Applied evolution: An integrated approach to studying life history traits in response to drug selection

The use of chemical control measures to reduce the impact of parasite and pest species has frequently resulted in the development of resistance. Thus, resistance management has become a key concern in human and veterinary medicine, and in agricultural production. Although it is known that factors such as gene flow between susceptible and resistant populations, drug type, application methods, and costs of resistance can affect the rate of resistance evolution, less is known about the impacts of density-dependent eco-evolutionary processes that could be altered by drug-induced mortality. The overall aim of this thesis was to take an experimental evolution approach to assess how life history traits respond to drug selection, using a free-living dioecious worm (Caenorhabditis remanei) as a model. In Chapter 2, I defined the relationship between C. remanei survival and Ivermectin dose over a range of concentrations, in order to control the intensity of selection used in the selection experiment described in Chapter 4. The dose-response data were also used to appraise curve-fitting methods, using Akaike Information Criterion (AIC) model selection to compare a series of nonlinear models. The type of model fitted to the dose response data had a significant effect on the estimates of LD50 and LD99, suggesting that failure to fit an appropriate model could give misleading estimates of resistance status. In addition, simulated data were used to establish that a potential cost of resistance could be predicted by comparing survival at the upper asymptote of dose-response curves for resistant and susceptible populations, even when differences were as low as 4%. This approach to dose-response modeling ensures that the maximum amount of useful information relating to resistance is gathered in one study. In Chapter 3, I asked how simulations could be used to inform important design choices used in selection experiments. Specifically, I focused on the effects of both within- and between-line variation on estimated power, when detecting small, medium and large effect sizes. Using mixed-effect models on simulated data, I demonstrated that commonly used designs with realistic levels of variation could be underpowered for substantial effect sizes. Thus, use of simulation-based power analysis provides an effective way to avoid under or overpowering a study designs incorporating variation due to random effects. In Chapter 4, I 3 investigated how Ivermectin dosage and changes in population density affect the rate of resistance evolution. I exposed replicate lines of C. remanei to two doses of Ivermectin (high and low) to assess relative survival of lines selected in drug-treated environments compared to untreated controls over 10 generations. Additionally, I maintained lines where mortality was imposed randomly to control for differences in density between drug treatments and to distinguish between the evolutionary consequences of drug treatment versus ecological processes affected by changes in density-dependent feedback. Intriguingly, both drug-selected and random-mortality lines showed an increase in survivorship when challenged with Ivermectin; the magnitude of this increase varied with the intensity of selection and life-history stage. The results suggest that interactions between density-dependent processes and life history may mediate evolved changes in susceptibility to control measures, which could result in misleading conclusions about the evolution of heritable resistance following drug treatment. In Chapter 5, I investigated whether the apparent changes in drug susceptibility found in Chapter 4 were related to evolved changes in life-history of C. remanei populations after selection in drug-treated and random-mortality environments. Rapid passage of lines in the drug-free environment had no effect on the measured life-history traits. In the drug-free environment, adult size and fecundity of drug-selected lines increased compared to the controls but drug selection did not affect lifespan. In the treated environment, drug-selected lines showed increased lifespan and fecundity relative to controls. Adult size of randomly culled lines responded in a similar way to drug-selected lines in the drug-free environment, but no change in fecundity or lifespan was observed in either environment. The results suggest that life histories of nematodes can respond to selection as a result of the application of control measures. Failure to take these responses into account when applying control measures could result in adverse outcomes, such as larger and more fecund parasites, as well as over-estimation of the development of genetically controlled resistance. In conclusion, my thesis shows that there may be a complex relationship between drug selection, density-dependent regulatory processes and life history of populations challenged with control measures. This relationship could have implications for how resistance is monitored and managed if life histories of parasitic species show such eco-evolutionary responses to drug application.

Using induced pluripotent stem cells (IPSCS) as a replacement for in vivo models to screen novel therapies in chronic myeloid leukaemia (CML)

Tyrpsine kinase inhibitors (TKIs) effectively target progenitors and mature leukaemic cells but prove less effective at eliminating leukaemic stem cells (LSCs) in patients with chronic myeloid leukaemia (CML). Several reports indicate that the TGFβ superfamily pathway is important for LSC survival and quiescence. We conducted extensive microarray analyses to compare expression patterns in normal haemopoietic stem cells (HSC) and progenitors with CML LSC and progenitor populations in chronic phase (CP), accelerated phase (AP) and blast crisis (BC) CML. The BMP/SMAD pathway and downstream signalling molecules were identified as significantly deregulated in all three phases of CML. The changes observed could potentiate altered autocrine signalling, as BMP2, BMP4 (p<0.05), and ACTIVIN A (p<0.001) were all down regulated, whereas BMP7, BMP10 and TGFβ (p<0.05) were up regulated in CP. This was accompanied by up regulation of BMPRI (p<0.05) and downstream SMADs (p<0.005). Interestingly, as CML progressed, the profile altered, with BC patients showing significant over-expression of ACTIVIN A and its receptor ACVR1C. To further characterise the BMP pathway and identify potential candidate biomarkers within a larger cohort, expression analysis of 42 genes in 60 newly diagnosed CP CML patient samples, enrolled on a phase III clinical trial (www.spirit-cml.org) with greater than 12 months follow-up data on their response to TKI was performed. Analysis revealed that the pathway was highly deregulated, with no clear distinction when patients were stratified into good, intermediate and poor response to treatment. One of the major issues in developing new treatments to target LSCs is the ability to test small molecule inhibitors effectively as it is difficult to obtain sufficient LSCs from primary patient material. Using reprogramming technologies, we generated induced pluripotent stem cells (iPSCs) from CP CML patients and normal donors. CML- and normal-derived iPSCs were differentiated along the mesodermal axis to generate haemopoietic and endothelial precursors (haemangioblasts). IPSC-derived haemangioblasts exhibited sensitivity to TKI treatment with increased apoptosis and reduction in the phosphorylation of downstream target proteins. 4 Dual inhibition studies were performed using BMP pathway inhibitors in combination with TKI on CML cell lines, primary cells and patient derived iPSCs. Results indicate that they act synergistically to target CML cells both in the presence and absence of BMP4 ligand. Inhibition resulted in decreased proliferation, irreversible cell cycle arrest, increased apoptosis, reduced haemopoietic colony formation, altered gene expression pattern, reduction in self-renewal and a significant reduction in the phosphorylation of downstream target proteins. These changes offer a therapeutic window in CML, with intervention using BMP inhibitors in combination with TKI having the potential to prevent LSC self-renewal and improve outcome for patients. By successfully developing and validating iPSCs for CML drug screening we hope to substantially reduce the reliance on animal models for early preclinical drug screening in leukaemia.