Artemisinin activity-based probes identify multiple molecular targets within the asexual stage of the malaria parasites Plasmodium falciparum 3D7

Abstract

The artemisinin (ART)-based antimalarials have contributed significantly to reducing global malaria deaths over the past decade, but we still do not know how they kill parasites. To gain greater insight into the potential mechanisms of ART drug action, we developed a suite of ART activity-based protein profiling probes to identify parasite protein drug targets in situ. Probes were designed to retain biological activity and alkylate the molecular target(s) of Plasmodium falciparum 3D7 parasites in situ. Proteins tagged with the ART probe can then be isolated using click chemistry before identification by liquid chromatography-MS/MS. Using these probes, we define an ART proteome that shows alkylated targets in the glycolytic, hemoglobin degradation, antioxidant defense, and protein synthesis pathways, processes essential for parasite survival. This work reveals the pleiotropic nature of the biological functions targeted by this important class of antimalarial drugs.

Keywords: antimalarial; artemisinin; bioactivation; chemical proteomics; molecular targets.

Fig. 1.

Rational design of the ART-ABPPs. (A) Conversion of ART to ART-ABPPs involves the addition of a clickable handle (i.e., an alkyne or azide to the ART drug pharmacophore by the peptide-coupling method illustrated in SI Text). The structures of the alkyne (P1) and azide (P2) probes and respective inactive deoxy controls CP1 and CP2 with in vitro IC₅₀ values are presented. (B) General workflow of copper-catalyzed and copper-free click chemistry approaches used in the identification of alkylated proteins after in situ treatment of P. falciparum parasite with alkyne and azide ART-ABPPs. The azide- and alkyne-modified proteins are tagged with biotin azide and biotin dibenzocyclooctyne (Biotin-DIBO), respectively, via click reactions followed by affinity purification tandem with LC-MS/MS for protein identification.

Fig. 2.

Heat map of the entire proteomic dataset identified by the alkyne and azide ART-ABPPs. Heat map analysis was carried out by plotting the average exponentially modified protein abundance index values for each protein. The data were acquired from multiple independent experiments for each probe [P1, n = 5; CP1, n = 5; DMSO, n = 5; P2, n = 2; CP2, n = 2; P2* (cellular homogenate)]. Each independent replicate was injected into the LC-MS/MS four times to improve the accuracy of protein identification. The heat map plot was clustered into four categories from high confidence to noise according to the hit frequency in all replicates. Protein hits found in seven to nine independent replicates were denoted as being of high confidence, hits seen in four to six replicates were denoted as medium confidence, three hits were denoted as low confidence, and hits seen in only one or two replicates were considered as noise. Proteins were manually searched for the presence (+) and absence (−) of a glutathione (GSH) binding motif according to data published by Kehr et al. (23). Complete datasets of ART proteomes are illustrated in Dataset S1. DMSO, dimethyl sulfoxide treatment; MW, protein molecular weight in kilodalton; ORF, open reading frame names.

Fig. 3.

Overview of ART-ABPP–tagged proteins. (A) Venn diagram showing overlap between proteins identified with the alkyne (P1) and azide (P2) ART probes. (B) Trend analysis between the confidence in protein identification as described in Fig. 2 and the proportion of those proteins carrying a glutathione (GSH) binding motif (χ² = 230.1 with a P value < 0.0001).

Fig. S1.

Proteins identified in a P. falciparum proteome using alkyne ART-ABPP (P1) vs. its deoxyether analog (control probe; CP1), illustrating the essentiality of the endoperoxide drug pharmacophore for successful protein labeling. (A) Identification SDS/PAGE gel showing alkylated proteins tagged with rhodamine-biotin trifunctional azide by click chemistry. Fluorescent protein bands are clearly visible with active probe P1 at 30, 20, 10, and 5 μg protein loading, with no labeling seen with the inactive control CP1 at 30 μg loading. (B) Chemical structure of the alkyne ART-ABPPs and their respective control.

Fig. S2.

Comparison between proteins identified with alkyne ART-ABPP (P1) vs. controls using LC-MS/MS. Multiple t test analysis was used to assess the significance of the labeling event seen with active probe treatment vs. controls (control probe and DMSO solvent). P values were corrected using the Holm–Sidak method. Data are the average of three independent replicates, and labeling with P1 probe was considered significant at P < 0.05. (A) Comparison of proteins identified from the parasite proteome extracted after treatment of the parasite culture in situ with 1 µM P1 vs. 1 µM deoxyartmesinin control probe (CP1). (B) Comparison of proteins identified from the parasite proteome treated with 1 µM P1 vs. DMSO solvent. emPAI, exponentially modified protein abundance index; HSP70, heat shock protein 70; MSP1, merozoite surface protein 1; ns, nonsignificant; RH3, reticulocyte-binding protein 3; TR, thioredoxin reductase.

Fig. S3.

Volcano plot illustrating differential protein abundance changes in response to preincubation of P. falciparum 3D7-extracted parasite proteins with the iron-chelating agent DFO. (A) Chemical structure of DFO. (B) Volcano plots with the uncorrected P value plotted against the log twofold change for P2 vs. P2 + DFO. The dotted red line represents P = 0.05; points above the line have P < 0.05, representing significant changes in protein abundance. Proteins of interest in the upper right quadrant (i.e., showing both a more than onefold change in abundance ratio on the x axis and a high statistical significance of P < 0.05 on the y axis) are assigned with abbreviated short names. A detailed description of these proteins and their respective P values is given in Table S1. The complete dataset used to create the volcano plot is in Dataset S4. EFA, elongation factor 1α; HGPRT, hypoxanthine-guanine-xanthine phosphoribosyltransferase; OAT, ornithine aminotransferase; PGI, glucose-6-phosphate isomerase.

Fig. S4.

Diagram of the main pathways targeted by ART-ABPPs in P. falciparum blood-stage trophozoites. The hemoglobin digestion pathway provides the amino acids to support rapid development and growth of the malaria parasite. Glycolysis provides the primary carbon sources and energy supply for the malaria parasites. Rapid glycolytic flux maintains rate-limiting glycolytic intermediates to support nucleotide biosynthesis, lipid biosynthesis, shikimic acid pathway, isoprenoid biosynthesis, and glutaminolysis. Arrows indicate metabolic steps, with multiple arrows showing various intervening steps not shown; dotted arrows indicate biochemical transport. CRT, chloroquine resistance; DHAP, dihydroxyacetone phosphate; ER, endoplasmic reticulum; G3PDH, glyceraldehyde 3 phosphate dehydrogenase; HK, hexokinase; LDH, lactate dehydrogenase; MDR, multidrug resistance pump; P, phosphate; PEP, phosphoenolpyruvate; PFK, phosphofructokinase; PGI, glucose-6-phosphate isomerase; PGK, phosphoglycerate kinase; PK, pyruvate kinase; PPP, pentose phosphate pathway; PRPP, phosphoribosyl pyrophosphate; TPI, triosephosphate isomerase. *Proteins detected and identified by ART-ABPPs. Modified from refs. and .

Fig. 4.

Gene Ontology (GO) analysis for biological pathways identified from ART proteome. (A) Black bars represent the numbers of proteins identified within each biological process (pathway) using the STITCH 4 web tool (stitch.embl.de). (B) Multiple testing corrected P values for enriched GO categories. The confidence view of ART–protein and protein–protein interactions networks built up using the STITCH 4 web tool is illustrated in Fig. S4. A complete dataset of ART interactome is in Datasets S2 and S3.

Fig. S5.

The confidence view of the ART–protein and protein–protein interaction network. The ART interactome was map built using the STITCH 4 web tool (stitch.embl.de) with default parameters and the accession codes for identified proteins. Proteins clustered into three main groups according to the confidence criteria explained in the text (Fig. 2). Predicted proteins using the protein–protein interaction analysis function were grouped by STITCH. Thicker lines represent stronger associations, protein–protein interactions are shown in blue, chemical–protein interactions are shown in green, and interactions between chemicals are shown in red. Network interaction analysis details can be found in Dataset S2.

Fig. S6.

ART-ABPPs targeted proteins throughout the parasite, including proteins from the cytosol and the parasites food vacuole as identified by Gene Ontology analysis using Cytoscape software version 3.3.0 (64) and CluGO App (65).