Plasmodium falciparum remains one of the greatest global public health challenges. Current malaria control efforts face the persistent challenge of drug resistance and, to date, the malaria parasite has been able to develop resistance to almost every drug with which it has been challenged. One strategy to tackle the problem of drug resistance is to systematically identify new antimalarials to which the parasite has not yet developed resistance. Many novel compounds with antimalarial activity are in development. However, in most cases, the targets and the mechanisms of action of these compounds are unknown. Understanding which compounds are targeting new pathways for which no resistance exists is critical to prioritising novel drug candidates. Understanding drug resistance in more depth has the added advantage that targeting pathways for which resistance comes at a high fitness cost could prolong drug efficacy in the field. There may also be cases where drug resistance mutations confer hypersensitivity to new antimalarials compounds. Identifying cases where this occurs would allow us to develop better combination therapies. Currently, it is only practical to test the effectiveness of new antimalarials against a handful drug- resistant mutants at a time. To develop a new platform to address this problem, I set out to use BarSeq technology and CRISPR-Cas9 editing to create a pool of barcoded drug resistant mutants that covered a diverse representation of the currently known malaria resistome. These mutants would include three main groups:

1. Lines with clinical significance: mutations that confer resistance to antimalarial compounds currently being used in the field.
2. Lines with significance to compounds in the Medicines for Malaria Venture (MMV) pipeline: mutations that confer resistance to compounds currently in the drug development pipeline.
3. Putative future drug targets: resistance mutations generated to “toolbox” compounds not currently in the drug development pipeline. By utilising CRISPR-Cas9 genome editing technology, barcode cassettes were inserted into the genome of drug-resistant lines. These lines were then combined into one master pool that would allow all lines to be interrogated in parallel. Amplicon sequencing was then used to compare the relative growth of each mutant over time in the presence and absence of query compounds. This would allow multiplex competition assays to identify the targets or mechanisms of resistance to novel antimalarials. The ultimate aim is to develop a comprehensive cross-resistance screening platform for rapid compound profiling. The initial part of my work focused on improving the efficiency of the CRISPR-based barcode insertion reaction, targeting the Pfrh3 locus, that had previously been developed in the lab. When I attempted this approach on a larger scale, it was found that editing was too inefficient with only 5% of transfections being successfully barcoded. Several different editing strategies to increase barcoding efficiency were explored. In the end, a positive and negative selection approach was used to enrich for edited parasites and to insert barcodes into a new nonessential locus, Pfpare. This increased the percentage of successfully barcoded transfections to over 50% with all parasites being edited in these successful transfections. This increase in efficiency has allowed me to insert 53 barcodes into 47 different drug-resistant lines, covering the lines of clinical significance, lines with significance to compounds in the MMV pipeline, and the majority of the putative future antimalarial targets Drug resistant mutants were pooled together and grown in competition in the absence of the drug to measure fitness costs of resistance. The pool was tested with well-characterised antimalarials GNF179 and NITD609 to optimise screening parameters including selection concentration and time of harvest. The pool was then validated using a panel of blinded compounds with different mechanisms of action supplied by the MMV. Testing of the pool revealed that it could be used to select out specific mutant lines that had been shown to confer resistance to these compounds. Antimalarial compounds with novel mechanisms of action were successfully identified using the BarSeq approach because of their profile of total killing of the pool, indicating no relevant resistant lines were present. However, to provide insight into their mechanism of action, in vitro evolution experiments or other compound profiling experiments would still need to take place. The rate at which resistant mutations can be generated is limited by the mutation rate of the parasites in the selection. The use of so called hyper-mutator lines that have lost the ability to proofread during transcription has been established as a way to increase mutation rate and therefore the rate at which resistance is able to develop in yeast and bacteria. Recently our lab generated a Plasmodium falciparum line with this phenotype. Drug selection assays have shown that it is capable of developing resistance at a lower inoculum of parasites than its wildtype parent line. In this thesis, I investigated whether introducing a second source of mutation, in this case UV radiation, could accelerate this process even further. I established a protocol for treating parasites with UV radiation, then challenged UV and non-UV treated hyper-mutator and wildtype parasites with the antimalarial compound NITD609. The hyper- mutator line once again proved more able to develop resistance at lower concentrations than the wildtype parental line. The UV treated parasites returned more quickly after selection, however with only a modest improvement in time after selection. Nonetheless, this approach may be beneficial for compounds for which resistance is more challenging to generate.