The use of agricultural fungicides exerts very strong selection pressures on plant pathogens. This can lead to the spread of fungicide resistance in the pathogen population, which leads to a reduction in efficacy of disease control and loss of yield. In this thesis, we use mathematical modelling to investigate how the spread of fungicide resistant pathogen strains can be slowed, using epidemiological models to understand how application strategies can be optimised. A range of different fungicide application strategies have been proposed as anti-resistance strategies. Two of the most often considered strategies rely on combining two fungicides with different modes of action. The first involves spraying the two fungicides at the same time (mixture) and the second spraying them alternately at different times (alternation). These strategies have been compared both experimentally and by mathematical modellers for decades, but no firm conclusion as to which is better has been reached, although mixtures have in general often been favoured. We use mathematical models of septoria leaf blotch (Zymoseptoria tritici) on winter wheat and powdery mildew on grapevine (Erysiphe necator) to investigate the relative performance of these two strategies. We show that depending on the exact way in which the strategies are compared and the exact case, either strategy can be the more effective. However, when aiming to optimise yield in the long-term, we show that mixtures are very likely to be the most effective strategy in any given case. The structure of mathematical models clearly impacts on the conclusions of those models. As well as investigating the sensitivity of our conclusions to the structure of the models, we use a range of nested models to isolate mechanisms driving the differential performance of fungicide mixtures and alternation. Although the fine detail of a model’s predictions depends on its exact structure, we find a number of conserved patterns. In particular we find no case in which mixtures do not produce the overall largest yield over the time for which the fungicide remains effective. We also investigate the effects of the timing of an individual fungicide spray on its contribution toward resistance development and disease control. A set of so-called “governing principles” to understand the performance of resistance-management strategies was recently introduced by van den Bosch et al., formalising concepts from earlier literature. These quantify selection rates by examining the difference between the growth rates of fungicide-sensitive and fungicide resistant pathogen strains. Throughout the thesis, we concentrate on the extent to which these governing principles can be used to explain the relative performance of the resistance-management strategies that are considered.

Antimicrobial resistance (AMR) poses a substantial threat to public health and clinical medicine. By 2050, it’s predicted that AMR will be responsible for a yearly mortality rate of 10 million people, surpassing the mortality of cancer. Despite this daunting future we face, there are many efforts currently employed to combat the growth of AMR. One significant effort involves surveillance and early identification of novel resistant bacteria circulating in high antibiotic exposure environments. The second chapter of this thesis focuses on sampling 25 patients from a hospital environment, rich with antibiotics, to build a collection of AMR bacteria that will be tested and added to surveillance efforts/future study. This chapter allowed for the identification of several worrying AMR bacteria that provide greater insights into circulating AMR in Canadian hospitals and their patients. From the AMR collection created in chapter 2, we are also able to advance our scientific understanding of how antibiotic resistance develops within us and causes issues with treatment. In chapter 3, we looked at the effects of antibiotic administration routes on the level of AMR observed in our patient sample. We saw that current approaches to limit selection for AMR in the gut still resulted in clinically significant and concerning increases in AMR. Furthermore, this chapter allowed greater understanding of contributors to increased AMR in patients. AMR increases are not fully explained by exposure/colonization in hospital settings, but also by evolution of AMR originating from non-resistant bacteria in the gut. Additionally, analysis of these bacteria will inform expected AMR evolutionary trajectories and help us plan against them. During analysis of patient data, we also came across evolution of a less understood resistance phenotype, hetero-resistance, to a very important antibiotic, colistin. We investigated a commonly prescribed antifungal, fluconazole, for its ability to promote this resistance phenotype; however, it appeared that fluconazole did not promote this phenotype. Ultimately, this thesis serves as a valuable reservoir of AMR bacteria for future study and contributes to a greater understanding of AMR development in patients, one day leading to more informed clinical decision making. / Thesis / Master of Science (MSc)

Nowell (1976) first proposed that unless cytotoxic cancer therapy eradicates all tumor cells, genetic or heritable variation within heterogeneous tumors will inevitably lead to the evolution of chemotherapeutic resistance through clonal selection. This evolutionary hypothesis was formalized by Goldie and Coldman (1979), who developed one of the earliest mathematical kinetic models of resistance evolution in neoplasms. Their model predicted that the likelihood of response and cure would be increased in combination vs single agent cytotoxic therapies. In a later study, Gardner (2002) developed a computational kinetic model to predict chemotherapeutic combinations, doses, and schedules most likely to result in patient response and prolonged life. This model predicts that combination therapy involving both cytotoxic and cytostatic drugs will be more effective than combination therapy involving only cytotoxic drugs. Thus far, no systematic evaluation of the Goldie and Coldman and Gardner hypotheses have been conducted in the metastatic clinical trial setting. Here I test these hypotheses using the results of over 700 phase II, III and II/III clinical trials. I show that, as predicted by Goldie and Coldman, both overall response rate and overall survival were greater in combination arms. Moreover, median duration of response – the key indicator of the rate of resistance evolution - was also greater in combination vs single agent arms. These results suggest that generally combination chemotherapy is more effective than single agent therapy for advanced solid tumors as predicted by Goldie and Coldman (1979) hypothesis and that, at least in the metastatic setting, the potential disadvantages of combination therapy with respect to accelerated resistance evolution are outweighed by the greater waiting times for resistance mutations to arise. By contrast, although combination cytotoxic and cytostatic therapy is associated with a greater average overall response rate than multi agent cytotoxic therapy, this is not the case for both median duration of response and overall survival. Hence, there is no evidence that, in contrast to the predictions of the Gardner (2002) model, combination cytotoxic and cytostatic therapy decreases the rate of resistance evolution relative to that obtaining under combination cytotoxic therapy.

Nowell (1976) first proposed that unless cytotoxic cancer therapy eradicates all tumor cells, genetic or heritable variation within heterogeneous tumors will inevitably lead to the evolution of chemotherapeutic resistance through clonal selection. This evolutionary hypothesis was formalized by Goldie and Coldman (1979), who developed one of the earliest mathematical kinetic models of resistance evolution in neoplasms. Their model predicted that the likelihood of response and cure would be increased in combination vs single agent cytotoxic therapies. In a later study, Gardner (2002) developed a computational kinetic model to predict chemotherapeutic combinations, doses, and schedules most likely to result in patient response and prolonged life. This model predicts that combination therapy involving both cytotoxic and cytostatic drugs will be more effective than combination therapy involving only cytotoxic drugs. Thus far, no systematic evaluation of the Goldie and Coldman and Gardner hypotheses have been conducted in the metastatic clinical trial setting. Here I test these hypotheses using the results of over 700 phase II, III and II/III clinical trials. I show that, as predicted by Goldie and Coldman, both overall response rate and overall survival were greater in combination arms. Moreover, median duration of response – the key indicator of the rate of resistance evolution - was also greater in combination vs single agent arms. These results suggest that generally combination chemotherapy is more effective than single agent therapy for advanced solid tumors as predicted by Goldie and Coldman (1979) hypothesis and that, at least in the metastatic setting, the potential disadvantages of combination therapy with respect to accelerated resistance evolution are outweighed by the greater waiting times for resistance mutations to arise. By contrast, although combination cytotoxic and cytostatic therapy is associated with a greater average overall response rate than multi agent cytotoxic therapy, this is not the case for both median duration of response and overall survival. Hence, there is no evidence that, in contrast to the predictions of the Gardner (2002) model, combination cytotoxic and cytostatic therapy decreases the rate of resistance evolution relative to that obtaining under combination cytotoxic therapy.

Antibiotic resistance is a quickly emerging public health crisis, accounting for more than 700,000 annual global deaths. Global human antibiotic overuse and misuse has significantly expedited the rate at which bacteria become resistant to antibiotics. A renewed focus on discovering new antibiotics is one approach to addressing this crisis. However, it alone cannot solve the problem: historically, the introduction of a new antibiotic has consistently, and at times rapidly, been followed by the appearance and dissemination of resistant bacteria. It is thus crucial to develop strategies to improve how we select and deploy antibiotics so that we can control and prevent the emergence and transmission of antibiotic resistance. Current gold-standard antibiotic susceptibility tests measure bacterial growth, which can take up to 72 hours. However, bacteria exhibit more immediate measurable phenotypes of antibiotic susceptibility, including changes in transcription, after brief antibiotic exposure. In this dissertation I develop a framework for building a paper-based cell-free toehold sensor antibiotic susceptibility test that can detect differential mRNA expression. I also explore how long-term lab evolution experiments can be used to prospectively uncover transcriptional signatures of antibiotic susceptibility. Paper-based cell-free systems provide an opportunity for developing clinically tractable nucleic-acid based diagnostics that are low-cost, rapid, and sensitive. I develop a computational workflow to rapidly and easily design toehold switch sensors, amplification primers, and synthetic RNAs. I develop an experimental workflow, based on existing paper-based cell-free technology, for screening toehold sensors, amplifying bacterial mRNA, and deploying sensors for differential mRNA detection. I combine this work to introduce a paper-based cell-free toehold sensor antibiotic susceptibility test that can detect fluoroquinolone-susceptible E. coli. Next, I describe a methodology for long-term lab evolution and how it can be used to explore the relationship between a phenotype, such as gene expression, and antibiotic resistance acquisition. Using a set of E. coli strains evolved to acquire tetracycline resistance, I explore how each strain's transcriptome changes as resistance increases. Together, this work provides a set of computational and experimental methods that can be used to study the emergence of antibiotic resistance, and improve upon available methods for properly selecting and deploying antibiotics. / 2023-03-17T00:00:00Z

To combat the ever-growing clinical burden imposed by antibiotic-resistant pathogens, multiple-antibiotic treatments are increasingly being considered as promising treatment options. The impact of multiple-antibiotic treatments on the evolution of resistance is not well understood however, and debate is ongoing about the effectiveness of various multiple-antibiotic treatments. In this thesis, I investigate how aspects of multiple-antibiotic treatments impact the rate of evolution of antibiotic resistance in the opportunistic human pathogen Pseudomonas aeruginosa. In particular, I look at the impact of interactions between antibiotics in combination on the evolution of resistance, and how creating heterogeneity in the antibiotic environment by rotating the antibiotics used may change the rate of evolution of resistance. I characterise the interactions present in 120 combinations of antibiotics and find that the type of interaction can be predicted by the mechanism of action of the antibiotics involved. I investigate the effect of a subset of these combinations on the evolution of antibiotic resistance. My results refute the influential but poorly-evidenced hypothesis that synergistic combinations accelerate the evolution of resistance, even when synergistic combinations have the same inhibitory effect on sensitive bacteria as additive or antagonistic antibiotic combinations. I focus on a combination of the antibiotics ceftriaxone and sulfamethoxazole and test whether it is more effective in preventing the evolution of resistance than predicted by the inhibitory effect of the combination on sensitive bacteria. I do not find the combination to be more effective than predicted. Finally, I create heterogeneous antibiotic environments by rotating the antibiotic present at different rates. For the first time in a laboratory setting, I test how varying the rate of fluctuation in the antibiotics present in a heterogeneous antibiotic environment impacts the rate of evolution of resistance. Unexpectedly, I find the rate of evolution of resistance increases with increasing levels of antibiotic heterogeneity.

La tuberculose (TB), provoquée par Mycobacterium tuberculosis, est une des trois maladies prioritaires dans le monde. Les TB multi-résistantes (MDR) et ultra-résistantes (XDR-TB) représentent des obstacles majeurs pour la lutte antituberculeuse. Dans les pays à MDR-TB élevée, comme le Vietnam, la détection insuffisante de la résistance aux antibiotiques est un des facteurs principaux qui favorisent la transmission des souches résistantes. De plus, dans ces pays, encore très peu de choses sont connues sur la résistance à la pyrazinamide et aux antibiotiques de seconde ligne et sur les déterminants génétiques liés à ces résistances. Dans ce contexte, ce travail vise donc à acquérir des connaissances sur la résistance aux antibiotiques au Vietnam et à étudier comment M. tuberculosis évolue de l’état sensible à l’état ultra-résistant.260 isolats cliniques collectés au Vietnam entre 2005 et 2009 ont été inclus. Diverses techniques et analyses ont été utilisées: tests de sensibilité aux médicaments (développement d'un test à temps réduit), spoligotypage et MIRU-VNTR (24 loci) et séquençage de gènes. Les données ont été analysées par des analyses statistiques et phylogénétiques. Ce travail s’est d’abord focalisé sur la caractérisation d’isolats hautement résistants et sur la résistance à la pyrazinamide. Une forte proportion d'isolats quadruple résistants aux antibiotiques de première ligne a été identifiée comme pré-XDR et XDR et en majorité appartenant à la famille Beijing. L'analyse moléculaire a également révélé une forte proportion d'isolats, en particulier MDR, quadruple résistants et de la famille Beijing, portant des mutations associées à la résistance à la pyrazinamide.L'analyse génétique et phylogénétique globale a ensuite montré une grande diversité de profils de mutations dans chaque famille et chaque cluster MIRU-VNTR. Ces données suggèrent que M. tuberculosis peut suivre des chemins évolutifs variés pour devenir ultra-résistant. La prédominance de mutations et de combinaisons de mutations associées à un haut niveau de résistance et à un faible coût en termes de fitness suggère un effet cumulatif des mutations et un rôle de l’épistasie dans l'acquisition de la résistance multiple. De plus, une fréquence élevée de mutations compensatoires associées à la résistance à la rifampicine a été détectée chez les isolats très résistants. Ces processus semblent donc influencer fortement l'évolution de la résistance dans notre échantillon. Il est à noter que les mutations liées à des niveaux de résistance élevée et à de faibles coûts en termes de fitness, ainsi que les mutations compensatoires étaient plus particulièrement associées à la famille Beijing.En conclusion, ce travail fournit des connaissances uniques sur la résistance aux antibiotiques chez M. tuberculosis au Vietnam. En particulier, ces données prédisent une évolution de la résistance vers une situation de plus en plus préoccupante. Premièrement, la famille Beijing, en cours d’invasion au Vietnam, apparaît associée à de hauts niveaux de résistance, de faible coût en termes de fitness et aux mutations compensatoires. Deuxièmement, le risque élevé de résistance à la pyrazinamide remet en question son efficacité et son utilisation dans les traitements contre la MDR et la XDR-TB. Troisièmement, les données suggèrent une évolution de M. tuberculosis vers un potentiel de résistance plus élevé par effet cumulatif des mutations associés à la résistance et l’existence de phénomènes d’épistasie. Comme les échantillons étudiés dans ce travail ont été collectés, l’étape suivante est de valider nos hypothèses sur des données actualisées.Enfin, ce travail avec les données déjà publiées a permis d’établir, pour la première fois, un inventaire des mutations associées à la résistance aux antibiotiques chez M. tuberculosis au Vietnam. Cette base de données sera utilisée pour le développement d'une puce à ADN pour la détection rapide de la résistance aux antibiotiques au Vietnam. / Tuberculosis (TB) is one of the deadliest infectious diseases worldwide, mainly caused by Mycobacterium tuberculosis. Multidrug resistant (MDR) and extensively drug resistant (XDR) TB are currently main challenges for TB control. In high MDR-TB burden countries like Vietnam, one of the main factors of drug resistant strain spread is the insufficient capacity of drug resistance detection. Besides, still little is known in these countries about the resistance to second line and pyrazinamide drugs (key drugs in the MDR-TB treatment) and the genetic determinants linked to these resistances. In this context, this work aimed to acquire knowledge on drug resistance in Vietnam and to understand how M. tuberculosis evolved from sensitive to highly drug resistance form by molecular analysis.260 clinical isolates collected in Vietnam between 2005 and 2009 were included. Various techniques and analyses were used: drug susceptibility testing (development of a test with a reduced turn-around time), spoligotyping and 24-MIRU-VNTR typing and gene sequencing. The data were analyzed by statistical and phylogenetic analyses.First, this work was focused on highly drug resistant M. tuberculosis clinical isolates and pyrazinamide resistance. A high proportion of quadruple first-line drug resistant isolates (resistant to isoniazid, rifampicin, streptomycin and ethambutol) have been characterized as pre-XDR and XDR isolates, belonging especially to Beijing family. The molecular analysis revealed also high proportion of drug resistant isolates carrying highly confident pyrazinamide resistance-associated mutations, particularly in MDR and quadruple resistant isolates and in Beijing family.Second, the genetic and phylogenetic analyses showed high diversity of mutation patterns within each family and each MIRU-VNTR cluster suggesting various evolutionary trajectories towards first and second-line drug resistance. The predominance of specific mutations and combinations of mutations associated with high level of resistance and low fitness cost suggests a cumulative effect of mutations and a role for epistasis in multiple-drug resistance acquisition. In addition, high frequency of fitness-compensatory mutations associated with rifampicin resistant mutations was detected in highly drug resistant isolates. These processes may drive the evolution of drug resistance in this sample and lead to a successful spread of highly drug resistant strains. It is worth noting that Beijing family was specifically linked to high-level drug resistance and low fitness cost mutations and to compensatory mutations.In conclusion, this work provides knowledge on the resistance to the first and second-line anti-TB drugs in clinical M. tuberculosis samples collected in Vietnam between 2005 and 2009. These data predict an evolution towards a more problematic situation in terms of drug resistance. First, because the Beijing family, which is currently invading Vietnam, is associated with highly drug resistance, mutations linked to high-level drug resistance and low fitness cost and compensatory mutations. Second, the high risk of pyrazinamide resistance in our sample challenges the efficacy and the use of this drug in MDR-TB treatment. Third, our data suggest an evolution of M. tuberculosis towards a higher potential of drug resistance because of a probable cumulative effect of drug resistant mutations and epistatic interactions. Since the samples under study were collected between 2005-2009, the next step is to test our hypotheses on a recent sampling. Finally, this study together with published data allowed making, for the first time, an inventory of the drug resistance associated mutations in M. tuberculosis isolates from Vietnam.