High-throughput whole-cell screens of millions of proprietary and publicly available compounds have led to the discovery of thousands of hits with confirmed anti-malarial activity. These compounds represent a rich starting point for drug discovery. Drug discovery efforts increasingly rely on whole-cell screening, mostly conducted against the asexual blood-stages of malaria parasites. The advantage of whole-cell screens is that compounds gain access to the parasite where they can hit simple or multifactorial targets. A routine and cost-effective method to elucidating mechanisms of action (MoA) of these hits would greatly enhance the drug discovery process. Consequently, there is an urgent need for robust, high-throughput technologies with the sensitivity to identify, validate and prioritize drug effects on targets and pathways in the parasite, with no a priori expectation of how the drug works. Here we present a systematic approach to uncover the functional connections among drug actions by generating a reference collection of gene expression profiles from two different parasite lines treated with an array of drugs/small molecules with known or suspected targets and chosen to span a wide range of biological space (Aim 1). We will then capture these induced transcriptional states as response signatures that do not depend on large effects by any one or few genes, but rather can discern subtle relationships among drug effects based on the pathway fingerprints derived from these drug-specific transcriptional responses (Aim 2). This collection can be easily probed with new drugs to be placed in this drug-drug network as a framework for validation and hypothesis testing (Aim 3).
Recent large-scale malaria parasite drug screens have elucidated many exciting hits and novel scaffolds. Now the emphasis has turned to identifying the targets of these bioactive compounds. We propose to build a query- able reference set of transcript response profiles of parasites to many drugs and small molecules that span the basic biology of the parasite. We will develop a data analysis platform for the community to match transcript profiles of drugs of unknown targets, identify their mechanisms of action, and build drug-drug networks.