Predictive Modeling of Complex Malaria Phenotypes

Malaria remains the leading cause of death in children under the age of 5 years in sub-Saharan Africa. While massive efforts to control the disease have recently been launched leading to a reduction in the number of deaths from the disease, the success of these efforts is at risk of being thwarted by an emerging resistance to the first line anti-malarial drug artemisinin. Without a vaccine, the fight against the disease in the foreseeable future will require effective medicines. There is need for new unbiased approaches to understand the basic biology of the parasite, enhance the pace at which new drugs can be discovered and reduce the rate of drug resistance emergence.

Gene expression is a fundamental step in the decoding of information in DNA into phenotypes. Therefore, transcriptional profiling provides an unbiased foundation for understanding a wide array of biological processes and complex phenotypes. Transcriptional responses in the malaria parasite are poorly understood and their roles, if any in drug resistance and mechanism of action (MOA) have been largely inconclusive. This thesis makes key contributions in methods for transcriptional profiling, its application in understanding drug resistance evolution, MOA and how complex transcriptional properties are encoded in DNA in the human malaria parasite, Plasmodium falciparum. Specifically, Chapter 2 of this thesis develops and validates a custom exon microarray platform for the simultaneous profiling of transcript levels of protein coding genes, non-coding RNAs (ncRNAs) and transcript isoforms. In Chapter 3, this thesis develops a novel computational approach for predicting diverging gene interactions in chloroquine resistant (CQR) and sensitive (CQS) recombinant clones using transcriptional data sets. The diverging co-expression of the chloroquine resistance transporter gene (pfcrt) provides insights into the normal biological functions of the gene and highlights diverging biological processes in CQR and CQS clones, including diverging small molecule responses that are validated by dose response profiling and quantitative trait loci (QTL) analysis. Chapter 4 of this thesis performs the most extensive profiling to date of the malaria parasite's transcriptional responses to perturbations using a set of 31 chemically and functionally diverse small molecules in two laboratory clones. The chapter develops methods for minimizing non-specific small molecule effects by normalizing the transcriptional responses of each small molecule to those obtained from other perturbations, enabling the detection of subtle associations between transcriptional responses and small molecule chemistry. The systems approaches developed in the chapter allow the prediction of small molecule effects on specific biological processes, laying a foundation for future prediction of drug MOA. Finally, Chapter 5 examines whether complex gene expression patterns are encoded in promoter sequences and reports associations between the intrinsic properties of promoters (DNA rigidity, nucleosome binding potential and sequencing statistics), and dynamic gene expression properties.

Collectively, the results presented in this thesis offer new ways for understanding malaria parasite biology and uncover an underappreciated complexity of the parasites transcriptional landscape.