An integrative analysis of small molecule transcriptional responses in the human malaria parasite Plasmodium falciparum

Abstract

Background: Transcriptional responses to small molecules can provide insights into drug mode of action (MOA). The capacity of the human malaria parasite, Plasmodium falciparum, to respond specifically to transcriptional perturbations has been unclear based on past approaches. Here, we present the most extensive profiling to date of the parasite's transcriptional responsiveness to thirty-one chemically and functionally diverse small molecules.

Methods: We exposed two laboratory strains of the human malaria parasite P. falciparum to brief treatments of thirty-one chemically and functionally diverse small molecules associated with biological effects across multiple pathways based on various levels of evidence. We investigated the impact of chemical composition and MOA on gene expression similarities that arise between perturbations by various compounds. To determine the target biological pathways for each small molecule, we developed a novel framework for encoding small molecule effects on a spectra of biological processes or GO functions that are enriched in the differentially expressed genes of a given small molecule perturbation.

Results: We find that small molecules associated with similar transcriptional responses contain similar chemical features, and/ or have a shared MOA. The approach also revealed complex relationships between drugs and biological pathways that are missed by most exisiting approaches. For example, the approach was able to partition small molecule responses into drug-specific effects versus non-specific effects.

Conclusions: Our work provides a new framework for linking transcriptional responses to drug MOA in P. falciparum and can be generalized for the same purpose in other organisms.

Fig. 1

Hierarchical clustering of compounds based on their genome-wide response indices for each gene across two parasite samples. The respond index for each gene in a given perturbation is obtained as the ratio between the average transcript level of the gene following a 2 h exposure to the compound in two parasite samples and the average transcript level of the same gene across all perturbations performed in the same batch of experiments. The clustering of the compounds reveals two broad groupings (Class I- red labels and Class II- green labels) in which compounds within a group are positively correlated to each other but negatively correlated to compounds in a different group

Fig. 2

Key substructure difference between Class I and II compounds identified using the rule induction algorithm OneR. Two Class I compounds (chloroquine and apicidin) and two Class II compounds (MMS and cerulenin) are shown as an example. The substructures are numbered from 0 to 880, with the position of a given substructure referred to as its bit position. The bit structure represents a chemical element, group, ring structure or atom pairs based on the PubChem substructure fingerprints. The presence of a given substructure is encoded by 1 and the absence by a 0. Class I and II compounds are largely differentiated at bit position 185 which encodes the substructure fingerprint ‘≥2 any ring size 6’

Fig. 3

Principle component analysis (PCA) of transcriptional correlations between the small molecules. The first component of variation (Dim 1) splits the compounds into two clusters identical to those observed by hierarchical clustering of the compounds (Fig. 1). The PCA plot reveals complex drug relationships involving both chemical similarity and MOA. Compounds lacking a ring system (PPMP, olomucine, MMS, cerulenin, epoxomicin and z-Val-Asp) occupy the upper left quadrant of the plot in spite of their distinct MOAs, supporting a dominant influence of chemical structure on global small molecule relationships. An exception is E64 which occupies a position on the plot next to other hemoglobin digestion inhibitors (shown in red- CQ, TQ and E64). *Although the MOA of artemisinin is still unknown, the drug has been shown to require activation by heme released during hemoglobin digestion [56]

Fig. 4

Representation of small molecule transcriptional effects as binary fingerprints in which “1” encodes an enrichment of a given biological process in the top 100 induced genes while a “0” represents lack of enrichment of a given process. a Two examples of partial small molecule-GO fingerprints for chloroquine and artemisnin. b Visualization of the small molecule–GO fingerprints for the 31 compounds as a heatmap in which the enrichment of a biological process in top 100 up-regulated genes for each compound is represented in red and the lack of enrichment by green. c The small molecule-GO fingerprints can be visualized in a bipartite network composed of small molecule nodes and biological process nodes. A focused view of the up-regulated small molecule-GO relationships between rapamycin and PPMP shows that both compounds are associated with autophagy but only rapamycin is associated with protein import into nucleus and co-translational protein folding

Fig. 5

Representation of small molecule-GO network for artemisinin. GO biological processes enriched in the top 100 up-regulated genes are shown as red nodes and down-regulated genes are shown as green nodes.