Acquisition of antibodies against endothelial protein C receptor-binding domains of <i>Plasmodium falciparum</i> erythrocyte membrane protein 1 in children with severe malaria

BACKGROUND: Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1) mediates parasite sequestration in postcapillary venules in P. falciparum malaria. PfEMP1 types can be classified based on their cysteine-rich interdomain region (CIDR) domains. Antibodies to different PfEMP1 types develop gradually after repeated infections as children age, and antibodies to specific CIDR types may confer protection. METHODS: Levels of immunoglobulin G to 35 recombinant CIDR domains were measured by means of Luminex assay in acute-stage (baseline) and convalescent-stage plasma samples from Papua New Guinean children with severe or uncomplicated malaria and in healthy age-matched community controls. RESULTS: At baseline, antibody levels were similar across the 3 groups. After infection, children with severe malaria had higher antibody levels than those with uncomplicated malaria against the endothelial protein C receptor (EPCR) binding CIDRα1 domains, and this difference was largely confined to older children. Antibodies to EPCR-binding domains increased from presentation to follow-up in severe malaria, but not in uncomplicated malaria. CONCLUSIONS: The acquisition of antibodies against EPCR-binding CIDRα1 domains of PfEMP1 after a severe malaria episode suggest that EPCR-binding PfEMP1 may have a role in the pathogenesis of severe malaria in Papua New Guinea

The <i>Plasmodium falciparum</i> transcriptome in severe malaria reveals altered expression of genes involved in important processes including surface antigen–encoding <i>var</i> genes

<div><p>Within the human host, the malaria parasite <i>Plasmodium falciparum</i> is exposed to multiple selection pressures. The host environment changes dramatically in severe malaria, but the extent to which the parasite responds to—or is selected by—this environment remains unclear. From previous studies, the parasites that cause severe malaria appear to increase expression of a restricted but poorly defined subset of the PfEMP1 variant, surface antigens. PfEMP1s are major targets of protective immunity. Here, we used RNA sequencing (RNAseq) to analyse gene expression in 44 parasite isolates that caused severe and uncomplicated malaria in Papuan patients. The transcriptomes of 19 parasite isolates associated with severe malaria indicated that these parasites had decreased glycolysis without activation of compensatory pathways; altered chromatin structure and probably transcriptional regulation through decreased histone methylation; reduced surface expression of PfEMP1; and down-regulated expression of multiple chaperone proteins. Our RNAseq also identified novel associations between disease severity and PfEMP1 transcripts, domains, and smaller sequence segments and also confirmed all previously reported associations between expressed PfEMP1 sequences and severe disease. These findings will inform efforts to identify vaccine targets for severe malaria and also indicate how parasites adapt to—or are selected by—the host environment in severe malaria.</p></div

Gene sets enriched in deregulated genes in severe malaria.

<p>(A) Summary of highly ranked GO and KEGG gene annotation pathways that included significantly deregulated genes in severe malaria. Only gene sets that contained more than 1 deregulated gene are shown; deregulated gene set data available in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004328#pbio.2004328.s017" target="_blank">S3 Data</a>, deregulated genes available in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004328#pbio.2004328.s015" target="_blank">S1 Data</a>. (B) The glycolysis pathway in <i>P</i>. <i>falciparum</i> in severe malaria. Fold-change in gene expression in severe malaria relative to uncomplicated malaria (x) and <i>p</i>-value for the fold-change are indicated beside genes. Genes that were significantly (adjusted <i>p <</i> 0.1) down-regulated in severe malaria are indicated in red. (C) LC-MS metabolomic analysis of plasma samples from patients with severe and uncomplicated malaria. Ion counts for metabolites commonly affected by malaria are presented; data available in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004328#pbio.2004328.s018" target="_blank">S4 Data</a>. adj-p, adjusted <i>p</i>; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; LC-MS, liquid chromatography–mass spectrometry; logFC, log fold-change; uncompl, uncomplicated.</p

Analysis of RNAseq data via de novo assembly at the level of <i>var</i> gene domains.

<p>(A) Expression levels of domain subfamilies from [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004328#pbio.2004328.ref014" target="_blank">14</a>] found to be up-regulated in severe disease as identified using HMMER3 models. These models were built from the domain sequences of [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004328#pbio.2004328.ref014" target="_blank">14</a>]. Samples and clusters have been grouped using complete linkage hierarchical clustering. (B) PCA plot of read counts that align to domain regions of the de novo–assembled transcripts identified using HMMER3 models. There is less separation by phenotypes in this plot than was observed at the whole-transcript–and all-gene–analysis levels. Read count data for Fig 7 is available in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004328#pbio.2004328.s022" target="_blank">S8 Data</a>. (C) An example of the hierarchical clustering tree. Colours represent significance, with red indicating a significant difference in expression after multiple testing correction and blue indicating not significant. Nodes are coloured grey if there is insufficient evidence for them to be considered in the testing either because they have less than 5 samples present or they are marked by DESeq2’s prefilter step. At the 60% identity level, cluster 670_X0.6 becomes significant. This significance is then obscured at the 50% identity level, demonstrating the importance of considering different levels of the hierarchy. (D) Clustering the domain level counts at 50% sequence identity rather than using the previous classifications of [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004328#pbio.2004328.ref014" target="_blank">14</a>] improves the grouping of severe samples. At 50% identity, the severe samples are grouped more closely together, suggesting that they have more in common than the nonsevere samples; transformed read count data available in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004328#pbio.2004328.s023" target="_blank">S9 Data</a>. PCA, principal component analysis; RNAseq, RNA sequencing.</p

Analysis of RNAseq data via de novo assembly at the level of <i>var</i> gene segments.

<p>Expression levels of novel conserved segment clusters found to be up-regulated in severe disease. Samples and segment clusters have been grouped using complete linkage hierarchical clustering. The raw read counts that were transformed for this figure are available in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004328#pbio.2004328.s026" target="_blank">S12 Data</a>.</p

Analysis of RNAseq data at the level of <i>var</i> gene transcripts: Separate assembly.

<p>(A) Expression levels of clusters identified by Corset found to be up-regulated in severe disease. Samples and clusters have been grouped using complete linkage hierarchical clustering. Raw read counts are available in S7 data. (B) Expression levels of clusters identified by Corset found to be up-regulated in severe disease. Values for all samples and the IQR and median are indicated and are available in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004328#pbio.2004328.s021" target="_blank">S7 Data</a>. RPKM is reads per kb of transcript per million reads mapped to total <i>var</i> transcripts. IQR, interquartile range; RPKM, Reads Per Kilobase of transcript per Million mapped reads.</p

Genome-wide analysis of RNAseq data using 3D7 annotation.

<p>(A) Estimated stage proportions for each sample. The mixture model was constrained to require that each sample be made up of a combination of ring, early trophozoite, late trophozoite, schizont, and gametocyte stages. Consequently, the columns in this barplot must add to 1 for each sample. A small bias towards the early trophozoite appears in the nonsevere malaria samples. Sample SFC21 also appears to be an outlier due to its higher proportion of late-stage and gametocyte parasites, a finding which was confirmed by microscopy. Plotted proportions are available in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004328#pbio.2004328.s016" target="_blank">S2 Data</a>. (B) A PCA plot of read counts normalised for library size (read counts are available in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004328#pbio.2004328.s016" target="_blank">S2 Data</a>). Samples are coloured by phenotype, red for severe and blue for nonsevere. Some separation by disease severity phenotype is evident; however, staging effects are apparent as is seen in the outlying position of sample SFC21, which has been identified as having more late-stage and gametocyte parasites. (C) A PCA plot of read counts normalised for library size, staging effects, and other unwanted batch effects using the novel mixture model along with 3 unwanted factors of variation estimated by RUV4 (normalised read counts are available in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004328#pbio.2004328.s016" target="_blank">S2 Data</a>). Sample SFC21 has been appropriately dealt with and a better separation of the samples by disease phenotype can be observed. PC, principal component; PCA, principal component analysis; RUV, Remove Unwanted Variation.</p

Analysis of RNAseq data at the level of <i>var</i> gene transcripts: Combined assembly.

<p>(A) Expression levels of transcripts from the combined sample assembly found to be up-regulated in severe disease. Samples and clusters have been grouped using complete linkage hierarchical clustering (raw read counts available in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004328#pbio.2004328.s020" target="_blank">S6 Data</a>). (B) Expression levels of transcripts from the combined sample assembly found to be up-regulated in severe disease; values for all samples and the IQR and median are indicated. RPKM is reads per kb of transcript per million reads mapped to total <i>var</i> transcripts (RPKM available in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004328#pbio.2004328.s020" target="_blank">S6 Data</a>). IQR, interquartile range; RPKM, Reads Per Kilobase of transcript per Million mapped reads.</p

Summary of PfEMP1 transcripts, domains, and segments that were up-regulated in severe malaria.

<p>Sequences up-regulated in severe malaria are organised in columns for each analysis method separated by grey bars. Multiple domains found in the same single transcripts from the combined or separate assemblies are on a single row. Closely related sequences found in multiple analyses are colour coded for each of the major domain types and are grouped together across analyses by unbroken horizontal lines. Domains and/or segments that clustered together by expression profile in multiple individuals within a single analysis are also grouped by unbroken horizontal lines. Grey shaded sequences at the bottom of the diagram are unrelated to each other. For example, in the case of DC4, 2 transcripts from the combined assembly were amongst the closest BLAST hits to the DC4-like transcripts from the CORSET cluster of the separate assembly; 6 domains and 5 blocks identified by HMM in the separate assembly are found in DC4 domains; and clusters for 1 domain and 4 segments identified by hierarchical analysis contained DC4 domain sequences, including those from the DC4-like transcripts from the CORSET cluster of the separate assembly. ^aCombined assembly transcripts up-regulated in severe malaria were all adjusted <i>p <</i> 0.05 except for domains marked ^b (adjusted <i>p <</i> 0.153). Domains HMM and blocks HMM were identified using the HMM of [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004328#pbio.2004328.ref014" target="_blank">14</a>]. Domains and segments %ID were identified using the novel hierarchical approach developed for this study. ^cNon–DC8-like DBLδ1 and non–DC4-like DBLβ3 that clustered by expression profile in the same patients with a highly conserved CIDRβ1. A dashed line separates DBLβ12 from DC8 because DC8 typically contain DBLβ12, but these DBLβ12 formed a phylogenetic cluster with non-DC8 DBLβ12. Dashed lines separate putative DC9 components because transcripts containing all components were not up-regulated in the combined assembly or the Corset analysis, but the clusters from which the up-regulated segments were identified contained multiple transcripts carrying the DC9 domains. ATS, acidic terminal sequence; CIDR, cysteine-rich interdomain region; DBL, Duffy binding-like; DC, domain cassette; HMM, Hidden Markov Model; PfEMP1, <i>Plasmodium falciparum</i> Erythrocyte Membrane Protein 1; TM, transmembrane.</p