Severe adult malaria is associated with specific PfEMP1 adhesion types and high parasite biomass

Abstract

The interplay between cellular and molecular determinants that lead to severe malaria in adults is unexplored. Here, we analyzed parasite virulence factors in an infected adult population in India and investigated whether severe malaria isolates impair endothelial protein C receptor (EPCR), a protein involved in coagulation and endothelial barrier permeability. Severe malaria isolates overexpressed specific members of the Plasmodium falciparum var gene/PfEMP1 (P. falciparum erythrocyte membrane protein 1) family that bind EPCR, including DC8 var genes that have previously been linked to severe pediatric malaria. Machine learning analysis revealed that DC6- and DC8-encoding var transcripts in combination with high parasite biomass were the strongest indicators of patient hospitalization and disease severity. We found that DC8 CIDRα1 domains from severe malaria isolates had substantial differences in EPCR binding affinity and blockade activity for its ligand activated protein C. Additionally, even a low level of inhibition exhibited by domains from two cerebral malaria isolates was sufficient to interfere with activated protein C-barrier protective activities in human brain endothelial cells. Our findings demonstrate an interplay between parasite biomass and specific PfEMP1 adhesion types in the development of adult severe malaria, and indicate that low impairment of EPCR function may contribute to parasite virulence.

Keywords: EPCR; PfEMP1; Plasmodium falciparum; malaria; var.

Fig. 1.

Association between PfHRP2 plasma concentrations and disease severity. PfHRP2 levels were compared between patient groups and by number of WHO SM criteria. (A) PfHRP2 plasma concentrations among disease groups. Horizontal lines indicate median for each group. Pairwise comparisons were analyzed using the Mann–Whitney U test. Significant higher concentration is represented by **P < 0.01 and ***P < 0.001. (B) Spearman’s rank correlation coefficient (ρ) and P value for the association between PfHRP2 plasma concentrations and number of severity criteria.

Fig. 2.

Transcription of UpsA, DC6, and DC8 var is elevated in SM patients. The transcript abundances of var gene subtypes were investigated among patients. (A) Transcript levels of A, B, and C var gene groups and (B) domain subtypes of DC8 and DC6 in SM and OP groups. Horizontal lines indicate median for each group. Differences among groups were compared by using the Mann–Whitney U test. Significant higher transcription is represented by *P ≤ 0.05 and FDR ≤ 0.2, ***P ≤ 0.005 and FDR ≤ 0.05. (C) Heat map showing transcription levels of DC8 and DC6 domain subtypes and VarA genes. Maximum transcription levels are represented in red, minimum transcription in blue, and median transcription levels in white. Color equivalents were set by comparing each primer transcript among all patients analyzed. FDR, Benjamini–Hochberg adjustment for false discovery rate.

Fig. S1.

The relationship between the C-terminal DC6 cassette and PfEMP1 head structures. (A) Schematic showing the chromosomal location of group A, B, and C var genes. The classification and predicted binding properties of PfEMP1 in 3D7 genome reference isolate are shown based on the PfEMP1 head structures. PfEMP1-containing DC8 are classified as B/A. (B) DC6 C-terminal subtypes are found in combination with rosetting (group A), EPCR-binding (group B and B\A), and CD36-binding (group B and C) head structures among the seven Plasmodium falciparum annotated genomes (13).

Fig. S2.

In silico predicted performance of the var domain primers on seven annotated parasite genotypes. Genes that presented annealing of both primers (with no more than one mismatch, which was not present at the 3′ end of the primer) are shown. Rosetting (yellow), EPCR (blue and violet), and CD36-binding (orange) phenotypes were predicted by examining the CIDR domain in the PfEMP1 semiconserved head structure (rosetting = CIDRβ/γ/δ, EPCR-binding = CIDRα1.1/4–8, CD36 binding = CIDRα2–6). DBLβ3 and DBLβ5 domains are associated with ICAM-1 binding (–64) and some DBLε/ζ subtypes with IgM/α2-macroglobulin binding (54, 55).

Fig. 3.

Machine-learning approach to understand disease severity. Parasite factors associated with a higher risk of patient hospitalization and disease severity were revealed by machine-learning approaches. (A) Summary of RF feature selection strategy to identify parasite virulence factors that discriminate between hospitalized patients (SM + MSM) (Left), SM patients (Right), and OP. The top 10 parasite factors with the highest MDCA are shown. Positive correlation with disease severity is shown with a 1, negative with a −1, and no association with a 0. To adjust for false discovery, familywise error rates (RF mProbes FWER) were estimated using mProbes algorithm and values ≤ 0.2 were considered significant. The predicted binding phenotype was determined as described in Fig. S2. The CMI P value is used to find primers that are significantly informative even after PfHRP2 is accounted for. Var features with a P ≤ 0.05 presented virulence not explained by parasite biomass. (B) evTrees illustrate disease pathways to patient hospitalization (Left) or severe disease (Right) after PfHRP2 filtration (P ≤ 0.20). The percentages in the boxes represent the probability of each pathway to classify patients into hospitalized (H), severe malaria (S), or outpatients (OP). The number of patients in each pathway is indicated below. The percentages beneath the lines show the proportion of the total severe patients classified by each pathway. (C) Var primers were grouped according to binding phenotype or var group (Fig. S2 and Table S4) and ranked by MDCA (15, 16). The association with patient hospitalization (Left) and disease severity (Right) was determined using a Mann–Whitney U. P ≤ 0.05 and FDR ≤ 0.2 are considered significant. FDR, Benjamini–Hochberg adjustment for FDR.

Fig. S3.

Pairwise comparison correlation of DC8 and DC6 subtypes expression among all patients. Spearman ρ values are depicted and ranked from highest (black) to lowest (light gray). All of the ρ values presented a P ≤ 0.05 except for the correlation DBLγ of DC6-DBLγ4/6 of DC8.

Fig. 4.

Inhibition of the APC–EPCR interaction by DC8 CIDRα from severe isolates. DC8 CIDRα domains expressed from SM isolates were analyzed for EPCR binding affinity and blocking the interaction with its ligand APC. (A) Neighbor-joining tree (bootstrap n = 100) of 66 previously classified CIDRα1 sequences (14) and 7 Indian CIDRα1 transcripts amplified from adult SM patients in this study (black dots). (B) The first column shows the dissociation constant (K_d) for rCIDRα1.1-EPCR measured by biolayer interferometry (see Fig. S5 for detailed kinetics). Histograms show APC binding to CHO-EPCR cells in the presence or absence of 250 μg/mL Indian CIDRα1 domains. The vertical line shows the primary and secondary antibody background used to set the gate for APC⁺ cells. Red: strong inhibition; blue: medium; green: low; light gray: no inhibition. The bar graphs show the percentage of APC binding in the presence of CIDR domains relative to APC alone (mean and SD, n = 4 independent experiments). (C) Inhibition by rCIDRα1.1 of APC-dependent protection of endothelial barrier properties. (C, Left) Kinetics showing APC (50 nM)-mediated protection of thrombin (2 nM) induced barrier disruption in human brain endothelial cell monolayers, and examples of rCIDR1.1 that do or do not inhibit APC barrier protection activity. (Right) Bar graph showing the barrier protection (%) activity of APC on human brain endothelial cells and HUVEC cells (EA.hy926 cells) pretreated with rCIDRα1.1 (mean and SD, n = 6 independent experiments for all CIDR domains, except n = 3 for rPFE1640wCIDRα1.3). P values were calculated using a one-way ANOVA and Dunnet’s multiple comparison test. Significant values are represented by *P ≤ 0.05, **P ≤ 0.01, and ***P ≤ 0.001.

Fig. S4.

Identification and expression of DC8 CIDRα1 expressed by SM isolates. (A) PCR strategy to amplify the full-length DC8 CIDRα1 domain from SM patients using var domain primers (15). (B) CIDRα1 recombinant proteins were analyzed under nonreducing conditions in a SDS/PAGE gel and stained by GelCode Blue Protein Stain.

Fig. S5.

Binding kinetics between recombinant CIDRα1 domains and EPCR determined by BLI and flow cytometry. (A) Representative sensograms with corresponding kinetic fits to the data (red). (B) Summary of binding kinetics. *Binding not detected. (C) Dose-dependent binding titration of CIDRα1 domains to CHO745-EPCR cells analyzed by flow cytometry, median levels depicted (n = 4). Binding levels to the negative control, untransfected CHO745 cells is shown only for the highest CIDRα1 concentration analyzed.