Summary Brain swelling is the major predictor of mortality in pediatric cerebral malaria (CM). However, the mechanisms leading to swelling remain poorly defined. Here, we combined neuroimaging, parasite transcript profiling, and laboratory blood profiles to develop machine learning models of malarial retinopathy and brain swelling. We found that parasite var transcripts encoding endothelial protein C receptor (EPCR) binding domains, in combination with high parasite biomass and low platelet levels, are strong indicators of CM cases with malarial retinopathy. Swelling cases presented low platelet levels and increased transcript abundance of parasite PfEMP1 DC8 and group A EPCR-binding domains. Remarkably, the dominant transcript in 50% of swelling cases encoded PfEMP1 group A CIDRα1.7 domains. Furthermore, a recombinant CIDRα1.7 domain from a pediatric CM brain autopsy inhibited the barrier protective properties of EPCR in human brain endothelial cells in vitro. Together, these findings suggest a detrimental role for EPCR-binding CIDRα1 domains in brain swelling.

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. malaria | Plasmodium falciparum | var | PfEMP1 | EPCR

Many protein engineering problems involve finding mutations that produce proteins with a particular function. Computational active learning is an attractive approach to discover desired biological activities. Traditional active learning techniques have been optimized to iteratively improve classifier accuracy, not to quickly discover biologically significant results. We report here a novel active learning technique, Most Informative Positive (MIP), which is tailored to biological problems because it seeks novel and informative positive results. MIP active learning differs from traditional active learning methods in two ways: (1) it preferentially seeks Positive (functionally active) examples; and (2) it may be effectively extended to select gene regions suitable for high throughput combinatorial mutagenesis. We applied MIP to discover mutations in the tumor suppressor protein p53 that reactivate mutated p53 found in human cancers. This is an important biomedical goal because p53 mutants have been implicated in half of all human cancers, and restoring active p53 in tumors leads to tumor regression. MIP found Positive (cancer rescue) p53 mutants in silico using 33% fewer experiments than traditional non-MIP active learning, with only a minor decrease in classifier accuracy. Applying MIP to in vivo experimentation yielded immediate Positive results. Ten different p53 mutations found in human cancers were paired in silico with all possible single amino acid rescue mutations, from which MIP was used to select a Positive Region predicted to be enriched for p53 cancer rescue mutants. In vivo assays showed that the predicted Positive Region: (1) had significantly more (p<0.01) new strong cancer rescue mutants than control regions (Negative, and non-MIP active learning); (2) had slightly more new strong cancer rescue mutants than an Expert region selected for purely biological considerations; and (3) rescued for the first time the previously unrescuable p53 cancer mutant P152L.