resistance is conferred by the loss of KatG catalase-peroxidase activity and the resistance mutations are associated with decreased fitness, as KatG function is essential for survival in phagocytic cells (165). Clinical isolates of isoniazid resistant M. tuberculosis was observed to have increased expression of AhpC alkyl hydroperoxidase, which could substitute for the loss of KatG function (223).

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Figure 3. Compensatory mutations can compensate for the fitness cost of antibiotic resistance via several different mechanisms. Intragenic and intergenic mutations can restore a stable conformation of a mutated protein or protein complex. The activity of a cellular process can be restored by over expression of a malfunctioning component or by a bypass mechanism. The figure is adapted after Maisnier-Patin et. al. 2004.

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EVOLUTION TOWARDS REDUCED GENOME SIZE Bacterial genome sizes vary from 0.6 Mbp to approximately 10Mbp (48). The smallest genomes are encountered among bacteria with an endosymbiotic (e.g. Buchnera, Wigglesworthia) or parasitic (e.g. Mycoplasma, Rickettsia) life-style (3, 12, 103, 224). A small genome size was earlier thought to reflect an evolutionary primitive state from which free-living species with large genome sizes emerged (251). Recent year’s research and completion of many bacterial genome sequencing projects have proven this to be wrong. The small genomes have in fact evolved from ancestral bacteria with larger genomes through extensive gene loss (10, 129, 174, 252). Small genomes are found among distantly related bacteria, all living in continuous close contact with eukaryotic host cells. Thus, a small genome seems to be associated with a specific life-style, rather than being an attribute of certain phylogenetic groups (10, 129, 174, 252).

Figure 4. Bacterial genome size is determined by the relative rates of gene acquisition and gene loss. The figure is adapted after Mira et. al. 2001.

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A genome can increase in size by DNA duplications or by acquisition of external DNA through horizontal gene transfer (139, 170). Deletions of large segments of DNA and gene inactivation, followed by erosion due to deletional bias, constitute opposing forces that decrease genome size (Figure 4) (139, 170). Deletion events are more common than insertion events and genome size is determined by a balance between selection maintaining sequences in the genome and deletional bias removing non-coding or superfluous DNA (139, 170). Bacterial genomes are therefore normally densely packed and approximately 80% of fully sequenced genomes consist of intact open reading frames (ORFs). Also, gene length is effectively constant (approximately 1 kbp in length) across genomes. A reduction in genome size therefore implies massive gene loss (129, 174, 252).

The Eukaryotic Cell as a Growth Niche Obligate parasites and endosymbionts replicate and grow inside eukaryotic cells (129, 174, 252). Several features associated with this life-style are thought to be important in the evolution towards a reduced genome size (178). The eukaryotic host cell is a nutrient rich environment that can provide many of the metabolic products needed to sustain growth, e.g. amino acids, vitamins, co-factors, nucleotides and carbohydrates (177, 237, 262). A large number of genes present in free-living bacterial species are involved in biosynthesis of many of these growth intermediates and these genes are made redundant in the intracellular growth niche. As a result these genes are no longer maintained by selection and can be lost from the shrinking genome (177, 237, 262). The intracellular location of the bacteria also results in small population sizes and repeated bottle-necks in each transfer between eukaryotic cells (169). The population structure subjects the populations to increased levels of genetic drift and mutations in genes that provide beneficial, but not essential, functions can become fixed in the populations due to stochastic events (173, 253). Once gene function has been lost due to mutation, this gene is targeted for extinction via deletional bias (170). The intracellular growth environment is a relatively benign growth niche. As a result, selection on efficiency of basic cellular processes is less stringent and mutations can become fixed in populations due to inadequate purifying selection. This sheltered growth environment also decreases contact with other bacteria and phages, resulting in a decreased rate of horizontal gene transfer that could function as a balancing force to genome shrinkage through deletions (129, 174, 252).

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The Process of Genome Reduction

Reductive evolution of genome size is a combination of large deletions removing functionally unrelated genes in one single event, and of smaller deletions gradually eroding inactivated genes (175, 176, 225). The relative contribution of these two processes to genome reduction has been investigated using the endosymbiont Buchnera aphidicola as a model organism. B. aphidicola belong to the γ-proteobacteria, the same group as the well characterized free-living enterobacteria. By comparing the small genome of B. aphidicola to the close relative E. coli K12, the putative genome of a common free-living ancestor has been reconstructed (175, 176, 225). From this comparison, genome reduction appears initially to have taken place through a few large deletions occurring soon after establishment of endosymbiosis with the aphid host. The initial phase of rapid genome shrinkage was then followed by gene inactivation and gradual erosion of DNA (175, 176, 225). Each aphid species carries its own specific strain of B. aphidicola and the phylogeny of each B. aphidicola strain follows the phylogeny of its host (26). It is therefore possible to date the divergence of different strains of B. aphidicola, a task that is otherwise problematic since bacteria are not well represented in the fossil record. The result shows that the major reduction in genome size took place soon after establishment of endosymbiosis, followed by a drastic decrease in reduction rate. The last 50 million years have been characterized by evolutionary stasis and different strains of B. aphidicola are remarkably similar in genome content and organization (226, 236). Reductive evolution through gene inactivation followed by deletions can be observed in different stages of the process as pseudogenes, or as long spacer regions between intact ORFs (11, 82, 170). The genome of Mycobacterium leprae represents a genome in the early stages of reductive evolution. Only 50 % of the genome consists of intact ORFs and the M leprae genome contains an unusually high number of pseudogenes (51). In late stages of genome reduction a high percentage of the DNA in the genome again consist of functional ORFs and all pseudogenes have been completely eroded. This is the case for the reduced genome of B. aphidicola (224). Bacteria of the genus Rickettsia have a parasitic life-style and are associated with small genome sizes. Whole genome sequencing of R. prowazekii revealed an unusually high proportion of non-coding DNA of 24%, corresponding to different stages of gene degradation (12). Sequence comparisons of different members of the genus Rickettsia has made it possible to elucidate rates and patterns of sequence evolution in reduced genomes (4, 5, 9, 11, 82). Non-coding DNA is especially informative for this kind of analysis. The non-coding DNA is not expected to be subjected to purifying selection and mutation rates and nucleotide substitution patterns in these sequences therefore reflect the

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spectra of mutational processes in the cell. Deletions predominate over insertions in Rickettsia genomes reflecting the bias towards deletions in reduced genomes (11). Reduced genomes also experience elevated evolution rates that appear to be mainly due to increased mutation rate (112). Genes encoding DNA repair functions are to a large degree absent from reduced genomes and this can explain the high rate of mutagenesis observed. Also, reduce genomes have an unusually high A-T content, probably as a result of the absence of specialized DNA repair systems (9, 12, 112, 224). Genome rearrangements are common in the evolution of many free-living bacteria. The reduced genomes of different strains of B. aphidicola are remarkably similar in gene organization, indicating that recombination events are uncommon (226, 236). Several potential explanations for this phenomenon have been put forward. First, prokaryotic genomes often contain long repeat sequences, for example rRNA operons and IS-elements (33, 162). These long repeat sequences are virtually absent in reduced genomes. B. aphidicola and R. prowazekii for example only harbors one copy of each rRNA gene and repeat sequences in the form of IS-elements and prophages are absent (4, 12, 82, 224). Homologous recombination between repeat elements resulting in deletions has been important in the evolution of reduced genome size. In this process repeat elements once present in the genome has been consumed. Second, the recA gene, together with several other genes involved in recombination, is absent from many reduced genomes (12, 224). The absence of suitable repeat sequences and the loss of recA have been suggested as important contributors to the low level of recombination observed in reduced genomes (129, 226, 252). Much emphasis has been put on the lack of functional RecA-dependent homologous recombination (106). However, experimental evidence from E. coli and S. typhimurium suggest that deletions and rearrangements frequently arise via mechanisms that are not dependent on RecA functions (2, 100, 111, 215). The low rate of recombination observed in reduced genomes can therefore be the result of a more general loss of recombinational capacity due to the absence of several components of DNA repair and recombination (226). Third, the absence of recombination events may not be a reflection of mechanistic constraints. Rather, gene content and genome organization can be maintained due to selection (140). For example, the distance between the origin and terminus of replication is important for efficient DNA replication. Also, highly expressed genes are often situated close to the origin of replication and they are transcribed in the same direction as DNA replication. Recombination events resulting in disruption of gene order can then significantly alter the expression level of such genes (106).

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The Minimal Genome Concept Closely associated with evolution toward a small genome size is the concept of a minimal genome. How many genes and what functions are needed to sustain a living cell? (136) Experimental approaches to determine which genes are essential under a defined set of growth conditions include massive transposon mutagenesis, use of antisense RNA to inhibit gene expression and systematic inactivation of single genes (79, 110, 114, 132-134). However, an essential gene set is not the same as a minimal gene set. Bacterial genomes display a high degree of redundancy where several genes can perform similar functions. Genes that are not essential when inactivated singly can become essential when other functions are absent. Similarly, an essential gene function can be made redundant when other genes are deleted simultaneously. Another way to address the question of a minimal gene set is by computational analysis (89, 136). In a pioneering analysis, Mushegian and Koonin compared the genomes of the first two bacterial species to be sequenced, Mycoplasma pneumoniae and Haemophilus influenzae (180). The two bacterial species are only distantly related to each other and genes common to the two genomes were proposed to be conserved in evolution and to be essential for cell function. In the study 256 genes were identified as conserved between the two species and suggested to approximate a minimal gene set (180). The addition of more bacterial genomes to the analysis has since then reduced this number substantially (89, 136). One draw back of computational analysis is that it does not take into account gene functions that are performed by unrelated gene copies in different bacteria. Minimal gene sets defined in these kinds of analyses therefore tend to underestimate the size of a minimal gene set. Recent analyses have focused on defining gene functions that are essential to maintain a living cell (90). Among these function are (i) replication and a rudimentary DNA repair system, (ii) interpretation (transcription) and expression (translation) of the genetic information, (iii) RNA and protein processing, (iv) maintenance of cell structure and cell division, (v) transport of substrates over the cell membrane, (vi) generation of energy and (vii) capacity to synthesize metabolites not provided by the environment (90).

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PRESENT INVESTIGATION The aim of this thesis is to describe patterns and mechanisms of bacterial adaptation to novel environments. The specific aims are;

• to determine the mutation rate among natural isolates of E. coli. (paper I)

• to determine how mutation rate and population size influence the rate

and extent of adaptation of S. typhimurium LT2 to mice. (paper II)

• to determine the mechanisms of fosfomycin resistance in E. coli. Also to estimate the biological cost of fosfomycin resistance in E. coli and evaluate its impact on clinical resistance development. (paper III)

• to identify mutations conferring resistance to actinonin in S.

typhimurium LT2 and to estimate their effect on bacterial fitness. Further to perform compensatory evolution and identify the compensatory mutations. (paper IV)

• to reduce the genome size of S. typhimurium LT2 by means of

spontaneous deletions. Also to identify the deletion junctions and evaluate the role of sequence homologies in deletion formation. (paper V)

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RESULTS AND DISCUSSION The following presentation is a summary of the main findings in the papers included in this thesis. For a detailed description of the methods used and graphical presentation of the data, the reader is referred to the specific papers.

Weak Mutators are Enriched in Clinical Isolates (paper I) In paper I we determined the frequency of strains with elevated mutation rates among natural isolates of E. coli. Strains were collected in three different European countries; Spain, Denmark and Sweden. In Spain 100 strains were collected from positive urine cultures, 100 strains from blood cultures and 100 strains from stools of healthy volunteers. 170 strains were collected in Denmark from blood cultures and the Swedish collection consisted of 226 strains from urine cultures. In total 696 E. coli strains were included in the study. For all strains we determined the mutation frequency to rifampicin resistance, which is a common method to estimate mutation rates in bacteria. Based on their estimated mutation frequencies, the strains could be categorized into four different groups. Most strains (63.3 %) had mutation frequencies in a narrow interval between 8x10-9– 4x10-8, corresponding to the modal peak of the distribution. These strains were considered as normo-mutable and the mutation frequency to rifampicin resistance observed in this group corresponds well to the mutation frequency expected for a repair proficient E. coli strain (85). 13 % of the strains had a lower mutation frequency than 8x10-9 and were termed hypomutable. Among the E. coli isolates we observed an enrichment of strains with a moderate increase in mutation rate (4x10-8 – 4x10-7). This group constituted 23% of all isolates and was designated as weak mutators. Also, five strains with a strong mutator phenotype were identified, where the mutation frequency to rifampicin resistance was above 4x10-7. This high mutation frequency corresponds to that observed for MMR-deficient E. coli, however the genetic basis for the high mutation rate was not investigated in these strains (105, 166, 171, 172). A clear difference in enrichment of weak mutators could be observed depending on the source of isolation. In the collection of Spanish isolates, weak mutators were more common among pathogenic E. coli isolated from blood (38%) and urine cultures (25%) than among commensal E. coli isolated from stool samples (11%). There was also a difference between pathogenic E. coli depending on the site of isolation. Enrichment of mutator strains among clinical isolates have been observed in several studies (65, 98, 135, 141, 157, 188, 218). Among Pseudomonas aeruginosa isolated from cystic fibrosis patients as many as 20% of the strains displayed a strong mutator phenotype (188). The life-style of

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pathogenic bacteria seems to be especially prone to enrich for elevated mutation rates and pathogenic bacteria encounter a highly dynamic and heterogeneous environment in its host (194). Disease progression, with tissue destruction and activation of the immune system, contribute to a rapidly changing environment. Such environmental fluctuations are known to enrich for mutator strains due to their increased rate of formation of beneficial mutations (64, 228). Population structure is also important in the enrichment for mutators. Strains with a high mutation rate are more rapidly enriched in small populations and in populations passing through severe bottle-necks (64, 228). Both of these conditions may very well exist in the host environment. Strong mutators have been the focus of interest in most studies and little attention has been paid to weak mutators. In our study we identified weak mutators at far higher frequencies than strong mutators. There are several possible explanations for this finding. First, weak mutators might simply form at a higher rate than strong mutators due to a larger mutational target. Due to their abundance, weak mutators can therefore experience a higher probability than strong mutators of acquiring beneficial mutations with which they hitch-hike to higher frequencies in a population. Second, strong mutators could be efficiently counter-selected because of their increased rate of fixation of deleterious mutations. Due to their lower rate of mutation accumulation, the evolutionary success of a weak mutator could therefore be higher over time than for a strong mutator. A high mutation rate has been suggested as a risk factor for antibiotic resistance development and the enrichment of mutator strains among clinical isolates could potentially speed up the rate of resistance development (50, 91, 153). If so, mutator strains should be more common among antibiotic resistant isolates than among fully susceptible strains. We determined the resistance level to ciprofloxacin resistance in a sub-set of E. coli isolates and compared the resistance level with mutation rate. We could not detect any correlation between mutation rate and ciprofloxacin resistance. These results are in concordance with data from a French collection of E. coli strains (65). Increased frequencies of mutator strains have however been identified among ciprofloxacin resistant E. coli isolates in other strain collections (135). The reasons for the discrepancies observed between different strain collections are not clear. The trivial explanation would be that an increased mutation rate is not a risk factor for resistance development and the correlation observed in some studies is purely incidental. Alternatively, a correlation between resistance and mutation rate could be obscured by other factors, for example mutation rate modifiers arising after resistance development.

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Mutation Supply Rate is Limiting for Adaptation of Salmonella to Mice

(paper II) In paper II we investigated the influence of mutation rate and population size on the rate and extent of adaptation of S. typhimurium to mice. The laboratory strain S. typhimurium LT2 cause a typhoid fever-like infection in BALB/c mice (156, 186). In the animal host, the bacteria replicate in macrophages and, a few days after infection, bacteria can be harvested from liver and spleen in large numbers. We evolved 18 different lineages of S. typhimurium in mice by serial passage for approximately 130 generations. Four different combinations of population size and mutation rate were used to vary mutation supply rate and fitness of the evolving populations was estimated at the end-point (132 generations) and mid-point (66 generations) of the experiment by performing competition experiments in mice. At the end point all populations had increased fitness. However, the fitness increase (s=0.1) was small for the populations evolved with the lowest mutation supply rate (i.e. small population size-low mutation rate) whereas the other evolved populations had reached the same high fitness level in mice (s=0.4). In populations evolved with a high mutation rate, the time of ascent of beneficial mutations seemed to be limiting for the rate of adaptation. Increasing mutation supply rate in these populations, by increasing population size, had little effect on the rate of adaptation. In the populations evolved with a low mutation rate however, mutation supply rate became limiting. The populations evolved with a small population size increased in fitness only marginally and increasing the mutation supply rate, by increasing the population size, significantly accelerated the rate of adaptation. By mathematical modeling we could estimate an apparent mutation rate for beneficial mutations in these lineages to be larger than 10-6/cell/generation. The experimental data is consistent with the fixation of two successive beneficial mutations, each with a selection coefficient of 0.2, and a target for beneficial mutations as large as 104 base pairs. The large target for beneficial mutation suggests that adaptation to mice can be conferred via a large number of single nucleotide substitutions or loss-of-function mutations in single genes.

Mutator Strains Evolved in Mice Display Fitness Trade-Offs due to

Mutation Accumulation (paper II) To determine if adaptation to mice resulted in decreased fitness in secondary environments we investigated the metabolic potential of the evolved populations. First, we screened the evolved populations for auxotrophic mutants that had lost the ability to synthesize specific amino acids. In the populations evolved with a high mutation rate, 0.7-39% of the bacteria was auxotrophs, whereas the frequency of auxotrophic mutants in the populations evolved with a low mutation rate was below 0.08%. We also investigated the capacity to metabolize

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46 different carbohydrates for one random clone from each evolved population. All clones from populations evolved with a low mutation rate had the same metabolic potential as the unevolved ancestral strain, whereas clones from the evolved mutator populations had defects on at least one of the substrates tested. In conclusion, populations evolved with a high mutation rate displayed fitness trade-offs in secondary environments whereas populations evolved with a low mutation rate did not. Fitness trade-offs in secondary environments are thought to occur either via antagonistic pleiotrophy effects, where a beneficial mutation by itself causes decreased fitness in a secondary environment, or via mutation accumulation. In this case, antagonistic pleiotrophy is an unlikely explanation, since populations evolved with a low mutation rate displayed no fitness trade-offs and the fitness trade-offs observed in the mutator lineages were most likely caused by a higher rate of mutation accumulation.

Partially Different Spectra of Mutations Confer Resistance to Fosfomycin

in Resistant Strains Isolated in the Laboratory and in Clinical Fosfomycin

Resistant Isolates (paper III) Fosfomycin is a cell-wall inhibitor mainly used in treatment of UTIs (119, 168). Under laboratory conditions fosfomycin resistance develops rapidly with a mutation frequency to resistance between 10-7-10-8. However, despite many years of usage the frequency of fosfomycin resistant clinical isolates is low, indicating that factors other than the rate with which resistant variants form are important for clinical resistance development (122). In paper III we isolated a set of fosfomycin resistant mutants of E. coli NU14 under laboratory conditions, identified the resistance mutations and estimated their effect on bacterial fitness. The results from the in vitro isolated strains were then compared with a set of fosfomycin resistant clinical isolates. Fosfomycin resistance in E. coli is mainly conferred via inhibited uptake of the antibiotic into the cell through the GlpT and UhpT transport systems. The expression of the two transporters is positively regulated by the cAMP levels in the cell and full expression of the UhpT system also requires the transcriptional regulators UhpABC and an external inducer in the form of glucose-6-phosphate (G6P) (117, 164, 187, 221). Fosfomycin resistance is conferred by loss-of-function mutations in the genes encoding either the transporters themselves, or in regulatory genes like cyaA and ptsI that control the cAMP levels in the cell (118, 119, 168). In the in vitro isolated fosfomycin resistant strains, resistance mutations were identified both in genes encoding the transporters as well as in cyaA and ptsI genes whereas in the clinical isolates only mutations in the transporter genes could be identified. To exclude the possibility that the clinical isolates carried unidentified mutations that interfere with the cAMP levels in the cell

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we tested the ability of the fosfomycin resistant strains to grow on minimal medium supplemented with five different carbohydrates. Mutations in genes regulating cAMP levels in the cell confer many pleiotrophic effects and mutant strains become defective for uptake of several different carbohydrates (199). All fosfomycin resistant mutants isolated in vitro with mutations in cyaA or ptsI were unable to growth on at least one of the carbohydrates tested whereas no growth defects could be identified in the clinical fosfomycin resistant isolates. The results show that fosfomycin resistance is conferred by partially different spectra of mutations in the in vitro isolated strains as compared to the clinical fosfomycin resistant isolates. The absence of mutations in genes regulating cAMP expression in the clinical isolates suggests that this class of mutations is counter-selected under in vivo conditions. Reduced cAMP levels are known to decrease adhesion to epithelial cells, since pilus biosynthesis is reduced (22). Also, defects in uptake of carbohydrates could reduce growth rates both in the bladder and in the intestine.

Fosfomycin Resistance Generally Confers a High Fitness Cost that

Contributes to the Low Prevalence of Fosfomycin Resistance among

Clinical Isolates (paper III) To estimate the fitness costs associated with fosfomycin resistance we measured exponential growth rates in LB and urine. Also, a mathematical model was developed to estimate the effect of fitness costs on the probability of resistance development in the bladder during antibiotic therapy. The model takes into account known parameters of bladder dynamics, e.g. flow rate of urine into the bladder and maximal and minimal volume of urine in the bladder before and after emptying (96). Microbial parameters, such as growth rate of resistant and susceptible strains and the mutation rate to resistance, were estimated in the laboratory. The mathematical modeling showed that fosfomycin resistance would develop with a high rate during antibiotic therapy, if the resistance mutations did not have any effect on bacterial fitness. However, the probability of resistance development was very sensitive to changes in growth rate and even a moderate decrease in fitness would drastically reduce the probability of resistance development. By measuring exponential growth rates we showed that fosfomycin resistance generally confers a fitness cost to the resistant bacteria. Growth rates of the in vitro isolated fosfomycin resistant mutants were between 10-25% slower than for the susceptible parental strain, both in LB and in urine. For a resistant strain to establish an infection in the bladder during fosfomycin therapy growth rate in the presence of the antibiotic is also important. We therefore measured exponential growth rates in the presence of a range of fosfomycin concentrations. The addition of the antibiotic to the growth medium reduced growth rates of the resistant

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strains even further compared to when fosfomycin was absent. For the severe fitness costs observed, the mathematical model showed that the probability of resistance development in the bladder during fosfomycin therapy is improbable. For the clinical fosfomycin resistant isolates we lacked access to an isogenic susceptible strain for comparisons. Instead, we compared growth rates between a set of fosfomycin resistant and fosfomycin susceptible clinical isolates. No significant differences in growth rate could be detected between the two groups. The results indicated that either the fitness cost of fosfomycin resistance in the clinical isolates had been ameliorated via compensatory mutations, or the clinical isolates carry resistance mutations that do not confer a high fitness cost. Exponential growth rate is only one parameter affecting fitness of uropathogenic E. coli. The ability to adhere to uroepithelial cells is also an important virulence factor and other studies have shown that fosfomycin resistant E. coli display reduced adherence to epithelial cells (47, 52, 63, 93, 130). However, rapid growth in urine is an important virulence factor and reduced growth rates in urine will, therefore, have significant effects on the virulence of fosfomycin resistant E. coli. Expression profiling of E. coli growing in the bladder of infected mice show that genes encoding ribosomal proteins are highly expressed, suggesting that the bacteria are growing rapidly (230). Also, uropathogenic E. coli have been demonstrated to grow significantly faster in urine than E. coli isolated from feces, indicating that rapid growth in urine is a specific trait for these strains (96). The low prevalence of fosfomycin resistance observed clinically has been suggested to be dependent on several parameters. For many antibiotics, resistance is rapidly enriched in the normal flora after treatment and can persist for many years. In these cases, the normal flora can function as a reservoir of resistance determinants that can then be transferred to pathogenic strains. Fosfomycin therapy does not seem to enrich for resistant strains in the intestinal flora and resistance is mainly conferred by chromosomal mutations that do not spread horizontally (14, 101). We suggest that in addition to these factors, the biological cost of resistance, in terms of reduced growth rates, is an important contributor to the low prevalence of fosfomycin resistance observed clinically.

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Actinonin Resistance Mutations are Associated with High Fitness Costs

(paper IV)

For the success of any new antibiotic a low rate of clinical resistance development is essential. To evaluate the risk of resistance development clinically, the biological costs of resistance, and the ability to compensate for such costs, are important factors to estimate. Deformylase inhibitors are a new class of antibiotics targeting a unique feature of bacterial translation (88, 154, 155). In paper IV we estimated the biological cost of resistance to the classic deformylase inhibitor actinonin, and the capacity to compensate for any such fitness costs. We also determined the genetic basis for resistance and compensation. Initiation of translation with a formylated methionine is an evolutionary conserved feature of bacterial translation. After translation is completed, the formyl group is removed by the enzyme peptide deformylase (PDF), a step that is essential for subsequent protein maturation (13, 24, 88, 183). Actinonin binds to and interferes with the activity of PDF, resulting in accumulation of formylated polypeptides in the cell and subsequent cell death. We isolated a set of actinonin resistant mutants in the laboratory from S. typhimurium LT2. Resistance mutations were identified by sequencing the fmt and folD genes. The fmt gene encodes formyl-methyl transferase (FMT), catalyzing the addition of the formyl group to the methionyl initiator-tRNA complex (13). The resistance mutations identified in this gene were mainly loss-of function mutations that disrupt FMT function. As a consequence, the ribosome is forced to initiate translation with an unformylated methionine and the need for a functional PDF is thereby by-passed. The folD gene encodes a bi-functional enzyme important for the production of the precursor molecule 10-formyl-H4folate, the donor of the formyl group (159). Similar to fmt mutations, loss of folD function is thought to reduce formylation of the methionyl initiator-tRNA complex. To determine the effect of the resistance mutations on bacterial fitness we measured exponential growth rates in LB. The majority of all actinonin resistant mutants displayed severely reduced growth rates and the effect was especially pronounced in strains carrying mutations in the fmt and folD genes. For a subset of the actinonin resistant mutations, fitness was also estimated in a mouse model and the results showed that for all but one strain, virulence in mice was severely perturbed. The one strain where no cost was identified in the mouse model also had similar fitness as the parental susceptible strain in LB, indicating that this strain might carry a resistance mutation that does not confer any detectable fitness cost. The severe fitness costs observed can be attributed to a decreased translation rate, since initiation of translation with an unformylated methionine is a highly inefficient process (97). Taken together, the data show that actinonin resistance generally confers a heavy fitness burden on the resistant bacteria.

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Over-expression of the Initiator tRNA via Gene Amplifications can

Compensate for the Cost of Actinonin Resistance (paper IV) The severe fitness costs associated with actinonin resistance suggests that the rate and extent of compensatory evolution will be important parameters to determine when evaluating the risk for clinical resistance development. We evolved in total 39 lineages from five different ancestral, actinonin resistant strains by serial passage in LB. The ancestral strains carried different resistance mutations in either fmt or folD, conferring severe fitness costs to the resistant strains (growth rates in LB were reduced between 42% - 76%). The first growth-compensated mutant to reach fixation in each lineage was isolated and used for further analysis and we could isolate growth-compensated mutants in all evolved lineages. Some mutants increased fitness to levels similar to the susceptible wild-type strain, but the majority of growth-compensated mutants still displayed a significant fitness cost after acquisition of one compensatory mutation. The result indicates that restoring fitness to the same level as for the susceptible wild-type strain generally requires a sequential acquisition of several compensatory mutations. Intragenic compensatory mutations were identified in 12 strains by sequencing the gene carrying the resistance mutation. The intragenic compensatory mutations identified were mainly in the same codon as the resistance mutations, reverting a stop codon to a sense codon. In 24 of the growth compensated strains, we could not identify any intragenic compensatory mutations and here we used transposon mutagenesis to identify the unknown extragenic mutations. In eight strains the transposon linked to the compensatory mutation was inserted close to the metZW genes that encode for the initiator-tRNA (tRNAi). Southern hybridization revealed gene amplifications spanning the metZW genes in all eight strains with a 5-fold to 40-fold increase in copy number. We could also show, by Northern hybridizations, that the increase in copy number was accompanied by a similar increase in the level of the tRNAi itself. An overproduction of the tRNAi can potentially compensate for the decreased translation rate of the resistant strains via either of two different mechanisms, or by a combination of both. First, if the mutated FMT enzyme retains some activity, over production of the tRNAi might increase the rate of formylation by increasing the concentration of one of the substrates (tRNAi) in the enzymatic reaction. Second, an over production of the tRNAi can potentially increase the rate of translation initiation with an unformylated methionine by simply increasing the proportion of tRNAi relative to the elongator tRNAs in the cell. Presently, we can not distinguish between the two mechanisms, but the results will have important implications for resistance

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development. A compensatory mechanism that requires restoration of formylation capacity will also concomitantly decrease the resistance level to actinonin. We further wanted to determine the size of the amplified arrays and the structure of the amplification junctions in the growth-compensated strains. The formation of the first duplication is the rate limiting step in generating gene amplifications (231). Once the first duplication is formed, RecA can act on the large stretches of sequence identity and catalyze subsequent amplification via homologous recombination. The metZ and metW genes are identical and situated in close proximity to one another on the Salmonella chromosome, only separated by a spacer region of 31 bp. This tandem localization of the two genes constitutes an already preformed duplication that could potentially have been used to create the amplifications identified in the growth compensated strains. To investigate if this was the case, we performed PCRs with primers located directly upstream and downstream of the two genes. If the homologies in the two identical genes were used as substrates for the amplifications, the primers would be expected to produce a PCR product of the same size as the amplified array.

Figure 5. Model for the evolution of new gene functions by means of gene amplification.

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However, the PCR products obtained were all of the same size as for the wild type S. typhimurium strain and the metZW genes could therefore not have been used as substrates in the amplification process. In one of the eight compensated strains carrying gene amplifications, we could identify the duplication junction to 460 bp upstream and 1440 bp downstream of the start of the metZ gene, giving rise to an amplified array of 1908 bp. Here, we could not detect any sequence homologies at the junction. Gene duplications have been suggested to constitute a first step in evolution of new gene functions (128, 240). At the same time as one gene copy performs the original function, the second copy can evolve new functions through mutagenesis. There are two main problems with this theory. First, how can elevated copy numbers of a gene be maintained in a population long enough for genetic and phenotypic divergence to take place. Long stretches of amplified DNA are intrinsically unstable and rapidly segregate via homologous recombination, which rarely allows for enough time for diversification. Second, since most mutations will inactivate gene function the probability of evolving a new function is very low compared to the probability of gene inactivation if there is no selection for functionality of all gene copies. Here, we describe a system where gene amplifications can compensate for the cost of a deleterious mutation. In this system, both gene number and gene functionality is maintained by selection (Figure 5).

Bacterial Genome is a Rapid Process under Laboratory Conditions

(paper V) Evolution towards endosymbiosis is associated with extensive gene loss and concomitant reduction in genome size (129, 174, 252). In paper V we set up a simple experimental system to study the rate and mechanisms of DNA loss in real time (Figure 6). Different lineages of S. typhimurium were serially passaged on hematin agar plates, and with regular intervals samples from the evolving lineages were analyzed for DNA loss by performing PFGE and DNA microarrays. The evolving lineages were serially passaged by each day picking one random colony and streaking it on a fresh agar plate. By introducing a bottle-neck of one cell in each passage the effect of purifying selection was effectively reduced. The majority of mutations, including deletions, are deleterious to bacterial fitness and would therefore be lost if purifying selection were not eliminated in the evolving lineages. Reduction in population size and passage through severe bottle-necks are also characteristic features of evolution towards reduced genome size (129, 174, 252). The intracellular growth environment of endosymbionts provides many of the metabolic products needed to sustain bacterial growth and genes encoding metabolic functions are almost absent from reduced

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Figure 6. Schematic presentation of the experimental system used for studying genome reduction under laboratory conditions.

genomes as they have been made redundant during evolution toward endosymbiosis. In our experimental set-up we used hematin agar plates to mimic the nutrient rich growth environment provided by the eukaryotic cell. The growth medium provides many of the amino acids, vitamins, co-factors and carbohydrates needed for bacterial growth and thereby selection for many of the metabolic functions encoded for on the Salmonella chromosome was thereby relaxed.

We evolved in total 72 lineages of S. typhimurium using this experimental set-up. Twelve lineages were evolved with a low mutation rate and 60 lineages with a high mutation rate. The MMR system has a stabilizing effect on maintaining genome structure and the MutS- mutator strain used in this experiment displayed a 50-fold higher deletion rate than the wild-type strain in the mob-gal deletion assay system (143). After 60 cycles of serial passage (corresponding to approximately 1500 generations), deletions were identified in four of the mutator lineages, whereas no deletions could be detected in the wild type lineages after 300 cycles of serial passage (~7500 generations). The deletions identified varied in size from approximately 173 kbp to 1.2 kbp. From the data we calculated an average rate of DNA loss of 2.5 bp/chromosome/generation for the mutator strain and 0.05 bp/chromosome/generation for the wild-type strain. These rates most likely reflect maximal DNA loss rates, since successive deletions will decrease the target size for future deletions as the fraction of non-essential genes decreases. The data however, indicate that

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genome size can be drastically reduced in a, from an evolutionary perspective, very short time period. If a generation time of one day is assumed, the above rates suggests that a genome can decrease in size by 1 Mbp in 50,000 years. The above calculations are obviously associated with a large degree of uncertainty since many factors like generation time, the extent of purifying selection and genetic drift, and the decline in deletion rate are unknown. Reduction of genome size in endosymbionts is however known initially to have been a rapid process with large deletions occurring early in evolution, followed by a gradual reduction in deletion rate (236). To evaluate the dependence of deletion formation on RecA-dependent homologous recombination and sequence homologies we identified the deletion end-points in two of the spontaneous deletions isolated in the evolution experiment and in four independent deletions isolated using the mob-gal deletion assay system. The homologies identified were 40 bp, 12 bp, 4 bp, 2 bp, 1 bp and 0 bp respectively. Since the RecA system requires sequence homologies of at least 25 bp, the results indicate that 5/6 deletions were probably formed by RecA-independent processes (222). Reduced genomes typically lack the recA gene and they are devoid of repeat sequences (12, 224). The lack of homologous recombination functions have been suggested as one explanation for the extraordinary genome stability observed in different strains of Buchnera (129, 252). Our data show that spontaneous deletions form at high frequencies via other pathways than classical RecA-dependent homologous recombination. Therefore, loss of repeat sequences and RecA function might not be enough to explain the genome stability observed in endosymbionts. Reduced genomes have lost many genes involved in DNA repair and recombination, apart from RecA, and might therefore carry more general defects in recombination that affects several pathways of deletion formation (226). Alternatively, genome size and organization can be maintained effectively by selection (106). The evolutionary stasis observed for many reduced genomes may therefore be a reflection of selection acting to preserve genome organization.

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CONCLUDING REMARKS

Bacterial adaptation to novel environments is dependent on the rate of formation of genetic variation and on how selection acts on the genetic variation formed. In this thesis I have investigated the prevalence of strains with increased mutation rate in natural isolates of E. coli and further investigated how mutation rate influences the rate and extent of bacterial adaptation under experimental conditions. I have also investigated how strong selection pressures influence antibiotic resistance development and determined the genetic basis for the changes observed. In addition, I have set up an experimental to study the process of bacterial genome reduction. Increased mutation rate can speed up the rate of bacterial adaptation under conditions where the supply of genetic variants is limited (paper II) and bacterial strains with increased mutation rates are enriched among natural isolates of E. coli (paper I). The result indicates that the supply of genetic variants is often limiting for bacterial adaptation under natural conditions. Enrichment of bacteria with increased mutation rates is especially prominent among pathogenic bacteria (65, 98, 135, 141, 157, 188). These bacteria experience a highly variable environment in their hosts and such environments are known to enrich for increased mutation rates (64, 194, 228). A high mutation rate, though, comes at a cost in the form of rapid accumulation of mutations that can prove deleterious in secondary environments (paper II) (64, 228). The evolutionary success of a mutator strain is therefore determined by a balance between the beneficial effect of generating advantageous mutations at a high rate and the cost of more rapid accumulation of deleterious mutations. Increased frequencies of mutator strains among pathogenic bacteria have been suggested to be a risk factor for antibiotic resistance development, due to a more rapid rate of formation of resistance mutations (50). The rate of formation of resistant variants is only one critical parameter for antibiotic resistance development. Also the biological cost of resistance and the ability to genetically compensate for such costs are of importance. Most resistance mutations confer a biological cost on the resistant bacteria (8, 29). We show that resistance to the antibiotics fosfomycin and actinonin generally confers a heavy fitness burden on the resistant bacteria (paper III, IV). Fosfomycin is used clinically to treat UTI:s and the cost of fosfomycin resistance can significantly reduce the probability of resistance development during antibiotic therapy (paper III). Also, the spectra of fosfomycin resistance mutations are partially different between resistant strains isolated in the laboratory and clinical fosfomycin resistant isolates (paper III). Only a subset of the resistant strains isolated in the laboratory are represented among the clinical isolates, indicating that many of the resistance mutations identified in the laboratory are counter-

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selected under natural conditions. In many cases the fitness burden of resistance mutations can be compensated for via acquisition of second-site mutations (151). Actinonin resistance generally confers a high fitness cost to the resistant bacteria that can be compensated for via both intragenic and extragenic mutations (paper IV). The intragenic compensatory mutations restore function of the mutated protein, whereas the extragenic mutations identified are gene amplifications that result in over-production of a limiting component in translation initiation. Contrary to the evolution of antibiotic resistance, which is characterized by the effects of strong selective pressures, evolution toward a reduced genome size is to a large extent influenced by genetic drift (129, 174, 252). Many of the characteristics of evolution towards a reduced genome size can be mimicked in the laboratory and using such an experimental set-up we have observed a very rapid rate of DNA loss in S. typhimurium under laboratory conditions (paper V). The results agree well with observations from the reduced genomes of endosymbiotic bacteria where genome reduction was initially a rapid process followed by long periods of evolutionary stasis (236). Reduced genomes typically lack repeat sequences and RecA functions (12, 224). This has been suggested as one of the reasons for exceptional stability of genome organization observed in reduced genomes (12, 129, 252). Many of the deletions formed under laboratory conditions however, did not depend on RecA functions (paper V). Loss of RecA functions alone might therefore not be enough to explain the exceptional genome stability observed.

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ACKNOWLEDGEMENTS The work presented in this thesis has been performed at the bacteriology department at the Swedish institute for infections disease control. Many people have in different ways supported me in my work as a PhD-student and contributed to the completion of this thesis. I would especially like to extend my gratitude to the following people. My supervisor Dan Andersson for teaching me microbiology and molecular genetics. Oh captain my captain, you have been a big support for me during these years. I have greatly appreciated the possibility you have given me to test my own ideas and choose my own projects. Thank you for introducing me to the wonders of replica printing and I am genuinely impressed by your skills at solving crosswords. My co-supervisor Mikael Rhen for sharing your vast scientific knowledge and for including sailing in the Swedish archipelago as an important part of my education as a PhD-student. The heads of department of Bacteriology Gunilla Källenius, Lars Engstrand and Birgitta Henriques-Normark for providing a space for me at SMI. My co-authors on the papers included in this thesis. This thesis would not have been possible without your contributions. Former and present members of the Dan Andersson group at SMI. Especially; Bente

Larsen for sharing both good and bad days in the lab with me during my first years. My partner in crime Elisabeth Kugelberg for your optimistic time planning that ended up in us leaving the lab very early in the morning. Sophie Maisnier-Patin for ideas and feed-back on my work. Anna Syk for all excellent technical advise and help and for all the care you have shown me. My personal sushi provider Cecilia Dahlberg for sharing your knowledge in evolutionary biology, and for your genuine interest in my projects. Vilhelm

Paulander for your provocative way of annoying me and for being a good friend. Sanna

Koskiniemi for adding substantially to the Finnish touch of the group. Professor Diarmaid Hughes and his group in Uppsala. Our group-meetings have been a source of inspiration and new ideas to test in the lab.

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My dear friend Sofia Eriksson for teaching me microarrays and many more things. You fully appreciate that all problems of a PhD-student are best solved over a glass of wine. Fredrik Söderbom and Andrea Hinas in Uppsala for helping me with the Northern blots. Pyloripinglorna –past and present - for all nice coffee breaks, lunches and parties. Britt-Marie Hoffman and Lena Gezelius for all support during difficult times.

Solomon Ghebremichael for being part of the nice atmosphere in lab 420 when I first started working at SMI.

Thomas Åkerlund and Sture Karlsson for being the funniest travelling companions in Argentina. Emma Huitric for critical reading of my thesis. All the people at the department of bacteriology for creating a nice working atmosphere. My friends, especially Linda&Paco for creating a nice atmosphere for me in “pensionat Uppsala” when I have needed a break from work, Jenny for helping me finding a solution to every problem and for never underestimating the power of “terapi-shopping”, Anna for understanding the ups and downs in the world of a PhD-student, Ramona for simply being a good friend, my friend from home Ulrika for reminding me that there actually is a world outside science. My parents, Claes&Kirsti, my sisters Karin, Johanna & Christina and a large number of relatives for always supporting me in my work and believing in my capabilities. A special thanks to Johanna for all the help with the graphical design of my thesis.

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REFERENCES 1. Acharya, S., P. L. Foster, P. Brooks, and R. Fishel. 2003. The coordinated

functions of the E. coli MutS and MutL proteins in mismatch repair. Mol Cell 12:233-46.

2. Agemizu, Y., N. Uematsu, and K. Yamamoto. 1999. DNA sequence analysis of spontaneous tonB deletion mutations in a polA1 strain of Escherichia coli K12. Biochem Biophys Res Commun 261:584-9.

3. Akman, L., A. Yamashita, H. Watanabe, K. Oshima, T. Shiba, M. Hattori, and S. Aksoy. 2002. Genome sequence of the endocellular obligate symbiont of tsetse flies, Wigglesworthia glossinidia. Nat Genet 32:402-7.

4. Amiri, H., C. M. Alsmark, and S. G. Andersson. 2002. Proliferation and deterioration of Rickettsia palindromic elements. Mol Biol Evol 19:1234-43.

5. Amiri, H., W. Davids, and S. G. Andersson. 2003. Birth and death of orphan genes in Rickettsia. Mol Biol Evol 20:1575-87.

6. Andersson, D. I., J. Bjorkman, and D. Hughes. 2001. Fitness and Virulence of Antibiotic Resistant Bacteria. In D. Hughes and D. I. Andersson (ed.), Antibiotic Development and Resistance. Taylor and Francis, London.

7. Andersson, D. I., and D. Hughes. 1996. Muller's ratchet decreases fitness of a DNA-based microbe. Proc Natl Acad Sci U S A 93:906-7.

8. Andersson, D. I., and B. R. Levin. 1999. The biological cost of antibiotic resistance. Curr Opin Microbiol 2:489-93.

9. Andersson, J. O., and S. G. Andersson. 1999. Genome degradation is an ongoing process in Rickettsia. Mol Biol Evol 16:1178-91.

10. Andersson, J. O., and S. G. Andersson. 1999. Insights into the evolutionary process of genome degradation. Curr Opin Genet Dev 9:664-71.

11. Andersson, J. O., and S. G. Andersson. 2001. Pseudogenes, junk DNA, and the dynamics of Rickettsia genomes. Mol Biol Evol 18:829-39.

12. Andersson, S. G., A. Zomorodipour, J. O. Andersson, T. Sicheritz-Ponten, U. C. Alsmark, R. M. Podowski, A. K. Naslund, A. S. Eriksson, H. H. Winkler, and C. G. Kurland. 1998. The genome sequence of Rickettsia prowazekii and the origin of mitochondria. Nature 396:133-40.

13. Apfel, C. M., H. Locher, S. Evers, B. Takacs, C. Hubschwerlen, W. Pirson, M. G. Page, and W. Keck. 2001. Peptide deformylase as an antibacterial drug target: target validation and resistance development. Antimicrob Agents Chemother 45:1058-64.

14. Arca, P., G. Reguera, and C. Hardisson. 1997. Plasmid-encoded fosfomycin resistance in bacteria isolated from the urinary tract in a multicentre survey. J Antimicrob Chemother 40:393-9.

15. Arjan, J. A., M. Visser, C. W. Zeyl, P. J. Gerrish, J. L. Blanchard, and R. E. Lenski. 1999. Diminishing returns from mutation supply rate in asexual populations. Science 283:404-6.

16. Atwood, K. C., L. K. Schneider, and F. J. Ryan. 1951. Periodic selection in Escherichia coli. Proc Natl Acad Sci U S A 37:146-55.

17. Atwood, K. C., L. K. Schneider, and F. J. Ryan. 1951. Selective mechanisms in bacteria. Cold Spring Harb Symp Quant Biol 16:345-55.

18. Au, K. G., M. Cabrera, J. H. Miller, and P. Modrich. 1988. Escherichia coli mutY gene product is required for specific A-G----C.G mismatch correction. Proc Natl Acad Sci U S A 85:9163-6.

19. Au, K. G., K. Welsh, and P. Modrich. 1992. Initiation of methyl-directed mismatch repair. J Biol Chem 267:12142-8.

20. Austin, D. J., K. G. Kristinsson, and R. M. Anderson. 1999. The relationship between the volume of antimicrobial consumption in human communities and the frequency of resistance. Proc Natl Acad Sci U S A 96:1152-6.

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21. Bachtrog, D., and I. Gordo. 2004. Adaptive evolution of asexual populations under Muller's ratchet. Evolution Int J Org Evolution 58:1403-13.

22. Baga, M., M. Goransson, S. Normark, and B. E. Uhlin. 1985. Transcriptional activation of a pap pilus virulence operon from uropathogenic Escherichia coli. Embo J 4:3887-93.

23. Baird-Parker, A. C. 1990. Foodborne salmonellosis. Lancet 336:1231-5. 24. Bandow, J. E., D. Becher, K. Buttner, F. Hochgrafe, C. Freiberg, H. Brotz,

and M. Hecker. 2003. The role of peptide deformylase in protein biosynthesis: a proteomic study. Proteomics 3:299-306.

25. Barras, F., and M. G. Marinus. 1989. The great GATC: DNA methylation in E. coli. Trends Genet 5:139-43.

26. Baumann, P., L. Baumann, C. Y. Lai, D. Rouhbakhsh, N. A. Moran, and M. A. Clark. 1995. Genetics, physiology, and evolutionary relationships of the genus Buchnera: intracellular symbionts of aphids. Annu Rev Microbiol 49:55-94.

27. Bjedov, I., O. Tenaillon, B. Gerard, V. Souza, E. Denamur, M. Radman, F. Taddei, and I. Matic. 2003. Stress-induced mutagenesis in bacteria. Science 300:1404-9.

28. Bjorkholm, B., M. Sjolund, P. G. Falk, O. G. Berg, L. Engstrand, and D. I. Andersson. 2001. Mutation frequency and biological cost of antibiotic resistance in Helicobacter pylori. Proc Natl Acad Sci U S A 98:14607-12.

29. Bjorkman, J., and D. I. Andersson. 2000. The cost of antibiotic resistance from a bacterial perspective. Drug Resist Updat 3:237-245.

30. Bjorkman, J., D. Hughes, and D. I. Andersson. 1998. Virulence of antibiotic-resistant Salmonella typhimurium. Proc Natl Acad Sci U S A 95:3949-53.

31. Bjorkman, J., I. Nagaev, O. G. Berg, D. Hughes, and D. I. Andersson. 2000. Effects of environment on compensatory mutations to ameliorate costs of antibiotic resistance. Science 287:1479-82.

32. Bjorkman, J., P. Samuelsson, D. I. Andersson, and D. Hughes. 1999. Novel ribosomal mutations affecting translational accuracy, antibiotic resistance and virulence of Salmonella typhimurium. Mol Microbiol 31:53-8.

33. Blattner, F. R., G. Plunkett, 3rd, C. A. Bloch, N. T. Perna, V. Burland, M. Riley, J. Collado-Vides, J. D. Glasner, C. K. Rode, G. F. Mayhew, J. Gregor, N. W. Davis, H. A. Kirkpatrick, M. A. Goeden, D. J. Rose, B. Mau, and Y. Shao. 1997. The complete genome sequence of Escherichia coli K-12. Science 277:1453-74.

34. Boe, L., M. Danielsen, S. Knudsen, J. B. Petersen, J. Maymann, and P. R. Jensen. 2000. The frequency of mutators in populations of Escherichia coli. Mutat Res 448:47-55.

35. Bohman, K., T. Ruusala, P. C. Jelenc, and C. G. Kurland. 1984. Kinetic impairment of restrictive streptomycin-resistant ribosomes. Mol Gen Genet 198:90-9.

36. Bridges, B. A. 2001. Hypermutation in bacteria and other cellular systems. Philos Trans R Soc Lond B Biol Sci 356:29-39.

37. Bronzwaer, S. L., O. Cars, U. Buchholz, S. Molstad, W. Goettsch, I. K. Veldhuijzen, J. L. Kool, M. J. Sprenger, and J. E. Degener. 2002. A European study on the relationship between antimicrobial use and antimicrobial resistance. Emerg Infect Dis 8:278-82.

38. Brotcorne-Lannoye, A., and G. Maenhaut-Michel. 1986. Role of RecA protein in untargeted UV mutagenesis of bacteriophage lambda: evidence for the requirement for the dinB gene. Proc Natl Acad Sci U S A 83:3904-8.

39. Bryan, L. E. 1988. General mechanisms of resistance to antibiotics. J Antimicrob Chemother 22 Suppl A:1-15.

40. Buckling, A., M. A. Wills, and N. Colegrave. 2003. Adaptation limits diversification of experimental bacterial populations. Science 302:2107-9.

41. Burch, C. L., and L. Chao. 2000. Evolvability of an RNA virus is determined by its mutational neighbourhood. Nature 406:625-8.

42. Burchall, J. J. 1973. Mechanism of action of trimethoprim-sulfamethoxazole. II. J Infect Dis 128:Suppl: 437-41.

53

ANNIKA NILSSON _

43. Burgos, M., K. DeRiemer, P. M. Small, P. C. Hopewell, and C. L. Daley. 2003. Effect of drug resistance on the generation of secondary cases of tuberculosis. J Infect Dis 188:1878-84.

44. Burman, L. G., and B. Olsson-Liljequist. 2001. A Global pespective on Bacterial Infections, Antibiotic Usage and the Antibiotic Resistance Problem. In D. Hughes and D. I. Andersson (ed.), Antibiotic Development and Resistance. Taylor and Francis, London.

45. Bush, K., M. Macielag, and M. Weidner-Wells. 2004. Taking inventory: antibacterial agents currently at or beyond phase 1. Curr Opin Microbiol 7:466-76.

46. Caley, M. J., and P. L. Munday. 2003. Growth trades off with habitat specialization. Proc R Soc Lond B Biol Sci 270 Suppl 2:S175-7.

47. Carlone, N. A., M. Borsotto, A. M. Cuffini, and D. Savoia. 1987. Effect of fosfomycin trometamol on bacterial adhesion in comparison with other chemotherapeutic agents. Eur Urol 13 Suppl 1:86-91.

48. Casjens, S. 1998. The diverse and dynamic structure of bacterial genomes. Annu Rev Genet 32:339-77.

49. Chao, L., and C. E. Cox. 1983. Competition between high and low mutating strains of Escherichia coli. Evolution 37:125-134.

50. Chopra, I., A. J. O'Neill, and K. Miller. 2003. The role of mutators in the emergence of antibiotic-resistant bacteria. Drug Resist Updat 6:137-45.

51. Cole, S. T., K. Eiglmeier, J. Parkhill, K. D. James, N. R. Thomson, P. R. Wheeler, N. Honore, T. Garnier, C. Churcher, D. Harris, K. Mungall, D. Basham, D. Brown, T. Chillingworth, R. Connor, R. M. Davies, K. Devlin, S. Duthoy, T. Feltwell, A. Fraser, N. Hamlin, S. Holroyd, T. Hornsby, K. Jagels, C. Lacroix, J. Maclean, S. Moule, L. Murphy, K. Oliver, M. A. Quail, M. A. Rajandream, K. M. Rutherford, S. Rutter, K. Seeger, S. Simon, M. Simmonds, J. Skelton, R. Squares, S. Squares, K. Stevens, K. Taylor, S. Whitehead, J. R. Woodward, and B. G. Barrell. 2001. Massive gene decay in the leprosy bacillus. Nature 409:1007-11.

52. Connell, I., W. Agace, P. Klemm, M. Schembri, S. Marild, and C. Svanborg. 1996. Type 1 fimbrial expression enhances Escherichia coli virulence for the urinary tract. Proc Natl Acad Sci U S A 93:9827-32.

53. Cooper, T. F., D. E. Rozen, and R. E. Lenski. 2003. Parallel changes in gene expression after 20,000 generations of evolution in Escherichia coli. Proc Natl Acad Sci U S A 100:1072-7.

54. Cooper, V. S., and R. E. Lenski. 2000. The population genetics of ecological specialization in evolving Escherichia coli populations. Nature 407:736-9.

55. Courcelle, J., A. Khodursky, B. Peter, P. O. Brown, and P. C. Hanawalt. 2001. Comparative gene expression profiles following UV exposure in wild-type and SOS-deficient Escherichia coli. Genetics 158:41-64.

56. Cox, E. C., and T. C. Gibson. 1974. Selection for high mutation rates in chemostats. Genetics 77:169-84.

57. Damagnez, V., M. P. Doutriaux, and M. Radman. 1989. Saturation of mismatch repair in the mutD5 mutator strain of Escherichia coli. J Bacteriol 171:4494-7.

58. Dao, V., and P. Modrich. 1998. Mismatch-, MutS-, MutL-, and helicase II-dependent unwinding from the single-strand break of an incised heteroduplex. J Biol Chem 273:9202-7.

59. Datta, N., and V. M. Hughes. 1983. Plasmids of the same Inc groups in Enterobacteria before and after the medical use of antibiotics. Nature 306:616-7.

60. Davies, J. 1994. Inactivation of antibiotics and the dissemination of resistance genes. Science 264:375-82.

61. Davis, B. D. 1987. Mechanism of bactericidal action of aminoglycosides. Microbiol Rev 51:341-50.

62. Davison, J. 1999. Genetic exchange between bacteria in the environment. Plasmid 42:73-91.

63. De Man, P., U. Jodal, C. Van Kooten, and C. Svanborg. 1990. Bacterial adherence as a virulence factor in urinary tract infection. Apmis 98:1053-60.

64. de Visser, J. A. 2002. The fate of microbial mutators. Microbiology 148:1247-52.

54


65. Denamur, E., S. Bonacorsi, A. Giraud, P. Duriez, F. Hilali, C. Amorin, E. Bingen, A. Andremont, B. Picard, F. Taddei, and I. Matic. 2002. High frequency of mutator strains among human uropathogenic Escherichia coli isolates. J Bacteriol 184:605-9.

66. Denamur, E., G. Lecointre, P. Darlu, O. Tenaillon, C. Acquaviva, C. Sayada, I. Sunjevaric, R. Rothstein, J. Elion, F. Taddei, M. Radman, and I. Matic. 2000. Evolutionary implications of the frequent horizontal transfer of mismatch repair genes. Cell 103:711-21.

67. Drake, J. W. 1991. A constant rate of spontaneous mutation in DNA-based microbes. Proc Natl Acad Sci U S A 88:7160-4.

68. Drake, J. W. 1993. General antimutators are improbable. J Mol Biol 229:8-13. 69. Drlica, K. 1999. Mechanism of fluoroquinolone action. Curr Opin Microbiol 2:504-

8. 70. Duncan, B. K., and B. Weiss. 1982. Specific mutator effects of ung (uracil-DNA

glycosylase) mutations in Escherichia coli. J Bacteriol 151:750-5. 71. Dykhuizen, D. E. 1990. Experimental Studies of Natural Selection in Bacteria.

Annu Rev Ecol Syst 21:373-98. 72. Dykhuizen, D. E., and A. M. Dean. 2004. Evolution of specialists in an

experimental microcosm. Genetics 167:2015-26. 73. Echols, H., C. Lu, and P. M. Burgers. 1983. Mutator strains of Escherichia coli,

mutD and dnaQ, with defective exonucleolytic editing by DNA polymerase III holoenzyme. Proc Natl Acad Sci U S A 80:2189-92.

74. Elena, S. F., and R. E. Lenski. 2003. Evolution experiments with microorganisms: the dynamics and genetic bases of adaptation. Nat Rev Genet 4:457-69.

75. Emody, L., M. Kerenyi, and G. Nagy. 2003. Virulence factors of uropathogenic Escherichia coli. Int J Antimicrob Agents 22 Suppl 2:29-33.

76. Evans, E., and E. Alani. 2000. Roles for mismatch repair factors in regulating genetic recombination. Mol Cell Biol 20:7839-44.

77. Fernandez De Henestrosa, A. R., T. Ogi, S. Aoyagi, D. Chafin, J. J. Hayes, H. Ohmori, and R. Woodgate. 2000. Identification of additional genes belonging to the LexA regulon in Escherichia coli. Mol Microbiol 35:1560-72.

78. Fijalkowska, I. J., R. L. Dunn, and R. M. Schaaper. 1993. Mutants of Escherichia coli with increased fidelity of DNA replication. Genetics 134:1023-30.

79. Forsyth, R. A., R. J. Haselbeck, K. L. Ohlsen, R. T. Yamamoto, H. Xu, J. D. Trawick, D. Wall, L. Wang, V. Brown-Driver, J. M. Froelich, K. G. C, P. King, M. McCarthy, C. Malone, B. Misiner, D. Robbins, Z. Tan, Z. Y. Zhu Zy, G. Carr, D. A. Mosca, C. Zamudio, J. G. Foulkes, and J. W. Zyskind. 2002. A genome-wide strategy for the identification of essential genes in Staphylococcus aureus. Mol Microbiol 43:1387-400.

80. Foster, P. L. 2004. Adaptive mutation in Escherichia coli. J Bacteriol 186:4846-52. 81. Fowler, R. G., and R. M. Schaaper. 1997. The role of the mutT gene of Escherichia

coli in maintaining replication fidelity. FEMS Microbiol Rev 21:43-54. 82. Frank, A. C., H. Amiri, and S. G. Andersson. 2002. Genome deterioration: loss

of repeated sequences and accumulation of junk DNA. Genetica 115:1-12. 83. Fuchs, R. P., N. Koffel-Schwartz, S. Pelet, R. Janel-Bintz, R. Napolitano, O.

J. Becherel, T. H. Broschard, D. Y. Burnouf, and J. Wagner. 2001. DNA polymerases II and V mediate respectively mutagenic (-2 frameshift) and error-free bypass of a single N-2-acetylaminofluorene adduct. Biochem Soc Trans 29:191-5.

84. Funchain, P., A. Yeung, J. L. Stewart, R. Lin, M. M. Slupska, and J. H. Miller. 2000. The consequences of growth of a mutator strain of Escherichia coli as measured by loss of function among multiple gene targets and loss of fitness. Genetics 154:959-70.

85. Garibyan, L., T. Huang, M. Kim, E. Wolff, A. Nguyen, T. Nguyen, A. Diep, K. Hu, A. Iverson, H. Yang, and J. H. Miller. 2003. Use of the rpoB gene to determine the specificity of base substitution mutations on the Escherichia coli chromosome. DNA Repair (Amst) 2:593-608.

55

ANNIKA NILSSON _

86. Gaynor, M., and A. S. Mankin. 2003. Macrolide antibiotics: binding site, mechanism of action, resistance. Curr Top Med Chem 3:949-61.

87. Gerrish, P. J., and R. E. Lenski. 1998. The fate of competing beneficial mutations in an asexual population. Genetica 102-103:127-44.

88. Giglione, C., M. Pierre, and T. Meinnel. 2000. Peptide deformylase as a target for new generation, broad spectrum antimicrobial agents. Mol Microbiol 36:1197-205.

89. Gil, R., B. Sabater-Munoz, A. Latorre, F. J. Silva, and A. Moya. 2002. Extreme genome reduction in Buchnera spp.: toward the minimal genome needed for symbiotic life. Proc Natl Acad Sci U S A 99:4454-8.

90. Gil, R., F. J. Silva, J. Pereto, and A. Moya. 2004. Determination of the core of a minimal bacterial gene set. Microbiol Mol Biol Rev 68:518-37, table of contents.

91. Giraud, A., I. Matic, M. Radman, M. Fons, and F. Taddei. 2002. Mutator bacteria as a risk factor in treatment of infectious diseases. Antimicrob Agents Chemother 46:863-5.

92. Giraud, A., I. Matic, O. Tenaillon, A. Clara, M. Radman, M. Fons, and F. Taddei. 2001. Costs and benefits of high mutation rates: adaptive evolution of bacteria in the mouse gut. Science 291:2606-8.

93. Gismondo, M. R., L. Drago, C. Fassina, M. L. Garlaschi, M. Rosina, and A. Lombardi. 1994. Escherichia coli: effect of fosfomycin trometamol on some urovirulence factors. J Chemother 6:167-72.

94. Goldman, R. C., and F. Scaglione. 2004. The macrolide-bacterium interaction and its biological basis. Curr Drug Targets Infect Disord 4:241-60.

95. Goodman, M. F. 2002. Error-prone repair DNA polymerases in prokaryotes and eukaryotes. Annu Rev Biochem 71:17-50.

96. Gordon, D. M., and M. A. Riley. 1992. A theoretical and experimental analysis of bacterial growth in the bladder. Mol Microbiol 6:555-62.

97. Guillon, J. M., Y. Mechulam, J. M. Schmitter, S. Blanquet, and G. Fayat. 1992. Disruption of the gene for Met-tRNA(fMet) formyltransferase severely impairs growth of Escherichia coli. J Bacteriol 174:4294-301.

98. Gustafsson, I., M. Sjolund, E. Torell, M. Johannesson, L. Engstrand, O. Cars, and D. I. Andersson. 2003. Bacteria with increased mutation frequency and antibiotic resistance are enriched in the commensal flora of patients with high antibiotic usage. J Antimicrob Chemother 52:645-50.

99. Guttman, D. S., and D. E. Dykhuizen. 1994. Detecting selective sweeps in naturally occurring Escherichia coli. Genetics 138:993-1003.

100. Halliday, J. A., and B. W. Glickman. 1991. Mechanisms of spontaneous mutation in DNA repair-proficient Escherichia coli. Mutat Res 250:55-71.

101. Hendlin, D., E. Celozzi, B. Weissberger, and E. L. Foltz. 1977. Effect of fosfomycin on the fecal microflora of man. Chemotherapy 23 Suppl 1:117-26.

102. Hersh, M. N., R. G. Ponder, P. J. Hastings, and S. M. Rosenberg. 2004. Adaptive mutation and amplification in Escherichia coli: two pathways of genome adaptation under stress. Res Microbiol 155:352-9.

103. Himmelreich, R., H. Hilbert, H. Plagens, E. Pirkl, B. C. Li, and R. Herrmann. 1996. Complete sequence analysis of the genome of the bacterium Mycoplasma pneumoniae. Nucleic Acids Res 24:4420-49.

104. Hiramatsu, K., Y. Katayama, H. Yuzawa, and T. Ito. 2002. Molecular genetics of methicillin-resistant Staphylococcus aureus. Int J Med Microbiol 292:67-74.

105. Horst, J. P., T. H. Wu, and M. G. Marinus. 1999. Escherichia coli mutator genes. Trends Microbiol 7:29-36.

106. Hughes, D. 2000. Evaluating genome dynamics: the constraints on rearrangements within bacterial genomes. Genome Biol 1:REVIEWS0006.

107. Hughes, D., and D. I. Andersson. 2001. Target Mutations Mediating Antibiotic Resistance. In D. Hughes and D. I. Andersson (ed.), Antibiotic Development and Resistance. Taylor and Francis, London.

108. Humayun, M. Z. 1998. SOS and Mayday: multiple inducible mutagenic pathways in Escherichia coli. Mol Microbiol 30:905-10.

56


109. Hutchinson, D. K. 2003. Oxazolidinone antibacterial agents: a critical review. Curr Top Med Chem 3:1021-42.

110. Hutchison, C. A., S. N. Peterson, S. R. Gill, R. T. Cline, O. White, C. M. Fraser, H. O. Smith, and J. C. Venter. 1999. Global transposon mutagenesis and a minimal Mycoplasma genome. Science 286:2165-9.

111. Ishiura, M., N. Hazumi, H. Shinagawa, A. Nakata, T. Uchida, and Y. Okada. 1990. RecA-independent high-frequency deletion of recombinant cosmid DNA in Escherichia coli. J Gen Microbiol 136 ( Pt 1):69-79.

112. Itoh, T., W. Martin, and M. Nei. 2002. Acceleration of genomic evolution caused by enhanced mutation rate in endocellular symbionts. Proc Natl Acad Sci U S A 99:12944-8.

113. Janion, C. 2001. Some aspects of the SOS response system--a critical survey. Acta Biochim Pol 48:599-610.

114. Ji, Y., G. Woodnutt, M. Rosenberg, and M. K. Burnham. 2002. Identification of essential genes in Staphylococcus aureus using inducible antisense RNA. Methods Enzymol 358:123-8.

115. Johnson, J. R. 1991. Virulence factors in Escherichia coli urinary tract infection. Clin Microbiol Rev 4:80-128.

116. Junop, M. S., W. Yang, P. Funchain, W. Clendenin, and J. H. Miller. 2003. In vitro and in vivo studies of MutS, MutL and MutH mutants: correlation of mismatch repair and DNA recombination. DNA Repair (Amst) 2:387-405.

117. Kadner, R. J. 1995. Expression of the Uhp sugar-phosphate transport system in Escherichia coli. In J. A. Hoch and T. J. Silhavy (ed.), Two-component signal transduction. ASM Press, Washington, D. C.

118. Kadner, R. J., and H. H. Winkler. 1973. Isolation and characterization of mutations affecting the transport of hexose phosphates in Escherichia coli. J Bacteriol 113:895-900.

119. Kahan, F. M., J. S. Kahan, P. J. Cassidy, and H. Kropp. 1974. The mechanism of action of fosfomycin (phosphonomycin). Ann N Y Acad Sci 235:364-86.

120. Kahlmeter, G. 2000. The ECO.SENS Project: a prospective, multinational, multicentre epidemiological survey of the prevalence and antimicrobial susceptibility of urinary tract pathogens--interim report. J Antimicrob Chemother 46 Suppl 1:15-22; discussion 63-5.

121. Kahlmeter, G. 2003. An international survey of the antimicrobial susceptibility of pathogens from uncomplicated urinary tract infections: the ECO.SENS Project. J Antimicrob Chemother 51:69-76.

122. Kahlmeter, G. 2003. Prevalence and antimicrobial susceptibility of pathogens in uncomplicated cystitis in Europe. The ECO.SENS study. Int J Antimicrob Agents 22 Suppl 2:49-52.

123. Kaper, J. B., J. P. Nataro, and H. L. Mobley. 2004. Pathogenic Escherichia coli. Nat Rev Microbiol 2:123-40.

124. Kenyon, C. J., and G. C. Walker. 1980. DNA-damaging agents stimulate gene expression at specific loci in Escherichia coli. Proc Natl Acad Sci U S A 77:2819-23.

125. Kibota, T. T., and M. Lynch. 1996. Estimate of the genomic mutation rate deleterious to overall fitness in E. coli. Nature 381:694-6.

126. Kim, S. R., G. Maenhaut-Michel, M. Yamada, Y. Yamamoto, K. Matsui, T. Sofuni, T. Nohmi, and H. Ohmori. 1997. Multiple pathways for SOS-induced mutagenesis in Escherichia coli: an overexpression of dinB/dinP results in strongly enhancing mutagenesis in the absence of any exogenous treatment to damage DNA. Proc Natl Acad Sci U S A 94:13792-7.

127. Kim, S. R., K. Matsui, M. Yamada, P. Gruz, and T. Nohmi. 2001. Roles of chromosomal and episomal dinB genes encoding DNA pol IV in targeted and untargeted mutagenesis in Escherichia coli. Mol Genet Genomics 266:207-15.

128. Kimura, M., and T. Ohta. 1970. Genetic loads at a polymorphic locus which is maintained by frequency-dependent selection. Genet Res 16:145-50.

129. Klasson, L., and S. G. Andersson. 2004. Evolution of minimal-gene-sets in host-dependent bacteria. Trends Microbiol 12:37-43.

57

ANNIKA NILSSON _

130. Klein, U., M. Pawelzik, and W. Opferkuch. 1985. Influence of beta-lactam antibiotics, fosfomycin and vancomycin on the adherence (hemagglutination) of Escherichia coli-containing different adhesins. Chemotherapy 31:138-45.

131. Knowles, D. J., N. Foloppe, N. B. Matassova, and A. I. Murchie. 2002. The bacterial ribosome, a promising focus for structure-based drug design. Curr Opin Pharmacol 2:501-6.

132. Knuth, K., H. Niesalla, C. J. Hueck, and T. M. Fuchs. 2004. Large-scale identification of essential Salmonella genes by trapping lethal insertions. Mol Microbiol 51:1729-44.

133. Kobayashi, K., S. D. Ehrlich, A. Albertini, G. Amati, K. K. Andersen, M. Arnaud, K. Asai, S. Ashikaga, S. Aymerich, P. Bessieres, F. Boland, S. C. Brignell, S. Bron, K. Bunai, J. Chapuis, L. C. Christiansen, A. Danchin, M. Debarbouille, E. Dervyn, E. Deuerling, K. Devine, S. K. Devine, O. Dreesen, J. Errington, S. Fillinger, S. J. Foster, Y. Fujita, A. Galizzi, R. Gardan, C. Eschevins, T. Fukushima, K. Haga, C. R. Harwood, M. Hecker, D. Hosoya, M. F. Hullo, H. Kakeshita, D. Karamata, Y. Kasahara, F. Kawamura, K. Koga, P. Koski, R. Kuwana, D. Imamura, M. Ishimaru, S. Ishikawa, I. Ishio, D. Le Coq, A. Masson, C. Mauel, R. Meima, R. P. Mellado, A. Moir, S. Moriya, E. Nagakawa, H. Nanamiya, S. Nakai, P. Nygaard, M. Ogura, T. Ohanan, M. O'Reilly, M. O'Rourke, Z. Pragai, H. M. Pooley, G. Rapoport, J. P. Rawlins, L. A. Rivas, C. Rivolta, A. Sadaie, Y. Sadaie, M. Sarvas, T. Sato, H. H. Saxild, E. Scanlan, W. Schumann, J. F. Seegers, J. Sekiguchi, A. Sekowska, S. J. Seror, M. Simon, P. Stragier, R. Studer, H. Takamatsu, T. Tanaka, M. Takeuchi, H. B. Thomaides, V. Vagner, J. M. van Dijl, K. Watabe, A. Wipat, H. Yamamoto, M. Yamamoto, Y. Yamamoto, K. Yamane, K. Yata, K. Yoshida, H. Yoshikawa, U. Zuber, and N. Ogasawara. 2003. Essential Bacillus subtilis genes. Proc Natl Acad Sci U S A 100:4678-83.

134. Kolisnychenko, V., G. Plunkett, 3rd, C. D. Herring, T. Feher, J. Posfai, F. R. Blattner, and G. Posfai. 2002. Engineering a reduced Escherichia coli genome. Genome Res 12:640-7.

135. Komp Lindgren, P., A. Karlsson, and D. Hughes. 2003. Mutation rate and evolution of fluoroquinolone resistance in Escherichia coli isolates from patients with urinary tract infections. Antimicrob Agents Chemother 47:3222-32.

136. Koonin, E. V. 2000. How many genes can make a cell: the minimal-gene-set concept. Annu Rev Genomics Hum Genet 1:99-116.

137. Korona, R., C. H. Nakatsu, L. J. Forney, and R. E. Lenski. 1994. Evidence for multiple adaptive peaks from populations of bacteria evolving in a structured habitat. Proc Natl Acad Sci U S A 91:9037-41.

138. Krishnaswamy, S., J. A. Rogers, R. J. Isbell, and R. G. Fowler. 1993. The high mutator activity of the dnaQ49 allele of Escherichia coli is medium-dependent and results from both defective 3'-->5' proofreading and methyl-directed mismatch repair. Mutat Res 288:311-9.

139. Kunin, V., and C. A. Ouzounis. 2003. The balance of driving forces during genome evolution in prokaryotes. Genome Res 13:1589-94.

140. Lawrence, J. G., and H. Ochman. 1998. Molecular archaeology of the Escherichia coli genome. Proc Natl Acad Sci U S A 95:9413-7.

141. LeClerc, J. E., B. Li, W. L. Payne, and T. A. Cebula. 1996. High mutation frequencies among Escherichia coli and Salmonella pathogens. Science 274:1208-11.

142. Lehtinen, D. A., and F. W. Perrino. 2004. Dysfunctional proofreading in the Escherichia coli DNA polymerase III core. Biochem J 384:337-48.

143. Lejeune, P., and A. Danchin. 1990. Mutations in the bglY gene increase the frequency of spontaneous deletions in Escherichia coli K-12. Proc Natl Acad Sci U S A 87:360-3.

144. Lenski, R. E. 1991. Quantifying fitness and gene stability in microorganisms. Biotechnology 15:173-92.

145. Lenski, R. E., and M. Travisano. 1994. Dynamics of adaptation and diversification: a 10,000-generation experiment with bacterial populations. Proc Natl Acad Sci U S A 91:6808-14.

58


146. Levin, B. R., M. Lipsitch, V. Perrot, S. Schrag, R. Antia, L. Simonsen, N. M. Walker, and F. M. Stewart. 1997. The population genetics of antibiotic resistance. Clin Infect Dis 24 Suppl 1:S9-16.

147. Livermore, D. M. 2003. Linezolid in vitro: mechanism and antibacterial spectrum. J Antimicrob Chemother 51 Suppl 2:ii9-16.

148. Livermore, D. M. 1987. Mechanisms of resistance to cephalosporin antibiotics. Drugs 34 Suppl 2:64-88.

149. MacLean, R. C., G. Bell, and P. B. Rainey. 2004. The evolution of a pleiotropic fitness tradeoff in Pseudomonas fluorescens. Proc Natl Acad Sci U S A 101:8072-7.

150. Macvanin, M., J. Bjorkman, S. Eriksson, M. Rhen, D. I. Andersson, and D. Hughes. 2003. Fusidic acid-resistant mutants of Salmonella enterica serovar Typhimurium with low fitness in vivo are defective in RpoS induction. Antimicrob Agents Chemother 47:3743-9.

151. Maisnier-Patin, S., and D. I. Andersson. 2004. Adaptation to the deleterious effects of antimicrobial drug resistance mutations by compensatory evolution. Res Microbiol 155:360-9.

152. Maisnier-Patin, S., O. G. Berg, L. Liljas, and D. I. Andersson. 2002. Compensatory adaptation to the deleterious effect of antibiotic resistance in Salmonella typhimurium. Mol Microbiol 46:355-66.

153. Mao, E. F., L. Lane, J. Lee, and J. H. Miller. 1997. Proliferation of mutators in A cell population. J Bacteriol 179:417-22.

154. Margolis, P., C. Hackbarth, S. Lopez, M. Maniar, W. Wang, Z. Yuan, R. White, and J. Trias. 2001. Resistance of Streptococcus pneumoniae to deformylase inhibitors is due to mutations in defB. Antimicrob Agents Chemother 45:2432-5.

155. Margolis, P. S., C. J. Hackbarth, D. C. Young, W. Wang, D. Chen, Z. Yuan, R. White, and J. Trias. 2000. Peptide deformylase in Staphylococcus aureus: resistance to inhibition is mediated by mutations in the formyltransferase gene. Antimicrob Agents Chemother 44:1825-31.

156. Mastroeni, P., and M. Sheppard. 2004. Salmonella infections in the mouse model: host resistance factors and in vivo dynamics of bacterial spread and distribution in the tissues. Microbes Infect 6:398-405.

157. Matic, I., M. Radman, F. Taddei, B. Picard, C. Doit, E. Bingen, E. Denamur, and J. Elion. 1997. Highly variable mutation rates in commensal and pathogenic Escherichia coli. Science 277:1833-4.

158. Matic, I., F. Taddei, and M. Radman. 2004. Survival versus maintenance of genetic stability: a conflict of priorities during stress. Res Microbiol 155:337-41.

159. Matthews, R. G. 1996. One-Carbon Metabolism, p. 600-611. In F. V. e. a. Neidhardt (ed.), Escherichia coli and Salmonella Cellular and Molecular Biology, 2 ed, vol. 1. ASM Press, Washington, DC.

160. Maynard-Smith, J., and J. Haigh. 1974. Hitchhiking effect of a favourable gene. Genet Res 23:23-25.

161. Mazodier, P., and J. Davies. 1991. Gene transfer between distantly related bacteria. Annu Rev Genet 25:147-71.

162. McClelland, M., K. E. Sanderson, J. Spieth, S. W. Clifton, P. Latreille, L. Courtney, S. Porwollik, J. Ali, M. Dante, F. Du, S. Hou, D. Layman, S. Leonard, C. Nguyen, K. Scott, A. Holmes, N. Grewal, E. Mulvaney, E. Ryan, H. Sun, L. Florea, W. Miller, T. Stoneking, M. Nhan, R. Waterston, and R. K. Wilson. 2001. Complete genome sequence of Salmonella enterica serovar Typhimurium LT2. Nature 413:852-6.

163. McKenzie, G. J., and S. M. Rosenberg. 2001. Adaptive mutations, mutator DNA polymerases and genetic change strategies of pathogens. Curr Opin Microbiol 4:586-94.

164. Merkel, T. J., J. L. Dahl, R. H. Ebright, and R. J. Kadner. 1995. Transcription activation at the Escherichia coli uhpT promoter by the catabolite gene activator protein. J Bacteriol 177:1712-8.

165. Miesel, L., D. A. Rozwarski, J. C. Sacchettini, and W. R. Jacobs, Jr. 1998. Mechanisms for isoniazid action and resistance. Novartis Found Symp 217:209-20; discussion 220-1.

59

ANNIKA NILSSON _

166. Miller, J. H. 1996. Spontaneous mutators in bacteria: insights into pathways of mutagenesis and repair. Annu Rev Microbiol 50:625-43.

167. Miller, J. H., A. Suthar, J. Tai, A. Yeung, C. Truong, and J. L. Stewart. 1999. Direct selection for mutators in Escherichia coli. J Bacteriol 181:1576-84.

168. Minassian, M. A., D. A. Lewis, D. Chattopadhyay, B. Bovill, G. J. Duckworth, and J. D. Williams. 1998. A comparison between single-dose fosfomycin trometamol (Monuril) and a 5-day course of trimethoprim in the treatment of uncomplicated lower urinary tract infection in women. Int J Antimicrob Agents 10:39-47.

169. Mira, A., and N. A. Moran. 2002. Estimating population size and transmission bottlenecks in maternally transmitted endosymbiotic bacteria. Microb Ecol 44:137-43.

170. Mira, A., H. Ochman, and N. A. Moran. 2001. Deletional bias and the evolution of bacterial genomes. Trends Genet 17:589-96.

171. Modrich, P. 1991. Mechanisms and biological effects of mismatch repair. Annu Rev Genet 25:229-53.

172. Modrich, P. 1989. Methyl-directed DNA mismatch correction. J Biol Chem 264:6597-600.

173. Moran, N. A. 1996. Accelerated evolution and Muller's rachet in endosymbiotic bacteria. Proc Natl Acad Sci U S A 93:2873-8.

174. Moran, N. A. 2002. Microbial minimalism: genome reduction in bacterial pathogens. Cell 108:583-6.

175. Moran, N. A. 2003. Tracing the evolution of gene loss in obligate bacterial symbionts. Curr Opin Microbiol 6:512-8.

176. Moran, N. A., and A. Mira. 2001. The process of genome shrinkage in the obligate symbiont Buchnera aphidicola. Genome Biol 2:RESEARCH0054.

177. Moran, N. A., G. R. Plague, J. P. Sandstrom, and J. L. Wilcox. 2003. A genomic perspective on nutrient provisioning by bacterial symbionts of insects. Proc Natl Acad Sci U S A 100 Suppl 2:14543-8.

178. Moran, N. A., and J. J. Wernegreen. 2000. Lifestyle evolution in symbiotic bacteria: insights from genomics. Trends in Ecology and Evolution 15:321-326.

179. Muller, H. J. 1964. The Relation of Recombination to Mutational Advance. Mutat Res 106:2-9.

180. Mushegian, A. R., and E. V. Koonin. 1996. A minimal gene set for cellular life derived by comparison of complete bacterial genomes. Proc Natl Acad Sci U S A 93:10268-73.

181. Nagaev, I., J. Bjorkman, D. I. Andersson, and D. Hughes. 2001. Biological cost and compensatory evolution in fusidic acid-resistant Staphylococcus aureus. Mol Microbiol 40:433-9.

182. Napolitano, R., R. Janel-Bintz, J. Wagner, and R. P. Fuchs. 2000. All three SOS-inducible DNA polymerases (Pol II, Pol IV and Pol V) are involved in induced mutagenesis. Embo J 19:6259-65.

183. Newton, D. T., C. Creuzenet, and D. Mangroo. 1999. Formylation is not essential for initiation of protein synthesis in all eubacteria. J Biol Chem 274:22143-6.

184. Notley-McRobb, L., and T. Ferenci. 2000. Experimental analysis of molecular events during mutational periodic selections in bacterial evolution. Genetics 156:1493-501.

185. Notley-McRobb, L., S. Seeto, and T. Ferenci. 2002. Enrichment and elimination of mutY mutators in Escherichia coli populations. Genetics 162:1055-62.

186. Ohl, M. E., and S. I. Miller. 2001. Salmonella: a model for bacterial pathogenesis. Annu Rev Med 52:259-74.

187. Olekhnovich, I. N., J. L. Dahl, and R. J. Kadner. 1999. Separate contributions of UhpA and CAP to activation of transcription of the uhpT promoter of Escherichia coli. J Mol Biol 292:973-86.

188. Oliver, A., R. Canton, P. Campo, F. Baquero, and J. Blazquez. 2000. High frequency of hypermutable Pseudomonas aeruginosa in cystic fibrosis lung infection. Science 288:1251-4.

60


189. Oosterom, J. 1991. Epidemiological studies and proposed preventive measures in the fight against human salmonellosis. Int J Food Microbiol 12:41-51.

190. Orr, H. A. 2003. The distribution of fitness effects among beneficial mutations. Genetics 163:1519-26.

191. Palmer, B. R., and M. G. Marinus. 1994. The dam and dcm strains of Escherichia coli--a review. Gene 143:1-12.

192. Parkhill, J., G. Dougan, K. D. James, N. R. Thomson, D. Pickard, J. Wain, C. Churcher, K. L. Mungall, S. D. Bentley, M. T. Holden, M. Sebaihia, S. Baker, D. Basham, K. Brooks, T. Chillingworth, P. Connerton, A. Cronin, P. Davis, R. M. Davies, L. Dowd, N. White, J. Farrar, T. Feltwell, N. Hamlin, A. Haque, T. T. Hien, S. Holroyd, K. Jagels, A. Krogh, T. S. Larsen, S. Leather, S. Moule, P. O'Gaora, C. Parry, M. Quail, K. Rutherford, M. Simmonds, J. Skelton, K. Stevens, S. Whitehead, and B. G. Barrell. 2001. Complete genome sequence of a multiple drug resistant Salmonella enterica serovar Typhi CT18. Nature 413:848-52.

193. Parry, C. M., T. T. Hien, G. Dougan, N. J. White, and J. J. Farrar. 2002. Typhoid fever. N Engl J Med 347:1770-82.

194. Pfennig, K. S. 2001. Evolution of pathogen virulence: the role of variation in host phenotype. Proc R Soc Lond B Biol Sci 268:755-60.

195. Pham, P., J. G. Bertram, M. O'Donnell, R. Woodgate, and M. F. Goodman. 2001. A model for SOS-lesion-targeted mutations in Escherichia coli. Nature 409:366-70.

196. Pham, P., S. Rangarajan, R. Woodgate, and M. F. Goodman. 2001. Roles of DNA polymerases V and II in SOS-induced error-prone and error-free repair in Escherichia coli. Proc Natl Acad Sci U S A 98:8350-4.

197. Poole, K. 2004. Resistance to beta-lactam antibiotics. Cell Mol Life Sci 61:2200-23. 198. Poon, A., and S. P. Otto. 2000. Compensating for our load of mutations: freezing

the meltdown of small populations. Evolution Int J Org Evolution 54:1467-79. 199. Postma, P. W., J. W. Lengeler, and G. R. Jacobson. 1993.

Phosphoenolpyruvate:carbohydrate phosphotransferase systems of bacteria. Microbiol Rev 57:543-94.

200. Radman, M., I. Matic, and F. Taddei. 1999. Evolution of evolvability. Ann N Y Acad Sci 870:146-55.

201. Radman, M., F. Taddei, and I. Matic. 2000. DNA repair systems and bacterial evolution. Cold Spring Harb Symp Quant Biol 65:11-9.

202. Radman, M., F. Taddei, and I. Matic. 2000. Evolution-driving genes. Res Microbiol 151:91-5.

203. Rangarajan, S., R. Woodgate, and M. F. Goodman. 2002. Replication restart in UV-irradiated Escherichia coli involving pols II, III, V, PriA, RecA and RecFOR proteins. Mol Microbiol 43:617-28.

204. Rayssiguier, C., D. S. Thaler, and M. Radman. 1989. The barrier to recombination between Escherichia coli and Salmonella typhimurium is disrupted in mismatch-repair mutants. Nature 342:396-401.

205. Reid, S. D., C. J. Herbelin, A. C. Bumbaugh, R. K. Selander, and T. S. Whittam. 2000. Parallel evolution of virulence in pathogenic Escherichia coli. Nature 406:64-7.

206. Reynolds, M. G. 2000. Compensatory evolution in rifampin-resistant Escherichia coli. Genetics 156:1471-81.

207. Roth, J. R., and D. I. Andersson. 2004. Amplification-mutagenesis--how growth under selection contributes to the origin of genetic diversity and explains the phenomenon of adaptive mutation. Res Microbiol 155:342-51.

208. Rozen, D. E., J. A. de Visser, and P. J. Gerrish. 2002. Fitness effects of fixed beneficial mutations in microbial populations. Curr Biol 12:1040-5.

209. Rupp, W. D. 1996. DNA Repair Mechanisms. In F. C. Neidhardt (ed.), Escherichia coli and Salmonella Cellular and Molecular Biology. ASM Press, Washington, D. C.

210. Said-Salim, B., B. Mathema, and B. N. Kreiswirth. 2003. Community-acquired methicillin-resistant Staphylococcus aureus: an emerging pathogen. Infect Control Hosp Epidemiol 24:451-5.

61

ANNIKA NILSSON _

211. Sander, P., B. Springer, T. Prammananan, A. Sturmfels, M. Kappler, M. Pletschette, and E. C. Bottger. 2002. Fitness cost of chromosomal drug resistance-conferring mutations. Antimicrob Agents Chemother 46:1204-11.

212. Schaaper, R. M. 1998. Antimutator mutants in bacteriophage T4 and Escherichia coli. Genetics 148:1579-85.

213. Schaaper, R. M. 1993. Base selection, proofreading, and mismatch repair during DNA replication in Escherichia coli. J Biol Chem 268:23762-5.

214. Schaaper, R. M. 1989. Escherichia coli mutator mutD5 is defective in the mutHLS pathway of DNA mismatch repair. Genetics 121:205-12.

215. Schaaper, R. M., and R. L. Dunn. 1991. Spontaneous mutation in the Escherichia coli lacI gene. Genetics 129:317-26.

216. Schaaper, R. M., and M. Radman. 1989. The extreme mutator effect of Escherichia coli mutD5 results from saturation of mismatch repair by excessive DNA replication errors. Embo J 8:3511-6.

217. Schaechter, M. 2001. Escherichia coli and Salmonella 2000: the view from here. Microbiol Mol Biol Rev 65:119-30.

218. Schofield, M. J., and P. Hsieh. 2003. DNA mismatch repair: molecular mechanisms and biological function. Annu Rev Microbiol 57:579-608.

219. Schofield, M. J., S. Nayak, T. H. Scott, C. Du, and P. Hsieh. 2001. Interaction of Escherichia coli MutS and MutL at a DNA mismatch. J Biol Chem 276:28291-9.

220. Schrag, S. J., V. Perrot, and B. R. Levin. 1997. Adaptation to the fitness costs of antibiotic resistance in Escherichia coli. Proc R Soc Lond B Biol Sci 264:1287-91.

221. Schumacher, G., and K. Bussmann. 1978. Cell-free synthesis of proteins related to sn-glycerol-3-phosphate transport in Escherichia coli. J Bacteriol 135:239-50.

222. Shen, P., and H. V. Huang. 1986. Homologous recombination in Escherichia coli: dependence on substrate length and homology. Genetics 112:441-57.

223. Sherman, D. R., K. Mdluli, M. J. Hickey, T. M. Arain, S. L. Morris, C. E. Barry, 3rd, and C. K. Stover. 1996. Compensatory ahpC gene expression in isoniazid-resistant Mycobacterium tuberculosis. Science 272:1641-3.

224. Shigenobu, S., H. Watanabe, M. Hattori, Y. Sakaki, and H. Ishikawa. 2000. Genome sequence of the endocellular bacterial symbiont of aphids Buchnera sp. APS. Nature 407:81-6.

225. Silva, F. J., A. Latorre, and A. Moya. 2001. Genome size reduction through multiple events of gene disintegration in Buchnera APS. Trends Genet 17:615-8.

226. Silva, F. J., A. Latorre, and A. Moya. 2003. Why are the genomes of endosymbiotic bacteria so stable? Trends Genet 19:176-80.

227. Smith, K. C., L. Armitige, and A. Wanger. 2003. A review of tuberculosis: reflections on the past, present and future of a global epidemic disease. Expert Rev Anti Infect Ther 1:483-91.

228. Sniegowski, P. D., P. J. Gerrish, T. Johnson, and A. Shaver. 2000. The evolution of mutation rates: separating causes from consequences. Bioessays 22:1057-66.

229. Sniegowski, P. D., P. J. Gerrish, and R. E. Lenski. 1997. Evolution of high mutation rates in experimental populations of E. coli. Nature 387:703-5.

230. Snyder, J. A., B. J. Haugen, E. L. Buckles, C. V. Lockatell, D. E. Johnson, M. S. Donnenberg, R. A. Welch, and H. L. Mobley. 2004. Transcriptome of uropathogenic Escherichia coli during urinary tract infection. Infect Immun 72:6373-81.

231. Sonti, R. V., and J. R. Roth. 1989. Role of gene duplications in the adaptation of Salmonella typhimurium to growth on limiting carbon sources. Genetics 123:19-28.

232. Stambuk, S., and M. Radman. 1998. Mechanism and control of interspecies recombination in Escherichia coli. I. Mismatch repair, methylation, recombination and replication functions. Genetics 150:533-42.

233. Sutton, M. D., B. T. Smith, V. G. Godoy, and G. C. Walker. 2000. The SOS response: recent insights into umuDC-dependent mutagenesis and DNA damage tolerance. Annu Rev Genet 34:479-497.

234. Svanborg, C., and G. Godaly. 1997. Bacterial virulence in urinary tract infection. Infect Dis Clin North Am 11:513-29.

62


235. Taddei, F., M. Radman, J. Maynard-Smith, B. Toupance, P. H. Gouyon, and B. Godelle. 1997. Role of mutator alleles in adaptive evolution. Nature 387:700-2.

236. Tamas, I., L. Klasson, B. Canback, A. K. Naslund, A. S. Eriksson, J. J. Wernegreen, J. P. Sandstrom, N. A. Moran, and S. G. Andersson. 2002. 50 million years of genomic stasis in endosymbiotic bacteria. Science 296:2376-9.

237. Tamas, I., L. M. Klasson, J. P. Sandstrom, and S. G. Andersson. 2001. Mutualists and parasites: how to paint yourself into a (metabolic) corner. FEBS Lett 498:135-9.

238. Tang, M., P. Pham, X. Shen, J. S. Taylor, M. O'Donnell, R. Woodgate, and M. F. Goodman. 2000. Roles of E. coli DNA polymerases IV and V in lesion-targeted and untargeted SOS mutagenesis. Nature 404:1014-8.

239. Tang, M., X. Shen, E. G. Frank, M. O'Donnell, R. Woodgate, and M. F. Goodman. 1999. UmuD'(2)C is an error-prone DNA polymerase, Escherichia coli pol V. Proc Natl Acad Sci U S A 96:8919-24.

240. Taylor, J. S., and J. Raes. 2004. Duplication and Divergence: The Evolution of New Genes and Old Ideas. Annu Rev Genet.

241. Tenaillon, O., E. Denamur, and I. Matic. 2004. Evolutionary significance of stress-induced mutagenesis in bacteria. Trends Microbiol 12:264-70.

242. Tenaillon, O., H. Le Nagard, B. Godelle, and F. Taddei. 2000. Mutators and sex in bacteria: conflict between adaptive strategies. Proc Natl Acad Sci U S A 97:10465-70.

243. Tenaillon, O., F. Taddei, M. Radmian, and I. Matic. 2001. Second-order selection in bacterial evolution: selection acting on mutation and recombination rates in the course of adaptation. Res Microbiol 152:11-6.

244. Tenaillon, O., B. Toupance, H. Le Nagard, F. Taddei, and B. Godelle. 1999. Mutators, population size, adaptive landscape and the adaptation of asexual populations of bacteria. Genetics 152:485-93.

245. Travisano, M., J. A. Mongold, A. F. Bennett, and R. E. Lenski. 1995. Experimental tests of the roles of adaptation, chance, and history in evolution. Science 267:87-90.

246. Trobner, W., and R. Piechocki. 1984. Competition between isogenic mutS and mut+ populations of Escherichia coli K12 in continuously growing cultures. Mol Gen Genet 198:175-6.

247. Trobner, W., and R. Piechocki. 1981. Competition growth between Escherichia coli mutL and mut+ in continuously growing cultures. Z Allg Mikrobiol 21:347-9.

248. Trobner, W., and R. Piechocki. 1984. Selection against hypermutability in Escherichia coli during long term evolution. Mol Gen Genet 198:177-8.

249. Wagner, J., P. Gruz, S. R. Kim, M. Yamada, K. Matsui, R. P. Fuchs, and T. Nohmi. 1999. The dinB gene encodes a novel E. coli DNA polymerase, DNA pol IV, involved in mutagenesis. Mol Cell 4:281-6.

250. Walker, G. C. 1996. The SOS Response of Escherichia coli. In F. C. Neidhardt (ed.), Escherichia coli and Salmonella Cellular and Molecular Biology. ASM Press, Washington, D. C.

251. Wallace, D. C., and H. J. Morowitz. 1973. Genome size and evolution. Chromosoma 40:121-6.

252. Wernegreen, J. J. 2002. Genome evolution in bacterial endosymbionts of insects. Nat Rev Genet 3:850-61.

253. Wernegreen, J. J., and N. A. Moran. 1999. Evidence for genetic drift in endosymbionts (Buchnera): analyses of protein-coding genes. Mol Biol Evol 16:83-97.

254. Whitlock, M. C. 2000. Fixation of new alleles and the extinction of small populations: drift load, beneficial alleles, and sexual selection. Evolution Int J Org Evolution 54:1855-61.

255. Winfield, M. D., and E. A. Groisman. 2003. Role of nonhost environments in the lifestyles of Salmonella and Escherichia coli. Appl Environ Microbiol 69:3687-94.

256. Worth, L., Jr., T. Bader, J. Yang, and S. Clark. 1998. Role of MutS ATPase activity in MutS,L-dependent block of in vitro strand transfer. J Biol Chem 273:23176-82.

63

ANNIKA NILSSON _

257. Worth, L., Jr., S. Clark, M. Radman, and P. Modrich. 1994. Mismatch repair proteins MutS and MutL inhibit RecA-catalyzed strand transfer between diverged DNAs. Proc Natl Acad Sci U S A 91:3238-41.

258. Yamaguchi, M., V. Dao, and P. Modrich. 1998. MutS and MutL activate DNA helicase II in a mismatch-dependent manner. J Biol Chem 273:9197-201.

259. Yang, W. 2000. Structure and function of mismatch repair proteins. Mutat Res 460:245-56.

260. Zahrt, T. C., N. Buchmeier, and S. Maloy. 1999. Effect of mutS and recD mutations on Salmonella virulence. Infect Immun 67:6168-72.

261. Zahrt, T. C., and S. Maloy. 1997. Barriers to recombination between closely related bacteria: MutS and RecBCD inhibit recombination between Salmonella typhimurium and Salmonella typhi. Proc Natl Acad Sci U S A 94:9786-91.

262. Zientz, E., T. Dandekar, and R. Gross. 2004. Metabolic interdependence of obligate intracellular bacteria and their insect hosts. Microbiol Mol Biol Rev 68:745-70.

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